Automatically identify input data type

examples/analyze-experimental-data.ipynb

@@ -10,7 +10,7 @@
  "\n",
  "For the purposes of this notebook, example data has been included in a directory called ``eg-230-data/``. This is experimental data that was taken from an SIS device illuminated by a local-oscillator at 230 GHz (see Garrett 2018).\n",
  "\n",
- "QMix includes two classes that can be used to analyze these experimental data:\n",
+ "QMix includes two classes that can be used to analyze this experimental data:\n",
  "\n",
  " - [qmix.exp.RawData0](https://garrettj403.github.io/QMix/qmix.exp.html#qmix.exp.exp_data.RawData0), which is used to analyze DC data (no LO illumination), and\n",
  " - [qmix.exp.RawData](https://garrettj403.github.io/QMix/qmix.exp.html#qmix.exp.exp_data.RawData), which is used to analyze pumped data (with LO illumination).\n",
@@ -51,7 +51,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 4,
+ "execution_count": 2,
  "metadata": {},
  "outputs": [
  {
@@ -107,17 +107,16 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "In order to import these files properly, we will define the properties of the CSV files, which will then be passed on to the ``RawData0`` and ``RawData`` classes."
+ "In order to import these files properly, we will define the properties of the CSV files, which will then be passed on to the ``RawData0`` class."
  ]
  },
  {
  "cell_type": "code",
- "execution_count": 10,
+ "execution_count": 4,
  "metadata": {},
  "outputs": [],
  "source": [
- "csv_params = dict(input_type = 'csv', # pass data as CSV data files\n",
- " delimiter = ',', # delimiter used by CSV files\n",
+ "csv_params = dict(delimiter = ',', # delimiter used by CSV files\n",
  " usecols = (0,1), # columns to import\n",
  " skip_header = 1, # skip the first row (the header)\n",
  " v_fmt = 'mV', # units for voltage data\n",
@@ -133,7 +132,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 11,
+ "execution_count": 5,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -150,7 +149,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 12,
+ "execution_count": 6,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -162,7 +161,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 13,
+ "execution_count": 7,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -187,7 +186,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 14,
+ "execution_count": 8,
  "metadata": {},
  "outputs": [
  {
@@ -221,7 +220,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 15,
+ "execution_count": 9,
  "metadata": {},
  "outputs": [
  {
@@ -245,7 +244,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 16,
+ "execution_count": 10,
  "metadata": {},
  "outputs": [
  {
@@ -270,7 +269,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 17,
+ "execution_count": 11,
  "metadata": {},
  "outputs": [
  {
@@ -316,7 +315,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 18,
+ "execution_count": 12,
  "metadata": {},
  "outputs": [
  {
@@ -343,7 +342,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 19,
+ "execution_count": 13,
  "metadata": {},
  "outputs": [
  {
@@ -370,7 +369,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 20,
+ "execution_count": 14,
  "metadata": {},
  "outputs": [
  {
@@ -458,7 +457,6 @@
  "source": [
  "pump = qmix.exp.RawData(iv_data, dciv, hot_data, cold_data,\n",
  " freq = 230.2, # LO frequency in [GHz]\n",
- " input_type = \"Numpy\", # Pass in Numpy arrays\n",
  " **params)"
  ]
  },

qmix/exp/exp_data.py

@@ -103,17 +103,16 @@ class RawData0(object):
  Args:
  dciv: DC I-V curve. Either a CSV data file or a Numpy array. The data
  should have two columns: the first for voltage, and the second
- for current. To pass a Numpy array, set the ``input_type`` keyword
- argument to ``"numpy"``. To pass a CSV data file, set the 
- ``input_type`` keyword argument to ``"csv"``. The properties of 
- the CSV file can be set through additional keyword arguments.
- (See below).
+ for current. If you are using CSV files, the properties of 
+ the CSV file can be set through additional keyword arguments
+ (see below).
  dcif: DC IF data. Either a CSV data file or a Numpy array. The 
  data should have two columns: the first for voltage, and the 
- second for IF power.
+ second for IF power. If you are using CSV files, the properties of 
+ the CSV file can be set through additional keyword arguments
+ (see below).
 
  Keyword arguments:
- input_type (str): Input type ('csv' or 'numpy').
  delimiter (str): Delimiter for CSV files.
  usecols (tuple): List of columns to import (tuple of length 2).
  skip_header (int): Number of rows to skip, used to skip the header.
@@ -176,12 +175,12 @@ def __init__(self, dciv, dcif=None, **kw):
  self.comment = comment
  self.vleak = vleak
 
- if kw['input_type'].lower() == 'csv':
+ if isinstance(dciv, str): # input type: CSV file
  self.file_path = dciv
- elif kw['input_type'].lower() == 'numpy':
+ elif isinstance(dciv, np.ndarray): # input type: Numpy array
  self.file_path = 'Numpy array'
  else:
- raise ValueError('Input type not recognized.')
+ raise ValueError('Input data type not recognized.')
 
  # Get DC I-V data
  self.voltage, self.current, self.dc = dciv_curve(dciv, **kw)
@@ -640,11 +639,12 @@ class RawData(object):
  Args:
  ivdata: I-V data. Either a CSV data file or a Numpy array. The data
  should have two columns: the first for voltage, and the second
- for current.
+ for current. If you are using CSV files, the properties of 
+ the CSV file can be set through additional keyword arguments
+ (see below).
  dciv (qmix.exp.iv_data.DCIVData): DC I-V metadata
 
  Keyword arguments:
- input_type (str): Input type ('csv' or 'numpy').
  delimiter (str): Delimiter for CSV files.
  usecols (tuple): List of columns to import (tuple of length 2).
  skip_header (int): Number of rows to skip, used to skip the header.
@@ -719,7 +719,7 @@ def __init__(self, ivdata, dciv, if_hot=None, if_cold=None, **kw):
  analyze_if = analyze
 
  # Sort out file paths
- if kw['input_type'].lower() == 'csv':
+ if isinstance(ivdata, str): # input type: CSV file
  self.iv_file = ivdata
  self.directory = os.path.dirname(ivdata)
  self.iv_filename = os.path.basename(ivdata)
@@ -729,7 +729,7 @@ def __init__(self, ivdata, dciv, if_hot=None, if_cold=None, **kw):
  else:
  self.filename_hot = None
  self.filename_cold = None
- elif kw['input_type'].lower() == 'numpy':
+ elif isinstance(ivdata, np.ndarray): # input type: Numpy array
  self.iv_file = 'Numpy array'
  self.directory = 'Numpy array'
  self.iv_filename = 'Numpy array'
@@ -740,7 +740,7 @@ def __init__(self, ivdata, dciv, if_hot=None, if_cold=None, **kw):
  self.filename_hot = None
  self.filename_cold = None
  else:
- raise ValueError("Input type not recognized.")
+ raise ValueError("Input data type not recognized.")
 
  # Unpack DC I-V metadata
  self.dciv = dciv
@@ -755,8 +755,8 @@ def __init__(self, ivdata, dciv, if_hot=None, if_cold=None, **kw):
  self.current_dc = dciv.current
 
  # Get LO frequency
- if kw['input_type'] == 'numpy' and freq is None:
- str1 = 'If input_type is set to \'numpy\', '
+ if isinstance(ivdata, np.ndarray) and freq is None:
+ str1 = 'If input data is in the form of Numpy arrays, '
  str2 = 'you must define the frequency of the LO signal.'
  raise ValueError(str1 + str2)
  self.freq, self.freq_str = _get_freq(freq, ivdata)

qmix/exp/if_data.py

@@ -55,15 +55,12 @@ def dcif_data(ifdata, dc, **kwargs):
  Args:
  ifdata: IF data. Either a CSV data file or a Numpy array. The data
  should have two columns: the first for voltage, and the second
- for IF power. To pass a Numpy array, set the ``input_type`` 
- keyword argument to ``"numpy"``. To pass a CSV data file, set the 
- ``input_type`` keyword argument to ``"csv"``. The properties of 
- the CSV file can be set through additional keyword arguments.
- (See below).
+ for IF power. If you are passing a CSV file, the properties of 
+ the CSV file can be set through additional keyword arguments
+ (see below).
  dc (qmix.exp.iv_data.DCIVData): DC I-V metadata.
 
  Keyword Args:
- input_type (str): Input type ('csv' or 'numpy').
  delimiter (str): Delimiter for CSV files.
  usecols (tuple): List of columns to import (tuple of length 2).
  skip_header (int): Number of rows to skip, used to skip the header.
@@ -110,7 +107,6 @@ def if_data(if_hot, if_cold, dc, **kwargs):
  dc (qmix.exp.iv_data.DCIVData): DC I-V metadata.
 
  Keyword Args:
- input_type (str): Input type ('csv' or 'numpy').
  delimiter (str): Delimiter for CSV files.
  usecols (tuple): List of columns to import (tuple of length 2).
  skip_header (int): Number of rows to skip, used to skip the header.
@@ -360,11 +356,9 @@ def _load_if(ifdata, dc, **kwargs):
  Args:
  ifdata: IF data. Either a CSV data file or a Numpy array. The data
  should have two columns: the first for voltage, and the second
- for IF power. To pass a Numpy array, set the ``input_type`` 
- keyword argument to ``"numpy"``. To pass a CSV data file, set the 
- ``input_type`` keyword argument to ``"csv"``. The properties of 
- the CSV file can be set through additional keyword arguments.
- (See below).
+ for IF power. If you are using a CSV file, the properties of 
+ the CSV file can be set through additional keyword arguments
+ (see below).
  dc (qmix.exp.iv_data.DCIVData): DC I-V metadata. 
 
  Keyword arguments:
@@ -377,7 +371,6 @@ def _load_if(ifdata, dc, **kwargs):
  points
  rseries (float): series resistance of measurement system
  skip_header: number of rows to skip at the beginning of the file
- input_type (str):
 
  Returns: IF data (in matrix form)
 
@@ -386,7 +379,6 @@ def _load_if(ifdata, dc, **kwargs):
  # Unpack keyword arguments
  v_multiplier = kwargs.get('v_multiplier', PARAMS['v_multiplier'])
  skip_header = kwargs.get('skip_header', PARAMS['skip_header'])
- input_type = kwargs.get('input_type', PARAMS['input_type'])
  sigma = kwargs.get('ifdata_sigma', PARAMS['ifdata_sigma'])
  vmax = kwargs.get('ifdata_vmax', PARAMS['ifdata_vmax'])
  npts = kwargs.get('ifdata_npts', PARAMS['ifdata_npts'])
@@ -396,16 +388,16 @@ def _load_if(ifdata, dc, **kwargs):
  v_fmt = kwargs.get('v_fmt', PARAMS['v_fmt'])
 
  # Import raw IF data
- if input_type.lower() == 'csv':
+ if isinstance(ifdata, str): # assume CSV data file
  ifdata = np.genfromtxt(ifdata, delimiter=delim, usecols=usecols,
  skip_header=skip_header)
- elif input_type.lower() == 'numpy':
+ elif isinstance(ifdata, np.ndarray): # Numpy array
  assert isinstance(ifdata, np.ndarray), \
  'I-V data should be a Numpy array.'
  assert ifdata.ndim == 2, 'I-V data should be 2-dimensional.'
  assert ifdata.shape[1] == 2, 'I-V data should have 2 columns.'
  else:
- raise ValueError("Input type not recognized.")
+ raise ValueError("Input data type not recognized.")
 
  # Clean
  ifdata = remove_nans_matrix(ifdata)

qmix/exp/iv_data.py

@@ -71,14 +71,11 @@ def dciv_curve(ivdata, **kwargs):
  Args:
  ivdata: DC I-V data. Either a CSV data file or a Numpy array. The data
  should have two columns: the first for voltage, and the second
- for current. To pass a Numpy array, set the ``input_type`` keyword
- argument to ``"numpy"``. To pass a CSV data file, set the 
- ``input_type`` keyword argument to ``"csv"``. The properties of 
+ for current. If you are using CSV files, the properties of 
  the CSV file can be set through additional keyword arguments.
  (See below).
 
  Keyword Args:
- input_type (str): Input type ('csv' or 'numpy').
  delimiter (str): Delimiter for CSV files.
  usecols (tuple): List of columns to import (tuple of length 2).
  skip_header (int): Number of rows to skip, used to skip the header.
@@ -206,16 +203,13 @@ def iv_curve(ivdata, dc, **kwargs):
  Args:
  ivdata: I-V data. Either a CSV data file or a Numpy array. The data
  should have two columns: the first for voltage, and the second
- for current. To pass a Numpy array, set the ``input_type`` keyword
- argument to ``"numpy"``. To pass a CSV data file, set the 
- ``input_type`` keyword argument to ``"csv"``. The properties of 
- the CSV file can be set through additional keyword arguments.
- (See below).
+ for current. If you are using a CSV file, the properties of 
+ the CSV file can be set through additional keyword arguments
+ (see below).
  dc (qmix.exp.iv_data.DCIVData): DC I-V data metadata. Generated 
  previously by ``dciv_curve``.
 
  Keyword Args:
- input_type (str): Input type ('csv' or 'numpy').
  delimiter (str): Delimiter for CSV files.
  usecols (tuple): List of columns to import (tuple of length 2).
  skip_header (int): Number of rows to skip, used to skip the header.
@@ -327,7 +321,6 @@ def _load_iv(ivdata, **kw):
  for current.
 
  Keyword Arguments:
- input_type: input type ('csv' or 'numpy')
  v_fmt: voltage units ('uV', 'mV', 'V')
  i_fmt: current units ('uA', 'mA', 'A')
  usecols: list of columns to use (tuple of length 2)
@@ -341,24 +334,23 @@ def _load_iv(ivdata, **kw):
 
  # Unpack keyword arguments
  skip_header = kw.get('skip_header', PARAMS['skip_header'])
- input_type = kw.get('input_type', PARAMS['input_type'])
  delimiter = kw.get('delimiter', PARAMS['delimiter'])
  usecols = kw.get('usecols', PARAMS['usecols'])
  v_fmt = kw.get('v_fmt', PARAMS['v_fmt'])
  i_fmt = kw.get('i_fmt', PARAMS['i_fmt'])
 
  # Import raw I-V data
- if input_type.lower() == 'csv':
+ if isinstance(ivdata, str): # input: CSV file
  vraw, iraw = np.genfromtxt(ivdata, delimiter=delimiter,
  usecols=usecols, skip_header=skip_header).T
- elif input_type.lower() == 'numpy':
+ elif isinstance(ivdata, np.ndarray): # input: Numpy array
  assert isinstance(ivdata, np.ndarray), \
  'I-V data should be a Numpy array.'
  assert ivdata.ndim == 2, 'I-V data should be 2-dimensional.'
  assert ivdata.shape[1] == 2, 'I-V data should have 2 columns.'
  vraw, iraw = ivdata.T
  else:
- raise ValueError("Input type not recognized.")
+ raise ValueError("Input data type not recognized.")
 
  # Set units
  volt_v = vraw * _vfmt_dict[v_fmt]

qmix/exp/parameters.py

@@ -20,8 +20,6 @@
  ``RawData`` either as CSV data files or as Numpy arrays. The CSV
  files should have two columns: one for voltage and one for current
  or IF power. The Numpy arrays should also have two columns.
- - ``input_type == "csv"`` : Input type. Either ``"csv"`` or 
- ``"numpy"``.
  - CSV files:
  - **Note:** All of the experimental data is expected to be stored in
  CSV data files. These parameters control how the data is read in 
@@ -162,8 +160,6 @@ class will determine the best bias voltage automatically.
 """
 
 params = dict(
- # Experimental data data
- input_type = 'csv',
  # CSV files
  delimiter = ',',
  usecols = (0, 1),