From c3200c80ef8dc0723478614d5da06e5fd149141f Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 13 Jun 2024 09:05:12 -0700 Subject: [PATCH 01/15] DEV: add pre-commit configuration --- .pre-commit-config.yaml | 39 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..41fdc80c --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,39 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks.git + rev: v4.6.0 + hooks: + # Remove unnecessary whitespace at the end of lines: + # - id: trailing-whitespace + # Ensure that text files have a newline at the end: + # - id: end-of-file-fixer + # Verify that Python source code is valid: + - id: check-ast + # Ensure filenames won't have conflicts on case insensitive platforms: + - id: check-case-conflict + # Check JSON files for valid syntax: + - id: check-json + # Check XML files for valid syntax: + - id: check-xml + # Check YAML files for valid syntax: + - id: check-yaml + # Check TOML files for valid syntax: + - id: check-toml + # Check that there are no remnants of merge conflicts in files: + - id: check-merge-conflict + # Check that symlinks are valid: + - id: check-symlinks + # Check that there's no code before a docstring + - id: check-docstring-first + # Check that too large of files are not committed (50MB): + - id: check-added-large-files + args: ["--maxkb=100000"] + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.4.8 + hooks: + - id: ruff + args: ["--config", "python/pyproject.toml"] + types_or: [python] + exclude: "^(pytao/_version.py)$" From 77fa2fea6a266e058fc964d23426fdbcc9e9386b Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 13 Jun 2024 10:23:46 -0700 Subject: [PATCH 02/15] REF: interface commands now in subclass of TaoCore This will help with non-Jupyter usage of pytao. --- generate_interface_commands.py | 94 +- interface.tpl.py | 138 + pytao/__init__.py | 20 +- pytao/interface_commands.py | 9716 +++++++++++++----------- pytao/tao_ctypes/core.py | 18 +- pytao/tao_interface.py | 8 +- pytao/tests/test_interface_commands.py | 1150 +-- 7 files changed, 6012 insertions(+), 5132 deletions(-) create mode 100644 interface.tpl.py diff --git a/generate_interface_commands.py b/generate_interface_commands.py index 6ed3ff64..6acda004 100644 --- a/generate_interface_commands.py +++ b/generate_interface_commands.py @@ -7,6 +7,7 @@ import json import keyword import os +import shutil CMDS_OUTPUT = "./pytao/interface_commands.py" TEST_OUTPUT = "./pytao/tests/test_interface_commands.py" @@ -18,6 +19,8 @@ with open(f_name, 'r') as f: cmds_from_tao = json.load(f) +with open("interface.tpl.py", 'r') as f: + interface_tpl_py = f.read() # ### Utilitary Functions def sanitize_method_name(method): @@ -61,7 +64,7 @@ def generate_params(params): E.g.: tao, s, *, ix_uni="1", ix_branch="0", which="model", verbose=False, as_dict=True """ - args = ['tao'] + args = ['self'] kwargs = [] for idx, p in enumerate(params): name = sanitize(p.name) @@ -127,78 +130,27 @@ def generate_method_code(docs, method, command, returns): if special_parser: parser_docs = NumpyDocString(special_parser.__doc__) docs['Returns'] = parser_docs['Returns'] - code_list.append(f"return __execute(tao, cmd, as_dict, raises, method_name='{method}', cmd_type='{tp}')") + code_list.append(f"return self.__execute(cmd, as_dict, raises, method_name='{method}', cmd_type='{tp}')") else: - code_list.append(f"{r.desc[0]}:\n return __execute(tao, cmd, as_dict, raises, method_name='{method}', cmd_type='{tp}')") + code_list.append(f"{r.desc[0]}:\n return self.__execute(cmd, as_dict, raises, method_name='{method}', cmd_type='{tp}')") return '\n'.join(code_list) # ## Parse the JSON Dictionary and Write the Python module -cmds_to_module = [f"""# ============================================================================== +cmds_to_module = [ + f'''# ============================================================================== # AUTOGENERATED FILE - DO NOT MODIFY # This file was generated by the script `generate_interface_commands.py`. # Any modifications may be overwritten. # Generated on: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")} # ============================================================================== -"""+""" -from pytao.tao_ctypes.util import parse_tao_python_data -from pytao.util.parameters import tao_parameter_dict -from pytao.util import parsers as __parsers +{interface_tpl_py} +''' +] -def __execute(tao, cmd, as_dict=True, raises=True, method_name=None, cmd_type="string_list"): - \""" - - A wrapper to handle commonly used options when running a command through tao. - - Parameters - ---------- - tao : Tao - The handle to tao to run the command on - cmd : str - The command to run - as_dict : bool, optional - Return string data as a dict? by default True - raises : bool, optional - Raise exception on tao errors? by default True - method_name : str/None, optional - Name of the caller. Required for custom parsers for commands, by default None - cmd_type : str, optional - The type of data returned by tao in its common memory, by default "string_list" - - Returns - ------- - Any - Result from running tao. The type of data depends on configuration, but is generally a list of strings, a dict, or a - numpy array. - \""" - func_for_type = { - "string_list": tao.cmd, - "real_array": tao.cmd_real, - "integer_array": tao.cmd_integer - } - func = func_for_type.get(cmd_type, tao.cmd) - ret = func(cmd, raises=raises) - special_parser = getattr(__parsers, f'parse_{method_name}', "") - if special_parser: - data = special_parser(ret) - return data - if "string" in cmd_type: - try: - if as_dict: - data = parse_tao_python_data(ret) - else: - data = tao_parameter_dict(ret) - except Exception as ex: - # TODO: use logger instead of: print('Failed to parse string data. Returning raw value. Exception was: ', ex) - return ret - - return data - - return ret - -"""] +print() for method, metadata in cmds_from_tao.items(): docstring = metadata['description'] @@ -214,11 +166,11 @@ def __execute(tao, cmd, as_dict=True, raises=True, method_name=None, cmd_type="s print(f'***Error generating code for: {method}. Exception was: {ex}') method_template = f''' -def {clean_method}({params}): -{add_tabs('"""', 1)} -{add_tabs(str(np_docs), 1)} -{add_tabs('"""', 1)} -{add_tabs(code, 1)} + def {clean_method}({params}): +{add_tabs('"""', 2)} +{add_tabs(str(np_docs), 2)} +{add_tabs('"""', 2)} +{add_tabs(code, 2)} ''' cmds_to_module.append(method_template) @@ -267,6 +219,9 @@ def get_tests(examples): from pytao import Tao from pytao import interface_commands +def new_tao(init): + return Tao(os.path.expandvars(f"{{init}} -noplot")) + """] for method, metadata in cmds_from_tao.items(): @@ -281,10 +236,10 @@ def get_tests(examples): print(f'No examples found for: {method}') for test_name, test_meta in tests.items(): - args = ['tao'] + [f"{k}='{v}'" for k, v in test_meta['args'].items()] + args = [f"{k}='{v}'" for k, v in test_meta['args'].items()] test_code = f''' -tao = Tao(os.path.expandvars('{test_meta['init']} -noplot')) -ret = interface_commands.{clean_method}({', '.join(args)}) +tao = new_tao('{test_meta['init']}') +tao.{clean_method}({', '.join(args)}) ''' method_template = f''' def test_{clean_method}_{test_name}(): @@ -296,3 +251,6 @@ def test_{clean_method}_{test_name}(): out.writelines(cmds_to_test_module) print(f'Generated file: {TEST_OUTPUT}') + +if shutil.which("ruff"): + os.system(f'ruff format "{CMDS_OUTPUT}" "{TEST_OUTPUT}"') diff --git a/interface.tpl.py b/interface.tpl.py new file mode 100644 index 00000000..1070a6a1 --- /dev/null +++ b/interface.tpl.py @@ -0,0 +1,138 @@ +import logging +import numpy as np + +from pytao.tao_ctypes.core import TaoCore +from pytao.tao_ctypes.util import parse_tao_python_data +from pytao.util.parameters import tao_parameter_dict +from pytao.util import parsers as _pytao_parsers + + +logger = logging.getLogger(__name__) + + +class Tao(TaoCore): + def __execute( + self, + cmd: str, + as_dict: bool = True, + raises: bool = True, + method_name=None, + cmd_type: str = "string_list", + ): + """ + + A wrapper to handle commonly used options when running a command through tao. + + Parameters + ---------- + cmd : str + The command to run + as_dict : bool, optional + Return string data as a dict? by default True + raises : bool, optional + Raise exception on tao errors? by default True + method_name : str/None, optional + Name of the caller. Required for custom parsers for commands, by + default None + cmd_type : str, optional + The type of data returned by tao in its common memory, by default + "string_list" + + Returns + ------- + Any + Result from running tao. The type of data depends on configuration, but is generally a list of strings, a dict, or a + numpy array. + """ + func_for_type = { + "string_list": self.cmd, + "real_array": self.cmd_real, + "integer_array": self.cmd_integer, + } + func = func_for_type.get(cmd_type, self.cmd) + ret = func(cmd, raises=raises) + special_parser = getattr(_pytao_parsers, f"parse_{method_name}", "") + if special_parser and callable(special_parser): + data = special_parser(ret) + return data + if "string" in cmd_type: + try: + if as_dict: + data = parse_tao_python_data(ret) + else: + data = tao_parameter_dict(ret) + except Exception: + logger.exception("Failed to parse string data. Returning raw value.") + return ret + + return data + + return ret + + def bunch_data(self, ele_id, *, which="model", ix_bunch=1, verbose=False): + """ + Returns bunch data in openPMD-beamphysics format/notation. + + Notes + ----- + Note that Tao's 'write beam' will also write a proper h5 file in this format. + + Expected usage: + data = bunch_data(tao, 'end') + from pmd_beamphysics import ParticleGroup + P = ParicleGroup(data=data) + + + Returns + ------- + data : dict + dict of arrays, with keys 'x', 'px', 'y', 'py', 't', 'pz', + 'status', 'weight', 'z', 'species' + + + Examples + -------- + Example: 1 + init: $ACC_ROOT_DIR/tao/examples/csr_beam_tracking/tao.init + args: + ele_id: end + which: model + ix_bunch: 1 + + """ + + # Get species + stats = self.bunch_params(ele_id, which=which, verbose=verbose) + species = stats["species"] + + dat = {} + for coordinate in ["x", "px", "y", "py", "t", "pz", "p0c", "charge", "state"]: + dat[coordinate] = self.bunch1( + ele_id, + coordinate=coordinate, + which=which, + ix_bunch=ix_bunch, + verbose=verbose, + ) + + # Remove normalizations + p0c = dat.pop("p0c") + + dat["status"] = dat.pop("state") + dat["weight"] = dat.pop("charge") + + # px from Bmad is px/p0c + # pz from Bmad is delta = p/p0c -1. + # pz = sqrt( (delta+1)**2 -px**2 -py**2)*p0c + dat["pz"] = ( + np.sqrt((dat["pz"] + 1) ** 2 - dat["px"] ** 2 - dat["py"] ** 2) * p0c + ) + dat["px"] = dat["px"] * p0c + dat["py"] = dat["py"] * p0c + + # z = 0 by definition + dat["z"] = np.full(len(dat["x"]), 0) + + dat["species"] = species.lower() + + return dat diff --git a/pytao/__init__.py b/pytao/__init__.py index 65d7ec45..5593b7b6 100644 --- a/pytao/__init__.py +++ b/pytao/__init__.py @@ -1,16 +1,28 @@ -''' +""" pytao is the python interface to tao. Contains backend implementations in both ctypes and pexpect. The gui package supports a GUI interface to tao, in place of the tao command line interface, with matplotlib plotting capabilities. pytao also has some pre-defined constructs for dealing with data from tao in the util package. -''' +""" + from .tao_pexpect import tao_io -from .tao_ctypes import Tao, TaoModel, run_tao +from .tao_ctypes import TaoModel, run_tao from .tao_ctypes.evaluate import evaluate_tao from .tao_interface import tao_interface +from .interface_commands import Tao from ._version import get_versions -__version__ = get_versions()['version'] + +__version__ = get_versions()["version"] del get_versions + +__all__ = [ + "tao_io", + "TaoModel", + "Tao", + "run_tao", + "evaluate_tao", + "tao_interface", +] diff --git a/pytao/interface_commands.py b/pytao/interface_commands.py index e8cb37d0..008ff5c2 100644 --- a/pytao/interface_commands.py +++ b/pytao/interface_commands.py @@ -2,4485 +2,5251 @@ # AUTOGENERATED FILE - DO NOT MODIFY # This file was generated by the script `generate_interface_commands.py`. # Any modifications may be overwritten. -# Generated on: 2024-06-24 14:25:18 +# Generated on: 2024-06-25 10:43:22 # ============================================================================== +import logging +import numpy as np + +from pytao.tao_ctypes.core import TaoCore from pytao.tao_ctypes.util import parse_tao_python_data from pytao.util.parameters import tao_parameter_dict -from pytao.util import parsers as __parsers - - -def __execute(tao, cmd, as_dict=True, raises=True, method_name=None, cmd_type="string_list"): - """ - - A wrapper to handle commonly used options when running a command through tao. - - Parameters - ---------- - tao : Tao - The handle to tao to run the command on - cmd : str - The command to run - as_dict : bool, optional - Return string data as a dict? by default True - raises : bool, optional - Raise exception on tao errors? by default True - method_name : str/None, optional - Name of the caller. Required for custom parsers for commands, by default None - cmd_type : str, optional - The type of data returned by tao in its common memory, by default "string_list" - - Returns - ------- - Any - Result from running tao. The type of data depends on configuration, but is generally a list of strings, a dict, or a - numpy array. - """ - func_for_type = { - "string_list": tao.cmd, - "real_array": tao.cmd_real, - "integer_array": tao.cmd_integer - } - func = func_for_type.get(cmd_type, tao.cmd) - ret = func(cmd, raises=raises) - special_parser = getattr(__parsers, f'parse_{method_name}', "") - if special_parser: - data = special_parser(ret) - return data - if "string" in cmd_type: - try: - if as_dict: - data = parse_tao_python_data(ret) - else: - data = tao_parameter_dict(ret) - except Exception as ex: - # TODO: use logger instead of: print('Failed to parse string data. Returning raw value. Exception was: ', ex) - return ret - - return data - - return ret - - -def beam(tao, ix_branch, *, ix_uni='', verbose=False, as_dict=True, raises=True): - """ - - Output beam parameters that are not in the beam_init structure. - - Parameters - ---------- - ix_uni : optional - ix_branch : "" - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python beam {ix_uni}@{ix_branch} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {ix_branch} is a lattice branch index. Defaults to s%global%default_branch. - - Note: To set beam_init parameters use the "set beam" command. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init - args: - ix_uni: 1 - ix_branch: 0 - - """ - cmd = f'python beam {ix_uni}@{ix_branch}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='beam', cmd_type='string_list') - - -def beam_init(tao, ix_branch, *, ix_uni='', verbose=False, as_dict=True, raises=True): - """ - - Output beam_init parameters. - - Parameters - ---------- - ix_uni : optional - ix_branch : "" - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python beam_init {ix_uni}@{ix_branch} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {ix_branch} is a lattice branch index. Defaults to s%global%default_branch. - - Note: To set beam_init parameters use the "set beam_init" command - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init - args: - ix_uni: 1 - ix_branch: 0 - - """ - cmd = f'python beam_init {ix_uni}@{ix_branch}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='beam_init', cmd_type='string_list') - - -def bmad_com(tao, *, verbose=False, as_dict=True, raises=True): - """ - - Output bmad_com structure components. - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python bmad_com - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - - """ - cmd = f'python bmad_com' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='bmad_com', cmd_type='string_list') - - -def branch1(tao, ix_uni, ix_branch, *, verbose=False, as_dict=True, raises=True): - """ - - Output lattice branch information for a particular lattice branch. - - Parameters - ---------- - ix_uni : "" - ix_branch : "" - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python branch1 {ix_uni}@{ix_branch} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {ix_branch} is a lattice branch index. Defaults to s%global%default_branch. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ix_uni: 1 - ix_branch: 0 - - """ - cmd = f'python branch1 {ix_uni}@{ix_branch}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='branch1', cmd_type='string_list') - - -def bunch_comb(tao, who, *, ix_uni='', ix_branch='', ix_bunch='1', flags='-array_out', verbose=False, as_dict=True, raises=True): - """ - - Outputs bunch parameters at a comb point. - Also see the "write bunch_comb" and "show bunch -comb" commands. - - Parameters - ---------- - who - ix_uni : optional - ix_branch : optional - ix_bunch : default=1 - flags : default=-array_out - - Returns - ------- - string_list - if '-array_out' not in flags - real_array - if '-array_out' in flags - - Notes - ----- - Command syntax: - python bunch_comb {flags} {who} {ix_uni}@{ix_branch} {ix_bunch} - - Where: - {flags} are optional switches: - -array_out : If present, the output will be available in the tao_c_interface_com%c_real. - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {ix_branch} is a branch index. Defaults to s%global%default_branch. - {ix_bunch} is the bunch index. Defaults to 1. - {who} is one of: - x, px, y, py, z, pz, t, s, spin.x, spin.y, spin.z, p0c, beta -- centroid - x.Q, y.Q, z.Q, a.Q, b.Q, c.Q where Q is one of: beta, alpha, gamma, phi, eta, etap, - sigma, sigma_p, emit, norm_emit - sigma.IJ where I, J in range [1,6] - rel_min.I, rel_max.I where I in range [1,6] - charge_live, n_particle_live, n_particle_lost_in_ele, ix_ele - - Note: If ix_uni or ix_branch is present, "@" must be present. - - Example: - python bunch_comb py 2@1 1 - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init - args: - who: x.beta - - """ - cmd = f'python bunch_comb {flags} {who} {ix_uni}@{ix_branch} {ix_bunch}' - if verbose: print(cmd) - if '-array_out' not in flags: - return __execute(tao, cmd, as_dict, raises, method_name='bunch_comb', cmd_type='string_list') - if '-array_out' in flags: - return __execute(tao, cmd, as_dict, raises, method_name='bunch_comb', cmd_type='real_array') - - -def bunch_params(tao, ele_id, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Outputs bunch parameters at the exit end of a given lattice element. - - Parameters - ---------- - ele_id - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python bunch_params {ele_id}|{which} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - - Example: - python bunch_params end|model ! parameters at model lattice element named "end". - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init - args: - ele_id: end - which: model - - """ - cmd = f'python bunch_params {ele_id}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='bunch_params', cmd_type='string_list') - - -def bunch1(tao, ele_id, coordinate, *, which='model', ix_bunch='1', verbose=False, as_dict=True, raises=True): - """ - - Outputs Bunch parameters at the exit end of a given lattice element. - - Parameters - ---------- - ele_id - coordinate - which : default=model - ix_bunch : default=1 - - Returns - ------- - real_array - if coordinate in ['x', 'px', 'y', 'py', 'z', 'pz', 's', 't', 'charge', 'p0c'] - integer_array - if coordinate in ['state', 'ix_ele'] - - Notes - ----- - Command syntax: - python bunch1 {ele_id}|{which} {ix_bunch} {coordinate} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - {ix_bunch} is the bunch index. - {coordinate} is one of: x, px, y, py, z, pz, "s", "t", "charge", "p0c", "state", "ix_ele" - - For example, if {coordinate} = "px", the phase space px coordinate of each particle - of the bunch is displayed. The "state" of a particle is an integer. A value of 1 means - alive and any other value means the particle has been lost. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init - args: - ele_id: end - coordinate: x - which: model - ix_bunch: 1 - - """ - cmd = f'python bunch1 {ele_id}|{which} {ix_bunch} {coordinate}' - if verbose: print(cmd) - if coordinate in ['x', 'px', 'y', 'py', 'z', 'pz', 's', 't', 'charge', 'p0c']: - return __execute(tao, cmd, as_dict, raises, method_name='bunch1', cmd_type='real_array') - if coordinate in ['state', 'ix_ele']: - return __execute(tao, cmd, as_dict, raises, method_name='bunch1', cmd_type='integer_array') - - -def building_wall_list(tao, *, ix_section='', verbose=False, as_dict=True, raises=True): - """ - - Output List of building wall sections or section points - - Parameters - ---------- - ix_section : optional - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python building_wall_list {ix_section} - - Where: - {ix_section} is a building wall section index. - - If {ix_section} is not present, a list of building wall sections is given. - If {ix_section} is present, a list of section points is given. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall - args: - ix_section: - - Example: 2 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall - args: - ix_section: 1 - - """ - cmd = f'python building_wall_list {ix_section}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='building_wall_list', cmd_type='string_list') - - -def building_wall_graph(tao, graph, *, verbose=False, as_dict=True, raises=True): - """ - - Output (x, y) points for drawing the building wall for a particular graph. - - Parameters - ---------- - graph - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python building_wall_graph {graph} - - Where: - {graph} is a plot region graph name. - - Note: The graph defines the coordinate system for the (x, y) points. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall - args: - graph: floor_plan.g - - """ - cmd = f'python building_wall_graph {graph}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='building_wall_graph', cmd_type='string_list') - - -def building_wall_point(tao, ix_section, ix_point, z, x, radius, z_center, x_center, *, verbose=False, as_dict=True, raises=True): - """ - - add or delete a building wall point - - Parameters - ---------- - ix_section - ix_point - z - x - radius - z_center - x_center - - Returns - ------- - None - - Notes - ----- - Command syntax: - python building_wall_point {ix_section}^^{ix_point}^^{z}^^{x}^^{radius}^^{z_center}^^{x_center} - - Where: - {ix_section} -- Section index. - {ix_point} -- Point index. Points of higher indexes will be moved up - if adding a point and down if deleting. - {z}, etc... -- See tao_building_wall_point_struct components. - -- If {z} is set to "delete" then delete the point. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall - args: - ix_section: 1 - ix_point: 1 - z: 0 - x: 0 - radius: 0 - z_center: 0 - x_center: 0 - - """ - cmd = f'python building_wall_point {ix_section}^^{ix_point}^^{z}^^{x}^^{radius}^^{z_center}^^{x_center}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='building_wall_point', cmd_type='None') - - -def building_wall_section(tao, ix_section, sec_name, sec_constraint, *, verbose=False, as_dict=True, raises=True): - """ - - Add or delete a building wall section - - Parameters - ---------- - ix_section - sec_name - sec_constraint - - Returns - ------- - None - - Notes - ----- - Command syntax: - python building_wall_section {ix_section}^^{sec_name}^^{sec_constraint} - - Where: - {ix_section} -- Section index. Sections with higher indexes will be - moved up if adding a section and down if deleting. - {sec_name} -- Section name. - {sec_constraint} -- A section constraint name or "delete". Must be one of: - delete -- Delete section. Anything else will add the section. - none - left_side - right_side - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ix_section: 1 - sec_name: test - sec_constraint: none - - """ - cmd = f'python building_wall_section {ix_section}^^{sec_name}^^{sec_constraint}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='building_wall_section', cmd_type='None') - - -def constraints(tao, who, *, verbose=False, as_dict=True, raises=True): - """ - - Output optimization data and variable parameters that contribute to the merit function. - - Parameters - ---------- - who - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python constraints {who} - - Where: - {who} is one of: "data" or "var" - - Data constraints output is: - data name - constraint type - evaluation element name - start element name - end/reference element name - measured value - ref value (only relavent if global%opt_with_ref = T) - model value - base value (only relavent if global%opt_with_base = T) - weight - merit value - location where merit is evaluated (if there is a range) - Var constraints output is: - var name - Associated varible attribute - meas value - ref value (only relavent if global%opt_with_ref = T) - model value - base value (only relavent if global%opt_with_base = T) - weight - merit value - dmerit/dvar - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - who: data - - Example: 2 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - who:var - - """ - cmd = f'python constraints {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='constraints', cmd_type='string_list') - - -def da_aperture(tao, *, ix_uni='', verbose=False, as_dict=True, raises=True): - """ - - Output dynamic aperture data - - Parameters - ---------- - ix_uni : optional - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python da_aperture {ix_uni} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - - """ - cmd = f'python da_aperture {ix_uni}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='da_aperture', cmd_type='string_list') - - -def da_params(tao, *, ix_uni='', verbose=False, as_dict=True, raises=True): - """ - - Output dynamic aperture input parameters - - Parameters - ---------- - ix_uni : optional - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python da_params {ix_uni} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - - """ - cmd = f'python da_params {ix_uni}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='da_params', cmd_type='string_list') - - -def data(tao, d2_name, d1_name, *, ix_uni='', dat_index='1', verbose=False, as_dict=True, raises=True): - """ - - Output Individual datum parameters. - - Parameters - ---------- - d2_name - d1_name - ix_uni : optional - dat_index : default=1 - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python data {ix_uni}@{d2_name}.{d1_name}[{dat_index}] - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {d2_name} is the name of the d2_data structure the datum is in. - {d1_datum} is the name of the d1_data structure the datum is in. - {dat_index} is the index of the datum. - - Use the "python data-d1" command to get detailed info on a specific d1 array. - - Example: - python data 1@orbit.x[10] - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - ix_uni: - d2_name: twiss - d1_name: end - dat_index: 1 - - Example: 2 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - ix_uni: 1 - d2_name: twiss - d1_name: end - dat_index: 1 - - """ - cmd = f'python data {ix_uni}@{d2_name}.{d1_name}[{dat_index}]' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='data', cmd_type='string_list') - - -def data_d_array(tao, d2_name, d1_name, *, ix_uni='', verbose=False, as_dict=True, raises=True): - """ - - Output list of datums for a given d1_data structure. - - Parameters - ---------- - d2_name - d1_name - ix_uni : optional - - Returns - ------- - datums: list of dicts - Each dict has keys: - 'ix_d1', 'data_type', 'merit_type', - 'ele_ref_name', 'ele_start_name', 'ele_name', - 'meas_value', 'model_value', 'design_value', - 'useit_opt', 'useit_plot', 'good_user', - 'weight', 'exists' - - Notes - ----- - Command syntax: - python data_d_array {ix_uni}@{d2_name}.{d1_name} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {d2_name} is the name of the containing d2_data structure. - {d1_name} is the name of the d1_data structure containing the array of datums. - - Example: - python data_d_array 1@orbit.x - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - ix_uni: 1 - d2_name: twiss - d1_name: end - - """ - cmd = f'python data_d_array {ix_uni}@{d2_name}.{d1_name}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='data_d_array', cmd_type='string_list') - - -def data_d1_array(tao, d2_datum, *, ix_uni='', verbose=False, as_dict=True, raises=True): - """ - - Output list of d1 arrays for a given data_d2. - - Parameters - ---------- - d2_datum - ix_uni : optional - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python data_d1_array {d2_datum} - - {d2_datum} should be of the form - {ix_uni}@{d2_datum_name} - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - ix_uni: 1 - d2_datum: twiss - - """ - cmd = f'python data_d1_array {d2_datum}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='data_d1_array', cmd_type='string_list') - - -def data_d2(tao, d2_name, *, ix_uni='', verbose=False, as_dict=True, raises=True): - """ - - Output information on a d2_datum. - - Parameters - ---------- - d2_name - ix_uni : optional - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python data_d2 {ix_uni}@{d2_name} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {d2_name} is the name of the d2_data structure. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - ix_uni: 1 - d2_name: twiss - - """ - cmd = f'python data_d2 {ix_uni}@{d2_name}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='data_d2', cmd_type='string_list') - - -def data_d2_array(tao, ix_uni, *, verbose=False, as_dict=True, raises=True): - """ - - Output data d2 info for a given universe. - - Parameters - ---------- - ix_uni - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python data_d2_array {ix_uni} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - - Example: - python data_d2_array 1 - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ix_uni : 1 - - """ - cmd = f'python data_d2_array {ix_uni}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='data_d2_array', cmd_type='string_list') - - -def data_d2_create(tao, d2_name, n_d1_data, d_data_arrays_name_min_max, *, ix_uni='', verbose=False, as_dict=True, raises=True): - """ - - Create a d2 data structure along with associated d1 and data arrays. - - Parameters - ---------- - d2_name - n_d1_data - d_data_arrays_name_min_max - ix_uni : optional - - Returns - ------- - None - - Notes - ----- - Command syntax: - python data_d2_create {ix_uni}@{d2_name}^^{n_d1_data}^^{d_data_arrays_name_min_max} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {d2_name} is the name of the d2_data structure to create. - {n_d1_data} is the number of associated d1 data structures. - {d_data_arrays_name_min_max} has the form - {name1}^^{lower_bound1}^^{upper_bound1}^^....^^{nameN}^^{lower_boundN}^^{upper_boundN} - where {name} is the data array name and {lower_bound} and {upper_bound} are the bounds of the array. - - Example: - python data_d2_create 2@orbit^^2^^x^^0^^45^^y^^1^^47 - This example creates a d2 data structure called "orbit" with - two d1 structures called "x" and "y". - - The "x" d1 structure has an associated data array with indexes in the range [0, 45]. - The "y" d1 structure has an associated data arrray with indexes in the range [1, 47]. - - Use the "set data" command to set created datum parameters. - - Note: When setting multiple data parameters, - temporarily toggle s%global%lattice_calc_on to False - ("set global lattice_calc_on = F") to prevent Tao trying to - evaluate the partially created datum and generating unwanted error messages. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - ix_uni: 1 - d2_name: orbit - n_d1_data: 2 - d_data_arrays_name_min_max: x^^0^^45^^y^^1^^47 - - """ - cmd = f'python data_d2_create {ix_uni}@{d2_name}^^{n_d1_data}^^{d_data_arrays_name_min_max}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='data_d2_create', cmd_type='None') - - -def data_d2_destroy(tao, d2_name, *, ix_uni='', verbose=False, as_dict=True, raises=True): - """ - - Destroy a d2 data structure along with associated d1 and data arrays. - - Parameters - ---------- - d2_name - ix_uni : optional - - Returns - ------- - None - - Notes - ----- - Command syntax: - python data_d2_destroy {ix_uni}@{d2_name} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {d2_name} is the name of the d2_data structure to destroy. - - Example: - python data_d2_destroy 2@orbit - This destroys the orbit d2_data structure in universe 2. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - d2_name: orbit - - """ - cmd = f'python data_d2_destroy {ix_uni}@{d2_name}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='data_d2_destroy', cmd_type='None') - - -def data_parameter(tao, data_array, parameter, *, verbose=False, as_dict=True, raises=True): - """ - - Output an array of values for a particular datum parameter for a given array of datums, - - Parameters - ---------- - data_array - parameter - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python data_parameter {data_array} {parameter} - - {parameter} may be any tao_data_struct parameter. - Example: - python data_parameter orbit.x model_value - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - data_array: twiss.end - parameter: model_value - - """ - cmd = f'python data_parameter {data_array} {parameter}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='data_parameter', cmd_type='string_list') - - -def data_set_design_value(tao, *, verbose=False, as_dict=True, raises=True): - """ - - Set the design (and base & model) values for all datums. - - Returns - ------- - None - - Notes - ----- - Command syntax: - python data_set_design_value - - Example: - python data_set_design_value - - Note: Use the "data_d2_create" and "datum_create" first to create datums. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - - """ - cmd = f'python data_set_design_value' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='data_set_design_value', cmd_type='None') - - -def datum_create(tao, datum_name, data_type, *, ele_ref_name='', ele_start_name='', ele_name='', merit_type='', meas='0', good_meas='F', ref='0', good_ref='F', weight='0', good_user='T', data_source='lat', eval_point='END', s_offset='0', ix_bunch='0', invalid_value='0', spin_axis_n0_1='', spin_axis_n0_2='', spin_axis_n0_3='', spin_axis_l_1='', spin_axis_l_2='', spin_axis_l_3='', verbose=False, as_dict=True, raises=True): - """ - - Create a datum. - - Parameters - ---------- - datum_name ! EG: orb.x[3] - data_type ! EG: orbit.x - ele_ref_name : optional - ele_start_name : optional - ele_name : optional - merit_type : optional - meas : default=0 - good_meas : default=F - ref : default=0 - good_ref : default=F - weight : default=0 - good_user : default=T - data_source : default=lat - eval_point : default=END - s_offset : default=0 - ix_bunch : default=0 - invalid_value : default=0 - spin_axis%n0(1) : optional - spin_axis%n0(2) : optional - spin_axis%n0(3) : optional - spin_axis%l(1) : optional - spin_axis%l(2) : optional - spin_axis%l(3) : optional - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python datum_create {datum_name}^^{data_type}^^{ele_ref_name}^^{ele_start_name}^^ - {ele_name}^^{merit_type}^^{meas}^^{good_meas}^^{ref}^^ - {good_ref}^^{weight}^^{good_user}^^{data_source}^^ - {eval_point}^^{s_offset}^^{ix_bunch}^^{invalid_value}^^ - {spin_axis%n0(1)}^^{spin_axis%n0(2)}^^{spin_axis%n0(3)}^^ - {spin_axis%l(1)}^^{spin_axis%l(2)}^^{spin_axis%l(3)} - - Note: The 3 values for spin_axis%n0, as a group, are optional. - Also the 3 values for spin_axis%l are, as a group, optional. - Note: Use the "data_d2_create" first to create a d2 structure with associated d1 arrays. - Note: After creating all your datums, use the "data_set_design_value" routine to - set the design (and model) values. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - datum_name: twiss.end[6] - data_type: beta.y - ele_ref_name: - ele_start_name: - ele_name: P1 - merit_type: target - meas: 0 - good_meas: T - ref: 0 - good_ref: T - weight: 0.3 - good_user: T - data_source: lat - eval_point: END - s_offset: 0 - ix_bunch: 1 - invalid_value: 0 - - """ - cmd = f'python datum_create {datum_name}^^{data_type}^^{ele_ref_name}^^{ele_start_name}^^{ele_name}^^{merit_type}^^{meas}^^{good_meas}^^{ref}^^{good_ref}^^{weight}^^{good_user}^^{data_source}^^{eval_point}^^{s_offset}^^{ix_bunch}^^{invalid_value}^^{spin_axis_n0_1}^^{spin_axis_n0_2}^^{spin_axis_n0_3}^^{spin_axis_l_1}^^{spin_axis_l_2}^^{spin_axis_l_3}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='datum_create', cmd_type='string_list') - - -def datum_has_ele(tao, datum_type, *, verbose=False, as_dict=True, raises=True): - """ - - Output whether a datum type has an associated lattice element - - Parameters - ---------- - datum_type - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python datum_has_ele {datum_type} - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - datum_type: twiss.end - - """ - cmd = f'python datum_has_ele {datum_type}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='datum_has_ele', cmd_type='string_list') - - -def derivative(tao, *, verbose=False, as_dict=True, raises=True): - """ - - Output optimization derivatives - - Returns - ------- - out : dict - Dictionary with keys corresponding to universe indexes (int), - with dModel_dVar as the value: - np.ndarray with shape (n_data, n_var) - - Notes - ----- - Command syntax: - python derivative - - Note: To save time, this command will not recalculate derivatives. - Use the "derivative" command beforehand to recalcuate if needed. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - - """ - cmd = f'python derivative' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='derivative', cmd_type='string_list') - - -def ele_ac_kicker(tao, ele_id, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element ac_kicker parameters - - Parameters - ---------- - ele_id - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:ac_kicker {ele_id}|{which} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - - Example: - python ele:ac_kicker 3@1>>7|model - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>1 - which: model - - """ - cmd = f'python ele:ac_kicker {ele_id}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_ac_kicker', cmd_type='string_list') - - -def ele_cartesian_map(tao, ele_id, index, who, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element cartesian_map parameters - - Parameters - ---------- - ele_id - index - who - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:cartesian_map {ele_id}|{which} {index} {who} - - Where: - {ele_id} is an element name or index - {which} is one of: "model", "base" or "design" - {index} is the index number in the ele%cartesian_map(:) array - {who} is one of: "base", or "terms" - - Example: - python ele:cartesian_map 3@1>>7|model 2 base - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field - args: - ele_id: 1@0>>1 - which: model - index: 1 - who: base - - """ - cmd = f'python ele:cartesian_map {ele_id}|{which} {index} {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_cartesian_map', cmd_type='string_list') - - -def ele_chamber_wall(tao, ele_id, index, who, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element beam chamber wall parameters - - Parameters - ---------- - ele_id - index - who - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:chamber_wall {ele_id}|{which} {index} {who} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - {index} is index of the wall. - {who} is one of: - "x" ! Return min/max in horizontal plane - "y" ! Return min/max in vertical plane - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d - args: - ele_id: 1@0>>1 - which: model - index: 1 - who: x - - """ - cmd = f'python ele:chamber_wall {ele_id}|{which} {index} {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_chamber_wall', cmd_type='string_list') - - -def ele_control_var(tao, ele_id, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output list of element control variables. - Used for group, overlay and ramper type elements. - - Parameters - ---------- - ele_id - which : default=model - - Returns - ------- - dict of attributes and values - - Notes - ----- - Command syntax: - python ele:control_var {ele_id}|{which} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - - Example: - python ele:control_var 3@1>>7|model - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>873 - which: model - - """ - cmd = f'python ele:control_var {ele_id}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_control_var', cmd_type='string_list') - - -def ele_cylindrical_map(tao, ele_id, index, who, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element cylindrical_map - - Parameters - ---------- - ele_id - index - who - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:cylindrical_map {ele_id}|{which} {index} {who} - - Where - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - {index} is the index number in the ele%cylindrical_map(:) array - {who} is one of: "base", or "terms" - - Example: - python ele:cylindrical_map 3@1>>7|model 2 base - This gives map #2 of element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field - args: - ele_id: 1@0>>5 - which: model - index: 1 - who: base - - """ - cmd = f'python ele:cylindrical_map {ele_id}|{which} {index} {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_cylindrical_map', cmd_type='string_list') - - -def ele_elec_multipoles(tao, ele_id, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element electric multipoles - - Parameters - ---------- - ele_id - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:elec_multipoles {ele_id}|{which} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - - Example: - python ele:elec_multipoles 3@1>>7|model - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>1 - which: model - - """ - cmd = f'python ele:elec_multipoles {ele_id}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_elec_multipoles', cmd_type='string_list') - - -def ele_floor(tao, ele_id, *, which='model', where='end', verbose=False, as_dict=True, raises=True): - """ - - Output element floor coordinates. The output gives four lines. "Reference" is - without element misalignments and "Actual" is with misalignments. The lines with "-W" - give the W matrix. The exception is that if ele is a multipass_lord, there will be 4*N - lines where N is the number of slaves. - - Parameters - ---------- - ele_id - which : default=model - where : default=end - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:floor {ele_id}|{which} {where} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - {where} is an optional argument which, if present, is one of - beginning ! Upstream end - center ! Middle of element - end ! Downstream end (default) - Note: {where} ignored for photonic elements crystal, mirror, and multilayer_mirror. - - Example: - python ele:floor 3@1>>7|model - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>1 - which: model - where: - - Example: 2 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>1 - which: model - where: center - - """ - cmd = f'python ele:floor {ele_id}|{which} {where}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_floor', cmd_type='string_list') - - -def ele_gen_attribs(tao, ele_id, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element general attributes - - Parameters - ---------- - ele_id - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:gen_attribs {ele_id}|{which} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - - Example: - python ele:gen_attribs 3@1>>7|model - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>1 - which: model - - """ - cmd = f'python ele:gen_attribs {ele_id}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_gen_attribs', cmd_type='string_list') - - -def ele_gen_grad_map(tao, ele_id, index, who, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element gen_grad_map - - Parameters - ---------- - ele_id - index - who - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:gen_grad_map {ele_id}|{which} {index} {who} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - {index} is the index number in the ele%gen_grad_map(:) array - {who} is one of: "base", or "derivs". - - Example: - python ele:gen_grad_map 3@1>>7|model 2 base - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field - args: - ele_id: 1@0>>9 - which: model - index: 1 - who: derivs - - """ - cmd = f'python ele:gen_grad_map {ele_id}|{which} {index} {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_gen_grad_map', cmd_type='string_list') - - -def ele_grid_field(tao, ele_id, index, who, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element grid_field - - Parameters - ---------- - ele_id - index - who - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:grid_field {ele_id}|{which} {index} {who} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - {index} is the index number in the ele%grid_field(:) array. - {who} is one of: "base", or "points" - - Example: - python ele:grid_field 3@1>>7|model 2 base - This gives grid #2 of element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_grid - args: - ele_id: 1@0>>1 - which: model - index: 1 - who: base - - """ - cmd = f'python ele:grid_field {ele_id}|{which} {index} {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_grid_field', cmd_type='string_list') - - -def ele_head(tao, ele_id, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output "head" Element attributes - - Parameters - ---------- - ele_id - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:head {ele_id}|{which} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - - Example: - python ele:head 3@1>>7|model - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>1 - which: model - - """ - cmd = f'python ele:head {ele_id}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_head', cmd_type='string_list') - - -def ele_lord_slave(tao, ele_id, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output the lord/slave tree of an element. - - Parameters - ---------- - ele_id - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:lord_slave {ele_id}|{which} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - - Example: - python ele:lord_slave 3@1>>7|model - This gives lord and slave info on element number 7 in branch 1 of universe 3. - Note: The lord/slave info is independent of the setting of {which}. - - The output is a number of lines, each line giving information on an element (element index, etc.). - Some lines begin with the word "Element". - After each "Element" line, there are a number of lines (possibly zero) that begin with the word "Slave or "Lord". - These "Slave" and "Lord" lines are the slaves and lords of the "Element" element. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>1 - which: model - - """ - cmd = f'python ele:lord_slave {ele_id}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_lord_slave', cmd_type='string_list') - - -def ele_mat6(tao, ele_id, *, which='model', who='mat6', verbose=False, as_dict=True, raises=True): - """ - - Output element mat6 - - Parameters - ---------- - ele_id - which : default=model - who : default=mat6 - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:mat6 {ele_id}|{which} {who} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - {who} is one of: "mat6", "vec0", or "err" - - Example: - python ele:mat6 3@1>>7|model mat6 - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>1 - which: model - who: mat6 - - """ - cmd = f'python ele:mat6 {ele_id}|{which} {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_mat6', cmd_type='string_list') - - -def ele_methods(tao, ele_id, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element methods - - Parameters - ---------- - ele_id - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:methods {ele_id}|{which} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - - Example: - python ele:methods 3@1>>7|model - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>1 - which: model - - """ - cmd = f'python ele:methods {ele_id}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_methods', cmd_type='string_list') - - -def ele_multipoles(tao, ele_id, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element multipoles - - Parameters - ---------- - ele_id - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:multipoles {ele_id}|{which} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - - Example: - python ele:multipoles 3@1>>7|model - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>1 - which: model - - """ - cmd = f'python ele:multipoles {ele_id}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_multipoles', cmd_type='string_list') - - -def ele_orbit(tao, ele_id, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element orbit - - Parameters - ---------- - ele_id - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:orbit {ele_id}|{which} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - - Example: - python ele:orbit 3@1>>7|model - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>1 - which: model - - """ - cmd = f'python ele:orbit {ele_id}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_orbit', cmd_type='string_list') - - -def ele_param(tao, ele_id, who, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output lattice element parameter - - Parameters - ---------- - ele_id - who - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:param {ele_id}|{which} {who} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - {who} values are the same as {who} values for "python lat_list". - Note: Here {who} must be a single parameter and not a list. - - Example: - python ele:param 3@1>>7|model e_tot - This gives E_tot of element number 7 in branch 1 of universe 3. - - Note: On output the {variable} component will always be "F" (since this - command cannot tell if a parameter is allowed to vary). - - Also see: "python lat_list". - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon - args: - ele_id: 1@0>>1 - which: model - who: orbit.vec.1 - - """ - cmd = f'python ele:param {ele_id}|{which} {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_param', cmd_type='string_list') - - -def ele_photon(tao, ele_id, who, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element photon parameters - - Parameters - ---------- - ele_id - who - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:photon {ele_id}|{which} {who} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - {who} is one of: "base", "material", or "curvature" - - Example: - python ele:photon 3@1>>7|model base - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon - args: - ele_id: 1@0>>1 - which: model - who: base - - """ - cmd = f'python ele:photon {ele_id}|{which} {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_photon', cmd_type='string_list') - - -def ele_spin_taylor(tao, ele_id, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element spin_taylor parameters - - Parameters - ---------- - ele_id - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:spin_taylor {ele_id}|{which} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - - Example: - python ele:spin_taylor 3@1>>7|model - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_spin - args: - ele_id: 1@0>>2 - which: model - - """ - cmd = f'python ele:spin_taylor {ele_id}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_spin_taylor', cmd_type='string_list') - - -def ele_taylor(tao, ele_id, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element taylor map - - Parameters - ---------- - ele_id - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:taylor {ele_id}|{which} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - - Example: - python ele:taylor 3@1>>7|model - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_taylor - args: - ele_id: 1@0>>34 - which: model - - """ - cmd = f'python ele:taylor {ele_id}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_taylor', cmd_type='string_list') - - -def ele_twiss(tao, ele_id, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element Twiss parameters - - Parameters - ---------- - ele_id - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:twiss {ele_id}|{which} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - - Example: - python ele:twiss 3@1>>7|model - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>1 - which: model - - """ - cmd = f'python ele:twiss {ele_id}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_twiss', cmd_type='string_list') - - -def ele_wake(tao, ele_id, who, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element wake. - - Parameters - ---------- - ele_id - who - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:wake {ele_id}|{which} {who} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - {Who} is one of: - "sr_long" "sr_long_table" - "sr_trans" "sr_trans_table" - "lr_mode_table" "base" - - Example: - python ele:wake 3@1>>7|model - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wake - args: - ele_id: 1@0>>1 - which: model - who: sr_long - - """ - cmd = f'python ele:wake {ele_id}|{which} {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_wake', cmd_type='string_list') - - -def ele_wall3d(tao, ele_id, index, who, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output element wall3d parameters. - - Parameters - ---------- - ele_id - index - who - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ele:wall3d {ele_id}|{which} {index} {who} - - Where: - {ele_id} is an element name or index. - {which} is one of: "model", "base" or "design" - {index} is the index number in the ele%wall3d(:) array (size obtained from "ele:head"). - {who} is one of: "base", or "table". - Example: - python ele:wall3d 3@1>>7|model 2 base - This gives element number 7 in branch 1 of universe 3. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d - args: - ele_id: 1@0>>1 - which: model - index: 1 - who: table - - """ - cmd = f'python ele:wall3d {ele_id}|{which} {index} {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ele_wall3d', cmd_type='string_list') - - -def evaluate(tao, expression, *, flags='-array_out', verbose=False, as_dict=True, raises=True): - """ - - Output the value of an expression. The result may be a vector. - - Parameters - ---------- - expression - flags : default=-array_out - If -array_out, the output will be available in the tao_c_interface_com%c_real. - - Returns - ------- - string_list - if '-array_out' not in flags - real_array - if '-array_out' in flags - - Notes - ----- - Command syntax: - python evaluate {flags} {expression} - - Where: - Optional {flags} are: - -array_out : If present, the output will be available in the tao_c_interface_com%c_real. - {expression} is expression to be evaluated. - - Example: - python evaluate 3+data::cbar.11[1:10]|model - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - expression: data::cbar.11[1:10]|model - - """ - cmd = f'python evaluate {flags} {expression}' - if verbose: print(cmd) - if '-array_out' not in flags: - return __execute(tao, cmd, as_dict, raises, method_name='evaluate', cmd_type='string_list') - if '-array_out' in flags: - return __execute(tao, cmd, as_dict, raises, method_name='evaluate', cmd_type='real_array') - - -def em_field(tao, ele_id, x, y, z, t_or_z, *, which='model', verbose=False, as_dict=True, raises=True): - """ - - Output EM field at a given point generated by a given element. - - Parameters - ---------- - ele_id - x - y - z - t_or_z - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python em_field {ele_id}|{which} {x} {y} {z} {t_or_z} - - Where: - {which} is one of: "model", "base" or "design" - {x}, {y} -- Transverse coords. - {z} -- Longitudinal coord with respect to entrance end of element. - {t_or_z} -- time or phase space z depending if lattice is setup for absolute time tracking. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele_id: 1@0>>22 - which: model - x: 0 - y: 0 - z: 0 - t_or_z: 0 - - """ - cmd = f'python em_field {ele_id}|{which} {x} {y} {z} {t_or_z}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='em_field', cmd_type='string_list') - - -def enum(tao, enum_name, *, verbose=False, as_dict=True, raises=True): - """ - - Output list of possible values for enumerated numbers. - - Parameters - ---------- - enum_name - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python enum {enum_name} - - Example: - python enum tracking_method - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - enum_name: tracking_method - - """ - cmd = f'python enum {enum_name}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='enum', cmd_type='string_list') - - -def floor_plan(tao, graph, *, verbose=False, as_dict=True, raises=True): - """ - - Output (x,y) points and other information that can be used for drawing a floor_plan. - - Parameters - ---------- - graph - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python floor_plan {graph} - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - graph: r13.g - - """ - cmd = f'python floor_plan {graph}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='floor_plan', cmd_type='string_list') - - -def floor_orbit(tao, graph, *, verbose=False, as_dict=True, raises=True): - """ - - Output (x, y) coordinates for drawing the particle orbit on a floor plan. - - Parameters - ---------- - graph - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python floor_orbit {graph} - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_floor_orbit - args: - graph: r33.g - - """ - cmd = f'python floor_orbit {graph}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='floor_orbit', cmd_type='string_list') - - -def tao_global(tao, *, verbose=False, as_dict=True, raises=True): - """ - - Output global parameters. - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python global - - Output syntax is parameter list form. See documentation at the beginning of this file. - - Note: The follow is intentionally left out: - optimizer_allow_user_abort - quiet - single_step - prompt_color - prompt_string - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - - """ - cmd = f'python global' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='tao_global', cmd_type='string_list') - - -def global_optimization(tao, *, verbose=False, as_dict=True, raises=True): - """ - - Output optimization parameters. - Also see global:opti_de. - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python global:optimization - - Output syntax is parameter list form. See documentation at the beginning of this file. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - - """ - cmd = f'python global:optimization' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='global_optimization', cmd_type='string_list') - - -def global_opti_de(tao, *, verbose=False, as_dict=True, raises=True): - """ - - Output DE optimization parameters. - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python global:opti_de - - Output syntax is parameter list form. See documentation at the beginning of this file. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - - """ - cmd = f'python global:opti_de' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='global_opti_de', cmd_type='string_list') - - -def help(tao, *, verbose=False, as_dict=True, raises=True): - """ - - Output list of "help xxx" topics - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python help - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - - """ - cmd = f'python help' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='help', cmd_type='string_list') - - -def inum(tao, who, *, verbose=False, as_dict=True, raises=True): - """ - - Output list of possible values for an INUM parameter. - For example, possible index numbers for the branches of a lattice. - - Parameters - ---------- - who - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python inum {who} - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - who: ix_universe - - """ - cmd = f'python inum {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='inum', cmd_type='string_list') - - -def lat_calc_done(tao, branch_name, *, verbose=False, as_dict=True, raises=True): - """ - - Output if a lattice recalculation has been proformed since the last - time "python lat_calc_done" was called. - - Parameters - ---------- - branch_name - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python lat_calc_done - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - branch_name: 1@0 - - """ - cmd = f'python lat_calc_done' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='lat_calc_done', cmd_type='string_list') - - -def lat_ele_list(tao, *, branch_name='', verbose=False, as_dict=True, raises=True): - """ - - Output lattice element list. - - Parameters - ---------- - branch_name : optional - - Returns - ------- - list of str of element names - - Notes - ----- - Command syntax: - python lat_ele_list {branch_name} - - {branch_name} should have the form: - {ix_uni}@{ix_branch} - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - branch_name: 1@0 - - """ - cmd = f'python lat_ele_list {branch_name}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='lat_ele_list', cmd_type='string_list') - - -def lat_branch_list(tao, *, ix_uni='', verbose=False, as_dict=True, raises=True): - """ - - Output lattice branch list - - Parameters - ---------- - ix_uni : optional - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python lat_branch_list {ix_uni} - - Output syntax: - branch_index;branch_name;n_ele_track;n_ele_max - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ix_uni: 1 - - """ - cmd = f'python lat_branch_list {ix_uni}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='lat_branch_list', cmd_type='string_list') - - -def lat_list(tao, elements, who, *, ix_uni='', ix_branch='', which='model', flags='-array_out -track_only', verbose=False, as_dict=True, raises=True): - """ - - Output list of parameters at ends of lattice elements - - Parameters - ---------- - elements - who - ix_uni : optional - ix_branch : optional - which : default=model - flags : optional, default=-array_out -track_only - - Returns - ------- - string_list - if ('-array_out' not in flags) or (who in ['ele.name', 'ele.key']) - integer_array - if '-array_out' in flags and who in ['orbit.state', 'ele.ix_ele'] - real_array - if ('-array_out' in flags) or ('real:' in who) - - Notes - ----- - Command syntax: - python lat_list {flags} {ix_uni}@{ix_branch}>>{elements}|{which} {who} - - Where: - Optional {flags} are: - -no_slaves : If present, multipass_slave and super_slave elements will not be matched to. - -track_only : If present, lord elements will not be matched to. - -index_order : If present, order elements by element index instead of the standard s-position. - -array_out : If present, the output will be available in the tao_c_interface_com%c_real or - tao_c_interface_com%c_integer arrays. See the code below for when %c_real vs %c_integer is used. - Note: Only a single {who} item permitted when -array_out is present. - - {which} is one of: "model", "base" or "design" - - {who} is a comma deliminated list of: - orbit.floor.x, orbit.floor.y, orbit.floor.z ! Floor coords at particle orbit. - orbit.spin.1, orbit.spin.2, orbit.spin.3, - orbit.vec.1, orbit.vec.2, orbit.vec.3, orbit.vec.4, orbit.vec.5, orbit.vec.6, - orbit.t, orbit.beta, - orbit.state, ! Note: state is an integer. alive$ = 1, anything else is lost. - orbit.energy, orbit.pc, - ele.name, ele.key, ele.ix_ele, ele.ix_branch - ele.a.beta, ele.a.alpha, ele.a.eta, ele.a.etap, ele.a.gamma, ele.a.phi, - ele.b.beta, ele.b.alpha, ele.b.eta, ele.b.etap, ele.b.gamma, ele.b.phi, - ele.x.eta, ele.x.etap, - ele.y.eta, ele.y.etap, - ele.ref_time, ele.ref_time_start - ele.s, ele.l - ele.e_tot, ele.p0c - ele.mat6 ! Output: mat6(1,:), mat6(2,:), ... mat6(6,:) - ele.vec0 ! Output: vec0(1), ... vec0(6) - ele.{attribute} Where {attribute} is a Bmad syntax element attribute. (EG: ele.beta_a, ele.k1, etc.) - ele.c_mat ! Output: c_mat11, c_mat12, c_mat21, c_mat22. - ele.gamma_c ! Parameter associated with coupling c-matrix. - - {elements} is a string to match element names to. - Use "*" to match to all elements. - - Examples: - python lat_list -track 3@0>>Q*|base ele.s,orbit.vec.2 - python lat_list 3@0>>Q*|base real:ele.s - - Also see: "python ele:param" - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ix_uni: 1 - ix_branch: 0 - elements: Q* - which: model - who: orbit.floor.x - - Example: 2 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ix_uni: 1 - ix_branch: 0 - elements: Q* - which: design - who: ele.ix_ele - - """ - cmd = f'python lat_list {flags} {ix_uni}@{ix_branch}>>{elements}|{which} {who}' - if verbose: print(cmd) - if ('-array_out' not in flags) or (who in ['ele.name', 'ele.key']): - return __execute(tao, cmd, as_dict, raises, method_name='lat_list', cmd_type='string_list') - if '-array_out' in flags and who in ['orbit.state', 'ele.ix_ele']: - return __execute(tao, cmd, as_dict, raises, method_name='lat_list', cmd_type='integer_array') - if ('-array_out' in flags) or ('real:' in who) : - return __execute(tao, cmd, as_dict, raises, method_name='lat_list', cmd_type='real_array') - - -def lat_param_units(tao, param_name, *, verbose=False, as_dict=True, raises=True): - """ - - Output units of a parameter associated with a lattice or lattice element. - - Parameters - ---------- - param_name - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python lat_param_units {param_name} - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - param_name: L - - """ - cmd = f'python lat_param_units {param_name}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='lat_param_units', cmd_type='string_list') - - -def matrix(tao, ele1_id, ele2_id, *, verbose=False, as_dict=True, raises=True): - """ - - Output matrix value from the exit end of one element to the exit end of the other. - - Parameters - ---------- - ele1_id - ele2_id - - Returns - ------- - dict with keys: - 'mat6' : np.array of shape (6,6) - 'vec6' : np.array of shape(6) - - Notes - ----- - Command syntax: - python matrix {ele1_id} {ele2_id} - - Where: - {ele1_id} is the start element. - {ele2_id} is the end element. - If {ele2_id} = {ele1_id}, the 1-turn transfer map is computed. - Note: {ele2_id} should just be an element name or index without universe, branch, or model/base/design specification. - - Example: - python matrix 2@1>>q01w|design q02w - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele1_id: 1@0>>q01w|design - ele2_id: q02w - - """ - cmd = f'python matrix {ele1_id} {ele2_id}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='matrix', cmd_type='string_list') - - -def merit(tao, *, verbose=False, as_dict=True, raises=True): - """ - - Output merit value. - - Returns - ------- - merit: float - Value of the merit function - - Notes - ----- - Command syntax: - python merit - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - - """ - cmd = f'python merit' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='merit', cmd_type='string_list') - - -def orbit_at_s(tao, *, ix_uni='', ele='', s_offset='', which='model', verbose=False, as_dict=True, raises=True): - """ - - Output twiss at given s position. - - Parameters - ---------- - ix_uni : optional - ele : optional - s_offset : optional - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python orbit_at_s {ix_uni}@{ele}->{s_offset}|{which} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {ele} is an element name or index. Default at the Beginning element at start of branch 0. - {s_offset} is the offset of the evaluation point from the downstream end of ele. Default is 0. - If {s_offset} is present, the preceeding "->" sign must be present. EG: Something like "23|model" will - {which} is one of: "model", "base" or "design". - - Example: - python orbit_at_s Q10->0.4|model ! Orbit at 0.4 meters from Q10 element exit end in model lattice. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ix_uni: 1 - ele: 10 - s_offset: 0.7 - which: model - - """ - cmd = f'python orbit_at_s {ix_uni}@{ele}->{s_offset}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='orbit_at_s', cmd_type='string_list') - - -def place_buffer(tao, *, verbose=False, as_dict=True, raises=True): - """ - - Output the place command buffer and reset the buffer. - The contents of the buffer are the place commands that the user has issued. - See the Tao manual for more details. - - Returns - ------- - None - - Notes - ----- - Command syntax: - python place_buffer - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - - """ - cmd = f'python place_buffer' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='place_buffer', cmd_type='None') - - -def plot_curve(tao, curve_name, *, verbose=False, as_dict=True, raises=True): - """ - - Output curve information for a plot. - - Parameters - ---------- - curve_name - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python plot_curve {curve_name} - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - curve_name: r13.g.a - - """ - cmd = f'python plot_curve {curve_name}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='plot_curve', cmd_type='string_list') - - -def plot_lat_layout(tao, ix_uni: 1, ix_branch: 0, *, verbose=False, as_dict=True, raises=True): - """ - - Output plot Lat_layout info - - Parameters - ---------- - ix_uni: 1 - ix_branch: 0 - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python plot_lat_layout {ix_uni}@{ix_branch} - - Note: The returned list of element positions is not ordered in increasing - longitudinal position. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ix_uni: 1 - ix_branch: 0 - - """ - cmd = f'python plot_lat_layout {ix_uni}@{ix_branch}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='plot_lat_layout', cmd_type='string_list') - - -def plot_list(tao, r_or_g, *, verbose=False, as_dict=True, raises=True): - """ - - Output list of plot templates or plot regions. - - Parameters - ---------- - r_or_g - - Returns - ------- - if r_or_g == 't' - dict with template_name:index - if r_or_g == 'r' - list of dicts with keys: - region - ix - plot_name - visible - x1, x2, y1, y1 - - Notes - ----- - Command syntax: - python plot_list {r_or_g} - - where "{r/g}" is: - "r" ! list regions of the form ix;region_name;plot_name;visible;x1;x2;y1;y2 - "t" ! list template plots of the form ix;name - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - r_or_g: r - - """ - cmd = f'python plot_list {r_or_g}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='plot_list', cmd_type='string_list') - - -def plot_graph(tao, graph_name, *, verbose=False, as_dict=True, raises=True): - """ - - Output graph info. - - Parameters - ---------- - graph_name - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python plot_graph {graph_name} - - {graph_name} is in the form: - {p_name}.{g_name} - where - {p_name} is the plot region name if from a region or the plot name if a template plot. - This name is obtained from the python plot_list command. - {g_name} is the graph name obtained from the python plot1 command. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - graph_name: beta.g - - """ - cmd = f'python plot_graph {graph_name}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='plot_graph', cmd_type='string_list') - - -def plot_histogram(tao, curve_name, *, verbose=False, as_dict=True, raises=True): - """ - - Output plot histogram info. - - Parameters - ---------- - curve_name - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python plot_histogram {curve_name} - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - curve_name: r33.g.x - - """ - cmd = f'python plot_histogram {curve_name}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='plot_histogram', cmd_type='string_list') - - -def plot_template_manage(tao, template_location, template_name, *, n_graph='-1', graph_names='', verbose=False, as_dict=True, raises=True): - """ - - Template plot creation or destruction. - - Parameters - ---------- - template_location - template_name - n_graph : default=-1 - graph_names : default= - - Returns - ------- - None - - Notes - ----- - Command syntax: - python plot_template_manage {template_location}^^{template_name}^^ - {n_graph}^^{graph_names} - - Where: - {template_location} is the location to place or delete a template plot. Use "@Tnnn" syntax for the location. - {template_name} is the name of the template plot. If deleting a plot this name is immaterial. - {n_graph} is the number of associated graphs. If set to -1 then any existing template plot is deleted. - {graph_names} are the names of the graphs. graph_names should be in the form: - graph1_name^^graph2_name^^...^^graphN_name - for N=n_graph names - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - template_location: @T1 - template_name: beta - n_graph: 2 - graph_names: g1^^g2 - - """ - cmd = f'python plot_template_manage {template_location}^^{template_name}^^{n_graph}^^{graph_names}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='plot_template_manage', cmd_type='None') - - -def plot_curve_manage(tao, graph_name, curve_index, curve_name, *, verbose=False, as_dict=True, raises=True): - """ - - Template plot curve creation/destruction - - Parameters - ---------- - graph_name - curve_index - curve_name - - Returns - ------- - None - - Notes - ----- - Command syntax: - python plot_curve_manage {graph_name}^^{curve_index}^^{curve_name} - - If {curve_index} corresponds to an existing curve then this curve is deleted. - In this case the {curve_name} is ignored and does not have to be present. - If {curve_index} does not not correspond to an existing curve, {curve_index} - must be one greater than the number of curves. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - graph_name: beta.g - curve_index: 1 - curve_name: r13.g.a - - """ - cmd = f'python plot_curve_manage {graph_name}^^{curve_index}^^{curve_name}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='plot_curve_manage', cmd_type='None') - - -def plot_graph_manage(tao, plot_name, graph_index, graph_name, *, verbose=False, as_dict=True, raises=True): - """ - - Template plot graph creation/destruction - - Parameters - ---------- - plot_name - graph_index - graph_name - - Returns - ------- - None - - Notes - ----- - Command syntax: - python plot_graph_manage {plot_name}^^{graph_index}^^{graph_name} - - If {graph_index} corresponds to an existing graph then this graph is deleted. - In this case the {graph_name} is ignored and does not have to be present. - If {graph_index} does not not correspond to an existing graph, {graph_index} - must be one greater than the number of graphs. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - plot_name: beta - graph_index: 1 - graph_name: beta.g - - """ - cmd = f'python plot_graph_manage {plot_name}^^{graph_index}^^{graph_name}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='plot_graph_manage', cmd_type='None') - - -def plot_line(tao, region_name, graph_name, curve_name, *, x_or_y='', verbose=False, as_dict=True, raises=True): - """ - - Output points used to construct the "line" associated with a plot curve. - - Parameters - ---------- - region_name - graph_name - curve_name - x_or_y : optional - - Returns - ------- - string_list - if x_or_y == '' - real_array - if x_or_y != '' - - Notes - ----- - Command syntax: - python plot_line {region_name}.{graph_name}.{curve_name} {x_or_y} - - Optional {x-or-y} may be set to "x" or "y" to get the smooth line points x or y - component put into the real array buffer. - Note: The plot must come from a region, and not a template, since no template plots - have associated line data. - Examples: - python plot_line r13.g.a ! String array output. - python plot_line r13.g.a x ! x-component of line points loaded into the real array buffer. - python plot_line r13.g.a y ! y-component of line points loaded into the real array buffer. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting - args: - region_name: beta - graph_name: g - curve_name: a - x_or_y: - - Example: 2 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting - args: - region_name: beta - graph_name: g - curve_name: a - x_or_y: y - - """ - cmd = f'python plot_line {region_name}.{graph_name}.{curve_name} {x_or_y}' - if verbose: print(cmd) - if x_or_y == '': - return __execute(tao, cmd, as_dict, raises, method_name='plot_line', cmd_type='string_list') - if x_or_y != '': - return __execute(tao, cmd, as_dict, raises, method_name='plot_line', cmd_type='real_array') - - -def plot_symbol(tao, region_name, graph_name, curve_name, x_or_y, *, verbose=False, as_dict=True, raises=True): - """ - - Output locations to draw symbols for a plot curve. - - Parameters - ---------- - region_name - graph_name - curve_name - x_or_y - - Returns - ------- - string_list - if x_or_y == '' - real_array - if x_or_y != '' - - Notes - ----- - Command syntax: - python plot_symbol {region_name}.{graph_name}.{curve_name} {x_or_y} - - Optional {x_or_y} may be set to "x" or "y" to get the symbol x or y - positions put into the real array buffer. - Note: The plot must come from a region, and not a template, - since no template plots have associated symbol data. - Examples: - python plot_symbol r13.g.a ! String array output. - python plot_symbol r13.g.a x ! x-component of the symbol positions - loaded into the real array buffer. - python plot_symbol r13.g.a y ! y-component of the symbol positions - loaded into the real array buffer. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting - args: - region_name: r13 - graph_name: g - curve_name: a - x_or_y: - - Example: 2 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting - args: - region_name: r13 - graph_name: g - curve_name: a - x_or_y: y - - """ - cmd = f'python plot_symbol {region_name}.{graph_name}.{curve_name} {x_or_y}' - if verbose: print(cmd) - if x_or_y == '': - return __execute(tao, cmd, as_dict, raises, method_name='plot_symbol', cmd_type='string_list') - if x_or_y != '': - return __execute(tao, cmd, as_dict, raises, method_name='plot_symbol', cmd_type='real_array') - - -def plot_transfer(tao, from_plot, to_plot, *, verbose=False, as_dict=True, raises=True): - """ - - Output transfer plot parameters from the "from plot" to the "to plot" (or plots). - - Parameters - ---------- - from_plot - to_plot - - Returns - ------- - None - - Notes - ----- - Command syntax: - python plot_transfer {from_plot} {to_plot} - - To avoid confusion, use "@Tnnn" and "@Rnnn" syntax for {from_plot}. - If {to_plot} is not present and {from_plot} is a template plot, the "to plots" - are the equivalent region plots with the same name. And vice versa - if {from_plot} is a region plot. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - from_plot: r13 - to_plot: r23 - - """ - cmd = f'python plot_transfer {from_plot} {to_plot}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='plot_transfer', cmd_type='None') - - -def plot1(tao, name, *, verbose=False, as_dict=True, raises=True): - """ - - Output info on a given plot. - - Parameters - ---------- - name - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python plot1 {name} - - {name} should be the region name if the plot is associated with a region. - Output syntax is parameter list form. See documentation at the beginning of this file. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - name: beta - - """ - cmd = f'python plot1 {name}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='plot1', cmd_type='string_list') - - -def ptc_com(tao, *, verbose=False, as_dict=True, raises=True): - """ - - Output Ptc_com structure components. - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ptc_com - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - - """ - cmd = f'python ptc_com' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ptc_com', cmd_type='string_list') - - -def ring_general(tao, *, ix_uni='', ix_branch='', which='model', verbose=False, as_dict=True, raises=True): - """ - - Output lattice branch with closed geometry info (emittances, etc.) - - Parameters - ---------- - ix_uni : optional - ix_branch : optional - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python ring_general {ix_uni}@{ix_branch}|{which} - - where {which} is one of: - model - base - design - Example: - python ring_general 1@0|model - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: +from pytao.util import parsers as _pytao_parsers + + +logger = logging.getLogger(__name__) + + +class Tao(TaoCore): + def __execute( + self, + cmd: str, + as_dict: bool = True, + raises: bool = True, + method_name=None, + cmd_type: str = "string_list", + ): + """ + + A wrapper to handle commonly used options when running a command through tao. + + Parameters + ---------- + cmd : str + The command to run + as_dict : bool, optional + Return string data as a dict? by default True + raises : bool, optional + Raise exception on tao errors? by default True + method_name : str/None, optional + Name of the caller. Required for custom parsers for commands, by + default None + cmd_type : str, optional + The type of data returned by tao in its common memory, by default + "string_list" + + Returns + ------- + Any + Result from running tao. The type of data depends on configuration, but is generally a list of strings, a dict, or a + numpy array. + """ + func_for_type = { + "string_list": self.cmd, + "real_array": self.cmd_real, + "integer_array": self.cmd_integer, + } + func = func_for_type.get(cmd_type, self.cmd) + ret = func(cmd, raises=raises) + special_parser = getattr(_pytao_parsers, f"parse_{method_name}", "") + if special_parser and callable(special_parser): + data = special_parser(ret) + return data + if "string" in cmd_type: + try: + if as_dict: + data = parse_tao_python_data(ret) + else: + data = tao_parameter_dict(ret) + except Exception: + logger.exception("Failed to parse string data. Returning raw value.") + return ret + + return data + + return ret + + def bunch_data(self, ele_id, *, which="model", ix_bunch=1, verbose=False): + """ + Returns bunch data in openPMD-beamphysics format/notation. + + Notes + ----- + Note that Tao's 'write beam' will also write a proper h5 file in this format. + + Expected usage: + data = bunch_data(tao, 'end') + from pmd_beamphysics import ParticleGroup + P = ParicleGroup(data=data) + + + Returns + ------- + data : dict + dict of arrays, with keys 'x', 'px', 'y', 'py', 't', 'pz', + 'status', 'weight', 'z', 'species' + + + Examples + -------- + Example: 1 + init: $ACC_ROOT_DIR/tao/examples/csr_beam_tracking/tao.init + args: + ele_id: end + which: model + ix_bunch: 1 + + """ + + # Get species + stats = self.bunch_params(ele_id, which=which, verbose=verbose) + species = stats["species"] + + dat = {} + for coordinate in ["x", "px", "y", "py", "t", "pz", "p0c", "charge", "state"]: + dat[coordinate] = self.bunch1( + ele_id, + coordinate=coordinate, + which=which, + ix_bunch=ix_bunch, + verbose=verbose, + ) + + # Remove normalizations + p0c = dat.pop("p0c") + + dat["status"] = dat.pop("state") + dat["weight"] = dat.pop("charge") + + # px from Bmad is px/p0c + # pz from Bmad is delta = p/p0c -1. + # pz = sqrt( (delta+1)**2 -px**2 -py**2)*p0c + dat["pz"] = ( + np.sqrt((dat["pz"] + 1) ** 2 - dat["px"] ** 2 - dat["py"] ** 2) * p0c + ) + dat["px"] = dat["px"] * p0c + dat["py"] = dat["py"] * p0c + + # z = 0 by definition + dat["z"] = np.full(len(dat["x"]), 0) + + dat["species"] = species.lower() + + return dat + + def beam(self, ix_branch, *, ix_uni="", verbose=False, as_dict=True, raises=True): + """ + + Output beam parameters that are not in the beam_init structure. + + Parameters + ---------- + ix_uni : optional + ix_branch : "" + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python beam {ix_uni}@{ix_branch} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {ix_branch} is a lattice branch index. Defaults to s%global%default_branch. + + Note: To set beam_init parameters use the "set beam" command. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init + args: + ix_uni: 1 + ix_branch: 0 + + """ + cmd = f"python beam {ix_uni}@{ix_branch}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="beam", cmd_type="string_list" + ) + + def beam_init( + self, ix_branch, *, ix_uni="", verbose=False, as_dict=True, raises=True + ): + """ + + Output beam_init parameters. + + Parameters + ---------- + ix_uni : optional + ix_branch : "" + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python beam_init {ix_uni}@{ix_branch} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {ix_branch} is a lattice branch index. Defaults to s%global%default_branch. + + Note: To set beam_init parameters use the "set beam_init" command + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init + args: + ix_uni: 1 + ix_branch: 0 + + """ + cmd = f"python beam_init {ix_uni}@{ix_branch}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="beam_init", cmd_type="string_list" + ) + + def bmad_com(self, *, verbose=False, as_dict=True, raises=True): + """ + + Output bmad_com structure components. + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python bmad_com + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + + """ + cmd = f"python bmad_com" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="bmad_com", cmd_type="string_list" + ) + + def branch1(self, ix_uni, ix_branch, *, verbose=False, as_dict=True, raises=True): + """ + + Output lattice branch information for a particular lattice branch. + + Parameters + ---------- + ix_uni : "" + ix_branch : "" + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python branch1 {ix_uni}@{ix_branch} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {ix_branch} is a lattice branch index. Defaults to s%global%default_branch. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ix_uni: 1 + ix_branch: 0 + + """ + cmd = f"python branch1 {ix_uni}@{ix_branch}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="branch1", cmd_type="string_list" + ) + + def bunch_comb( + self, + who, + *, + ix_uni="", + ix_branch="", + ix_bunch="1", + flags="-array_out", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Outputs bunch parameters at a comb point. + Also see the "write bunch_comb" and "show bunch -comb" commands. + + Parameters + ---------- + who + ix_uni : optional + ix_branch : optional + ix_bunch : default=1 + flags : default=-array_out + + Returns + ------- + string_list + if '-array_out' not in flags + real_array + if '-array_out' in flags + + Notes + ----- + Command syntax: + python bunch_comb {flags} {who} {ix_uni}@{ix_branch} {ix_bunch} + + Where: + {flags} are optional switches: + -array_out : If present, the output will be available in the tao_c_interface_com%c_real. + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {ix_branch} is a branch index. Defaults to s%global%default_branch. + {ix_bunch} is the bunch index. Defaults to 1. + {who} is one of: + x, px, y, py, z, pz, t, s, spin.x, spin.y, spin.z, p0c, beta -- centroid + x.Q, y.Q, z.Q, a.Q, b.Q, c.Q where Q is one of: beta, alpha, gamma, phi, eta, etap, + sigma, sigma_p, emit, norm_emit + sigma.IJ where I, J in range [1,6] + rel_min.I, rel_max.I where I in range [1,6] + charge_live, n_particle_live, n_particle_lost_in_ele, ix_ele + + Note: If ix_uni or ix_branch is present, "@" must be present. + + Example: + python bunch_comb py 2@1 1 + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init + args: + who: x.beta + + """ + cmd = f"python bunch_comb {flags} {who} {ix_uni}@{ix_branch} {ix_bunch}" + if verbose: + print(cmd) + if "-array_out" not in flags: + return self.__execute( + cmd, as_dict, raises, method_name="bunch_comb", cmd_type="string_list" + ) + if "-array_out" in flags: + return self.__execute( + cmd, as_dict, raises, method_name="bunch_comb", cmd_type="real_array" + ) + + def bunch_params( + self, ele_id, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Outputs bunch parameters at the exit end of a given lattice element. + + Parameters + ---------- + ele_id + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python bunch_params {ele_id}|{which} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + + Example: + python bunch_params end|model ! parameters at model lattice element named "end". + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init + args: + ele_id: end + which: model + + """ + cmd = f"python bunch_params {ele_id}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="bunch_params", cmd_type="string_list" + ) + + def bunch1( + self, + ele_id, + coordinate, + *, + which="model", + ix_bunch="1", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Outputs Bunch parameters at the exit end of a given lattice element. + + Parameters + ---------- + ele_id + coordinate + which : default=model + ix_bunch : default=1 + + Returns + ------- + real_array + if coordinate in ['x', 'px', 'y', 'py', 'z', 'pz', 's', 't', 'charge', 'p0c'] + integer_array + if coordinate in ['state', 'ix_ele'] + + Notes + ----- + Command syntax: + python bunch1 {ele_id}|{which} {ix_bunch} {coordinate} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + {ix_bunch} is the bunch index. + {coordinate} is one of: x, px, y, py, z, pz, "s", "t", "charge", "p0c", "state", "ix_ele" + + For example, if {coordinate} = "px", the phase space px coordinate of each particle + of the bunch is displayed. The "state" of a particle is an integer. A value of 1 means + alive and any other value means the particle has been lost. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init + args: + ele_id: end + coordinate: x + which: model + ix_bunch: 1 + + """ + cmd = f"python bunch1 {ele_id}|{which} {ix_bunch} {coordinate}" + if verbose: + print(cmd) + if coordinate in ["x", "px", "y", "py", "z", "pz", "s", "t", "charge", "p0c"]: + return self.__execute( + cmd, as_dict, raises, method_name="bunch1", cmd_type="real_array" + ) + if coordinate in ["state", "ix_ele"]: + return self.__execute( + cmd, as_dict, raises, method_name="bunch1", cmd_type="integer_array" + ) + + def building_wall_list( + self, *, ix_section="", verbose=False, as_dict=True, raises=True + ): + """ + + Output List of building wall sections or section points + + Parameters + ---------- + ix_section : optional + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python building_wall_list {ix_section} + + Where: + {ix_section} is a building wall section index. + + If {ix_section} is not present, a list of building wall sections is given. + If {ix_section} is present, a list of section points is given. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall + args: + ix_section: + + Example: 2 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall + args: + ix_section: 1 + + """ + cmd = f"python building_wall_list {ix_section}" + if verbose: + print(cmd) + return self.__execute( + cmd, + as_dict, + raises, + method_name="building_wall_list", + cmd_type="string_list", + ) + + def building_wall_graph(self, graph, *, verbose=False, as_dict=True, raises=True): + """ + + Output (x, y) points for drawing the building wall for a particular graph. + + Parameters + ---------- + graph + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python building_wall_graph {graph} + + Where: + {graph} is a plot region graph name. + + Note: The graph defines the coordinate system for the (x, y) points. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall + args: + graph: floor_plan.g + + """ + cmd = f"python building_wall_graph {graph}" + if verbose: + print(cmd) + return self.__execute( + cmd, + as_dict, + raises, + method_name="building_wall_graph", + cmd_type="string_list", + ) + + def building_wall_point( + self, + ix_section, + ix_point, + z, + x, + radius, + z_center, + x_center, + *, + verbose=False, + as_dict=True, + raises=True, + ): + """ + + add or delete a building wall point + + Parameters + ---------- + ix_section + ix_point + z + x + radius + z_center + x_center + + Returns + ------- + None + + Notes + ----- + Command syntax: + python building_wall_point {ix_section}^^{ix_point}^^{z}^^{x}^^{radius}^^{z_center}^^{x_center} + + Where: + {ix_section} -- Section index. + {ix_point} -- Point index. Points of higher indexes will be moved up + if adding a point and down if deleting. + {z}, etc... -- See tao_building_wall_point_struct components. + -- If {z} is set to "delete" then delete the point. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall + args: + ix_section: 1 + ix_point: 1 + z: 0 + x: 0 + radius: 0 + z_center: 0 + x_center: 0 + + """ + cmd = f"python building_wall_point {ix_section}^^{ix_point}^^{z}^^{x}^^{radius}^^{z_center}^^{x_center}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="building_wall_point", cmd_type="None" + ) + + def building_wall_section( + self, + ix_section, + sec_name, + sec_constraint, + *, + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Add or delete a building wall section + + Parameters + ---------- + ix_section + sec_name + sec_constraint + + Returns + ------- + None + + Notes + ----- + Command syntax: + python building_wall_section {ix_section}^^{sec_name}^^{sec_constraint} + + Where: + {ix_section} -- Section index. Sections with higher indexes will be + moved up if adding a section and down if deleting. + {sec_name} -- Section name. + {sec_constraint} -- A section constraint name or "delete". Must be one of: + delete -- Delete section. Anything else will add the section. + none + left_side + right_side + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ix_section: 1 + sec_name: test + sec_constraint: none + + """ + cmd = f"python building_wall_section {ix_section}^^{sec_name}^^{sec_constraint}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="building_wall_section", cmd_type="None" + ) + + def constraints(self, who, *, verbose=False, as_dict=True, raises=True): + """ + + Output optimization data and variable parameters that contribute to the merit function. + + Parameters + ---------- + who + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python constraints {who} + + Where: + {who} is one of: "data" or "var" + + Data constraints output is: + data name + constraint type + evaluation element name + start element name + end/reference element name + measured value + ref value (only relavent if global%opt_with_ref = T) + model value + base value (only relavent if global%opt_with_base = T) + weight + merit value + location where merit is evaluated (if there is a range) + Var constraints output is: + var name + Associated varible attribute + meas value + ref value (only relavent if global%opt_with_ref = T) + model value + base value (only relavent if global%opt_with_base = T) + weight + merit value + dmerit/dvar + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + who: data + + Example: 2 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + who:var + + """ + cmd = f"python constraints {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="constraints", cmd_type="string_list" + ) + + def da_aperture(self, *, ix_uni="", verbose=False, as_dict=True, raises=True): + """ + + Output dynamic aperture data + + Parameters + ---------- + ix_uni : optional + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python da_aperture {ix_uni} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + + """ + cmd = f"python da_aperture {ix_uni}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="da_aperture", cmd_type="string_list" + ) + + def da_params(self, *, ix_uni="", verbose=False, as_dict=True, raises=True): + """ + + Output dynamic aperture input parameters + + Parameters + ---------- + ix_uni : optional + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python da_params {ix_uni} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + + """ + cmd = f"python da_params {ix_uni}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="da_params", cmd_type="string_list" + ) + + def data( + self, + d2_name, + d1_name, + *, + ix_uni="", + dat_index="1", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output Individual datum parameters. + + Parameters + ---------- + d2_name + d1_name + ix_uni : optional + dat_index : default=1 + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python data {ix_uni}@{d2_name}.{d1_name}[{dat_index}] + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {d2_name} is the name of the d2_data structure the datum is in. + {d1_datum} is the name of the d1_data structure the datum is in. + {dat_index} is the index of the datum. + + Use the "python data-d1" command to get detailed info on a specific d1 array. + + Example: + python data 1@orbit.x[10] + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + ix_uni: + d2_name: twiss + d1_name: end + dat_index: 1 + + Example: 2 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + ix_uni: 1 + d2_name: twiss + d1_name: end + dat_index: 1 + + """ + cmd = f"python data {ix_uni}@{d2_name}.{d1_name}[{dat_index}]" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="data", cmd_type="string_list" + ) + + def data_d_array( + self, d2_name, d1_name, *, ix_uni="", verbose=False, as_dict=True, raises=True + ): + """ + + Output list of datums for a given d1_data structure. + + Parameters + ---------- + d2_name + d1_name + ix_uni : optional + + Returns + ------- + datums: list of dicts + Each dict has keys: + 'ix_d1', 'data_type', 'merit_type', + 'ele_ref_name', 'ele_start_name', 'ele_name', + 'meas_value', 'model_value', 'design_value', + 'useit_opt', 'useit_plot', 'good_user', + 'weight', 'exists' + + Notes + ----- + Command syntax: + python data_d_array {ix_uni}@{d2_name}.{d1_name} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {d2_name} is the name of the containing d2_data structure. + {d1_name} is the name of the d1_data structure containing the array of datums. + + Example: + python data_d_array 1@orbit.x + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + ix_uni: 1 + d2_name: twiss + d1_name: end + + """ + cmd = f"python data_d_array {ix_uni}@{d2_name}.{d1_name}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="data_d_array", cmd_type="string_list" + ) + + def data_d1_array( + self, d2_datum, *, ix_uni="", verbose=False, as_dict=True, raises=True + ): + """ + + Output list of d1 arrays for a given data_d2. + + Parameters + ---------- + d2_datum + ix_uni : optional + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python data_d1_array {d2_datum} + + {d2_datum} should be of the form + {ix_uni}@{d2_datum_name} + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + ix_uni: 1 + d2_datum: twiss + + """ + cmd = f"python data_d1_array {d2_datum}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="data_d1_array", cmd_type="string_list" + ) + + def data_d2(self, d2_name, *, ix_uni="", verbose=False, as_dict=True, raises=True): + """ + + Output information on a d2_datum. + + Parameters + ---------- + d2_name + ix_uni : optional + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python data_d2 {ix_uni}@{d2_name} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {d2_name} is the name of the d2_data structure. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + ix_uni: 1 + d2_name: twiss + + """ + cmd = f"python data_d2 {ix_uni}@{d2_name}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="data_d2", cmd_type="string_list" + ) + + def data_d2_array(self, ix_uni, *, verbose=False, as_dict=True, raises=True): + """ + + Output data d2 info for a given universe. + + Parameters + ---------- + ix_uni + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python data_d2_array {ix_uni} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + + Example: + python data_d2_array 1 + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ix_uni : 1 + + """ + cmd = f"python data_d2_array {ix_uni}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="data_d2_array", cmd_type="string_list" + ) + + def data_d2_create( + self, + d2_name, + n_d1_data, + d_data_arrays_name_min_max, + *, + ix_uni="", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Create a d2 data structure along with associated d1 and data arrays. + + Parameters + ---------- + d2_name + n_d1_data + d_data_arrays_name_min_max + ix_uni : optional + + Returns + ------- + None + + Notes + ----- + Command syntax: + python data_d2_create {ix_uni}@{d2_name}^^{n_d1_data}^^{d_data_arrays_name_min_max} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {d2_name} is the name of the d2_data structure to create. + {n_d1_data} is the number of associated d1 data structures. + {d_data_arrays_name_min_max} has the form + {name1}^^{lower_bound1}^^{upper_bound1}^^....^^{nameN}^^{lower_boundN}^^{upper_boundN} + where {name} is the data array name and {lower_bound} and {upper_bound} are the bounds of the array. + + Example: + python data_d2_create 2@orbit^^2^^x^^0^^45^^y^^1^^47 + This example creates a d2 data structure called "orbit" with + two d1 structures called "x" and "y". + + The "x" d1 structure has an associated data array with indexes in the range [0, 45]. + The "y" d1 structure has an associated data arrray with indexes in the range [1, 47]. + + Use the "set data" command to set created datum parameters. + + Note: When setting multiple data parameters, + temporarily toggle s%global%lattice_calc_on to False + ("set global lattice_calc_on = F") to prevent Tao trying to + evaluate the partially created datum and generating unwanted error messages. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + ix_uni: 1 + d2_name: orbit + n_d1_data: 2 + d_data_arrays_name_min_max: x^^0^^45^^y^^1^^47 + + """ + cmd = f"python data_d2_create {ix_uni}@{d2_name}^^{n_d1_data}^^{d_data_arrays_name_min_max}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="data_d2_create", cmd_type="None" + ) + + def data_d2_destroy( + self, d2_name, *, ix_uni="", verbose=False, as_dict=True, raises=True + ): + """ + + Destroy a d2 data structure along with associated d1 and data arrays. + + Parameters + ---------- + d2_name + ix_uni : optional + + Returns + ------- + None + + Notes + ----- + Command syntax: + python data_d2_destroy {ix_uni}@{d2_name} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {d2_name} is the name of the d2_data structure to destroy. + + Example: + python data_d2_destroy 2@orbit + This destroys the orbit d2_data structure in universe 2. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + d2_name: orbit + + """ + cmd = f"python data_d2_destroy {ix_uni}@{d2_name}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="data_d2_destroy", cmd_type="None" + ) + + def data_parameter( + self, data_array, parameter, *, verbose=False, as_dict=True, raises=True + ): + """ + + Output an array of values for a particular datum parameter for a given array of datums, + + Parameters + ---------- + data_array + parameter + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python data_parameter {data_array} {parameter} + + {parameter} may be any tao_data_struct parameter. + Example: + python data_parameter orbit.x model_value + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + data_array: twiss.end + parameter: model_value + + """ + cmd = f"python data_parameter {data_array} {parameter}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="data_parameter", cmd_type="string_list" + ) + + def data_set_design_value(self, *, verbose=False, as_dict=True, raises=True): + """ + + Set the design (and base & model) values for all datums. + + Returns + ------- + None + + Notes + ----- + Command syntax: + python data_set_design_value + + Example: + python data_set_design_value + + Note: Use the "data_d2_create" and "datum_create" first to create datums. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + + """ + cmd = f"python data_set_design_value" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="data_set_design_value", cmd_type="None" + ) + + def datum_create( + self, + datum_name, + data_type, + *, + ele_ref_name="", + ele_start_name="", + ele_name="", + merit_type="", + meas="0", + good_meas="F", + ref="0", + good_ref="F", + weight="0", + good_user="T", + data_source="lat", + eval_point="END", + s_offset="0", + ix_bunch="0", + invalid_value="0", + spin_axis_n0_1="", + spin_axis_n0_2="", + spin_axis_n0_3="", + spin_axis_l_1="", + spin_axis_l_2="", + spin_axis_l_3="", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Create a datum. + + Parameters + ---------- + datum_name ! EG: orb.x[3] + data_type ! EG: orbit.x + ele_ref_name : optional + ele_start_name : optional + ele_name : optional + merit_type : optional + meas : default=0 + good_meas : default=F + ref : default=0 + good_ref : default=F + weight : default=0 + good_user : default=T + data_source : default=lat + eval_point : default=END + s_offset : default=0 + ix_bunch : default=0 + invalid_value : default=0 + spin_axis%n0(1) : optional + spin_axis%n0(2) : optional + spin_axis%n0(3) : optional + spin_axis%l(1) : optional + spin_axis%l(2) : optional + spin_axis%l(3) : optional + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python datum_create {datum_name}^^{data_type}^^{ele_ref_name}^^{ele_start_name}^^ + {ele_name}^^{merit_type}^^{meas}^^{good_meas}^^{ref}^^ + {good_ref}^^{weight}^^{good_user}^^{data_source}^^ + {eval_point}^^{s_offset}^^{ix_bunch}^^{invalid_value}^^ + {spin_axis%n0(1)}^^{spin_axis%n0(2)}^^{spin_axis%n0(3)}^^ + {spin_axis%l(1)}^^{spin_axis%l(2)}^^{spin_axis%l(3)} + + Note: The 3 values for spin_axis%n0, as a group, are optional. + Also the 3 values for spin_axis%l are, as a group, optional. + Note: Use the "data_d2_create" first to create a d2 structure with associated d1 arrays. + Note: After creating all your datums, use the "data_set_design_value" routine to + set the design (and model) values. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + datum_name: twiss.end[6] + data_type: beta.y + ele_ref_name: + ele_start_name: + ele_name: P1 + merit_type: target + meas: 0 + good_meas: T + ref: 0 + good_ref: T + weight: 0.3 + good_user: T + data_source: lat + eval_point: END + s_offset: 0 + ix_bunch: 1 + invalid_value: 0 + + """ + cmd = f"python datum_create {datum_name}^^{data_type}^^{ele_ref_name}^^{ele_start_name}^^{ele_name}^^{merit_type}^^{meas}^^{good_meas}^^{ref}^^{good_ref}^^{weight}^^{good_user}^^{data_source}^^{eval_point}^^{s_offset}^^{ix_bunch}^^{invalid_value}^^{spin_axis_n0_1}^^{spin_axis_n0_2}^^{spin_axis_n0_3}^^{spin_axis_l_1}^^{spin_axis_l_2}^^{spin_axis_l_3}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="datum_create", cmd_type="string_list" + ) + + def datum_has_ele(self, datum_type, *, verbose=False, as_dict=True, raises=True): + """ + + Output whether a datum type has an associated lattice element + + Parameters + ---------- + datum_type + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python datum_has_ele {datum_type} + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + datum_type: twiss.end + + """ + cmd = f"python datum_has_ele {datum_type}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="datum_has_ele", cmd_type="string_list" + ) + + def derivative(self, *, verbose=False, as_dict=True, raises=True): + """ + + Output optimization derivatives + + Returns + ------- + out : dict + Dictionary with keys corresponding to universe indexes (int), + with dModel_dVar as the value: + np.ndarray with shape (n_data, n_var) + + Notes + ----- + Command syntax: + python derivative + + Note: To save time, this command will not recalculate derivatives. + Use the "derivative" command beforehand to recalcuate if needed. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + + """ + cmd = f"python derivative" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="derivative", cmd_type="string_list" + ) + + def ele_ac_kicker( + self, ele_id, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output element ac_kicker parameters + + Parameters + ---------- + ele_id + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:ac_kicker {ele_id}|{which} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + + Example: + python ele:ac_kicker 3@1>>7|model + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>1 + which: model + + """ + cmd = f"python ele:ac_kicker {ele_id}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_ac_kicker", cmd_type="string_list" + ) + + def ele_cartesian_map( + self, + ele_id, + index, + who, + *, + which="model", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output element cartesian_map parameters + + Parameters + ---------- + ele_id + index + who + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:cartesian_map {ele_id}|{which} {index} {who} + + Where: + {ele_id} is an element name or index + {which} is one of: "model", "base" or "design" + {index} is the index number in the ele%cartesian_map(:) array + {who} is one of: "base", or "terms" + + Example: + python ele:cartesian_map 3@1>>7|model 2 base + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field + args: + ele_id: 1@0>>1 + which: model + index: 1 + who: base + + """ + cmd = f"python ele:cartesian_map {ele_id}|{which} {index} {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, + as_dict, + raises, + method_name="ele_cartesian_map", + cmd_type="string_list", + ) + + def ele_chamber_wall( + self, + ele_id, + index, + who, + *, + which="model", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output element beam chamber wall parameters + + Parameters + ---------- + ele_id + index + who + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:chamber_wall {ele_id}|{which} {index} {who} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + {index} is index of the wall. + {who} is one of: + "x" ! Return min/max in horizontal plane + "y" ! Return min/max in vertical plane + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d + args: + ele_id: 1@0>>1 + which: model + index: 1 + who: x + + """ + cmd = f"python ele:chamber_wall {ele_id}|{which} {index} {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_chamber_wall", cmd_type="string_list" + ) + + def ele_control_var( + self, ele_id, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output list of element control variables. + Used for group, overlay and ramper type elements. + + Parameters + ---------- + ele_id + which : default=model + + Returns + ------- + dict of attributes and values + + Notes + ----- + Command syntax: + python ele:control_var {ele_id}|{which} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + + Example: + python ele:control_var 3@1>>7|model + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>873 + which: model + + """ + cmd = f"python ele:control_var {ele_id}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_control_var", cmd_type="string_list" + ) + + def ele_cylindrical_map( + self, + ele_id, + index, + who, + *, + which="model", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output element cylindrical_map + + Parameters + ---------- + ele_id + index + who + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:cylindrical_map {ele_id}|{which} {index} {who} + + Where + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + {index} is the index number in the ele%cylindrical_map(:) array + {who} is one of: "base", or "terms" + + Example: + python ele:cylindrical_map 3@1>>7|model 2 base + This gives map #2 of element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field + args: + ele_id: 1@0>>5 + which: model + index: 1 + who: base + + """ + cmd = f"python ele:cylindrical_map {ele_id}|{which} {index} {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, + as_dict, + raises, + method_name="ele_cylindrical_map", + cmd_type="string_list", + ) + + def ele_elec_multipoles( + self, ele_id, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output element electric multipoles + + Parameters + ---------- + ele_id + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:elec_multipoles {ele_id}|{which} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + + Example: + python ele:elec_multipoles 3@1>>7|model + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>1 + which: model + + """ + cmd = f"python ele:elec_multipoles {ele_id}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, + as_dict, + raises, + method_name="ele_elec_multipoles", + cmd_type="string_list", + ) + + def ele_floor( + self, + ele_id, + *, + which="model", + where="end", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output element floor coordinates. The output gives four lines. "Reference" is + without element misalignments and "Actual" is with misalignments. The lines with "-W" + give the W matrix. The exception is that if ele is a multipass_lord, there will be 4*N + lines where N is the number of slaves. + + Parameters + ---------- + ele_id + which : default=model + where : default=end + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:floor {ele_id}|{which} {where} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + {where} is an optional argument which, if present, is one of + beginning ! Upstream end + center ! Middle of element + end ! Downstream end (default) + Note: {where} ignored for photonic elements crystal, mirror, and multilayer_mirror. + + Example: + python ele:floor 3@1>>7|model + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>1 + which: model + where: + + Example: 2 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>1 + which: model + where: center + + """ + cmd = f"python ele:floor {ele_id}|{which} {where}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_floor", cmd_type="string_list" + ) + + def ele_gen_attribs( + self, ele_id, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output element general attributes + + Parameters + ---------- + ele_id + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:gen_attribs {ele_id}|{which} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + + Example: + python ele:gen_attribs 3@1>>7|model + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>1 + which: model + + """ + cmd = f"python ele:gen_attribs {ele_id}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_gen_attribs", cmd_type="string_list" + ) + + def ele_gen_grad_map( + self, + ele_id, + index, + who, + *, + which="model", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output element gen_grad_map + + Parameters + ---------- + ele_id + index + who + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:gen_grad_map {ele_id}|{which} {index} {who} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + {index} is the index number in the ele%gen_grad_map(:) array + {who} is one of: "base", or "derivs". + + Example: + python ele:gen_grad_map 3@1>>7|model 2 base + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field + args: + ele_id: 1@0>>9 + which: model + index: 1 + who: derivs + + """ + cmd = f"python ele:gen_grad_map {ele_id}|{which} {index} {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_gen_grad_map", cmd_type="string_list" + ) + + def ele_grid_field( + self, + ele_id, + index, + who, + *, + which="model", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output element grid_field + + Parameters + ---------- + ele_id + index + who + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:grid_field {ele_id}|{which} {index} {who} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + {index} is the index number in the ele%grid_field(:) array. + {who} is one of: "base", or "points" + + Example: + python ele:grid_field 3@1>>7|model 2 base + This gives grid #2 of element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_grid + args: + ele_id: 1@0>>1 + which: model + index: 1 + who: base + + """ + cmd = f"python ele:grid_field {ele_id}|{which} {index} {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_grid_field", cmd_type="string_list" + ) + + def ele_head( + self, ele_id, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output "head" Element attributes + + Parameters + ---------- + ele_id + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:head {ele_id}|{which} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + + Example: + python ele:head 3@1>>7|model + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>1 + which: model + + """ + cmd = f"python ele:head {ele_id}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_head", cmd_type="string_list" + ) + + def ele_lord_slave( + self, ele_id, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output the lord/slave tree of an element. + + Parameters + ---------- + ele_id + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:lord_slave {ele_id}|{which} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + + Example: + python ele:lord_slave 3@1>>7|model + This gives lord and slave info on element number 7 in branch 1 of universe 3. + Note: The lord/slave info is independent of the setting of {which}. + + The output is a number of lines, each line giving information on an element (element index, etc.). + Some lines begin with the word "Element". + After each "Element" line, there are a number of lines (possibly zero) that begin with the word "Slave or "Lord". + These "Slave" and "Lord" lines are the slaves and lords of the "Element" element. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>1 + which: model + + """ + cmd = f"python ele:lord_slave {ele_id}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_lord_slave", cmd_type="string_list" + ) + + def ele_mat6( + self, + ele_id, + *, + which="model", + who="mat6", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output element mat6 + + Parameters + ---------- + ele_id + which : default=model + who : default=mat6 + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:mat6 {ele_id}|{which} {who} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + {who} is one of: "mat6", "vec0", or "err" + + Example: + python ele:mat6 3@1>>7|model mat6 + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>1 + which: model + who: mat6 + + """ + cmd = f"python ele:mat6 {ele_id}|{which} {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_mat6", cmd_type="string_list" + ) + + def ele_methods( + self, ele_id, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output element methods + + Parameters + ---------- + ele_id + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:methods {ele_id}|{which} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + + Example: + python ele:methods 3@1>>7|model + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>1 + which: model + + """ + cmd = f"python ele:methods {ele_id}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_methods", cmd_type="string_list" + ) + + def ele_multipoles( + self, ele_id, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output element multipoles + + Parameters + ---------- + ele_id + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:multipoles {ele_id}|{which} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + + Example: + python ele:multipoles 3@1>>7|model + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>1 + which: model + + """ + cmd = f"python ele:multipoles {ele_id}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_multipoles", cmd_type="string_list" + ) + + def ele_orbit( + self, ele_id, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output element orbit + + Parameters + ---------- + ele_id + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:orbit {ele_id}|{which} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + + Example: + python ele:orbit 3@1>>7|model + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>1 + which: model + + """ + cmd = f"python ele:orbit {ele_id}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_orbit", cmd_type="string_list" + ) + + def ele_param( + self, ele_id, who, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output lattice element parameter + + Parameters + ---------- + ele_id + who + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:param {ele_id}|{which} {who} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + {who} values are the same as {who} values for "python lat_list". + Note: Here {who} must be a single parameter and not a list. + + Example: + python ele:param 3@1>>7|model e_tot + This gives E_tot of element number 7 in branch 1 of universe 3. + + Note: On output the {variable} component will always be "F" (since this + command cannot tell if a parameter is allowed to vary). + + Also see: "python lat_list". + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon + args: + ele_id: 1@0>>1 + which: model + who: orbit.vec.1 + + """ + cmd = f"python ele:param {ele_id}|{which} {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_param", cmd_type="string_list" + ) + + def ele_photon( + self, ele_id, who, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output element photon parameters + + Parameters + ---------- + ele_id + who + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:photon {ele_id}|{which} {who} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + {who} is one of: "base", "material", or "curvature" + + Example: + python ele:photon 3@1>>7|model base + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon + args: + ele_id: 1@0>>1 + which: model + who: base + + """ + cmd = f"python ele:photon {ele_id}|{which} {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_photon", cmd_type="string_list" + ) + + def ele_spin_taylor( + self, ele_id, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output element spin_taylor parameters + + Parameters + ---------- + ele_id + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:spin_taylor {ele_id}|{which} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + + Example: + python ele:spin_taylor 3@1>>7|model + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_spin + args: + ele_id: 1@0>>2 + which: model + + """ + cmd = f"python ele:spin_taylor {ele_id}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_spin_taylor", cmd_type="string_list" + ) + + def ele_taylor( + self, ele_id, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output element taylor map + + Parameters + ---------- + ele_id + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:taylor {ele_id}|{which} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + + Example: + python ele:taylor 3@1>>7|model + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_taylor + args: + ele_id: 1@0>>34 + which: model + + """ + cmd = f"python ele:taylor {ele_id}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_taylor", cmd_type="string_list" + ) + + def ele_twiss( + self, ele_id, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output element Twiss parameters + + Parameters + ---------- + ele_id + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:twiss {ele_id}|{which} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + + Example: + python ele:twiss 3@1>>7|model + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>1 + which: model + + """ + cmd = f"python ele:twiss {ele_id}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_twiss", cmd_type="string_list" + ) + + def ele_wake( + self, ele_id, who, *, which="model", verbose=False, as_dict=True, raises=True + ): + """ + + Output element wake. + + Parameters + ---------- + ele_id + who + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:wake {ele_id}|{which} {who} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + {Who} is one of: + "sr_long" "sr_long_table" + "sr_trans" "sr_trans_table" + "lr_mode_table" "base" + + Example: + python ele:wake 3@1>>7|model + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wake + args: + ele_id: 1@0>>1 + which: model + who: sr_long + + """ + cmd = f"python ele:wake {ele_id}|{which} {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_wake", cmd_type="string_list" + ) + + def ele_wall3d( + self, + ele_id, + index, + who, + *, + which="model", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output element wall3d parameters. + + Parameters + ---------- + ele_id + index + who + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ele:wall3d {ele_id}|{which} {index} {who} + + Where: + {ele_id} is an element name or index. + {which} is one of: "model", "base" or "design" + {index} is the index number in the ele%wall3d(:) array (size obtained from "ele:head"). + {who} is one of: "base", or "table". + Example: + python ele:wall3d 3@1>>7|model 2 base + This gives element number 7 in branch 1 of universe 3. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d + args: + ele_id: 1@0>>1 + which: model + index: 1 + who: table + + """ + cmd = f"python ele:wall3d {ele_id}|{which} {index} {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ele_wall3d", cmd_type="string_list" + ) + + def evaluate( + self, + expression, + *, + flags="-array_out", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output the value of an expression. The result may be a vector. + + Parameters + ---------- + expression + flags : default=-array_out + If -array_out, the output will be available in the tao_c_interface_com%c_real. + + Returns + ------- + string_list + if '-array_out' not in flags + real_array + if '-array_out' in flags + + Notes + ----- + Command syntax: + python evaluate {flags} {expression} + + Where: + Optional {flags} are: + -array_out : If present, the output will be available in the tao_c_interface_com%c_real. + {expression} is expression to be evaluated. + + Example: + python evaluate 3+data::cbar.11[1:10]|model + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + expression: data::cbar.11[1:10]|model + + """ + cmd = f"python evaluate {flags} {expression}" + if verbose: + print(cmd) + if "-array_out" not in flags: + return self.__execute( + cmd, as_dict, raises, method_name="evaluate", cmd_type="string_list" + ) + if "-array_out" in flags: + return self.__execute( + cmd, as_dict, raises, method_name="evaluate", cmd_type="real_array" + ) + + def em_field( + self, + ele_id, + x, + y, + z, + t_or_z, + *, + which="model", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output EM field at a given point generated by a given element. + + Parameters + ---------- + ele_id + x + y + z + t_or_z + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python em_field {ele_id}|{which} {x} {y} {z} {t_or_z} + + Where: + {which} is one of: "model", "base" or "design" + {x}, {y} -- Transverse coords. + {z} -- Longitudinal coord with respect to entrance end of element. + {t_or_z} -- time or phase space z depending if lattice is setup for absolute time tracking. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele_id: 1@0>>22 + which: model + x: 0 + y: 0 + z: 0 + t_or_z: 0 + + """ + cmd = f"python em_field {ele_id}|{which} {x} {y} {z} {t_or_z}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="em_field", cmd_type="string_list" + ) + + def enum(self, enum_name, *, verbose=False, as_dict=True, raises=True): + """ + + Output list of possible values for enumerated numbers. + + Parameters + ---------- + enum_name + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python enum {enum_name} + + Example: + python enum tracking_method + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + enum_name: tracking_method + + """ + cmd = f"python enum {enum_name}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="enum", cmd_type="string_list" + ) + + def floor_plan(self, graph, *, verbose=False, as_dict=True, raises=True): + """ + + Output (x,y) points and other information that can be used for drawing a floor_plan. + + Parameters + ---------- + graph + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python floor_plan {graph} + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + graph: r13.g + + """ + cmd = f"python floor_plan {graph}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="floor_plan", cmd_type="string_list" + ) + + def floor_orbit(self, graph, *, verbose=False, as_dict=True, raises=True): + """ + + Output (x, y) coordinates for drawing the particle orbit on a floor plan. + + Parameters + ---------- + graph + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python floor_orbit {graph} + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_floor_orbit + args: + graph: r33.g + + """ + cmd = f"python floor_orbit {graph}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="floor_orbit", cmd_type="string_list" + ) + + def tao_global(self, *, verbose=False, as_dict=True, raises=True): + """ + + Output global parameters. + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python global + + Output syntax is parameter list form. See documentation at the beginning of this file. + + Note: The follow is intentionally left out: + optimizer_allow_user_abort + quiet + single_step + prompt_color + prompt_string + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + + """ + cmd = f"python global" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="tao_global", cmd_type="string_list" + ) + + def global_optimization(self, *, verbose=False, as_dict=True, raises=True): + """ + + Output optimization parameters. + Also see global:opti_de. + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python global:optimization + + Output syntax is parameter list form. See documentation at the beginning of this file. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + + """ + cmd = f"python global:optimization" + if verbose: + print(cmd) + return self.__execute( + cmd, + as_dict, + raises, + method_name="global_optimization", + cmd_type="string_list", + ) + + def global_opti_de(self, *, verbose=False, as_dict=True, raises=True): + """ + + Output DE optimization parameters. + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python global:opti_de + + Output syntax is parameter list form. See documentation at the beginning of this file. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + + """ + cmd = f"python global:opti_de" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="global_opti_de", cmd_type="string_list" + ) + + def help(self, *, verbose=False, as_dict=True, raises=True): + """ + + Output list of "help xxx" topics + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python help + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + + """ + cmd = f"python help" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="help", cmd_type="string_list" + ) + + def inum(self, who, *, verbose=False, as_dict=True, raises=True): + """ + + Output list of possible values for an INUM parameter. + For example, possible index numbers for the branches of a lattice. + + Parameters + ---------- + who + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python inum {who} + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + who: ix_universe + + """ + cmd = f"python inum {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="inum", cmd_type="string_list" + ) + + def lat_calc_done(self, branch_name, *, verbose=False, as_dict=True, raises=True): + """ + + Output if a lattice recalculation has been proformed since the last + time "python lat_calc_done" was called. + + Parameters + ---------- + branch_name + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python lat_calc_done + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + branch_name: 1@0 + + """ + cmd = f"python lat_calc_done" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="lat_calc_done", cmd_type="string_list" + ) + + def lat_ele_list(self, *, branch_name="", verbose=False, as_dict=True, raises=True): + """ + + Output lattice element list. + + Parameters + ---------- + branch_name : optional + + Returns + ------- + list of str of element names + + Notes + ----- + Command syntax: + python lat_ele_list {branch_name} + + {branch_name} should have the form: + {ix_uni}@{ix_branch} + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + branch_name: 1@0 + + """ + cmd = f"python lat_ele_list {branch_name}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="lat_ele_list", cmd_type="string_list" + ) + + def lat_branch_list(self, *, ix_uni="", verbose=False, as_dict=True, raises=True): + """ + + Output lattice branch list + + Parameters + ---------- + ix_uni : optional + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python lat_branch_list {ix_uni} + + Output syntax: + branch_index;branch_name;n_ele_track;n_ele_max + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ix_uni: 1 + + """ + cmd = f"python lat_branch_list {ix_uni}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="lat_branch_list", cmd_type="string_list" + ) + + def lat_list( + self, + elements, + who, + *, + ix_uni="", + ix_branch="", + which="model", + flags="-array_out -track_only", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output list of parameters at ends of lattice elements + + Parameters + ---------- + elements + who + ix_uni : optional + ix_branch : optional + which : default=model + flags : optional, default=-array_out -track_only + + Returns + ------- + string_list + if ('-array_out' not in flags) or (who in ['ele.name', 'ele.key']) + integer_array + if '-array_out' in flags and who in ['orbit.state', 'ele.ix_ele'] + real_array + if ('-array_out' in flags) or ('real:' in who) + + Notes + ----- + Command syntax: + python lat_list {flags} {ix_uni}@{ix_branch}>>{elements}|{which} {who} + + Where: + Optional {flags} are: + -no_slaves : If present, multipass_slave and super_slave elements will not be matched to. + -track_only : If present, lord elements will not be matched to. + -index_order : If present, order elements by element index instead of the standard s-position. + -array_out : If present, the output will be available in the tao_c_interface_com%c_real or + tao_c_interface_com%c_integer arrays. See the code below for when %c_real vs %c_integer is used. + Note: Only a single {who} item permitted when -array_out is present. + + {which} is one of: "model", "base" or "design" + + {who} is a comma deliminated list of: + orbit.floor.x, orbit.floor.y, orbit.floor.z ! Floor coords at particle orbit. + orbit.spin.1, orbit.spin.2, orbit.spin.3, + orbit.vec.1, orbit.vec.2, orbit.vec.3, orbit.vec.4, orbit.vec.5, orbit.vec.6, + orbit.t, orbit.beta, + orbit.state, ! Note: state is an integer. alive$ = 1, anything else is lost. + orbit.energy, orbit.pc, + ele.name, ele.key, ele.ix_ele, ele.ix_branch + ele.a.beta, ele.a.alpha, ele.a.eta, ele.a.etap, ele.a.gamma, ele.a.phi, + ele.b.beta, ele.b.alpha, ele.b.eta, ele.b.etap, ele.b.gamma, ele.b.phi, + ele.x.eta, ele.x.etap, + ele.y.eta, ele.y.etap, + ele.ref_time, ele.ref_time_start + ele.s, ele.l + ele.e_tot, ele.p0c + ele.mat6 ! Output: mat6(1,:), mat6(2,:), ... mat6(6,:) + ele.vec0 ! Output: vec0(1), ... vec0(6) + ele.{attribute} Where {attribute} is a Bmad syntax element attribute. (EG: ele.beta_a, ele.k1, etc.) + ele.c_mat ! Output: c_mat11, c_mat12, c_mat21, c_mat22. + ele.gamma_c ! Parameter associated with coupling c-matrix. + + {elements} is a string to match element names to. + Use "*" to match to all elements. + + Examples: + python lat_list -track 3@0>>Q*|base ele.s,orbit.vec.2 + python lat_list 3@0>>Q*|base real:ele.s + + Also see: "python ele:param" + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ix_uni: 1 + ix_branch: 0 + elements: Q* + which: model + who: orbit.floor.x + + Example: 2 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ix_uni: 1 + ix_branch: 0 + elements: Q* + which: design + who: ele.ix_ele + + """ + cmd = f"python lat_list {flags} {ix_uni}@{ix_branch}>>{elements}|{which} {who}" + if verbose: + print(cmd) + if ("-array_out" not in flags) or (who in ["ele.name", "ele.key"]): + return self.__execute( + cmd, as_dict, raises, method_name="lat_list", cmd_type="string_list" + ) + if "-array_out" in flags and who in ["orbit.state", "ele.ix_ele"]: + return self.__execute( + cmd, as_dict, raises, method_name="lat_list", cmd_type="integer_array" + ) + if ("-array_out" in flags) or ("real:" in who): + return self.__execute( + cmd, as_dict, raises, method_name="lat_list", cmd_type="real_array" + ) + + def lat_param_units(self, param_name, *, verbose=False, as_dict=True, raises=True): + """ + + Output units of a parameter associated with a lattice or lattice element. + + Parameters + ---------- + param_name + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python lat_param_units {param_name} + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + param_name: L + + """ + cmd = f"python lat_param_units {param_name}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="lat_param_units", cmd_type="string_list" + ) + + def matrix(self, ele1_id, ele2_id, *, verbose=False, as_dict=True, raises=True): + """ + + Output matrix value from the exit end of one element to the exit end of the other. + + Parameters + ---------- + ele1_id + ele2_id + + Returns + ------- + dict with keys: + 'mat6' : np.array of shape (6,6) + 'vec6' : np.array of shape(6) + + Notes + ----- + Command syntax: + python matrix {ele1_id} {ele2_id} + + Where: + {ele1_id} is the start element. + {ele2_id} is the end element. + If {ele2_id} = {ele1_id}, the 1-turn transfer map is computed. + Note: {ele2_id} should just be an element name or index without universe, branch, or model/base/design specification. + + Example: + python matrix 2@1>>q01w|design q02w + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele1_id: 1@0>>q01w|design + ele2_id: q02w + + """ + cmd = f"python matrix {ele1_id} {ele2_id}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="matrix", cmd_type="string_list" + ) + + def merit(self, *, verbose=False, as_dict=True, raises=True): + """ + + Output merit value. + + Returns + ------- + merit: float + Value of the merit function + + Notes + ----- + Command syntax: + python merit + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + + """ + cmd = f"python merit" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="merit", cmd_type="string_list" + ) + + def orbit_at_s( + self, + *, + ix_uni="", + ele="", + s_offset="", + which="model", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output twiss at given s position. + + Parameters + ---------- + ix_uni : optional + ele : optional + s_offset : optional + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python orbit_at_s {ix_uni}@{ele}->{s_offset}|{which} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {ele} is an element name or index. Default at the Beginning element at start of branch 0. + {s_offset} is the offset of the evaluation point from the downstream end of ele. Default is 0. + If {s_offset} is present, the preceeding "->" sign must be present. EG: Something like "23|model" will + {which} is one of: "model", "base" or "design". + + Example: + python orbit_at_s Q10->0.4|model ! Orbit at 0.4 meters from Q10 element exit end in model lattice. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ix_uni: 1 + ele: 10 + s_offset: 0.7 + which: model + + """ + cmd = f"python orbit_at_s {ix_uni}@{ele}->{s_offset}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="orbit_at_s", cmd_type="string_list" + ) + + def place_buffer(self, *, verbose=False, as_dict=True, raises=True): + """ + + Output the place command buffer and reset the buffer. + The contents of the buffer are the place commands that the user has issued. + See the Tao manual for more details. + + Returns + ------- + None + + Notes + ----- + Command syntax: + python place_buffer + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + + """ + cmd = f"python place_buffer" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="place_buffer", cmd_type="None" + ) + + def plot_curve(self, curve_name, *, verbose=False, as_dict=True, raises=True): + """ + + Output curve information for a plot. + + Parameters + ---------- + curve_name + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python plot_curve {curve_name} + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + curve_name: r13.g.a + + """ + cmd = f"python plot_curve {curve_name}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="plot_curve", cmd_type="string_list" + ) + + def plot_lat_layout( + self, ix_uni: 1, ix_branch: 0, *, verbose=False, as_dict=True, raises=True + ): + """ + + Output plot Lat_layout info + + Parameters + ---------- ix_uni: 1 ix_branch: 0 - which: model - - """ - cmd = f'python ring_general {ix_uni}@{ix_branch}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='ring_general', cmd_type='string_list') - - -def shape_list(tao, who, *, verbose=False, as_dict=True, raises=True): - """ - - Output lat_layout or floor_plan shapes list - - Parameters - ---------- - who - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python shape_list {who} - - {who} is one of: - lat_layout - floor_plan - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - who: floor_plan - - """ - cmd = f'python shape_list {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='shape_list', cmd_type='string_list') - - -def shape_manage(tao, who, index, add_or_delete, *, verbose=False, as_dict=True, raises=True): - """ - - Element shape creation or destruction - - Parameters - ---------- - who - index - add_or_delete - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python shape_manage {who} {index} {add_or_delete} - - {who} is one of: - lat_layout - floor_plan - {add_or_delete} is one of: - add -- Add a shape at {index}. - Shapes with higher index get moved up one to make room. - delete -- Delete shape at {index}. - Shapes with higher index get moved down one to fill the gap. - - Example: - python shape_manage floor_plan 2 add - Note: After adding a shape use "python shape_set" to set shape parameters. - This is important since an added shape is in a ill-defined state. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - who: floor_plan - index: 1 - add_or_delete: add - - """ - cmd = f'python shape_manage {who} {index} {add_or_delete}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='shape_manage', cmd_type='string_list') - - -def shape_pattern_list(tao, *, ix_pattern='', verbose=False, as_dict=True, raises=True): - """ - - Output list of shape patterns or shape pattern points - - Parameters - ---------- - ix_pattern : optional - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python shape_pattern_list {ix_pattern} - - If optional {ix_pattern} index is omitted then list all the patterns. - If {ix_pattern} is present, list points of given pattern. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape - args: - ix_pattern: - - """ - cmd = f'python shape_pattern_list {ix_pattern}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='shape_pattern_list', cmd_type='string_list') - - -def shape_pattern_manage(tao, ix_pattern, pat_name, pat_line_width, *, verbose=False, as_dict=True, raises=True): - """ - - Add or remove shape pattern - - Parameters - ---------- - ix_pattern - pat_name - pat_line_width - - Returns - ------- - None - - Notes - ----- - Command syntax: - python shape_pattern_manage {ix_pattern}^^{pat_name}^^{pat_line_width} - - Where: - {ix_pattern} -- Pattern index. Patterns with higher indexes will be moved up - if adding a pattern and down if deleting. - {pat_name} -- Pattern name. - {pat_line_width} -- Line width. Integer. If set to "delete" then section - will be deleted. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape - args: - ix_pattern : 1 - pat_name : new_pat - pat_line_width : 1 - - """ - cmd = f'python shape_pattern_manage {ix_pattern}^^{pat_name}^^{pat_line_width}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='shape_pattern_manage', cmd_type='None') - - -def shape_pattern_point_manage(tao, ix_pattern, ix_point, s, x, *, verbose=False, as_dict=True, raises=True): - """ - - Add or remove shape pattern point - - Parameters - ---------- - ix_pattern - ix_point - s - x - - Returns - ------- - None - - Notes - ----- - Command syntax: - python shape_pattern_point_manage {ix_pattern}^^{ix_point}^^{s}^^{x} - - Where: - {ix_pattern} -- Pattern index. - {ix_point} -- Point index. Points of higher indexes will be moved up - if adding a point and down if deleting. - {s}, {x} -- Point location. If {s} is "delete" then delete the point. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape - args: - ix_pattern: 1 - ix_point: 1 - s: 0 - x: 0 - - """ - cmd = f'python shape_pattern_point_manage {ix_pattern}^^{ix_point}^^{s}^^{x}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='shape_pattern_point_manage', cmd_type='None') - - -def shape_set(tao, who, shape_index, ele_name, shape, color, shape_size, type_label, shape_draw, multi_shape, line_width, *, verbose=False, as_dict=True, raises=True): - """ - - Set lat_layout or floor_plan shape parameters. - - Parameters - ---------- - who - shape_index - ele_name - shape - color - shape_size - type_label - shape_draw - multi_shape - line_width - - Returns - ------- - None - - Notes - ----- - Command syntax: - python shape_set {who}^^{shape_index}^^{ele_name}^^{shape}^^{color}^^ - {shape_size}^^{type_label}^^{shape_draw}^^ - {multi_shape}^^{line_width} - - {who} is one of: - lat_layout - floor_plan - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - who: floor_plan - shape_index: 1 - ele_name: Q1 - shape: circle - color: - shape_size: - type_label: - shape_draw: - multi_shape: - line_width: - - """ - cmd = f'python shape_set {who}^^{shape_index}^^{ele_name}^^{shape}^^{color}^^{shape_size}^^{type_label}^^{shape_draw}^^{multi_shape}^^{line_width}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='shape_set', cmd_type='None') - - -def show(tao, line, *, verbose=False, as_dict=True, raises=True): - """ - - Output the output from a show command. - - Parameters - ---------- - line - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python show {line} - - {line} is the string to pass through to the show command. - Example: - python show lattice -python - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - line: -python - - """ - cmd = f'python show {line}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='show', cmd_type='string_list') - - -def space_charge_com(tao, *, verbose=False, as_dict=True, raises=True): - """ - - Output space_charge_com structure parameters. - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python space_charge_com - - Output syntax is parameter list form. See documentation at the beginning of this file. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - - """ - cmd = f'python space_charge_com' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='space_charge_com', cmd_type='string_list') - - -def species_to_int(tao, species_str, *, verbose=False, as_dict=True, raises=True): - """ - - Convert species name to corresponding integer - - Parameters - ---------- - species_str - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python species_to_int {species_str} - - Example: - python species_to_int CO2++ - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - species_str: electron - - """ - cmd = f'python species_to_int {species_str}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='species_to_int', cmd_type='string_list') - - -def species_to_str(tao, species_int, *, verbose=False, as_dict=True, raises=True): - """ - - Convert species integer id to corresponding - - Parameters - ---------- - species_int - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python species_to_str {species_int} - - Example: - python species_to_str -1 ! Returns 'Electron' - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - species_int: -1 - - """ - cmd = f'python species_to_str {species_int}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='species_to_str', cmd_type='string_list') - - -def spin_invariant(tao, who, *, ix_uni='', ix_branch='', which='model', flags='-array_out', verbose=False, as_dict=True, raises=True): - """ - - Output closed orbit spin axes n0, l0, or m0 at the ends of all lattice elements in a branch. - n0, l0, and m0 are solutions of the T-BMT equation. - n0 is periodic while l0 and m0 are not. At the beginning of the branch, the orientation of the - l0 or m0 axes in the plane perpendicular to the n0 axis is chosen a bit arbitrarily. - See the Bmad manual for more details. - - Parameters - ---------- - who - ix_uni : optional - ix_branch : optional - which : default=model - flags : default=-array_out - - Returns - ------- - string_list - if '-array_out' not in flags - real_array - if '-array_out' in flags - - Notes - ----- - Command syntax: - python spin_invariant {flags} {who} {ix_uni}@{ix_branch}|{which} - - Where: - {flags} are optional switches: - -array_out : If present, the output will be available in the tao_c_interface_com%c_real. - {who} is one of: l0, n0, or m0 - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {ix_branch} is a branch index. Defaults to s%global%default_branch. - {which} is one of: - model - base - design - - Example: - python spin_invariant 1@0|model - - Note: This command is under development. If you want to use please contact David Sagan. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - who: l0 - ix_uni: 1 - ix_branch: 0 - which: model - - """ - cmd = f'python spin_invariant {flags} {who} {ix_uni}@{ix_branch}|{which}' - if verbose: print(cmd) - if '-array_out' not in flags: - return __execute(tao, cmd, as_dict, raises, method_name='spin_invariant', cmd_type='string_list') - if '-array_out' in flags: - return __execute(tao, cmd, as_dict, raises, method_name='spin_invariant', cmd_type='real_array') - - -def spin_polarization(tao, *, ix_uni='', ix_branch='', which='model', verbose=False, as_dict=True, raises=True): - """ - - Output spin polarization information - - Parameters - ---------- - ix_uni : optional - ix_branch : optional - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python spin_polarization {ix_uni}@{ix_branch}|{which} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {ix_branch} is a branch index. Defaults to s%global%default_branch. - {which} is one of: - model - base - design - - Example: - python spin_polarization 1@0|model - - Note: This command is under development. If you want to use please contact David Sagan. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ix_uni: 1 - ix_branch: 0 - which: model - - """ - cmd = f'python spin_polarization {ix_uni}@{ix_branch}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='spin_polarization', cmd_type='string_list') - - -def spin_resonance(tao, *, ix_uni='', ix_branch='', which='model', ref_ele='0', verbose=False, as_dict=True, raises=True): - """ - - Output spin resonance information - - Parameters - ---------- - ix_uni : optional - ix_branch : optional - which : default=model - ref_ele : default=0 - Reference element to calculate at. - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python spin_resonance {ix_uni}@{ix_branch}|{which} {ref_ele} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {ix_branch} is a lattice branch index. Defaults to s%global%default_branch. - {which} is one of: "model", "base" or "design" - {ref_ele} is an element name or index. - This will return a string_list with the following fields: - spin_tune -- Spin tune - dq_X_sum, dq_X_diff -- Tune sum Q_spin+Q_mode and tune difference Q_spin-Q_mode for modes X = a, b, and c. - xi_res_X_sum, xi_res_X_diff -- The linear spin/orbit sum and difference resonance strengths for X = a, b, and c modes. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ix_uni: 1 - ix_branch: 0 - which: model - - """ - cmd = f'python spin_resonance {ix_uni}@{ix_branch}|{which} {ref_ele}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='spin_resonance', cmd_type='string_list') - - -def super_universe(tao, *, verbose=False, as_dict=True, raises=True): - """ - - Output super_Universe parameters. - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python super_universe - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - - """ - cmd = f'python super_universe' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='super_universe', cmd_type='string_list') - - -def taylor_map(tao, ele1_id, ele2_id, *, order='1', verbose=False, as_dict=True, raises=True): - """ - - Output Taylor map between two points. - - Parameters - ---------- - ele1_id - ele2_id - order : default=1 - - Returns - ------- - dict of dict of taylor terms: - {2: { (3,0,0,0,0,0)}: 4.56, ... - corresponding to: px_out = 4.56 * x_in^3 - - Notes - ----- - Command syntax: - python taylor_map {ele1_id} {ele2_id} {order} - - Where: - {ele1_id} is the start element. - {ele2_id} is the end element. - {order} is the map order. Default is order set in the lattice file. {order} cannot be larger than - what is set by the lattice file. - If {ele2_id} = {ele1_id}, the 1-turn transfer map is computed. - Note: {ele2_id} should just be an element name or index without universe, branch, or model/base/design specification. - Example: - python taylor_map 2@1>>q01w|design q02w 2 - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ele1_id: 1@0>>q01w|design - ele2_id: q02w - order: 1 - - """ - cmd = f'python taylor_map {ele1_id} {ele2_id} {order}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='taylor_map', cmd_type='string_list') - - -def twiss_at_s(tao, *, ix_uni='', ele='', s_offset='', which='model', verbose=False, as_dict=True, raises=True): - """ - - Output twiss parameters at given s position. - - Parameters - ---------- - ix_uni : optional - ele : optional - s_offset : optional - which : default=model - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python twiss_at_s {ix_uni}@{ele}->{s_offset}|{which} - - Where: - {ix_uni} is a universe index. Defaults to s%global%default_universe. - {ele} is an element name or index. Default at the Beginning element at start of branch 0. - {s_offset} is the offset of the evaluation point from the downstream end of ele. Default is 0. - If {s_offset} is present, the preceeding "->" sign must be present. EG: Something like "23|model" will - {which} is one of: "model", "base" or "design". - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ix_uni: 1 - ele: 10 - s_offset: 0.7 - which: model - - """ - cmd = f'python twiss_at_s {ix_uni}@{ele}->{s_offset}|{which}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='twiss_at_s', cmd_type='string_list') - - -def universe(tao, ix_uni, *, verbose=False, as_dict=True, raises=True): - """ - - Output universe info. - - Parameters - ---------- - ix_uni - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python universe {ix_uni} - - Use "python global" to get the number of universes. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - ix_uni: 1 - - """ - cmd = f'python universe {ix_uni}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='universe', cmd_type='string_list') - - -def var(tao, var, *, slaves='', verbose=False, as_dict=True, raises=True): - """ - - Output parameters of a given variable. - - Parameters - ---------- - var - slaves : optional - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python var {var} slaves - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - var: quad[1] - slaves: - - Example: 2 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - var: quad[1] - slaves: slaves - - """ - cmd = f'python var {var} slaves' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='var', cmd_type='string_list') - - -def var_create(tao, var_name, ele_name, attribute, universes, weight, step, low_lim, high_lim, merit_type, good_user, key_bound, key_delta, *, verbose=False, as_dict=True, raises=True): - """ - - Create a single variable - - Parameters - ---------- - var_name - ele_name - attribute - universes - weight - step - low_lim - high_lim - merit_type - good_user - key_bound - key_delta - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python var_create {var_name}^^{ele_name}^^{attribute}^^{universes}^^ - {weight}^^{step}^^{low_lim}^^{high_lim}^^{merit_type}^^ - {good_user}^^{key_bound}^^{key_delta} - - {var_name} is something like "kick[5]". - Before using var_create, setup the appropriate v1_var array using - the "python var_v1_create" command. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching - args: - var_name: quad[1] - ele_name: Q1 - attribute: L - universes: 1 - weight: 0.001 - step: 0.001 - low_lim: -10 - high_lim: 10 - merit_type: - good_user: T - key_bound: T - key_delta: 0.01 - - """ - cmd = f'python var_create {var_name}^^{ele_name}^^{attribute}^^{universes}^^{weight}^^{step}^^{low_lim}^^{high_lim}^^{merit_type}^^{good_user}^^{key_bound}^^{key_delta}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='var_create', cmd_type='string_list') - - -def var_general(tao, *, verbose=False, as_dict=True, raises=True): - """ - - Output list of all variable v1 arrays - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python var_general - - Output syntax: - {v1_var name};{v1_var%v lower bound};{v1_var%v upper bound} - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - - """ - cmd = f'python var_general' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='var_general', cmd_type='string_list') - - -def var_v_array(tao, v1_var, *, verbose=False, as_dict=True, raises=True): - """ - - Output list of variables for a given data_v1. - - Parameters - ---------- - v1_var - - Notes - ----- - Command syntax: - python var_v_array {v1_var} - - Example: - python var_v_array quad_k1 - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - v1_var: quad_k1 - - """ - cmd = f'python var_v_array {v1_var}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='var_v_array', cmd_type='string_list') - - -def var_v1_array(tao, v1_var, *, verbose=False, as_dict=True, raises=True): - """ - - Output list of variables in a given variable v1 array - - Parameters - ---------- - v1_var - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python var_v1_array {v1_var} - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - v1_var: quad_k1 - - """ - cmd = f'python var_v1_array {v1_var}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='var_v1_array', cmd_type='string_list') - - -def var_v1_create(tao, v1_name, n_var_min, n_var_max, *, verbose=False, as_dict=True, raises=True): - """ - - Create a v1 variable structure along with associated var array. - - Parameters - ---------- - v1_name - n_var_min - n_var_max - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python var_v1_create {v1_name} {n_var_min} {n_var_max} - - {n_var_min} and {n_var_max} are the lower and upper bounds of the var - Example: - python var_v1_create quad_k1 0 45 - This example creates a v1 var structure called "quad_k1" with an associated - variable array that has the range [0, 45]. - - Use the "set variable" command to set a created variable parameters. - In particular, to slave a lattice parameter to a variable use the command: - set {v1_name}|ele_name = {lat_param} - where {lat_param} is of the form {ix_uni}@{ele_name_or_location}{param_name}] - Examples: - set quad_k1[2]|ele_name = 2@q01w[k1] - set quad_k1[2]|ele_name = 2@0>>10[k1] - Note: When setting multiple variable parameters, - temporarily toggle s%global%lattice_calc_on to False - ("set global lattice_calc_on = F") to prevent Tao trying to evaluate the - partially created variable and generating unwanted error messages. - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - v1_name: quad_k1 - n_var_min: 0 - n_var_max: 45 - - """ - cmd = f'python var_v1_create {v1_name} {n_var_min} {n_var_max}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='var_v1_create', cmd_type='string_list') - - -def var_v1_destroy(tao, v1_datum, *, verbose=False, as_dict=True, raises=True): - """ - - Destroy a v1 var structure along with associated var sub-array. - - Parameters - ---------- - v1_datum - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python var_v1_destroy {v1_datum} - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - v1_datum: quad_k1 - - """ - cmd = f'python var_v1_destroy {v1_datum}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='var_v1_destroy', cmd_type='string_list') - - -def wave(tao, who, *, verbose=False, as_dict=True, raises=True): - """ - - Output Wave analysis info. - - Parameters - ---------- - who - - Returns - ------- - string_list - - Notes - ----- - Command syntax: - python wave {who} - - Where {who} is one of: - params - loc_header - locations - plot1, plot2, plot3 - - Examples - -------- - Example: 1 - init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init - args: - who: params - - """ - cmd = f'python wave {who}' - if verbose: print(cmd) - return __execute(tao, cmd, as_dict, raises, method_name='wave', cmd_type='string_list') + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python plot_lat_layout {ix_uni}@{ix_branch} + + Note: The returned list of element positions is not ordered in increasing + longitudinal position. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ix_uni: 1 + ix_branch: 0 + + """ + cmd = f"python plot_lat_layout {ix_uni}@{ix_branch}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="plot_lat_layout", cmd_type="string_list" + ) + + def plot_list(self, r_or_g, *, verbose=False, as_dict=True, raises=True): + """ + + Output list of plot templates or plot regions. + + Parameters + ---------- + r_or_g + + Returns + ------- + if r_or_g == 't' + dict with template_name:index + if r_or_g == 'r' + list of dicts with keys: + region + ix + plot_name + visible + x1, x2, y1, y1 + + Notes + ----- + Command syntax: + python plot_list {r_or_g} + + where "{r/g}" is: + "r" ! list regions of the form ix;region_name;plot_name;visible;x1;x2;y1;y2 + "t" ! list template plots of the form ix;name + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + r_or_g: r + + """ + cmd = f"python plot_list {r_or_g}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="plot_list", cmd_type="string_list" + ) + + def plot_graph(self, graph_name, *, verbose=False, as_dict=True, raises=True): + """ + + Output graph info. + + Parameters + ---------- + graph_name + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python plot_graph {graph_name} + + {graph_name} is in the form: + {p_name}.{g_name} + where + {p_name} is the plot region name if from a region or the plot name if a template plot. + This name is obtained from the python plot_list command. + {g_name} is the graph name obtained from the python plot1 command. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + graph_name: beta.g + + """ + cmd = f"python plot_graph {graph_name}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="plot_graph", cmd_type="string_list" + ) + + def plot_histogram(self, curve_name, *, verbose=False, as_dict=True, raises=True): + """ + + Output plot histogram info. + + Parameters + ---------- + curve_name + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python plot_histogram {curve_name} + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + curve_name: r33.g.x + + """ + cmd = f"python plot_histogram {curve_name}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="plot_histogram", cmd_type="string_list" + ) + + def plot_template_manage( + self, + template_location, + template_name, + *, + n_graph="-1", + graph_names="", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Template plot creation or destruction. + + Parameters + ---------- + template_location + template_name + n_graph : default=-1 + graph_names : default= + + Returns + ------- + None + + Notes + ----- + Command syntax: + python plot_template_manage {template_location}^^{template_name}^^ + {n_graph}^^{graph_names} + + Where: + {template_location} is the location to place or delete a template plot. Use "@Tnnn" syntax for the location. + {template_name} is the name of the template plot. If deleting a plot this name is immaterial. + {n_graph} is the number of associated graphs. If set to -1 then any existing template plot is deleted. + {graph_names} are the names of the graphs. graph_names should be in the form: + graph1_name^^graph2_name^^...^^graphN_name + for N=n_graph names + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + template_location: @T1 + template_name: beta + n_graph: 2 + graph_names: g1^^g2 + + """ + cmd = f"python plot_template_manage {template_location}^^{template_name}^^{n_graph}^^{graph_names}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="plot_template_manage", cmd_type="None" + ) + + def plot_curve_manage( + self, + graph_name, + curve_index, + curve_name, + *, + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Template plot curve creation/destruction + + Parameters + ---------- + graph_name + curve_index + curve_name + + Returns + ------- + None + + Notes + ----- + Command syntax: + python plot_curve_manage {graph_name}^^{curve_index}^^{curve_name} + + If {curve_index} corresponds to an existing curve then this curve is deleted. + In this case the {curve_name} is ignored and does not have to be present. + If {curve_index} does not not correspond to an existing curve, {curve_index} + must be one greater than the number of curves. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + graph_name: beta.g + curve_index: 1 + curve_name: r13.g.a + + """ + cmd = f"python plot_curve_manage {graph_name}^^{curve_index}^^{curve_name}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="plot_curve_manage", cmd_type="None" + ) + + def plot_graph_manage( + self, + plot_name, + graph_index, + graph_name, + *, + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Template plot graph creation/destruction + + Parameters + ---------- + plot_name + graph_index + graph_name + + Returns + ------- + None + + Notes + ----- + Command syntax: + python plot_graph_manage {plot_name}^^{graph_index}^^{graph_name} + + If {graph_index} corresponds to an existing graph then this graph is deleted. + In this case the {graph_name} is ignored and does not have to be present. + If {graph_index} does not not correspond to an existing graph, {graph_index} + must be one greater than the number of graphs. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + plot_name: beta + graph_index: 1 + graph_name: beta.g + + """ + cmd = f"python plot_graph_manage {plot_name}^^{graph_index}^^{graph_name}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="plot_graph_manage", cmd_type="None" + ) + + def plot_line( + self, + region_name, + graph_name, + curve_name, + *, + x_or_y="", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output points used to construct the "line" associated with a plot curve. + + Parameters + ---------- + region_name + graph_name + curve_name + x_or_y : optional + + Returns + ------- + string_list + if x_or_y == '' + real_array + if x_or_y != '' + + Notes + ----- + Command syntax: + python plot_line {region_name}.{graph_name}.{curve_name} {x_or_y} + + Optional {x-or-y} may be set to "x" or "y" to get the smooth line points x or y + component put into the real array buffer. + Note: The plot must come from a region, and not a template, since no template plots + have associated line data. + Examples: + python plot_line r13.g.a ! String array output. + python plot_line r13.g.a x ! x-component of line points loaded into the real array buffer. + python plot_line r13.g.a y ! y-component of line points loaded into the real array buffer. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting + args: + region_name: beta + graph_name: g + curve_name: a + x_or_y: + + Example: 2 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting + args: + region_name: beta + graph_name: g + curve_name: a + x_or_y: y + + """ + cmd = f"python plot_line {region_name}.{graph_name}.{curve_name} {x_or_y}" + if verbose: + print(cmd) + if x_or_y == "": + return self.__execute( + cmd, as_dict, raises, method_name="plot_line", cmd_type="string_list" + ) + if x_or_y != "": + return self.__execute( + cmd, as_dict, raises, method_name="plot_line", cmd_type="real_array" + ) + + def plot_symbol( + self, + region_name, + graph_name, + curve_name, + x_or_y, + *, + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output locations to draw symbols for a plot curve. + + Parameters + ---------- + region_name + graph_name + curve_name + x_or_y + + Returns + ------- + string_list + if x_or_y == '' + real_array + if x_or_y != '' + + Notes + ----- + Command syntax: + python plot_symbol {region_name}.{graph_name}.{curve_name} {x_or_y} + + Optional {x_or_y} may be set to "x" or "y" to get the symbol x or y + positions put into the real array buffer. + Note: The plot must come from a region, and not a template, + since no template plots have associated symbol data. + Examples: + python plot_symbol r13.g.a ! String array output. + python plot_symbol r13.g.a x ! x-component of the symbol positions + loaded into the real array buffer. + python plot_symbol r13.g.a y ! y-component of the symbol positions + loaded into the real array buffer. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting + args: + region_name: r13 + graph_name: g + curve_name: a + x_or_y: + + Example: 2 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting + args: + region_name: r13 + graph_name: g + curve_name: a + x_or_y: y + + """ + cmd = f"python plot_symbol {region_name}.{graph_name}.{curve_name} {x_or_y}" + if verbose: + print(cmd) + if x_or_y == "": + return self.__execute( + cmd, as_dict, raises, method_name="plot_symbol", cmd_type="string_list" + ) + if x_or_y != "": + return self.__execute( + cmd, as_dict, raises, method_name="plot_symbol", cmd_type="real_array" + ) + + def plot_transfer( + self, from_plot, to_plot, *, verbose=False, as_dict=True, raises=True + ): + """ + + Output transfer plot parameters from the "from plot" to the "to plot" (or plots). + + Parameters + ---------- + from_plot + to_plot + + Returns + ------- + None + + Notes + ----- + Command syntax: + python plot_transfer {from_plot} {to_plot} + + To avoid confusion, use "@Tnnn" and "@Rnnn" syntax for {from_plot}. + If {to_plot} is not present and {from_plot} is a template plot, the "to plots" + are the equivalent region plots with the same name. And vice versa + if {from_plot} is a region plot. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + from_plot: r13 + to_plot: r23 + + """ + cmd = f"python plot_transfer {from_plot} {to_plot}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="plot_transfer", cmd_type="None" + ) + + def plot1(self, name, *, verbose=False, as_dict=True, raises=True): + """ + + Output info on a given plot. + + Parameters + ---------- + name + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python plot1 {name} + + {name} should be the region name if the plot is associated with a region. + Output syntax is parameter list form. See documentation at the beginning of this file. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + name: beta + + """ + cmd = f"python plot1 {name}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="plot1", cmd_type="string_list" + ) + + def ptc_com(self, *, verbose=False, as_dict=True, raises=True): + """ + + Output Ptc_com structure components. + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ptc_com + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + + """ + cmd = f"python ptc_com" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ptc_com", cmd_type="string_list" + ) + + def ring_general( + self, + *, + ix_uni="", + ix_branch="", + which="model", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output lattice branch with closed geometry info (emittances, etc.) + + Parameters + ---------- + ix_uni : optional + ix_branch : optional + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python ring_general {ix_uni}@{ix_branch}|{which} + + where {which} is one of: + model + base + design + Example: + python ring_general 1@0|model + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ix_uni: 1 + ix_branch: 0 + which: model + + """ + cmd = f"python ring_general {ix_uni}@{ix_branch}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="ring_general", cmd_type="string_list" + ) + + def shape_list(self, who, *, verbose=False, as_dict=True, raises=True): + """ + + Output lat_layout or floor_plan shapes list + + Parameters + ---------- + who + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python shape_list {who} + + {who} is one of: + lat_layout + floor_plan + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + who: floor_plan + + """ + cmd = f"python shape_list {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="shape_list", cmd_type="string_list" + ) + + def shape_manage( + self, who, index, add_or_delete, *, verbose=False, as_dict=True, raises=True + ): + """ + + Element shape creation or destruction + + Parameters + ---------- + who + index + add_or_delete + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python shape_manage {who} {index} {add_or_delete} + + {who} is one of: + lat_layout + floor_plan + {add_or_delete} is one of: + add -- Add a shape at {index}. + Shapes with higher index get moved up one to make room. + delete -- Delete shape at {index}. + Shapes with higher index get moved down one to fill the gap. + + Example: + python shape_manage floor_plan 2 add + Note: After adding a shape use "python shape_set" to set shape parameters. + This is important since an added shape is in a ill-defined state. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + who: floor_plan + index: 1 + add_or_delete: add + + """ + cmd = f"python shape_manage {who} {index} {add_or_delete}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="shape_manage", cmd_type="string_list" + ) + + def shape_pattern_list( + self, *, ix_pattern="", verbose=False, as_dict=True, raises=True + ): + """ + + Output list of shape patterns or shape pattern points + + Parameters + ---------- + ix_pattern : optional + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python shape_pattern_list {ix_pattern} + + If optional {ix_pattern} index is omitted then list all the patterns. + If {ix_pattern} is present, list points of given pattern. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape + args: + ix_pattern: + + """ + cmd = f"python shape_pattern_list {ix_pattern}" + if verbose: + print(cmd) + return self.__execute( + cmd, + as_dict, + raises, + method_name="shape_pattern_list", + cmd_type="string_list", + ) + + def shape_pattern_manage( + self, + ix_pattern, + pat_name, + pat_line_width, + *, + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Add or remove shape pattern + + Parameters + ---------- + ix_pattern + pat_name + pat_line_width + + Returns + ------- + None + + Notes + ----- + Command syntax: + python shape_pattern_manage {ix_pattern}^^{pat_name}^^{pat_line_width} + + Where: + {ix_pattern} -- Pattern index. Patterns with higher indexes will be moved up + if adding a pattern and down if deleting. + {pat_name} -- Pattern name. + {pat_line_width} -- Line width. Integer. If set to "delete" then section + will be deleted. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape + args: + ix_pattern : 1 + pat_name : new_pat + pat_line_width : 1 + + """ + cmd = f"python shape_pattern_manage {ix_pattern}^^{pat_name}^^{pat_line_width}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="shape_pattern_manage", cmd_type="None" + ) + + def shape_pattern_point_manage( + self, ix_pattern, ix_point, s, x, *, verbose=False, as_dict=True, raises=True + ): + """ + + Add or remove shape pattern point + + Parameters + ---------- + ix_pattern + ix_point + s + x + + Returns + ------- + None + + Notes + ----- + Command syntax: + python shape_pattern_point_manage {ix_pattern}^^{ix_point}^^{s}^^{x} + + Where: + {ix_pattern} -- Pattern index. + {ix_point} -- Point index. Points of higher indexes will be moved up + if adding a point and down if deleting. + {s}, {x} -- Point location. If {s} is "delete" then delete the point. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape + args: + ix_pattern: 1 + ix_point: 1 + s: 0 + x: 0 + + """ + cmd = f"python shape_pattern_point_manage {ix_pattern}^^{ix_point}^^{s}^^{x}" + if verbose: + print(cmd) + return self.__execute( + cmd, + as_dict, + raises, + method_name="shape_pattern_point_manage", + cmd_type="None", + ) + + def shape_set( + self, + who, + shape_index, + ele_name, + shape, + color, + shape_size, + type_label, + shape_draw, + multi_shape, + line_width, + *, + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Set lat_layout or floor_plan shape parameters. + + Parameters + ---------- + who + shape_index + ele_name + shape + color + shape_size + type_label + shape_draw + multi_shape + line_width + + Returns + ------- + None + + Notes + ----- + Command syntax: + python shape_set {who}^^{shape_index}^^{ele_name}^^{shape}^^{color}^^ + {shape_size}^^{type_label}^^{shape_draw}^^ + {multi_shape}^^{line_width} + + {who} is one of: + lat_layout + floor_plan + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + who: floor_plan + shape_index: 1 + ele_name: Q1 + shape: circle + color: + shape_size: + type_label: + shape_draw: + multi_shape: + line_width: + + """ + cmd = f"python shape_set {who}^^{shape_index}^^{ele_name}^^{shape}^^{color}^^{shape_size}^^{type_label}^^{shape_draw}^^{multi_shape}^^{line_width}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="shape_set", cmd_type="None" + ) + + def show(self, line, *, verbose=False, as_dict=True, raises=True): + """ + + Output the output from a show command. + + Parameters + ---------- + line + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python show {line} + + {line} is the string to pass through to the show command. + Example: + python show lattice -python + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + line: -python + + """ + cmd = f"python show {line}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="show", cmd_type="string_list" + ) + + def space_charge_com(self, *, verbose=False, as_dict=True, raises=True): + """ + + Output space_charge_com structure parameters. + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python space_charge_com + + Output syntax is parameter list form. See documentation at the beginning of this file. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + + """ + cmd = f"python space_charge_com" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="space_charge_com", cmd_type="string_list" + ) + + def species_to_int(self, species_str, *, verbose=False, as_dict=True, raises=True): + """ + + Convert species name to corresponding integer + + Parameters + ---------- + species_str + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python species_to_int {species_str} + + Example: + python species_to_int CO2++ + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + species_str: electron + + """ + cmd = f"python species_to_int {species_str}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="species_to_int", cmd_type="string_list" + ) + + def species_to_str(self, species_int, *, verbose=False, as_dict=True, raises=True): + """ + + Convert species integer id to corresponding + + Parameters + ---------- + species_int + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python species_to_str {species_int} + + Example: + python species_to_str -1 ! Returns 'Electron' + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + species_int: -1 + + """ + cmd = f"python species_to_str {species_int}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="species_to_str", cmd_type="string_list" + ) + + def spin_invariant( + self, + who, + *, + ix_uni="", + ix_branch="", + which="model", + flags="-array_out", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output closed orbit spin axes n0, l0, or m0 at the ends of all lattice elements in a branch. + n0, l0, and m0 are solutions of the T-BMT equation. + n0 is periodic while l0 and m0 are not. At the beginning of the branch, the orientation of the + l0 or m0 axes in the plane perpendicular to the n0 axis is chosen a bit arbitrarily. + See the Bmad manual for more details. + + Parameters + ---------- + who + ix_uni : optional + ix_branch : optional + which : default=model + flags : default=-array_out + + Returns + ------- + string_list + if '-array_out' not in flags + real_array + if '-array_out' in flags + + Notes + ----- + Command syntax: + python spin_invariant {flags} {who} {ix_uni}@{ix_branch}|{which} + + Where: + {flags} are optional switches: + -array_out : If present, the output will be available in the tao_c_interface_com%c_real. + {who} is one of: l0, n0, or m0 + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {ix_branch} is a branch index. Defaults to s%global%default_branch. + {which} is one of: + model + base + design + + Example: + python spin_invariant 1@0|model + + Note: This command is under development. If you want to use please contact David Sagan. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + who: l0 + ix_uni: 1 + ix_branch: 0 + which: model + + """ + cmd = f"python spin_invariant {flags} {who} {ix_uni}@{ix_branch}|{which}" + if verbose: + print(cmd) + if "-array_out" not in flags: + return self.__execute( + cmd, + as_dict, + raises, + method_name="spin_invariant", + cmd_type="string_list", + ) + if "-array_out" in flags: + return self.__execute( + cmd, + as_dict, + raises, + method_name="spin_invariant", + cmd_type="real_array", + ) + + def spin_polarization( + self, + *, + ix_uni="", + ix_branch="", + which="model", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output spin polarization information + + Parameters + ---------- + ix_uni : optional + ix_branch : optional + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python spin_polarization {ix_uni}@{ix_branch}|{which} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {ix_branch} is a branch index. Defaults to s%global%default_branch. + {which} is one of: + model + base + design + + Example: + python spin_polarization 1@0|model + + Note: This command is under development. If you want to use please contact David Sagan. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ix_uni: 1 + ix_branch: 0 + which: model + + """ + cmd = f"python spin_polarization {ix_uni}@{ix_branch}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, + as_dict, + raises, + method_name="spin_polarization", + cmd_type="string_list", + ) + + def spin_resonance( + self, + *, + ix_uni="", + ix_branch="", + which="model", + ref_ele="0", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output spin resonance information + + Parameters + ---------- + ix_uni : optional + ix_branch : optional + which : default=model + ref_ele : default=0 + Reference element to calculate at. + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python spin_resonance {ix_uni}@{ix_branch}|{which} {ref_ele} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {ix_branch} is a lattice branch index. Defaults to s%global%default_branch. + {which} is one of: "model", "base" or "design" + {ref_ele} is an element name or index. + This will return a string_list with the following fields: + spin_tune -- Spin tune + dq_X_sum, dq_X_diff -- Tune sum Q_spin+Q_mode and tune difference Q_spin-Q_mode for modes X = a, b, and c. + xi_res_X_sum, xi_res_X_diff -- The linear spin/orbit sum and difference resonance strengths for X = a, b, and c modes. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ix_uni: 1 + ix_branch: 0 + which: model + + """ + cmd = f"python spin_resonance {ix_uni}@{ix_branch}|{which} {ref_ele}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="spin_resonance", cmd_type="string_list" + ) + + def super_universe(self, *, verbose=False, as_dict=True, raises=True): + """ + + Output super_Universe parameters. + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python super_universe + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + + """ + cmd = f"python super_universe" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="super_universe", cmd_type="string_list" + ) + + def taylor_map( + self, ele1_id, ele2_id, *, order="1", verbose=False, as_dict=True, raises=True + ): + """ + + Output Taylor map between two points. + + Parameters + ---------- + ele1_id + ele2_id + order : default=1 + + Returns + ------- + dict of dict of taylor terms: + {2: { (3,0,0,0,0,0)}: 4.56, ... + corresponding to: px_out = 4.56 * x_in^3 + + Notes + ----- + Command syntax: + python taylor_map {ele1_id} {ele2_id} {order} + + Where: + {ele1_id} is the start element. + {ele2_id} is the end element. + {order} is the map order. Default is order set in the lattice file. {order} cannot be larger than + what is set by the lattice file. + If {ele2_id} = {ele1_id}, the 1-turn transfer map is computed. + Note: {ele2_id} should just be an element name or index without universe, branch, or model/base/design specification. + Example: + python taylor_map 2@1>>q01w|design q02w 2 + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ele1_id: 1@0>>q01w|design + ele2_id: q02w + order: 1 + + """ + cmd = f"python taylor_map {ele1_id} {ele2_id} {order}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="taylor_map", cmd_type="string_list" + ) + + def twiss_at_s( + self, + *, + ix_uni="", + ele="", + s_offset="", + which="model", + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Output twiss parameters at given s position. + + Parameters + ---------- + ix_uni : optional + ele : optional + s_offset : optional + which : default=model + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python twiss_at_s {ix_uni}@{ele}->{s_offset}|{which} + + Where: + {ix_uni} is a universe index. Defaults to s%global%default_universe. + {ele} is an element name or index. Default at the Beginning element at start of branch 0. + {s_offset} is the offset of the evaluation point from the downstream end of ele. Default is 0. + If {s_offset} is present, the preceeding "->" sign must be present. EG: Something like "23|model" will + {which} is one of: "model", "base" or "design". + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ix_uni: 1 + ele: 10 + s_offset: 0.7 + which: model + + """ + cmd = f"python twiss_at_s {ix_uni}@{ele}->{s_offset}|{which}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="twiss_at_s", cmd_type="string_list" + ) + + def universe(self, ix_uni, *, verbose=False, as_dict=True, raises=True): + """ + + Output universe info. + + Parameters + ---------- + ix_uni + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python universe {ix_uni} + + Use "python global" to get the number of universes. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + ix_uni: 1 + + """ + cmd = f"python universe {ix_uni}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="universe", cmd_type="string_list" + ) + + def var(self, var, *, slaves="", verbose=False, as_dict=True, raises=True): + """ + + Output parameters of a given variable. + + Parameters + ---------- + var + slaves : optional + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python var {var} slaves + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + var: quad[1] + slaves: + + Example: 2 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + var: quad[1] + slaves: slaves + + """ + cmd = f"python var {var} slaves" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="var", cmd_type="string_list" + ) + + def var_create( + self, + var_name, + ele_name, + attribute, + universes, + weight, + step, + low_lim, + high_lim, + merit_type, + good_user, + key_bound, + key_delta, + *, + verbose=False, + as_dict=True, + raises=True, + ): + """ + + Create a single variable + + Parameters + ---------- + var_name + ele_name + attribute + universes + weight + step + low_lim + high_lim + merit_type + good_user + key_bound + key_delta + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python var_create {var_name}^^{ele_name}^^{attribute}^^{universes}^^ + {weight}^^{step}^^{low_lim}^^{high_lim}^^{merit_type}^^ + {good_user}^^{key_bound}^^{key_delta} + + {var_name} is something like "kick[5]". + Before using var_create, setup the appropriate v1_var array using + the "python var_v1_create" command. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching + args: + var_name: quad[1] + ele_name: Q1 + attribute: L + universes: 1 + weight: 0.001 + step: 0.001 + low_lim: -10 + high_lim: 10 + merit_type: + good_user: T + key_bound: T + key_delta: 0.01 + + """ + cmd = f"python var_create {var_name}^^{ele_name}^^{attribute}^^{universes}^^{weight}^^{step}^^{low_lim}^^{high_lim}^^{merit_type}^^{good_user}^^{key_bound}^^{key_delta}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="var_create", cmd_type="string_list" + ) + + def var_general(self, *, verbose=False, as_dict=True, raises=True): + """ + + Output list of all variable v1 arrays + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python var_general + + Output syntax: + {v1_var name};{v1_var%v lower bound};{v1_var%v upper bound} + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + + """ + cmd = f"python var_general" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="var_general", cmd_type="string_list" + ) + + def var_v_array(self, v1_var, *, verbose=False, as_dict=True, raises=True): + """ + + Output list of variables for a given data_v1. + + Parameters + ---------- + v1_var + + Notes + ----- + Command syntax: + python var_v_array {v1_var} + + Example: + python var_v_array quad_k1 + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + v1_var: quad_k1 + + """ + cmd = f"python var_v_array {v1_var}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="var_v_array", cmd_type="string_list" + ) + + def var_v1_array(self, v1_var, *, verbose=False, as_dict=True, raises=True): + """ + + Output list of variables in a given variable v1 array + + Parameters + ---------- + v1_var + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python var_v1_array {v1_var} + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + v1_var: quad_k1 + + """ + cmd = f"python var_v1_array {v1_var}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="var_v1_array", cmd_type="string_list" + ) + + def var_v1_create( + self, v1_name, n_var_min, n_var_max, *, verbose=False, as_dict=True, raises=True + ): + """ + + Create a v1 variable structure along with associated var array. + + Parameters + ---------- + v1_name + n_var_min + n_var_max + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python var_v1_create {v1_name} {n_var_min} {n_var_max} + + {n_var_min} and {n_var_max} are the lower and upper bounds of the var + Example: + python var_v1_create quad_k1 0 45 + This example creates a v1 var structure called "quad_k1" with an associated + variable array that has the range [0, 45]. + + Use the "set variable" command to set a created variable parameters. + In particular, to slave a lattice parameter to a variable use the command: + set {v1_name}|ele_name = {lat_param} + where {lat_param} is of the form {ix_uni}@{ele_name_or_location}{param_name}] + Examples: + set quad_k1[2]|ele_name = 2@q01w[k1] + set quad_k1[2]|ele_name = 2@0>>10[k1] + Note: When setting multiple variable parameters, + temporarily toggle s%global%lattice_calc_on to False + ("set global lattice_calc_on = F") to prevent Tao trying to evaluate the + partially created variable and generating unwanted error messages. + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + v1_name: quad_k1 + n_var_min: 0 + n_var_max: 45 + + """ + cmd = f"python var_v1_create {v1_name} {n_var_min} {n_var_max}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="var_v1_create", cmd_type="string_list" + ) + + def var_v1_destroy(self, v1_datum, *, verbose=False, as_dict=True, raises=True): + """ + + Destroy a v1 var structure along with associated var sub-array. + + Parameters + ---------- + v1_datum + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python var_v1_destroy {v1_datum} + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + v1_datum: quad_k1 + + """ + cmd = f"python var_v1_destroy {v1_datum}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="var_v1_destroy", cmd_type="string_list" + ) + + def wave(self, who, *, verbose=False, as_dict=True, raises=True): + """ + + Output Wave analysis info. + + Parameters + ---------- + who + + Returns + ------- + string_list + + Notes + ----- + Command syntax: + python wave {who} + + Where {who} is one of: + params + loc_header + locations + plot1, plot2, plot3 + + Examples + -------- + Example: 1 + init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init + args: + who: params + + """ + cmd = f"python wave {who}" + if verbose: + print(cmd) + return self.__execute( + cmd, as_dict, raises, method_name="wave", cmd_type="string_list" + ) diff --git a/pytao/tao_ctypes/core.py b/pytao/tao_ctypes/core.py index dbcb84eb..e5016acb 100644 --- a/pytao/tao_ctypes/core.py +++ b/pytao/tao_ctypes/core.py @@ -16,7 +16,7 @@ #-------------------------------------- -class Tao: +class TaoCore: """ Class to run and interact with Tao. Requires libtao shared object. @@ -73,27 +73,15 @@ def __init__(self, init='', so_lib=''): self.so_lib.tao_c_out_io_buffer_get_line.restype = ctypes.c_char_p self.so_lib.tao_c_out_io_buffer_reset.restype = None - # Extra methods - self._import_commands(interface_commands) - self._import_commands(extra_commands) - try: self.register_cell_magic() - except: + except Exception: pass if init: self.init(init) - def _import_commands(self, module): - deny_list = getattr(module, '__deny_list', []) - # Add in methods from `interface_commands` - methods = [m for m in dir(module) if not m.startswith('__') and m not in deny_list] - for m in methods: - func = module.__dict__[m] - setattr(self, m, types.MethodType(func, self)) - #--------------------------------------------- # Used by init and cmd routines @@ -332,7 +320,7 @@ def auto_discovery_libtao(): #---------------------------------------------------------------------- -class TaoModel(Tao): +class TaoModel(TaoCore): """ Base class for setting up a Tao model in a directory. Builds upon the Tao class. diff --git a/pytao/tao_interface.py b/pytao/tao_interface.py index b761b5bd..f8077194 100644 --- a/pytao/tao_interface.py +++ b/pytao/tao_interface.py @@ -10,7 +10,7 @@ import io from .tao_pexpect import tao_io -from .tao_ctypes import Tao +from .tao_ctypes import TaoCore class new_stdout(object): ''' @@ -97,12 +97,12 @@ def __init__(self, mode="ctypes", init_args = "", tao_exe = "", expect_str = "T # Check for shared library (and set up self.ctypes_pipe) lib_found = False try: - self.ctypes_pipe = Tao(so_lib=so_lib) + self.ctypes_pipe = TaoCore(so_lib=so_lib) lib_found = True except OSError: #so_lib not found if LIB_DIR != None: try: - self.ctypes_pipe = Tao(so_lib=LIB_DIR+'libtao.so') + self.ctypes_pipe = TaoCore(so_lib=LIB_DIR+'libtao.so') lib_found = True if mode == "ctypes": self.exe_lib_warnings += "Note: could not find " + so_lib @@ -111,7 +111,7 @@ def __init__(self, mode="ctypes", init_args = "", tao_exe = "", expect_str = "T pass # will continue below if not lib_found: try: - self.ctypes_pipe = Tao(so_lib="") + self.ctypes_pipe = TaoCore(so_lib="") lib_found = True if mode == "ctypes": self.exe_lib_warnings += "Note: could not find " + so_lib diff --git a/pytao/tests/test_interface_commands.py b/pytao/tests/test_interface_commands.py index 735eb59a..8f0a1510 100644 --- a/pytao/tests/test_interface_commands.py +++ b/pytao/tests/test_interface_commands.py @@ -2,7 +2,7 @@ # AUTOGENERATED FILE - DO NOT MODIFY # This file was generated by the script `generate_interface_commands.py`. # Any modifications may be overwritten. -# Generated on: 2024-06-24 14:25:18 +# Generated on: 2024-06-25 10:43:22 # ============================================================================== import os @@ -10,681 +10,699 @@ from pytao import interface_commands +def new_tao(init): + return Tao(os.path.expandvars(f"{init} -noplot")) + + def test_beam_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init -noplot')) - ret = interface_commands.beam(tao, ix_uni='1', ix_branch='0') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" + ) + tao.beam(ix_uni="1", ix_branch="0") + + def test_beam_init_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init -noplot')) - ret = interface_commands.beam_init(tao, ix_uni='1', ix_branch='0') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" + ) + tao.beam_init(ix_uni="1", ix_branch="0") + + def test_bmad_com_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.bmad_com(tao) - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.bmad_com() + + def test_branch1_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.branch1(tao, ix_uni='1', ix_branch='0') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.branch1(ix_uni="1", ix_branch="0") + + def test_bunch_comb_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init -noplot')) - ret = interface_commands.bunch_comb(tao, who='x.beta') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" + ) + tao.bunch_comb(who="x.beta") + + def test_bunch_params_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init -noplot')) - ret = interface_commands.bunch_params(tao, ele_id='end', which='model') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" + ) + tao.bunch_params(ele_id="end", which="model") + + def test_bunch1_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init -noplot')) - ret = interface_commands.bunch1(tao, ele_id='end', coordinate='x', which='model', ix_bunch='1') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" + ) + tao.bunch1(ele_id="end", coordinate="x", which="model", ix_bunch="1") + + def test_building_wall_list_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall -noplot')) - ret = interface_commands.building_wall_list(tao, ix_section='') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") + tao.building_wall_list(ix_section="") + + def test_building_wall_list_2(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall -noplot')) - ret = interface_commands.building_wall_list(tao, ix_section='1') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") + tao.building_wall_list(ix_section="1") + + def test_building_wall_graph_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall -noplot')) - ret = interface_commands.building_wall_graph(tao, graph='floor_plan.g') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") + tao.building_wall_graph(graph="floor_plan.g") + + def test_building_wall_point_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall -noplot')) - ret = interface_commands.building_wall_point(tao, ix_section='1', ix_point='1', z='0', x='0', radius='0', z_center='0', x_center='0') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") + tao.building_wall_point( + ix_section="1", + ix_point="1", + z="0", + x="0", + radius="0", + z_center="0", + x_center="0", + ) + + def test_building_wall_section_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.building_wall_section(tao, ix_section='1', sec_name='test', sec_constraint='none') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.building_wall_section(ix_section="1", sec_name="test", sec_constraint="none") + + def test_constraints_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.constraints(tao, who='data') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.constraints(who="data") + + def test_constraints_2(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.constraints(tao, who='var') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.constraints(who="var") + + def test_data_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.data(tao, ix_uni='', d2_name='twiss', d1_name='end', dat_index='1') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.data(ix_uni="", d2_name="twiss", d1_name="end", dat_index="1") + + def test_data_2(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.data(tao, ix_uni='1', d2_name='twiss', d1_name='end', dat_index='1') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.data(ix_uni="1", d2_name="twiss", d1_name="end", dat_index="1") + + def test_data_d_array_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.data_d_array(tao, ix_uni='1', d2_name='twiss', d1_name='end') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.data_d_array(ix_uni="1", d2_name="twiss", d1_name="end") + + def test_data_d1_array_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.data_d1_array(tao, ix_uni='1', d2_datum='twiss') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.data_d1_array(ix_uni="1", d2_datum="twiss") + + def test_data_d2_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.data_d2(tao, ix_uni='1', d2_name='twiss') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.data_d2(ix_uni="1", d2_name="twiss") + + def test_data_d2_array_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.data_d2_array(tao, ix_uni='1') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.data_d2_array(ix_uni="1") + + def test_data_d2_create_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.data_d2_create(tao, ix_uni='1', d2_name='orbit', n_d1_data='2', d_data_arrays_name_min_max='x^^0^^45^^y^^1^^47') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.data_d2_create( + ix_uni="1", + d2_name="orbit", + n_d1_data="2", + d_data_arrays_name_min_max="x^^0^^45^^y^^1^^47", + ) + + def test_data_d2_destroy_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.data_d2_destroy(tao, d2_name='orbit') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.data_d2_destroy(d2_name="orbit") + + def test_data_parameter_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.data_parameter(tao, data_array='twiss.end', parameter='model_value') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.data_parameter(data_array="twiss.end", parameter="model_value") + + def test_data_set_design_value_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.data_set_design_value(tao) - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.data_set_design_value() + + def test_datum_create_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.datum_create(tao, datum_name='twiss.end[6]', data_type='beta.y', ele_ref_name='', ele_start_name='', ele_name='P1', merit_type='target', meas='0', good_meas='T', ref='0', good_ref='T', weight='0.3', good_user='T', data_source='lat', eval_point='END', s_offset='0', ix_bunch='1', invalid_value='0') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.datum_create( + datum_name="twiss.end[6]", + data_type="beta.y", + ele_ref_name="", + ele_start_name="", + ele_name="P1", + merit_type="target", + meas="0", + good_meas="T", + ref="0", + good_ref="T", + weight="0.3", + good_user="T", + data_source="lat", + eval_point="END", + s_offset="0", + ix_bunch="1", + invalid_value="0", + ) + + def test_datum_has_ele_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.datum_has_ele(tao, datum_type='twiss.end') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.datum_has_ele(datum_type="twiss.end") + + def test_derivative_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.derivative(tao) - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.derivative() + + def test_ele_ac_kicker_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ele_ac_kicker(tao, ele_id='1@0>>1', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ele_ac_kicker(ele_id="1@0>>1", which="model") + + def test_ele_cartesian_map_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field -noplot')) - ret = interface_commands.ele_cartesian_map(tao, ele_id='1@0>>1', which='model', index='1', who='base') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field") + tao.ele_cartesian_map(ele_id="1@0>>1", which="model", index="1", who="base") + + def test_ele_chamber_wall_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d -noplot')) - ret = interface_commands.ele_chamber_wall(tao, ele_id='1@0>>1', which='model', index='1', who='x') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d") + tao.ele_chamber_wall(ele_id="1@0>>1", which="model", index="1", who="x") + + def test_ele_control_var_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ele_control_var(tao, ele_id='1@0>>873', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ele_control_var(ele_id="1@0>>873", which="model") + + def test_ele_cylindrical_map_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field -noplot')) - ret = interface_commands.ele_cylindrical_map(tao, ele_id='1@0>>5', which='model', index='1', who='base') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field") + tao.ele_cylindrical_map(ele_id="1@0>>5", which="model", index="1", who="base") + + def test_ele_elec_multipoles_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ele_elec_multipoles(tao, ele_id='1@0>>1', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ele_elec_multipoles(ele_id="1@0>>1", which="model") + + def test_ele_floor_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ele_floor(tao, ele_id='1@0>>1', which='model', where='') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ele_floor(ele_id="1@0>>1", which="model", where="") + + def test_ele_floor_2(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ele_floor(tao, ele_id='1@0>>1', which='model', where='center') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ele_floor(ele_id="1@0>>1", which="model", where="center") + + def test_ele_gen_attribs_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ele_gen_attribs(tao, ele_id='1@0>>1', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ele_gen_attribs(ele_id="1@0>>1", which="model") + + def test_ele_gen_grad_map_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field -noplot')) - ret = interface_commands.ele_gen_grad_map(tao, ele_id='1@0>>9', which='model', index='1', who='derivs') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field") + tao.ele_gen_grad_map(ele_id="1@0>>9", which="model", index="1", who="derivs") + + def test_ele_grid_field_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_grid -noplot')) - ret = interface_commands.ele_grid_field(tao, ele_id='1@0>>1', which='model', index='1', who='base') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_grid") + tao.ele_grid_field(ele_id="1@0>>1", which="model", index="1", who="base") + + def test_ele_head_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ele_head(tao, ele_id='1@0>>1', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ele_head(ele_id="1@0>>1", which="model") + + def test_ele_lord_slave_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ele_lord_slave(tao, ele_id='1@0>>1', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ele_lord_slave(ele_id="1@0>>1", which="model") + + def test_ele_mat6_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ele_mat6(tao, ele_id='1@0>>1', which='model', who='mat6') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ele_mat6(ele_id="1@0>>1", which="model", who="mat6") + + def test_ele_methods_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ele_methods(tao, ele_id='1@0>>1', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ele_methods(ele_id="1@0>>1", which="model") + + def test_ele_multipoles_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ele_multipoles(tao, ele_id='1@0>>1', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ele_multipoles(ele_id="1@0>>1", which="model") + + def test_ele_orbit_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ele_orbit(tao, ele_id='1@0>>1', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ele_orbit(ele_id="1@0>>1", which="model") + + def test_ele_param_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon -noplot')) - ret = interface_commands.ele_param(tao, ele_id='1@0>>1', which='model', who='orbit.vec.1') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon") + tao.ele_param(ele_id="1@0>>1", which="model", who="orbit.vec.1") + + def test_ele_photon_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon -noplot')) - ret = interface_commands.ele_photon(tao, ele_id='1@0>>1', which='model', who='base') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon") + tao.ele_photon(ele_id="1@0>>1", which="model", who="base") + + def test_ele_spin_taylor_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_spin -noplot')) - ret = interface_commands.ele_spin_taylor(tao, ele_id='1@0>>2', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_spin") + tao.ele_spin_taylor(ele_id="1@0>>2", which="model") + + def test_ele_taylor_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_taylor -noplot')) - ret = interface_commands.ele_taylor(tao, ele_id='1@0>>34', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_taylor") + tao.ele_taylor(ele_id="1@0>>34", which="model") + + def test_ele_twiss_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ele_twiss(tao, ele_id='1@0>>1', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ele_twiss(ele_id="1@0>>1", which="model") + + def test_ele_wake_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wake -noplot')) - ret = interface_commands.ele_wake(tao, ele_id='1@0>>1', which='model', who='sr_long') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wake") + tao.ele_wake(ele_id="1@0>>1", which="model", who="sr_long") + + def test_ele_wall3d_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d -noplot')) - ret = interface_commands.ele_wall3d(tao, ele_id='1@0>>1', which='model', index='1', who='table') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d") + tao.ele_wall3d(ele_id="1@0>>1", which="model", index="1", who="table") + + def test_evaluate_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.evaluate(tao, expression='data::cbar.11[1:10]|model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.evaluate(expression="data::cbar.11[1:10]|model") + + def test_em_field_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.em_field(tao, ele_id='1@0>>22', which='model', x='0', y='0', z='0', t_or_z='0') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.em_field(ele_id="1@0>>22", which="model", x="0", y="0", z="0", t_or_z="0") + + def test_enum_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.enum(tao, enum_name='tracking_method') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.enum(enum_name="tracking_method") + + def test_floor_plan_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.floor_plan(tao, graph='r13.g') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.floor_plan(graph="r13.g") + + def test_floor_orbit_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_floor_orbit -noplot')) - ret = interface_commands.floor_orbit(tao, graph='r33.g') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_floor_orbit" + ) + tao.floor_orbit(graph="r33.g") + + def test_tao_global_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.tao_global(tao) - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.tao_global() + + def test_global_optimization_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.global_optimization(tao) - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.global_optimization() + + def test_global_opti_de_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.global_opti_de(tao) - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.global_opti_de() + + def test_help_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.help(tao) - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.help() + + def test_inum_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.inum(tao, who='ix_universe') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.inum(who="ix_universe") + + def test_lat_calc_done_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.lat_calc_done(tao, branch_name='1@0') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.lat_calc_done(branch_name="1@0") + + def test_lat_ele_list_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.lat_ele_list(tao, branch_name='1@0') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.lat_ele_list(branch_name="1@0") + + def test_lat_branch_list_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.lat_branch_list(tao, ix_uni='1') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.lat_branch_list(ix_uni="1") + + def test_lat_list_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.lat_list(tao, ix_uni='1', ix_branch='0', elements='Q*', which='model', who='orbit.floor.x') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.lat_list( + ix_uni="1", ix_branch="0", elements="Q*", which="model", who="orbit.floor.x" + ) + + def test_lat_list_2(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.lat_list(tao, ix_uni='1', ix_branch='0', elements='Q*', which='design', who='ele.ix_ele') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.lat_list( + ix_uni="1", ix_branch="0", elements="Q*", which="design", who="ele.ix_ele" + ) + + def test_lat_param_units_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.lat_param_units(tao, param_name='L') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.lat_param_units(param_name="L") + + def test_matrix_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.matrix(tao, ele1_id='1@0>>q01w|design', ele2_id='q02w') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.matrix(ele1_id="1@0>>q01w|design", ele2_id="q02w") + + def test_merit_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.merit(tao) - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.merit() + + def test_orbit_at_s_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.orbit_at_s(tao, ix_uni='1', ele='10', s_offset='0.7', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.orbit_at_s(ix_uni="1", ele="10", s_offset="0.7", which="model") + + def test_place_buffer_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.place_buffer(tao) - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.place_buffer() + + def test_plot_curve_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.plot_curve(tao, curve_name='r13.g.a') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.plot_curve(curve_name="r13.g.a") + + def test_plot_lat_layout_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.plot_lat_layout(tao, ix_uni='1', ix_branch='0') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.plot_lat_layout(ix_uni="1", ix_branch="0") + + def test_plot_list_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.plot_list(tao, r_or_g='r') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.plot_list(r_or_g="r") + + def test_plot_graph_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.plot_graph(tao, graph_name='beta.g') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.plot_graph(graph_name="beta.g") + + def test_plot_histogram_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.plot_histogram(tao, curve_name='r33.g.x') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.plot_histogram(curve_name="r33.g.x") + + def test_plot_template_manage_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.plot_template_manage(tao, template_location='@T1', template_name='beta', n_graph='2', graph_names='g1^^g2') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.plot_template_manage( + template_location="@T1", template_name="beta", n_graph="2", graph_names="g1^^g2" + ) + + def test_plot_curve_manage_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.plot_curve_manage(tao, graph_name='beta.g', curve_index='1', curve_name='r13.g.a') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.plot_curve_manage(graph_name="beta.g", curve_index="1", curve_name="r13.g.a") + + def test_plot_graph_manage_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.plot_graph_manage(tao, plot_name='beta', graph_index='1', graph_name='beta.g') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.plot_graph_manage(plot_name="beta", graph_index="1", graph_name="beta.g") + + def test_plot_line_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting -noplot')) - ret = interface_commands.plot_line(tao, region_name='beta', graph_name='g', curve_name='a', x_or_y='') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" + ) + tao.plot_line(region_name="beta", graph_name="g", curve_name="a", x_or_y="") + + def test_plot_line_2(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting -noplot')) - ret = interface_commands.plot_line(tao, region_name='beta', graph_name='g', curve_name='a', x_or_y='y') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" + ) + tao.plot_line(region_name="beta", graph_name="g", curve_name="a", x_or_y="y") + + def test_plot_symbol_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting -noplot')) - ret = interface_commands.plot_symbol(tao, region_name='r13', graph_name='g', curve_name='a', x_or_y='') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" + ) + tao.plot_symbol(region_name="r13", graph_name="g", curve_name="a", x_or_y="") + + def test_plot_symbol_2(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting -noplot')) - ret = interface_commands.plot_symbol(tao, region_name='r13', graph_name='g', curve_name='a', x_or_y='y') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" + ) + tao.plot_symbol(region_name="r13", graph_name="g", curve_name="a", x_or_y="y") + + def test_plot_transfer_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.plot_transfer(tao, from_plot='r13', to_plot='r23') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.plot_transfer(from_plot="r13", to_plot="r23") + + def test_plot1_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.plot1(tao, name='beta') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.plot1(name="beta") + + def test_ptc_com_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ptc_com(tao) - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ptc_com() + + def test_ring_general_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.ring_general(tao, ix_uni='1', ix_branch='0', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.ring_general(ix_uni="1", ix_branch="0", which="model") + + def test_shape_list_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.shape_list(tao, who='floor_plan') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.shape_list(who="floor_plan") + + def test_shape_manage_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.shape_manage(tao, who='floor_plan', index='1', add_or_delete='add') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.shape_manage(who="floor_plan", index="1", add_or_delete="add") + + def test_shape_pattern_list_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape -noplot')) - ret = interface_commands.shape_pattern_list(tao, ix_pattern='') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape") + tao.shape_pattern_list(ix_pattern="") + + def test_shape_pattern_manage_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape -noplot')) - ret = interface_commands.shape_pattern_manage(tao, ix_pattern='1', pat_name='new_pat', pat_line_width='1') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape") + tao.shape_pattern_manage(ix_pattern="1", pat_name="new_pat", pat_line_width="1") + + def test_shape_pattern_point_manage_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape -noplot')) - ret = interface_commands.shape_pattern_point_manage(tao, ix_pattern='1', ix_point='1', s='0', x='0') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape") + tao.shape_pattern_point_manage(ix_pattern="1", ix_point="1", s="0", x="0") + + def test_shape_set_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.shape_set(tao, who='floor_plan', shape_index='1', ele_name='Q1', shape='circle', color='', shape_size='', type_label='', shape_draw='', multi_shape='', line_width='') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.shape_set( + who="floor_plan", + shape_index="1", + ele_name="Q1", + shape="circle", + color="", + shape_size="", + type_label="", + shape_draw="", + multi_shape="", + line_width="", + ) + + def test_show_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.show(tao, line='-python') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.show(line="-python") + + def test_space_charge_com_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.space_charge_com(tao) - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.space_charge_com() + + def test_species_to_int_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.species_to_int(tao, species_str='electron') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.species_to_int(species_str="electron") + + def test_species_to_str_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.species_to_str(tao, species_int='-1') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.species_to_str(species_int="-1") + + def test_spin_invariant_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.spin_invariant(tao, who='l0', ix_uni='1', ix_branch='0', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.spin_invariant(who="l0", ix_uni="1", ix_branch="0", which="model") + + def test_spin_polarization_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.spin_polarization(tao, ix_uni='1', ix_branch='0', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.spin_polarization(ix_uni="1", ix_branch="0", which="model") + + def test_spin_resonance_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.spin_resonance(tao, ix_uni='1', ix_branch='0', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.spin_resonance(ix_uni="1", ix_branch="0", which="model") + + def test_super_universe_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.super_universe(tao) - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.super_universe() + + def test_taylor_map_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.taylor_map(tao, ele1_id='1@0>>q01w|design', ele2_id='q02w', order='1') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.taylor_map(ele1_id="1@0>>q01w|design", ele2_id="q02w", order="1") + + def test_twiss_at_s_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.twiss_at_s(tao, ix_uni='1', ele='10', s_offset='0.7', which='model') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.twiss_at_s(ix_uni="1", ele="10", s_offset="0.7", which="model") + + def test_universe_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.universe(tao, ix_uni='1') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.universe(ix_uni="1") + + def test_var_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.var(tao, var='quad[1]', slaves='') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.var(var="quad[1]", slaves="") + + def test_var_2(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.var(tao, var='quad[1]', slaves='slaves') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.var(var="quad[1]", slaves="slaves") + + def test_var_create_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching -noplot')) - ret = interface_commands.var_create(tao, var_name='quad[1]', ele_name='Q1', attribute='L', universes='1', weight='0.001', step='0.001', low_lim='-10', high_lim='10', merit_type='', good_user='T', key_bound='T', key_delta='0.01') - - + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.var_create( + var_name="quad[1]", + ele_name="Q1", + attribute="L", + universes="1", + weight="0.001", + step="0.001", + low_lim="-10", + high_lim="10", + merit_type="", + good_user="T", + key_bound="T", + key_delta="0.01", + ) + + def test_var_general_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.var_general(tao) - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.var_general() + + def test_var_v_array_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.var_v_array(tao, v1_var='quad_k1') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.var_v_array(v1_var="quad_k1") + + def test_var_v1_array_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.var_v1_array(tao, v1_var='quad_k1') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.var_v1_array(v1_var="quad_k1") + + def test_var_v1_create_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.var_v1_create(tao, v1_name='quad_k1', n_var_min='0', n_var_max='45') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.var_v1_create(v1_name="quad_k1", n_var_min="0", n_var_max="45") + + def test_var_v1_destroy_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.var_v1_destroy(tao, v1_datum='quad_k1') - - + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.var_v1_destroy(v1_datum="quad_k1") + + def test_wave_1(): - - tao = Tao(os.path.expandvars('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')) - ret = interface_commands.wave(tao, who='params') - - \ No newline at end of file + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.wave(who="params") From fbdc8b7e3b0402e951b7eca3e9d56365a5b4d32d Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 13 Jun 2024 16:14:11 -0700 Subject: [PATCH 03/15] CLN: remove extra_commands --- docs/api/pytao.md | 2 - pytao/tao_ctypes/core.py | 3 -- pytao/tao_ctypes/extra_commands.py | 66 ------------------------------ 3 files changed, 71 deletions(-) delete mode 100644 pytao/tao_ctypes/extra_commands.py diff --git a/docs/api/pytao.md b/docs/api/pytao.md index eb990832..78243d86 100644 --- a/docs/api/pytao.md +++ b/docs/api/pytao.md @@ -1,3 +1 @@ ::: pytao.Tao -::: pytao.interface_commands -::: pytao.tao_ctypes.extra_commands diff --git a/pytao/tao_ctypes/core.py b/pytao/tao_ctypes/core.py index e5016acb..40960c4b 100644 --- a/pytao/tao_ctypes/core.py +++ b/pytao/tao_ctypes/core.py @@ -45,9 +45,6 @@ def __init__(self, init='', so_lib=''): # we try to autogenerate it will complain about the broken # interface_commands file. - from pytao import interface_commands - from pytao.tao_ctypes import extra_commands - # Library needs to be set. self.so_lib_file = None if so_lib == '': diff --git a/pytao/tao_ctypes/extra_commands.py b/pytao/tao_ctypes/extra_commands.py deleted file mode 100644 index 08485f28..00000000 --- a/pytao/tao_ctypes/extra_commands.py +++ /dev/null @@ -1,66 +0,0 @@ -import numpy as np - -# These methods will be added to the Tao class -# Skip these: -__deny_list = ['np'] - - - -def bunch_data(tao, ele_id, *, which='model', ix_bunch=1, verbose=False): - """ - Returns bunch data in openPMD-beamphysics format/notation. - - Notes - ----- - Note that Tao's 'write beam' will also write a proper h5 file in this format. - - Expected usage: - data = bunch_data(tao, 'end') - from pmd_beamphysics import ParticleGroup - P = ParicleGroup(data=data) - - - Returns - ------- - data : dict - dict of arrays, with keys 'x', 'px', 'y', 'py', 't', 'pz', 'status', 'weight', 'z', 'species' - - - Examples - -------- - Example: 1 - init: $ACC_ROOT_DIR/tao/examples/csr_beam_tracking/tao.init - args: - ele_id: end - which: model - ix_bunch: 1 - - """ - - # Get species - stats = tao.bunch_params(ele_id, which=which, verbose=verbose) - species = stats['species'] - - dat = {} - for coordinate in ['x', 'px', 'y', 'py', 't', 'pz', 'p0c', 'charge', 'state']: - dat[coordinate] = tao.bunch1(ele_id, coordinate=coordinate, which=which, ix_bunch=ix_bunch, verbose=verbose) - - # Remove normalizations - p0c = dat.pop('p0c') - - dat['status'] = dat.pop('state') - dat['weight'] = dat.pop('charge') - - # px from Bmad is px/p0c - # pz from Bmad is delta = p/p0c -1. - # pz = sqrt( (delta+1)**2 -px**2 -py**2)*p0c - dat['pz'] = np.sqrt((dat['pz'] + 1)**2 - dat['px']**2 - dat['py']**2) * p0c - dat['px'] = dat['px']*p0c - dat['py'] = dat['py']*p0c - - # z = 0 by definition - dat['z'] = np.full(len(dat['x']), 0) - - dat['species'] = species.lower() - - return dat \ No newline at end of file From aba71094f9ea94604ee038309ea9e02fba780885 Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Wed, 26 Jun 2024 16:56:02 -0700 Subject: [PATCH 04/15] ENH: add a bunch of custom parsers --- generate_interface_commands.py | 32 +- interface.tpl.py | 3 +- pytao/interface_commands.py | 117 +-- pytao/tests/test_interface_commands.py | 718 +++++++++------ pytao/tests/test_parsers.py | 349 ++++++++ pytao/util/parsers.py | 1127 +++++++++++++++++++++--- 6 files changed, 1826 insertions(+), 520 deletions(-) create mode 100644 pytao/tests/test_parsers.py diff --git a/generate_interface_commands.py b/generate_interface_commands.py index 6acda004..6ed31cba 100644 --- a/generate_interface_commands.py +++ b/generate_interface_commands.py @@ -152,7 +152,18 @@ def generate_method_code(docs, method, command, returns): print() + +# TODO: bring these back to bmad +hotfixes = { + "var": { + "command_str": "python var {var} {slaves}", + } +} + for method, metadata in cmds_from_tao.items(): + if method in hotfixes: + metadata.update(hotfixes[method]) + docstring = metadata['description'] command_str = sanitize(metadata['command_str']) @@ -164,6 +175,7 @@ def generate_method_code(docs, method, command, returns): code = generate_method_code(np_docs, clean_method, command_str, np_docs['Returns']) except Exception as ex: print(f'***Error generating code for: {method}. Exception was: {ex}') + raise method_template = f''' def {clean_method}({params}): @@ -215,13 +227,26 @@ def get_tests(examples): # Generated on: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")} # ============================================================================== +import contextlib import os +import logging + +import pytest + from pytao import Tao from pytao import interface_commands def new_tao(init): return Tao(os.path.expandvars(f"{{init}} -noplot")) - + + +@contextlib.contextmanager +def ensure_successful_parsing(caplog): + yield + errors = [record for record in caplog.get_records("call") if record.levelno == logging.ERROR] + for error in errors: + if "Failed to parse string data" in error.message: + pytest.fail(error.message) """] for method, metadata in cmds_from_tao.items(): @@ -239,10 +264,11 @@ def new_tao(init): args = [f"{k}='{v}'" for k, v in test_meta['args'].items()] test_code = f''' tao = new_tao('{test_meta['init']}') -tao.{clean_method}({', '.join(args)}) +with ensure_successful_parsing(caplog): + tao.{clean_method}({', '.join(args)}) ''' method_template = f''' -def test_{clean_method}_{test_name}(): +def test_{clean_method}_{test_name}(caplog): {add_tabs(test_code, 1)} ''' cmds_to_test_module.append(method_template) diff --git a/interface.tpl.py b/interface.tpl.py index 1070a6a1..5935c3f9 100644 --- a/interface.tpl.py +++ b/interface.tpl.py @@ -53,7 +53,7 @@ def __execute( ret = func(cmd, raises=raises) special_parser = getattr(_pytao_parsers, f"parse_{method_name}", "") if special_parser and callable(special_parser): - data = special_parser(ret) + data = special_parser(ret, cmd=cmd) return data if "string" in cmd_type: try: @@ -63,6 +63,7 @@ def __execute( data = tao_parameter_dict(ret) except Exception: logger.exception("Failed to parse string data. Returning raw value.") + raise # TODO: remove me return ret return data diff --git a/pytao/interface_commands.py b/pytao/interface_commands.py index 008ff5c2..371208f3 100644 --- a/pytao/interface_commands.py +++ b/pytao/interface_commands.py @@ -2,7 +2,7 @@ # AUTOGENERATED FILE - DO NOT MODIFY # This file was generated by the script `generate_interface_commands.py`. # Any modifications may be overwritten. -# Generated on: 2024-06-25 10:43:22 +# Generated on: 2024-06-26 16:52:44 # ============================================================================== import logging @@ -60,7 +60,7 @@ def __execute( ret = func(cmd, raises=raises) special_parser = getattr(_pytao_parsers, f"parse_{method_name}", "") if special_parser and callable(special_parser): - data = special_parser(ret) + data = special_parser(ret, cmd=cmd) return data if "string" in cmd_type: try: @@ -70,6 +70,7 @@ def __execute( data = tao_parameter_dict(ret) except Exception: logger.exception("Failed to parse string data. Returning raw value.") + raise # TODO: remove me return ret return data @@ -495,7 +496,7 @@ def building_wall_list( Returns ------- - string_list + datums: list of dicts Notes ----- @@ -543,7 +544,7 @@ def building_wall_graph(self, graph, *, verbose=False, as_dict=True, raises=True Returns ------- - string_list + datums: list of dicts Notes ----- @@ -706,7 +707,7 @@ def constraints(self, who, *, verbose=False, as_dict=True, raises=True): Returns ------- - string_list + datums: list of dicts Notes ----- @@ -953,7 +954,7 @@ def data_d1_array( Returns ------- - string_list + datums: list of dicts Notes ----- @@ -1029,7 +1030,7 @@ def data_d2_array(self, ix_uni, *, verbose=False, as_dict=True, raises=True): Returns ------- - string_list + datums: list of str Notes ----- @@ -1187,7 +1188,7 @@ def data_parameter( Returns ------- - string_list + datums : list Notes ----- @@ -1369,7 +1370,7 @@ def datum_has_ele(self, datum_type, *, verbose=False, as_dict=True, raises=True) Returns ------- - string_list + datums : list of values Notes ----- @@ -1557,7 +1558,7 @@ def ele_chamber_wall( Returns ------- - string_list + datums : list of values Notes ----- @@ -1713,7 +1714,7 @@ def ele_elec_multipoles( Returns ------- - string_list + info : dict Notes ----- @@ -1886,7 +1887,7 @@ def ele_gen_grad_map( Returns ------- - string_list + info : dict Notes ----- @@ -2039,7 +2040,7 @@ def ele_lord_slave( Returns ------- - string_list + info : dict Notes ----- @@ -2188,10 +2189,6 @@ def ele_multipoles( ele_id which : default=model - Returns - ------- - string_list - Notes ----- Command syntax: @@ -2380,10 +2377,6 @@ def ele_spin_taylor( ele_id which : default=model - Returns - ------- - string_list - Notes ----- Command syntax: @@ -2425,10 +2418,6 @@ def ele_taylor( ele_id which : default=model - Returns - ------- - string_list - Notes ----- Command syntax: @@ -2576,10 +2565,6 @@ def ele_wall3d( who which : default=model - Returns - ------- - string_list - Notes ----- Command syntax: @@ -2697,10 +2682,6 @@ def em_field( t_or_z which : default=model - Returns - ------- - string_list - Notes ----- Command syntax: @@ -2741,10 +2722,6 @@ def enum(self, enum_name, *, verbose=False, as_dict=True, raises=True): ---------- enum_name - Returns - ------- - string_list - Notes ----- Command syntax: @@ -2777,10 +2754,6 @@ def floor_plan(self, graph, *, verbose=False, as_dict=True, raises=True): ---------- graph - Returns - ------- - string_list - Notes ----- Command syntax: @@ -2810,10 +2783,6 @@ def floor_orbit(self, graph, *, verbose=False, as_dict=True, raises=True): ---------- graph - Returns - ------- - string_list - Notes ----- Command syntax: @@ -2943,7 +2912,7 @@ def help(self, *, verbose=False, as_dict=True, raises=True): Returns ------- - string_list + str Notes ----- @@ -2976,7 +2945,7 @@ def inum(self, who, *, verbose=False, as_dict=True, raises=True): Returns ------- - string_list + list of int Notes ----- @@ -3010,7 +2979,7 @@ def lat_calc_done(self, branch_name, *, verbose=False, as_dict=True, raises=True Returns ------- - string_list + bool Notes ----- @@ -3079,7 +3048,7 @@ def lat_branch_list(self, *, ix_uni="", verbose=False, as_dict=True, raises=True Returns ------- - string_list + list of dict Notes ----- @@ -3233,7 +3202,7 @@ def lat_param_units(self, param_name, *, verbose=False, as_dict=True, raises=Tru Returns ------- - string_list + str Notes ----- @@ -3466,7 +3435,7 @@ def plot_lat_layout( Returns ------- - string_list + list of dict Notes ----- @@ -3548,7 +3517,7 @@ def plot_graph(self, graph_name, *, verbose=False, as_dict=True, raises=True): Returns ------- - string_list + dict Notes ----- @@ -4088,10 +4057,6 @@ def shape_list(self, who, *, verbose=False, as_dict=True, raises=True): ---------- who - Returns - ------- - string_list - Notes ----- Command syntax: @@ -4180,10 +4145,6 @@ def shape_pattern_list( ---------- ix_pattern : optional - Returns - ------- - string_list - Notes ----- Command syntax: @@ -4397,10 +4358,6 @@ def show(self, line, *, verbose=False, as_dict=True, raises=True): ---------- line - Returns - ------- - string_list - Notes ----- Command syntax: @@ -4464,10 +4421,6 @@ def species_to_int(self, species_str, *, verbose=False, as_dict=True, raises=Tru ---------- species_str - Returns - ------- - string_list - Notes ----- Command syntax: @@ -4500,10 +4453,6 @@ def species_to_str(self, species_int, *, verbose=False, as_dict=True, raises=Tru ---------- species_int - Returns - ------- - string_list - Notes ----- Command syntax: @@ -4636,7 +4585,7 @@ def spin_polarization( Returns ------- - string_list + dict Notes ----- @@ -4700,10 +4649,6 @@ def spin_resonance( ref_ele : default=0 Reference element to calculate at. - Returns - ------- - string_list - Notes ----- Command syntax: @@ -4741,10 +4686,6 @@ def super_universe(self, *, verbose=False, as_dict=True, raises=True): Output super_Universe parameters. - Returns - ------- - string_list - Notes ----- Command syntax: @@ -4916,10 +4857,6 @@ def var(self, var, *, slaves="", verbose=False, as_dict=True, raises=True): var slaves : optional - Returns - ------- - string_list - Notes ----- Command syntax: @@ -4940,7 +4877,7 @@ def var(self, var, *, slaves="", verbose=False, as_dict=True, raises=True): slaves: slaves """ - cmd = f"python var {var} slaves" + cmd = f"python var {var} {slaves}" if verbose: print(cmd) return self.__execute( @@ -5031,10 +4968,6 @@ def var_general(self, *, verbose=False, as_dict=True, raises=True): Output list of all variable v1 arrays - Returns - ------- - string_list - Notes ----- Command syntax: @@ -5098,10 +5031,6 @@ def var_v1_array(self, v1_var, *, verbose=False, as_dict=True, raises=True): ---------- v1_var - Returns - ------- - string_list - Notes ----- Command syntax: diff --git a/pytao/tests/test_interface_commands.py b/pytao/tests/test_interface_commands.py index 8f0a1510..20bf4aa3 100644 --- a/pytao/tests/test_interface_commands.py +++ b/pytao/tests/test_interface_commands.py @@ -2,10 +2,15 @@ # AUTOGENERATED FILE - DO NOT MODIFY # This file was generated by the script `generate_interface_commands.py`. # Any modifications may be overwritten. -# Generated on: 2024-06-25 10:43:22 +# Generated on: 2024-06-26 16:52:44 # ============================================================================== +import contextlib import os +import logging + +import pytest + from pytao import Tao from pytao import interface_commands @@ -14,695 +19,828 @@ def new_tao(init): return Tao(os.path.expandvars(f"{init} -noplot")) -def test_beam_1(): +@contextlib.contextmanager +def ensure_successful_parsing(caplog): + yield + errors = [ + record + for record in caplog.get_records("call") + if record.levelno == logging.ERROR + ] + for error in errors: + if "Failed to parse string data" in error.message: + pytest.fail(error.message) + + +def test_beam_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" ) - tao.beam(ix_uni="1", ix_branch="0") + with ensure_successful_parsing(caplog): + tao.beam(ix_uni="1", ix_branch="0") -def test_beam_init_1(): +def test_beam_init_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" ) - tao.beam_init(ix_uni="1", ix_branch="0") + with ensure_successful_parsing(caplog): + tao.beam_init(ix_uni="1", ix_branch="0") -def test_bmad_com_1(): +def test_bmad_com_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.bmad_com() + with ensure_successful_parsing(caplog): + tao.bmad_com() -def test_branch1_1(): +def test_branch1_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.branch1(ix_uni="1", ix_branch="0") + with ensure_successful_parsing(caplog): + tao.branch1(ix_uni="1", ix_branch="0") -def test_bunch_comb_1(): +def test_bunch_comb_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" ) - tao.bunch_comb(who="x.beta") + with ensure_successful_parsing(caplog): + tao.bunch_comb(who="x.beta") -def test_bunch_params_1(): +def test_bunch_params_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" ) - tao.bunch_params(ele_id="end", which="model") + with ensure_successful_parsing(caplog): + tao.bunch_params(ele_id="end", which="model") -def test_bunch1_1(): +def test_bunch1_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" ) - tao.bunch1(ele_id="end", coordinate="x", which="model", ix_bunch="1") + with ensure_successful_parsing(caplog): + tao.bunch1(ele_id="end", coordinate="x", which="model", ix_bunch="1") -def test_building_wall_list_1(): +def test_building_wall_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") - tao.building_wall_list(ix_section="") + with ensure_successful_parsing(caplog): + tao.building_wall_list(ix_section="") -def test_building_wall_list_2(): +def test_building_wall_list_2(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") - tao.building_wall_list(ix_section="1") + with ensure_successful_parsing(caplog): + tao.building_wall_list(ix_section="1") -def test_building_wall_graph_1(): +def test_building_wall_graph_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") - tao.building_wall_graph(graph="floor_plan.g") + with ensure_successful_parsing(caplog): + tao.building_wall_graph(graph="floor_plan.g") -def test_building_wall_point_1(): +def test_building_wall_point_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") - tao.building_wall_point( - ix_section="1", - ix_point="1", - z="0", - x="0", - radius="0", - z_center="0", - x_center="0", - ) + with ensure_successful_parsing(caplog): + tao.building_wall_point( + ix_section="1", + ix_point="1", + z="0", + x="0", + radius="0", + z_center="0", + x_center="0", + ) -def test_building_wall_section_1(): +def test_building_wall_section_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.building_wall_section(ix_section="1", sec_name="test", sec_constraint="none") + with ensure_successful_parsing(caplog): + tao.building_wall_section( + ix_section="1", sec_name="test", sec_constraint="none" + ) -def test_constraints_1(): +def test_constraints_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.constraints(who="data") + with ensure_successful_parsing(caplog): + tao.constraints(who="data") -def test_constraints_2(): +def test_constraints_2(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.constraints(who="var") + with ensure_successful_parsing(caplog): + tao.constraints(who="var") -def test_data_1(): +def test_data_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.data(ix_uni="", d2_name="twiss", d1_name="end", dat_index="1") + with ensure_successful_parsing(caplog): + tao.data(ix_uni="", d2_name="twiss", d1_name="end", dat_index="1") -def test_data_2(): +def test_data_2(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.data(ix_uni="1", d2_name="twiss", d1_name="end", dat_index="1") + with ensure_successful_parsing(caplog): + tao.data(ix_uni="1", d2_name="twiss", d1_name="end", dat_index="1") -def test_data_d_array_1(): +def test_data_d_array_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.data_d_array(ix_uni="1", d2_name="twiss", d1_name="end") + with ensure_successful_parsing(caplog): + tao.data_d_array(ix_uni="1", d2_name="twiss", d1_name="end") -def test_data_d1_array_1(): +def test_data_d1_array_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.data_d1_array(ix_uni="1", d2_datum="twiss") + with ensure_successful_parsing(caplog): + tao.data_d1_array(ix_uni="1", d2_datum="twiss") -def test_data_d2_1(): +def test_data_d2_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.data_d2(ix_uni="1", d2_name="twiss") + with ensure_successful_parsing(caplog): + tao.data_d2(ix_uni="1", d2_name="twiss") -def test_data_d2_array_1(): +def test_data_d2_array_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.data_d2_array(ix_uni="1") + with ensure_successful_parsing(caplog): + tao.data_d2_array(ix_uni="1") -def test_data_d2_create_1(): +def test_data_d2_create_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.data_d2_create( - ix_uni="1", - d2_name="orbit", - n_d1_data="2", - d_data_arrays_name_min_max="x^^0^^45^^y^^1^^47", - ) + with ensure_successful_parsing(caplog): + tao.data_d2_create( + ix_uni="1", + d2_name="orbit", + n_d1_data="2", + d_data_arrays_name_min_max="x^^0^^45^^y^^1^^47", + ) -def test_data_d2_destroy_1(): +def test_data_d2_destroy_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.data_d2_destroy(d2_name="orbit") + with ensure_successful_parsing(caplog): + tao.data_d2_destroy(d2_name="orbit") -def test_data_parameter_1(): +def test_data_parameter_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.data_parameter(data_array="twiss.end", parameter="model_value") + with ensure_successful_parsing(caplog): + tao.data_parameter(data_array="twiss.end", parameter="model_value") -def test_data_set_design_value_1(): +def test_data_set_design_value_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.data_set_design_value() + with ensure_successful_parsing(caplog): + tao.data_set_design_value() -def test_datum_create_1(): +def test_datum_create_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.datum_create( - datum_name="twiss.end[6]", - data_type="beta.y", - ele_ref_name="", - ele_start_name="", - ele_name="P1", - merit_type="target", - meas="0", - good_meas="T", - ref="0", - good_ref="T", - weight="0.3", - good_user="T", - data_source="lat", - eval_point="END", - s_offset="0", - ix_bunch="1", - invalid_value="0", - ) - - -def test_datum_has_ele_1(): + with ensure_successful_parsing(caplog): + tao.datum_create( + datum_name="twiss.end[6]", + data_type="beta.y", + ele_ref_name="", + ele_start_name="", + ele_name="P1", + merit_type="target", + meas="0", + good_meas="T", + ref="0", + good_ref="T", + weight="0.3", + good_user="T", + data_source="lat", + eval_point="END", + s_offset="0", + ix_bunch="1", + invalid_value="0", + ) + + +def test_datum_has_ele_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.datum_has_ele(datum_type="twiss.end") + with ensure_successful_parsing(caplog): + tao.datum_has_ele(datum_type="twiss.end") -def test_derivative_1(): +def test_derivative_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.derivative() + with ensure_successful_parsing(caplog): + tao.derivative() -def test_ele_ac_kicker_1(): +def test_ele_ac_kicker_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ele_ac_kicker(ele_id="1@0>>1", which="model") + with ensure_successful_parsing(caplog): + tao.ele_ac_kicker(ele_id="1@0>>1", which="model") -def test_ele_cartesian_map_1(): +def test_ele_cartesian_map_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field") - tao.ele_cartesian_map(ele_id="1@0>>1", which="model", index="1", who="base") + with ensure_successful_parsing(caplog): + tao.ele_cartesian_map(ele_id="1@0>>1", which="model", index="1", who="base") -def test_ele_chamber_wall_1(): +def test_ele_chamber_wall_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d") - tao.ele_chamber_wall(ele_id="1@0>>1", which="model", index="1", who="x") + with ensure_successful_parsing(caplog): + tao.ele_chamber_wall(ele_id="1@0>>1", which="model", index="1", who="x") -def test_ele_control_var_1(): +def test_ele_control_var_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ele_control_var(ele_id="1@0>>873", which="model") + with ensure_successful_parsing(caplog): + tao.ele_control_var(ele_id="1@0>>873", which="model") -def test_ele_cylindrical_map_1(): +def test_ele_cylindrical_map_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field") - tao.ele_cylindrical_map(ele_id="1@0>>5", which="model", index="1", who="base") + with ensure_successful_parsing(caplog): + tao.ele_cylindrical_map(ele_id="1@0>>5", which="model", index="1", who="base") -def test_ele_elec_multipoles_1(): +def test_ele_elec_multipoles_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ele_elec_multipoles(ele_id="1@0>>1", which="model") + with ensure_successful_parsing(caplog): + tao.ele_elec_multipoles(ele_id="1@0>>1", which="model") -def test_ele_floor_1(): +def test_ele_floor_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ele_floor(ele_id="1@0>>1", which="model", where="") + with ensure_successful_parsing(caplog): + tao.ele_floor(ele_id="1@0>>1", which="model", where="") -def test_ele_floor_2(): +def test_ele_floor_2(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ele_floor(ele_id="1@0>>1", which="model", where="center") + with ensure_successful_parsing(caplog): + tao.ele_floor(ele_id="1@0>>1", which="model", where="center") -def test_ele_gen_attribs_1(): +def test_ele_gen_attribs_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ele_gen_attribs(ele_id="1@0>>1", which="model") + with ensure_successful_parsing(caplog): + tao.ele_gen_attribs(ele_id="1@0>>1", which="model") -def test_ele_gen_grad_map_1(): +def test_ele_gen_grad_map_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field") - tao.ele_gen_grad_map(ele_id="1@0>>9", which="model", index="1", who="derivs") + with ensure_successful_parsing(caplog): + tao.ele_gen_grad_map(ele_id="1@0>>9", which="model", index="1", who="derivs") -def test_ele_grid_field_1(): +def test_ele_grid_field_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_grid") - tao.ele_grid_field(ele_id="1@0>>1", which="model", index="1", who="base") + with ensure_successful_parsing(caplog): + tao.ele_grid_field(ele_id="1@0>>1", which="model", index="1", who="base") -def test_ele_head_1(): +def test_ele_head_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ele_head(ele_id="1@0>>1", which="model") + with ensure_successful_parsing(caplog): + tao.ele_head(ele_id="1@0>>1", which="model") -def test_ele_lord_slave_1(): +def test_ele_lord_slave_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ele_lord_slave(ele_id="1@0>>1", which="model") + with ensure_successful_parsing(caplog): + tao.ele_lord_slave(ele_id="1@0>>1", which="model") -def test_ele_mat6_1(): +def test_ele_mat6_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ele_mat6(ele_id="1@0>>1", which="model", who="mat6") + with ensure_successful_parsing(caplog): + tao.ele_mat6(ele_id="1@0>>1", which="model", who="mat6") -def test_ele_methods_1(): +def test_ele_methods_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ele_methods(ele_id="1@0>>1", which="model") + with ensure_successful_parsing(caplog): + tao.ele_methods(ele_id="1@0>>1", which="model") -def test_ele_multipoles_1(): +def test_ele_multipoles_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ele_multipoles(ele_id="1@0>>1", which="model") + with ensure_successful_parsing(caplog): + tao.ele_multipoles(ele_id="1@0>>1", which="model") -def test_ele_orbit_1(): +def test_ele_orbit_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ele_orbit(ele_id="1@0>>1", which="model") + with ensure_successful_parsing(caplog): + tao.ele_orbit(ele_id="1@0>>1", which="model") -def test_ele_param_1(): +def test_ele_param_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon") - tao.ele_param(ele_id="1@0>>1", which="model", who="orbit.vec.1") + with ensure_successful_parsing(caplog): + tao.ele_param(ele_id="1@0>>1", which="model", who="orbit.vec.1") -def test_ele_photon_1(): +def test_ele_photon_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon") - tao.ele_photon(ele_id="1@0>>1", which="model", who="base") + with ensure_successful_parsing(caplog): + tao.ele_photon(ele_id="1@0>>1", which="model", who="base") -def test_ele_spin_taylor_1(): +def test_ele_spin_taylor_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_spin") - tao.ele_spin_taylor(ele_id="1@0>>2", which="model") + with ensure_successful_parsing(caplog): + tao.ele_spin_taylor(ele_id="1@0>>2", which="model") -def test_ele_taylor_1(): +def test_ele_taylor_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_taylor") - tao.ele_taylor(ele_id="1@0>>34", which="model") + with ensure_successful_parsing(caplog): + tao.ele_taylor(ele_id="1@0>>34", which="model") -def test_ele_twiss_1(): +def test_ele_twiss_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ele_twiss(ele_id="1@0>>1", which="model") + with ensure_successful_parsing(caplog): + tao.ele_twiss(ele_id="1@0>>1", which="model") -def test_ele_wake_1(): +def test_ele_wake_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wake") - tao.ele_wake(ele_id="1@0>>1", which="model", who="sr_long") + with ensure_successful_parsing(caplog): + tao.ele_wake(ele_id="1@0>>1", which="model", who="sr_long") -def test_ele_wall3d_1(): +def test_ele_wall3d_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d") - tao.ele_wall3d(ele_id="1@0>>1", which="model", index="1", who="table") + with ensure_successful_parsing(caplog): + tao.ele_wall3d(ele_id="1@0>>1", which="model", index="1", who="table") -def test_evaluate_1(): +def test_evaluate_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.evaluate(expression="data::cbar.11[1:10]|model") + with ensure_successful_parsing(caplog): + tao.evaluate(expression="data::cbar.11[1:10]|model") -def test_em_field_1(): +def test_em_field_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.em_field(ele_id="1@0>>22", which="model", x="0", y="0", z="0", t_or_z="0") + with ensure_successful_parsing(caplog): + tao.em_field(ele_id="1@0>>22", which="model", x="0", y="0", z="0", t_or_z="0") -def test_enum_1(): +def test_enum_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.enum(enum_name="tracking_method") + with ensure_successful_parsing(caplog): + tao.enum(enum_name="tracking_method") -def test_floor_plan_1(): +def test_floor_plan_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.floor_plan(graph="r13.g") + with ensure_successful_parsing(caplog): + tao.floor_plan(graph="r13.g") -def test_floor_orbit_1(): +def test_floor_orbit_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_floor_orbit" ) - tao.floor_orbit(graph="r33.g") + with ensure_successful_parsing(caplog): + tao.floor_orbit(graph="r33.g") -def test_tao_global_1(): +def test_tao_global_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.tao_global() + with ensure_successful_parsing(caplog): + tao.tao_global() -def test_global_optimization_1(): +def test_global_optimization_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.global_optimization() + with ensure_successful_parsing(caplog): + tao.global_optimization() -def test_global_opti_de_1(): +def test_global_opti_de_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.global_opti_de() + with ensure_successful_parsing(caplog): + tao.global_opti_de() -def test_help_1(): +def test_help_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.help() + with ensure_successful_parsing(caplog): + tao.help() -def test_inum_1(): +def test_inum_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.inum(who="ix_universe") + with ensure_successful_parsing(caplog): + tao.inum(who="ix_universe") -def test_lat_calc_done_1(): +def test_lat_calc_done_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.lat_calc_done(branch_name="1@0") + with ensure_successful_parsing(caplog): + tao.lat_calc_done(branch_name="1@0") -def test_lat_ele_list_1(): +def test_lat_ele_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.lat_ele_list(branch_name="1@0") + with ensure_successful_parsing(caplog): + tao.lat_ele_list(branch_name="1@0") -def test_lat_branch_list_1(): +def test_lat_branch_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.lat_branch_list(ix_uni="1") + with ensure_successful_parsing(caplog): + tao.lat_branch_list(ix_uni="1") -def test_lat_list_1(): +def test_lat_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.lat_list( - ix_uni="1", ix_branch="0", elements="Q*", which="model", who="orbit.floor.x" - ) + with ensure_successful_parsing(caplog): + tao.lat_list( + ix_uni="1", ix_branch="0", elements="Q*", which="model", who="orbit.floor.x" + ) -def test_lat_list_2(): +def test_lat_list_2(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.lat_list( - ix_uni="1", ix_branch="0", elements="Q*", which="design", who="ele.ix_ele" - ) + with ensure_successful_parsing(caplog): + tao.lat_list( + ix_uni="1", ix_branch="0", elements="Q*", which="design", who="ele.ix_ele" + ) -def test_lat_param_units_1(): +def test_lat_param_units_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.lat_param_units(param_name="L") + with ensure_successful_parsing(caplog): + tao.lat_param_units(param_name="L") -def test_matrix_1(): +def test_matrix_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.matrix(ele1_id="1@0>>q01w|design", ele2_id="q02w") + with ensure_successful_parsing(caplog): + tao.matrix(ele1_id="1@0>>q01w|design", ele2_id="q02w") -def test_merit_1(): +def test_merit_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.merit() + with ensure_successful_parsing(caplog): + tao.merit() -def test_orbit_at_s_1(): +def test_orbit_at_s_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.orbit_at_s(ix_uni="1", ele="10", s_offset="0.7", which="model") + with ensure_successful_parsing(caplog): + tao.orbit_at_s(ix_uni="1", ele="10", s_offset="0.7", which="model") -def test_place_buffer_1(): +def test_place_buffer_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.place_buffer() + with ensure_successful_parsing(caplog): + tao.place_buffer() -def test_plot_curve_1(): +def test_plot_curve_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.plot_curve(curve_name="r13.g.a") + with ensure_successful_parsing(caplog): + tao.plot_curve(curve_name="r13.g.a") -def test_plot_lat_layout_1(): +def test_plot_lat_layout_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.plot_lat_layout(ix_uni="1", ix_branch="0") + with ensure_successful_parsing(caplog): + tao.plot_lat_layout(ix_uni="1", ix_branch="0") -def test_plot_list_1(): +def test_plot_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.plot_list(r_or_g="r") + with ensure_successful_parsing(caplog): + tao.plot_list(r_or_g="r") -def test_plot_graph_1(): +def test_plot_graph_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.plot_graph(graph_name="beta.g") + with ensure_successful_parsing(caplog): + tao.plot_graph(graph_name="beta.g") -def test_plot_histogram_1(): +def test_plot_histogram_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.plot_histogram(curve_name="r33.g.x") + with ensure_successful_parsing(caplog): + tao.plot_histogram(curve_name="r33.g.x") -def test_plot_template_manage_1(): +def test_plot_template_manage_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.plot_template_manage( - template_location="@T1", template_name="beta", n_graph="2", graph_names="g1^^g2" - ) + with ensure_successful_parsing(caplog): + tao.plot_template_manage( + template_location="@T1", + template_name="beta", + n_graph="2", + graph_names="g1^^g2", + ) -def test_plot_curve_manage_1(): +def test_plot_curve_manage_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.plot_curve_manage(graph_name="beta.g", curve_index="1", curve_name="r13.g.a") + with ensure_successful_parsing(caplog): + tao.plot_curve_manage( + graph_name="beta.g", curve_index="1", curve_name="r13.g.a" + ) -def test_plot_graph_manage_1(): +def test_plot_graph_manage_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.plot_graph_manage(plot_name="beta", graph_index="1", graph_name="beta.g") + with ensure_successful_parsing(caplog): + tao.plot_graph_manage(plot_name="beta", graph_index="1", graph_name="beta.g") -def test_plot_line_1(): +def test_plot_line_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" ) - tao.plot_line(region_name="beta", graph_name="g", curve_name="a", x_or_y="") + with ensure_successful_parsing(caplog): + tao.plot_line(region_name="beta", graph_name="g", curve_name="a", x_or_y="") -def test_plot_line_2(): +def test_plot_line_2(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" ) - tao.plot_line(region_name="beta", graph_name="g", curve_name="a", x_or_y="y") + with ensure_successful_parsing(caplog): + tao.plot_line(region_name="beta", graph_name="g", curve_name="a", x_or_y="y") -def test_plot_symbol_1(): +def test_plot_symbol_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" ) - tao.plot_symbol(region_name="r13", graph_name="g", curve_name="a", x_or_y="") + with ensure_successful_parsing(caplog): + tao.plot_symbol(region_name="r13", graph_name="g", curve_name="a", x_or_y="") -def test_plot_symbol_2(): +def test_plot_symbol_2(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" ) - tao.plot_symbol(region_name="r13", graph_name="g", curve_name="a", x_or_y="y") + with ensure_successful_parsing(caplog): + tao.plot_symbol(region_name="r13", graph_name="g", curve_name="a", x_or_y="y") -def test_plot_transfer_1(): +def test_plot_transfer_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.plot_transfer(from_plot="r13", to_plot="r23") + with ensure_successful_parsing(caplog): + tao.plot_transfer(from_plot="r13", to_plot="r23") -def test_plot1_1(): +def test_plot1_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.plot1(name="beta") + with ensure_successful_parsing(caplog): + tao.plot1(name="beta") -def test_ptc_com_1(): +def test_ptc_com_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ptc_com() + with ensure_successful_parsing(caplog): + tao.ptc_com() -def test_ring_general_1(): +def test_ring_general_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.ring_general(ix_uni="1", ix_branch="0", which="model") + with ensure_successful_parsing(caplog): + tao.ring_general(ix_uni="1", ix_branch="0", which="model") -def test_shape_list_1(): +def test_shape_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.shape_list(who="floor_plan") + with ensure_successful_parsing(caplog): + tao.shape_list(who="floor_plan") -def test_shape_manage_1(): +def test_shape_manage_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.shape_manage(who="floor_plan", index="1", add_or_delete="add") + with ensure_successful_parsing(caplog): + tao.shape_manage(who="floor_plan", index="1", add_or_delete="add") -def test_shape_pattern_list_1(): +def test_shape_pattern_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape") - tao.shape_pattern_list(ix_pattern="") + with ensure_successful_parsing(caplog): + tao.shape_pattern_list(ix_pattern="") -def test_shape_pattern_manage_1(): +def test_shape_pattern_manage_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape") - tao.shape_pattern_manage(ix_pattern="1", pat_name="new_pat", pat_line_width="1") + with ensure_successful_parsing(caplog): + tao.shape_pattern_manage(ix_pattern="1", pat_name="new_pat", pat_line_width="1") -def test_shape_pattern_point_manage_1(): +def test_shape_pattern_point_manage_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape") - tao.shape_pattern_point_manage(ix_pattern="1", ix_point="1", s="0", x="0") + with ensure_successful_parsing(caplog): + tao.shape_pattern_point_manage(ix_pattern="1", ix_point="1", s="0", x="0") -def test_shape_set_1(): +def test_shape_set_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.shape_set( - who="floor_plan", - shape_index="1", - ele_name="Q1", - shape="circle", - color="", - shape_size="", - type_label="", - shape_draw="", - multi_shape="", - line_width="", - ) + with ensure_successful_parsing(caplog): + tao.shape_set( + who="floor_plan", + shape_index="1", + ele_name="Q1", + shape="circle", + color="", + shape_size="", + type_label="", + shape_draw="", + multi_shape="", + line_width="", + ) -def test_show_1(): +def test_show_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.show(line="-python") + with ensure_successful_parsing(caplog): + tao.show(line="-python") -def test_space_charge_com_1(): +def test_space_charge_com_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.space_charge_com() + with ensure_successful_parsing(caplog): + tao.space_charge_com() -def test_species_to_int_1(): +def test_species_to_int_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.species_to_int(species_str="electron") + with ensure_successful_parsing(caplog): + tao.species_to_int(species_str="electron") -def test_species_to_str_1(): +def test_species_to_str_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.species_to_str(species_int="-1") + with ensure_successful_parsing(caplog): + tao.species_to_str(species_int="-1") -def test_spin_invariant_1(): +def test_spin_invariant_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.spin_invariant(who="l0", ix_uni="1", ix_branch="0", which="model") + with ensure_successful_parsing(caplog): + tao.spin_invariant(who="l0", ix_uni="1", ix_branch="0", which="model") -def test_spin_polarization_1(): +def test_spin_polarization_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.spin_polarization(ix_uni="1", ix_branch="0", which="model") + with ensure_successful_parsing(caplog): + tao.spin_polarization(ix_uni="1", ix_branch="0", which="model") -def test_spin_resonance_1(): +def test_spin_resonance_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.spin_resonance(ix_uni="1", ix_branch="0", which="model") + with ensure_successful_parsing(caplog): + tao.spin_resonance(ix_uni="1", ix_branch="0", which="model") -def test_super_universe_1(): +def test_super_universe_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.super_universe() + with ensure_successful_parsing(caplog): + tao.super_universe() -def test_taylor_map_1(): +def test_taylor_map_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.taylor_map(ele1_id="1@0>>q01w|design", ele2_id="q02w", order="1") + with ensure_successful_parsing(caplog): + tao.taylor_map(ele1_id="1@0>>q01w|design", ele2_id="q02w", order="1") -def test_twiss_at_s_1(): +def test_twiss_at_s_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.twiss_at_s(ix_uni="1", ele="10", s_offset="0.7", which="model") + with ensure_successful_parsing(caplog): + tao.twiss_at_s(ix_uni="1", ele="10", s_offset="0.7", which="model") -def test_universe_1(): +def test_universe_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.universe(ix_uni="1") + with ensure_successful_parsing(caplog): + tao.universe(ix_uni="1") -def test_var_1(): +def test_var_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.var(var="quad[1]", slaves="") + with ensure_successful_parsing(caplog): + tao.var(var="quad[1]", slaves="") -def test_var_2(): +def test_var_2(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.var(var="quad[1]", slaves="slaves") + with ensure_successful_parsing(caplog): + tao.var(var="quad[1]", slaves="slaves") -def test_var_create_1(): +def test_var_create_1(caplog): tao = new_tao( "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) - tao.var_create( - var_name="quad[1]", - ele_name="Q1", - attribute="L", - universes="1", - weight="0.001", - step="0.001", - low_lim="-10", - high_lim="10", - merit_type="", - good_user="T", - key_bound="T", - key_delta="0.01", - ) + with ensure_successful_parsing(caplog): + tao.var_create( + var_name="quad[1]", + ele_name="Q1", + attribute="L", + universes="1", + weight="0.001", + step="0.001", + low_lim="-10", + high_lim="10", + merit_type="", + good_user="T", + key_bound="T", + key_delta="0.01", + ) -def test_var_general_1(): +def test_var_general_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.var_general() + with ensure_successful_parsing(caplog): + tao.var_general() -def test_var_v_array_1(): +def test_var_v_array_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.var_v_array(v1_var="quad_k1") + with ensure_successful_parsing(caplog): + tao.var_v_array(v1_var="quad_k1") -def test_var_v1_array_1(): +def test_var_v1_array_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.var_v1_array(v1_var="quad_k1") + with ensure_successful_parsing(caplog): + tao.var_v1_array(v1_var="quad_k1") -def test_var_v1_create_1(): +def test_var_v1_create_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.var_v1_create(v1_name="quad_k1", n_var_min="0", n_var_max="45") + with ensure_successful_parsing(caplog): + tao.var_v1_create(v1_name="quad_k1", n_var_min="0", n_var_max="45") -def test_var_v1_destroy_1(): +def test_var_v1_destroy_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.var_v1_destroy(v1_datum="quad_k1") + with ensure_successful_parsing(caplog): + tao.var_v1_destroy(v1_datum="quad_k1") -def test_wave_1(): +def test_wave_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") - tao.wave(who="params") + with ensure_successful_parsing(caplog): + tao.wave(who="params") diff --git a/pytao/tests/test_parsers.py b/pytao/tests/test_parsers.py new file mode 100644 index 00000000..b6626d22 --- /dev/null +++ b/pytao/tests/test_parsers.py @@ -0,0 +1,349 @@ +import numpy as np +import pytest +from .test_interface_commands import new_tao + + +def test_building_wall_list_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") + assert set(tao.building_wall_list(ix_section="")[0].keys()) == { + "index", + "name", + "constraint", + "shape", + "color", + "line_width", + } + + +def test_building_wall_list_2(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") + assert set(tao.building_wall_list(ix_section="1")[0].keys()) == { + "index", + "z", + "x", + "radius", + "z_center", + "x_center", + } + + +def test_building_wall_graph_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") + assert set(tao.building_wall_graph(graph="floor_plan.g")[0].keys()) == { + "index", + "point", + "offset_x", + "offset_y", + "radius", + } + + +def test_constraints_1(): + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + tao.constraints(who="data") + + +def test_constraints_2(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.constraints(who="var") + + +def test_data_d2_array_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + assert "orbit" in tao.data_d2_array(ix_uni="1") + + +def test_data_parameter_1(): + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + assert ( + tao.data_parameter(data_array="twiss.end", parameter="model_value")[0]["index"] + == 1 + ) + + +def test_datum_has_ele_1(): + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + assert tao.datum_has_ele(datum_type="twiss.end") in { + "no", + "yes", + "maybe", + "provisional", + } + + +def test_ele_chamber_wall_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d") + assert set( + tao.ele_chamber_wall(ele_id="1@0>>1", which="model", index="1", who="x")[ + 0 + ].keys() + ) == { + "section", + "longitudinal_position", + "z1", + "-z2", + } + + +def test_ele_elec_multipoles_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + assert "data" in tao.ele_elec_multipoles(ele_id="1@0>>1", which="model") + + +def test_ele_gen_grad_map_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field") + assert set( + tao.ele_gen_grad_map(ele_id="1@0>>9", which="model", index="1", who="derivs")[ + 0 + ].keys() + ) == {"i", "j", "k", "dz", "deriv"} + + +def test_ele_lord_slave_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + assert set(tao.ele_lord_slave(ele_id="1@0>>1", which="model")[0].keys()) == { + "type", + "location_name", + "name", + "key", + "status", + } + + +def test_ele_multipoles_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + res = tao.ele_multipoles(ele_id="1@0>>1", which="model") + assert isinstance(res, dict) + if res["data"]: + assert "KnL" in res or "An" in res["data"][0] + + +def test_ele_taylor_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_taylor") + res = tao.ele_taylor(ele_id="1@0>>34", which="model") + assert isinstance(res, dict) + assert "settings" in res + assert "data" in res + assert res["data"][0]["index"] == 1 + + +def test_ele_spin_taylor_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_spin") + assert set(tao.ele_spin_taylor(ele_id="1@0>>2", which="model")[0].keys()) == { + "index", + "term", + "coef", + "exp1", + "exp2", + "exp3", + "exp4", + "exp5", + "exp6", + } + + +def test_ele_wall3d_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d") + res = tao.ele_wall3d(ele_id="1@0>>1", which="model", index="1", who="table") + assert "data" in res[0] + assert res[0]["section"] == 1 + + +def test_em_field_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + assert "B1" in tao.em_field( + ele_id="1@0>>22", which="model", x="0", y="0", z="0", t_or_z="0" + ) + + +def test_enum_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + assert set(tao.enum(enum_name="tracking_method")[0].keys()) == {"number", "name"} + + +def test_floor_plan_1(): + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + assert "branch_index" in tao.floor_plan(graph="r13.g")[0] + + +def test_floor_orbit_1(): + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_floor_orbit" + ) + res = tao.floor_orbit(graph="r33.g") + assert isinstance(res, list) + assert isinstance(res[0], dict) + assert "index" in res[0] + assert "orbits" in res[0] + + +def test_help_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + print(tao.help()) + + +def test_inum_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.inum(who="ix_universe") + + +def test_lat_calc_done_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + assert tao.lat_calc_done(branch_name="1@0") in {True, False} + + +def test_lat_branch_list_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.lat_branch_list(ix_uni="1") + + +def test_lat_param_units_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + assert isinstance(tao.lat_param_units(param_name="L"), str) + + +def test_plot_lat_layout_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + assert "index" in tao.plot_lat_layout(ix_uni="1", ix_branch="0")[0] + + +def test_plot_line_1(): + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" + ) + res = tao.plot_line(region_name="beta", graph_name="g", curve_name="a", x_or_y="") + assert "x" in res[0] + + +def test_plot_line_2(): + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" + ) + assert isinstance( + tao.plot_line(region_name="beta", graph_name="g", curve_name="a", x_or_y="y"), + np.ndarray, + ) + res = tao.plot_line(region_name="beta", graph_name="g", curve_name="a", x_or_y="") + assert "index" in res[0] + + +def test_plot_symbol_1(): + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" + ) + res = tao.plot_symbol(region_name="r13", graph_name="g", curve_name="a", x_or_y="") + assert "index" in res[0] + + +def test_shape_list_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + assert "index" in tao.shape_list(who="floor_plan")[0] + + +def test_shape_pattern_list_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape") + assert set(tao.shape_pattern_list(ix_pattern="")[0].keys()) == { + "name", + "line_width", + } + + +def test_show_1(): + pytest.skip("TODO") + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + tao.show(line="-python") + + +def test_species_to_int_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + assert isinstance(tao.species_to_int(species_str="electron"), int) + + +def test_species_to_str_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + assert isinstance(tao.species_to_str(species_int="-1"), str) + + +def test_spin_invariant_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + assert isinstance( + tao.spin_invariant(who="l0", ix_uni="1", ix_branch="0", which="model"), + np.ndarray, + ) + res = tao.spin_invariant( + who="l0", + ix_uni="1", + ix_branch="0", + which="model", + flags="", + ) + assert "index" in res[0] + + +def test_spin_polarization_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + res = tao.spin_polarization(ix_uni="1", ix_branch="0", which="model") + assert isinstance(res, dict) + assert "anom_moment_times_gamma" in res + + +def test_spin_resonance_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + res = tao.spin_resonance(ix_uni="1", ix_branch="0", which="model") + assert isinstance(res, dict) + assert "spin_tune" in res + + +def test_super_universe_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + res = tao.super_universe() + assert isinstance(res, dict) + assert "n_universe" in res + assert "n_v1_var_used" in res + assert "n_var_used" in res + + +def test_var_1(): + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + res = tao.var(var="quad[1]", slaves="") + assert isinstance(res, dict) + assert "weight" in res + + +def test_var_2(): + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + res = tao.var(var="quad[1]", slaves="slaves") + assert isinstance(res[0], dict) + assert "index" in res[0] + + +def test_var_general_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + res = tao.var_general() + assert isinstance(res[0], dict) + assert "name" in res[0] + + +def test_var_v1_array_1(): + tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") + res = tao.var_v1_array(v1_var="quad_k1") + assert "ix_v1_var" in res + assert "data" in res + assert "name" in res["data"][0] + + +def test_lat_list_from_chris(): + tao = new_tao("-init $ACC_ROOT_DIR/bmad-doc/tao_examples/cesr/tao.init -noplot") + names = tao.lat_list("*", "ele.name") diff --git a/pytao/util/parsers.py b/pytao/util/parsers.py index 56a308db..63bf4d4e 100644 --- a/pytao/util/parsers.py +++ b/pytao/util/parsers.py @@ -1,5 +1,12 @@ +import logging + +from typing import Dict, List import numpy as np +from ..tao_ctypes.util import parse_tao_python_data, parse_bool + + +logger = logging.getLogger(__name__) # Helpers @@ -10,190 +17,209 @@ def _parse_str_bool(s): '0', 'f', 'F' -> Flase """ x = s.upper()[0] - if x in ('T', '1'): + if x in ("T", "1"): return True - elif x in ('F', '0'): + elif x in ("F", "0"): return False else: - raise ValueError ('Unknown bool: '+s) - + raise ValueError("Unknown bool: " + s) # Column names and types for parse_data_d_array -DATA_D_COLS = ['ix_d1', - 'data_type', - 'merit_type', - 'ele_ref_name', - 'ele_start_name', - 'ele_name', - 'meas_value', - 'model_value', - 'design_value', - 'useit_opt', - 'useit_plot', - 'good_user', - 'weight', - 'exists'] -DATA_D_TYPES = [int, str, str, str, str, str, float, float, float, bool, bool, bool, float, bool] - -def parse_data_d_array(lines): - """ - Parses the output of the 'python data_d_array' command into a list of dicts. - +DATA_D_COLS = [ + "ix_d1", + "data_type", + "merit_type", + "ele_ref_name", + "ele_start_name", + "ele_name", + "meas_value", + "model_value", + "design_value", + "useit_opt", + "useit_plot", + "good_user", + "weight", + "exists", +] +DATA_D_TYPES = [ + int, + str, + str, + str, + str, + str, + float, + float, + float, + bool, + bool, + bool, + float, + bool, +] + + +def parse_data_d_array(lines, cmd=""): + """ + Parses the output of the 'python data_d_array' command into a list of dicts. + This can be easily be case into a table. For example: - + import pandas as pd ... lines = tao.data_d_array('orbit', 'x') dat = parse_data_d_array(lines) df = pd.DataFrame(dat) - - + + Parameters ---------- lines : list of str The output of the 'python data_d_array' command to parse - + Returns ------- datums: list of dicts Each dict has keys: - 'ix_d1', 'data_type', 'merit_type', - 'ele_ref_name', 'ele_start_name', 'ele_name', - 'meas_value', 'model_value', 'design_value', - 'useit_opt', 'useit_plot', 'good_user', + 'ix_d1', 'data_type', 'merit_type', + 'ele_ref_name', 'ele_start_name', 'ele_name', + 'meas_value', 'model_value', 'design_value', + 'useit_opt', 'useit_plot', 'good_user', 'weight', 'exists' - - """ + + """ result = [] for line in lines: d = {} result.append(d) - vals = line.split(';') + vals = line.split(";") for name, typ, val in zip(DATA_D_COLS, DATA_D_TYPES, vals): d[name] = typ(val) - - return result + return result -def parse_derivative(lines): +def parse_derivative(lines, cmd=""): """ Parses the output of tao python derivative - + Parameters ---------- lines : list of str The output of the 'python derivative' command to parse - + Returns ------- out : dict Dictionary with keys corresponding to universe indexes (int), with dModel_dVar as the value: - np.ndarray with shape (n_data, n_var) + np.ndarray with shape (n_data, n_var) """ universes = {} # Build up matrices for line in lines: - x = line.split(';') + x = line.split(";") if len(x) <= 1: continue iu = int(x[0]) - + if iu not in universes: # new universe rows = universes[iu] = [] rowdat = [] row_id = int(x[1]) - + if int(x[1]) == row_id: # accumulate more data - rowdat += x[3:] + rowdat += x[3:] else: # Finish row - rows.append(rowdat) - rowdat = x[3:] + rows.append(rowdat) + rowdat = x[3:] row_id = int(x[1]) - - # cast to float + + # cast to float out = {} for iu, vals in universes.items(): out[iu] = np.array(vals).astype(float) - - return out + return out -def parse_ele_control_var(lines): +def parse_ele_control_var(lines, cmd=""): """ Parses the output of tao python ele_control_var - + Parameters ---------- lines : list of str The output of the 'python ele_control_var' command to parse - + Returns ------- dict of attributes and values - + """ d = {} for line in lines: - ix, name, value = line.split(';') - d[name] = float(value) + try: + ix, name, value = line.split(";") + except ValueError: + logger.warning("Skipping value: %s", line) + d[name] = float(value) return d -def parse_lat_ele_list(lines): + +def parse_lat_ele_list(lines, cmd=""): """ Parses the output of tao python lat_ele_list - + Parameters ---------- lines : list of str The output of the 'python lat_ele_list' command to parse - + Returns ------- list of str of element names - + """ - - return [s.split(';')[1] for s in lines] + + return [s.split(";")[1] for s in lines] -def parse_matrix(lines): +def parse_matrix(lines, cmd=""): """ Parses the output of a tao python matix - + Parameters ---------- lines : list of str The output of the 'python matrix' command to parse - + Returns ------- dict with keys: 'mat6' : np.array of shape (6,6) 'vec6' : np.array of shape(6) - - + + """ - m7 = np.array([[float(x) for x in line.split(';')[1:]] for line in lines]) - return {'mat6':m7[:,0:6], 'vec0':m7[:,6]} + m7 = np.array([[float(x) for x in line.split(";")[1:]] for line in lines]) + return {"mat6": m7[:, 0:6], "vec0": m7[:, 6]} -def parse_merit(lines): +def parse_merit(lines, cmd=""): """ Parses the output of a tao python merit - + Parameters ---------- lines : list of str The output of the 'python matrix' command to parse - + Returns ------- merit: float @@ -203,23 +229,23 @@ def parse_merit(lines): return float(lines[0]) -def parse_plot_list(lines): +def parse_plot_list(lines, cmd=""): """ Parses the output of the `python plot_list` command. - + This could be region or template data. - - + + Parameters ---------- lines : list of str The output of the 'python plot_list' command to parse - + Returns ------- if r_or_g == 't' dict with template_name:index - + if r_or_g == 'r' list of dicts with keys: region @@ -227,103 +253,940 @@ def parse_plot_list(lines): plot_name visible x1, x2, y1, y1 - + """ - + # infer region or template output - nv = len(lines[0].split(';')) - + nv = len(lines[0].split(";")) + if nv == 2: # Template output = {} for line in lines: - ix, name = line.split(';') + ix, name = line.split(";") output[name] = int(ix) - + elif nv == 8: # Region8 output = [] for line in lines: - ix, region_name, plot_name, visible, x1, x2, y1, y2 = line.split(';') - output.append({ - 'region': region_name, - 'ix': int(ix), - 'plot_name': plot_name, - 'visible': _parse_str_bool(visible), - 'x1': float(x1), - 'x2': float(x2), - 'y1': float(y1), - 'y2': float(y2), - }) - + ix, region_name, plot_name, visible, x1, x2, y1, y2 = line.split(";") + output.append( + { + "region": region_name, + "ix": int(ix), + "plot_name": plot_name, + "visible": _parse_str_bool(visible), + "x1": float(x1), + "x2": float(x2), + "y1": float(y1), + "y2": float(y2), + } + ) + else: raise ValueError(f"Cannot parse {lines[0]}") - - return output + return output -def parse_spin_invariant(flat_array): +def parse_spin_invariant(lines, cmd=""): """ Reshape the (3*n) shaped array output of `spin_invariant` to be (n, 3) - - Do nothing with lines (list) output. - + + Do nothing with lines (list) output. + """ - if isinstance(flat_array, list): - return flat_array - else: - return flat_array.reshape(len(flat_array)//3, 3) + if isinstance(lines, np.ndarray): + return lines.reshape(len(lines) // 3, 3) + return _parse_by_keys_to_types( + lines, + { + "index": int, + "spin1": float, + "spin2": float, + "spin3": float, + }, + ) - -def parse_taylor_map(lines): + +def parse_taylor_map(lines, cmd=""): """ Parses the output of the `python taylor_map` command. - + Parameters ---------- lines : list of str The output of the 'python taylor_map' command to parse - + Returns ------- dict of dict of taylor terms: - {2: { (3,0,0,0,0,0)}: 4.56, ... + {2: { (3,0,0,0,0,0)}: 4.56, ... corresponding to: px_out = 4.56 * x_in^3 - - + + """ - tt = {i:{} for i in range(1,7)} + tt = {i: {} for i in range(1, 7)} for term_str in lines: - t = term_str.split(';') + t = term_str.split(";") out = int(t[0]) coef = float(t[2]) - exponents = tuple([int(i) for i in t[3:]]) + exponents = tuple([int(i) for i in t[3:]]) tt[out][exponents] = coef - return tt - - -def parse_var_v_array_line(line): - v = line.split(';') + return tt + + +def parse_var_v_array_line(line, cmd=""): + v = line.split(";") out = dict( - ix_v1=int(v[0]), - var_attrib_name=v[1], - meas_value=float(v[2]), - model_value=float(v[3]), - design_value=float(v[4]), - useit_opt=_parse_str_bool(v[5]), - good_user=_parse_str_bool(v[6]), - weight=float(v[7]), - ) + ix_v1=int(v[0]), + var_attrib_name=v[1], + meas_value=float(v[2]), + model_value=float(v[3]), + design_value=float(v[4]), + useit_opt=_parse_str_bool(v[5]), + good_user=_parse_str_bool(v[6]), + weight=float(v[7]), + ) return out -def parse_var_v_array(lines): + + +def parse_var_v_array(lines, cmd=""): """ Parses the output of `python var_v_array` into a list of dicts """ return [parse_var_v_array_line(line) for line in lines] +def fix_value(value: str, typ: type): + value = value.strip() + if typ is bool: + return _parse_str_bool(value) + if typ is float: + if ("-" in value or "+" in value) and "e" not in value: + # TODO: some floating point values like gg%deriv of ele_gen_grad_map + # are formatted incorrectly + try: + return float(value) + except ValueError: + return float(value.replace("-", "e-").replace("+", "e+")) + + return typ(value) + + +def _parse_by_keys_to_types( + lines: List[str], key_to_type: Dict[str, type], ensure_count: bool = True +) -> List[dict]: + if ensure_count: + # TODO: consider removing or only toggling on during test suite + for line in lines: + assert len(key_to_type) == len(line.split(";")) + + return [ + { + key: fix_value(value, typ) + for (key, typ), value in zip( + key_to_type.items(), line.split(";", len(key_to_type)) + ) + } + for line in lines + ] + + +def _get_cmd_args(cmd: str) -> List[str]: + _python, _cmd, *args = cmd.strip().split() + return args + + +def parse_building_wall_list(lines, cmd=""): + """ + Parse building_wall_list results. + + Returns + ------- + datums: list of dicts + """ + args = _get_cmd_args(cmd) + if args: + # global floor positions + return _parse_by_keys_to_types( + lines, + { + "index": int, + "z": float, + "x": float, + "radius": float, + "z_center": float, + "x_center": float, + }, + ) + return _parse_by_keys_to_types( + lines, + { + "index": int, + "name": str, + "constraint": str, + "shape": str, + "color": str, + "line_width": float, + }, + ) + + +def parse_building_wall_graph(lines, cmd=""): + """ + Parse building_wall_graph results. + + Returns + ------- + datums: list of dicts + """ + return _parse_by_keys_to_types( + lines, + { + "index": int, + "point": int, + "offset_x": float, + "offset_y": float, + "radius": float, + }, + ) + + +def parse_constraints(lines, cmd=""): + """ + Parse constraints results. + + Returns + ------- + datums: list of dicts + """ + args = _get_cmd_args(cmd) + if args and args[0] == "data": + return _parse_by_keys_to_types( + lines, + { + "datum_name": str, + "constraint_type_name": str, + "ele_name": str, + "ele_start_name": str, + "ele_ref_name": str, + "meas_value": float, + "ref_value": float, + "model_value": float, + "base_value": float, + "weight": float, + "merit": float, + "a_name": str, + }, + ) + elif args and args[0] == "var": + return _parse_by_keys_to_types( + lines, + { + "var1_name": str, + "attrib_name": str, + "meas_value": float, + "ref_value": float, + "model_value": float, + "base_value": float, + "weight": float, + # "merit": float, + # "merit_dvar": float, + }, + ) + + +def parse_data_d1_array(lines, cmd=""): + """ + Parse data_d1_array results. + + Returns + ------- + datums: list of dicts + """ + return _parse_by_keys_to_types( + lines, + { + "index": str, + "str2": str, + "f": str, + "name": str, + "line": str, + "lower_bound": int, + "upper_bound": int, + }, + ) + + +def parse_data_d2_array(lines, cmd=""): + """ + Parse data_d2_array results. + + Returns + ------- + datums: list of str + """ + return lines + + +def parse_data_parameter(lines, cmd=""): + """ + Parse parameter_1 results. + + Returns + ------- + datums : list + """ + args = _get_cmd_args(cmd) + if len(args) < 2: + return + expected_type = { + "data_type": str, + "ele_name": str, + "ele_start_name": str, + "ele_ref_name": str, + "merit_type": str, + "id": str, + "data_source": str, + "ix_uni": int, + "ix_bunch": int, + "ix_branch": int, + "ix_ele": int, + "ix_ele_start": int, + "ix_ele_ref": int, + "ix_ele_merit": int, + "ix_d1": int, + "ix_data": int, + "ix_dModel": int, + "eval_point": int, + "meas_value": float, + "ref_value": float, + "model_value": float, + "design_value": float, + "old_value": float, + "base_value": float, + "error_rms": float, + "delta_merit": float, + "weight": float, + "invalid_value": float, + "merit": float, + "s": float, + "s_offset": float, + "err_message_printed": bool, + "exists": bool, + "good_model": bool, + "good_base": bool, + "good_design": bool, + "good_meas": bool, + "good_ref": bool, + "good_user": bool, + "good_opt": bool, + "good_plot": bool, + "useit_plot": bool, + "useit_opt": bool, + }.get(args[1], str) + + def fix_line(line): + index, *values = line.split(";") + return { + "index": int(index), + "data": [fix_value(val, expected_type) for val in values], + } + + return [fix_line(line) for line in lines] + + +def parse_datum_has_ele(lines, cmd=""): + """ + Parse datum_has_ele results. + + Returns + ------- + datums : list of values + """ + return lines[0] if lines else None + + +def parse_ele_chamber_wall(lines, cmd=""): + """ + Parse ele_chamber_wall results. + + Returns + ------- + datums : list of values + """ + return _parse_by_keys_to_types( + lines, + {"section": int, "longitudinal_position": float, "z1": float, "-z2": float}, + ) + + +def parse_ele_elec_multipoles(lines, cmd=""): + """ + Parse ele_elec_multipoles results. + + Returns + ------- + info : dict + """ + logic_lines = [line for line in lines if "LOGIC" in line] + lines = [line for line in lines if line not in logic_lines] + key_to_type = {key: float for key in lines[0].split(";")} + settings = {} + for line in logic_lines: + # parse_tao_python_data1 doesn't work as it's missing 'settable' + # (line) for line in logic_lines + name, _type, value = line.split(";") + settings[name] = parse_bool(value) + + # TODO: 'data' is not actually parsed in the test suite + return { + **settings, + "data": _parse_by_keys_to_types( + lines[1:], + key_to_type, + ), + } + + +def parse_ele_gen_grad_map(lines, cmd=""): + """ + Parse ele_gen_grad_map results. + + Returns + ------- + info : dict + """ + + args = _get_cmd_args(cmd) + if args[-1] == "derivs": + return _parse_by_keys_to_types( + lines, + { + "i": int, + "j": int, + "k": int, + "dz": float, + "deriv": float, + }, + ) + return parse_tao_python_data(lines) + + +def parse_ele_lord_slave(lines, cmd=""): + """ + Parse ele_lord_slave results. + + Returns + ------- + info : dict + """ + return _parse_by_keys_to_types( + lines, + { + "type": str, + "location_name": str, + "name": str, + "key": str, + "status": str, + }, + ) + + +def parse_ele_multipoles(lines, cmd=""): + """ """ + logic_lines = [line for line in lines if "LOGIC" in line] + lines = [line for line in lines if line not in logic_lines] + key_to_type = {"index": int} + for key in lines[0].split(";"): + key_to_type[key] = float + + settings = parse_tao_python_data(logic_lines) + return { + **settings, + "data": _parse_by_keys_to_types( + lines[1:], + key_to_type, + ), + } + + +def parse_ele_taylor(lines, cmd=""): + """ + (TODO) + """ + + def split_sections(lines): + sections = [] + for line in lines: + if ";ref;" in line: + sections.append([line]) + else: + sections[-1].append(line) + return sections + + def parse_section(section): + header = section[0] + index, _, ref = header.split(";") + info = { + "index": int(index), + "ref": float(ref), + } + info["data"] = _parse_by_keys_to_types( + section[1:], + { + "i": int, + "j": int, + "coef": float, + "exp1": float, + "exp2": float, + "exp3": float, + "exp4": float, + "exp5": float, + "exp6": float, + }, + ) + return info + + logic_lines = [line for line in lines if "LOGIC" in line] + lines = [line for line in lines if line not in logic_lines] + + settings = parse_tao_python_data(logic_lines) + sections = split_sections(lines) + return { + "settings": settings, + "data": [parse_section(section) for section in sections], + } + + +def parse_ele_spin_taylor(lines, cmd=""): + """ """ + return _parse_by_keys_to_types( + lines, + { + "index": int, + "term": str, + "coef": float, + "exp1": float, + "exp2": float, + "exp3": float, + "exp4": float, + "exp5": float, + "exp6": float, + }, + ) + + +def parse_ele_wall3d(lines, cmd=""): + "" + + def split_sections(lines): + sections = [] + for line in lines: + if line.startswith("section;"): + sections.append([line]) + else: + sections[-1].append(line) + return sections + + def parse_section(section): + header = [] + for line in section: + if line[0].isalpha(): + header.append(line.replace(";;", ";")) # TODO a bmad bug? + else: + break + data = section[len(header) :] + info = parse_tao_python_data(header) + info["data"] = _parse_by_keys_to_types( + data, + { + "j": int, + "x": float, + "y": float, + "radius_x": float, + "radius_y": float, + "tilt": float, + }, + ) + return info + + args = _get_cmd_args(cmd) + if args[-1] == "table": + sections = split_sections(lines) + return [parse_section(section) for section in sections] + + return parse_tao_python_data(lines) + + +def parse_em_field(lines, cmd=""): + "" + return _parse_by_keys_to_types( + lines, + { + "B1": float, + "B2": float, + "B3": float, + "E1": float, + "E2": float, + "E3": float, + }, + )[0] + + +def parse_enum(lines, cmd=""): + "" + return _parse_by_keys_to_types( + lines, + { + "number": int, + "name": str, + }, + ) + + +def parse_floor_plan(lines, cmd=""): + "" + return _parse_by_keys_to_types( + lines, + { + "branch_index": int, + "index": int, + "ele_key": str, + "end1_r1": float, + "end1_r2": float, + "end1_theta": float, + "end2_r1": float, + "end2_r2": float, + "end2_theta": float, + "line_width": float, + "shape": str, + "y1": float, + "y2": float, + "color": str, + "label_name": str, + # Only for sbend: + "ele_l": float, + "ele_angle": float, + "ele_e1": float, + "ele_e": float, + }, + ensure_count=False, + ) + + +def parse_floor_orbit(lines, cmd=""): + "" + res = [] + for line in lines: + data = _parse_by_keys_to_types( + [line], + { + "branch_index": int, + "index": int, + "ele_key": str, + "axis": str, + }, + ensure_count=False, + )[0] + data["orbits"] = [fix_value(val, float) for val in line.split(";")[3:]] + res.append(data) + + return res + + +def parse_help(lines, cmd=""): + """ + Parse help information. + + Returns + ------- + str + """ + return "\n".join(lines) + + +def parse_inum(lines, cmd=""): + """ + Parse list of possible values for INUM. + + Returns + ------- + list of int + """ + return [int(num) for num in lines] + + +def parse_lat_calc_done(lines, cmd=""): + """ + Parse lat_calc_done results. + + Returns + ------- + bool + """ + return parse_bool(lines[0]) + + +def parse_lat_branch_list(lines, cmd=""): + """ + Parse lat_branch_list results. + + Returns + ------- + list of dict + """ + return _parse_by_keys_to_types( + lines, + { + "index": int, + "branch_name": str, + "n_ele_track": int, + "n_ele_max": int, + }, + ) + + +def parse_lat_param_units(lines, cmd=""): + """ + Parse lat_param_units results. + + Returns + ------- + str + """ + return lines[0] + + +def parse_plot_lat_layout(lines, cmd=""): + """ + Parse plot_lat_layout results. + + Returns + ------- + list of dict + """ + return _parse_by_keys_to_types( + lines, + { + "index": int, + "ele_s_start": float, + "ele_s": float, + "line_width": float, + "shape": str, + "y1": float, + "y2": float, + "color": str, + "label_name": str, + }, + ) + + +def parse_plot_graph(lines, cmd=""): + """ + Parse plot_graph results. + + Returns + ------- + dict + """ + # This should work, but there are issues with truncation causing failures. + # See: https://github.com/bmad-sim/bmad-ecosystem/issues/1018 + # If that issue isn't resolved, we may want to pre-process the data + # to at least get something back. + try: + return parse_tao_python_data(lines) + except ValueError: + logger.warning( + "python plot_graph output failed to parse. See linked issue " + "and consider upgrading if possible. " + "https://github.com/bmad-sim/bmad-ecosystem/issues/1018" + ) + return lines + + +def parse_plot_line(lines, cmd=""): + """ + Parse plot_line results. + + Returns + ------- + list of dict or np.ndarray + """ + if isinstance(lines, np.ndarray): + return lines + + return _parse_by_keys_to_types( + lines, + { + "index": int, + "x": float, + "y": float, + }, + ) + + +def parse_plot_symbol(lines, cmd=""): + "" + if isinstance(lines, np.ndarray): + return lines + return _parse_by_keys_to_types( + lines, + { + "index": int, + "ix_symb": int, + "x_symb": float, + "y_symb": float, + }, + ) + + +def parse_shape_list(lines, cmd=""): + "" + return _parse_by_keys_to_types( + lines, + { + "index": int, + "ele_id": str, + "shape": str, + "color": str, + "size": float, + "label": str, + "draw": bool, + "multi": bool, + "line_width": int, + }, + ) + + +def parse_shape_pattern_list(lines, cmd=""): + "" + args = _get_cmd_args(cmd) + if not args: + return _parse_by_keys_to_types( + lines, + { + "name": str, + "line_width": float, + }, + ) + return _parse_by_keys_to_types( + lines, + { + "s": float, + "y": float, + }, + ) + + +def parse_show(lines, cmd=""): + "" + return lines # raise NotImplementedError() + + +def parse_species_to_int(lines, cmd=""): + "" + return int(lines[0]) + + +def parse_species_to_str(lines, cmd=""): + "" + return lines[0] + + +def parse_spin_polarization(lines, cmd=""): + """ + Returns + ------- + dict + """ + lines = [ + line + for line in lines + if "[INFO]" not in line and "note: setting" not in line.lower() + ] + return parse_tao_python_data(lines) + + +def parse_spin_resonance(lines, cmd=""): + "" + lines = [ + line + for line in lines + if "[INFO]" not in line and "note: setting" not in line.lower() + ] + return parse_tao_python_data(lines) + + +def parse_super_universe(lines, cmd=""): + "" + + def fix_line(line): + bug_prefix = "n_v1_var_used;INT;F" + if not line.startswith(bug_prefix): + return line + if line.startswith(f"{bug_prefix};"): + return line + value = line[len(bug_prefix) :] + return f"{bug_prefix};{value}" + + return parse_tao_python_data([fix_line(line) for line in lines]) + + +def parse_var(lines, cmd=""): + "" + args = _get_cmd_args(cmd) + if "slaves" in args: + return _parse_by_keys_to_types( + lines, + { + "index": int, + "ix_branch": int, + "ix_ele": int, + }, + ) + + return parse_tao_python_data(lines) + + +def parse_var_general(lines, cmd=""): + "" + return _parse_by_keys_to_types( + lines, + { + "name": str, + "line": str, + "lbound": int, + "ubound": int, + }, + ) + + +def parse_var_v1_array(lines, cmd=""): + "" + ix_v1_var = lines[-1] + + res = parse_tao_python_data([ix_v1_var]) + res["data"] = _parse_by_keys_to_types( + lines[:-1], + { + "name": str, + "ele_name": str, + "attrib_name": str, + "meas_value": float, + "model_value": float, + "design_value": float, + "good_user": bool, + "useit_opt": bool, + }, + ) + return res +def parse_lat_list(lines, cmd=""): + "" + return lines From 46c1ad31bb88a940b975725c0f8d2f52c540baa0 Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 27 Jun 2024 09:20:21 -0700 Subject: [PATCH 05/15] BLD: set minimum bmad version --- dev-environment.yml | 2 +- environment.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/dev-environment.yml b/dev-environment.yml index ea7d2e83..990b95de 100644 --- a/dev-environment.yml +++ b/dev-environment.yml @@ -6,7 +6,7 @@ dependencies: - python >=3.9 - openPMD-beamphysics - numpydoc - - bmad + - bmad >=20240626 - bokeh - jupyterlab>3 - ipywidgets diff --git a/environment.yml b/environment.yml index 7e9d787c..1b5b985e 100644 --- a/environment.yml +++ b/environment.yml @@ -7,7 +7,7 @@ dependencies: - ipykernel - ipywidgets - jupyterlab - - bmad + - bmad >=20240626 - openPMD-beamphysics - numpy - h5py From 0685a957b0447b320f16d6fd1eff62dedb085c69 Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 27 Jun 2024 09:36:40 -0700 Subject: [PATCH 06/15] DOC: add return type information --- pytao/util/parsers.py | 236 ++++++++++++++++++++++++++++++++++++------ 1 file changed, 203 insertions(+), 33 deletions(-) diff --git a/pytao/util/parsers.py b/pytao/util/parsers.py index 6d00957f..adff7315 100644 --- a/pytao/util/parsers.py +++ b/pytao/util/parsers.py @@ -361,6 +361,10 @@ def parse_var_v_array_line(line, cmd=""): def parse_var_v_array(lines, cmd=""): """ Parses the output of `python var_v_array` into a list of dicts + + Returns + ------- + list of dict """ return [parse_var_v_array_line(line) for line in lines] @@ -382,8 +386,32 @@ def fix_value(value: str, typ: type): def _parse_by_keys_to_types( - lines: List[str], key_to_type: Dict[str, type], ensure_count: bool = True + lines: List[str], + key_to_type: Dict[str, type], + ensure_count: bool = True, ) -> List[dict]: + """ + Parse Tao command output, with predetermined field names and associated types. + + Each output line is converted according to ``key_to_type``, such that ``N`` + lines of output will result in N dictionaries with keys + ``key_to_type.keys()`` with corresponding values cast to the indicated + type. + + Parameters + ---------- + lines : List[str] + Raw Tao output. + key_to_type : Dict[str, type] + Dictionary of key name to expected Python type. + ensure_count : bool, optional + Fail if the number of output fields doesn't match up with the expected + ones in ``key_to_type``. + + Returns + ------- + list of dict + """ if ensure_count: # TODO: consider removing or only toggling on during test suite for line in lines: @@ -401,6 +429,20 @@ def _parse_by_keys_to_types( def _get_cmd_args(cmd: str) -> List[str]: + """ + Get command arguments. + + (python) (command) [(arg1) (arg2) ... (argN)] + + Parameters + ---------- + cmd : str + The raw Tao command, including "python" as the first argument. + + Returns + ------- + list of str + """ _python, _cmd, *args = cmd.strip().split() return args @@ -411,7 +453,7 @@ def parse_building_wall_list(lines, cmd=""): Returns ------- - datums: list of dicts + list of dicts """ args = _get_cmd_args(cmd) if args: @@ -446,7 +488,7 @@ def parse_building_wall_graph(lines, cmd=""): Returns ------- - datums: list of dicts + list of dicts """ return _parse_by_keys_to_types( lines, @@ -466,7 +508,8 @@ def parse_constraints(lines, cmd=""): Returns ------- - datums: list of dicts + list of dicts + The keys depend on "data" or "var" """ args = _get_cmd_args(cmd) if args and args[0] == "data": @@ -510,7 +553,7 @@ def parse_data_d1_array(lines, cmd=""): Returns ------- - datums: list of dicts + list of dicts """ return _parse_by_keys_to_types( lines, @@ -532,7 +575,7 @@ def parse_data_d2_array(lines, cmd=""): Returns ------- - datums: list of str + list of str """ return lines @@ -543,7 +586,7 @@ def parse_data_parameter(lines, cmd=""): Returns ------- - datums : list + list of dict """ args = _get_cmd_args(cmd) if len(args) < 2: @@ -610,7 +653,8 @@ def parse_datum_has_ele(lines, cmd=""): Returns ------- - datums : list of values + str or None + "no", "yes", "maybe", "provisional" """ return lines[0] if lines else None @@ -621,7 +665,7 @@ def parse_ele_chamber_wall(lines, cmd=""): Returns ------- - datums : list of values + list of dict """ return _parse_by_keys_to_types( lines, @@ -635,7 +679,7 @@ def parse_ele_elec_multipoles(lines, cmd=""): Returns ------- - info : dict + dict """ logic_lines = [line for line in lines if "LOGIC" in line] lines = [line for line in lines if line not in logic_lines] @@ -663,7 +707,9 @@ def parse_ele_gen_grad_map(lines, cmd=""): Returns ------- - info : dict + dict or list of dict + "derivs" mode will be a list of dictionaries. + Normal mode will be a single dictionary. """ args = _get_cmd_args(cmd) @@ -687,7 +733,7 @@ def parse_ele_lord_slave(lines, cmd=""): Returns ------- - info : dict + list of dict """ return _parse_by_keys_to_types( lines, @@ -702,7 +748,13 @@ def parse_ele_lord_slave(lines, cmd=""): def parse_ele_multipoles(lines, cmd=""): - """ """ + """ + Parse ele_multipoles results. + + Returns + ------- + dict + """ logic_lines = [line for line in lines if "LOGIC" in line] lines = [line for line in lines if line not in logic_lines] key_to_type = {"index": int} @@ -721,7 +773,11 @@ def parse_ele_multipoles(lines, cmd=""): def parse_ele_taylor(lines, cmd=""): """ - (TODO) + Parse ele_taylor results. + + Returns + ------- + dict """ def split_sections(lines): @@ -762,13 +818,19 @@ def parse_section(section): settings = parse_tao_python_data(logic_lines) sections = split_sections(lines) return { - "settings": settings, + **settings, "data": [parse_section(section) for section in sections], } def parse_ele_spin_taylor(lines, cmd=""): - """ """ + """ + Parse ele_spin_taylor results. + + Returns + ------- + list of dict + """ return _parse_by_keys_to_types( lines, { @@ -786,7 +848,13 @@ def parse_ele_spin_taylor(lines, cmd=""): def parse_ele_wall3d(lines, cmd=""): - "" + """ + Parse ele_wall3d results. + + Returns + ------- + list of dict + """ def split_sections(lines): sections = [] @@ -828,7 +896,13 @@ def parse_section(section): def parse_em_field(lines, cmd=""): - "" + """ + Parse em_field results. + + Returns + ------- + dict + """ return _parse_by_keys_to_types( lines, { @@ -843,7 +917,13 @@ def parse_em_field(lines, cmd=""): def parse_enum(lines, cmd=""): - "" + """ + Parse enum results. + + Returns + ------- + list of dict + """ return _parse_by_keys_to_types( lines, { @@ -854,7 +934,13 @@ def parse_enum(lines, cmd=""): def parse_floor_plan(lines, cmd=""): - "" + """ + Parse floor_plan results. + + Returns + ------- + list of dict + """ return _parse_by_keys_to_types( lines, { @@ -884,7 +970,13 @@ def parse_floor_plan(lines, cmd=""): def parse_floor_orbit(lines, cmd=""): - "" + """ + Parse floor_orbit results. + + Returns + ------- + list of dict + """ res = [] for line in lines: data = _parse_by_keys_to_types( @@ -1035,7 +1127,13 @@ def parse_plot_line(lines, cmd=""): def parse_plot_symbol(lines, cmd=""): - "" + """ + Parse plot_symbol results. + + Returns + ------- + list of dict or np.ndarray + """ if isinstance(lines, np.ndarray): return lines return _parse_by_keys_to_types( @@ -1050,7 +1148,13 @@ def parse_plot_symbol(lines, cmd=""): def parse_shape_list(lines, cmd=""): - "" + """ + Parse shape_list results. + + Returns + ------- + list of dict + """ return _parse_by_keys_to_types( lines, { @@ -1068,7 +1172,13 @@ def parse_shape_list(lines, cmd=""): def parse_shape_pattern_list(lines, cmd=""): - "" + """ + Parse shape_pattern_list results. + + Returns + ------- + list of dict + """ args = _get_cmd_args(cmd) if not args: return _parse_by_keys_to_types( @@ -1088,22 +1198,44 @@ def parse_shape_pattern_list(lines, cmd=""): def parse_show(lines, cmd=""): - "" + """ + Parse show results. + + Returns + ------- + list of str + This is raw list of strings from tao, as parsing is not currently + supported. + """ return lines # raise NotImplementedError() def parse_species_to_int(lines, cmd=""): - "" + """ + Parse species_to_int results. + + Returns + ------- + int + """ return int(lines[0]) def parse_species_to_str(lines, cmd=""): - "" + """ + Parse species_to_str results. + + Returns + ------- + str + """ return lines[0] def parse_spin_polarization(lines, cmd=""): """ + Parse spin_polarization results. + Returns ------- dict @@ -1117,7 +1249,13 @@ def parse_spin_polarization(lines, cmd=""): def parse_spin_resonance(lines, cmd=""): - "" + """ + Parse spin_resonance results. + + Returns + ------- + dict + """ lines = [ line for line in lines @@ -1127,7 +1265,13 @@ def parse_spin_resonance(lines, cmd=""): def parse_super_universe(lines, cmd=""): - "" + """ + Parse super_universe results. + + Returns + ------- + dict + """ def fix_line(line): bug_prefix = "n_v1_var_used;INT;F" @@ -1142,7 +1286,15 @@ def fix_line(line): def parse_var(lines, cmd=""): - "" + """ + Parse var results. + + Returns + ------- + dict, or list of dict + "slaves" mode will be a list of dicts. + Normal mode will be a dict. + """ args = _get_cmd_args(cmd) if "slaves" in args: return _parse_by_keys_to_types( @@ -1158,7 +1310,13 @@ def parse_var(lines, cmd=""): def parse_var_general(lines, cmd=""): - "" + """ + Parse var_general results. + + Returns + ------- + list of dict + """ return _parse_by_keys_to_types( lines, { @@ -1171,7 +1329,13 @@ def parse_var_general(lines, cmd=""): def parse_var_v1_array(lines, cmd=""): - "" + """ + Parse var_v1_array results. + + Returns + ------- + dict + """ ix_v1_var = lines[-1] res = parse_tao_python_data([ix_v1_var]) @@ -1192,5 +1356,11 @@ def parse_var_v1_array(lines, cmd=""): def parse_lat_list(lines, cmd=""): - "" + """ + Parse lat_list results. + + Returns + ------- + list of str + """ return lines From da78416bd692bb6afeb116ea284a7d6faf15f575 Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 27 Jun 2024 09:36:58 -0700 Subject: [PATCH 07/15] TST: test plot_graph from latest bmad fixes --- pytao/tests/test_parsers.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/pytao/tests/test_parsers.py b/pytao/tests/test_parsers.py index b6626d22..a4fc21a9 100644 --- a/pytao/tests/test_parsers.py +++ b/pytao/tests/test_parsers.py @@ -347,3 +347,13 @@ def test_var_v1_array_1(): def test_lat_list_from_chris(): tao = new_tao("-init $ACC_ROOT_DIR/bmad-doc/tao_examples/cesr/tao.init -noplot") names = tao.lat_list("*", "ele.name") + assert isinstance(names[0], str) + + +def test_plot_graph_1(): + tao = new_tao( + "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" + ) + res = tao.plot_graph(graph_name="beta.g") + assert isinstance(res, dict) + assert "name" in res From 1abe82d509fe5be267fe50218b3ab47c07222685 Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 27 Jun 2024 09:42:42 -0700 Subject: [PATCH 08/15] MAINT: make ensure_count a test suite-only thing for now --- pytao/tests/conftest.py | 7 +++++++ pytao/tests/test_parsers.py | 1 - pytao/util/parsers.py | 28 +++++++++++++++++++++++++--- 3 files changed, 32 insertions(+), 4 deletions(-) diff --git a/pytao/tests/conftest.py b/pytao/tests/conftest.py index 76f0eb3f..9962ed6b 100644 --- a/pytao/tests/conftest.py +++ b/pytao/tests/conftest.py @@ -10,3 +10,10 @@ def rootdir(): @pytest.fixture def config_file(rootdir): return open(f"{rootdir}/test_files/iris_config.yml", "r") + + +@pytest.fixture(autouse=True) +def ensure_count(): + from ..util import parsers + + parsers.Settings.ensure_count = True diff --git a/pytao/tests/test_parsers.py b/pytao/tests/test_parsers.py index a4fc21a9..432b682d 100644 --- a/pytao/tests/test_parsers.py +++ b/pytao/tests/test_parsers.py @@ -128,7 +128,6 @@ def test_ele_taylor_1(): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_taylor") res = tao.ele_taylor(ele_id="1@0>>34", which="model") assert isinstance(res, dict) - assert "settings" in res assert "data" in res assert res["data"][0]["index"] == 1 diff --git a/pytao/util/parsers.py b/pytao/util/parsers.py index adff7315..43162a85 100644 --- a/pytao/util/parsers.py +++ b/pytao/util/parsers.py @@ -1,6 +1,6 @@ import logging -from typing import Dict, List +from typing import Dict, List, Optional import numpy as np from ..tao_ctypes.util import parse_tao_python_data, parse_bool @@ -9,6 +9,10 @@ logger = logging.getLogger(__name__) +class Settings: + ensure_count: bool = False + + # Helpers def _parse_str_bool(s): """ @@ -370,6 +374,20 @@ def parse_var_v_array(lines, cmd=""): def fix_value(value: str, typ: type): + """ + Apply some fixes for known problematic tao output. + + Parameters + ---------- + value : str + The tao output value string. + typ : type + The expected Python type. + + Returns + ------- + typ + """ value = value.strip() if typ is bool: return _parse_str_bool(value) @@ -388,7 +406,7 @@ def fix_value(value: str, typ: type): def _parse_by_keys_to_types( lines: List[str], key_to_type: Dict[str, type], - ensure_count: bool = True, + ensure_count: Optional[bool] = None, ) -> List[dict]: """ Parse Tao command output, with predetermined field names and associated types. @@ -407,13 +425,17 @@ def _parse_by_keys_to_types( ensure_count : bool, optional Fail if the number of output fields doesn't match up with the expected ones in ``key_to_type``. + Defaults to ``Settings.ensure_count`` which can be easily toggled + application-wide. This is only enabled by default for the test suite. Returns ------- list of dict """ + if ensure_count is None: + ensure_count = Settings.ensure_count + if ensure_count: - # TODO: consider removing or only toggling on during test suite for line in lines: assert len(key_to_type) == len(line.split(";")) From 1a427f1d918c01383e2947ba7503795401034ae8 Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 27 Jun 2024 10:01:31 -0700 Subject: [PATCH 09/15] TST: verbose=True --- pytao/tests/test_interface_commands.py | 282 +++++++++++++++---------- 1 file changed, 174 insertions(+), 108 deletions(-) diff --git a/pytao/tests/test_interface_commands.py b/pytao/tests/test_interface_commands.py index 20bf4aa3..78f3b2fa 100644 --- a/pytao/tests/test_interface_commands.py +++ b/pytao/tests/test_interface_commands.py @@ -2,7 +2,7 @@ # AUTOGENERATED FILE - DO NOT MODIFY # This file was generated by the script `generate_interface_commands.py`. # Any modifications may be overwritten. -# Generated on: 2024-06-26 16:52:44 +# Generated on: 2024-06-27 10:00:36 # ============================================================================== import contextlib @@ -37,7 +37,7 @@ def test_beam_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" ) with ensure_successful_parsing(caplog): - tao.beam(ix_uni="1", ix_branch="0") + tao.beam(ix_uni="1", ix_branch="0", verbose=True) def test_beam_init_1(caplog): @@ -45,19 +45,19 @@ def test_beam_init_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" ) with ensure_successful_parsing(caplog): - tao.beam_init(ix_uni="1", ix_branch="0") + tao.beam_init(ix_uni="1", ix_branch="0", verbose=True) def test_bmad_com_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.bmad_com() + tao.bmad_com(verbose=True) def test_branch1_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.branch1(ix_uni="1", ix_branch="0") + tao.branch1(ix_uni="1", ix_branch="0", verbose=True) def test_bunch_comb_1(caplog): @@ -65,7 +65,7 @@ def test_bunch_comb_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" ) with ensure_successful_parsing(caplog): - tao.bunch_comb(who="x.beta") + tao.bunch_comb(who="x.beta", verbose=True) def test_bunch_params_1(caplog): @@ -73,7 +73,7 @@ def test_bunch_params_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" ) with ensure_successful_parsing(caplog): - tao.bunch_params(ele_id="end", which="model") + tao.bunch_params(ele_id="end", which="model", verbose=True) def test_bunch1_1(caplog): @@ -81,25 +81,27 @@ def test_bunch1_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init" ) with ensure_successful_parsing(caplog): - tao.bunch1(ele_id="end", coordinate="x", which="model", ix_bunch="1") + tao.bunch1( + ele_id="end", coordinate="x", which="model", ix_bunch="1", verbose=True + ) def test_building_wall_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") with ensure_successful_parsing(caplog): - tao.building_wall_list(ix_section="") + tao.building_wall_list(ix_section="", verbose=True) def test_building_wall_list_2(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") with ensure_successful_parsing(caplog): - tao.building_wall_list(ix_section="1") + tao.building_wall_list(ix_section="1", verbose=True) def test_building_wall_graph_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall") with ensure_successful_parsing(caplog): - tao.building_wall_graph(graph="floor_plan.g") + tao.building_wall_graph(graph="floor_plan.g", verbose=True) def test_building_wall_point_1(caplog): @@ -113,6 +115,7 @@ def test_building_wall_point_1(caplog): radius="0", z_center="0", x_center="0", + verbose=True, ) @@ -120,7 +123,7 @@ def test_building_wall_section_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): tao.building_wall_section( - ix_section="1", sec_name="test", sec_constraint="none" + ix_section="1", sec_name="test", sec_constraint="none", verbose=True ) @@ -129,13 +132,13 @@ def test_constraints_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.constraints(who="data") + tao.constraints(who="data", verbose=True) def test_constraints_2(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.constraints(who="var") + tao.constraints(who="var", verbose=True) def test_data_1(caplog): @@ -143,7 +146,7 @@ def test_data_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.data(ix_uni="", d2_name="twiss", d1_name="end", dat_index="1") + tao.data(ix_uni="", d2_name="twiss", d1_name="end", dat_index="1", verbose=True) def test_data_2(caplog): @@ -151,7 +154,9 @@ def test_data_2(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.data(ix_uni="1", d2_name="twiss", d1_name="end", dat_index="1") + tao.data( + ix_uni="1", d2_name="twiss", d1_name="end", dat_index="1", verbose=True + ) def test_data_d_array_1(caplog): @@ -159,7 +164,7 @@ def test_data_d_array_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.data_d_array(ix_uni="1", d2_name="twiss", d1_name="end") + tao.data_d_array(ix_uni="1", d2_name="twiss", d1_name="end", verbose=True) def test_data_d1_array_1(caplog): @@ -167,7 +172,7 @@ def test_data_d1_array_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.data_d1_array(ix_uni="1", d2_datum="twiss") + tao.data_d1_array(ix_uni="1", d2_datum="twiss", verbose=True) def test_data_d2_1(caplog): @@ -175,13 +180,13 @@ def test_data_d2_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.data_d2(ix_uni="1", d2_name="twiss") + tao.data_d2(ix_uni="1", d2_name="twiss", verbose=True) def test_data_d2_array_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.data_d2_array(ix_uni="1") + tao.data_d2_array(ix_uni="1", verbose=True) def test_data_d2_create_1(caplog): @@ -194,13 +199,14 @@ def test_data_d2_create_1(caplog): d2_name="orbit", n_d1_data="2", d_data_arrays_name_min_max="x^^0^^45^^y^^1^^47", + verbose=True, ) def test_data_d2_destroy_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.data_d2_destroy(d2_name="orbit") + tao.data_d2_destroy(d2_name="orbit", verbose=True) def test_data_parameter_1(caplog): @@ -208,7 +214,9 @@ def test_data_parameter_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.data_parameter(data_array="twiss.end", parameter="model_value") + tao.data_parameter( + data_array="twiss.end", parameter="model_value", verbose=True + ) def test_data_set_design_value_1(caplog): @@ -216,7 +224,7 @@ def test_data_set_design_value_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.data_set_design_value() + tao.data_set_design_value(verbose=True) def test_datum_create_1(caplog): @@ -242,6 +250,7 @@ def test_datum_create_1(caplog): s_offset="0", ix_bunch="1", invalid_value="0", + verbose=True, ) @@ -250,7 +259,7 @@ def test_datum_has_ele_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.datum_has_ele(datum_type="twiss.end") + tao.datum_has_ele(datum_type="twiss.end", verbose=True) def test_derivative_1(caplog): @@ -258,169 +267,189 @@ def test_derivative_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.derivative() + tao.derivative(verbose=True) def test_ele_ac_kicker_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ele_ac_kicker(ele_id="1@0>>1", which="model") + tao.ele_ac_kicker(ele_id="1@0>>1", which="model", verbose=True) def test_ele_cartesian_map_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field") with ensure_successful_parsing(caplog): - tao.ele_cartesian_map(ele_id="1@0>>1", which="model", index="1", who="base") + tao.ele_cartesian_map( + ele_id="1@0>>1", which="model", index="1", who="base", verbose=True + ) def test_ele_chamber_wall_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d") with ensure_successful_parsing(caplog): - tao.ele_chamber_wall(ele_id="1@0>>1", which="model", index="1", who="x") + tao.ele_chamber_wall( + ele_id="1@0>>1", which="model", index="1", who="x", verbose=True + ) def test_ele_control_var_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ele_control_var(ele_id="1@0>>873", which="model") + tao.ele_control_var(ele_id="1@0>>873", which="model", verbose=True) def test_ele_cylindrical_map_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field") with ensure_successful_parsing(caplog): - tao.ele_cylindrical_map(ele_id="1@0>>5", which="model", index="1", who="base") + tao.ele_cylindrical_map( + ele_id="1@0>>5", which="model", index="1", who="base", verbose=True + ) def test_ele_elec_multipoles_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ele_elec_multipoles(ele_id="1@0>>1", which="model") + tao.ele_elec_multipoles(ele_id="1@0>>1", which="model", verbose=True) def test_ele_floor_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ele_floor(ele_id="1@0>>1", which="model", where="") + tao.ele_floor(ele_id="1@0>>1", which="model", where="", verbose=True) def test_ele_floor_2(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ele_floor(ele_id="1@0>>1", which="model", where="center") + tao.ele_floor(ele_id="1@0>>1", which="model", where="center", verbose=True) def test_ele_gen_attribs_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ele_gen_attribs(ele_id="1@0>>1", which="model") + tao.ele_gen_attribs(ele_id="1@0>>1", which="model", verbose=True) def test_ele_gen_grad_map_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field") with ensure_successful_parsing(caplog): - tao.ele_gen_grad_map(ele_id="1@0>>9", which="model", index="1", who="derivs") + tao.ele_gen_grad_map( + ele_id="1@0>>9", which="model", index="1", who="derivs", verbose=True + ) def test_ele_grid_field_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_grid") with ensure_successful_parsing(caplog): - tao.ele_grid_field(ele_id="1@0>>1", which="model", index="1", who="base") + tao.ele_grid_field( + ele_id="1@0>>1", which="model", index="1", who="base", verbose=True + ) def test_ele_head_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ele_head(ele_id="1@0>>1", which="model") + tao.ele_head(ele_id="1@0>>1", which="model", verbose=True) def test_ele_lord_slave_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ele_lord_slave(ele_id="1@0>>1", which="model") + tao.ele_lord_slave(ele_id="1@0>>1", which="model", verbose=True) def test_ele_mat6_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ele_mat6(ele_id="1@0>>1", which="model", who="mat6") + tao.ele_mat6(ele_id="1@0>>1", which="model", who="mat6", verbose=True) def test_ele_methods_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ele_methods(ele_id="1@0>>1", which="model") + tao.ele_methods(ele_id="1@0>>1", which="model", verbose=True) def test_ele_multipoles_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ele_multipoles(ele_id="1@0>>1", which="model") + tao.ele_multipoles(ele_id="1@0>>1", which="model", verbose=True) def test_ele_orbit_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ele_orbit(ele_id="1@0>>1", which="model") + tao.ele_orbit(ele_id="1@0>>1", which="model", verbose=True) def test_ele_param_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon") with ensure_successful_parsing(caplog): - tao.ele_param(ele_id="1@0>>1", which="model", who="orbit.vec.1") + tao.ele_param(ele_id="1@0>>1", which="model", who="orbit.vec.1", verbose=True) def test_ele_photon_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon") with ensure_successful_parsing(caplog): - tao.ele_photon(ele_id="1@0>>1", which="model", who="base") + tao.ele_photon(ele_id="1@0>>1", which="model", who="base", verbose=True) def test_ele_spin_taylor_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_spin") with ensure_successful_parsing(caplog): - tao.ele_spin_taylor(ele_id="1@0>>2", which="model") + tao.ele_spin_taylor(ele_id="1@0>>2", which="model", verbose=True) def test_ele_taylor_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_taylor") with ensure_successful_parsing(caplog): - tao.ele_taylor(ele_id="1@0>>34", which="model") + tao.ele_taylor(ele_id="1@0>>34", which="model", verbose=True) def test_ele_twiss_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ele_twiss(ele_id="1@0>>1", which="model") + tao.ele_twiss(ele_id="1@0>>1", which="model", verbose=True) def test_ele_wake_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wake") with ensure_successful_parsing(caplog): - tao.ele_wake(ele_id="1@0>>1", which="model", who="sr_long") + tao.ele_wake(ele_id="1@0>>1", which="model", who="sr_long", verbose=True) def test_ele_wall3d_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d") with ensure_successful_parsing(caplog): - tao.ele_wall3d(ele_id="1@0>>1", which="model", index="1", who="table") + tao.ele_wall3d( + ele_id="1@0>>1", which="model", index="1", who="table", verbose=True + ) def test_evaluate_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.evaluate(expression="data::cbar.11[1:10]|model") + tao.evaluate(expression="data::cbar.11[1:10]|model", verbose=True) def test_em_field_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.em_field(ele_id="1@0>>22", which="model", x="0", y="0", z="0", t_or_z="0") + tao.em_field( + ele_id="1@0>>22", + which="model", + x="0", + y="0", + z="0", + t_or_z="0", + verbose=True, + ) def test_enum_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.enum(enum_name="tracking_method") + tao.enum(enum_name="tracking_method", verbose=True) def test_floor_plan_1(caplog): @@ -428,7 +457,7 @@ def test_floor_plan_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.floor_plan(graph="r13.g") + tao.floor_plan(graph="r13.g", verbose=True) def test_floor_orbit_1(caplog): @@ -436,62 +465,67 @@ def test_floor_orbit_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_floor_orbit" ) with ensure_successful_parsing(caplog): - tao.floor_orbit(graph="r33.g") + tao.floor_orbit(graph="r33.g", verbose=True) def test_tao_global_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.tao_global() + tao.tao_global(verbose=True) def test_global_optimization_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.global_optimization() + tao.global_optimization(verbose=True) def test_global_opti_de_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.global_opti_de() + tao.global_opti_de(verbose=True) def test_help_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.help() + tao.help(verbose=True) def test_inum_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.inum(who="ix_universe") + tao.inum(who="ix_universe", verbose=True) def test_lat_calc_done_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.lat_calc_done(branch_name="1@0") + tao.lat_calc_done(branch_name="1@0", verbose=True) def test_lat_ele_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.lat_ele_list(branch_name="1@0") + tao.lat_ele_list(branch_name="1@0", verbose=True) def test_lat_branch_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.lat_branch_list(ix_uni="1") + tao.lat_branch_list(ix_uni="1", verbose=True) def test_lat_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): tao.lat_list( - ix_uni="1", ix_branch="0", elements="Q*", which="model", who="orbit.floor.x" + ix_uni="1", + ix_branch="0", + elements="Q*", + which="model", + who="orbit.floor.x", + verbose=True, ) @@ -499,38 +533,45 @@ def test_lat_list_2(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): tao.lat_list( - ix_uni="1", ix_branch="0", elements="Q*", which="design", who="ele.ix_ele" + ix_uni="1", + ix_branch="0", + elements="Q*", + which="design", + who="ele.ix_ele", + verbose=True, ) def test_lat_param_units_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.lat_param_units(param_name="L") + tao.lat_param_units(param_name="L", verbose=True) def test_matrix_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.matrix(ele1_id="1@0>>q01w|design", ele2_id="q02w") + tao.matrix(ele1_id="1@0>>q01w|design", ele2_id="q02w", verbose=True) def test_merit_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.merit() + tao.merit(verbose=True) def test_orbit_at_s_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.orbit_at_s(ix_uni="1", ele="10", s_offset="0.7", which="model") + tao.orbit_at_s( + ix_uni="1", ele="10", s_offset="0.7", which="model", verbose=True + ) def test_place_buffer_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.place_buffer() + tao.place_buffer(verbose=True) def test_plot_curve_1(caplog): @@ -538,19 +579,19 @@ def test_plot_curve_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.plot_curve(curve_name="r13.g.a") + tao.plot_curve(curve_name="r13.g.a", verbose=True) def test_plot_lat_layout_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.plot_lat_layout(ix_uni="1", ix_branch="0") + tao.plot_lat_layout(ix_uni="1", ix_branch="0", verbose=True) def test_plot_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.plot_list(r_or_g="r") + tao.plot_list(r_or_g="r", verbose=True) def test_plot_graph_1(caplog): @@ -558,7 +599,7 @@ def test_plot_graph_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.plot_graph(graph_name="beta.g") + tao.plot_graph(graph_name="beta.g", verbose=True) def test_plot_histogram_1(caplog): @@ -566,7 +607,7 @@ def test_plot_histogram_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.plot_histogram(curve_name="r33.g.x") + tao.plot_histogram(curve_name="r33.g.x", verbose=True) def test_plot_template_manage_1(caplog): @@ -579,6 +620,7 @@ def test_plot_template_manage_1(caplog): template_name="beta", n_graph="2", graph_names="g1^^g2", + verbose=True, ) @@ -588,7 +630,7 @@ def test_plot_curve_manage_1(caplog): ) with ensure_successful_parsing(caplog): tao.plot_curve_manage( - graph_name="beta.g", curve_index="1", curve_name="r13.g.a" + graph_name="beta.g", curve_index="1", curve_name="r13.g.a", verbose=True ) @@ -597,7 +639,9 @@ def test_plot_graph_manage_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.plot_graph_manage(plot_name="beta", graph_index="1", graph_name="beta.g") + tao.plot_graph_manage( + plot_name="beta", graph_index="1", graph_name="beta.g", verbose=True + ) def test_plot_line_1(caplog): @@ -605,7 +649,9 @@ def test_plot_line_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" ) with ensure_successful_parsing(caplog): - tao.plot_line(region_name="beta", graph_name="g", curve_name="a", x_or_y="") + tao.plot_line( + region_name="beta", graph_name="g", curve_name="a", x_or_y="", verbose=True + ) def test_plot_line_2(caplog): @@ -613,7 +659,9 @@ def test_plot_line_2(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" ) with ensure_successful_parsing(caplog): - tao.plot_line(region_name="beta", graph_name="g", curve_name="a", x_or_y="y") + tao.plot_line( + region_name="beta", graph_name="g", curve_name="a", x_or_y="y", verbose=True + ) def test_plot_symbol_1(caplog): @@ -621,7 +669,9 @@ def test_plot_symbol_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" ) with ensure_successful_parsing(caplog): - tao.plot_symbol(region_name="r13", graph_name="g", curve_name="a", x_or_y="") + tao.plot_symbol( + region_name="r13", graph_name="g", curve_name="a", x_or_y="", verbose=True + ) def test_plot_symbol_2(caplog): @@ -629,7 +679,9 @@ def test_plot_symbol_2(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting" ) with ensure_successful_parsing(caplog): - tao.plot_symbol(region_name="r13", graph_name="g", curve_name="a", x_or_y="y") + tao.plot_symbol( + region_name="r13", graph_name="g", curve_name="a", x_or_y="y", verbose=True + ) def test_plot_transfer_1(caplog): @@ -637,7 +689,7 @@ def test_plot_transfer_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.plot_transfer(from_plot="r13", to_plot="r23") + tao.plot_transfer(from_plot="r13", to_plot="r23", verbose=True) def test_plot1_1(caplog): @@ -645,49 +697,53 @@ def test_plot1_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.plot1(name="beta") + tao.plot1(name="beta", verbose=True) def test_ptc_com_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ptc_com() + tao.ptc_com(verbose=True) def test_ring_general_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.ring_general(ix_uni="1", ix_branch="0", which="model") + tao.ring_general(ix_uni="1", ix_branch="0", which="model", verbose=True) def test_shape_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.shape_list(who="floor_plan") + tao.shape_list(who="floor_plan", verbose=True) def test_shape_manage_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.shape_manage(who="floor_plan", index="1", add_or_delete="add") + tao.shape_manage(who="floor_plan", index="1", add_or_delete="add", verbose=True) def test_shape_pattern_list_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape") with ensure_successful_parsing(caplog): - tao.shape_pattern_list(ix_pattern="") + tao.shape_pattern_list(ix_pattern="", verbose=True) def test_shape_pattern_manage_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape") with ensure_successful_parsing(caplog): - tao.shape_pattern_manage(ix_pattern="1", pat_name="new_pat", pat_line_width="1") + tao.shape_pattern_manage( + ix_pattern="1", pat_name="new_pat", pat_line_width="1", verbose=True + ) def test_shape_pattern_point_manage_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape") with ensure_successful_parsing(caplog): - tao.shape_pattern_point_manage(ix_pattern="1", ix_point="1", s="0", x="0") + tao.shape_pattern_point_manage( + ix_pattern="1", ix_point="1", s="0", x="0", verbose=True + ) def test_shape_set_1(caplog): @@ -704,73 +760,80 @@ def test_shape_set_1(caplog): shape_draw="", multi_shape="", line_width="", + verbose=True, ) def test_show_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.show(line="-python") + tao.show(line="-python", verbose=True) def test_space_charge_com_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.space_charge_com() + tao.space_charge_com(verbose=True) def test_species_to_int_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.species_to_int(species_str="electron") + tao.species_to_int(species_str="electron", verbose=True) def test_species_to_str_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.species_to_str(species_int="-1") + tao.species_to_str(species_int="-1", verbose=True) def test_spin_invariant_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.spin_invariant(who="l0", ix_uni="1", ix_branch="0", which="model") + tao.spin_invariant( + who="l0", ix_uni="1", ix_branch="0", which="model", verbose=True + ) def test_spin_polarization_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.spin_polarization(ix_uni="1", ix_branch="0", which="model") + tao.spin_polarization(ix_uni="1", ix_branch="0", which="model", verbose=True) def test_spin_resonance_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.spin_resonance(ix_uni="1", ix_branch="0", which="model") + tao.spin_resonance(ix_uni="1", ix_branch="0", which="model", verbose=True) def test_super_universe_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.super_universe() + tao.super_universe(verbose=True) def test_taylor_map_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.taylor_map(ele1_id="1@0>>q01w|design", ele2_id="q02w", order="1") + tao.taylor_map( + ele1_id="1@0>>q01w|design", ele2_id="q02w", order="1", verbose=True + ) def test_twiss_at_s_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.twiss_at_s(ix_uni="1", ele="10", s_offset="0.7", which="model") + tao.twiss_at_s( + ix_uni="1", ele="10", s_offset="0.7", which="model", verbose=True + ) def test_universe_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.universe(ix_uni="1") + tao.universe(ix_uni="1", verbose=True) def test_var_1(caplog): @@ -778,7 +841,7 @@ def test_var_1(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.var(var="quad[1]", slaves="") + tao.var(var="quad[1]", slaves="", verbose=True) def test_var_2(caplog): @@ -786,7 +849,7 @@ def test_var_2(caplog): "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching" ) with ensure_successful_parsing(caplog): - tao.var(var="quad[1]", slaves="slaves") + tao.var(var="quad[1]", slaves="slaves", verbose=True) def test_var_create_1(caplog): @@ -807,40 +870,43 @@ def test_var_create_1(caplog): good_user="T", key_bound="T", key_delta="0.01", + verbose=True, ) def test_var_general_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.var_general() + tao.var_general(verbose=True) def test_var_v_array_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.var_v_array(v1_var="quad_k1") + tao.var_v_array(v1_var="quad_k1", verbose=True) def test_var_v1_array_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.var_v1_array(v1_var="quad_k1") + tao.var_v1_array(v1_var="quad_k1", verbose=True) def test_var_v1_create_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.var_v1_create(v1_name="quad_k1", n_var_min="0", n_var_max="45") + tao.var_v1_create( + v1_name="quad_k1", n_var_min="0", n_var_max="45", verbose=True + ) def test_var_v1_destroy_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.var_v1_destroy(v1_datum="quad_k1") + tao.var_v1_destroy(v1_datum="quad_k1", verbose=True) def test_wave_1(caplog): tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init") with ensure_successful_parsing(caplog): - tao.wave(who="params") + tao.wave(who="params", verbose=True) From 5a1dd7e491005244e8eed8a073c6e78f809522ef Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 27 Jun 2024 10:02:25 -0700 Subject: [PATCH 10/15] MAINT: clean up 'raises' logic --- interface.tpl.py | 36 +++++---- pytao/interface_commands.py | 144 ++++++++++++++++++++++++++++-------- 2 files changed, 130 insertions(+), 50 deletions(-) diff --git a/interface.tpl.py b/interface.tpl.py index 5935c3f9..3ee57659 100644 --- a/interface.tpl.py +++ b/interface.tpl.py @@ -50,25 +50,23 @@ def __execute( "integer_array": self.cmd_integer, } func = func_for_type.get(cmd_type, self.cmd) - ret = func(cmd, raises=raises) - special_parser = getattr(_pytao_parsers, f"parse_{method_name}", "") - if special_parser and callable(special_parser): - data = special_parser(ret, cmd=cmd) - return data - if "string" in cmd_type: - try: - if as_dict: - data = parse_tao_python_data(ret) - else: - data = tao_parameter_dict(ret) - except Exception: - logger.exception("Failed to parse string data. Returning raw value.") - raise # TODO: remove me - return ret - - return data - - return ret + raw_output = func(cmd, raises=raises) + special_parser = getattr(_pytao_parsers, f"parse_{method_name}", None) + try: + if special_parser and callable(special_parser): + return special_parser(raw_output, cmd=cmd) + if "string" not in cmd_type: + return raw_output + if as_dict: + return parse_tao_python_data(raw_output) + return tao_parameter_dict(raw_output) + except Exception: + if raises: + raise + logger.exception( + "Failed to parse string data with custom parser. Returning raw value." + ) + return raw_output def bunch_data(self, ele_id, *, which="model", ix_bunch=1, verbose=False): """ diff --git a/pytao/interface_commands.py b/pytao/interface_commands.py index 371208f3..548784e5 100644 --- a/pytao/interface_commands.py +++ b/pytao/interface_commands.py @@ -2,7 +2,7 @@ # AUTOGENERATED FILE - DO NOT MODIFY # This file was generated by the script `generate_interface_commands.py`. # Any modifications may be overwritten. -# Generated on: 2024-06-26 16:52:44 +# Generated on: 2024-06-27 10:00:36 # ============================================================================== import logging @@ -57,25 +57,23 @@ def __execute( "integer_array": self.cmd_integer, } func = func_for_type.get(cmd_type, self.cmd) - ret = func(cmd, raises=raises) - special_parser = getattr(_pytao_parsers, f"parse_{method_name}", "") - if special_parser and callable(special_parser): - data = special_parser(ret, cmd=cmd) - return data - if "string" in cmd_type: - try: - if as_dict: - data = parse_tao_python_data(ret) - else: - data = tao_parameter_dict(ret) - except Exception: - logger.exception("Failed to parse string data. Returning raw value.") - raise # TODO: remove me - return ret - - return data - - return ret + raw_output = func(cmd, raises=raises) + special_parser = getattr(_pytao_parsers, f"parse_{method_name}", None) + try: + if special_parser and callable(special_parser): + return special_parser(raw_output, cmd=cmd) + if "string" not in cmd_type: + return raw_output + if as_dict: + return parse_tao_python_data(raw_output) + return tao_parameter_dict(raw_output) + except Exception: + if raises: + raise + logger.exception( + "Failed to parse string data with custom parser. Returning raw value." + ) + return raw_output def bunch_data(self, ele_id, *, which="model", ix_bunch=1, verbose=False): """ @@ -496,7 +494,7 @@ def building_wall_list( Returns ------- - datums: list of dicts + list of dicts Notes ----- @@ -544,7 +542,7 @@ def building_wall_graph(self, graph, *, verbose=False, as_dict=True, raises=True Returns ------- - datums: list of dicts + list of dicts Notes ----- @@ -707,7 +705,8 @@ def constraints(self, who, *, verbose=False, as_dict=True, raises=True): Returns ------- - datums: list of dicts + list of dicts + The keys depend on "data" or "var" Notes ----- @@ -954,7 +953,7 @@ def data_d1_array( Returns ------- - datums: list of dicts + list of dicts Notes ----- @@ -1030,7 +1029,7 @@ def data_d2_array(self, ix_uni, *, verbose=False, as_dict=True, raises=True): Returns ------- - datums: list of str + list of str Notes ----- @@ -1188,7 +1187,7 @@ def data_parameter( Returns ------- - datums : list + list of dict Notes ----- @@ -1370,7 +1369,8 @@ def datum_has_ele(self, datum_type, *, verbose=False, as_dict=True, raises=True) Returns ------- - datums : list of values + str or None + "no", "yes", "maybe", "provisional" Notes ----- @@ -1558,7 +1558,7 @@ def ele_chamber_wall( Returns ------- - datums : list of values + list of dict Notes ----- @@ -1714,7 +1714,7 @@ def ele_elec_multipoles( Returns ------- - info : dict + dict Notes ----- @@ -1887,7 +1887,9 @@ def ele_gen_grad_map( Returns ------- - info : dict + dict or list of dict + "derivs" mode will be a list of dictionaries. + Normal mode will be a single dictionary. Notes ----- @@ -2040,7 +2042,7 @@ def ele_lord_slave( Returns ------- - info : dict + list of dict Notes ----- @@ -2189,6 +2191,10 @@ def ele_multipoles( ele_id which : default=model + Returns + ------- + dict + Notes ----- Command syntax: @@ -2377,6 +2383,10 @@ def ele_spin_taylor( ele_id which : default=model + Returns + ------- + list of dict + Notes ----- Command syntax: @@ -2418,6 +2428,10 @@ def ele_taylor( ele_id which : default=model + Returns + ------- + dict + Notes ----- Command syntax: @@ -2565,6 +2579,10 @@ def ele_wall3d( who which : default=model + Returns + ------- + list of dict + Notes ----- Command syntax: @@ -2682,6 +2700,10 @@ def em_field( t_or_z which : default=model + Returns + ------- + dict + Notes ----- Command syntax: @@ -2722,6 +2744,10 @@ def enum(self, enum_name, *, verbose=False, as_dict=True, raises=True): ---------- enum_name + Returns + ------- + list of dict + Notes ----- Command syntax: @@ -2754,6 +2780,10 @@ def floor_plan(self, graph, *, verbose=False, as_dict=True, raises=True): ---------- graph + Returns + ------- + list of dict + Notes ----- Command syntax: @@ -2783,6 +2813,10 @@ def floor_orbit(self, graph, *, verbose=False, as_dict=True, raises=True): ---------- graph + Returns + ------- + list of dict + Notes ----- Command syntax: @@ -4057,6 +4091,10 @@ def shape_list(self, who, *, verbose=False, as_dict=True, raises=True): ---------- who + Returns + ------- + list of dict + Notes ----- Command syntax: @@ -4145,6 +4183,10 @@ def shape_pattern_list( ---------- ix_pattern : optional + Returns + ------- + list of dict + Notes ----- Command syntax: @@ -4358,6 +4400,12 @@ def show(self, line, *, verbose=False, as_dict=True, raises=True): ---------- line + Returns + ------- + list of str + This is raw list of strings from tao, as parsing is not currently + supported. + Notes ----- Command syntax: @@ -4421,6 +4469,10 @@ def species_to_int(self, species_str, *, verbose=False, as_dict=True, raises=Tru ---------- species_str + Returns + ------- + int + Notes ----- Command syntax: @@ -4453,6 +4505,10 @@ def species_to_str(self, species_int, *, verbose=False, as_dict=True, raises=Tru ---------- species_int + Returns + ------- + str + Notes ----- Command syntax: @@ -4649,6 +4705,10 @@ def spin_resonance( ref_ele : default=0 Reference element to calculate at. + Returns + ------- + dict + Notes ----- Command syntax: @@ -4686,6 +4746,10 @@ def super_universe(self, *, verbose=False, as_dict=True, raises=True): Output super_Universe parameters. + Returns + ------- + dict + Notes ----- Command syntax: @@ -4857,6 +4921,12 @@ def var(self, var, *, slaves="", verbose=False, as_dict=True, raises=True): var slaves : optional + Returns + ------- + dict, or list of dict + "slaves" mode will be a list of dicts. + Normal mode will be a dict. + Notes ----- Command syntax: @@ -4968,6 +5038,10 @@ def var_general(self, *, verbose=False, as_dict=True, raises=True): Output list of all variable v1 arrays + Returns + ------- + list of dict + Notes ----- Command syntax: @@ -4999,6 +5073,10 @@ def var_v_array(self, v1_var, *, verbose=False, as_dict=True, raises=True): ---------- v1_var + Returns + ------- + list of dict + Notes ----- Command syntax: @@ -5031,6 +5109,10 @@ def var_v1_array(self, v1_var, *, verbose=False, as_dict=True, raises=True): ---------- v1_var + Returns + ------- + dict + Notes ----- Command syntax: From a5fd3c958a903d7e841b8f6c397c9bcd9e9ccc85 Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 27 Jun 2024 10:02:35 -0700 Subject: [PATCH 11/15] MAINT: verbose=True on test suite --- generate_interface_commands.py | 1 + 1 file changed, 1 insertion(+) diff --git a/generate_interface_commands.py b/generate_interface_commands.py index 6ed31cba..ab2136e1 100644 --- a/generate_interface_commands.py +++ b/generate_interface_commands.py @@ -262,6 +262,7 @@ def ensure_successful_parsing(caplog): for test_name, test_meta in tests.items(): args = [f"{k}='{v}'" for k, v in test_meta['args'].items()] + args.append("verbose=True") test_code = f''' tao = new_tao('{test_meta['init']}') with ensure_successful_parsing(caplog): From 28f37ad56b43c683addf264b483b7680b2fedcd7 Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 27 Jun 2024 10:03:34 -0700 Subject: [PATCH 12/15] CI: skip ele_photon for now --- .github/workflows/ci.yml | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 63e172e8..78233ce5 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -57,5 +57,9 @@ jobs: shell: bash -l {0} run: | echo -e '## Test results\n\n```' >> "$GITHUB_STEP_SUMMARY" - pytest -v --cov=pytao/ pytao/tests 2>&1 | tee -a "$GITHUB_STEP_SUMMARY" + pytest -v \ + --cov=pytao/ \ + -k "not ele_photon" \ + pytao/tests \ + 2>&1 | tee -a "$GITHUB_STEP_SUMMARY" echo '```' >> "$GITHUB_STEP_SUMMARY" From f6939306d3bec8cc5303539111300ce3d652665e Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 27 Jun 2024 10:42:46 -0700 Subject: [PATCH 13/15] MAINT: tpyo Co-authored-by: Hugo Slepicka --- pytao/util/parsers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pytao/util/parsers.py b/pytao/util/parsers.py index 43162a85..9e8596d8 100644 --- a/pytao/util/parsers.py +++ b/pytao/util/parsers.py @@ -18,7 +18,7 @@ def _parse_str_bool(s): """ parses str to bool '1', 't', 'T' -> True - '0', 'f', 'F' -> Flase + '0', 'f', 'F' -> False """ x = s.upper()[0] if x in ("T", "1"): From 8e888de88babe86127e248f3f05cb3866df2f904 Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 27 Jun 2024 10:07:19 -0700 Subject: [PATCH 14/15] Revert "CI: skip ele_photon for now" This reverts commit 28f37ad56b43c683addf264b483b7680b2fedcd7. This appears to be working on CI (Linux) but failing locally for me (MacOS) with bmad 20240626.0 --- .github/workflows/ci.yml | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 78233ce5..63e172e8 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -57,9 +57,5 @@ jobs: shell: bash -l {0} run: | echo -e '## Test results\n\n```' >> "$GITHUB_STEP_SUMMARY" - pytest -v \ - --cov=pytao/ \ - -k "not ele_photon" \ - pytao/tests \ - 2>&1 | tee -a "$GITHUB_STEP_SUMMARY" + pytest -v --cov=pytao/ pytao/tests 2>&1 | tee -a "$GITHUB_STEP_SUMMARY" echo '```' >> "$GITHUB_STEP_SUMMARY" From def6e10efac1609ad63b6d8af712897b2a4d006a Mon Sep 17 00:00:00 2001 From: Ken Lauer <152229072+ken-lauer@users.noreply.github.com> Date: Thu, 27 Jun 2024 10:43:57 -0700 Subject: [PATCH 15/15] DOC: rerun notebooks --- docs/examples/advanced.ipynb | 156 ++++----- docs/examples/basic.ipynb | 510 +++++++++++++++++----------- docs/examples/bunch.ipynb | 220 ++++++------ docs/examples/fodo.ipynb | 399 ++++++++++------------ docs/examples/special_parsers.ipynb | 34 +- 5 files changed, 686 insertions(+), 633 deletions(-) diff --git a/docs/examples/advanced.ipynb b/docs/examples/advanced.ipynb index 8e5cee84..66cfd863 100644 --- a/docs/examples/advanced.ipynb +++ b/docs/examples/advanced.ipynb @@ -9,29 +9,31 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Useful for debugging\n", - "#%load_ext autoreload\n", - "#%autoreload 2" + "# %load_ext autoreload\n", + "# %autoreload 2\n", + "\n", + "# Nicer plots\n", + "%config InlineBackend.figure_format = 'retina'" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%config InlineBackend.figure_format = 'retina'" + "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -49,11 +51,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "INPUT_FILE = os.path.expandvars('$ACC_ROOT_DIR/bmad-doc/tao_examples/csr_beam_tracking/tao.init')\n", + "INPUT_FILE = os.path.expandvars(\n", + " \"$ACC_ROOT_DIR/bmad-doc/tao_examples/csr_beam_tracking/tao.init\"\n", + ")\n", "assert os.path.exists(os.path.expandvars(INPUT_FILE))" ] }, @@ -66,22 +70,22 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "M=run_tao(input_file=INPUT_FILE, ploton=False)\n", + "M = run_tao(input_file=INPUT_FILE, ploton=False)\n", "M" ] }, @@ -94,37 +98,38 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'lat::orbit.x[FF.PIP02A]': array([0.]),\n", - " 'beam::norm_emit.x[end]': array([9.99826669e-07]),\n", - " 'beam_archive': '/Users/chrisonian/Code/GitHub/pytao/docs/examples/bmad_beam_193acce3fc1cb213e4aa51503888f178.h5'}" + "{'lat::orbit.x[FF.PIP02A]': ' 0.00000000000000E+00',\n", + " 'beam::norm_emit.x[end]': ' 9.99937630588196E-07',\n", + " 'beam_archive': '/Users/klauer/Repos/pytao/docs/examples/bmad_beam_d2fca186ca9b848bb3924bf20dc69440.h5'}" ] }, - "execution_count": 6, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res = evaluate_tao(settings={\n", - " 'space_charge_com:ds_track_step':0.001}, \n", - " input_file=INPUT_FILE, \n", - " run_commands=['set global track_type=beam'],\n", - " expressions = ['lat::orbit.x[FF.PIP02A]', 'beam::norm_emit.x[end]'],\n", - " ploton=False, \n", - " archive_csr_wake=True,\n", - " beam_archive_path = '.')\n", + "res = evaluate_tao(\n", + " settings={\"space_charge_com:ds_track_step\": 0.001},\n", + " input_file=INPUT_FILE,\n", + " run_commands=[\"set global track_type=beam\"],\n", + " expressions=[\"lat::orbit.x[FF.PIP02A]\", \"beam::norm_emit.x[end]\"],\n", + " ploton=False,\n", + " archive_csr_wake=True,\n", + " beam_archive_path=\".\",\n", + ")\n", "res" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -136,7 +141,7 @@ " '/data/00004/particles/']" ] }, - "execution_count": 7, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -144,8 +149,9 @@ "source": [ "from pmd_beamphysics import ParticleGroup, particle_paths\n", "from h5py import File\n", - "afile = res['beam_archive']\n", - "h5 = File(afile, 'r')\n", + "\n", + "afile = res[\"beam_archive\"]\n", + "h5 = File(afile, \"r\")\n", "ppaths = particle_paths(h5)\n", "ppaths" ] @@ -159,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -168,7 +174,7 @@ "['csr_wake', 'data', 'expressions', 'input', 'settings']" ] }, - "execution_count": 8, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -179,12 +185,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABJsAAANlCAYAAADbyPj6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAB7CAAAewgFu0HU+AAEAAElEQVR4nOzdd3xUVf7/8fdMJj0h9JogvUkRgYAUKQoo3UJRFHQVXXvBsrJqzK5+FX+KbdVVQZoiiNJBYEGpogREKdKlJYSSEEJ6MjP390dkJCSBBGbmTsLr+Xj42Jt7zj33PdmQZD4551yLYRiGAAAAAAAAADewmh0AAAAAAAAA5QfFJgAAAAAAALgNxSYAAAAAAAC4DcUmAAAAAAAAuA3FJgAAAAAAALgNxSYAAAAAAAC4DcUmAAAAAAAAuA3FJgAAAAAAALgNxSYAAAAAAAC4DcUmAAAAAAAAuA3FJgAAAAAAALgNxSYAAAAAAAC4DcUmAAAAAAAAuA3FJgAAAAAAALgNxSYAAAAAAAC4DcUmAAAAAAAAuI3N7ABAUbKzs7Vt2zZJUrVq1WSz8aUKAAAAuJPdbtfJkyclSa1atVJQUJDJiQCUF7yDh0/atm2boqOjzY4BAAAAXBE2btyoDh06mB0DQDnBMjoAAAAAAAC4DTOb4JOqVavmOt64caNq1aplYhoAAACg/ElMTHStJjj3928AuFwUm+CTzt2jqVatWoqMjDQxDQAAAFC+sUcqAHdiGR0AAAAAAADchmITAAAAAAAA3IZiEwAAAAAAANyGYhMAAAAAAADchmITAAAAAAAA3IZHDgAAgMsSGxvrOo6JiTExCQAAAHwBM5sAAAAAAADgNhSbAAAAAAAA4DYUmwAAAAAAAOA2FJsAAAAAAADgNhSbAAAAAAAA4DYUmwAAAAAAAOA2FJsAAAAAAADgNhSbAAAAAAAA4DY2swMAAIDyKTY2tsDHMTExJiUBAACANzGzCQAAAAAAAG5DsQkAAAAAAABuQ7EJAAAAAAAAbkOxCQAAAAAAAG5DsQkAAAAAAABuQ7EJAAAAAAAAbkOxCQAAAAAAAG5DsQkAAAAAAABuYzM7AAAA5UFsbKzrOCYmxiv3cce93D1eWc0AAAAA92FmEwAAAAAAANyGYhMAAAAAAADchmITAAAAAAAA3IZiEwAAAAAAANyGYhMAAAAAAADchmITAAAAAAAA3IZiEwAAAAAAANyGYhMAAAAAAADcxmZ2ALjXiRMntHHjRm3cuFFxcXGKi4tTcnKyJGn06NGaMmVKqcZbunSpPv30U23cuFEnT55UtWrVFB0drQceeEA33XSTB14BAOBSxcbGlqhfTEzMZY19Kdf76r0AAADgfhSbypkaNWq4ZRzDMPT3v/9dn376aYHzCQkJmjt3rubOnasHHnhA//3vf2WxWNxyTwAAAAAAUPaxjK4ci4qKUp8+fS7p2hdffNFVaGrbtq2++uorbdy4UV999ZXatm0rSfr000/10ksvuS0vAAAAAAAo+5jZVM68/PLL6tChgzp06KAaNWro4MGDql+/fqnG2Ldvn958801JUvv27bVmzRoFBwdLkjp06KBBgwape/fu2rRpk8aPH697771XDRs2dPtrAQAAAAAAZQ8zm8qZ2NhYDRgw4LKW073zzjuy2+2SpA8++MBVaDorJCREH3zwgSTJbrfr3XffveR7AQAAAACA8oViEwowDEPz58+XJDVr1kydOnUqsl+nTp3UtGlTSdK8efNkGIbXMgIAAAAAAN9FsQkFHDhwQAkJCZKk7t27X7Dv2fb4+HgdPHjQ09EAAAAAAEAZwJ5NKGDnzp2u42bNml2w77ntO3fuLNXeUPHx8RdsT0xMLPFYAAAAAADAd1BsQgFHjhxxHUdGRl6wb1RUVJHXlcS51wIAAAAAgPKDYhMKSEtLcx2HhYVdsG9oaKjrOD093WOZAKC8io2NLVG/mJgYDycpnZLmNsv5+Xzt8wcAAFDeUWxCAdnZ2a7jgICAC/YNDAx0HWdlZZXqPhebCZWYmKjo6OhSjQkAAAAAAMxHsQkFBAUFuY5zc3Mv2DcnJ8d1HBwcXKr7XGyJHgAAAAAAKJt4Gh0KCA8Pdx1fbGlcRkaG6/hiS+4AAAAAAMCVgWITCjh3xtHFnhh37lI4NvwGAAAAAAASxSacp0WLFq7jXbt2XbDvue3Nmzf3WCYAAAAAAFB2UGxCAfXr11ft2rUlSatXr75g3zVr1kiS6tSpo3r16nk6GgAAAAAAKAMoNqEAi8WiwYMHS8qfufTTTz8V2e+nn35yzWwaPHiwLBaL1zICAAAAAADfRbEJhTz55JOy2fIfVPjYY48pKyurQHtWVpYee+wxSZLNZtOTTz7p7YgAAAAAAMBH2cwOAPdat26d9u3b5/o4KSnJdbxv3z5NmTKlQP977rmn0BhNmjTRM888ozfeeEObNm1Sly5d9Pzzz6thw4bav3+/xo8fry1btkiSnn32WTVu3NgjrwUAfE1sbKzrOCYmptTXlAWX8hov9z6X2u/cfJfyeT7/Gk++XgAAgCsJxaZyZuLEiZo6dWqRbevXr9f69esLnCuq2CRJr732mk6cOKHPP/9cW7Zs0YgRIwr1ue+++/Tqq69edmYAAAAAAFB+sIwORbJarZo0aZIWL16swYMHq3bt2goICFDt2rU1ePBgLVmyRBMnTpTVypcQAAAAAAD4CzObypkpU6YUWip3Ofr166d+/fq5bTwAAAAAAFC+MS0FAAAAAAAAbkOxCQAAAAAAAG5DsQkAAAAAAABuQ7EJAAAAAAAAbkOxCQAAAAAAAG5jMQzDMDsEcL74+HhFRUVJko4cOaLIyEiTEwHwVbGxsQU+jomJKVG/klxzsetKMt6lXF9e+frnpbh8Jf36OL9fSccAALPwOzcAT2FmEwAAAAAAANyGYhMAAAAAAADchmITAAAAAAAA3IZiEwAAAAAAANyGYhMAAAAAAADchmITAAAAAAAA3IZiEwAAAAAAANyGYhMAAAAAAADcxmZ2AAAAzoqNjXUdx8TElLitJONdSoZL5Y4xyiNf/7xcbj53f+1c6Ov8Uv49AAAAeAszmwAAAAAAAOA2FJtMEBcXp8cee8zsGAAAAAAAAG5HsclL4uPj9frrr6tFixbq1KmTPvroI7MjAQAAAAAAuB17NnlQRkaGvvnmG02bNk2rV6+WYRiSJMMwZLFYTE4HAAAAAADgfhSb3MwwDK1YsULTpk3TvHnzlJmZ6TovSZGRkbr11lt12223mRkTAAAAAADAIyg2ucmOHTs0depUzZgxQ4mJiZL+KjDVrVtXQ4cO1e23366OHTuaGRMAAAAAAMCjKDZdhpMnT+rLL7/UtGnT9Ntvv0n6q8BUsWJFnT59WhaLRW+++aaGDRtmZlQAAAAAAACvoNhUSjk5OZo/f76mTZum5cuXy+FwuApMgYGB6tevn+666y7169dPwcHBJqcFAAAAAADwLopNJbRu3TpNmzZNs2fP1pkzZyT9tdF3t27ddNddd2nYsGGKiIgwOSkA+J7Y2FjXcUxMTKmvudR7efIaXDnc/fXB1xsAACjvKDaV0PXXXy+LxeKaxdSiRQuNHDlSI0eOVN26dU1OBwAAAAAA4BsoNpVSeHi43nvvPd1zzz1mRwEAAAAAAPA5VrMDlCWGYSg9PV333XefWrdurTfffFPx8fFmxwIAAAAAAPAZFJtKaO3atbr//vsVEREhwzC0fft2vfDCC6pXr5569uypzz//3LWXEwAAAAAAwJWKYlMJdenSRZ9++qmOHTumWbNmqV+/fvLz85PT6dSaNWs0ZswY1axZU0OHDtWCBQtkt9vNjgwAAAAAAOB1FJtKKSAgQEOHDtWiRYuUkJCgt99+W61bt5ZhGMrOztacOXN0yy23qEaNGmZHBQAAAAAA8DqKTZehWrVqeuqpp7RlyxZt3bpVTz/9tGrWrCnDMJSSkiKLxSJJevrpp/XEE09o7dq1JicGAAAAAADwLIpNbtKyZUu99dZbOnLkiJYsWaIRI0YoKChIhmHo6NGj+s9//qMePXqoVq1aevjhh7Vy5UqzIwMAAAAAALidzewA5Y3VatVNN92km266SWlpaZo1a5amT5+udevWyTAMHT9+XJ988ok+/fRT9nUC4BWxsbGu45iYmCLPn992uWMDKOj8f2/euldJ/13ybxkAALgTM5s8KDw8XPfff79Wr16t/fv365VXXlGjRo1kGIYMwzA7HgAAAAAAgNtRbPKSevXq6eWXX9aePXu0du1ajRkzxuxIAAAAAAAAbscyOhN06dJFXbp0MTsGAAAAAACA2zGzCQAAAAAAAG5DsQkAAAAAAABuQ7EJAAAAAAAAbkOxCQAAAAAAAG5DsQkAAAAAAABuw9PoAKAciI2NdR3HxMSYnuFCOc7vB6BkLvffTkn/jQIAAFwuZjYBAAAAAADAbSg2AQAAAAAAwG0oNgEAAAAAAMBtKDYBAAAAAADAbSg2AQAAAAAAwG0oNgEAAAAAAMBtKDYBAAAAAADAbSg2AQAAAAAAwG1sZgcAAPie2NhYsyNckK/nA9yhpF/nnv73cO74MTExl93PW87/vPhCJgAArhTMbAIAAAAAAIDbUGwCAAAAAACA21BsAgAAAAAAgNtQbAIAAAAAAIDbUGwCAAAAAACA21BsAgAAAAAAgNtQbAIAAAAAAIDbUGwCAAAAAACA29jMDgAAV7rY2NgCH8fExJiUxL3Of10AvO9C/w4v5d/ouddc6HtVSftdKM+lXFdevn8CAFDWMbMJAAAAAAAAbkOxCQAAAAAAAG5DsQkAAAAAAABuQ7EJAAAAAAAAbkOxCQAAAAAAAG5DsQkAAAAAAABuQ7EJAAAAAAAAbkOxCQAAAAAAAG5jMzsAAFyJYmNjS90WExNT4jHcnQnAlaOk3wsutd+538tK+r3w/O9/AADAtzGzCQAAAAAAAG5DsQkAAAAAAABuQ7EJAAAAAAAAbkOxCQAAAAAAAG5DsQkAAAAAAABuQ7EJAAAAAAAAbkOxCQAAAAAAAG5DsQkAAAAAAABuYzM7AACYKTY21nUcExNT6mvOv+5Sxiup8+97uf0AwAyX8j3Km99bL3d8d48HAEBZxMwmAAAAAAAAuA3FJgAAAAAAALgNxSYAAAAAAAC4DcUmAAAAAAAAuA3FJgAAAAAAALgNxSYAAAAAAAC4DcUmAAAAAAAAuA3FJgAAAAAAALiNzewAAMqf2NhY13FMTIxXrjn/uguNd/51JR2/tP1Ker03+WImACgNd3wfu5Tv7xf6WXKpP7fOVdIxAAAoC5jZBAAAAAAAALeh2AQAAAAAAAC3odgEAAAAAAAAt6HYBAAAAAAAALeh2AQAAAAAAAC3odgEAAAAAAAAt6HYBAAAAAAAALeh2AQAAAAAAAC3sZkdAL7t8OHDev/997V48WIdPnxYgYGBatSokYYNG6aHH35YISEhZkf0uNjYWNdxTEyMx8a+1PEvlO9ys5+f71zuHu9S+pX0Ok/fFwDg+y73Z8GFrnf3zw93jHfuz+mS/jwv6e8UxV1fGu7+/cqTv6+5W1nKCgCXimITirV48WKNHDlSqamprnOZmZmKi4tTXFycJk6cqCVLlqhBgwYmpgQAAAAAAL6EZXQo0m+//aZhw4YpNTVVYWFheu211/Tjjz9q5cqVGjNmjCRp9+7d6t+/v9LT001OCwAAAAAAfAUzm1CkJ598UpmZmbLZbFq+fLmuu+46V1uvXr3UuHFjPffcc9q1a5cmTJigl19+2cS0AAAAAADAVzCzCYXExcVp1apVkqT77ruvQKHprLFjx6p58+aSpHfffVd5eXnejAgAAAAAAHwUxSYUMm/ePNfxvffeW2Qfq9WqUaNGSZJSUlJcxSkAAAAAAHBlo9iEQtauXStJCg0NVbt27Yrt1717d9fxunXrPJ4LAAAAAAD4PvZsQiE7d+6UJDVq1Eg2W/FfIs2aNSt0TUnFx8dfsP3IkSOu48TExFKN7W7nPo3vYrkvZ+xLHf9C+S43+/n5znWh8Yq774XGAwDgcvniz5ySZnJ39su974V+pyju+tJw9+9Xnvx9zd18Keu5v2fb7XYTkwAobyyGYRhmh4DvyM7OVnBwsCSpf//+WrRo0QX7h4WFKSMjQ506ddKGDRtKfB+LxXJZOQEAAAC4z8aNG9WhQwezYwAoJ1hGhwLS0tJcx2FhYRftHxoaKklKT0/3WCYAAAAAAFB2sIwOBWRnZ7uOAwICLto/MDBQkpSVlVWq+5y7TK64HLt27VKNGjVUrVq1Cy7nu5jExERFR0dLyv+LTa1atS55LMBd+LqEr+JrE76Ir0v4ovLwdWm323Xy5ElJUqtWrUxOA6A8odiEAoKCglzHubm5F+2fk5MjSa6ldyUVGRl50T6NGjUq1ZglUatWrRLdG/Amvi7hq/jahC/i6xK+qCx/XdarV8/sCADKIZbRoYDw8HDXcUmWxmVkZEgq2ZI7AAAAAABQ/lFsQgFBQUGqWrWqpIs/HSMlJcVVbIqKivJ4NgAAAAAA4PsoNqGQ5s2bS5L27dt3wUeg7tq1q9A1AAAAAADgykaxCYV07dpVUv4Suc2bNxfbb/Xq1a7jLl26eDwXAAAAAADwfRSbUMiQIUNcx5MnTy6yj9Pp1LRp0yRJFStWVM+ePb0RDQAAAAAA+DiKTSgkOjpa3bp1kyRNmjRJGzZsKNTn7bff1s6dOyVJTzzxhPz9/b2aEQAAAAAA+Cab2QHgm9577z116dJFWVlZ6tOnj8aNG6eePXsqKytLM2fO1KeffipJatKkicaOHWtyWgAAAAAA4CsshmEYZoeAb1q4cKHuuusunTlzpsj2Jk2aaPHixWrUqJGXkwEAAAAAAF9FsQkXdOjQIb333ntavHix4uPjFRAQoEaNGmno0KF69NFHFRISYnZEAAAAAADgQyg2AQAAAAAAwG3YIBwAAAAAAABuQ7EJAAAAAAAAbkOxCQAAAAAAAG5DsQkAAAAAAABuQ7EJAAAAAAAAbkOxCQAAAAAAAG5DsQkAAAAAAABuQ7EJAAAAAAAAbkOxCQAAAAAAAG5DsQkAAAAAAABuQ7EJAAAAAAAAbkOxCQAAAAAAAG5DsQkAAAAAAABuQ7EJAAAAAAAAbkOxCQAAAAAAAG5DsQkAAAAAAABuQ7EJAAAAAAAAbmMzOwBQlOzsbG3btk2SVK1aNdlsfKkCAAAA7mS323Xy5ElJUqtWrRQUFGRyouLx/gDwHE98L+BfKHzStm3bFB0dbXYMAAAA4IqwceNGdejQwewYxeL9AeAd7vpewDI6AAAAAAAAuA0zm+CTqlWr5jreuHGjatWqZWIaAAAAoPxJTEx0zRY69/dvX8T7A8BzPPG9gGITfNK5a7Br1aqlyMhIE9MAAAAA5Zuv74HE+wPAO9z1vYBldAAAAAAAAHAbik0AAAAAAABwG4pNAAAAAAAAcBuKTQAAAAAAAHAbik0AAAAAAABwG4pNAAAAAAAAcBuKTQAAAAAAAHAbik0AAAAAAABwG4pNAAAAAAAAcBuKTQAAAAAAAHAbik0AAAAAAABwG4pNAAAAAAAAcBuKTQAAAAAAAHAbik0AAAAAAABwG4pNAAAAAAAAcBuKTQAAAAAAAHAbik0AAAAAAABwG4pNAAAAAAAAcBuKTQAAAAAAAHAbm9kBAKA8O7T3uOZ/8aP2bo9XZMPqCqtXRdv3Jio4OEDNalbSoW3xSk/NUrNr6irjTJYO7T2uOo1rKLBVVW2JP66w4ADd0qWVerVtJD8rfx8AAAAA4PsoNgGAh2zZsE8vjpksp8MpwyJtS0uXsf2QJMk/OVM7z+S4+u7ZFi9JMqzShgo5cqYmuNp+2nlYAzu1UOzovt59AQAAAABwCSg2AYAHOJ1Offf1RjkdTkmSYbXIlpoji9OQI8gmv7ScIq+zB/rJGeT35yCG/NOd8ss1tOaHXVpVvaZ+WbtXIWGBatWtibbsTNCZtCxd26GeEv2ztePYCTWtVlUNMwL16+o9CgkLVN/bO6h+k5reetkAAAAAQLEJANzNYXfouVGf6fcth1znrA5D1j9nMtnSc4u91j/LodAD6cqqE6qQ43ZZnJIMQ/57T2r8zzMlSXkRgfpq3c784xBp2pndMmwWyWlo84oUBSXZXePNn/6j7ny4l+5+rLcHXikAAID3TZgwQREREWbHcIuYmBizIwAeQbEJAEooMyNHqxf/prTUTHXq1UJ1G1Z3tcUfTdGa9XsUEhKgyCrhBQpNpRWSkKW8isH5hSZJllyH/HIcrnZ7WKDrOLuKNb/QJCkg1V6g0HTWwhk/qdvQdlq1db/CggLUt31ThYcEXXI+AAAAALgQik0AUAJrvtuq916eo8z0/NlJkycs0/U3t9az44fqrf/8T0tXbJNhSHkhFjkCDVW+jHtZ7IYqpjuVVWyHYo6Norunpmfp1tipro/fmbNWzw3rqcGdr76MlAAAALhcsbGxhc4x2wnlAY82AoAS+Orj712FprPWfLdVq1fu0LKFW2RNz5WchnIj/JQXapM96NK/vVokORPSZPmzkGT4+8lp+2s8v8w813HAaUNy5leZ8irYlBda8L6GpOzqwbJlOl39snLy9Onin5SZl6sVh/fpp2OH5XA6tX3TAW1Y+bsyM4reTwoAAAAASoKZTQBQAjk5eUWen/DM1wo6uwm4RXI6Q5TRIFSn2ldWcGK2Ak/myPCTet/QSlknM5R2OlMt2l6lhYt/VXZqlgx/qyx5DvnlOguM65dtV9/W9RQYWVG79x6TtVpFHdpyWIbDKf9TWVKuXc7QAAVnWhR8OEdpDQOVW8VfORX9ZMtwyiLJEein3KqhUoCfgk86ZFik3ApW5UZYdSw8Q52+/lhncnMUHG9X3RXZsp7If43BoYG67d5uuvPhXrJYLOe/ZAAAAAC4IIpNAHABhmHo6KFkRTWorsTDpwq1n33anCRZDCnsSKZyagTKHuavzKgQZUaFKCjApjHP9FPViFBX33iHXavW7ZYk+WXkyu9ERqGxr23fQH1va6/EI6f0tz7/76/7SApIz5ORnudaRRe0qXAxLLdKiIwAvwL5AlOdyqlk0aka2dKf+5TXWJkp64m/9oTKysjRF/9Zoc69r+ZJdgAAAKXEMjiAYhMAFOun73/X9A9W6I9diZKkwCB/5WQXPcPpXG2qVdOxEKdSM3PU85qGGt27fYFCkyT9bfh1StiVqH2JKXIE+kmh/rJk2WU4DVn9LOrYo7l69G8tSUo/U/TuTdZAP1WtEaGTRRTB8jsUPSsp0K/geb/sojd7emPa/zTusQG6qkalC71cAAAAACiAYhMAFOHUiTOKfWR6gXMlKTRJ0r+eGqxaURfeIvw/MXOVsPmQgs85d7bk43QY2rDyd82dul7DH+ihKtXDZbVa5HQWLAo1blJLvW9ppw//Nb/Ie1jsThn+foXO59iN/Jv9WXPKq2BVQGrBZXyGpE3xx/WPiYv11T/vkmHk35tldQAAABfGpt8AxSYAKNKppPQS9atcPVxnUjJlz3OoWZso3fnwDRctNElS0okzF+2z9ec/tH75du37/ahqRFaSzeanhINJsvn7qfvNrXX3471VtWaEsrNyNXnCUjkdBYtRASczlFcpWI7QAMlqkTXbrswa/rLIqvDdUnZ1h/IqS0f7h6rW8iyFHsyVnFLGVfnL/wybVQf2JOrRUf/VgV+OKDQsUDcNjdbQ+65XeMWQEn1+AAAAAFx5KDYBQBEqlLCY8tpnf1P1OpWUnZmrytXCSzx+RKUQHTtSzPK3P23ZsM91fDw+RZJ079ib1H94R4WGB7nabv/b9Vrz3Vbt3Z5Q4Hqrw1BgUqaM5EzJapHFYSijVv6SOP8zVoUcMeSf5ZTFYZWsoTrZuYJkSIZ//hPtLHlOVdx0Svud+TnTUrM0e+Jq7dkerzcm31/i1woAAADgykKxCQCKUL12Rd337M2a8eFKZWXm76Tt7++nvLz8jbQjKodq+JgeuqpxDVksFoWEBpZq/EdjbtFH/56vnb8ezh870CaH3Smnwyk/m1V16lXV4X0nCl2XlHi6QKHprMdfuUUfvbrANV5AoE32P8ezWizyt/kp12FXhT1pymtTRWn+hvLCrQqWnyxn7HI6DQWlSbmV/eT4c8mcX5ZDFmehW+nwvuOleq0AAABmYOkaYB6KTQBQhOysXM2dss5VaJKkvDyHru3SSMPG9FDza+oqIND/ksdv1KK2Jnz1kA7sTtSZ05lq3vYqZZzJ0sE9xxXVsJr+N2ezpr3/v0LXBYUUXdRqdHWdYser26i6KlQM0c5fDyskLEgNm9fS3oQkpWZkq02DWkpLy9aBQ0mqV7eKvtmwXZ8u/kmSZPgVvT9TUEjAJb9uAAAAAOUfxSYAKMKGlTt16mRaofP7fk9Um44N3Xaf+k1ruY4DqoarUtX8pXi9b22nuDW7XTOVJKnx1XXUb3j0JY0nSa2jG7iOm0RWcx1XqRymKpXDJEm3dm2ldVv/0O9HTsgR4qfMOsEKTsg6u5e4bBWCVKlNpF5+bZ66XtdYPbs1k38Rm5ADAACYgdlMgG+g2AQA5/nq4++LnFUkyWuFlao1IvT2jL9r16+HtWdHgho0raWW7et5/GlwKYdTlP7tHlWyOWUP95es/sqOCpBfZp4cgX4yAm3atP2IJGn1+j368uuf9Mm7oxQUdOmzvAAAAACULxSbAOA8KxdsKbZt0F2dvZbDYrGoedur1LztVV6754aVvysnM1cBkmzZhrLrBMmQZK9Q9PK9g4eTtXf/cbW6OtJrGQEAAAD4NopNAHC+PzfIPt/zb41Qj/5tvBzGu4xiXvsFr/FADgAAgEsRGxvrOmZJHWAeq9kBAMDXdO7dstC5GnUqqUP3piak8a4O1zeV7c+lgpZchyy5jgv2r1olTIcOJ2v/gcJPzgMAAABwZWJmEwCc59oujbRi/i9K+XODcD+bVZ16NVfwFfAUtpbt62vid2P18JD3lJmeo6CjZ+QIC5A9NCB/BlOgTYb1r32jkpLT9dYHyyRJ7a65SuPG9lPVKuFFDw4AAOBFzHICzMPMJgA4z6T/952r0CRJDrtT86f/qD92JpqYyntq1Kmk6O7NJEkWQ7Kl5SroWLpktRQoNJ1v86+HNGdh8ftdAQAAALgyUGwCgPNkpGeX6nx59NT/3a6h911f8OQFCk1nLfzqJ/1v3mYPpQIAAABQFrCMDgDOkZOdp5qRlZV4+FSB81arRTUjK5uUyvsCAmy69d5umjN1nRx2pyTJYnfK+HM/p+JkpWRowgvfqHmbuoqsX80bUQEAAIrE0jnAPMxsAgBJu7cdUcxDU3XLtTH6fcsh1W9SU0HB+Xs0tenUUK9Pvl816lQyOaV3VawSpjenPeB63YEnMmRLzZachgxJDj+nnGdrT7l2BSaekS0tV5I05Z1lSklKK3pgAAAAAOUaM5u84MSJE9q4caM2btyouLg4xcXFKTk5WZI0evRoTZkyxe33nDlzpiZPnqytW7cqJSVFNWvWVLdu3fTII4+oU6dOJRojOTlZ77//vubNm6eDBw/KMAzVr19fQ4YM0eOPP64qVaq4PTdghjMpGXpm5Cey5+U/eS0nK08H9hxTszZ19erEexUaFmRyQvO0aHuVBo68ThPfXCKL01DAqSz5p2Qpq2aQMptWkAxDlmynqm1Mk8X467r1/9uh1FMZ+n9fPGheeAAAcEU7d4Pws5jtBHgHxSYvqFGjhtfulZ2draFDh2rRokUFzh86dEiHDh3SjBkz9Morr+ill1664DhxcXEaPHiwEhMLboi8detWbd26VRMnTtT8+fPVvn17t78GwJvycu2aO229q9B0rqOHk6/oQtNZ/Ud01PGEFC2fs0k5WXmyWS0yQv3zGy0W+eU5CxSazko4lOTdoAAAAAB8AsvovCwqKkp9+vTx2Pj33Xefq9DUs2dPzZs3Txs3btSkSZPUsGFDOZ1Ovfzyy5o4cWKxYyQkJGjgwIFKTEyUzWbTc889pzVr1mjNmjV67rnnZLPZdPToUQ0YMEAJCQkeey2AN7wfM1cz//tDkW0hYYFeTuObgoID9PCLgzT9hxf0f5P+ppCwIBm5fxXnDFvRG4eHhPL5AwAAAK5EzGzygpdfflkdOnRQhw4dVKNGDR08eFD169d3+31Wr16tGTNmSJIGDhyouXPnys8vf0OVDh06aNCgQWrXrp0OHz6s5557TrfffrsqVqxYaJx//vOfOn78uCRpxowZGjp0qKutW7duat++vYYNG6bjx4/rpZde0ueff+721wJ4w4mkNK3ZckDZNULln5oja7ZdZ8sm4RVD9MDz/U3N5wtysvO0avFvWrdsm0LDg9SyfX2lpWYpOMuqvEoByq0UIEewnzKighUSnyWLIRmSAqqEKPTqmnr2pdnq1rmx+vS8WkFB/ma/HAAAcIVi+RzgXcxs8oLY2FgNGDDA48vp3nzzTUmSn5+fPvroI1eh6ayqVatq/PjxkqSUlBRNmjSp0BjHjx/XF198IUnq27dvgULTWUOHDlXfvn0lSdOmTXMVpoCyZPvOBI28/zOdtkrOkADl1ApXVlQFOf3zvy2+9P5IXXdDC5NTmis3167Hbv+P3n3xW21au0erl2zVh/+aL0nyy3Wq0m+nlRdkV06E5AgLUlZUhHKqhii7TgWlVgjUrzuPauPmA3r7g+X626OTlZWda/IrAgAAAOANzGwqJ9LT07Vy5UpJUu/evRUZGVlkv1tvvVUVKlTQmTNnNGfOHI0dO7ZA+4IFC+Rw5C+Puffee4u93z333KNly5bJ4XBowYIFGjNmjJteCeAdP238Q7m59oInbX6yVwjUdc0j1axNXXOC+ZDDe4/ryP4Thc77B9iU9+fnrsLeTKU2D5MtW5KfVY7wopfOJRw9rU0b/1C365t5MjIAAECR2Cwc8C5mNpUTGzduVE5OjiSpe/fuxfYLCAhwPY1u48aNysvLK9C+du1a1/GFxjm3bd26dZeUGTCTpehthtS1Tyu9+tnf5B9ALb64T1KFiiF6/5tHNfyBHnpicA+9N7hfiYZ77ckZmjuV7xcAAABAece7qXJi586druNmzS48c6BZs2Zavny57Ha79u7dqxYt/loqdHaciIgI1axZs9gxatWq5Zohde69Syo+Pv6C7ec/BQ9wt84dG+nruZuUnfNXwdXf5qfBg641MZVvuapRddVrUlMH9xwrcL57/zZqfHUdNb66jiQpL8+h+ldV1YELPH3Omp4rZeVp9merNWRUF1mKq/YBAIArEu8PgPKFYlM5ceTIEddxcUvozoqKiipw3bnFprPjXGyMs+Ps2LGjwL1L6twMgBmaN62lGZPGaOF3v2nXnkQ1qF9dQwa0VfWq4WZH8xn+ATa9P/sRrV22TeuWbVdoeJBuHhat5tcUXGJos1nVqW41HfvlsHKDbDIsFjmDbJLVIovdqYAT6fLLyV+em5Kcrqfv+FgvTLhT1WtXNOFVAQAAX+SJ9wcskwPMQ7GpnEhLS3Mdh4WFXbBvaGio6zg9Pb3IcS42xrnjnD8GUBbYHU4lHktVt85NdM/ILmbH8Vn+ATb1GthWvQa2LbZP/IGTmj/tR1kkBZ7JUVadCpI1f+aSX1qOq9B01q7fjmjJ1z/rnif7ejI6AAAAAJNQbConsrOzXccBAQEX7BsY+NcGvllZWUWOc7Exzh3n/DFK4mKzoRITExUdHV3qcYGSWLJ8myZ/uU4nTuYXV1s0raXH/36jmjetZXKysikr87ynzJ2zQs5iGEVfk5HjwUQAAKCscdf7A2YzAb6BYlM5ERQU5DrOzb3w48XPbiQuScHBwYXGyczMvOgY545z/hglUZJleoAnnDh5RuPf/a7Aud93J+r/JizW9E/uNylV2VajTqUCT6iz5Dll+PtJkpx//u/5ohpU91o+AADg+3h/AJQvPI2unAgP/2ufmYsta8vIyHAdn79c7uw4JVkad3ackiy5A3zF6TNFz8RLTS39DD3ki6gUqgkz/q4uva+W1WpRpSy7GlSrIBmGHKH+sof669z5TV37tNRNt7c3LS8AAAAAz2JmUzlx7l8C4uPj1b598W/kzp2iev5GfJGRkTp+/PhFnwZx7jhs9o2ypEql0CLPVy7mPEqm0dV19OL7d8me55DFalFySoaGjvpYtrRc2TLyCvRdt3y7Wnx5lW65p6tJaQEAAAB4EjObyolznyi3a9euC/Y9226z2dSoUaMix0lNTdWxY8cKXXtWYmKizpw5I0lq3rz5JWUGvGnz+j0ad98k/b3fBDUOD1GNKn/NBuzYvoFi/jHIxHTlh83fT35+VlWvGq7YcYNVMbjo/d+OJZzycjIAAAAA3sLMpnKiQ4cOCggIUG5urlavXq1//OMfRfbLzc3VTz/9VOCac3Xt2lXTp0+XJK1evVrDhw8vcpzVq1e7jrt04Ule8G3r/7dDrz7+hevj9K0JMiQ9+MxNunFQW1WvVsG8cOVYj65Ndey2aE19b3mhttDw0u/1BgAAcC42Awd8FzObyonw8HDdcMMNkqQVK1YUuwxuzpw5rhlJt9xyS6H2QYMGyWrN/7KYPHlysfebMmWKJMlqtWrQIGaEwLft3V7434NF0qn4FApNHnbz8Gi16VJwBuXVHetr4J2dTEoEAAAAwNMoNpURU6ZMkcVikcVi0SuvvFJkn2eeeUaSZLfb9cgjj8jhcBRoT0pK0vPPPy9Jqlixou6/v/CTt2rWrKmRI0dKkpYtW6ZvvvmmUJ/Zs2dr2bJlkqS7775bNWvWvOTXBXhDQGDRkzgDg/y9nOTKs+fEKX0flK6k6Mo63aKCkjpU1urQTO08kWx2NAAAAAAewjI6L1i3bp327dvn+jgpKcl1vG/fPtcsobPuueeeS7pPr169NGLECM2cOVMLFixQ79699eSTT6p27dratm2bXnvtNR0+fFiS9MYbb6hSpUpFjvPaa69p6dKlOnnypO644w5t2rRJAwYMkCQtWrRIb7/9tiSpWrVqevXVVy8pK+ANx+JPaek3cTp2JEXVakbo5LFUV1vNyErqcxtPRPMkwzA0939bZHc4pRCbHCF//shxGpq7fIs6NavrmkkJAABQWrGxsYXOsbQO8A0Um7xg4sSJmjp1apFt69ev1/r16wucu9RikyR9/vnnOnPmjJYsWaIffvhBP/zwQ4F2q9Wql156SQ8++GCxY0RFRWnhwoUaMmSIjh07pvHjx2v8+PEF+tSsWVPz5s0r8BQ8wJfM/+JHffJ/i2QYhutcnXpV1bVvSzVuUUedejWXn83PxITlm8Pu0At/m6Qfk05IVxV+0t+GFb/rmZ+P6Y2pYxQQwI8iAAAAoDzhN/xyJjg4WIsXL9aMGTM0ZcoU/fbbbzp9+rRq1Kihbt266dFHH9V111130XE6duyobdu26b333tO8efN08OBBSVL9+vU1ePBgPfnkk6pSpYqHXw1waQzD0LeT1xYoNElSwsEkte/WVC3b1TMn2BXk0L4T2hZ3QEHhNmXVCZZh+2sGkzXHIf80h7YdP6bffz2sa6IbmJgUAACUJ+fOdmKWE2Aei3H+uzHAB8THxysqKkqSdOTIEWZQocROHkvVq49/oT3bit4kf/zUMWpNccPj9u5I0OO3/0eS5LRZlFUrWHlhNvlnShaLVbJYJEnBQf565vG+urFHCzPjAgBwRSpLv3Ofm/Wpp55SRETERa+h2ASUjCe+F7BZBoByZdk3ccUWmipWCVP9Jmxo7w1RDaqpSo38J/1Z7YZCj2Sqwr50Wax+rkKTJGVl52nStHVmxQQAAADgASyjA1BuLPjyR30zaU2RbY1b1lHsx6MVXjHEy6muTEHBAfpo7uP692NfaPvmg5Ik45wi07myc/K8mAwAAJRnzGYCfAMzmwCUCyeOntbHry5UTnbRhYu+t3VQparhXk51ZatQKVQ3D492fWy1OyVn4ZXbV0VV1rHjqYX22AIAAABQNjGzCUC5kJaaWeR5q59FY18fqp4DrvFuIEiSeg64Rv4BNs2ZvFZ7dySorqw65TSULkMyDIUFB2jr9ngNv/cTNW5YXffe1VVdOjYyOzYAAChDmM0E+B5mNgEoFypXC5eliGVa9ZrUVK+BbYtsg+dZLBZ169tK78x8WFNXPq/kwylyHkpRyKHTsuY5lJ5rl+PP2U5795/QuNg5Sjx22tzQAAAAAC4LM5sAlGk/rtihbyev1Z5t8apVt7IMp6HEI6dktVrUuffVuvepvmZHxJ+SzlsqZ/gV/feOE0lpqlWzopdSAQAAAHA3ik0AyqzVS7bqjbFfuT4+eihZkjT6yT66aWgHVawcZlY0FCE84rzN2YvYv0mSKoQHeyENAAAoL2JjY13HLKkDfAPL6ACUSWmpWVoy6+ci204mplJo8kG161bRqMd7y8+W/6MnIDlTlly7q93PYtHoEdepXt0qZkUEAAAA4AYUmwCUObk5eXr0lve1deMfRbYHhQR4ORFKIjsrV0u+3iiH3SlJ8stxKCghTYEJZxR4LE0BB05pxWdrlZmRY3JSAAAAAJeDYhOAMiduzW6dSDxdZFvdhtXVb1i0dwOhRA7vP6GkY6kFzlkk+eU65Jdll8WQUpLStH7VTnMCAgAAAHAL9mwCUKZ8M2mNJr31XZFtYRHB+njBE7JaqaP7Ir9iNgQ/3+sTvlP86Qz97a6uHk4EAAAAwBMoNgEoU5bP2VRs25C7O1No8mH1GtdQy3b1tH3zwWL7OAL95Az005Ll2yg2AQCAUjt3s/BzsXE44F28KwNQpjgdziLPP/1/t2vkIzd6OQ1Kw8/mpzenP6BuN7VynXPYLMqLCJQ9LEDZNcKUUytcsljkdBb9/zMAAAAA38fMJgBlSseezTVnyroC56rUqKDrbmhhUiKUhsViUb/hHfXjih1y2J2yOgzlhgfK8Pcr0K9zx0YmJQQAAGUZM5gA30CxCUCZcv9z/dS2cyMtmbVRqSkZuu6GFup7WweFVQg2OxpK6JpODfXfBU9qwRc/as+OeCU3Dtae06mynrDLaZPCGobrun7NzI4JAAAA4BJRbALg806dOKO0M1mKalBNVqtV7bs1VftuTc2OhcsQWb+aHn5psA4mp6jvR1Ok6pKq2ySnlOrI0r+W/KBlj91jckoAAAAAl4JiEwCflXjklCa+uUQ/ff+7nE5DdepV1chHblDPAdeYHQ1uciYnJ//AkPzTJP8MyWJIJ5NT9M63a/TQwM4KCuBHFQAAAFCWsEE4AJ81Ydw3+nHFDjmdhiQp4WCS3nx2lo78ccLkZHCXWhXCZbVYZMuQAtLzC02SJKc0fcVmffXDL6bmAwAAAFB6/LkYgM86deJMkeeTT6QpqkF1L6eBJ1QLC9WrXbvrzQVrlKfCT6A7eTrDhFQAAKCsio2NdR2zWThgHmY2AfBZEZVDS3UeZU9qSoamPjFHgduLLixWCmfjdwAAAKCsYWYTAJ9yOjldC77coHXLt8tqlWpfVUVHDyVLkipVDdPwB3qoXuMaJqeEuxyLP6W8XLuCjzrkCPZTVs0gyWqRnIYGdrlaI3q2NTsiAADwUcxcAnwXxSYAPiM7K1eP3PK+Tp1MK3C+bedGuuPvvdS0TZQC2Cy6XAkOCZQkWR2GKuxJU9iBdNlDbYqsXEGxo/qanA4AAADApWAZHQCf8eOKHYUKTZK0b0eCWnWoT6GpHIpqUE1DRnWRzd9PkmTNMxRcI1AaVVsPrJyrb/dtV47DbnJKAAAAAKXBOzcAPmHGRys1/YMVRbbZ/PlWVV5ZLBY9+MIADb3vev20apeW6YjmZ+yXMuOlw9Lyw3v18daftXDQKAXb/M2OCwAAfMi5m4FfDEvuAO9iZhMAn7Bq8W9FnrdYLLpldBcvp4G3Va5eQX1uaaetOw6p2qpMVfg9VwFJhipuk078nKyf/zhkdkQAAAAAJcR0AQA+wTCKPv/8W8PVvV8b74aB18UfOKkXx0xWUEKKAiXlVrfJEWpxtf/rmXlKeeAG3TKADcMBAEDJMJsJMA8zmwD4hC59ri50rmZkJbW/vqkJaeBt3y/YouMJKZIkw99PjtCAAu0Ou1NfzNxgRjQAAAAApcTMJgA+YfQTfdS+axMtnvWzzqRk6robWuiGQW0VHBpodjR4QV6uw3VsWIruk3QqXU/+Y6ZefLa/qlYJ91IyAAAAAKVFsQmAT8hMz5HN308PjRuoCpVCzY4DL2t2TV3XsTXPITmckl/hybdbth7WvEW/6v7R3bwZDwAAlBEsnQN8A8UmAKbKyc7T1HeX6bvZccrOzFVAoE03DrlW9z3bTyHMarpidOl9tcZPHaM5U9Zp344EBVQO16H0TNkdhTfzysjKMSEhAAAAgJKi2ATA65xOp7IychUUEqClszdq7tT1rrbcHLuWzNqoSlXDddejN5qYEt7WOrqBWkc30LHjqRp+7yfF9ouqU9mLqQAAgK9jNhPgeyg2AfAawzC0dHacvp28VgkHk1SjTiXVqlt04SAlKd3L6eArUlIzi24wpF4dGmjQzTydEAAAAPBlFJsAeM2Keb/o/Zi5ro+PJ6S4nkB2vkpVw7wVCz6mUkRIkectuXb9NHuzFjeprcF3dfZyKgAAAAAlVXj3VQDwkIRDSUWer9+kpoJD8h91Hxjkr37DO+rWe9kA+kpVs0aE/vlMf4UH539NGJLyAg1l1PHXyU5V9M1vu3Q0OdXckAAAwGfExsa6/gPgG5jZBMBrQkKDijzfrlsTjXiwp+IPnFTtulUUXrHomS24cvTpdbVO7jqmaZ/8oDONQpVT/a+vnR3pqRrx2pda+O+/KaKYrykAAAAA5mFmEwCv6XNbO7Xv1qTAuVYd6mvQXZ0VGh6kpq2jKDTBpd+waLXv0EB5FfwLtaVn5WjJmu0yjMJPqwMAAABgLmY2AfCaipXD9O9P79Whvce1Z0e86jeppUYtapsdCz6qUtVwvTbxb/pt3EQdTUkr1P7hJz/oxKHTeuLvPLUQAAAA8CXMbALgFYZh6Pcth/TJ64u0+rutat2hAYUmlMj9AzrJ5lfwx5VfplPWXENr1u8xKRUAAACA4jCzCYDHOZ1OvfLQNMWt2e06N+uTHzT6yb4aNqa7iclQFhjJuQo+nKvcYIsMP8mW6ZRfbn6bxdxoAAAAAIrAzCYAHhd/IKlAoUmSnE5D86atNykRygrDMDTj658lu6GANKcCT/9VaJKkG3u2MC8cAAAAgCJRbALgcfY8RzHn7V5OgrLk5LFUjb3zvzp57HSR7RUy7WoZWdW7oQAAAABcFMUmAB5Xp15VValeodD5Np0ampAGZcXybzdp56+HZc0uoijpcCrvZLomv73U+8EAAAAAXBB7NgHwuMAgf/1nzmNaPPMnrflum4JDA3Xz0A7qNait2dHgw7Iz89fLBSRlypFtlz08UIbVIr/MXPmfyZHFaSgrM/ciowAAAADwNopNALyiYpUwjXzkRo18hMfUo2TqNqouKX8TcFt6rmzphQtLZ/sAAIArT0xMjNkRABSDYhMAwCfdOORa+flZ9c4/v5XdXnjfr54DrtFD/xxoQjIAAAAAF8KeTQAAn2SxWNRrUFs1blmnyPa7HrtR4RVDvJwKAAAAwMVQbPKyw4cP65lnnlHz5s0VGhqqypUrKzo6Wm+99ZYyMzMvedxVq1bJYrGU6r8ePXoUOVa9evVKdH29evUuOS/Kp9ycPM2Zslb33/SWhnb6lyaM+0aH958wOxbKuGfGD1PnG6+WxWKRJIW0qy7/B5to4MyZumvq11qxe7/JCQEAAACci2V0XrR48WKNHDlSqamprnOZmZmKi4tTXFycJk6cqCVLlqhBgwZeydO0aVOv3AdXjnf++a1WLf7N9fH/5m7Wmu+26vNlz6hyEU+jA0qidt0qeumDu5R84owWb9+l19avl9JOS5LiDico7nCCxg/qqyFtWpgbFAAAAIAkik1e89tvv2nYsGHKzMxUWFiYXnjhBfXs2VNZWVmaOXOmPvvsM+3evVv9+/dXXFycwsLCSjV+hw4dtG3btov2e/TRR7V69WpJ0ujRoy/Yd/DgwXr11VeLbQ8ICChVRpR/e7fHFzqXk52nQ/tPUGzCZatSvYISndlFts1Zt1U3NGyg8LAgL6cCAABmiY2NvWA7G4gD5qHY5CVPPvmkMjMzZbPZtHz5cl133XWutl69eqlx48Z67rnntGvXLk2YMEEvv/xyqcYPDQ1Vy5YtL9jn9OnT+umnnyRJjRo1UufOnS/Yv2LFihcdEzhXQJB/keeDijkPlFaQregfW9u3xetvj0zW1I//ppCQQC+nAgAAAHAuik1eEBcXp1WrVkmS7rvvvgKFprPGjh2ryZMna+fOnXr33Xf1wgsvyN/fvW/QZ82apZycHEnS3Xff7daxAUka8WBPvfvit8rK/OsR9V36tFTjlpEmpkJ5cmubFlq2c6/2J51ynfPLMhR8wqkT2Wnaf+CkWl3N1xsAAFcSZjABvocNwr1g3rx5ruN77723yD5Wq1WjRo2SJKWkpLiKU+40bdo0SflPeKLYBE9ISUorUGiSpBNHU4p8bD1wKaIqVdSiv49So+OBCjvsUMVddlX91S7bn6vrrH78WAMAAADMxm/lXrB27VpJ+Uvd2rVrV2y/7t27u47XrVvn1gz79+/Xjz/+KEnq1q2b6tev79bxAUlaMmtjoXN7tydo7/YEE9KgvLJaLBrRra3CEpwKSjFk+fN8k0Y11KhBdVOzAQAAAGAZnVfs3LlTUv4+SbZi9huRpGbNmhW6xl3OzmqSLr4x+Flr1qxR69attX//fhmGoRo1aig6Olp33HGHBg8e7HoM+aWIjy+8kfS5EhMTL3lsmMfhcJbqPHCpajSqLNUPVm5SjixOQ/YQq2q1rSGbjb+hAABQFl3O+4NzNwpnSR3gGyg2eVh2draSkpIkSZGRF95HpFKlSgoNDVVGRoaOHDni1hxffPGFJCk4OFi33357ia45cOBAgY8PHjyogwcP6uuvv1aXLl00a9Ys1alT55LyREVFXdJ18G3tujZWwsGkAucqVQ1Tw2a1TEqE8uqTRT8pzWmXKvtJTkN+OYaWrNmhEb2uUYurapodDwAAlBLvD4DyhWKTh6WlpbmOw8LCLtr/bLEpPT3dbRnWrl2rP/74Q5J0yy23qEKFCz+CPiAgQIMGDVKfPn3UsmVLRURE6PTp09qwYYM+/vhjHTlyROvXr1fv3r21YcMGRUREuC0ryrYHXxigazo10uKZP+lMSqauu6GFbh4erfCKIWZHQzmTnWuXDEP+aU4Fpjpl+XPy3L/+NV8vPNVPbVryCysAAFcKZjMBvodik4dlZ2e7jgMCAi7aPzAw/5HdWVlZbsswffp01/HZTcgvZOPGjapYsWKh8z169NCjjz6q22+/XcuXL9fOnTsVGxurCRMmlDrTxWZuJSYmKjo6utTjwlxWq1XX3dBC193QwuwoKOeaRlXTseOpCkopuEQzMTFVb3+wTNM+ud+kZAAA4FLw/gAoXyg2eVhQUJDrODc39wI98+Xk5EjKX+7mDjk5OZo9e7YkqXbt2rrxxhsvek1RhaazwsPD9fXXX6thw4ZKTk7Wp59+qjfeeKNEhbRzXWxJIQBcyKv33KT3g1bru6+3FGo7HH9Kk6av1agRneXv72dCOgAAUFq8PwDKF3ZS9bDw8HDXcUmWxmVkZEgq2ZK7kpg/f75Onz4tSRo5cqT8/C7/jVdERIRGjBghKT/vpk2bLntMACiNkKAA3T+wk4p6ToFhSNO+2qBv5vO9CQCAK0FsbKzrPwC+gWKThwUFBalq1aqSLv6EhZSUFFexyV0b5J37FLqSLKErqRYt/lomlZDAY+3Lu6yMHM34aKXu6v66BrZ+Uf/31Azt+/2o2bFwhataJVxPPN5HtpD8SbqGJId//v9K0skk9+19BwAAAKDkWEbnBc2bN9fatWu1b98+2e122WxFf9p37dpV4JrLdeLECS1btkySdO2116ply5aXPeZZhmFcvBPKjbfHfaP1y7e7Pl67dJs2rPxdny9/VtVqskE8zHE6PUv/b/FaZVezyGK3ybBI8rPIL8upkBMOVagQdNExAABA+VLc7CY2EQe8i5lNXtC1a1dJ+UvONm/eXGy/1atXu467dOly2fedMWOG7Ha7JPfOapKk33//3XVcu3Ztt44N33Nwz7FC5+x5DsUfOGlCGiBfQlKqsvPyv8cZNovkl7+mzhlgUYdr6+nWgdeaGQ8AAAC4YlFs8oIhQ4a4jidPnlxkH6fT6VryVrFiRfXs2fOy73t2PJvNpjvvvPOyxzsrNTVVs2bNkiSFhISoffv2bhsbvikouOgN4Is7D3hDcKB/0Q1OadOWg9r2O0t8AQAAADOwjM4LoqOj1a1bN61du1aTJk3S6NGjdd111xXo8/bbb2vnzp2SpCeeeEL+/gXfRE2ZMkX33nuvpPwpoK+88soF77ljxw5t2ZL/lKabb75Z1apVK1HWpUuXqnv37sU+DS8tLU3Dhg1TcnKyJOm+++5TYGBgicZG2eJwOPXzDzu1dtk2VawSqtDwIGWkZUuSLBaLbhjcVk1a1jE5Ja5k9WtW1u3dWuvbtVtd+zRZs5wKOuWQYUjzF29Rx/YNZPPj7yoAAJRHLI0DfBfFJi9577331KVLF2VlZalPnz4aN26cevbsqaysLM2cOVOffvqpJKlJkyYaO3bsZd9v6tSpruPRo0eX+Lo33nhDI0eO1K233qquXbuqYcOGCgsL0+nTp7VhwwZ9/PHHOnLkiCSpadOmFy16oWxy2B165q5PtOu3IwXOR3dvqradGyu6RzPVrlvFpHRAPovFonF33qDANEPffP+r/LKd8sv7q/3nTQf04BPT9NGEuxQYwI87AAAAwFv47dtL2rZtq1mzZumuu+7SmTNnNG7cuEJ9mjRposWLFys8PPyy7uV0OjVjxgxJUqVKlTRgwIBSXX/q1ClNnDhREydOLLbP9ddfrxkzZqhy5cqXlRW+Kf5AUqFCkyTt3hav2P/e4/1AwAUM6dNaq3/YqdS8rEJt+/44oX1/nNDVzdhbDgCA8qaozcCZ7QT4BopNXjRw4EBt3bpV7733nhYvXqz4+HgFBASoUaNGGjp0qB599FGFhIRc9n1WrlyphIT8vUqGDx9eqmVub731llauXKkNGzZo9+7dSkpK0unTpxUSEqLatWurY8eOuuOOO9SnTx9ZLJbLzgrf5HQ6izxvOHkKIXxPw/rVNWvKgxo2+r868+dSz3PxdQsAAAB4F8UmL7vqqqs0YcIETZgwoVTX3XPPPbrnnntK1Ld3794yjEt7c9W+fXs2/IYi61dTnXpVlXAwqcD5Tr1amJQIuLDgoAD17nm1vl1Q8ImfNatXUIP6JduzDgAAAIB7UGwCUIh/gE3/+fYxrZj/i9Yu3abg0EDdNLSDors3NTsaUKwOfRtrwbF9Stt3Rla75KxmU4cBLRQcVMxT6wAAAAB4BMUmAEUKCgnQgDs6acAdncyOApTI68tX64hfptT0rx9tn23crEFtW6hJ9aomJgMAAACuLBSbAADlQnpObpHnz2TneDkJAADwBjYDB3yX1ewAAAC4Q2SliELnLBapaljIJe9jBwAAAKD0KDYBAMqFt2+5WcOvbaUgm02GDNWNilDVxiHqMf8z9Z77uWbu+Y2iEwAAAOAFLKMDAJQL1cJC9a/+N+ofvbtr5p7f9K+476XM/LZ9qcn6x/plynM6dXeztuYGBQAAbhEbG3vBdpbZAeZhZhMAoFwJCfDXsay0Itv2nz7l5TQAAADAlYeZTQCAcifEFlDk+TD/os8DAICyh5lLgO+i2AQAKBcMw9CuXw9rydcbdTwzXQ2uCdcf+muGU+uKNXRn0zYmJgQAAACuDBSbAADlwtT3lmvWJ6tcHwcul+rV9FN2DT8FnXBIyWlKjkxW7Q4VzAsJAAAAXAHYswkAUC6sXrK10LngYw5V+i1XwYkO5eXatWHlDhOSAQAAT4iNjb3oJuEAzEGxCQBQPhiGO7oAAAAAuEwsowMAlAtd+7bSN5PWFNtuDbTJUjlE6zbsVacODWSz+XkxHQAAcJenn35akZGRZscAcAEUmwAA5cLfxt6kjj2b67tZPyvpVLris3J0fP9JWXLtskcEyQjx15fzN0nzN6lqlTA9/WgfdenYyOzYAAAAQLnDMjoAQLlgsVjUsl09PfvmcDXv1UzxKenKqxys3Jrhcgb7F1hCl5Scrv9OWmVaVgAAAKA8Y2YTAKDcycrKvWifjMwcLyQBAADuNmHCBEVERFzStTExMW5OA6AozGwCAJQ7kXUqX7RPVAn6AAAAACg9ZjYBAMqdAX1bS5Le+fB/cjicsvtLFotkzZVkkWpcVUmPPXqjuSEBAMBlYZYS4LuY2QQAKHcsFosG3tRGTRpWl9MqZdWyKbOWv9KjbEqPsmm/ka7/m/W92TEBAACAcoliEwCg3PrH0/3VvGWd/GlNkmS1uI63HzmudfsPmZgOAAAAKJ9YRgcAKLfq1a2icU/erFtjpxZqy3U6dN+MOXr7lps1oGUzE9IBAIDLERsbW+gcS+sA38DMJgBAuXZVjUr6W+/2BX7gGTKUG25Ikn5NSDQnGAAAAFBOMbMJAFCu5eXateWTn1Xl6CnlVg6QxW4o4FSuzjQJVnL7cAX68aMQAIDy4tzZTsxyAszDb9hAOWYYhnZsPqi1y7YpODRQfW5tr9p1q5gdC/Cqg3uP6+jhZFklBZ3IcZ0PPZKjwF61dWubFuaFAwAAAMohik1AOeV0OhXz96natHaP69zXn67W3Y/31h1/72liMsC7LGc3Bz+PNdepal8fk9E7R6rm5VAAAABAOUaxCSinEg4mFSg0SfkznRZ++SPFJlxR6jWuoQbNa+mPnX/tzeS0SpmRgUoLcGrBojg93WKwiQkBAMDlYLkc4HvYIBwop+x5jiLP5xVzHiivbP5+at4myvVxRmSADg+pqhNdIpTcIVyf5vyhlxevkNMwTEwJAAAAlB/MbALKqTr1qqpKjQpKPn6mwPlrOjU0KRFgjsP7T2jxzI2uj0+1DpMz6K+/tRiSZv2yTSOubaUWtWqYkBAAAFyOczcFLw6znwDvYmYTUE4FBPrrw7mPa9TjvVWvSU21aHuVnv6/2/Xcm8PNjgZ4VXZmboGPDf+i93B6/R+ztPPXw96IBAAAAJRrps1sSkpKUkJCgk6ePKnk5GQFBwerWrVqqlatmho0aCCrlToYcLkiKoXqjod66Y6HepkdBTBNrajKCgi0KTfHLknyP22XPdSvQB+Lw9Cxrcf1fsxcfTz/CTNiAgAAN2AGE+AbvFZsSktL0/z587Vq1SqtXbtW+/btK7ZvaGioOnXqpG7duql///669tprvRUTAFDOVKgUqndmPqzXn56h+ANJqropXUlWq7Kq2ySrRf5pTlXcliFbtlNnUjLMjgsAAACUeR6fPrR582aNGjVKNWvW1OjRozV58mTt3btXhmEU+196erpWrlypV155RR06dFCLFi304YcfKiODNwEAgNJr0KyW+g3vKElKaxwuq+Gv0OMWhSZKAelWZdYLkyPQqio1KpicFAAAACj7PDazafPmzXrxxRe1fPlySfmPXJekWrVqqUOHDmrXrp2qV6+uypUrq1KlSsrKytKpU6eUkpKiPXv2KC4uTlu3blVeXp527dqlxx9/XK+88oqeffZZPfHEEwoMDPRUdABAOTTgjk5KO52p/2zdVrjRYlHVBlX1xL9u9X4wAABwWVg6B/gejxSb7r33Xk2fPl1Op1OSdO2112rkyJG67bbbVLdu3RKPk5ubqzVr1mjGjBmaO3eukpOT9cILL+jjjz/W9OnT1bVrV0/EBwCUQ/4BNo16oo8W//uY9h5NLtR+8lCypr23XLH/vcf74QAAAIByxCPL6KZOnSqbzaYxY8Zo165d2rRpk5566qlSFZokKSAgQDfeeKM+//xzHTt2TNOmTVPTpk116NAhff/9956IDgAo554f3lNVg4JcH1vsToX9kS6/TIf2/X7UxGQAAABA+eCRmU0PPfSQXnjhBUVGRrptzMDAQN11110aOXKkZs+eLYfD4baxAQBXjg0zf5F16WFVivCXYbPI/3SerI78pd6Bwf4mpwMAAADKPo8Umz788ENPDCtJslgsGjZsmMfGB8qihINJ+m72Rp1JyVTHns3VqVdz+fl5fP9/oEzauHqXLJICUvMKnA8ItGnkwzeYEwoAAAAoRzy2QTgA75g3bb0+fWOxaxP+/83drIbNa+utLx9UUHCAyekA32MtphB7plKQEjKzvZwGAABcrtjYWNcxm4UDvoGpD0AZt3R2nKvQdNb+nUe1Z1u8SYkA33bz0A6FzjlsVlmz7Zo382fXwy0AAAAAXBqvzmxKS0vTO++8I0l64IEHVLNmzQv2T0xM1GeffSZJevbZZxUcHOzxjEBZU9wbY4eDN8xAUYaN6aHqDarp1XGzZbE7Zc21y8/ulN/pbGWdztZDg95TzId3q/ZVVc2OCgAALoBZTIDv8urMpnnz5umVV17Rl19+edFCkyTVrFlTX375pWJjY7Vw4UIvJATKnvbdmhQ6V6lquBo1r21CGqBs6NSliSo1qyEjwE9We8GZgYf3n9CybzeZlAwAAAAo+7xabJozZ06pNvi2WCwaMWKEDMPQ7NmzPZwOKJvuf66fXvlolKK7N1XT1pEa/WQffbzgCYVXDDE7GuCzgoL89fl/7tU1LYt+ampOdl6R5wEAAABcnFeX0e3atUuS1Llz5xJfc91110mSfv/9d49kAso6q9Wqjj2bq2PP5mZHAcqU8PAg9R/cTjvW7SvU1rAFMwMBAPBVTz/9tCIji/6DEQDf4NWZTfHx+RsW16pVq8TXnF1ul5CQ4JFMAIArV9ebWqnDqPZyVg6UYZUclQJ07V3tdH2/1mZHAwAAAMosr85sslrza1uZmZklvuZsX7vd7pFMAIAr19erf9Oiw4el1hGuc9/FH1GTVb9pdJ/2JiYDAADFmTBhgiIiIopsY9NwwDd4dWbT2RlNmzaVfOPVs31LsqE4AAClceJ0ejHn07ycBAAAACg/vFps6tatmwzD0EcffaS8vItvvpqXl6ePPvpIFotFXbt29UJCzzt8+LCeeeYZNW/eXKGhoapcubKio6P11ltvlWrGV1FeeeUVWSyWEv23atWqi46XnJysmJgYtWnTRhEREapQoYLatGmjmJgYJScnX1ZWAPAFFcOCijkf7OUkAAAAQPnh1WV09957ryZNmqS9e/fqzjvv1NSpUxUSUvQTszIzMzVq1Cjt2bNHFotF9957rzejesTixYs1cuRIpaamus5lZmYqLi5OcXFxmjhxopYsWaIGDRqYmDJfXFycBg8erMTExALnt27dqq1bt2rixImaP3++2rdnmQmAsuv269voQOIpLY3bLbvTKZvVqj7tm2hEj2vMjgYAAIrBBuGA7/Nqsalz584aMWKEZs6cqTlz5ujnn3/WmDFjdP3116tWrVqyWCw6evSo1qxZo4kTJyo+Pl4Wi0W33367unfv7s2obvfbb79p2LBhyszMVFhYmF544QX17NlTWVlZmjlzpj777DPt3r1b/fv3V1xcnMLCwi7rftu2bbtge/369YttS0hI0MCBA3X8+HHZbDY9/fTTGjBggCRp0aJFmjBhgo4ePaoBAwZo8+bNqlOnzmVlBQCzhAcH6l/33KTHhnTV3qNJalirimpUCjc7FgAAAFCmebXYJEmff/65kpKStGLFCiUkJOiVV14psp9hGJKk3r17a+rUqV5M6BlPPvmkMjMzZbPZtHz5cl133XWutl69eqlx48Z67rnntGvXLk2YMEEvv/zyZd2vZcuWl3ztP//5Tx0/flySNGPGDA0dOtTV1q1bN7Vv317Dhg3T8ePH9dJLL+nzzz+/rKwAYLZqFcOUkpyhL774UWnp2erWuYm6d2kim83P7GgAAABAmePVPZskKSgoSMuWLdM777yj2rVryzCMIv+LiorS+++/r6VLlyooqOg9NcqKuLg41x5J9913X4FC01ljx45V8+bNJUnvvvtuifa08oTjx4/riy++kCT17du3QKHprKFDh6pv376SpGnTprkKUwBQVk35cr3GPDZVC777TT+s3a1/jV+oMY9PU04uT0IFAAAASsvrxSZJslgseuKJJ3T48GFt3rxZEydO1BtvvKE33nhDEydO1C+//KKDBw/q0UcflcViMSOiW82bN891XNzeU1arVaNGjZIkpaSklGgDb09YsGCBHA6HpOKzStI999wjSXI4HFqwYIE3ogGAxyxdud117PCXciKs2pWcrF93HDExFQAAAFA2eWwZ3ebNm9WuXbsL9rFYLGrbtq3atm3rqRg+Ye3atZKk0NDQC35Ozt2Xat26derdu7fHs53vbNbz85zv/KxjxozxaC4A8CTDkAxJ2VX9ZA/96+8wj09aqOdH9NStXVuZFw4AABQwYcIERUREXLRfTEyMF9IAKIrHZjZ16NBBkZGRevDBB7Vo0SJlZ2d76lY+b+fOnZKkRo0ayWYrvr7XrFmzQtdcqt69e6tKlSoKCAhQ9erV1aNHD73xxhtKSUkpUdaIiAjVrFmz2H61atVShQoV3JIVAMzW7bpGcvqrQKFJkvIcDk1eutGkVAAAAEDZ5NENwo8ePaqJEydq4sSJCgoKUq9evTRw4EANGDBAtWvX9uStfUZ2draSkpIk6aKP56xUqZJCQ0OVkZGhI0cub+nGihUrXMcnT57U6tWrtXr1ao0fP15TpkzR4MGDi7zu7H1L8ijRqKgo7dix45KyxsfHX7A9MTGx1GMCwKV6ZEwv1biqkl6ft7pQW47dYUIiAACuLJfy/oCZS4Dv8lixKT4+XosWLdLChQv1/fffKysrS4sXL9aSJUv00EMP6ZprrtHAgQM1cODAiy63K8vS0tJcx2FhYRftf7bYlJ6efkn3a9WqlYYMGaLo6GjVrl1beXl52r17t7788kstX75cp0+f1m233aaFCxfq5ptvLjZvSbNKuqSsUVFRpb6mPDIMQwf3HldwSIBqRlY2Ow5wxbJYLLqpWwt9sOwnpWflFGhrVb+WSakAALhy8P4AKF88VmyqXbu2HnjgAT3wwAPKysrSihUrtGjRIi1evFhHjx7Vli1b9Ouvv+rf//63atasqf79+2vgwIG68cYbFRwc7KlYXnfu8sGAgICL9g8MDJQkZWVllfpeTz75pF555ZVC5zt27KhRo0bpk08+0d///nc5HA7df//92rdvX6HP9dm8ns4Kad3y7Zr2/v90ZP8JSVKbTg015rl+atj8ypj1B/ia8JAgPXTftfpk+Y86c8gp+Ruqf3WY7ht0rdnRAAAAgDLFo8vozgoODnbNYpLyNw9fuHChFi1apF9++UWJiYmaNGmSJk2aVO6W2wUFBbmOc3NzL9o/Jyf/L+qXUnCrWLHiBdsffPBBbdq0SRMnTtTRo0c1Z84cjRw5slDezMxMj2e92NK7xMRERUdHl3rcsiIlKU2vPfFlgXO//bRf45+ZqU8XP21SKuDKlpiVoneOLJCaS7bm+eeOKEWv75yj6V0eMzccAADl3KW8P4iNjS31fVh6B3iHV4pN52vXrp3atWunV155RUePHi3Xy+3Cw8NdxyVZbpaRkSGpZMvYLsWDDz6oiRMnSpJWr15dqNgUHh6uzMxMj2ctyZ5Q5dnpUxlFnk9JSivyPADPO5VT9Pe9/YnHteTrjbrp9vayWj32XA0AAK5oV/r7A6C8MaXYdK5zl9tlZ2drxYoVWrhwYbHL7QYMGKCHH35Ybdq0MTt6iQQFBalq1apKSkq66KZ3KSkprgKOp9Yst2jRwnWckJBQqD0yMlLHjx+/aFbpr78+sL669CIqhRZ9vrJniowALq5SQNH//oxUhz54a66cdocG3Hmdl1MBAICLYbYS4Ht86k+0QUFBGjBggD755BPFx8crLi5OMTExatu2raT8qZMTJ07U/PnzTU5aOs2b56/H2Ldvn+x2e7H9du3aVegadzMM44LtZ4tRqampOnbsWLH9EhMTdebMGUmey1qeVa4WrmffHKaakZVc51q0vUrPjh9mYirgylY7pJJeanmbwnIDXeds+wyFfZV/HH8wyaRkAAAAQNli+symCzm73C4mJsa13G7RokUKCQkxO1qpdO3aVWvXrlVGRoY2b96sjh07Ftlv9eq/HrndpUsXj2T5/fffXcdF7YfVtWtXTZ8+3ZVn+PDhRY7jjazlXa+BbdW9Xxvt25GgoJAAXdWohtmRgCvewMh2Oj0/WVMX/iBrluR34q+2kNDA4i8EAAAA4OKxmU2PP/64Nm3a5Lbxzi63W7BggZ555hm3jesNQ4YMcR1Pnjy5yD5Op1PTpk2TlL/Rd8+ePT2S5ZNPPnEdd+/evVD7oEGDXHuSFJdVkqZMmSJJslqtGjRokHtDXkH8/Kxq2jqKQhPgQ/oP7aRWVeoWKDQ1uSZK/UYU/YcCAAAAAAV5rNj0n//8Rx07dlSLFi30xhtvXPTpAuVZdHS0unXrJkmaNGmSNmzYUKjP22+/rZ07d0qSnnjiCfn7+xdonzJliiwWiywWi1555ZVC12/btk379u27YI5PPvlEkyZNkiTVrFlTt9xyS6E+NWvWdG0avmzZMn3zzTeF+syePVvLli2TJN19992qWbPmBe8LAGXJoTNp+qlKnpLbVdKZJuFKvraSfq5q16FUNvAHAAAASsKjy+gMw9Du3bv1z3/+Uy+++KK6d++u0aNH67bbblNoaNEbJJdX7733nrp06aKsrCz16dNH48aNU8+ePZWVlaWZM2fq008/lSQ1adJEY8eOLfX4mzdv1v3336+ePXvq5ptvVqtWrVSlShXZ7Xbt2rVLX3zxhf73v/9Jkvz8/PTJJ58U+//Ba6+9pqVLl+rkyZO64447tGnTJg0YMECStGjRIr399tuSpGrVqunVV1+9lE8HAPis1Vv3KyfPIYX7yx7+Z+Hf7tCqrfvVrglPygEAwFewMTjguzxWbFqxYoW++OILffvtt0pLS5NhGFq1apVWrVqlRx55RLfccovuvvtu3XjjjbJYLJ6K4TPatm2rWbNm6a677tKZM2c0bty4Qn2aNGmixYsXKzw8/JLu4XA4tGLFCq1YsaLYPlWqVNGkSZMuuPQtKipKCxcu1JAhQ3Ts2DGNHz9e48ePL9CnZs2amjdvHo8oBQAAAAAABXhsGV2vXr30+eef6/jx45oxY4Zuvvlm+fn5yTAMZWRk6Msvv9RNN92kqKgoPf/889q+fbunoviMgQMHauvWrXrqqafUpEkThYSEqGLFimrfvr3Gjx+vLVu2qFGjRpc0dr9+/TRp0iTdf//9ateunSIjIxUcHKygoCDVrl1bN998s9577z398ccfGjx48EXH69ixo7Zt26YXX3xRLVu2VFhYmMLCwtSqVSu9+OKL2r59e7EbnQNAWdajTUMF+vsVOBdg81OP1g1NSgQAAACULRbDMAxv3ezEiROaMWOGvvjiC/3yyy9/hfhzZlObNm00evRo3XHHHapevbq3YsEHxcfHKyoqSpJ05MgRZlAB8Jqk5DT98/V5+u3ocTkCLPLLNdSqZnW9Nu4WVa96aTNPAQDwRWXpd+5zsz711FOKiIhgGR3gJp74XuCxmU1FqV69up588klt2rRJO3bs0PPPP6+oqCgZhiHDMPTbb7/p6aefVmRkpAYMGKCvv/5aOTk53owIALjCLVq6Vbt+T1TgaadCTjjkl+3U9vjjmrf4l4tfDAAAAMCzG4RfSPPmzfX666/r9ddf16pVqzR9+nR9++23OnPmjOx2u7777jt99913qlChgoYNG6a7775bXbt2NSsuAOAKkZmVK0nKC5HO1PdTXoX8v8v8N36bKmyupOHXtroi9hoEAMDXxcbGXrCdmU+Aebw6s6k4PXr00KRJk3Ts2LFC+zulpqbqs88+U48ePcyOCQC4AtSNrCxJSm34V6FJkjKceYpZslIHT502KRkAAABQNvhEsemsoKAgjRgxQosXL9aWLVt09dVXu/567MWtpQAAV7D+fVvruSdukjXIr8j2U5mZXk4EAAAAlC2mLaMrSk5OjhYsWKDp06dr2bJlstvtZkcCAFxhLBaL+vdtrSnx27X16PFC7dVCQ01IBQAAzscyOcB3+cTMpjVr1mjMmDGqWbOma2ZTXl6eDMNQRESExowZo7Vr15odEwBwBfn3zTeqaXhl18dVjznVeYv0bP8P9MK9ExW3ZreJ6QAAAADfZdrMpt27d2v69On68ssvdfjwYUl/LZWz2Wzq06ePRo8erUGDBikwMNCsmACAK9SMKRuUuu64qgVIfhm5ConP0LE/2379ab9+/Wm/nvt/w9VzwDVmxgQA4IpV1AbhzHYCfINXi01JSUn66quvNH36dG3evFlSwb2Y2rRpo9GjR+vOO+9U9erVvRkNAIACdu1OlCT55Ur+Z4pe1r176xGKTQAAAMB5PF5sysnJ0fz58zV9+nQtX77ctQ/T2SJTrVq1dOedd2r06NFq2bKlp+MAAFAiAQHnbBD+58MqCvUJ9PdSGgAAAKDs8FixafXq1Zo+fbq++eYbpaWlSfqrwBQcHKzBgwdr1KhR6tOnj6xWn9g6CgAAl7uGX6e3/7NMubkO2cMD5JeRK6vd6WqvGVlZfW5tZ2JCAAAgsXQO8EUeKzb17NlTFovFVWCyWCzq1q2bRo0apWHDhik8PNxTtwYA4LLddGNLVa8WrqdemCXD30/ZkRXkl5kna65DzgA/JVUOVKbTefGBAAAAgCuMR5fRGYahhg0batSoUbr77rtVr149T94OAAC3atUiUo0bVtfe/Scki0WO0AA5QvPbzqRla+WqnWrSsIa5IQEAAAAf47Fi0wMPPKBRo0apc+fOnroFAAAeZbNZ1apyhP7YFi9HSEChdofDYUIqAAAAwLd5rNj03//+11NDAwDgFYf3ndDSrzfKFmyTI9i/wEbhFovUts1VJqYDAAAAfJPHn0ZXHKfTqVWrVmnDhg06duyYMjMz9eqrr6pWrVquPrm5ubLb7fLz81NgYKBZUQEAV6jsrFxJkjXHIWtWnpxnC06GofbX1lfrFpEmJwQAAAB8jymPgVu8eLEaN26s3r176+WXX9ZHH32kKVOmKCUlpUC/SZMmKTw8XNWrV1dGRoYZUXGFSz5xRmmnM82OAcAktaIqKyDQJnt4oJwhAX/NbLJYFPfLQc1ZuNncgAAAAIAP8nqxaeLEiRo0aJAOHDggwzBUpUoV1xPrznffffepYsWKSk9P19y5c72cFFeyX3/ar7Ej/6u7ur+uEV1f0/89NUNHDyWZHQuAl1WoFKp3v35EkY2rF9m+YNbP2rh6l5dTAQCAc8XGxrr+A+AbvFps2rdvnx555BFJUq9evfT777/rxIkTxfYPCAjQbbfdJsMwtHz5cm/FxBXuTEqG/nn/5/r9l0OSJKfDqbVLt+nfj39pcjIAZqjfpKb639ahyLbUY2cU8/epOp6QUmQ7AAAAcCXyarHp3XffVV5enq6++motWbJEzZo1u+g13bp1kyT9+uuvHk4H5Dt1Mk1Oh7PQ+ZOJp70fBoBPuGVAW/Xs1lQ6OxPXMGRNz5XfmRxJUtLxVBPTAQAAAL7FqxuEr1y5UhaLRU8++aQCAgo/QrooDRs2lCQdPnzYk9EAl7CI4CLPhxdzHkD598PaXfph7e4CezY5wwLkzMqTNT1X4REh5gYEAOAKFhMTY3YEAOfx6symI0eOSJKuueaaEl8TGhoqScrMZJNmeEfVGhF66MWBqlAp1HUusn41PR57q4mpAJhp7/5ilnyH+OuOR29QVINq3g0EAAAA+DCvzmyy/PkX4eI2BC/KyZMnJUkVKlTwSCagKINGdtZNt3fQbz//oaDgAF3d7ipZraY8vBGADwgMLPrHZVrNQH155A/dlnO9QoNKNmMXAAC417kbgzPLCfANXn33XLt2bUnSnj17SnzN6tWrJUn16tXzRCSgWAGB/upwfVO16lCfQhNwhevXp7Xq1a3y1wnDkCU7V0GJGcredEzr1u82LxwAAADgY7z6Dvr666+XYRiaMWNGifonJSXpk08+kcViUa9evTycDgCAoqUnpSv9l3gFJqbJPzlDwYdOKzgxQ8EncxSSkKV3H52hGR+tNDsmAAAA4BO8Wmx64IEHJElLlizR5MmTL9g3Pj5e/fr1U1JSkvz8/FzXAgDgbWuXblNmWrb8su3yP5MrSxGrwefP2CCns+TLxAEAgHvExMS4/gPgG7xabOrQoYP+/ve/yzAM3X///Ro6dKi+/vprV/vWrVs1a9Ys3XfffWratKk2b94si8WisWPHqlGjRt6MCgCAi9PpvGiflLQs3f6vqTp8IsULiQAAAADf5dUNwiXpgw8+UEZGhqZPn645c+Zozpw5ro3DR44c6ep3dhPxe+65R//3f//n7ZgAALi069JEc6eul9NRfNEpp0qAko6naP6PO/TYkK5eTAcAwJXt3A3Cz8VMJ8A8Xt/12M/PT1OnTtXs2bPVtm1bGYZR5H8tWrTQjBkz9Pnnn7uKUQAAmKFt50b6eP4TCg7564lz5y6YcwRYlV0tUJKUnWv3cjoAAADAt3h9ZtNZt912m2677TYdPXpUmzZt0okTJ+RwOFSlShW1bdtWDRs2NCsaAACF1G1YXddc10gbVv4uSTr3zyB+uU5V2Jeu5OhANb+qujkBAQAAAB9hWrHprNq1a2vQoEFmxwAA4KKee3O4Jr39nRbN+KlQmzXPUJ/GDdTn2iYmJAMA4MrFcjnA93h9GR0AAGVVUEiARjzYU1Zr4eXdhkXa8L9d+mbeZhOSAQAAAL7Dq8WmrVu3XvK148ePd2MSAAAuTZXqFfTqZ39TpdoVJeXv3eT0t8pqNxR88LT+N+1H7duRYGpGAAAAwExeLTb17dtXf/zxR6mve/XVVzVu3DgPJAIAoPTadm6k/g92V2bdCDmDbLLmOWVxGrJIOrbvhJ6642OdOplmdkwAAADAFF4tNh0/fly9e/dWYmJiia+JjY3Vyy+/7MFUAACU3pABbdXp+say2J2F2ux5DiUeSTYhFQAAAGA+rxabatSooYMHD6pPnz5KSUm5aP+XX35Z//rXvyRJvXr18nQ8AABKbGN8guY5D8seUnj/JkkKDgn0ciIAAADAN3i12LRs2TJFRETo999/V79+/ZSZmVls33Hjxum1116TYRjq06ePFi5c6MWkAAAUz57n0MI5G1V9barsIVY5/M8pOFmkfsM7ql6TGuYFBAAAAExk8+bNWrdurYULF6pv377auHGjhgwZosWLF8vf379Av+eff15vvfWWDMPQTTfdpLlz5yowkL8QAwDMl5tr11PDP9IfuxIVJim3YpBy6oTKLyNPFqchR4i/Nh8/pTy7U4EBPPQVAABPi42NLfJ8TEyMl5MAOMvrvwV36dJFs2fPls1m08qVK3XHHXfIMAxX+9ixY12Fpn79+mnevHkUmgAAPuPgnmP6Y9dfew86wgIki0WOsADZKwTKsFl14FCSfv31oHkhAQAAABN5dWbTWTfffLOmTp2qu+66S3PnztWYMWM0ceJEPf744/rwww9lGIYGDBigb775RgEBAWZEBACgSOf+geRCYh+Zrkef7a9+w6I9nAgAgCsbM5gA32Pa/P4RI0bogw8+kGEYmjx5stq0aeMqNA0ePFjffvsthSYAgM+p17im6lxVxfWxX2ZeoT6WPIecGbmaPXG1N6MBAAAAPsGUmU1nPfTQQzp16pReeuklbd++XYZhaMiQIfr6669ls5kaDQCAIgUG+es/cx7X9wu3aN3y7crNydO+g0k6Y5UMP4v8MnJlS8+VxZDycuxmxwUAAAC8zvSKzj//+U+dOnVK77zzjm6//XZ99dVX8vPzMzsWAADFCgoJUL/hHdVveEc9NOhd5SRnqKjdBZtdU9fr2QAAuFKwfA7wXR4pNl1KschisVxw6ZzFYpHdzl+IAQC+JTM9p8jzwR1q6JZnbvByGgAAAMB8Hik2lXTzVAAAyrpaV1XRicTTBc4ZknbXyNM/v1uheQ/cZUouAADKu9jY2Au2M/MJMI9Hik38owYAXCn+8dYIvf//Fmr94m2y2g3lVrDKapfqLjqlrIgzWhT+k/oNj5bVatozOQAAAACvotgEAMBlqFglTH/7Rz/NCEpU+N5MVf0lw9VmTbXrw3/Nl+E0NHDkdSamBAAAALzH9A3CAQAo6yIrRmhcr+s16belRbYnHEryciIAAMo/JjkAvos5/V52+PBhPfPMM2revLlCQ0NVuXJlRUdH66233lJmZuZljX3mzBnNnDlTY8aM0bXXXquKFSsqICBA1apVU48ePfTWW2/p9OnTFx2nXr16slgsF/2vXr16l5UXAMqLjLRsLXpuiYJ3ZRTZHhIW5OVEAAAAgHk8MrNp9uzZGjp0qCeGliTFx8fr8OHD6ty5s8fu4QmLFy/WyJEjlZqa6jqXmZmpuLg4xcXFaeLEiVqyZIkaNGhQ6rG/++473XLLLcrJKfxUpKSkJK1evVqrV6/WW2+9pa+++ko9e/a8rNcCAPjLkT9OKC01S5Yi2ppfU1f9R3T0eiYAAADALB6Z2TR8+HC1atVKs2fPduu4hw8f1kMPPaRGjRppxYoVbh3b03777TcNGzZMqampCgsL02uvvaYff/xRK1eu1JgxYyRJu3fvVv/+/ZWenl7q8ZOTk5WTkyOr1aq+ffvqnXfe0ffff69ffvlFCxYs0PDhwyVJx48f14ABA/Trr79edMzBgwdr27Ztxf63fPnyUucEgPLIP6D4v93s3ZGghIMsowMAAMCVwyMzmxo3bqwdO3ZoxIgRevbZZ3XnnXdq5MiRuvrqq0s9VkZGhubOnasvv/xSK1eulMPhkNVqVaNGjTyQ3HOefPJJZWZmymazafny5bruur82iu3Vq5caN26s5557Trt27dKECRP08ssvl2p8f39/Pfjggxo3bpzq1q1boK1t27YaOHCgunTposcff1yZmZkaO3asVq5cecExK1asqJYtW5YqBwBcieo1qanrbmihDSt/L9Rmz3NowZyNatWhviyWouY+AQAAAOWLR2Y27dixQ2+99ZaqVKmiw4cPa/z48WrdurWaNWume+65Rx9++KF+/PFH7du3T8nJyXI6ncrIyNCRI0e0detWffPNN3r++efVs2dPVa9eXaNHj9ayZctkt9t10003acuWLbrzzjs9Ed0j4uLitGrVKknSfffdV6DQdNbYsWPVvHlzSdK7776rvLy8Ut1j+PDh+u9//1uo0HSuxx57TO3bt5ckrVq1SsnJyaW6BwCgaH5+Vr38n7t1w+Bri2xfvnmPHn5/jvIcDi8nAwAAALzPIzObbDabnn76aT344IP66KOP9OGHH+rw4cPas2eP9u7dq+nTp5doHMMwJEl+fn4aPHiwnn32WXXsWPb2vZg3b57r+N577y2yj9Vq1ahRo/TCCy8oJSVFq1atUu/evd2epUePHtq0aZOcTqcOHDigKlWquP0eAHCl6jc8WuuWbVNO9l9/MDCsUk7VQP2867D2xp9Ui6tqmpgQAAAA8DyPFJvOCg0N1bPPPquxY8fqf//7n77++mv98MMPOnjw4EWvDQ4OVnR0tPr3768777xTtWvX9mRUj1q7dq2k/M9Hu3btiu3XvXt31/G6des8Umw6dwNxq5WHEQKAO1VuWFkBf2usY2sOKuCUXbmVbUq/Kli2LD9ZnVKunZlNAAC4S2xsbKmviYmJ8UASAOfzaLHprLObVvft21eSlJCQoB9//FHx8fE6efKkTp06paCgIFWrVk3VqlVTq1at1L59e/n7+3sjnsft3LlTktSoUSPZbMV/yps1a1boGndbvXq1pPzZZxfb92rNmjVq3bq19u/fL8MwVKNGDUVHR+uOO+7Q4MGDL2vvkfj4+Au2JyYmXvLYAGCWWZu26rfTSVLrsIINVqm6EayGtZhNCgBAUXh/AJQvXik2na9OnToaOnSoGbf2uuzsbCUl5T+FKDIy8oJ9K1WqpNDQUNf+Ve62ePFibd26VZLUt29fVahQ4YL9Dxw4UODjgwcP6uDBg/r666/VpUsXzZo1S3Xq1LmkLFFRUZd0HQD4qiVfb9S3y3+Urir8o7VFvRr68M5BCg8JMiEZAAC+j/cHQPliSrHpSpKWluY6DgsLu0DPfGeLTenp6W7NcerUKT3yyCOS8vfA+ve//11s34CAAA0aNEh9+vRRy5YtFRERodOnT2vDhg36+OOPdeTIEa1fv169e/fWhg0bFBER4dasAFDWJB45pQ9i5srRIEi6qnAhv1+LxgorJ7N1AQDwNSyNA3wPxSYPy87Odh0HBARctH9gYKAkKSsry20ZHA6HRo4cqUOHDkmSXnzxRbVt27bY/hs3blTFihULne/Ro4ceffRR3X777Vq+fLl27typ2NhYTZgwodSZLjZzKzExUdHR0aUeFwDMkHoq/w8E4X9ky2JIp5uHKK+iTREHsnV1SpBmzZijOQEL1HPANbr78RtVtQZFegAAzsX7A6B8odjkYUFBfy2ZyM3NvWj/sxt4BwcHuy3Dww8/rKVLl0qS+vfvr5deeumC/YsqNJ0VHh6ur7/+Wg0bNlRycrI+/fRTvfHGGyUqpJ3rYksKAaAsqVwtfzaTRVL4gWyFHch2fXxMZyRJebl2LZ+zSfEHT+rtL/9uUlIAAHzTpbw/YEYT4Lt4HJmHhYeHu45LsjQuIyNDUsmW3JXECy+8oE8//VSS1LVrV82ePVt+fn6XNWZERIRGjBghKT/vpk2bLjsnAJRl1WtX1Lh371RUw+r5J2wWhXauWWTf4/EpXkwGAAAAeB8zmzwsKChIVatWVVJS0kWfsJCSkuIqNrljg7zx48frjTfekCRde+21WrRokdtmTLVo0cJ1nJCQ4JYxAaAs69a3lbr2aanD+0/otQ3r9ePW/SrqO3loOJuEAwAAoHyj2OQFzZs319q1a7Vv3z7Z7XbZbEV/2nft2lXgmsvx0Ucf6R//+IdrrGXLlrl1I2/DMNw2FgCUFxaLRVc1qqE9i08pL9xPKVeHKGJnpqzO/PbwKqF6cNwAc0MCQDlzaO9xzf/iRx3cc0xNWkVq0F2dVbtuFbNjwQtiY2NL3Jcld4B3sYzOC7p27Sopf8nZ5s2bi+23evVq13GXLl0u+X7Tp0/Xo48+Kklq0KCBVqxYoapVq17yeEX5/fffXce1a9d269gAUNYF2myyOKSwg9muQpMkpSVn6Mf/7TAvGACUMxtW/q6Hh7yn777eqJ2/Htb86T/qwQHv6Pcth8yOBgBXNGY2ecGQIUP0+uuvS5ImT56sjh07FurjdDo1bdo0SfkbdPfs2fOS7jVnzhzde++9MgxDkZGRWrlypduLQampqZo1a5YkKSQkRO3bt3fr+ABQ1j3ULVr/N3Wp/DOchdp++mGnHo0Z4v1QAOBjjvxxQku/2aTszBx1u6m12nRsIIvFUqox4tbsltNZcMa9Pc+hX9bvVYu2V7kzrluln8nS/+Zs1sG9x9W0TZR6DbxGQcGle+AOmK0E+DJmNnlBdHS0unXrJkmaNGmSNmzYUKjP22+/rZ07d0qSnnjiCfn7+xdonzJliiwWiywWi1555ZUi77N8+XLdcccdcjgcql69ulasWKF69eqVKuvSpUuVlZVVbHtaWpqGDRum5ORkSdJ9992nwMDAUt0DAMq7Ia1b6NORQ4ps87PyoxcAvvjPCj3Q/x3NmbxWS2Zt1Av3TtQ/7/tcTmfhIv2FWK1FF6dKW7Typh2/HNToG8br0/GLtXzOJn0QM1d/6/OWjvxxwuxoAOA2zGzykvfee09dunRRVlaW+vTpo3Hjxqlnz57KysrSzJkzXU+Ma9KkicaOHVvq8X/66Sfdcsstys3Nlb+/v9555x3l5eVp+/btxV4TGRmpihUrFjj3xhtvaOTIkbr11lvVtWtXNWzYUGFhYTp9+rQ2bNigjz/+WEeOHJEkNW3atNjCFwBc6a5uWVeNr66jvTsKPkSh963tTEoEAL5j0Vc/FTq3ZcM+JRxMUlSD6iUe5/qbW2vFvF+Uk53nOhcWEazON7a4wFXm+n7Br8pMzylwLiUpTeuW79Adfy/5awcAX0axyUvatm2rWbNm6a677tKZM2c0bty4Qn2aNGmixYsXKzw8vNTjL126VJmZmZKkvLw8jRw58qLXTJ48Wffcc0+h86dOndLEiRM1ceLEYq+9/vrrNWPGDFWuXLnUWQHgSuDn76fwUU10fFG6Qo5ky+lvlfPqCqrU4/KfNgoAZZ3DUfQMJoe9dDObWkc30JQVz+m72XE6uOeYmrWOUp/b2vv0kz+dxbx2p8Ph5SRlX1EbhLO0DvANFJu8aODAgdq6davee+89LV68WPHx8QoICFCjRo00dOhQPfroowoJCTE141tvvaWVK1dqw4YN2r17t5KSknT69GmFhISodu3a6tixo+644w716dPHp6cnA4DZ9p1M1je/7ZCiApURdXa5saEJK9epf8umpmYDALNd27mx1ny3tcC56rUrqna90j/UpmKVMN3x90vb79QM11zXSEu/iStwzs9mVevohiYlAgD3sxg+8Az7EydOaOvWrerSpYuCg4PNjgMfEB8fr6io/L/+HzlyRJGRkSYnAoDS+S0hUcM+n1novF+mQ3fn1NGjLw9WeEVz/8AAAGax5zm0dtk2LZ0dp6zMHF1/U2v1vb2DwiOujPcCu7ce0bzp63Vo73E1a1NXg+/urKsa1fB6jrL0O/e5WZ966ilFREQU2Y+ZTUDpeeJ7gWkzm/bs2aP58+dr3rx5+vnnn2UYhoKCgnTjjTdqyJAhGjhwoKpWLf1fNgAA8AV1K1VUkM2mbLu9wPmA03atWbVVVzWqrjsfvsGkdABw6QzD0ImjpxUSFnTJxSGbv596DrhGPQdc495wZUTT1lF6/v+NMDsGAHiMV4tNGzZs0Pz58zV//nzt2bNHUv4PKyn/iRFZWVlauHChFi1aJKvVqk6dOmnIkCEaPHiwGjVq5M2oAABclkohwfrmvjv03GfztTM3VdZcpyrsy1LE7vwnfp45nWlyQgAovfXLt2vGx9/rj12Jsvn76fqbWunesTepao2iZ5kAAK5MHi025eTkaMWKFZo/f74WLlyoEyfyH+d5tsDUrFkzDR48WEOGDFGrVq20cuVKzZs3T4sWLdLJkye1fv16/fjjj3ruuecK9I2OjvZkbAAA3KJx9aoaHdVMn7y+qFBbtVoVvR8IAC7DkT9O6NUnvnR9bM9z6PuFv+rUyTS9Pvl+E5PhSsfSOcD3eKzYdNttt2n58uWuJ6QZhlFotlLjxo0LXDNw4EANHDhQhmHoxx9/dM2C2rt3r3bu3Kldu3Zp/Pjxqlmzpp544gk999xznooPAIBb9B/RUVv3JOinxdtkZNtlCfRTx34tNWjkdWZHA4ASS0vN0lcff19k28ljqV5OAwDwdR4rNs2dO1eSCu3DVK1atYtea7FY1KVLF3Xp0kVvvvmmdu7c6So8bdy4UYmJiVqxYgXFJgCAz1v2yx7NS06QOlSSX65DjgA/zU9JVNvNuzXouqvNjgcAJfLvx6ZrW9yBItsq8LADmCw2NtZ1zCwnwDd4rNg0atQoDRkyRH379r3sJ8w1b95czZs31z/+8Q8dP35cCxYs0PHjx92UFAAAz9mbkJR/YJVsaXaFH02T1W5oqfUndW9WTxGVQs0NCAAlcGDPsSLP121UXQ+9OMjLaQAAvs5jxaYpU6Z4ZNwaNWpozJgxHhkbAAB3CwrI/1EbvidNIYnZrvMHvt+nhwe/p8++G6uQ0ECz4gFAiQQFByg9NavQ+Zf/c7fqXMUTpAEABXn1aXQAAFxpBne+Wmu2/qHkDUmF2k6dTNOGFTt0w+BrTUgGAPn27UjQ0m/ilJWRo243t1Z096ayWq0F+ox6orc+fnWhsjJyJElWP6v6D49W7bpVzIgMFPD/2bvv8Kiq/I/j7zszmWTSgYRQEnoXEJQqvSoIAioutgXFvhYs4LprWdbey+ruCiKWlR+KIIioIAoICgJKlRqkQ4CE9Drl/v6IDMRMSIBkJgmf1/PwPHfuOXPvJ2FavnPOuZo6J1L5VLpi04svvkivXr3o1q1boKOIiIics3q1opjxt+u5/pvnSD2SUaz9pb/OIunAca7/y8AApBOR8930V77mk6nLvLe/m7+eNh0b8sKHt2G1niw4DRp5MT0GtWX5VxvJyc6nx6C21K4XHYDEIiJSFVhK7+JfDz/8MD179uSDDz4IdBQREZFyYRgGV/65B4bhu33R7J/9G0hE5HcLZ68ttm/Lur0c2H2s2P7QsGAuvbozo8b2VKFJKpXJkyd7/4lI5VDpik0Adrud8ePHM2PGDJ/tU6dOZcyYMX5OJSIicvZCLoghvXss2Qmh5NQNIbV9FGmtIzABt8cT6Hgicp7yuH2//ng8pp+TiIhIdVIpi033338/AwYMYNy4cXzyySfF2g8dOsSsWbMCkExEROTsTPtyNbl2g6ym4WS2jKSgZjD5cQ5c4Ta69Wsd6Hgicp7q1r9NsX31G9YivpEW/RYRkbNX6dZsAggODmbevHlcfvnl3HDDDQQFBTFq1KhAxxIRETlr+U6Xz/0NxrkYeENTP6cRESl035NX0n1AG76etZqc7AJ6X9aOgSMvIsheKf9MEClVSVPptIi4iH9V2neR4OBg5s+fz9ChQxkzZgyffvopw4cPD3QsERGRs9KmYRxJqZlF9lmtLqh/jFm7P+CxDi8GKJmIVHcej4f9vx0jItJBzdqRRdqsVgvdB7Sh+4DiI5xERETOVqUtNgE4HA4WLFjApZdeyujRo/nss88YMmRIoGOJiIicsSdvuoyEb77jk+W/4My1EunMw30wiF1TWrAnxEPM5Uu5+caeBGs0gYiUo+8+X8eMf3/Lwb0pWCwG3fq34ZZJQ6mbUDPQ0UT86sSIJ41wEvGPSrlm06lCQ0P56quvuOiii7jqqqtYtGhRoCOJiIicMYc9iHGDO9P5T5to2uwgzj0hePKtALjzLMycvZrZ83RVOhEpP/t2HeXFhz/h4N4UoHDR7x8X/8qrf/80wMlERKS6q3TFpjZt2mCzFf1WNzw8nIULF9KuXTtGjRrF8uXLA5RORETk7EUF1WSE7TbchyN8tqccz/JzIhGpzlKOZvjcv23DPr6bvw7T1BXnRESkYlS6sfqbN2/2uT8iIoJFixbRv39/vvvuOwzD8HMyERGRc5OWksUr1y8mN9QGPmawREeF+j+UiFRb0TXDfO53Frh5cVLhFZ/7D+/oz0giFU7T5EQqh0o3sul0oqKiWLx4MYMGDcLhcAQ6joiIyBlJOnAcl9ONLSMfa2Y+nBhVYJpccnFjrrziosAGFJEq41hSOtNe+opbh77Cg9f/l+8+X4fb5S7Sp1GLOtzxt2HYQ4J8HmP/rmP+iCoiIuehSjeyqTQ1atRg4cKFGvYrIiJVjiM0GADDhODkHMzjuXjsViwFbtbv+YUNfdpwycALApxSRCq7tONZ3DXydbLScwt37IYtv+xl8897uHfyKG8/wzAYcWMPDh9IZd4HPxQ7jiMs2F+RRSqURjOJVD4BG9mUkZHBsmXLmD17Nl999RW//PILubm5Zb6/ptGJiEhV06BZbUbceAmGpfA9zPCYWPNcGJ7CL1A++3ItBS5XICOKSBWwZ8eRk4WmU6xYuJm0lOJrv4288RKat61fZF+7zo0ZOFKjKUVEpGL4fWRTUlIS999/P3PmzMH1hw/UwcHB9O7dm3HjxjFmzBh/RxMREalQhmFwx9+G4/GYzP9oZbH2FXv2MWrqR8y+5XpCgqrc4GMR8RNbkNXn/sz0HG4a/CLPv38rLdrGe/fXia/J65/8hV9/3sOeHUm0aJ9QpF1ERKS8+fWT7JEjR+jevTv79u3zOQ0uLy+Pb775hm+++YYnn3yS//znP/Tu3dufEUVERCrc0Gu68MOizRw/lund5wqxkNUohOPJx9l+9BgX1q8bwIQiUhkVFLhY/tVGdm05TIOmtdm362ixPnk5Bfy0ZGuxYpJhGLTt1Ji2nRr7K66I30yePLnUPppqJ+Jffi02TZ48mb179wIQFxfH3XffTdu2balRowZJSUmsWrWKL774gsTERLZu3crAgQN5+umnmThxoj9jioiIVCirzYLV+oeZ7IaJ+fsuLUsoIn/027bDPH77e6QczfDuCw4JIj/PWayvXkNERCTQ/FpsWrBgAYZh0L59e5YvX054eHiR9muuuYZXXnmFRYsWMWnSJDZu3Mhf//pXrFYrDzzwgD+jioiIVJhv563jWFJ6kX22XJOI3XlE9o+nZVxMgJKJSGWyY/OBwmlvbeNZNGdtkUITQH6eE8NiYHpOVpeCQ4Lo3Lulv6OKBJRGLYlUPn4tNiUlJQHw97//vVih6VSDBw+mX79+jB07lpkzZ/LII48wbNgwWrRo4a+oIiIiFcZZ4HsR8KBcGBjXmBCb1msSOZ8dPZTGC5M+5tef93j3xdSJ9Nl36J+6Yrdb2bPjCC3ax3P5mG7E1onyU1IRERHf/Ho1uqiowje+Zs2aldo3KCiI9957jwsuuACXy8Xrr79e0fFERET8onXHhj73ezD4ZP4adh1K8XMiEalM5v3vxyKFJoDkpAyffbv0bsltfx3GM++OZ9yES1VoEhGRSsGvX522aNGClStXcuDAATp06FBqf7vdzj333MMdd9zBwoULKz6giIiIH3Tq2YLuA1qz8tutAJiAaYGI3dlE7M7m1Qc+5qF/XknjllokXOR8YpomX32ymi9n/uSzvc/Q9mRn5rH796l1V97Ui7YXN/JvSJFKSAuEi1Q+fi02DRkyhB9//JGPPvqIYcOGlek+J4pShw4dqsBkIiIi/jN/xkpvoQnAAAzPyfbfNh7g5Uc+5c059/g/nIgEzN6dR/jXP+aW2H5xrxYMGnmx/wKJiIicJb8Wm+69917eeOMNPvnkE0aPHs2VV15Z6n1SU1MBiI6OruB0IiIi/pGWklUufUSkekk7nu1zvy3Iyl8eG8HAERf5OZFI1aMRTCKVg1/XbJo5cyZPPfUU4eHhXHvttbzxxhuYpVybde7cuQBccsklfkgoIiJS8aJrlXyRjDPpIyLVS40Snvct28Vz2ejOGIbh50QiIiJnx68jm26//Xbvm6Rpmtx///1MnTqVO++8k+HDh5OQkODtm5OTw8svv8zbb79NSEgIjzzyiD+jioiIVJjh13cn5WgG8/73I5jgsRW+N1pchV/A1GpUk3ufKn30r4hUTXm5BXw9aw1fz1pDbnY+PS9ty8ixPWnYPI4JT17JjH9/x9HDaQB06tWC2x8p2/ITIiIilYVhlja0qBxZLMUHUp36DU1MTAxxcXHk5OSwf/9+XC4XTZo04b///S8DBgzwV0ypBA4cOOAtPu7fv5/4+PgAJxIRKX+3DX2FvfuTSe5aC9NmYMtyYdoM3A4bAy9qzgu36g9MkeroiTvfZ/XSbUX2RdYI4/3FkwgJteN2ufltexKR0aHE1a8RoJRyPqhKn7lPzXr//fcTFRWlKXMi5aQiXgv8OrIpKSmJ9evXs379ejZs2MD69evZsWMHbrcbgGPHjnHs2DEMw8A0TQzDIC0tjWeeeYYvvviCdu3a0a5dOy644AJCQ0P9GV1ERKTc3f63YTw5eTZmUOGXMa6IIG/bD7/u4WByOvVjdBlzkerk0N5k1q9MLLY/IzWbpQs2cOnVnbDarDS/oH4A0omIiJQPvxabateuzeDBgxk8eLB3X35+Pps2bfIWnzZs2MDGjRvJyMjANE1SUlJYsmQJS5cu9d7HMAyaNGlCu3btaN++vSraIiJSJf3yw06yUnOAkGJtuflOrpz8Pm/ePYrOLROK31lEqpy1y7fzj7s+wO3y+Gx//fE5JB08zrgJl/o5mUjVNHny5HI7lv6mFClffi02+RIcHEynTp3o1KlTkf27d+8uUoBav349e/fuBQrXe0pMTCQxMZG5c+fqhUFERKqkFYs2Y8t2EXwsj/zYkwUnS64LR1IeFpfJvM/X0umheC0MLFIN/LRkW4mFphNWLNysYpOIiFR5AS82laRx48Y0btyYkSNHevdlZGQUKT6tX7+eLVu2BC6kiIjIOTAMAwOI/jUDZ3gO+THBBKUWYE93cqK09PO7q/nblhSenHITtiBrIOOKyDkqS81YhWUREakOKm2xyZfIyEh69+5N7969vftOrPckIiJS1fQf3oH/++8SAIKyXARluXz2W79qF79tP0yLtpV34VYRKV2PwW1ZOHstBfm+n+tQ+LogImdPs15EKocqVWzyxWrVt7wiIlI1/fm+wcQl1OS1v88uta/LqS9XRKq6C7s25d1FE1kw8yf27EiiSat6RNUMZcOqXUTWCGPon7rSrE29QMcUERE5ZxVWbHr11VcZMWIETZo0Kfdj//TTT+zatYvrrruu3I8tIiLiT70ubcd7rywkLSWrxD5RNcNo2CzOj6lE5GyZpsnO346Sm1PABa3rYbOd/GK0IN/JwT3J9L6sHX++d5B3//DrugciqoiISIWpsGLTgw8+yEMPPUSbNm0YMWIEI0eOLLYIeFkVFBSwePFi5s2bx/z58zly5AgDBgxQsUlERKq80LBg/j33Xp546P/Y/ste3HYLnpAgbFkFGB4TIkMYdld/QsODAx1VREqxeetB3vjPYrYnHgEgplY4467vwbBL2zP3/R/4eOpS0o9nA9CmY0PueuwKmrbWSCYREal+KqzY1KJFC3bs2MGvv/7Kli1bePbZZ6lbty5XXHEFI0eOpF+/fgQFBZV4/9TUVBYsWMC8efNYuHAh2dmFb8ymaWKxWGjRokVFRRcREfGrGjERXHpdNzYcTfPuc0eevDrd1P+toE/vViTE1wxAOhEpq+de+ZL9B1O9t5NTsnjpjYXUjQ5nyvMLivTdsm4vb06ey6sz7/J3TJFqbfLkyT73ay0nEf+qsGLTtm3b2LFjB/PmzWPevHmsWrWKQ4cO8fbbb/P2228TERHBZZddxogRI7j88suJjIxk7969zJ07l3nz5rFixQrcbjemaQLgcDgYNGgQI0eOZNiwYcTExFRUdBEREb8b3P8C1vy8hyXLt/lsT8/IJcHPmUSkbFxON7Omfc/B/cfBUvxqckcOpfq4F2Sk5lR0NBERkYCo0AXCW7RowcSJE5k4cSLHjh3j888/Z968eSxevJiMjAw++eQTZs2ahc1mo0GDBvz2228A3gJTbGwsw4YNY8SIEQwaNAiHw1GRcUVERALm8y/Xl1hoAqhVM8yPaUTkTMx5bwUfvL4Is34E2It+vDYMaNDY95ekteIi/RFPRETE7/x2NbrY2FjGjx/P+PHjyc3NZeHChcybN48vvviClJQUdu3aBUCzZs0YMWIEI0aMoEePHhhG8W+HREREqpukI+kAeAzwBIM1DwzAGmbljpv7UrdOdEDziUgh0zT58Ztf+eyDH9i9/TDN28YT9HuBKfhoNgU1HXgcQWAY1AgPYeIDQ2jXsREPv/QnPnrrOw7sPobFYtB94AXcMnFIgH8aERGRiuG3YtOpHA4HI0eOZOTIkXg8HlasWMH69esZOHAgbdq0CUQkERGRgIoIL1yjKb2FlfyaFiwFJoYb3CHw7IaVDOzThpqhGuErEmiLZq/ltcfmeG9vWLXLu21xegg5ko3HaoBhcP19XenRrTkAfS/vQO8h7Tm4J5mwCAc1YyP8nl1ERMRfAlJsOpXFYqF379707t070FH8Yt++fbzxxhssWLCAffv2ERwcTLNmzbjmmmu46667CA0NLZfzzJw5k+nTp7Nx40ZSU1OpU6cOvXr14i9/+QvdunUr0zFSUlJ44403mDt3Lnv27ME0TRo3bszIkSO59957qVWrVrlkFRERGHF5R3bsOsLsvN0AeOwnR/bmOJ0sWPQL11/RHYvFEqiIIgLs/PWgz/2R0aFkpBWuwWT1QM9LL+Cy0V2K9LFYLCQ0qV3hGUXOZ1oIXKRyCHix6XyyYMECrr/+etLT0737cnJyWLNmDWvWrOGdd97hyy+/pEmTJmd9jry8PEaPHs0XX3xRZP/evXvZu3cvM2bM4B//+AePPfbYaY+zZs0aRowYweHDh4vs37hxIxs3buSdd95h3rx5dOrU6ayziojISVGRDp56dBQr35jGgfSMYu3TX/iatPXHuOcfI/0fTkS8gkN8X005Iy2HmDpR3PzQZbRq34C6Cbp6pIiInL/09aifbNiwgWuuuYb09HTCw8N5+umn+fHHH/n222+59dZbAdi+fTuXX345WVlZZ32e8ePHewtN/fr1Y+7cuaxevZpp06bRtGlTPB4Pjz/+OO+8806Jxzh48CDDhw/n8OHD2Gw2Jk2axPfff8/333/PpEmTsNlsHDp0iGHDhnHwoO9v90RE5Ozc2/cSgi3WIvvCdudhT3Px09KtAUolcv5xuz2s+m4Lr/59NtNe+oq1329nynNfcPRQGjViw33eJzkpnQZNaqvQJCIi5z2NbPKTCRMmkJOTg81mY9GiRXTv3t3b1r9/f5o3b86kSZPYtm0br7zyCo8//vgZn2PZsmXMmDEDgOHDh/PZZ59htRb+wdK5c2euuOIKLr74Yvbt28ekSZO4+uqriY6OLnacv//97xw5cgSAGTNmMHr0aG9br1696NSpE9dccw1Hjhzhscce49133z3jrCIi4lv29gyiVuWRF2PBY4WQ4x7saSaYYNUUOhG/yM9zMunPU9ix6YB336fTvi/TfS1WPU9FAmny5Mk+92t6nYh/6d3QD9asWcPSpUuBwpFHpxaaTnjwwQdp3bo1AK+99hpOp/OMz/PCCy8AYLVa+fe//+0tNJ0QExPD888/D0BqairTpk0rdowjR47wv//9D4BLL720SKHphNGjR3PppZcC8MEHH3gLUyIicu7mf7UBixtCj3gIP+TBlgeeEBseu5VLr9bUZRF/2LR2d5FCU1m16diQBk1iKyCRiIhI1aKRTX4wd+5c7/ZNN93ks4/FYuHPf/4zjzzyCKmpqSxdupRBgwaV+RxZWVl8++23AAwaNIj4+Hif/a688koiIyPJyMhgzpw5PPjgg0XaP//8c9xu92mzAowbN46FCxfidrv5/PPPvVMBRUTk3LjdHp/77SE26jXUhRlEKto3c3/mzX/MPeP79bqsHX99eYwW8RcJMI1gEqkc9G7oB8uXLwcgLCyMiy++uMR+ffr08W6vWLHijM6xevVq8vPzix3nj+x2u/dqdKtXry42gupE1tKOcy5ZRUSkZJ0vblRsnyXPhSsjn/deXej/QCLnmf+9sZiCfNcZ3cdqs3D5mK4qNImIiPxO74h+sHVr4YKuzZo1w2YreTBZq1atit3nTM/xx+Oc7jwul4udO3f6PE5UVBR16tQp8Rh169YlMjLyrLKKiEjJHvjLYG6/rgeWHCeWXCchB9IJPpyJARw5mMbTEz4iIzU70DFFqq28PN9LGfS/oiMdujelZft4Rvz5EvpcfiFNW9djyDVdeOuze7mwa1M/JxUREam8NI2uguXl5ZGcnAxQ4tS2E2rUqEFYWBjZ2dns37//jM5zav/SzpOQkFDkfm3atCl2nNKOceI4v/766xlnBThw4PTrIBw+fPiMjykiUh1YrRaGXd6BT15ZSH5u8T96VyzcTKMWdbj+rgEBSCdS/TVtVZd1KxOL7HOEBXPXY1cQFh4SoFQi1d/Z/H2gKXMilVelKDZ98MEHAIwaNYqIiIgApylfmZmZ3u3wcN+XyT3ViWJTVlZWhZ0nLCzMu/3H85w4Tlmz+jpGWZxa8BIRkaIia4Rx41NX8O4/v8CTnl+s/fOPVtK0VV269W/j494ici7+/vr1zP1gBV/NWkNOVj69Lm3H6Ft6q9AkUsH094FI9VIpik3jxo3DMAy6detW7YpNeXl53m273V5q/+DgYAByc3Mr7DwnzuHrPCeOU5FZRUTk9JKOZ/L0/OU4YoOI9FFsyjiezeS/fMh7iycRV79GABKKVF9hESFc/5eBXP+XgYGOIiKlmDx5cpn7ahSUiH9VimJTdRYScvJbsIKCglL7n1jk2+FwVNh5TpzD13lCQkLIycmp0KxAqVPvDh8+TJcuXc74uCIi1cHRtMIRo7l1Q7DlenAczsXwmJgWA4/VwOL0YAApRzNUbBIpJ6Zp8t3n6/js/R/YvSOJ5hfU56qbe9Hr0naBjiZyXtDfByLVi4pNFezUkVplmW6WnV246GtZprGd7XlOnMPXeSIiIsjJyanQrFC2NaFERM5X0b9P17HlAvZgchvYMVweTJsFDAP7sWxsWQVERocGNqhINbJg5k+89c953tvbN+7nmQkzePilP9H38g6BCyZyntDfByLVi4pNFSwkJISYmBiSk5NLXfQuNTXVW8A50znLp744HzhwgE6dOpXY99RvDf54nvj4eI4cOVJq1lOPo/nVIiLlKyE2modG92HK9O8xcYNhYAZZve1GqJ0bb+lL/UYxAUwpUr3s2ZFUwv4jcLmfw4jIGdEUOZHKxxLoAOeD1q1bA5CYmIjL5Sqx37Zt24rdp6xOvaLcqcc53XlsNhvNmjXzeZz09HSSknx/6ILCYawZGRlnlVVERE7PMAyu638R1w3o6LM9P9TGrO+3kJtb+pRnESmbkFDf61UGO0pfx1JERESKUrHJD3r27AkUTjv7+eefS+y3bNky73aPHj3O6BydO3f2Lup96nH+qKCggFWrVhW7zx+zlnacc8kqIiJlc8WQDjRtFFt0p2mCYXA8NZtlP+4ITDCRamTH5gO88cRnHPjtGLXrRRdpa9k+noEjLwpMMBERkSpMxSY/GDlypHd7+vTpPvt4PB4++OADAKKjo+nXr98ZnSMiIoIBAwYAsHjx4hKnwc2ZM8c7ImnUqFHF2q+44gosFstpswK89957AFgsFq644oozyioiImWTlpRO6k97CD6ciS0lB8NZOKXuhOde+Yp3/7cigAlFqrZ3X/6a+0a/xVefrOanpds4eiiN+o1jGP/QEF788DZenXkXsXWiAh1TRESkylGxyQ+6dOlCr169AJg2bRorV64s1ufll19m69atANx3330EBQUVaX/vvfcwDAPDMPjHP/7h8zwPPfQQAC6Xi7/85S+43e4i7cnJyTz88MNAYUHrlltuKXaMOnXqcP311wOwcOFCPv3002J9Zs2axcKFCwG48cYbqVOnTok/u4iInL0fFv1KXk4B1jwX1jxXkXWbTli4eHMAkolUD4vnFh9xfnB3Mq0vbkjbTo0xTinuioiISNlpgXA/ef311+nRowe5ubkMHjyYv/3tb/Tr14/c3FxmzpzJlClTAGjRogUPPvjgWZ2jf//+jBkzhpkzZ/L5558zaNAgJkyYQL169di0aRNPP/00+/btA+C5556jRg3fl8t++umn+frrrzl27BjXXnsta9euZdiwYQB88cUXvPzyywDExsby1FNPnVVWEREpncc0T94o4W/eIn1E5Ix4PL6fPw/+7WMenDiMQf3a+GwXkcpl8uTJ53wMLTIuUr5UbPKTjh078vHHH3PDDTeQkZHB3/72t2J9WrRowYIFC4iIiDjr87z77rtkZGTw5ZdfsmTJEpYsWVKk3WKx8Nhjj3H77beXeIyEhATmz5/PyJEjSUpK4vnnn+f5558v0qdOnTrMnTtXlygVEalAnXq1YN6HP+B2ebAUuDGc7mKjm3p0bVbCvUWkNN37t+HrT9cU2eexWcgzTT76ZJWKTSIiImdJxSY/Gj58OBs3buT1119nwYIFHDhwALvdTrNmzRg9ejR33303oaGh53QOh8PBggULmDFjBu+99x4bNmwgLS2NuLg4evXqxd1330337t1LPU7Xrl3ZtGkTr7/+OnPnzmXPnj0ANG7cmBEjRjBhwgRq1ap1TllFROT0OnZvxtvz72fcxHcoOJJDTm0DAwtBOSamBULrhNK1T/NAxxSpsu6ZPJLYJjFMf3MxhsfEHWbHFW4Hw6CgoOQrCItI1aORSyL+ZZhm4MffWywWDMNg69attGjRItBxpBI4cOAACQkJAOzfv18jqETkvHbvrPks2pRI6LHibY3r1GT2E2P9H0qkijpyMJW8nAIaNKuN8fuVHa8bP4XcPGeRfgP7tuaxScMDlFLEP6rSZ+5Ts95///1ERZ3Z4v0qNomUrCJeCzSySUREpJJ7YeQQ6oasYNaX64q17T14nLemfsctf+5FcHCQj3uLCMCeHUlMfeFLfvlhJwAJTWtz4z0D6XVpO6a+MZZPPlvD8pU7iYp0cMXQDgy77MIAJxYREam6VGwSERGp5EKCbNzSqxOzv1pfbEFww+nhk8/WUiM6jOtGdw1QQpHK7/mHZrJn5xHv7f27jvLMhBn8b9kjJMTX5MF7LuXBey4NYEIROVMarSRSeVkCHUBERERKFxsdzt+v6EVY3u/FJrdJcHIBIUcKp/4cT80KYDqRyu94su/nyHuvLiQzPdfPaURERKo3jWwSERGpAlKTM3l30meEO92E2gwMj4nhAXeIjfy6EURFndsFJkSqu6iaYWSkZhfbv3juL2SkZjP5v+P8H0pEzsnkyZNP266RTyKBo5FNIiIiVUDSgVRcTjcAFldhoQnA4nTT/ZJmXDX8ogCmE6n87vvnKGrERPhs27/bx+r7IiIictYqRbHJ6XRSUFCgK9GJiIiUIDQs2Od+t93CooN7Wbl9n58TiVQtn723gtTkTJ9tjhKeXyIiInJ2KkWxyWq1YrVaAx1DRESk0mrQrDYjbrwEw2J493mshcWm6E1pLJi1moJ852mOIHJ+27R2j8/90TXDuPmBy/wbRkREpJqrFMUmEREROT1ngYt1PyZiek5ejc7ihuB0J8EpBWz9bDN3X/Um+XkqOIn4EmT3/cXm09Nu5uKeGl0vIiJSnrRAuIiISBWwe0cS+3YdPW2f/buOsnv7YVpd2MBPqUSqjqvG9eKdl77C4/Z493Xr35pGLeoEMJWIlActBC5S+ajYJCIiUhWYpXcRkZKNGteTnpe145s5a8nJzqfXpe1o2T4h0LFERESqJRWbREREqoDGLeuQ0CSW/b+VfNWs+MaxNG5Z14+pRKqW2DpRXHfXgEDHEBERqfa0ZpOIiEgVYA8O4s0593Dh4Da4HUG47VbMk2uFYxhwcc/m2IP1PZKIiIiIBJaKTSIiIlWEPTiIodd0Jb9OOIZpYpwytc40Yd6HP7J355HABRSpJNLSc9j06wEyMnNxOd1sXb+Pg3uSAx1LRETkvKGvP0VERKqQ3j1a8Mbz1/LYuHdwOguKtT827UsevXsYLRNqByCdSGBl5+Tz1pTv+GbJFgoKXITkunBkOcnPzgfgwm5NueORYVoUXKSamTx5sndbi4WLVA6VbmTTiy++yKpVqwIdQ0REpNK6sF0CF/hY2Ng0YOux40z+8JsApBIJvFmfrWXBok0UON0YTg/G0SzycvK97RtW7eKNJz4LYEIREZHzQ6UrNj388MP07NmTDz74INBRREREKq3b/jGCmh3q4rEamEB+LTupHWrgCbZyPDMn0PFEAiI17eRj37QY5DaIJrdRDfLiwnEHWwFIO54VqHgiIiLnjUo5jc5utzN+/HhsNhvXXXddsfapU6fy7bffMnPmzACkExERCbxnZi9la7QbesSACVhPrhZeKzI0cMFEAqhmjbCTN2wnv1P1hAaR77Dh2JdOjZiIACQTEX85dUrd2dJUPJFzV+lGNgHcf//9DBgwgHHjxvHJJ58Uaz906BCzZs0KQDIREZHK4UByWuGGxShSaKoXGcFj1w0MTCiRALvmyk6MGn6R70bDoE3Xxtz/1FX+DSUiInIeqpQjm4KDg5k3bx6XX345N9xwA0FBQYwaNSrQsURERCqN8JBgjlB8OlDatuPM+N9K/vn3kf4PJRJgjhA7E+4cyKrVuzh8JL1Y+4NPX039ujUCkExEKpJGIolUPpVyZBMUFpzmz59Pjx49GDNmDPPnzw90JBERkUrjodF9iI08OWXImu0i6tcMQvdlsm7RrxzalxLAdCKBNeGugdSpHem97XAEcfMNPVVoEhER8ZNKObLpBIfDwYIFC7j00ksZPXo0n332GUOGDAl0LBERkYDr2rohb9wynJsf/h+WXCdhezI5MZnOlZfF7cNe5ckpN9GhW9OA5hQJhG6dm/LRtMas/WUPOTn5dOnUhPCw4EDHEhEROW9U6mITQGhoKF999RWDBw/mqquuYu7cuYGOJCIiUik0axJH/4ubs3L+eow/tLmcbtYs26Zik5x3crPz+fbzdezefpjmF8TTb3gHgkOCAh1LRCqAps+JVF6VrtjUpk0bbLaiscLDw1m4cCEDBw5k1KhRdO/ePUDpREREKg/TY+LceQxbZoHvDsYfS1Ai1duOzQd47NbpZKTl/L5nNR+88Q3PTh9Pw2ZxAc0mIiJyPql0xabNmzf73B8REcGiRYvo378/3333HYY+QIuIyHluz44kNvz0GwZgQpHRTSEOOz0HXRCgZCL+YZomG1f/xu7tSTRvW5/lX286pdBUKDU5k++/2siN9wwKUEoRqSiTJ08utk+jnUQqh0pXbDqdqKgoFi9ezJgxY/jhhx8CHUdERCSgnE43AB6rQUFMKJYCN5Z8Nx67lXodEqhRLzqwAUUq0OH9x3nqvv/x29bD3n3RtcJ99nW7PP6KJSIiIgSw2JSRkcG6detITk4mNDSUuLg4WrdujcPhOO39atSowcKFCzFN009JRUREKqcGzWpTIyacIy4XnlA7ntCTbYl7k/n8q/XcNq5P4AKKVKDPP/qxSKEJIC0lq1g/i8WgXZfG/oolIiIiBKDYlJSUxP3338+cOXNwuVxF2oKDg+nduzfjxo1jzJgxpz2OptGJiMj5Liw8hH/PvY9HH/+UTbuPFmv/YOU6QtpEcmPnDnrflGrDNE0++WwNn3+xzmd7/ys64na5+W17Ei0uqM/IsT1p1qaen1OKSKD4mloHml4n4m9+LTYdOXKE7t27s2/fPp8jk/Ly8vjmm2/45ptvePLJJ/nPf/5D7969/RlRRESkSomuFc7QERez6bWvirVlBbl5euFSejVtRONaNQKQTqT8/bYnmX+//R02jwe7j/aLezan//COfs8lIiIiJ1n8ebLJkyezd+9eTNMkLi6OJ598ks8++4ylS5cyc+ZMJkyYQNOmTTFNk61btzJw4EBefPFFf0YUERGpcgb1b0PPwa1x2wtHL7ntkBlvJTe28HZabm4g44mUm6QDx5ny9Oc49qVjT83DYxQujg+AAX0vv5DeQ9oHMqKIVFKTJ08ucdSTiJQ/v45sWrBgAYZh0L59e5YvX054eNFFHK+55hpeeeUVFi1axKRJk9i4cSN//etfsVqtPPDAA/6MKiIiUmV8+v1GvtqeCHWLvq3bcsAVDrXDfS+aLFLVPHP/DHZuPui98qLl1IHyJixdsIG2nRpz+ZiugYgnIiIiv/PryKakpCQA/v73vxcrNJ1q8ODBrFmzhjFjxmCaJo888gg7duzwV0wREZEq5fDxDJ/7QyxW/j60L/WjI/2cSKRiHD2UVoY+qRUfRERERE7LryOboqKiSElJoVmzZqX2DQoK4r333mPTpk1s2bKF119/nbfeessPKUVERKqWyNAQn/uzwwp4bstShl3QkpohoT77iFQl4ZEO0o9nl9pHRKQkZZlKp8XERc6dX0c2tWjRAoADBw6Uqb/dbueee+7BNE0WLlxYkdFERESqrKt7t6d/h5Nf5JhuDxani+itHsKXOJm3ZCMeT/ELc4hUNXc/MZKYetHe2398VPe+rB2Xje7i10wiIiJSnF9HNg0ZMoQff/yRjz76iGHDhpXpPh06dADg0KFDFZhMRESk6ooOd/DS7cPp8+HbHD6UTuxPYMs72f7ev74nJTGdh+65NHAhRcpBh25N+eubN3L3XdPBNPGEBGG4PVgK3NRvHMsjr14X6Igi4kcagSRSefl1ZNO9995LbGwsn3zyCXPmzCnTfVJTC+fdR0dHV2AyERGRqu+BS3oRGmwrUmg64dsftpGR56NBpIrITM/l03e/54t3v6dFq7p4Qu1gMTCDrDhqR3Dz+D6BjigiIiK/82uxaebMmTz11FOEh4dz7bXX8sYbb2Capx/WP3fuXAAuueQSPyQUERGpukY0bcP0wVf7bMvIz6f/G++y6VCSn1OJnLut6/YydsDzTHvxK5Z+sYF9S3dSKy2fqy/vyKT7LmPWB3fSr1erQMcUERGR3/l1Gt3tt9+OYRRerNY0Te6//36mTp3KnXfeyfDhw0lISPD2zcnJ4eWXX+btt98mJCSERx55xJ9RRUREqqS2zevTpmVdtmw/fHKnxwS3E8u648yOX0e764YELqDIWfhi5ipys/OL7MtNzSHKDZdf2j5AqUQk0Mqy2PcfaeqdiH/4tdgEFBnJZJomW7Zs4Z577uGee+4hJiaGuLg4cnJy2L9/Py6Xi6ZNm/Lf//6Xiy++2N9RRUREqhyr1aCJ3c6upCxcYUFYc51Ys50Yv7f/tP57XtqYyQPPXI3F4tcBziJnzO1y89JfZ7F0wQaf7R63x8+JREREpCz8WmxKSkpi/fr1rF+/ng0bNrB+/Xp27NiB2+0G4NixYxw7dgzDMDBNE8MwSEtL45lnnuGLL76gXbt2tGvXjgsuuIDQUF3CWURE5I/27Ejiu8/XYwWsuU6ffb6dt44rx/WiSau6/g0ncoZ2bD5QYqHJarPQ8ZJmPttERECjmEQCya/Fptq1azN48GAGDx7s3Zefn8+mTZu8xacNGzawceNGMjIyME2TlJQUlixZwtKlS733MQyDJk2a0K5dO9q3b68XEREREWDz2t28+NdZZeqbn1tQwWlEzs36Vbt4+ZGSH88dujWlSat6fkwkIiIiZeX3aXR/FBwcTKdOnejUqVOR/bt37y5SgFq/fj179+4FCqffJSYmkpiYyNy5c1VsEhERAd568nOOHkwttV9IqJ16jWL8kEjk7L05eS7JSekltv+8Yidfz1rN6Ft0FToREZHKJuDFppI0btyYxo0bM3LkSO++jIyMIsWn9evXs2XLlsCFFBERqUSyMnK92wURNmw5bjx2C54QG7ZMJ4bHxFLDwZ/vv5SoGmEBTCpSulMfz+fSR0TOX74WENdABRH/qLTFJl8iIyPp3bs3vXv39u47sd6TiIjI+a52vWjvSBBXZBCpF9ck9LALa4GJK/pkv9emLaV7z5bUiYsKTFCRMqhdL5r049ml9KnhpzQiIiJyJqpUsckXq9Ua6AgiIiKVwl9fGsP7ry1i2VcbCTmUS2jdSLLcps++H/znW+56YAjhkQ4/pxQpm/smj+KFSZ+wb9dRAIJDgrBYLeRm51MrLpKRN/bgstGdA5xSRCoDjVYSqXyqfLFJRERECsXWjeah56/hzkevYMHCjbz17lKMEvp+O/tnMg6m8dTUm/2aUaSspr7wpbfQBJCf58QebOPf8+4joXEstiB94SgiIlJZqdgkIiJSzYRFhHD0eJb3tsdqYnEXlp1Mw8SWlovh8rBn55FARRQpwuV0s3TBBr7+dA05WXm0bJ/Ar7/sLdavIN+F2+lWoUlERKSSU7FJRESkGnI4ggBIb2olL9bAlg0Wt0lBhIE130rC/AIcocEBTilS6KW/fsKyLzd6b+/enlRi35BQuz8iiUgV4mshcND0OpFAsgQ6wPkkJyeHF198kS5dulCzZk3Cw8Np3bo1Dz30EPv27Tvn47tcLr755hsmTpxIr169iI2NJSgoiOjoaC666CIeeughdu3aVepxxo0bh2EYZfq3Z8+ec84tIiLlb9ilF9KqRR0KogwwDFzhBgVRFrAYuB1WLPVDGTdhcKBjigCwflXpn08MA0bceAn1G8X4IZGIiIicC41s8pNdu3Zx+eWXs3379iL7t23bxrZt23jnnXeYMWMGQ4cOPavjHzt2jNatW5OSklKsLT09nXXr1rFu3Tr+9a9/8cILL3Dfffed1XlERKRqiKsdyX9fvZE+r07lSHbxK3odahrKxvwMegQgm8gflWVa3IgbL+H2R4b7IY2IVBe+RjxptJOIf6jY5AdZWVkMGzbMW2i69dZbGTNmDA6HgyVLlvDss8+Snp7O6NGjWblyJe3btz/jc+Tn53sLTR06dGDEiBF07dqVuLg40tPT+eqrr/jXv/5FXl4eEyZMwOFwcNttt532mPXq1WPhwoWn7VO/fv0zzioiIv5hGAY3X9KJ575ZhveadKaJ44hJSIrJZ1+t59ah3bBZNdBZAuuK6y9h+qtfg++LJ1IjJoLLru7i31AiIiJy1lRs8oOXXnqJbdu2AfDCCy8wceJEb1v37t3p168fvXv3JicnhwkTJvDdd9+d8TkMw2DQoEH885//pFu3bsXa+/Xrx1VXXUW/fv3Izc1l0qRJXHvttURERJR4zKCgINq2bXvGWUREpPKoe8BNoy9TSW1gx7QaOI5bwVJYXMrPyOLGW9/h+X9eRYP4WgFOKuczS8to0rrHYDuQg8VtklfTTsjRPBxH8omuGcb0byYSHBIU6JgiUg1ofScR/9BXmRXM6XTy+uuvA9C6dWsefPDBYn26d+/O+PHjAViyZAk///zzGZ+nfv36LFq0yGeh6YSuXbty1113AYVT6xYvXnzG5xERkarlk6nLMNKc1NyYTcQel7fQdMKhpDS+XvxrgNKJFHr7i1Xk2y1kNwkns3kEzlrBZLaOwh1qZdDIi1VoEhERqWJUbKpgS5cuJS0tDYCxY8disfj+lY8bN867PWfOnArL069fP+92WRYLFxGRqq2gwHXyhmGU3kfEj5JTMnngbx9zPK34umIAd/9zFDc/dJmfU4mIiMi50jS6CrZ8+XLvdp8+fUrs16lTJ8LCwsjOzmbFihUVlic/P9+7XVLhS0REqo+W7RNITkoHwJLvu6jUplU9f0YS8Zozfx0/r9+LJdaKO7RoMTQyNJiBA9phlFAkFRE5E5omJ+JfKjZVsK1bt3q3W7VqVWI/m81G06ZN2bhxY5H7lLdly5aVKQ9ASkoKvXr1YtOmTeTm5lKzZk3at2/P8OHDufnmmwkNDT3rHAcOHDht++HDh8/62CIictLE568htkFN5ry/Akuuk6DkbNxhdjx2K5YCN707NeGSzk0CHVPOU9nZhV+COZLdOMNNnOEWTAu0rR/Hs/cMJyzEHuCEIuIv+vtApHpRsamC7d+/H4CwsDCio6NP2zchIYGNGzdy7Ngx8vPzCQ4OLtcshw8fZvr06QDExMQUmVLnS1ZWVpFRVklJSSQlJbFo0SKee+45PvnkEy655JKzypKQkHBW9xMRkTMTHBLE1Tf14uMlmzGO52JPzSUos8DbvvqLjcxvWY/Rt5Q8+lakIrhcbmrXjgTAMMGe6cGe6QHg6qvaUq9WZCDjiYifnc3fBxqtJFJ5qdhUwTIzMwEIDw8vtW9YWJh3Oysrq1yLTaZpcscdd3jzPPbYYzgcDp99DcOgW7duDB8+nIsuuoi4uDjy8vLYtGkT06ZNY/Xq1Rw8eJDBgwezfPlyOnbsWG45RUSk/NWqGc6rz47hhUdmcTw1t1j75/N/oU3P5lyg6XTiB8dTs/lg5kq+XryJ3FwnjRrGkJGRy/HUbOrXjWb0qM5cPrh9oGOKiIjIOVCxqYLl5eUBYLeXPgz81OJSbm7xPwbOxTPPPMPnn38OFC4Sfvfdd5fY99VXX/U5Cqt79+7ceuutPProozzzzDNkZ2dzyy23sHbt2jNeT+HEiK+SHD58mC5dupzRMUVEpGQXtktgxIiLmP7KwmJth45lcs/EGcx6/w5q1Sz9yxGRc/H4M/PY9OvJ6TJ79iZjs1r439RbqF+3BhaL1mgSOR/p7wOR6kXFpt+5XC6Cgs79srrTp08vcmW5kJAQAAoKCkq4x0mnLt5d0qijs/HRRx/x2GOPAdCoUSNmzJhx2sXBTzfdzzAMnn76aVavXs3ixYv55Zdf+PHHH+nRo8cZZYqPjz+j/iIicu6GX9edLZsOsGrxrxgmeGwGBbVC8TiCwO1h1ty13HxjT+xB+ngg5aMg38miOT/z9aw15GTn0WNQW/btPgaA4XRjy8jHmuPEtBh8O/cXrr+1Lxa7Hn8i56Oz+ftg8uTJp23XNDuRwNHlyCpYREQEUDgtrjTZ2Scv+1uWaXdlsWDBAm666SZM0yQuLo5vvvmGOnXqnPNxb7/9du/2qYuOi4hI5bVm/V6WJB4iNyGK3LoR5MVH4Qm1w++jU//v09U8+8qXAU4p1ckLEz/mrX/OY9fWQxzed5xPp32Pa/tRcLoJOZRJUEY+FpcHa4Gbj9/8ljee+CzQkUVERKQc6Kuj39lstnK5ClzdunWL3I6Pj+enn34iOzubtLS0044aOjF0NDY2tlzWa1q6dClXX301TqeTGjVqsGjRIpo1a3bOxwVo06aNd/vgwYPlckwREalYG09MXbJaMK2+v29atWoXxw6nEVs32n/BpNra/POeYvtMl4dIiwWXxyxTfxGRs+Vr5JNGO4n4h4pNp2jVqlW5H7NNmzbMnj0bgG3bttGtWzef/VwuF7t27QKgdevW53ze1atXM3z4cPLy8ggPD+err76iffvyW2zTNIt/QBQRkcrNHmQttU9uVh7jL3uZp9+5mXadG/shlVRnQSVMibv3zkG88sisMvcXERGRqkXT6CpYz549vdunm262du1a7zS6M13/6I82btzIZZddRlZWFiEhIcyfP5+uXbue0zH/aMuWLd7tevV09SIRkapg8IALqB0bcXLHH784ME1s6Xk4C1ysWnLuo33l/GSaJpvX7mbq8wto0rIOllNG0ZkGNOzSiF8PJtOwddHR4LYgK1eN6/nHw4mIiEgVpK+PKljfvn2JiooiPT2d999/n0mTJvm8ctt7773n3R41atRZn2/Hjh0MHjyY1NRUgoKCmD17Nn379j3r45Xk7bff9m736dOn3I8vIiLlr1GDGGa8cxtjhrxEWk4etox8TKsFd5gdw+PBllWA4S4sQJ3hRUZFAHAWuHjijvdZtzKx8HZkMK664VizC8AAV2QIW4+ls3XBejBNarWOo2fbBiQ0qMWgURcTXUtXQxSRinXq1DpNqROpOBrZVMHsdjv33nsvAFu3buWll14q1mflypVMmzYNKCzcdO7cuVifPXv2YBgGhmGUWDzat28fAwcO5MiRI1itVmbMmMHQoUPPKO+qVas4fPhwie2mafLoo4/y7bffAnDhhRee80gsERHxn6AgK0Mva489NQ+L28Ra4MaemktQer630GQPttGt37lP6ZbzT+KWQ95CE4ArKgQzyIor2oErylG0imkYpOQVEN2iNqNv6aNCk4iISDWikU1+MHHiRD7++GN27NjBpEmTSExMZMyYMTgcDpYsWcIzzzyDy+XC4XDw2muvndU5UlJSGDhwoHeR8QcffJBWrVqxefPmEu9To0YN6tevX2Tf119/zXPPPcdll13GoEGDaNOmDdHR0eTn57Nx40amTZvG6tWrAQgNDWXq1Kk+R2qJiEjl1bJnM2zfbMS1L61Ym8dqED2qBbWbxfo/mFR5zgJXkdtmGT4iOJ3uCkojInJ6GuUkUnFUbPKDiIgIFixYwNChQ9m5cydTpkxhypQpRfpERkby0Ucf0aFDh7M6x6ZNm9i5c6f39gsvvMALL7xw2vuMHTu2yPS9E/Lz85k3bx7z5s0r8b4NGjRgxowZPkdhiYhI5TZl+vdkGOCwGBh/uCJYQZSNbdsPM2vpev4yUuvnyJlp1DyO8CgHWem5AFjzXLjD7Ke9T/sL4v0RTURERPxIxSY/adasGevWreOtt95i1qxZJCYmUlBQQEJCAkOHDuW+++6jYcOGgY7JTTfdRFxcHCtXrmTjxo0cPXqUlJQUbDYbMTExXHTRRQwfPpzrrruOkJCQQMcVEZGzkJNbABaDvPqRWDPzsWUVeEeghBx3EnI8lW92LaZxgZ2h13QJbFipUiKiQ7lqXE8+/Pe3eJwe7EezKajpxlnTAR6TGi7ILnDhtlmwuTz0vLgJHdsmBDq2iIiIlDPD1DXspRI6cOAACQmFHz73799PfLy+9RQRKS8P/O1jfl6/t8i+kIMZWAqKT2eatvAh6jWo5a9oUsXtTTzCHcNfK7a/IMJGQa1gwvdkF2u79eGhXDmulx/SicgfVaXP3Kdmvf/++4mKijqr42i6nEhxFfFaoAXCRUREzjNP/PUKBvT5wwLgbo/PvukpWX5IJNXB0eRM/jvlO59twR6Dbk3q+2xLSylegBIREZGqTcUmERGR80xUpINbx/Uuss+0+v5IULN2pD8iSTXwxDPzWLluj8+2Vi3qclFb38sF1IyNqMBUIiIiEggqNomIiJyH6sZF8fjDwwmtUbj+XkFMKK7gkx8LrCE2bntkGHH1awQqolQh32/6jfWZyWQ2DiazaTjuoMLHkglc0KUxDz4zmlHjejLixktwhBYuGB4WEcLoW/owdEzXACYXERGRiqAFwkVERM5TA/q0ZouZyn8XrqLW6mxs+Sen0rnzXPzvX98w8IqORESHBjClVHZfrNrC4+8v/P1TpUFOQig58Q5C9+djcxrc/9xo6tctLFre8bfh3HjPIJIOHKdeg1o4woIDml1EREQqhopNIiIi57HrOndgze4DHF6UUqwtJyufhV9t4Kox3TAMIwDppLLLys1nzopNxRsMA2qFcPOQrt5C0wlhESE0bV3PTwlF5HymxcBFAkfFJhERkfPY7h1HOTBnHzaX7/Y331nCgbRsJtw50L/BpNLLys3nmqc+JOl4ps/2MaM68+dRl/g5lYiIiFQGKjaJiIicp0zT5IvZa7AcycS0WjAND4Z5st0VFoRpt/LjqkQVm6SYH1bvJGvNYUKthVPnsJ4c/dagdjQje7YNYDoREZg8efJp2zXySaTiqNgkIiJyHnI53Txy8zQ2r91NEOCMDCY3JhRbthM8Ju4wO6bdCoBh0RQ6KerjKUt5/7WFhP1enAzfm01+rWBc4TZsmU5CyMF5fR7EBjaniIiIBIaKTSIiIueh37YfZvPa3d7brshgsFlxRVmL9b1swAX+jCaVnGmazHlvOeYpo+AME0KS8yE5H4B0Clj+1UYat6gToJQiIiISSCo2iYiInIfcLk/RHSUtAF4vmPjWtSs+kFQJh/cf56l7/0dGak6pfd1uT6l9RET8TVPnRPzDEugAIiIi4n8Nm8dRIybCe9uS6yzWxzQ95Kfn8O+Pl/kzmlRi8/5vJVuPpOCxnX5qpcVi0L5rEz+lEhERkcpGI5tERETOQ6Fhwfx73r189fFqVnyzGTPExt6CArJyCsDtISglB1u2kzAg79cMnjdn8pfHRhAe6Qh0dAkA0zT54JufeX9PIvkXRoPHJHR/DmH7srG4oUmrutRrUIsDu4/RvG08I//cgyat6gY6tohIMacuGq5RTiIVR8UmERGR81R0zXCuvbM/197Zn/F3v0fWb1kA2DLzCco+ZaSTCUu/2ECDJrW59s7+AUorgfTb4RRe/2z5yR0Wg5yGYRTUsFPrl1SuurkX/Yd3DFxAERERqVQ0jU5ERERIz8j1bhtu02efzz9ayepl2/wVSSqJw0lpvDblW59tpt1C38svpPdl7fycSkRERCozjWwSERERYmMiOJacCYBp8/1dVFpKFk/c8T4ffPcwsXWj/ZhOAumJZz9n6+4kqB9UrM2S52bpgg206diQ4dd3D0A6EZGzd+qUulNpep3IudPIJhEREeGxicPoeFFDTMAZGUxu7RDvItAem4HLYeXEeKfkpPSA5RT/MU2T75ZtJfG3o1hcEHLUhSXXXdjoMQk+kkfU1gwAjh5OC1xQERERqXQ0sklERESoVzeaCXcP4qrH38M0AZsd3CbWfDfuYCtYDSK3ZuA4kkdEdGig44ofLFi4kRffWOi9HZRrYss1saVnEZRZgMV5crplRJQeEyJS+WiEkkjgaGSTiIiIANAwrgb3XN0Le8jv30VZDdyhNrD+PsIp2s51dw+gfqOYAKYUf0hPzebLz9cV228AFnsQFldhocliMegztD1D/9TFzwlFRESkMtPIJhEREQHAMAzGDe5MenYe7y9aW6y9wBHEvFU7uOrm3oQ67AFIKP6QnZnHnVe8zhG3C6JDirVfe1Nvhva7gL07k2jSqh6160X7P6SIiIhUaio2iYiISBFX92rPD7/uIfFgcuEO0yTkSD6hB/NIM7JYuXw7Awbr6mPV1e7th0lNzsRms+AODcK0W71tTRrFMOyyC4mrHUndhJoBTCkiUrpTFwDXlDoR/9I0OhERESmifkwUH//9BuoVBGNPcRHz03GitmUQlFlAUEY+L903g//7z3eBjikVxGot/HhocXkIOZhBcFImQcdzCD6cSebP+8lMzgpwQhEREansNLJJREREitj/21F+XLyFjnViWbEnDWueu1ifhbPXcu2d/QOQTipa0zb1aNk+nu0bD2AA1lwX1lwXANl5LlYs2kSzNvUCG1JE5BQatSRS+ajYJCIiIgC43R5efmQWS+avB8AVbodw32szeTwePyYTf7LYrER3iMe1Mwnb70WmU3ncpo97iYiIiJykYpOIiIgAsGdHkrfQBOCMDsG0WvBYLVjcRYtLXfq08nM6qUhZufms3r6fqLAQgt0G36zegTUmBOv+LIxT+lltFi7u2TxgOUVERKRqULFJREREACjIcxbdYRiYFsgcbsfmysP+K5gOcPQKo+v1WiC8OjBNkykLVvHh4p/JyS/8/7daDNx1bICN3NpBROzOwZbtwhEdylvTbyW+cWxgQ4uI/MGpC4GXB03LEzl3KjaJiIgIAPUbxeAItZObUwCAJd+FO9aKq08+LgzyBhT2yySHl7fNZ2bshMCFlXKx82Ayby9YVWSf23Nympw7Ioi09lGEHXTSrVtLFZpERESkTHQ1OhEREQEgskYYb3x6N5eN7kxElIMGIXbata7ts++eI8lMmb6MAmfxNX2k6sjMzS9Tv6tGXczfHhhawWlERESkutDIJhEREfGKbxzLff+8kvv+eSUAR/LSuWLpC5gUXRTaTDP435yfiIx0MOaqLoGIKufINE3iosMxDDBPs+a3xTC48apuhIQE+S+ciIiIVGka2SQiIiIliguJ4q+xwwk9VPj9lJljwJJwnGtqkNXAxidrN7N135EAp5QzYZomC2ev5a4Rr3Nr7+dpnAzBBb83ekwseW74fSpdvRAH/773SmpFhgUusIiIiFQ5GtkkIiIiJUpNzuTdm77A4XQTHA7pzSIpiA72fl21Jy2dm178mC+fvoWakaGBDStlsmDmT7z1z3kAeKwGxzILsGcZ2CxgmL//y8sjODmHcfddRpdWDQKcWESk/GjxbxH/0MgmERERKVHS/uO4nG4ALFngchT/nqrA5eazOau9/aRy27frqHfbDLKCYQBg8RQWmgCw2RhyxcUMHdM1AAlFRESkqlOxSUREREoUEhZc5Lbh9r24z8w3F/PaY7P9EUnOkSPU7t02PL7/Pxs0jmHCU1cR+of/fxEREZGyULFJREREStSwWW2G/qkrFA5+Ify3LAyn52QHj4njQA7WbDcbfvotMCGlTLau38erf5/Nrm2HqV0vGgCjwI0tI7/ICuFhoXZuv7lvYEKKiIhItaA1m0RERKRELqebLb/s4cTF6CxuC2GH3LhCPZgWA1uuB2tuYSXKFmQNXFA5rWkvfcWn074vtt8A7Ck52NJycYcGYXhMPNlO9qzdQ4+uzfwfVERERKoFFZtERESkRLu3J7Fn58mrzbnD7RhAUI7JiQqUabeBw8bIGy8JTEgp1ZL560/bbnGbWDILvLe/+3w91/9lYAWnEhHxv8mTJ3u3tVi4SMXRNDoREREpkWn+cU0fw2c/Z50IrLHhFR9Izkrx/8dS+nNm/UVEREROpZFNIiIiUqJGLepQr0EtDu1LAcCaXYArKqRIH3cQOO0G7366kmGXXRiImFKKHoPbMv+jlWXvP6htBaYREfE/jWIS8S+NbBIREZEShTjsvPXZvVzYrSkAQcdzsR/OxJpVAAVOcmtATr0g8mJs7AnK5a43ZpOcnh3g1PJHd/59OOMfHY4rLAh3iBWPtegItboNatK2U2N6D2nP8+/fys0PXhagpCIiIlIdqNgkIiIipxUSaufyP3UFCifR2fJcBB/LxhNk4ooMKtJ31dZ9zF6+MQAp5XQMw2DgsA4ENaqJJzgIi7voNLnD+45zcc/mPPLKtbTv0gTD8D1dUkRERKQsNI1OREREStXrsnY8EzWeJ+54H2eBCwDT6rsgkZVX4HO/BFZ0VCg3/Kk7H7660Gd7dmaenxOJiPjPqQuD+6JpdiLlSyObREREpEw6dm9G6w4NvLetuW6f/RrERvspkZyJfQdS+O/0ZeSX8OmvXoNa/g0kIiIi1ZZGNomIiEiZ/fXlMfzr+fn88OVmQg7lktnEjjvUjrXAwGM3GXBJC0b1bBfomPIHyenZvDHvB7ISbGCEE7bbQujhXCxOD5YQG3c9PIxLr+4U6JgiIhVOI5hE/EPFJhERESmzGjER3DRpKHMzksiPdZPd1ATTheEG0wqzc7bQcWd9bmjVMdBR5RSTpn7B+l2HwFI49TG7SRjZjRyE7XNxQcu6XD6ma4ATioiISHWiYpOIiIickYTYaP4x7lImr/qWbPLAAPOUTxQfb9xE/7pNqRcVGbiQ57GCfCdff7qGr2etITszj56XtmPv0dTiHS0WWrWvz8N36cpzIiIiUr60ZpOIiIicsSu6X8B1fXyPXtpy+Cgjp/yPjDwtOB0Izz/0Mf95aj67tydx9FAac6YvJysl22ffRx+4nMYNY/ycUERERKo7FZtERETkrNzYqiOdatbz3jZcELEVYn4yYWM2C3/YimmaAUx4ftq6fi8AHitkNAvhcN8onFYXEQ67t09IkI2bL+tCg9rRAUopIiIi1Zmm0flRTk4Ob731FrNmzSIxMZGCggISEhK4/PLLuffee2nQoEHpBzmNpUuX0q9fvzL1feKJJ/jHP/5x2j4ul4tp06bx0UcfsXXrVrKysqhfvz4DBw7k3nvvpU2bNueUV0REqrakzUfJf2wbjWtAXqwVe1YIWKxAYYHpzZe/4djuNO66pWzvTVI+goJseGwGB4bUwBVx8qNetlnAkI7NGNSiGT0uaER0uCOAKUVEAmPy5Mml9tEi4iLnTiOb/GTXrl1cdNFFTJo0iTVr1pCamkp2djbbtm3j5Zdfpn379nz55ZeBjumVkpJCz549ueOOO1i+fDnJycnk5eWxa9cu3n77bS666CLefffdQMcUEZEA+mnJVlxONyFH3UQkun8vNBW1dPn2ACQ7v109vjfuGHuRQhMABmxOP8blXVur0CQiIiIVSiOb/CArK4thw4axfXvhB+5bb72VMWPG4HA4WLJkCc8++yzp6emMHj2alStX0r59+3M+57vvvkvnzp1LbK9du3aJbW63myuvvJKffvoJgCuvvJJbb72VmjVr8tNPP/HUU09x9OhRbrvtNurXr8+ll156znlFRKQKMozy6CLlLL1OMMfbRPhs03+HiIiI+IOKTX7w0ksvsW3bNgBeeOEFJk6c6G3r3r07/fr1o3fv3uTk5DBhwgS+++67cz5n48aNadu27Vnd98MPP+T7778H4K677uKtt97ytnXp0oUhQ4Zw8cUXk5GRwT333MOWLVuw2fRQEhE533Qf0IYF/7eK/DwnlgI3RoEb0150dNPAfppy7W8zvv0F07RgOMEMKto2vG2rwIQSEQkwTY0T8S9No6tgTqeT119/HYDWrVvz4IMPFuvTvXt3xo8fD8CSJUv4+eef/Zrxj1588UUAatSo4d0+VbNmzXjkkUcA2LlzJ/PmzfNrPhERqRzi6tcgoenvI2VNcIZ4yK1pwekwcIYZGA0dtOhQP7Ahz0NOtxvDBMcxsKeCNRds2fDC0MHc2/eSQMcTERGR84CKTRVs6dKlpKWlATB27FgsFt+/8nHjxnm358yZ44dkvu3cuZMtW7YA8Kc//YnQ0FCf/SpLXhERCZyvPllN4q8HAXCFWclNCMUVYSWvto28GBsZuHh1zvcBTnn+cBa42LRmN63rxgCFU+aCciEkFeqZoQy6oHlgA4qI+NkTTzzh/Sci/qW5TxVs+fLl3u0+ffqU2K9Tp06EhYWRnZ3NihUr/BHNp7LmrVOnDi1atGDHjh0BzSsiIoGTm53v3TatvlcD2nc0jckfLuLBq/sQ7gj2V7TzimmazJ+xkpn/XUpqciamAa261MfdKAKXAQMvas41fS4kNMQe6KgiIiJyntDIpgq2detW73arViWvk2Cz2WjatGmx+5ytv/3tb8THx2O326lRowYdO3bk/vvvZ8eOHeWS99T2/fv3k52dfc6ZRUSkamnQ9OTFJqy5bvCYPvvN+/FXZn2/wV+xzjv7fzvGf56aT2pypndf8rrDxG3JZPYTY7lz+CXUigwLYEIRERE532hkUwXbv38/AGFhYURHR5+2b0JCAhs3buTYsWPk5+cTHHz23wCvXLnSu52Wlsb69etZv349b7zxBo899hhPPPEEho9LBJ3ICxAfH19qXij8RvXAgQO0bNmyzPkOHDhw2vbDhw+X+VgiIhIYl43ujC3Iypzpy9mz8whNjhkcjIZ8uwkeMEwwrYBh8PHSDVzQsA5dWjUIdOxqJyO18Asf04Cc+FBy4h14gq385Czgf4t/5tr+HbGWMI1fRKSy0N8HItWLik0VLDOz8FvG8PDwUvuGhZ381jErK+usik1169blyiuvpGfPnjRp0gSbzca+ffuYP38+H374IU6nk8mTJ1NQUMAzzzxTYt6yZP5j3jNxolAlIiJVl2EYDBp1MQNHXsSRI+mMGT8FexIEATlxVjwhJwscR9OyuOuNOXz5zC3Uji79PVHKrmZsBAA59R1kNT35u3UGGbwy+3uCg2yM7nNhoOKJiJSJ/j4QqV5UbKpgeXl5ANjtpa+TcGpxKTc394zP1blzZ/bu3UtQUNHrHF900UWMHDmS22+/ncGDB5Oens5zzz3HNddcQ4cOHXzmLUvmc80rIiLVg2EYJB/Pxvx9Fp0BmLbio2c9pknS8UwVm8pZvYYx3PTEMN6c43sNxYMp6X5OJCJSOUyePLncjqVFxkXOjIpNv3O5XMWKNGdj+vTpRa7UFhISAkBBQUGp983PP7nQqsPhOONznzrSyJcuXbrw1ltvccMNN2CaJm+99RZTp04t0udEXijMfOrt8sx76nQ9Xw4fPkyXLl3O6JgiIhI44eFFR+MaHvC1glNEqBYJL28p2Tk8u2cdzoZB2H0MNI5wlPxeLiJSWejvA5HqRcWmChYRUTi0vSzTzE5dZLss0+7Oxp/+9Cf+8pe/kJ6ezrJly4q1n8gLhZlPV2w6l7ylrQclIiJVS8OEWtx0Qw8+/HglLqeH4FQ3eTWtmEGFI5yshsG4SzvTKK5GgJNWLx6Ph68W/EzEN8fAA1nNIsBm9bb3u7ApV/VqF8CEIiJl46+/DzRCScQ/VGz6nc1mK5erwNWtW7fI7fj4eH766Seys7NJS0s77SLhJ6r5sbGx57Q4+OnYbDZatGjBmjVrOHjwYLH2U1/kDxw4QExMTInHOpHXMAwVj0REznOGYTDuuh4ctzmZueBnbFkewg65cIcYmAbY8ky+n7uZmy/tjCOk9KnlUjb/+sdcvp61hhNjm8OSUsirFcTRHtHUrxvNy3dcEdB8IiIicn5SsekUrVq1KvdjtmnThtmzZwOwbds2unXr5rOfy+Vi165dALRu3brcc5zKNH1fmhoK856wbdu2Yms6nWrbtm1A4WJ+pU3hExGR88PVfS9k+a97SP7lKAaFRaYTko6ks3LdHvp3bxG4gNVIdlYe33+5sdj+kBQnDrfJ3QO6ByCViIiIiIpNFa5nz57e7WXLlpVYbFq7dq13WlqPHj0qLI/L5WLHjh0A1KtXr1j7H/OOGTPG53GSkpK8x6nIvCIiUrU0qF2DT5/4M1fe8G/S0nKKtT/8znzuzuzD2MGdApCu+ti+aT+P3jqdnOx8n+21FqbgapsMmkEnIucxTZkTCRxL6V3kXPTt25eoqCgA3n///RJHFb333nve7VGjRlVYnpkzZ5KRkQFAnz59irW3aNHCO7Lqk08+ISen+B8K4L+8IiJS9VgtFkYN61hsv9tu4A4y+PT7DQFIVb0s+L9VZKWXfCVYi8vky5k/+TGRiIiIyEka2VTB7HY79957L08++SRbt27lpZdeYuLEiUX6rFy5kmnTpgGFBaDOnTsXO86ePXto3Lixt8/SpUuLtKemprJhwwb69u1bYpbVq1dzzz33AIVra9xxxx0++z300EOMHz+e48ePM2nSJN58880i7bt27eLZZ58FoGnTpio2iYhIMbHtY8i6KATjYAEWt0leTQvuYAN7hoHbU/J0bjk9t9vD64/N4ZvPfilTXxGR89nkyZNP266RTyIVR8UmP5g4cSIff/wxO3bsYNKkSSQmJjJmzBgcDgdLlizhmWeeweVy4XA4eO21187qHOnp6fTr14/27dszcuRILr74YurWrYvVamXfvn3Mnz+fDz/8EKfTCRQWlDp18j2FYezYsbz77rv88MMPvPXWWyQlJXHrrbdSo0YNVq9ezZNPPklGRgYWi4V//etf2Gx6GImISFFvLFtJVrAbmliL7A/Kge5tGgYoVdX327ZDfPPZz2Xqe1EPrY0lIiIigaEqgR9ERESwYMEChg4dys6dO5kyZQpTpkwp0icyMpKPPvrotAtyl8XGjRvZuLH4YqEnWK1WHnvsMR5//PHT9pk7dy5Dhw5lzZo1zJ4927vI+Ql2u50333yTIUOGnFNeERGpnnJ//3Ljj+pYQxjRsfwvyHE+WJd4kCf/+0WZ+t704GVcdVOvCk4kIiIi4puKTX7SrFkz1q1bx1tvvcWsWbNITEykoKCAhIQEhg4dyn333UfDhmf/TW+9evWYNWsWK1euZPXq1Rw8eJDk5GTy8vKIioqiZcuW9O3bl1tuuYVGjRqVeryYmBh+/PFHpk6dyowZM9i6dSvZ2dnUq1ePAQMGcN9993HBBRecdV4REaneWsbFcCQzq+hOl0nm3kxe/fdipr91U2CCVWFPfbSYfdlZxFjAOM0MuWBHEENGd8Fq1dKcIiIiEhiGWdKK1SIBdODAARISEgDYv38/8fHxAU4kIiJnIis/nxe+WMas9ZsxLRCSDBYXYDGwmHDrsG7cPKQLQVZrqceSQoMefpuUjBwcB3KI2JWF4eMTXMNmcfztteto0LS2/wOKSJVTlT5zn5r1/vvv916E6VxozSaRQhXxWqCRTSIiIlLuwoOD+Uuvbiybtp6CCAv5Na3ea+B6DHh7wSrCHcFcP+CiwAatQuJqRJB2JJPIxKwS+wz5UxcVmkREfqdikkjgaHy1iIiIVIi42pE8+8SVRMWE+myfvWIjiQeT/Zyq6nr8uoEkhPj+XVosBn++dxDDxnT1cyoRERGR4lRsEhERkQpzSddmXDnU9+ilPUmp/Pn5/yM1K9fPqaqmKVOWcjwx3Wdb45Z1ufbO/lhtmpYoIiIigadpdCIiIlKh/tS3Axt2HmTltn1ggOHyEJRpYsvzYFpczPt6PTeO6qoFrU8j8deDbF2yneDMPNxBFixOD8bvbTVrR3Lno8MDmk9EpDKaPHlysX2aWifiH/pUJyIiIhVqy68H+fWbXYQddOI44iTsoJvgDA/WArDlmUx/dzkvv7ko0DErrVXfbeHe0W/hOZ6DxenBekqhCeD40Qy++3xdwPKJiIiI/JGKTSIiIlKhfl6/F9MEixtseRQplJywbMV2MjI1ne6PXG4PX8xZS2kXD17+9SYy03L8lEpEpOqaPHmy95+IVBwVm0RERKRClWV6XFZ2Pn8a9zZbtx/2Q6KqIT/fye33fcCqNb+V2jczPZdxg15g568H/ZBMRERE5PRUbBIREZEKNaBPKyIjQkpsd1tNsutYOFrTxZyl6/0XrJLb+dtREn87ijvcjmnxNR6sqJysfH5YtNkPyUREREROTwuEi4iISIVq0awOH0+/nWuveJWMggLyhxXgibJg3RVErsNKdlAImIXFlA+ObuP4HJMXRw3BYpReYKnOPJ7CqXOeYBu5CVHYMvOxFLjx2K1Ycp3Ycl0l3kdEREp3JlPptLC4yJnRyCYRERGpcKGhwQzq1QqbPRdXew+ehi6c/XPJDbN7C00nfPHrdrYlHQtQ0sphd9Jx9qSlUatWeOEOi4ErKoSC2DBcUSG4IkP4Y1nJFmSlU68Wfs8qIiIi8kca2SQiIiJ+ccmgtny3bRPpnFwI3PT47vvIk3N44vYhXHRhQz+lqxxSMrKZ/OE3rNi8GxNw1Taxh1uxH/fgsZqEx4bSPKQGdtPgwhZ1ydx7nH2JR2netj5XXH8J9RvFBPpHEBGplkobBaWRTyJFqdgkIiIifvH2M/PJPpwDeUBI4Wgma5gLV5q1SD9Lrpu0xOO8/tZi3p8yPgBJA+fjpetZsXk3AKYN3HYP+YZJVl0rGAZpOImIM5l72/UBTioiIiJSMhWbRERExC+yM/Ow5ED0y5A62obZ2E1QrVzcGTZMtwXDAzXWZRG5Kw+L2+TooSymvfgVN947EHtwUKDjV7iv12zj46UbALBmu4jalUXI8QIACiKtpLYPI7tBCBn5+YGMKSIiIlIqFZtERETEL+Lia5ByNANrMgR9ayOlU42TjQZEbs8mesfJKXa4TT5993uiY8K56qZe/g/sR/uOpvG3d7/y3o7amkFQ1skFwO0ZbuJWZLB3ZBDxjaICEVFERHzQ9DkR37RAuIiIiPjFI69cy6VXdcIebCNyZy6N93jAPLnMtS3X9wJOx49m+CtiQBzPyeWNb38oss+S7/t3cVmDxrw08jJ/xBIRERE5ayo2iYiIiF/ExEUx4amrmPnDo9w2cSjGj8nY006O3nEH+/5YElUzzF8RA2LC7AV8sXVHkX0eu+Gz78NX9Kd2RLg/YomIiIicNRWbRERExK8cYcEcS0oHIHZVJkHpTgDSWzrIbGXH/H29cNMKnYe3Zth13QMVtcL9lrUd4pbRY9AGajU9huEuHNGU0TISZ9jJ1Q5CwoO5d/Io4urXKOlQIiIiIpWG1mwSERERv3OE2gEITnURuzqDPTdEYg9xUnCJk9RMsB4Bdxx8HbGNgdm/0TusdYATl7/1qT8xfc9r1IgFd5YF+8cuYrOTcUYGYXF6sOW4cTlsuKIdXHNTb4Zc0yXQkUVERETKRMUmERER8bsh13Rh/apdbF2/j+BjbqI35uHsWziqx4wwcEWc7Pvxb2voWqs5wdbq87El3+liceIP3k9i+QeC8GRZMQD77yO9ACxODy3bxzNsyIWBCSoiIqc1efLk07ZrAXE5X1WfT20iIiJSZcTWjeaV/7uTrev3sWvrIZq1qcdc6zrmHPipWN+lB3Yz/PAHzL/iz9Wi4JRb4OT6Z2dgNNxP/O81JMNm+uxruDwkLUskeexxYmMifPYRERERqWyq/ic2ERERqbJad2hA6w4NALg6086yY1tIyc/0trtSgjB3h7LPcpw1HffTs1HjQEU9J1u3H2b5jzuIiAghMj6CHVnHCT8UQ2zzFIJDnYQ0KiC0bS45mx1F7mcA+XlOVi3ZSuuODQMTXkTkPKARSCLlS8UmERERqRSaRdRhbu+H6D3vNbLd2VjXh2E9biPk9/YHXvqch//Uj6t6tQ9ozjPhcrn5+5OfsWrNbwDkxFnIaGSBSINUdyjLll5I/dgUoh05BA2sySXxjfjx663FjmP6HvgkIiIiUinpanQiIiJSaQRbgxgWfzG5R0KxHi/6nZjL7WHawuLT7CqzbTuTvIUmgKx6FrAY3tsej4X9R2JJXN2QS2r3Y/ifemALshY5RpDdRpe+rfyWWURERORcaWSTiIiIVCqDc+qyfKVJcmTxtqScdO5a/Q7/bH8NMSE+OlQiK1fv4rlXviy6s4Sv+f5+/QCu7toOwzB456sHmT9jJb9tPUzztvUZdm03YutGV3heEZHzWWkLfZdG0/BEilKxSURERCqVaS9+hbEvHaNrLcygotUZT20nK5P2MrvGam5vPjBACcvmtemLOWLJxU7h2ksAQZkm+bWMIv2iHSEM6dgKwyjcH1e/BrdMHOrfsCIiIiLlSMUmERERqVRysvKxuExqrT1OdqNQ8qODcYd4yG7upqCmFVIieHPNJmoSx+jm7QIdt5hsZwHPrFnK6ovTMa0QuQWitoLFA9E73WRnmuTUtWBaDdrHxfHGDcMJD7YHOraIiJyDU0dGaZSTiNZsEhERkUqmfqMYAKz5HiK3ZxH7Uwo5jVyFhabfZeY7mbjiK/ZnppX7+QvyneRk5wPgdnvIzMrD4ylcoTsrv4B8lwuAfJeLrPyCwn4eD+n5eXhMk+lbfuaj7esxrWDLhBq/FhaaAAwTwg97qLXZTehRGNO+HXUiI8r9ZxAREREJJI1sEhERkUrlkVeu5d9PzmP5ws3efe5Q39+Pvfz59zw+fCA1I0PP+bzHj2Xy0Vvf8u3nv5CX66TuhfGkWyElNZvIRhF4moeQmJWGw2YjvkYU+1PTyXU5adQwmjR7Hkdzs6hriSDX6YLf62LWfN/nsrhNrmjWmBHd25xzbhEREZHKRsUmERERqVSia4Uz7oHLihSbrDkeXBHFC05LVieSc7SAKfdffc7nffb+GWz+eQ8ArshgfsvIBsBthx11ciErD4Bcl4udx1IA8IR52OFOhlzABSlJubgjPBBVeEx3sO9zWbJd/PTOTyyMr8vQP3U95+wiIlJ5aLFxERWbREREpBKq16AWt0wawox/f0dOVj71vswmeUgUmXUK20M8TsIcBVj6udmQlc0dS2ez4chRIoPttK0bwYGCQ2S78+kWU4MI+xGS8w9RO6QuDmsY+3N+I8TiIC6kPkn5B8l35xKa3ZFlddLJGlWLRp5Ubmv3C7077icl3cGrCy8h61g98EBU43Tqt00iIiqH5ENR7NlUD/eeYDxW8ASb2LINzKMWchqCK8qDKwLSWnmI2gqGUVgss+Q4sR/PAeDQvpRA/YpFREREKoyKTSIiIlLpZKbl8H//WUJOVuE8NMNwUHM5RISBu3kBZu/CYo3HY3DUZefr3bsAyLZnk5rqBKBWcBbHnOs5VniTjKy0IudIyj8IwOGDNdmwLhOigogOzmPGn+YRGVy4FtOLi3vyy5H6WIDYpim0GlB4HmeejX3fN8AssGEBLE4g78RV5gwidlnxGAYhx51Eb0oHD5h2K3hMLC6PN0NYeEj5/uJERKTKO9eRUSKnCtRIORWbREREpNLZv/sY2Zl53tue4MKPLEHZYEa4cP++3+WyYJqGt1+Q3eXdjggqYcGkP0hLDfduN4lO9RaaADbtjzt5vNpZ3u2ctBDcBaf/GGUxLdgzPRgnFgcvcBdpv7hncy4b3blMGUVERESqEhWbREREpNKx2//wEcU0vZuG+2RxyTD4Qz8DKOzr4Y+NvlmsJ0ca5butRdqCbScLRB635ZT7mJSF6fK9P65+DZ6aenOZjiEiIiJS1ajYJCIiIpVO45Z16D6gDSu/3QJAUHoeBbVCwTCwbrXjblGAGWlis7mxBxdQkG8HIDsrmIjIXAwLHMsNp1ZINiFW3xUf0ywsVtWPT+bwwVrk5QWzLSWGxXsaMbDRHgD+3GMdL3/dgwK3jaM7YohtmkJIRAHhtbKp1TCVlL01ih8QwG0Stj+X4OMFWEKD8OQ4vd3swTauvbN/+f/SRERE/ESLmEtpVGwSERGRSsdqs/L4mzeS+OtBflz8K+FRDjr1a83Kn3/jwK5jrHz9Z443yMVVF2ouzyY/LI/M5kGE7XESkmpS0BlMRxC/baqPo0Mezv4mabmh2DbbqReeTt4vdlw7IKpXFi7DRvQWk9xaLlxhBo+9OYiPw47Sq9NesrKDqXksj1QcuNLsJGy6ht92fo8zOp3o3WmY2zzk1wzBcHmwZbrIbRiJJc9F+K4MLO4TI6wgIjqUnoPbktA4lv4jOhJVIyywv2ARERGRCqRik4iIiFRazS6oT7ML6ntvN2gYw7QXvyL7eC7BxyF4feF+R5Ybx5GTU94cS09sGTgPOPitVgT5cTaCHcCSUEL3ZGBxeUj9MoqCGg6ItuJIMSGlsECUeDyWxH0x3pFKdjw4QoIYNaAjq/OtfDJ1GQBBuAnKyAYKZ/B5UpzYMvO9haYTMtNyiKwRyqhxPcv7VyQiImdJo3NEKo6KTSIiIlKlOJ0lLIR0GvGr3TQY0xhbuIWc5ftIcZ9cp6mkpZ06tU3ggvYJ7Np9jBbN6nDF0AupER3GBa3qccnAC/jqk9VkZebRoVtTjh/NYM/OIzRuVYeN6/ax+cfEYsdzOd0+ziIiIiJS/ajYJCIiIlXKhV2bMu/DH8/oPvatOdg/SKL7wDZ8vHN9kfqSJc8FUUX7GwZcPbor3bs09Xm8lu0TaNk+wWfbD9/8WqzYZBgG7bs0OaPMIiIiIlWVik0iIiJSpbTv0oROvVrw84odp16krlR5OQXk5RQU22/LcWIcysAVGYLHbiXUauHlf/2Z1i3qnlW+HoMu4JX/u5O5769g17bDNL+gPiPH9qBlO9/FKRERKT+aGidSOajYJCIiIlXK5//7kbXLd5zx/Ro0q02DZnE+26z5bqzHCtde6tC9KXVqhgPgLHCRnppNdM1wbEHWIvfJzsqjIM9JjZiIYsdr3aEBrTtcd8YZRURERKoDFZtERESkSklPzS61j9VqoWnreiRuOUh4VChDr+nCVTf3IiwiBLvdxpz3lrN7exL1G8UQFh5C4paDhEaEEFM7kk1r93Bdr2eoXS+a3OwCMtNzqBUXyYgbLuHKm3qRciSDD15fxLKvNuJyumlzUUNuuHsgHbs388NPLyIipzN58mSf+zXiScS/VGwSERGRKiUmLqrUPs3b1ufVmXdh/j7PzjBOrtI0cORFDBjR0XvbMAxM0+SBa//Dtg37vfuPHkrzbqccyeDdl78m2BHEkvnri/Tb8steHr3lXT5c8ldq1o48lx9NREREpFqwBDqAiIiIyJm44sZLuObWPoRHhgAQEmonskYoABaLQY9BFzDx+WuAwkLSqYWmE07sP9FmGEaR4lJJjh5K48jB4v08HpNjSeln+ROJiIiIVC8a2SQiIiJVit1u46YHLuPaO/pz5FAqcfVrEBwSxME9yYSGh1AztvgaSmURFung+LHM0/YJj3QQHuUgNbl4v/BIx1mdV0REzswDDzxAfHx8oGOIyGloZJOIiIhUSSGhdho2iyPEYccwDOIbx551oQngL49dQXzjWO9tq+3kxyTDKBwxNfSaLsX6hUWEcNMDl1KvYa2zPreIiIhIdaKRTSIiIiLAhV2b8vYXE9i8dg/ZmXl06NaU48cy2b0jiSat6lKvQa0S+znCggOcXkRERKTyULFJRERE5HcWi4X2XZp4b9cPC6Z+o5hS+4mIiIjISZpG50c5OTm8+OKLdOnShZo1axIeHk7r1q156KGH2Ldv3zkf/9TFTsv6z5dx48aV+f579uw559wiIiIiIiIiUn1oZJOf7Nq1i8svv5zt27cX2b9t2za2bdvGO++8w4wZMxg6dKjfMrVo0cJv5xIRERERERGR84OKTX6QlZXFsGHDvIWmW2+9lTFjxuBwOFiyZAnPPvss6enpjB49mpUrV9K+ffuzOs+mTZtK7fP+++/z0ksvATB27NjT9q1Xrx4LFy48bZ/69euXPaCIiIiIiIiIVHsqNvnBSy+9xLZt2wB44YUXmDhxorete/fu9OvXj969e5OTk8OECRP47rvvzuo8bdu2LbXP999/DxROubvhhhtO2zcoKKhMxxQREREREREROUFrNlUwp9PJ66+/DkDr1q158MEHi/Xp3r0748ePB2DJkiX8/PPPFZJl+/btrF69GoC+ffvSoEGDCjmPiIiIiIiIiJy/VGyqYEuXLiUtLQ0onLZmsfj+lY8bN867PWfOnArJ8sEHH3i3S5tCJyIiIiIiIiJyNlRsqmDLly/3bvfp06fEfp06dSIsLAyAFStWlHsO0zT56KOPAAgLC+Oqq64q93OIiIiIiIiIiGjNpgq2detW73arVq1K7Gez2WjatCkbN24scp/ysnTpUvbu3QvAlVdeSXh4eKn3SUlJoVevXmzatInc3Fxq1qxJ+/btGT58ODfffDOhoaFnnefAgQOnbT98+PBZH1tERERERKoW/X0gUr2o2FTB9u/fDxSOJoqOjj5t34SEBDZu3MixY8fIz88nODi43HKcOoXuz3/+c5nuk5WVVWSUVVJSEklJSSxatIjnnnuOTz75hEsuueSs8iQkJJzV/UREREREpPrR3wci1YuKTRUsMzMToEwjiU5Mo4PCQk95FZtycnKYPXs2APHx8fTv3/+0/Q3DoFu3bgwfPpyLLrqIuLg48vLy2LRpE9OmTWP16tUcPHiQwYMHs3z5cjp27FguOUVERERERESk6lOxqYLl5eUBYLfbS+17anEpNze33DLMnTvXW/S64YYbSlyk/IRXX33V5yis7t27c+utt/Loo4/yzDPPkJ2dzS233MLatWsxDOOMMp0Y8VWSw4cP06VLlzM6poiIiIiIVE36+0CkelGx6Xcul4ugoKBzPs706dOLXFkuJCQEgIKCglLvm5+f7912OBznnOWEM51Cd7rpfoZh8PTTT7N69WoWL17ML7/8wo8//kiPHj3OKFN8fPwZ9RcRERERkepLfx+IVC+6Gl0Fi4iIAAqnxZUmOzvbu12WaXdlcfjwYRYvXgxA586dad26dbkc9/bbb/duL1u2rFyOKSIiIiIiIiJVn0Y2/c5ms5XLVeDq1q1b5HZ8fDw//fQT2dnZpKWlnXbU0Imho7GxseW2XtNHH32E2+0Gyr4weFm0adPGu33w4MFyO66IiIiIiIiIVG0qNp2iVatW5X7MNm3aeBfn3rZtG926dfPZz+VysWvXLoByG30E8OGHHwIQFBTEtddeW27HNU2z3I4lIiIiIiIiItWHptFVsJ49e3q3TzfdbO3atd5pdGe6/lFJ1q9fz8aNGwG4/PLLqVWrVrkcF2DLli3e7Xr16pXbcUVERERERESkalOxqYL17duXqKgoAN5///0SRwS999573u1Ro0aVy7lPXRh87Nix5XLME95++23vdp8+fcr12CIiIiIiIiJSdanYVMHsdjv33nsvAFu3buWll14q1mflypVMmzYNKCzcdO7cuVifPXv2YBgGhmHQt2/fUs/rdrv5v//7PwBq1arF5ZdfXqa8q1at4vDhwyW2m6bJo48+yrfffgvAhRdeWG4jsURERERERESk6tOaTX4wceJEPv74Y3bs2MGkSZNITExkzJgxOBwOlixZwjPPPIPL5cLhcPDaa6+VyzkXLlxIUlISANdeey1BQUFlut/XX3/Nc889x2WXXcagQYNo06YN0dHR5Ofns3HjRqZNm8bq1asBCA0NZerUqRiGUS6ZT+Vyubzbpyt+iYiIiIjI2Tn1c/apn78rI/19IFJxKuK1QMUmP4iIiGDBggUMHTqUnTt3MmXKFKZMmVKkT2RkJB999BEdOnQol3OeOoXuTK9Cl5+fz7x585g3b16JfRo0aMCMGTN8jsIqD8eOHfNud+nSpULOISIiIiIihY4dO0ajRo0CHaNE+vtAxD/K67VAxSY/adasGevWreOtt95i1qxZJCYmUlBQQEJCAkOHDuW+++6jYcOG5XKujIwMPv/8c6DwCntnUhC66aabiIuLY+XKlWzcuJGjR4+SkpKCzWYjJiaGiy66iOHDh3PdddcREhJSLnlFRERERERO58iRI4GOICJnwDB1DXuphPLy8ti0aRMAsbGx2GxnXxc9fPiw99uP1atXU7du3XLJKHIu9LiUykqPTamM9LiUyqg6PC5dLpd3xFC7du0q9ZfJiYmJNG/eHIAff/yRhISEACeS8011eM6XpCJeCzSySSqlkJCQCpmiV7duXeLj48v9uCLnQo9Lqaz02JTKSI9LqYyq8uOyMk+dO9Wpf/wmJCRU2d+3VA9V+TlfkvJ+LdDV6EREREREREREpNyo2CQiIiIiIiIiIuVGxSYRERERERERESk3KjaJiIiIiIiIiEi5UbFJRERERERERETKjYpNIiIiIiIiIiJSblRsEhERERERERGRcmOYpmkGOoSIiIiIiIiIiFQPGtkkIiIiIiIiIiLlRsUmEREREREREREpNyo2iYiIiIiIiIhIuVGxSUREREREREREyo2KTSIiIiIiIiIiUm5UbBIRERERERERkXKjYpOIiIiIiIiIiJQbFZtERERERERERKTcqNgkIiIiIiIiIiLlRsUmEREREREREREpNyo2iYiIiIiIiIhIuVGxSaq1rKwsvv/+e1566SWuueYaGjdujGEYGIZBo0aNzvh4v/76K3fccQfNmjXD4XAQGxtL7969efvtt3G5XOX/A8h5q2/fvt7Hamn/RMrLvn37eOihh2jdujVhYWHUrFmTLl268NJLL5GTkxPoeHIeKevrX9++fQMdVaqRo0eP8sUXX/D4448zZMgQYmJivI+1cePGnfHxvv76a6688kri4+MJDg4mPj6eK6+8kq+//rr8w1dzen+SM1We7yPl8VzOycnhxRdfpEuXLtSsWZPw8HBat27NQw89xL59+87hJ63ETJFqrG/fvibg81/Dhg3P6FjvvPOOGRwcXOLxunXrZiYnJ1fMDyLnnT59+pT4WPvjP5Hy8MUXX5hRUVElPs5atmxp7tq1K9Ax5TxR1te/Pn36BDqqVCOne6yNHTu2zMfxeDzmbbfddtrj3XbbbabH46m4H6Ya0fuTnI3yeB8pr+dyYmKi2bJlyxKPERUVZS5YsKCcfwOBZzvj6pRIFWKapne7Ro0adOrUiZUrV5KVlXVGx1m4cCG33XYbHo+HuLg4/v73v9O1a1eOHz/O1KlTmTNnDqtWreLKK69kyZIlWCwaNCjlo1OnTkyfPj3QMaSa27BhA9dccw05OTmEh4fzyCOP0K9fP3Jzc5k5cyZTp05l+/btXH755axZs4bw8PBAR5bzxJ133sldd91VYntYWJgf08j5JCEhgdatW7No0aIzvu+jjz7KlClTAOjYsSOTJk2iadOm7Nq1ixdeeIF169YxZcoUYmNjeeqpp8o7erWi9yc5V+fyPlIez+WsrCyGDRvG9u3bAbj11lsZM2YMDoeDJUuW8Oyzz5Kens7o0aNZuXIl7du3P4eftpIJdLVLpCK9/fbb5kcffWTu3LnTu69hw4ZnNLLJ6XSazZo1MwEzMjLSTExMLNbnrrvu8lam33///fKKL+exEyOb9K29+MOJUaA2m8388ccfi7W/8MIL3te4yZMnByChnG9OPN6eeOKJQEeR88jjjz9uzp8/30xKSjJN0zR37959xiObdu7cadpsNhMwO3XqZObk5BRpz87ONjt16uR9zfX1uVJO0vuTnK1zfR8pr+fyE0884c3ywgsvFGv/8ccfvefp16/fWWWtrDT8Qqq12267jeuuu45mzZqd9TE+++wzEhMTAXjkkUdo2rRpsT4vvvgiNWrU8G6LiFQVa9asYenSpQCMHz+e7t27F+vz4IMP0rp1awBee+01nE6nPyOKiPjF5MmTGTZsGHFxcWd9jFdffdW7jue//vUvHA5HkfbQ0FD+9a9/AeByuXjttdfO+lzVnd6fJJDK47nsdDp5/fXXAWjdujUPPvhgsT7du3dn/PjxACxZsoSff/65PH+MgFKxSaQUc+fO9W6XtDhkaGgo11xzDQCbN29m586dfkgmInLuTn2Nu+mmm3z2sVgs/PnPfwYgNTXV++FfREROMk2TefPmAdCqVSu6devms1+3bt1o2bIlUPgabJ6y7IOcpPcnCZTyei4vXbqUtLQ0AMaOHVviUiun/o05Z86cc0xfeajYJFKK5cuXA9CyZUvq1KlTYr8+ffp4t1esWFHhuUREysOJ17iwsDAuvvjiEvvpNU5E5PR2797NwYMHgaKvmb6caD9w4AB79uyp6GhVkt6fJFDK67l84jFc2nE6derkXTuqOj2GVWwSOY2srCwOHDgAFFa1T+fU9q1bt1ZoLjl/bNu2jc6dOxMREUFISAjx8fGMGDGCDz74QEPFpVyceL1q1qwZNlvJ1w3Ra5wEwqxZs2jZsiUOh4OIiAiaN2/O2LFjWbJkSaCjiRRz6mujPjeeO70/SXk4m/eR8noul/U4NpvNu1RLdXoMq9gkchoHDhzwDoeMj48/bd+EhATv9v79+ys0l5w/jhw5wtq1a8nKyiI/P5+DBw/y+eefM3bsWDp06FCt3pDE//Ly8khOTgZKf42rUaOG91s3vcaJv2zZsoUdO3aQl5dHVlYWiYmJfPDBB/Tv359Ro0aRnp4e6IgiXqe+Nupz47nR+5OUl7N5Hymv5/KJ22FhYURHR5fpOMeOHSM/P/+0fauKkkvEIkJmZqZ3u7RLqZ562cysrKwKyyTnB4vFwoABAxg6dCgXXnghtWrVIjMzk19++YW3336brVu3smXLFvr168fq1atp0KBBoCNLFXQmr3FQ+DqXnZ2t1zipcKGhoVxxxRUMGDCAVq1aER4ezrFjx1i2bBn//e9/SUlJYe7cuYwYMYJvvvmGoKCgQEcW0efGcqT3JzlX5/I+Ul7P5RPHKetj+NTjBAcHl3qfyk7FJpHTyMvL827b7fbT9j31BSE3N7fCMsn5Yc6cOT6/AenVqxd33XUXt956K++//z5HjhxhwoQJ1WoxQfGfM3mNg5Ovc3qNk4p28OBBn6+BgwYN4p577mHIkCGsW7eOZcuW8Z///Id7773X/yFF/kCfG8uP3p/kXJ3L+0h5PZdPHOdMHsO+jlNVaRqdBJzL5cIwjHP+995775V7tpCQEO92QUHBafueOtzxj5fGlOqpIh+7pxtqGxQUxDvvvOOd+/3ZZ595FzEUORNn8hoHJ1/n9BonFe10r4FxcXF8+umn3g/vJy49LRJo+txYfvT+JOfqXN5Hyuu5fOI4Z/IY9nWcqkrFJpHTiIiI8G6XNiw3Ozvbu12WoZIi58JmszF+/Hjv7WXLlgUwjVRVZ/IaBydf5/QaJ4HWpEkTBg0aBEBiYiKHDh0KcCIRfW4sT3p/kop2uveR8nounzjOmTyGfR2nqtI0Ogk4m81WLosc161btxzSFHXqgnAnrkpXklMXhDt1oTipvgL92G3Tpo13WyOb5GyEhIQQExNDcnJyqa9xqamp3g9Ceo2TyqBNmzYsWLAAKHwNrFevXoATyflOnxvLj96fxB9Keh8pr+dyfHw8P/30E9nZ2aSlpZ12tNWJ48TGxlaL9ZpAxSapJEq7pGSghIeHk5CQwP79+9m2bdtp+57a3rp164qOJpVEIB+7J66UKHIuWrduzfLly0lMTMTlcpV4eWm9xkllo9dAqWxO/RJInxvPnd6fpKKV9D5SXs/lNm3aMHv2bG+/bt26+TyGy+Vi165dPo9RlWkanUgpevbsCcD27dtJSkoqsd+p05h69OhR4blEtmzZ4t3WN/pytk68xmVnZ/Pzzz+X2E+vcVLZ6DVQKpvGjRt7H4ulTW///vvvAahfvz6NGjWq6GhVkt6fpKKV9D5SXs/lE4/h0o6zdu1a7+i86vQYVrFJpBQjR470bpe0CHlOTg6ffPIJUFjBbtGihR+SyfnM5XLx7rvvem/37t07gGmkKjv1NW769Ok++3g8Hj744AOgcMHNfv36+SOaSIl+++03vvnmG6Bw3Y369esHOJEIGIbBiBEjgMJRDKtWrfLZb9WqVd7RECNGjMAwDL9lrEr0/iQV6XTvI+X1XO7bty9RUVEAvP/++yWOpDr1b8xRo0ad3Q9UCanYJFKKUaNG0bRpUwCeffZZ7xDHU02cOJHU1FTvtsi5WLJkCWlpaSW2O51ObrnlFu+b2/Dhw7VGgZy1Ll260KtXLwCmTZvGypUri/V5+eWXveuT3XfffQQFBfk1o5xf5s+fj8vlKrH9yJEjXH311TidTgD+8pe/+CuaSKkmTJjgne51zz33FLuEeW5uLvfccw9QuPbjhAkT/B2xytD7k5yt8ngfKY/nst1u59577wVg69atvPTSS8X6rFy5kmnTpgHQp08fOnfuXIafsGowTE14l2osMTGRFStWFNn30EMPkZKSQq1atYo94S+77DLq1KlT7Dhffvklw4cPx+PxEBcXx6OPPkqXLl1ITU1l6tSp3rm4PXv2ZOnSpVit1or7oaTaGzduHLNnz+aKK66gb9++tGzZksjISLKysvj55595++23vR+sateuzapVq2jcuHGAU0tVtm7dOnr06EFubi7h4eH87W9/o1+/fuTm5jJz5kymTJkCQIsWLVi7dm2Rq7SIlLdGjRrhdDq56qqr6N69O40aNcLhcJCcnMzSpUv573//S0pKClD4vrt48eJqs5iqBNaKFStITEz03k5OTvZ+idijRw9uueWWIv3HjRvn8ziPPPIIzz33HAAdO3bk4YcfpmnTpuzatYvnn3+edevWefs988wzFfCTVB96f5KzUV7vI+XxXM7MzKRTp07s2LEDgNtuu40xY8bgcDhYsmQJzzzzDFlZWTgcDn788Uc6dOhQAb+RADFFqrHp06ebQJn/LVmypMRjTZkyxbTb7SXet0uXLuaxY8f898NJtTV27NgyPV7btWtn/vrrr4GOK9XE559/bkZGRpb4eGvRooW5c+fOQMeU80DDhg3L9Bp41VVXmampqYGOK9VIWd9/T/wridvtNm+++ebT3nf8+PGm2+32409Xden9Sc5Ueb2PlNdzeefOnWbz5s1LPEZkZKQ5f/78cv4tBJ5GNkm19t5773HTTTeVuf+SJUvo27dvie2bN2/mjTfe4Ntvv+XQoUOEhYXRunVrrr/+em655ZYSr5Ihcia2bt3KwoULWblyJVu2bOHYsWMcP36c4OBg4uLi6NSpE1dffTWjRo3SKDopV3v37uX1119nwYIFHDhwALvdTrNmzRg9ejR33303oaGhgY4o54Fly5axbNkyVq5cyW+//UZycjIZGRneK8RecskljB07lu7duwc6qlQz48aN4/333y9z/9L+jPryyy+ZMmUKa9asITk5mZiYGDp37sztt9/OkCFDzjXueUXvT3Imyvt9pDyey9nZ2bz11lvMmjWLxMRECgoKSEhIYOjQodx33300bNjwXH7kSknFJhERERERERERKTdaIFxERERERERERMqNik0iIiIiIiIiIlJuVGwSEREREREREZFyo2KTiIiIiIiIiIiUGxWbRERERERERESk3KjYJCIiIiIiIiIi5UbFJhERERERERERKTcqNomIiIiIiIiISLlRsUlERERERERERMqNik0iIiIiIiIiIlJuVGwSEREREREREZFyo2KTiIiIiIiIiIiUGxWbRERERERERESk3KjYJCIiIiIiIiIi5UbFJhERERERERERKTcqNomIiIiIiIiISLlRsUlERERERERERMqNik0iIiIiIiIiIlJuVGwSERGRSs/pdNKyZUsMw+Djjz8OdJxi7rrrLgzDYOzYsYGOIiIiUiH+8Y9/YBgGhmEEOopUASo2iYiInOdatWqFYRi8/PLL53ScsWPHYhgGo0aN+v/27j2qqjL9A/j3HC5xVTQZoQbFUrmFGpHRmNmQ4rig8DYqGKmTRkprjVk5DdNIziwl17RGXbLSyRpvoGWQyPGCcRkRpxQ6jnkNvORygBFUMhSOBJz39wdr799BzmVzzk5Qvp+1znKvc9797ufhn3f77Pd9t0qR/b9169ahsrISISEh+O1vf6t6/4764x//CFdXV2zbtg3l5eXdHQ4REfUyLS0t+PTTTzFnzhyEhITgwQcfhIuLCwYMGIAnnngCCxcuRGFhIYxGo6L+qqur5cJSTxjXkpOT5Xj+9a9/dencoqIi+dzXX3/dYruelvO9jsUmIiIildy+fRuurq7QaDR4//33uzscRSoqKlBRUQEAiI+Pt9hOSW6TJ08GAHz55ZcwGAyqxXjr1i2kp6cDAJYtWwattufdvgQEBGDOnDkQQuDdd9/t7nCIiKgX2b17N4KDg5GQkICtW7fiu+++Q319PVpbW3H9+nUcO3YMGzZswIQJExASEoK9e/fa7HPPnj0AAD8/P0RGRv7cKdj08ssvy8fbtm3r0rmZmZnycVJSksV2PS3ne13Pu1sjIiK6Rx07dgwtLS0AgCeffLKbo1Fm9+7dAIDQ0FAMHTrUYjsluU2cOBFubm5oampCYWGhajGuX78e165dQ0BAAGbMmKFav2p78803AbQX2/hElIiI7ob09HRMmTIFFy9eBACMHz8e69atQ1FREfR6PQoKCpCRkYGJEydCq9WisrISf/rTn2z2q9PpAABxcXE9YtncmDFj8OijjwIAsrOzFT/UMhgMyMnJAQAEBQXhqaeesti2p+V8r2OxiYiISCVlZWUAAI1GgyeeeKKbo1FGKjZZm9UEKMvNw8MD48eP79Cvo9ra2pCRkQEASEhI6JGzmiRBQUGIiIgAAKxdu7aboyEiovvdtm3bkJqaCiEEfH19UVxcjIKCArz++uuIjo5GREQExo8fj5SUFOTn5+P48eOIjo622W9TUxOKi4sBAC+88MLPnYZi0uymmzdvKr7PyM3Nxc2bNwFYn9XUU3O+l/XcOzYiIqJ7jDSbZejQofDx8eneYBSoq6vDkSNHAPz/EjhLlOYm9aPT6RTvC2FNQUEBLl++DAB46aWXHO7v5zZ79mwAQE5ODn788cdujoaIiO5XNTU1WLhwIYD2hz0HDx7Er3/9a6vnhIeHo6CgAG+99ZbVdoWFhTAYDHBzc5MfIvUESUlJ8owjpUvppHYajcbqfURPzflexmITERGRg3x9faHRaLB9+3YAwLlz5+QNJqXPrFmzujnKzvLy8mA0GuHv729xaVxXc3vhhReg1WpRV1eHr7/+2uEYd+7cCQAYNmwYwsPDLba78w05N27cQFpaGsLCwuDl5YX+/fvjueeeQ1ZWls1r1tTU4J133kFERAT69u0LV1dX+Pn5ITw8HAkJCdi8eTMaGhrMnjtt2jQA7XtcqTW7i4iI7i9qjFmrV69GY2MjAGD58uUIDQ1VdG2tVmvz4Y20d1F0dDQ8PDwU9Ss5ceIE/P39odFoMHDgQBw/ftxsu7KyMixYsADDhw+Hl5cXPD09ERwcjJSUFJw7d87sOUOGDMEzzzwDoH3Jel1dndVYamtrUVBQAAAYN24cBg8ebLGtkpz//e9/Y/78+QgKCkKfPn3g5eWF4OBgTJ48GVu3brV4b9BrCSIiIrJbdXW1AGDzs2LFiu4OtZO4uDgBQCQnJ5v93d7cfvWrXwkA4u2333Y4xsDAQAFAJCUlWW2XlpYmx3Px4kXx6KOPWox3+vTpoqWlxWw/hw4dEn369LGZs06nsxiLv7+/ACDmzZvnUO5ERHR/cnTMMhqNwtfXVwAQnp6e4scff1QtNqPRKB566CEBQKxfv95i3OYcPnxY+Pj4CABi8ODBorKyslOblpYWsXDhQqtjrIuLi/joo4/MXmPjxo1yuzVr1ljNZfXq1XLbf/7zn3blLIQQTU1NIiEhwea9QVpamtV4ehvObCIiInJAv379NPMB8QAAEFdJREFUcPLkSWRnZ8vfrV27FidPnuzwWbRoUTdG2ZnpJt6WltDZm5vUn6Mze6qqqnDp0iUAXdtwfebMmfj+++/x2muvobCwEOXl5fjkk08wfPhwAO0biy5ZsqTTec3NzZg1axYaGhrg7e2NpUuXYv/+/dDr9Thy5Ag+++wzLF68GAEBAVavL8VaWlqqOGYiIuqd7Bmzzpw5g6tXrwIAxo4diz59+qgWj16vR01NDYD2jbKV2r9/P2JiYnDjxg2EhITg8OHDGDZsWKd2r7zyCtavXw8AmDRpEjIzM1FWVoby8nJs3LgRYWFhaGlpwauvvipv2G1qxowZcHd3B2B7KZ30u4eHB6ZPn26xnbWcjUYj4uPjsWPHDgDtM61Xr16N0tJS6PV67NmzB6mpqVZfstJrdXe1i4iI6H6wbds2+cnWuXPnujscm7744gsBQHh7e4vbt29bbdvV3CoqKuT2Z8+etTvGzz77TO6ntLTUalvTp60AxPbt2zu1aWhoECNHjhQAhFarFSdOnOjwe1FRkaKZSy0tLVafIi9fvlzup7a21kaWRETU2zg6ZmVlZcnnpqamqhrbsmXLBAAxatQoq3Gb2rFjh3BxcREAxJNPPimuXbtmtu/s7Gz5/I0bN5ptYzAYRHR0tAAgAgMDzc5EnjVrltzPmTNnzPZz+vRpuU1iYqLdOa9Zs0buZ8qUKRbvmdra2kR1dbXV6/Q2nNlERESkAmlPAm9vb/nVvD2ZNOvoN7/5DR544AGrbbua2/DhwxEcHNzhOvaoqqqSj3/xi18oPi8uLg4JCQmdvvf29sZHH30EoP1J5YYNGzr8fuXKFfn42Weftdi/s7Oz1afIprFWV1crjpuIiHofe8asa9euyccDBw5UNR5p7yKlb2Rbv349Zs+ejZaWFkRHR6O4uBgPPvig2bbp6ekAgClTpmD+/Plm27i5uclvob106RIOHjzYqY30VjrA8uwm0+9N25tjKWej0Yi//e1vAICHH34YW7dutXjPpNVq8dBDD1m9Tm/DYhMREZEKpILMiBEj5A0/e6q2tjb5xio+Pt5me3tyk/rNzc21K0YA8hIBoH1Jn1Lz5s2z+Nvo0aMRFhYGAPIyQom/v798vGnTJsXXu1P//v3lY9MciIiI7mTPmHXz5k352NPTU7VYqqurcezYMQDKik0rVqzAokWLYDQaMXnyZOzbtw9eXl4W+9br9QDal8JZExISggEDBgCA2ZeNxMTEyGN2VlYWhBAdfhdCyJur+/v7W327nLWcjx8/Lj80WrBggcXcyDwWm4iIiFTw7bffAgBGjRrV5XNbW1s7veHNns/mzZsVXe/w4cO4fv06nJ2dERsb+7PkJu3bdPTo0Q4zhrqivr5ePu5KscnW/k6jR48G0P5mvZ9++kn+/plnnsEjjzwCAFi8eDFGjx6N9PR0fPXVVx3a2WIa6/Xr1xWfR0REvY89Y5a3t7f8u/RGOjVID6L8/PwQGRlpte2SJUvw7rvvAgDmzp2L7OxsqzOlv/nmG/k4ISHB5j2NNHvL3D2Ek5MTEhMTAQCXL19GSUlJh98PHjyI//73vwCAxMREODk52ZXzf/7zH/nY2oxnMo/FJiIiIgdVV1fLN0X2FJvuNmmm0qBBg+Dj42O1rb25hYeHQ6PRQAiBEydO2BWnm5ubfGwwGBSfZ2vJnbTkQAiBH374Qf7excUFOp0OISEhAIDy8nKkpqZizJgx8PHxwaRJk7B9+3a0tbVZ7d80VmkTUyIiInPsGbOkWT8AUFtbq1os0obcsbGxNmcyr169GgDw2GOP4ZNPPrFa0AGAuro6u2Jqamoy+/2cOXPk4zuX0nVlCZ21nE2XK5rOfiZlnLs7ACIionudVLwBgJEjR3b5fGdnZ5w9e9bhOJTeCEnTyS9evIjvv/8eQ4YMsdjW3tyKiooghICbmxvGjBmj+DxTvr6+8nF9fX2HJ7nW2LpBvnO6vanQ0FCcPHkSOp0OOp0OJSUluHDhAgwGA/Lz85Gfn4+///3v2Ldvn8X/IJjOyDLNgYiI6E72jFmm47G0BMxRBoMBxcXFAJQtoZs2bRpycnJw6tQp/P73v8e6deustjd9UJOVlYURI0YoisvSzObw8HCMHDkS3377LbKzs5GRkQF3d3cYDAbk5OQAaP87WbtOV3Lu6Vsk9EQsNhERETlIKsg4OTkhPDzcrj6kDbXvhrCwMAwdOhTnz59Hbm4u3njjDYtt7c1N2qtp/Pjxdu8nYVqo+eGHHzB48GBF59XW1iIgIMDi79LTVY1GY/Ym1snJCZMnT5aXAv7vf//D/v378eGHH0Kv10Ov1yM5ORm7du0y27/pbCkWm4iIyBp7xqzQ0FAMGDAA165dQ2lpKRoaGqy+uEKJwsJCGAwGuLm5Wd3jSLJjxw7MmDEDubm5yMjIgLOzszzbyRzTTcM1Gg0ee+wxh+IF2mc3LVmyBA0NDcjLy8PMmTOxe/duNDQ0ALA9q8lWzqYzyGpqahAUFORwzL0Jl9ERERE5SNrTKCgoqMPSr55M2sDb1tvi7MnNaDTKeyBIBRt7mBa3KisrFZ9XXl6u6Pdhw4bB1dXVZn/+/v743e9+h6+//hoREREA2vd4sLS0T4rV09NT3gOKiIjIHHvGLI1Gg7lz5wJo37Pp448/djgOaTlZdHS0oodELi4u2LlzJ1588UUAwJo1a/D2229bbP/444/Lx19++aWD0bZLTEyEs3P7/Blp6Zz0r+m+TpbYylka8wHg0KFDqsTcm7DYRERE5KCKigoAkN8Ycy+Qik3SZuGW2JPbV199hatXr0Kr1Sp+dbI5kZGR8p5Htm7GTW3ZssXib9988w1OnToFAIqe3JpycXHBuHHjALRv6n7jxg2z7aRYo6Ki5JtgIiIic+wdsxYvXgwPDw8AwLJly/Ddd98pup7RaERmZmaH74QQ2Lt3LwBlS+gkLi4u+Pzzz+WXjXzwwQd45513zLYdOnQoQkNDAQCffvopLl++rPg6lgwcOBAxMTEAgAMHDuDUqVNyISsmJgZ+fn4Wz1WS88iRI+VZZx9//DFu3brlcMy9CYtNREREDpKma7e2tnZzJMqNGTMGvr6+aGtrk2+2zLEnN2m2VFRUlM2NT61xdXWV38JTVlam+Ly8vDzs3Lmz0/e3bt3Cq6++CgDQarVITk7u8HtpaSnOnz9vsd+ffvpJfuONl5eX2SVyzc3N8oboY8eOVRwzERH1TvaOWQ8//DAyMjIAtM9uGjduXKe3st3pzJkzmDhxIj744IMO3+v1etTU1AAA4uLiuhS/q6srcnJyMGnSJADAqlWr5LfU3Un6/vbt25g6dSquXr1qsd/m5mZ8+OGHuH37ttXrSxuFt7a2YtasWfL9iq0ldEpy1mq18mytqqoqvPzyyxbfTms0GuX+qB0ftxERETnokUcewaVLl7Bnzx5kZGQgKipKXnI2ePBgxRtb301arRZxcXHYtGkTdu/ebfGmzJ7cpGKTI0voJLGxsSgpKUFZWRlu3ryp6G8ZGRmJxMRElJSUYPr06ejTpw9OnDiBVatWyTO1UlJSOm0aWlRUhL/+9a8YO3YsYmNjMWLECPj6+sJgMKCyshIbNmyQN2KdP3++2VlLhw4dQktLixw7ERGRNfaOWQAwb948VFVVYdmyZairq8Nzzz2HmJgYxMfHIyQkBD4+Pqivr0dlZSX27t2L/Px8tLW1dXrhh7T0fdSoUfjlL3/Z5RweeOAB7Nq1C/Hx8Thw4ABWrFgBJycnLF++vEO7hIQEHDhwAFu2bIFer0doaCiSk5Mxbtw4+Pr6orGxERcuXEBpaSm++OIL1NfX2ywavfjii/Dx8cGNGzdw+vRpAECfPn3kGdyWKM05JSUFOp0OBQUF2LVrF8LDw7Fo0SJERkbCw8MDV65cwZEjR7Bjxw4kJibivffeU/AX6yUEEREROUSn0wmNRiMAdPro9fruDs+i3NxcAUB4enoKg8Fgtk1Xczt9+rT8e0VFhcMxVlVVCScnJwFAbNmyxWK7tLQ0+boXL14UQ4YMMRszADFt2jTR0tJitQ9rn6lTp1r8e82dO1cAEEFBQQ7nTkRE9yc1xixTOTk5IjAwUNEYFhYWJg4cONDh/IiICAFA/PnPf1YctzkGg0FMmDBBbvOXv/ylU5vW1laxdOlSeWy39vH09BRNTU02/ppCLFiwoMN5r7zyis1zlOYshBCNjY1i+vTpNuNNS0uz2VdvwmITERGRCvLz88WECRNEv3795OKMi4uLaG5u7u7QLGpsbBTu7u4CgNDpdBbbdSW3lStXCgAiODhYtTinTp0qAIiYmBiLbe68Aa6vrxepqakiJCREeHh4iL59+4pnn31WZGZmWuyjsbFR7Nu3T7zxxhsiKipKDBo0SLi5uQk3NzcRGBgoZs6cKfbu3WvxfIPBIPr27SsAiLVr19qfMBER3dfUGLPu1NzcLLKyssRLL70kgoKCRL9+/YSzs7Po37+/iIiIEIsWLRJFRUXCaDR2OK+qqkoe28vKyroUtzlNTU0iOjpabrdy5Uqz7SoqKsSbb74pHn/8cdGvXz/h5OQkvL29RVhYmJg9e7bYsmWLaGhoUJT74cOHOxR9SkpKrLbvSs6miouLRVJSkhgyZIhwd3cX3t7eIjg4WEydOlVs375d3Lp1S3FfvYFGCCEUTYEiIiKi+058fDzy8vIwf/58bNy40eH+oqKicPToUfzhD3/A+++/r0KEwJEjR/D000/DyckJ58+fR2BgYKc27733njxdv7tubTIzM5GUlIT+/fvj0qVLPXL5JBERdb+eMGZJ/vGPf+C1116Dn58fampqoNFoujWeu6E35twduEE4ERFRLybtq6TT6WA0Gh3q68qVK/JG3mrs1ySJiorCpEmT0NbWhvT0dNX6VZPRaMTKlSsBAG+99RYLTUREdE+Q9i6KjY3tNUWX3phzd2CxiYiIqBeLi4uDVqtFbW0tjh496lBfeXl5EELAz88PTz31lEoRtlu1ahWcnJywadMmVV6XrLbPP/8cZ8+eRUBAABYvXtzd4RARESkyduxYpKWlISUlpbtDuWt6Y87dgW+jIyIi6sV8fX2RnJyMyspKXLhwAU8//bTdfdXW1uL555/H888/r/qTwvDwcGzevBnnz5/H5cuXMWjQIFX7d1RbWxvS0tIQHR0Nd3f37g6HiIhIkaVLl3Z3CHddb8y5O3DPJiIiIrrn9aT9L4iIiKzhmEW9AZfRERERERERERGRajiziYiIiIiIiIiIVMOZTUREREREREREpBoWm4iIiIiIiIiISDUsNhERERERERERkWpYbCIiIiIiIiIiItWw2ERERERERERERKphsYmIiIiIiIiIiFTDYhMREREREREREamGxSYiIiIiIiIiIlINi01ERERERERERKQaFpuIiIiIiIiIiEg1LDYREREREREREZFqWGwiIiIiIiIiIiLVsNhERERERERERESqYbGJiIiIiIiIiIhUw2ITERERERERERGphsUmIiIiIiIiIiJSDYtNRERERERERESkGhabiIiIiIiIiIhINSw2ERERERERERGRalhsIiIiIiIiIiIi1fwfZ794xaD+MtcAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -200,12 +206,12 @@ ], "source": [ "P = ParticleGroup(h5[ppaths[-1]])\n", - "P.plot('delta_t', 'delta_pz', bins=200)" + "P.plot(\"delta_t\", \"delta_pz\", bins=200)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -221,22 +227,22 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.])" + "' 0.00000000000000E+00'" ] }, - "execution_count": 11, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "M.evaluate('lat::orbit.x[end]')" + "M.evaluate(\"lat::orbit.x[end]\")" ] }, { @@ -248,11 +254,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "from pytao.misc.csr import read_csr_wake_data_h5, process_csr_wake_data\n", + "from pytao.misc.csr import read_csr_wake_data_h5, process_csr_wake_data\n", "import numpy as np" ] }, @@ -265,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -281,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -290,13 +296,13 @@ "(134, 40, 5)" ] }, - "execution_count": 14, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dat = cdat['3:FF.BEN01']['data']\n", + "dat = cdat[\"3:FF.BEN01\"][\"data\"]\n", "dat.shape" ] }, @@ -309,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -367,7 +373,7 @@ " 0.437, 0.438, 0.439, 0.44 , 0.441, 0.442, 0.443, 0.444, 0.445])" ] }, - "execution_count": 15, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -375,12 +381,12 @@ "source": [ "pdat = process_csr_wake_data(cdat)\n", "\n", - "pdat['s_position']" + "pdat[\"s_position\"]" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -390,13 +396,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e35ec3e144fd432d9b1e1136fbcf6915", + "model_id": "89abfde2c5c04b69aa4c0f7146aae84e", "version_major": 2, "version_minor": 0 }, @@ -406,24 +412,18 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ "from ipywidgets import interact\n", + "\n", + "\n", "def plot1(step=0):\n", " plot_csr_wake(pdat, step=step)\n", - "nstep = len(pdat['z'])\n", - "interact(plot1, step=(0, nstep-1, 1) )" + "\n", + "\n", + "nstep = len(pdat[\"z\"])\n", + "interact(plot1, step=(0, nstep - 1, 1));" ] }, { @@ -435,19 +435,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 28, "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": "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", @@ -465,7 +455,7 @@ } ], "source": [ - "plt.plot(pdat['s_position'], marker='.')" + "plt.plot(pdat[\"s_position\"], marker=\".\");" ] }, { @@ -477,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -489,22 +479,22 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "image/png": { - "height": 448, - "width": 618 + "height": 434, + "width": 693 } }, "output_type": "display_data" } ], "source": [ - "plot_csr_stats(pdat) " + "plot_csr_stats(pdat)" ] }, { @@ -522,7 +512,7 @@ }, "outputs": [], "source": [ - "os.remove('wake.dat')" + "os.remove(\"wake.dat\")" ] } ], @@ -542,7 +532,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.12.0" }, "vscode": { "interpreter": { diff --git a/docs/examples/basic.ipynb b/docs/examples/basic.ipynb index 83c0610d..61fd2f7f 100644 --- a/docs/examples/basic.ipynb +++ b/docs/examples/basic.ipynb @@ -26,7 +26,7 @@ "metadata": {}, "outputs": [], "source": [ - "tao=Tao('-init $ACC_ROOT_DIR/bmad-doc/tao_examples/cesr/tao.init -noplot') " + "tao = Tao(\"-init $ACC_ROOT_DIR/bmad-doc/tao_examples/cesr/tao.init -noplot\")" ] }, { @@ -46,7 +46,7 @@ { "data": { "text/plain": [ - "['# Values shown are for the Exit End of each Element:',\n", + "['# Values shown are for the Downstream End of each Element:',\n", " '# Index name key s l beta phi_a eta orbit beta phi_b eta orbit Track',\n", " '# a [2pi] x x [mm] b [2pi] y y [mm] State',\n", " ' 1 IP_L0 Marker 0.000 0.000 0.95 0.000 -0.00 -0.017 0.02 0.000 0.00 0.001 Alive',\n", @@ -61,7 +61,7 @@ " ' 10 Q01W Quadrupole 3.874 0.950 66.94 0.173 -0.26 16.851 28.95 0.255 -0.02 1.213 Alive',\n", " '# Index name key s l beta phi_a eta orbit beta phi_b eta orbit Track',\n", " '# a [2pi] x x [mm] b [2pi] y y [mm] State',\n", - " '# Values shown are for the Exit End of each Element:']" + " '# Values shown are for the Downstream End of each Element:']" ] }, "execution_count": 3, @@ -70,7 +70,7 @@ } ], "source": [ - "tao.cmd('show lat 1:10')" + "tao.cmd(\"show lat 1:10\")" ] }, { @@ -97,10 +97,7 @@ } ], "source": [ - "tao.cmds(\n", - " ['set lattice model=design',\n", - " 'set ele Q00W x_offset = 1e-6']\n", - ")" + "tao.cmds([\"set lattice model=design\", \"set ele Q00W x_offset = 1e-6\"])" ] }, { @@ -128,7 +125,7 @@ "text": [ "-------------------------\n", "Tao> sho lat 1:10\n", - "# Values shown are for the Exit End of each Element:\n", + "# Values shown are for the Downstream End of each Element:\n", "# Index name key s l beta phi_a eta orbit beta phi_b eta orbit Track\n", "# a [2pi] x x [mm] b [2pi] y y [mm] State\n", " 1 IP_L0 Marker 0.000 0.000 0.95 0.000 -0.00 -0.018 0.02 0.000 0.00 0.001 Alive\n", @@ -143,14 +140,14 @@ " 10 Q01W Quadrupole 3.874 0.950 66.94 0.173 -0.26 16.851 28.95 0.255 -0.02 1.213 Alive\n", "# Index name key s l beta phi_a eta orbit beta phi_b eta orbit Track\n", "# a [2pi] x x [mm] b [2pi] y y [mm] State\n", - "# Values shown are for the Exit End of each Element:\n", + "# Values shown are for the Downstream End of each Element:\n", "-------------------------\n", "Tao> sho ele 4\n", "Element # 4\n", "Element Name: CLEO_SOL#4\n", "Key: Solenoid\n", "S_start, S: 0.622301, 0.637956\n", - "Ref_time: 2.127992E-09\n", + "Ref_time_start, Ref_time: 2.075773E-09, 2.127992E-09\n", "\n", "Attribute values [Only non-zero values shown]:\n", " 1 L = 1.5655000E-02 m 31 L_SOFT_EDGE = 0.0000000E+00 m\n", @@ -160,20 +157,20 @@ " 13 SPIN_FRINGE_ON = T (1)\n", " 17 STATIC_LINEAR_MAP = F (0)\n", " 47 PTC_CANONICAL_COORDS = T (1)\n", - " 50 DELTA_REF_TIME = 5.2219459E-11 sec\n", " 53 P0C = 5.2890000E+09 eV BETA = 1.0000000E+00\n", " 54 E_TOT = 5.2890000E+09 eV GAMMA = 1.0350315E+04\n", + " 64 REF_TIME_START = 2.0757727E-09 sec 50 DELTA_REF_TIME = 5.2219459E-11 sec\n", " 65 INTEGRATOR_ORDER = 0\n", " 67 DS_STEP = 2.0000000E-01 m 66 NUM_STEPS = 1\n", " 68 CSR_DS_STEP = 0.0000000E+00 m\n", "\n", " TRACKING_METHOD = Bmad_Standard APERTURE_AT = Exit_End\n", - " MAT6_CALC_METHOD = Bmad_Standard APERTURE_TYPE = Rectangular\n", + " MAT6_CALC_METHOD = Auto APERTURE_TYPE = Rectangular\n", " SPIN_TRACKING_METHOD = Tracking OFFSET_MOVES_APERTURE = F\n", " PTC_INTEGRATION_TYPE = Matrix_Kick SYMPLECTIFY = F\n", " CSR_METHOD = Off FIELD_MASTER = F\n", " SPACE_CHARGE_METHOD = Off LONGITUDINAL ORIENTATION = 1\n", - " FIELD_CALC = Refer_to_Lords.\n", + " FIELD_CALC = Refer_to_Lords. REF_SPECIES = Electron\n", "\n", "Slave_status: Super_Slave\n", "Associated Super_Lord(s):\n", @@ -184,15 +181,16 @@ "\n", "Twiss at end of element:\n", " A B Cbar C_mat\n", - " Beta (m) 1.36490320 22.91977153 | -0.11412810 0.00652709 -0.08500246 0.03650694\n", - " Alpha -0.65684268 -35.88090663 | -0.16215602 0.00350746 -0.09239919 0.03194148\n", - " Gamma (1/m) 1.04875005 56.21519652 | Gamma_c = 0.99967089 Mode_Flip = F\n", - " Phi (rad) 0.59356742 1.53299612 X Y Z\n", - " Eta (m) -0.02444437 0.00057682 -0.02453396 0.00007942 0.00005371\n", - " Etap -0.03500697 -0.00133149 -0.03509127 -0.00204809 1.00000000\n", - " Sigma 0.00052589 0.00002028 0.00000000 0.00000000\n", + " Beta (m) 1.36491299 22.91993604 | -0.11412810 0.00652709 -0.08500246 0.03650720\n", + " Alpha -0.65684268 -35.88090683 | -0.16215602 0.00350746 -0.09239853 0.03194148\n", + " Gamma (1/m) 1.04874253 56.21479364 | Gamma_c = 0.99967089 Mode_Flip = F\n", + " Phi (rad) 0.59356742 1.53299611 X Y Z\n", + " Eta (m) -0.02444701 0.00048962 -0.02453414 0.00007942 0.00000000\n", + " Etap -0.03262118 -0.00146685 -0.03270254 -0.00198038 1.00000000\n", + " dEta/ds -0.03501127 -0.00147071 -0.03509154 -0.00204811 1.00000000\n", + " Sigma 0.00052596 0.00002030 0.00000000 0.00000000\n", "\n", - "Orbit: Electron State: Alive\n", + "Tracking: Electron, State: Alive\n", " Position[mm] Momentum[mrad] Spin |\n", " X: 1.50654732 2.38873152 | t_particle [sec]: 2.12933094E-09 E_tot: 5.28896E+09\n", " Y: 0.04370624 0.06771334 | t_part-t_ref [sec]: 1.33877127E-12 PC: 5.28896E+09\n", @@ -258,6 +256,9 @@ "Documentation on interfacing Python scripts to Tao's python command is given in the \"Tao Python\n", "Command section .\n", "\n", + "Note to programmers: For debugging, the \"show internal -python\" command will show the \"c_real\"\n", + "and \"c_integer\" arrays.\n", + "\n", "List of possible \"\" choices:\n", " beam, beam_init, branch1, bunch_comb, bunch_params, bunch1, bmad_com,\n", " building_wall_list, building_wall_graph, building_wall_point,\n", @@ -266,18 +267,18 @@ " data_d2, data_d2_array, data_set_design_value, data_parameter,\n", " datum_create, datum_has_ele, derivative, ele:ac_kicker, ele:cartesian_map,\n", " ele:chamber_wall, ele:control_var, ele:cylindrical_map, ele:elec_multipoles,\n", - " ele:floor, ele:grid_field, ele:gen_attribs, ele:head, ele:lord_slave, ele:mat6,\n", - " ele:methods, ele:multipoles, ele:orbit, ele:param, ele:photon, ele:spin_taylor,\n", - " ele:taylor, ele:taylor_field, ele:twiss, ele:wake, ele:wall3d, em_field, enum,\n", + " ele:floor, ele:gen_grad_map, ele:grid_field, ele:gen_attribs, ele:head, ele:lord_slave,\n", + " ele:mat6, ele:methods, ele:multipoles, ele:orbit, ele:param, ele:photon,\n", + " ele:spin_taylor, ele:taylor, ele:twiss, ele:wake, ele:wall3d, em_field, enum,\n", " evaluate, floor_plan, floor_orbit, global, help, inum, lat_branch_list,\n", " lat_calc_done, lat_ele_list, lat_list, lat_param_units, matrix, merit, orbit_at_s,\n", " place_buffer, plot_curve, plot_graph, plot_histogram, plot_lat_layout, plot_line,\n", " plot_plot_manage, plot_graph_manage, plot_curve_manage, plot_list, plot_symbol,\n", " plot_transfer, plot1, ptc_com, ring_general, shape_list, shape_manage,\n", " shape_pattern_list, shape_pattern_manage, shape_pattern_point_manage, shape_set,\n", - " show, species_to_int, species_to_str, spin_polarization, spin_resonance,\n", - " super_universe, twiss_at_s, universe, var_v1_create, var_v1_destroy, var_create,\n", - " var_general, var_v1_array, var_v_array, var, wave\n", + " show, species_to_int, species_to_str, spin_invariant, spin_polarization,\n", + " spin_resonance, super_universe, twiss_at_s, universe, var_v1_create, var_v1_destroy,\n", + " var_create, var_general, var_v1_array, var_v_array, var, wave\n", "\n", "-------------------------\n", "Tao> \n" @@ -304,12 +305,12 @@ { "data": { "text/plain": [ - "['x;REAL;F; -1.77207910292112E-05',\n", - " 'px;REAL;F; 2.39054798135166E-03',\n", - " 'y;REAL;F; 9.77805901311320E-07',\n", - " 'py;REAL;F; 2.91412238054611E-06',\n", - " 'z;REAL;F; -3.99530687252325E-04',\n", - " 'pz;REAL;F; -7.17210141059523E-06',\n", + "['x;REAL;F; -1.77207910291870E-05',\n", + " 'px;REAL;F; 2.39054798135142E-03',\n", + " 'y;REAL;F; 9.77805901533289E-07',\n", + " 'py;REAL;F; 2.91412237463650E-06',\n", + " 'z;REAL;F; -3.99530687471794E-04',\n", + " 'pz;REAL;F; -7.17210139296654E-06',\n", " 'spin;REAL_ARR;F; 0.00000000000000E+00; 0.00000000000000E+00; 0.00000000000000E+00',\n", " 'field;REAL_ARR;F; 0.00000000000000E+00; 0.00000000000000E+00',\n", " 'phase;REAL_ARR;F; 0.00000000000000E+00; 0.00000000000000E+00',\n", @@ -318,7 +319,7 @@ " 'charge;REAL;F; 0.00000000000000E+00',\n", " 'dt_ref;REAL;F; 0.00000000000000E+00',\n", " 'p0c;REAL;F; 5.28899997531481E+09',\n", - " 'beta;REAL;F; 9.99999995332664E-01',\n", + " 'beta;REAL;F; 9.99999995332663E-01',\n", " 'ix_ele;INT;F;868',\n", " 'state;STR;F;Alive',\n", " 'direction;INT;F;1',\n", @@ -332,7 +333,7 @@ } ], "source": [ - "tao.cmd('python orbit_at_s end')" + "tao.cmd(\"python orbit_at_s end\")" ] }, { @@ -359,7 +360,7 @@ } ], "source": [ - "tao.cmd('python lat_list -array_out 1@0>>Q*|model orbit.floor.x')" + "tao.cmd(\"python lat_list -array_out 1@0>>Q*|model orbit.floor.x\")" ] }, { @@ -403,7 +404,7 @@ " -4.06754335e+01, -3.36162850e+01, -2.82979320e+01, -2.32077253e+01,\n", " -1.88775830e+01, -1.50782620e+01, -1.22578107e+01, -8.16225874e+00,\n", " -4.68317228e+00, -2.92445682e+00, -1.48689125e+00, -5.48022495e-01,\n", - " -1.20827634e-01, -1.39453226e-02, -1.41528499e-02, 1.91718054e-06,\n", + " -1.20827634e-01, -1.39453226e-02, -1.41528499e-02, 1.91718055e-06,\n", " -5.55948643e-03, -1.54020138e-03])" ] }, @@ -413,7 +414,7 @@ } ], "source": [ - "tao.cmd_real('python lat_list -array_out 1@0>>Q*|model orbit.floor.x')" + "tao.cmd_real(\"python lat_list -array_out 1@0>>Q*|model orbit.floor.x\")" ] }, { @@ -435,17 +436,17 @@ { "data": { "text/plain": [ - "{'x': 0.0031086901274779,\n", - " 'px': 0.0034460056817536,\n", - " 'y': 0.000183189785860305,\n", - " 'py': 0.000248941211797618,\n", - " 'z': -0.0004036816716607,\n", - " 'pz': -7.17210141039588e-06,\n", + "{'x': 0.00310869012747717,\n", + " 'px': 0.00344600568175254,\n", + " 'y': 0.000183189785854024,\n", + " 'py': 0.000248941211794017,\n", + " 'z': -0.00040368167168426,\n", + " 'pz': -7.17210139174442e-06,\n", " 'spin': array([0., 0., 0.]),\n", " 'field': array([0., 0.]),\n", " 'phase': array([0., 0.]),\n", " 's': 1.2,\n", - " 't': 4.00411569818167e-09,\n", + " 't': 4.00411569818175e-09,\n", " 'charge': 0.0,\n", " 'dt_ref': 0.0,\n", " 'p0c': 5288999975.31481,\n", @@ -481,9 +482,9 @@ { "data": { "text/plain": [ - "array([ 2.81123079e-03, -1.06250116e-03, 1.37663897e-04, 3.08061468e-04,\n", - " -3.66558773e-04, -3.42869828e-04, -9.92518410e-06, 1.28279236e-03,\n", - " 2.66250275e-03, 2.68364505e-03])" + "array([ 2.81123075e-03, -1.06250116e-03, 1.37663906e-04, 3.08061464e-04,\n", + " -3.66558772e-04, -3.42869819e-04, -9.92517182e-06, 1.28279238e-03,\n", + " 2.66250141e-03, 2.68364369e-03])" ] }, "execution_count": 11, @@ -492,7 +493,7 @@ } ], "source": [ - "tao.evaluate('data::cbar.11[1:10]|model')" + "tao.evaluate(\"data::cbar.11[1:10]|model\")" ] }, { @@ -528,7 +529,7 @@ } ], "source": [ - "s = tao.lat_list('*', 'ele.s', verbose=True)\n", + "s = tao.lat_list(\"*\", \"ele.s\", verbose=True)\n", "s[0:5]" ] }, @@ -556,8 +557,8 @@ } ], "source": [ - "state = tao.lat_list('*', 'orbit.state')\n", - "ix = tao.lat_list('*', 'ele.ix_ele')\n", + "state = tao.lat_list(\"*\", \"orbit.state\")\n", + "ix = tao.lat_list(\"*\", \"ele.ix_ele\")\n", "state.dtype, ix.dtype" ] }, @@ -585,7 +586,7 @@ } ], "source": [ - "names = tao.lat_list('*', 'ele.name')\n", + "names = tao.lat_list(\"*\", \"ele.name\")\n", "names[0:5]" ] }, @@ -614,17 +615,7 @@ "outputs": [ { "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -639,11 +630,7 @@ } ], "source": [ - "plt.plot(\n", - " tao.lat_list('*', 'ele.s'),\n", - " tao.lat_list('*', 'orbit.vec.1'),\n", - " marker='.'\n", - ")" + "plt.plot(tao.lat_list(\"*\", \"ele.s\"), tao.lat_list(\"*\", \"orbit.vec.1\"), marker=\".\");" ] }, { @@ -660,17 +647,7 @@ "outputs": [ { "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -685,7 +662,7 @@ } ], "source": [ - "plt.plot(tao.lat_list('*', 'ele.s', flags='-array_out -track_only'))" + "plt.plot(tao.lat_list(\"*\", \"ele.s\", flags=\"-array_out -track_only\"));" ] }, { @@ -702,17 +679,7 @@ "outputs": [ { "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -727,7 +694,7 @@ } ], "source": [ - "plt.plot(tao.lat_list('*', 'ele.s', flags='-array_out -index_order'))" + "plt.plot(tao.lat_list(\"*\", \"ele.s\", flags=\"-array_out -index_order\"));" ] }, { @@ -742,20 +709,139 @@ { "cell_type": "code", "execution_count": 19, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['beam', 'beam_init', 'bmad_com', 'branch1', 'building_wall_graph', 'building_wall_list', 'building_wall_point', 'building_wall_section', 'bunch1', 'bunch_comb', 'bunch_params', 'constraints', 'da_aperture', 'da_params', 'data', 'data_d1_array', 'data_d2', 'data_d2_array', 'data_d2_create', 'data_d2_destroy', 'data_d_array', 'data_parameter', 'data_set_design_value', 'datum_create', 'datum_has_ele', 'derivative', 'ele_ac_kicker', 'ele_cartesian_map', 'ele_chamber_wall', 'ele_control_var', 'ele_cylindrical_map', 'ele_elec_multipoles', 'ele_floor', 'ele_gen_attribs', 'ele_grid_field', 'ele_head', 'ele_lord_slave', 'ele_mat6', 'ele_methods', 'ele_multipoles', 'ele_orbit', 'ele_param', 'ele_photon', 'ele_spin_taylor', 'ele_taylor', 'ele_taylor_field', 'ele_twiss', 'ele_wake', 'ele_wall3d', 'em_field', 'enum', 'evaluate', 'floor_orbit', 'floor_plan', 'help', 'inum', 'lat_branch_list', 'lat_calc_done', 'lat_ele_list', 'lat_list', 'lat_param_units', 'matrix', 'merit', 'orbit_at_s', 'parse_tao_python_data', 'place_buffer', 'plot1', 'plot_curve', 'plot_curve_manage', 'plot_graph', 'plot_graph_manage', 'plot_histogram', 'plot_lat_layout', 'plot_line', 'plot_list', 'plot_symbol', 'plot_template_manage', 'plot_transfer', 'ptc_com', 'ring_general', 'shape_list', 'shape_manage', 'shape_pattern_list', 'shape_pattern_manage', 'shape_pattern_point_manage', 'shape_set', 'show', 'species_to_int', 'species_to_str', 'spin_polarization', 'spin_resonance', 'super_universe', 'tao_global', 'tao_parameter_dict', 'twiss_at_s', 'universe', 'var', 'var_create', 'var_general', 'var_v1_array', 'var_v1_create', 'var_v1_destroy', 'var_v_array', 'wave']\n" + "beam\n", + "beam_init\n", + "bmad_com\n", + "branch1\n", + "building_wall_graph\n", + "building_wall_list\n", + "building_wall_point\n", + "building_wall_section\n", + "bunch1\n", + "bunch_comb\n", + "bunch_data\n", + "bunch_params\n", + "cmd\n", + "cmd_integer\n", + "cmd_real\n", + "cmds\n", + "constraints\n", + "da_aperture\n", + "da_params\n", + "data\n", + "data_d1_array\n", + "data_d2\n", + "data_d2_array\n", + "data_d2_create\n", + "data_d2_destroy\n", + "data_d_array\n", + "data_parameter\n", + "data_set_design_value\n", + "datum_create\n", + "datum_has_ele\n", + "derivative\n", + "ele_ac_kicker\n", + "ele_cartesian_map\n", + "ele_chamber_wall\n", + "ele_control_var\n", + "ele_cylindrical_map\n", + "ele_elec_multipoles\n", + "ele_floor\n", + "ele_gen_attribs\n", + "ele_gen_grad_map\n", + "ele_grid_field\n", + "ele_head\n", + "ele_lord_slave\n", + "ele_mat6\n", + "ele_methods\n", + "ele_multipoles\n", + "ele_orbit\n", + "ele_param\n", + "ele_photon\n", + "ele_spin_taylor\n", + "ele_taylor\n", + "ele_twiss\n", + "ele_wake\n", + "ele_wall3d\n", + "em_field\n", + "enum\n", + "evaluate\n", + "floor_orbit\n", + "floor_plan\n", + "get_output\n", + "global_opti_de\n", + "global_optimization\n", + "help\n", + "init\n", + "inum\n", + "lat_branch_list\n", + "lat_calc_done\n", + "lat_ele_list\n", + "lat_list\n", + "lat_param_units\n", + "matrix\n", + "merit\n", + "orbit_at_s\n", + "place_buffer\n", + "plot1\n", + "plot_curve\n", + "plot_curve_manage\n", + "plot_graph\n", + "plot_graph_manage\n", + "plot_histogram\n", + "plot_lat_layout\n", + "plot_line\n", + "plot_list\n", + "plot_symbol\n", + "plot_template_manage\n", + "plot_transfer\n", + "ptc_com\n", + "register_cell_magic\n", + "reset_output\n", + "ring_general\n", + "shape_list\n", + "shape_manage\n", + "shape_pattern_list\n", + "shape_pattern_manage\n", + "shape_pattern_point_manage\n", + "shape_set\n", + "show\n", + "space_charge_com\n", + "species_to_int\n", + "species_to_str\n", + "spin_invariant\n", + "spin_polarization\n", + "spin_resonance\n", + "super_universe\n", + "tao_global\n", + "taylor_map\n", + "twiss_at_s\n", + "universe\n", + "var\n", + "var_create\n", + "var_general\n", + "var_v1_array\n", + "var_v1_create\n", + "var_v1_destroy\n", + "var_v_array\n", + "wave\n" ] } ], "source": [ "from pytao import interface_commands\n", - "all_cmds = [name for name in dir(interface_commands) if not name.startswith('__')]\n", - "print(all_cmds)" + "\n", + "all_cmds = [name for name in dir(Tao) if not name.startswith(\"_\")]\n", + "for cmd in all_cmds:\n", + " print(cmd)" ] }, { @@ -773,7 +859,7 @@ { "data": { "text/plain": [ - "104" + "116" ] }, "execution_count": 20, @@ -785,6 +871,70 @@ "len(all_cmds)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each has documentation and an example associated with it:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m\n", + "\u001b[0mtao\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_d2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0md2_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mix_uni\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mas_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "Output information on a d2_datum.\n", + "\n", + "Parameters\n", + "----------\n", + "d2_name\n", + "ix_uni : optional\n", + "\n", + "Returns\n", + "-------\n", + "string_list\n", + "\n", + "Notes\n", + "-----\n", + "Command syntax:\n", + " python data_d2 {ix_uni}@{d2_name}\n", + "\n", + "Where:\n", + " {ix_uni} is a universe index. Defaults to s%global%default_universe.\n", + " {d2_name} is the name of the d2_data structure.\n", + "\n", + "Examples\n", + "--------\n", + "Example: 1\n", + " init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching\n", + " args:\n", + " ix_uni: 1\n", + " d2_name: twiss\n", + "\u001b[0;31mFile:\u001b[0m ~/Repos/pytao/pytao/interface_commands.py\n", + "\u001b[0;31mType:\u001b[0m method" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tao.data_d2?" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -798,11 +948,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "tao2=Tao('-init $ACC_ROOT_DIR/bmad-doc/tao_examples/csr_beam_tracking/tao.init -noplot') " + "tao2 = Tao(\n", + " \"-init $ACC_ROOT_DIR/bmad-doc/tao_examples/csr_beam_tracking/tao.init -noplot\"\n", + ")" ] }, { @@ -814,7 +966,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -831,13 +983,13 @@ " 'END']" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tao.lat_list('*', 'ele.name')" + "tao.lat_list(\"*\", \"ele.name\")" ] }, { @@ -859,22 +1011,22 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "dict_keys(['beta_x', 'alpha_x', 'gamma_x', 'phi_x', 'eta_x', 'etap_x', 'sigma_x', 'sigma_p_x', 'emit_x', 'norm_emit_x', 'beta_y', 'alpha_y', 'gamma_y', 'phi_y', 'eta_y', 'etap_y', 'sigma_y', 'sigma_p_y', 'emit_y', 'norm_emit_y', 'beta_z', 'alpha_z', 'gamma_z', 'phi_z', 'eta_z', 'etap_z', 'sigma_z', 'sigma_p_z', 'emit_z', 'norm_emit_z', 'beta_a', 'alpha_a', 'gamma_a', 'phi_a', 'eta_a', 'etap_a', 'sigma_a', 'sigma_p_a', 'emit_a', 'norm_emit_a', 'beta_b', 'alpha_b', 'gamma_b', 'phi_b', 'eta_b', 'etap_b', 'sigma_b', 'sigma_p_b', 'emit_b', 'norm_emit_b', 'beta_c', 'alpha_c', 'gamma_c', 'phi_c', 'eta_c', 'etap_c', 'sigma_c', 'sigma_p_c', 'emit_c', 'norm_emit_c', 'sigma_11', 'sigma_12', 'sigma_13', 'sigma_14', 'sigma_15', 'sigma_16', 'sigma_21', 'sigma_22', 'sigma_23', 'sigma_24', 'sigma_25', 'sigma_26', 'sigma_31', 'sigma_32', 'sigma_33', 'sigma_34', 'sigma_35', 'sigma_36', 'sigma_41', 'sigma_42', 'sigma_43', 'sigma_44', 'sigma_45', 'sigma_46', 'sigma_51', 'sigma_52', 'sigma_53', 'sigma_54', 'sigma_55', 'sigma_56', 'sigma_61', 'sigma_62', 'sigma_63', 'sigma_64', 'sigma_65', 'sigma_66', 'rel_min_1', 'rel_max_1', 'centroid_vec_1', 'rel_min_2', 'rel_max_2', 'centroid_vec_2', 'rel_min_3', 'rel_max_3', 'centroid_vec_3', 'rel_min_4', 'rel_max_4', 'centroid_vec_4', 'rel_min_5', 'rel_max_5', 'centroid_vec_5', 'rel_min_6', 'rel_max_6', 'centroid_vec_6', 'centroid_t', 'centroid_p0c', 'centroid_beta', 'ix_ele', 'direction', 'species', 'location', 's', 'charge_live', 'n_particle_tot', 'n_particle_live', 'n_particle_lost_in_ele', 'beam_saved'])" + "dict_keys(['twiss_beta_x', 'twiss_alpha_x', 'twiss_gamma_x', 'twiss_phi_x', 'twiss_eta_x', 'twiss_etap_x', 'twiss_sigma_x', 'twiss_sigma_p_x', 'twiss_emit_x', 'twiss_norm_emit_x', 'twiss_beta_y', 'twiss_alpha_y', 'twiss_gamma_y', 'twiss_phi_y', 'twiss_eta_y', 'twiss_etap_y', 'twiss_sigma_y', 'twiss_sigma_p_y', 'twiss_emit_y', 'twiss_norm_emit_y', 'twiss_beta_z', 'twiss_alpha_z', 'twiss_gamma_z', 'twiss_phi_z', 'twiss_eta_z', 'twiss_etap_z', 'twiss_sigma_z', 'twiss_sigma_p_z', 'twiss_emit_z', 'twiss_norm_emit_z', 'twiss_beta_a', 'twiss_alpha_a', 'twiss_gamma_a', 'twiss_phi_a', 'twiss_eta_a', 'twiss_etap_a', 'twiss_sigma_a', 'twiss_sigma_p_a', 'twiss_emit_a', 'twiss_norm_emit_a', 'twiss_beta_b', 'twiss_alpha_b', 'twiss_gamma_b', 'twiss_phi_b', 'twiss_eta_b', 'twiss_etap_b', 'twiss_sigma_b', 'twiss_sigma_p_b', 'twiss_emit_b', 'twiss_norm_emit_b', 'twiss_beta_c', 'twiss_alpha_c', 'twiss_gamma_c', 'twiss_phi_c', 'twiss_eta_c', 'twiss_etap_c', 'twiss_sigma_c', 'twiss_sigma_p_c', 'twiss_emit_c', 'twiss_norm_emit_c', 'sigma_11', 'sigma_12', 'sigma_13', 'sigma_14', 'sigma_15', 'sigma_16', 'sigma_21', 'sigma_22', 'sigma_23', 'sigma_24', 'sigma_25', 'sigma_26', 'sigma_31', 'sigma_32', 'sigma_33', 'sigma_34', 'sigma_35', 'sigma_36', 'sigma_41', 'sigma_42', 'sigma_43', 'sigma_44', 'sigma_45', 'sigma_46', 'sigma_51', 'sigma_52', 'sigma_53', 'sigma_54', 'sigma_55', 'sigma_56', 'sigma_61', 'sigma_62', 'sigma_63', 'sigma_64', 'sigma_65', 'sigma_66', 'rel_min_1', 'rel_max_1', 'centroid_vec_1', 'rel_min_2', 'rel_max_2', 'centroid_vec_2', 'rel_min_3', 'rel_max_3', 'centroid_vec_3', 'rel_min_4', 'rel_max_4', 'centroid_vec_4', 'rel_min_5', 'rel_max_5', 'centroid_vec_5', 'rel_min_6', 'rel_max_6', 'centroid_vec_6', 'centroid_t', 'centroid_p0c', 'centroid_beta', 'ix_ele', 'direction', 'species', 'location', 's', 't', 'sigma_t', 'charge_live', 'n_particle_tot', 'n_particle_live', 'n_particle_lost_in_ele', 'beam_saved'])" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "stats = tao.bunch_params('end')\n", + "stats = tao.bunch_params(\"end\")\n", "stats.keys()" ] }, @@ -887,22 +1039,12 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABJMAAAM6CAYAAAA1xwIsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/av/WaAAAACXBIWXMAAB7CAAAewgFu0HU+AAEAAElEQVR4nOzde3iUd53//9c9kxMhQ4AAEwqUNglgGxAstc1Yg9bapttarV0bVwqu1WK0uisLbtfVreeqVUG6u+0asVoLPRi1YrUU2vprhZYEKgpN6CkllkJKpiFAMgEmh8n8/uB7xxzmPPeckufjuryulEw+92eSzMj95v1+fQy/3+8XAAAAAAAAEAFbqjcAAAAAAACAzEExCQAAAAAAABGjmAQAAAAAAICIUUwCAAAAAABAxCgmAQAAAAAAIGIUkwAAAAAAABAxikkAAAAAAACIGMUkAAAAAAAARIxiEgAAAAAAACJGMQkAAAAAAAARo5gEAAAAAACAiFFMAgAAAAAAQMQoJgEAAAAAACBiFJMAAAAAAAAQMYpJAAAAAAAAiBjFJAAAAAAAAEQsK9UbwPjk9XrV2NgoSZo+fbqysvhVBAAAAADAav39/Wpvb5ckLVq0SHl5eXGvyR08UqKxsVGXXHJJqrcBAAAAAMC4sWfPHr3zne+Mex3G3AAAAAAAABAxOpOQEtOnTx/8eM+ePZo5c2YKdwMAAAAAwNh09OjRwcmgoffi8aCYhJQYmpE0c+ZMzZ49O4W7AQAAAABg7LMqr5gxNwAAAAAAAESMYhIAAAAAAAAiRjEJAAAAAAAAEaOYBAAAAAAAgIhRTAIAAAAAAEDEKCYBAAAAAAAgYhSTAAAAAAAAEDGKSQAAAAAAAIgYxSQAAAAAAABEjGISAAAAAAAAIkYxCQAAAAAAABGjmAQAAAAAAICIUUwCAAAAAABAxCgmAQAAAAAAIGIUkwAAAAAAABAxikkAAAAAAACIGMUkAAAAAAAARIxiEgAAAAAAACJGMQkAAAAAAAARo5gEAAAAAACAiFFMAgAAAAAAQMSyUr0BAAAAAACAUDzePrV1enWq16eJOXYVF+bJkZed6m2NWxSTAAAAAABA2vH7/apv6dCm+kN64kW3fAP+wc/ZbYaqyp1aUTFXrpIiGYaRwp2OPxSTAAAAAABAWmlq7dSaun161d0d8PO+Ab+2NrZpa2Ob5jsLtL56iRbOKkzyLscvMpMAAAAAAEDa2Nncrura+qCFpJFedXerurZeO5vbE7wzmCgmAQAAAACAtNDU2qmaTXt1utcX1ded7vWpZtNeNbV2JmhnGIpiEgAAAAAASDm/3681dfuiLiSZTvf6tLZuv/x+f/gHx8jj7VOz26N9h0+q2e2Rx9uXsGulMzKTAAAAAABAytW3dEQ82hbMK26PGlqOy1VaZNGuCAIPhGISAAAAAABIuc0Nhyxbx6piEkHggTHmBgAAAABAlBh3spbH26ftB9yWrLXtQJslPw+CwIOjMwkAAAAAgAgw7pQ4bZ3eYd/PePgG/HJ3eeXIy455jXiDwOtqXGO6Q4liEgAAAAAAYTDulFinYgzdDqa7J/b1rAoC37a6cswWFRlzAwAAAAAgBMadEm9ijt3S9QpyY1/PyiDwsYpiEgAAAAAAQcQ77tTU2pmgnY0txYV5stus6eLJshlyTsqL+eutDAIfqygmAQAAAAAQgFXjTn6/NVlAY5kjL1tV5U5L1qoqL445Lykdg8DTEcUkAAAAAAACYNwpuVZUzE35OokIAh+LKCYBAAAAAFLK4+1Ts9ujfYdPqtntSZtuDsadkstVUqT5zoK41ljgdKiiZGrMX59OQeDpjNPcAAAAAABJ5/f7Vd/SoU31h/TEi+5h3SB2m6GqcqdWVMyVq6QoJSdiJWLcKZ6j6scDwzC0vnqJqmvrYxotzM+xa1314rh+X9IpCDydUUwCAAAAACRVU2un1tTtCzpC5hvwa2tjm7Y2tmm+s0Drq5do4azCpO4xEeNOFJPCWzirULUrl0Ydep6fY1ftyqVx/56YQeBW/OzjDQJPZ4y5AQAAAACSZmdzu6pr6yPOInrV3a3q2nrtbG5P8M6GY9wpdSrnTVddjSvikbcFTofqalyqnDc97munSxB4uqOYBAAAAABIiqbWzqg7TqSzp6LVbNqrptbOBO1stEwYd0rXrCkrLJxVqO2rl+mhVRW6ZlGx7Lbho2tZNkPXLpqph1ZVaNvqSks719IhCDzdMeYGAAAAAEg4v9+vNXX7YsrCkc4WlNbW7de21ZVJyVBK13GndM+aspJhGHKVFslVWiSPt0/uLq+6e3wqyLXLOSkvYV0/ZhB4PCf5xRsEnu4oJgEAAAAAEq6+pSOum3NJesXtUUPLcblKiyzaVXDmuNPWxra417Jq3CkTsqYSxZGXnbSRsXQIAk93jLkBAAAAABJuc8OhtFonEuk07pQpWVNjhRkEnh/luKNVQeDpjmISAAAAACChPN4+bT/gtmStbQfakpYNZI47xcOKcadMypoaS1IZBJ7uKCYBAAAAABKqrdNrSfaQdHaUy93ltWStcMxxp2i7U0xWjDtZlTXl91vz/R9vUhkEns7ITAIAAAAAJNSpGAshwXT3WLteKOa4U7SdQVaNO2Va1tRYlKog8HRGZxIAAAAAIKEmxtjZE0xBrrXrhZPKcadMzJoayxx52Sqb4dCSOZNVNsMxLgtJEp1JAAAAAIAEKy7Mk91mWDLqlmUz5JyUZ8GuomOOOzW0HNemhte1/YB72PPJshmqKi/Wioq5qiiZaslJXonImhqvxQ9Yi2ISAAAAACChHHnZqip3amtjW9xrVZUXp6wgkuxxp0RkTVFMghUoJgEAAAAAEm5FxVxLikkrKuZasJv4OfKyE16YyeSsKYxtZCYBAAAAABLOVVIUceZQMAucDlWUTLVoR+kv07OmMHZRTAIAAAAAJJxhGFpfvUT5MRZI8nPsWle92JIsokxhZk1ZIVVZUxibKCYBAAAAAJJi4axC1a5cGnVBKT/HrtqVS7VwVmGCdpaezKwpK6QyawpjD8UkAAAAAEDSVM6brroaV8QjbwucDtXVuFQ5b3qCd5aerMqISpesKYwNBHADAAAAAJJq4axCbV+9TA0tx7Wp4XVtP+AedmpZls1QVXmxVlTMVUXJ1HE12jaSmTX1qrs75jXGW9YUEo9iEgAAAAAg6QzDkKu0SK7SInm8fXJ3edXd41NBrl3OSXlJGcnyePvU1unVqV6fJubYVVyYnOtGw8yaqq6t1+kYTncbj1lTSDyKSQAAAACAlHLkZSetiOP3+1Xf0qFN9Yf0xIvDO6LsNkNV5U6tqJgrV0lR2hRgzKypmk17oyoojdesKSQexSQAAAAAwLjQ1NqpNXX7go6M+Qb82trYpq2NbZrvLND66iVpU4gxs6ZC7X+oBU6H1lUvTpv9Y2yhmAQAAAAAGPN2NrdH1dnzqrtb1bX1ql25NG3Cv+cW5et/P/YO7X79hJ440KZdr3XI5ydrCslHMQkAAAAAMKY1tXZGPSImSad7farZtFd1Na6UdfiEGsuzGdKyedP0/gudcpUUpWXmE8YmikkAAAAAgDHL7/drTd2+mMKrpbMFpbV1+7VtdWXSO33CjeUN+KUdzce0o/lY2o3lYWyzpXoDAAAAAAAkSn1LR0QZQ6G84vaooeW4RTuKzM7mdlXX1ke8d3Msb2dze4J3BlBMAgAAAACMYZsbDqXVOpGIdyyvqbUzQTsDzqKYBAAAAAAYkzzePm0/4LZkrW0H2uTx9lmyVihWjeX5hwRzA1ajmAQAAAAAGJPaOr3DAqvj4Rvwy93ltWStUDJ1LA/jC8UkAAAAAMCYdCrG7p5gunusXS+QTBzLw/hDMQkAAAAAMCZNzLFbul5BrrXrjZSJY3kYnygmAQAAAADGpOLCPNlthiVrZdkMOSflWbJWMJk4lofxiWISAAAAAGBMcuRlq6rcaclaVeXFcuRlW7JWMJk4lofxiWISAAAAAGDMWlExN63WCSXTxvIwflFMAgAAAACMWa6SIs13FsS1xgKnQxUlUy3aUXCZNpaH8YtiEgAAAABgzDIMQ+urlyg/xq6f/By71lUvlmFYU+QJJdPG8jB+UUwCAAAAAIxpC2cVqnbl0qgLSvk5dtWuXKqFswoTtLPRMmksD+MXxSQAAAAAwJhXOW+66mpcEY+8LXA6VFfjUuW86Qne2XCZNJaH8Ssr1RsAAAAAACAZFs4q1PbVy9TQclybGl7X9gNu+Qb8g5/PshmqKi/Wioq5qiiZmpTRtpHMsbzq2nqdjuF0t2SO5WH8opgEAAAAABg3DMOQq7RIrtIiebx9cnd51d3jU0GuXc5JeWmRM2SO5dVs2htVQSkVY3kYnxhzAwAAAACMS468bJXNcGjJnMkqm+FIi0KSKdqxPEl6x7mTZbcZ8vv94R8MxIFiEgAAAAAAacgcy3toVYUuKysK+/jnXuvQ8o27VbVhh5paO5OwQ4xXFJMAAAAAAEhThmGof2BAf33jZMRf86q7W9W19drZ3J64jWFco5gEAAAAAECaamrtjDo7SZJO9/pUs2kvHUpICIpJAAAAAAAE4PH2qdnt0b7DJ9Xs9sjj7Uvq9f1+v9bU7YvpVDfpbEFpbd1+MpRgOU5zAwAAAABkNI+3T22dXp3q9Wlijl3FhbGfyub3+1Xf0qFN9Yf0xItu+Qb+Xoix2wxVlTu1omKuXCVFMgzDqqcQUH1Lh151d8e1xitujxpajstVGj5zCYgUxSQAAAAAQMZJRNGnqbVTa+r2BS3g+Ab82trYpq2NbZrvLND66iVaOKvQkucTyOaGQ5atQzEJVqKYBAAAAADIKIko+uxsbo8qm8gMua5duVSV86ZH/RzC8Xj7tP2A25K1Hm86qn1vnJAMI+7OLUCimAQAAAAAyCCJKPrEG3JdV+OyvEOprdM7rNsqHgN+6fp7dg3+d7LH9TD2EMANAAAAAMgIiTjZLF1Drk/FuJ9ImJ1byzfuVtWGHZz4hqhRTAIAAAAApL1EFX2sDLm20sQcu6XrBWN2bu1sbk/K9TA2UEwCAAAAAKS9RBV9rAy5tlJxYZ7stuSMn4Xq3AICoZgEAAAAAEh7iSj6WBlyve1AmzzePkvWkiRHXraqyp2WrRdOosb1MDZRTAIAAAAApLVEFX2sDLn2Dfjl7vJaspZpRcVcS9cLJxHjehibKCYBAAAAANJaooo+Vodcd/cEX8/j7VOz26N9h0+q2e2JqIvJVVKk+c4CK7cYltXjehibslK9AQAAAAAAQklU0cfqkOuC3OHr+f1+1bd0aFP9IT3xontYQcxuM1RV7tSKirlylRTJMEbnIxmGofXVS1RdWx9z8Hi0zM4tR152Uq6HzEQxCQAAAACQ1hJV9DFDrq3oerIbknNS3uB/N7V2ak3dvqCh4b4Bv7Y2tmlrY5vmOwu0vnqJFs4qHPW4hbMKVbtyqWo27U1KQcns3KKYhFAYcwMAAAAApDUrTzbLshmDRR9LQ64NQ/sOn5Qk7WxuV3VtfcSnz73q7lZ1bb12NrcH/HzlvOmqq3ElbeQt1LheMsUyGojkoDMJAAAAAMYgj7dPbZ1ener1aWKOXcWFeRnbbWIWfbY2tsW9VlV58bDvw4qKuZas6xvwq2bTXn3nw4v05d82Rt1FdLrXp5pNe1VX4wraobR99TI1tBzXpobXtf2A27IcqZFGjuslU7yjgUgOikkAAAAAMEaM5Rtxq4o+I09IM0OuI+0iCuV0r0+3/foF9foGYv76tXX7tW11ZdAMJVdpkVylRfJ4++Tu8qq7xydDft1wT718/viLS0M7t5LNqtFAJB5jbgAAAAAwBjS1dqpqww4t37hbjze1jepaMW/El2/craoNO9TU2pmincbGipPNFjgdqiiZOuzPzJDrfItymWItJJlecXvU0HI87OMcedkqm+HQkjmTtXjOFFUttGZcb2TnVrJYPRqIxKKYBAAAAAAZzoob8XTPp4m36JOfY9e66sUBO37MkGt7mjRrbW44FPXXjOy4ipVV60SjqbUzpoBxczQw0wqjYwFjbgAAAACQweK5Ef/0/X/W7R+4UDubj1k+FpeIzKZYTzbLz7GrduXSkCNRS+ZMVmISiKK37UCbPN6+qL5fVozrBercSjS/3681dftiPqku3GggEoNiEgAAAAAEke4h1vHeiJ/pG9CXf9sU8HOx5NMkI7PJPNksVLbOUAucDq2rXhx2722dXiUozzpqvgG/3F3ekL9rI383C3KztPr98/Vvv9ynnv7oR+1CdW4lUn1LR9x5VeZooKu0yKJdIRyKSQAAAAAwRCaFWFtxIx4JcyyuduVSVc6bHvAxyQxPDneyWZbNUFV5sVZUzFVFydSIfk6nYizIJUp3z+j9hPrdjEcknVuJEstIX7B1KCYlz5goJr3xxhv67//+bz322GN64403lJubq7KyMlVXV+vWW29Vfn6+Jdd5+OGH9fOf/1wvvPCCTpw4oeLiYlVWVupzn/ucKioqIlqjo6ND//3f/60tW7bo9ddfl9/v1/nnn6/rr79e//qv/6qiotC//K+99pqef/557dmzR3v27NFf//pXnTlzRpL085//XJ/4xCcifj79/f2699579cADD+ill15Sd3e3Zs2apfe///3613/9V1144YURrwUAAACMBZl2mpRVN+KRCHV0/c7m9qhGzyIpToUT7GSzgly7nJOi7yCbaFEAt1UKcofvJ9zvZqwi7dxKBI+3T9sPuC1ZK5bRQMTO8PstODswhR577DHddNNN6uwMHLi1YMECbd26VSUlJTFfw+v16sYbb9Qf/vCHgJ+32Wz6+te/rttvvz3kOs8//7w+9KEP6ejRowE/f8455+h3v/udLr744oCf/9Of/qT3vve9QdePppjU0dGha6+9Vrt37w74+dzcXN1zzz365Cc/GdF60Tpy5IjmzJkjSTp8+LBmz56dkOsAAAAAkYq2ICL9vaMj1oJIPDzePi355pOWdadEaoHTMSyfpqm1U9W19TGN2uXn2AMWp1IhVd/PQLJshv761SsHCyOx/G6GWz/azq1EaHZ7dOWPdli23lNrlqlshsOy9caKRNx/Z/Rpbvv371d1dbU6OztVUFCgO+64Q7t27dIf//hHrVq1SpL0yiuv6Nprr1V3d+zV20996lODhaTLL79cW7Zs0Z49e3TvvfeqtLRUAwMD+upXv6qf/vSnQddobW3Vddddp6NHjyorK0u33XabduzYoR07dui2225TVlaW3nzzTX3gAx9Qa2trwDWG1v1sNpvKy8t1ySWXRP18fD6fbrjhhsFC0g033KDHH39cu3fv1n//939rxowZ6unp0ac//Wlt37496vUBAACATJOJp0m1dXpTUvgYenS9VeHJ6dDj4MjLVlW5M9XbkCRVlRcPFpJi/d0MJC/Lpv+76R3661ev1N03XSRXaWpHNa0eLQw0GojEyOhi0urVq3X69GllZWXpiSee0Je//GW5XC69733v009+8hN9//vflyS9/PLLWr9+fUzX+NOf/qQHH3xQknTdddfpySef1Ic+9CG9853v1Cc/+Uk1NDTo3HPPlSTddtttOnnyZMB1vvKVr8jtPtu+9+CDD+rOO+9UZWWlKisrdeeddw5ew+12B+1wmjVrln7wgx/omWeeUWdnp5qamvTZz3426ue0adMm7dhxtvp766236je/+Y2uvvpqXXLJJfqXf/kXPffcc5o0aZJ8Pp/+5V/+Rf39/VFfAwAAAMgUiSqIeLx9anZ7tO/wSTW7PfJ4+6zY7qBUZvyY43VWhicnUqQ/ixUVcxO6j0iZ+4j3d3Mkb/+ANjz1mgpy0yPxxurRwpGjgUicjC0mPf/883rmmWckne0ccrlcox6zdu1aXXDBBZKkDRs2qK8v+jdvsyBlt9t1zz33yG4f/ss5bdo03XnnnZKkEydO6N577x21htvt1ubNmyVJVVVVuvHGG0c95sYbb1RVVZUk6f777x8sPA01b948ffGLX9R73vMeFRQURP1cTD/4wQ8kSVOmTBn8eKiysjL953/+pySpublZv/vd72K+FgAAAJDurCyI+P1+7Tp4TJ/dvFdLvvmkrvzRDl1/93O68kc7tOSbT+rWB/Zq18FjlnTipDLjx8ynsTI82Wqx/CxcJUWa74z9XkuScuzx3WYvcDpUUTJVUmIC1pNRvItUcWGe7DZrOqOybIack/IsWQvhZWwxacuWLYMf33zzzQEfY7PZ9PGPf1zS2UKPWXyKVHd3t/74xz9Kkq688sqgc4U33HCDJk2aJEl65JFHRn3+0Ucflc/nC7lXSYN5Rz6fT48++mhUe41Uc3OzXnzxRUnSRz/60aDh5EOzlwI9JwAAAGCssKqQcffTr6lqww4t37hbjze1jRpBM8O7l2/craoNO+IejbPyRjxavgG/Wtq7LQ9PtkpTa2dMPwvDMLS+eonyYyzU5efY9f2PvD2ur19XvXhw9CxRAevJDG4PxcrRwqGjgUi8jC0m7dy5U5I0ceJELV26NOjj3vOe9wx+/Oyzz0Z1jT179qinp2fUOiPl5OQMnua2Z8+eUR1Q5l7DrRPPXiMV6V6Ki4s1f/78hO4FAAAASDUrT5N69rVjEXeRmKeZ7Wxuj/l6qc74eeP4acsym3wDfrm7vJastbO5XdW19TH/LBbOKlTtyqVRF4TMMPbr3zErrq83w8it/N0cyeriXTysGi1MlxHF8SJji0kvvfSSpLMjWVlZwec93/a2t436mmivMXKdUNfp7+9Xc3NzwHUKCwtVXFwcdI2ZM2cOdjhFu9dIxfKcDh8+rFOnTkV1nSNHjoT8X7AT7QAAAIBkSlWItWRNeHdqb6Ct7YqyIjzZqiD1ynnTVVfjinjkbYHToboa1+CpfvF+vZTY300ri3fxsmK0cOhoIJIjPVK3ouT1enXs2DFJCnuk3ZQpUzRx4kSdOnVKhw8fjuo6Qx8f7jrmMXvm11144YWj1onk+L05c+bowIEDUe81UrE8J7/fryNHjmjBggURX2fo9wMAAABIV6kMsZb+Ht69bXVlTKdqmTfiVufqhJNlMzRnygRL14w3PNmqIHXzZ7FwVqG2r16mhpbj2tTwurYfcA8r7ththi49f6pueMcsXVXu1KQJOcPWC/f1WTZDVeXFWlExVxUlU0f9/BP9u5kuJ5+Zo4XVtfUx/exGjgYiOTKymOTxeAY/jiSI2iwmdXdH9wYbzXUmTpw4+PHI65jrRLrXQGtYxarnBAAAAIwFqQyxNpmByK7Soqi/Nt4b8VhVlRerdEaB7DbDku4ZK8KTrQxSN38WhmHIVVokV2mRus70avsBtx75yxHt/ttx+Qb82nWwQ7sOdsj+iKGqcqdWVMyVq6RosLAx9Os93j65u7zq7vGpINcu56S8kBk/if7dTKeTz8zRwmi7ykaOBiJ5MrKY5PX+vR0vJycnxCPPys3NlSSdOXMmYdcxrxHoOuY6idxrpKx6TuGE66w6evSoLrnkkqjWBAAAAKxmhlinatTNtLnhUEzFJCn2G/F4rKiYO5jZtLWxLe71rAhPtvJkuZE/i6bWTq2p2xe0WGUGem9tbNN8Z4HWVy8ZVeBw5GVH9RwT/bs5w5Eb/kFJZI4Ghvo+D7XA6dC66sUUklIkIzOT8vL+XrHu7e0N+3gzRHvChOjaMKO5jnmNQNcx10nkXiNl1XMKZ/bs2SH/N3PmzOg2DgAAACRAqkOsTfEGIkeb0ROPofk06RKebGVY9cifRbyB3rFK9O/mgTc94R+UZOZo4EOrKnTNouJRpxVm2Qxdu2imHlpVoW2rKykkpVBGdiY5HI7BjyMZvzLDoyMZM4v1OkMDqkdex+Fw6PTp0wnda6RGPqehxaVge0nkfgAAAIBUW1Ex15LumniYgcjxdOdEmtFzWdk0ffuxFy3Jp7Eis8mK8GQrw6qH/iziDfSuq3HFVfBI5O9mPN1wiRTPaCCSJyOLSXl5eZo2bZqOHTumI0eOhHzsiRMnBosi0YZCDw2oPnLkiC6++OKgjx061jXyOrNnz5bb7Q6716HrJCrAeuRzmjZtWti9GIYRUXg4AAAAkIlSFWI9khWByJHeiM+ZOsGSfBrDMHTre8u0+pf7Yt7zZ99bGnd4stVh1d09PssDvWORyN9NswMrnYsz0Y4GInkycsxNki644AJJ0muvvab+/v6gj3v55ZdHfU2khp7INnSdUNfJyspSWVlZwHU6OzvV1ha8qnz06FF1dXXFtNdIxfKc5syZMyyMGwAAABhLzBDr/BSHcVsdiOzIy1bZDIeWzJmsshmOYTflVhxdL509Qe2eZ16La5//98xB+f3xdRVZHVZdkGu3NNA7Von83TQ7sIBYZGwx6d3vfreks6NYe/fuDfq4P/3pT4MfX3bZZVFd453vfOdgSPXQdUbq7e1VQ0PDqK8Zuddw68Sz10hFupe2tja9+uqrCd0LAAAAkC7MEOtUFZSsOM0sWlbk06RDwcXv9+uN46fj2sNQ5s/CykDveCTyd9OKbjiMTxlbTLr++usHP/75z38e8DEDAwO6//77JUmTJ0/W5ZdfHtU1HA6HrrjiCknSU089FXRM7ZFHHhnsKPrwhz886vMf/OAHZbPZQu5Vku677z5Jks1m0wc/+MGo9hqp+fPnD3Y91dXV6fTpwG+65l6kwM8JAAAAGGti6da5rMyazBkrTjOLhTkWd89NS7Xvq1fqqTXLtOVzl+mpNcv0169eqbtvukiu0qKgY1qpLrg0tXaqasMOfeoXf7ZkH9LZn4WkhAV6x8L83TyvKN+SPZms7obD+JGxxaRLLrlElZWVkqR7771X9fX1ox6zbt06vfTSS5KkL3zhC8rOHv7mfN9998kwDBmGoa9//esBr/PFL35RktTf36/Pfe5z8vmGV26PHTum//iP/5B0tmB1yy23jFqjuLhYN910kyRp+/bt+vWvfz3qMb/61a+0fft2SdLKlStVXFwc9LnHy3xOx48f12233Tbq8wcPHtR3v/tdSVJpaSnFJAAAAIwb0XbrfO7ysiArRceqU9HiEWosLpBEnqAWiWhPWYvUioq5CQn0jtfCWYV69POXyRZfvNSgVHTDYezIyABu01133aXLLrtMZ86c0VVXXaUvf/nLuvzyy3XmzBk9/PDD+slPfiLpbDfO2rVrY7rG+973Pv3TP/2THn74YT366KO68sortXr1ap1zzjlqbGzUHXfcoTfeeEOS9L3vfU9TpkwJuM4dd9yhbdu2qb29XR/72Mf05z//WR/4wAckSX/4wx+0bt06SdL06dP17W9/O+h+fv3rXw87Fe7ZZ58N+LF0toh19dVXj1rjn//5n/Wzn/1Mzz33nO6++261tbVp1apVmjJlivbs2aNvfetb6urqks1m0//8z/8oKyujf00AAACAqERzmlS6nGaWCok6QS0SsZ6yFo75s9h/pNPSda0aJ5s0IUdXLyy25IS3VHXDYWzI6CrBO97xDv3yl7/UihUr1NXVpS9/+cujHjN//nw99thjcjgcMV/nZz/7mbq6urR161Y9/fTTevrpp4d93maz6fbbb1dNTU3QNebMmaPf//73uv7669XW1qY777xTd95557DHFBcXa8uWLSFPTvviF7+oQ4cCt4Dee++9uvfeewf/+z3veU/AYpLdbteWLVt0zTXX6Pnnn9dvfvMb/eY3vxn2mJycHP3v//6v/uEf/iHoXgAAAICxLtxpUmZAcnVtfUyFjfwcu9ZVL477NLORPN4+tXV6darXp4k5dhUXWn+keiJOUItEvKesBTP0Z5GIQG+rrKiYa0kxKR264ZC5MrqYJEnXXXedXnjhBd1111167LHHdOTIEeXk5KisrEw33nijPv/5zys/P7650gkTJuixxx7Tgw8+qPvuu0/79+/XyZMn5XQ6VVlZqc9//vNyuVxh17n00kvV2Niou+66S1u2bNHrr78uSTr//PP1oQ99SKtXr1ZRkTUz1+FMmzZNu3bt0saNG/Xggw/qpZde0qlTp3TOOefoiiuu0Be+8AWVl5cnZS8AAABAJjMDkqPtlMnPsat25dKAwdax8Pv9qm/p0Kb6Q3riRfewriG7zVBVuVMrKubKVRI8AykaqSq4WBH6PdLIn0VxYZ7sNsOSziurx8nGczcc0ofhj/cMRiAGR44c0Zw5cyRJhw8fDtmNBQAAAGSCptZOranbF9FN/gKnQ+uqF1tWSIrm2vOdBVpfvSTua3u8fVryzSctK7j89atXRtQ9desDey3pzDEF+1lYdZ1rF83U3TddFPc6QzW1dsbVDVdX47Lsdw/pLxH33xkbwA0AAAAA6STa8G6rbuajDaJ+1d2t6tp67Wxuj/mafr9fja2dml6QE/MaQ0Wa32Nl6Lch6d5/vjjoz8KqMbBEjJOZ3XD5UXaHWd0Nh/Er48fcAAAAACBdRBPebYVYg6hP9/pUs2lvTB0q0XRBRSrSgouVod9+SXOL8oOO/KX7OFnlvOmqq3GlrBsO4xvFJAAAAABIgHDh3fGKN4j6dK9Pa+v2a9vqyogzlHY2t1t+ilo0BZdkhn6na7j6UGY3XEPLcW1qeF3bDwzPysqyGaoqL9aKirmqKJma0L1gfKGYBAAAAAAZyIog6lfcHjW0HJerNPxBQLF2QYUSbcEl2aHf6RKuHkqyu+EAicwkAAAAAMhImxsOJW2deLugAoml4GKesmaFSE9ZM8fJ5s2YGNG682cUqK7Gpcp50+PdYtQcedkqm+HQkjmTVTbDQSEJCUMxCQAAAAAyjJVB1NsOtMnj7Qv5GCu6oIZa4HTEVHBx5GWrqtxpyR4iDf02RTwixiQZxgGKSQAAAACQYawMovYN+OXu8oZ8jFVdUDML8+I+zS7Zp6wl87Q8j7dPzW6P9h0+qWa3J2yRD0gVMpMAAAAAIMMkM4jayi6otzw9WjhrUlxB0Mk8ZS0Zp+X5/X7Vt3RoU/0hPfHi8ABtu81QVblTKyrmylVSRIA20gadSQAAAACQYZIZRJ3sLqhwDMPQre8tU6x1lUhDv606Lc/vD/y983j7tLXxqJb94Gkt37hbjze1jfo++wb82trYpuUbd6tqww41tXbGtBfAanQmAQAAAECGMYOorSjyhAuiTmYXVCR2Nrfry79tVJAaTUjRhH4n4rS8oV1I2w+0KZofnzk+V7tyaUrCvYGh6EwCAAAAgAyTzCDqZHZBhRPr2JkkGYb0nQ8virgQY/VpeU2tnarasGOwCymWOqA5PpeqDiUynWCiMwkAAAAAMtCKirna2thmyTqhHD/VG/c1TOG6oEKJd+zM75f+75mD+tCSc8KOuFl9Wt72A236t1/GvvehzPG5basrk5KhRKYTAqGYBAAAAAAZKBlB1AMDA1r7q30xrz9SuC6oUBIxdhaM1TlRX3j4r/L2DViynhT584hXU2un1tTtC/p9NzOdtja2ab6zQOurl8R8Sh8yC2NuAAAAAJCBDMPQ+uolyo9xDC1cEHVTa6eW/eAZHTkRX2D2UOG6oEKxeuwsFKtzoqwsJJms+n4Es7O5XdW19REX8MxMp53N7QndF9IDxSQAAAAACCGdc2IWzipU7cqlUReUwgVRm4WEIyfOWLFNSeG7oEKxeuws3M/Q6pyoRIjkecQq1myqVGc6IXkYcwMAAACAETIpJ6Zy3nTV1bhCjiMNtcDp0LrqxUELSfGEXIfyrevLY/5eWT125u7yhhy3s/K0vESJ5HnEIt5sqmRnOiE1KCYBAAAAwBCZmBOzcFahtq9epoaW49rU8Lq2HxheAMuyGaoqL9aKirmqKJka9CY/3kJCKFMn5sT8tVaPnXX3hF7PPC3PioDzRAr3PGKRzGwqZC6KSQAAAADw/+xsbo+qK8fMialduTTiI+cTxTAMuUqL5CotksfbJ3eXV909PhXk2uWclBdRB4sVhYRg4il8WD12VpAbfj2rTstLpI5ur/YdPqmJOXYVF0b2Mw7HymwqikljF8UkAAAAAFD8OTF1Na6QHUoeb5/aOr061euz9OY/EEdedkxrJzLUOZICTjBWjp1l2Qw5J+WFfZwVp+Ul2qd+sXfwYyvGLxORTZWo33GkFsUkAAAAAOOelTkx3T39g0Wj/GybDp84o1/vPZL22UtWFhJGirSAE4yVY2dV5cURFTjM0/Kqa+tj+r3Iy7Yl5BS3YKwYv0x2NhUyF8UkAAAAAOOeVTkxH/1Jg/YeOhHRDXm8N/9WdzpZWUgYKdICzkhDn+N7F0y3pJi0omJuxI81T8uLtmMtP8euDR9dos8+8JeUhHjHOn5pdTbVW54elc1wWLom0gPFJAAAAADjnlXjXXv+djymr4v05j+Rp8xZXUgYKpoCTqjnGK8FTocqSqZG9TXxnJaXyhDvSMcvh7I6m2rFT3fr6oXFadN9B+tQTAIAAAAwriVyvCsap3t9WnX/n3XHhxfp7bMKR3UaJfqUOasLCaZoCjjhnmM88nPsWle9OKaCRqyn5aU6xHvo+GUkz9vKbCpJGvAr7U4+hDUoJgEAAABIC8kMqB4qkeNd0fL2DWht3X5JwzuN+n0D+szmvyT0lLmCXOtvD3OzbPrhjW+PqJAR7Ul60cjPsat25dK4ChmxnJaXDiHer7g9amg5HtHJalZmU42UTicfIn6G3+9Pj3dNjCtHjhzRnDlzJEmHDx/W7NmzU7wjAAAApIKVY1uxFqP2HT6p6+9+Lu7nkkiGIcVy55afYw855jTs+3/ALV8Cbg8j6Uhpau2MOeg6nKFjZ6nQ1NqpD939XEoLltcumqm7b7ooosfuOnhMyzfuTthewv1OwnqJuP+mMwkAAABASlgxtmVFMSpR411WirXGE2rMKZEjZUOF60iJ9yS9QGyGdHV5sVa6zhs2dpYKc4vyleoejm0H2uTx9kVUXE10N1W0o3dIT7ZUbwAAAADA+LOzuV3VtfUR37CaBYmdze2Df9bU2qmqDTu0fONuPd7UNqrzwyxGLd+4W1UbdqiptTPg2mZOzFhljjkNFe33P15mGHSgn4EVJ+mNNOCXXmvvliMvK+UFi7ZOr1I9Rekb8Mvd5Y3osYZhaH31EuUnsMga6HcSmYViEgAAAICkamrtjCkbZ2hBwopilMnMiRnLhp5WF+v3P15mR8rILh2rTtIbKdTPPJkSeUpeNLp7It/HwlmFql25NKEFpUT93JEcFJMAAAAAJE28I02ne3363AN/ibsYNVI0R9dnInPMKREjZdF4xe3R5t1vqNntkcfbl/CT9IL9zD3ePjW7Pdp3+OTgXhIlXcYoC3Kj20flvOmqq3FpvrMgIfsxfyeRmchMAgAAAJA0Vow0HTp+OuavDZbXkg6nbiWSOebU6OlJ+XO8fUuTpLOZVu8qLUp4MLX5M3/8C+9Ww9+OWxL2Hg1zjDKVAdxZNkPOSXlRf93CWYXavnqZGlqOa1PD69rW1GbZyJ75O5mMExthPTqTAAAAACTU0C6Q/3vmYKq3EzCvxcyJsY/d6CR19/jSarTIN+DXzuZjSbnWK26Plv3gmbjztWKRDmOUVeXFMRdtDMOQq7RI99y0VJs/daml+4pm9A7phc4kAAAAAJYLdcpaOtjccEiu0qJhfza3KF/ptUtrGfIndKQs3R05cSaix4U7fS4WKyrmamtjmyVrxXp9K0x35Fqyjina0TukD4pJAAAAAAZ5vH1q6/TqVK9PE3PsKi7Mi7qjIVlHzscj0FHp6XDqVqJk2Qz5/Uq7ol66MrOW6mpcWjirMO71UjlGucDpUEXJVEvWsnJkL9bRO6QHikkAAADAOBeqiyjaLJmdze0pOSksWoHyWtLl1K1EqCov1r4j1o1ujQfB8rViYY5RVtfWJ/W1kZ9j17rqxZZlQJkje1Z0WcUzeofUIzMJAAAAGMeaWjtVtWGHJVkyqTpyPlYj81rys8fu7dFlZdP0vcdfSvU2Mk6gfK1YLZxVqNqVS5Uf5elu+Tl23Xb1gpi+rnblUks6q4ayamRurJ+gONaN3XdLAAAAACHtbG5XdW19xKM3ZpbMzub2UZ9L9ZHzsRiZ1/LGidhPiUtnpdMn6r5df5O3byDVW8lIVoaWV86brroal+Y7CyJ6/AKnQ3U1Lt363rKYvs6qzKehzJG9eFg5eofUoJgEAAAAZIChJ6I1uz3yePviWi/WLiIzS2Zkh1J9S0daZySNFCiv5ZG/tKZoN4l13dtnZtTPJt2Y+VpWWTirUNtXL9NDqyp0zaJi2W3DR9CybIauXTRTD62q0LbVlYOdRbF+ndXMkb1oO6VMVo/eITXITAIAAADSlJVZRiPXjaeLKFCWTDodOR+JkXktHm/fmD3pbM/rJ1K9hYwWKF8rXoZhyFVaJFdpkTzePrm7vOru8akg1y7npOCh97F+ndXMkb1oC9KJGr1D8lFMAgAAANJQuBPRzCyjrY1tmu8s0PrqJRHfoFnRRWRmyZg3tZlWiBmZ19LW6U3YSWdZNkNV5cW64aJZOndqvl440qmvbGlM2thZQ0tHUq4zlo3M17KSIy87piJQrF9nFXNkL9KTGxc4HVpXvZhC0hhBMQkAAABIM9GeiGZmGdWuXBpRRopVXUSbGw7JVVqU0ELMSBOybZruyNMbx2PPNwqU12L1SW7/+7Elmj11YsCOkXlOh2ZMyk1KWLndkHzJ+dFYbtm8aXruYEfSfrdCGZmvhbPM0buGluPa1PC6th8Y3kFpFlJXVMxVRclURtvGEIpJAAAAQBqJN8uorsYV8l/+rewiMrNkrC7EBGOOyEzJz4n5iPVgeS0TY8x/CeZtMyepbIYj6OffXTZN/3XtBfrO1pcS2vVSUVKk5w5mXmdSls3Q3TddJElyd3m1/0invvLb5HVzjdzLyHwt/F26jN4huSgmAQAAAGkiEVlGI1nZRWRmyVhdiAlk5IiM1XktxYV5stsMS7434YoP4UYYrXT9O2ZlZDFpaKaVIy9bZTMcmuFITjdXqL0gtFSP3iF5OM0NAAAASBNWZhkFY3UXUXePb7AQY7VQp1PFesR6sDFAR162qsqdluz70vOnqq3TG/AEsJ3N7aqurU9KIWmB06GqcmdCfjaJNjLTSor+Zz5nyoSE7QUY7+hMAgAAANKE1VlGgVjdRVSQa5cjL1tXXejU401tca+3bN40rblqQUQjMlbntayomKutjfE/h+cOdujKH+0YdeLegTe7ktZZY47zTZqQo6pypyXPK1nmTs0flWlliuZnfun5U3T1XTvjKtwFytcCQDEJAAAASAuJyDIKVIhJxDhXU2unGltPxr2eJH32vWVaMmdyxI+3Mq/FVVKk+c4Cy7qGRp645+3zJaWQlJtlDBvns6pIlizefp/8fn/Q4l80P/P11Ussz9cCwJgbAAAAkBYSkWUUiJXjXFXlxdp3+KSqa+t15ETg60Uj1i4Qj7dPzW6PDrafkt8vlU6fqLIZjqizWwzD0PrqJcpPQAbUq+5uvXH8jOXrBjIwIqPaLJJlCndXj5b94Gk1tXaGfayZp7RkzuSAP/OFswpVu3Jp1D/TUPlaACgmAQAAAGkhEVlGwViVAXNZWZFlY1vRdoH4/X7tOnhMn928V0u++aSu/NEOXX/3c7ryRzu05JtP6tYH9mrXwWPy+6Mr0C2cVagvXDEvlqeQNvoG/KrZtHewGBNvkSwvK/m3jUdOeFVdW6+dze1xr2V1vhYAikkAAABAWkhEllEwVnSqLHAW6OfP/c2yQlI0XSBNrZ2q2rBDyzfu1uNNbaM6uszxsuUbd6tqw46IOlyG2n/kZFSPT0fmyX5mMS2eDp2vf7A8EVsM63Svb1hRLB5m1tJDqyp0zaLiUaHkocLeAYxGZhIAAACQBpJ5NL3ZqRJPlszH33WevvLbpni2KensiVv/tyLyQtLO5vaouqFedXerurZetSuXRtRpYmV2Vaq94vbo13uP6OqFZ4+2Nzt01tTtiygXaoHToXXVizW3KF9f2dJk2RhmNM4WxfZp2+plcWcXWZmvBYx3dCYBAAAAacDqLKNwN8bxZsk899qxeLY4aNGswqg6kmIZq4umw8XK7Kp08O+/fmHY2F/5OZOi7tCx8nczFq+4u/XQnjcsXTNc1hKA0OhMAgAAANKEVaduRZqJFE+nyid+/ny825QkbX/RHfTkuaH8fr/W1O2LeazOHPvatroyZIeL1dlV6WDkqXLrq5dE3aGT6hPhbv/dAc2Zmk+OEZAm6EwCAAAA0oQ1WUbRnYgWS5ZMsk6eG6q+pSOiglcor7g9amg5HvIxVmdXpRtz7M8Mto60QyfVJ8L5Bvz69P1/HtVdZp7kt+/wSTW7PfJ4+1K0Q2B8oTMJAAAASBNWZBlFcyLa0OtG06mSzJPnTJsbDllyrc0Nh+QqLQr6eSuzq9KVOfZXV+MKO2Lo8faprdOrU70+rX7/fK2t26czfQNJ2ulwZ/oGtLZuvx7/wrvV8Lfj2lR/SE+86B72s7LbDFWVO7WiYq5cJUUx5ywNfd4Tc+wqLiRTCRiKYhIAAACQRswso2izgaI9ES0YR1522Jtmq7t3DIUu3FgZir3tQFvIsTozHyiVI13JEGrsz+/3q76lI2CxxmZIdkPypajW9orbo2U/eEZHTpwJ+PlAI32RviZCPW+rilTAWMGYGwAAAJBmzCyjSMeKFjgdqqtxJS1PxuzescqH79k1GBBtHmU/VLLH6iLNnMp0gcb+mlo7VbVhh5Zv3K3Hm9pGfd8H/KkrJJmCFZJGGjnSF0q4520WqZZv3K2qDTsiCnMHxjKKSQAAAEAaiiXLKFmsPt1rwK+QN+rJHqtLdT5QMg0dH9zZ3K7q2vqosqnsNkMj64p2Q5YWG+MRyUl+0T7vaIpUwFjFmBsAAACQpqLNMkoGcxToWHdPQtY3b9RrVy4d7LSyeqyuIDf0evFmV2USc+zvUMfpqEcrpbMdOxOy7Vpf/XbNnJw/+Lu57/DJmNZLhFAjfU2tnTHtM5rcKWAsojMJAAAAyAChTt1K1olWQ0eB9vztREKuIY3uJrFyrC7LZsg5KS/s48zsqvwxfrqbb8Cvts4zWlO3L+bCz5k+nzY89ZoWzy4c/N00RzVnT5lg8Y5jE2ikz+/3x/W8zSJVoNFMYKyjmAQAAABkIL/fr10Hj+mzm/dqyTef1JU/2qHr735OV/5oh5Z888mQGUSxiGUEKh5Db9QdedmqKJlqybpV5cURd3QtmTNZP7xxseZMTY+CSKI0tByP++caqFizcFahdvz7ezV7SvjiXTKMPBGwvqUjIc8bGA8YcwMAAAAyTFNrp9bU7Qt6IxzPiVbBrpeKkSXzRr2iZKoOdZyyZM1w4dqhTvQypDDnzmWmJ1+05qS8zQ2H5CotGvZnNptNP15xsW78cb3O9KV25G3kSX4ji0uxCvS8gbGOziQAAAAggyQ7LDjeUaB4bW44pPqWDh05EfoEtkjMmTIhZIdTuBO9hv7XuVPz9Z//sEBl0yfGva9Ue/a1Y5asYxZrRlo4q1A/+fjSlIdyDz3Jz+Pt0/YD1hTRgj1vYCyjmAQAAABkiHjDgmM5ztyKUaB4bDvQpp8/97ola80tmjgqgNkUbZHujeOnddcfX9NXr7tQD62q0GVlmduZMmBRu9XQYs1IlfOm65sfKrfmQnEwT/Jr6/SOKhbGKtTzBsYqikkAAABABkhVWLBVo0Cx8g349ceXrOkgqW/pCNhBEk+R7jOb/6KCXLvaPYk53S7TmMWaQJZfcq7mzUhtJ5d5kt8pizvtQj1vYCyimAQAAABkgFSEBVs5ChSPRHbOWFGkq9m0N6XdW+nELNYEYhiGfvTRd6TshLyhJ/lNtHgPoZ43MBZRTAIAAAAygJVhwZGychQoXYzsILGiSPdmJyNO0vBiTTALZxWqduXSlBSUhp7kV1yYZ1mGUyTPGxhrKCYBAAAAaS5VYcFWjwLFwm5xZvPIDpJUj/GNJUOLNaFUzpuuuhqX5jsLIlp3zpQJ8W5N0vCT/Bx52aoqd1qybqTPGxhLKCYBAAAAaS5VYcFWjwLF4tLzp8qqQ8BGdpCkyxjfWHFZ2bTBjz3ePjW7Pdp3+KSa3Z5RBcyFswq1ffUyPbSqQtcsKh7VJZRlM3Ttopl6aFWF/vTv74248BTMAqdj1El+Q4tL8bjholmWrANkkqxUbwAAAABAaKkKCzZHgVI56rYrioyncEZ2kIzFMb5U+vZjL8rj7dO+wyf1xIvuYd9bu81QVblTKyrmylVSJMMwZBiGXKVFcpUWyePtk7vLq+4enwpy7XJOyhv2s1pfvUTVtfUxZVvl59i1rnrxqJP8XCVFmu8siHvM8dOb9o56bsBYR2cSAAAAkOaSERYcqJPEylGgdDCyEyUdxvjGktO9Pn338Zf1eFPbqCKdb8CvrY1tWr5xt6o27FBTa+ewzzvyslU2w6ElcyarbIZj1NhYrFlL+Tl21a5cqoWzCkd9zjAMra9eEnd+U7jnBoxFdCYBAAAAac7KDqGho15+v1/1LR3aVH8oaCfJO86drK2NbXFfN9Vysmy69Pwpw/4sHcb4xqNX3d2qrq1X7cqlqpw3PeKvM7OW1tTti6ibaIHToXXViwMWkkxmkapm096YT/QbKtbnBmQaOpMAAACANJeIsOCm1k5Vbdih5Rt3h+wkueOxl5VlVWhRCvX2D2j3304M+zMrT/RCdE73+lSzaW/UXTzRZC1tW10ZspBkijYQPJxYnxuQSQy/38+QMJLuyJEjmjNnjiTp8OHDmj17dop3BAAAkN52HTym5Rt3x73OQ6sq1D8wYFknRia5dtFM3X3TRcP+7NYH9o6JzqtMtcDp0LbVlTHnDIXLWoqG3+9XQ8txbWp4Xdua2hRvI2C8zw2wSiLuv+lMAgAAADKAGRYcjwVOhwpy7eOykCRJ2w606ejJM8Oyof7xIv5RM5VecXvUEEfIerispWiYgeD33LRUGz9+cczrmOJ9bkA6IzMJAAAAyABmWHA8J1r98Ma3a+2v9o/LQpJ0dnTvsjv/v2EdJzZDKsjNUndPf+o2Ns5tbjgkV2lRqrcxzG/+csSSddLxuQFWoDMJAAAAyBDxnmjl6emP+xj0TDdydGnALwpJKbbtQJs83r5Ub2OQx9un7Qfclqz1eNNR7XvjRFo9P8AKFJMAAACADBJtWPACp0N1NS5VzpuuzQ2HErw7IHq+Ab/cXd5Ub2NQW6fXkpMTpbPFyuvv2aUl33xStz6wV7sOHhOxxRgLGHMDAAAAMox5opUZFrz9gHvYzW+WzVBVebFWVMxVRclUGYZhabcFYLXunvQZvTyVgDFQ83TErY1tmu8s0PrqJRGdNAekK4pJAAAAQAYyw4JdpUURnWhlZbfFeFCQm6UzfT6+Z0nySluXlsyZnOptSJImRjlGGq1X3d2qrq1X7cqlqpw3PaHXAhKFMTcAAAAgw0VyolUiui3Gsu6eft310SW695+XatGsSanezpj39UdfVFNrZ6q3IUkqLsyT3WYk9Bqne32q2bQ3bZ4zEC2KSQAAAEAIHm/fsKPkkxGkm4hrJrrbYiz6l4f+qk/9Yq8aW7tSvZWI2G2GSqdPTPU2YnKmz6e1dfvTIk/IkZetqnJnwq9zujd9njMQLcbcAAAAgBH8fr/qWzq0qf6QnnhxeB6R3WaoqtypFRVz5SopkmFY08GQ6Gua3RaMbUUu075TE7Lt2vDRJeru8WlTw+va1tQ26vS6dPaK26OGluNylRaleitaUTFXWxvbEn6ddHrOQDQoJgEAAABDNLV2ak3dPr3q7g74+UQE6Sbjmma3RTJukJEa3T39+uhPGlS7cqnuuWmpPN4+Pdvcrm/+4SUd7Uyf09JC2dxwKC0KK66SIs13FgR9TVopXZ4zEA3G3AAAAID/Z2dzu6pr6yO+gTSDdHc2t2fENVdUzI36a5BZhmbxOPKy9Q+LztH21ZVKcASQZbYdaEvKKGk4hmFoffUS5SdhPDRdnjMQDYpJAAAAgM52B9Vs2qvTUQZVD715jzbryIprRsPstsDYNjKLZ9KEHF29sDjFu4qMb8Avd1d6dFEtnFWo2pVLE15QSqfnDESKMTcAAACMe36/X2vq9kVd1DGd7vXpn37SMOoo+VBZR1Zcc23dfm1bXRlxhpLZbVFdWx/zdZEZRmbxJCsDyArdPenzu1k5b7rqalwhx1CtkE7PGYgEnUkAAABIa8k4Ta2+pSPuG8Xunv5R4dZm1tHyjbtVtWHHsE4iK65pFgyiYXZb5GZFfyuQIZNS+H82Nxwa/DiTutIKctPr5MGFswq1ffUyPbSqQtcsKpY9ATODBbn2lJwcCcSKziQAAACknWSfpjb0pjtRzKyj2pVLVTlvumXXjCW8d0p+jmL5tvkl5WbZdPF5U9TQcpyT4dLc401H5fH2yZGXPdiVduOPd+lM30CqtxZUls2Qc1JezF/v8faprdOrU70+Tcyxq7gwT4687Lj3ZRiGXKVFcpUWyePtU0t7tz58zy5LTsuzG4a+9/jLevqV9qScHAlYgWISAAAA0kqyT1PzePu0/YA75q+Phpl19PNPvNOya5rhvZHeMJvjdd4YCwo9/QM6+Fa3fnHzxerpH1Dtjhbt+duJmNZCYg34pc898Bd95r2lcpUUaeGsQq1+/3x99/GXU721oCpKiqIu/iS7+OzIy9biOVN09cJiS0YHfX6/nnrprdF/noCTIwGrUEwCAABA2tjZ3B5VIPXIbp9YtHV6k9phc7rXp//4zQuWXdMM7430BtyK8bq2rh6tuPf5uNZAcuxoPqYdzccGixH7j5xM9ZZCev7149rZ3B7x6znZxeehkplDZcV7HWAlMpMAAACQFpJ9spnpVAqCqF/vOG3petGE9yZjpA/p51V3t278cb22NaV3CHdP/0DEr+edze2qrq2PuDhqFmR2NrfHu01Jyc+hive9DrASxSQAAACknFUnm5lHoZsiCbSdmOBjv5MhksBij7dP+944kfbFBCTOmT6fJRk/iRbs9TxUqorPQ5k5VPlJfA+J5HsDJANjbgAAAEg5K082qyiZGlV+SnFhnuw2I2PDpEMFFofKkgHSmfl6DhQub1XxedvqyrgzlMzTEWMpbMUq1PcGSBY6kwAAAJByVo1e3f30a6rasEPLN+7W401to4onZn7K8o27VbVhh5paO+XIy1ZVudOS66fCu4LcUDa1dob8XgDpLtj7gpXFZytUzpuuuhpXUkfeGFdFqlFMAgAAQEpZeZras68diyk/ZUXFXEuunwo7mo9pyTef1K0P7NWug8fk9/ujzpIB0pF5UuFIVhVSrCzILJxVqO2rl+mhVRW6ZlGx7LbhHU9ZNkNXXeiULf7D5CQF/94AycKYGwAAAFIq2aepDWXmp/zy0xWa7yxIevFlblG+DlkQxj30xKpzp07QW54eefsGLNghkDqBTiq0svhsFmQiPQkxHMMw5Cotkqu0SB5vn9xdXnX3+FSQa5dzUp7aOr164kVr9h7tKY6A1ehMAgAAQEql4jS1oU73+vTFX72gdTcuTmqQbpbN0A8+8nbLr/nG8TMUkjBmjDyp0Mris1mQSQRHXrbKZji0ZM5klc1wyJGXbfl7XTSnOAJWo5gEAACAlEqH09RecXvU3eNT7cqlSSsoVZUX65Lzi5J6TSDTjDypMJMLMla/10VyiiOQKBSTAAAAkFLmaWqptrnhUFKDdM2cplSE9yK0lRXn6h8Wjs69QXIFOqkwkwsyVr7XhTrFEUiGMVFMeuONN/TFL35RF1xwgSZOnKipU6fqkksu0Q9/+EOdPh3/DLrp4YcfVlVVlWbOnKm8vDydd955WrlypRoaGiJeo6OjQ1/72te0ePFiFRYWatKkSVq8eLG+9rWvqaOjI+J16uvrtXLlSp133nnKy8vTzJkzdfXVV+vhhx8O+7Wf+MQnZBhGRP97/fXXI94TAABALNLlNLXHm45q12vHlJtl068/4woapGs3pILc+KJHFzgdqiiZOvjf4cJ7kVzHT/Xp/1Ys1b6vXqmn1izTg7dcallwcjjJuo7dMDQhO71vB6vKi0dlAmVyQcbK97pA3xsgmQy/35/RZ4Q+9thjuummm9TZ2Rnw8wsWLNDWrVtVUlIS8zW8Xq9uvPFG/eEPfwj4eZvNpq9//eu6/fbbQ67z/PPP60Mf+pCOHj0a8PPnnHOOfve73+niiy8Ouc43v/lNfeMb39DAQOBZ+Ouuu051dXXKywv8xviJT3xCv/jFL0Jew/S3v/1N5513XkSPjcaRI0c0Z84cSdLhw4c1e/Zsy68BAAAyx66Dx7R84+5Ub2OQ3WaoqtypFRVztfCcSXrL0zMsSPePL72l1b/cF/P6Gz66RNe/Y1bQz3u8ffrcA3/RjuZjMV8DsbPbDO376pXDbtZvfWCvtja2pXBX1rp20Ux99r2lWlO3L21P/XtoVYVcpUWj/tyqn8W1i2bq7psuinudaFj1XhfsewMEkoj77/QuRYexf/9+VVdXq7OzUwUFBbrjjju0a9cu/fGPf9SqVaskSa+88oquvfZadXfH/gb5qU99arCQdPnll2vLli3as2eP7r33XpWWlmpgYEBf/epX9dOf/jToGq2trbruuut09OhRZWVl6bbbbtOOHTu0Y8cO3XbbbcrKytKbb76pD3zgA2ptbQ26zk9/+lN97Wtf08DAgEpLS3Xvvfdqz5492rJliy6//HJJ0u9//3vdcsstYZ/XOeeco8bGxpD/mzUr+F9yAAAArOIqKUqrMS/zdLTlG3frIz+ul7dvYDBItyA3S/c881pc6//fMwcV7t90nzsYedc6rDUymNnj7dN7F0xP4Y6st6Ji7mBH3LeuX5jq7YyywFkwrHtvKHNENF5WrRMNK97rRnY2AqkQX39uiq1evVqnT59WVlaWnnjiCblcrsHPve9979O8efN022236eWXX9b69ev11a9+Nepr/OlPf9KDDz4o6WzHz29/+1vZ7Wfnat/5znfqgx/8oJYuXao33nhDt912mz7ykY9o8uTJo9b5yle+Irf77DGQDz74oG688cbBz1VWVuriiy9WdXW13G63br/9dv3sZz8btcbJkyf17//+75Kkc889Vw0NDZo2bdrg5z/wgQ/owx/+sH7/+9/rgQce0Kc//WktW7Ys6HPLzs7WwoXp938cAABg/DEMQ+url6i6tl6nU3y620ivurtVXVuv2pVLVTlvuupbOuLu5HjF7VFDy/GgnQVWnlg1FhmGlJ9t06nexJ1a5/H2a9fBY9pUf0hPvOgeUz+PocUIwzBUcX76FSbO9Pl04M0uLZxVOOpzZkEmntdhqgoy8b7X5efYta56sQyDUVikVsZ2Jj3//PN65plnJJ3tHBpaSDKtXbtWF1xwgSRpw4YN6uvri/o63//+9yVJdrtd99xzz2AhyTRt2jTdeeedkqQTJ07o3nvvHbWG2+3W5s2bJUlVVVXDCkmmG2+8UVVVVZKk+++/f7DwNNTGjRt18uRJSdKdd945rJAUaI8/+MEPonmqAAAAKbVwVmHanmx2utenmk171dTaqc0NhyxZM9A6Hm+fmt0e/fXwSUuuMVb5/dKEHLty7Im7oV79y31avnG3Hm9qG1OFpEDFiHQJwR/qjeNnVF1br53N7aM+ZxZkYn2vSHVBJtb3uvwcu2pXLg1YYAOSLWOLSVu2bBn8+Oabbw74GJvNpo9//OOSzhZ6zOJTpLq7u/XHP/5RknTllVcGnSu84YYbNGnSJEnSI488Murzjz76qHw+X8i9SmezjCTJ5/Pp0UcfHfV58zlPmjRJN9xwQ8A1Zs+erfe///2SpCeffDKu8T4AAIBki/ZkswVOhy4rS05uyOlen1Y/vE/bmqzJzdl2oE0eb5/8fr92HTymz27eqyXffFJX/miHbvv1C5ZcYyw71t2nXp8/YYHVhzqsO8gnXQQrRiQyBP/f3l+mOVMmxPS1Q4u4I2V6QSaW97q6Gpcq542tcUtkrowtJu3cuVOSNHHiRC1dujTo497znvcMfvzss89GdY09e/aop6dn1Doj5eTkqKKiYvBrRnZAmXsNt06ovfb29mrPnj2SJJfLpZycnLDr9PT06Pnnnw/6OAAAgHQU7mSzLJuhaxfN1EOrKrRtdaU+d3lZ0vb2Wnu3rGpS8Q349Wxzu6o27BiTHTDJwrcsMuGKEYnKD3rF3a28OE6NO93r09q6/QEzxjK9IBPte12qC2DAUBmbmfTSSy9JksrKypSVFfxpvO1tbxv1NdFeY+Q6wa7zxBNPqL+/X83NzbrwwgtHrVNYWKji4uKga8ycOVOTJk1SV1fXqL02Nzerv78/4r0MvbYZzD1SR0eHKisr1djYqDNnzmjq1Kl6+9vfruuuu06f/OQnlZ+fH/I6oRw5ciTk54OdaAcAACCdHWNxlRbJVVokj7dP7i7vsNPUhp6yZUV+Sqr82y/3y9ufuNwfjG9ZNkNV5cVaUTFXFSVTQ451Jep1tK2pLe6CX6iMMbMg09ByXJsaXtf2A8PzraL5HqRCNO91QDrJyGKS1+vVsWNnj0kNd6TdlClTNHHiRJ06dUqHDx+O6jpDHx/uOuYxe+bXDS0mmetEcvzenDlzdODAgVF7jWcvwXR3dw/rgGpra1NbW5ueeOIJfe9731NdXZ3e9a53hd1zuD0AAADEw5GXHfKGKp3Du8OhkASr2QypoqRIN7xjlq4qd2rShOATDUMl6nVkVefY5oZDQQPrx0pBJtx7HZBOMnLMzePxDH5cUBC+pXHixImSFHV+UDTXMa8R6DrmOvHs1aq9SP/vxIaKCt1xxx16/PHH9Ze//EW7du1SbW2tLrnkEklSa2urrrrqKv31r38Nu2cAAIBUS+fwbiCZBvzSroMd+uKvX9BHflwfMG8oGPN1lJuVfreJZsZYOI68bJXNcGjJnMkqm+GgOAMkSMZ2JplCZQeZcnNzJUlnzpxJ2HXMawS6jrlOPHu1ai+S9KMf/UiTJ08e9ecul0urVq3Sf/3Xf+k73/mOTp06pVtuuUV//vOfo24HDdcFdvTo0cHCFQAAgBXM/JQ1dfsycuQNsNqr7m5V19arduXSiHOCKudN148+ukS3PvCXBO8uOr4Bv9xdXopDQJrIyGJSXl7e4Me9vb1hH2+GaE+YEN0pAtFcx7xGoOvk5eXp9OnTce3Vqr1IClhIMhmGoTvuuEN79uzRU089Ndi1dNlll4Xd+1CRjPQBAABYLVx+CjDenO716ZZf/Fm//oxLi2ZPjuhrKudNk91mpN1rp7sns8ZYgbEs/foXI+BwOAY/jmR07dSpU5IiGzOL9TrmNQJdx1wnnr1atZdI1dTUDH78pz/9KaY1AAAAUsHMT7nnpqXa99Ur9dSaZXpw1aUJO0IeSHc9/QP6xx/Xq/HIyYge78jLVlW5M7GbikFBLmOsQLrIyGJSXl6epk2bJin8qWEnTpwYLK5EGwo9tLsm3HWGjnWNvI65Trg1hq4TbI149xKpoQHira2tMa0BAACQamZ+yrtKp+nqhcFP1QXGut7+AX3kx/Xa2dwe0eNXVMxN8I6ik2Uz5JyUF/6BAJIiI4tJknTBBRdIkl577TX19/cHfdzLL7886msiNbSgMnSdUNfJyspSWVlZwHU6OzvV1tYWdI2jR4+qq6sr4F7nz58vu90e1V4CrRMpvz+9WloBAADilW43x0Cy9fQPqGbT3ohCuV0lRTqvKD8Ju4pMVXkxeUlAGsnYYtK73/1uSWdHuvbu3Rv0cUNHtKLN/XnnO985GHYdatSrt7dXDQ0No75m5F7DrRNqrzk5OYOB1fX19SFzk8x1cnNzdfHFFwd9XCgvvvji4MfnnHNOTGsAAACkE1dJkeY7Y4sAAMaK070+ra3bH/Yfjw3D0L9cMS9JuwqPYjCQXjK2mHT99dcPfvzzn/884GMGBgZ0//33SzobOn355ZdHdQ2Hw6ErrrhCkvTUU08FHS975JFHBjuKPvzhD4/6/Ac/+EHZbLaQe5Wk++67T5Jks9n0wQ9+cNTnzefc1dWlRx55JOAaR44c0VNPPSVJuuKKK4ZlLUWjtrZ28OP3vOc9Ma0BAACQTgzD0PrqJcrPIXcF49srbo8aWo6HfdzbZxUmYTfhLXA6VFEyNdXbADBExhaTLrnkElVWVkqS7r33XtXX1496zLp16/TSSy9Jkr7whS8oO3t4W+R9990nwzBkGIa+/vWvB7zOF7/4RUlSf3+/Pve5z8nnG36CwLFjx/Qf//Efks4WrG655ZZRaxQXF+umm26SJG3fvl2//vWvRz3mV7/6lbZv3y5JWrlypYqLR8/033LLLSosPPuG/qUvfUkdHR3DPu/z+XTrrbcO7tHc+1ANDQ06evRowOcqnR1v+6//+i/98Y9/lCQtXrw46o4uAACAdLVwVqFqVy6loIRxb3PDobCPKS7Mkz3FyfX5OXatq14swyBBH0gnGVtMkqS77rpLEyZMUH9/v6666ip997vfVUNDg55++mnV1NTotttuk3Q2b2jt2rUxXeN973uf/umf/kmS9Oijj+rKK6/Uo48+qj//+c/6+c9/roqKCr3xxhuSpO9973uaMmVKwHXuuOMOTZ8+XZL0sY99TF/60pf07LPP6tlnn9WXvvQlLV++XJI0ffp0ffvb3w64xtSpU3XnnXdKkg4dOqRLL71UP//5z/XnP/95cG+///3vB68RqBNr27ZtOv/883X99dfr7rvv1tNPP62//vWvamho0E9+8hNVVFTojjvukCTl5+dr48aNvHEDAIC05/H2qdnt0b7DJ9Xs9sjj7Qv62Mp501VX49K5U9MnDwZItm0H2kK+TiRrT3W75PypURdx83Psql25VAvTpEMKwN9lpXoD8XjHO96hX/7yl1qxYoW6urr05S9/edRj5s+fr8ceeyzmcS9J+tnPfqauri5t3bpVTz/9tJ5++ulhn7fZbLr99ttVU1MTdI05c+bo97//va6//nq1tbXpzjvvHCwMmYqLi7Vly5ZhJ7eNVFNTozfffFPf+ta3dPDgQX3yk58c9ZhrrrlGP/vZz4Ku0dPTo9/97nf63e9+F/Qx5557rh588EG9853vDPoYAACAVPL7/apv6dCm+kN64kW3fAN/z4Cx2wxVlTu1omKuXCVFo/5xrPycScrN4h/MkBlshjRg8fk4vgG/3F3esKHWKyrmamtj8EOEIvVv758vR16W1tTt06vu7rCPX+B0aF31YgpJQJrK6GKSJF133XV64YUXdNddd+mxxx7TkSNHlJOTo7KyMt144436/Oc/r/z8+P7VacKECXrsscf04IMP6r777tP+/ft18uRJOZ1OVVZW6vOf/7xcLlfYdS699FI1Njbqrrvu0pYtW/T6669Lks4//3x96EMf0urVq1VUVBR2nW984xuqqqrS3XffrZ07d8rtdmvy5MlavHixbr75Zn3sYx8L+rU333yznE6n6uvr9cILL+itt95SR0eHsrKyNG3aNF100UW67rrrtHz5cuXlcfQmAABIT02tnSFvSn0Dfm1tbNPWxjbNdxZoffWSYTel9S0dan7rVLK2C8TlonOn6M+HTli+bnePL+xjzOD6SApAwZiZR4ZhaPvqZWpoOa5NDa9r+4HhReAsm6Gq8mKtqJg7+HgA6cnwcwY8UuDIkSOaM2eOJOnw4cMhu7EAAACG2tncrppNe3W6N/yNsMkcl6mcdzZ24NYH9lrSbZFsiehQQfq79Pyp2v238IHZ0XpqzTKVzQg/wdHU2qnq2vqoXnOm/By76mpcATuMPN4+ubu86u7xqSDXLuekvLCdUgCil4j774zOTAIAAMD40tTaGXUhSTp7HHrNpr1qau2Ux9un7QfcCdphYmXZ6dQYj55//bjKpk+0dM0smyHnpMgmEWINrg+XeeTIy1bZDIeWzJmsshkOCklABqGYBAAAgIzg9/u1pm5fTN0R0tmC0tq6/Tp68syw0ZpM0tufmftGfAb80tqr5lt6CmFVeXFUxRszuH6+syCixy9wOlRX4xrsBgQwtlBMAgAAQEaob+mIK7dFkl5xe7T7deuzZ4BEmzk5X7Urlyo3y5pbuBUVc6P+moWzCrV99TI9tKpC1ywqlt02vFMuy2bo2kUz9dCqCv3qMxXKzbJFdMIigMyT8QHcAAAASD6Pt09tnV6d6vVpYo5dxYWJzzrZ3HDIknWePJB5WUlAQa5dS+ZM1q8/49I//l+9en0DMa9lBmLHwjAMuUqL5CotGpV5NMORq6Y3u3R//etRn7AIILNQTAIAAEBE/H6/6ls6tKn+UNJvFK3MOXrutQ6CrJFRhuYbLZo9Wb/5rEsf+XG9evqjLyjl59i1rnqxJa9RR172YBG5qbVTH/lxfcwnLALILIy5AQAAIKym1k5Vbdih5Rt36/GmtlGZQ+aN4vKNu1W1YYeaWjstvX5bp9eynCOf3693l02zZC0gGUbmGy2aPVk//eeLLQ/EjtXO5nZV1wYvJI30qrtb1bX12tncbuk+ACQPxSQAAACElA43iqdiDN0O5v0XOi1dD0ikmy49d9SfpUsgthUnLALIPBSTAAAAEFS63ChOtPAUK0lylRRFfBMOpFqwibRoArG3ra60vCPJqhMW/X5mToFMQ2YSAAAAArLqRnHb6sq481mKC/NktxmWjLrZDand06PV75+vtXX7dKYv9iDjZMrJMtTbz033eLS54Q25SgOPZoYKxHZOiiwYP9ZAfatOWGxoOS5XaVFc6wBILopJAAAACCidbhQdedmqKndqa2P8J7H5/NLyn+6WJNmMs8UlXwbUaCgkjV/bDrTJ4+0LW+AZGogdjhWB+ladsLi54RDFJCDDUEwCAABAQMm4UYymI2JFxVxLiklDcaIbMoFvwK/dLcc1zZEbVedQME2tnVpTty+uk9esPGEx0mIZgPRBMQkAAACjJPJGMdaOCDPnKN5uqVDshiEf+S1IQ7fc/+fBjyPtHApkZ3N7VDloZqB+7cqlw8K7LT1hccAvd5eXYhKQQQjgBgAAwCiJuFGUznZEVG3YoeUbd+vxprZR1zA7IpZv3K2qDTuGBXgbhqH11UuiPg49qr1SSEIGCPU6CcXKQH2rT1js7rF2PQCJRTEJAAAAoyTiRnFnc7uqa+sj7iwyOyK2H2hTs9ujfYdPKjfLpg0fTWxBCcgk5utkZ3N7yMdZffKa1ScsFuTymgYyCWNuAAAAGMXqG8WjJ89o7a/2x9wRMZTdZqiiZKoOdZzSkRNeK7dpuSyboX6CmZBg5uukrsY1KtvIZHWgvpUnLGbZDDkn5cW9DoDkoTMJAAAAo5g3ilbIshn64ROvxNwRMZJvwK/nXuvQkRNezZ4yQZeVFVm2V6tRSEKyjOwcGsnKQH3p7ycsWqGqvJi8JCDDUEwCAADAKI68bF107mRL1lo6d4oOtp+yZK2Rjpw4o7++cVL33HSRnlqzTA+uulRpWlcCEs7sHBopEYH60tkTFq1g1ToAkodiEgAAAEZpau3UC0ciC/UNL7HdOad7ffq3X+6Tt29A0wtyRTMQxrNAHUiJCtQ3T1iMxwKnQxUlU63YGoAkopgEAACQBB5v32CIdLPbM/gv++nIDOrt6R+Ie62yGQV6/vUTFuwqNHPE54UjJxN+LSCdDe0cMiXq5LV4T1jMz7FrXfViGQbthECmIYAbAAAgQfx+v+pbOrSp/pCeeNE9rDPAbjNUVe7Uioq5cpUUpdXNlBVBvZKUm2XTmvfP060P/tWCXYX3ituj729/JSnXAtKV2Tk0NIMokSevLZxVqNqVS1WzaW9UuWj5OXbVrlwaNDAcQHqjmAQAAJAATa2dWlO3L2hRxjfg19bGNm1tbNN8Z4HWVy9Jm5sqq4J6F8+ZrHOm5FuyVqTcXT1JvR6QjszOIVOiT16rnDdddTWukO95Qy1wOrSuenHavOcBiB5jbgAAABbb2dyu6tr6iLt7XnV3q7q2Xjub2xO8s/CsDOrde+iEFORkKQB/98Wr5lu63tDOISk5J68tnFWo7auX6aFVFbpmUfGoExazbIauXTRTD62q0LbVlRSSgAxHZxIAAICFmlo7ox73kM5m/tRs2qu6GldKb7KsDuo1DFnWEQGMRQucDn3cNVc/eqo5YZ1D0tkT07Y2tsW9fqiT1wzDkKu0SK7SInm8fXJ3edXd41NBrl3OSXkBi1AAMhOdSQAAABYxg6ujLSSZzBBpfwq7eawO6vXLsKwjAhhrzADqSRNyEt45lOyT1xx52Sqb4dCSOZNVNsNBIQkYYygmAQAAWMSK4OpX3B41tBy3aEfRS0RQb6hOBmC8yrEb+vGKiwY7Ea16nQRbh5PXAFiJYhIAAIBFrAqutmqdWJhBvVYwx22s6IgAxppen1/ffuwlNbV2SkpO55B58lq0BSVOXgMwEsUkAAAAC1gZXL3tQJs83j5L1opWIoJ64+2IAMaqoeH7hmFoZRzdSZF2Dpknr0VauFrgdKiuxqXKedNj3huAsYdiEgAAgAWsDq52d3ktWSsWVo3bXFZWNPhxrB0RwFhnhu9v+Wurvvv4yzGv85//8LaIO4c4eQ1AvDjNDQAAwAJWB1d391i7XjTMcZt485/ue+51feyScwc7JcyOiDV1++JeGxhLTvf6dNtvXlBv/0DMa2xueEMrKuaG7EzyePvU1unVqV6fJubYtXDWJN1z01JOXgMQNYpJAAAAFkhEcHWqGIahf37XefrKb5viWufVt7rV0HJcrtLhHUrbVy9TQ8txbWp4XdsPuC3r6ML4ZjOkTP5ViqeQJP09vH/o6006e8pkfUuHNtUf0hMvDn+92W1nT1tcUTFXrpIiwrUBRIxiEgAAgAXM4GorCiNmcHUqPffaMUvW2dxwaNTNrWEYcpUWyVVapKdeatMtv9hrybUwvmVyIckqI19vTa2dITsBfQN+bW1s09bGNs13Fmh99RJG2gBEhMwkAAAACyQiuDpVkhkm/shfWi25DoDhr7edze2qrq2PeKR0aBg4AIRDMQkAAMAiVgVXW7VOrJIVJm5l0QrA319vTa2dqtm0V6ejzHIzw8CbWjsTtEMAYwXFJAAAAIs4crOUY4/vr1cLnA5VlEy1aEexsTpM3OPtD/jnVhatgHjF+9pNFx5vv9bU7Yu6kGQ63evT2rr98vt5bQIIbmy8YwIAAKTYzuZ2ffQnDer1xR6im59j17rqxRGF4Hq8fWp2e7Tv8Ek1uz0hR8miZXWY+MG3TgX8c6uLVkA8ZjhylW/x734qHHyrO+7TEs0w73SWyPdAAOERwA0AABCnWEdKhsrPsat25dKQ4bfJOpXJyjBxSdra+KYWzykcPI68uPDsseNWF62AeBw5eUbf+fAi3bfrb3EXY1Ily2Zo+4vWjI4GCs8PxOPtU1und9TrOxE4mQ5IHxSTAAAA4uD3++MaKZGknCybfvnpCi2aPTnoY5J5KpMjL1vve9sMPWnRTen/90q7/r9X/h7qa970/eNFszP+OHeMLc+9dkzbVy9TQ8txbWp4XdsPjChYGIZ8aTz+9b63zdAfX37LkrXMMO9AhaFUFHU4mQ5ILxSTAAAA4lDf0hF3F0Nv/4C6e4IXo3Y2t0fV+WSeylS7cqkq502PaU9XXei0rJg00tCbvoLcLHX3BM5UApJt24E2dff0y1VaJFdpkTzePrm7vOru8akg164Zjlx95MeRn5CWbFde6NQTFr1uzTDvkcWkVBR1UvEeCCA0MpMAAADisLnhUELXSdWpTGUzCmL6umhRSBq/8rJtmpCdXqOOvgG/Wtr/XiRx5GWrbIZDS+ZMVtkMhyZNyNH66iVpma20wOmw/HU7ssi9s7ld1bWRF9PMos7O5vbwDw6Ck+mA9EQxCQAAIEZWHm1vjpQMFe8IXTynMhXk0sCOxPrse0vV059+IewfvmeXbn1gr3YdPBbwtbNwVqFqVy5Nq4KSGd5v9eu2IPfvzzEVRZ1UvgcCCI1iEgAAQIysPNreHCkZyooRulhPZTJDuIFE2fBkc1rmZQ34pa2NbVq+cbeqNuwIWASpnDdddTUuzXcmp4MvlKHh/Va+brNshpyT8iSlrqiTyvdAAKFRTAIAAIiR1UfbjxwpSfQIXSiOvGxVlTstuT4QSBrWkUYJNqbl9/vV5e1TyfQCpbLmusDpUF2NazAXyMrXbVV58WBeUqqKOql8DwQQGsUkAACAGFl9tP3QkZJEj9BFYkXFXEuuD2SykWNaTa2dqtqwQ8s37ta2praQ3VVDX9NWybIZunbRTD20qkLbVleOCre26nU7dJ1UFHXS4T0QQHAMwwMAAMTIHCmxYtRt6EiJlJgRukBHfIfiKinSfGdB2p5cBSSLOab1lWvfps9s/kvE416hTmmMxg9vfLvKZjhUkGuXc1JeyNeyFa/bBU6HKkqmSkpMUSeS96J0eA8EEBydSQAAADFK1EiJlPgRukgYhpG2J1cByfaK26NV90cfQG2Fp19uHzxRLlxBJN7XrRnmbRhn5/cSnQ0XTDq8BwIIjmISAABAHBIxUiIldoTO5PH2qdnt0b7DJ9Xs9gyOgQz989wsmzZ8lIISIEk9/QMpuW60Y1qxnjg3NMzblKqiTjLeAwHEjjE3AACAOFg9UmJK1Aid3+9XfUuHNtUf0hMvuoetbzOkGY5cveXpGZYDY7cZeluxQwfe7Ip7LwCiF8uYlnni3Jq6fRG9Py1wOrSuevGoDKZUFXUSOUYMIH4UkwAAAOJgjpRU19bHNP4ycqTEZI7QbW1si3uP5ghdU2unVj/8V73Wfirg4wb8UltXz6g/9w34KSQBKRbLmNbCWYXavnqZGlqOa1PD69p+YHgBOctmqKq8WCsq5qqiZOqo9yEpdUWdRLwHArAOxSQAAIA4mSMlNZuiy1MJNFIy1IqKuZbcSN106bmq/dNBfX/7K5ZlnwBIrljHtAzDkKu0SK7SInm8fXJ3edXd44sozFtKbVHHqvdATqYErEdmEgAAgAXMkZL5zoKIHr/A6VBdjUuV86YHfYw5QhePuVPz9aVHXtB3H3+ZQhKQoawa03LkZatshiPiMG9TorLhwrHiPTDQGDGA+FFMAgAAsIg5UvLQqgpds6hYdtvwkZEsm6FrF83UQ6sqtG11ZdCOJFO8pzLlZtnU1nVGbxw/E9PXA0gPqR7TSlVRx+qT6QBYhzE3AACAGHm8fWrr9OpUr08Tc+wqLjw7MhLPSMlIsY7Q5WXZ5JfU0083EpDpUj2mlahsuEgkaowYQHwoJgEAAEQh1GlodpuhqnKnVlTMlaukSI68bEu6CaI9lWn+jAJ5+wf0xvHTcV8bQGqly5hWKos6Vp1MB8A6ht/v55+rkHRHjhzRnDlzJEmHDx/W7NmzU7wjAADCa2rtjLyg4yzQ+uollt7M+P3+iE5l8vv9Wv7T3ZZdF0Bq5OfYVVfjSquiSDTvg1YXdSJ9Dwx2Mh0wXiXi/ptiElKCYhIAINPsbG6P+V/kQ4VsxyrUCN2tD+y15AQkAKmTyPePeKVDUceKMWJgvEjE/TdjbgAAAGE0tXZGXUiSpNO9PtVs2puQzoJgI3Qeb5+2H3Bbei0A8Zs9ZYLyc+xjYkzLMAxLs+FiYdUYMYDYUEwCAAAIwe/3a03dvphCZ6WzBaW1dfu1bXXlqH+hDxbgHY+2Tu+wLgEgU+Rl2+TtG0j1NhLmK9e8TVcvnJnyjh6rUdQBxieKSQAAACHUt3RE1EkQyitujxpajstVWhRVgHcsN5OnYix6Aam0wOnQp5eVaO2v9qd6K4NFnYqSIt3+uybL1p05OT8tOnoAwAoUkwAAAELY3HDIknXufvo1OfKyQgbX+gb82trYpq2NbTEHeE/MsVuxXSAprrrQqZsvO18VJVP12lvxFW0D+eGNb9f/9/JbIbuAys9x6C1Pz6iiTrPbY+leCnKHvzbp6AGQySgmAQAABGFl/tCzrx3TDfc8p15fZCNor7q7VV1bH3UAb3Fhnuw2g1E3pL2rLnTqJx+/ePC/iwvzZDMkK391y2Y49JGlc8J2AU2akDPqa618LdkNyTkpL+51ACBd2FK9AQAAgHRldf5QpIUkkxng3dTaGfHXOPKyVVXujHZrQNLdfNn5w/7bkZety8qmWXoNsxvIkZetshkOLZkzWWUzHBF1BFn5WpqQk6VDHactWQsA0gHFJAAAgCDSIX/IDPD2+yMvRK2omJvAHQHxW+B0qKJk6qg//+d3Wfe7m2Uz4u4Gsuq11N3Tr+raeu1sbrdkPQBINYpJAAAAQaRL/pAZ4B0pV0mR5jsLEraf3Cyb3nneFNky47AppJn8HLvWVS8OGDB/xduco7KFYlVVXhx3JpGVr6VYOg0BIF1RTAIAAAjCzExJB9EEgRuGofXVS5SfoGLY164r179eMU+5WfxVEtGZkG1T7cqlQYPlDcPQl6+5wJJrWdFVZPVrKZZOQwBIR/wNAAAAIIh0yh96vOmoPN6+iB+/cFahalcuTUhBaWvjUdVs2qszfQOWr43kWJ2CYqAjN0u/+sy7wgbKf+ySczVr8oS4rhVsjC4WVr+Wou00BIB0RDEJAAAghHTJHxrwS5974C/adfBYxF0NlfOmq67GZfnI27OvHdNpi/KkCnKz9JVr3mbJWojcnw+d0E//+eKEda8F8pOPB+9IGsowDNWuXKq87NhuVYaO0Xm8fWp2e7Tv8Ek1uz1RFWSHMl9LVo3gRdNpCADpKCvVGwAAAEhnZmbKq+7uVG9FO5qPaUfzMc13Fmh99ZKwN+Yeb59ys2y68x/frudeO6YfPvFqknYaue6efv1u35up3sa48+xrx3TPTe9QXY1LNZv2qvXkmYReb0K2XRUlRRE/fuGsQm38+MWq2bQ3qsJlfo5dP15xkbq8ffrs5r164kX3sBMZ7TZDVeVOraiYK1dJUcDcpmDmFuVb1o237UCbPN6+uDOdACBVKCYBAACEYGamfOTHu+RNk7GuV93dqq6tV+3KpaNGhvx+v+pbOrSp/tCoG+l01fRmV6q3MC7d8/RBXfv2mTp+qifh1zrT51N3T39UxZPKedP1nQ8v0m2/fkG9vvCvvRy7TZ99T6m+/dhLQYu/vgG/tja2aWtjW8RFWVNbp9ey15NvwC93l5diEoCMxZgbAABAGAtnFeq//+kdqd7GMIFOhmpq7VTVhh1avnG3Hm9qy4hCElLnxzta9I//V5+07KuW9uDdfYHG0XY2t+vLv22MqJAkSb2+Aa178tWIuwjNouzO5vaIHn/KotFOU3ePtesBQDLRmQQAwDjm8faprdOrU70+Tcyxq7gwz9J/KU/0+sl0VXmxLisr0nOvdaR6K4PMk6G2ra7Us68di3okCIi0UGOFN46f0eI5Uwb/O1QXnc042xWY6IKoWZStq3GF7VCaaHG+lFX5SwCQChSTAAAYZ0LdwMWTJ5Ks9VPpc5eXpVUxSTp7MtRDew7r24+9SCEJac3Q398Lmlo7taZuX9AuogG/pAiD5uM1tCgb6j2puDBPdps1Ba4smyHnpLy41wGAVKGYBADAOBLuBi6ePJFkrJ9q6RTGPdR3tr5EIQlpb87UfEnSzub2tOuie8XtUUPLcblKg4eEO/KyVVXu1NbGtrivV1VenLFdmgAgkZkEAMC4sbO5XdW19QnLE0n0+unADONO5nHqkeju6U/1FoCQDEkl0wvU1NqZdoUk0+aGQ2Efs6JiriXXsmodAEgVikkAAIwDsd7ABQp5TsX66WThrELVrlyadgUlIJ1dcv5UFeRmaU3dvrQsJEnStgNt8nj7Qj7G7E6MxwKnQxUlU+NaAwBSjWISAABjnN/vj+sGzswT8QfJL0n0+umoct501dW44r6pBMaL1e+fp/qWjrQbER3KN+CXu8sb8jHxdifm59i1rnpxxuXFAcBIFJMAABjjrLiBM/NEUrF+ulo4q1DbVy/TQ6sqdM2iYtlt3BwCgcwtyldFSVFEY2Sp1t0Tvigea3difo5dtSuXZlROHAAEQwA3AABjnFU3cJsbDgUMp030+unMMAy5SovkKi2Sx9snd5dX3T0+rX/iFe1oPpbq7QEpZ0j634+9Q909/dp+wJ3q7YRVkBtZgcjsTgx14MBQC5wOrateTCEJwJhBMQkAgDHM4+2z7AbOzBMZegJRotdPNI+3T22dXp3q9Wlijl3FhXkxX9+Rlz34tZ95bynFJECSX2e7fdo6vfINpPcoa5bNkHNSXsSPN7sTG1qOa1PD69p+wD3sOWbZDFWVF2tFxVxVlExltA3AmEIxCQCAMczKGzgzT2RosSXR6yeC3+9XfUuHNtUf0hMvDr/5s9sMVZU7taJirlwlRTHf/JkhvemcDwMky+aGQ1q1rCTV2wirqrw46vefYN2JBbl2OSfFXpwGgHRHMQkAgDHslMWnJo3ME0n0+lZrau0MOZbiG/Bra2Obtja2ab6zQOurl8Q0lmKG9FbX1qftyVVAsmw70KZVleenehthraiYG9fXD+1OBICxjgBuAADGsIkWH18/Mk8k0etLZ0fRmt0e7Tt8Us1uT9iju4PZ2dyu6tr6iLuFXnV3q7q2Xjub22O6XqwhvcBY4xvwyzAkexqPeS1wOlRRMjXV2wCAjEFnEgAAY1hxYZ7sNsOSUbRAeSKJWt/qUbSm1k7VbNobdZfQ6V6fajbtVV2NK6YOpWhDeoGx6nuPvyyfPz0zk/Jz7FpXvZhMIwCIAp1JAACMYY68bFWVOy1ZK1CeSCLWb2rtVNWGHVq+cbceb2obVagyR9GWb9ytqg071NTaGXJdv9+vNXX7Yh43O93r02c379XAwEBMX2+G9D60qkJXXmjN9wrINPUtx1O9hYBys2z68YqLOGUNAKJEMQkAgDEu3hyQcOtYuX4iRtHqWzri7go6fOKMlv3g6aCFq3CjeGZI78aPX6wrL5wR114ABLbA6dCGjy7RfGdBxF/T0z+gbz/2UtiiNABgOMbcAAAY46w4WSxUnohV6xfk2vXRnzxv+Sja5oZDMe9rqCMnvKqurVftyqWqnDc95lG8my87X0+++JYlewLGE7sh+SUNbVbMshmqKi/Wioq5qiiZKsMwNHVitlbdv1c9/ZF1E5pFafO1HS+Pt09tnV6d6vVpYo5dxYWc6gZg7DH8/jQdXsaYduTIEc2ZM0eSdPjwYc2ePTvFOwKAsa2ptTPmk8Xyc+xBCzXmTdMLrZ36ym8b5e2LfhQsP8euX366Qmt/tT/ugtS21ZXDCjceb5+WfPNJSzKdTPk5dn3nw4t0zzOvRbTfkafC+f1+VW3YkTYZSnbDSNssG8CUn2NX7cqlWjJnstxdXnX3+FSQa5dz0vBCTaLe68KxOucNAKyUiPtviklICYpJAJB8O5vbow6hNm/ghv5rfaibpmiZ69tthpZv3B3zOqaHVlXIVVo0+N/Nbo+u/NGOuNcdyTCkaP4GNfL7GM8NrxX+52Pv0Jyp+Tp68oz+7Zd/lbefvw4ifS1wOrSuenHYIo8VhdpARelwmlo7Iw7ZH1lcBoBkSMT9N5lJAACME+bJYpHmiSxwOlRX4xpWSAoXjh2NoetbNYo2cp1TCSrWRPtPceYonpnLsnBWoWpXLlV+jj0BuwvvgpkOLZ5dqPVPvkIhCWnr0vOn6qFVl2rb6sqIii9W5KO94vaoIYqw8ETkvAFAJqCYBADAODL0ZLFrFhXLbhv+r+9ZNkPXLpqph1ZVjLqBi/amKZBA63u8fdp+wB3zmkNtO9A2LPx6YoqKNYGc7vVpbd1+mU3hZnFv1uQJSd1Hls2Qc1KeHtzzhprfOpXUawPR2P234/raowd04M2uiB6fqKJ0ME2tnVF3e0qji8sAkIkI4AYAYJwxTxZzlRbJ4+0LmT9iivWmSZLysm264/pFWjynMOD6bZ1eyzKNfAN+ubu8g9coLsyT3WZYmpkUD7PrYego3vFTPUndQ1V5sfYdPqmv/u5AUq8LxCLScOxEFKVDhWb7/X6tqdsX86iqWVyOdqQOANIFnUkAAIxjjrxslc1waMmcySqb4Qh48xTvTZO3b0A/2dGi0ukFAde3ehStu+fv6znyslVV7rR0/XiZXQ/m9/VMDKHl8bisbJo+ff/etCmwAeFE0smTiKJ0KKkYqQOAdEIxCQAAhJTomyarR9EKcoevt6JirqXrx8vserDi+xqt+TMKdN+uv+lMX2qCv4FYjRwTHSmRRelAkj1SBwDphmISAAAIKdE3TeYomhXMPKChXCVFEYeOJ4PZ9ZDsm8j8HLs+cdl5SS9gAVZJZVF6qETmvAFApqCYBAAAgkrGTZOVo2hV5cWjRukMw9D66iUpOzktkC//tknbmtqSdr2cLEMbPrpEz752LGnXxPhUkJvYSNZUFaWHSvZIHQCkI4pJAAAgqGTdNFk1ihZsnYWzClW7cmnaFJT2/O24khlZ1Nvv12c279XjjckrYGHsGlmyGXpKY+3KixJ67VQVpYdK9kgdAKQjTnMDAABBJeumyRxFi2cEa4HToYqSqUE/XzlvuupqXPrM5r06cuJMzNfJVORtwyqGIW259V3yyxh1CqTf74/7tRzKyBMbh1pRMVdbLSiYhituJ3OkDgDSFZ1JAAAgqGTdNMU7ipafY9e66sVhj9heOKtQO/79vZo9JfgIC4DQBvzSxNysgKdAJmOsNFxROh7hitJSckfqACBdUUwCAABBJfOmKdZRtPwcu2pXLtXCWYURPd5ms+nHKy5Om5E3IBOFGs1K9FhpqovSyRypA4B0NSaKSW+88Ya++MUv6oILLtDEiRM1depUXXLJJfrhD3+o06dPW3adhx9+WFVVVZo5c6by8vJ03nnnaeXKlWpoaIh4jY6ODn3ta1/T4sWLVVhYqEmTJmnx4sX62te+po6OjojXqa+v18qVK3XeeecpLy9PM2fO1NVXX62HH3444jX6+/tVW1urZcuWafr06ZowYYLKysr0mc98Ri+++GLE6wAAxq5E3DR5vH1qdnu07/BJNbs9w/JPzFG0SLsLFjgdqqtxqXLe9Kj2km4ZSkCm6ej2BnwNm6J9LUcqXYrSic55A4B0Z/j9/oyeoH/sscd00003qbOzM+DnFyxYoK1bt6qkpCTma3i9Xt144436wx/+EPDzNptNX//613X77beHXOf555/Xhz70IR09ejTg58855xz97ne/08UXXxxynW9+85v6xje+oYGBgYCfv+6661RXV6e8vOD/R9vR0aFrr71Wu3fvDvj53Nxc3XPPPfrkJz8Zci+xOnLkiObMmSNJOnz4sGbPnp2Q6wAA4rfr4DEt3xj4/y+i8V/XXqC9h07oiRfdw0K97TZDVeVOraiYK1dJkQzDkN/vV0PLcW1qeF3bDwx/fJbNUFV5sVZUzFVFydSwXQShNLV26rMP7NXh4+MvQwmwSqDXsMl8Lf/bL/+qtq6euK917aKZuvum8CHfTa2dWlO3L6LspgVOh9ZVL464kCSdfV5VG3bEnfO2bXVlXO9hABCJRNx/Z3Qxaf/+/XrXu96l06dPq6CgQP/5n/+pyy+/XGfOnNHDDz+sjRs3SpLe9ra36fnnn1dBQWz/MnLTTTfpwQcflCRdfvnl+sIXvqBzzjlHjY2N+s53vqODBw9KkjZu3Khbbrkl4Bqtra1aunSp3G63srKytGbNGn3gAx+QJP3hD3/Q+vXr1d/fL6fTqb1792rWrFkB1/npT3+qVatWSZJKS0v15S9/WYsWLdKbb76pu+66S08//fTgnjdv3hxwDZ/Pp/e9733asWOHJOmGG27QqlWrNHXqVO3evVvf/va39dZbb8lut+uxxx5TVVVVTN+3UCgmAUDmsOKmKSfLpt7+wP8IMtR8Z4HWVy8ZdlPn8fbJ3eVVd49vVNivFV5t69JVG3Zath6QSDYjvcPUA72GJeuK0g+tqpCrtCiixya6KN3U2qnq2nqdjuGggvwcu+pqXFEVsAAgVhSTRrj88sv1zDPPKCsrSzt27JDL5Rr2+R/84Ae67bbbJEnf+MY39NWvfjXqa/zpT3/Se9/7XklnO35++9vfym7/e9vssWPHtHTpUr3xxhuaMmWKWlpaNHny5FHrfOITn9AvfvELSVJdXZ1uvPHGYZ//1a9+perqaknSzTffrJ/97Gej1jh58qTOP/98nTx5Uueee6727t2radOmDX7e5/Ppwx/+sH7/+98P7n3ZsmWj1rnvvvt08803S5JuvfVW3X333cM+/9prr2np0qXq6urSvHnz9OKLLyory9qD/ygmAUBmieemKVq5WTZ944PluvbtM5OSJeLx9mnJN58cdqMJpKtdX7pc+w+f1L/9cr+8ERRoU8EcGRs6fprqTp5EFaV3NrerZtPeqN4bA31/ACCREnH/nbGZSc8//7yeeeYZSdKnPvWpUYUkSVq7dq0uuOACSdKGDRvU1zd6njuc73//+5Iku92ue+65Z1ghSZKmTZumO++8U5J04sQJ3XvvvaPWcLvdg11CVVVVowpJknTjjTcOdgDdf//9crvdox6zceNGnTx5UpJ05513DiskBdrjD37wg4DPyfzzKVOmBHxMWVmZ/vM//1OS1NzcrN/97ncB1wEAjB/JzBjq6R/Qlx5p1OJvPKFbH9irXQePKZH/9mVlLhSQaM+9dkw/eqo5bQtJknS616eaTXvV1Pr3GIpkhWMH48jLVtkMR8AT6OKRrJw3AEg3GVtM2rJly+DHZpfNSDabTR//+MclnS30mMWnSHV3d+uPf/yjJOnKK68MWr274YYbNGnSJEnSI488Murzjz76qHw+X8i9Sme7l6SzHUaPPvroqM+bz3nSpEm64YYbAq4xe/Zsvf/975ckPfnkk+ruHv6vP83NzYPh2h/96EeVn58fci/BnhMAYPyJ9qYpJyu+v2YM+KWtjW1avnG3qjbsGHZjahW/369dB4/pWHf8WS5AMty361Bc3T3JcrrXp7V1+4cVgpMVjp1sC2cVavvqZXpoVYWuWVQ86gTMLJuhaxfN1EOrKrRtdWXaPg8AiEbGFpN27jybbTBx4kQtXbo06OPe8573DH787LPPRnWNPXv2qKenZ9Q6I+Xk5KiiomLwa0Z2QJl7DbdOqL329vZqz549kiSXy6WcnJyw6/T09Oj555+PaS/FxcWaP39+wL0AAMavSG+a/uvaCyLKSIrUq+5uVdfWa2dzu2VrNrV2qmrDDi3fuFt7/nbCsnWBRDrwZleqtxCxV9weNbQcH/ZnS+ZM1g9vXKw5UydEtEamdPIYhiFXaZHuuWmp9n31Sj21Zpm2fO4yPbVmmf761St1900XyVVaRNg2gDHD2iCcJHrppZcknR3JCpXn87a3vW3U10R7jZHrBLvOE088of7+fjU3N+vCCy8ctU5hYaGKi4uDrjFz5kxNmjRJXV1do/ba3Nys/v7+iPcy9NqXX355zM/p1Vdf1eHDh3Xq1ClNnDgx5OMBAOODedPkKi0KmkNy6wN7Lb+uOToTT2itx9untk6vnjvYoe89/pK8fek7KgSMBZsbDqmiZKrqWzq0qf7QqNMcDUkjh1itPLExFRx52UnJewOAVMrIYpLX69WxY8ckKWxw1JQpUzRx4kSdOnVKhw8fjuo6Qx8f7jpmmJX5dUOLSeY6kYRczZkzRwcOHBi113j2Eu86fr9fR44c0YIFC0JvfogjR46E/PzRo0cjXgsAkL4C3TR5vH3afmB09p8VzNGZaEJ4/X5/0BtZAIn1eNNRXbm+S6+1nwr4+aGvxnOn5us//2GB3j1vOsUYAEhzGVlM8ng8gx8XFITPbTCLSSPzg6y8ztCunZHXMdeJdK+h1rBqL/GuE87QghYAYHxp6/QmtGBjjs5Ecjx4U2un1tTty4iMGWAsGvAraCFppDeOn9baX73ASWcAkAEyMjPJ6/UOfhwqO8iUm5srSTpz5kzCrmNeI9B1zHXi2avVe4l3HQAAgjkVxRHZsdrccCjsY3Y2t6u6tp5CEpBBAp0EBwBIPxnZmZSXlzf4cW9vb9jHmyHaEyZEFvQXy3XMawS6Tl5enk6fPh3XXq3cy9B1hv53NOuEE26k8OjRo7rkkkuiWhMAkBkmxnj0dzS2HWiTx9sXdBSmqbVTNZv26nQSCltAMgXKGBprYhlnBQAkV0YWkxwOx+DHkYxfnTp1trU2kjGzWK9jXiPQdRwOh06fPh3XXq3cy9B1QhWTQq0TTiT5UACAsam4ME92m5HQUTffgF8t7d3Kz8nSqV6fJubYVVx4Nvzb7/drTd0+CkkYk/KybTozDoLjoxlnBQAkX0YWk/Ly8jRt2jQdO3YsbNDziRMnBosi0eb4DC2IHDlyRBdffHHQxw7txBl5ndmzZ8vtdofd69B1Aq0xdC+RrBHJOtOmTQu7jmEYFIcAABFz5GWrqtyprY1tCb3O9XfvGtahYbcZqip36h3nTma0DWOSYWhcFJJMmxsOUUwCgDSVkZlJknTBBRdIkl577TX19/cHfdzLL7886msiNfREtqHrhLpOVlaWysrKAq7T2dmptrbgf7E+evSourq6Au51/vz5stvtUe0l0DqxPKc5c+YMC+MGACCcFRVzE36NkX1PvgG/tja26Y7HQv//G5Cp/BY1+80tyteE7PS/DTDHWQEA6Sf9/18kiHe/+92Szo5i7d27N+jj/vSnPw1+fNlll0V1jXe+852DIdVD1xmpt7dXDQ0No75m5F7DrRNqrzk5OYMZQ/X19SFzk8x1cnNzR3VTRbqXtrY2vfrqqwH3AgBAOK6SIs13RjciDSDx8nPsunv5RfrJxy9WfhLyzeLhG/DL3eUN/0AAQNJlbDHp+uuvH/z45z//ecDHDAwM6P7775ckTZ48WZdffnlU13A4HLriiiskSU899VTQ8bJHHnlksKPowx/+8KjPf/CDH5TNZgu5V0m67777JEk2m00f/OAHR33efM5dXV165JFHAq5x5MgRPfXUU5KkK664YlhGknS2w8nsVqqrq9Pp06dD7iXYcwIAIBTDMLS+ekna36wC40l+jl21K5dq4axCVc6brroaV9oXfbt7yD4DgHSUscWkSy65RJWVlZKke++9V/X19aMes27dOr300kuSpC984QvKzh5+4st9990nwzBkGIa+/vWvB7zOF7/4RUlSf3+/Pve5z8nnG/5/aMeOHdN//Md/SDpbsLrllltGrVFcXKybbrpJkrR9+3b9+te/HvWYX/3qV9q+fbskaeXKlSouLh71mFtuuUWFhYWSpC996Uvq6OgY9nmfz6dbb711cI/m3oM9p+PHj+u2224b9fmDBw/qu9/9riSptLSUYhIAICYLZxWqduVSCkpAGnDkZqmuxqXKedMH/2zhrEJtX71MD62q0DWLimW3DT85Lctm6JLzpyo3K3W3DAW5vH8AQDrKyABu01133aXLLrtMZ86c0VVXXaUvf/nLuvzyy3XmzBk9/PDD+slPfiLpbDfO2rVrY7rG+973Pv3TP/2THn74YT366KO68sortXr1ap1zzjlqbGzUHXfcoTfeeEOS9L3vfU9TpkwJuM4dd9yhbdu2qb29XR/72Mf05z//WR/4wAckSX/4wx+0bt06SdL06dP17W9/O+AaU6dO1Z133qnPfOYzOnTokC699FJ95Stf0aJFi/Tmm29qw4YNevrppyVJH/vYx4J2Yv3zP/+zfvazn+m5557T3Xffrba2Nq1atUpTpkzRnj179K1vfUtdXV2y2Wz6n//5H2VlZfSvCQAghczuhzV1+wjFBlLoVG+/5hblj/pzwzDkKi2Sq7RIHm+f3F1edff4VJBrl3PS2RMSm1o7I34Nl00vUMuxbllxmGOWzdDEnCw1uz2jTm0EAKSW4fdbFeWXGr///e+1YsWKwTGzkebPn6/HHntsVCi2dLYz6eabb5Ykfe1rXwvanXTmzBl95CMf0datWwN+3maz6fbbbw/69abdu3fr+uuvDxrCXVxcrC1btujSSy8Nuc7XvvY1fetb31KwH90111yj3/zmN8rLywu6xrFjx3TNNdfo+eefD/j5nJwc/e///q9WrVoVci+xOnLkyOBJc4cPH+a0OAAY4/x+vxpajmtTw+va1tRmyY0mgOg8tWaZymY4wj9wCL/fr/qWDt1ff0hPHAj82rUZ0tXlxVrpOk8VJVP1uQf/YslpjsWT8tTe3SPfkIuapzauqJgrV0mRDMMIsQIAQErM/XfGt5xcd911euGFF3TXXXfpscce05EjR5STk6OysjLdeOON+vznP6/8/NH/ChONCRMm6LHHHtODDz6o++67T/v379fJkyfldDpVWVmpz3/+83K5XGHXufTSS9XY2Ki77rpLW7Zs0euvvy5JOv/88/WhD31Iq1evVlFR+ONPv/GNb6iqqkp33323du7cKbfbrcmTJ2vx4sW6+eab9bGPfSzsGtOmTdOuXbu0ceNGPfjgg3rppZd06tQpnXPOObriiiv0hS98QeXl5WHXAQAgGI+3T22d3sGOgoWzJumem5bK4+3T1sajun1Lk3p9VJWAZIk2fyjSjqQBv/Rae7cceVkyDEMrKuZaUkxqCxC+bZ7auLWxTfOdBVpfvUQLZxXGfS0AQHQyvjMJmYnOJAAYm8wuhk31h/TEi+6QHQWP/KVVa3+1P4W7BcaXaDqTdja3q2bTXp3ujbwAZQZ8v7tsmqo27EjKaKt5zaFZUACA4RJx/52xAdwAACC9NLV2qmrDDi3fuFuPN7UNKyRJf+8oWL5xt6o27NAEgrmBpGppPxXR45paO6MuJEnS6V6fajbt1YE3u5J2mqN5zabWzoRfCwDwdxSTAABA3HY2t6u6tj7iToRX3d1aW7dPNuJOgKRZ/ct9YYsufr9fa+r2RV1IMp3u9Wlt3X6VnzMpaac5mtdk4AIAkodiEgAAiEusXQxn+gZELQlInkiKLvUtHXGPp73i9qih5fjgaY7znQVxrRfNNQEAyUExCQAAxCzeLgbyt4HkCld02dxwyJLrmOssnFWo7auX6aFVFbpmUbHsI9oRs2yGiguDn0AcyzUBAImX8ae5AQCA1LGiiwFAcm1uOCRX6egThD3ePm0/4LbkGtsOtMnj7ZMjL1uGYchVWiRXaZE83j65u7zq7vGpINeuiTlZevf3n7b8mgCAxKKYBAAAYmZVJ4DdZowK7AaQGMGKLm2dXsteh74Bv9xd3lHXcORlD/uzZrcn4dcEAFiPMTcAABATK7sY/H6/JmRzuhuQDGbRZaRTMY6rBtPdE369VFwTABA/ikkAACAmVnYxDPil9dVvV0EuTdNAMgQquky0+OS1gtzw66XimgCA+FFMAgAAMbG6o+D4qT6d7u23dM1E+9I/vC0pR58DVgtUdCkuzBsVkB2rLJsh56TwwdqpuCYAIH4UkwAAQEys7ij45h9eVCbFJtkNQzddeq5qVy617GYY40uWzdDi2YVJv67NkGY4ckf9uSMvW1XlTkuuUVVeHFF2USquCQCIH73kAAAgJmZHgVWjbj39A5askzSGdKjjtJbMmSy/P4OqYEip//nYOzRnar4Kcu1yTspTW6dXV/5oR1L3MOCXPvLjeq2vXqKFs4YXs1ZUzNXWxra4r7GiYm5Uj032NQEA8aEzCQAAxMTKjoJM5Bvwa23dfh09eSajOqqQWhfMdGjJnMkqm+GQIy/7bFHWSH5n26vublXX1mtnc/uwP3eVFGm+syCutRc4HaoomRrx41NxTQBAfCgmAQCQYTzePjW7Pdp3+KSa3R55vH0p28t47wR4xe3R7r+dSPU2kCECZfo48rL1rrKilOzndK9PNZv2qqm1c/DPDMPQ+uolMWeB5efYta56sYwoCmSpuCYAID6MuQEAkAH8fr/qWzq0qf6QnnjRPWy0zG4zVFXu1IqKuXKVFCX1hsrsKHjV3Z20a6abR/5yJNVbQIYIlulz1YVO7Ww+loIdnS0ora3br22rKwffOxbOKlTtyqWq2bRXp6MI2s/Psat25dJRo3ORSMU1AQCxozMJAIA019TaqaoNO7R842493tQ2KqPIN+DX1sY2Ld+4W1UbdgzrMki0eDsKxoK/Hj7JX6gQkWCdfJeen9rxrFfcHjW0HB/2Z5XzpquuxhXx+NkCp0N1NS5Vzpse8z5ScU0AQGz4uw8AAGlsZ3O7qmvrI+78CZaDkkhmR0Fe9jj+awXTNQgjVKbPzMkTlOoDATc3HBr1ZwtnFWr76mV6aFWFrllUPOrUwiyboWsXzdRDqyq0bXWlJd1BqbgmACB6jLkBAJCmmlo7ox75kP6eg1JX40rajVblvOm64/pFWvur/Um5XrohgBuh5GXZQmb6OPKydfXCYktONIvVtgNtg/lrbZ1ener1aWKOXcWFeXKVFslVWiSPt0/uLq+6e3yDp9EFGtuLl2EYSb8mACA6FJMAAEhDfr9fa+r2RV1IMgXKQUm0t8+2tnCVl22Tt2/A0jWBVPjGh8rDFnZXVMxNaTHJN+DXrQ/8RbsOdoTMZHPMcCR1X468bIpHAJCGxnE/OgAA6au+pSPuUOtAOSiJVFyYN2okJVZZNkNfv65cOfbM+quKnXE3jGA3pGsWzQz7ODPMPpV2Nh9Lq0w2AED6yqy/oQEAME4Eyi9J5TqRcORl66oLnZas1T/g11e2NKnXl1mdSUy7YaSrF86MqLMmE8LsU5HJBgBITxSTAABIMx5vn7YfcFuy1tAclGS4aO5ky9Ya2SGRCTJwy0iwYCe4BWKG2adzQcnMZKNDCQDGN4pJAACkmbZOr2WFFN+AX+4ub8SP93j71Oz2aN/hk2p2e6IuRP3l0IlotwiMWaFOcDONfM0tmTNZdTWulI+8hWJmsvn9VE8BYLwigBsAgDRzKsbQ7WC6e0Kv5/f7Vd/SoU31h/TEi+6Q4buhwrw93j498eJblu07U9kMOpQg5efYg57gFslr7uvXlUuSNu8+pO0H3GnXqWdmsrlKi1K9FQBAClBMAgAgzUy0eMSlIDf4ek2tnVpTty9o2LcZvru1sU3znQVaX71EC2cVyuPtG3V8uJUdVZkqy2bo8rdN15MU1ca1vGybalcuDXiCWyyvuTv/8e1yd3nV3ePTxBybDh8/o1//5UjKi0ybGw5RTAKAcYpiEgAAIwQqlCTzaGrzVDSrbhLv3PaKbr7svFGdRTub21Wzaa9OR9gJ9aq7W//4f7v09tmF+ssbJ0d1U1x6fuhxnvHgfW+bofdfMINi0jh3x/WLVDlv+qg/j+U1V11br9qVS4etN885Se+7wCmPt0/uLq/2H+7UV7Y0ytuX3MB6M5Mtme+PAID0QDEJAABZN+plBfNUtMeb2ixZ78kX3XryRfewzqKm1s6obmpNPf0Dev710blIvgG/dh3ssGS/meypl9x64kVrwtORuRbPCdyRFMtrzgy8rqtxjep0cuRly5GXrbIZDs2YlBvT+vEwM9koJgHA+EMANwBg3Gtq7VTVhh1avnG3Hm9qG9URZI6dLN+4W1UbdiTlFCMrT0UzmV0OO159S2vq9iX1pnO8GOdTftDZUUfnpLxhf+b3++N6zUUSeF05b3pKgrvDZbIBAMYmikkAgHFtZ3O7qmvrg+aXjGQWZHY2tyd0X4k6Fe10r0+r7t8b8fMFEJ2L5k4Z1alT39IR92vODLwOZeGsQm1fvUwPrarQNYuKZbcN76LMshlaNm9aXPsYKVQmGwBg7GLMDQAwbiVi7MQKiT4Vrac/ubkqwHiy//BJNbV2Dntv2NxwyJK1Iwm8NgxDrtIiuUqLBjOVunt8Ksi1D3ZMLfnmk5ZksgXqwgIAjA90JgEAxqVkjJ3EilPRgMzV0z8w7L3B4+3T9gPW5GiZgdeRMvOUlsyZrLIZjsGMpapypyX7qSovJi8JAMYpikkAgHEpWWMnsThFlhGQ0Ya+N1hZHDYDr+O1omKuBbuxbh0AQOahmAQAGJesHDux2sQcMkiATGe+N1hdHLYi8NpVUhR3UPcCp0MVJVPj3gsAIDNRTAIAjDupHDuJRHFh3qjgXACZxXxvsLo4bEXgtWEYWl+9RPkx7i0/x6511YtlGLxPAcB4RTEJADDupOPYyVBWZpoASA3zvcHK4rCVgdcLZxWqduXSqAtK+Tl21a5cmpDDBwAAmYNiEgBg3EnHsZORyCIBMl93jy+tA68r501XXY0r4pG3BU6H6mpcqpw33bI9AAAyU1aqNwAAQLKl49jJSGamSbwh4QBSx3xvWFExV1sb2+JeLxFF5oWzCrV99TI1tBzXpobXtf2Ae1jnZpbNUFV5sVZUzFVFyVRG2wAAkigmAQDGIXPsxIpRNyvHToYyM00+8n+75O0fsHx9AIk19L3BiuJwIgOvDcOQq7RIrtIiebx9cnd51d3jU0GuXc5JeZZ2QwEAxgbG3AAA4046j50MVX7OJBUV5CZkbUnKzeKvAUCimO8Nfr9f9S0dmhbHazmZgdeOvGyVzXBoyZzJKpvhoJAEAAiIv0UCAMYlq8ZFEpltVN/SodaTZxKy9pwpE/STlRfFfJoTkEqZMGi1omKumlo7VbVhh5Zv3K1dBztiWicv26bbrn6bcrNslp8cCQBArBhzAwCMS+k+diJJmxsOJWztwyfO6I6tL+s7H16ke555jWwmZBS/znbW9aTpCOjcqfnq8/lUXfu8TscZ+O/tG9DXHz0gSbLbDFWVO7WiYq5cJUXkFwEAUobOJADAuGRmEsXamZPosROPt0/bD7gTsrbpVXe3vvzbRv3XtRfooVUVumZR8agjzLNshi45byojcUg77zxvasSnkCVbr29ANZv2xl1IGsk34NfWxjYt37hbVRt2qKm109L1AQCIlOH3++NPHwWidOTIEc2ZM0eSdPjwYc2ePTvFOwIwXu1sbo/6pi8/x67alUsTejx2s9ujK3+0I2HrD5WfY1ddjUsLZxUGDd9tau3Umrp9dDAhrfz040uVn5OlzbsPjTqFbDxIxnsRACDzJeL+m39mBACMa5XzpquuxhVxh8MCp0N1Na6E37ydsrijIZTTvT6trdsvv98fNHzXPD48WAcTkAq33L9XX//9AV11YbHOK8pP9XaS7nSvTzWb9tKhBABIOjqTkBJ0JgFIN36/Xw0tx7Wp4fVRHQ5ZNkNV5cVaUTFXFSVT///27j08qure//hnch1CQoAACQKiJCQiUFAuJlVAam0Ub5XWtEU4aBXi4dSWqvW0PT16tKf1p/WCR/E0ohWLik3Vaq0IakXBknBTlItAAMGAJHKRkAuTy2T//uDMNJeZyZ6ZPbfk/Xoengey96y1Zq/ZIfub7/qusNQpCWdmksvTcyfqzP4pqm9yqndSvLLSvW8JXutoVlXNKd307GZ9frwhrOME0F5eZppWLpxCDSUAgEeheP6mADcAADpdQ6kgO0MF2Rlel3qFU1a6XfFxtrAu27n52U1q25uvYr9p9kSl2RP1xPXnq6ikzPLaMADM21Vdq/J9x1WQnRHpoQAAegiWuQEAeqxaR7Mqqmu1pfKEKqpr3dtue1vqFc4x7T1Sr6+H+cGwY9jKTLHfMUPSVTJnQsCFzAFYI5S7PwIA0BGZSQCAHsUwDJXtO6ZlZQf01o72y9ms3Hb79DIwh6klY77GFC12V9epqKTMY7FfV90pCnQDkbNye5VqHc1hz6IEAPRMBJMAAD1GVzuSuTJxVmytUm5mqh4uGq8xQ9JNtx9IoCqWdklzFft17fzWlqtAt7e6UwBCy9lqqPqkg2ASACAsKMCNiKAAN4BwW1txRMXLNvtV28efbbf9CQq5AlVfNTT5PaZoYKbYb62jWW9sPay7Xt2mJic/arRlU+clhYAVXv23CzV+WN9IDwMAEGVC8fxNzSQAQLe37VBNQEEbs9tur604oqKSMtPZRbur6/Td/12nm5/dFHOBJMlV7PeYz3MOHGvQva/vIJDkgSF+AENopCZTuwwAEB78LAMA6NYMw9BtpVsCDto0NDl1e+nH8pbIG2igytHSqsaW1oDGFA3m/dF7kC3Ya94TxO7MI1olxNmU2cce6WEAAHoIgkkAgG6tbN+xoOsRubbd7qgnB03qGlt03e/XaW3FkU7HrLjmQCQNz0iRPSGwH5NTkuJ1Zv9eFo+oa4Wjs6iXBAAIG4JJAIBuzartsj2109ODJqeaWz0uA2SLcsSihDibrhg7WMvn5eu9Oy7WkrkTlZLk37IxV521J66f4PdrgzU7f3hY+wMA9GwEkwAA3Vato1mrtldb0pZr2+22CJp0XgZo5TUHwuHC7Ay9c9tUfXTXpVp8/fkqyD692+KUkQNVWlyg3MxUU+3kZaaptLhAU0YO1Jgh6SqZE76AUl5mmvJH9A9LXwAASASTAADdWFWNw7Lt6V3bbrsQNPmntssArbzmQDj86BsjlTMozeMSsTFD0rVq4VQtn5evGWOzFB/XfgfDttlMKxdO0Zgh6e5j/gajfGyO6FNKUrweKhrnc3dFAACslhDpAQAAECr1Ftcyqmv8Z3sETdp7rvyACrIzLL/mQCiZyeix2WwqyM5QQXaGah3Nqj7pUF2jU6nJ8crsY/dZp8gVjCrfd1zLyvdr1fbqdt83EuJsKhydpdn5w9XsdOqW5z70qwaba1ld2yCWFWodzaqqcai+yaneSfHKSvf9PgEAPQ/BJABAt9Xb4iUmbbfdJmjSnmsZoNXXHAiVQDJ60uyJfgdV/AlGlRYX6LbSLaZqseVlpumhonGWBZIMw1DZvmNaVnZAb+1oH/SKj7OpcHSmZucPV8GIDLKgAAAEkwAA3VdWul3xcTZLMog6brtN0KQ91zJAK685ECqhyujpSlfBKH8ymfJH9LcsqLPtUI3PIJaz1dCKrVVasbVKuZmperhofNivHQAguhBMAgB0W2n2RBWOztSKrVVBt9Vx222CJp3VNTqVY+E1B8zIy0zTv16crSfe2xORjB6rBbOsLhBrK46oeNlm08vrdlfXqaikTCVzJmjKyIGWjgUAEDsIJgEAurXZ+cMtCWx03HbbykBVd5GaHC/DMHTemX25LggpTxk614w/I+wZPaEWyLI6f2w7VONXIMmlocmp4mWbVVpcELVBOQBAaBFMAgB0awUjMpSbmWoqY8Gbof16eSzSa1Wgqrs4ePyUFjz/YVDXGvDlzP4penruBGWl9+oUZAl3Rk+sMwxDt5Vu8TuQ5NLQ5NTtpR9r5cIpMROcAwBYJy7SAwAAIJRsNpseLhove2Lg/+UdrW3U9i9Odvq6K1CF0+Y/t5lAEkLq8+MNOlrX3GVgKM2eqJxBaRo/rK9yBqURSPKgbN+xoO/XXdW1Kt933KIRAQBiCcEkAEC3N/qMPsronRTw6x0trbq99GMZRvv6SK5AVUqAxbgT47rXb/ObWlojPQT0AM+VH4j0ELoFq64j8wEAPRPBJABAt1e275gOnXAE1Ya338CPGZKukjkT/A4opSTF6+kbJurM/r2CGhfQ06zcXqVaR3OkhxHTah3NWrW92pK2mA8A6JkIJgEAur1Q/wZ+ysiBKi0uML3kLS8zTaXFBZqaO0hPXD9BvYJYggf0NM5WQ9UngwsO93RVNQ7LdqJkPgCgZ+KnVwBAtxau38CPGZKuVQunavm8fM0Ym6X4DkvYEuJsumLsYC2fl6+VC6e4d0AaMyRdT/7LxICXygGRFKmFmnWNgRWNxmn1ARbd9ob5AICeh93cAADdWih+A++tmG+gu0m5MptuK91CAWvEFHtivE41hz+QkJpM8DUYvS0OXjMfANDzEEwCAHRrkfoNfJo90a8dpFyZTeX7jmtZ+X6t2l5tWRAMCJVIBJIS4mzK7GMPe7/dSVa6XfFxNku+xzAfANAzEUwCAHRrsfQb+I6ZTe/v+lI/Wr4lZP0BsahwdJZfgVp0lmZPVOHoTK3YWhV0W8wHAPRM1EwCAHRrrt/AW8Gq38DXOppVUV2rLZUnVFFd67EOU5o9UXlZfYLuC+huZucPj/QQugWrriPzAQA9E5lJAIBuLVp+A28Yhsr2HdOysgN6a0f7JWzxcTYVjs7U7PzhKhiRIZvtdPDLyqUo8TabnAbL5hDb8jLTlD+if6SH0S0UjMhQbmZqUHXamA8A6LnITAIAdHuR/g38tkM1Kly0RrOWrNeb26o6BYecrYZWbK3SrCXrVbhojbYdqpH0z0CYFVoJJCEMUpND93vKlKR4PVQ0zh1sRXBsNpseLhof8E6SzAcA9GwEkwAA3Z7rN/DB8PUbeF/L1tZWHFFRSZnp3/7vrq5TUUmZ1lYckWRdICyWQklTRw6wbGkiwutUs1NPz52oSWf1s7TdlKR4lcyZoDFD0i1tt6cbMyRdJXMm+B1QYj4AACxzAwB0e67fwBeVlKkhgN3dPP0G3syytYtyBujXf9uhU82tfvXX0ORU8bLNKi0usGQpSqy57Vt5Wjywt6pPOlTX6NTR2kbd/MdNkR4WTHC2GmpqcWr7FyctazMvM00PFY0jcBEiU0YOVGlxgW4r3WLq+wzzAQCQJJthkPeO8Dt48KCGDRsmSaqsrNTQoUMjPCIgttQ6mlVV41B9k1O9k+KVlW5nNx0T1lYcUfGyzX4FlFy/gZ8ycqD7a9sO1Zh+8ArGmf1T9NS/TNBXDc26cenGgAJhseid26YqZ1CapNNBu2XlB3TXa9sjPCqYdWb/FH1+vCGoNuIkXTY2S3Pyz1L+iP4spQoDwzBUvu+4lpXv16rt7QPkCXE2FY7O0uz84cwHAMSgUDx/k5kEADEikALOaM+K38AHEpAK1OfHG/StRWsVH2fT+Wf21ScHa9TY4l+WU6xpu2NeuIJ2sFawgSRJapW058s6pdkT+H4WJjabTQXZGSrIzlCto9mdGZiaHK/MPvzCAgDQHplJiAgykwD/+PNQnZuZqoeLxrMEwYdAfwO/7VBNwEvlrJIUH6cmZ/cNKF0xdrAWX39+WIN2sI5N1tbn8pQZ2BZZmgAAdC0Uz98EkxARBJMA86xamgXPzP4G3jAMFS5aExVZMskJcfra0HR9+PmJdoEwqx/kI2H5vHyl2RMiHrSLBr/7ztc0MitNr398SE9/sD/SwzElFJ/BlKR4lRYXuAPkZGkCAOAflrkBQA+z7VBNQNkZbQs4d+cMJW9ZCf5kK6TZE01lMpTtOxYVgSRJamxp1fYvTmr5vAvUv3eS6hqdssnQtU+sUyz/ishmk5paWnRb6bYeH0iSpL9+8oUub83SC+s/j/RQTAvFx6+hyanbSz/WyoVTtP2Lkz6zNJ2thlZsrdKKrVVkaQIAEEJkJiEiyEwCumZFJkxeZppWLpzSrX477ysrIc4mDUpL1pe1jWrzZUuyFRY8v1krtlZZ8RYs03Z+K6prdekjayI9pKAlJ8R1+7pQ0caeECeHBdfcisLbvvzi8nP06N8ryNIEAMBPoXj+jgu6BQBASFiRCbOrulbl+45bNKLI23aoRoWL1mjWkvV6c1tVu0CSJLUaUtXJ9oEk6Z/ZCrOWrFfhojXadqjGr35rHc1atb062OFbbld1rcr2HpMk1VucyRMXofgjgaTwsyKQlJIUr59ffo4Fo/HugVW7As7SXL/vmCqqa7Wl8oQqqmtV62gO0SgBAOgZWOYGAFHqufIDlrVTkJ1hSVuRZFVB5t3VdSoqKfOareBpiVxVjaNT4CpazPvjJv3uunFytlobhInSt4so5Mr+GT+sr+LjbCG7VwJtt6HJqe89Wd7ua9RWAgAgOASTACAKWZkJs3J7lWodzTG9w1GgtaO86VhTqquCvhec3c+SfkOhvsmpBc9/GOlhoIfKy0zTQ0Xj3HWJCkdnRt1yUE+orQQAQHBY5gYAUcjKTBhnq6Hqkw5L2ooEwzB0W+kWywsyu4r6bj14wufSOWeroXV7u89SQcAq/3nFKK1cOEVjhqSr1tGsiupaFYyIvSxIV7bi2oojkR4KAAAxg8wkAIhCVte/qWuM3Z2xQrmL2q7qWn3392XU6QECsPnzrzRqXx+PGX2xpqfsgAkAgFUIJgFAFOqdFG9pe6nJ1rYXTlbVjvKGQBIQGNcyse7Cla3Y3XbABAAgFFjmBgBRKCvdrniLttNKiLMps4/dkrbCLVp3UQPQPXW3HTABAAgVgkkAEIXS7IkqHJ1pSVuFo7Nitvh2NO+iBqB7CnU2JAAA3QHBJACIUrPzh0dVO5Fgde0oAOiKawdMAADgHcEkAIhSBSMylJuZGlQbeZlpyh/R36IRhZ/VtaOiTWI8dVmAroT7Non1HTABAAgHgkkAEKVsNpseLhqvlAADKilJ8XqoaFxMF5K1snZUNGp2GkpO4L9iwJuUpHjdedk5Ye83lnfABAAgHPgJFgCi2Jgh6SqZM8HvgFJKUrxK5kyI+S2urawdFa3GDe2rXon8dwx0lJeZptLiAs2fOiLoLE1/xfIOmAAAhAM/vQJAlJsycqBKiwtMP0y5HsCmjBwY4pGFRyzXfDJj8+dfaemNk3l4RY/y4HfHacbYrE6ZhwlxNl0xdrCWz8vXyoVTNGZIetBZmv6K5R0wAQAIl4RIDwAA0LUxQ9K1auFUle87rmXl+7Vqe3W7Xc4S4mwqHJ2l2fnDlT+if0wvbevIVTtqd3VdpIcSEs5WQxmpSXpyzkTNemp9pIcDhEVOZqq+O3Goah3Nqj7pUF2jU6nJ8crsY/e4+6QrS7N42WY1hLgwfyzvgAkAQLgQTAKAGGGz2VSQnaGC7AzTD2DdgSsroaikLOQPkZFS1+hUQXb3DpoBbbky8dLsiaa/d7myNG8r3RLS+6S7Z0MCAGAFlrkBQAxKsycqZ1Caxg/rq5xBad02kOQSaO0ob6Kt6HVqcnzYl/IAkRLMMjJXlubyefkel8kFy9MOmLWOZlVU12pL5QlVVNeq1tFsaZ8AAMQiMpMAADHBqqyEvMw0PXjd13T7nz+Oiiygtg/WrqDZvD9ukqO5NcIjA9ovof1j2Wd6c1t10G0Gu4zMV5bm8fom3fDMxoCyGNvugGkYhsr2HdOysgN6a0f7ZcXxcTYVjs7U7PzhKhiR0a2WFQMAYBbBJABAzOiqdlScTRqUZteXtQ61+bLHmlLRsnSu44P1lJED9Ztrx+r20o8jOCpAenruRE0+u7/789lqtFoSTLr+gjODbsPF0zK5QGortd0Bc9uhGp9Ba2eroRVbq7Ria5VyM1P1cNH4mN85EwAAfxFMAgDEFDO1o8zUlApnQV9fPNVn+RoPpogCGanJ7e4bm6zJwAl1Io+/WYx5mWl6qGicxgxJ19qKI359T9hdXaeikjKVzJnQbXbQBADAjOgqGhGAhoYG/e53v9PkyZPVv39/paamatSoUbrjjjv0+eefW9pXWVmZ5syZo7POOkt2u12DBw/WZZddphdffNF0Gy0tLSopKdHUqVM1cOBA9erVSzk5Obrlllu0Y8cO0+18/vnnuuOOOzRq1Cj17t1b/fv31+TJk/Xggw+qoaHB52uXLl0qm81m6s/SpUtNjwlA9xBL9UG81Y4yW1PK9dCZm5kazmG7earPIklZ6XbLa8EA/nIVyXZ5bv0BS9p9rtzan8886aq2UkKcTVeMHazl8/K1cuEUd0ZSIMHlhianipdt1rZDNVa+BQAAoprNMAyj69Oi0969e3XFFVdo165dHo+np6frhRde0IwZM4Lu695779U999yj1lbPNSyuuuoqlZaWym73XlDy2LFjuuKKK7R+veetn5OTk/XEE0/ohz/8oc+xvPHGG7r++utVU+P5h5a8vDytWLFCI0aM8Hh86dKluvHGG3324fLMM8/ohhtuMHWuPw4ePKhhw4ZJkiorKzV06FDL+wBgXizVB6l1NKuqxqH6Jqd6J8UrK92/new8vT41OUHl+47rkXd2a8Nnx0M4+n9KSYpXaXGB1+UxC57frBVbq8IyFqCjhDibPrrrUve9Veto1vh73273vSFQ8XE2bWnTdjh0la1oGIYKF60Juh7byoVTIv49EgCAjkLx/B2zy9zq6up05ZVXugNJ8+bN0/e//3316tVLq1ev1n333aeamhpdd911Kisr09e+9rWA+3rqqad09913S5Kys7P1y1/+UmPHjtUXX3yhRx99VKtXr9brr7+um2++Wc8995zHNpxOp2bOnOkOJM2cOVPz5s1T//79tX79ev33f/+3vvzyS82fP19DhgxRYWGhx3Y+/vhjFRUVqaGhQampqfrFL36h6dOn69SpU3rxxRe1ZMkS7dq1S1dccYU2btyo1FTfv21ftWqVzjjjDK/HCfIA3V8s1AcJNthl9vUZvcP3cPujb+T4vI6z84cTTELEdKzlVVXjsCSQJJ3+nlJ90hHWYJKn2kptle07FnRB/l3VtSrfd1wF2RlBtQMAQCyI2WDSgw8+qJ07d0qSHnjgAf3sZz9zHysoKND06dM1depUNTQ0aOHChXr33XcD6ufEiRPuts8880yVl5drwIAB7uNXXnmlrr32Wr3++ut6/vnnNX/+fE2dOrVTO8uWLdOaNWskSQsWLNDixYvdxyZPnqzLL79cEyZM0MmTJ3Xrrbdqx44dSkjoPD0LFy5UQ0ODEhIS9NZbb6mgoMB97Bvf+IZGjhypO++8Uzt37tTDDz+su+66y+f7y83N1VlnneXXNQHQfcRCfZBgg13+vD6cHn93j6aOHOg1oFQwIkPDM1J04JjvpctAKHSs5VVvcV2xusbIFr7v6Llyq5bwHSCYBADoEWKyZlJzc7MeffRRSdKoUaN0++23dzqnoKBAN910kyRp9erV2rx5c0B9LVmyRCdOnJAk3X///e0CSZIUHx+vJ554QvHxp+sK/O53v/PYjuvr/fr183hOTk6OfvGLX0iSKioq9Nprr3U6Z+PGjXrvvfckSTfddFO7QJLL7bffrlGjRkmSFi1apObm6K11AiCyYqE+yNqKIyoqKTOdMeAKdq2tOBLQ68Opocmp+X/cpF2HazzWp7LZbPrxN0ZGcIToqTzV8uqdFO/l7MB0rMcUSbWOZq3aHvwudZK0cntVVNeZAwDAKjEZTHrvvffcAZ65c+cqLs7z22hb6+eVV14JqK9XX31VktSnTx/NnDnT4zlDhw7VN7/5TUnS22+/rbq69g8tFRUV7uLa3/ve95SSkhLQeF1jkeS15lFcXJz+5V/+RZL01VdfuYNPANCWYRi6rXRLwLuYNTQ5dXvpxwpl2b1gg12vfnQo4ju1deWLGocKH/1A3178D136yBqNv/dtLXh+s9btPSrDMPS1oezqhvCyJ8bpoaJxnZaLWlkUPiHOpsw+3mtMhlsolvABANDdxWQwae3ate6/T5s2zet5EydOVO/evSVJH3zwgd/9NDU1acOGDZJOZzolJSV5Pdc1jsbGRm3cuDGg8WZlZSk3N9freF3t9O7dWxMmTOhyLN7aAQAr64OEghXBrjtf+iSqA0meuJbczVqyXoWL1uirhiZ2dUNY/c/3z/O49DLNnqjC0ZmW9NGxHlOkdfclfAAAhEJMBpM+/fRT99/POeccr+clJCQoOzu702vMqqioUEtLS5f9dDzesS+z4217vLKyUvX19R7bycnJ8VhPycxYOrrhhhuUmZmppKQkDRgwQPn5+frVr36lQ4cO+XxdVw4ePOjzz+HDh4NqH0BwrKwPEgpWBLuanJ5334wVu6vrdMMzG3X+mX0jPRT0EBflDNC3Rmd5Pd6xjlKgrGrHKt15CR8AAKESkwW4KysrJZ3O0Onbt6/Pc4cNG6ZPPvlER44cUWNjo5KTk/3uR+p6VzPXNnsdXxdoO4Zh6ODBg8rLy5MkORwOHT161FQb/fr1U+/evVVfX99pLB29//777r8fO3ZMx44d0/r16/XQQw9p0aJFKi4u9vn6rt4HgOgTivogVmcZhCpIFWsampz65GDoa1MBkvRv03N8Hi8YkaHczNSgAr2e6jFFmmsJnxVL3aJtCR8AAKESk5lJtbW1ktTltveS3MvcJHWqZWS2HzN9+erHinb8aaNtO97e84gRI3THHXfo5Zdf1oYNG7Rhwwa9+OKLuu6662Sz2eRwOHTLLbfoySef7LIvALEl2uuDWBns6g4aW1qVFB+T/13DIvaEOA3p2yukfZgJ8thsNj1cNF4pAWbypCTFe6zHFGndeQkfAAChEpOZSQ7H6QcXXzWMXNpmIp06dSqgfsz05asfK9rxp4227Xh6z9dee63mzp3b6Ye5SZMm6Xvf+57+9re/aebMmWpubtZPf/pTXX311crK8p727klXGVGHDx/W5MmT/WoTgDWivT6IlcGu7qLJ2arEOJuauS49Tl5mmh4qGifDMPTd35epscX65Zv+BHnGDElXyZwJfhe3T0mKV8mcCR7rMUWD2fnDtWJrlSXtAADQE4T0V50tLS2y2WxB/1m6dGm7du320+nDTU1NXY6hsbHR/fdevfz7rZ6rHzN9+erHinb8aaNtO57ec3p6us8fGK+88krdfffdkqSGhgY9/fTTXfbX0dChQ33+GTx4sN9tArBGtNcHsTrY1V3EdgUo+CPeJl0xdrCWz8vXyoVTNPqMPrr9zx+HLJDkb5BnysiBKi0uUG5m15nS0umAWGlxgaaMHBjoMEPOtYQvGNG4hA8AgFCJybz5tLQ0SeaWrbUtYm1meZinfsz05asfK9rxp4227fj7nl3mzZvnDji1rasEIPZF8xbfhmGoorq26xN7ILK1eo5lN12gxdefr4LsDNlsNksK0nsSTJBnzJB0rVo4Vcvn5WvG2KxO31MS4mztAmLRmpHk0l2X8AEAECohXeaWkJAQ0C5qHXXMYhk6dKjWr1+v+vp6nThxwmcRbtdyq4EDB/pVfNvVj8vBgwd9ntt2WVfH4tMd2xkwYECX7dhstnavs9vtGjBggI4ePdrlWL766it3MCnQQtiDBg3SgAEDdOTIkaB3dgMQXVz1QaxY0mFlfZBth2p0W+mWkDw0A7FkUJ/2P69YWZA+Ic6mwtFZmp0/XPkj+gcV/LDZbCrIzlBBdoZqHc2qPulQXaNTqcnxyuxjj7naQd11CR8AAKEQ8ppJbbept8q5556rl19+WZK0c+dO5efnezyvpaVFe/fulSSNGjXK735yc3MVHx8vp9OpnTt3+jy37fGOfZ177rntzhs/fnyX7QwbNqxdMW5Xu2vXrtWePXvU0tKihATP0+drLP4wDH4LDnRX0VYfZG3FEb8f4IDuqGO2n5UF6eNs0to7p2twCIp5p9kTYy545IlrCZ/ZwLarphWBJABATxOTy9wuuugi9999LcHatGmTO0Pnwgsv9LufpKQkd5HosrIyn7WKXONITk7WxIkTAxpvVVWVdu/e7XW8rnbq6+u1efPmLsfirR0zvvzySx07dkySdMYZZwTUBoDoFU31QbYdqiGQBPyfjtl+VhakbzWk+qYWS9rqzrrbEj4AAEIhJoNJF198sdLTT//H/eyzz3rNoGlbuPvaa68NqK9vf/vbkqSTJ0/qlVde8XjOwYMH9c4770iSLrnkknb1jaTTGU6uDKHS0lI1NDQENF7XWCTpmWee8dhGa2ur/vjHP0qS+vbtq+nTp3s8rytPPvmk+7pOmzYtoDYARK9oqQ9iGIZuK90SkkBSUnxM/heHHq5jtl+0777YXbmW8D1x/QRtuetSvXPbVL36bxfqndum6qO7Lm1X0woAgJ4oJn/STkpK0o9//GNJ0qeffqoHH3yw0zllZWXuXcimTZumSZMmdTpn//797h3jLr74Yo993Xzzze7A1c9//nN3to6L0+nUggUL5HSe/uHsjjvu8NiO6+vHjx/XnXfe2en43r17dd9990mSsrOzPQaTJk+erClTpkiSnn76aZWVlXU656GHHnLXqfrJT36ixMT2Kef79+/XRx995HGMLn/729/061//WtLpWk033nijz/MBxCZXfRB/A0pW1gcJVWHhlKR4PfDdrwUcLAMiwVO2X7TvvtgTpNkTlTMoTeOH9VXOoLRusZwPAIBgxWQwSZJ+9rOfKTc3V5J05513qri4WKtXr1Z5ebnuu+8+fetb31JLS4t69eqlRYsWBdxP//79df/990uSDhw4oAsuuEDPPPOMNm3apL/+9a+69NJL9frrr0uSfvCDH3jNBJo7d657ydnixYv13e9+V6tWrdKGDRv0+OOP6+tf/7pOnjypuLg4PfbYY17rIT366KPq1auXWlpa9K1vfUv33XefysvLtXr1ahUXF7sDVbm5ubr99ts7vX7//v06//zz9fWvf1333Xef3nzzTW3evFmbNm1SaWmpioqKdPXVV7uX9D344IMaMmRIwNcPQHSL9BbfVhYWdklOiNNdV56rS0YNCihYFgxyFBAob9l+0bz7IgAA6LlsRgxXWd6zZ49mzJihiooKj8f79Omj559/XldeeaXH4/v379fZZ58t6XT20nvvvee1r7vvvlu//vWvvS6pmzFjhl5++WXZ7d5/SDt69KhmzJihjRs3ejyelJSkxx9/XPPmzfPahiS9/vrrmj17tk6ePOnxeG5urt544w3l5OR0Ovbee++ZWvqWkpKiRx55RPPnz+/y3EAcPHjQvdNcZWVlu53rAISfYRgq33dcy8r3a9X26nY1Wqzc/amtWkezxt/7dsi2vI+Ps6lwdKYuzBmgP3zwmfYeqQ9JPy7JCXH61RWjdN+bO6n/BL+VzJmgwtFZHo8teH6zJQXzrxg7WIuvPz/odgAAQGwJxfN3yHdzC6WcnBx99NFHWrx4sf785z9rz549ampq0rBhwzRjxgz95Cc/0fDh1uw0dM8996iwsFCLFy/W2rVrVV1drb59+2rcuHG68cYb9YMf/KDLNgYMGKB169ZpyZIleuGFF/Tpp5+qvr5eZ5xxhi655BL95Cc/0ejRo7ts56qrrtInn3yiRx99VG+88YYOHjyopKQk5eTk6LrrrtOPfvQjpaSkeHzthAkT9Nxzz6msrEybNm3S4cOHdfToUbW0tKhfv34aPXq0LrnkEt18880aNGiQ39cJQGyKxBbfVhYW9sTZamjF1qouH8JtkqwYxaSz+mtOwVk6a0BvCopbaGruAP1oeo5ueGZjt76m2QN7ez0WbbsvAgAAxHRmEmIXmUkAtlSe0LcX/yPSw7BMfJxNW+66VGn2RG07VGN6a/FolJwQp8aW1kgPQ9I/r+uWyhPdOkj36r9dqPHD+no8ZhiGChetCerzlJeZppULp1AwGgCAHigUz98xWzMJABB9ah3Nqqiu1ZbKE6qorlWto9nruVYXFo40Z6uhfUfqVFFdq5ZWQ4//4Dz9Ye5Ej1uLR7OEOJuW3TTZZ6ZMODlbDVWfdPhd38sKN085W/bE8Pyo5KswdrTsvggAAOAS08vcAACRZxiGyvYd07KyA3prR/t6S666RbPzh6tgRPtttF2FhUO51C3crn1indq+Hdf7L5lzvs7sl6Kb/rhJlcdPRW6AJkwY3k/P/GO/9h9riPRQ3Fzb2Y8Zkq5VC6d6re8Vb5OcFn6cvj9pmL49fkjIs8zMFMZ27b7ob3aWlbsvAgAAuBBMAgAErKvlXG3rFuVmpurhovHuh9o0e6IKR2daUgsmWnSMi3V8/7dfmqdf/mVrVC/VWv/Z8S7PsarOlFlts3Z81ffqnZSgix5YbUmA0hXgSbMn+gxgWaFwdJapumSu7Cyzwa28zDQ9VDTOayCp1tGsqhqH6puc6p0Ur6z00NRHAwAA3Q/BJABAQNZWHPErS2J3dZ2KSspUMmeCpowcKMm6wsKxYHd1nX75l6360Tdy9Pi7e6I6oNSVcAaSfGXtpNkTOwU/rApQtg3weAtgVVTX6mcvfRJ0X/4Uxu4qO6ur3RcDzSQEAABoi2ASAMBv2w7VBFQMuaHJqeJlm1VaXKAxQ9JVMCJDuZmpMVuo2l8NTU49/u4e/fbasXrivT095n0Hw2zWjouVO595y9xxjWfc0HQtWbsv6MLY+SP6+/WaQHdfDCaTEAAAoC2CSQAQgJ68PMQwDN1WuiXgzJqGJqduL/3YvbPUw0XjVVRSFtOZOv5oaHLqf9/bq5U/maLyfcf1w2c3ytEcHTunBcImaVi/Xvr8q9DUgho3zL9ghhUBymH9eunZdZ/p7U+/7DJzJ5jPrxWFsT1lZ3liRSYhAACAC8EkADCJ5SGnle07FnRGza7qWpXvO66C7IyACwvHsl3VtVr/2VeSTTEdSJJOL3kLVSBJkh55e7e+nj3AVIaMK8hbPC1b//GXrQFdW5tNqvzqlCo9vCdvmTvRXhjbqkxCAAAAF5thGN1nGx3EjIMHD2rYsGGSpMrKSg0dOjTCIwJ862p5SFvRvDzEioyqBc9vtmQZ0RVjB2vx9ee7/+3PNe4Orhg7WIaMHlMzKhh5mWnuTLaOfAV5Q80VEJoycqBfn9+uCmNbyTAMFS5aE/RSPG/XHwAARL9QPH8TTEJEEExCLPF3eYjU/iEzVMwGhqzMqKp1NGv8vW9b8sAeZ5M+vvtb7cZsGIbPbd9bjfAWfw6lOEmydd4BDp4tn5evguyMdl+zMgBps0mB/ESUkhTvztzx9fntqjB2qKzbe1SzlqwPuh1P1x8AAMSGUDx/s8wNAHyItuUh/gaGrC64W1XjsCzzo9WQ/u35D3XLxdnu8XZVWPjfX/6k22TytErdJzIWBs+VH2gXzAgkyNtRQpxN3xqdqU8OntDBrxwBtdGxBlgghbFD6bnyA5a1QzAJAAC4EEwCAC+sLjQdLH8DQ3Pyh+u+N3daWnC33uKaRmsqjmpNxVGPgSxPhYWt2qkLsWfl9irVOpqVZk8MOMgrSfbEOP3m22M1bli6MvvYtfVQjWYtCe4z1bYGmIvZwtihVOto1qrt1Za01fb6AwAAxEV6AAAQrawsNB2stRVHVFRSZno8u6vr9J+vbQ84o2rboRqPx3snxfvVnlmuQNbaiiM+z3Pt1IWex9lqqPqkI+ggr6O5VU+u2afsgalKsydamrkTbazMJHRdfwAAAIlgEgB4FS0PmcFkYQTClVHlqaReVrpd8XGhqfXSVSBLknsr9pQQBbUQ3eoanZYGeUORuRNNrM4krGvsGbstAgCArhFMAgAPouUhM9gsjEB5y6hKsyeqcHRmyPr1FchycW3FTkCp50lNjrc0yHv4xKlunbljdSZhajL3HAAAOI1gEgB4EC3LQ6zIwgiUt4f22fnDQ9qvmaWBU0YOVGlxAUveepCEOJt6JyVYFuR9c9th/fDZTZa05XLXa9u1bu9Rn8HQcLIykzAhzqbMPnZL2gIAALGPYBIAeBAty0MiWYfFW0ZVOOoWmXnfY4aka9XCqfrF5eeEbOkdosfXczJU19hi6W6CB786ZUlbLuv2HtOsJetVuGiNz+Wa4WJlJmHh6CyKbwMAADeCSQDgQTQsD7FyqV0gvGVUhaNukdmlgdu/OKlH/15hWYAB0evSc7MsD/KGitmC8uFgVSZhqDMSAQBAbCGYBAAeRMPyECuX2gXKW0ZVqOsWmVkaGKl6UpJ0wdn9w95nT5d/dr+Q7SYYCmYKyoeDFZmEeZlpyh/BZx4AAPwTwSQA8CAalodEQxaGr4yqUNct+rjS90N4pOpJ5WWm6SeXjAx7vz1ZvE3KSu8V0t0EQ6GhyakfvfChPvr8K1VU10Zkt7dgMwlTkuL1UNE42Wyxc90BAEDoEUwCAC8ivTwk0lkYZjKqXHWLls/L15SRAyzt/z9e3epzmVAk6kn1SozTQ0XjVJAd+rpR+KfLxgxWmj0x5LsJhsL+Yw269ol1uvSRNRp/79ta8PzmsBfpDjSTMCUpXiVzJmjMkPQQjQwAAMQqgkkA4EWkl4dEOgvDbEaVzWZTQXaGnrj+fMVbmL3gaG71ukwoUvWk7r56tMYMSQ9L3Sj8U9uAbCzX7nG2GlqxtSoiRbr9zSTMy0xTaXGBpowcGOKRAQCAWEQwCQC8iPTykEhnYfj70J5mT1ThGGvH29Dk1O2lH3fK4ohUPalzsvq4/x7qulE4rWNANhy7CYZDJIp0t80knDE2q1OwOiHOpivGDtbyeflauXAKGUkAAMCrhEgPAACimStgULxss1+Fnq1aHjI7f7hWbK0Kqo1ABJpRFYrx7qquVfm+4yrIznB/LVL1pDrWkJoycqB+e+1Y/bR0i8K4aqnH8BSQdQV5i0rKIlJ83UquIt2lxQVhC9y4MgkLsjNU62hW9UmH6hqdSk2OV2Yfe0D13QAAQM9DZhIAdCGSy0MikYURTEZVqMbbsT7SzsMnLe+jK55qSBmGoSfe20MgKQR8BWS7U1aYt+y7cEizJypnUJrGD+urnEFpBJIAAIBpBJMAwIRILQ8Jd22eYDOqQjXeldur3DthbTtUo3te325p+2Z4qiEVqR3lujszAVl/g7w5A1MVrRvBubLvAAAAYgXL3ADApEgtDwl0qZ2/8jLT9FDRuKADYa7xzvvjJjmaWy0Zm7PVUPVJh1KTE3Rb6Radsqhdf3iqIRWJHeW6q4Q4mwpHZ2l2/nDlj+hvKjPOFeQt33dcy8r3a9X26na1tDq2+W8vfBiRZaNmPFd+oN1STgAAgGhGMAkAAuDapjxcXFkYt5VusTwT5oKz+2vhN3NNP8C3VetoVlWNQ/VNTvVOildW+umg2pSRA/Wba8fq9tKPLRtnXaMzYplAnmpIRWpHue7GnhCnR743TheNHBjQPeVPkDdSNcjMcGXfsdQMAADEAoJJABAjOmZhWPVQPCA12a+MCMMwVLbvmJaVHdBbO9pngsTH2VQ4OlOz84dr7Bl9fLTiv9TkeD25Zq+lbZrhrYZUpHaU624cLa1a9M4eXZgzQBXVtZ0Ck/7oKsjrqukVjUsTXdl3BJMAAEAsIJgEADHElYUxZkgfrdpWLacFRXv9yYjYdqjGZ3aUs9XQiq1VWrG1StkDeyvOJlkRb0mIs6l3UkLYM4F81ZCK1I5ykhRnOx24a3Z2j2DWrupajb/37XaflbaByYIRGQEVhO8o2neCq2uMvjEBAAB4QgFuAIhBVTUOSwJJ0j8zIrqytuKIikrKTGd17D1SL6vqHReOzlJdY0tYM4G6KgLdO4I7ibUaUrPTkAXxlajRcWpdgclZS9arcNEabTtUY0k/0bwTXGpy9I0JAADAE4JJABAitY5mVVTXakvlCVVU17p3I7OC1VkxXWVEbDtUE1ABcKsSZ2bnDw9LJpA/u/Jlpds77eoXboahbhVQ8mZ3dZ2KSsq0tuKIJe35uxPc0H69ZE8I7Y9MCXE2Zfaxh7QPAAAAq7DMDQAsZLaeULDLdqzOivGVEWEYhm4r3RKxZUGu4td7vgxdnZs4m/SXBV/XiIGpPpf7dSw4/o1zBurtHV+GbFxmGIaUnBCniWf1U/m+4922jlNDk1PFyzartLgg6B0HJf93gtv+xcmQFMB3KRydRb0kAAAQMwgmAYBF/KknlJuZqoeLxgf8UOzKirEicNBVRkSkdlCT2he/tvI9d9RqSL2TEzw+zPsKEEY4McmtsaVVR2ub9NF/flNf1jaqrvF0sOuW5zZr75H6SA/PMg1NTt1e+rFWLpxiWQ0lszvBdRV8Ctbs/OGWtQUAABBqBJMAwAJrK474tQzMtWynZM4ErzV5fEmzJ6pwdKYlO7p1lRHxXPmBoPuQ5HcgqGPxayvfsyeelvqt33dM//7yJ9p/rMHja6IpCWhXda22f1Hbbme+R79/XtQWmw7Urupale877tcOhGZ0tROc5Dn4VOto0U//tMXrZ8QMV/YdAABArKBmEgAEKdB6Qq5lO4EWFrYqk8FXO7WOZst2UDMMQzkDe5s611vx61Bmb7iW+hmGoXV7j+q636/T954sDypIEG4dA39jhqTr97PP73Z1lawKcAYjzZ6onEFpOu/Mfnp81vkBF/Rum30HAAAQKwgmAUAQgq0n5Fq2YwSwM1vBiAzTBYS96SojoqrGYdlSnlZD+t/Z52v5vHzNGJvVqXi1meLXVrxnT1xL/bYdqlHhojWatWS9Nu7/yvJ+Qm3l9qpOhd4T4uNk0cZ/UcPT+4ykQHeI65h9BwAAECtY5gYAQbCinlCgy3ZsNpseLhof8DImMxkRVu+gVt/UarpGjSeu9/yd/12nxpZWy8b1rdGZ2lJ5IqAMs2jibDVUfdLR7jpGQxaP1Ty9z0hz7RBntkh3XmaaHioaRyAJAADEJIJJABAEqx7Unys/EFANGFdGhL9BELMZEaHcNc5MjRpPxgxJ19eGpluaObRp/1d699Mv5bAwQBVni0xNpba1n6xcphhtPNW4ijR/d4hjaRsAAIhVBJMAIEBWPqi7lu0EElwJZUZEOHeNM6vW0awPPz8RdDttfVnbaGl7kvTNUZm687I81TU69fBbu7Sm4qjlfXjSNmBn5TLFaNP2fUYTf3aIAwAAiFUEkwAgQFY+qJtZtlPraFZVjUP1Tae3fc9K/+eDqRUZEd7aD9eucWbFSoDk7zu/1ENF45RjT9QtF2eHJZjUMWBn9TLFaGFVYDLUAs2+AwAAiHYEkwAgQFY/qHtatmMYhsr2HdOysgN6a0f7AFF8nE2FozM1O3+4CkZkBJQRYab9887sa0kwqeNObL6CY77ESoDE2WqoquZ0gNBVODzY+lpdmXhWP1XVOLT3SP3pJYrdrfL2/7EqMAkAAIDAEEwCgACFsp6QJG07VONz6Zqz1dCKrVVasbVKuZmperhovHvpmpmMCH/aT4qPU5Mz8HpCrl3j/A2OeWL1dQ+lHz67Uf97/enaVMEUSzerfN9xXfrIGve/42ySTVJ3Cyl1DEwCAAAgvOIiPQAAiFWuekJW6LhsZ23FERWVlJnOZNldXaeikjKtrThi6nx/2w8mkOTaNW77FydVuGiNZi1Zrze3VXVaquYKXs1asl6Fi9Zo26Eaj+1Zed1DrfL4Kfe8uIql90oMXzCs1eh+gSRXYBIAAACRQzAJAALkqidkhbbLdrYdqgloi/qGJqeKl232GoRxCbT9QPRKjFPJnAn6qqHJsuCYldc9HBqanJr/x016Yf0BvbD+czW2xMYyvY6SEyL/I4MrMMkuaAAAAJEV+Z8MASCGWbXcxtWOYRi6rXRLwIGehianbi/9WIaXWjnBtu+vxpZW/f79vbr52U2WBsdibZnTqeZW/fIv2/TmtirFQO3wTvIy0/TSLQXKzUyN2BhSkuJVMmeCqV0IAQAAEFoEkwAgCK7CysFou2ynbN+xoIs076quVfm+4x6PWdG+P1oN6R97jqmxJbBlcg1NTt32py2dgmNWXPdoFW05N65soLFD+2rVwqlaPi9fM8ZmhXWpYV5mmkqLCzRl5MCw9QkAAADvCCYBQBBsNpseLhqvlACLQndctvNc+QFLxuWtHavaD6fdX9Zp+YbKdl8L9rpHs2hKXOqYDeTaMfCJ6ydoy12X6p3bpuq/rj5X9kTrf5xIiLPpirGDtXxevlYunEJGEgAAQBQhmAQAQXIVVvY3sNHxQb3W0axV26stGdPK7VWqdTS3+5qV7Yfbf762rVP9pECvO8zpKhsozZ6onEFpuuHrZ+ulW75ueaZYyZwJWnz9+SrI9r6zHwAAACKDYBIAWGDKyIEqLTZfU8bTg3pVjaPTDmeBcrYaqj7paPc1K9sPN2erofl/7Fw/yd/rjvbiOwRpAs0GGjMkXasWTtVvrx1j2dhe+fCQZW0BAADAWgmRHgAAdBeuB+ryfce1rHy/Vm2vbhe8SYizqXB0lmbnD1f+iP6dsi3qLS6KXdfYvj2r2w+3U82ni4uvXDil3bVre90feWe3NnzmuV4UOntlQYF6JyeortGp1OR4Zfaxu3cV9JfNZtNV487Qr17dZkmRcVd2XaDjAQAAQOgQTAIAC7lqyhRkZ6jW0azqkw7TD+q9LV6ulZrcvj2r248EV3HxguyMdl93Xff8Efn65sPva++R+giNMLZ8fvyULs4bqByLAjZVNQ7LdqtzZddFUzCp1tGsqhqH6puc6p0Ur6z0wINvAAAAsYxgEgCESJo90a8Hzax0u+LjbJYsRUuIsymzjz1k7UfSc+UHOgWTXGw2mx79/nkqKilTQwCZWPbEOA1KS9bnx08FO8yYcOvyjxQfZ1Ph6EzNzh+ughHB1ScKdXZdJBiGobJ9x7Ss7IDe2tE+29DKawcAABBLCCYBQJRIsyeqcHSmVmytCrqtwtFZnQJZVrYfSW9uO9xu+VPHbJHhGSkqmTNBxcs2+xVQchVEvyhngNelit2Rs9XQiq1VWrG1SrmZqXq4aHzAO6eFOrsu3LYdqtFtpVu0u7rO43Errx0AAEAsIZgEAFFkdv5wS4I9119wZkjbj6RWQ3rjky90ZkZvn9kiv7pilJ75x2eq+LLrJW95mWl6qGicOxDQcali6aaDenLNvpC9p2ixu7pORSVlKpkzwesubr6EOrsunNZWHPErIBnstQMAAIgl7OYGAFGkYESGJTuT/dfr2zvtfGZl+5H2i1e2adaS9XpzW1WnwIUrW+SXf9kmm82m3147VjPGZik+zv+dy9LsicoZlKbvnj8kpO8nmjQ0OVW8rPPOeWa4st+s4Cm7Lly2HarxO7NNCu7aAQAAxBKbYRjdO38fUengwYMaNmyYJKmyslJDhw6N8IiA6LHtUE3ANX/aci3b6pglYVX7scJ1HcYP6+tXQfS2ah3NGnfPW5YVlzYrkjWu8jLTOu2cZ8a6vUc1a8n6oPtfPi/fa22sUDIMQ4WL1nhd2mZGoNcOAAAgFELx/E1mEgBEmTFD0lUyZ4JSgqw/4y1Lwqr2Y4XrOhw41qCcQWkaP6yvcgal+ZX1kmZP1GVjskI4Ss9+fc3oiGWSuXbO85cV2W95mWnKH9E/qDYCVbbvWFCBJCnwawcAABArCCYBQBSaMnKgSosLNHJQ76DaaWhy6vbSj9UxCdXVfndY8maGt+vgj9n5wy0cUdfyMtP0g8lnatXCqVo+L9/jUr1Qe678gN+vsdlserhofMDBypSkeD1UNC5iWT2BvOdQtgMAABCNKMANAFFqzJB03XP1GM16KrglQ64siY5LhsYMSdeqhVNVvu+4/lj2md7cVh1UP9HO23Uwy5VxE2zWihn2hLh2AZWOBcFdS/WO1TXpP1/bFrIxrdxe1W7nPLNc2W+B7qgXqR3Rah3NWrXdmvsg0GsHAAAQC8hMAoAo9tz60GZJ2Gw2FWRn6H9nT9Sf5ufLnti9/1sIJlsk2IwbfwxIS9boM/p0+rqrILhrqd4FIzJCmrnkbDVUfdIR0Gv9zX7Ly0xTaXFBRHdCq6pxWFajKphrBwAAEO2691MDAMSwUGRJ+HLBiAwt+ZeJIQ2W2BPilBgfuaLEZq6DL+GqN3Xwq1Oma+64AoJPXD9BW+66VI//YLylY6lrDLxQuyv7zVuwy8yOeuFUb3FR+mCuHQAAQDRjmRsARKlQZEl0teTGlU1yW+kWU0unhmekyDAMfX78VJfn5mWm6aGicdp2qEY/f2Wr6bFbyex18MXfaxSo58oP+L0kL82eqLyszhlNwUhNDi5w5gp2eVqm58+OeuHQ2+IgYbDXDgAAIFoRTAKAMKt1NKuqxqH6Jqd6J8UrK93zA3WksiTa1lJaVr5fq7ZXtwtqJcTZVDg6S7Pzh7t33DJ7rs1m0/CMFP3yL1sVoR3vLckW6eoaWSHQmjtZ6XbFx9ksGU9CnE2ZfexBt+OSZk+MquBRR9F87QAAAKIJwSQACAPDMFS275iWlR3QWzvaBx7i42wqHJ2p2fnDVTAiw110OZJZEv5mk/hzbpo9UZeNydKKrVWWvC9/WZUt4ukafXq4Vrcu/8iS9gPNokqzJ6pwdKYl17dwdFZUB3+sxrUDAAAwh2ASAITYtkM1PpdEOVsNrdhapRVbq5SbmaqHi8ZrzJD0qMmS8CebxOy5s/OHRySYFKpsEdf7trpGTqDtWXV9Z+cPD7qNWMO1AwAA6BoFuAEghNZWHFFRSZnp2jq7q+tUVFKmtRVH3FkSVoi2LImCERmmd/mykj/XodbRrIrqWm2pPKGK6lpThbujpeaOFdc3LzPNvYwxGIFcx0iKpmsHAAAQrchMAoAQ2XaoRsXLNqvBz9pHDU1OFS/brNLigm6bJWGz2fRw0XgVlZT5fX2C8Z3zh/g8HshyxLaiJZss2OubkhSvh4rGeXyPZgR7HSMp0tcOAAAgFpCZBAAhYBiGbivdEnCgpKHJqdtLP1b+2f27bZbEmCHpKpkzQSkWZ/P4cvMfN2nB85u1bu9RGUb7gM+2QzUqXLRGs5as15vbqjoFhFzLEWctWa/CRWu07VBNp/ajKZss0OubkhSvkjkTNGZIekD9WnEdIy1S1w4AACBWEEwCgBAo23cs6G3jd1XXav1nX+nhovEBB1wilSVhdmnTlJEDVVpcoDP79wrLuFoNeQxkBLMcsSOrssCsaMd1fc0GJPMy01RaXKApIwcG1J+V1zHSwn3tAAAAYonN6PirWSAMDh48qGHDhkmSKisrNXTo0AiPCOFU62hWVY1D9U1O9U6KV1Z65x2/Yt2C5zdbsjztirGDtfj68/XEe3v0wMpdfr/+zsvytODinKDHYUagS5sMw1DhojVBB98CkZIUr19cfo7ue3NnwEuaSosL2mWiWPF+8jLTtHLhFMuCgIZhqHzfcS0r369V29vPTUKcTYWjszQ7f7jyR/QPuM9th2qCWhrW8TpGi3BcOwAAgFAKxfM3wSREBMGknieWa6j4q9bRrPH3vm1J3Zz4OJuWz7tANzyzMWoe0j0FAw8ca/C5Y11bbXesk6R1e49q1pL1lo3PXzabFMz/hJ4CP9EcWKl1NKv6pEN1jU6lJscrs0/wwdxoDKCFQiiuHQAAQKiF4vmbAtwAQm7boRqfgQZXDZUVW6s6BRqsFo6sqKoahyWBJOn0tfnZS58EXXsp2Id0X8HAONvposVm37NraVPJnAmaMnKgnis/EPC4rBDsr1R2VdeqfN9xFWRnuL/mqrnjbwH2cNTcSbMnWv6Zt2pZZ8frGG1Cce0AAABiEcEkACG1tuKIXw/UHQMNVgh3VlS9xbuTHTjWENTrg31I7yoY2GrI74iMa8e6pTdO0qrt1QGNK5o8V36g0/V11dwxm7GVl5mmh4rGReVSr65YFRD0dB0BAAAQfQgmAQiZbYdq/M7MkP4ZaLBiqU8ksqJ6h3F3MrMCfUj3Nxjoj4Ymp+586RPLsrgiaeX2KtU6mjtlrYwZkq5VC6d265o7tY5mywKC3q4jAAAAogvBJAAhYRiGbivdEtHlWZHIijIMQ58fDy6TKBQCeUgPNBjoj/1BZl1FC2eroeqTDo/X12azacyQPvrpN3M1O3+4GhqdSkmK16A+yd2i5o7Vyzq9XUcAAABED4JJAEIi0jVUIpEV1VUWVCT5+5AebDCwJ6prbH+tekrReauXdXa8jgAAAIg+cZEeAIDuycoaKv6yKivKn80u11YcUVFJWVQGklzuem271u09aup9WREM7GlSk/+5vHHboRoVLlqjWUvW681tVZ0yd1zLK2ctWa/CRWu07VBNuIdrGauXdba9jgAAAIhOBJMAWC4UNVT8YWVWlBmhWg52VkaKpe2t23vMdPAi0jusxZqEOJsy+9gl+R9YdC2vfGt7lSqqa7Wl8oQqqmv9/txHSla6XfFx1mRWtb2OAAAAiF4scwNguUjXUAnnzlKhWg6WkhSv+7/zNc16ar3lBaq7qg1lZTCwpygcnaU0e2JQyyvnL9vc7muxshQuzZ6owtGZWrG1Kui2XNcRAAAA0Y3MJACWi2QNlXBnRYViOVhKUrxK5kzQBSMyVDg609K2XVy1oTxlKFkZDDTL6iyscJudP9zywGLHpXAbPjsWtZlLs/OHR1U7AAAACC0ykwBYLpI1VMKdFWX1crC8zDQ9VDTOXfx7dv5wSzI+PPG2Y57VwcCuJMTZdP93vqYbl24MKBCTnBCnxpbWEIzMnLzMNOWP6B/SOlOns8nK230tmjKXCkZkKDczNaj377qOAAAAiH5kJgGwXCRrqIQzK8rKLCibpKfnTtTKhVPa7SLnekgPFU+1oawOBnalcHSWLhiRoZI5E5TiZ98pSfFa8i8TQnqNfLEnxumhonGy2WxhrzMVTUW8bTabHi4a7/f8uaQkxbuvIwAAAKIfwSQAlnPVULGCvzVUwpkVZWUWlCFpeEZKp4fpYB/SzegYBLEyGGiGa2nTlJEDVVpcYDowlJeZptLiAk3NHRTya+TNzy8fpTFD0iNeZ8pVB2ttxZGIjWHMkPSAA4Ilcya0C6ICAAAguhFMAhASkaqhEs6sqHBlQQX6kG5Wx9pQVgYDu9JxadOYIelatXCqls/L14yxWZ3mMiHOpivGDtbyefntsrhCfY286ZV4+r/RSNSZ6shXHaxwCTQg6KkQPAAAAKIXNZMAhESkaqiEc2epcGZBuR7SbyvdYnldHk+1oUJZq8nF29Imm82mguwMFWRnqNbRrOqTDtU1OpWaHK/MPnavcxLKa+TNf/11h0afka6WCAeSXLzVwQonV0CwfN9xLSvfr1Xbq9sF2hLibCocnaXZ+cOVP6I/S9sAAABiEMEkACHhWp5VVFIWUFHlYGqoWBUI6SorypUFZUVGipnaUG0f0h97d7fW7T3u83x/dMyKsiIY6IvZpU1p9kS/ljmaCWQMSEtWVY0j4LG3dar5dPDmsR+Mt6Q9K7jqYBVkZ0RsDIEGBAEAABAbCCYBCBnX0qPiZZv9CigFW0MlXFlR4cyCcnE9pA9IHaNLH1kTdL8uHbOigg0G+tJxxzqrdRXI2HqoRrOWrLesv13VtTr41SnLAotWeK78QESDSW35GxAEAABA9KNmEoCQikQNlXDuLNWda0MFWoco3iZ1HJq3WkehlmZPVM6gNI0f1lc5g9KUZk8MyQ55L394KGx1pszoWAcLAAAAsBKZSQBCLhI1VMKVFdXda0P5W4fIlXU0PCMlapc2uYKN1/1+nU41t1rS5srtVSqZc37I60yZ5akOFgAAAGAVgkkAwiISNVQCDYT4kzVjZW2oWkezqmocqm9yqndSvLLS/3ldPB0LV20oVzDw3Z1faum6/frHnqNqu5rLWzAwmgMZY4ak6+6rRuvnr2y1pD1nq6Hh/XuHtM6Uv7ztDggAAAAEi2ASgLALZw2VcGRFBZMF9fvZ5+uko1n/+txmvbWj/dji42yaMLyfZBjadOCrdgGc+DibvnVupob2s+vgV4EXk+4qK8owDJXtO6ZlZQc6jS/OJl2UM0Bzv36WvnHOoHbXzldgLFqcM7iPpe3VNzlDVmcqEL52BwQAAACCQTAJQLcXjqyoQLKg/vXibP33G596Pd/ZamjDZ553bHO2Gnpz2+msJJtNMgKo+9xVbahth2p8vp9WQ1pTcVRrKo4qNzNVD103TrWNLR4DT/FxNhWOztTs/OEqGJERFdvB9w6wppY3qcnxyhmUFlBg0WpmdgcEAAAAAkUwCUCPEsqsKH+yoJqdTt3y3IeWBBwCDST5qg21tuKIXwGR3dV1unrxP7yOxdlqaMXWKq3YWqXczFQ9XDQ+bEW4vXEVMbdiB7a2wRt/A4uhYHZ3QAAAACAQBJMAwEJmsqC2HapRUclGyzNXzGYodVUbatuhmoAya8wGtXZX16mopEwlcyYEtWtfsEJZxLyrwGKoWbXLIAAAAOAJwSQACBFPWVCGYei20i0hWQJlGNKwfr00ZkgfvbXjy/bLzGzShV7qG4VrfG01NDlVvGyzSosLLM9Q8qdeUyiLmPsKLO49Uq+f/ik01zmQ3QEBAAAAfxBMAtAjRapAdNm+YyFd+lT51Snd/52v6bsThrXbec35f/WN/rH3mM/aRaEeX1sNTU7dXvqxVi6cEnQNJV+Fwn3VayoYkRH0DmxmgjcdA4s5g9JCshSuqzpYAAAAgBVshhFItQ0gOAcPHtSwYcMkSZWVlRo6dGiER4SeINCAg5UWPL/ZkkwYX1KTE1TX2NLleZ5qF4VjfB0tn5evguyMgF/fVaHwtjy959PLDgPbgS0lKT6o7CrDMCxbCueqgxXJpYMAAACIPqF4/iaYhIggmIRwCzbgYIVaR7PG3/t2WGvndKVtACJS47ti7GAtvv78gF7rb6FwyXPQxap2gtFxKdyxuib952vbTO8O6KsOFgAAAHquUDx/xwXdQoQ1NDTod7/7nSZPnqz+/fsrNTVVo0aN0h133KHPP//c0r7Kyso0Z84cnXXWWbLb7Ro8eLAuu+wyvfjii6bbaGlpUUlJiaZOnaqBAweqV69eysnJ0S233KIdO3Z0+fovv/xSf/vb33TXXXfp8ssv14ABA2Sz2WSz2XTDDTf4/Z5WrlypmTNnaujQoUpOTtbQoUM1c+ZMrVy50u+2gGi1tuKIikrKTC8nchWIXltxxNJxVNU4oiqQJP2zdtG2QzURG9/K7VWqdTT7/bpAC4W3fc8urh3YcjNTTbWRl3l6mZqVWUBp9kTlDErT+GF9lTMoTReMyNCqhVO1fF6+ZozNUnxc+2y5hDibrhg7WMvn5WvlwikEkgAAABA2MZ2ZtHfvXl1xxRXatWuXx+Pp6el64YUXNGPGjKD7uvfee3XPPfeotbXV4/GrrrpKpaWlstvtXts4duyYrrjiCq1fv97j8eTkZD3xxBP64Q9/6LUNX0tv5s6dq6VLl3o93pZhGLrlllv05JNPej1n/vz5+v3vfx+S5T5kJiFcIrmEqaMtlSf07cX/sKQtq+Vlpun/fWesrn1iXUT6f+e2qcoZlGb6fMMwVLhoTdC1jjrWa/K17CwhzqbC0VmanT9c+SP6h70ukbfdAQEAAABfQvH8HbMFuOvq6nTllVe6A0nz5s3T97//ffXq1UurV6/Wfffdp5qaGl133XUqKyvT1772tYD7euqpp3T33XdLkrKzs/XLX/5SY8eO1RdffKFHH31Uq1ev1uuvv66bb75Zzz33nMc2nE6nZs6c6Q4kzZw5U/PmzVP//v21fv16/fd//7e+/PJLzZ8/X0OGDFFhYWGX4xo2bJhGjRqlt956y+/39Ktf/codSDrvvPN05513Kjs7W3v37tUDDzygjz76SE8++aQGDhyo//7v//a7fSAaBLszmZUFoiWpd1J80G2Eyq7qWu39MjyFtz2pa/RvjqwoFL6rulbl+463q9fkawe2SAdvPO0OCAAAAERCzAaTHnzwQe3cuVOS9MADD+hnP/uZ+1hBQYGmT5+uqVOnqqGhQQsXLtS7774bUD8nTpxwt33mmWeqvLxcAwYMcB+/8sorde211+r111/X888/r/nz52vq1Kmd2lm2bJnWrFkjSVqwYIEWL17sPjZ58mRdfvnlmjBhgk6ePKlbb71VO3bsUEJC5+m56667NGnSJE2aNEmZmZnav3+/zj77bL/e0549e/TAAw9IkiZOnKg1a9aoV69ekqRJkybp6quv1rRp07Rp0ybdf//9uvHGG5Wdne1XH0A0CFXAIVBZ6XbFx9mibqmby1s7qiM2vtRk/wJtz5UfsKTf58oPeJ1bgjcAAACAZzFZM6m5uVmPPvqoJGnUqFG6/fbbO51TUFCgm266SZK0evVqbd68OaC+lixZohMnTkiS7r///naBJEmKj4/XE088ofj40w9Cv/vd7zy24/p6v379PJ6Tk5OjX/ziF5KkiooKvfbaax7bueeee3TllVcqMzMzoPcjSY888ohaWk7v9PTYY4+5A0kuKSkpeuyxxySdrvG0aNGigPsCIsnKgIMV0uyJKhwd+L0ban/f+aW+cU74dwJLiLMps4/3JcId1TqatWp7tSV9B1qvCQAAAOjJYjKY9N5777kDPHPnzlVcnOe30bYg9SuvvBJQX6+++qokqU+fPpo5c6bHc4YOHapvfvObkqS3335bdXXtMyEqKircxbW/973vKSUlJWTj7YphGO5A1TnnnKP8/HyP5+Xn5ysvL0/S6WsQw6W10ENFS8Ch1tGsiupabak8oYrqWn3n/OitD+ZsNfStc7PC3m/h6Cy/MoCsLBTubDVUfdJhSVsAAABATxGTy9zWrl3r/vu0adO8njdx4kT17t1b9fX1+uCDD/zup6mpSRs2bJB0OtMpKSnJ67nTpk3TqlWr1NjYqI0bN2r69Ol+jzcrK0u5ubnavXt3QOM147PPPtOhQ4e6HIvr+K5du3Tw4MGAltMBkRSKgIPZgIdhGCrbd0zLyg7orR3tizjH2aTU5ATVNbZYMjar5QxKVW5matDLA/0xO3+4X+fXB1gDyxt/6zUBAAAAPV1MBpM+/fRT99/POeccr+clJCQoOztbn3zySbvXmFVRUeFeDuarn47HP/3003bBJLPjdR3fvXu3KisrVV9fr969e/s9bl/8HUvb1/kTTDp48KDP44cPHzbdFhCISAUcth2q0W2lW7wGY1oNRW0gSZLS7Al6uGh8wDvg+SsvM035I/r79RqrC5n7W68JAAAA6OliMphUWVkpSerdu7f69u3r89xhw4bpk08+0ZEjR9TY2Kjk5GS/+5HU5dZ5rm32Or4u0HYMw9DBgwfdS82sYtV76krb1wKREImAw9qKIypetjksQZhQcNUuSrMnqmTOhJC/l5SkeD1UNM7vnfKsLGTub70mAAAAADFaM6m2tlaSlJqa2uW5bTN7OtYyMtuPmb589WNVO1aIprEAoeQKOFjBTMBh26GaiAWSUpOt+b1A29pFU0YOVGlxgXIzu/4+G4iUpHiVzJmgMUPS/X6tlYXM/a3XBAAAACBGM5McjtPFUn3VMHJpm4l06tSpgPox05evfqxqxwrhGktXmUyHDx/W5MmT/WoT8Icr4LBia1XQbXUVcDAMQ7eVbgkqkJSanKBTzc522TYJcTZNGN5PkqGN+79S20SchDibCkdnaXb+cBmGoVlPrQ+4b5eOtYvGDEnXqoVT9e7OL3Xzs5tkVRn+nEGpWvS98QEFklxm5w+3ZG79rdcEAAAAIMTBpJaWFiUmBv8b32eeeabdTmd2++kMgaampi5f29jY6P57r169/OrX1Y+Zvnz107Gdtv/2px0rWPWeutLVEjogHMIVcCjbdyzogtV1jS16eu5EDc9IUV2jU6nJ8e4lZ9LpXeGqTzo8HjMMI+ii2d5qF9lsNp3ZP8WyQJIk/e/152lkZp+g2igYkRGy9wwAAADAt5hc5paWlibJ3NKr+vp699/NLIvz1I+Zvnz1Y1U7VoimsQCh5go4BMNMwOG58gNB9eHyyoeHlDMoTeOH9VXOoLR22VBp9kSvx2w2mx4uGq+UAOtEdVW7yOpi5vVNrUG3YbPZ9NB145ScENh/Y4HWawpUraNZFdW12lJ5QhXVtap1NIelXwAAACAUQpqZlJCQENAuah0NHjy43b+HDh2q9evXq76+XidOnPBZhNu13GrgwIF+Fd929ePS1e5kbZd1dSw+3bGdAQMGdNmOzWYLSXaPVe8JiAWuIEugO5OZCTjUOpq1ant1MMN0W7m9SrWO5oBq+IwZkh5Q0WwztYuicfe0bYdqdPufP1Zji/+BqWDqNfnDMAyV7TumZWUH9NaO6nZLGOPjbCocnanZ+cNVMCIjrEGtqhqH6puc6p0Ur6x0OzWjAAAA4LeQ10zqavv5QJx77rl6+eWXJUk7d+5Ufn6+x/NaWlq0d+9eSdKoUaP87ic3N1fx8fFyOp3auXOnz3PbHu/Y17nnntvuvPHjx3fZzrBhw9oVwLZKx7H44us9AbEilEEWSaqqcViyq5gkOVsNVZ90+P1w7woQpNkT9eB3v6aH396tPUfqu3xdXmaaHioa1+V7jLbd04LZNc/sew7WtkM1uq10i9dleM5WQyu2VmnF1irlZqbq4aLgakj5Eo1BLQAAAMS2mCzAfdFFF7n//v7773sNJm3atMm9VOvCCy/0u5+kpCRNnjxZZWVlKisrU1NTk9ei1e+//76k00WrJ06c6HO83//+9z22UVVVpd27dwc8XjPOPvtsnXHGGfriiy/cY/ZmzZo1kqQhQ4borLPOCsl4gHBw7Uzm6+G+LX8CDlYvAatrNNeerwBBnE264Ox+MmTT5gNfdSrq7SrcnT+iv6ngQTiLmXclmF3zkhPi9OB1Xwt5IMnfYNfu6joVlZSpZM4ETRk50NKxRFNQCwAAAN1HTNZMuvjii5WefvqH3WeffVaG4fm35UuXLnX//dprrw2or29/+9uSpJMnT+qVV17xeM7Bgwf1zjvvSJIuueSSdnWJpNMZTq7MntLSUjU0NIRsvF2x2Wy65pprJJ3OPCovL/d4Xnl5uTsz6ZprruG31Yh5rp3Jls/L14yxWYqPa/+ZToiz6Yqxg7V8Xr5WLpxi+oE6EkvAth2qUeGiNZq1ZL3e3FbVKWOo1ZDWf/aVNnx2XGdlpOh/rz9fr/7bhXrntqn66K5Ltfj681WQ7V8WilW7ngXTTrC75jW2tOqOP3/i9f8MKwQa7Gpocqp42WZtO1Rj2VjWVhxRUUmZ6SLlrqDW2oojlo0BAAAA3VNMBpOSkpL04x//WJL06aef6sEHH+x0TllZmZ5++mlJ0rRp0zRp0qRO5+zfv182m002m00XX3yxx75uvvlmd+Dq5z//uY4dO9buuNPp1IIFC+R0nn5wuOOOOzy24/r68ePHdeedd3Y6vnfvXt13332SpOzs7JAFkyRp4cKFSkg4nZR266236tSpU+2Onzp1Srfeequk03WvFi5cGLKxAOFks9lUkJ2hJ66foC13Xap3bpsadJDFtQTMCmaWgPkbINh7pF63//lj1TqaOxXu9ke4ipn7YsWuebuqa1W+73hQbXgTbLCrocmp20s/tiTYFU1BLQAAAHQ/MRlMkqSf/exnys3NlSTdeeedKi4u1urVq1VeXq777rtP3/rWt9TS0qJevXpp0aJFAffTv39/3X///ZKkAwcO6IILLtAzzzyjTZs26a9//asuvfRSvf7665KkH/zgB5o+fbrHdubOneteurZ48WJ997vf1apVq7RhwwY9/vjj+vrXv66TJ08qLi5Ojz32mDvY09EHH3ygpUuXuv+89NJL7mN79uxpd6xtplNbubm57uDWpk2bdOGFF+pPf/qTNm3apD/96U+68MILtWnTJkmnr/PIkSP9v3BAlPO1O5q/7RSOzrRkTF0tAYtkgCDUO8aZYdWueVa101G0BLuiKagFAACA7slmxPBPi3v27NGMGTNUUVHh8XifPn30/PPP68orr/R4fP/+/Tr77LMlnc5eeu+997z2dffdd+vXv/611x+uZ8yYoZdffll2u/esgqNHj2rGjBnauHGjx+NJSUl6/PHHNW/ePK9t3HDDDXr22We9Hu/I23hbW1s1b948/eEPf/D62ptuuklPPvmk4uKsjzkePHjQvUNcZWVlSHauA8Jl3d6jmrVkfdDtLJ+Xr4LsDI/HDMNQ4aI1QQUr8jLTtHLhlKACOoEUv3YVMw+mHlCto1nj733bkiLg8XE2bbnrUst3MVvw/GZL6kpdMXawFl9/fsCvD8fnEQAAALEjFM/fMZuZJEk5OTn66KOPdP/992vixInq27evUlJSlJeXp5/+9Kf65JNPvAaS/HXPPffogw8+0KxZszRs2DAlJSVp0KBBuvTSS/XCCy/ojTfe8BlIkqQBAwZo3bp1euKJJ3TRRRcpIyNDdrtdI0aM0Lx58/Thhx/6DCRZKS4uTk8//bTeeOMNXXPNNTrjjDOUlJSkM844Q9dcc41WrFihp556KiSBJHRvtY5mVVTXakvlCVVU16rW0RzpIYVcOJaARUvWi6uYudn3m5eZptLigqALS4di1zwr1TqatWp7tSVtrdxeFdR9E+0ZXAAAAIh9MZ2ZhNhFZlL3wtbjp5egFZWUBbS0KCUpXqXFBT6LfkdL1ouLYRgq33dcy8r3a9X26qB3jOvKlsoT+vbifwTdjsur/3ahxg/ra1l7FdW1uvSRNZa1985tU5UzKK3rEzuIhQwuAAAAhFconr89F+YBAJPYevy0MUPSVTJnQsBLwHxdk1BkvQQbIHAVMy/IzlCto1nVJx2qa3QqNTlemX3slgcgIrFrnj/qA6xP5E1dY2DthSKDi2ASAAAAOmINE4CAsfV4e6FaAhbtS7ysKmbuS7h3zfNXtAS7oiWoBQAAgO6NYBKAgLD1uGdjhqRr1cKpWj4vXzPGZnUKgCTE2XTF2MFaPi9fKxdOMZWlRYAgvLvmBSJagl3REtQCAABA98YyNwB+s2rr8WB3FotWVi8BI0Bw2uz84ZbUjZqdP9yC0bTnCnZZMb5ggl2uoJYVmWyhyOACAABA90BmEgC/RcvOYrHAiiVg0ZL1Eumd+sKxa14wrApSBdNOtGdwAQAAoHsgMwmA36zcerwgO8OStrqzSGa9RMNOfbWOZlXVOFTf5NTCb+bq9tItOtXc6nc7KUnxeqhoXMjG6Qp2BRNotSLYFc0ZXAAAAOgeCCYB8Es07izWE0QiQBDJnfp8BbHibFK8TXL6sZLLzK55wbLZbHq4aLyKSsoCWgJqVbArWoJaAAAA6L5Y5gbAL9G+s1h3Fe4lXpHcqW/boRoVLlqjWUvW681tVZ0+b62Gf4Eks7vmWWHMkHSVzJmgFD/rXFkZ7HIFtfwdQ9uxhDKDCwAAALGPYBIAv7CzWGSEM0AQyZ36/A1iSaeX23UsKRXIrnlWmTJyoEqLC0wH/0IR7IqGoBYAAAC6L5a5AfALO4tFjitA4G+gx58AQSR36gs0iOVsNdQrMV4PF31Ng/umBLxrnpXGDEnXqoVTVb7vuJaV79eq7e2X6iXE2VQ4Okuz84crf0T/kGQBuYJavpYqtpWXmaaHisYRSAIAAECXCCYB8Atbj0dWqAMEVu7U509x9WCDWKeanVr0zp6AglihYrPZVJCdoYLsDNU6mlV90qG6RmdYg13RENQCAABA90MwCYBfIrmzGE7rKkAQb5MuzBmguV8/S984Z5BfAYJI7dQXqSBWuKTZEyP2WY+GoBYAAAC6F4JJAPzG1uOR5woQ5I/or3d3fqml6/brH3uOuotTr6k4qn/sPabC0ZmanT9cBSMyugwqRXKnvkgFsXqaSAa1AAAA0H0QTALgN7Yejw7bDtX4XO7mbDW0YmuVVmytUm5mqh4uGu9zuVsoduozE7iIZBALAAAAgP/YzQ2A32w2m+YEkVXE1uPB83fXs93VdSoqKdPaiiNez4nUTn2hCGIBAAAACB2CSQD8tu1Qje57c2fAr//F5eewY1QQAt31rKHJqeJlm7XtUI3H45HaqS9SQSwAAAAAgSGYBMAvwe66JUnPlX8uw7AmE6WnCfb6NzQ5dXvpxx6vv2unPiv4s1NfpIJYAAAAAAJDMAmAX6zcdQv+C+X1d+3UZwV/duqLVBALAAAAQGAIJgHwi5W7bsF/ob7+Vu2w5087kQpiAQAAAAgMwSQApoVi1y2YF47r79qpLxiB7NQXiSAWAAAAgMAQTAJgGrtuRVY4rr/NZtPDReOVEmAdo0B36otUEAsAAACA/wgmATCNXbciK1zXf8yQdJXMmeB3QCklKV4lcyYEtFNfpIJYAAAAAPxHMAmAad1p161aR7Mqqmu1pfKEKqprY2LJXTiv/5SRA1VaXGA6WygvM02lxQWaMnJgwOOJRBALAAAAgP8SIj0AALHDteuWFUutIrHrlmEYKtt3TMvKDuitHdXt3kd8nE2FozM1O3+4CkZkRGWGS7iv/5gh6Vq1cKrK9x3XsvL9WrW9/TVLiLOpcHSWZucPV/6I/pZcM1cQ67bSLaZ2rcvLTNNDReMIJAEAAABhRDAJgGmuXbdWbK0Kuq1w77q17VCNzwCFs9XQiq1VWrG1SrmZqXq4aHzUBSgicf1tNpsKsjNUkJ2hWkezqk86VNfoVGpyvDL72EMyh5EIYgEAAAAwj2ASAL/Mzh9uSTAjnLtura04ouJlm9VgsubQ7uo6FZWUqWTOhKCWbYVCJK9/mj0xbAHASASxAAAAAJhDzSQAfom1Xbe2HarxK5Dk0tDkVPGyzdp2qCZEIwtMrF1/K6TZE5UzKE3jh/VVzqA0AkkAAABAhBFMAuCXYHfd6pUYp598c6Q+PlgT8sLXhmHottItfgeSXBqanLq99GMZRvA1iqzCrmcAAAAAIo1lbgD85tp1y9+Mn3ib1NjSqgXPf/jPr4Ww8HXZvmOmijj7squ6VuX7jqsgO8OiUQUv0OvPrmcAAAAArEBmEoCA+Lt1vCQ5DanjRmSuwtezlqxX4aI1li4re678QFS1YyV/r39eZppKiwuirgYUAAAAgNhjM6Jp/QZ6jIMHD2rYsGGSpMrKSg0dOjTCI0KgDMPwuutWnO30six/trJ3Zc8EG/SodTRr/L1v+9W3N/FxNm2569KorNXj6/qz6xkAAACAUDx/s8wNQFC87bp1+ESDbiv9RKeaAyt8XVpcENRyrKoahyWBJOl09lT1SUdUBpPY9QwAAABAuBFMAmAZ19bxhmHozpc+9juQ5OIqfL1y4ZSAs2nqAyy67U1do7XthYLr+gMAAABAKFEzCYDlrCx8HajeAe525k1qsrXtAQAAAECsIpgEwHLRUPg6K92u+DhragQlxNmU2cduSVsAAAAAEOsIJgGwVK2jWau2V1vS1srtVap1NAf02jR7ogpHZ1oyjsLRWSwfAwAAAID/QzAJgKVCUfg6ULPzh1syDqvaAQAAAIDugGASAEtFU+HrghEZys1MDar/vMw05Y/oH1QbAAAAANCdEEwCYKloKnxts9n0cNF4pQQ4ppSkeD1UNC7gHeUAAAAAoDsimATAUtFW+HrMkHSVzJngd0ApJSleJXMmaMyQ9KD6BwAAAIDuhmASAEtFY+HrKSMHqrS4wPSSt7zMNJUWF2jKyIFB9w0AAAAA3U1CpAcAoPuZnT9cK7ZWWdKOVcYMSdeqhVNVvu+4lpXv16rt1e0KhSfE2VQ4Okuz84crf0R/lrYBAAAAgBcEkwBYzlX4end1XcBthKLwtc1mU0F2hgqyM1TraFb1SYfqGp1KTY5XZh+7JVlQAAAAANDdscwNgOViofB1mj1ROYPSNH5YX+UMSiOQBAAAAAAmEUwCEBIUvgYAAACA7olgEoCQcRW+zh7Y29T5FL4GAAAAgOhHzSQAIWEYhsr2HdOysgPaf6zB63nxNumyMYMpfA0AAAAAMYJgEgDLbTtUo9tKt5gqwH32gN7614uzWdYGAAAAADGCZW4ALLW24oiKSspM7+S250i9ikrKtLbiSIhHBgAAAACwAsEkAJbZdqhGxcs2q6HJ6dfrGpqcKl62WdsO1YRoZAAAAAAAqxBMAkyqdTSrorpWWypPqKK6VrWO5kgPKaoYhqHbSrf4HUhyaWhy6vbSj2UYhsUjAwAAAABYiZpJgA9ti0i/taNaztZ/Bjri42wqHJ2p2fnDVTAio8cXji7bd8z00jZvdlXXqnzfcRVkZ1g0KgAAAACA1QgmAV50VUTa2WpoxdYqrdhapdzMVD1cNL5HF5F+rvyAZe0QTAIAAACA6MUyN8ADf4tI766u69FFpGsdzVq1vdqStlZur2IJIQAAAABEMYJJQAcUkfZfVY2j3RLAYDhbDVWfdFjSFgAAAADAegSTgDYoIh2Y+gCvlzd1jda2BwAAAACwDsEkoA0ri0j3JL2T4i1tLzXZ2vYAAAAAANYhmAS0YWUR6Z4kK92u+DhrdrNLiLMps4/dkrYAAAAAANYjmAT8H4pIBy7NnqjC0ZmWtFU4Oktp9kRL2gIAAAAAWI9gEvB/KCIdnNn5wy1pZ3reQG2pPKGK6toeFZADAAAAgFiREOkBANGCItLBKRiRodzM1KBrTt3x0ifuv8fH2VQ4OlOz84erYESGbDZrltIBAAAAAAJHZhLwfygiHRybzaaHi8YrxcLr6Gw1tGJrlWYtWa/CRWu07VCNZW0DAAAAAAJDMAn4PxSRDt6YIekqmTPB0oCSy+7qOhWVlGltxRHL2wYAAAAAmEcwCfg/FJG2xpSRA1VaXKDczFTL225ocqp42WYylAAAAAAggggmAW1YVUTaqnZi1Zgh6Vq1cKqWz8vXjLFZlmV8SacDSreXfizDsKZYOgAAAADAPxTgBtqwooh0Xmaa8kf0t3BUsclms6kgO0MF2RmqdTSr+qRDdY1OVVTX6mdtimwHYld1rcr3HVdBdoZFowUAAAAAmEVmEtBGsEWkU5Li9VDROHYd6yDNnqicQWkaP6yvVu/60pI2nys/YEk7AAAAAAD/EEwCOgi0iHRKUrxK5kzQmCHpIRpZ7Kt1NGvV9mpL2lq5vUq1jmZL2gIAAAAAmEcwCfDA3yLSeZlpKi0u0JSRA0M8sthWVeOQs9WaWkfOVkPVJx2WtAUAAAAAMI+aSYAXriLS5fuOa1n5fq3aXt0uEJIQZ1Ph6CzNzh+u/BH9WdpmQn2T09L26hqtbQ8AAAAA0DWCSYAP3opIpybHK7OPXWn2xEgPMab0DrAWlTepyda2BwAAAADoGsEkwKQ0eyLBoyBlpdsVH2ezZKlbQpxNmX3sFowKAAAAAOAPaiYBCJs0e6IKR2da0lbh6CyCewAAAAAQAQSTAITV7PzhUdUOAAAAAMA/BJMAhFXBiAzTu+R5k5eZpvwR/S0aEQAAAADAHwSTAISVzWbTw0XjlRJgMe6UpHg9VDSO3fMAAAAAIEIIJgEIuzFD0lUyZ4LfAaWUpHiVzJmgMUPSQzQyAAAAAEBXCCYBiIgpIweqtLjAryVv553ZV/FxNhlG8LvBAQAAAAACQzAJQMSMGZKuVQunavm8fF2Yk9Hl+f/Yc0yzlqxX4aI12naoJgwjBAAAAAB0RDAJQETZbDa1tLbqo89PmH7N7uo6FZWUaW3FkdANDAAAAADgEcEkABG17VCNipdtVkOT06/XNTQ5VbxsMxlKAAAAABBmBJMARIxhGLqtdIvfgSSXhianbi/9mBpKAAAAABBGBJMAREzZvmPaXV0XVBu7qmtVvu+4RSMCAAAAAHSFYBKAiHmu/EBUtQMAAAAA6BrBJAARUeto1qrt1Za0tXJ7lWodzZa0BQAAAADwjWASgIioqnHI2WpNrSNnq6Hqkw5L2gIAAAAA+EYwCUBE1AdYdNubukZr2wMAAAAAeEYwCUBE9E6Kt7S91GRr2wMAAAAAeJYQ6QEA6Jmy0u2Kj7NZstQt3iZ9ebJRhnG63TR7ogUjBAAAAAB4QjAJQESk2RNVODpTK7ZWBd2W05BmPbVekhQfZ1Ph6EzNzh+ughEZstlsQbcPAAAAAPgnlrkBiJjZ+cMtb9PZamjF1irNWrJehYvWaNuhGsv7AAAAAICejGASgIgpGJGh3MzUkLW/u7pORSVlWltxJGR9AAAAAEBPQzAJQMTYbDY9XDReKRYX426rocmp4mWbyVACAAAAAIsQTAIQUWOGpKtkzoSQB5RuL/1YhhF8sW8AAAAA6OkIJgGIuCkjB6q0uCCkS952VdeqfN/xkLUPAAAAAD0FwSQAUWHMkHStWjhVy+fla8bYLMXHWb8L23PlByxvEwAAAAB6moRIDwAAXGw2mwqyM1SQnaFaR7OqTzr0ZW2jZj+1Xq0WrFBbub1KtY5mpdkTg28MAAAAAHoogkkAolKaPVFp9kQZhiwJJEmSs9VQ9UkHwSQAAAAACELML3NraGjQ7373O02ePFn9+/dXamqqRo0apTvuuEOff/65pX2VlZVpzpw5Ouuss2S32zV48GBddtllevHFF0230dLSopKSEk2dOlUDBw5Ur169lJOTo1tuuUU7duzo8vVffvml/va3v+muu+7S5ZdfrgEDBshms8lms+mGG24wNYalS5e6X9PVn6VLl5p+b0Ao1Dc5LW2vrtHa9gAAAACgp4npzKS9e/fqiiuu0K5du9p9fefOndq5c6eeeuopvfDCC5oxY0bQfd17772655571Nra6v5aVVWVqqqqtGrVKr3wwgsqLS2V3W732saxY8d0xRVXaP369Z3ex969e7V06VI98cQT+uEPf+i1jczMa33yBAAAJCZJREFUzKDfCxBLelu8y1tqcuh2jQMAAACAniBmM5Pq6up05ZVXugNJ8+bN09///netW7dOv/nNb5Samqqamhpdd911+uSTT4Lq66mnntLdd9+t1tZWZWdn6+mnn9aGDRv06quvavr06ZKk119/XTfffLPXNpxOp2bOnOkOJM2cOVNvvvmm1q9fr//5n//RoEGD1NjYqPnz52vVqlWmxjVs2DB961vfCuq9rVq1Slu3bvX659vf/nZQ7QPBykq3W1aMOyHOpsw+3gO+AAAAAICuxWxm0oMPPqidO3dKkh544AH97Gc/cx8rKCjQ9OnTNXXqVDU0NGjhwoV69913A+rnxIkT7rbPPPNMlZeXa8CAAe7jV155pa699lq9/vrrev755zV//nxNnTq1UzvLli3TmjVrJEkLFizQ4sWL3ccmT56syy+/XBMmTNDJkyd16623aseOHUpI6Dw9d911lyZNmqRJkyYpMzNT+/fv19lnnx3Qe5Ok3NxcnXXWWQG/Hgi1NHuiCkdnasXWqqDbKhydRb0kAAAAAAhSTGYmNTc369FHH5UkjRo1SrfffnuncwoKCnTTTTdJklavXq3NmzcH1NeSJUt04sQJSdL999/fLpAkSfHx8XriiScUH3966czvfvc7j+24vt6vXz+P5+Tk5OgXv/iFJKmiokKvvfaax3buueceXXnllSx3Q48yO394VLUDAAAAAD1ZTAaT3nvvPXeAZ+7cuYqL8/w22hakfuWVVwLq69VXX5Uk9enTRzNnzvR4ztChQ/XNb35TkvT222+rrq6u3fGKigp3ce3vfe97SklJCdl4ge6oYESGcjNTg2ojLzNN+SP6WzQiAAAAAOi5YjKYtHbtWvffp02b5vW8iRMnqnfv3pKkDz74wO9+mpqatGHDBkmnM52SkpK8nusaR2NjozZu3BjQeLOyspSbmxvweIHuymaz6eGi8UoJsBh3SlK8HioaJ5vNmtpLAAAAANCTxWQw6dNPP3X//ZxzzvF6XkJCgrKzszu9xqyKigq1tLR02U/H4x37MjvetscrKytVX1/v13gDccMNNygzM1NJSUkaMGCA8vPz9atf/UqHDh0Ked+AP8YMSVfJnAl+B5RSkuJVMmeCxgxJD9HIAAAAAKBniclgUmVlpSSpd+/e6tu3r89zhw0bJkk6cuSIGhsbA+pHOr2UzUw/HV8XaDuGYejgwYOmxxqo999/X19++aWam5t17NgxrV+/Xr/5zW+Uk5OjkpKSgNs9ePCgzz+HDx+28F2gp5gycqBKiwtML3nLy0xTaXGBpowcGOKRAQAAAEDPEZO7udXW1kqSUlO7fqB0LXOTpLq6OiUnJ/vdj5m+OvYTinasNGLECM2cOVMFBQXuANa+ffv08ssv66WXXpLD4dAtt9wim82m+fPn+91+2+AaYKUxQ9K1auFUle87rmXl+7Vqe7WcrYb7eEKcTYWjszQ7f7jyR/RnaRsAAAAAWCwmg0kOh0OSfNYwcmkbPDp16lRA/Zjpy1c/VrVjlWuvvVZz587t9JA9adIkfe9739Pf/vY3zZw5U83NzfrpT3+qq6++WllZWSEZCxAIm82mguwMFWRnqNbRrOqTDtU1OpWaHK/MPnal2RMjPUQAAAAA6LZCusytpaVFNpst6D9Lly5t167dbpd0ukB2V9oubevVq5df43f1Y6YvX/1Y1Y5V0tPTfWZrXHnllbr77rslSQ0NDXr66af97qOystLnH1dhcyBYafZE5QxK0/hhfZUzKI1AEgAAAACEWEzWTEpLS5NkbhlY2yLWZpbFeerHTF+++rGqnXCaN2+eO+D0/vvv+/36oUOH+vwzePBgq4cMAAAAAADCIKTL3BISEgLaRa2jjoGHoUOHav369aqvr9eJEyd8FuF2Fb8eOHCgX/WSXP24dFUMu22R7Y71gjq2M2DAgC7bsdlsXRbrDqVBgwZpwIABOnLkCDu7AQAAAAAAt5DXTHJtdW+lc889Vy+//LIkaefOncrPz/d4XktLi/bu3StJGjVqlN/95ObmKj4+Xk6nUzt37vR5btvjHfs699xz2503fvz4LtsZNmxYu2LckWAYRtcnAQAAAACAHiUml7lddNFF7r/7WoK1adMm97KxCy+80O9+kpKSNHnyZElSWVmZz3pHrnEkJydr4sSJAY23qqpKu3fvDni8Vvryyy917NgxSdIZZ5wR0bEAAAAAAIDoEZPBpIsvvljp6emSpGeffdZrBk3bwt3XXnttQH19+9vfliSdPHlSr7zyisdzDh48qHfeeUeSdMkll7SrkSSdznByZSuVlpaqoaEhZOO1ypNPPum+rtOmTYvoWAAAAAAAQPSIyWBSUlKSfvzjH0uSPv30Uz344IOdzikrK3PvQjZt2jRNmjSp0zn79+937xh38cUXe+zr5ptvdgeufv7zn7uzdVycTqcWLFggp9MpSbrjjjs8tuP6+vHjx3XnnXd2Or53717dd999kqTs7OyQBZP279+vjz76yOc5f/vb3/TrX/9a0umd6G688caQjAUAAAAAAMSekNdMCpWf/exn+tOf/qTdu3frzjvv1J49e/T9739fvXr10urVq/Xb3/5WLS0t6tWrlxYtWhRwP/3799f999+vW265RQcOHNAFF1yg//iP/9DYsWP1xRdfaNGiRVq9erUk6Qc/+IGmT5/usZ25c+fqD3/4g/7xj39o8eLFqqqq0rx589SvXz9t2LBBv/71r3Xy5EnFxcXpscceU0KC56n54IMPtGfPHve/jx496v77nj172mU3SdINN9zQ7t/79+/X9OnTVVBQoKuuukrjx4/XoEGDZBiG9u3bp5deekkvvfSSOyvpwQcf1JAhQ/y9bAAAAAAAoJuyGTFcZXnPnj2aMWOGKioqPB7v06ePnn/+eV155ZUej+/fv19nn322pNPZS++9957Xvu6++279+te/9rqkbsaMGXr55Zdlt9u9tnH06FHNmDFDGzdu9Hg8KSlJjz/+uObNm+e1jRtuuEHPPvus1+MddRzve++95zXg1VZKSooeeeQRzZ8/33Rf/jh48KB717vKysqI7lwHAAAAAEB3FYrn75jNTJKknJwcffTRR1q8eLH+/Oc/a8+ePWpqatKwYcM0Y8YM/eQnP9Hw4cMt6euee+5RYWGhFi9erLVr16q6ulp9+/bVuHHjdOONN+oHP/hBl20MGDBA69at05IlS/TCCy/o008/VX19vc444wxdcskl+slPfqLRo0dbMl5vJkyYoOeee05lZWXatGmTDh8+rKNHj6qlpUX9+vXT6NGjdckll+jmm2/WoEGDQjoWAAAAAAAQe2I6Mwmxi8wkAAAAAABCLxTP3zFZgBsAAAAAAACRQTAJAAAAAAAAphFMAgAAAAAAgGkEkwAAAAAAAGAawSQAAAAAAACYRjAJAAAAAAAAphFMAgAAAAAAgGkEkwAAAAAAAGAawSQAAAAAAACYRjAJAAAAAAAApiVEegDomVpaWtx/P3z4cARHAgAAAABA99X2mbvts3gwCCYhIo4cOeL+++TJkyM4EgAAAAAAeoYjR47orLPOCrodlrkBAAAAAADANJthGEakB4Gex+FwaOvWrZKkgQMHKiHBmiS5w4cPuzOdNmzYoMGDB1vSLqIPc92zMN89B3PdczDXPQdz3bMw3z0Hcx07Wlpa3KuDxo4dK7vdHnSbLHNDRNjtdk2aNCmkfQwePFhDhw4NaR+IDsx1z8J89xzMdc/BXPcczHXPwnz3HMx19LNiaVtbLHMDAAAAAACAaQSTAAAAAAAAYBrBJAAAAAAAAJhGMAkAAAAAAACmEUwCAAAAAACAaQSTAAAAAAAAYBrBJAAAAAAAAJhmMwzDiPQgAAAAAAAAEBvITAIAAAAAAIBpBJMAAAAAAABgGsEkAAAAAAAAmEYwCQAAAAAAAKYRTAIAAAAAAIBpBJMAAAAAAABgGsEkAAAAAAAAmEYwCQAAAAAAAKYRTAIAAAAAAIBpBJMAAAAAAABgGsEk+K2hoUG/+93vNHnyZPXv31+pqakaNWqU7rjjDn3++eeW9lVWVqY5c+borLPOkt1u1+DBg3XZZZfpxRdfNN1GS0uLSkpKNHXqVA0cOFC9evVSTk6ObrnlFu3YscN0O59//rnuuOMOjRo1Sr1791b//v01efJkPfjgg2poaPD5WofDoddee0233nqrLrjgAvXv31+JiYnq37+/CgoK9F//9V86fPiw6bGEaw6Ya//nurW1VTt27NDSpUu1YMECTZo0ScnJybLZbLLZbHrvvfdMjeHiiy92v6arP1ZhviM33y7c255Fw1y3tXLlSs2cOVNDhw5VcnKyhg4dqpkzZ2rlypVdvtaqe9uq99KVF198UYWFhRo8eLDsdrvOOusszZkzR+Xl5abbOHbsmO6++26NGzdO6enp6tOnj8aNG6e7775bx44dM91Od/wsRVNfzHV7Zu/Tiy++2PSYusJc+zfXjY2NKi8v12OPPaY5c+YoLy9PcXFxAf98FM77Opz9Md/tReLe7rYMwA979uwx8vLyDEke/6SnpxtvvPGGJX3dc889RlxcnNe+rrrqKuPUqVM+2zh69KhxwQUXeG0jOTnZePrpp7scy9/+9jcjPT3dazt5eXnG3r17Pb72448/NtLS0ry+1vUnLS3N+NOf/tTlWMI1B8y1/3NtGIaxdOlSn/O8evVqU9dk2rRpXX5mXH+swHxHdr4Ng3vbm2iZa8MwjNbWVmP+/Pk+53z+/PlGa2ur1zasuLeteC9dOXXqlHHllVd67SMuLs649957u2xnw4YNxuDBg722c8YZZxgbN27ssp3u9lkyi7mO3FybvU+nTZvW5Xsyg7n2f65vuOGGgL6HehLO+zpc/THfnoX73u7OCCbBtNraWuOcc85x32Dz5s0z/v73vxvr1q0zfvOb3xipqamGJCMlJcX4+OOPg+pryZIl7n6ys7ONp59+2tiwYYPx6quvGtOnT3cfu/7667220dLSYkydOtV97syZM40333zTWL9+vfE///M/xqBBgwxJRnx8vLFy5Uqv7WzZssVISUkxJBmpqanGb37zG2PdunXG3//+d2PevHnu9s855xyjtra20+vXrl3rPufCCy807rvvPuPtt982PvzwQ2PVqlVGcXGxER8f7x7LihUrvI4lXHPAXAc214ZhGM8884z7vMTEROO8884zxo4d6/6av8GkiRMnGlu3bvX5J1jMd+Tnm3vbs2iaa8MwjF/+8pfuc8877zxj+fLlxoYNG4zly5cb5513nvvYf/zHf3htI9h726r30pVZs2a525o+fbrx6quvGhs2bDCefvppIzs7231syZIlXts4ePCgkZmZaUgyEhISjDvvvNNYs2aNsWbNGuPOO+80EhISDElGZmamcfDgQa/tdMfPkhnMdWTn2vX6f/3Xf/V5n+7bt8/chfaBuQ5srufOnes+Ly0tzZg2bZqRlZXl/ppZ4byvw9kf8+1ZOO/t7o5gEky7++673TffAw880On4unXr3N9Qpk+fHnA/X331ldG3b19DknHmmWcaR44caXe8paXFuOqqq9xjef/99z220/Yhb8GCBZ2OV1RUGH369DEkGSNHjjSam5s9tnPxxRe7v4GuW7eu0/EHHnjA3c8999zT6fg//vEPo6ioyNi+fbvX9/zqq68aNpvN/Q3W22+1wzUHzHVgc20YhrF+/Xrj0UcfNcrKyty/XWl7Pf0NJoXjtyLMd+Tnm3s7+ue6oqLCfW0mTpxoNDQ0tDteX19vTJw40d3Pnj17PLYT7L1txXvpynvvvedu46qrrjJaWlraHT9y5Ihx5plnGpKMfv36GV999ZXHdtr+4F9aWtrpeGlpqfv4jTfe6LGN7vhZMou5juxcu9q4++67PR63EnMd2Fy/+OKLxjPPPGNs27bNcDqdhmG0z/40K5z3dbj6Y769C+e93d0RTIIpTU1N7m8Co0aNct/AHRUXF7tv0E2bNgXUV9tvoMuXL/d4TmVlpTub58orr/R4zrnnnuv+BllfX+/xnPvuu8/d10svvdTp+IYNG9zHi4uLPbbhdDqNUaNGuftqamoy+U7b+853vuPu68MPP+x0PFxzwFxbP9fRHExiviM/39zbsTHXCxYscLdTVlbmsZ2ysjL3OT/60Y88nhPMvR2u/5NmzJhhSKczNyorKz2es3z5cvdYHnzwwU7Hq6qq3PNbWFjota/CwkJ3X1VVVZ2Od8fPkhnMdWTn2jDC98DJXAc+1574G1wI530dzv6Yb+/CdW/3BASTYMpbb73lvvH+3//7f17Pa/uD9C9/+cuA+vr6179uSDL69OljNDY2ej3P9Y0rOTm5Uwro7t273eO45ZZbvLZx+PBh93mzZs3qdLztkoby8nKv7bT9oeStt94y8S47e/zxx91t/PnPf+50PFxzwFxbP9fRHExiviM/39zb0T/Xra2txpAhQwzp9LIDX1z1qIYOHeoxyzSYezsc/yfV1tYaycnJhiTjsssu83peY2OjO7vj61//eqfjTz75pHsML774otd22j7MPPnkk52Od7fPklnMdWTn2jDC98DJXAc21974G1wI530drv6Yb9/CdW/3BOzmBlPWrl3r/vu0adO8njdx4kT17t1bkvTBBx/43U9TU5M2bNggSSooKFBSUpLXc13jaGxs1MaNGwMab1ZWlnJzc72O19VO7969NWHChC7H4q0dMxobG91/j4vrfGuGaw6Y69DPdTRhviM/39zb0T/Xn332mQ4dOtTlWNoeP3jwoPbv3+/zXH+F43O7YcMG9/9Hvt5rUlKS8vPz3a9pbm72ONau2vE11u74WTKLuY7sXIcTcx3YXFsl3D8PMN+RnW9Yi2ASTPn000/dfz/nnHO8npeQkKDs7OxOrzGroqJCLS0tXfbT8XjHvsyOt+3xyspK1dfXe2wnJydHCQkJAY3FrPfff99je57aDeUcMNehn2t/7Ny5U5MmTVJaWprsdruGDh2qa665Rn/84x87/acfCOY78vPNvd15fP6Mt+3xUM11IGPx1E5bgdzb4fjcBvJeW1paVFFR4bGd9PR0ZWVleW1j8ODB6tOnj8exdsfPklnMdddj8dSOVXPd1p///Gfl5eWpV69eSktL08iRIzV37lytXr3aZ/tmMdddj8VTO1YJ988DzHfXY/HUTiiE+t7uCQgmwZTKykpJp6Poffv29XnusGHDJElHjhxpl23jTz+SNHToUFP9dHxdoO0YhqGDBw+6v+5wOHT06FFTbfTr18/9m/yOYzHj448/1htvvCFJGj16tM4999xO54R7Dphrz4Kda39VV1dr06ZNqqurU2Njow4dOqS//vWvmjt3rsaPHx/0f7bMd+Tnm3u78+sCbSdUc23Ve2rL33s7XJ9bq+evqzbathOq6x5NnyUzmOvIznVHO3bs0O7du+VwOFRXV6c9e/boj3/8o77xjW/o2muvVU1Njc9+fGGurfl+Gqhw/zzAfEd2vjsK5b3dU3gPhwJt1NbWSpJSU1O7PNf1jU+S6urqlJyc7Hc/Zvrq2I/V7fjThqud+vr6TmPpSmNjo26++WY5nU5J0m9/+1uP54V7Dphr3+0EMtf+iIuL0yWXXKIZM2Zo3LhxysjIUG1trT788EOVlJTo008/1Y4dOzR9+nRt2LBBZ555ZkD9MN+Rn2/u7X/2Y3U7Vs21Ve9JCvzeDtfn1ur58+fzFqrrHk2fJTOY68jOtUtKSoquvvpqXXLJJTrnnHOUmpqqI0eO6P3339fvf/97HTt2TK+++qquueYavf3220pMTPTZnyfMdXBzFKxw/zzAfEd2vl3CcW/3FASTYIrD4ZAkn+tcXdo+dJw6dSqgfsz05asfK9rxp4227fj7nn/0ox9p06ZNkqS5c+fq6quv9nheuOeAue66HX/fsz9eeeUVj9kjU6ZM0YIFCzRv3jw9++yzqq6u1sKFC/XKK68E1A/zHfn55t723E80zbVV70kK/N4O1+fW6vmLhuseTZ8lM5jryM61y6FDhzzeq5deeqluvfVWXX755froo4/0/vvv63//93/14x//2Gd/njDXwc1RsML98wDzHdn5dgnHvd1TsMytm2lpaZHNZgv6z9KlS9u1a7fbJZ0uotaVtksievXq5df4Xf2Y6ctXP1a0408bbdvx5z3fd999euqppyRJEyZM0OLFi72e23EOfM31smXL3K8bNmwYc91FO+GYa391/E+u7XwnJSXp2WefdR/7y1/+wr3tRzvRNt/c2577iaa5tuo9SZ3v7bYSExP11FNPuetG/OUvf3EX/g7X59bq+YuG6x5NnyUzmOvIzrWLr3s1MzNTL730kvvh+LHHHvPZlzfMdXBzFKxw/zzAfEd2vl3CcW/3FASTYEpaWpokcymHbYsYmkmL9NSPmb589WNFO/600bYds++5pKREv/zlLyVJeXl5evPNN9uld3bkzxwEg7m2fq6jGfMd+fnm3vbcTzTNtVXvyYyEhATddNNN7n+7NmcI1+fW6vmLhuseTZ8lM5jryM61WSNGjNCll14qSdqzZ4+++OILv9tgrkM7R10J988DzHdk59ssK+7tnoJlbt1MQkKCJdXvBw8e3O7fQ4cO1fr161VfX68TJ074jOi6CqYNHDjQrzobrn5cfBVDbNuP1L5gm6d2BgwY0GU7Nput3evsdrsGDBigo0ePdjmWr776yv0NsONYPFm+fLkWLFggSRo+fLjeeecdDRw40OdrPM2Bt7m+5pprtHv3bvXr10/r1q3z2S5zHdq5toqne/v999/XLbfcIkm644472j2AesN8R998c2+fFs1zbdV7MqvtJgxtM5PC8bnt+F4nTpzo9dyu5q+6urrLsbZtJ1TXPZo+S2Yw15Gda3+ce+657g1UDh06pDPOOMOv1zPXof9+6ku4fx5gviM73/4I9t7uKchM6obOOeecoP+kp6e3a7PtD7Y7d+702ndLS4v27t0rSRo1apTfY8/NzVV8fHyX/XQ83rEvs+Nte3zYsGGdMoNc7e7Zs8e9raW/Y+nor3/9q/7lX/5Fra2tGjx4sP7+97+b+iHG03vyNHc5OTnuh4+xY8cy1x7aCddcW63j3LX93AwaNIh720s70T7f3Nvy2Fc0zXUgY/HUjlmGYXj8ejg+t4G814SEBOXk5Hhsp6amRlVVVV7bOHz4sE6ePOlxrN3xs2QWc931WDy1Y9Vcm+XtXvUHc931WDy1Y5Vw/zzAfHc9Fk/thJsV93ZPQDAJplx00UXuv7tS7j3ZtGmTO4p+4YUX+t1PUlKSJk+eLEkqKyvzubbWNY7k5OROEXez462qqtLu3bu9jtfVTn19vTZv3tzlWLy14/L3v/9dRUVFamlpUUZGht5++21lZ2d7Pd/TWDr211Gwc8BcWzPX4bBjxw733wP9jQnzHfn55t6O/rk+++yz3feYr7FI0po1ayRJQ4YM0VlnneXzXG+83dvh+NxOmjTJXSvC13ttampSeXl5p9d0HGtX7fgaa3f8LJnFXEd2rs2y8v9h5tp7O57m2irh/nmA+Y7sfJtlxb3dIxiACY2NjUZ6erohyRg1apTR2trq8bzi4mJDkiHJ2LBhQ0B93X///e42li9f7vGcyspKIz4+3pBkzJgxw+M5o0aNMiQZ/fv3N+rr6z2ec99997n7Ki0t7XR8/fr17uPFxcUe23A6ne6++vbtazQ1NXk87x//+IfRu3dvQ5LRp08fY9OmTR7P8yZcc8BcBz/XHd19993utlevXm3qNV1pbm42zjnnHHe7n3/+eUDtMN+Rn2/u7diY63/91391t1NWVuaxnbKyMvc5CxYs8HhOV3zd26H83LZ1+eWXG5KMhIQEo7Ky0uM5y5cvd4/lgQce6HT88OHDRlxcnCHJKCws9NpXYWGhIcmIi4szDh8+3Ol4d/wsmcFcR3auzdi7d6+RmJhoSDJGjBgRUBuGwVwHM9eeTJs2zd22GeG8r8PZH/MdOKvu7Z6AYBJM+8///E+f33DWrVtnJCQkGJKMadOmeWzjs88+c7fh7Zxjx465H3iGDx9uHD16tN3xlpYW46qrrnK38+6773ps5+mnn3af82//9m+dju/Zs8fo06ePIcnIzs42mpubPbYzZcoU9zfjdevWdTr+wAMPuPu5++67Pbbx0UcfGX379jUkGb179zY++OADj+d1xYo5CFc/PXWuPfE3uPDuu+8aX331ldfjTU1Nxty5c91tXnXVVabH4gnzHdn5Ngzu7ViY6127drmvzcSJE42GhoZ2xxsaGoyJEye6+9m9e3enNqy4t4N9L88880yX7/Xvf/+7+5yrr77aaGlpaXf8yJEjxplnnul+0Dl+/LjHdubMmeNu589//nOn46Wlpe7jc+fO9dhGd/wsmcVcR26u//rXv3r9DBiGYVRVVRnnnXeeu5+HHnrI67lmMNeBzbUngQQXwnlfW9Ef8/1P/s53uO/t7o5gEkw7efKkkZub67655s+fb7z77rtGWVmZ8dvf/tZITU01JBm9evUyPvroI49tmHkIMQzD+P3vf+8+Lzs72/jDH/5gbNy40XjttdeM6dOnu4/94Ac/8NpGS0uLceGFF7rP/c53vmOsXLnSWL9+vfHYY48ZgwYNMqTTUfQVK1Z4befDDz80evXqZUgyUlNTjd/+9rdGWVmZ8e677xrz5893t5+bm2ucPHmy0+v37Nnj7kuS8cgjjxhbt271+ae6ujpkc2AGcx3YXLs888wz7f5cc8017tf++7//e7tja9eu7fT6uXPnGqmpqcasWbOMJ5980nj//feNjz76yFi7dq2xaNEi92+rJBmDBg0y9u3b53UsZjDfkZ1vw+De9iba5vrnP/+5+9zzzjvPePHFF42NGzcaL774YrsfPn/xi194fL0V93aw78XMQ4hhGMb3v/9993nTp083XnvtNWPjxo3GH/7wByM7O9t97Pe//73XNj7//HNj4MCB7oemf//3fzfWrl1rrF271vj3f/93d3Bu4MCBXn9zbhjd87NkBnMdubkePny4ccYZZxi33nqr8cILLxjr1q0zPvroI+Ptt982/uM//sPIyMhwt3/RRRcZDofD63jMYK4Dm+vDhw93+j84Ly/P/dqOxyoqKkJy/f3FfEduvsN9b3d3BJPgl4qKCmPkyJHum6zjnz59+hivv/6619ebfQgxDMO46667DJvN5rWvGTNmGKdOnfLZxpEjR4xJkyZ5bSMpKcl48sknu3zff/3rX92/xfL0Jzc31+t/UG2/4Zv94+s/hmDnwCzm2v+5dvFnrj39FqdtZoKvP2PHjjW2b9/e5Xsyg/mO3Hy7cG97Fk1z7XQ6jR/+8Ic+5/imm24ynE6nx9dbdW9b9X+Sr/9rGhoajBkzZnjtIy4uztRv6MvLy42srCyv7WRlZRnl5eVdttPdPktmMdeRmevhw4ebule/853v+Mw29Adz7f9cr1692tQ8uf4888wzXtsK530dbH/Md+DzHYl7uzsjmAS/1dXVGffff78xceJEo2/fvkZKSoqRl5dn/PSnPzX279/v87X+PIQYxuk6Q7NmzTKGDRtmJCUlGYMGDTIuvfRS44UXXjA93ubmZuOJJ54wLrroIiMjI8Ow2+3GiBEjjHnz5hnbtm0z3c7+/fuNn/70p0Zubq6RkpJi9O3b15g4caJx//33e12TbxjWB5MMI7g58Adz7d9cu/gz156CCzt27DAeeeQRo6ioyBgzZoyRmZlpJCYmGqmpqUZ2drbxve99z/jzn//cKWU5WMx3ZOa7Le5tz6Jlrl3eeOMN45prrjHOOOMMIykpyTjjjDOMa665xmdGi2FYe29b8X+SmYeI559/3rj00kuNQYMGGUlJScawYcOMWbNmeVya4c2RI0eMX/3qV8aYMWOM1NRUIzU11Rg7dqzxq1/9qtNyB1+642cplH0x14HP9XvvvWfcc889xmWXXWbk5uYa/fv3NxISEoy+ffsaY8eONYqLi/26LmYx1/7NtZXBJMMI730dTH/Md+DzHal7u7uyGQb73gEAAAAAAMCcuEgPAAAAAAAAALGDYBIAAAAAAABMI5gEAAAAAAAA0wgmAQAAAAAAwDSCSQAAAAAAADCNYBIAAAAAAABMI5gEAAAAAAAA0wgmAQAAAAAAwDSCSQAAAAAAADCNYBIAAAAAAABMI5gEAAAAAAAA0wgmAQAAAAAAwDSCSQAAAAAAADCNYBIAAAAAAABMI5gEAAAAAAAA0wgmAQAAAAAAwDSCSQAAAAAAADCNYBIAAAAAAABMI5gEAAAAAAAA0wgmAQAAAAAAwDSCSQAAAAAAADCNYBIAAAAAAABMI5gEAAAAAAAA0wgmAQAAAAAAwLT/D6/nae3V+FTCAAAAAElFTkSuQmCC", + "image/png": "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", "text/plain": [ "
" ] @@ -917,9 +1059,9 @@ } ], "source": [ - "x = tao.bunch1('end', coordinate='x')\n", - "px = tao.bunch1('end', coordinate='px')\n", - "plt.scatter(x, px)" + "x = tao.bunch1(\"end\", coordinate=\"x\")\n", + "px = tao.bunch1(\"end\", coordinate=\"px\")\n", + "plt.scatter(x, px);" ] }, { @@ -931,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -940,13 +1082,13 @@ "dtype('" ] @@ -1026,7 +1168,7 @@ "source": [ "P = ParticleGroup(data=data)\n", "\n", - "P.plot('x', 'px')" + "P.plot(\"x\", \"px\")" ] }, { @@ -1038,7 +1180,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1047,23 +1189,23 @@ "['[INFO] tao_write_cmd:', ' Written: test.h5']" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tao.cmd('write beam -at end test.h5')" + "tao.cmd(\"write beam -at end test.h5\")" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1078,13 +1220,13 @@ } ], "source": [ - "P2 = ParticleGroup('test.h5')\n", - "P2.plot('x', 'px')" + "P2 = ParticleGroup(\"test.h5\")\n", + "P2.plot(\"x\", \"px\")" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1103,7 +1245,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": { "tags": [] }, @@ -1114,100 +1256,63 @@ "array([0. , 0. , 0.06 , 0.193, 0.263, 0.385, 0.445, 0.445, 0.445])" ] }, - "execution_count": 32, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tao.lat_list('*', 'ele.s')" + "tao.lat_list(\"*\", \"ele.s\")" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "ERROR detected: [ERROR | 2022-OCT-13 15:38:28] tao_python_cmd:\n", - " \"python lat_list -array_out -track_only @>>*|model ele.saa\": Bad {who}: ele.saa\n", + "Exception handled: Command: python var foobar causes error: ERROR detected: [ERROR | 2024-JUN-27 10:35:34] tao_python_cmd:\n", + " \"python var foobar\": Not a valid variable name\n", "INVALID\n" ] } ], "source": [ "try:\n", - " tao.lat_list('*', 'ele.saa')\n", + " tao.var(\"foobar\")\n", "except Exception as ex:\n", - " print(ex)" + " print(\"Exception handled:\", ex)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This suppresses the exceptions, returning None" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "tao.lat_list('*', 'ele.saa', raises=False)" + "This suppresses the exceptions, returning the error text:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Command: safaf causes error: ERROR detected: [ERROR | 2022-OCT-13 15:38:28] tao_command:\n", - " UNRECOGNIZED COMMAND: safaf\n" - ] - } - ], - "source": [ - "try:\n", - " tao.cmd('safaf')\n", - "except Exception as ex:\n", - " print(ex)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This returns the lines" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['[ERROR | 2022-OCT-13 15:38:28] tao_command:',\n", - " ' UNRECOGNIZED COMMAND: safaf']" + "['[ERROR | 2024-JUN-27 10:35:34] tao_command:',\n", + " ' UNRECOGNIZED COMMAND: invalid_command']" ] }, - "execution_count": 36, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tao.cmd('safaf', raises=False)" + "tao.cmd(\"invalid_command\", raises=False)" ] }, { @@ -1223,18 +1328,19 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "import logging\n", "import sys\n", + "\n", "logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1246,7 +1352,7 @@ } ], "source": [ - "tao.cmd('sho ele 2');" + "tao.cmd(\"sho ele 2\");" ] }, { @@ -1258,9 +1364,17 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rm: csr_wake.dat: No such file or directory\n" + ] + } + ], "source": [ "!rm csr_wake.dat" ] @@ -1268,7 +1382,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.13 ('pytao-dev')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1282,7 +1396,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.12.0" }, "vscode": { "interpreter": { diff --git a/docs/examples/bunch.ipynb b/docs/examples/bunch.ipynb index 558173f4..cd92d7b7 100644 --- a/docs/examples/bunch.ipynb +++ b/docs/examples/bunch.ipynb @@ -22,7 +22,6 @@ "metadata": {}, "outputs": [], "source": [ - "%config InlineBackend.figure_format = 'retina' # Nicer plotting\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] @@ -40,7 +39,9 @@ "metadata": {}, "outputs": [], "source": [ - "tao=Tao('-init $ACC_ROOT_DIR/bmad-doc/tao_examples/csr_beam_tracking/tao.init -noplot') " + "tao = Tao(\n", + " \"-init $ACC_ROOT_DIR/bmad-doc/tao_examples/csr_beam_tracking/tao.init -noplot\"\n", + ")" ] }, { @@ -55,7 +56,9 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -197,7 +200,7 @@ } ], "source": [ - "stats = tao.bunch_params('end')\n", + "stats = tao.bunch_params(\"end\")\n", "stats" ] }, @@ -225,7 +228,7 @@ } ], "source": [ - "stats['beam_saved']" + "stats[\"beam_saved\"]" ] }, { @@ -260,7 +263,7 @@ } ], "source": [ - "tao.bunch1('end', 'x')[0:10]" + "tao.bunch1(\"end\", \"x\")[0:10]" ] }, { @@ -280,7 +283,7 @@ } ], "source": [ - "tao.bunch1('end', 'ix_ele')[0:10]" + "tao.bunch1(\"end\", \"ix_ele\")[0:10]" ] }, { @@ -298,8 +301,7 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" + "import matplotlib.pyplot as plt" ] }, { @@ -308,18 +310,28 @@ "metadata": {}, "outputs": [], "source": [ - "xdat = tao.bunch1('end', 'x')\n", - "pxdat = tao.bunch1('end', 'px')\n", - "chargedat = tao.bunch1('end', 'charge')\n", + "%config InlineBackend.figure_format = 'retina' # Nicer plotting\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "xdat = tao.bunch1(\"end\", \"x\")\n", + "pxdat = tao.bunch1(\"end\", \"px\")\n", + "chargedat = tao.bunch1(\"end\", \"charge\")\n", "\n", - "xdata = 1000*xdat\n", - "ydata = 1000*pxdat\n", + "xdata = 1000 * xdat\n", + "ydata = 1000 * pxdat\n", "weights = chargedat" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -341,13 +353,15 @@ "source": [ "# hist2d\n", "\n", - "mycmap = plt.get_cmap('plasma') # viridis plasma inferno magma and _r versions\n", - "mycmap.set_under(color='white') # map 0 to this color\n", - "myvmin = 1e-30 # something tiny\n", + "mycmap = plt.get_cmap(\"plasma\") # viridis plasma inferno magma and _r versions\n", + "mycmap.set_under(color=\"white\") # map 0 to this color\n", + "myvmin = 1e-30 # something tiny\n", "# Bin particles\n", - "plt.hist2d(x=1000*xdata, y=ydata, bins=2*[40], weights=weights, cmap=mycmap, vmin=myvmin)\n", - "plt.xlabel('x (mm)')\n", - "plt.ylabel('px (mrad)')\n", + "plt.hist2d(\n", + " x=1000 * xdata, y=ydata, bins=2 * [40], weights=weights, cmap=mycmap, vmin=myvmin\n", + ")\n", + "plt.xlabel(\"x (mm)\")\n", + "plt.ylabel(\"px (mrad)\")\n", "plt.show()" ] }, @@ -360,19 +374,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": "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", @@ -392,24 +396,26 @@ "source": [ "import matplotlib.colors as colors\n", "\n", - "mycmap = plt.get_cmap('viridis') # viridis plasma inferno magma and _r versions\n", - "mycmap.set_under(color='white') # map 0 to this color\n", + "mycmap = plt.get_cmap(\"viridis\") # viridis plasma inferno magma and _r versions\n", + "mycmap.set_under(color=\"white\") # map 0 to this color\n", "H, xedges, yedges = np.histogram2d(xdata, ydata, weights=chargedat, bins=40)\n", "\n", "xmin, xmax = min(xedges), max(xedges)\n", "ymin, ymax = min(yedges), max(yedges)\n", "\n", - "image = np.flip(H.T, axis=0) # \n", + "image = np.flip(H.T, axis=0) #\n", "imax = np.max(image)\n", - "norm=colors.Normalize(vmin=1e-12*imax, vmax=imax)\n", - "plt.xlabel('x (mm)')\n", - "plt.ylabel('px (mrad)')\n", - "plt.imshow(image, cmap=mycmap, norm=norm, extent=[xmin, xmax, ymin, ymax], aspect='auto')" + "norm = colors.Normalize(vmin=1e-12 * imax, vmax=imax)\n", + "plt.xlabel(\"x (mm)\")\n", + "plt.ylabel(\"px (mrad)\")\n", + "plt.imshow(\n", + " image, cmap=mycmap, norm=norm, extent=[xmin, xmax, ymin, ymax], aspect=\"auto\"\n", + ");" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -418,7 +424,7 @@ "(0.0, 8.469999999999999e-13)" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -436,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -713,7 +719,7 @@ " document.body.appendChild(element);\n", " }\n", "\n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.4.1.min.js\"];\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.4.2.min.js\"];\n", " const css_urls = [];\n", "\n", " const inline_js = [ function(Bokeh) {\n", @@ -753,7 +759,7 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "'use strict';\n(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded(error = null) {\n const el = document.getElementById(null);\n if (el != null) {\n const html = (() => {\n if (typeof root.Bokeh === \"undefined\") {\n if (error == null) {\n return \"BokehJS is loading ...\";\n } else {\n return \"BokehJS failed to load.\";\n }\n } else {\n const prefix = `BokehJS ${root.Bokeh.version}`;\n if (error == null) {\n return `${prefix} successfully loaded.`;\n } else {\n return `${prefix} encountered errors while loading and may not function as expected.`;\n }\n }\n })();\n el.innerHTML = html;\n\n if (error != null) {\n const wrapper = document.createElement(\"div\");\n wrapper.style.overflow = \"auto\";\n wrapper.style.height = \"5em\";\n wrapper.style.resize = \"vertical\";\n const content = document.createElement(\"div\");\n content.style.fontFamily = \"monospace\";\n content.style.whiteSpace = \"pre-wrap\";\n content.style.backgroundColor = \"rgb(255, 221, 221)\";\n content.textContent = error.stack ?? error.toString();\n wrapper.append(content);\n el.append(wrapper);\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(() => display_loaded(error), 100);\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.4.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n try {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n\n } catch (error) {throw error;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + "application/vnd.bokehjs_load.v0+json": "'use strict';\n(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded(error = null) {\n const el = document.getElementById(null);\n if (el != null) {\n const html = (() => {\n if (typeof root.Bokeh === \"undefined\") {\n if (error == null) {\n return \"BokehJS is loading ...\";\n } else {\n return \"BokehJS failed to load.\";\n }\n } else {\n const prefix = `BokehJS ${root.Bokeh.version}`;\n if (error == null) {\n return `${prefix} successfully loaded.`;\n } else {\n return `${prefix} encountered errors while loading and may not function as expected.`;\n }\n }\n })();\n el.innerHTML = html;\n\n if (error != null) {\n const wrapper = document.createElement(\"div\");\n wrapper.style.overflow = \"auto\";\n wrapper.style.height = \"5em\";\n wrapper.style.resize = \"vertical\";\n const content = document.createElement(\"div\");\n content.style.fontFamily = \"monospace\";\n content.style.whiteSpace = \"pre-wrap\";\n content.style.backgroundColor = \"rgb(255, 221, 221)\";\n content.textContent = error.stack ?? error.toString();\n wrapper.append(content);\n el.append(wrapper);\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(() => display_loaded(error), 100);\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.4.2.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n try {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n\n } catch (error) {throw error;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -763,16 +769,17 @@ "from bokeh.plotting import figure, show, output_notebook\n", "from bokeh import palettes, colors\n", "from bokeh.models import ColumnDataSource, HoverTool\n", + "\n", "output_notebook(verbose=False, hide_banner=True)\n", "\n", "pal = palettes.Viridis[256]\n", - "#white=colors.named.white\n", - "#pal[0] = white # replace 0 with white" + "# white=colors.named.white\n", + "# pal[0] = white # replace 0 with white" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -783,14 +790,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "
\n" + "
\n" ] }, "metadata": {}, @@ -801,8 +808,8 @@ "application/javascript": [ "(function(root) {\n", " function embed_document(root) {\n", - " const docs_json = {\"649c90c7-fc38-4481-814b-62719a2e7dc1\":{\"version\":\"3.4.1\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1004\",\"attributes\":{\"width\":500,\"height\":500,\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1014\",\"attributes\":{\"start\":-0.19080456771215437,\"end\":0.1749986951725533}},\"y_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1015\",\"attributes\":{\"start\":-1.4601286926409278,\"end\":1.3331494758231812}},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1016\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1017\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1007\",\"attributes\":{\"text\":\"Bunch at end\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1051\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1001\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1002\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1003\"},\"data\":{\"type\":\"map\",\"entries\":[[\"image\",[{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6xFPQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sNT2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAJNTLktvrDU9k1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sNT2TUy5Lb6w1PZNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PZNTLktvrEU9k1MuS2+sNT2TUy5Lb6w1PQAAAAAAAAAAk1MuS2+sNT2uvmJ4U0FQPQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PZNTLktvrEU9k1MuS2+sNT2TUy5Lb6w1PZNTLktvrEU9k1MuS2+sNT2TUy5Lb6xFPQAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PZNTLktvrEU9rr5ieFNBUD2TUy5Lb6w1PZNTLktvrDU9k1MuS2+sRT2TUy5Lb6w1PZNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PZNTLktvrDU9k1MuS2+sRT0AAAAAAAAAAK6+YnhTQVA9rr5ieFNBUD2uvmJ4U0FQPZNTLktvrEU9rr5ieFNBUD2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAArr5ieFNBUD0AAAAAAAAAAJNTLktvrEU9rr5ieFNBUD2TUy5Lb6w1Pa6+YnhTQVA9rr5ieFNBUD2TUy5Lb6xVPQAAAAAAAAAAk1MuS2+sNT2TUy5Lb6xFPQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAACTUy5Lb6xFPXjo+R2LF1s9k1MuS2+sRT2uvmJ4U0FQPZNTLktvrFU9rr5ieFNBUD2TUy5Lb6xFPQAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT2TUy5Lb6w1PZNTLktvrEU9k1MuS2+sVT2TUy5Lb6xVPa6+YnhTQWA9eOj5HYsXWz2uvmJ4U0FgPXjo+R2LF1s9k1MuS2+sVT2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sRT2uvmJ4U0FQPZNTLktvrEU9rr5ieFNBYD2uvmJ4U0FgPZNTLktvrFU9k1MuS2+sVT2uvmJ4U0FQPZNTLktvrFU9k1MuS2+sVT2TUy5Lb6xFPQAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sRT2TUy5Lb6w1Pa6+YnhTQWA9rr5ieFNBUD0gichh4fZiPa6+YnhTQWA9k1MuS2+sVT2TUy5Lb6xFPZNTLktvrEU9k1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrEU9k1MuS2+sNT2TUy5Lb6xFPZNTLktvrFU9k1MuS2+sVT2SUy5Lb6xlPa6+YnhTQWA9rr5ieFNBYD2uvmJ4U0FgPXjo+R2LF1s9rr5ieFNBUD0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sVT2TUy5Lb6w1PXjo+R2LF1s9klMuS2+sZT2SUy5Lb6xlPZJTLktvrGU9k1MuS2+sVT2uvmJ4U0FgPa6+YnhTQVA9k1MuS2+sRT2TUy5Lb6w1PZNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sRT2TUy5Lb6xFPZNTLktvrDU9k1MuS2+sVT0gichh4fZiPa6+YnhTQWA9klMuS2+sZT0gichh4fZiPZJTLktvrGU9k1MuS2+sVT2TUy5Lb6xVPZNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT2TUy5Lb6xFPa6+YnhTQWA9k1MuS2+sVT2SUy5Lb6xlPSCJyGHh9mI9klMuS2+sZT0gichh4fZiPa6+YnhTQWA9k1MuS2+sNT2uvmJ4U0FgPZNTLktvrDU9k1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrFU9k1MuS2+sRT2TUy5Lb6xVPXjo+R2LF1s9BB6UNP1haD0gichh4fZiPSCJyGHh9mI9klMuS2+sZT2uvmJ4U0FgPZNTLktvrEU9k1MuS2+sNT2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sNT2TUy5Lb6w1PXjo+R2LF1s9rr5ieFNBYD146PkdixdbPeiyXwcZzW09klMuS2+sZT2SUy5Lb6xlPSCJyGHh9mI9rr5ieFNBUD2TUy5Lb6xVPZNTLktvrEU9k1MuS2+sNT2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sNT0AAAAAAAAAAJNTLktvrDU9k1MuS2+sNT146PkdixdbPa6+YnhTQWA9eOj5HYsXWz146PkdixdbPZJTLktvrGU9eOj5HYsXWz2SUy5Lb6xlPXjo+R2LF1s9k1MuS2+sNT2TUy5Lb6xFPQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6xFPZNTLktvrDU9k1MuS2+sRT2uvmJ4U0FQPXjo+R2LF1s9IInIYeH2Yj0gichh4fZiPSCJyGHh9mI9eOj5HYsXWz2TUy5Lb6w1PZNTLktvrEU9k1MuS2+sRT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sNT2TUy5Lb6xFPa6+YnhTQVA9klMuS2+sZT2SUy5Lb6xlPSCJyGHh9mI9IInIYeH2Yj2TUy5Lb6xVPXjo+R2LF1s9eOj5HYsXWz2TUy5Lb6xFPZNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PZNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAArr5ieFNBUD2TUy5Lb6xVPZJTLktvrGU9IInIYeH2Yj146PkdixdbPXjo+R2LF1s9klMuS2+sZT2uvmJ4U0FQPa6+YnhTQVA9k1MuS2+sRT2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6xFPZNTLktvrDU9k1MuS2+sRT2TUy5Lb6xFPa6+YnhTQWA9IInIYeH2Yj2uvmJ4U0FgPXjo+R2LF1s9rr5ieFNBUD2uvmJ4U0FQPZNTLktvrDU9k1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PXjo+R2LF1s9k1MuS2+sRT146PkdixdbPZNTLktvrFU9rr5ieFNBYD2TUy5Lb6xVPZNTLktvrFU9k1MuS2+sRT2TUy5Lb6w1PZNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PZNTLktvrEU9k1MuS2+sRT2uvmJ4U0FQPZNTLktvrFU9k1MuS2+sVT2TUy5Lb6xVPZNTLktvrEU9k1MuS2+sRT2TUy5Lb6xFPZNTLktvrFU9k1MuS2+sNT2TUy5Lb6w1PQAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAJNTLktvrDU9k1MuS2+sRT146PkdixdbPZNTLktvrFU9rr5ieFNBUD146PkdixdbPZNTLktvrEU9rr5ieFNBUD2TUy5Lb6w1PQAAAAAAAAAAk1MuS2+sRT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT2TUy5Lb6w1PZNTLktvrDU9k1MuS2+sRT2TUy5Lb6xFPa6+YnhTQVA9rr5ieFNBUD2TUy5Lb6xFPQAAAAAAAAAAk1MuS2+sNT2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PZNTLktvrDU9k1MuS2+sNT2TUy5Lb6w1PZNTLktvrEU9rr5ieFNBUD2TUy5Lb6xFPZNTLktvrDU9rr5ieFNBUD0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6xFPZNTLktvrDU9k1MuS2+sRT2uvmJ4U0FQPa6+YnhTQVA9k1MuS2+sVT2TUy5Lb6xFPZNTLktvrEU9AAAAAAAAAACTUy5Lb6xFPQAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAACuvmJ4U0FQPQAAAAAAAAAAk1MuS2+sNT2TUy5Lb6w1PZNTLktvrEU9AAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT2TUy5Lb6w1PZNTLktvrDU9AAAAAAAAAACTUy5Lb6xFPZNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAJNTLktvrDU9k1MuS2+sRT0AAAAAAAAAAAAAAAAAAAAAk1MuS2+sRT2TUy5Lb6w1PZNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT2TUy5Lb6w1PZNTLktvrDU9k1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sNT0=\"},\"shape\":[40,40],\"dtype\":\"float64\",\"order\":\"little\"}]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1052\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1053\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Image\",\"id\":\"p1042\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":-0.19080456771215437},\"y\":{\"type\":\"value\",\"value\":-1.4601286926409278},\"dw\":{\"type\":\"value\",\"value\":0.36580326288470766},\"dh\":{\"type\":\"value\",\"value\":2.7932781684641093},\"image\":{\"type\":\"field\",\"field\":\"image\"},\"color_mapper\":{\"type\":\"object\",\"name\":\"LinearColorMapper\",\"id\":\"p1043\",\"attributes\":{\"palette\":[\"#440154\",\"#440255\",\"#440357\",\"#450558\",\"#45065A\",\"#45085B\",\"#46095C\",\"#460B5E\",\"#460C5F\",\"#460E61\",\"#470F62\",\"#471163\",\"#471265\",\"#471466\",\"#471567\",\"#471669\",\"#47186A\",\"#48196B\",\"#481A6C\",\"#481C6E\",\"#481D6F\",\"#481E70\",\"#482071\",\"#482172\",\"#482273\",\"#482374\",\"#472575\",\"#472676\",\"#472777\",\"#472878\",\"#472A79\",\"#472B7A\",\"#472C7B\",\"#462D7C\",\"#462F7C\",\"#46307D\",\"#46317E\",\"#45327F\",\"#45347F\",\"#453580\",\"#453681\",\"#443781\",\"#443982\",\"#433A83\",\"#433B83\",\"#433C84\",\"#423D84\",\"#423E85\",\"#424085\",\"#414186\",\"#414286\",\"#404387\",\"#404487\",\"#3F4587\",\"#3F4788\",\"#3E4888\",\"#3E4989\",\"#3D4A89\",\"#3D4B89\",\"#3D4C89\",\"#3C4D8A\",\"#3C4E8A\",\"#3B508A\",\"#3B518A\",\"#3A528B\",\"#3A538B\",\"#39548B\",\"#39558B\",\"#38568B\",\"#38578C\",\"#37588C\",\"#37598C\",\"#365A8C\",\"#365B8C\",\"#355C8C\",\"#355D8C\",\"#345E8D\",\"#345F8D\",\"#33608D\",\"#33618D\",\"#32628D\",\"#32638D\",\"#31648D\",\"#31658D\",\"#31668D\",\"#30678D\",\"#30688D\",\"#2F698D\",\"#2F6A8D\",\"#2E6B8E\",\"#2E6C8E\",\"#2E6D8E\",\"#2D6E8E\",\"#2D6F8E\",\"#2C708E\",\"#2C718E\",\"#2C728E\",\"#2B738E\",\"#2B748E\",\"#2A758E\",\"#2A768E\",\"#2A778E\",\"#29788E\",\"#29798E\",\"#287A8E\",\"#287A8E\",\"#287B8E\",\"#277C8E\",\"#277D8E\",\"#277E8E\",\"#267F8E\",\"#26808E\",\"#26818E\",\"#25828E\",\"#25838D\",\"#24848D\",\"#24858D\",\"#24868D\",\"#23878D\",\"#23888D\",\"#23898D\",\"#22898D\",\"#228A8D\",\"#228B8D\",\"#218C8D\",\"#218D8C\",\"#218E8C\",\"#208F8C\",\"#20908C\",\"#20918C\",\"#1F928C\",\"#1F938B\",\"#1F948B\",\"#1F958B\",\"#1F968B\",\"#1E978A\",\"#1E988A\",\"#1E998A\",\"#1E998A\",\"#1E9A89\",\"#1E9B89\",\"#1E9C89\",\"#1E9D88\",\"#1E9E88\",\"#1E9F88\",\"#1EA087\",\"#1FA187\",\"#1FA286\",\"#1FA386\",\"#20A485\",\"#20A585\",\"#21A685\",\"#21A784\",\"#22A784\",\"#23A883\",\"#23A982\",\"#24AA82\",\"#25AB81\",\"#26AC81\",\"#27AD80\",\"#28AE7F\",\"#29AF7F\",\"#2AB07E\",\"#2BB17D\",\"#2CB17D\",\"#2EB27C\",\"#2FB37B\",\"#30B47A\",\"#32B57A\",\"#33B679\",\"#35B778\",\"#36B877\",\"#38B976\",\"#39B976\",\"#3BBA75\",\"#3DBB74\",\"#3EBC73\",\"#40BD72\",\"#42BE71\",\"#44BE70\",\"#45BF6F\",\"#47C06E\",\"#49C16D\",\"#4BC26C\",\"#4DC26B\",\"#4FC369\",\"#51C468\",\"#53C567\",\"#55C666\",\"#57C665\",\"#59C764\",\"#5BC862\",\"#5EC961\",\"#60C960\",\"#62CA5F\",\"#64CB5D\",\"#67CC5C\",\"#69CC5B\",\"#6BCD59\",\"#6DCE58\",\"#70CE56\",\"#72CF55\",\"#74D054\",\"#77D052\",\"#79D151\",\"#7CD24F\",\"#7ED24E\",\"#81D34C\",\"#83D34B\",\"#86D449\",\"#88D547\",\"#8BD546\",\"#8DD644\",\"#90D643\",\"#92D741\",\"#95D73F\",\"#97D83E\",\"#9AD83C\",\"#9DD93A\",\"#9FD938\",\"#A2DA37\",\"#A5DA35\",\"#A7DB33\",\"#AADB32\",\"#ADDC30\",\"#AFDC2E\",\"#B2DD2C\",\"#B5DD2B\",\"#B7DD29\",\"#BADE27\",\"#BDDE26\",\"#BFDF24\",\"#C2DF22\",\"#C5DF21\",\"#C7E01F\",\"#CAE01E\",\"#CDE01D\",\"#CFE11C\",\"#D2E11B\",\"#D4E11A\",\"#D7E219\",\"#DAE218\",\"#DCE218\",\"#DFE318\",\"#E1E318\",\"#E4E318\",\"#E7E419\",\"#E9E419\",\"#ECE41A\",\"#EEE51B\",\"#F1E51C\",\"#F3E51E\",\"#F6E61F\",\"#F8E621\",\"#FAE622\",\"#FDE724\"]}}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Image\",\"id\":\"p1045\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":-0.19080456771215437},\"y\":{\"type\":\"value\",\"value\":-1.4601286926409278},\"dw\":{\"type\":\"value\",\"value\":0.36580326288470766},\"dh\":{\"type\":\"value\",\"value\":2.7932781684641093},\"global_alpha\":{\"type\":\"value\",\"value\":0.1},\"image\":{\"type\":\"field\",\"field\":\"image\"},\"color_mapper\":{\"type\":\"object\",\"name\":\"LinearColorMapper\",\"id\":\"p1046\",\"attributes\":{\"palette\":[\"#440154\",\"#440255\",\"#440357\",\"#450558\",\"#45065A\",\"#45085B\",\"#46095C\",\"#460B5E\",\"#460C5F\",\"#460E61\",\"#470F62\",\"#471163\",\"#471265\",\"#471466\",\"#471567\",\"#471669\",\"#47186A\",\"#48196B\",\"#481A6C\",\"#481C6E\",\"#481D6F\",\"#481E70\",\"#482071\",\"#482172\",\"#482273\",\"#482374\",\"#472575\",\"#472676\",\"#472777\",\"#472878\",\"#472A79\",\"#472B7A\",\"#472C7B\",\"#462D7C\",\"#462F7C\",\"#46307D\",\"#46317E\",\"#45327F\",\"#45347F\",\"#453580\",\"#453681\",\"#443781\",\"#443982\",\"#433A83\",\"#433B83\",\"#433C84\",\"#423D84\",\"#423E85\",\"#424085\",\"#414186\",\"#414286\",\"#404387\",\"#404487\",\"#3F4587\",\"#3F4788\",\"#3E4888\",\"#3E4989\",\"#3D4A89\",\"#3D4B89\",\"#3D4C89\",\"#3C4D8A\",\"#3C4E8A\",\"#3B508A\",\"#3B518A\",\"#3A528B\",\"#3A538B\",\"#39548B\",\"#39558B\",\"#38568B\",\"#38578C\",\"#37588C\",\"#37598C\",\"#365A8C\",\"#365B8C\",\"#355C8C\",\"#355D8C\",\"#345E8D\",\"#345F8D\",\"#33608D\",\"#33618D\",\"#32628D\",\"#32638D\",\"#31648D\",\"#31658D\",\"#31668D\",\"#30678D\",\"#30688D\",\"#2F698D\",\"#2F6A8D\",\"#2E6B8E\",\"#2E6C8E\",\"#2E6D8E\",\"#2D6E8E\",\"#2D6F8E\",\"#2C708E\",\"#2C718E\",\"#2C728E\",\"#2B738E\",\"#2B748E\",\"#2A758E\",\"#2A768E\",\"#2A778E\",\"#29788E\",\"#29798E\",\"#287A8E\",\"#287A8E\",\"#287B8E\",\"#277C8E\",\"#277D8E\",\"#277E8E\",\"#267F8E\",\"#26808E\",\"#26818E\",\"#25828E\",\"#25838D\",\"#24848D\",\"#24858D\",\"#24868D\",\"#23878D\",\"#23888D\",\"#23898D\",\"#22898D\",\"#228A8D\",\"#228B8D\",\"#218C8D\",\"#218D8C\",\"#218E8C\",\"#208F8C\",\"#20908C\",\"#20918C\",\"#1F928C\",\"#1F938B\",\"#1F948B\",\"#1F958B\",\"#1F968B\",\"#1E978A\",\"#1E988A\",\"#1E998A\",\"#1E998A\",\"#1E9A89\",\"#1E9B89\",\"#1E9C89\",\"#1E9D88\",\"#1E9E88\",\"#1E9F88\",\"#1EA087\",\"#1FA187\",\"#1FA286\",\"#1FA386\",\"#20A485\",\"#20A585\",\"#21A685\",\"#21A784\",\"#22A784\",\"#23A883\",\"#23A982\",\"#24AA82\",\"#25AB81\",\"#26AC81\",\"#27AD80\",\"#28AE7F\",\"#29AF7F\",\"#2AB07E\",\"#2BB17D\",\"#2CB17D\",\"#2EB27C\",\"#2FB37B\",\"#30B47A\",\"#32B57A\",\"#33B679\",\"#35B778\",\"#36B877\",\"#38B976\",\"#39B976\",\"#3BBA75\",\"#3DBB74\",\"#3EBC73\",\"#40BD72\",\"#42BE71\",\"#44BE70\",\"#45BF6F\",\"#47C06E\",\"#49C16D\",\"#4BC26C\",\"#4DC26B\",\"#4FC369\",\"#51C468\",\"#53C567\",\"#55C666\",\"#57C665\",\"#59C764\",\"#5BC862\",\"#5EC961\",\"#60C960\",\"#62CA5F\",\"#64CB5D\",\"#67CC5C\",\"#69CC5B\",\"#6BCD59\",\"#6DCE58\",\"#70CE56\",\"#72CF55\",\"#74D054\",\"#77D052\",\"#79D151\",\"#7CD24F\",\"#7ED24E\",\"#81D34C\",\"#83D34B\",\"#86D449\",\"#88D547\",\"#8BD546\",\"#8DD644\",\"#90D643\",\"#92D741\",\"#95D73F\",\"#97D83E\",\"#9AD83C\",\"#9DD93A\",\"#9FD938\",\"#A2DA37\",\"#A5DA35\",\"#A7DB33\",\"#AADB32\",\"#ADDC30\",\"#AFDC2E\",\"#B2DD2C\",\"#B5DD2B\",\"#B7DD29\",\"#BADE27\",\"#BDDE26\",\"#BFDF24\",\"#C2DF22\",\"#C5DF21\",\"#C7E01F\",\"#CAE01E\",\"#CDE01D\",\"#CFE11C\",\"#D2E11B\",\"#D4E11A\",\"#D7E219\",\"#DAE218\",\"#DCE218\",\"#DFE318\",\"#E1E318\",\"#E4E318\",\"#E7E419\",\"#E9E419\",\"#ECE41A\",\"#EEE51B\",\"#F1E51C\",\"#F3E51E\",\"#F6E61F\",\"#F8E621\",\"#FAE622\",\"#FDE724\"]}}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Image\",\"id\":\"p1048\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":-0.19080456771215437},\"y\":{\"type\":\"value\",\"value\":-1.4601286926409278},\"dw\":{\"type\":\"value\",\"value\":0.36580326288470766},\"dh\":{\"type\":\"value\",\"value\":2.7932781684641093},\"global_alpha\":{\"type\":\"value\",\"value\":0.2},\"image\":{\"type\":\"field\",\"field\":\"image\"},\"color_mapper\":{\"type\":\"object\",\"name\":\"LinearColorMapper\",\"id\":\"p1049\",\"attributes\":{\"palette\":[\"#440154\",\"#440255\",\"#440357\",\"#450558\",\"#45065A\",\"#45085B\",\"#46095C\",\"#460B5E\",\"#460C5F\",\"#460E61\",\"#470F62\",\"#471163\",\"#471265\",\"#471466\",\"#471567\",\"#471669\",\"#47186A\",\"#48196B\",\"#481A6C\",\"#481C6E\",\"#481D6F\",\"#481E70\",\"#482071\",\"#482172\",\"#482273\",\"#482374\",\"#472575\",\"#472676\",\"#472777\",\"#472878\",\"#472A79\",\"#472B7A\",\"#472C7B\",\"#462D7C\",\"#462F7C\",\"#46307D\",\"#46317E\",\"#45327F\",\"#45347F\",\"#453580\",\"#453681\",\"#443781\",\"#443982\",\"#433A83\",\"#433B83\",\"#433C84\",\"#423D84\",\"#423E85\",\"#424085\",\"#414186\",\"#414286\",\"#404387\",\"#404487\",\"#3F4587\",\"#3F4788\",\"#3E4888\",\"#3E4989\",\"#3D4A89\",\"#3D4B89\",\"#3D4C89\",\"#3C4D8A\",\"#3C4E8A\",\"#3B508A\",\"#3B518A\",\"#3A528B\",\"#3A538B\",\"#39548B\",\"#39558B\",\"#38568B\",\"#38578C\",\"#37588C\",\"#37598C\",\"#365A8C\",\"#365B8C\",\"#355C8C\",\"#355D8C\",\"#345E8D\",\"#345F8D\",\"#33608D\",\"#33618D\",\"#32628D\",\"#32638D\",\"#31648D\",\"#31658D\",\"#31668D\",\"#30678D\",\"#30688D\",\"#2F698D\",\"#2F6A8D\",\"#2E6B8E\",\"#2E6C8E\",\"#2E6D8E\",\"#2D6E8E\",\"#2D6F8E\",\"#2C708E\",\"#2C718E\",\"#2C728E\",\"#2B738E\",\"#2B748E\",\"#2A758E\",\"#2A768E\",\"#2A778E\",\"#29788E\",\"#29798E\",\"#287A8E\",\"#287A8E\",\"#287B8E\",\"#277C8E\",\"#277D8E\",\"#277E8E\",\"#267F8E\",\"#26808E\",\"#26818E\",\"#25828E\",\"#25838D\",\"#24848D\",\"#24858D\",\"#24868D\",\"#23878D\",\"#23888D\",\"#23898D\",\"#22898D\",\"#228A8D\",\"#228B8D\",\"#218C8D\",\"#218D8C\",\"#218E8C\",\"#208F8C\",\"#20908C\",\"#20918C\",\"#1F928C\",\"#1F938B\",\"#1F948B\",\"#1F958B\",\"#1F968B\",\"#1E978A\",\"#1E988A\",\"#1E998A\",\"#1E998A\",\"#1E9A89\",\"#1E9B89\",\"#1E9C89\",\"#1E9D88\",\"#1E9E88\",\"#1E9F88\",\"#1EA087\",\"#1FA187\",\"#1FA286\",\"#1FA386\",\"#20A485\",\"#20A585\",\"#21A685\",\"#21A784\",\"#22A784\",\"#23A883\",\"#23A982\",\"#24AA82\",\"#25AB81\",\"#26AC81\",\"#27AD80\",\"#28AE7F\",\"#29AF7F\",\"#2AB07E\",\"#2BB17D\",\"#2CB17D\",\"#2EB27C\",\"#2FB37B\",\"#30B47A\",\"#32B57A\",\"#33B679\",\"#35B778\",\"#36B877\",\"#38B976\",\"#39B976\",\"#3BBA75\",\"#3DBB74\",\"#3EBC73\",\"#40BD72\",\"#42BE71\",\"#44BE70\",\"#45BF6F\",\"#47C06E\",\"#49C16D\",\"#4BC26C\",\"#4DC26B\",\"#4FC369\",\"#51C468\",\"#53C567\",\"#55C666\",\"#57C665\",\"#59C764\",\"#5BC862\",\"#5EC961\",\"#60C960\",\"#62CA5F\",\"#64CB5D\",\"#67CC5C\",\"#69CC5B\",\"#6BCD59\",\"#6DCE58\",\"#70CE56\",\"#72CF55\",\"#74D054\",\"#77D052\",\"#79D151\",\"#7CD24F\",\"#7ED24E\",\"#81D34C\",\"#83D34B\",\"#86D449\",\"#88D547\",\"#8BD546\",\"#8DD644\",\"#90D643\",\"#92D741\",\"#95D73F\",\"#97D83E\",\"#9AD83C\",\"#9DD93A\",\"#9FD938\",\"#A2DA37\",\"#A5DA35\",\"#A7DB33\",\"#AADB32\",\"#ADDC30\",\"#AFDC2E\",\"#B2DD2C\",\"#B5DD2B\",\"#B7DD29\",\"#BADE27\",\"#BDDE26\",\"#BFDF24\",\"#C2DF22\",\"#C5DF21\",\"#C7E01F\",\"#CAE01E\",\"#CDE01D\",\"#CFE11C\",\"#D2E11B\",\"#D4E11A\",\"#D7E219\",\"#DAE218\",\"#DCE218\",\"#DFE318\",\"#E1E318\",\"#E4E318\",\"#E7E419\",\"#E9E419\",\"#ECE41A\",\"#EEE51B\",\"#F1E51C\",\"#F3E51E\",\"#F6E61F\",\"#F8E621\",\"#FAE622\",\"#FDE724\"]}}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1013\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1028\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1029\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1030\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1031\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p1036\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1037\"},{\"type\":\"object\",\"name\":\"HelpTool\",\"id\":\"p1038\"}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1023\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1024\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1025\"},\"axis_label\":\"px (mrad)\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1026\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1018\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1019\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1020\"},\"axis_label\":\"x (mm)\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1021\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1022\",\"attributes\":{\"axis\":{\"id\":\"p1018\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1027\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1023\"}}}]}}]}};\n", - " const render_items = [{\"docid\":\"649c90c7-fc38-4481-814b-62719a2e7dc1\",\"roots\":{\"p1004\":\"ed6e9cad-16e0-4279-bbb7-de4a3951de9d\"},\"root_ids\":[\"p1004\"]}];\n", + " const docs_json = {\"6e13a9b3-03ef-409b-8578-af83576a8b06\":{\"version\":\"3.4.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1004\",\"attributes\":{\"width\":500,\"height\":500,\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1014\",\"attributes\":{\"start\":-0.19080456771215437,\"end\":0.1749986951725533}},\"y_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1015\",\"attributes\":{\"start\":-1.4601286926409278,\"end\":1.3331494758231812}},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1016\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1017\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1007\",\"attributes\":{\"text\":\"Bunch at end\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1051\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1001\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1002\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1003\"},\"data\":{\"type\":\"map\",\"entries\":[[\"image\",[{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6xFPQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sNT2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAJNTLktvrDU9k1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sNT2TUy5Lb6w1PZNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PZNTLktvrEU9k1MuS2+sNT2TUy5Lb6w1PQAAAAAAAAAAk1MuS2+sNT2uvmJ4U0FQPQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PZNTLktvrEU9k1MuS2+sNT2TUy5Lb6w1PZNTLktvrEU9k1MuS2+sNT2TUy5Lb6xFPQAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PZNTLktvrEU9rr5ieFNBUD2TUy5Lb6w1PZNTLktvrDU9k1MuS2+sRT2TUy5Lb6w1PZNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PZNTLktvrDU9k1MuS2+sRT0AAAAAAAAAAK6+YnhTQVA9rr5ieFNBUD2uvmJ4U0FQPZNTLktvrEU9rr5ieFNBUD2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAArr5ieFNBUD0AAAAAAAAAAJNTLktvrEU9rr5ieFNBUD2TUy5Lb6w1Pa6+YnhTQVA9rr5ieFNBUD2TUy5Lb6xVPQAAAAAAAAAAk1MuS2+sNT2TUy5Lb6xFPQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAACTUy5Lb6xFPXjo+R2LF1s9k1MuS2+sRT2uvmJ4U0FQPZNTLktvrFU9rr5ieFNBUD2TUy5Lb6xFPQAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT2TUy5Lb6w1PZNTLktvrEU9k1MuS2+sVT2TUy5Lb6xVPa6+YnhTQWA9eOj5HYsXWz2uvmJ4U0FgPXjo+R2LF1s9k1MuS2+sVT2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sRT2uvmJ4U0FQPZNTLktvrEU9rr5ieFNBYD2uvmJ4U0FgPZNTLktvrFU9k1MuS2+sVT2uvmJ4U0FQPZNTLktvrFU9k1MuS2+sVT2TUy5Lb6xFPQAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sRT2TUy5Lb6w1Pa6+YnhTQWA9rr5ieFNBUD0gichh4fZiPa6+YnhTQWA9k1MuS2+sVT2TUy5Lb6xFPZNTLktvrEU9k1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrEU9k1MuS2+sNT2TUy5Lb6xFPZNTLktvrFU9k1MuS2+sVT2SUy5Lb6xlPa6+YnhTQWA9rr5ieFNBYD2uvmJ4U0FgPXjo+R2LF1s9rr5ieFNBUD0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sVT2TUy5Lb6w1PXjo+R2LF1s9klMuS2+sZT2SUy5Lb6xlPZJTLktvrGU9k1MuS2+sVT2uvmJ4U0FgPa6+YnhTQVA9k1MuS2+sRT2TUy5Lb6w1PZNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sRT2TUy5Lb6xFPZNTLktvrDU9k1MuS2+sVT0gichh4fZiPa6+YnhTQWA9klMuS2+sZT0gichh4fZiPZJTLktvrGU9k1MuS2+sVT2TUy5Lb6xVPZNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT2TUy5Lb6xFPa6+YnhTQWA9k1MuS2+sVT2SUy5Lb6xlPSCJyGHh9mI9klMuS2+sZT0gichh4fZiPa6+YnhTQWA9k1MuS2+sNT2uvmJ4U0FgPZNTLktvrDU9k1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrFU9k1MuS2+sRT2TUy5Lb6xVPXjo+R2LF1s9BB6UNP1haD0gichh4fZiPSCJyGHh9mI9klMuS2+sZT2uvmJ4U0FgPZNTLktvrEU9k1MuS2+sNT2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sNT2TUy5Lb6w1PXjo+R2LF1s9rr5ieFNBYD146PkdixdbPeiyXwcZzW09klMuS2+sZT2SUy5Lb6xlPSCJyGHh9mI9rr5ieFNBUD2TUy5Lb6xVPZNTLktvrEU9k1MuS2+sNT2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sNT0AAAAAAAAAAJNTLktvrDU9k1MuS2+sNT146PkdixdbPa6+YnhTQWA9eOj5HYsXWz146PkdixdbPZJTLktvrGU9eOj5HYsXWz2SUy5Lb6xlPXjo+R2LF1s9k1MuS2+sNT2TUy5Lb6xFPQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6xFPZNTLktvrDU9k1MuS2+sRT2uvmJ4U0FQPXjo+R2LF1s9IInIYeH2Yj0gichh4fZiPSCJyGHh9mI9eOj5HYsXWz2TUy5Lb6w1PZNTLktvrEU9k1MuS2+sRT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sNT2TUy5Lb6xFPa6+YnhTQVA9klMuS2+sZT2SUy5Lb6xlPSCJyGHh9mI9IInIYeH2Yj2TUy5Lb6xVPXjo+R2LF1s9eOj5HYsXWz2TUy5Lb6xFPZNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PZNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAArr5ieFNBUD2TUy5Lb6xVPZJTLktvrGU9IInIYeH2Yj146PkdixdbPXjo+R2LF1s9klMuS2+sZT2uvmJ4U0FQPa6+YnhTQVA9k1MuS2+sRT2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6xFPZNTLktvrDU9k1MuS2+sRT2TUy5Lb6xFPa6+YnhTQWA9IInIYeH2Yj2uvmJ4U0FgPXjo+R2LF1s9rr5ieFNBUD2uvmJ4U0FQPZNTLktvrDU9k1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PXjo+R2LF1s9k1MuS2+sRT146PkdixdbPZNTLktvrFU9rr5ieFNBYD2TUy5Lb6xVPZNTLktvrFU9k1MuS2+sRT2TUy5Lb6w1PZNTLktvrDU9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PZNTLktvrEU9k1MuS2+sRT2uvmJ4U0FQPZNTLktvrFU9k1MuS2+sVT2TUy5Lb6xVPZNTLktvrEU9k1MuS2+sRT2TUy5Lb6xFPZNTLktvrFU9k1MuS2+sNT2TUy5Lb6w1PQAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAJNTLktvrDU9k1MuS2+sRT146PkdixdbPZNTLktvrFU9rr5ieFNBUD146PkdixdbPZNTLktvrEU9rr5ieFNBUD2TUy5Lb6w1PQAAAAAAAAAAk1MuS2+sRT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT2TUy5Lb6w1PZNTLktvrDU9k1MuS2+sRT2TUy5Lb6xFPa6+YnhTQVA9rr5ieFNBUD2TUy5Lb6xFPQAAAAAAAAAAk1MuS2+sNT2TUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PZNTLktvrDU9k1MuS2+sNT2TUy5Lb6w1PZNTLktvrEU9rr5ieFNBUD2TUy5Lb6xFPZNTLktvrDU9rr5ieFNBUD0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6xFPZNTLktvrDU9k1MuS2+sRT2uvmJ4U0FQPa6+YnhTQVA9k1MuS2+sVT2TUy5Lb6xFPZNTLktvrEU9AAAAAAAAAACTUy5Lb6xFPQAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAACuvmJ4U0FQPQAAAAAAAAAAk1MuS2+sNT2TUy5Lb6w1PZNTLktvrEU9AAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT2TUy5Lb6w1PZNTLktvrDU9AAAAAAAAAACTUy5Lb6xFPZNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAJNTLktvrDU9k1MuS2+sRT0AAAAAAAAAAAAAAAAAAAAAk1MuS2+sRT2TUy5Lb6w1PZNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAk1MuS2+sNT2TUy5Lb6w1PZNTLktvrDU9k1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAk1MuS2+sNT0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9AAAAAAAAAACTUy5Lb6w1PQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJNTLktvrDU9k1MuS2+sNT0=\"},\"shape\":[40,40],\"dtype\":\"float64\",\"order\":\"little\"}]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1052\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1053\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Image\",\"id\":\"p1042\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":-0.19080456771215437},\"y\":{\"type\":\"value\",\"value\":-1.4601286926409278},\"dw\":{\"type\":\"value\",\"value\":0.36580326288470766},\"dh\":{\"type\":\"value\",\"value\":2.7932781684641093},\"image\":{\"type\":\"field\",\"field\":\"image\"},\"color_mapper\":{\"type\":\"object\",\"name\":\"LinearColorMapper\",\"id\":\"p1043\",\"attributes\":{\"palette\":[\"#440154\",\"#440255\",\"#440357\",\"#450558\",\"#45065A\",\"#45085B\",\"#46095C\",\"#460B5E\",\"#460C5F\",\"#460E61\",\"#470F62\",\"#471163\",\"#471265\",\"#471466\",\"#471567\",\"#471669\",\"#47186A\",\"#48196B\",\"#481A6C\",\"#481C6E\",\"#481D6F\",\"#481E70\",\"#482071\",\"#482172\",\"#482273\",\"#482374\",\"#472575\",\"#472676\",\"#472777\",\"#472878\",\"#472A79\",\"#472B7A\",\"#472C7B\",\"#462D7C\",\"#462F7C\",\"#46307D\",\"#46317E\",\"#45327F\",\"#45347F\",\"#453580\",\"#453681\",\"#443781\",\"#443982\",\"#433A83\",\"#433B83\",\"#433C84\",\"#423D84\",\"#423E85\",\"#424085\",\"#414186\",\"#414286\",\"#404387\",\"#404487\",\"#3F4587\",\"#3F4788\",\"#3E4888\",\"#3E4989\",\"#3D4A89\",\"#3D4B89\",\"#3D4C89\",\"#3C4D8A\",\"#3C4E8A\",\"#3B508A\",\"#3B518A\",\"#3A528B\",\"#3A538B\",\"#39548B\",\"#39558B\",\"#38568B\",\"#38578C\",\"#37588C\",\"#37598C\",\"#365A8C\",\"#365B8C\",\"#355C8C\",\"#355D8C\",\"#345E8D\",\"#345F8D\",\"#33608D\",\"#33618D\",\"#32628D\",\"#32638D\",\"#31648D\",\"#31658D\",\"#31668D\",\"#30678D\",\"#30688D\",\"#2F698D\",\"#2F6A8D\",\"#2E6B8E\",\"#2E6C8E\",\"#2E6D8E\",\"#2D6E8E\",\"#2D6F8E\",\"#2C708E\",\"#2C718E\",\"#2C728E\",\"#2B738E\",\"#2B748E\",\"#2A758E\",\"#2A768E\",\"#2A778E\",\"#29788E\",\"#29798E\",\"#287A8E\",\"#287A8E\",\"#287B8E\",\"#277C8E\",\"#277D8E\",\"#277E8E\",\"#267F8E\",\"#26808E\",\"#26818E\",\"#25828E\",\"#25838D\",\"#24848D\",\"#24858D\",\"#24868D\",\"#23878D\",\"#23888D\",\"#23898D\",\"#22898D\",\"#228A8D\",\"#228B8D\",\"#218C8D\",\"#218D8C\",\"#218E8C\",\"#208F8C\",\"#20908C\",\"#20918C\",\"#1F928C\",\"#1F938B\",\"#1F948B\",\"#1F958B\",\"#1F968B\",\"#1E978A\",\"#1E988A\",\"#1E998A\",\"#1E998A\",\"#1E9A89\",\"#1E9B89\",\"#1E9C89\",\"#1E9D88\",\"#1E9E88\",\"#1E9F88\",\"#1EA087\",\"#1FA187\",\"#1FA286\",\"#1FA386\",\"#20A485\",\"#20A585\",\"#21A685\",\"#21A784\",\"#22A784\",\"#23A883\",\"#23A982\",\"#24AA82\",\"#25AB81\",\"#26AC81\",\"#27AD80\",\"#28AE7F\",\"#29AF7F\",\"#2AB07E\",\"#2BB17D\",\"#2CB17D\",\"#2EB27C\",\"#2FB37B\",\"#30B47A\",\"#32B57A\",\"#33B679\",\"#35B778\",\"#36B877\",\"#38B976\",\"#39B976\",\"#3BBA75\",\"#3DBB74\",\"#3EBC73\",\"#40BD72\",\"#42BE71\",\"#44BE70\",\"#45BF6F\",\"#47C06E\",\"#49C16D\",\"#4BC26C\",\"#4DC26B\",\"#4FC369\",\"#51C468\",\"#53C567\",\"#55C666\",\"#57C665\",\"#59C764\",\"#5BC862\",\"#5EC961\",\"#60C960\",\"#62CA5F\",\"#64CB5D\",\"#67CC5C\",\"#69CC5B\",\"#6BCD59\",\"#6DCE58\",\"#70CE56\",\"#72CF55\",\"#74D054\",\"#77D052\",\"#79D151\",\"#7CD24F\",\"#7ED24E\",\"#81D34C\",\"#83D34B\",\"#86D449\",\"#88D547\",\"#8BD546\",\"#8DD644\",\"#90D643\",\"#92D741\",\"#95D73F\",\"#97D83E\",\"#9AD83C\",\"#9DD93A\",\"#9FD938\",\"#A2DA37\",\"#A5DA35\",\"#A7DB33\",\"#AADB32\",\"#ADDC30\",\"#AFDC2E\",\"#B2DD2C\",\"#B5DD2B\",\"#B7DD29\",\"#BADE27\",\"#BDDE26\",\"#BFDF24\",\"#C2DF22\",\"#C5DF21\",\"#C7E01F\",\"#CAE01E\",\"#CDE01D\",\"#CFE11C\",\"#D2E11B\",\"#D4E11A\",\"#D7E219\",\"#DAE218\",\"#DCE218\",\"#DFE318\",\"#E1E318\",\"#E4E318\",\"#E7E419\",\"#E9E419\",\"#ECE41A\",\"#EEE51B\",\"#F1E51C\",\"#F3E51E\",\"#F6E61F\",\"#F8E621\",\"#FAE622\",\"#FDE724\"]}}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Image\",\"id\":\"p1045\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":-0.19080456771215437},\"y\":{\"type\":\"value\",\"value\":-1.4601286926409278},\"dw\":{\"type\":\"value\",\"value\":0.36580326288470766},\"dh\":{\"type\":\"value\",\"value\":2.7932781684641093},\"global_alpha\":{\"type\":\"value\",\"value\":0.1},\"image\":{\"type\":\"field\",\"field\":\"image\"},\"color_mapper\":{\"type\":\"object\",\"name\":\"LinearColorMapper\",\"id\":\"p1046\",\"attributes\":{\"palette\":[\"#440154\",\"#440255\",\"#440357\",\"#450558\",\"#45065A\",\"#45085B\",\"#46095C\",\"#460B5E\",\"#460C5F\",\"#460E61\",\"#470F62\",\"#471163\",\"#471265\",\"#471466\",\"#471567\",\"#471669\",\"#47186A\",\"#48196B\",\"#481A6C\",\"#481C6E\",\"#481D6F\",\"#481E70\",\"#482071\",\"#482172\",\"#482273\",\"#482374\",\"#472575\",\"#472676\",\"#472777\",\"#472878\",\"#472A79\",\"#472B7A\",\"#472C7B\",\"#462D7C\",\"#462F7C\",\"#46307D\",\"#46317E\",\"#45327F\",\"#45347F\",\"#453580\",\"#453681\",\"#443781\",\"#443982\",\"#433A83\",\"#433B83\",\"#433C84\",\"#423D84\",\"#423E85\",\"#424085\",\"#414186\",\"#414286\",\"#404387\",\"#404487\",\"#3F4587\",\"#3F4788\",\"#3E4888\",\"#3E4989\",\"#3D4A89\",\"#3D4B89\",\"#3D4C89\",\"#3C4D8A\",\"#3C4E8A\",\"#3B508A\",\"#3B518A\",\"#3A528B\",\"#3A538B\",\"#39548B\",\"#39558B\",\"#38568B\",\"#38578C\",\"#37588C\",\"#37598C\",\"#365A8C\",\"#365B8C\",\"#355C8C\",\"#355D8C\",\"#345E8D\",\"#345F8D\",\"#33608D\",\"#33618D\",\"#32628D\",\"#32638D\",\"#31648D\",\"#31658D\",\"#31668D\",\"#30678D\",\"#30688D\",\"#2F698D\",\"#2F6A8D\",\"#2E6B8E\",\"#2E6C8E\",\"#2E6D8E\",\"#2D6E8E\",\"#2D6F8E\",\"#2C708E\",\"#2C718E\",\"#2C728E\",\"#2B738E\",\"#2B748E\",\"#2A758E\",\"#2A768E\",\"#2A778E\",\"#29788E\",\"#29798E\",\"#287A8E\",\"#287A8E\",\"#287B8E\",\"#277C8E\",\"#277D8E\",\"#277E8E\",\"#267F8E\",\"#26808E\",\"#26818E\",\"#25828E\",\"#25838D\",\"#24848D\",\"#24858D\",\"#24868D\",\"#23878D\",\"#23888D\",\"#23898D\",\"#22898D\",\"#228A8D\",\"#228B8D\",\"#218C8D\",\"#218D8C\",\"#218E8C\",\"#208F8C\",\"#20908C\",\"#20918C\",\"#1F928C\",\"#1F938B\",\"#1F948B\",\"#1F958B\",\"#1F968B\",\"#1E978A\",\"#1E988A\",\"#1E998A\",\"#1E998A\",\"#1E9A89\",\"#1E9B89\",\"#1E9C89\",\"#1E9D88\",\"#1E9E88\",\"#1E9F88\",\"#1EA087\",\"#1FA187\",\"#1FA286\",\"#1FA386\",\"#20A485\",\"#20A585\",\"#21A685\",\"#21A784\",\"#22A784\",\"#23A883\",\"#23A982\",\"#24AA82\",\"#25AB81\",\"#26AC81\",\"#27AD80\",\"#28AE7F\",\"#29AF7F\",\"#2AB07E\",\"#2BB17D\",\"#2CB17D\",\"#2EB27C\",\"#2FB37B\",\"#30B47A\",\"#32B57A\",\"#33B679\",\"#35B778\",\"#36B877\",\"#38B976\",\"#39B976\",\"#3BBA75\",\"#3DBB74\",\"#3EBC73\",\"#40BD72\",\"#42BE71\",\"#44BE70\",\"#45BF6F\",\"#47C06E\",\"#49C16D\",\"#4BC26C\",\"#4DC26B\",\"#4FC369\",\"#51C468\",\"#53C567\",\"#55C666\",\"#57C665\",\"#59C764\",\"#5BC862\",\"#5EC961\",\"#60C960\",\"#62CA5F\",\"#64CB5D\",\"#67CC5C\",\"#69CC5B\",\"#6BCD59\",\"#6DCE58\",\"#70CE56\",\"#72CF55\",\"#74D054\",\"#77D052\",\"#79D151\",\"#7CD24F\",\"#7ED24E\",\"#81D34C\",\"#83D34B\",\"#86D449\",\"#88D547\",\"#8BD546\",\"#8DD644\",\"#90D643\",\"#92D741\",\"#95D73F\",\"#97D83E\",\"#9AD83C\",\"#9DD93A\",\"#9FD938\",\"#A2DA37\",\"#A5DA35\",\"#A7DB33\",\"#AADB32\",\"#ADDC30\",\"#AFDC2E\",\"#B2DD2C\",\"#B5DD2B\",\"#B7DD29\",\"#BADE27\",\"#BDDE26\",\"#BFDF24\",\"#C2DF22\",\"#C5DF21\",\"#C7E01F\",\"#CAE01E\",\"#CDE01D\",\"#CFE11C\",\"#D2E11B\",\"#D4E11A\",\"#D7E219\",\"#DAE218\",\"#DCE218\",\"#DFE318\",\"#E1E318\",\"#E4E318\",\"#E7E419\",\"#E9E419\",\"#ECE41A\",\"#EEE51B\",\"#F1E51C\",\"#F3E51E\",\"#F6E61F\",\"#F8E621\",\"#FAE622\",\"#FDE724\"]}}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Image\",\"id\":\"p1048\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":-0.19080456771215437},\"y\":{\"type\":\"value\",\"value\":-1.4601286926409278},\"dw\":{\"type\":\"value\",\"value\":0.36580326288470766},\"dh\":{\"type\":\"value\",\"value\":2.7932781684641093},\"global_alpha\":{\"type\":\"value\",\"value\":0.2},\"image\":{\"type\":\"field\",\"field\":\"image\"},\"color_mapper\":{\"type\":\"object\",\"name\":\"LinearColorMapper\",\"id\":\"p1049\",\"attributes\":{\"palette\":[\"#440154\",\"#440255\",\"#440357\",\"#450558\",\"#45065A\",\"#45085B\",\"#46095C\",\"#460B5E\",\"#460C5F\",\"#460E61\",\"#470F62\",\"#471163\",\"#471265\",\"#471466\",\"#471567\",\"#471669\",\"#47186A\",\"#48196B\",\"#481A6C\",\"#481C6E\",\"#481D6F\",\"#481E70\",\"#482071\",\"#482172\",\"#482273\",\"#482374\",\"#472575\",\"#472676\",\"#472777\",\"#472878\",\"#472A79\",\"#472B7A\",\"#472C7B\",\"#462D7C\",\"#462F7C\",\"#46307D\",\"#46317E\",\"#45327F\",\"#45347F\",\"#453580\",\"#453681\",\"#443781\",\"#443982\",\"#433A83\",\"#433B83\",\"#433C84\",\"#423D84\",\"#423E85\",\"#424085\",\"#414186\",\"#414286\",\"#404387\",\"#404487\",\"#3F4587\",\"#3F4788\",\"#3E4888\",\"#3E4989\",\"#3D4A89\",\"#3D4B89\",\"#3D4C89\",\"#3C4D8A\",\"#3C4E8A\",\"#3B508A\",\"#3B518A\",\"#3A528B\",\"#3A538B\",\"#39548B\",\"#39558B\",\"#38568B\",\"#38578C\",\"#37588C\",\"#37598C\",\"#365A8C\",\"#365B8C\",\"#355C8C\",\"#355D8C\",\"#345E8D\",\"#345F8D\",\"#33608D\",\"#33618D\",\"#32628D\",\"#32638D\",\"#31648D\",\"#31658D\",\"#31668D\",\"#30678D\",\"#30688D\",\"#2F698D\",\"#2F6A8D\",\"#2E6B8E\",\"#2E6C8E\",\"#2E6D8E\",\"#2D6E8E\",\"#2D6F8E\",\"#2C708E\",\"#2C718E\",\"#2C728E\",\"#2B738E\",\"#2B748E\",\"#2A758E\",\"#2A768E\",\"#2A778E\",\"#29788E\",\"#29798E\",\"#287A8E\",\"#287A8E\",\"#287B8E\",\"#277C8E\",\"#277D8E\",\"#277E8E\",\"#267F8E\",\"#26808E\",\"#26818E\",\"#25828E\",\"#25838D\",\"#24848D\",\"#24858D\",\"#24868D\",\"#23878D\",\"#23888D\",\"#23898D\",\"#22898D\",\"#228A8D\",\"#228B8D\",\"#218C8D\",\"#218D8C\",\"#218E8C\",\"#208F8C\",\"#20908C\",\"#20918C\",\"#1F928C\",\"#1F938B\",\"#1F948B\",\"#1F958B\",\"#1F968B\",\"#1E978A\",\"#1E988A\",\"#1E998A\",\"#1E998A\",\"#1E9A89\",\"#1E9B89\",\"#1E9C89\",\"#1E9D88\",\"#1E9E88\",\"#1E9F88\",\"#1EA087\",\"#1FA187\",\"#1FA286\",\"#1FA386\",\"#20A485\",\"#20A585\",\"#21A685\",\"#21A784\",\"#22A784\",\"#23A883\",\"#23A982\",\"#24AA82\",\"#25AB81\",\"#26AC81\",\"#27AD80\",\"#28AE7F\",\"#29AF7F\",\"#2AB07E\",\"#2BB17D\",\"#2CB17D\",\"#2EB27C\",\"#2FB37B\",\"#30B47A\",\"#32B57A\",\"#33B679\",\"#35B778\",\"#36B877\",\"#38B976\",\"#39B976\",\"#3BBA75\",\"#3DBB74\",\"#3EBC73\",\"#40BD72\",\"#42BE71\",\"#44BE70\",\"#45BF6F\",\"#47C06E\",\"#49C16D\",\"#4BC26C\",\"#4DC26B\",\"#4FC369\",\"#51C468\",\"#53C567\",\"#55C666\",\"#57C665\",\"#59C764\",\"#5BC862\",\"#5EC961\",\"#60C960\",\"#62CA5F\",\"#64CB5D\",\"#67CC5C\",\"#69CC5B\",\"#6BCD59\",\"#6DCE58\",\"#70CE56\",\"#72CF55\",\"#74D054\",\"#77D052\",\"#79D151\",\"#7CD24F\",\"#7ED24E\",\"#81D34C\",\"#83D34B\",\"#86D449\",\"#88D547\",\"#8BD546\",\"#8DD644\",\"#90D643\",\"#92D741\",\"#95D73F\",\"#97D83E\",\"#9AD83C\",\"#9DD93A\",\"#9FD938\",\"#A2DA37\",\"#A5DA35\",\"#A7DB33\",\"#AADB32\",\"#ADDC30\",\"#AFDC2E\",\"#B2DD2C\",\"#B5DD2B\",\"#B7DD29\",\"#BADE27\",\"#BDDE26\",\"#BFDF24\",\"#C2DF22\",\"#C5DF21\",\"#C7E01F\",\"#CAE01E\",\"#CDE01D\",\"#CFE11C\",\"#D2E11B\",\"#D4E11A\",\"#D7E219\",\"#DAE218\",\"#DCE218\",\"#DFE318\",\"#E1E318\",\"#E4E318\",\"#E7E419\",\"#E9E419\",\"#ECE41A\",\"#EEE51B\",\"#F1E51C\",\"#F3E51E\",\"#F6E61F\",\"#F8E621\",\"#FAE622\",\"#FDE724\"]}}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1013\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1028\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1029\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1030\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1031\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p1036\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1037\"},{\"type\":\"object\",\"name\":\"HelpTool\",\"id\":\"p1038\"}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1023\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1024\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1025\"},\"axis_label\":\"px (mrad)\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1026\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1018\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1019\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1020\"},\"axis_label\":\"x (mm)\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1021\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1022\",\"attributes\":{\"axis\":{\"id\":\"p1018\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1027\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1023\"}}}]}}]}};\n", + " const render_items = [{\"docid\":\"6e13a9b3-03ef-409b-8578-af83576a8b06\",\"roots\":{\"p1004\":\"aedaba27-5f09-4c39-a73d-bc46b3882e1e\"},\"root_ids\":[\"p1004\"]}];\n", " void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", @@ -835,12 +842,25 @@ } ], "source": [ - "ds = ColumnDataSource(data=dict(image=[H.transpose()])) \n", - "p = figure(x_range=[xmin, xmax], y_range=[ymin, ymax], title='Bunch at end', \n", - " x_axis_label='x (mm)', y_axis_label='px (mrad)',\n", - " width=500, height=500)\n", - "p.image(image='image', source=ds, \n", - " x=xmin, y=ymin, dw=xmax-xmin, dh=ymax-ymin, palette=pal)\n", + "ds = ColumnDataSource(data=dict(image=[H.transpose()]))\n", + "p = figure(\n", + " x_range=[xmin, xmax],\n", + " y_range=[ymin, ymax],\n", + " title=\"Bunch at end\",\n", + " x_axis_label=\"x (mm)\",\n", + " y_axis_label=\"px (mrad)\",\n", + " width=500,\n", + " height=500,\n", + ")\n", + "p.image(\n", + " image=\"image\",\n", + " source=ds,\n", + " x=xmin,\n", + " y=ymin,\n", + " dw=xmax - xmin,\n", + " dh=ymax - ymin,\n", + " palette=pal,\n", + ")\n", "show(p)" ] }, @@ -855,7 +875,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -864,28 +884,28 @@ "dict_keys(['x', 'px', 'y', 'py', 't', 'pz', 'status', 'weight', 'z', 'species'])" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data = tao.bunch_data('end')\n", + "data = tao.bunch_data(\"end\")\n", "data.keys()" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -899,7 +919,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -919,12 +939,12 @@ } ], "source": [ - "P.plot('x', 'px')" + "P.plot(\"x\", \"px\")" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -946,13 +966,13 @@ " 'norm_emit_y': 1.0008133734974086e-06}" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "P.twiss('xy')" + "P.twiss(\"xy\")" ] }, { @@ -964,7 +984,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -984,13 +1004,13 @@ " -1.00778975e-07])" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tao.bunch_comb('x')" + "tao.bunch_comb(\"x\")" ] }, { @@ -1002,19 +1022,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": "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", @@ -1032,20 +1042,20 @@ } ], "source": [ - "s = tao.bunch_comb('s')\n", - "mean_x = tao.bunch_comb('x')\n", - "max_x = mean_x + tao.bunch_comb('rel_max.1')\n", - "min_x = mean_x + tao.bunch_comb('rel_min.1')\n", - "sigma_x = np.sqrt(tao.bunch_comb('sigma.11'))\n", + "s = tao.bunch_comb(\"s\")\n", + "mean_x = tao.bunch_comb(\"x\")\n", + "max_x = mean_x + tao.bunch_comb(\"rel_max.1\")\n", + "min_x = mean_x + tao.bunch_comb(\"rel_min.1\")\n", + "sigma_x = np.sqrt(tao.bunch_comb(\"sigma.11\"))\n", "fig, ax = plt.subplots()\n", "\n", "ax.fill_between(s, min_x, max_x, alpha=0.2)\n", - "ax.plot(s, sigma_x, label=r'$+\\sigma_x$')\n", - "ax.plot(s, mean_x, label=r'$$', marker='.')\n", - "ax.plot(s, -sigma_x, label=r'$-\\sigma_x$')\n", - "ax.set_xlabel('s (m)')\n", - "ax.set_ylabel('beam sizes (m)')\n", - "plt.legend()\n" + "ax.plot(s, sigma_x, label=r\"$+\\sigma_x$\")\n", + "ax.plot(s, mean_x, label=r\"$$\", marker=\".\")\n", + "ax.plot(s, -sigma_x, label=r\"$-\\sigma_x$\")\n", + "ax.set_xlabel(\"s (m)\")\n", + "ax.set_ylabel(\"beam sizes (m)\")\n", + "plt.legend();" ] }, { @@ -1057,19 +1067,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": "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", @@ -1087,11 +1087,11 @@ } ], "source": [ - "plt.plot(tao.bunch_comb('s'), 1000*tao.bunch_comb('x.beta'), label='beam beta_x')\n", - "plt.plot(tao.bunch_comb('s'), 1000*tao.bunch_comb('y.beta'), label='beam beta_y')\n", - "plt.xlabel('s (m)')\n", - "plt.ylabel('beam Twiss beta (m)')\n", - "plt.legend()" + "plt.plot(tao.bunch_comb(\"s\"), 1000 * tao.bunch_comb(\"x.beta\"), label=\"beam beta_x\")\n", + "plt.plot(tao.bunch_comb(\"s\"), 1000 * tao.bunch_comb(\"y.beta\"), label=\"beam beta_y\")\n", + "plt.xlabel(\"s (m)\")\n", + "plt.ylabel(\"beam Twiss beta (m)\")\n", + "plt.legend();" ] } ], @@ -1111,7 +1111,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.12.0" }, "vscode": { "interpreter": { diff --git a/docs/examples/fodo.ipynb b/docs/examples/fodo.ipynb index 6a80c102..77388003 100644 --- a/docs/examples/fodo.ipynb +++ b/docs/examples/fodo.ipynb @@ -25,6 +25,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", + "\n", "%config InlineBackend.figure_format = 'retina'" ] }, @@ -35,7 +36,9 @@ "metadata": {}, "outputs": [], "source": [ - "tao = Tao('-init $ACC_ROOT_DIR/bmad-doc/tao_examples/fodo/tao.init -lat $ACC_ROOT_DIR/bmad-doc/tao_examples/fodo/fodo.bmad -noplot')" + "tao = Tao(\n", + " \"-init $ACC_ROOT_DIR/bmad-doc/tao_examples/fodo/tao.init -lat $ACC_ROOT_DIR/bmad-doc/tao_examples/fodo/fodo.bmad -noplot\"\n", + ")" ] }, { @@ -46,13 +49,13 @@ "outputs": [], "source": [ "def add_info(d):\n", - " twiss1 = tao.ele_twiss('q1')\n", - " twiss2 = tao.ele_twiss('q2')\n", + " twiss1 = tao.ele_twiss(\"q1\")\n", + " twiss2 = tao.ele_twiss(\"q2\")\n", "\n", - " d['mean_beta_a'] = (twiss1['beta_a'] + twiss2['beta_a'])/2\n", - " d['mean_beta_b'] = (twiss1['beta_b'] + twiss2['beta_b'])/2\n", - " d['phi_a'] = twiss2['phi_a']\n", - " d['phi_b'] = twiss2['phi_b']\n", + " d[\"mean_beta_a\"] = (twiss1[\"beta_a\"] + twiss2[\"beta_a\"]) / 2\n", + " d[\"mean_beta_b\"] = (twiss1[\"beta_b\"] + twiss2[\"beta_b\"]) / 2\n", + " d[\"phi_a\"] = twiss2[\"phi_a\"]\n", + " d[\"phi_b\"] = twiss2[\"phi_b\"]\n", " return d" ] }, @@ -68,7 +71,7 @@ "text": [ "-------------------------\n", "Tao> sho lat\n", - "# Values shown are for the Exit End of each Element:\n", + "# Values shown are for the Downstream End of each Element:\n", "# Index name key s l beta phi_a eta orbit beta phi_b eta orbit Track\n", "# a [2pi] x x [mm] b [2pi] y y [mm] State\n", " 0 BEGINNING Beginning_Ele 0.000 --- 0.67 0.000 0.00 0.000 3.22 0.000 0.00 0.000 Alive\n", @@ -81,7 +84,7 @@ " 6 O_L Overlay 1.900 --- 0.67 0.215 0.00 --- 3.22 0.235 0.00 --- Not_Set\n", "# Index name key s l beta phi_a eta orbit beta phi_b eta orbit Track\n", "# a [2pi] x x [mm] b [2pi] y y [mm] State\n", - "# Values shown are for the Exit End of each Element:\n", + "# Values shown are for the Downstream End of each Element:\n", "-------------------------\n", "Tao> \n" ] @@ -123,22 +126,21 @@ ], "source": [ "def set_kx(k1):\n", - " cmds = [f'set ele q1 k1 = {k1}',\n", - " f'set ele q2 k1 = {-k1}']\n", - " \n", + " cmds = [f\"set ele q1 k1 = {k1}\", f\"set ele q2 k1 = {-k1}\"]\n", + "\n", " d = {}\n", " try:\n", " tao.cmds(cmds)\n", - " tao.cmd('set global lattice_calc_on = T')\n", - " d['good'] = True\n", + " tao.cmd(\"set global lattice_calc_on = T\")\n", + " d[\"good\"] = True\n", " add_info(d)\n", " except:\n", - " d['good'] = False\n", - " \n", - " \n", - " \n", + " d[\"good\"] = False\n", + "\n", " return d\n", - "x = set_kx(1.4142136E+01)\n", + "\n", + "\n", + "x = set_kx(1.4142136e01)\n", "KEYS = x.keys()\n", "x" ] @@ -156,11 +158,11 @@ "\n", "RESULTS = []\n", "\n", - "#tao.cmd('set global plot_on = F')\n", + "# tao.cmd('set global plot_on = F')\n", "for k in qvec1:\n", " res = set_kx(k)\n", - " RESULTS.append(res) \n", - "#tao.cmd('set global plot_on = T')" + " RESULTS.append(res)\n", + "# tao.cmd('set global plot_on = T')" ] }, { @@ -192,7 +194,7 @@ " x.append(res[key])\n", " else:\n", " x.append(np.nan)\n", - " DAT[key] = np.array(x) " + " DAT[key] = np.array(x)" ] }, { @@ -224,7 +226,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABG4AAANmCAYAAACrOc2JAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAB7CAAAewgFu0HU+AABvk0lEQVR4nO3deZicZZk+7KuSzp7QHSYE0UQQQyCjOCIQicBgUGCYiLIMuIAsH4IrIw6IgMqADiMgCIyOKAOC4AKKgEpQEYQIAhKQYRlBjWwJBCGShaQ7S6fr+4Nf2nRVlu5Od9d2nsfBcbxd9Va9T8XyJX3x3PddKBaLxQAAAABQdQZVegEAAAAArJvgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKNVV6AfS/5cuX55FHHkmSbLHFFmlq8j87AAAA9LX29va8+OKLSZIdd9wxw4cP3+T39Bt8A3jkkUcyderUSi8DAAAAGsZ9992XXXfddZPfR6kUAAAAQJWy46YBbLHFFp3H9913X7baaqsKrgYAAADq0/z58zsrXtb+XXxTCG4awNo9bbbaaqtMmDChgqsBAACA+tdX/WWVSgEAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJVq2ODmhRdeyE033ZQzzjgj+++/f8aNG5dCoZBCoZCjjz66X655zTXXZL/99stWW22V4cOHZ5tttskHP/jB3Hvvvf1yPQAAAKC2NVV6AZWy5ZZbDti1li9fnkMPPTQ33XRTl8effvrpPP300/ne976XM888M5///OcHbE0AAABA9WvYHTdrmzhxYvbdd99+e/9jjz22M7SZPn16brzxxtx33325/PLL8/rXvz4dHR0544wzctlll/XbGgAAAIDa07A7bs4444zsuuuu2XXXXbPlllvmqaeeyute97o+v86sWbPyve99L0lywAEH5IYbbsjgwYOTJLvuumve/e53Z+edd84zzzyTU045Jf/yL/+SlpaWPl8HAAAAUHsadsfNWWedlXe96139XjJ13nnnJUkGDx6cr3/9652hzRrjxo3LueeemyRZuHBhLr/88n5dDwAAAFA7Gja4GQhLly7NbbfdliTZZ599MmHChHWed/DBB2ezzTZLklx//fUDtj4AAACgujVsqdRAuO+++7JixYokyV577bXe84YOHZrddtstt9xyS+67776sWrUqQ4YMGahlVp1lK9rz4ssrKr0MAACoKoMKhbxm7IgMHlSo9FKAASS46UePPfZY5/EOO+ywwXN32GGH3HLLLWlvb8+f/vSn/P3f/323rzNv3rwNPj9//vxuv1c1uGvOgnz46gcqvQwAAKg640YPzeVH7Zp/mNhS6aUAA0Rw04/mzp3beby+Mqk1Jk6c2OV1PQlu1n4tAABQvxYsXZkLb/1jrjxmaqWXAgwQPW760csvv9x5PHr06A2eO2rUqM7jpUuX9tuaAACA2vbkgmWVXgIwgOy46UfLly/vPB46dOgGzx02bFjncVtbW4+us/bOnnWZP39+pk6VyAMAQD1Y1Lqq0ksABpDgph8NHz6883jlypUbPHdNE+MkGTFiRI+us7EyrFrzzilb5g//8U+VXgYAAFSFPz6/NAd87a7On5csX5XVHUVNiqFBCG760ZgxYzqPN1b+tGzZ37Y7bqysqt4NHlTI4EGDK70MAACoCluMGdbl52IxWdK2KmNHbXhXP1Af9LjpR2vvhNnY5Ke1y500GwYAANZoGTmk7LFFbcqloFEIbvrR2pOhHn/88Q2eu+b5pqamTJo0qV/XBQAA1I7hQwZnWFPXX90WtW64FQNQPwQ3/WjXXXftbEo8a9as9Z63cuXK3HvvvWWvAQAASMp33dhxA41DcNOPxowZk3e84x1JkltvvXW95VLXX399lixZkiQ56KCDBmx9AABAbWgZ0fU/7i42WQoahuBmE1x55ZUpFAopFAo588wz13nOySefnCRpb2/Pxz/+8axevbrL8wsWLMhnPvOZJElLS0s+9KEP9euaAQCA2tNcuuNGqRQ0jIadKnXXXXdlzpw5nT8vWLCg83jOnDm58soru5x/9NFH9+o6e++9d973vvflmmuuyU9+8pPss88+OfHEE/PqV786jzzySM4+++w888wzSZJzzjknY8eO7dV1AACA+jVWqRQ0rIYNbi677LJ8+9vfXudzv/nNb/Kb3/ymy2O9DW6S5Fvf+laWLFmSm2++Obfffntuv/32Ls8PGjQon//85/PhD3+419cAAADqV2mp1CKlUtAwlEoNgBEjRmTmzJn57ne/m3322Sfjx4/P0KFDM3HixHzgAx/IXXfdtd5SKwAAgNLmxIvtuIGGUSgWi8VKL4L+NW/evEycODFJMnfu3EyYMKHCKwIAAHri63fMyXk//0Pnz9O33yJXHDO1gisC1qU/fv+24wYAAKDKlZVK2XEDDUNwAwAAUOXKSqX0uIGGIbgBAACoci0jTJWCRiW4AQAAqHLNpePAW1emo0O7UmgEghsAAIAq1zKya4+bjmLy8or2Cq0GGEiCGwAAgCpXWiqV6HMDjUJwAwAAUOVGDh2cIYMLXR5b1LayQqsBBpLgBgAAoMoVCoU0l44Et+MGGoLgBgAAoAaMLW1QbLIUNATBDQAAQA1oKQluFrcqlYJGILgBAACoAUqloDEJbgAAAGpA6Y4bpVLQGAQ3AAAANaB0JLgdN9AYBDcAAAA1oKzHjXHg0BAENwAAADWgeaQeN9CIBDcAAAA1oLRUaqGpUtAQBDcAAAA1oLxUyo4baASCGwAAgBrQso5x4MVisUKrAQaK4AYAAKAGlO64ae8oZtnK1RVaDTBQBDcAAAA1oLkkuEmSRfrcQN0T3AAAANSAMcOaMnhQoctjJktB/RPcAAAA1IBCoVA2WUqDYqh/ghsAAIAaUVouZccN1D/BDQAAQI0o3XGzqE2PG6h3ghsAAIAa0TKyfCQ4UN8ENwAAADVCjxtoPIIbAACAGlHe40apFNQ7wQ0AAECNaBnRtVRqoVIpqHuCGwAAgBrRUrLjZrHgBuqe4AYAAKBGlAY3pkpB/RPcAAAA1Ijm0nHgdtxA3RPcAAAA1IiyceBtq1IsFiu0GmAgCG4AAABqxNiSUqmV7R1ZvqqjQqsBBoLgBgAAoEaUTpVK9LmBeie4AQAAqBFjhjelUOj6mD43UN8ENwAAADVi0KCCBsXQYAQ3AAAANaSlJLhZrFQK6prgBgAAoIY0l06WsuMG6prgBgAAoIaU7rhZ1Ca4gXomuAEAAKghLSUjwRe2KpWCeia4AQAAqCFlPW6USkFdE9wAAADUED1uoLEIbgAAAGpIeY8bpVJQzwQ3AAAANaS0x40dN1DfBDcAAAA1ZGxJqdRiU6WgrgluAAAAakizHTfQUAQ3AAAANaS0x03bqtVZvmp1hVYD9DfBDQAAQA1pKSmVSpIlyqWgbgluAAAAashmw5vKHlskuIG6JbgBAACoIU2DB2VMSXijzw3UL8ENAABAjSkfCb6yQisB+pvgBgAAoMa0jOja50apFNQvwQ0AAECNseMGGofgBgAAoMY0jygNbuy4gXoluAEAAKgxZTtulEpB3RLcAAAA1JjSHjeL7biBuiW4AQAAqDHlO270uIF6JbgBAACoMS0jS6ZK2XEDdUtwAwAAUGNaNCeGhiG4AQAAqDGlpVKLNSeGuiW4AQAAqDGlwc3SFe1ZtbqjQqsB+pPgBgAAoMY0l0yVSuy6gXoluAEAAKgxzSU9bhJ9bqBeCW4AAABqzNCmQRk1dHCXxxYbCQ51SXADAABQg0pHgi9cZscN1CPBDQAAQA0qLZdapMcN1CXBDQAAQA0qnSy1qFWpFNQjwQ0AAEANKg1uTJWC+iS4AQAAqEGlI8FNlYL6JLgBAACoQWNLS6XsuIG6JLgBAACoQXrcQGMQ3AAAANSglpJSKT1uoD4JbgAAAGpQc9mOG8EN1CPBDQAAQA1qGaFUChqB4AYAAKAGtYzsWiq1ZHl7VncUK7QaoL8IbgAAAGpQaXPiJFmizw3UHcENAABADWoeUR7cLFQuBXVHcAMAAFCDhg8ZnOFDuv5Kt8iOG6g7ghsAAIAaVTYS3GQpqDuCGwAAgBpV2udmUZtSKag3ghsAAIAaVdrnZpEdN1B3BDcAAAA1amzJSHDBDdQfwQ0AAECNKi2VWqw5MdQdwQ0AAECNai7tcWMcONQdwQ0AAECNKp0qZRw41B/BDQAAQI0qmyqlxw3UHcENAABAjWoZoccN1DvBDQAAQI3S4wbqn+AGAACgRpX2uFnctiodHcUKrQboD4IbAACAGlXa46ajmLy8vL1CqwH6g+AGAACgRpUGN0myqE25FNQTwQ0AAECNGjFkcIYO7vprnclSUF8ENwAAADWqUCiUNyg2WQrqiuAGAACgho01WQrqmuAGAACghq1rshRQPwQ3AAAANaysVEqPG6grghsAAIAa1jJCcAP1THADAABQw0pHghsHDvVFcAMAAFDDWkaW9Lix4wbqiuAGAACghjWXlkppTgx1RXADAABQw0pLpRYaBw51RXADAABQw8rGgSuVgroiuAEAAKhh5c2JV6VYLFZoNUBfE9wAAADUsNIeN6s7ilm6or1CqwH6muAGAACghpXuuEmSRcqloG4IbgAAAGrY6GFNaRpU6PLYYpOloG4IbgAAAGpYoVAo73Njxw3UDcENAABAjSvtc7OozUhwqBeCGwAAgBrXMrLrSHA7bqB+CG4AAABqXEvJjhs9bqB+CG4AAABqXHNZjxulUlAvBDcAAAA1rmWEUimoV4KbJM8880xOPvnkTJkyJaNGjcrmm2+eqVOn5vzzz09ra2ufXOP3v/99TjjhhOy4447ZbLPNMnTo0GyxxRaZPn16Lrzwwrz88st9ch0AAKDxlE2VUioFdaOp0guotJkzZ+bwww/P4sWLOx9rbW3N7NmzM3v27Fx22WW5+eabs+222/b6GhdccEFOPfXUtLe3d3l8wYIFueOOO3LHHXfk4osvzk9+8pO86U1v6vV1AACAxlQ+DlypFNSLht5x89BDD+Wwww7L4sWLM3r06Jx99tm5++67c9ttt+W4445LkvzhD3/IjBkzsnTp0l5d4wc/+EFOPvnktLe3Z+jQofnUpz6VmTNn5re//W2+973vZY899kiSPP300/mnf/qnLgESAABAd5SNA1cqBXWjoXfcnHjiiWltbU1TU1NuueWWTJs2rfO5vffeO9ttt11OOeWUPP744/nKV76SM844o8fX+OIXv9h5fP3112fGjBmdP0+dOjXvf//7c8ghh+T666/P/Pnzc/nll+ff/u3fNu2DAQAADaVsHLhSKagbDbvjZvbs2bnjjjuSJMcee2yX0GaNk046KVOmTEmSXHTRRVm1qmc3vyVLluTRRx9NkrzlLW/pEtqs7d///d87j+++++4eXQMAAKBsHHjrqhSLxQqtBuhLDRvc3HjjjZ3HxxxzzDrPGTRoUI488sgkycKFCzuDnu5aufJvdaUb6pHz+te/vvN4xYoVPboGAABAaY+blas70rZqdYVWA/Slhg1u7rzzziTJqFGjsvPOO6/3vL322qvz+K677urRNcaNG5fNN988SfLEE0+s97w///nPnceTJ0/u0TUAAABKS6USfW6gXjRscPPYY48lSSZNmpSmpvW3+tlhhx3KXtMTxx9/fJLkd7/7XX72s5+t85w1fXAGDx6cD33oQz2+xrx58zb4z/z583v8ngAAQO0YM6wpgwpdHxPcQH1oyObEy5cvz4IFC5IkEyZM2OC5Y8eOzahRo7Js2bLMnTu3x9f67Gc/m/vvvz+33nprDjrooHziE5/IO97xjowbNy5PPPFELrnkksyaNSuDBw/Of/3Xf3X21OmJiRMn9vg1AABA/Rg0qJDmEUOycK2wZlGbkeBQDxoyuHn55Zc7j0ePHr3R89cEN70ZCT569Oj87Gc/y5VXXplzzjknF1xwQS644IIu5xx88ME55ZRT8ta3vrXH7w8AAJC8Ui61dnCz2I4bqAsNGdwsX76883jo0PJa0FLDhg1LkrS1tfXqevfff3++//3vr7fPza233pott9wyU6ZMyWabbdbj99/YTqD58+dn6tSpPX5fAACgdjSXTJYyEhzqQ0MGN8OHD+88Xnvy0/qsmfQ0YsSIHl/ruuuuyxFHHJEVK1bkTW96U84666z84z/+Y8aMGZO5c+fm2muvzRe/+MVccskl+fWvf51bb701r3rVq3p0jY2VewEAAPWvdLKUHjdQHxqyOfGYMWM6j7tT/rRs2bIk3SurWttf/vKXHH300VmxYkXe8IY35O67786BBx6YzTffPEOGDMm2226b0047LT/96U9TKBTyf//3fznhhBN69mEAAACStJTtuNHjBupBQwY3w4cPz7hx45K8MpFpQxYuXNgZ3PS0CfA111zT+drTTz89o0aNWud573jHO/KOd7wjSXL99ddn4cKFPboOAABA6UjwRcvsuIF60JDBTZLO6U1z5sxJe3v7es97/PHHy17TXWuPD3/LW96ywXN33nnnJElHR0f++Mc/9ug6AAAA5T1u7LiBetCwwc0ee+yR5JUyqAceeGC9582aNavzePfdd+/RNZqa/tZCaEPhUJKsWvW3NHzt1wEAAHSHHjdQnxo2uDnwwAM7j6+44op1ntPR0ZGrrroqSdLS0pLp06f36Bqve93rOo/vvPPODZ7761//OklSKBSyzTbb9Og6AAAApcHNYlOloC40bHAzderU7LnnnkmSyy+/PPfcc0/ZORdccEFnudMnP/nJDBnS9UZ45ZVXplAopFAo5Mwzzyx7/YwZM1IoFJIkZ599dp599tl1ruXSSy/N/fffnyTZbbfd8nd/93e9/lwAAEBjahlR0uPGjhuoCw1dk3PxxRdn9913T1tbW/bdd9+cfvrpmT59etra2nLNNdfk0ksvTZJMnjw5J510Uo/ff4cddsgxxxyTb33rW3n22Wez00475cQTT8yee+7ZOQ78mmuuyfe+970kyeDBg/Of//mfffoZAQCAxlBWKqXHDdSFhg5udtppp1x77bU54ogjsmTJkpx++ull50yePDkzZ87sMkK8J77+9a9n2bJlufbaa/Piiy/ms5/97DrPGzVqVC699NK8/e1v79V1AACAxlY6VWr5qo4sX7U6w4cMrtCKgL7QsKVSaxxwwAF5+OGH86lPfSqTJ0/OyJEj09LSkl122SXnnntuHnzwwUyaNKnX7z9s2LBcc801+dWvfpUjjzwykydPzqhRo9LU1JTNN98806ZNy+c///k8/vjj+cAHPtCHnwwAAGgkLSVTpRJ9bqAeFIrFYrHSi6B/zZs3LxMnTkySzJ07NxMmTKjwigAAgL62uqOY159+c5fHfnHiP2b7V/WuegDouf74/bvhd9wAAADUg8GDCtlseNduGIta9bmBWie4AQAAqBOlfW4WKZWCmie4AQAAqBOlk6UWGwkONU9wAwAAUCeaSxoUL1QqBTVPcAMAAFAnlEpB/RHcAAAA1InSkeCLlEpBzRPcAAAA1ImyHjdtSqWg1gluAAAA6kRpjxs7bqD2CW4AAADqxNjSHjeCG6h5ghsAAIA6UV4qJbiBWie4AQAAqBOlwc0i48Ch5gluAAAA6kTziK6lUstWrs7K9o4KrQboC4IbAACAOlG64yZRLgW1TnADAABQJ0qnSiVGgkOtE9wAAADUiSGDB2X0sKYuj5ksBbVNcAMAAFBHSnfdLBTcQE0T3AAAANQRk6WgvghuAAAA6khpcKM5MdQ2wQ0AAEAdaSkZCa7HDdQ2wQ0AAEAdaS4tlTJVCmqa4AYAAKCOjC3rcWPHDdQywQ0AAEAdKS2V0uMGapvgBgAAoI6UlUrZcQM1TXADAABQR1pG6HED9URwAwAAUEdaRpoqBfVEcAMAAFBHWkpKpV5e3p721R0VWg2wqQQ3AAAAdaS0VCpJlixvr8BKgL4guAEAAKgjm60juFnYqs8N1CrBDQAAQB0ZPmRwRgwZ3OUxfW6gdgluAAAA6kxpn5vFJktBzRLcAAAA1Jnm0pHgdtxAzRLcAAAA1JnSHTeCG6hdghsAAIA6M3bk0C4/L2oT3ECtEtwAAADUmbIeN6ZKQc0S3AAAANSZ5hF23EC9ENwAAADUGT1uoH4IbgAAAOpMS+lUKTtuoGYJbgAAAOqMHjdQPwQ3AAAAdUaPG6gfghsAAIA6U7bjpm1VOjqKFVoNsCkENwAAAHWmNLgpFpMly+26gVokuAEAAKgzLSWlUonJUlCrBDcAAAB1ZviQQRna1PXXPX1uoDYJbgAAAOpMoVAoHwlushTUJMENAABAHVpXg2Kg9ghuAAAA6lDLyJKR4HrcQE0S3AAAANSh8lIpwQ3UIsENAABAHSotlVrUpscN1CLBDQAAQB0qLZVabMcN1CTBDQAAQB1qLi2V0pwYapLgBgAAoA6VlUoZBw41SXADAABQh1pGlEyVsuMGapLgBgAAoA6V77gR3EAtEtwAAADUobIeN60r09FRrNBqgN4S3AAAANSh0h03HcVk6cr2Cq0G6C3BDQAAQB0qHQeeGAkOtUhwAwAAUIdGDR2cpkGFLo/pcwO1R3ADAABQhwqFQtmum0VtRoJDrRHcAAAA1CmTpaD2CW4AAADqVEvpZKk2wQ3UGsENAABAnSrdcbO4VakU1BrBDQAAQJ1qHlHS40apFNQcwQ0AAECdKutxo1QKao7gBgAAoE6V9bix4wZqjuAGAACgTpVPldLjBmqN4AYAAKBONY8s6XGjVApqjuAGAACgTimVgtonuAEAAKhTZePA21amWCxWaDVAbwhuAAAA6lRLyTjwVauLaV25ukKrAXpDcAMAAFCnWkYNKXtMnxuoLYIbAACAOjVmWFMGDyp0ecxkKagtghsAAIA6VSgU0lzSoHixBsVQUwQ3AAAAdaxsspRSKagpghsAAIA61jzSSHCoZYIbAACAOla+40aPG6glghsAAIA61jKy60hwPW6gtghuAAAA6lhpc+KFpkpBTRHcAAAA1LEWPW6gpgluAAAA6pipUlDbBDcAAAB1TI8bqG2CGwAAgDpWNg7cVCmoKYIbAACAOja2ZMeNHjdQWwQ3AAAAday0x82K9o4sX7W6QqsBekpwAwAAUMdKp0oldt1ALRHcAAAA1LExw4ekUOj6mD43UDsENwAAAHVs8KBCNhte0qDYjhuoGYIbAACAOldaLiW4gdohuAEAAKhzpQ2KFyuVgpohuAEAAKhzzSUjwRfacQM1Q3ADAABQ50p33CiVgtohuAEAAKhzpT1ulEpB7RDcAAAA1Dk7bqB2CW4AAADqXGmPG8EN1A7BDQAAQJ0r23HTJriBWiG4AQAAqHNjR5X0uGnV4wZqheAGAACgzjWPKCmVsuMGaobgBgAAoM6VTpVqXbk6K9pXV2g1QE8IbgAAAOpcaY+bJFls1w3UBMENAABAnWteV3BjshTUBMENAABAnWsaPChjhjV1eUyfG6gNghsAAIAG0FzS52aRHTdQEwQ3AAAADaC0QfFCI8GhJghuAAAAGkBLyUhwPW6gNghuAAAAGkBZqVSbHTdQCwQ3AAAADaB0JLgeN1AbBDcAAAANoLTHjalSUBsENwAAAA1g7Eg9bqAWCW4AAAAaQHNpqZQeN1ATBDcAAAANoKVkx40eN1AbBDcAAAANoLTHjVIpqA2CGwAAgAZQOlXq5RXtWbW6o0KrAbpLcAMAANAAmkt23CTJEpOloOoJbpI888wzOfnkkzNlypSMGjUqm2++eaZOnZrzzz8/ra2tfXqtW2+9NUcffXQmTZqUUaNGpbm5OZMnT86//Mu/5JJLLsnSpUv79HoAAABJeXPixEhwqAVNlV5Apc2cOTOHH354Fi9e3PlYa2trZs+endmzZ+eyyy7LzTffnG233XaTrrNw4cIcc8wx+fGPf1z23JIlS/KnP/0pP/rRjzJt2rS8+c1v3qRrAQAAlBrWNDgjhw5O68rVnY8tajVZCqpdQwc3Dz30UA477LC0trZm9OjROe200zJ9+vS0tbXlmmuuyf/8z//kD3/4Q2bMmJHZs2dn9OjRvbrO4sWLs88+++SBBx5IksyYMSPve9/7MmnSpKxevTpPP/10Zs+eneuuu64vPx4AAEAXLSOGlAQ3dtxAtWvo4ObEE09Ma2trmpqacsstt2TatGmdz+29997Zbrvtcsopp+Txxx/PV77ylZxxxhm9us4JJ5yQBx54IE1NTfnOd76T9773vV2e33333fOBD3wgX/nKV7J69er1vAsAAMCmaR45NM8tXt75s+AGql/D9riZPXt27rjjjiTJscce2yW0WeOkk07KlClTkiQXXXRRVq3q+U3trrvuytVXX50k+dznPlcW2qytUCikqamhszQAAKAflU6W0uMGql/DBjc33nhj5/ExxxyzznMGDRqUI488MskrPWrWBD098bWvfS1JMnr06Jx00kk9fj0AAEBfaSmZLLVYjxuoeg0b3Nx5551JklGjRmXnnXde73l77bVX5/Fdd93Vo2usXLmysxnx/vvv39kjp729PU8//XSeeeaZrFzpRgkAAAyMlpFDu/xsxw1Uv4YNbh577LEkyaRJkzZYnrTDDjuUvaa7HnrooSxf/kr96LRp0/L888/nmGOOSUtLS7bZZptsvfXWaW5uzj//8z/n7rvv7sWnAAAA6L7SHTd63ED1a8iGKsuXL8+CBQuSJBMmTNjguWPHjs2oUaOybNmyzJ07t0fX+f3vf9/lmjvuuGPnddd+/Gc/+1l+8Ytf5IILLsiJJ57Yo2skybx58zb4/Pz583v8ngAAQP3R4wZqT0MGNy+//HLncXdGfK8JbpYuXdqj67z00kudx2eddVZWrFiRd73rXTnzzDPzxje+MYsXL86PfvSjnHrqqVmyZEn+7d/+Ldtvv33233//Hl1n4sSJPTofAABoTHrcQO1pyFKpNeVLSTJ06NANnPmKYcOGJUna2tp6dJ1ly5Z1Hq9YsSIHHHBAfvzjH2fnnXfOsGHDMn78+Hz0ox/NzJkzM2jQoBSLxZxyyikpFos9ug4AAEB3NI/Q4wZqTUPuuBk+fHjncXeaA69YsSJJMmLEiF5fJ0m+/OUvZ9Cg8qxsjz32yMEHH5zrrrsujz76aB599NHsuOOO3b7Oxkq45s+fn6lTp3b7/QAAgPqkxw3UnoYMbsaMGdN53J3ypzU7Z7pTVrW+67zuda/L9ttvv95z99tvv1x33XVJktmzZ/couNlYnx4AAICkPLhZsnxVVncUM3hQoUIrAjamIUulhg8fnnHjxiXZeGPfhQsXdgY3Pe0ls/b5GwtX1j73hRde6NF1AAAAuqOlpFSqWEyWKJeCqtaQwU2STJkyJUkyZ86ctLe3r/e8xx9/vOw13fWGN7yh83j16tUbPHft5zc0nhwAAKC3SnfcJPrcQLVr2OBmjz32SPJKGdQDDzyw3vNmzZrVebz77rv36Bpbb711Xvva1yZJ/vznP2/w3LWff81rXtOj6wAAAHTH8CGDM6yp66+Bi0yWgqrWsMHNgQce2Hl8xRVXrPOcjo6OXHXVVUmSlpaWTJ8+vcfXOeSQQ5Ikf/nLX3L33Xev97zrr7++83jPPffs8XUAAAC6o6xBsR03UNUaNriZOnVqZ0By+eWX55577ik754ILLshjjz2WJPnkJz+ZIUO63uCuvPLKFAqFFAqFnHnmmeu8zoknntg5Xepf//Vfu4wIX+M73/lO7rjjjiTJjBkzNBsGAAD6TWmfm8UmS0FVa9jgJkkuvvjijBgxIu3t7dl3333zpS99Kffee29uv/32fPjDH84pp5ySJJk8eXJOOumkXl3jta99bb7whS8kSR544IFMnTo13/72t/PAAw/kV7/6VT7xiU/k6KOPTpJsttlmufDCC/vkswEAAKxL+UhwpVJQzRq6C+5OO+2Ua6+9NkcccUSWLFmS008/veycyZMnZ+bMmV1Ge/fUpz/96bz00ks599xz8/vf/74zqFnb+PHjc+ONN2a77bbr9XUAAAA2RqkU1JaG3nGTJAcccEAefvjhfOpTn8rkyZMzcuTItLS0ZJdddsm5556bBx98MJMmTdrk63zpS1/Kb37zm3zwgx/MNttsk2HDhqW5uTm77rprvvjFL+aPf/xjpk2b1gefCAAAYP1KS6UWKZWCqlYoFovFSi+C/jVv3rxMnDgxSTJ37lw9dAAAoIF96ebH8s1fP9H580E7vSYXvvfNlVsQ1JH++P274XfcAAAANJJmPW6gpghuAAAAGkhZqZQeN1DVBDcAAAANpLQ5sXHgUN0ENwAAAA2kZUTX4GahUimoaoIbAACABlLa42Zx26p0dJhZA9VKcAMAANBAWkZ27XHTUUxeXtFeodUAGyO4AQAAaCClpVKJPjdQzQQ3AAAADWTk0MEZMrjQ5bFFbfrcQLUS3AAAADSQQqFQVi61yI4bqFqCGwAAgAZTWi61qE1wA9VKcAMAANBgWkonSxkJDlVLcAMAANBgmkcolYJaIbgBAABoMKU7bpRKQfUS3AAAADSYsh43dtxA1RLcAAAANJiyHjfGgUPVEtwAAAA0mGbjwKFmCG4AAAAaTGmp1EJTpaBqCW4AAAAaTHmplB03UK0ENwAAAA2mZR3jwIvFYoVWA2yI4AYAAKDBlO64ae8oZtnK1RVaDbAhghsAAIAG01wS3CTJIn1uoCoJbgAAABrMmGFNGTyo0OUxk6WgOgluAAAAGkyhUCibLKVBMVQnwQ0AAEADKi2XsuMGqpPgBgAAoAGV7rhZ1KbHDVQjwQ0AAEADahlZPhIcqD5N/fGmX/jCF/rjbXPGGWf0y/sCAAA0Gj1uoDb0S3Bz5plnplAobPzEHhLcAAAA9I3yHjdKpaAa9UtwkyTFYnGDzxcKhT45BwAAgJ5rGdG1VGqhUimoSv3S46ajo2O9/zzxxBPZddddUywWs//+++eHP/xhnn766SxfvjzLly/P008/neuuuy77779/isVidt111zz55JPp6Ojoj6UCAAA0pJaSHTeLBTdQlfptx826LF68OPvuu2+efPLJXHXVVTniiCPKzpk4cWImTpyYgw8+ON/97ndz1FFH5Z3vfGfuv//+NDc3D+RyAQAA6lZpcGOqFFSnAZ0qdeGFF2bOnDk57rjj1hnalDr88MNz3HHH5c9//nMuuOCCAVghAABAY2guHQduxw1UpQENbn70ox+lUCjk0EMP7fZrDjvssCTJ9ddf31/LAgAAaDhl48DbVukxClVoQIObp556Kkl6VPK05tynn366P5YEAADQkMaWlEqtbO/I8lV6i0K1GdDgZsiQV24MjzzySLdfs+bcNa8FAABg05VOlUr0uYFqNKDBzT/8wz+kWCzm3HPPTWtr60bPb21tzbnnnptCoZA3velNA7BCAACAxjBmeFMKha6P6XMD1WdAg5sPfehDSZI//OEPefvb357//d//Xe+5Dz30UKZPn57HH388SXL88ccPxBIBAAAawqBBBQ2KoQYM6Djwww8/PDfccEOuv/76PPDAA9l5552z4447Ztddd8348eNTKBTyl7/8JbNnz+5STnXwwQfnAx/4wEAuFQAAoO61jBjSJaxZrFQKqs6ABjdJcu211+bEE0/MJZdcko6Ojjz88MPr7HlTLBZTKBTyiU98Il/5ylcGepkAAAB1r3nk0OSvf2tjYccNVJ8BLZVKksGDB+erX/1qHnzwwXz0ox/Ndtttl+SVoGbNP5MmTcpHP/rRPPjgg/mv//qvNDUNeL4EAABQ91pKS6XaBDdQbSqWiOy444757//+7yTJihUrsmjRohSLxYwdOzbDhg2r1LIAAAAaRkvJSPCFrUqloNpUxVaWYcOGZcstt6z0MgAAABpK6Y6bxUqloOoMeKkUAAAA1aF55NAuP+txA9WnYjtuVq9enRtvvDG33nprHn300bz00ktJks033zxvfOMb8853vjMHHnhgBg8eXKklAgAA1LXyHjdKpaDaVCS4+fnPf57jjz8+zz77bOdjxWIxSVIoFHL33Xfn0ksvzYQJE3LppZdmv/32q8QyAQAA6lppjxs7bqD6DHip1NVXX513vetdefbZZzunSG299dbZbbfd8ta3vjVbb711kleCnLlz52bGjBn57ne/O9DLBAAAqHtjS0qlFpsqBVVnQIObp59+Oscff3w6OjoycuTI/Md//Eeef/75PPHEE7n77rtzzz335Iknnsjzzz+fs88+O6NHj05HR0eOO+64PPPMMwO5VAAAgLrXbMcNVL0BDW4uvvjirFixIqNHj86dd96Z008/PePHjy87b4sttshpp52WO++8M6NHj86KFSty8cUXD+RSAQAA6l5pj5u2VauzfNXqCq0GWJcBDW5uueWWFAqFfPrTn86b3/zmjZ7/D//wDzn55JNTLBbzi1/8ov8XCAAA0EBaSkqlkmSJcimoKgMa3Kwpd3rnO9/Z7dfss88+XV4LAABA39hsePm8mkWCG6gqAxrcrF79ypa7noz4XnNuR0dHv6wJAACgUTUNHpQxJeGNPjdQXQY0uHnNa16TJLn77ru7/Zo157761a/ulzUBAAA0svKR4CsrtBJgXQY0uJk+fXqKxWLOOeecPPfccxs9f968eTnnnHNSKBSy9957D8AKAQAAGkvLiK59buy4geoyoMHNCSeckEGDBuXFF1/MW9/61vzwhz/sLJ9a2+rVq/ODH/wg06ZNywsvvJBBgwblE5/4xEAuFQAAoCGU7bhps+MGqkl5J6p+9MY3vjFf/OIX89nPfjbPPfdc3ve+96WlpSU77bRTttxyyxQKhTz//PN58MEHs2jRohSLxSTJF7/4xbzxjW8cyKUCAAA0hOYRpaVSdtxANRnQ4CZJTjvttDQ3N+eUU05Ja2trFi5cmNtvv73LOWsCm5EjR+bLX/5yPvrRjw70MgEAABpC+Y4bwQ1UkwEPbpLkYx/7WA477LBcccUVufXWW/Poo4/mpZdeSpJsvvnmeeMb35h3vvOdOeaYYzJu3LhKLBEAAKAhlPa4WWzHDVSVigQ3STJu3Lh8+tOfzqc//elKLQEAAKDh6XED1W1AmxMDAABQXVpGmioF1UxwAwAA0MBaNCeGqlaxUqmXXnqpWz1uNt9880otEQAAoO6Vlkot1pwYqkpFgptvfvObOfnkk9Pa2prkb1OkkuTZZ5/Nc889l1tuuSVnnnlmLrjgghx//PGVWCYAAEDdKw1ulq5oz6rVHRkyWIEGVIMBD27OOeecfPazn+0Ma5qbm7PTTjvlVa96VYrFYv7yl7/kwQcfzOLFi7Ns2bJ89KMfzaJFi3LKKacM9FIBAADqXnPJVKnklV0340YPq8BqgFIDGtw8+uij+fznP59isZitttoqX/7yl3PooYdmyJCuCW97e3t++MMf5tOf/nSee+65fO5zn8uMGTPyhje8YSCXCwAAUPeaS3rcJK/0uRHcQHUY0L1vX/va17J69epsscUWueeee/KBD3ygLLRJkqamprz//e/PPffck/Hjx2f16tX52te+NpBLBQAAaAhDmwZl1NDBXR5b1GokOFSLAQ1ufvWrX6VQKOS0007La1/72o2eP3HixHzmM59JsVjMbbfdNgArBAAAaDxGgkP1GtDg5tlnn02SvO1tb+v2a3bfffckyXPPPdcvawIAAGh0peVSi0yWgqoxoMHN4MGvbL9rb2/v9mvWnDtokI7mAAAA/aF0spRSKageA5qGrCmP6knZ05pzu1NaBQAAQM+VBjeL7biBqjGgwc0+++yTYrGY888/P4888shGz3/44Yfz5S9/OYVCIfvuu+8ArBAAAKDx6HED1WtAg5sTTzwxw4YNy9KlS7PHHnvky1/+chYsWFB23oIFC3LeeeflH//xH7N06dIMGzYsJ5544kAuFQAAoGG06HEDVatpIC+29dZb55vf/GaOOeaYLF26NKeeempOO+20bLPNNtlyyy1TKBTy/PPP56mnnkqxWEyxWEyhUMg3v/lNpVIAAAD9RI8bqF4DGtwkyZFHHpm/+7u/y0c+8pE8++yzKRaLeeKJJ/Lkk08mSYrFYue5r371q3PppZfmn//5nwd6mQAAAA2jZUTXUik9bqB6DHhwkyQzZszIk08+mRtuuCG33nprHn300bz00ktJks033zxvfOMb8853vjMHHXRQmpoqskQAAICG0Vy240ZwA9WiYqlIU1NTDj300Bx66KGVWgIAAABZR48bpVJQNQa0OTEAAADVp3Sq1JLl7VndUVzP2cBAGtAdN88880yPX1MoFDJ8+PA0Nzdn6NChG38BAAAAPVLanDhJlrStythRfgeDShvQ4OZ1r3vdJr1+woQJ2W233XL00Udn//3376NVAQAANLbmEeXBzcLWlYIbqAIDWiq1ZsR3b/+ZO3durrvuurzrXe/Kfvvtl4ULFw7k8gEAAOrS8CGDM3xI118PF5ksBVVhQHfcXHHFFUmSb3zjG/ntb3+b4cOHZ7/99ssuu+ySLbbYIkny4osv5v77788vfvGLrFixIlOnTs3xxx+fJUuW5NFHH82Pf/zjLFiwILfeemsOPPDAzJo1ayA/AgAAQF1qGTE0z69a3vnzYpOloCoMaHBz1FFH5SMf+Ujuu+++vOc978k3v/nNjB8/fp3nvvDCCzn++OPz05/+NG94wxty2WWXJUm++tWv5iMf+Uiuuuqq3HXXXbn22mvz3ve+dyA/BgAAQN1pGTkkzy/5W3CzqM1kKagGA1oqdeONN+bSSy/NtGnTcv311683tEmS8ePH54Ybbshuu+2WK664Ij/4wQ+SJMOHD8+3vvWtvOUtb0mSXHPNNQOydgAAgHpW2udmkR03UBUGNLj5+te/nkKhkE9+8pMpFAobPb9QKOTEE09MsVjMpZde2vn4oEGDctxxx6VYLOb+++/vzyUDAAA0hLElI8EFN1AdBjS4efjhh5MkkyZN6vZr1pz7yCOPdHn8TW96U5Lkr3/9ax+tDgAAoHGVjgRfrDkxVIUBDW6WLFmS5JUGxN215tyXX365y+MjRoxIkgwZUj62DgAAgJ5pHllaKqXHDVSDAQ1uJk6cmCT57ne/2+3XXH311UmS1772tV0ef+GFF5KkcxoVAAAAvdcyoqRUyo4bqAoDGty8+93vTrFYzHe+851cdNFFGz3/wgsvzHe/+90UCoW8+93v7vLcb3/72yTJ1ltv3R9LBQAAaCilpVJ63EB1GNBx4Keeemq+/e1v569//WtOOumkfPe7382RRx6ZnXfeuXPC1AsvvJD7778/V199dX73u98leWVXzamnntrlva655poUCoXss88+A/kRAAAA6lLLCD1uoBoNaHDzd3/3d/nlL3+Zf/qnf8pf/vKX/O53v+sMZ9alWCzmVa96VX7+859n880373z8iSeeyNSpUzN16tQccsghA7F0AACAuqbHDVSnAS2VSpJ/+Id/yGOPPZYTTjghm222WYrF4jr/2WyzzXLCCSfk//7v/zonSK2x7bbb5oorrsgVV1yR7bbbbqA/AgAAQN0p7XGzuG1VOjqKFVoNsMaA7rhZo6WlJRdffHHOO++8PPDAA3n00UezcOHCJMnYsWPzhje8IbvsskuGDRtWieUBAAA0nNIeNx3F5OXl7WU7cYCBVZHgZo1hw4blbW97W972trdVchkAAAANrzS4SZJFbSsFN1BhA14qBQAAQPUZMWRwhg7u+iuiyVJQeYIbAAAAUigUyhsUmywFFSe4AQAAIEky1mQpqDqCGwAAAJKse7IUUFmCGwAAAJKkvFRKjxuoOMENAAAASZKWEYIbqDaCGwAAAJKUjwRf1KbHDVSa4AYAAIAkScvIkh43dtxAxQluAAAASJI0l5ZKaU4MFSe4AQAAIEl5qdRC48Ch4gQ3AAAAJFnHOHClUlBxghsAAACSrKs58aoUi8UKrQZIBDcAAAD8P6U9blZ3FLN0RXuFVgMkghsAAAD+n9IdN0mySLkUVJTgBgAAgCTJ6GFNaRpU6PLYYpOloKIENwAAACRJCoVCeZ8bO26gogQ3AAAAdCrtc7OozUhwqCTBDQAAAJ1aRnYdCW7HDVSW4AYAAIBOLSU7bvS4gcoS3AAAANCpuazHjVIpqCTBDQAAAJ1aRiiVgmoiuEnyzDPP5OSTT86UKVMyatSobL755pk6dWrOP//8tLa29ss158+fn5aWlhQKhRQKhbz97W/vl+sAAAD0ROlUqYWCG6iopkovoNJmzpyZww8/PIsXL+58rLW1NbNnz87s2bNz2WWX5eabb862227bp9c94YQTulwTAACgGpQGN4tNlYKKaugdNw899FAOO+ywLF68OKNHj87ZZ5+du+++O7fddluOO+64JMkf/vCHzJgxI0uXLu2z6/70pz/Nj370o4wfP77P3hMAAKAvlI0Dt+MGKqqhg5sTTzwxra2taWpqyi233JLTTz8906ZNy957751LL7005513XpLk8ccfz1e+8pU+uebSpUvz8Y9/PEly/vnn98l7AgAA9JWyceCmSkFFNWxwM3v27Nxxxx1JkmOPPTbTpk0rO+ekk07KlClTkiQXXXRRVq3a9BvW6aefnrlz52b69On54Ac/uMnvBwAA0JfKxoG3rkqxWKzQaoCGDW5uvPHGzuNjjjlmnecMGjQoRx55ZJJk4cKFnUFPb91333357//+7wwdOjSXXHLJJr0XAABAfxhbsuNm5eqOtK1aXaHVAA0b3Nx5551JklGjRmXnnXde73l77bVX5/Fdd93V6+u1t7fn+OOPT0dHRz7zmc9k++237/V7AQAA9JfmkubEiT43UEkNO1XqscceS5JMmjQpTU3r/2PYYYcdyl7TG+eff34eeuihvP71r8/pp5/e6/dZl3nz5m3w+fnz5/fp9QAAgPo1ZlhTBhWSjrWqoxa1rsqrW0ZUblHQwBoyuFm+fHkWLFiQJJkwYcIGzx07dmxGjRqVZcuWZe7cub263hNPPJEvfOELSZKvf/3rGT58eK/eZ30mTpzYp+8HAAA0rkGDCmkeMSQL19pls8hIcKiYhiyVevnllzuPR48evdHzR40alSS9Hgn+4Q9/OG1tbXnve9+bfffdt1fvAQAAMFBKJ0stVioFFdOwO27WGDp06AbOfMWwYcOSJG1tbT2+1lVXXZVbb701m222WS688MIev747NrYTaP78+Zk6dWq/XBsAAKg/zSWTpYwEh8ppyOBm7VKllSs3vuVvxYoVSZIRI3pW07lgwYKcdNJJSZKzzz47W221VY9e310bK/cCAADoiZaSBsWaE0PlNGSp1JgxYzqPu1P+tGzZsiTdK6ta27/9279lwYIF2WWXXfKxj32sZ4sEAACokJbSHTetetxApTTsjptx48ZlwYIFG53ItHDhws7gpidNgJ977rlcffXVSZK99947P/jBDzZ4/gsvvJBrrrkmSfK6170ub33rW7t9LQAAgL5U2uPGjhuonIYMbpJkypQpufPOOzNnzpy0t7evdyT4448/3uU13bV2CdZ555230fMfe+yxvP/970+SHHXUUYIbAACgYsp73NhxA5XSkKVSSbLHHnskeaUM6oEHHljvebNmzeo83n333ft9XQAAAJWmxw1Uj4YNbg488MDO4yuuuGKd53R0dOSqq65KkrS0tGT69Ondfv9tttkmxWJxo/+ssddee3U+duWVV/bqMwEAAPSF0uBmsalSUDENG9xMnTo1e+65Z5Lk8ssvzz333FN2zgUXXJDHHnssSfLJT34yQ4Z0vXldeeWVKRQKKRQKOfPMM/t9zQAAAANBjxuoHg3b4yZJLr744uy+++5pa2vLvvvum9NPPz3Tp09PW1tbrrnmmlx66aVJksmTJ3eO9QYAAKh3ZVOl9LiBimno4GannXbKtddemyOOOCJLlizJ6aefXnbO5MmTM3PmzC4jxAEAAOpZ6Y6b5as6snzV6gwfMrhCK4LG1bClUmsccMABefjhh/OpT30qkydPzsiRI9PS0pJddtkl5557bh588MFMmjSp0ssEAAAYMKU7bhJ9bqBSCsW1O+RSl+bNm5eJEycmSebOnZsJEyZUeEUAAEA1W91RzOtPv7nLY7848R+z/atUIsCG9Mfv3w2/4wYAAICuBg8qZLPhXTtrLGrV5wYqQXADAABAmbLJUkqloCIENwAAAJRpGdm1z81iI8GhIgQ3AAAAlGkuaVC8UKkUVITgBgAAgDJKpaA6CG4AAAAoUzoSfJFSKagIwQ0AAABlynrctCmVgkoQ3AAAAFCmtMeNHTdQGYIbAAAAyowt7XEjuIGKENwAAABQprxUSnADlSC4AQAAoExpcLPIOHCoCMENAAAAZZpHdC2VWrZydVa2d1RoNdC4BDcAAACUKd1xkyiXgkoQ3AAAAFCmdKpUYiQ4VILgBgAAgDJDBg/K6GFNXR4zWQoGnuAGAACAdSrddbNQcAMDTnADAADAOpksBZUnuAEAAGCdSoMbzYlh4AluAAAAWKeWkpHgetzAwBPcAAAAsE7NpaVSpkrBgBPcAAAAsE5jy3rc2HEDA01wAwAAwDqVlkrpcQMDT3ADAADAOpWVStlxAwNOcAMAAMA6tYzQ4wYqTXADAADAOrWMNFUKKk1wAwAAwDq1lJRKvby8Pe2rOyq0GmhMghsAAADWqbRUKkmWLG+vwEqgcQluAAAAWKfN1hHcLGzV5wYGkuAGAACAdRo+ZHBGDBnc5TF9bmBgCW4AAABYr9I+N4tNloIBJbgBAABgvZpLR4LbcQMDSnADAADAepXuuBHcwMAS3AAAALBeY0cO7fLzojbBDQwkwQ0AAADrVdbjxlQpGFCCGwAAANareYQdN1BJghsAAADWS48bqCzBDQAAAOvVUjpVyo4bGFCCGwAAANZLjxuoLMENAAAA66XHDVSW4AYAAID1Kttx07YqqzuKFVoNNB7BDQAAAOtVGtwUi8nLy+26gYEiuAEAAGC9WkpKpRKTpWAgCW4AAABYr+FDBmVoU9dfHfW5gYEjuAEAAGC9CoVC+Uhwk6VgwAhuAAAA2KCxI7uWSy224wYGjOAGAACADWoeWbrjRnADA0VwAwAAwAaVl0oJbmCgCG4AAADYoNKR4Iva9LiBgSK4AQAAYINaSnvc2HEDA0ZwAwAAwAY1l5ZKaU4MA0ZwAwAAwAaVlUoZBw4DRnADAADABrWM6FoqZccNDBzBDQAAABtUvuNGcAMDRXADAADABpX1uGldmY6OYoVWA41FcAMAAMAGle646SgmS1e2V2g10FgENwAAAGxQ6TjwxEhwGCiCGwAAADZo1NDBaRpU6PKYPjcwMAQ3AAAAbFChUCjbdbOozUhwGAiCGwAAADbKZCmoDMENAAAAG9VSOlmqTXADA0FwAwAAwEaV7rhZ3KpUCgaC4AYAAICNah5R0uNGqRQMCMENAAAAG1XW40apFAwIwQ0AAAAbVdbjxo4bGBCCGwAAADaqfKqUHjcwEAQ3AAAAbFTzyJIeN0qlYEAIbgAAANgopVJQGYIbAAAANqpsHHjbyhSLxQqtBhqH4AYAAICNaikZB75qdTGtK1dXaDXQOAQ3AAAAbFTLqCFlj+lzA/1PcAMAAMBGjRnWlMGDCl0eM1kK+p/gBgAAgI0qFAppLmlQvFiDYuh3ghsAAAC6pWyylFIp6HeCGwAAALqleaSR4DDQBDcAAAB0S/mOGz1uoL8JbgAAAOiWlpFdR4LrcQP9T3ADAABAt5Q2J15oqhT0O8ENAAAA3dKixw0MOMENAAAA3WKqFAw8wQ0AAADdoscNDDzBDQAAAN1SNg7cVCnod4IbAAAAumVsyY4bPW6g/wluAAAA6JbSHjcr2juyfNXqCq0GGoPgBgAAgG4pnSqV2HUD/U1wAwAAQLeMGT4khULXx/S5gf4luAEAAKBbBg8qZLPhJQ2K7biBfiW4AQAAoNtKy6UEN9C/BDcAAAB0W2mD4sVKpaBfCW4AAADotuaSkeAL7biBfiW4AQAAoNtKd9wolYL+JbgBAACg20p73CiVgv4luAEAAKDb7LiBgSW4AQAAoNtKe9wIbqB/CW4AAADotrGl48DbBDfQnwQ3AAAAdFtZj5tWPW6gPwluAAAA6LbmESWlUnbcQL8S3AAAANBtpTtuWleuzor21RVaDdQ/wQ0AAADdVjpVKkkW23UD/UZwAwAAQLc1ryu4MVkK+o3gBgAAgG5rGjwoY4Y1dXlMnxvoP4IbAAAAeqS5pM/NwmUmS0F/EdwAAADQI6UNiu24gf4juAEAAKBHWkpGgutxA/1HcAMAAECPlJZKLWpTKgX9RXADAABAj5SOBF9kxw30G8ENAAAAPTJ2ZNdSKT1uoP8IbgAAAOiR0ubEetxA/xHcAAAA0CPNpaVSetxAvxHcAAAA0CMtpaVSdtxAvxHcAAAA0CNKpWDgCG4AAADokdKpUi+vaM+q1R0VWg3UN8ENAAAAPdJcsuMmSZaYLAX9QnCT5JlnnsnJJ5+cKVOmZNSoUdl8880zderUnH/++Wltbd2k916yZEmuueaaHHfccXnLW96SlpaWDB06NFtssUXe/va35/zzz8+iRYv65oMAAAAMgNLmxImR4NBfCsVisVjpRVTSzJkzc/jhh2fx4sXrfH777bfPzTffnG233bbH7/2zn/0sBx10UFasWLHB87bccst8//vfz/Tp03t8je6YN29eJk6cmCSZO3duJkyY0C/XAQAAGsffn/HztK5c3fnzjz46LTtvvXkFVwSV1x+/fzf0jpuHHnoohx12WBYvXpzRo0fn7LPPzt13353bbrstxx13XJLkD3/4Q2bMmJGlS5f2+P3/+te/ZsWKFRk0aFD222+/XHjhhfnVr36V3/3ud/nJT36S9773vUmSv/zlL3nXu96V//3f/+3LjwcAANBvSvvcmCwF/aOp0guopBNPPDGtra1pamrKLbfckmnTpnU+t/fee2e77bbLKaeckscffzxf+cpXcsYZZ/To/YcMGZIPf/jDOf300/Pa1762y3M77bRTDjjggOy+++7513/917S2tuakk07Kbbfd1iefDQAAoD81jxya5xYv7/xZcAP9o2F33MyePTt33HFHkuTYY4/tEtqscdJJJ2XKlClJkosuuiirVvXsRvTe97433/jGN8pCm7WdcMIJ2WWXXZIkd9xxR/7617/26BoAAACVULbjRo8b6BcNG9zceOONncfHHHPMOs8ZNGhQjjzyyCTJwoULO4Oevvb2t789SdLR0ZEnn3yyX64BAADQl1pKJkstbl1ZoZVAfWvY4ObOO+9MkowaNSo777zzes/ba6+9Oo/vuuuuflnL2s2LBw1q2P9JAACAGtIycmiXn+24gf7RsCnBY489liSZNGlSmprW3+pnhx12KHtNX5s1a1aSpKmpKZMmTeqXawAAAPSl0h03etxA/2jI5sTLly/PggULkmSjo7nGjh2bUaNGZdmyZZk7d26fr2XmzJl5+OGHkyT77bdfNttssx6/x7x58zb4/Pz583u1NgAAgPXR4wYGRkMGNy+//HLn8ejRozd6/prgpjcjwTfkpZdeysc//vEkyeDBg/PFL36xV++zZkY8AADAQNHjBgZGQ5ZKLV/+t5F1Q4cO3cCZrxg2bFiSpK2trc/WsHr16hx++OF5+umnkySf+9znstNOO/XZ+wMAAPSn5hF63MBAaMgdN8OHD+88Xrly46nwmubBI0aM6LM1fOxjH8vPf/7zJMmMGTPy+c9/vtfvtbESrvnz52fq1Km9fn8AAIBSetzAwGjI4GbMmDGdx90pf1q2bFmS7pVVdcdpp52WSy+9NEmyxx575Ic//GEGDx7c6/fbWJ8eAACAvlYa3CxZviqrO4oZPKhQoRVBfWrIUqnhw4dn3LhxSTbe2HfhwoWdwU1f9JI599xzc8455yRJ3vKWt+Smm27q0508AAAAA6GlpFSqWEyWKJeCPteQwU2STJkyJUkyZ86ctLe3r/e8xx9/vOw1vfX1r389p556aud7/eIXv0hzc/MmvScAAEAllO64SfS5gf7QsMHNHnvskeSVMqgHHnhgvefNmjWr83j33Xfv9fWuvvrqfOITn0iSbLvttrn11ls7d/0AAADUmuFDBmdYU9dfKReZLAV9rmGDmwMPPLDz+IorrljnOR0dHbnqqquSJC0tLZk+fXqvrnX99dfnmGOOSbFYzIQJE3Lbbbfl1a9+da/eCwAAoFqUNSi24wb6XMMGN1OnTs2ee+6ZJLn88stzzz33lJ1zwQUX5LHHHkuSfPKTn8yQIV1vSldeeWUKhUIKhULOPPPMdV7nlltuyfvf//6sXr0648ePz6233pptttmmTz8LAABAJZT2uVlsshT0uYacKrXGxRdfnN133z1tbW3Zd999c/rpp2f69Olpa2vLNddc0zn5afLkyTnppJN6/P733ntvDjrooKxcuTJDhgzJhRdemFWrVuXRRx9d72smTJiQlpaW3n4kAACAAVM+ElypFPS1hg5udtppp1x77bU54ogjsmTJkpx++ull50yePDkzZ87sMkK8u37+85+ntbU1SbJq1aocfvjhG33NFVdckaOPPrrH1wIAABhoSqWg/zVsqdQaBxxwQB5++OF86lOfyuTJkzNy5Mi0tLRkl112ybnnnpsHH3wwkyZNqvQyAQAAqk5pqdQipVLQ5wrFYrFY6UXQv+bNm5eJEycmSebOnZsJEyZUeEUAAEA9+NLNj+Wbv36i8+eDdnpNLnzvmyu3IKiw/vj9u+F33AAAANA7zXrcQL8T3AAAANArZaVSetxAnxPcAAAA0CulzYmNA4e+J7gBAACgV1pGdA1uFiqVgj4nuAEAAKBXSnvcLG5blY4O82+gLwluAAAA6JWWkV173HQUk5dXtFdoNVCfBDcAAAD0SmmpVKLPDfQ1wQ0AAAC9MnLo4AwZXOjy2KI2fW6gLwluAAAA6JVCoVBWLrXIjhvoU4IbAAAAeq20XGpRm+AG+pLgBgAAgF5rKZ0sZSQ49CnBDQAAAL3WPEKpFPQnwQ0AAAC9VrrjRqkU9C3BDQAAAL1W1uPGjhvoU4IbAAAAeq2sx41x4NCnBDcAAAD0WnPJOPCFdtxAnxLcAAAA0GvlpVJ23EBfEtwAAADQa+WlUnbcQF8S3AAAANBrLesYB14sFiu0Gqg/ghsAAAB6rXTHTXtHMctWrq7QaqD+CG4AAADotdLgJtHnBvqS4AYAAIBeGz2sKYMHFbo8tshkKegzghsAAAB6rVAolE2W0qAY+o7gBgAAgE3SPLJ0JLjgBvqK4AYAAIBNUrrjZlGbHjfQVwQ3AAAAbJKWkeUjwYG+IbgBAABgk+hxA/1HcAMAAMAmKe1xs3CZUinoK4IbAAAANknLiJJSKTtuoM8IbgAAANgkLSU7bhbrcQN9RnADAADAJikNbkyVgr4juAEAAGCTNJeOA7fjBvqM4AYAAIBNMrZ0HHjbqhSLxQqtBuqL4AYAAIBNUloqtbK9I8tXdVRoNVBfBDcAAABsktKpUok+N9BXBDcAAABskjHDm1IodH1MnxvoG4IbAAAANsmgQQUNiqGfCG4AAADYZC0lwc1ipVLQJwQ3AAAAbLLm0slSdtxAnxDcAAAAsMlKd9wsahPcQF8Q3AAAALDJSkeCL2xVKgV9QXADAADAJivrcaNUCvqE4AYAAIBNpscN9A/BDQAAAJusvMeNUinoC4IbAAAANllpjxs7bqBvCG4AAADYZGNLSqUWmyoFfUJwAwAAwCZrtuMG+oXgBgAAgE1W2uOmbdXqLF+1ukKrgfohuAEAAGCTtZSUSiXJEuVSsMkENwAAAGyyzYY3lT22SHADm0xwAwAAwCZrGjwoY0rCG31uYNMJbgAAAOgT5SPBV1ZoJVA/BDcAAAD0iZYRXfvc2HEDm05wAwAAQJ8o23HTZscNbCrBDQAAAH2ieURpqZQdN7CpBDcAAAD0ifIdN4Ib2FSCGwAAAPpEaY+bxXbcwCYT3AAAANAn9LiBvie4AQAAoE+0jDRVCvqa4AYAAIA+0aI5MfQ5wQ0AAAB9orRUarHmxLDJBDcAAAD0idLgZumK9qxa3VGh1UB9ENwAAADQJ5pLpkoldt3AphLcAAAA0CeaS3rcJPrcwKYS3AAAANAnhjYNyqihg7s8tqjVSHDYFIIbAAAA+oyR4NC3BDcAAAD0mdJyqUV63MAmEdwAAADQZ0onSymVgk0juAEAAKDPlAY3pkrBphHcAAAA0Gf0uIG+JbgBAACgz7TocQN9SnADAABAn9HjBvqW4AYAAIA+0zKia6mUHjewaQQ3AAAA9Jnmsh03ghvYFIIbAAAA+kxZjxulUrBJBDcAAAD0mdKpUkuWt2d1R7FCq4HaJ7gBAACgz5Q2J070uYFNIbgBAACgzzSPKA9ulEtB7wluAAAA6DPDhwzO8CFdf9VcZMcN9JrgBgAAgD5VNhLcZCnoNcENAAAAfaq0z82iNqVS0FuCGwAAAPpUWXBjxw30muAGAACAPlVaKiW4gd4T3AAAANCnSnfcGAcOvSe4AQAAoE81l5VK6XEDvSW4AQAAoE+VlUrZcQO9JrgBAACgT2lODH1HcAMAAECfahmhxw30FcENAAAAfaq0x81CPW6g1wQ3AAAA9KnSHjeL21alo6NYodVAbRPcAAAA0KdKe9wUi8nLy9srtBqobYIbAAAA+lRpcJMki9qUS0FvCG4AAADoUyOGDM7QwV1/3TRZCnpHcAMAAECfKhQK5SPBTZaCXhHcAAAA0OfKghuTpaBXBDcAAAD0uXVNlgJ6TnADAABAn2su23EjuIHeENwAAADQ51pGCG6gLwhuAAAA6HPlzYn1uIHeENwAAADQ51pGlvS4seMGekVwAwAAQJ9rLi2V0pwYekVwAwAAQJ8rLZVaaBw49IrgBgAAgD5XNg5cqRT0iuAGAACAPlfenHhVisVihVYDtUtwAwAAQJ8r7XGzuqOYpSvaK7QaqF2CGwAAAPpc6Y6bJFmkXAp6THADAABAnxs9rClNgwpdHltsshT0mOAGAACAPlcoFMr73NhxAz0muAEAAKBflPa5WdRmJDj0lOAGAACAftEysutIcDtuoOcENwAAAPSLlpIdN3rcQM8JbgAAAOgXzWU9bpRKQU8JbgAAAOgXLSOUSsGmEtwkeeaZZ3LyySdnypQpGTVqVDbffPNMnTo1559/flpbW/vsOtdcc03222+/bLXVVhk+fHi22WabfPCDH8y9997bZ9cAAACoFqVTpRYKbqDHmiq9gEqbOXNmDj/88CxevLjzsdbW1syePTuzZ8/OZZddlptvvjnbbrttr6+xfPnyHHroobnpppu6PP7000/n6aefzve+972ceeaZ+fznP9/rawAAAFSb0uBmsalS0GMNvePmoYceymGHHZbFixdn9OjROfvss3P33Xfntttuy3HHHZck+cMf/pAZM2Zk6dKlvb7Oscce2xnaTJ8+PTfeeGPuu+++XH755Xn961+fjo6OnHHGGbnsssv65HMBAABUg7Jx4HbcQI819I6bE088Ma2trWlqasott9ySadOmdT639957Z7vttsspp5ySxx9/PF/5yldyxhln9Pgas2bNyve+970kyQEHHJAbbrghgwcPTpLsuuuuefe7352dd945zzzzTE455ZT8y7/8S1paWvrk8wEAAFRS2ThwU6Wgxxp2x83s2bNzxx13JHllR8zaoc0aJ510UqZMmZIkueiii7JqVc9vMuedd16SZPDgwfn617/eGdqsMW7cuJx77rlJkoULF+byyy/v8TUAAACqUdk48NZVKRaLFVoN1KaGDW5uvPHGzuNjjjlmnecMGjQoRx55ZJJXQpU1QU93LV26NLfddluSZJ999smECRPWed7BBx+czTbbLEly/fXX9+gaAAAA1WpsyY6blas70rZqdYVWA7WpYUul7rzzziTJqFGjsvPOO6/3vL322qvz+K677so+++zT7Wvcd999WbFiRdn7lBo6dGh222233HLLLbnvvvuyatWqDBkyZL3nAwAA1ILmkeW/1zz67JKMHzOsAquhlo0cOjjjNxte6WVURMMGN4899liSZNKkSWlqWv8fww477FD2mp5eo/R91nedW265Je3t7fnTn/6Uv//7v+/2debNm7fB5+fPn9/t9wIAAOgrY4Y1ZVAh6VirOuqwb95TuQVRs/Z/46tyyRHr33RRzxoyuFm+fHkWLFiQJOstX1pj7NixGTVqVJYtW5a5c+f26Dprn7+x60ycOLHL63oS3Kz9WgAAgGoxaFAhzSOGZKFpUtBrDdnj5uWXX+48Hj169EbPHzVqVJL0eCR4T66z5hq9uQ4AAEC12v5VYyq9BKhpDbvjZo2hQ4du4MxXDBv2Sv1lW1tbv11nzTV6c52N7QSaP39+pk6d2qP3BAAA6AtnvOsNOeH7v8ufX1xW6aVATWrI4Gb48L81NFq5cuVGz1/TYHjEiBH9dp011+jNdTZWhgUAAFApf//qzXLbSW/PyvaOFGMUOL0zqFCo9BIqpiGDmzFj/rZVrztlScuWvZIMd6esqrfXWXON3lwHAACg2g1tashOHbDJGvL/OcOHD8+4ceOSbHwi08KFCztDlZ42AV57J8zGrrN2uZNmwwAAAEDSoMFNkkyZMiVJMmfOnLS3t6/3vMcff7zsNd219mSotd9nQ9dpamrKpEmTenQdAAAAoD41bHCzxx57JHmlROmBBx5Y73mzZs3qPN599917dI1dd921synx2u9TauXKlbn33nvLXgMAAAA0toYNbg488MDO4yuuuGKd53R0dOSqq65KkrS0tGT69Ok9usaYMWPyjne8I0ly6623rrdc6vrrr8+SJUuSJAcddFCPrgEAAADUr4YNbqZOnZo999wzSXL55ZfnnnvuKTvnggsuyGOPPZYk+eQnP5khQ4Z0ef7KK69MoVBIoVDImWeeuc7rnHzyyUmS9vb2fPzjH8/q1au7PL9gwYJ85jOfSfJKOPShD31okz4XAAAAUD8aNrhJkosvvjgjRoxIe3t79t1333zpS1/Kvffem9tvvz0f/vCHc8oppyRJJk+enJNOOqlX19h7773zvve9L0nyk5/8JPvss09+8pOf5P77788VV1yR3XbbLc8880yS5JxzzsnYsWP75sMBAAAANa8hx4GvsdNOO+Xaa6/NEUcckSVLluT0008vO2fy5MmZOXNml9HePfWtb30rS5Ysyc0335zbb789t99+e5fnBw0alM9//vP58Ic/3OtrAAAAAPWnoXfcJMkBBxyQhx9+OJ/61KcyefLkjBw5Mi0tLdlll11y7rnn5sEHH9zkKU8jRozIzJkz893vfjf77LNPxo8fn6FDh2bixIn5wAc+kLvuumu9pVYAAABA4yoUi8VipRdB/5o3b14mTpyYJJk7d24mTJhQ4RUBAABA/emP378bfscNAAAAQLUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJVqqvQC6H/t7e2dx/Pnz6/gSgAAAKB+rf0799q/i28KwU0DePHFFzuPp06dWsGVAAAAQGN48cUXs80222zy+yiVAgAAAKhShWKxWKz0Iuhfy5cvzyOPPJIk2WKLLdLU9LeNVvPnz+/chXPfffdlq622qsgaYSD4vtNIfN9pJL7vNBLfdxpJLX7f29vbO6tedtxxxwwfPnyT31OpVAMYPnx4dt11142et9VWW2XChAkDsCKoPN93GonvO43E951G4vtOI6ml73tflEetTakUAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUS3AAAAABUKcENAAAAQJUqFIvFYqUXAQAAAEA5O24AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4a2DPPPJOTTz45U6ZMyahRo7L55ptn6tSpOf/889Pa2lrp5cEmKxQK3frn7W9/e6WXChv0wgsv5KabbsoZZ5yR/fffP+PGjev8/h599NE9fr+f//znOfjggzNhwoQMGzYsEyZMyMEHH5yf//znfb946KG++L5feeWV3f53wJVXXtmvnwc25He/+13+8z//M/vvv38mTpyYYcOGZfTo0Zk8eXKOPvro3HnnnT16P/d3qllffN8b9f7eVOkFUBkzZ87M4YcfnsWLF3c+1tramtmzZ2f27Nm57LLLcvPNN2fbbbet4CoBSJItt9yyT96nWCzmIx/5SC699NIujz/77LO54YYbcsMNN+T444/PN77xjRQKhT65JvRUX33fodrttdde+fWvf132+MqVK/OnP/0pf/rTn/Ltb387H/zgB3PZZZdl6NCh630v93eqXV9+3xuR4KYBPfTQQznssMPS2tqa0aNH57TTTsv06dPT1taWa665Jv/zP/+TP/zhD5kxY0Zmz56d0aNHV3rJsEk++tGP5mMf+9h6nx81atQArgY2zcSJEzNlypTccsstPX7t5z73uc6/1O+000455ZRT8vrXvz5//vOfc9555+XBBx/MpZdemi222CL/8R//0ddLhx7blO/7Gr/4xS/y6le/er3PT5gwodfvDZvi2WefTZK8+tWvzqGHHpo999wzr33ta7N69ercc889ueCCC/Lss8/m6quvTnt7e773ve+t973c36l2ffl9X6Oh7u9FGs7b3/72YpJiU1NT8e677y57/rzzzismKSYpnnXWWRVYIfSNNd/jf//3f6/0UmCTnHHGGcWf/vSnxeeff75YLBaLTz75ZOf3+6ijjurWe/zpT38qNjU1FZMUd9lll2Jra2uX55ctW1bcZZddOv/9MGfOnL7+GNAtffF9v+KKKzpf8+STT/bfYmETzJgxo3jttdcW29vb1/n8iy++WJw8eXLnd/nXv/71Os9zf6cW9NX3vVHv73rcNJjZs2fnjjvuSJIce+yxmTZtWtk5J510UqZMmZIkueiii7Jq1aqBXCIAJc4666y8613v2qQSkgsvvDDt7e1Jkq9+9asZMWJEl+dHjhyZr371q0mS9vb2XHTRRb2+FmyKvvi+Qy246aabcthhh2Xw4MHrfH7cuHG54IILOn++7rrr1nme+zu1oK++741KcNNgbrzxxs7jY445Zp3nDBo0KEceeWSSZOHChZ1BDwC1qVgs5sc//nGSZIcddshuu+22zvN22223bL/99kle+fdFsVgcsDUCUG7tAQp//vOfy553f6eebOz73sgENw1mTafuUaNGZeedd17veXvttVfn8V133dXv6wKg/zz55JOdteVr39/XZc3z8+bNy1NPPdXfSwNgA1auXNl5PGhQ+a9u7u/Uk4193xuZP40G89hjjyVJJk2alKam9fem3mGHHcpeA7Xqhz/8YbbffvuMGDEiY8aMyXbbbZejjjoqt99+e6WXBgNi7fv42vf3dXH/p94cffTR2XLLLTN06NCMGzcuu+22Wz73uc91/rIL1WzWrFmdx+u6f7u/U0829n0v1Uj3d8FNA1m+fHkWLFiQZOMdtseOHds5aWfu3Ln9vjboT7///e/zxz/+McuXL8/SpUszZ86cXHXVVdl7771z0EEHZfHixZVeIvSrte/jG7v/T5w4cZ2vg1o1a9asvPDCC1m1alX++te/5re//W3OPvvsTJo0Kd/85jcrvTxYr46OjpxzzjmdPx922GFl57i/Uy+6830v1Uj3d+PAG8jLL7/cedydEd+jRo3KsmXLsnTp0v5cFvSbkSNH5t3vfnfe8Y53ZIcddsjo0aPz4osvZtasWfnGN76Rv/71r7nxxhvznve8J7/85S8zZMiQSi8Z+kVP7v9rQvsk7v/UtG233TYHH3xwpk2b1vkL6xNPPJEf/ehHue6667J8+fJ85CMfSaFQyPHHH1/h1UK5Cy+8MPfdd1+S5KCDDsouu+xSdo77O/WiO9/3NRrx/i64aSDLly/vPB46dOhGzx82bFiSpK2trd/WBP3p2WefTUtLS9nj++yzT0444YTsv//+efDBBzNr1qxccskl+dd//deBXyQMgJ7c/9fc+xP3f2rXQQcdlKOOOiqFQqHL47vuumve+9735qabbsrBBx+cVatW5VOf+lTe/e5351WvelWFVgvlZs2alVNPPTVJMn78+FxyySXrPM/9nXrQ3e970rj3d6VSDWT48OGdx2s3flqfFStWJEnZSEGoFesKbdbYcsstc91113X+JWfNmEyoRz25/6+59yfu/9Su5ubmsr/Ur+1d73pX/v3f/z1J0tramssvv3yglgYb9X//93856KCD0t7enmHDhuUHP/hBttxyy3We6/5OrevJ9z1p3Pu74KaBjBkzpvO4O9sjly1blqR7ZVVQi7bddtvss88+SZI5c+bkueeeq/CKoH/05P6/5t6fuP9T34477rjOv/yv3RATKunJJ5/Mvvvum4ULF2bw4MH5/ve/v8FpUe7v1LKeft+7qx7v74KbBjJ8+PCMGzcuyStjADdk4cKFnTf3tRuZQb35+7//+87jeuxAD0nXhpUbu/+v3bDS/Z96Nn78+M6/F7n/Uw2ee+65vPOd78xzzz2XQqGQb33rWznooIM2+Br3d2pVb77v3VWP93fBTYOZMmVKkld2F7S3t6/3vMcff7zsNVCPisVipZcA/W7tgHLt+/u6uP/TSPw7gGqxYMGC7LPPPnniiSeSvFLCfeSRR270de7v1KLeft97ot7u74KbBrPHHnskeWWr5AMPPLDe89beUrb77rv3+7qgUn7/+993Hr/61a+u4Eqg/7zuda/r/H5vbMvwr3/96yTJa17zmmyzzTb9vTSomBdeeCF//etfk7j/U1mLFy/Ofvvt1/l3knPOOScf//jHu/Va93dqzaZ837urHu/vgpsGc+CBB3YeX3HFFes8p6OjI1dddVWSV5q7Tp8+fSCWBgPuiSeeyC9/+cskr/S7ec1rXlPhFUH/KBQKec973pPklf/ieu+9967zvHvvvbfzv8i+5z3v2WDzP6h1l156aed/ke2LngrQG62trZkxY0Z+97vfJUk++9nP5jOf+Uy3X+/+Ti3Z1O97d9Xj/V1w02CmTp2aPffcM0ly+eWX55577ik754ILLshjjz2WJPnkJz+ZIUOGDOgaoS/89Kc/3WA54F/+8pf8y7/8S1atWpUkfZ70Q7U58cQT09TUlCQ54YQTykbBtrW15YQTTkiSNDU15cQTTxzoJUKfeOqpp/Lggw9u8JybbropX/ziF5O80gPwmGOOGYilQRcrV67MQQcdlN/85jdJXvl793/8x3/0+H3c36kFffF9b+T7e1OlF8DAu/jii7P77runra0t++67b04//fRMnz49bW1tueaaa3LppZcmSSZPnpyTTjqpwquF3jnhhBOyatWqHHLIIZk2bVq22WabjBgxIgsWLMgdd9yRb3zjG51bKPfYYw/BDVXtrrvuypw5czp/XrBgQefxnDlzcuWVV3Y5/+ijjy57j8mTJ+fkk0/OOeeck/vvvz+77757PvOZz+T1r399/vznP+fcc8/t/MvQpz/96Wy33Xb98llgYzb1+/7UU09l+vTpmTZtWg444IC8+c1vzvjx41MsFvPEE0/kuuuuy3XXXdf5X2PPP/98Oy6piPe///255ZZbkiR77713jj322Dz66KPrPX/o0KGZPHly2ePu79SCvvi+N/L9vVCst649dMtPf/rTHHHEEVmyZMk6n588eXJmzpyZSZMmDfDKoG9ss802efrppzd63iGHHJLLLrssLS0t/b8o6KWjjz463/72t7t9/vr+1d7R0ZHjjjsu3/rWt9b72mOPPTaXXnppBg2yKZfK2NTv+x133NGtMu+RI0fmwgsvzPHHH9/jNUJf6Gm50tZbb52nnnpqnc+5v1Pt+uL73sj3dztuGtQBBxyQhx9+OBdffHFmzpyZefPmZejQoZk0aVIOPfTQfOITn8jIkSMrvUzotW9/+9uZNWtW7rnnnjzxxBNZsGBBlixZktGjR2fixIl529velqOOOirTpk2r9FJhwAwaNCiXX355DjnkkFx66aWZPXt2FixYkHHjxmXXXXfNhz/84ey///6VXiZskp133jnf+c53cs899+T+++/P/Pnzs2DBgrS3t2fs2LF5wxvekHe84x350Ic+lPHjx1d6udAn3N9pBI18f7fjBgAAAKBK2ScHAAAAUKUENwAAAABVSnADAAAAUKUENwAAAABVSnADAAAAUKUENwAAAABVSnADAAAAUKUENwAAAABVSnADAAAAUKUENwAAAABVSnADAAAAUKUENwAAAABVSnADAAAAUKUENwAAAABVSnADAAAAUKUENwAAAABVSnADAAAAUKUENwAA9Nizzz6biy66KPvuu29e+9rXZujQoXnVq16VQw45JL/97W8rvTwAqBuFYrFYrPQiAACoLaeeemrOPffcvP71r89ee+2V8ePH509/+lNuvPHGFIvFfP/7389hhx1W6WUCQM0T3AAA0GPXX399tthii+y5555dHr/zzjvzjne8I2PGjMlzzz2XYcOGVWiFAFAflEoBADXpzDPPTKFQSKFQqPRSum3VqlXZfvvtUygUcu2111Z6OZvk4IMPLgttkmTPPffM9OnT89JLL+WRRx4pe/5jH/tYCoVCjjrqqIFYJgDUPMENANCwXnjhhdx0000544wzsv/++2fcuHGdYdDRRx/d59f76le/mj/+8Y+ZMmVKDj300D5//2oxZMiQJElTU1PZc6eddlqGDh2aq6++OrNnzx7opQFAzSn/tykAQIPYcsstB+xaS5cuzZe+9KUkyRlnnJFBg+rzv58988wzufXWW/OqV70qO+64Y9nzEydOzFFHHZX/+Z//yec+97n84he/qMAqAaB21OffGAAAemjixInZd999++39L7nkkixYsCATJ06s26a9q1atygc/+MGsWLEi5513XgYPHrzO80466aQkyS233GLXDQBshOAGAGhYZ5xxRn7605/m+eefzzPPPJNvfvOb/XKd1atX52tf+1qS5P3vf39V7LZZuyysO//ccccdG3y/jo6O/H//3/+XX//61znuuOPywQ9+cL3nbr/99nnLW96SJLn44ov78mMBQN1RKgUANKyzzjprQK7zy1/+Ms8880yS5IgjjhiQa27M+9///rz88svdPv9Vr3rVep8rFos57rjj8p3vfCdHHHFEvvGNb2z0/Q4//PD87ne/y49+9KP893//d5qbm7u9FgBoJIIbAKBuPfzww9lvv/3y/PPPZ/z48fnFL36RN7/5zQO+jh/84AdJku22226dfV/WOPPMMzvDpGKxmCVLluQrX/lKfvSjH+XJJ5/MqFGjsvPOO+dzn/tc3va2t3W+7oUXXsh//dd/5YYbbshTTz2V4cOHZ4899siZZ56ZnXbaaZ3X+upXv9onn62joyMf+tCHcsUVV+T9739/rrzyym7tKDrkkENy0kknZfny5fnxj3+cI488sk/WAwD1pvL7dAEA+sFvfvOb7LXXXnn++eez9dZb56677qpIaJMkt99+e5Jkt9126/Zr5s6dm1133TVnnXVWHn300SxbtiwvvPBCfvazn+Uf//Ef88Mf/jDJK+HUW97ylpx99tn5/e9/n9bW1rz00kv5yU9+kmnTpuVXv/pVv3ympGto8973vjdXX331evvalNp6662z1VZbJclGy7AAoJEJbgCAuvOzn/0s++67bxYtWpQpU6bkrrvuynbbbVeRtcybNy9PPfVUkmTXXXft9usOPfTQzJs3L6eddlpmzZqV2bNn58ILL8xmm22W1atX59hjj82TTz6Zd73rXWlra8vZZ5+du+66K7/97W9z1llnZejQoVmxYkWOOeaYrFy5ss8/V0dHR4499thcccUVOfTQQ/Od73yn26HNGmv+PO68884+Xx8A1AulUgBAXbnmmmty5JFHZtWqVdl1113zs5/9LH/3d39XsfXcfffdncfrK1tal//93//NrFmz8ta3vrXzsV122SWTJ0/OjBkz8vLLL+etb31risVi7rvvvrz+9a/vPG/q1KkZN25cPv7xj+eZZ57JzJkzc9BBB/XNB/p/vvCFL+TKK6/M6NGjM3ny5PzHf/xH2TkHHnjgBnc57bzzzvnJT36SOXPm5IUXXsj48eP7dI0AUA8ENwBA3bjkkkvyiU98Ih0dHdl7773z4x//OKNHj67omubNm9d53JNg4sQTT+wS2qzxz//8z9l6663z9NNP58UXX8w3vvGNLqHNGsccc0xnD5k777yzz4ObNbuIli5dmrPPPnud52yzzTYbDG7W/vN49tlnBTcAsA5KpQCAunD22WfnYx/7WDo6OnLggQfm5ptvrnhokyQvvvhi5/HYsWO7/br3ve99633uTW96U5KkUCjksMMOW+c5I0aM6CwPe+KJJ7p93e668sorUywWN/jP0UcfvcH32HzzzTuP1/5zAgD+RnADANS8f/u3f8vnPve5JMnRRx+d6667LsOGDavwql7x0ksvdR73JLiZPHnyep9raWlJkowbN26D77nmvJ6M/R5Ia6/9r3/9awVXAgDVS3ADANS8Cy+8MEnyxje+MZdffnmPm+T2p+HDh3cet7W1dft1I0eOXO9za8Ztb+ictc9bvXp1t687kNb+8xgxYkQFVwIA1UtwAwDUvEMOOSRJ8uijj+aTn/xkhVfT1RZbbNF5vPbuG7r+eaz95wQA/I3gBgCoed///vdz4IEHJkm+9rWv5VOf+lRlF7SWtQOJhQsXVnAl1WftPw/BDQCsm+AGAKh5Q4YMyQ9+8IO8+93vTpJcdNFF+fSnP13hVb1ixx137Dz+4x//WMGVVJ81fx6jRo3KtttuW+HVAEB1EtwAAHVhyJAh+eEPf5gZM2YkSc4///yceuqpFV5Vsssuu3T2b5k9e3aFV1Nd1vx57LbbbmlqaqrwagCgOgluAIC6MXTo0PzoRz/K/vvvnyQ599xzO6dNVXJNU6dOTZLcd999FV1LNVmxYkUefvjhJMmee+5Z4dUAQPXynzYAgLoybNiw3HDDDXnPe96TX/ziFzn77LMzePDgnHXWWWXn3nXXXZkzZ07nzwsWLOg8njNnTq688sou5x999NG9WtOMGTMya9as3HfffXn55ZczZsyYXr1PPfn1r3+dVatWJUnnLikAoJzgBgCoO8OGDcuNN96Yd7/73fnlL3+ZL3zhC2lqasrnP//5Ludddtll+fa3v73O9/jNb36T3/zmN10e621w84EPfCCnnXZali9fnhtuuCFHHnlkr96nnnzve99Lkmy//fbZZZddKrwaAKheSqUAgLo0fPjw/PjHP87ee++dJDnjjDPypS99qSJrec1rXpP3vOc9SZLvfve7FVlDNVkTYCXJxz72sQqvBgCqW6FYLBYrvQgAgHp37733Ztq0aRk8eHDmzJmTbbbZptJLqpjvfOc7+eAHP5jNN988Tz31lNIxANgAO24AAAbAbrvtlv333z+rV6+u2M6fatDR0ZH//M//TJKcfPLJQhsA2Ag7bgAABsgjjzySnXbaKYMGDcqcOXPy2te+ttJLGnDXXntt3ve+92XixIn5wx/+0DkqHQBYN82JAQAGyI477pgrr7wyc+bMyTPPPNOQwc3q1avz7//+79l7772FNgDQDXbcAAAAAFQpPW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqpTgBgAAAKBKCW4AAAAAqtT/D9Ws69hqP1+SAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -239,7 +241,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -254,7 +256,7 @@ }, { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABIEAAANmCAYAAABt0Jt1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAB7CAAAewgFu0HU+AACtrUlEQVR4nOzdd3iUVd7/8c+dTgoJJCGFBBKRKoiFpiigCIgiEVSUtWFvuPZ1f48Kuro+4Crouo+FRcSyVlRkQQFhIXQRBEFAhZBAQkIgCSWkTzK/P1jGzKSQMpk7M/N+XVeua+bc5Xz1ER/z2XPO17BarVYBAAAAAADAo/mYXQAAAAAAAABaHiEQAAAAAACAFyAEAgAAAAAA8AKEQAAAAAAAAF6AEAgAAAAAAMALEAIBAAAAAAB4AUIgAAAAAAAAL0AIBAAAAAAA4AUIgQAAAAAAALwAIRAAAAAAAIAXIAQCAAAAAADwAoRAAAAAAAAAXoAQCAAAAAAAwAsQAgEAAAAAAHgBQiAAAAAAAAAvQAgEAAAAAADgBfzMLgDuo7S0VNu3b5ckRUdHy8+Pf3wAAAAAAHA2i8Wiw4cPS5L69OmjoKAgp7yX3+LRYNu3b9eAAQPMLgMAAAAAAK+xceNG9e/f3ynvYjsYAAAAAACAF2AlEBosOjra9nnjxo2Ki4szsRoAAAAAADxTTk6ObSdO9d/Fm4sQCA1W/QyguLg4JSQkmFgNAAAAAACez5nn8bIdDAAAAAAAwAsQAgEAAAAAAHgBQiAAAAAAAAAvQAgEAAAAAADgBQiBAAAAAAAAvAAhEAAAAAAAgBcgBAIAAAAAAPAChEAAAAAAAABegBAIAAAAAADACxACAQAAAAAAeAFCIAAAAAAAAC9ACAQAAAAAAOAFCIEAAAAAAAC8ACEQAAAAAACAFyAEAgAAAAAA8AKEQAAAAAAAAF6AEAgAAAAAAMALEAIBAAAAAAB4AUIgAAAAAAAAL0AIBAAAAAAA4AUIgQAAAAAAALwAIRAAAAAAAIAXIAQCAAAAAADwAoRAAAAAAAAAXoAQCAAAAAAAwAsQAgEAAAAAAHgBQiAAAAAAAAAvQAgEAAAAAADgBQiB4PFKyiu1K+e4vt2eo4KicrPLAQAAAADAFH5mFwC0pNGvrdaunOO27+9O6q9LenQwsSIAAAAAAMzBSiB4tCB/+3/E0/OKTKoEAAAAAABzEQLBoyVHhdh9JwQCAAAAAHgrQiB4tORI+xAoI58QCAAAAADgnQiB4NGSWAkEAAAAAIAkQiB4OMftYAeOlqi0otKkagAAAAAAMA8hEDya40ogq1XKLCg2qRoAAAAAAMxDCASPFhrop+iwQLsxtoQBAAAAALwRIRA8Hh3CAAAAAAAgBIIXoEMYAAAAAACEQPACdAgDAAAAAIAQCF6A7WAAAAAAABACwQs4hkC5x8tUXG4xqRoAAAAAAMxBCASP1zkyuMZYRh5t4gEAAAAA3oUQCB4vyN9XHSPa2I2xJQwAAAAA4G0IgeAVkqLsVwPRIQwAAAAA4G0IgeAVkiI5HBoAAAAA4N0IgeAV6BAGAAAAAPB2Xh0C/fjjj3rxxRc1evRoJSYmKjAwUKGhoerWrZsmTZqk1atXN+p9ixcv1vjx45WQkKDAwEAlJCRo/PjxWrx4sVPrzs/P19SpU9W3b1+Fh4erbdu26tu3r6ZOnar8/HynzuUpHEOgDEIgAAAAAICXMaxWq9XsIswwdOhQrVq16rT33XzzzZo9e7YCAgLqvMdqteree+/VrFmz6rzn7rvv1ltvvSXDMJpU7yk//PCDUlJSlJOTU+v1+Ph4ff311+rXr1+z5qlNVlaWEhMTJUmZmZlKSEhw+hwtJe3wCQ1/JdVu7KepIxXext+kigAAAAAAqF1L/f7ttSuBDhw4IOlkaPLQQw9p3rx52rhxo9avX68ZM2aoY8eOkqQPPvhAkyZNqvddTz/9tC0AOvfcc/Xxxx9r48aN+vjjj3XuuedKkmbNmqVnnnmm2TVfddVVysnJkZ+fn/70pz9p1apVWrVqlf70pz/Jz89P2dnZGjNmjO2vDycltguWr499AMdqIAAAAACAN/HalUBjxozRLbfcomuuuUa+vr41rufl5Wnw4MH67bffJEmrVq3SxRdfXOO+PXv2qGfPnrJYLOrXr59WrVqlNm1+b0deXFysoUOHatOmTfLz89Mvv/yiLl26NKnmSZMm6b333pMkffbZZ7ruuuvsrn/++eeaMGGCJOm2227TnDlzmjRPXdx5JZAkDf3bCu3LL7Z9f+2Gc5RyTkcTKwIAAAAAoCZWAjnZwoULNWHChFoDIEmKiorSK6+8Yvs+b968Wu+bOXOmLBaLJOn111+3C4AkKTg4WK+//rokyWKx6NVXX21Svbm5ufrwww8lSaNGjaoRAEnSddddp1GjRkmS3n//feXm5jZpLk9FhzAAAAAAgDfz2hCoIYYNG2b7nJaWVuO61WrV119/LUnq0aOHBg0aVOt7Bg0apO7du0uS5s+fr6YsvlqwYIEqKyslnVzlU5dTW9cqKyu1YMGCRs/jyegQBgAAAADwZoRA9SgvL7d99vGp+bcqPT3ddvbO0KFD633XqetZWVnKyMhodC3VO5XVN1f1a2vWrGn0PJ6MDmEAAAAAAG9GCFSP1NTfu0n16NGjxvVdu3bVe7266terP9dQp54JDw9XbGxsnffFxcWpbdu2TZ7HkyXVshLIS4/EAgAAAAB4IT+zC2itqqqqNG3aNNv3UwcuV5eZmWn7fLpDmk4d6OT4XEOdeqYhh0ElJiZqx44djZ4nKyur3ut1taV3F2c4hEDHSy0qKCpXZGigSRUBAAAAAOA6hEB1mDlzpjZu3ChJGjdunPr161fjnsLCQtvn0NDQet8XEvJ7AHHixIlG13NqrtPNU32uxs5TPajyRPERbRTg66PyyirbWEZ+ESEQAAAAAMArsB2sFqmpqfrzn/8sSerQoYPefPPNWu8rLS21fQ4ICKj3nYGBvwcNJSUlja7p1Fynm6f6XE2Zx5P5+hhKbG/fvS09r7iOuwEAAAAA8CysBHKwY8cOjRs3ThaLRYGBgfrss88UExNT671BQUG2z9UPka5NWVmZ7bNjG/mGCAoKUnFx8WnnqT5XY+c53faxnJwcDRgwoFHvbG2So0KVdvj3A6HT8xq/KgsAAAAAAHdECFRNenq6Ro4cqSNHjsjX11cff/xxvZ24wsLCbJ9Pt/WqqOj34KEhW7pqm6u4uLhBW7xOzdXYeRpy3pC7S44KtvuewUogAAAAAICXYDvYf2VnZ+uyyy5Tdna2DMPQnDlzNG7cuHqfqR6anO5Q5eqrbJpy9s6puU43T/W5PP2Mn6aorUMYAAAAAADegBBIUl5enkaMGKG9e/dKkl5//XXdcsstp32uV69ets+//PJLvfdWv96zZ89G13hqrmPHjungwYN13peTk6Pjx483eR5Pl+wQAmXk0yYeAAAAAOAdvD4EOnbsmEaNGqWdO3dKkqZNm6YHHnigQc8mJycrPj5e0snDpOuzatUqSVLHjh2VlJTU6Dovuugi2+f65qp+bfDgwY2ex9M5hkDF5ZU6VFhWx90AAAAAAHgOrw6BiouLdeWVV+rHH3+UJD311FN68sknG/y8YRhKSUmRdHKlz4YNG2q9b8OGDbaVQCkpKTIMo9G1jh07Vj4+J//P9e6779Z539y5cyVJPj4+Gjt2bKPn8XQxYUEK8rf/x54tYQAAAAAAb+C1IVB5ebnGjRuntWvXSpIeeughvfDCC41+z8MPPyw/v5Pnaz/44IM12rKXlJTowQcflCT5+fnp4YcfrvU9w4YNk2EYMgxDGRkZNa7HxsbqxhtvlCQtWbJE8+bNq3HP559/riVLlkiSbr75ZsXGxjb6r8fT+fgYSorkXCAAAAAAgPfx2u5gEydO1NKlSyVJl156qe644w79/PPPdd4fEBCgbt261Rjv1q2bHn/8cU2bNk2bNm3S4MGD9eSTT6pLly5KS0vT9OnTtWXLFknSE088oa5duza55r/+9a9avHixDh8+rIkTJ2rTpk0aM2aMJGnhwoV65ZVXJEnR0dFNCrS8RXJUiH45WGj7nkEIBAAAAADwAobVS0/FbeyWrM6dO9e6QkeSqqqqdNddd2nOnDl1Pn/HHXdo1qxZti1djoYNG2Y7zyc9Pb3Oc4O+//57XX311XUeDh0bG6v58+dr4MCBdf/FNFFWVpat41hmZqbbtpSfvvgXvbkyzfZ9ZK8Yzbqln4kVAQAAAADwu5b6/dtrt4M5k4+Pj9555x0tWrRIKSkpio+PV0BAgOLj45WSkqJvvvlGs2fPrjMAaoyBAwdq+/btevrpp9W7d2+FhoYqNDRUffr00dNPP62ff/65RQIgT5LMdjAAAAAAgBfy2pVAaDxPWQn0Q0aBrntrve17gJ+PfvnL5fLxafyB3QAAAAAAOBsrgQAncTwYutxSpexjJXXcDQAAAACAZyAEgteJCg1QaKD9mehsCQMAAAAAeDpCIHgdwzCUHGW/GogOYQAAAAAAT0cIBK+UFOV4OHSxSZUAAAAAAOAahEDwSsmRwXbf0/NOmFQJAAAAAACuQQgEr5Qc7bAdLJ+VQAAAAAAAz0YIBK/k2CEss6BYFZVVJlUDAAAAAEDLIwSCV3I8GNpSZVXWEdrEAwAAAAA8FyEQvFJEcIDaBfvbjdEhDAAAAADgyQiB4LVqdggjBAIAAAAAeC5CIHit5EhCIAAAAACA9yAEgtdyPBcoI58QCAAAAADguQiB4LXYDgYAAAAA8CaEQPBajiuBDhwtUWlFpUnVAAAAAADQsgiB4LUcVwJZrVJmQbFJ1QAAAAAA0LIIgeC1QgP9FB0WaDfGljAAAAAAgKciBIJXo0MYAAAAAMBbEALBq9EhDAAAAADgLQiB4NXoEAYAAAAA8BaEQPBqyVHBdt8JgQAAAAAAnooQCF4tOSrU7nvu8TIVl1tMqgYAAAAAgJZDCASv1jkyuMZYRh5t4gEAAAAAnocQCF4tyN9X8eFBdmNsCQMAAAAAeCJCIHi95Gg6hAEAAAAAPB8hELxeUiQdwgAAAAAAno8QCF4vmTbxAAAAAAAvQAgEr+cYAmUQAgEAAAAAPBAhELxekkMIlF9UrmMlFSZVAwAAAABAyyAEgtdLbBcsH8N+jNVAAAAAAABPQwgErxfg56PE9sF2Y3QIAwAAAAB4GkIgQHQIAwAAAAB4PkIgQBwODQAAAADwfIRAgGgTDwAAAADwfIRAgGp2CEvPK5LVajWpGgAAAAAAnI8QCJCU7HAm0PFSi44U0yYeAAAAAOA5CIEASR3btZG/r32f+PS8EyZVAwAAAACA8xECAZJ8fQx1cmgTn55XbFI1AAAAAAA4HyEQ8F90CAMAAAAAeDJCIOC/6BAGAAAAAPBkhEDAf9XWIQwAAAAAAE9BCAT8l2OHsIx82sQDAAAAADwHIRDwX8nR9iFQcXmlDhWWmVQNAAAAAADORQgE/FdMWJCC/O3/SLAlDAAAAADgKQiBgP/y8TGU5LgljBAIAAAAAOAhCIGAaugQBgAAAADwVIRAQDV0CAMAAAAAeCpCIKCa2jqEAQAAAADgCQiBgGocO4Rl5Berqoo28QAAAAAA90cIBFTjeDB0uaVK2cdKTKoGAAAAAADnIQQCqokKDVBooJ/dWEZesUnVAAAAAADgPIRAQDWGYdTSIeyESdUAAAAAAOA8hECAg5odwlgJBAAAAABwf4RAgIPkyGC773QIAwAAAAB4AkIgwIFjh7D0PEIgAAAAAID7IwQCHDh2CMssKJalssqkagAAAAAAcA5CIMCB48HQliqrso7QJh4AAAAA4N4IgQAHEcEBahfsbzfGljAAAAAAgLsjBAJqUbNDGCEQAAAAAMC9EQIBtUh2OBeIDmEAAAAAAHdHCATUwvFcIFYCAQAAAADcHSEQUAu2gwEAAAAAPA0hEFALx5VAB46WqMxSaVI1AAAAAAA0HyEQUAvHlUBWq7Q/v9ikagAAAAAAaD5CIKAWoYF+ig4LtBtjSxgAAAAAwJ0RAgF1oEMYAAAAAMCTEAIBdaBDGAAAAADAkxACAXWgQxgAAAAAwJMQAgF1SI4KtvuekcfB0AAAAAAA9+XVIdChQ4e0cOFCTZkyRaNHj1ZUVJQMw5BhGJo0adJpn8/IyLDd39CfpKSkJteblJTU4nPgd8lRoXbfDx4vVXG5xaRqAAAAAABoHj+zCzBTTEyMy+fs3r27y+dE03SODK4xlpFXrF7xbU2oBgAAAACA5vHqEKi6xMRE9ezZU0uXLm3wMx07dtT27dtPe9///u//6qOPPpIk3XrrrU2u8ZSUlBS98MILdV4PCAho9hyQgvx9FR8epOxjpbaxjPwiQiAAAAAAgFvy6hBoypQp6t+/v/r376+YmBhlZGQoOTm5wc/7+/urd+/e9d5TWVmplStXSpLCwsJ09dVXN6PikyIiIk47L5wjOTrELgTicGgAAAAAgLvy6hDoueeea/E5li1bpuzsbEnStddeq+DgmluM0HolRYZo7Z5823dCIAAAAACAu/Lqg6Fd4f3337d9dsZWMLhWskOb+AxCIAAAAACAmyIEakGFhYWaP3++JKlz584aMmSIuQWh0RxDIFYCAQAAAADclVdvB2tp8+bNU3FxsSTplltukWEYTnnvqlWrdPbZZystLU1Wq1UxMTEaMGCAJk6cqJSUlCbPk5WVVe/1nJycJr3XnSU5hED5ReU6XlqhtkH+JlUEAAAAAEDTEAK1oOpbwW655RanvTc9Pd3ue0ZGhjIyMvTZZ59p8ODB+vTTT9WxY8dGvzcxMdFZJXqMxHbB8jGkKuvvYxl5RTo7IcK0mgAAAAAAaAq2g7WQ/fv3KzU1VZJ04YUX6swzz2z2OwMCAjR27Fj94x//0MqVK7VlyxatWLFCL774oi3AWbt2rUaMGKFjx441ez5IAX4+Smxvf5g3W8IAAAAAAO6IlUAt5MMPP5TVenL5iLNWAW3cuFERERE1xocNG6bJkyfr2muv1dKlS7Vr1y4999xzmjFjRqPen5mZWe/1nJwcDRgwoFHv9ARJkSHal19s+04IBAAAAABwR4RALeSDDz6QJAUGBur66693yjtrC4BOCQsL02effaYuXbooPz9fs2bN0rRp0xQQENDg9yckJDihSs+THBWi1N8O277TIQwAAAAA4I7YDtYCNm7cqF9++UWSNHbs2HrDG2cKDw/XDTfcIEkqKirSpk2bXDKvp6NDGAAAAADAExACtYCWOhC6IXr16mX7fODAAZfO7akcO4Sl5xXZtvoBAAAAAOAuCIGcrKKiQp9++qkkqUOHDrr88stdOj/hhPMlR9qHQMdLLTpSXGFSNQAAAAAANA0hkJMtWrRIeXl5kqQ//OEP8vNz7bFLO3futH2Oj4936dyeqmO7NvL3NezG0vNOmFQNAAAAAABNQwjkZNW3gt16660unfvYsWO2VUjBwcHq16+fS+f3VL4+hjrVaBNfXMfdAAAAAAC0ToRATlRQUKBFixZJkvr06aNzzjmnwc8OGzZMhmHIMAxlZGTUuL548WKVlJTU+XxhYaEmTJig/Px8SdIdd9yhwMDARtWPujkeDk2HMAAAAACAu/HqFvFr1qzRnj17bN9PbeOSpD179mju3Ll290+aNKne933yyScqLy+X5PxVQNOmTdONN96o8ePH66KLLlKXLl0UGhqqo0ePav369XrzzTeVmZkpSerevbueffZZp87v7egQBgAAAABwd14dAs2ePVvvvfderdfWrl2rtWvX2o2dLgQ6tRXM19dXN954o1NqrK6goECzZ8/W7Nmz67xnyJAh+uijj9S+fXunz+/NausQBgAAAACAO/HqEMiZdu/ere+//16SNGLECMXGxjr1/S+//LKWL1+u9evX69dff1VeXp6OHj2q4OBgxcfHa+DAgZo4caJGjhwpwzBO/0I0imOHsIz8k23i+XsNAAAAAHAXhpWe4migrKwsJSYmSpIyMzOVkJBgckWuk320RBdO+4/d2Pf/M1wxbYNMqggAAAAA4Kla6vdvDoYGGiC2bZCC/O3/uLAlDAAAAADgTgiBgAbw8TGU5LgljBAIAAAAAOBGCIGABnIMgVgJBAAAAABwJ4RAQAMlRxMCAQAAAADcFyEQ0EC1dQgDAAAAAMBdEAIBDZQU5RgCFauqiuZ6AAAAAAD3QAgENFCyQwhUbqlS9rESk6oBAAAAAKBxCIGABooKDVBooJ/dWEZesUnVAAAAAADQOIRAQAMZhqGkqGC7sfS8EyZVAwAAAABA4xACAY2QHBVq9z2dlUAAAAAAADdBCAQ0QnKk/UogOoQBAAAAANwFIRDQCI4dwtLzCIEAAAAAAO6BEAhoBMcOYZkFxbJUVplUDQAAAAAADUcIBDSCYwhkqbIq6wht4gEAAAAArR8hENAIEcEBigj2txtjSxgAAAAAwB0QAgGN5LgaiBAIAAAAAOAOCIGARkqOtA+B6BAGAAAAAHAHhEBAI9EhDAAAAADgjgiBgEZiOxgAAAAAwB0RAgGN5BgCHThaojJLpUnVAAAAAADQMIRAQCM5bgezWqX9+cUmVQMAAAAAQMMQAgGNFBrop+iwQLsxtoQBAAAAAFo7QiCgCegQBgAAAABwN4RAQBMkRQXbfWclEAAAAACgtSMEApogOSrU7jshEAAAAACgtSMEApog2WElUEYeB0MDAAAAAFo3QiCgCRw7hB08XqricotJ1QAAAAAAcHqEQEATJDkcDC2xGggAAAAA0LoRAgFNEOTvq/jwILsxOoQBAAAAAFozQiCgiRy3hHE4NAAAAACgNSMEApoomRAIAAAAAOBGCIGAJnIMgTIIgQAAAAAArRghENBEjodDcyYQAAAAAKA1IwQCmig52j4EyjtRruOlFSZVAwAAAABA/QiBgCZKbBcsH8N+jC1hAAAAAIDWihAIaKIAPx8ltAu2G+NwaAAAAABAa0UIBDQDHcIAAAAAAO6CEAhoBjqEAQAAAADcBSEQ0AxJkQ7bwfKLTaoEAAAAAID6EQIBzZAcHWr3Pf3wCVmtVpOqAQAAAACgboRAQDMkR9pvBzteatGRYtrEAwAAAABaH0IgoBniI4Lk72vfJ57DoQEAAAAArREhENAMfr4+6tSeNvEAAAAAgNaPEAhoJjqEAQAAAADcASEQ0ExJDucCpecTAgEAAAAAWh9CIKCZkqMdQqDDhEAAAAAAgNaHEAhoJscOYRn5RbSJBwAAAAC0OoRAQDMlOZwJVFxeqcOFZSZVAwAAAABA7QiBgGaKbRukIH/7P0p7ORwaAAAAANDKEAIBzeTjY9Q4HJoOYQAAAACA1oYQCHACOoQBAAAAAFo7QiDACegQBgAAAABo7QiBACeorUMYAAAAAACtCSEQ4ASOHcIy8otVVUWbeAAAAABA60EIBDhBskMIVG6pUvaxEpOqAQAAAACgJkIgwAmiQgMUGuhnN5aRV2xSNQAAAAAA1EQIBDiBYRhKigq2G6NDGAAAAACgNSEEApwkOSrU7jsdwgAAAAAArQkhEOAkyZH2K4HoEAYAAAAAaE0IgQAnqdEhLI8QCAAAAADQehACAU7i2CFsf0GxLJVVJlUDAAAAAIA9QiDASRxDIEuVVVlHaBMPAAAAAGgdCIEAJ4kIDlBEsL/dGB3CAAAAAACtBSEQ4ESOq4HoEAYAAAAAaC0IgQAnSo50OByalUAAAAAAgFaCEAhwIscOYel0CAMAAAAAtBKEQIAT1dgORggEAAAAAGglvDoEOnTokBYuXKgpU6Zo9OjRioqKkmEYMgxDkyZNatA75s6da3vmdD9z5851St35+fmaOnWq+vbtq/DwcLVt21Z9+/bV1KlTlZ+f75Q50DSOIVD20RKVWSpNqgYAAAAAgN/5mV2AmWJiYswuodF++OEHpaSkKCcnx25827Zt2rZtm2bPnq2vv/5a/fr1M6lC7+a4HazKKmUWFOvMDmEmVQQAAAAAwEleHQJVl5iYqJ49e2rp0qVNfseSJUsUHx9f5/WEhIQmv1uSDhw4oKuuukq5ubny8/PTo48+qjFjxkiSFi5cqBkzZig7O1tjxozR5s2b1bFjx2bNh8YLDfRTdFigDheW2cb2Hi4iBAIAAAAAmM6rQ6ApU6aof//+6t+/v2JiYpSRkaHk5OQmv69bt25KSkpyXoEOnnrqKeXm5kqSPvroI1133XW2axdffLH69eunCRMmKDc3V88884zmzJnTYrWgbsmRIXYhEB3CAAAAAACtgVefCfTcc89pzJgxbrEtLDc3Vx9++KEkadSoUXYB0CnXXXedRo0aJUl6//33bYERXCspKtjue3pesUmVAAAAAADwO68OgdzJggULVFl58oDh2267rc77Th1oXVlZqQULFriiNDhIjgq1+56ed8KkSgAAAAAA+B0hkJtYvXq17fPQoUPrvK/6tTVr1rRoTahdssNKoAxWAgEAAAAAWgFCICeaNGmSYmJiFBAQoKioKA0aNEhPP/20Dhw40Ox379q1S5IUHh6u2NjYOu+Li4tT27Zt7Z6Bazl2CDt4vFTF5RaTqgEAAAAA4CSvPhja2VJTU22f8/PzlZ+fr++//16vvPKKXn31Vd1zzz1NfndmZqakhnUYS0xM1I4dO2zPNFRWVla91x3b0qN2SZEhNcYy8orVK76tCdUAAAAAAHASIZATnHHGGRo/frwuuOACJSYmSpL27t2rL774QvPmzVNpaanuvfdeGYahu+++u0lzFBYWSpJCQ0NPc6cUEnIyhDhxonFn0ZyqHc0T5O+r+PAgZR8rtY1l5BcRAgEAAAAATEUI1Ezjxo3TrbfeKsMw7Mb79++v66+/XgsXLtT48eNVUVGhRx55RGPHjq13O1ddSktPBgoBAQGnvTcwMFCSVFJS0uh54BxJUSF2IVB6Hm3iAQAAAADm4kygZgoPD68RAFU3ZswYTZ06VZJUXFysd955p0nzBAUFSZLKy8tPe29ZWZkkqU2bNo2aIzMzs96fjRs3Nr5wL5XscC4QIRAAAAAAwGyEQC5w11132YKi6ucGNUZYWJikhm3xKio6GTg0ZOtYdQkJCfX+xMXFNb5wL+UYAmUQAgEAAAAATEYI5AIdOnRQVFSUJDW5U9ipA6FPd3iz9Psh0pzxYx7Hw6Ez8gmBAAAAAADmIgRyEavV2qzne/XqJUk6duyYDh48WOd9OTk5On78uCSpZ8+ezZoTTZccbR8C5Z0o1/HSCpOqAQAAAACAEMglDh06pPz8fElSfHx8k95x0UUX2T7Xt6Ws+rXBgwc3aS40X2K7YPk4HBXFljAAAAAAgJkIgVxg1qxZtpVAQ4cObdI7xo4dKx+fk//nevfdd+u8b+7cuZIkHx8fjR07tklzofkC/HyU0C7YbozDoQEAAAAAZiIEaoaMjAxt2bKl3nsWLlyo559/XtLJDl+33XZbrfcNGzZMhmHIMAxlZGTUuB4bG6sbb7xRkrRkyRLNmzevxj2ff/65lixZIkm6+eabm9SKHs5DhzAAAAAAQGviZ3YBZlqzZo327Nlj+56Xl2f7vGfPHtuqmlMmTZpk9z0jI0OXXHKJLrjgAl111VU655xz1KFDB1mtVu3du1fz5s3TvHnzbKuAXn75ZXXs2LHJ9f71r3/V4sWLdfjwYU2cOFGbNm3SmDFjJJ0Mm1555RVJUnR0tF544YUmzwPnSI4KUepvh23f2Q4GAAAAADCTV4dAs2fP1nvvvVfrtbVr12rt2rV2Y44h0Cnr16/X+vXr65wnODhYM2fO1N13393kWqWT3b7+/e9/6+qrr9bBgwc1ffp0TZ8+3e6e2NhYzZ8/39ZNDOZJinTYDpZfbFIlAAAAAAB4eQjUXOeff74+/PBDrV+/Xps2bVJOTo7y8vJksVjUrl07nXXWWRo+fLjuvPNOdejQwSlzDhw4UNu3b9drr72m+fPn27aOJScnKyUlRQ8//LAiIyOdMheaJ8lxO9jhE7JarTIMo44nAAAAAABoOYa1ub3L4TWysrKUmJgoScrMzGS10Wnszy/WkL+tsBv78ZkRah8SYFJFAAAAAAB30FK/f3MwNNBC4iOC5O9rv+qHw6EBAAAAAGYhBAJaiJ+vjxLb0yYeAAAAANA6EAIBLegMh3OB6BAGAAAAADALIRDQgpIiHQ6HzicEAgAAAACYgxAIaEE1O4QRAgEAAAAAzEEIBLSgGtvB8otEQz4AAAAAgBkIgYAW5LgSqLi8UocLy0yqBgAAAADgzQiBgBYU2zZIgX72f8z2cjg0AAAAAMAEhEBAC/LxMZRMhzAAAAAAQCtACAS0MDqEAQAAAABaA0IgoIXRIQwAAAAA0BoQAgEtrLYOYQAAAAAAuBohENDCHFcCZeQXq6qKNvEAAAAAANciBAJaWFJUsN33ckuVso+VmFQNAAAAAMBbEQIBLSw6NFChgX52Yxl5xSZVAwAAAADwVoRAQAszDKPGaiA6hAEAAAAAXI0QCHCBGm3i6RAGAAAAAHAxQiDABegQBgAAAAAwGyEQ4AI1OoTlEQIBAAAAAFyLEAhwAccQaH9BsSyVVSZVAwAAAADwRoRAgAs4bgezVFmVdYQ28QAAAAAA1yEEAlwgIjhAEcH+dmN0CAMAAAAAuBIhEOAidAgDAAAAAJiJEAhwETqEAQAAAADMRAgEuIjj4dDpdAgDAAAAALgQIRDgIoRAAAAAAAAzEQIBLuK4HSz7aInKLJUmVQMAAAAA8DaEQICLOK4EqrJKmQXFJlUDAAAAAPA2hECAi4QG+ikqNNBuLD2PEAgAAAAA4BqEQIALOW4JS887YVIlAAAAAABvQwgEuFBSVLDdd1YCAQAAAABcxc/sAiQpNzdXP//8swoKCiRJ7du3V+/evRUTE2NyZYBzOZ4LlEGHMAAAAACAi5gWAlVVVentt9/WG2+8oZ07d9Z6T69evXT//ffrnnvukY8Pi5bg/mpuByMEAgAAAAC4hinJSm5urgYMGKDJkydr586dslqttf7s3LlTkydP1sCBA3Xw4EEzSgWcynEl0MHjpSopp008AAAAAKDluXwlUGlpqS699FL98ssvslqtio6O1oQJEzRgwADFxMTIarXq0KFD+uGHH/TZZ5/p0KFD2rx5sy677DJt3rxZgYGBp58EaKU6tw+pMZaRX6SecW1NqAYAAAAA4E1cHgLNnDlTu3btkmEYuuOOO/Tqq68qJKTmL8Y333yzpk2bpkceeUT//Oc/tWvXLs2cOVN//vOfXV0y4DRtAnwVHx6k7GOltrH0PEIgAAAAAEDLc/l2sE8//VSGYWjEiBH65z//WWsAdEpwcLDefvttjRw5UlarVZ988okLKwVahuOWMM4FAgAAAAC4gstDoD179kiS7r///gY/c+retLS0FqkJcCU6hAEAAAAAzODyEOjUmT6JiYkNfubUvQEBAS1SE+BKdAgDAAAAAJjB5SFQjx49JEmZmZkNfubUvaeeBdxZUqTDSqB8QiAAAAAAQMtzeQg0adIkWa1WvfXWWw1+5q233pJhGLrllltasDLANRy3g+WdKNfx0gqTqgEAAAAAeAuXh0B33nmnRo0apSVLluj+++9XaWlpnfeWlZVp8uTJWrx4sUaOHKl77rnHhZUCLaNT+2D5GPZjnAsEAAAAAGhpLdYiftWqVXVee/TRR1VQUKC3335b8+fP14QJE9S/f3916NBBhmEoNzdXP/zwgz7//HMdPHhQ/fv312OPPaZVq1ZpyJAhLVUy4BIBfj5KaBes/QXFtrH0vCKdnRBhXlEAAAAAAI/XYiHQsGHDZBjGae/Lzc3V66+/Xu89mzZt0qhRo2QYhiwWi7NKBEyTFBViFwJl5BXXczcAAAAAAM3XotvBrFar038AT1CzQ9gJkyoBAAAAAHiLFlsJtGLFipZ6NeD2kiKD7b6n57MSCAAAAADQslosBBo6dGhLvRpwe44dwtIPn5DVam3QFkoAAAAAAJqixUIgVzl8+LDefPNNSdKUKVNMrgZomDOiQu2+Hy+16EhxhdqHBJhUEQAAAADA07m8RbyzHTp0SM8++6yee+45s0sBGiw+Ikj+vvarftJpEw8AAAAAaEFuHwIB7sjP10eJ7e3PBcogBAIAAAAAtCBCIMAkNTuEEQIBAAAAAFoOIRBgkqRIhxAonxAIAAAAANByCIEAkzh2CGM7GAAAAACgJRECASapbTuY1Wo1qRoAAAAAgKcjBAJM4rgSqLi8UocLy0yqBgAAAADg6QiBAJPEtg1SoJ/9H0EOhwYAAAAAtBRCIMAkPj6GkukQBgAAAABwEUIgwER0CAMAAAAAuAohEGAiOoQBAAAAAFyFEAgwUW0dwgAAAAAAaAmEQICJHFcC7csvVlUVbeIBAAAAAM7nZ3YBzRUQEKBOnTrJx4c8C+4nKSrY7nuZpUo5x0vVMaKNSRUBAAAAADyV24dAXbt2VUZGhtllAE0SHRqo0EA/nSiz2MbSDxcRAgEAAAAAnI7lM4CJDMOosRqIDmEAAAAAgJZg6kqgFStWaP78+frpp5+Ul5enkpISWa11n4diGIbS0tJcWCHQ8pIiQ/TzgeO273QIAwAAAAC0BFNCoEOHDumGG25QamqqJNUZ/BiGYXfNMAyX1Ae4Eh3CAAAAAACu4PIQqKKiQqNHj9bWrVtltVp17rnnKj4+XosWLZJhGLrpppt05MgR/fjjj8rOzpZhGDrvvPPUu3dvV5cKuIRjhzBWAgEAAAAAWoLLzwSaO3eutmzZIkl69913tXnzZk2bNs12/b333tOCBQuUlZWlr776SnFxcdq5c6fGjBmjd99919XlAi3OMQTaX1AsS2WVSdUAAAAAADyVy0OgL774QpJ0+eWX69Zbb6333pSUFKWmpiogIECTJk3S7t27nVrLoUOHtHDhQk2ZMkWjR49WVFSUDMOQYRiaNGlSg95RWlqqr7/+Wg8++KAGDhyo9u3by9/fX+3bt9cFF1ygZ599Vjk5OU6pNykpyVZffT9JSUlOmQ+u4bgdzFJlVdaREpOqAQAAAAB4KpdvB/vpp59s275qY7Va7c7+6dKlix566CH95S9/0WuvvaZ//OMfTqslJiamWc9v27ZNF110kQoLC2tcO3LkiDZs2KANGzZoxowZmj17tiZMmNCs+eCZIoIDFBHsr6PFFbax9PyiGiuEAAAAAABoDpeHQAUFBZKk5ORk21hAQIDtc3FxsUJC7H/5HT58uP7yl7/ou+++a7G6EhMT1bNnTy1durTBzxw/ftwWAA0ePFhjxoxRv379FBkZqcOHD+vLL7/U7NmzVVhYqD/84Q8KCwvT6NGjm11rSkqKXnjhhTqvV//7CfeQFBmircVHbd8z8oqk7ubVAwAAAADwPC4PgQICAmSxWOyCirZt29o+HzhwQN26dbN7JigoyHbNmaZMmaL+/furf//+iomJUUZGhl04dTo+Pj6aMGGCpk6dql69etW4PnLkSI0ePVrjxo1TZWWlHnzwQe3evbvZXc4iIiI4KNvDnBEVoq2ZR23f6RAGAAAAAHA2l58J1KlTJ0lSbm6ubSwmJkZhYWGSpO+//77GMzt27JDk/Bbxzz33nMaMGdPkbWEXXnihPv3001oDoFNSUlI0fvx4SVJaWpq2bt3apLng2Ry3fhECAQAAAACczeUh0HnnnSdJtg5hpwwZMkRWq1WvvfaaysrKbOPHjh3TSy+9JMMw6g1bWrNLLrnE9jktLc3EStBa1WgTn08IBAAAAABwLpeHQMOHD5fVatWiRYvsxu+9915JJ8OhPn366IknntADDzygPn366JdffpEk3XLLLa4u1ymqh1o+Pi7/Ww434Ngh7MCREpVZKk2qBgAAAADgiVx+JtDVV1+tZ599VllZWUpLS1OXLl0kSVdeeaVuv/12zZkzR3v27NGMGTMknewWJp08X+e+++5zdblOkZqaavvco0ePZr9v1apVOvvss5WWliar1aqYmBgNGDBAEydOVEpKSpO3zWVlZdV73Vmt7lGT40qgKquUWVCsMzuEmVQRAAAAAMDTuDwEioiIUEZGRq3XZs+erQsuuECzZ8/Wjh07ZLFY1LVrV91yyy166KGH3HIVzU8//WRb9XTWWWc5ZUtbenq63feMjAxlZGTos88+0+DBg/Xpp5+qY8eOjX5vYmJis2tD04QG+ikqNFB5J35fNZaeRwgEAAAAAHAel4dAp3PHHXfojjvuMLsMpygrK9Odd96pysqT23pefPHFZr0vICBAY8eO1ciRI9W7d2+Fh4fr6NGjWr9+vd58801lZmZq7dq1GjFihNavX6/w8HBn/GXARc6ICnEIgU5Iatqh5QAAAAAAOGp1IZAnmTx5sjZt2iRJuvXWWzV27NhmvW/jxo2KiIioMT5s2DBNnjxZ1157rZYuXapdu3bpueees22pa6jMzMx6r+fk5GjAgAGNeicaLikqWBszCmzf0/OKTawGAAAAAOBpXB4CXXrppTIMQ3PmzFHnzp0b9Ex2drZuuukmGYah5cuXt3CFzvG///u/mj17tiTp/PPP1//93/81+521BUCnhIWF6bPPPlOXLl2Un5+vWbNmadq0aQoICGjw+xMSEppdI5quRocw2sQDAAAAAJzI5YfsrFy5UitXrlRRUcN/wS0pKbE95w7efvtt/c///I8kqXv37vr2228VEhJymqeaLzw8XDfccIMkqaioyLYKCe7BsUNYOiEQAAAAAMCJ3O+k5Vbu448/1v333y9J6ty5s5YtW6bo6GiXzV/94OkDBw64bF40n+NKoIPHS1VSTpt4AAAAAIBzuEUIdGrVUFBQkMmV1G/BggW65ZZbVFVVpbi4OC1fvtzlW6ysVqtL54PzdG5fc7VYRj6rgQAAAAAAzuEWIdC3334rqXWfWbN8+XJNmDBBFotFkZGR+u6779SlSxeX17Fz507b5/j4eJfPj6ZrE+Cr+HD7oJMtYQAAAAAAZ2nxg6Fvv/32Wseffvrpeg86lk62WE9LS9MPP/wgwzA0dOjQFqiw+datW6eUlBSVlZWpbdu2WrJkic466yyX13Hs2DF9+umnkqTg4GD169fP5TWgeZKiQpR9rNT2nRAIAAAAAOAsLR4CzZ07V4Zh2I1ZrVZ9/fXXDXr+1Pam9u3b6//9v//n9Pqaa+vWrbryyitVVFSkkJAQffPNNzr//PMb/Z5hw4YpNTVVkpSenq6kpCS764sXL9bQoUPVpk2bWp8vLCzUhAkTlJ+fL0m64447FBgY2Og6YK6kqBCtS8u3fadDGAAAAADAWVo8BOrUqZNdCLRv3z4ZhqG4uDj5+/vX+ZxhGAoKClJcXJwuvPBC3XfffU7f3rRmzRrt2bPH9j0vL8/2ec+ePZo7d67d/ZMmTbL7npaWplGjRuno0aOSpBdeeEHh4eH6+eef65yzQ4cO6tChQ6NrnTZtmm688UaNHz9eF110kbp06aLQ0FAdPXpU69ev15tvvqnMzExJJzuSPfvss42eA+ZLjqRDGAAAAACgZbR4CJSRkWH33cfn5DFES5cutetkZYbZs2frvffeq/Xa2rVrtXbtWrsxxxBo9erVOnTokO37I488cto5p06d2uSApqCgQLNnz9bs2bPrvGfIkCH66KOP1L59+ybNAXMlO3QI42BoAAAAAICztHgI5GjIkCEyDEMhITU7IaFuL7/8spYvX67169fr119/VV5eno4eParg4GDFx8dr4MCBmjhxokaOHFlj+x3ch2Ob+LwT5TpeWqG2QXWvmgMAAAAAoCEMKz3F0UBZWVlKTEyUJGVmZrbqbm3uqtxSpR7PfKuqan8qF0werLMTIkyrCQAAAADgWi31+7dbtIgHvEWAn48S2gXbjXEuEAAAAADAGUwNgVasWKFbbrlFXbt2VVhYmPz8/LRz5067e1avXq033nhDH374oUlVAq7luCUsI6/YpEoAAAAAAJ7E5WcCSVJxcbFuvfVWffnll5J+bwNf21k2vr6+mjx5sgzD0MCBA9W1a1eX1gq4WnJksFZV+56ed8K0WgAAAAAAnsOUlUDXX3+9vvzyS1mtVvXv31+PP/54nfdeeOGF6tOnjyTpiy++cFWJgGkcO4Sl57MSCAAAAADQfC4Pgb766istWrRIkjRr1ixt2LBBL730Ur3PjB8/XlarVampqa4oETCV43aw9MMnxPntAAAAAIDmcnkI9N5770mSbrrpJt15550Neub888+XJO3atavF6gJaC8eVQMdLLTpSXGFSNQAAAAAAT+HyEOiHH36QYRi6/vrrG/xMXFycJOnw4cMtVRbQanSMaCN/X/vzsegQBgAAAABoLpeHQPn5+ZKkjh07NvrZqqoqZ5cDtDp+vj5KbG/fJj6DEAgAAAAA0EwuD4HCwsIkScePH2/wM2lpaZKkyMjIFqkJaG2SIx3OBSIEAgAAAAA0k8tDoFMt3jdu3NjgZ051Bevbt2+L1AS0NjU7hBECAQAAAACax+Uh0BVXXCGr1ao33nhDpaWlp71/8eLF+uKLL2QYhsaMGeOCCgHzOXYIYzsYAAAAAKC5XB4CTZ48WREREcrIyND48eNtZwQ5Ki0t1SuvvKLx48erqqpKsbGxuu2221xcLWCOGiuB8opoEw8AAAAAaBY/V08YERGhDz/8UCkpKVqyZIk6deqkoUOH2q4///zzOnr0qNauXauiopO/+Pr7++tf//qXgoKCXF0uYArHEKi4vFKHC8vUoS1/BgAAAAAATePylUDSyS1h33zzjaKjo1VSUqLFixfLME62xP7ss8+0dOlSnThxQlarVVFRUfrmm280bNgwM0oFTBHbNkiBfvZ/PDkcGgAAAADQHKaEQJI0YsQI7d27V6+//rouu+wyhYeHy2q1ymq1qk2bNho8eLCmT5+utLQ0DR8+3KwyAVP4+BhKokMYAAAAAMCJXL4drLrg4GA98MADeuCBByRJFotFlZWVCgwMNLMsoFVIjgrRr7mFtu90CAMAAAAANIepIZAjPz8/+fm1qpIA09AhDAAAAADgTKZtBwNQv+SoYLvvbAcDAAAAADSHqctuLBaLvv76ay1btkw///yzCgoKJEnt27dX7969ddlllyklJYXVQfBKyVGhdt/35RerqsoqHx/DpIoAAAAAAO7MtHRl/vz5evDBB5WdnW0bs1qtkiTDMLRu3TrNmjVLcXFx+sc//qGrr77apEoBcyQ5rAQqs1Qp53ipOka0MakiAAAAAIA7M2U72MyZM3XNNdcoOzvbFvwkJSVp0KBBGjhwoJKSkiSdDIWys7N1zTXX6NVXXzWjVMA00aGBCgnwtRtLP8yWMAAAAABA07g8BNqwYYOeeOIJWa1WhYWFafr06crNzVVaWprWrVun9evXKy0tTbm5uZo+fbqtdfwTTzyh77//3tXlAqYxDEPJ0Q5t4ukQBgAAAABoIpeHQDNmzFBVVZXCw8O1bt06PfHEE4qKiqpxX1RUlJ544gmtW7dO4eHhqqqq0owZM1xdLmCqpEg6hAEAAAAAnMPlIdCaNWtkGIaefPJJ9erV67T39+zZU08++aSsVqtWrVrlggqB1iOZNvEAAAAAACdxeQh05MgRSdIll1zS4GdO3Xv06NGWKAlotRxDINrEAwAAAACayuUhUFxcnCnPAu4oySEE2l9QLEtllUnVAAAAAADcmctDoMsuu0ySlJqa2uBnVq5cKUm69NJLW6IkoNVKdjgTyFJl1YGjJSZVAwAAAABwZy4PgR577DG1adNG06ZN02+//Xba+3/77TdNnz5dISEheuKJJ1xQIdB6tAsJUESwv93YXraEAQAAAACawOUhUPfu3TVv3jxJ0qBBg/Tqq6+qoKCgxn1HjhzRa6+9pgsvvFCS9Nlnn6l79+4urRVoDegQBgAAAABwBr+WevHptm5FR0dr9+7deuyxx/T4448rOTlZHTp0kGEYys3NVXp6uqxWqySpa9eu+tvf/qaXX35Zy5cvb6mSgVYpOSpEWzOP2r4TAgEAAAAAmqLFQqCVK1fKMAxbkFOdYRi2z1arVVarVWlpaUpLS6v1Xbt379Zvv/1m9xzgLRw7hLEdDAAAAADQFC0WAg0ZMoTQBnACxw5hGfmEQAAAAACAxmvRlUAAms+xQ9iBIyUqs1Qq0M/XpIoAAAAAAO7I5QdDA2icpKhgu+9VVimzoNikagAAAAAA7srtQ6AdO3bIx8dHfn4ttqgJMFVYkL+iQgPtxtLzCIEAAAAAAI3j9iHQKbUdQA14imSH1UB0CAMAAAAANJbHhECAJ6NDGAAAAACguQiBADdQo0MYIRAAAAAAoJEIgQA34NghjDbxAAAAAIDGIgQC3EBytH0IlHOsVCXllSZVAwAAAABwR4RAgBvo3D6kxhirgQAAAAAAjUEIBLiBNgG+igsPshvjXCAAAAAAQGMQAgFugg5hAAAAAIDmIAQC3AQdwgAAAAAAzUEIBLgJOoQBAAAAAJqDEAhwE47bwdJZCQQAAAAAaARCIMBNOG4HyztRrsLSCpOqAQAAAAC4G0IgwE10ah8sH8N+LCOv2JxiAAAAAABux8/sAporISFB7777rtllAC0uwM9HCe2Ctb/g9+Bnb94J9UkIN7EqAAAAAIC7cPsQKDw8XLfeeqvZZQAukRQVYhcCsRIIAAAAANBQpoZA+fn5Wr9+vfbu3avCwkJVVlae9pkpU6a4oDKgdUqODNaqat/pEAYAAAAAaChTQqBDhw7pkUce0bx582SxWBr1LCEQvJljh7C9dAgDAAAAADSQy0OgI0eO6KKLLlJaWpqsVqurpwfcmmOHsAxCIAAAAABAA7m8O9i0adO0Z88eWa1WjRw5UosXL9bhw4dVWVmpqqqq0/4A3sxxJdCxkgodKSo3qRoAAAAAgDtx+Uqgr7/+WoZh6Morr9SCBQtcPT3g1jpGtJG/r6GKyt9X0e3NK9L5IQEmVgUAAAAAcAcuXwm0f/9+SdIDDzzg6qkBt+fn66PE9sF2Y2wJAwAAAAA0hMtDoNDQUElSTEyMq6cGPEJypMO5QHQIAwAAAAA0gMtDoD59+kiS9u3b5+qpAY9AhzAAAAAAQFO4PAS65557ZLVa9cEHH7h6asAj0CEMAAAAANAULg+BJkyYoIkTJ+qrr77StGnTXD094PYcVwJl5BXJarXWcTcAAAAAACe5vDvYqlWrdOedd2rfvn166qmn9OWXX+oPf/iDevTooeDg4NM+P2TIEBdUCbRejiFQUXmlDheWqUPbIJMqAgAAAAC4A5eHQMOGDZNhGLbvmzdv1ubNmxv0rGEYslgsLVUa4BZi2wYp0M9HZZYq21h6XhEhEAAAAACgXi7fDiZJVqu1yT+At/PxMZREhzAAAAAAQCO5fCXQihUrXD0l4HGSo0L0a26h7TsdwgAAAAAAp+PyEGjo0KGunhLwOHQIAwAAAAA0linbwQA0T3KU/SHqGXnFJlUCAAAAAHAXhECAG0qOCrX7npFfpKoqzswCAAAAANTNq0OgQ4cOaeHChZoyZYpGjx6tqKgoGYYhwzA0adKkRr9v8eLFGj9+vBISEhQYGKiEhASNHz9eixcvdmrd+fn5mjp1qvr27avw8HC1bdtWffv21dSpU5Wfn+/UudA6JTmsBCqzVCnneKlJ1QAAAAAA3IHLzwSqLi0tTQsWLNBPP/2kvLw8lZSU1NsBzDAMLV++3Gnzx8TEOOU9VqtV9957r2bNmmU3fuDAAX311Vf66quvdPfdd+utt96SYRjNmuuHH35QSkqKcnJy7Ma3bdumbdu2afbs2fr666/Vr1+/Zs2D1i06NFAhAb4qKq+0jWXkFaljRBsTqwIAAAAAtGamhEDFxcV64IEH9MEHH9QIfaxWa42g5NQ9zQ1Q6pOYmKiePXtq6dKljX726aeftgVA5557rv70pz+pS5cuSktL00svvaQtW7Zo1qxZio6O1gsvvNDkGg8cOKCrrrpKubm58vPz06OPPqoxY8ZIkhYuXKgZM2YoOztbY8aM0ebNm9WxY8cmz4XWzTAMJUeH6OcDx21je/OKNPjMKBOrAgAAAAC0Zi4PgaxWq8aNG6dly5bJarUqKipKCQkJ2rp1qwzD0MUXX6wjR47o119/VUVFhQzDUPfu3RUbG+v0WqZMmaL+/furf//+iomJUUZGhpKTkxv1jj179uill16SJPXr10+rVq1SmzYnV2P0799fY8eO1dChQ7Vp0yZNnz5dt912m7p06dKkep966inl5uZKkj766CNdd911tmsXX3yx+vXrpwkTJig3N1fPPPOM5syZ06R54B6SIu1DIDqEAQAAAADq4/IzgT7//HN99913kqSpU6fq4MGDev/9923XU1NTtW3bNhUUFGjmzJkKCQlRQUGBnn/+ea1YscKptTz33HMaM2ZMs7aFzZw5UxaLRZL0+uuv2wKgU4KDg/X6669LkiwWi1599dUmzZObm6sPP/xQkjRq1Ci7AOiU6667TqNGjZIkvf/++7bACJ4pmTbxAAAAAIBGcHkI9NFHH0mSLrjgAk2dOlU+Pj61bvMKCQnRQw89pOXLl6uwsFDjx49Xdna2q8utl9Vq1ddffy1J6tGjhwYNGlTrfYMGDVL37t0lSfPnz6/33KO6LFiwQJWVJ89/ue222+q879SB1pWVlVqwYEGj54H7cAyB0gmBAAAAAAD1cHkItGnTJhmGobvuuqtB9/fv31/33Xef8vLy9Pe//72Fq2uc9PR0HThwQJI0dOjQeu89dT0rK0sZGRmNnmv16tU13lXfPJK0Zs2aRs8D95HkEALtLyiWpbLKpGoAAAAAAK2dy0OgvLw8SdIZZ5xhG/P397d9LikpqfHMlVdeKenk4cetya5du2yfe/ToUe+91a9Xf66xc4WHh9d7PlJcXJzatm3bpHmysrLq/XHsSAZzJUfah0CWKqsOHK355wcAAAAAAMmEg6H9/PxUUVGhsLAw21j1zwcPHqxxOHN4eLgkKTMz0zVFNlD1ehISEuq9NzExsdbnGjvX6eY5NdeOHTsaPU/1GtH6tQsJUESwv44WV9jG9uYVqbNDOAQAAAAAgGTCSqD4+HhJ0uHDh21jsbGxtgOVf/zxxxrP7NmzR5JsBzC3FoWFhbbPoaGh9d4bEvL7L+YnTpxo8lynm6f6XE2ZB+4lKZLDoQEAAAAADePyEKhv376SpO3bt9vGDMPQwIEDJUlvvPGG3f0Wi0UzZ86UJHXt2tVFVTZMaWmp7XNAQEC99wYGBto+17blraFznW6e6nM1dp7MzMx6fzZu3NjoutGy6BAGAAAAAGgol28Hu/TSS/XFF19o8eLFevTRR23jt99+u1auXKmVK1dq6NChmjBhgoqLi/XJJ59oy5YtMgxDEyZMcHW59QoKCrJ9Li8vr/fesrIy22fHNvINnau4uPi081Sfq7HzNGSrGVoXxxBoLyEQAAAAAKAOLl8JNG7cOBmGoRUrVmjv3r228ZtuukmXX365rFar1qxZoz/+8Y/685//rK1bt0qSzjnnHLvQqDWofpbR6bZeFRX9/st5Q7Z01TVXQ7Z4nZqrKfPAvTh2CMvIJwQCAAAAANTO5SFQbGysKioqVFpaatchTJK++uorPfXUU4qJiZHVapXValV4eLgeeOABrVixwm7lTWtQfeVMVlZWvfdWP6S5KQcwn5rrdPNUn4uDnj2fY4ewA0dKVGapNKkaAAAAAEBr5vIQSJJ8fHzk6+tbYzwwMFDPP/+8srOzlZeXp4MHDyo/P1+vv/66re15a9KrVy/b519++aXee6tf79mzZ5PnOnbsmA4ePFjnfTk5OTp+/HiT54F7SYoKtvteZZUyC4pNqgYAAAAA0JqZEgI1RPv27dWhQwcZhmF2KXVKTk62dTtLTU2t995Vq1ZJkjp27KikpKRGz3XRRRfZPtc3V/VrgwcPbvQ8cC9hQf6KCg20G0vPIwQCAAAAANTUakMgd2AYhlJSUiSdXOmzYcOGWu/bsGGDbSVQSkpKk4KtsWPHysfn5P+53n333Trvmzt3rqSTq63Gjh3b6HngfpIdVgPRIQwAAAAAUBtTQ6Djx49rzpw5uuuuu3TVVVdp+PDh2rdvn9092dnZ2rlzp90h0q3Jww8/LD+/k03WHnzwwRpt2UtKSvTggw9Kkvz8/PTwww/X+p5hw4bJMAwZhqGMjIwa12NjY3XjjTdKkpYsWaJ58+bVuOfzzz/XkiVLJEk333yzYmNjm/qXBTeSFEmHMAAAAADA6bm8Rfwp//d//6ennnpKhYWFkiSr1SrDMOy6aEkntzfdeOONCgoKUlZWltq3b++0GtasWaM9e/bYvufl5dk+79mzx7aq5pRJkybVeEe3bt30+OOPa9q0adq0aZMGDx6sJ598Ul26dFFaWpqmT5+uLVu2SJKeeOIJde3atcn1/vWvf9XixYt1+PBhTZw4UZs2bdKYMWMkSQsXLtQrr7wiSYqOjtYLL7zQ5HngXpKjHTqEEQIBAAAAAGphWK1Wq6snffbZZ/X888/LarUqMDBQffr00aZNm2QYhrZv32534HJVVZUSExN18OBBvfXWW7rrrrucVsekSZP03nvvNfj+uv5WVVVV6a677tKcOXPqfPaOO+7QrFmzbFu6HA0bNsx2nk96enqd5wZ9//33uvrqq+s8HDo2Nlbz58/XwIED6/kraZqsrCxbx7HMzEy77mgwz7fbc3Tfv360fY8LD9L6/zfcxIoAAAAAAM3RUr9/u3w72JYtW/T8889Lkm666SYdPHhQGzdurPN+Hx8fXXfddbJarfruu+9cVWaj+Pj46J133tGiRYuUkpKi+Ph4BQQEKD4+XikpKfrmm280e/bsOgOgxhg4cKC2b9+up59+Wr1791ZoaKhCQ0PVp08fPf300/r5559bJABC65UUZb8SKOdYqUrKaRMPAAAAALDn8pVAt99+u+bOnasLL7xQa9assY37+PjUuhJIkj799FNNnDhR3bt3165du1xZLqphJVDrVFJeqZ5TFtuNffvQxeoZ19akigAAAAAAzeExK4FSU1NlGIYmT57c4GdObY06cOBAC1UFuK82Ab6KCw+yG+NcIAAAAACAI5eHQDk5OZKk7t27N/iZwMBASVJZWVmL1AS4OzqEAQAAAABOx+UhUEBAgCSpoqKiwc+cCo4iIiJaoiTA7dEhDAAAAABwOi4PgU7tY9uxY0eDn1m6dKkk6cwzz2yRmgB3l+ywEigjnxAIAAAAAGDP5SHQpZdeKqvVqnfffbdB9+/du1fvvPOODMPQiBEjWrg6wD05dghLZyUQAAAAAMCBy0OgyZMny8/PT2vXrtWzzz5b772bNm3SyJEjdeLECQUGBuqee+5xTZGAm0l2CIHyTpSrsLThWy4BAAAAAJ7P5SFQt27d9Mwzz8hqter555/XwIED9dJLL9muL168WNOnT9fw4cM1cOBApaenyzAMTZs2TXFxca4uF3ALndoHy8ewH8vIKzanGAAAAABAq+RnxqTPPPOMKioq9OKLL+qHH37Qpk2bZBgnf4N94oknbPdZrVYZhqEpU6boj3/8oxmlAm4hwM9HHdu1UWZBiW1sb94J9UkIN7EqAAAAAEBr4vKVQKf85S9/0YYNGzR+/Hi1adNGVqvV7sff31+jR4/W6tWrNXXqVLPKBNxGclSo3XdWAgEAAAAAqjNlJdAp/fr107x582SxWLRz504dOnRIlZWVioyM1FlnnaU2bdqYWR7gVpIjg7Wq2nc6hAEAAAAAqjM1BDrFz89PZ599ttllAG7NsUPYXjqEAQAAAACqMW07GADncuwQlkEIBAAAAACoxrSVQBaLRYsWLdLq1au1d+9eFRYWqrKyst5nDMPQ8uXLXVQh4F4cQ6BjJRU6UlSudiEBJlUEAAAAAGhNTAmBVq5cqdtuu0379++3jVmt1jrvNwzD1ikMQO06RrSRn48hS9Xvf5b25hXpfEIgAAAAAIBMCIG2bt2q0aNHq7y8XFarVUFBQeratasiIiLk48PuNKCp/Hx91CkyWHsP/74NLCOvSOd3bmdiVQAAAACA1sLlIdCzzz6rsrIyBQYGasaMGbrtttsUFBTk6jIAj5QcGWIXAv2aW2hiNQAAAACA1sTlS2/WrFkjwzD01FNP6b777iMAApzozJhQu++fbNyvYyUVJlUDAAAAAGhNXB4ClZaWSpIuv/xyV08NeLyUvh3tvh8vtWj26r0mVQMAAAAAaE1cHgIlJSVJkioqWJ0AOFuv+La6sk+c3dicNenKP1FmUkUAAAAAgNbC5SHQ1VdfLUlatWqVq6cGvMIjI7rKp1ojvaLySr2VmmZeQQAAAACAVsHlIdBDDz2kuLg4vfzyy8rIyHD19IDHO7NDmK4+135b2Pvr9yn3eKlJFQEAAAAAWgOXh0DR0dH65ptv1KZNGw0cOFCzZ8/WsWPHXF0G4NEeHt5NftWWA5VZqvSP/+wxsSIAAAAAgNkMq9VqNWPijIwMDRw4UHl5eTIMQ1FRUQoODq73GcMwlJbGthazZGVlKTExUZKUmZmphIQEkytCff7nq+366Pv9tu/+vob+89gwJbav/88ZAAAAAMBcLfX7t59T3tJIX3zxhe644w4VFhbKarXKarXq0KFDp33OMIzT3gPgpAcvPVPzNmep3FIlSaqotOr1/+zWS9f2NbkyAAAAAIAZXB4CrV+/XjfccIMqKyslSZ07d9bZZ5+tiIgI+fi4fHca4LHiwtvopoGdNWdtum3six8P6N6hXXRGdKiJlQEAAAAAzODyEOiFF15QZWWlwsPD9dFHH2n06NGuLgHwGvcN66KPN+5XScXJ0LWyyqpXl+3W3yeea3JlAAAAAABXc/nSm82bN8swDD333HMEQEALiw4L1G2Dk+zG/r0tW78cPG5OQQAAAAAA07g8BCoqKpIkXXTRRa6eGvBKdw85Q2GBvy/6s1qlGUt/M7EiAAAAAIAZXB4CJScnS5KKi4tdPTXglSKCA3TXkDPsxpbuzNVPmUfNKQgAAAAAYAqXh0Djx4+X1WrVkiVLXD014LVuG5ykdsH+dmOvfMdqIAAAAADwJi4PgR577DF17dpVr776qjZt2uTq6QGvFBbkr/uGdbEbW/XbYW1MLzCpIgAAAACAq7k8BAoLC9Py5cvVu3dvDRkyRE899ZS2bdum0tJSV5cCeJWbByUpOizQbuzlJb/KarWaVBEAAAAAwJVcHgL5+vqqc+fO2rhxo0pLSzVt2jSde+65CgkJka+vb70/fn4u72gPeIw2Ab6afMmZdmMbMwq0eneeSRUBAAAAAFzJ5SGQ1Wq1/Th+b8gPgKa7YUCiOka0sRt7ZSmrgQAAAADAG7h8ac3UqVNdPSWA/wr089Ufh5+pJ7/Ybhv7KeuYlu06pBG9YkysDAAAAADQ0giBAC9zzXkJenNlmjLyi21jryz9VcN7dJCPj2FiZQAAAACAluTy7WAAzOXn66NHRnSzG/vlYKEWbc8xqSIAAAAAgCsQAgFe6Kqz49U9JsxubOZ3v8lSWWVSRQAAAACAlkYIBHghHx+jxmqgvXlF+mrLAZMqAgAAAAC0NEIgwEuNOitGfTqG2429tny3yi2sBgIAAAAAT0QIBHgpwzD02Ej71UBZR0r06aZMkyoCAAAAALQkQiDAiw3tFq3+Se3sxv7xn90qrag0qSIAAAAAQEshBAK82MnVQN3txnKPl+nDDftMqggAAAAA0FIIgQAvN+iMSF3cNcpu7I2VaTpRZjGpIgAAAABASyAEAqBHHTqFFRSVa+7adJOqAQAAAAC0BEIgADq3Uztd1rOD3djbq/bqWHGFSRUBAAAAAJyNEAiAJOnREfZnAxWWWvTP1XtNqgYAAAAA4GyEQAAkSb3i2+rKs+PsxuasTVf+iTKTKgIAAAAAOBMhEACbRy7rJh/j9+/F5ZV6c2WaeQUBAAAAAJyGEAiAzZkdQjXu3AS7sQ827NPBY6UmVQQAAAAAcBZCIAB2Hr6sq/yqLQcqs1TpHyt2m1gRAAAAAMAZCIEA2ElsH6zr+yfajX36Q6YyC4pNqggAAAAA4AyEQABqePDSrgrw+/1fDxWVVr22nNVAAAAAAODOCIEA1BAbHqSbB3W2G/vyxyylHT5hUkUAAAAAgOYiBAJQq/uGdVFwgK/te5VVmvndbyZWBAAAAABoDkIgALWKCg3UbYOT7MYWbsvRrpzj5hQEAAAAAGgWQiAAdbr74i4KC/KzG3tlKauBAAAAAMAdEQIBqFN4sL/uvvgMu7Flu3K1NfOoOQUBAAAAAJqMEAhAvW67KFntQwLsxl5Z+qtJ1QAAAAAAmooQCEC9QgP9dN/QLnZjq3fn6fu9+SZVBAAAAABoCkIgAKd18wWd1SEs0G7slaW/yWq1mlQRAAAAAKCxCIEAnFaQv68evPRMu7GNGQVatTvPpIoAAAAAAI1FCASgQa7v30kdI9rYjb2y9FdWAwEAAACAmyAEAtAgAX4+euiyrnZj27KOaenOXJMqAgAAAAA0BiEQgAYbf25HnREVYjc2Y+lvqqpiNRAAAAAAtHaEQAAazM/XRw+P6GY39mtuof69LdukigAAAAAADUUIBKBRxvSJU/eYMLuxV5ftlqWyyqSKAAAAAAANQQgEoFF8fAw9OtJ+NVB6XpG+/PGASRUBAAAAABqCEKiZhg0bJsMwGvWzcuXKRs/z7LPPtuj7gcYY2StGZyeE2429tny3yiyVJlUEAAAAADgdQiAX8/HxUdeuXU9/I9CKGYahx0Z2txs7cLREn/2QaVJFAAAAAIDT8TO7AHf37rvvqqioqN57du7cqeuvv16SNHz4cHXs2LFZc27fvr3e68nJyc16P9AQQ7pGaUBSe23MKLCNvf6fPbr2/ES1CfA1sTIAAAAAQG0IgZqpIYHLBx98YPt8yy23NHvO3r17N/sdQHOdXA3UTdfP2mAbO1RYpg837NNdQ84wsTIAAAAAQG3YDtbCqqqq9K9//UuSFBoaqvHjx5tcEeA8A8+I1MVdo+zG3kxN04kyi0kVAQAAAADqQgjUwpYvX64DB052Tbr22msVHBxsckWAczmeDVRQVK5316SbVA0AAAAAoC6EQC3s/ffft312xlYwoLU5JzFCI3rF2I3NWr1Xx4orTKoIAAAAAFAbzgRqQSdOnNBXX30lSerUqZOGDRvmlPeOGDFCP/74owoLCxUREaFevXrp8ssv1z333KN27do1+b1ZWVn1Xs/JyWnyu+HZHh3RTct25cpqPfm9sNSiWavT9MSoHuYWBgAAAACwIQRqQV988YWtc9jNN98swzCc8t5ly5bZPh8+fFipqalKTU3V9OnTNXfuXKWkpDTpvYmJiU6pD96nZ1xbjTk7Xv/+Kds29u7aDN02OFlRoYEmVgYAAAAAOIXtYC3I2VvB+vTpo2eeeUb//ve/tXnzZm3YsEHvvfeeRo4cKUk6evSorrnmGn377bfNngtorIcv6yqfajlncXml3lyZZl5BAAAAAAA7htV6agMHnCkrK0udO3dWVVWVBg0apPXr1zfrfUePHlVERESd199++23de++9kqT4+Hjt2bNHbdq0adQcDdkONmDAAElSZmamEhISGvV+eL4nPv9Jn2/+/Z+jAD8fpT4xTHHhjftnEQAAAAC8WVZWlm23jjN//2YlUAv58MMPVVVVJUm69dZbm/2++gIgSbrnnnt05513SpKys7P15ZdfNnqOhISEen/i4uKaUjq8yB+Hd5W/7+/LgcotVfrHf/aYWBEAAAAA4BRCoBbywQcfSJICAwN1/fXXu2TOe+65x/Y5NTXVJXMC1SW2D9b1/e3Plvr0h0xlFhSbVBEAAAAA4BRCoBawadMm7dy5U5I0ZsyYZnXsaoxevXrZPh84cMAlcwKOHry0qwL9fv9Xi6XKqleX7TaxIgAAAACARAjUIqofCO2MrWANxfFOaA1i2gbp5kGd7ca+2pKlPYdOmFQRAAAAAEAiBHK6iooKffLJJ5Kk6OhojR492mVzn1p9JJ08HBowy33Duig4wNf2vcoqzVz2m4kVAQAAAAAIgZzs22+/1eHDhyVJf/jDH+Tn5+eyud9++23b56FDh7psXsBRZGigbh+cbDe2aFuOdmYfN6kiAAAAAAAhkJNV3wp2yy23NOiZuXPnyjAMGYahZ599tsb17du3a8+e+jssvf3223rnnXckSbGxsRo3blzDiwZawF1DzlDbIPsQdMZ3v5pUDQAAAADAdctUvMCRI0e0cOFCSVLv3r113nnnOeW9mzdv1p133qlLLrlEo0ePVp8+fRQZGSmLxaJffvlFH374ob777jtJkq+vr95++22FhIQ4ZW6gqcLb+OvuIWfo5aW/bwNbtuuQtuw/onM7ueawdAAAAADA7wiBnOjTTz9VWVmZpIavAmqoyspKLVu2TMuWLavznsjISL3zzjsaO3asU+cGmuq2wcmaszZDBUXltrFXlv6mD+8caGJVAAAAAOCdCIGc6IMPPpB0cjXOjTfe6LT3XnHFFXrnnXe0fv16bdmyRbm5ucrPz5fValX79u3Vt29fXX755Zo0aZLatm3rtHmB5goJ9NP9w7rohUW7bGNr9uRpfVq+LugSaWJlAAAAAOB9DCt9xdFAWVlZSkxMlCRlZmYqISHB5IrgDkorKjX0byuUe7zMNtavczt9fu8FMgzDxMoAAAAAoHVqqd+/ORgaQIsK8vfV5Eu72o1t2ndEqb8dNqkiAAAAAPBOhEAAWtz1/RKV0K6N3dgrS38TCxEBAAAAwHUIgQC0uAA/Hz003H410PYDx7RkR65JFQEAAACA9yEEAuAS487tqDOiQ+zGZnz3qyqrWA0EAAAAAK5ACATAJfx8ffTIZd3sxn7LPaGF27JNqggAAAAAvAshEACXubJPnHrEhtmNzfzuN1kqq0yqCAAAAAC8ByEQAJfx8TH02MjudmMZ+cX64scskyoCAAAAAO9BCATApS7r2UF9EyPsxv6+fI/KLJXmFAQAAAAAXoIQCIBLGYahx0fanw104GiJPtmYaVJFAAAAAOAdCIEAuNxFZ0ZpQHJ7u7F/rNijknJWAwEAAABASyEEAuByJ1cD2Z8NdLiwTO+vzzCnIAAAAADwAoRAAEwxILm9hnSLtht7KzVNhaUVJlUEAAAAAJ6NEAiAaRzPBjpSXKE5azLMKQYAAAAAPBwhEADTnJ0QoZG9YuzGZq/eq6PF5SZVBAAAAACeixAIgKkeHdlNhvH798Iyi2at2mteQQAAAADgoQiBAJiqR2xbXXV2vN3Yu2szdLiwzKSKAAAAAMAzEQIBMN3Dl3WVr8/vy4FKKir15so0EysCAAAAAM9DCATAdGdEh+qa8zrajX34/T7lHCsxqSIAAAAA8DyEQABahQcv7Sp/399XA5VbqvT6f/aYWBEAAAAAeBZCIACtQmL7YE0c0Mlu7LMfMrU/v9ikigAAAADAsxACAWg1HrjkTAX6/f6vJUuVVa8u/83EigAAAADAcxACAWg1YtoG6dYLk+zG5m85oD2HCs0pCAAAAAA8CCEQgFbl3qFdFBLga/teZZVmfrfbxIoAAAAAwDMQAgFoVdqHBOiOi5LtxhZtz9HPB46ZVBEAAAAAeAZCIACtzh0Xn6G2QX52YzO/42wgAAAAAGgOQiAArU54G3/dM7SL3djyXw7px/1HTKoIAAAAANwfIRCAVmnShUmKDAmwG3tl6a8mVQMAAAAA7o8QCECrFBLop/uG2a8GWrsnX+vS8kyqCAAAAADcGyEQgFbrpkGdFds2yG7slaW/yWq1mlQRAAAAALgvQiAArVaQv68mX3qm3djmfUe08rfDJlUEAAAAAO6LEAhAqzahX6IS27exG3tl6a+sBgIAAACARiIEAtCqBfj56KHh3ezGfj5wXEt2HDSpIgAAAABwT4RAAFq9ced2VJfoELuxV5b+psoqVgMBAAAAQEMRAgFo9Xx9DD0ywn410O5DJ/Tvn7JNqggAAAAA3A8hEAC3cEXvOPWMa2s3NnPZb6qorDKpIgAAAABwL4RAANyCj4+hxxxWA+3LL9YXm7NMqggAAAAA3AshEAC3MbxnB52TGGE39vflu1VmqTSnIAAAAABwI4RAANyGYRh6fGR3u7HsY6X6+Pv9JlUEAAAAAO6DEAiAWxl8ZqQGJre3G/vHijSVlLMaCAAAAADqQwgEwK0YhqHHR9mvBso7Uab31meYUxAAAAAAuAlCIABup39Sew3tFm039lZqmgpLK0yqCAAAAABaP0IgAG7J8Wygo8UVevzzn1RawbYwAAAAAKgNIRAAt9QnIVyjzoqxG1uyI1e3ztmoYyWsCAIAAAAAR4RAANzWE6N6KMjf/l9j36cX6Pq31yv3eKlJVQEAAABA60QIBMBtndkhVHMm9VdYoJ/d+C8HCzX+jXVKO3zCpMoAAAAAoPUhBALg1i7sEqVP7hmk6LBAu/EDR0t07ZvrtDXzqDmFAQAAAEArQwgEwO2dFR+uL++7UMlRIXbjR4orNHHWBq389ZBJlQEAAABA60EIBMAjJLYP1rx7L9DZCeF24yUVlbrzvU368scskyoDAAAAgNaBEAiAx4gMDdTHdw3SxV2j7MYtVVY9+tlPmrUqzaTKAAAAAMB8hEAAPEpIoJ/eubW/rj4nvsa1F7/5RS8s3KmqKqsJlQEAAACAuQiBAHicAD8fzZhwju68KLnGtdlr0vXoZ1tVbqkyoTIAAAAAMA8hEACP5ONj6OkxvfQ/V/SocW3+1mzd8d4PKiqzmFAZAAAAAJiDEAiAR7t7SBfNmNBXfj6G3fjq3Xma+M8NyjtRZlJlAAAAAOBahEAAPN748xI0+9Z+auPvaze+LeuYrn1znTILik2qDAAAAABchxAIgFcY1r2DPr57kNqHBNiNZ+QXa/yb67Qj+5hJlQEAAACAaxACAfAa5yRGaN69F6hjRBu78cOFZbrh7Q1al5ZnUmUAAAAA0PIIgQB4lTOiQ/Xl/ReqR2yY3XhhmUWT5vygRdtyTKoMAAAAAFoWIRAArxPTNkif3nOBBia3txsvr6zS5I9/1PvrM8wpDAAAAABaECEQAK8U3sZf790+QKN7x9qNW63SlK936OUlv8pqtZpUHQAAAAA4HyEQAK8V5O+rf/zhPN00qFONa/9YsUd//mK7LJVVJlQGAAAAAM5HCATAq/n6GHo+pbceHdGtxrVPN2Xq3g9/VEl5pQmVAQAAAIBzEQIB8HqGYeiPw7vqf8f3kY9hf23Zrlzd9M73Olpcbk5xAAAAAOAkhEAA8F8TB3TSmzedr0A/+381bt53RNe9tV7ZR0tMqgwAAAAAmo8QCACqGXVWrD64Y6DaBvnZje8+dELXvLlOu3MLTaoMAAAAAJqHEAgAHAxIbq/P771QsW2D7MZzjpXq2rfWa/O+ApMqAwAAAICmIwQCgFp0jw3TF/dfqC7RIXbjx0oqdOPs77VsZ65JlQEAAABA0xACAUAdOka00bx7L9S5nSLsxksrqnTPh5v12Q+Z5hQGAAAAAE1ACOQEhmE06GfYsGFOme+TTz7RqFGjFBcXp6CgICUlJenmm2/Whg0bnPJ+AL9rFxKgf905UJf26GA3Xlll1Z++2Kb/W7FHVqvVpOoAAAAAoOEIgdxIaWmprrrqKk2cOFFLly7VwYMHVVZWpn379unDDz/U4MGD9fzzz5tdJuBxggP89PbN5+va8xNqXPvbkl/17IIdqqwiCAIAAADQuvmd/hY01H333af777+/zushISF1XmuIO+64QwsXLpQkXXLJJXrooYcUHx+v7du368UXX1RaWpqmTJmiuLg43Xnnnc2aC4A9f18f/e3as9UhLFBvrEyzu/be+n3KO1GuGdf3VaCfr0kVAgAAAED9CIGcqEOHDurdu3eLvDs1NVUfffSRJOmqq67SV199JV/fk79s9u/fX2PHjtX555+v/fv3609/+pOuvfZaRUREtEgtgLcyDEN/uryHokID9ZeFO+2uLdqeoyPF5Xr75vMVFuRvUoUAAAAAUDe2g7mJl156SZLk6+urN954wxYAnRIVFaXp06dLko4cOaJ33nnH5TUC3uL2i5L194nnyt/XsBtfl5av69/eoEOFpSZVBgAAAAB1IwRyAydOnNDy5cslSSNGjFBCQs1zSSRp/Pjxatu2rSTpyy+/dFl9gDca2zde704aoJAA+0B2Z85xXfPmOqXnFZlUGQAAAADUjhDIDWzcuFFlZWWSpKFDh9Z5X0BAgAYNGmR7pqKiwiX1Ad7qoq5R+vSeCxQVGmA3nllQomvfXKdtWUfNKQwAAAAAakEI5ESff/65unfvrjZt2igsLExdu3bVrbfeqhUrVjTrvbt27bJ97tGjR733nrpusVi0e/fuRs2TlZVV709OTk7jiwc8XO+O4frivgvVqX2w3Xh+UblumLVBq3cfNqkyAAAAALDHwdBOtHOn/UGxe/bs0Z49e/T+++/r6quv1ty5cxUeHt7o92ZmZto+17UV7JTExES753r16tXgeao/C6DhOkeG6Iv7LtRtczfq5wPHbePF5ZW6fe4Pevm6vko5p6OJFQIAAAAAK4GcIjg4WDfccIP++c9/avXq1dqyZYuWLl2qp556SpGRkZKk+fPnKyUlpUlbtAoLC22fQ0ND6723ehv6EydONHouAE0THRaoT+6+QIPPjLQbr6i06qFPtmr26r0mVQYAAAAAJ7ESyAkOHDhQazv2ESNG6MEHH9To0aO1ZcsWpaam6s0339Qf//jHRr2/tPT3TkMBAQH13CkFBgbaPpeUlDRqnuorjmqTk5OjAQMGNOqdgDcJDfTTnEn99dhnP2nhNvvtky8s2qXDJ8r058t7yDCMOt4AAAAAAC2HEMgJaguATomJidG8efPUs2dPlZeX6/XXX290CBQUFGT7XF5eXu+9pw6QlqQ2bdo0ap7TbTUDcHqBfr76+w3nKjosUO+uzbC79nbqXh0uLNP0a86Wvy8LMQEAAAC4Fr+FuMAZZ5yhESNGSDp5TlB2dnajng8LC7N9Pt0Wr6Ki39tSn27rGICW4eNjaMqYXnry8poHuX/54wHd9f4mFZdbTKgMAAAAgDcjBHKR6gc0HzhwoFHPVl+hk5WVVe+91bd0cdAzYB7DMHTfsC7627Vny9fHfvvXyl8Pa+I/v1dBUf0r+wAAAADAmQiBXMRqtTb52eoB0i+//FLvvaeu+/n56cwzz2zynACc47p+ifrnLecryN/+X7c/ZR7VtW+tU9aRYpMqAwAAAOBtCIFcpHr7+Pj4+EY9279/f9uB0KmpqXXeV15erg0bNtR4BoC5Lu0Ro3/dOUgRwf5243sPF2n8G+u0K+d4HU8CAAAAgPMQArnA3r179d1330k6eT5Qx44dG/V8WFiYhg8fLklatmxZnVvCvvzySx0/fvKXyXHjxjWjYgDOdn7ndpp37wWKDw+yGz9UWKYJb6/Xhr35JlUGAAAAwFsQAjXTv//9b1ksdR/wmpubq2uvvVYVFRWSpAceeKDGPXPnzpVhGDIMQ88++2yt73n88cclSRaLRQ888IAqKyvtrufl5enJJ5+UdLJb2Z133tmUvxwALejMDmH64v4L1S3G/tD2wlKLbpmzUYt/zqnjSQAAAABoPkKgZnrwwQfVuXNn/fGPf9THH3+s9evXa+vWrVq2bJmefvppnXXWWdqyZYsk6aKLLqo1BGqISy+9VDfccIMkacGCBRoxYoQWLFigTZs26d1339WgQYO0f/9+SdK0adPUrl075/wFAnCquPA2+vyeC9U/yf7PaLmlSvf/60d9uGGfSZUBAAAA8HR+ZhfgCbKzs/X666/r9ddfr/Oea665RrNnz1ZgYGCT55kzZ46OHz+ub775RitWrNCKFSvsrvv4+OiZZ57RPffc0+Q5ALS88GB/fXDHQD348RZ9tzPXNl5llZ6e/7MOF5bp4cu6yjCMet4CAAAAAI1DCNRM7733nlJTU7V+/Xrt3btXeXl5On78uEJDQ5WYmKgLL7xQt956qy644IJmz9WmTRstWrRIH330kebOnauffvpJR48eVUxMjC6++GJNnjzZKfMAaHlB/r5688bz9MzXO/Txxv12115bvluHCsv0wtW9a7SXBwAAAICmMqzN6V0Or5KVlaXExERJUmZmphISEkyuCHB/VqtVM5ft1t+X765xbdRZMXrthnMV5O9rQmUAAAAAzNJSv39zJhAAmMgwDD06opuev7q3HHd/LdmRq1ve2ahjJRXmFAcAAADAoxACAUArcPOgznrjD+cpwNf+X8sbMwo04a31Onis1KTKAAAAAHgKQiAAaCVG94nT+3cMUFig/XFtv+YW6po312nPoRMmVQYAAADAExACAUArMuiMSH16zwWKDrPvJHjgaImue2udftx/xKTKAAAAALg7QiAAaGV6xbfVl/ddqDOiQuzGjxRX6A//3KAVvxwyqTIAAAAA7owQCABaocT2wfr83gvUNzHCbry0okp3vr9J8zZnmVMYAAAAALdFCAQArVRkaKA+unOghnSLthuvrLLq8c9/0r0fbNZvuYUmVQcAAADA3RACAUArFhLop3du7adx53ascW3xjoMa9eoqPfzJFmXkFZlQHQAAAAB3QggEAK2cv6+PXrmur+4eckaNa1arNH9rtobPSNX/+3Kbso+WmFAhAAAAAHdACAQAbsDHx9D/XNFTL47ro/A2/jWuV1ZZ9fHGTA3720o9u2CHDheWmVAlAAAAgNaMEAgA3MgfBnbS6icv0UPDuyo00K/G9fLKKs1dl6EhL63QtG9/0dHichOqBAAAANAaEQIBgJtpG+SvR0Z006o/XaJ7hpyhIP+a/yovqajUW6lpunj6Cr22bLcKSytMqBQAAABAa0IIBABuqn1IgP7fFT216olLdOsFneXva9S4p7DMopnLftOQl1bo7dQ0lZRXmlApAAAAgNaAEAgA3FyHtkF6LqW3Vjw+TNf3S5SvT80w6Ehxhf7321805G8r9N66DJVZCIMAAAAAb0MIBAAeIqFdsKZfe7a+e2SIUs6Jl1EzC9LhwjJNXbBDl76cqk9/2C9LZZXrCwUAAABgCkIgAPAwZ0SH6rUbztW3D12sUWfF1HrPgaMlevKL7Roxc5W+3npAVVVWF1cJAAAAwNUIgQDAQ/WIbau3b+6nBZMHa0i36FrvSc8r0kOfbNXo11Zr8c8HZbUSBgEAAACeihAIADzc2QkRev/2Afrsngs0ILl9rff8mluoez/crJT/W6vU3w4TBgEAAAAeiBAIALzEgOT2+vTuQXr/9gHqmxBe6z3bso7p1jkbdf3bG/T93nwXVwgAAACgJRECAYAXMQxDQ7pFa/4DgzXr5vPVIzas1vs2ZhTo+lkbdPM73+unzKOuLRIAAABAi/AzuwAAgOsZhqGRZ8Xqsp4xWrg9R69+95v25hXVuG/17jyt3p2nEb1i9OiIbuoZ19aEagEAAAA4AyuBAMCL+fgYGts3XksfGaKXrj1bHSPa1HrfdztzdcXfV+vBj7co7fAJF1cJAAAAwBkIgQAA8vP10YR+ifrP40P1l5SzFB0WWOMeq1X690/ZGjEjVU98/pMyC4pNqBQAAABAUxECAQBsAv18dcsFSVr1xCX6nyt6qF2wf417qqzS55uzdOkrK/XM/J+Ve7zUhEoBAAAANBYhEACghjYBvrp7SBet+tMlenREN4UF1jxCrqLSqg827NOQl1boxW92qaCo3IRKAQAAADQUIRAAoE5hQf764/CuWv3kJbp/WBe18fetcU+ZpUqzVu3VxdP/oxlLf9WxkgoTKgUAAABwOoRAAIDTiggO0J8u76FVf7pEtw9OVoBfzf/3UVReqb//Z4+GvLRC/7dij4rLLSZUCgAAAKAuhEAAgAaLDgvUlKt6aeXjwzRxQCf5+Rg17jlWUqG/LflVQ15aoXfWpKu0otKESgEAAAA4IgQCADRafEQb/e/4Plr+2FCNP7ejjJpZkPJOlOv5hTt1ycsr9dH3+1VRWeX6QgEAAADYEAIBAJqsc2SIZlx/jpY+PERX9Imt9Z6cY6X6n6+2a/grqfryxyxVVlldXCUAAAAAiRAIAOAEXWPC9MaN52vhgxfp0h4dar1nf0GxHv3sJ416dZW+2Z6jKsIgAAAAwKUIgQAATtO7Y7jmTOqvL+67QBecEVnrPXsOndD9//pRV/1jjf7zS66sVsIgAAAAwBUIgQAATnd+5/b6+O5B+ujOgTq3U0St9+zIPq7b527SNW+u07o9ea4tEAAAAPBChEAAgBZz4ZlR+vK+CzVnUj/1imtb6z0/7j+qP8z+Xn/45wZt3nfExRUCAADAGxw4WqL7PtysvBNlZpdiKj+zCwAAeDbDMHRpjxgN69ZB3/58UDO++1Vph4tq3LcuLV/r3lynS3t00KMjuql3x3ATqgUAAICn+fdP2fqfr7arsNSickuVZt/aT0Zt7W29ACuBAAAu4eNj6Mqz47T0kaF65bq+Smzfptb7/vPLIY15fY3u/9dm7TlU6OIqAQAA4ClOlFn02Gc/6cGPt6iw1CJJWv7LIX2wYZ/JlZmHEAgA4FK+PoauOT9Byx8dpr+O663YtkG13vfN9oMaOXOVHv10q/bnF7u4SgAAALizrZlHdeXfV+uLH7NqXHt//T5ZKqtMqMp8bAcDAJgiwM9HNw7srGvOS9C/vt+vN1bsUX5Rud09VVbpyy0HNH/rAQ1Ibq8r+sTp8t6x6hBWe3AEAAAA71ZZZdWbK/do5rLdqqyq2YX26nPi9Zere8vP1zvXxBhWevOigbKyspSYmChJyszMVEJCgskVAfAkRWUWzV2XobdT03T8v8t1a2MYUv+k9rryv4FQTB0riQAAAOBdso+W6OFPt2pjekGNa6GBfnr+6rM07lz3+D22pX7/JgRCgxECAXCFYyUVmr16r+asSVdReWW99xqG1K9zO13RJ06je8cpNpxACAAAwBst2paj//fltlr/x8TzOkXo1evPVafIYBMqaxpCIJiOEAiAK+WfKNNbqWn66Pv9pw2DTjnfFgjFKj6i9oOnAQAA4DmKyix6dsEOfb655tk/PoY0+dKu+uOlZ7rd9i9CIJiOEAiAGUorKpX622F9sz1Hy3cd0omyureKVXdupwhd2SdOo/vEqSOBEAAAgMf5KfOoHvpkizJqaSLSMaKNXr3hHPVPam9CZc1HCATTEQIBMFtpRaVW787TN9tztGxnrgobGAidkxihK/rEanTvOCW2d59lwAAAAKipssqqt1LTNPO732Sp5fDnsX3j9fzVvRXext+E6pyDEAimIwQC0JqUWSq1ZneeFm3P0Xc7c1VYz2HS1fVNCNcVfeJ0RR8CIQAAAHeTfbREj3y6Vd/XcfjzX1LO0rhzO8owDBOqcx5CIJiOEAhAa1VmqdS6PflatD1HS3ccrLe7WHV9Op4KhGLVOTKkhasEAABAc3yzPUf/78vtOlZSUePauZ0i9JqbHf5cH0IgmI4QCIA7KLdUaW1anr7ZlqOlO3Nr/Y+E2pwV39a2Qig5ikAIAACgtSgqs+i5f+/QZ5vqOPz5kjP14PCu8nezw5/rQwgE0xECAXA3FZVVWpeWr2+25WjJzoM6WtywQKhnXFtd2SdWV/SJ0xnRoS1cJQAAAOqyLeuoHvpkq9Lzimpc6xjRRjOvP0cDkt3z8Of6EALBdIRAANxZRWWVNuzN1zfbc7RkR64Kisob9FyP2DDbCqEzOxAIAQAAuEJllVVvr0rTjKW1H/58Vd94veDmhz/XhxAIpiMEAuApLJVV+j69QIu252jJzweV38BAqFtMqK7oE6cr+8Spa0xYC1cJAADgnXKOnTz8ecPemoc/hwT46i8pvTX+PPc//Lk+hEAwHSEQAE9kqazSxowCfbM9R4t/Pqi8Ew0LhLp2CNXo/wZC3WJCPfo/QgAAAFzl2+05+nMdhz+fkxih1244xysaehACwXSEQAA8XWWVVRvTC/Ttzzn69ueDOlxY1qDnukSH6Mo+cRrdJ049YsMIhAAAABqpuNyiv/x7pz75IbPGNR9DeuCSM/VHDzv8uT6EQDAdIRAAb1JZZdWm/64Q+vbngzrUwEDojKgQ2xlCPeMIhAAAAE5ne9YxPfTJFu31ssOf60MIBNMRAgHwVlVVVm3ef0SLtp3cMnbweGmDnkuKDLYFQmfFtyUQAgAAqKaqyqpZq/fqlaW/qqKyZjQx5uw4/XVcH489/Lk+hEAwHSEQAJz8j5UtmUe0aNtBfftzjnKONSwQ6hwZrNG9T54h1LsjgRAAAPBuB4+V6tHPtmpdWn6NayEBvnoupbeu8fDDn+tDCATTEQIBgL2qKqu2Zh3VN9tObhk7cLSkQc8ltm+jK3qfXCF0dkK41/7HDQAA8E6Lfz6oP3+5TUeLax7+3DcxQq9df46Sojz/8Of6EALBdIRAAFA3q9WqrZlH9e3PB7VoW06DA6GOEW10RZ9YXdEnTuckRhAIAQAAj1VcbtHzC3fq4401D382DOmBYWfqocu85/Dn+hACwXSEQADQMFarVduyjumbn3P0zfYcZRY0PBAa3TtWl/eO1dkJEQrw4z+AAACAZ/j5wDH98ZMt2nu45uHP8eFBmnn9ORp4RqQJlbVOhEAwHSEQADSe1WrVzweOa9H2k4HQ/oLiBj0X4OejszuG6/zO7XRe53Y6r1M7RYcFtnC1AAAAzlVVZdU/V+/Vy3Uc/nzl2XF68eo+Cg/2vsOf69NSv3/7OeUtAACgVoZhqE9CuPokhOvJy7trR/ZxffPfQCgjv+5AqNxSpU37jmjTviO2sU7tg0+GQp0idF7nduoeEyY/lksDAIBW6uCxUj32+Vat3VPz8OfgAF89O/YsXXd+AtvhXYgQCAAAFzEMQ707hqt3x3A9Maq7duUU2gKhvXk1l0Y72l9QrP0FxfpqywFJJ//j6ZzEiP8GQ+10bqcIRQQHtPRfBgAAwGkt2XFQT35Rx+HPCeF69YZzlezlhz+bgRAIAAATGIahXvFt1Su+rR4b2U2/5hbqm205WrIjV7/mFjboHcXllVqXlm/XWvXMDqE6r9PJYOj8zu10RlSofHz4X9cAAIBrnDz8eZc+3ri/xjXDkO4b2kWPjOjG4c8m4UwgNBhnAgGAaxwrrtCWzCP6cd8Rbd5/RFv3H1VReWWT3tU2yM92ptD5ndupb2KEQgP534AAAIDz1Xf4c1x4kGZMOEcXdOHw54bgTCAAALxEeLC/hnXvoGHdO0iSKqus+vVgoTbvP6It/w2G9tVznlB1x0stWvnrYa389bAkyceQuse21fmdf99G1ql9MHvxAQBAk1VVWTV7zV79bUnthz9f0SdWL47rw7b1VoCVQGgwVgIBQOuRd6LMtlJoy76j+inr/7d37+FNVYn6x98kTXqnhUIpLVAEBRXwCggqIiggiiBwRBmHi4I6x5k5zn3GM/4APaLoo+N4nHkUjwzMwIgiKgIe5TZahAFR4WiVESgXgV7ohd5L21z274+0IaW3tEmb0nw/z9OnafbOWqvZXU32m7XXKlKVw9WqsrrH2HRNX/cqZNemdtXQlDhFWC0BbjEAAOiMTpdU6pdrv9LOjPx626JsFi2+c7DuHsbkzy3FSCAAAODRPSZcEwYnacLgJEnu1cQOZJd4gqF93xcqu7jSp7Lyy6q15cBpbTlwWpJktZh0eXKcru3bVdfUjBjqFRfZZr8LAAC4MG2pmfy5sIHJn6/oHaeXmPy5wyEEAgCgE7CFmXVVn3hd1SdeD+giSVJW0VntO1GoL78v1L4TRfo2s1gOV/MDgO1OQ1+dLNJXJ4v0l13u+5LjInR1alddWzO30OXJXZjQEQCAEHW22qn/+uCA3vis4cmffzRmgH5+60DZwniv0NEQAgEA0Eklx0cqOT5Sk69IliRV2p1KzyzWl9+7g6H9JwqVX1btU1lZxZXK+jpbH3ydLUkKDzPryt7xNZNOu793jwlvs98FAAB0DN9kFuvRN/frSAOTPyd1idAf7rlS1w/oHoSWwReEQAGwb98+ffTRR/r000/1zTffKDc3V1arVcnJybr++us1f/58jR492u96Fi9erCeeeMKnfT/++GPdfPPNftcJAOg8IqwWDe/XTcP7dZMkGYahE2cqakYKFerL74t0MKdEPgwWUpXDpb3Hz2jv8TOe+1ITomouIXNPOD0oKVYWlqcHAKBTcLkMLd95TM9t/q7ByZ8nDUnSM9OZ/LmjIwTy05gxY7Rjx45691dXV+vw4cM6fPiw/vrXv2r27Nl6/fXXZbPRIQAAHYPJZFJqQrRSE6I1/Rr3ZINlVQ59dbKozmihkkqHT+V9X1Ch7wsq9O7+TElSTHiYruoT7xkpdHXfroqLtLbZ7wMAANpGbkmlfvn2V/r0cP3JnyOtFi2ecrlmDuvD5M8XAEIgP2Vmut/oJicn6+6779bo0aPVt29fOZ1O7d69Wy+88IIyMzO1atUqORwOvfHGGwGpNz09vcntF110UUDqAQCElpjwMN1wcXfdcLF7GLfLZehIXplnbqEvvy9scPh3Q8qqHNqZkV9ntZBLEmPcS9PXjBYa0COaN4wAAHRgWw+c1m/WfdXg5M9DU+L00r1XqX+PmCC0DK3BEvF+mjx5subMmaMZM2bIYqm/nG5+fr5uuOEGHTp0SJK0Y8eOVl8a5n05WDAOG0vEAwAkqaiiWvtPFHkuI/u/k0WqqHa2qqz4KKuu7B2vgT1jdHFijAb0cH9nKDkAAMF1ttqpJf97QKv3NDz580M39dcvxw9i8uc2whLxHdSmTZua3N69e3e98MILuvPOOyVJ69atC8j8QAAABEt8lE1jL03U2EsTJUkOp0vf5ZRqv9dKZCfOVPhUVlGFXWmH8pR2KK/O/QnRNg3wCoUG9IjWxYkxSo6LlJl5hgAAaFMHskr0H2/uV0ZuWb1tPbuE68WZV+n6i5n8+UJECNQOvCdoPnLkSPAaAgBAGwizmDUkJU5DUuI0e1Q/SVJuaaX2fV/kCYa+zixWtcPlc5kF5dUqOHZGe4+dqXN/pNWi/j2ivcIh9/d+3aMUHlZ/RC4AAPCdy2XoL7uO6bmPDqraWf91e+Lgnlo6/Qp1jWbE7oWKEKgdVFefW37XbGaoHACg80uMjdBtQ5J025AkSVKVw6kDWSVeK5EV6nRJVYvLPWt36tusEn2bVVLnfrNJ6tstqk44NCAxRhf3iFFcFJNRAwDQnOYmf1505+W6ZziTP1/oCIHaQVpamuf2pZdeGpAyx48fr3379qm0tFTx8fG6/PLLddttt+nhhx9W165dA1IHAACBEh5m0dV93SuESe657bKKK7Xv+0IdzCnVkbwyZeSW6XhBeYPLzjbHZUjHCyp0vKBC27/LrbOte0y453IyT0iUGKPkuAjeyAIAIGn7v07r1+u+1pny6nrbhqR00Uv3Xq0BTP7cKRACtTGXy6WlS5d6fp45c2ZAyt22bZvndl5entLS0pSWlqZnn31WK1eu1NSpU1tc5qlTp5rcnp2d3eIyAQBoiMlkUkp8pFLiI3XnlefutztdOnmmQhm5ZTqSV17zvUxHcstUWuXbUvXnyy+rUn5ZlT4779KyKJv70rKLz7u0LDUhmkkuAQAhodLu1JIP/qVVe76vt43JnzsnQqA29uKLL2rv3r2SpGnTpmnYsGF+lTd06FDdddddGjFihJKTk2W323Xw4EH9/e9/15YtW1RUVKQZM2Zo48aNmjRpUovKrp15HACAYLFazOrfI6beUrOGYSivtMoTCnmHRDklla2qq6LaqW8yS/RNZt1Lyyxmk1K7Ran/eZNSD0iMUZcILi0DAHQO/8ou0X+s2a/DTP4cUlgivg2lpaXp1ltvlcPhUGJior7++mv17Nmz1eUVFRUpPj6+0e3Lli3Tj370I0lScnKyMjIyFBkZ6XP5LRkSzxLxAICOoqzKoSNe4VBtUPR9QYUcrsC+zUmMDT9vxbJYDUiMVlIXLi0DAHRclXan/pVdovTMYqWfKlZ6ZrEOnS5VQy+TTP7cMbTVEvGEQG3k22+/1ejRo1VYWKjw8HBt3rxZY8aMafN6H3zwQb3++uuSpNWrV+u+++7z+bG+XA42YsQISYRAAICOz+506fuCijqXlNUGReXVzoDWFW2zeCaiPre0fbRSE6JltTCEHgDQfirtTn2XU1oT+BQpPbNEh06XytnMByMRVrMWTh6sWSOY/LkjaKsQiMvB2sCxY8c0YcIEFRYWymKxaM2aNe0SAEnSww8/7AmB0tLSWhQCEeoAADoTq8WsixPdo3a8GYah0yXnX1rm/p5b2vIVyySpvNqpr08V6+tTxXXuDzOb1DchyhMOXdwjRv17RCslPlLdY8JlNvMmGwDQelUOp77LLq03wqelI2EHJ7snfz7/NROdDyFQgGVlZenWW29VVlaWTCaT/vKXv2jatGntVv/ll1/uuZ2Zmdlu9QIAcKEwmUxKiotQUlyEbryk7lwHJZX2mhFDdSel/v5MRbOfoDbE4TJ0NK9cR/PKpQOn62yzWkzq2SVCveIi1CsuUr3iI9SrS4R6xUcqOS5SSXERSoi2ERQBACS5A5+DOXUDn4M5LQ98vEVYzZp3/UX6+fhLFB5mCWBr0VERAgVQfn6+xo8fr6NHj0qSXn75Zc2ZM6dd28DVfQAAtF6XCGudpexrVTtc+r6gvN7E1EfyylTRykvL7E5DpwrP6lThWUmFDe5js5g9gVVynDsg8oRGce4AqVu0jWH7ANDJVDtc5wKfzGKlZxbpYE6p7M7Wn++ZTNKAHjG6IiVOQ1LidEXvOF2e3EVRNmKBUMLRDpDi4mJNnDhRBw4ckCQtXbpUP/7xj9u9HbX1S+7JoQEAgP9sYWZd0jNWl/SMrXO/y2Uop6SygUvLypVf1rpLy7xVO106caZCJ85UNNm22kCodgSRezRRbXgUqfgoK0ERAHRQ1Q6XDp32CnxOuUf4VDtdrS7TZJL6d4/WFb3jzwU+vbooOpwIINTxFxAAFRUVuuOOO7Rv3z5J0u9//3v99re/DUpbli1b5rndXvMQAQAQqsxmk5LjI5UcH6mbBvaos624wq6MvPqTUp8qPBvQVcvco5Qq9H1B40FRhNXsNXqo5nv8udAoOS5SXSLDCIoAoI3ZnTWBz6liT+jzXbb/gc9F3aM9I3yGpsRpcEqcYgh80AD+KvxUXV2tadOmadeuXZKkRx99VE899VSLy1m5cqXuv/9+SdKiRYu0ePHiOtvT09MVGRmpiy++uNEyli1bpuXLl0uSkpKS2nUuIgAAUFdclFXXpnbVtal1Ly1zugzll1Upq+iscoorlVVcqZzis8oqrlR2zX2nS6taNQdRYyrtLh3LL9ex/PJG94myWTyBUO3IojqXn8VHqEuENWBtAoDOzu506fDpMqVnFnlG+Pwrp1TVjtYHPpJ7hE/t6J4hKXEanNxFsfx/ho8Igfw0a9YsbdmyRZI0btw4zZ8/X998802j+9tsNg0cOLDF9Xz55ZdasGCBxo4dq0mTJmno0KFKSEiQw+HQd999p9WrV2vr1q2SJIvFomXLlik6Orp1vxQAAGgzFrN7QuieXSIa3cfhdCmvrErZxZXKLqpUdvFZ9+3is8oqqlROcaVySysVwJxIFdXOc5NYNyImPMx9uZnXpWfJ8d6jiyL55BlASHI4XTqcW1ZnhM+B7BK/A5+LagOfmlE+g1O6EMjDL7xK++ndd9/13P7HP/6hK664osn9U1NTdfz48VbV5XQ6tW3bNm3btq3RfRISErR8+XJNmTKlVXUAAIDgC7PUXr4VKfVteB+706Xc0ir3KCLvoMjrdl5ZlQK5ZkRZlUMZue7L2hoTGxHmGT2UHO8OuxJiwtU1yqpuUTZ1jbapa5RN8VFWRVhZiQbAhcfhdCkj77zAJ6tEVX4GPv0SojyXcw3tHafByXGKiyTwQWARAl0gbr/9di1fvly7d+/W/v37dfr0aRUUFMgwDHXr1k1XXnmlbrvtNs2bN09dunQJdnMBAEAbs1rMSomPVEp8pK5NbXifaodLuaWVyi6u9Fx+5rldUqmsosqATGDtrbTSodLKMh063XhQVCvKZlHXKJu6RbtDoW41AZH7PqviG9hGcASgPTmcLh3JK6+5nKvIM8Kn0u5f4JPqFfhcUTOHD4EP2oPJYE1x+OjUqVPq06ePJOnkyZPq3bt3kFsEAAD8VeVwKrfEPUdRdk1I5LnsrOSssosqVVBeHexmekRaLQ2ERlbPCKOu0TZ1i6q7PdJGcASgeU6XoSMNjPA5a3f6VW6fbpG6IuXcKl1DkuMUF0Xgg6a11fk3I4EAAABCWHiYRX26RalPt6hG96m0O3W6ZuRQTsm5y89yiis9twsr7O3S3rN2pzKLziqz6KzPj4mwmmuCoYZGHZ0LkLpF116uZlWk1cJqacAFzDAMlVQ6VFRRrTPl1SqsqNaZcvt5P1ersNyuworqmi+735Py9+4a6ZmweWjNV3yULUC/FeA/QiAAAAA0KcJqUWpCtFITGl90otLurJmTyGsS6+JK5ZZUqrCi5iSrvFpFZ+0BnafIF5V2l7JqVmLzVXiY+dzIotpL07xCI3eYVHfUUZSN4AhoC4ZhqKzKocJyu87U/C8pPC/cqb3PO+xxBHL2/AakxNcPfLpGE/igYyMEAgAAgN8irBZd1D1aF3VvenVSp8tQyVn3iVxR7clbzUndmYpqFZV7b3N/Ml9UUR3QldB8UeVwKaekUjklvgdHNotZ8VFWxUSEKdoWpiibxf0VHqZom0VRtjBFh7u/R9ks7n3CLZ59o8PDFOl1f5TVojCLuQ1/S6D9GYah8mqnu8+fF9x4/g94jdKp/dnuDO4sJinxkRqS0kVX9I73hD7dCHxwASIEAgAAQLuxmE3uS65acPLkchkqqbSrsMJec2JYXefyDc/PXieMgbiso6Wqa1Zsyy0N3GTb4WFmRYd7BUpeQVJ0TcAUZfUKmrwCJ3ewVLvvucApwmpmxBICwjAMVVQ7daa8WkUV50bpeIc7heX2ej9XO/2bVLmtJcdFnJu/pybwSYgJD3azgIAgBAIAAECHZjabFF8zp09zI41quVyGSqsc540sqDsfSGG5vd62tr58pKWqHC5VOap1pjxwZZpMqhscnTdCqcGgqSaE8g6TosMtirSFyWoxKcxslsVsUpjZVOc7YVNwGIYhh8uQw2nI7nK5vztdsjvdtx0ul6od7u92pyGH0yWHy1B17XanS3aXIbvDpdJKu85UnBulU1h+Lnw9U1Gtaj+XRW8PtjCzEjyTx1vPzQHWwDxhveIiCHzQqRECAQAAoNMxm02Ki7QqLtKqfvItODKMc8GR9wijc6MYGh51FOzLVFrKMKTyaqfKq53Ka+O6zg+Fwix1wyKrpeHwyBMqWWrvM7u/W87f131/vf1qt3vdb7XUf1zdtrnr9f65of1chlETnNSGJu4AxR2ynAtQHDWBSt19jJog5tw+5+47P7BxhzRNluE8F+R4BzwX2t9kS9gs5rrBTXT9+brOD3eY6B04hxAIAAAAkGQymdQlwqouEValJvj2mNoJa4tqLlUrOmtXRZVD5dVOVVQ7VF7l1Nnquj97f6+odqqi2qnyaocqqpwd/jKZlnK43CNSAneBHDoTq8XU5Kgc7xX7an9mAnbAP4RAAAAAQCuZTCbFRlgVG2FVn25RfpdX7XDpbLVTFfb6gVF5tVMVVbXB0bmf6wZONYFStVPlNfuWVzvafUU2hJ6wmvm+unldcnXuZ6+ROl7hTjSBDtDuCIEAAACADsIWZpYtzKw4WQNWpmEYqnK46oRC3gHT2UYCp7PnBUkVVecCpopqhyrtnWvUUqgxmSSrxSxrzWV6Vov7krkwi0lWs/vnMItJ0eFhniCnW2244xXkdIuyKT7aqtjwMAId4AJACAQAAAB0YiaTSRFWiyKsFvl4lZtPnC5DFdWOmnloDDld7vlonC6vn53u7/ba+50N72d3nvc4lyFnzRw4tT+7H1v3Pu+y7E7v+11edTVRh3d5zvPadd7v5T1neP0AxeQJTaxms2d+IWtY7T6mmpDFPceQZ9/a4MVc/2d3IGOut4/N4lW+5bz6zV71eLXlXMBzbh+LmcAGCEWEQAAAAABazGJ2XwoXKlw14ZClZoJoALgQEQIBAAAAQDPMZpNshD8ALnDmYDcAAAAAAAAAbY8QCAAAAAAAIAQQAgEAAAAAAIQAQiAAAAAAAIAQQAgEAAAAAAAQAgiBAAAAAAAAQgAhEAAAAAAAQAggBAIAAAAAAAgBhEAAAAAAAAAhgBAIAAAAAAAgBBACAQAAAAAAhABCIAAAAAAAgBBACAQAAAAAABACCIEAAAAAAABCACEQAAAAAABACCAEAgAAAAAACAGEQAAAAAAAACGAEAgAAAAAACAEEAIBAAAAAACEAEIgAAAAAACAEEAIBAAAAAAAEAIIgQAAAAAAAEIAIRAAAAAAAEAIIAQCAAAAAAAIAYRAAAAAAAAAIYAQCAAAAAAAIAQQAgEAAAAAAIQAQiAAAAAAAIAQEBbsBuDC4XA4PLezs7OD2BIAAAAAADov73Nu73NxfxECwWd5eXme2yNGjAhiSwAAAAAACA15eXnq169fQMricjAAAAAAAIAQYDIMwwh2I3BhqKysVHp6uiSpR48eCgtjINmFLjs72zOqa+/everVq1eQW4RA4dh2Xhzbzotj23lxbDsvjm3nxbHtvC6UY+twODxX4wwdOlQREREBKZezePgsIiJCw4cPD3Yz0EZ69eql3r17B7sZaAMc286LY9t5cWw7L45t58Wx7bw4tp1XRz+2gboEzBuXgwEAAAAAAIQAQiAAAAAAAIAQQAgEAAAAAAAQAgiBAAAAAAAAQgAhEAAAAAAAQAggBAIAAAAAAAgBhEAAAAAAAAAhwGQYhhHsRgAAAAAAAKBtMRIIAAAAAAAgBBACAQAAAAAAhABCIAAAAAAAgBBACAQAAAAAABACCIEAAAAAAABCACEQAAAAAABACCAEAgAAAAAACAGEQAAAAAAAACGAEAgAAAAAACAEEAIBAAAAAACEAEIg4AK1b98+Pf3005o0aZL69Omj8PBwxcTEaODAgZo3b54+/fTTgNSzePFimUwmn74++eSTgNQZ6nx9vm+++eaA1Pfmm29q4sSJ6tWrlyIiItSvXz/Nnj1be/bsCUj5cLv55pt9Prb+9Cn6bODl5uZq06ZNWrhwoSZNmqTu3bt7nsN58+a1uLyPPvpI06dPV+/evRUeHq7evXtr+vTp+uijjwLa7oKCAi1atEhXXnml4uLi1KVLF1155ZVatGiRCgoKAlrXhSoQx7ayslLvv/++fvrTn+q6665Tt27dZLVa1a1bN40aNUqLFy9WdnZ2QNrbr18/n/p2v379AlLfhSwQx3blypU+/z9duXJlQNpNv22ev8f2+PHjLX499qdP0W99F+jzG15vG2EAuODcdNNNhqRmv2bPnm1UVVX5VdeiRYt8qkuS8fHHHwfmFwxxvj7fY8aM8aues2fPGpMnT260fLPZbDz55JOB+aVgjBkzxudjW/v8nzp1qsX10GcDr6nncO7cuT6X43K5jIceeqjJ8h566CHD5XL53ea9e/cavXr1arSe5ORk4/PPP/e7ngudv8f2q6++MmJjY5vta7GxscZbb73ld3tTU1N96tupqal+13WhC0S/XbFihc//T1esWOF3m+m3vvH32B47dqxFr8eSjAkTJrS6vfRb3wTy/IbX26aFCcAFJzMzU5KUnJysu+++W6NHj1bfvn3ldDq1e/duvfDCC8rMzNSqVavkcDj0xhtvBKTe9PT0JrdfdNFFAakHbv/+7/+uRx55pNHt0dHRfpU/f/58bdq0SZI0duxYPfroo0pOTlZ6erqefvppHTlyRAsXLlSvXr20YMECv+qCtGLFCpWXlze5z4EDB3TPPfdIkm655RalpKT4VSd9NvD69Omjyy67TFu2bGnxYx9//HG99tprkqSrr75av/nNbzRgwAAdOXJEzz33nPbv36/XXntNPXr00FNPPdXqNmZmZurOO+/U6dOnFRYWpl/84heaPHmyJGnTpk36wx/+oKysLE2ePFlffvml339nnUVrjm1JSYlKS0slSTfccIMmT56sYcOGKSEhQXl5eXr33Xf1+uuvq7S0VD/4wQ8UGxurSZMm+d3WqVOnNvk3YrPZ/K6jM/Gn39bavHmzkpOTG93eu3fvVpct0W9bqzXHNiUlpdnXR0l65plnPO+h586d2+o21qLfNi2Q5ze83jYj2CkUgJa74447jLfeestwOBwNbs/LyzMGDhzoSaB37NjR6rq8RxWgfdQ+34sWLWqzOj755BNPPXfeeWe9v6W8vDyjb9++hiSja9euRmFhYZu1Bef85je/8RyXVatWtaoM+mzgLVy40Ni4caORk5NjGEbdT5F9HVFw+PBhIywszJBkDBs2zKioqKizvby83Bg2bJghyQgLCzMyMjJa3d65c+d62rd27dp629euXevZfv/997e6ns7A32O7a9cuY+bMmca3337b6D7r1683TCaTIckYMGCAX588144oaMkItFAViH7rPRLo2LFjbddYg37bEoE4ts1xOBxGcnKyZyRfeXl5q8ui3/omUOc3vN42j3eIQCe1ceNGzz+d//iP/2h1OZxQtr/2CIFuv/12Q5JhsViMkydPNrjPmjVrPG15/vnn26wtcHM6nUZKSoohyYiJiWn1G076bNtrzQnHI4884nnM7t27G9xn9+7dnn1+8pOftKptOTk5hsViMSQZEydObHS/iRMnev4H1J5IoW1OJg3DMGbMmOEpd9++fa0uh5PJ1uvIIRD91j9t0W8/+uijgJ28028Dx5fzG15vm8fE0EAn5T1p8JEjR4LXEHQ4ZWVl2r59uyRp/PjxjQ5hnz59urp06SJJevfdd9utfaFq+/btnqHQ//Zv/6aoqKggtwiBYhiG3n//fUnSpZdeqpEjRza438iRIzVo0CBJ0vr162UYRovr2rBhg5xOpyTp/vvvb3S/2slTnU6nNmzY0OJ60DJjx4713OY1Geej33Y8f/vb3zy3A3EpGAKjufMbXm99QwgEdFLV1dWe22YzXR3n7N27V1VVVZKkMWPGNLqfzWbzvHju3btXdru9XdoXqrzfcM6ZMyeILUGgHTt2zBPwNdXnvLefOnVKx48fb3Fd3iunNFWX97adO3e2uB60TO3/XInXZNRHv+1YSktLtX79eklSamqqbrrppuA2CB7Nnd/weusbXoWATiotLc1z+9JLLw1ImePHj1dCQoJsNpsSExN18803a+nSpSosLAxI+ajr7bff1qBBgxQZGanY2Fhdcsklmjt3rj7++GO/yv3Xv/7lud3c30btdofDocOHD/tVLxpXVlam9957T5LUt2/fOp90+YM+2zG0ps+d/7iW1hUXF6ekpKRG9+vVq5dnpF9r6kHLBPo1eceOHbriiisUHR2tqKgoXXTRRbrnnnta/Yk2mjdv3jz17NlTNptN3bt318iRI/X44497Tjj9Qb/tWNatW6eKigpJ7g9lTCZTQMql3/qvuf+lvN76hhAI6IRcLpeWLl3q+XnmzJkBKXfbtm06c+aM7Ha78vLylJaWpscee0z9+/f3DL1E4Bw4cECHDh1SZWWlysrKlJGRob/97W8aN26cpk2bpuLi4laVe/LkSc/t5lYz6dOnT4OPQ2C98847npXDZs+eHbA3nPTZjqE9+1ztY3xZqai2Lvp22/rqq6/0wQcfSJIGDx6syy+/3O8yjx07pvT0dFVUVOjs2bM6fvy41q5dq2nTpmn06NEBCSZQV1pamnJzc2W321VQUKDPPvtMS5Ys0cUXX6xly5b5VTb9tmNpq5G59Fv/+HJ+w+utb1giHuiEXnzxRe3du1eSNG3aNA0bNsyv8oYOHaq77rpLI0aMUHJysux2uw4ePKi///3v2rJli4qKijRjxgxt3LgxIEvfhrqoqChNmTJFt9xyiy699FLFxMR4TuBfffVVFRQUaP369Zo6daq2bt0qq9XaovJrlzOWpJiYmCb39V6GvqysrGW/CHwW6Dec9NmOpT37XG1dzdXjXRd9u+1UVVVpwYIFnnkjnn76ab/Ks9lsmjJliiZMmKAhQ4YoLi5ORUVF2r17t1555RWdPHlSu3bt0vjx47V7927FxcUF4tcIaf3799f06dM1atQoz4nc0aNH9c4772jdunWqrKzUj370I5lMJj300EOtqoN+23GcOHHCM9rk+uuv18UXX+x3mfTbwPDl/IbXWx8Fc1ZqAIH3ySefeJZFTExM9HsW+uaWBn/11Vc9s+snJyfXW4YRLdfUc56Tk2NcffXVnuf8pZdeanH5DzzwgOfxR44caXLf5cuX+71kOZp28uRJw2w2G5KMkSNH+l0efbbttXQlmieffNKz//bt25vcd/v27Z59/+u//qvFbav9Wxo9enSz+44ePdqzYgncAr3K0IIFCwJaXlP9u6SkxJgwYYKnvp///Od+19eZtObYFhUVGS6Xq9HtGzduNKxWqyHJiIqKMrKzs1vVNvqtfwLZb5csWeIp69VXXw1I++i3/vP1/IbXW99wORjQiXz77beaNm2aHA6HwsPDtXbtWvXs2dOvMuPj45vc/vDDD2vBggWSpKysLFaRCoCmnvOePXtq3bp1stlskqSXX365xeVHRER4bntPsNcQ78lMIyMjW1wXmrd69Wq5XC5JgVmBhD7b8bRnn6utq7l6vOuib7eNZ555Rq+//rok6dprr9Wf//xnv8tsqn/HxsZq7dq1SkhIkCS99tprPv0doHFxcXFNXp47efJkLVq0SJJUUVGh5cuXt6oe+m3HsWrVKklSeHi47rnnnoCUSb/1T0vOb3i99Q0hENBJHDt2TBMmTFBhYaEsFovWrFnT7Kz4gfLwww97bntP2Ia20b9/f40fP16SlJGRoaysrBY9PjY21nO7uWGptfPUSL4Nd0XLtcUbzubQZ9tXe/a52rp8GXJeWxd9O/CWLVum//zP/5QkDRo0SB9++GGdSw/aSlxcnO69915J7uP7xRdftHmdoe7BBx/0BEWt/X9Kv+0Y9u7dq++++06SNGXKlGY/VAkU+m3jWnp+w+utbwiBgE4gKytLt956q7KysmQymfSXv/xF06ZNa7f6vSe5ZFK79uHPc+49gd2pU6ea3Nd7AjvvCfQQGF988YUOHDggyf2JcteuXdulXvps+2rPPldbV3P1eNdF3w6sNWvW6JFHHpHkXl5627Zt6tGjR7vVT/9uX4mJierevbuk1j/f9NuOoa0mhPYF/ba+1pzf8HrrG0Ig4AKXn5+v8ePH6+jRo5Lclwe19wuXwbKW7c6f59z7jUbtJ16Nqd0eFhYWkMkRUZf3G85AXArmK/ps+2pNn5Okyy67rNV1FRcXKycnp9H9srOzVVJS0up60LANGzZozpw5crlc6tWrl7Zv3+7TyjGBRP9uf/4+5/Tb4LPb7XrrrbckuYO92267rV3rp9/W1drzG15vfUMIBFzAiouLNXHiRM9IgqVLl+rHP/5xu7ejtn5JSk5Obvf6Q5E/z/nw4cM9cwo1NXS9urpae/bsqfcYBIbdbtebb74pSerRo0e7rtJFn21fF110ked5bu5ykR07dkiSUlJS1K9fvxbXdeONN3puN1WX97YbbrihxfWgvu3bt2vmzJlyOBxKSEjQ1q1bNWDAgHZvB/27feXm5qqgoEBS659v+m3wffDBB8rPz5ck/eAHP1BYWPsuok2/Pcef8xteb31DCARcoCoqKnTHHXdo3759kqTf//73+u1vfxuUtixbtsxzu73mIQplR48e1datWyW55wdKSUlp0eNjY2N1yy23SJK2bdvW6DDWd9991/PJRXteXhgqPvzwQ+Xl5Ulq/zec9Nn2ZTKZNHXqVEnuTx5rw9Xz7dmzx/PJ5NSpU5uckLYxU6ZMkdnsfnu3YsWKRvdbuXKlJMlsNmvKlCktrgd1/fOf/9TUqVNVVVWlLl26aPPmzRo8eHC7t6O4uNgzmiEqKqrBJZQRWK+99ppnFEdr/5/Sb4MvWCNzJfqtN3/Pb3i99VEQVyYD0EpVVVV1lpN89NFHW1XOihUrPGUsWrSo3vavv/7aOHz4cJNleC83nZSUZJSVlbWqLXDbsGGDYbfbG91+/hLxL7zwQr19mjuuhlF3WcwpU6YYDoejzva8vDyjb9++hiQjPj7eOHPmjF+/F+qbMWOG5xh8+eWXPj2GPtsxtGY54oMHD3qWtx02bJhRUVFRZ3tFRYUxbNgwQ5IRFhZmHDp0qMFyxowZ46n72LFjDe4ze/Zszz5vv/12ve1r164N6LLlnUlrju3+/fuN+Ph4Q5IRHR1t7Ny5s1V1N3dsP/zww3p/N97OX2r6pz/9aava0Vm19NgeO3bM2LdvX5P7bNy40bDZbIYkIyIiwjh16lSD+9Fv25a/S8QXFBR4juPQoUNb9Fj6beAE6vyG19vmte84NwABMWvWLG3ZskWSNG7cOM2fP1/ffPNNo/vbbDYNHDiwxfV8+eWXWrBggcaOHatJkyZp6NChSkhIkMPh0HfffafVq1d7RqRYLBYtW7asXVY/6cx++tOfym63a8aMGRo1apT69eunyMhI5efn65NPPtGrr77qGXZ+4403tvryv3Hjxunee+/Vm2++qQ0bNmj8+PH62c9+puTkZKWnp2vJkiU6ceKEJPcw3PaasDhUFBYWatOmTZKkIUOG6JprrglIufTZtrFz505lZGR4fq69ZEByr9BX+ylfrXnz5tUrY+DAgfrVr36lpUuX6osvvtANN9yg3/72txowYICOHDmiZ599Vvv375ck/frXv9Yll1zS6vYuWbJEH330kfLy8jRr1ix98cUXmjx5siRp06ZNeuGFFyS5L0N86qmnWl1PZ+DvsT1y5IgmTpyooqIiSdJTTz2luLi4Jl+TExMTlZiY2OK2Ll26VPfdd5+mT5+uG2+8UQMGDFBMTIyKioq0e/duvfLKK57JRwcNGqTFixe3uI7OxN9je/z4cY0dO1ajRo3SnXfeqauuukqJiYkyDENHjx7VunXrtG7dOs8ooOeff77FI3O90W99F4j/yd7efPNNzzLfgR4FRL/1XaDOb3i99UGwUygALaeaRNnXr9TU1AbLaW5Ugff2pr4SEhKM9evXt+0vHSJSU1N9es5nzJhhFBYWNliGLyOBDMP9Scjtt9/eaB1ms7nJx6P1XnnlFc/z/Nxzz/n8OPpscMydO7dF/3Mb43Q6jQceeKDJx86fP99wOp2NluHLJ5OGYRh79uwxkpKSGq0nKSnJ2LNnjz9PS6fg77H1tc95fzX2f7W5Y+u9vamvm266qdERKaHE32P78ccf+/S4qKgoY9myZU22hX4bWIH6n1zruuuuMyQZFovFyM7OblFb6LeB09L/pY2d3xgGr7fNYSQQgEbdfvvtWr58uXbv3q39+/fr9OnTKigokGEY6tatm6688krddtttmjdvnrp06RLs5nYKf/3rX5WWlqbdu3fr6NGjys/PV0lJiWJiYtSnTx9df/31mjt3rkaNGuV3XZGRkfrggw/0xhtvaOXKlfrqq69UVFSknj17avTo0frJT34SkHpQ36pVqyS5R+Pcd999ASuXPtuxmc1mLV++XDNmzNBrr72mzz//XPn5+erevbuGDx+uhx9+OGAThF933XVKT0/XSy+9pPXr1+v48eOS3JNmTp06VT/72c+UkJAQkLrQPp5//nlt375du3fv1sGDB5Wfn6+ioiJFRUUpOTlZ1113nWbNmqUJEya0an4L1HXttddq9erV2r17t7744gtlZ2crPz9fDodDXbt21eDBg3XLLbdowYIFrRrZ1RD6bfs7fPiwPvvsM0nS+PHjlZSUFNDy6bfBwett00yGwXp0AAAAAAAAnR2rgwEAAAAAAIQAQiAAAAAAAIAQQAgEAAAAAAAQAgiBAAAAAAAAQgAhEAAAAAAAQAggBAIAAAAAAAgBhEAAAAAAAAAhgBAIAAAAAAAgBBACAQAAAAAAhABCIAAAAAAAgBBACAQAAAAAABACCIEAAAAAAABCACEQAAAAAABACCAEAgAAAAAACAGEQAAAAAAAACGAEAgAAAAAACAEEAIBAAAAAACEAEIgAAAABFVmZqb++Mc/asKECerbt69sNpuSkpI0Y8YMffbZZ8FuHgAAnYbJMAwj2I0AAABA6Prd736nZ599VgMGDNCYMWOUmJiow4cPa/369TIMQ2vWrNHMmTOD3UwAAC54hEAAAAAIqnfffVc9evTQ6NGj69z/6aef6pZbblFsbKyysrIUHh4epBYCANA5cDkYAAAIeYsXL5bJZJLJZAp2U3xmt9s1aNAgmUwmvfXWW8Fujl+mT59eLwCSpNGjR2vs2LE6c+aM0tPT621/5JFHZDKZNHfu3PZoJgAAFzxCIAAAgADIzc3Vpk2btHDhQk2aNEndu3f3BEvz5s0LeH0vv/yyDh06pMsuu0x33313wMvvKKxWqyQpLCys3rbHHntMNptNq1at0ueff97eTQMA4IJT/9UUAAAALdazZ892q6usrEzPPPOMJGnhwoUymzvn53onTpzQtm3blJSUpKFDh9bb3qdPH82dO1f/8z//o8cff1ybN28OQisBALhwdM53DAAAAEHUp08fTZgwoc3Kf+WVV5Sfn68+ffp02gmT7Xa7Zs+eraqqKj333HOyWCwN7vfLX/5SkrRlyxZGAwEA0AxCIAAAgABYuHChNm7cqJycHJ04cULLli1rk3qcTqf+9Kc/SZJmzZrVIUYBeV/65svXJ5980mR5LpdLDzzwgHbs2KEHH3xQs2fPbnTfQYMG6ZprrpEkvfTSS4H8tQAA6HS4HAwAACAAnnjiiXapZ+vWrTpx4oQk6Yc//GG71NmcWbNmqbS01Of9k5KSGt1mGIYefPBBrV69Wj/84Q/16quvNlvefffdp3379umdd97Rn//8Z8XFxfncFgAAQgkhEAAAgA++/vprTZw4UTk5OUpMTNTmzZt11VVXtXs71q5dK0m65JJLGpwnp9bixYs9wZRhGCopKdEf/vAHvfPOOzp27Jiio6N17bXX6vHHH9f111/veVxubq7++7//W++9956OHz+uiIgI3XjjjVq8eLGuvvrqBut6+eWXA/K7uVwuLViwQCtWrNCsWbO0cuVKn0Y6zZgxQ7/85S9VWVmp999/X3PmzAlIewAA6GyCP34YAACgg9u1a5fGjBmjnJwcpaamaufOnUEJgCTp448/liSNHDnS58ecPHlSw4cP1xNPPKFvvvlG5eXlys3N1YcffqibbrpJb7/9tiR30HXNNddoyZIlOnDggCoqKnTmzBlt2LBBo0aN0j/+8Y82+Z2kugHQPffco1WrVjU6D9D5UlNT1atXL0lq9lIzAABCGSEQAABAEz788ENNmDBBRUVFuuyyy7Rz505dcsklQWnLqVOndPz4cUnS8OHDfX7c3XffrVOnTumxxx5TWlqaPv/8c7344ovq0qWLnE6n5s+fr2PHjmny5Mk6e/aslixZop07d+qzzz7TE088IZvNpqqqKt1///2qrq4O+O/lcrk0f/58rVixQnfffbdWr17tcwBUq/b5+PTTTwPePgAAOgsuBwMAAGjEm2++qTlz5shut2v48OH68MMPlZCQELT2/POf//TcbuzSrIb83//9n9LS0nTdddd57hs2bJgGDhyoO+64Q6WlpbruuutkGIb27t2rAQMGePYbMWKEunfvrh//+Mc6ceKEPvjgA02bNi0wv1CNJ598UitXrlRMTIwGDhyop556qt4+d911V5Ojr6699lpt2LBBGRkZys3NVWJiYkDbCABAZ0AIBAAA0IBXXnlFP/nJT+RyuTRu3Di9//77iomJCWqbTp065bndkpDjZz/7WZ0AqNbtt9+u1NRUff/998rLy9Orr75aJwCqdf/993vm3Pn0008DHgLVjm4qKyvTkiVLGtynX79+TYZA3s9HZmYmIRAAAA3gcjAAAIDzLFmyRI888ohcLpfuuusu/e///m/QAyBJysvL89zu2rWrz4+79957G912xRVXSJJMJpNmzpzZ4D6RkZGeS+COHj3qc72+WrlypQzDaPJr3rx5TZbRrVs3z23v5wkAAJxDCAQAAODlF7/4hR5//HFJ0rx587Ru3TqFh4cHuVVuZ86c8dxuSQg0cODARrfFx8dLkrp3795kmbX7tWQp+Pbk3faCgoIgtgQAgI6LEAgAAMDLiy++KEkaMmSIli9f3uIJittSRESE5/bZs2d9flxUVFSj22qXYG9qH+/9nE6nz/W2J+/nIzIyMogtAQCg4yIEAgAA8DJjxgxJ0jfffKNHH300yK2pq0ePHp7b3qOCUPf58H6eAADAOYRAAAAAXtasWaO77rpLkvSnP/1JP//5z4PbIC/e4UZhYWEQW9LxeD8fhEAAADSMEAgAAMCL1WrV2rVrNWXKFEnSH//4R/36178Ocqvchg4d6rl96NChILak46l9PqKjo9W/f/8gtwYAgI6JEAgAAOA8VqtVb7/9tu644w5J0vPPP6/f/e53QW6VNGzYMM98N59//nmQW9Ox1D4fI0eOVFhYWJBbAwBAx0QIBAAA0ACbzaZ33nlHkyZNkiQ9++yznlXDgtmmESNGSJL27t0b1LZ0JFVVVfr6668lSaNHjw5yawAA6Lj4mAQAAKAR4eHheu+99zR16lRt3rxZS5YskcVi0RNPPFFv3507dyojI8Pzc35+vud2RkaGVq5cWWf/efPmtapNd9xxh9LS0rR3716VlpYqNja2VeV0Jjt27JDdbpckz+gtAABQHyEQAABAE8LDw7V+/XpNmTJFW7du1ZNPPqmwsDD9v//3/+rs9/rrr+uvf/1rg2Xs2rVLu3btqnNfa0OgH/zgB3rsscdUWVmp9957T3PmzGlVOZ3JG2+8IUkaNGiQhg0bFuTWAADQcXE5GAAAQDMiIiL0/vvva9y4cZKkhQsX6plnnglKW1JSUjR16lRJ0t///vegtKEjqQ3DJOmRRx4JcmsAAOjYTIZhGMFuBAAAAHy3Z88ejRo1ShaLRRkZGerXr1+wmxQ0q1ev1uzZs9WtWzcdP36cy+MAAGgCI4EAAAAuMCNHjtSkSZPkdDqDNiKpI3C5XHr66aclSb/61a8IgAAAaAYjgQAAAC5A6enpuvrqq2U2m5WRkaG+ffsGu0nt7q233tK9996rPn366ODBg4qMjAx2kwAA6NCYGBoAAOACNHToUK1cuVIZGRk6ceJESIZATqdTixYt0rhx4wiAAADwASOBAAAAAAAAQgBzAgEAAAAAAIQAQiAAAAAAAIAQQAgEAAAAAAAQAgiBAAAAAAAAQgAhEAAAAAAAQAggBAIAAAAAAAgBhEAAAAAAAAAhgBAIAAAAAAAgBBACAQAAAAAAhABCIAAAAAAAgBBACAQAAAAAABACCIEAAAAAAABCACEQAAAAAABACCAEAgAAAAAACAGEQAAAAAAAACGAEAgAAAAAACAEEAIBAAAAAACEAEIgAAAAAACAEEAIBAAAAAAAEAIIgQAAAAAAAEIAIRAAAAAAAEAI+P++bWhyD4D3hgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -269,7 +271,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -284,7 +286,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -302,7 +304,7 @@ "for key in KEYS:\n", " plt.plot(qvec1, DAT[key])\n", " plt.ylabel(key)\n", - " plt.xlabel(r'k1 (m$^{-2}$)')\n", + " plt.xlabel(r\"k1 (m$^{-2}$)\")\n", " plt.show()" ] }, @@ -339,21 +341,21 @@ ], "source": [ "def set_k(k1, k2):\n", - " cmds = [f'set ele q1 k1 = {k1}', f'set ele q2 k1 = {-k2}']\n", - " \n", + " cmds = [f\"set ele q1 k1 = {k1}\", f\"set ele q2 k1 = {-k2}\"]\n", + "\n", " d = {}\n", " try:\n", " tao.cmds(cmds)\n", - " tao.cmd('set global lattice_calc_on = T')\n", - " d['good'] = True\n", + " tao.cmd(\"set global lattice_calc_on = T\")\n", + " d[\"good\"] = True\n", " add_info(d)\n", " except:\n", - " d['good'] = False\n", - " \n", - " \n", - " \n", + " d[\"good\"] = False\n", + "\n", " return d\n", - "x = set_k(1.4142136E+01, 1.4142136E+01)\n", + "\n", + "\n", + "x = set_k(1.4142136e01, 1.4142136e01)\n", "KEYS = x.keys()\n", "x" ] @@ -368,10 +370,10 @@ "data": { "text/plain": [ "{'good': True,\n", - " 'mean_beta_a': 20.723056201983,\n", + " 'mean_beta_a': 20.7230562019829,\n", " 'mean_beta_b': 20.7230562019829,\n", - " 'phi_a': 0.0966467384116863,\n", - " 'phi_b': 0.096646738411687}" + " 'phi_a': 0.0966467384116868,\n", + " 'phi_b': 0.0966467384116869}" ] }, "execution_count": 11, @@ -380,7 +382,7 @@ } ], "source": [ - "set_k(1,1)" + "set_k(1, 1)" ] }, { @@ -394,7 +396,7 @@ "n2 = 60\n", "qvec1 = np.linspace(1, 15, n1)\n", "qvec2 = np.linspace(1, 15, n2)\n", - "K1, K2 = np.meshgrid(qvec1, qvec2, indexing='ij')\n", + "K1, K2 = np.meshgrid(qvec1, qvec2, indexing=\"ij\")\n", "\n", "fK1 = K1.flatten()\n", "fK2 = K2.flatten()" @@ -412,8 +414,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 1.1 s, sys: 627 ms, total: 1.73 s\n", - "Wall time: 1.91 s\n" + "CPU times: user 996 ms, sys: 606 ms, total: 1.6 s\n", + "Wall time: 1.94 s\n" ] } ], @@ -421,16 +423,16 @@ "%%time\n", "# Make data\n", "\n", - "tao.cmd('set global plot_on = F')\n", + "tao.cmd(\"set global plot_on = F\")\n", "\n", "RESULTS = []\n", "for k1, k2 in zip(fK1, fK2):\n", " res = set_k(k1, k2)\n", - "# print(res)\n", + " # print(res)\n", " RESULTS.append(res)\n", "\n", - " \n", - "#tao.cmd('set global plot_on = T')" + "\n", + "# tao.cmd('set global plot_on = T')" ] }, { @@ -462,7 +464,7 @@ " x.append(res[key])\n", " else:\n", " x.append(np.nan)\n", - " \n", + "\n", " DAT[key] = np.array(x).reshape(n1, n2)" ] }, @@ -482,8 +484,10 @@ "outputs": [], "source": [ "NICE = {}\n", - "NICE['mean_beta_a'] = r'$<\\beta_x>$'\n", - "NICE['mean_beta_b'] = r'$<\\beta_y>$'\n", + "NICE[\"mean_beta_a\"] = r\"$<\\beta_x>$\"\n", + "NICE[\"mean_beta_b\"] = r\"$<\\beta_y>$\"\n", + "\n", + "\n", "def nice(key):\n", " if key in NICE:\n", " return NICE[key]\n", @@ -498,7 +502,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -513,7 +517,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -528,19 +532,25 @@ } ], "source": [ - "#fig, ax = plt.subplots(figsize=(10,8))\n", + "# fig, ax = plt.subplots(figsize=(10,8))\n", + "\n", "\n", "def plot1(key):\n", - " plt.imshow(DAT[key], origin='lower',\n", - " extent=[qvec1.min(), qvec1.max(), qvec2.min(), qvec2.max()], \n", - " cmap='jet',\n", - " vmax = 10)\n", - " plt.xlabel('Q1 (+)k1 (1/m$^2$)')\n", - " plt.ylabel('Q2 (-)k1 (1/m$^2$)')\n", + " plt.imshow(\n", + " DAT[key],\n", + " origin=\"lower\",\n", + " extent=[qvec1.min(), qvec1.max(), qvec2.min(), qvec2.max()],\n", + " cmap=\"jet\",\n", + " vmax=10,\n", + " )\n", + " plt.xlabel(\"Q1 (+)k1 (1/m$^2$)\")\n", + " plt.ylabel(\"Q2 (-)k1 (1/m$^2$)\")\n", " plt.colorbar(label=nice(key))\n", " plt.show()\n", - "plot1('mean_beta_a')\n", - "plot1('mean_beta_b')" + "\n", + "\n", + "plot1(\"mean_beta_a\")\n", + "plot1(\"mean_beta_b\")" ] }, { @@ -560,23 +570,7 @@ { "data": { "text/plain": [ - "{'mode_flip': False,\n", - " 'beta_a': 19.8980601747808,\n", - " 'alpha_a': 20.8824960367293,\n", - " 'gamma_a': 21.9658919957421,\n", - " 'phi_a': 0.688888454799636,\n", - " 'eta_a': 0.0,\n", - " 'etap_a': 0.0,\n", - " 'beta_b': 8.56179989648874,\n", - " 'alpha_b': -8.68869255013973,\n", - " 'gamma_b': 8.93426372440964,\n", - " 'phi_b': 0.0669702646497206,\n", - " 'eta_b': 0.0,\n", - " 'etap_b': 0.0,\n", - " 'eta_x': 0.0,\n", - " 'etap_x': 0.0,\n", - " 'eta_y': 0.0,\n", - " 'etap_y': 0.0}" + "{}" ] }, "execution_count": 17, @@ -598,12 +592,19 @@ "run\n", "show var -bmad -good\n", " \"\"\"\n", - " lines = tao.cmds(cmds.split('\\n'), suppress_lattice_calc=False, suppress_plotting=False, raises=False)\n", - " \n", + " lines = tao.cmds(\n", + " cmds.split(\"\\n\"),\n", + " suppress_lattice_calc=False,\n", + " suppress_plotting=False,\n", + " raises=False,\n", + " )\n", + "\n", " # Twiss at Q1\n", - " T = tao.ele_twiss('Q1')\n", + " T = tao.ele_twiss(\"Q1\")\n", " return T\n", - "optimize(10, 20) " + "\n", + "\n", + "optimize(10, 20)" ] }, { @@ -615,7 +616,7 @@ { "data": { "text/plain": [ - "[' 1.76010645172912E-24']" + "0.0" ] }, "execution_count": 18, @@ -637,7 +638,7 @@ { "data": { "text/plain": [ - "(10.0000000000001, 20.0000000000004)" + "(0.0, 0.0)" ] }, "execution_count": 19, @@ -647,8 +648,8 @@ ], "source": [ "# Check that the optimization worked\n", - "average_beta_a = tao.data('fodo', 'betas', dat_index=1)['model_value']\n", - "average_beta_b = tao.data('fodo', 'betas', dat_index=2)['model_value']\n", + "average_beta_a = tao.data(\"fodo\", \"betas\", dat_index=1)[\"model_value\"]\n", + "average_beta_b = tao.data(\"fodo\", \"betas\", dat_index=2)[\"model_value\"]\n", "average_beta_a, average_beta_b" ] }, @@ -661,7 +662,7 @@ { "data": { "text/plain": [ - "(20.6297896339797, -10.5500557883925)" + "(-455.891092342075, 529.901800996265)" ] }, "execution_count": 20, @@ -671,8 +672,8 @@ ], "source": [ "# These are the K\n", - "kq1 = tao.ele_gen_attribs('Q1')['K1']\n", - "kq2 = tao.ele_gen_attribs('Q2')['K1']\n", + "kq1 = tao.ele_gen_attribs(\"Q1\")[\"K1\"]\n", + "kq2 = tao.ele_gen_attribs(\"Q2\")[\"K1\"]\n", "kq1, kq2" ] }, @@ -695,7 +696,7 @@ { "data": { "text/plain": [ - "[' 7.64007369085481E-23']" + "0.0" ] }, "execution_count": 21, @@ -704,11 +705,13 @@ } ], "source": [ - "tao.cmd('alias setbetas veto var *;veto dat *;use dat fodo.betas[1,2];set dat fodo.betas[1]|meas=[[1]];set dat fodo.betas[2]|meas=[[2]];use var quad;run;show var -bmad -good')\n", - "#tao.cmd('call SetBetas.tao', raises=False)\n", + "tao.cmd(\n", + " \"alias setbetas veto var *;veto dat *;use dat fodo.betas[1,2];set dat fodo.betas[1]|meas=[[1]];set dat fodo.betas[2]|meas=[[2]];use var quad;run;show var -bmad -good\"\n", + ")\n", + "# tao.cmd('call SetBetas.tao', raises=False)\n", "\n", - "lines = tao.cmd('setbetas 40 25', raises=False)\n", - "lines[-3:];\n", + "lines = tao.cmd(\"setbetas 40 25\", raises=False)\n", + "lines[-3:]\n", "tao.merit()" ] }, @@ -721,23 +724,7 @@ { "data": { "text/plain": [ - "{'mode_flip': False,\n", - " 'beta_a': 79.7708116314783,\n", - " 'alpha_a': 83.989771805627,\n", - " 'gamma_a': 88.4444024533056,\n", - " 'phi_a': 0.213283369019596,\n", - " 'eta_a': 0.0,\n", - " 'etap_a': 0.0,\n", - " 'beta_b': 10.6890160749887,\n", - " 'alpha_b': -10.8776594910372,\n", - " 'gamma_b': 11.1631861310562,\n", - " 'phi_b': 0.0536009562200223,\n", - " 'eta_b': 0.0,\n", - " 'etap_b': 0.0,\n", - " 'eta_x': 0.0,\n", - " 'etap_x': 0.0,\n", - " 'eta_y': 0.0,\n", - " 'etap_y': 0.0}" + "{}" ] }, "execution_count": 22, @@ -746,7 +733,7 @@ } ], "source": [ - "T = tao.ele_twiss('Q1')\n", + "T = tao.ele_twiss(\"Q1\")\n", "T" ] }, @@ -777,17 +764,19 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[0;31mSignature:\u001b[0m \u001b[0mmake_markers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "Makes markers relative to ref ele.\n", - "\n", - "If filename is given, the lines will be written to ta file. \n", - "\u001b[0;31mFile:\u001b[0m ~/Code/GitHub/pytao/pytao/misc/markers.py\n", - "\u001b[0;31mType:\u001b[0m function\n" - ] + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m \u001b[0mmake_markers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "Makes markers relative to ref ele.\n", + "\n", + "If filename is given, the lines will be written to ta file. \n", + "\u001b[0;31mFile:\u001b[0m ~/Repos/pytao/pytao/misc/markers.py\n", + "\u001b[0;31mType:\u001b[0m function" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -812,15 +801,14 @@ } ], "source": [ - "smax = 20.0 # m\n", + "smax = 20.0 # m\n", "\n", "# Alternatively, if the lattice were already loaded\n", - "#smax = tao.lat_list('*', who='ele.s').max()\n", + "# smax = tao.lat_list('*', who='ele.s').max()\n", "\n", "slist = np.linspace(0, smax, 200)\n", "\n", - "make_markers(slist, filename='markers.bmad');\n", - "\n", + "make_markers(slist, filename=\"markers.bmad\")\n", "smax" ] }, @@ -844,7 +832,7 @@ "source": [ "# Make a lattice and write to a local file\n", "\n", - "latfile = os.path.join(os.getcwd(), 'fodo10.bmad')\n", + "latfile = os.path.join(os.getcwd(), \"fodo10.bmad\")\n", "\n", "LAT2 = f\"\"\"\n", "\n", @@ -859,7 +847,7 @@ "use, lat\n", "\n", "\"\"\"\n", - "open(latfile, 'w').write(LAT2)" + "open(latfile, \"w\").write(LAT2)" ] }, { @@ -867,36 +855,39 @@ "execution_count": 27, "id": "24532821-6d8d-4ace-84e7-21748ee98af0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "Command: reinit tao -clear -init $ACC_ROOT_DIR/bmad-doc/tao_examples/fodo/tao.init -lat /Users/klauer/Repos/pytao/docs/examples/fodo10.bmad -noplot causes error: ERROR detected: [ERROR | 2024-JUN-27 10:40:04] twiss_from_mat6:\n BAD 1-TURN MATRIX: UNSTABLE.\n TWISS PARAMETERS NOT COMPUTED\n[ERROR | 2024-JUN-27 10:40:04] twiss_from_mat6:\n BAD 1-TURN MATRIX: UNSTABLE.\n TWISS PARAMETERS NOT COMPUTED\n[ERROR | 2024-JUN-27 10:40:04] tao_set_invalid:\n UNSTABLE 1-TURN MATRIX\n FOR DATUM: fodo.betas[1] with data_type: beta.a\n[ERROR | 2024-JUN-27 10:40:04] tao_set_invalid:\n UNSTABLE 1-TURN MATRIX\n FOR DATUM: fodo.betas[2] with data_type: beta.b", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[27], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Run with this lattice\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m tao \u001b[38;5;241m=\u001b[39m \u001b[43mTao\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m-init $ACC_ROOT_DIR/bmad-doc/tao_examples/fodo/tao.init -lat \u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mlatfile\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m -noplot\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 4\u001b[0m \u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Repos/pytao/pytao/tao_ctypes/core.py:79\u001b[0m, in \u001b[0;36mTaoCore.__init__\u001b[0;34m(self, init, so_lib)\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 78\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m init:\n\u001b[0;32m---> 79\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minit\u001b[49m\u001b[43m(\u001b[49m\u001b[43minit\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Repos/pytao/pytao/tao_ctypes/core.py:116\u001b[0m, in \u001b[0;36mTaoCore.init\u001b[0;34m(self, cmd)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_output()\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 115\u001b[0m \u001b[38;5;66;03m# Reinit\u001b[39;00m\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcmd\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mreinit tao -clear \u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mcmd\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mraises\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Repos/pytao/pytao/tao_ctypes/core.py:142\u001b[0m, in \u001b[0;36mTaoCore.cmd\u001b[0;34m(self, cmd, raises)\u001b[0m\n\u001b[1;32m 140\u001b[0m err \u001b[38;5;241m=\u001b[39m error_in_lines(lines)\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m err:\n\u001b[0;32m--> 142\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCommand: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcmd\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m causes error: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00merr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lines\n", + "\u001b[0;31mRuntimeError\u001b[0m: Command: reinit tao -clear -init $ACC_ROOT_DIR/bmad-doc/tao_examples/fodo/tao.init -lat /Users/klauer/Repos/pytao/docs/examples/fodo10.bmad -noplot causes error: ERROR detected: [ERROR | 2024-JUN-27 10:40:04] twiss_from_mat6:\n BAD 1-TURN MATRIX: UNSTABLE.\n TWISS PARAMETERS NOT COMPUTED\n[ERROR | 2024-JUN-27 10:40:04] twiss_from_mat6:\n BAD 1-TURN MATRIX: UNSTABLE.\n TWISS PARAMETERS NOT COMPUTED\n[ERROR | 2024-JUN-27 10:40:04] tao_set_invalid:\n UNSTABLE 1-TURN MATRIX\n FOR DATUM: fodo.betas[1] with data_type: beta.a\n[ERROR | 2024-JUN-27 10:40:04] tao_set_invalid:\n UNSTABLE 1-TURN MATRIX\n FOR DATUM: fodo.betas[2] with data_type: beta.b" + ] + } + ], "source": [ "# Run with this lattice\n", - "tao = Tao(f'-init $ACC_ROOT_DIR/bmad-doc/tao_examples/fodo/tao.init -lat {latfile} -noplot')" + "tao = Tao(\n", + " f\"-init $ACC_ROOT_DIR/bmad-doc/tao_examples/fodo/tao.init -lat {latfile} -noplot\"\n", + ")" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "b29e148b-053a-4666-90d7-be7b47ee4b86", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['',\n", - " 'Tao: set global track_type = beam',\n", - " '',\n", - " 'Tao: set global track_type = single']" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Toggle the beam on and off\n", - "tao.cmd('set beam_init n_particle = 1000')\n", - "tao.cmd('set global track_type = beam;set global track_type = single')" + "tao.cmd(\"set beam_init n_particle = 1000\")\n", + "tao.cmd(\"set global track_type = beam;set global track_type = single\")" ] }, { @@ -909,7 +900,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "8047a9d2-d92d-4dcc-af75-3d98f9bd53a9", "metadata": {}, "outputs": [], @@ -917,10 +908,9 @@ "import h5py\n", "from pmd_beamphysics import ParticleGroup, particle_paths\n", "\n", - "with h5py.File('beam_dump.h5', 'r') as h5:\n", + "with h5py.File(\"beam_dump.h5\", \"r\") as h5:\n", " pp = particle_paths(h5)\n", - " Plist = [ParticleGroup(h5[g]) for g in pp]\n", - " " + " Plist = [ParticleGroup(h5[g]) for g in pp]" ] }, { @@ -935,50 +925,35 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "067d3f0a-7a7b-4c97-bb24-05d75222df4d", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 685, - "width": 1028 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "\n", - "skip = 1 # make larger for faster plotting\n", - "fig, axes = plt.subplots(2, figsize=(12,8))\n", + "skip = 1 # make larger for faster plotting\n", + "fig, axes = plt.subplots(2, figsize=(12, 8))\n", "\n", "axes[0].plot(\n", - " [P.t[::skip]*299792458 for P in Plist],\n", - " [P.x[::skip]*1e6 for P in Plist],\n", - " alpha=0.01, color='black'\n", + " [P.t[::skip] * 299792458 for P in Plist],\n", + " [P.x[::skip] * 1e6 for P in Plist],\n", + " alpha=0.01,\n", + " color=\"black\",\n", ")\n", "\n", "axes[1].plot(\n", - " [P.t[::skip]*299792458 for P in Plist],\n", - " [P.y[::skip]*1e6 for P in Plist],\n", - " alpha=0.01, color='black'\n", + " [P.t[::skip] * 299792458 for P in Plist],\n", + " [P.y[::skip] * 1e6 for P in Plist],\n", + " alpha=0.01,\n", + " color=\"black\",\n", ")\n", "\n", - "axes[0].set_ylabel(r'$x$ (µm)')\n", - "axes[1].set_ylabel(r'$y$ (µm)')\n", + "axes[0].set_ylabel(r\"$x$ (µm)\")\n", + "axes[1].set_ylabel(r\"$y$ (µm)\")\n", "\n", - "axes[1].set_xlabel(r'$ct$ (m)')\n", + "axes[1].set_xlabel(r\"$ct$ (m)\")\n", "\n", "for ax in axes:\n", - " ax.set_ylim(-2000,2000)" + " ax.set_ylim(-2000, 2000)" ] }, { @@ -991,49 +966,23 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "78e1f010-5bef-47a9-af3c-7d488df46717", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 371, - "width": 1006 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "k1 = 'sigma_x'\n", - "k2 = 'sigma_y'\n", + "k1 = \"sigma_x\"\n", + "k2 = \"sigma_y\"\n", "\n", - "x = np.array([P['mean_t']*299792458 for P in Plist])\n", + "x = np.array([P[\"mean_t\"] * 299792458 for P in Plist])\n", "y1 = np.array([P[k1] for P in Plist])\n", "y2 = np.array([P[k2] for P in Plist])\n", "\n", - "fig, ax = plt.subplots(figsize=(12,4))\n", - "ax.plot(x, y1*1e6, label=k1)\n", - "ax.plot(x, y2*1e6, label=k2)\n", - "ax.set_xlabel(' (m)')\n", - "ax.set_ylabel(f'{k1}, {k2} (µm)')\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "ax.plot(x, y1 * 1e6, label=k1)\n", + "ax.plot(x, y2 * 1e6, label=k2)\n", + "ax.set_xlabel(\" (m)\")\n", + "ax.set_ylabel(f\"{k1}, {k2} (µm)\")\n", "plt.legend()" ] }, @@ -1047,7 +996,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "3bf14254-fca3-4a2d-82b6-48f65774398b", "metadata": {}, "outputs": [], @@ -1061,7 +1010,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.13 ('pytao-dev')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1075,7 +1024,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.12.0" }, "vscode": { "interpreter": { diff --git a/docs/examples/special_parsers.ipynb b/docs/examples/special_parsers.ipynb index 6c60f9e0..18669e84 100644 --- a/docs/examples/special_parsers.ipynb +++ b/docs/examples/special_parsers.ipynb @@ -27,7 +27,7 @@ "metadata": {}, "outputs": [], "source": [ - "tao=Tao('-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot')" + "tao = Tao(\"-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot\")" ] }, { @@ -69,7 +69,7 @@ } ], "source": [ - "tao.data_d_array('orbit', 'x')[7]" + "tao.data_d_array(\"orbit\", \"x\")[7]" ] }, { @@ -87,10 +87,10 @@ "metadata": {}, "outputs": [], "source": [ - "tao.cmd('veto var *;veto dat *;')\n", - "tao.cmd('use var quad_k1[3:5]')\n", - "tao.cmd('set dat orbit.x[1:5]|meas=0')\n", - "tao.cmd('use dat orbit.x[1:5]');" + "tao.cmd(\"veto var *;veto dat *;\")\n", + "tao.cmd(\"use var quad_k1[3:5]\")\n", + "tao.cmd(\"set dat orbit.x[1:5]|meas=0\")\n", + "tao.cmd(\"use dat orbit.x[1:5]\");" ] }, { @@ -173,7 +173,7 @@ } ], "source": [ - "tao.ele_control_var('H01W')" + "tao.ele_control_var(\"H01W\")" ] }, { @@ -246,7 +246,7 @@ } ], "source": [ - "tao.matrix('beginning', 'end')" + "tao.matrix(\"beginning\", \"end\")" ] }, { @@ -319,7 +319,7 @@ } ], "source": [ - "result = tao.plot_list('r')\n", + "result = tao.plot_list(\"r\")\n", "\n", "result[0:2]" ] @@ -343,9 +343,9 @@ ], "source": [ "# 't' gives a mapping of template plot to index\n", - "result = tao.plot_list('t')\n", + "result = tao.plot_list(\"t\")\n", "\n", - "result['cbar']" + "result[\"cbar\"]" ] }, { @@ -380,7 +380,7 @@ } ], "source": [ - "tao.spin_invariant('l0')" + "tao.spin_invariant(\"l0\")" ] }, { @@ -578,7 +578,7 @@ } ], "source": [ - "tt = tao.taylor_map('beginning', 'end', order=2)\n", + "tt = tao.taylor_map(\"beginning\", \"end\", order=2)\n", "tt" ] }, @@ -603,7 +603,7 @@ ], "source": [ "# Compare some terms with the matrix calc:\n", - "tao.matrix('beginning', 'end')['mat6'][0,0], tt[1][(1,0,0,0,0,0)]" + "tao.matrix(\"beginning\", \"end\")[\"mat6\"][0, 0], tt[1][(1, 0, 0, 0, 0, 0)]" ] }, { @@ -626,7 +626,7 @@ } ], "source": [ - "tao.matrix('beginning', 'end')['mat6'][1,0], tt[2][(1,0,0,0,0,0)]" + "tao.matrix(\"beginning\", \"end\")[\"mat6\"][1, 0], tt[2][(1, 0, 0, 0, 0, 0)]" ] }, { @@ -670,7 +670,7 @@ } ], "source": [ - "result = tao.var_v_array('quad_k1')\n", + "result = tao.var_v_array(\"quad_k1\")\n", "result[0:2]" ] } @@ -691,7 +691,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.0" }, "vscode": { "interpreter": {