From 4879544ff59d96e9b33a0767543c5020b82f167a Mon Sep 17 00:00:00 2001
From: Philipp Heinrich <philipp.heinrich@fau.de>
Date: Wed, 23 Nov 2022 13:38:07 +0100
Subject: [PATCH 1/4] introduce corpus.quick_conc()

---
 ccc/concordances.py          |   5 +-
 ccc/cwb.py                   | 116 ++++++++++++++++++++++++-
 ccc/discoursemes.py          | 143 ++-----------------------------
 ccc/utils.py                 | 161 ++++++++++++++++++++++++++++++++++-
 tests/conftest.py            |  23 +++++
 tests/test_05_concordance.py |  57 +++++++++++++
 6 files changed, 362 insertions(+), 143 deletions(-)

diff --git a/ccc/concordances.py b/ccc/concordances.py
index bcb2175..1574e4e 100644
--- a/ccc/concordances.py
+++ b/ccc/concordances.py
@@ -7,7 +7,7 @@
 """
 import itertools
 import logging
-from random import sample
+from random import sample, seed
 
 # requirements
 from pandas import DataFrame
@@ -287,6 +287,9 @@ def lines(self, form='simple', p_show=['word'], s_show=[],
         if (cut_off is None) or (len(matches) < cut_off):
             cut_off = len(matches)
         # order
+        if isinstance(order, int):
+            seed(order)
+            order = 'random'
         if order == 'random':
             matches = sample(list(matches), cut_off)
         elif order == 'first':
diff --git a/ccc/cwb.py b/ccc/cwb.py
index 07473b3..70ba5cc 100644
--- a/ccc/cwb.py
+++ b/ccc/cwb.py
@@ -18,16 +18,21 @@
 # part of module
 from .cache import Cache, generate_idx, generate_library_idx
 from .cl import Corpus as Attributes
+from .concordances import Concordance
 from .counts import Counts, cwb_scan_corpus
 from .cqp import CQP
 from .dumps import Dump
-from .utils import chunk_anchors, correct_anchors, preprocess_query
+from .utils import (chunk_anchors, correct_anchors, dump_left_join,
+                    format_roles, group_lines, preprocess_query)
 from .version import __version__
 
 logger = logging.getLogger(__name__)
 
 
 def decode(text):
+    """savely decode a string catching common errors
+
+    """
     try:
         text = text.decode('utf-8')
     except (UnicodeDecodeError, AttributeError):
@@ -1101,8 +1106,7 @@ def query_cqp(self, cqp_query, context=20, context_left=None,
         )
 
         # if dump has been retrieved from cache, NQR might not exist
-        if self.show_nqr().empty or \
-           name not in self.show_nqr()['subcorpus'].values:
+        if save and (self.show_nqr().empty or name not in self.show_nqr()['subcorpus'].values):
             # undump the dump and save to disk
             cqp = self.start_cqp()
             cqp.nqr_from_dump(df_dump, name)
@@ -1160,3 +1164,109 @@ def query(self, cqp_query=None, context=20, context_left=None,
 
         else:
             raise NotImplementedError()
+
+    def quick_query(self, topic_query, s_context, filter_queries=[], match_strategy='longest'):
+        """makes sure query result is defined as subcorpus.
+
+        finds all s_context spans that contain topic_query and all filter_queries
+
+        :return: identifier (name of NQR on disk)
+        :rtype: str
+
+        """
+
+        # IDENTIFY
+        topic_identifier = generate_idx([self.subcorpus, topic_query, s_context, match_strategy], prefix='Query')
+        filter_identifier = generate_idx([self.subcorpus, topic_query, s_context, match_strategy, filter_queries], prefix='Query')
+
+        # CHECK CQP
+        cqp = self.start_cqp()
+        cqp.Exec(f'set MatchingStrategy "{match_strategy}";')
+        size = int(cqp.Exec(f'size {filter_identifier};'))
+
+        if size == 0:
+
+            # TODO: avoid saving twice if there's no filter
+            size = int(cqp.Exec(f'size {topic_identifier};'))
+
+            logger.info(f'topic query: {topic_query}')
+            if size == 0:
+                # TOPIC
+                cqp.Query(f'{topic_identifier} = {topic_query} expand to {s_context};')
+                logger.info(f'.. saving {topic_identifier} in CWB binary format')
+                cqp.Exec(f'save {topic_identifier};')
+            logger.info('.. size: ' + cqp.Exec(f'size {topic_identifier};'))
+
+            # FILTER
+            cqp.Exec(f'{filter_identifier} = {topic_identifier};')
+            for query in filter_queries:
+                logger.info(f'filter query: {query}')
+                cqp.Exec(f'{filter_identifier};')
+                cqp.Query(f'{filter_identifier} = {query} expand to {s_context};')
+                logger.info('.. size: ' + cqp.Exec(f'size {filter_identifier};'))
+
+            # SAVE
+            logger.info(f'.. saving {filter_identifier} in CWB binary format')
+            cqp.Exec(f'save {filter_identifier};')
+
+        cqp.__kill__()
+
+        return filter_identifier
+
+    def quick_conc(self, topic_query, s_context, window, order=42,
+                   cut_off=100, highlight_queries=dict(),
+                   filter_queries=dict(), p_show=['word'], s_show=[],
+                   match_strategy='longest'):
+        """
+
+        :return: concordance lines, each one a dict
+        :rtype: list(dict)
+        """
+
+        # CHECK CQP
+        identifier = self.quick_query(topic_query, s_context, filter_queries.values(), match_strategy)
+        cqp = self.start_cqp()
+
+        # init CONTEXT (TextConstellation)
+        cqp.Exec(f'{identifier};')
+        df_context = cqp.Dump(f'{identifier};')
+        dump_context = Dump(self.copy(), df_context, None)
+        dump_context = dump_context.set_context(window, s_context)
+        df_context = dump_context.df[['contextid', 'context', 'contextend']]
+
+        # index by TOPIC MATCHES
+        cqp.Exec(f'Temp = {topic_query};')
+        df_query = cqp.Dump('Temp;')
+        dump_query = Dump(self.copy(), df_query, None)
+        dump_query = dump_query.set_context(window, s_context)
+        df_context = dump_left_join(df_context, dump_query.df, 'topic', drop=True, window=window)
+        df_context = df_context.set_index(['match_topic', 'matchend_topic'])
+        df_context.index.names = ['match', 'matchend']
+
+        # FILTER according to window size
+        for name, query in filter_queries.items():
+            cqp.Exec(f'Temp = {query};')
+            df_query = cqp.Dump('Temp;')
+            dump_query = Dump(self.copy(), df_query, None)
+            dump_query = dump_query.set_context(window, s_context)
+            df_context = dump_left_join(df_context, dump_query.df, name, drop=True, window=window)
+            df_context = df_context.drop([c + "_" + name for c in ['match', 'matchend', 'offset']], axis=1)
+
+        # HIGHLIGHT
+        for name, query in highlight_queries.items():
+            cqp.Exec(f'Temp = {query};')
+            df_query = cqp.Dump('Temp;')
+            dump_query = Dump(self.copy(), df_query, None)
+            dump_query = dump_query.set_context(window, s_context)
+            df_context = dump_left_join(df_context, dump_query.df, name, drop=False, window=window)
+
+        cqp.__kill__()
+
+        # ACTUAL CONCORDANCING
+        hkeys = list(highlight_queries.keys())
+        df = group_lines(df_context, hkeys)
+        conc = Concordance(self.copy(), df)
+        lines = conc.lines(form='dict', p_show=p_show, s_show=s_show, order=order, cut_off=cut_off)
+        output = lines.apply(lambda row: format_roles(row, hkeys, s_show, window, htmlify_meta=True), axis=1)
+
+        return list(output)
diff --git a/ccc/discoursemes.py b/ccc/discoursemes.py
index c211a94..7618242 100644
--- a/ccc/discoursemes.py
+++ b/ccc/discoursemes.py
@@ -9,14 +9,14 @@
 
 # requirements
 from association_measures.measures import score
-from pandas import NA, DataFrame, concat
+from pandas import DataFrame, concat
 
 # part of module
 from . import Corpus
 from .collocates import Collocates, dump2cooc
 from .concordances import Concordance
 from .dumps import Dump
-from .utils import format_cqp_query
+from .utils import dump_left_join, format_cqp_query, format_roles
 
 logger = logging.getLogger(__name__)
 
@@ -115,57 +115,6 @@ def __init__(self):
         pass
 
 
-def constellation_left_join(df1, df2, name, drop=True, window=None):
-    """join an additional dump df2 to an existing constellation
-
-    :param DataFrame df1: constellation dump === (m, me) ci c ce m_t* me_t* o_t* m_1* me_1* o_1* ... ==
-    :param DataFrame df2: additional dump === (m, me) ci c ce ==
-    :param str name: name for additional discourseme
-    :param bool drop: drop all rows that do not contain all discoursemes within topic context?
-    :return: constellation dump including additional dump  === (m, me) ci c ce m_t me_t o_t m_1 me_1 o_1 ... m_name me_name o_name ==
-    :rtype: DataFrame
-    """
-
-    # merge dumps via contextid ###
-    df1 = df1.reset_index()
-    df2 = df2.reset_index()[['contextid', 'match', 'matchend']].astype("Int64")
-    m = df1.merge(df2, on='contextid', how='left')
-
-    # calculate offset ###
-    m['offset_y'] = 0       # init as overlap
-    # y .. x
-    m.loc[m['match_x'] > m['matchend_y'], 'offset_y'] = m['matchend_y'] - m['match_x']
-    # x .. y
-    m.loc[m['matchend_x'] < m['match_y'], 'offset_y'] = m['match_y'] - m['matchend_x']
-    # missing y
-    m.loc[m['match_y'].isna(), 'offset_y'] = NA
-
-    # restrict to complete constellation ###
-    if drop:
-        m = m.dropna()
-        if window is None:
-            # only keep co-occurrences that are within context
-            m = m.loc[
-                (m['matchend_y'] >= m['context']) & (m['match_y'] < m['contextend'])
-            ]
-        else:
-            m = m.loc[abs(m['offset_y']) <= window]
-
-    # rename columns ###
-    m = m.rename(columns={
-        'match_x': 'match',
-        'matchend_x': 'matchend',
-        'match_y': 'match_' + name,
-        'matchend_y': 'matchend_' + name,
-        'offset_y': 'offset_' + name
-    })
-
-    # set index ###
-    m = m.set_index(['match', 'matchend'])
-
-    return m
-
-
 def aggregate_matches(df, name, context_col='contextid',
                       match_cols=['match', 'matchend']):
 
@@ -185,88 +134,6 @@ def aggregate_matches(df, name, context_col='contextid',
     return table
 
 
-def format_roles(row, names, s_show, window, htmlify_meta=False):
-    """Take a row of a dataframe indexed by match, matchend of the node,
-    columns for each discourseme with sets of tuples indicating discourseme positions,
-    columns for each s in s_show,
-    and a column 'dict' containing the pre-formatted concordance line.
-
-    creates a list (aligned with other lists) of lists of roles; roles are:
-    - 'node' (if cpos in index)
-    - 'out_of_window' (if offset of cpos from node > window)
-    - discourseme names
-
-    :return: concordance line for MMDA frontend
-    :rtype: dict
-
-    """
-
-    # TODO directly create relevant objects, no need for frontend to take care of it
-    # init
-    d = row['dict']
-    roles = list()
-
-    # 'out_of_window' | None | 'node'
-    role = ['out_of_window' if abs(t) > window else None for t in d['offset']]
-    for i in range(d['cpos'].index(row.name[0]), d['cpos'].index(row.name[1]) + 1):
-        role[i] = 'node'
-    roles.append(role)
-
-    # discourseme names
-    for name in names:
-
-        role = [None] * len(d['offset'])
-
-        if not isinstance(row[name], float):
-            for t in row[name]:
-
-                # check match information
-                if len(t) == 2:
-                    # lazy definition without offset
-                    start = 0
-                    end = 1
-                elif len(t) == 3:
-                    # with offset
-                    start = 1
-                    end = 2
-                else:
-                    continue
-
-                # skip NAs
-                if not isinstance(t[start], int):
-                    continue
-
-                # skip the ones too far away
-                try:
-                    start = d['cpos'].index(t[start])
-                    end = d['cpos'].index(t[end]) + 1
-                except ValueError:
-                    continue
-
-                for i in range(start, end):
-                    role[i] = name
-
-        roles.append(role)
-
-    # combine individual roles into one list of lists
-    d['role'] = [[a for a in set(r) if a is not None] for r in list(zip(*roles))]
-
-    # add s-attributes
-    if htmlify_meta:
-        meta = {key: row[key] for key in s_show if not key.startswith("BOOL")}
-        d['meta'] = DataFrame.from_dict(
-            meta, orient='index'
-        ).to_html(bold_rows=False, header=False)
-        for s in s_show:
-            if s.startswith("BOOL"):
-                d[s] = row[s]
-    else:
-        for s in s_show:
-            d[s] = row[s]
-
-    return d
-
-
 class Constellation:
 
     def __init__(self, dump, name='topic'):
@@ -309,8 +176,8 @@ def add_discourseme(self, dump, name='discourseme', drop=True, window=None):
             return
 
         self.discoursemes[name] = dump
-        self.df = constellation_left_join(self.df, dump.df, name,
-                                          drop=drop, window=window)
+        self.df = dump_left_join(self.df, dump.df, name,
+                                 drop=drop, window=window)
 
     def group_lines(self):
         """
@@ -371,7 +238,7 @@ def concordance(self, window=5,
         elif order == 'first':
             df = df.head(cut_off)
         elif order == 'last':
-            df = df.head(cut_off)
+            df = df.tail(cut_off)  # TODO
         else:
             raise NotImplementedError
 
diff --git a/ccc/utils.py b/ccc/utils.py
index 15a2523..c0f5056 100644
--- a/ccc/utils.py
+++ b/ccc/utils.py
@@ -13,7 +13,7 @@
 
 # requirements
 import numpy as np
-from pandas import NA
+from pandas import NA, DataFrame
 from unidecode import unidecode
 
 logger = logging.getLogger(__name__)
@@ -351,6 +351,165 @@ def fold_df(df, flags="%cd"):
 
 
 def filter_df(df, path):
+
     data = pkgutil.get_data(__name__, path)
     items = set(data.decode().split("\n"))
     return df.loc[~df.index.isin(items)]
+
+
+##########################
+# concordance dataframes #
+##########################
+def dump_left_join(df1, df2, name, drop=True, window=None):
+    """join an additional dump df2 to an existing constellation
+
+    :param DataFrame df1: constellation dump === (m, me) ci c ce m_t* me_t* o_t* m_1* me_1* o_1* ... ==
+    :param DataFrame df2: additional dump === (m, me) ci c ce ==
+    :param str name: name for additional discourseme
+    :param bool drop: drop all rows that do not contain all discoursemes within topic context?
+    :return: constellation dump including additional dump  === (m, me) ci c ce m_t me_t o_t m_1 me_1 o_1 ... m_name me_name o_name ==
+    :rtype: DataFrame
+    """
+
+    # merge dumps via contextid ###
+    df1 = df1.reset_index()
+    df2 = df2.reset_index()[['contextid', 'match', 'matchend']].astype("Int64")
+    m = df1.merge(df2, on='contextid', how='left')
+
+    # calculate offset ###
+    m['offset_y'] = 0       # init as overlap
+    # y .. x
+    m.loc[m['match_x'] > m['matchend_y'], 'offset_y'] = m['matchend_y'] - m['match_x']
+    # x .. y
+    m.loc[m['matchend_x'] < m['match_y'], 'offset_y'] = m['match_y'] - m['matchend_x']
+    # missing y
+    m.loc[m['match_y'].isna(), 'offset_y'] = NA
+
+    # restrict to complete constellation ###
+    if drop:
+        m = m.dropna()
+        # only keep co-occurrences that are within context
+        m = m.loc[(m['matchend_y'] >= m['context']) & (m['match_y'] < m['contextend'])]
+        if window:
+            # only keep co-occurrences that are within window
+            m = m.loc[abs(m['offset_y']) <= window]
+
+    # rename columns ###
+    m = m.rename(columns={'match_x': 'match',
+                          'matchend_x': 'matchend',
+                          'match_y': 'match_' + name,
+                          'matchend_y': 'matchend_' + name,
+                          'offset_y': 'offset_' + name})
+
+    # set index ###
+    m = m.set_index(['match', 'matchend'])
+
+    return m
+
+
+def group_lines(df, names):
+    """
+    convert dataframe:
+    === (m, me) ci c ce m0 m0e o0 m1 me1 o1 m2 me2 o2 ===
+    with duplicate indices to
+    === (m, me) ci c ce m0 m1 m2 ===
+    without duplicate indices
+    where
+    m0 = {(o0, m0, m0e), (o0, m0, m0e), ...}
+    m1 = {(o1, m1, m1e), ...}
+
+    """
+
+    df = df.copy()
+    # TODO: deduplication necessary?
+    df_reduced = df[~df.index.duplicated(keep='first')][['contextid', 'context', 'contextend']]
+    for name in names:
+        columns = [m + "_" + name for m in ['offset', 'match', 'matchend']]
+        df[name] = df[columns].values.tolist()
+        df[name] = df[name].apply(tuple)
+        df = df.drop(columns, axis=1)
+        df_reduced[name] = df.groupby(level=['match', 'matchend'], group_keys=False)[name].apply(set)
+
+    return df_reduced
+
+
+def format_roles(row, names, s_show, window, htmlify_meta=False):
+    """Take a row of a dataframe indexed by match, matchend of the node,
+    columns for each discourseme with sets of tuples indicating discourseme positions,
+    columns for each s in s_show,
+    and a column 'dict' containing the pre-formatted concordance line.
+
+    creates a list (aligned with other lists) of lists of roles; roles are:
+    - 'node' (if cpos in index)
+    - 'out_of_window' (if offset of cpos from node > window)
+    - discourseme names
+
+    :return: concordance line for MMDA frontend
+    :rtype: dict
+
+    """
+
+    # TODO directly create relevant objects, no need for frontend to take care of it
+    # init
+    d = row['dict']
+    roles = list()
+
+    # 'out_of_window' | None | 'node'
+    role = ['out_of_window' if abs(t) > window else None for t in d['offset']]
+    for i in range(d['cpos'].index(row.name[0]), d['cpos'].index(row.name[1]) + 1):
+        role[i] = 'node'
+    roles.append(role)
+
+    # discourseme names
+    for name in names:
+
+        role = [None] * len(d['offset'])
+
+        if not isinstance(row[name], float):
+            for t in row[name]:
+
+                # check match information
+                if len(t) == 2:
+                    # lazy definition without offset
+                    start = 0
+                    end = 1
+                elif len(t) == 3:
+                    # with offset
+                    start = 1
+                    end = 2
+                else:
+                    continue
+
+                # skip NAs
+                if not isinstance(t[start], int):
+                    continue
+
+                # skip the ones too far away
+                try:
+                    start = d['cpos'].index(t[start])
+                    end = d['cpos'].index(t[end]) + 1
+                except ValueError:
+                    continue
+
+                for i in range(start, end):
+                    role[i] = name
+
+        roles.append(role)
+
+    # combine individual roles into one list of lists
+    d['role'] = [[a for a in set(r) if a is not None] for r in list(zip(*roles))]
+
+    # add s-attributes
+    if htmlify_meta:
+        meta = {key: row[key] for key in s_show if not key.startswith("BOOL")}
+        d['meta'] = DataFrame.from_dict(
+            meta, orient='index'
+        ).to_html(bold_rows=False, header=False)
+        for s in s_show:
+            if s.startswith("BOOL"):
+                d[s] = row[s]
+    else:
+        for s in s_show:
+            d[s] = row[s]
+
+    return d
diff --git a/tests/conftest.py b/tests/conftest.py
index f20ee3d..52b9c26 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -82,6 +82,29 @@ def germaparl():
     }
 
 
+@pytest.fixture
+def germaparl_full():
+    """ settings for local germaparl corpus """
+
+    corpus_name = "GERMAPARL-1949-2021"
+    registry_path = "/usr/local/share/cwb/registry"
+    lib_path = None
+
+    context = 50
+    s_context = 'speaker_node'
+
+    s_query = 's'
+
+    return {
+        'registry_path': registry_path,
+        'corpus_name': corpus_name,
+        'context': context,
+        's_context': s_context,
+        's_query': s_query,
+        'lib_path': lib_path,
+    }
+
+
 @pytest.fixture
 def discoursemes():
     """ discoursemes """
diff --git a/tests/test_05_concordance.py b/tests/test_05_concordance.py
index 0e83b61..fa268fd 100644
--- a/tests/test_05_concordance.py
+++ b/tests/test_05_concordance.py
@@ -17,6 +17,63 @@ def get_corpus(corpus_settings, data_path=DATA_PATH):
     )
 
 
+###############
+# CONCORDANCE #
+###############
+@pytest.mark.concordance
+def test_ccc_quick_conc(germaparl):
+
+    corpus = get_corpus(germaparl)
+
+    topic_query = '[lemma="die"]'
+    s_context = 's'
+    window = 5
+    filter_queries = highlight_queries = {
+        'CSU': '[lemma="CSU"]',
+        'CDU': '[lemma="CDU"]'
+    }
+
+    conc = corpus.quick_conc(
+        topic_query=topic_query,
+        filter_queries=filter_queries,
+        highlight_queries=highlight_queries,
+        window=window,
+        s_context=s_context,
+        cut_off=100,
+        order='first'
+    )
+
+    assert len(conc) == 100
+    assert 'cpos' in conc[0]
+
+
+@pytest.mark.concordance
+def test_ccc_quick_conc_random(germaparl):
+
+    corpus = get_corpus(germaparl)
+
+    topic_query = '[lemma="die"]'
+    s_context = 's'
+    window = 5
+    filter_queries = highlight_queries = {
+        'CSU': '[lemma="CSU"]',
+        'CDU': '[lemma="CDU"]'
+    }
+
+    conc = corpus.quick_conc(
+        topic_query=topic_query,
+        filter_queries=filter_queries,
+        highlight_queries=highlight_queries,
+        window=window,
+        s_context=s_context,
+        cut_off=100,
+        order=50
+    )
+
+    assert len(conc) == 100
+    assert 'cpos' in conc[0]
+
+
 @pytest.mark.raw
 def test_concordance_simple(germaparl):
     corpus = get_corpus(germaparl)

From c3e25062db6949cfed88e5618f25ee543479f339 Mon Sep 17 00:00:00 2001
From: Philipp Heinrich <philipp.heinrich@fau.de>
Date: Fri, 25 Nov 2022 13:25:42 +0100
Subject: [PATCH 2/4] simplify quick_query and quick_conc; integrate into
 discoursemes

---
 ccc/cwb.py                    | 150 +++++++++++++++++++-------
 ccc/discoursemes.py           | 194 +++++++---------------------------
 ccc/utils.py                  |  37 ++++++-
 tests/test_04_cwb.py          |  41 +++++++
 tests/test_05_concordance.py  |  26 +++++
 tests/test_11_discoursemes.py |   5 +-
 6 files changed, 248 insertions(+), 205 deletions(-)

diff --git a/ccc/cwb.py b/ccc/cwb.py
index 70ba5cc..887243c 100644
--- a/ccc/cwb.py
+++ b/ccc/cwb.py
@@ -23,7 +23,7 @@
 from .cqp import CQP
 from .dumps import Dump
 from .utils import (chunk_anchors, correct_anchors, dump_left_join,
-                    format_roles, group_lines, preprocess_query)
+                    format_roles, group_lines, preprocess_query, aggregate_matches)
 from .version import __version__
 
 logger = logging.getLogger(__name__)
@@ -1165,16 +1165,35 @@ def query(self, cqp_query=None, context=20, context_left=None,
         else:
             raise NotImplementedError()
 
-    def quick_query(self, topic_query, s_context, filter_queries=[], match_strategy='longest'):
+    def quick_query(self, s_context, topic_query="", filter_queries=[], match_strategy='longest'):
         """makes sure query result is defined as subcorpus.
 
-        finds all s_context spans that contain topic_query and all filter_queries
+        without topic query:
+        - finds all s_context spans that contain at least one filter_query
+        with topic query:
+        - finds all s_context spans that contain topic_query and all filter_queries
 
         :return: identifier (name of NQR on disk)
         :rtype: str
 
         """
 
+        if len(topic_query) == 0:
+            identifier = generate_idx([self.subcorpus, filter_queries, s_context, match_strategy], prefix='Query')
+
+            cqp = self.start_cqp()
+            cqp.Exec(f'set MatchingStrategy "{match_strategy}";')
+            size = int(cqp.Exec(f'size {identifier};'))
+
+            if size == 0:
+                disjunction = " | ".join(['(' + q + ')' for q in filter_queries])
+                logger.info(f'disjunction query: {disjunction}')
+                cqp.Query(f'{identifier} = {disjunction} within {s_context} expand to {s_context};')
+                logger.info(f'.. saving {identifier} in CWB binary format')
+                cqp.Exec(f'save {identifier};')
+
+            return identifier
+
         # IDENTIFY
         topic_identifier = generate_idx([self.subcorpus, topic_query, s_context, match_strategy], prefix='Query')
         filter_identifier = generate_idx([self.subcorpus, topic_query, s_context, match_strategy, filter_queries], prefix='Query')
@@ -1223,50 +1242,99 @@ def quick_conc(self, topic_query, s_context, window, order=42,
         :rtype: list(dict)
         """
 
-        # CHECK CQP
-        identifier = self.quick_query(topic_query, s_context, filter_queries.values(), match_strategy)
-        cqp = self.start_cqp()
+        if len(topic_query) == 0:
 
-        # init CONTEXT (TextConstellation)
-        cqp.Exec(f'{identifier};')
-        df_context = cqp.Dump(f'{identifier};')
-        dump_context = Dump(self.copy(), df_context, None)
-        dump_context = dump_context.set_context(window, s_context)
-        df_context = dump_context.df[['contextid', 'context', 'contextend']]
-
-        # index by TOPIC MATCHES
-        cqp.Exec(f'Temp = {topic_query};')
-        df_query = cqp.Dump('Temp;')
-        dump_query = Dump(self.copy(), df_query, None)
-        dump_query = dump_query.set_context(window, s_context)
-        df_context = dump_left_join(df_context, dump_query.df, 'topic', drop=True, window=window)
-        df_context = df_context.set_index(['match_topic', 'matchend_topic'])
-        df_context.index.names = ['match', 'matchend']
-
-        # FILTER according to window size
-        for name, query in filter_queries.items():
-            cqp.Exec(f'Temp = {query};')
-            df_query = cqp.Dump('Temp;')
-            dump_query = Dump(self.copy(), df_query, None)
-            dump_query = dump_query.set_context(window, s_context)
-            df_context = dump_left_join(df_context, dump_query.df, name, drop=True, window=window)
-            df_context = df_context.drop([c + "_" + name for c in ['match', 'matchend', 'offset']], axis=1)
+            queries = {**highlight_queries, **filter_queries}
+
+            # INIT CQP
+            identifier = self.quick_query(s_context, topic_query="", filter_queries=queries.values(), match_strategy=match_strategy)
+            cqp = self.start_cqp()
 
-        # HIGHLIGHT
-        for name, query in highlight_queries.items():
-            cqp.Exec(f'Temp = {query};')
+            # init CONTEXT (TextConstellation)
+            cqp.Exec(f'cut {identifier} {cut_off};')
+            df_context = cqp.Dump(f'{identifier};')
+            dump_context = Dump(self.copy(), df_context, None)
+            dump_context = dump_context.set_context(context_break=s_context)
+            df_context = dump_context.df[['contextid']]
+            df_context = df_context.reset_index().set_index('contextid')
+
+            # HIGHLIGHT
+            cqp.Exec(f'{identifier};')
+            for name, query in queries.items():
+                cqp.Exec(f'Temp = {query};')
+                df_query = cqp.Dump('Temp;')
+                if len(df_query) > 0:
+                    dump_query = Dump(self.copy(), df_query, None)
+                    dump_query = dump_query.set_context(context_break=s_context)
+                    df_query = dump_query.df[['contextid']]
+                    df_agg = aggregate_matches(df_query, name)
+                    df_context = df_context.join(df_agg)
+                else:
+                    df_context[name] = None
+                    df_context[name + '_BOOL'] = False
+                    df_context[name + '_COUNTS'] = 0
+            cqp.__kill__()
+
+            # index by CONTEXT MATCHES
+            df = df_context.set_index(['match', 'matchend'])
+            names = list(queries.keys())
+            names_bool = [n + '_BOOL' for n in names]
+            names_count = [n + '_COUNTS' for n in names]
+            for b, c in zip(names_bool, names_count):
+                df[b] = df[b].fillna(False)
+                df[c] = df[c].fillna(0)
+
+            # ACTUAL CONCORDANCING
+            conc = Concordance(self.copy(), df)
+            lines = conc.lines(form='dict', p_show=p_show, s_show=s_show, order=order, cut_off=cut_off)
+            output = lines.apply(lambda row: format_roles(row, names, s_show=names_bool+s_show, window=0, htmlify_meta=True), axis=1)
+
+        else:
+
+            # INIT CQP
+            identifier = self.quick_query(s_context, topic_query, filter_queries.values(), match_strategy)
+            cqp = self.start_cqp()
+
+            # init CONTEXT (TextConstellation)
+            cqp.Exec(f'{identifier};')
+            df_context = cqp.Dump(f'{identifier};')
+            dump_context = Dump(self.copy(), df_context, None)
+            dump_context = dump_context.set_context(window, s_context)
+            df_context = dump_context.df[['contextid', 'context', 'contextend']]
+
+            # index by TOPIC MATCHES
+            cqp.Exec(f'Temp = {topic_query};')
             df_query = cqp.Dump('Temp;')
             dump_query = Dump(self.copy(), df_query, None)
             dump_query = dump_query.set_context(window, s_context)
-            df_context = dump_left_join(df_context, dump_query.df, name, drop=False, window=window)
+            df_context = dump_left_join(df_context, dump_query.df, 'topic', drop=True, window=window)
+            df_context = df_context.set_index(['match_topic', 'matchend_topic'])
+            df_context.index.names = ['match', 'matchend']
+
+            # FILTER according to window size
+            for name, query in filter_queries.items():
+                cqp.Exec(f'Temp = {query};')
+                df_query = cqp.Dump('Temp;')
+                dump_query = Dump(self.copy(), df_query, None)
+                dump_query = dump_query.set_context(window, s_context)
+                df_context = dump_left_join(df_context, dump_query.df, name, drop=True, window=window)
+                df_context = df_context.drop([c + "_" + name for c in ['match', 'matchend', 'offset']], axis=1)
+
+            # HIGHLIGHT
+            for name, query in highlight_queries.items():
+                cqp.Exec(f'Temp = {query};')
+                df_query = cqp.Dump('Temp;')
+                dump_query = Dump(self.copy(), df_query, None)
+                dump_query = dump_query.set_context(window, s_context)
+                df_context = dump_left_join(df_context, dump_query.df, name, drop=False, window=window)
 
-        cqp.__kill__()
+            cqp.__kill__()
 
-        # ACTUAL CONCORDANCING
-        hkeys = list(highlight_queries.keys())
-        df = group_lines(df_context, hkeys)
-        conc = Concordance(self.copy(), df)
-        lines = conc.lines(form='dict', p_show=p_show, s_show=s_show, order=order, cut_off=cut_off)
-        output = lines.apply(lambda row: format_roles(row, hkeys, s_show, window, htmlify_meta=True), axis=1)
+            # ACTUAL CONCORDANCING
+            hkeys = list(highlight_queries.keys())
+            df = group_lines(df_context, hkeys)
+            conc = Concordance(self.copy(), df)
+            lines = conc.lines(form='dict', p_show=p_show, s_show=s_show, order=order, cut_off=cut_off)
+            output = lines.apply(lambda row: format_roles(row, hkeys, s_show, window, htmlify_meta=True), axis=1)
 
         return list(output)
diff --git a/ccc/discoursemes.py b/ccc/discoursemes.py
index 7618242..ab54477 100644
--- a/ccc/discoursemes.py
+++ b/ccc/discoursemes.py
@@ -16,7 +16,7 @@
 from .collocates import Collocates, dump2cooc
 from .concordances import Concordance
 from .dumps import Dump
-from .utils import dump_left_join, format_cqp_query, format_roles
+from .utils import dump_left_join, format_cqp_query, aggregate_matches, group_lines, format_roles
 
 logger = logging.getLogger(__name__)
 
@@ -24,7 +24,6 @@
 ########################################################
 # (1) FOCUS ON TOPIC: DIRECTED (IE INDEXED) DATAFRAMES #
 ########################################################
-
 # init(dump, name='topic')
 # input : dump.df: === (m, me) ci c ce ==
 # output: self.df: === (m, me) ci c ce m_topic me_topic o_topic ===
@@ -38,27 +37,9 @@
 # 1202 1202      36     1195     1218     1202   1202     0
 # ...   ...     ...      ...      ...      ...    ...   ...
 
-# #### 1 occurrence
-# ... die [CDU]:m-me+m_t-me_t will eigentlich ...
-
-# #### 2 occurrences
-# ... die [CDU]:m-me+m_t-me_t und [CSU] gehen da ...  0
-# ... die [CDU]:m-me und [CSU]:m_t-me_t gehen da ...  2
-# ... die [CDU]:m_t-me_t und [CSU]:m-me gehen da ... -2
-# ... die [CDU] und [CSU]:m-me+m_t-me_t gehen da ...  0
-
-# #### 3 occurrences
-# ... die [Union]:0+1, d.h. die [CDU] und [CSU] gehen da ...
-# ... die [Union]:0, d.h. die [CDU]:1 und [CSU] gehen da ...
-# ... die [Union]:0, d.h. die [CDU] und [CSU]:1 gehen da ...
-# ... die [Union]:1, d.h. die [CDU]:0 und [CSU] gehen da ...
-# ... die [Union], d.h. die [CDU]:0+1 und [CSU] gehen da ...
-# ... die [Union], d.h. die [CDU]:0 und [CSU]:1 gehen da ...
-# ... die [Union]:1, d.h. die [CDU] und [CSU]:0 gehen da ...
-# ... die [Union], d.h. die [CDU]:1 und [CSU]:0 gehen da ...
-# ... die [Union], d.h. die [CDU] und [CSU]:0+1 gehen da ...
-
+##########################################################
 # add_discourseme(dump, name, drop=True)
+##########################################################
 # input:  === (m, me) ci c ce m_t me_t o_t ==
 #         === (m_d, me_d) ci ==
 # output: === (m, me) ci c ce m_t me_t o_t m_d me_d o_d ==
@@ -71,19 +52,12 @@
 # 1202 1202      36    1195    1218   1202   1202      0    1205   1205     3
 #  ...  ...     ...     ...     ...    ...    ...    ...     ...    ...   ...
 
-# #### t . d
-# #### d . t
-# ... die [CDU]:0+1 [will]:2 eigentlich ...
-
-# #### t . t . d
-# #### t . d . t
-# #### d . t . t
-# ... die [CDU]:0+1 und [CSU] [will]:2 eigentlich ...
-# ... die [CDU]:0 und [CSU]:1 [will]:2 eigentlich ...
-# ... die [CDU]:1 und [CSU]:0 [will]:2 eigentlich ...
-# ... die [CDU] und [CSU]:0+1 [will]:2 eigentlich ...
-
+##########################################################
 # add_discourseme(dump, name, drop=False)
+##########################################################
+# input:  === (m, me) ci c ce m_t me_t o_t ==
+#         === (m_d, me_d) ci ==
+# output: === (m, me) ci c ce m_t me_t o_t m_d me_d o_d ==
 
 #     (m     me)     ci        c       ce     m_t    me_t      o_t    m_d    me_d    o_d
 #    638    638      18      629      672     638     638        0    636     636     -2
@@ -95,7 +69,9 @@
 # 149292 149292    7317   149286   149306  149290  149290       -2   <NA>    <NA>   <NA>
 #        149292    7317   149286   149306  149292  149292        0   <NA>    <NA>   <NA>
 
+##########################################################
 # group_lines()
+##########################################################
 # === (m, me) ci c ce m0 m0e o0 m1 me1 o1 m2 me2 o2 ===
 # with duplicate indices to
 # === (m, me) ci c ce d0 d1 d2 ===
@@ -115,25 +91,6 @@ def __init__(self):
         pass
 
 
-def aggregate_matches(df, name, context_col='contextid',
-                      match_cols=['match', 'matchend']):
-
-    # counts
-    counts = DataFrame(df[context_col].value_counts()).astype("Int64")
-    counts.columns = ['COUNTS_' + name]
-
-    # matches
-    matches = df.reset_index()
-    matches['MATCHES_' + name] = matches[match_cols].values.tolist()
-    matches['MATCHES_' + name] = matches['MATCHES_' + name].apply(tuple)
-    matches = matches.groupby('contextid', group_keys=True)['MATCHES_' + name].apply(set)
-
-    # combine
-    table = counts.join(matches)
-
-    return table
-
-
 class Constellation:
 
     def __init__(self, dump, name='topic'):
@@ -179,32 +136,6 @@ def add_discourseme(self, dump, name='discourseme', drop=True, window=None):
         self.df = dump_left_join(self.df, dump.df, name,
                                  drop=drop, window=window)
 
-    def group_lines(self):
-        """
-        convert dataframe:
-        === (m, me) ci c ce m0 m0e o0 m1 me1 o1 m2 me2 o2 ===
-        with duplicate indices to
-        === (m, me) ci c ce m0 m1 m2 ===
-        without duplicate indices
-        where
-        m0 = {(o0, m0, m0e), (o0, m0, m0e), ...}
-        m1 = {(o1, m1, m1e), ...}
-
-        """
-
-        df = self.df.copy()
-        df_reduced = df[~df.index.duplicated(keep='first')][
-            ['contextid', 'context', 'contextend']
-        ]
-        for name in self.discoursemes.keys():
-            columns = [m + "_" + name for m in ['offset', 'match', 'matchend']]
-            df[name] = df[columns].values.tolist()
-            df[name] = df[name].apply(tuple)
-            df = df.drop(columns, axis=1)
-            df_reduced[name] = df.groupby(level=['match', 'matchend'])[name].apply(set)
-
-        return df_reduced
-
     def breakdown(self, p_atts=['word'], flags=""):
 
         breakdowns = list()
@@ -229,30 +160,11 @@ def concordance(self, window=5,
         """
 
         # convert dataframe
-        df = self.group_lines()
-
-        # cut off and sampling (done here to be able to use random_seed)
-        cut_off = len(df) if cut_off is None or cut_off > len(df) else cut_off
-        if order == 'random':
-            df = df.sample(cut_off, random_state=random_seed)
-        elif order == 'first':
-            df = df.head(cut_off)
-        elif order == 'last':
-            df = df.tail(cut_off)  # TODO
-        else:
-            raise NotImplementedError
-
-        # retrieve concordance lines
+        hkeys = list(self.discoursemes.keys())
+        df = group_lines(self.df, hkeys)
         conc = Concordance(self.corpus.copy(), df)
-        lines = conc.lines(form='dict', p_show=p_show, s_show=s_show,
-                           order=order, cut_off=cut_off)
-
-        # map roles
-        output = lines.apply(
-            lambda row: format_roles(
-                row, self.discoursemes.keys(), s_show, window, htmlify_meta
-            ), axis=1
-        )
+        lines = conc.lines(form='dict', p_show=p_show, s_show=s_show, order=order, cut_off=cut_off)
+        output = lines.apply(lambda row: format_roles(row, hkeys, s_show, window, htmlify_meta=True), axis=1)
 
         return list(output)
 
@@ -366,79 +278,49 @@ def breakdown(self, p_atts=['word'], flags=""):
 
         return breakdown
 
-    def concordance(self, window=0,
-                    p_show=['word', 'lemma'], s_show=[],
-                    order='random', cut_off=100, random_seed=42,
-                    htmlify_meta=False, form='list'):
-
-        # cut off and sampling
-        cut_off = len(self.df) if cut_off is None or cut_off > len(self.df) else cut_off
-        if order == 'random':
-            df = self.df.sample(cut_off, random_state=random_seed)
-        elif order == 'first':
-            df = self.df.head(cut_off)
-        elif order == 'last':
-            df = self.df.head(cut_off)
-        else:
-            raise NotImplementedError
+    def concordance(self, window=0, p_show=['word', 'lemma'],
+                    s_show=[], order='random', cut_off=100,
+                    random_seed=42, htmlify_meta=False):
 
         # join context..contextend
         contexts = self.corpus.dump_from_s_att(self.s_context, annotation=False)
         contexts.columns = ['contextid']
         contexts = contexts.reset_index().set_index('contextid')
-        df = df.join(contexts).set_index(['match', 'matchend'])
-
-        # retrieve concordance lines
+        df = self.df.join(contexts)
+
+        # index by CONTEXT MATCHES
+        df = df.set_index(['match', 'matchend'])
+        names = list(self.discoursemes.keys())
+        names_bool = [n + '_BOOL' for n in names]
+        names_count = [n + '_COUNTS' for n in names]
+        for b, c in zip(names_bool, names_count):
+            df[b] = df[b].fillna(False)
+            df[c] = df[c].fillna(0)
+
+        # ACTUAL CONCORDANCING
         conc = Concordance(self.corpus.copy(), df)
-        lines = conc.lines(form='dict', p_show=p_show, s_show=s_show,
-                           order=order, cut_off=cut_off)
-
-        # get boolean columns for each discourseme
-        names_bool = list()
-        for name in [c for c in df.columns if c.startswith("COUNTS_")]:
-            name_bool = '_'.join(['BOOL', name.split("COUNTS_")[-1]])
-            names_bool.append(name_bool)
-            lines[name_bool] = (lines[name] > 0)
-            lines[name_bool] = lines[name_bool].fillna(False)
-
-        # format roles
-        match_cols = [c for c in lines.columns if c.startswith("MATCHES_")]
-        match_names = [c.split("MATCHES_")[-1] for c in match_cols]
-        col_mapper = dict(zip(match_cols, match_names))
-        lines = lines.rename(columns=col_mapper)
-
-        if form == 'table':
-            return lines
-        elif form == 'list':
-            output = lines.apply(
-                lambda row: format_roles(
-                    row, match_names, s_show=names_bool+s_show, window=window, htmlify_meta=htmlify_meta
-                ), axis=1
-            )
-            return list(output)
-        else:
-            raise NotImplementedError()
+        lines = conc.lines(form='dict', p_show=p_show, s_show=s_show, order=order, cut_off=cut_off)
+        output = lines.apply(lambda row: format_roles(row, names, s_show=names_bool+s_show, window=0, htmlify_meta=True), axis=1)
+
+        return list(output)
 
     def associations(self, ams=None, frequencies=True,
                      min_freq=2, order='log_likelihood',
                      cut_off=None):
 
-        counts = self.df[[c for c in self.df.columns if c.startswith("COUNTS_")]]
-        counts.columns = [c.split("COUNTS_")[-1] for c in counts.columns]
-        cooc = counts > 0
+        counts = self.df[[c for c in self.df.columns if c.endswith("_BOOL")]]
+        counts.columns = [c.split("_BOOL")[0] for c in counts.columns]
+        cooc = counts.fillna(False)
 
         # TODO triangular matrix
         tables = DataFrame()
         for name in counts.columns:
-            table = round(textual_associations(
-                cooc, self.N, name
-            ).reset_index(), 2)
+            table = round(textual_associations(cooc, self.N, name).reset_index(), 2)
             table['node'] = name
             tables = concat([tables, table])
 
         tables = tables[
-            ['node', 'candidate'] +
-            [d for d in tables.columns if d not in ['node', 'candidate']]
+            ['node', 'candidate'] + [d for d in tables.columns if d not in ['node', 'candidate']]
         ]
 
         return tables
diff --git a/ccc/utils.py b/ccc/utils.py
index c0f5056..50166d6 100644
--- a/ccc/utils.py
+++ b/ccc/utils.py
@@ -433,6 +433,35 @@ def group_lines(df, names):
     return df_reduced
 
 
+def aggregate_matches(df, name, context_col='contextid',
+                      match_cols=['match', 'matchend']):
+    """
+    convert dataframe:
+    === (m, me) ci ===
+    to
+    === (ci) {name} COUNTS_{name} BOOL_{name} ===
+
+    """
+    # counts
+    counts = DataFrame(df[context_col].value_counts()).astype("Int64")
+    counts.columns = [name + '_COUNTS']
+
+    # matches
+    matches = df.reset_index()
+    matches[name] = matches[match_cols].values.tolist()
+    matches[name] = matches[name].apply(tuple)
+    matches = matches.groupby('contextid', group_keys=True)[name].apply(set)
+
+    # combine
+    table = counts.join(matches)
+
+    # bool
+    table[name + '_BOOL'] = table[name + '_COUNTS'] > 0
+    table[name + '_BOOL'] = table[name + '_BOOL'].fillna(False)
+
+    return table
+
+
 def format_roles(row, names, s_show, window, htmlify_meta=False):
     """Take a row of a dataframe indexed by match, matchend of the node,
     columns for each discourseme with sets of tuples indicating discourseme positions,
@@ -501,12 +530,10 @@ def format_roles(row, names, s_show, window, htmlify_meta=False):
 
     # add s-attributes
     if htmlify_meta:
-        meta = {key: row[key] for key in s_show if not key.startswith("BOOL")}
-        d['meta'] = DataFrame.from_dict(
-            meta, orient='index'
-        ).to_html(bold_rows=False, header=False)
+        meta = {key: row[key] for key in s_show if not key.endswith("_BOOL")}
+        d['meta'] = DataFrame.from_dict(meta, orient='index').to_html(bold_rows=False, header=False)
         for s in s_show:
-            if s.startswith("BOOL"):
+            if s.endswith("_BOOL"):
                 d[s] = row[s]
     else:
         for s in s_show:
diff --git a/tests/test_04_cwb.py b/tests/test_04_cwb.py
index 79bfffc..e6ceece 100644
--- a/tests/test_04_cwb.py
+++ b/tests/test_04_cwb.py
@@ -517,3 +517,44 @@ def test_query_anchor(germaparl):
         corrections={2: 1}
     )
     assert (d.df[2] == d.df['contextend']).all()
+
+
+@pytest.mark.now
+def test_ccc_quick_query(germaparl):
+
+    corpus = get_corpus(germaparl)
+
+    topic_query = '[lemma="die"]'
+    s_context = 's'
+    filter_queries = {
+        'CSU': '[lemma="CSU"]',
+        'CDU': '[lemma="CDU"]'
+    }
+
+    identifier = corpus.quick_query(
+        s_context=s_context,
+        topic_query=topic_query,
+        filter_queries=filter_queries.values()
+    )
+
+    assert identifier in corpus.show_nqr()['subcorpus'].values
+
+
+@pytest.mark.now
+def test_ccc_quick_query_2(germaparl):
+
+    corpus = get_corpus(germaparl)
+
+    s_context = 's'
+    queries = {
+        'Angela': '[lemma="Angela"]',
+        'CSU': '[lemma="CSU"]',
+        'CDU': '[lemma="CDU"]'
+    }
+
+    identifier = corpus.quick_query(
+        s_context=s_context,
+        filter_queries=queries.values()
+    )
+
+    assert identifier in corpus.show_nqr()['subcorpus'].values
diff --git a/tests/test_05_concordance.py b/tests/test_05_concordance.py
index fa268fd..4a6e60d 100644
--- a/tests/test_05_concordance.py
+++ b/tests/test_05_concordance.py
@@ -21,6 +21,7 @@ def get_corpus(corpus_settings, data_path=DATA_PATH):
 # CONCORDANCE #
 ###############
 @pytest.mark.concordance
+@pytest.mark.now
 def test_ccc_quick_conc(germaparl):
 
     corpus = get_corpus(germaparl)
@@ -47,6 +48,31 @@ def test_ccc_quick_conc(germaparl):
     assert 'cpos' in conc[0]
 
 
+@pytest.mark.concordance
+def test_ccc_quick_conc_2(germaparl):
+
+    corpus = get_corpus(germaparl)
+
+    s_context = 's'
+    queries = {
+        'Herr': '[lemma="Herr"]',
+        'CSU': '[lemma="CSU"]',
+        'CDU': '[lemma="CDU"]'
+    }
+
+    conc = corpus.quick_conc(
+        s_context=s_context,
+        topic_query={},
+        window=0,
+        filter_queries=queries,
+        cut_off=100,
+        order='first'
+    )
+
+    assert len(conc) == 100
+    assert 'cpos' in conc[0]
+
+
 @pytest.mark.concordance
 def test_ccc_quick_conc_random(germaparl):
 
diff --git a/tests/test_11_discoursemes.py b/tests/test_11_discoursemes.py
index b6bce93..af02646 100644
--- a/tests/test_11_discoursemes.py
+++ b/tests/test_11_discoursemes.py
@@ -537,7 +537,7 @@ def test_textual_constellation(germaparl, discoursemes):
         s_context=discoursemes['parameters']['s_context']
     )
     assert len(const.df) == 624
-    assert 'MATCHES_topic' in const.df.columns
+    assert 'topic' in const.df.columns
 
 
 def test_textual_constellation_add(germaparl, discoursemes):
@@ -580,7 +580,7 @@ def test_textual_constellation_add(germaparl, discoursemes):
     )
 
     assert len(const.df) == 2156
-    assert 'MATCHES_discourseme' in const.df.columns
+    assert 'discourseme' in const.df.columns
 
 
 def test_textual_constellation_association(germaparl, discoursemes):
@@ -730,7 +730,6 @@ def test_textual_constellation_concordance(germaparl, discoursemes):
     assert len(lines) == 2198
 
 
-@pytest.mark.now
 def test_textual_constellation_breakdown(germaparl, discoursemes):
 
     corpus_name = germaparl['corpus_name']

From 4cc7b23cacc66061559a30af2b7325cb55fe733d Mon Sep 17 00:00:00 2001
From: Philipp Heinrich <philipp.heinrich@fau.de>
Date: Fri, 25 Nov 2022 13:28:22 +0100
Subject: [PATCH 3/4] update pytest to fix vulnerability issue

---
 Pipfile      |   2 +-
 Pipfile.lock | 446 +++++++++++++++++++++++++--------------------------
 2 files changed, 222 insertions(+), 226 deletions(-)

diff --git a/Pipfile b/Pipfile
index b82c0f9..faea78c 100644
--- a/Pipfile
+++ b/Pipfile
@@ -5,7 +5,7 @@ verify_ssl = true
 
 [dev-packages]
 cython = "==0.29.30"
-pytest = "==7.0.1"
+pytest = "==7.2.0"
 pylint = "==2.13.9"
 pytest-cov = "==3.0.0"
 tabulate = "==0.8.9"
diff --git a/Pipfile.lock b/Pipfile.lock
index 13ef03b..8cf1757 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "d26e6bff5573b47b197bfafb3f0fdba42137ab7421374b6071268a87f878d39b"
+            "sha256": "a3c7ad0e282b40813900650a31138b3da99db3b4d2b1de0e8f01bcf824d57a67"
         },
         "pipfile-spec": 6,
         "requires": {},
@@ -65,67 +65,73 @@
         },
         "numexpr": {
             "hashes": [
-                "sha256:052ec3a55cc1ccc447580ee5b828b2bd0bc14fea0756ddb81d9617b5472c77b5",
-                "sha256:08d8f8e31647815d979185eb455cb5b4d845e20ff808bd6f7f4edf5e0a35e2f6",
-                "sha256:1575a35190d650bf64d2efd8590a8ef3ca564ef20b9f8727428b57759712becb",
-                "sha256:19cd7563421862de85404bd5de06bee8a3ebff4fc9f718de09cc704bc3348f08",
-                "sha256:33be3bbbad71d97d14a39d84957c2bcc368fec775369664d0c24be030c50c359",
-                "sha256:3a1ce79b7d32c55cce334566e3c6716f7b646f6eceb2ace38adaa795848f3583",
-                "sha256:4ddc46c1e5d726b57d008169b75074ab66869e1827098614ebafa45d152f81b7",
-                "sha256:4f291f0df7b25d9530991f880cc232a644a7a722d130c61b43e593b98fb6523f",
-                "sha256:5532bd7164eb8a05410771faf94a661fc69e5ca72deb8612f1bedc26311ed3c8",
-                "sha256:5b014d1c426c444102fb9eea6438052ee86c82684d27dd20b531caf2c60bc4c9",
-                "sha256:5c660dea90935b963db9529d963493c40fabb2343684b52083fb86b2547d60c8",
-                "sha256:828926f5d4dc9ace2bebd2eec56bee852518afa31e6df175d1706e6631dfd1a2",
-                "sha256:854541cf4214d747ab2f87229e9dde052fddc52c379f59047d64f9b7e2f4d578",
-                "sha256:99b9a91811de8cd24bd7d7fbc1883653dad6485e8c683d85b1007a13868713f6",
-                "sha256:a6954d65d7140864d9bb2302b7580c60c88c4d12e00c59a0a53f1660573e922b",
-                "sha256:b127b0d0e1665b94adcc658c5f9d688ac4903ef81da5d8f4e956c995cf69d5c7",
-                "sha256:ba9aa03f4213f4e0c0d964afd6a920c9000e73d22b88c72c46b151d292ee5581",
-                "sha256:be9b8309a4a2f785197b1a29f7767a5ff217ea505e5a751b237336f3b50b7e48",
-                "sha256:c35669268f602ac6412c8c6244b256ebb4f31ffc926b936ca0d9cffda251db8a",
-                "sha256:cb647c9d9c785dae0759bf6c875cde2bec472b5c3f7a6015734b161ae766d141",
-                "sha256:cbd75ac287923bd0c5b95143915648c62d97f994b06dacd770bd205da014f6bd",
-                "sha256:eba7fad925e3063a0434844a667fbdea30b53fe1344efef73475b32d33aa0fec",
-                "sha256:f29b882a21b5381c0e472bc66e8d1c519b8920edc2522b8b4ede79e314d31d20",
-                "sha256:fe6b49631c3bf54e92b0fb334c8e59694685924492d80c325e1b44ecbbc0f22d"
+                "sha256:059546e8f6283ccdb47c683101a890844f667fa6d56258d48ae2ecf1b3875957",
+                "sha256:17ac9cfe6d0078c5fc06ba1c1bbd20b8783f28c6f475bbabd3cad53683075cab",
+                "sha256:3f039321d1c17962c33079987b675fb251b273dbec0f51aac0934e932446ccc3",
+                "sha256:5538b30199bfc68886d2be18fcef3abd11d9271767a7a69ff3688defe782800a",
+                "sha256:655d84eb09adfee3c09ecf4a89a512225da153fdb7de13c447404b7d0523a9a7",
+                "sha256:6931b1e9d4f629f43c14b21d44f3f77997298bea43790cfcdb4dd98804f90783",
+                "sha256:6c368aa35ae9b18840e78b05f929d3a7b3abccdba9630a878c7db74ca2368339",
+                "sha256:6ee9db7598dd4001138b482342b96d78110dd77cefc051ec75af3295604dde6a",
+                "sha256:77898fdf3da6bb96aa8a4759a8231d763a75d848b2f2e5c5279dad0b243c8dfe",
+                "sha256:7bca95f4473b444428061d4cda8e59ac564dc7dc6a1dea3015af9805c6bc2946",
+                "sha256:7d71add384adc9119568d7e9ffa8a35b195decae81e0abf54a2b7779852f0637",
+                "sha256:845a6aa0ed3e2a53239b89c1ebfa8cf052d3cc6e053c72805e8153300078c0b1",
+                "sha256:90f12cc851240f7911a47c91aaf223dba753e98e46dff3017282e633602e76a7",
+                "sha256:9400781553541f414f82eac056f2b4c965373650df9694286b9bd7e8d413f8d8",
+                "sha256:9e34931089a6bafc77aaae21f37ad6594b98aa1085bb8b45d5b3cd038c3c17d9",
+                "sha256:9f096d707290a6a00b6ffdaf581ee37331109fb7b6c8744e9ded7c779a48e517",
+                "sha256:a38664e699526cb1687aefd9069e2b5b9387da7feac4545de446141f1ef86f46",
+                "sha256:a6d2d7740ae83ba5f3531e83afc4b626daa71df1ef903970947903345c37bd03",
+                "sha256:a75967d46b6bd56455dd32da6285e5ffabe155d0ee61eef685bbfb8dafb2e484",
+                "sha256:b076db98ca65eeaf9bd224576e3ac84c05e451c0bd85b13664b7e5f7b62e2c70",
+                "sha256:b318541bf3d8326682ebada087ba0050549a16d8b3fa260dd2585d73a83d20a7",
+                "sha256:b96334fc1748e9ec4f93d5fadb1044089d73fb08208fdb8382ed77c893f0be01",
+                "sha256:c867cc36cf815a3ec9122029874e00d8fbcef65035c4a5901e9b120dd5d626a2",
+                "sha256:d5432537418d18691b9115d615d6daa17ee8275baef3edf1afbbf8bc69806147",
+                "sha256:db93cf1842f068247de631bfc8af20118bf1f9447cd929b531595a5e0efc9346",
+                "sha256:df35324666b693f13a016bc7957de7cc4d8801b746b81060b671bf78a52b9037",
+                "sha256:df3a1f6b24214a1ab826e9c1c99edf1686c8e307547a9aef33910d586f626d01",
+                "sha256:eaec59e9bf70ff05615c34a8b8d6c7bd042bd9f55465d7b495ea5436f45319d0",
+                "sha256:f3a920bfac2645017110b87ddbe364c9c7a742870a4d2f6120b8786c25dc6db3",
+                "sha256:ff5835e8af9a212e8480003d731aad1727aaea909926fd009e8ae6a1cba7f141"
             ],
             "index": "pypi",
-            "version": "==2.8.3"
+            "version": "==2.8.4"
         },
         "numpy": {
             "hashes": [
-                "sha256:0fe563fc8ed9dc4474cbf70742673fc4391d70f4363f917599a7fa99f042d5a8",
-                "sha256:12ac457b63ec8ded85d85c1e17d85efd3c2b0967ca39560b307a35a6703a4735",
-                "sha256:2341f4ab6dba0834b685cce16dad5f9b6606ea8a00e6da154f5dbded70fdc4dd",
-                "sha256:296d17aed51161dbad3c67ed6d164e51fcd18dbcd5dd4f9d0a9c6055dce30810",
-                "sha256:488a66cb667359534bc70028d653ba1cf307bae88eab5929cd707c761ff037db",
-                "sha256:4d52914c88b4930dafb6c48ba5115a96cbab40f45740239d9f4159c4ba779962",
-                "sha256:5e13030f8793e9ee42f9c7d5777465a560eb78fa7e11b1c053427f2ccab90c79",
-                "sha256:61be02e3bf810b60ab74e81d6d0d36246dbfb644a462458bb53b595791251911",
-                "sha256:7607b598217745cc40f751da38ffd03512d33ec06f3523fb0b5f82e09f6f676d",
-                "sha256:7a70a7d3ce4c0e9284e92285cba91a4a3f5214d87ee0e95928f3614a256a1488",
-                "sha256:7ab46e4e7ec63c8a5e6dbf5c1b9e1c92ba23a7ebecc86c336cb7bf3bd2fb10e5",
-                "sha256:8981d9b5619569899666170c7c9748920f4a5005bf79c72c07d08c8a035757b0",
-                "sha256:8c053d7557a8f022ec823196d242464b6955a7e7e5015b719e76003f63f82d0f",
-                "sha256:926db372bc4ac1edf81cfb6c59e2a881606b409ddc0d0920b988174b2e2a767f",
-                "sha256:95d79ada05005f6f4f337d3bb9de8a7774f259341c70bc88047a1f7b96a4bcb2",
-                "sha256:95de7dc7dc47a312f6feddd3da2500826defdccbc41608d0031276a24181a2c0",
-                "sha256:a0882323e0ca4245eb0a3d0a74f88ce581cc33aedcfa396e415e5bba7bf05f68",
-                "sha256:a8365b942f9c1a7d0f0dc974747d99dd0a0cdfc5949a33119caf05cb314682d3",
-                "sha256:a8aae2fb3180940011b4862b2dd3756616841c53db9734b27bb93813cd79fce6",
-                "sha256:c237129f0e732885c9a6076a537e974160482eab8f10db6292e92154d4c67d71",
-                "sha256:c67b833dbccefe97cdd3f52798d430b9d3430396af7cdb2a0c32954c3ef73894",
-                "sha256:ce03305dd694c4873b9429274fd41fc7eb4e0e4dea07e0af97a933b079a5814f",
-                "sha256:d331afac87c92373826af83d2b2b435f57b17a5c74e6268b79355b970626e329",
-                "sha256:dada341ebb79619fe00a291185bba370c9803b1e1d7051610e01ed809ef3a4ba",
-                "sha256:ed2cc92af0efad20198638c69bb0fc2870a58dabfba6eb722c933b48556c686c",
-                "sha256:f260da502d7441a45695199b4e7fd8ca87db659ba1c78f2bbf31f934fe76ae0e",
-                "sha256:f2f390aa4da44454db40a1f0201401f9036e8d578a25f01a6e237cea238337ef",
-                "sha256:f76025acc8e2114bb664294a07ede0727aa75d63a06d2fae96bf29a81747e4a7"
+                "sha256:01dd17cbb340bf0fc23981e52e1d18a9d4050792e8fb8363cecbf066a84b827d",
+                "sha256:06005a2ef6014e9956c09ba07654f9837d9e26696a0470e42beedadb78c11b07",
+                "sha256:09b7847f7e83ca37c6e627682f145856de331049013853f344f37b0c9690e3df",
+                "sha256:0aaee12d8883552fadfc41e96b4c82ee7d794949e2a7c3b3a7201e968c7ecab9",
+                "sha256:0cbe9848fad08baf71de1a39e12d1b6310f1d5b2d0ea4de051058e6e1076852d",
+                "sha256:1b1766d6f397c18153d40015ddfc79ddb715cabadc04d2d228d4e5a8bc4ded1a",
+                "sha256:33161613d2269025873025b33e879825ec7b1d831317e68f4f2f0f84ed14c719",
+                "sha256:5039f55555e1eab31124a5768898c9e22c25a65c1e0037f4d7c495a45778c9f2",
+                "sha256:522e26bbf6377e4d76403826ed689c295b0b238f46c28a7251ab94716da0b280",
+                "sha256:56e454c7833e94ec9769fa0f86e6ff8e42ee38ce0ce1fa4cbb747ea7e06d56aa",
+                "sha256:58f545efd1108e647604a1b5aa809591ccd2540f468a880bedb97247e72db387",
+                "sha256:5e05b1c973a9f858c74367553e236f287e749465f773328c8ef31abe18f691e1",
+                "sha256:7903ba8ab592b82014713c491f6c5d3a1cde5b4a3bf116404e08f5b52f6daf43",
+                "sha256:8969bfd28e85c81f3f94eb4a66bc2cf1dbdc5c18efc320af34bffc54d6b1e38f",
+                "sha256:92c8c1e89a1f5028a4c6d9e3ccbe311b6ba53694811269b992c0b224269e2398",
+                "sha256:9c88793f78fca17da0145455f0d7826bcb9f37da4764af27ac945488116efe63",
+                "sha256:a7ac231a08bb37f852849bbb387a20a57574a97cfc7b6cabb488a4fc8be176de",
+                "sha256:abdde9f795cf292fb9651ed48185503a2ff29be87770c3b8e2a14b0cd7aa16f8",
+                "sha256:af1da88f6bc3d2338ebbf0e22fe487821ea4d8e89053e25fa59d1d79786e7481",
+                "sha256:b2a9ab7c279c91974f756c84c365a669a887efa287365a8e2c418f8b3ba73fb0",
+                "sha256:bf837dc63ba5c06dc8797c398db1e223a466c7ece27a1f7b5232ba3466aafe3d",
+                "sha256:ca51fcfcc5f9354c45f400059e88bc09215fb71a48d3768fb80e357f3b457e1e",
+                "sha256:ce571367b6dfe60af04e04a1834ca2dc5f46004ac1cc756fb95319f64c095a96",
+                "sha256:d208a0f8729f3fb790ed18a003f3a57895b989b40ea4dce4717e9cf4af62c6bb",
+                "sha256:dbee87b469018961d1ad79b1a5d50c0ae850000b639bcb1b694e9981083243b6",
+                "sha256:e9f4c4e51567b616be64e05d517c79a8a22f3606499941d97bb76f2ca59f982d",
+                "sha256:f063b69b090c9d918f9df0a12116029e274daf0181df392839661c4c7ec9018a",
+                "sha256:f9a909a8bae284d46bbfdefbdd4a262ba19d3bc9921b1e76126b1d21c3c34135"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==1.23.4"
+            "version": "==1.23.5"
         },
         "packaging": {
             "hashes": [
@@ -137,36 +143,36 @@
         },
         "pandas": {
             "hashes": [
-                "sha256:0d8d7433d19bfa33f11c92ad9997f15a902bda4f5ad3a4814a21d2e910894484",
-                "sha256:1642fc6138b4e45d57a12c1b464a01a6d868c0148996af23f72dde8d12486bbc",
-                "sha256:171cef540bfcec52257077816a4dbbac152acdb8236ba11d3196ae02bf0959d8",
-                "sha256:1b82ccc7b093e0a93f8dffd97a542646a3e026817140e2c01266aaef5fdde11b",
-                "sha256:1d34b1f43d9e3f4aea056ba251f6e9b143055ebe101ed04c847b41bb0bb4a989",
-                "sha256:207d63ac851e60ec57458814613ef4b3b6a5e9f0b33c57623ba2bf8126c311f8",
-                "sha256:2504c032f221ef9e4a289f5e46a42b76f5e087ecb67d62e342ccbba95a32a488",
-                "sha256:33a9d9e21ab2d91e2ab6e83598419ea6a664efd4c639606b299aae8097c1c94f",
-                "sha256:3ee61b881d2f64dd90c356eb4a4a4de75376586cd3c9341c6c0fcaae18d52977",
-                "sha256:41aec9f87455306496d4486df07c1b98c15569c714be2dd552a6124cd9fda88f",
-                "sha256:4e30a31039574d96f3d683df34ccb50bb435426ad65793e42a613786901f6761",
-                "sha256:5cc47f2ebaa20ef96ae72ee082f9e101b3dfbf74f0e62c7a12c0b075a683f03c",
-                "sha256:62e61003411382e20d7c2aec1ee8d7c86c8b9cf46290993dd8a0a3be44daeb38",
-                "sha256:73844e247a7b7dac2daa9df7339ecf1fcf1dfb8cbfd11e3ffe9819ae6c31c515",
-                "sha256:85a516a7f6723ca1528f03f7851fa8d0360d1d6121cf15128b290cf79b8a7f6a",
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-                "sha256:8a4fc04838615bf0a8d3a03ed68197f358054f0df61f390bcc64fbe39e3d71ec",
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-            "version": "==1.5.0"
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@@ -186,10 +192,10 @@
         },
         "pytz": {
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-            "version": "==2022.4"
+            "version": "==2022.6"
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         "pyyaml": {
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@@ -239,30 +245,30 @@
         },
         "scipy": {
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         "six": {
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@@ -282,11 +288,11 @@
         },
         "wheel": {
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@@ -315,11 +321,11 @@
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@@ -416,11 +422,11 @@
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-            "version": "==0.4.5"
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@@ -483,35 +489,35 @@
         },
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@@ -561,11 +567,11 @@
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@@ -583,6 +589,14 @@
             "index": "pypi",
             "version": "==0.7.1"
         },
+        "exceptiongroup": {
+            "hashes": [
+                "sha256:542adf9dea4055530d6e1279602fa5cb11dab2395fa650b8674eaec35fc4a828",
+                "sha256:bd14967b79cd9bdb54d97323216f8fdf533e278df937aa2a90089e7d6e06e5ec"
+            ],
+            "markers": "python_version < '3.11'",
+            "version": "==1.0.4"
+        },
         "idna": {
             "hashes": [
                 "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4",
@@ -601,11 +615,10 @@
         },
         "importlib-metadata": {
             "hashes": [
-                "sha256:da31db32b304314d044d3c12c79bd59e307889b287ad12ff387b3500835fc2ab",
-                "sha256:ddb0e35065e8938f867ed4928d0ae5bf2a53b7773871bfe6bcc7e4fcdc7dea43"
+                "sha256:d84d17e21670ec07990e1044a99efe8d615d860fd176fc29ef5c306068fda313"
             ],
             "markers": "python_version < '3.10'",
-            "version": "==5.0.0"
+            "version": "==5.1.0"
         },
         "iniconfig": {
             "hashes": [
@@ -648,54 +661,36 @@
         },
         "keyring": {
             "hashes": [
-                "sha256:69732a15cb1433bdfbc3b980a8a36a04878a6cfd7cb99f497b573f31618001c0",
-                "sha256:69b01dd83c42f590250fe7a1f503fc229b14de83857314b1933a3ddbf595c4a5"
+                "sha256:3dd30011d555f1345dec2c262f0153f2f0ca6bca041fb1dc4588349bb4c0ac1e",
+                "sha256:ad192263e2cdd5f12875dedc2da13534359a7e760e77f8d04b50968a821c2361"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==23.9.3"
+            "version": "==23.11.0"
         },
         "lazy-object-proxy": {
             "hashes": [
-                "sha256:043651b6cb706eee4f91854da4a089816a6606c1428fd391573ef8cb642ae4f7",
-                "sha256:07fa44286cda977bd4803b656ffc1c9b7e3bc7dff7d34263446aec8f8c96f88a",
-                "sha256:12f3bb77efe1367b2515f8cb4790a11cffae889148ad33adad07b9b55e0ab22c",
-                "sha256:2052837718516a94940867e16b1bb10edb069ab475c3ad84fd1e1a6dd2c0fcfc",
-                "sha256:2130db8ed69a48a3440103d4a520b89d8a9405f1b06e2cc81640509e8bf6548f",
-                "sha256:39b0e26725c5023757fc1ab2a89ef9d7ab23b84f9251e28f9cc114d5b59c1b09",
-                "sha256:46ff647e76f106bb444b4533bb4153c7370cdf52efc62ccfc1a28bdb3cc95442",
-                "sha256:4dca6244e4121c74cc20542c2ca39e5c4a5027c81d112bfb893cf0790f96f57e",
-                "sha256:553b0f0d8dbf21890dd66edd771f9b1b5f51bd912fa5f26de4449bfc5af5e029",
-                "sha256:677ea950bef409b47e51e733283544ac3d660b709cfce7b187f5ace137960d61",
-                "sha256:6a24357267aa976abab660b1d47a34aaf07259a0c3859a34e536f1ee6e76b5bb",
-                "sha256:6a6e94c7b02641d1311228a102607ecd576f70734dc3d5e22610111aeacba8a0",
-                "sha256:6aff3fe5de0831867092e017cf67e2750c6a1c7d88d84d2481bd84a2e019ec35",
-                "sha256:6ecbb350991d6434e1388bee761ece3260e5228952b1f0c46ffc800eb313ff42",
-                "sha256:7096a5e0c1115ec82641afbdd70451a144558ea5cf564a896294e346eb611be1",
-                "sha256:70ed0c2b380eb6248abdef3cd425fc52f0abd92d2b07ce26359fcbc399f636ad",
-                "sha256:8561da8b3dd22d696244d6d0d5330618c993a215070f473b699e00cf1f3f6443",
-                "sha256:85b232e791f2229a4f55840ed54706110c80c0a210d076eee093f2b2e33e1bfd",
-                "sha256:898322f8d078f2654d275124a8dd19b079080ae977033b713f677afcfc88e2b9",
-                "sha256:8f3953eb575b45480db6568306893f0bd9d8dfeeebd46812aa09ca9579595148",
-                "sha256:91ba172fc5b03978764d1df5144b4ba4ab13290d7bab7a50f12d8117f8630c38",
-                "sha256:9d166602b525bf54ac994cf833c385bfcc341b364e3ee71e3bf5a1336e677b55",
-                "sha256:a57d51ed2997e97f3b8e3500c984db50a554bb5db56c50b5dab1b41339b37e36",
-                "sha256:b9e89b87c707dd769c4ea91f7a31538888aad05c116a59820f28d59b3ebfe25a",
-                "sha256:bb8c5fd1684d60a9902c60ebe276da1f2281a318ca16c1d0a96db28f62e9166b",
-                "sha256:c19814163728941bb871240d45c4c30d33b8a2e85972c44d4e63dd7107faba44",
-                "sha256:c4ce15276a1a14549d7e81c243b887293904ad2d94ad767f42df91e75fd7b5b6",
-                "sha256:c7a683c37a8a24f6428c28c561c80d5f4fd316ddcf0c7cab999b15ab3f5c5c69",
-                "sha256:d609c75b986def706743cdebe5e47553f4a5a1da9c5ff66d76013ef396b5a8a4",
-                "sha256:d66906d5785da8e0be7360912e99c9188b70f52c422f9fc18223347235691a84",
-                "sha256:dd7ed7429dbb6c494aa9bc4e09d94b778a3579be699f9d67da7e6804c422d3de",
-                "sha256:df2631f9d67259dc9620d831384ed7732a198eb434eadf69aea95ad18c587a28",
-                "sha256:e368b7f7eac182a59ff1f81d5f3802161932a41dc1b1cc45c1f757dc876b5d2c",
-                "sha256:e40f2013d96d30217a51eeb1db28c9ac41e9d0ee915ef9d00da639c5b63f01a1",
-                "sha256:f769457a639403073968d118bc70110e7dce294688009f5c24ab78800ae56dc8",
-                "sha256:fccdf7c2c5821a8cbd0a9440a456f5050492f2270bd54e94360cac663398739b",
-                "sha256:fd45683c3caddf83abbb1249b653a266e7069a09f486daa8863fb0e7496a9fdb"
+                "sha256:0c1c7c0433154bb7c54185714c6929acc0ba04ee1b167314a779b9025517eada",
+                "sha256:14010b49a2f56ec4943b6cf925f597b534ee2fe1f0738c84b3bce0c1a11ff10d",
+                "sha256:4e2d9f764f1befd8bdc97673261b8bb888764dfdbd7a4d8f55e4fbcabb8c3fb7",
+                "sha256:4fd031589121ad46e293629b39604031d354043bb5cdf83da4e93c2d7f3389fe",
+                "sha256:5b51d6f3bfeb289dfd4e95de2ecd464cd51982fe6f00e2be1d0bf94864d58acd",
+                "sha256:6850e4aeca6d0df35bb06e05c8b934ff7c533734eb51d0ceb2d63696f1e6030c",
+                "sha256:6f593f26c470a379cf7f5bc6db6b5f1722353e7bf937b8d0d0b3fba911998858",
+                "sha256:71d9ae8a82203511a6f60ca5a1b9f8ad201cac0fc75038b2dc5fa519589c9288",
+                "sha256:7e1561626c49cb394268edd00501b289053a652ed762c58e1081224c8d881cec",
+                "sha256:8f6ce2118a90efa7f62dd38c7dbfffd42f468b180287b748626293bf12ed468f",
+                "sha256:ae032743794fba4d171b5b67310d69176287b5bf82a21f588282406a79498891",
+                "sha256:afcaa24e48bb23b3be31e329deb3f1858f1f1df86aea3d70cb5c8578bfe5261c",
+                "sha256:b70d6e7a332eb0217e7872a73926ad4fdc14f846e85ad6749ad111084e76df25",
+                "sha256:c219a00245af0f6fa4e95901ed28044544f50152840c5b6a3e7b2568db34d156",
+                "sha256:ce58b2b3734c73e68f0e30e4e725264d4d6be95818ec0a0be4bb6bf9a7e79aa8",
+                "sha256:d176f392dbbdaacccf15919c77f526edf11a34aece58b55ab58539807b85436f",
+                "sha256:e20bfa6db17a39c706d24f82df8352488d2943a3b7ce7d4c22579cb89ca8896e",
+                "sha256:eac3a9a5ef13b332c059772fd40b4b1c3d45a3a2b05e33a361dee48e54a4dad0",
+                "sha256:eb329f8d8145379bf5dbe722182410fe8863d186e51bf034d2075eb8d85ee25b"
             ],
-            "markers": "python_version >= '3.6'",
-            "version": "==1.7.1"
+            "markers": "python_version >= '3.7'",
+            "version": "==1.8.0"
         },
         "markupsafe": {
             "hashes": [
@@ -753,11 +748,11 @@
         },
         "more-itertools": {
             "hashes": [
-                "sha256:1bc4f91ee5b1b31ac7ceacc17c09befe6a40a503907baf9c839c229b5095cfd2",
-                "sha256:c09443cd3d5438b8dafccd867a6bc1cb0894389e90cb53d227456b0b0bccb750"
+                "sha256:250e83d7e81d0c87ca6bd942e6aeab8cc9daa6096d12c5308f3f92fa5e5c1f41",
+                "sha256:5a6257e40878ef0520b1803990e3e22303a41b5714006c32a3fd8304b26ea1ab"
             ],
-            "markers": "python_version >= '3.5'",
-            "version": "==8.14.0"
+            "markers": "python_version >= '3.7'",
+            "version": "==9.0.0"
         },
         "packaging": {
             "hashes": [
@@ -777,11 +772,11 @@
         },
         "platformdirs": {
             "hashes": [
-                "sha256:027d8e83a2d7de06bbac4e5ef7e023c02b863d7ea5d079477e722bb41ab25788",
-                "sha256:58c8abb07dcb441e6ee4b11d8df0ac856038f944ab98b7be6b27b2a3c7feef19"
+                "sha256:1006647646d80f16130f052404c6b901e80ee4ed6bef6792e1f238a8969106f7",
+                "sha256:af0276409f9a02373d540bf8480021a048711d572745aef4b7842dad245eba10"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==2.5.2"
+            "version": "==2.5.4"
         },
         "pluggy": {
             "hashes": [
@@ -801,9 +796,10 @@
         },
         "py-cpuinfo": {
             "hashes": [
-                "sha256:5f269be0e08e33fd959de96b34cd4aeeeacac014dd8305f70eb28d06de2345c5"
+                "sha256:3cdbbf3fac90dc6f118bfd64384f309edeadd902d7c8fb17f02ffa1fc3f49690",
+                "sha256:859625bc251f64e21f077d099d4162689c762b5d6a4c3c97553d56241c9674d5"
             ],
-            "version": "==8.0.0"
+            "version": "==9.0.0"
         },
         "pycparser": {
             "hashes": [
@@ -845,19 +841,19 @@
         },
         "pytest": {
             "hashes": [
-                "sha256:9ce3ff477af913ecf6321fe337b93a2c0dcf2a0a1439c43f5452112c1e4280db",
-                "sha256:e30905a0c131d3d94b89624a1cc5afec3e0ba2fbdb151867d8e0ebd49850f171"
+                "sha256:892f933d339f068883b6fd5a459f03d85bfcb355e4981e146d2c7616c21fef71",
+                "sha256:c4014eb40e10f11f355ad4e3c2fb2c6c6d1919c73f3b5a433de4708202cade59"
             ],
             "index": "pypi",
-            "version": "==7.0.1"
+            "version": "==7.2.0"
         },
         "pytest-benchmark": {
             "hashes": [
-                "sha256:36d2b08c4882f6f997fd3126a3d6dfd70f3249cde178ed8bbc0b73db7c20f809",
-                "sha256:40e263f912de5a81d891619032983557d62a3d85843f9a9f30b98baea0cd7b47"
+                "sha256:fb0785b83efe599a6a956361c0691ae1dbb5318018561af10f3e915caa0048d1",
+                "sha256:fdb7db64e31c8b277dff9850d2a2556d8b60bcb0ea6524e36e28ffd7c87f71d6"
             ],
             "index": "pypi",
-            "version": "==3.4.1"
+            "version": "==4.0.0"
         },
         "pytest-cov": {
             "hashes": [
@@ -869,18 +865,18 @@
         },
         "pytz": {
             "hashes": [
-                "sha256:2c0784747071402c6e99f0bafdb7da0fa22645f06554c7ae06bf6358897e9c91",
-                "sha256:48ce799d83b6f8aab2020e369b627446696619e79645419610b9facd909b3174"
+                "sha256:222439474e9c98fced559f1709d89e6c9cbf8d79c794ff3eb9f8800064291427",
+                "sha256:e89512406b793ca39f5971bc999cc538ce125c0e51c27941bef4568b460095e2"
             ],
-            "version": "==2022.4"
+            "version": "==2022.6"
         },
         "readme-renderer": {
             "hashes": [
-                "sha256:d3f06a69e8c40fca9ab3174eca48f96d9771eddb43517b17d96583418427b106",
-                "sha256:e8ad25293c98f781dbc2c5a36a309929390009f902f99e1798c761aaf04a7923"
+                "sha256:cd653186dfc73055656f090f227f5cb22a046d7f71a841dfa305f55c9a513273",
+                "sha256:f67a16caedfa71eef48a31b39708637a6f4664c4394801a7b0d6432d13907343"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==37.2"
+            "version": "==37.3"
         },
         "requests": {
             "hashes": [
@@ -892,11 +888,11 @@
         },
         "requests-toolbelt": {
             "hashes": [
-                "sha256:64c6b8c51b515d123f9f708a29743f44eb70c4479440641ed2df8c4dea56d985",
-                "sha256:f695d6207931200b46c8ef6addbc8a921fb5d77cc4cd209c2e7d39293fcd2b30"
+                "sha256:18565aa58116d9951ac39baa288d3adb5b3ff975c4f25eee78555d89e8f247f7",
+                "sha256:62e09f7ff5ccbda92772a29f394a49c3ad6cb181d568b1337626b2abb628a63d"
             ],
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
-            "version": "==0.10.0"
+            "version": "==0.10.1"
         },
         "rfc3986": {
             "hashes": [
@@ -1035,11 +1031,11 @@
         },
         "urllib3": {
             "hashes": [
-                "sha256:3fa96cf423e6987997fc326ae8df396db2a8b7c667747d47ddd8ecba91f4a74e",
-                "sha256:b930dd878d5a8afb066a637fbb35144fe7901e3b209d1cd4f524bd0e9deee997"
+                "sha256:47cc05d99aaa09c9e72ed5809b60e7ba354e64b59c9c173ac3018642d8bb41fc",
+                "sha256:c083dd0dce68dbfbe1129d5271cb90f9447dea7d52097c6e0126120c521ddea8"
             ],
-            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5' and python_version < '4'",
-            "version": "==1.26.12"
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5'",
+            "version": "==1.26.13"
         },
         "webencodings": {
             "hashes": [
@@ -1120,11 +1116,11 @@
         },
         "zipp": {
             "hashes": [
-                "sha256:3a7af91c3db40ec72dd9d154ae18e008c69efe8ca88dde4f9a731bb82fe2f9eb",
-                "sha256:972cfa31bc2fedd3fa838a51e9bc7e64b7fb725a8c00e7431554311f180e9980"
+                "sha256:4fcb6f278987a6605757302a6e40e896257570d11c51628968ccb2a47e80c6c1",
+                "sha256:7a7262fd930bd3e36c50b9a64897aec3fafff3dfdeec9623ae22b40e93f99bb8"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==3.9.0"
+            "version": "==3.10.0"
         }
     }
 }

From 8523bbe08ed8769b234f6e8301d781d4c892738b Mon Sep 17 00:00:00 2001
From: Philipp Heinrich <philipp.heinrich@fau.de>
Date: Fri, 25 Nov 2022 13:33:00 +0100
Subject: [PATCH 4/4] add citation file

---
 CITATION.cff | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 CITATION.cff

diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..bd9f9e0
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,10 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+- family-names: "Heinrich"
+  given-names: "Philipp"
+  orcid: "https://orcid.org/0000-0002-4785-9205"
+title: "cwb-ccc"
+version: 0.11.2
+date-released: 2022-11-25
+url: "https://github.com/ausgerechnet/cwb-ccc"