Update versions of dependencies and constraints (#65)

* fixed some linting

* address a warning raised during tests

* update deps

* bump fcs_parser version

* allow also pandas 1.*

* poetry upgrade

* Update pyproject.toml

---------

Co-authored-by: Eugene Yurtsev <[email protected]>

fcsparser/__init__.py

@@ -1,7 +1,7 @@
 import os
 
-from .version import __version__
 from .api import parse
+from .version import __version__
 
 test_sample_path = os.path.join(
  os.path.abspath(os.path.dirname(__file__)),

fcsparser/api.py

@@ -3,18 +3,18 @@
 Distributed under the MIT License.
 
 Useful documentation for dtypes in numpy
-http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.byteswap.html?highlight=byteswap#numpy.ndarray.byteswap # noqa
+http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.byteswap.html?highlight=byteswap#numpy.ndarray.byteswap
 http://docs.scipy.org/doc/numpy/user/basics.types.html
 http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
 """
 from __future__ import division
 
 import contextlib
 import logging
-from io import BytesIO
 import string
 import sys
 import warnings
+from io import BytesIO
 
 import numpy
 import pandas as pd
@@ -132,8 +132,10 @@ def __init__(
  Compatible with most FCS 2.0, 3.0, 3.1 files.
 
  self.annotation: a dictionary holding the parsed content of the TEXT segment
- In addition, a key called __header__ has been added to this dictionary
- It specifies the information parsed from the FCS file HEADER segment.
+ In addition, a key called __header__ 
+ has been added to this dictionary
+ It specifies the information parsed 
+ from the FCS file HEADER segment.
  (This won't be necessary for most users.)
 
  self.data holds the parsed DATA segment
@@ -152,7 +154,8 @@ def __init__(
  channel_naming: '$PnS' | '$PnN'
  Determines which meta data field is used for naming the channels.
 
- The default should be $PnS (even though it is not guaranteed to be unique)
+ The default should be $PnS 
+ (even though it is not guaranteed to be unique)
 
  $PnN stands for the short name (guaranteed to be unique).
  Will look like 'FL1-H'
@@ -165,7 +168,8 @@ def __init__(
  The program attempts to use the alternative field by default.
 
  Note: These names are not flipped in the implementation.
- It looks like they were swapped for some reason in the official FCS specification.
+ It looks like they were swapped 
+ for some reason in the official FCS specification.
  data_set: int
  Index of retrieved data set in the fcs file.
  This value specifies the data set being retrieved from an fcs file with
@@ -239,12 +243,14 @@ def from_data(cls, data):
  def read_header(self, file_handle, nextdata_offset=0):
  """Read the header of the FCS file.
 
- The header specifies where the annotation, data and analysis are located inside the binary
+ The header specifies where the annotation, data and analysis 
+ are located inside the binary
  file.
 
  Args:
  file_handle: buffer containing FCS file.
- nextdata_offset: byte offset of a set header from file start specified by $NEXTDATA
+ nextdata_offset: byte offset of a set header 
+ from file start specified by $NEXTDATA
  """
  header = {"FCS format": file_handle.read(6)}
 
@@ -309,7 +315,8 @@ def _extract_text_dict(raw_text):
  raw_text = raw_text.strip()
  if raw_text[-1] != delimiter:
  msg = (
- "The first two characters were:\n {}. The last two characters were: {}\n"
+ "The first two characters were:\n {}. "
+ "The last two characters were: {}\n"
  "Parser expects the same delimiter character in beginning "
  "and end of TEXT segment. "
  "This file may be parsed incorrectly!".format(
@@ -323,18 +330,20 @@ def _extract_text_dict(raw_text):
  else:
  raw_text = raw_text[1:-1]
 
- # 1:-1 above removes the first and last characters which are reserved for the delimiter.
+ # 1:-1 above removes the first and last characters which are reserved 
+ # for the delimiter.
 
- # The delimiter is escaped by being repeated (two consecutive delimiters). This code splits
- # on the escaped delimiter first, so there is no need for extra logic to distinguish
+ # The delimiter is escaped by being repeated (two consecutive delimiters). 
+ # This code splits on the escaped delimiter first, 
+ # so there is no need for extra logic to distinguish
  # actual delimiters from escaped delimiters.
  nested_split_list = [x.split(delimiter) for x in raw_text.split(delimiter * 2)]
 
  # Flatten the nested list to a list of elements (alternating keys and values)
  raw_text_elements = nested_split_list[0]
  for partial_element_list in nested_split_list[1:]:
- # Rejoin two parts of an element that was split by an escaped delimiter (the end and
- # start of two successive sub-lists in nested_split_list)
+ # Rejoin two parts of an element that was split by an escaped delimiter 
+ # (the end and start of two successive sub-lists in nested_split_list)
  raw_text_elements[-1] += delimiter + partial_element_list[0]
  raw_text_elements.extend(partial_element_list[1:])
 
@@ -397,7 +406,9 @@ def read_text(self, file_handle):
  self.channel_names_s = tuple(channel_names_s)
 
  # Convert some of the fields into integer values
- keys_encoding_bits = [f"$P{channel_number}B" for channel_number in self.channel_numbers]
+ keys_encoding_bits = [
+ f"$P{channel_number}B" for channel_number in self.channel_numbers
+ ]
 
  add_keys_to_convert_to_int = ["$NEXTDATA", "$PAR", "$TOT"]
 
@@ -544,7 +555,8 @@ def read_data(self, file_handle):
  # and a type; i.e.,
  # https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html
  # The names are assigned automatically.
- # In order for this code to work correctly with the pandas DataFrame constructor,
+ # In order for this code to work correctly 
+ # with the pandas DataFrame constructor,
  # we convert the *names* of the dtypes to the channel names we want to use.
 
  names = self.get_channel_names()
@@ -615,7 +627,8 @@ def analysis(self):
  def reformat_meta(self):
  """Collect the meta data information in a more user friendly format.
 
- Function looks through the meta data, collecting the channel related information into a
+ Function looks through the meta data, 
+ collecting the channel related information into a
  dataframe and moving it into the _channels_ key.
  """
  meta = self.annotation # For shorthand (passed by reference)
@@ -626,7 +639,8 @@ def reformat_meta(self):
  if key[3] not in string.digits:
  channel_properties.append(key[3:])
 
- # Capture all the channel information in a list of lists -- used to create a data frame
+ # Capture all the channel information in a list of lists --
+ # used to create a data frame
  channel_matrix = [
  [meta.get("$P{0}{1}".format(ch, p)) for p in channel_properties]
  for ch in self.channel_numbers
@@ -681,8 +695,11 @@ def parse(
  path: str
  Path of .fcs file
  meta_data_only: bool
- If True, the parse_fcs only returns the meta_data (the TEXT segment of the FCS file)
- compensate: bool, reserved parameter to indicate whether the FCS data should be compensated, unimplemented.
+ If True, the parse_fcs only returns the meta_data 
+ (the TEXT segment of the FCS file)
+ compensate: bool, 
+ reserved parameter to indicate whether the FCS data 
+ should be compensated, unimplemented.
  channel_naming: '$PnS' | '$PnN'
  Determines which meta data field is used for naming the channels.
  The default should be $PnS (even though it is not guaranteed to be unique)
@@ -695,17 +712,21 @@ def parse(
  The chosen field will be used to population self.channels
 
  Note: These names are not flipped in the implementation.
- It looks like they were swapped for some reason in the official FCS specification.
+ It looks like they were swapped for some reason 
+ in the official FCS specification.
  reformat_meta: bool
  If true, the meta data is reformatted with the channel information organized
  into a DataFrame and moved into the '_channels_' key
  data_set: int
  Index of retrieved data set in the fcs file.
- This value specifies the data set being retrieved from an fcs file with multiple data sets.
+ This value specifies the data set being retrieved from an fcs file 
+ with multiple data sets.
  dtype: str | None
  If provided, will force convert all data into this dtype.
- This is set by default to auto-convert to float32 to deal with cases in which the original
- data has been stored using a smaller data type (e.g., unit8). This modifies the original
+ This is set by default to auto-convert to float32 
+ to deal with cases in which the original
+ data has been stored using a smaller data type (e.g., unit8). 
+ This modifies the original
  data, but should make follow up analysis safer in basically all cases.
  encoding: str
  Provide encoding type of the text section.

fcsparser/tests/test_fcs_reader.py

@@ -499,7 +499,7 @@ def test_parsed_into_dataframe_correctly(self):
  # ['<u2', '<u2', '<u2', '<u2', '<u2', '<u2', '<u2', '<u2', '<u4', '<u1']
  fcsparser = FCSParser(path=fname)
  # Make sure that data gets parsed as 2-dimensional
- self.assertEquals(fcsparser.dataframe.shape, (725, 10))
+ self.assertEqual(fcsparser.dataframe.shape, (725, 10))
  # Verify that the values are correct.
  assert_array_equal(fcsparser.dataframe.values[:2, :], np.array(
  [[8, 7, 15, 15, 5, 8, 7, 6, 23, 0], [6, 7, 13, 14, 6, 9, 10, 4, 23, 0]]))