holgi
/
sensospot_parser
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity
Browse Source

moved main code into .parser submodule 

this should lead to a cleaner structur when a cli module is added.
Also the public facing methods are now clearly defined.
xmlparsing
Holger Frey 5 years ago
parent
3c20598927
commit
ef00e80700
4 changed files with 776 additions and 723 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 202
      sensovation_data_parser/__init__.py
  2. 211
      sensovation_data_parser/parser.py
  3. 552
      tests/test_parser.py
  4. 534
      tests/test_sensovation_data_parser.py

202
sensovation_data_parser/__init__.py
Unescape View File

@ -6,200 +6,10 @@ Parsing the numerical output from Sensovation image analysis. @@ -6,200 +6,10 @@ Parsing the numerical output from Sensovation image analysis.
__version__ = "0.0.1"
import re
from pathlib import Path
from collections import namedtuple
import pandas
from defusedxml import ElementTree
REGEX_WELL = re.compile(
r"""
(?P<row>([A-Z]+)) # row name containing one or more letters
(?P<column>(\d+)) # column, one or more decimals
""",
re.VERBOSE | re.IGNORECASE,
from .parser import ( # noqa: F401
ExposureInfo,
parse_file,
parse_folder,
process_folder,
parse_multiple_files,
)
COLUMNS_TO_DROP = ["Rect.", "Contour"]
COLUMNS_RENAME_MAP = {
" ID ": "Pos.Id",
"Found": "Spot.Found",
"Dia.": "Spot.Diameter",
}
CACHE_FILE_NAME = "cached_data.h5"
CACHE_TABLE_NAME = f"raw_data_v{__version__}"
FileInfo = namedtuple("FileInfo", ["row", "column", "exposure"])
ExposureInfo = namedtuple("ExposureInfo", ["channel", "time"])
def _guess_decimal_separator(file_handle):
""" guesses the decimal spearator of a opened data file """
file_handle.seek(0)
headers = next(file_handle) # noqa: F841
data = next(file_handle)
separator = "," if data.count(",") > data.count(".") else "."
file_handle.seek(0)
return separator
def _parse_csv(data_file):
""" parse a csv sensovation data file """
data_path = Path(data_file)
with data_path.open("r") as handle:
decimal_sep = _guess_decimal_separator(handle)
return pandas.read_csv(handle, sep="\t", decimal=decimal_sep)
def _extract_measurement_info(data_file):
""" extract measurement meta data from a file name """
data_path = Path(data_file)
*rest, well, exposure = data_path.stem.rsplit("_", 2) # noqa: F841
matched = REGEX_WELL.match(well)
if matched is None:
raise ValueError(f"not a valid well: '{well}'")
row = matched["row"].upper()
column = int(matched["column"])
exposure = int(exposure)
return FileInfo(row, column, exposure)
def _cleanup_data_columns(data_frame):
""" renames some data columns for consistency and drops unused columns """
renamed = data_frame.rename(columns=COLUMNS_RENAME_MAP)
return renamed.drop(columns=COLUMNS_TO_DROP)
def parse_file(data_file):
""" parses one data file and adds metadata to result """
measurement_info = _extract_measurement_info(data_file)
data_frame = _parse_csv(data_file)
data_frame["Field.Row"] = measurement_info.row
data_frame["Field.Column"] = measurement_info.column
data_frame["Exposure.Id"] = measurement_info.exposure
return _cleanup_data_columns(data_frame)
def parse_multiple_files(file_list):
""" parses a list of file paths to one combined dataframe """
if not file_list:
raise ValueError("Empty file list provided")
collection = (parse_file(path) for path in file_list)
data_frame = next(collection)
for next_frame in collection:
data_frame = data_frame.append(next_frame, ignore_index=True)
return data_frame
def _list_csv_files(folder):
""" returns all csv files in a folder """
folder_path = Path(folder)
files = (item for item in folder_path.iterdir() if item.is_file())
visible = (item for item in files if not item.stem.startswith("."))
return (item for item in visible if item.suffix.lower() == ".csv")
def _sanity_check(data_frame):
""" checks some basic constrains of a combined data frame """
field_rows = len(data_frame["Field.Row"].unique())
field_cols = len(data_frame["Field.Column"].unique())
exposures = len(data_frame["Exposure.Id"].unique())
spot_positions = len(data_frame["Pos.Id"].unique())
expected_rows = field_rows * field_cols * exposures * spot_positions
if expected_rows != len(data_frame):
raise ValueError("Measurements are missing")
return data_frame
def parse_folder(folder):
""" parses all csv files in a folder to one large dataframe """
file_list = _list_csv_files(folder)
data_frame = parse_multiple_files(file_list)
return data_frame
def _search_channel_info_file(folder):
""" searches for a exposure settings file in a folder """
folder_path = Path(folder)
params_folder = folder_path / "Parameters"
if not params_folder.is_dir():
return None
param_files = list(params_folder.glob("**/*.svexp"))
if len(param_files) == 1:
return param_files[0]
else:
return None
def _parse_channel_info(channel_file):
""" parses the cannel informations from a settings file """
file_path = Path(channel_file)
with file_path.open("r") as file_handle:
tree = ElementTree.parse(file_handle)
result = {}
for child in tree.find("Channels"):
# child.tag == "ChannelConfig1"
exposure = int(child.tag[-1])
channel_description = child.attrib["Description"]
# channel_description == "Cy3/Cy5 Green"
channel = channel_description.rsplit(" ", 1)[-1]
time = int(child.attrib["ExposureTimeMs"])
result[exposure] = ExposureInfo(channel.lower(), time)
return result
def _get_valid_exposure_info(folder, data_frame, exposure_info=None):
""" returns valid exposure information """
available_exposures = set(data_frame["Exposure.Id"].unique())
if exposure_info is None:
params_file = _search_channel_info_file(folder)
if params_file is not None:
exposure_info = _parse_channel_info(params_file)
if exposure_info is not None:
if available_exposures == set(exposure_info.keys()):
return exposure_info
return {c: ExposureInfo(None, None) for c in available_exposures}
def _augment_exposure_info(data_frame, exposure_info):
data_frame["Exposure.Channel"] = ""
data_frame["Exposure.Time"] = 0
for exposure_id, info in exposure_info.items():
mask = data_frame["Exposure.Id"] == exposure_id
data_frame.loc[mask, "Exposure.Channel"] = info.channel
data_frame.loc[mask, "Exposure.Time"] = info.time
return data_frame
def _process_folder(folder, exposures=None):
""" parses all csv files in a folder, adds some checks and more data """
data_frame = parse_folder(folder)
exposures = _get_valid_exposure_info(folder, data_frame, exposures)
data_frame = _augment_exposure_info(data_frame, exposures)
data_frame["Field.Row"] = data_frame["Field.Row"].astype("category")
data_frame["Exposure.Channel"] = data_frame["Exposure.Channel"].astype(
"category"
)
return data_frame
def process_folder(folder, exposures=None, use_cache=True):
""" parses all csv files in a folder, adds some checks and more data """
hdf5_path = folder / CACHE_FILE_NAME
if use_cache:
try:
return pandas.read_hdf(hdf5_path, CACHE_TABLE_NAME)
except (FileNotFoundError, KeyError):
# either file or table doesn't exist
pass
data_frame = _process_folder(folder, exposures)
if use_cache:
try:
data_frame.to_hdf(hdf5_path, CACHE_TABLE_NAME, format="table")
except OSError:
# capturing high level OSError
# read only filesystems don't throw a more specific exception
pass
return data_frame

211
sensovation_data_parser/parser.py
Unescape View File

@ -0,0 +1,211 @@ @@ -0,0 +1,211 @@
""" Sensovation Data Parser
Parsing the numerical output from Sensovation image analysis.
"""
import re
from pathlib import Path
from collections import namedtuple
import pandas
from defusedxml import ElementTree
REGEX_WELL = re.compile(
r"""
(?P<row>([A-Z]+)) # row name containing one or more letters
(?P<column>(\d+)) # column, one or more decimals
""",
re.VERBOSE | re.IGNORECASE,
)
COLUMNS_TO_DROP = ["Rect.", "Contour"]
COLUMNS_RENAME_MAP = {
" ID ": "Pos.Id",
"Found": "Spot.Found",
"Dia.": "Spot.Diameter",
}
CACHE_FILE_NAME = "raw_data.h5"
FileInfo = namedtuple("FileInfo", ["row", "column", "exposure"])
ExposureInfo = namedtuple("ExposureInfo", ["channel", "time"])
def _get_cache_table_name():
""" automatic hdf5 table name, avoids a circular import """
from . import __version__
return f"v{__version__}"
def _guess_decimal_separator(file_handle):
""" guesses the decimal spearator of a opened data file """
file_handle.seek(0)
headers = next(file_handle) # noqa: F841
data = next(file_handle)
separator = "," if data.count(",") > data.count(".") else "."
file_handle.seek(0)
return separator
def _parse_csv(data_file):
""" parse a csv sensovation data file """
data_path = Path(data_file)
with data_path.open("r") as handle:
decimal_sep = _guess_decimal_separator(handle)
return pandas.read_csv(handle, sep="\t", decimal=decimal_sep)
def _extract_measurement_info(data_file):
""" extract measurement meta data from a file name """
data_path = Path(data_file)
*rest, well, exposure = data_path.stem.rsplit("_", 2) # noqa: F841
matched = REGEX_WELL.match(well)
if matched is None:
raise ValueError(f"not a valid well: '{well}'")
row = matched["row"].upper()
column = int(matched["column"])
exposure = int(exposure)
return FileInfo(row, column, exposure)
def _cleanup_data_columns(data_frame):
""" renames some data columns for consistency and drops unused columns """
renamed = data_frame.rename(columns=COLUMNS_RENAME_MAP)
return renamed.drop(columns=COLUMNS_TO_DROP)
def parse_file(data_file):
""" parses one data file and adds metadata to result """
measurement_info = _extract_measurement_info(data_file)
data_frame = _parse_csv(data_file)
data_frame["Field.Row"] = measurement_info.row
data_frame["Field.Column"] = measurement_info.column
data_frame["Exposure.Id"] = measurement_info.exposure
return _cleanup_data_columns(data_frame)
def parse_multiple_files(file_list):
""" parses a list of file paths to one combined dataframe """
if not file_list:
raise ValueError("Empty file list provided")
collection = (parse_file(path) for path in file_list)
data_frame = next(collection)
for next_frame in collection:
data_frame = data_frame.append(next_frame, ignore_index=True)
return data_frame
def _list_csv_files(folder):
""" returns all csv files in a folder """
folder_path = Path(folder)
files = (item for item in folder_path.iterdir() if item.is_file())
visible = (item for item in files if not item.stem.startswith("."))
return (item for item in visible if item.suffix.lower() == ".csv")
def _sanity_check(data_frame):
""" checks some basic constrains of a combined data frame """
field_rows = len(data_frame["Field.Row"].unique())
field_cols = len(data_frame["Field.Column"].unique())
exposures = len(data_frame["Exposure.Id"].unique())
spot_positions = len(data_frame["Pos.Id"].unique())
expected_rows = field_rows * field_cols * exposures * spot_positions
if expected_rows != len(data_frame):
raise ValueError("Measurements are missing")
return data_frame
def parse_folder(folder):
""" parses all csv files in a folder to one large dataframe """
file_list = _list_csv_files(folder)
data_frame = parse_multiple_files(file_list)
return data_frame
def _search_channel_info_file(folder):
""" searches for a exposure settings file in a folder """
folder_path = Path(folder)
params_folder = folder_path / "Parameters"
if not params_folder.is_dir():
return None
param_files = list(params_folder.glob("**/*.svexp"))
if len(param_files) == 1:
return param_files[0]
else:
return None
def _parse_channel_info(channel_file):
""" parses the cannel informations from a settings file """
file_path = Path(channel_file)
with file_path.open("r") as file_handle:
tree = ElementTree.parse(file_handle)
result = {}
for child in tree.find("Channels"):
# child.tag == "ChannelConfig1"
exposure = int(child.tag[-1])
channel_description = child.attrib["Description"]
# channel_description == "Cy3/Cy5 Green"
channel = channel_description.rsplit(" ", 1)[-1]
time = int(child.attrib["ExposureTimeMs"])
result[exposure] = ExposureInfo(channel.lower(), time)
return result
def _get_valid_exposure_map(folder, data_frame, exposure_map=None):
""" returns valid exposure information """
available_exposures = set(data_frame["Exposure.Id"].unique())
if exposure_map is None:
params_file = _search_channel_info_file(folder)
if params_file is not None:
exposure_map = _parse_channel_info(params_file)
if exposure_map is not None:
if available_exposures == set(exposure_map.keys()):
return exposure_map
return {c: ExposureInfo(None, None) for c in available_exposures}
def _augment_exposure_map(data_frame, exposure_map):
data_frame["Exposure.Channel"] = ""
data_frame["Exposure.Time"] = 0
for exposure_id, info in exposure_map.items():
channel, time = info
mask = data_frame["Exposure.Id"] == exposure_id
data_frame.loc[mask, "Exposure.Channel"] = channel
data_frame.loc[mask, "Exposure.Time"] = time
return data_frame
def _process_folder(folder, exposures=None):
""" parses all csv files in a folder, adds some checks and more data """
data_frame = parse_folder(folder)
exposures = _get_valid_exposure_map(folder, data_frame, exposures)
data_frame = _augment_exposure_map(data_frame, exposures)
data_frame["Field.Row"] = data_frame["Field.Row"].astype("category")
data_frame["Exposure.Channel"] = data_frame["Exposure.Channel"].astype(
"category"
)
return data_frame
def process_folder(folder, exposures=None, use_cache=True):
""" parses all csv files in a folder, adds some checks and more data """
hdf5_path = folder / CACHE_FILE_NAME
if use_cache:
try:
return pandas.read_hdf(hdf5_path, _get_cache_table_name())
except (FileNotFoundError, KeyError):
# either file or table doesn't exist
pass
data_frame = _process_folder(folder, exposures)
if use_cache:
try:
data_frame.to_hdf(
hdf5_path, _get_cache_table_name(), format="table"
)
except OSError:
# capturing high level OSError
# read only filesystems don't throw a more specific exception
pass
return data_frame

552
tests/test_parser.py
Unescape View File

@ -0,0 +1,552 @@ @@ -0,0 +1,552 @@
""" Stub file for testing the project """
from pathlib import Path
import numpy
import pytest
EXAMPLE_DIR_WO_PARAMS = "mtp_wo_parameters"
EXAMPLE_DIR_WITH_PARAMS = "mtp_with_parameters"
@pytest.fixture
def example_dir(request):
root_dir = Path(request.config.rootdir)
yield root_dir / "example_data"
@pytest.fixture
def example_file(example_dir):
data_dir = example_dir / EXAMPLE_DIR_WO_PARAMS
yield data_dir / "160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv"
@pytest.fixture
def exposure_df():
from pandas import DataFrame
yield DataFrame(data={"Exposure.Id": [1, 2, 3]})
@pytest.fixture
def dir_for_caching(tmpdir, example_file):
import shutil
temp_path = Path(tmpdir)
dest = temp_path / example_file.name
shutil.copy(example_file, dest)
yield temp_path
@pytest.mark.parametrize(
"sub_dir, file_name",
[
(
EXAMPLE_DIR_WO_PARAMS,
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv",
),
(
EXAMPLE_DIR_WITH_PARAMS,
"160210_SG2-010-001_Regen_cy3100_1_A1_1.csv",
),
],
)
def test_parse_csv(example_dir, sub_dir, file_name):
from sensovation_data_parser.parser import _parse_csv
result = _parse_csv(example_dir / sub_dir / file_name)
columns = {
" ID ": numpy.int64,
"Pos.X": numpy.int64,
"Pos.Y": numpy.int64,
"Bkg.Mean": float,
"Spot.Mean": float,
"Bkg.Median": float,
"Spot.Median": float,
"Bkg.StdDev": float,
"Spot.StdDev": float,
"Bkg.Sum": numpy.int64,
"Spot.Sum": numpy.int64,
"Bkg.Area": numpy.int64,
"Spot.Area": numpy.int64,
"Spot.Sat. (%)": numpy.int64,
"Found": numpy.bool_,
"Pos.Nom.X": numpy.int64,
"Pos.Nom.Y": numpy.int64,
"Dia.": numpy.int64,
"Rect.": str,
"Contour": object, # ignore the type of contour
}
assert set(result.columns) == set(columns.keys())
assert len(result[" ID "].unique()) == 100
assert len(result) == 100
for column, value_type in columns.items():
assert isinstance(result[column][0], value_type)
def test_parse_csv_no_array(example_dir):
from sensovation_data_parser.parser import _parse_csv
result = _parse_csv(example_dir / "no_array_A1_1.csv")
assert len(result) == 1
assert result[" ID "][0] == 0
@pytest.mark.parametrize(
"input, expected", [("", "."), ("..,", "."), (".,,", ","), ("..,,", "."),]
)
def test_guess_decimal_separator_returns_correct_separator(input, expected):
from sensovation_data_parser.parser import _guess_decimal_separator
from io import StringIO
handle = StringIO(f"header\n{input}\n")
result = _guess_decimal_separator(handle)
assert result == expected
def test_guess_decimal_separator_rewinds_handle():
from sensovation_data_parser.parser import _guess_decimal_separator
from io import StringIO
handle = StringIO(f"header\n{input}\n")
_guess_decimal_separator(handle)
assert next(handle) == "header\n"
def test_well_regex_ok():
from sensovation_data_parser.parser import REGEX_WELL
result = REGEX_WELL.match("AbC123")
assert result["row"] == "AbC"
assert result["column"] == "123"
@pytest.mark.parametrize("input", ["", "A", "1", "1A", "-1", "A-"])
def test_well_regex_no_match(input):
from sensovation_data_parser.parser import REGEX_WELL
result = REGEX_WELL.match(input)
assert result is None
@pytest.mark.parametrize(
"filename, expected",
[("A1_1.csv", ("A", 1, 1)), ("test/measurement_1_H12_2", ("H", 12, 2)),],
)
def test_extract_measurement_info_ok(filename, expected):
from sensovation_data_parser.parser import _extract_measurement_info
result = _extract_measurement_info(filename)
assert result == expected
@pytest.mark.parametrize("filename", ["wrong_exposure_A1_B", "no_well_XX_1"])
def test_extract_measurement_info_raises_error(filename):
from sensovation_data_parser.parser import _extract_measurement_info
with pytest.raises(ValueError):
_extract_measurement_info(filename)
def test_cleanup_data_columns():
from sensovation_data_parser.parser import _cleanup_data_columns
from pandas import DataFrame
columns = ["Rect.", "Contour", " ID ", "Found", "Dia."]
data = {col: [i] for i, col in enumerate(columns)}
data_frame = DataFrame(data=data)
result = _cleanup_data_columns(data_frame)
assert set(result.columns) == {"Pos.Id", "Spot.Found", "Spot.Diameter"}
assert result["Pos.Id"][0] == 2
assert result["Spot.Found"][0] == 3
assert result["Spot.Diameter"][0] == 4
def test_parse_file(example_file):
from sensovation_data_parser.parser import parse_file
result = parse_file(example_file)
columns = {
"Pos.Id",
"Pos.X",
"Pos.Y",
"Bkg.Mean",
"Spot.Mean",
"Bkg.Median",
"Spot.Median",
"Bkg.StdDev",
"Spot.StdDev",
"Bkg.Sum",
"Spot.Sum",
"Bkg.Area",
"Spot.Area",
"Spot.Sat. (%)",
"Spot.Found",
"Pos.Nom.X",
"Pos.Nom.Y",
"Spot.Diameter",
"Field.Row",
"Field.Column",
"Exposure.Id",
}
assert set(result.columns) == columns
assert result["Field.Row"][0] == "A"
assert result["Field.Column"][0] == 1
assert result["Exposure.Id"][0] == 1
@pytest.mark.parametrize(
"file_list",
[
[
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv",
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_2.csv",
],
["160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv"],
],
)
def testparse_multiple_files_ok(example_dir, file_list):
from sensovation_data_parser.parser import parse_multiple_files
sub_dir = example_dir / EXAMPLE_DIR_WO_PARAMS
files = [sub_dir / file for file in file_list]
data_frame = parse_multiple_files(files)
print(data_frame["Exposure.Id"].unique())
assert len(data_frame) == 100 * len(files)
assert len(data_frame["Exposure.Id"].unique()) == len(files)
def testparse_multiple_files_empty_file_list():
from sensovation_data_parser.parser import parse_multiple_files
with pytest.raises(ValueError):
parse_multiple_files([])
def testparse_multiple_files_empty_array(example_dir):
from sensovation_data_parser.parser import parse_multiple_files
files = [example_dir / "no_array_A1_1.csv"]
data_frame = parse_multiple_files(files)
print(data_frame["Exposure.Id"].unique())
assert len(data_frame) == 1
def test_list_csv_files(example_dir):
from sensovation_data_parser.parser import _list_csv_files
result = list(_list_csv_files(example_dir / EXAMPLE_DIR_WITH_PARAMS))
assert len(result) == 36 * 3
assert all(str(item).endswith(".csv") for item in result)
assert all(not item.stem.startswith(".") for item in result)
def test_parse_folder(example_dir):
from sensovation_data_parser.parser import parse_folder
data_frame = parse_folder(example_dir / EXAMPLE_DIR_WITH_PARAMS)
assert len(data_frame) == 36 * 3 * 100
assert len(data_frame["Field.Row"].unique()) == 3
assert len(data_frame["Field.Column"].unique()) == 12
assert len(data_frame["Exposure.Id"].unique()) == 3
assert len(data_frame["Pos.Id"].unique()) == 100
def test_sanity_check_ok(example_dir):
from sensovation_data_parser.parser import (
_sanity_check,
parse_multiple_files,
)
sub_dir = example_dir / EXAMPLE_DIR_WO_PARAMS
file_list = [
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv",
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_2.csv",
]
files = [sub_dir / file for file in file_list]
data_frame = parse_multiple_files(files)
result = _sanity_check(data_frame)
assert len(result) == len(data_frame)
def test_sanity_check_raises_value_error(example_dir):
from sensovation_data_parser.parser import (
_sanity_check,
parse_multiple_files,
)
sub_dir = example_dir / EXAMPLE_DIR_WO_PARAMS
file_list = [
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv",
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_2.csv",
]
files = [sub_dir / file for file in file_list]
data_frame = parse_multiple_files(files)
data_frame = data_frame.drop(data_frame.index[1])
with pytest.raises(ValueError):
_sanity_check(data_frame)
def test_search_channel_info_file_ok(example_dir):
from sensovation_data_parser.parser import _search_channel_info_file
result = _search_channel_info_file(example_dir / EXAMPLE_DIR_WITH_PARAMS)
assert result.suffix == ".svexp"
def test_search_channel_info_file_no_parameters_folder(example_dir):
from sensovation_data_parser.parser import _search_channel_info_file
result = _search_channel_info_file(example_dir / EXAMPLE_DIR_WO_PARAMS)
assert result is None
def test_search_channel_info_file_no_parameters_file(tmpdir):
from sensovation_data_parser.parser import _search_channel_info_file
params_dir = tmpdir / "Parameters"
params_dir.mkdir()
result = _search_channel_info_file(tmpdir)
assert result is None
def test_parse_channel_info(example_dir):
from sensovation_data_parser.parser import (
_search_channel_info_file,
_parse_channel_info,
)
params = _search_channel_info_file(example_dir / EXAMPLE_DIR_WITH_PARAMS)
result = _parse_channel_info(params)
assert set(result.keys()) == {1, 2, 3}
assert result[1] == ("green", 100)
assert result[2] == ("red", 150)
assert result[3] == ("red", 15)
def test_get_valid_exposure_map_provided_ok(exposure_df):
from sensovation_data_parser.parser import (
_get_valid_exposure_map,
ExposureInfo,
)
dummy_value = ExposureInfo(None, None)
exposure_map = {1: dummy_value, 2: dummy_value, 3: dummy_value}
result = _get_valid_exposure_map(
"/nonexistent", exposure_df, exposure_map=exposure_map
)
assert result == exposure_map
def test_get_valid_exposure_map_provided_not_ok(exposure_df):
from sensovation_data_parser.parser import _get_valid_exposure_map
exposure_map = {1: None, 2: None}
result = _get_valid_exposure_map(
"/nonexistent", exposure_df, exposure_map=exposure_map
)
assert set(result.keys()) == {1, 2, 3}
assert all(v == (None, None) for v in result.values())
def test_get_valid_exposure_map_info_from_file_ok(example_dir, exposure_df):
from sensovation_data_parser.parser import _get_valid_exposure_map
result = _get_valid_exposure_map(
example_dir / EXAMPLE_DIR_WITH_PARAMS, exposure_df, exposure_map=None
)
assert set(result.keys()) == {1, 2, 3}
assert result[1] == ("green", 100)
assert result[2] == ("red", 150)
assert result[3] == ("red", 15)
def test_get_valid_exposure_map_info_from_file_not_ok(
example_dir, exposure_df
):
from sensovation_data_parser.parser import _get_valid_exposure_map
data_frame = exposure_df.drop(exposure_df.index[1])
result = _get_valid_exposure_map(
example_dir / EXAMPLE_DIR_WITH_PARAMS, data_frame, exposure_map=None
)
assert set(result.keys()) == {1, 3}
assert all(v == (None, None) for v in result.values())
def test_augment_exposure_map(exposure_df):
from sensovation_data_parser.parser import (
_augment_exposure_map,
ExposureInfo,
)
exposure_map = {
1: ExposureInfo("red", 10),
2: ExposureInfo("green", 20),
3: ExposureInfo("blue", 50),
}
result = _augment_exposure_map(exposure_df, exposure_map)
assert result["Exposure.Id"][0] == 1
assert result["Exposure.Channel"][0] == "red"
assert result["Exposure.Time"][0] == 10
assert result["Exposure.Id"][1] == 2
assert result["Exposure.Channel"][1] == "green"
assert result["Exposure.Time"][1] == 20
assert result["Exposure.Id"][2] == 3
assert result["Exposure.Channel"][2] == "blue"
assert result["Exposure.Time"][2] == 50
def test_process_folder_with_exposure_map(example_dir):
from sensovation_data_parser.parser import _process_folder
result = _process_folder(example_dir / EXAMPLE_DIR_WITH_PARAMS)
assert len(result) == 36 * 100 * 3
expected = [(1, "green", 100), (2, "red", 150), (3, "red", 15)]
for exposure_id, channel, time in expected:
mask = result["Exposure.Id"] == exposure_id
example_row = result.loc[mask].iloc[1]
assert example_row["Exposure.Channel"] == channel
assert example_row["Exposure.Time"] == time
def test_process_folder_without_exposure_map(example_dir):
from sensovation_data_parser.parser import _process_folder
from pandas import isnull
result = _process_folder(example_dir / EXAMPLE_DIR_WO_PARAMS)
assert len(result) == 96 * 100 * 3
for exposure_id in range(1, 4):
mask = result["Exposure.Id"] == exposure_id
example_row = result.loc[mask].iloc[1]
print(type(example_row["Exposure.Channel"]))
assert isnull(example_row["Exposure.Channel"])
assert isnull(example_row["Exposure.Time"])
def test_process_folder_creates_cache(dir_for_caching):
from sensovation_data_parser.parser import (
process_folder,
CACHE_FILE_NAME,
)
cache_path = dir_for_caching / CACHE_FILE_NAME
assert not cache_path.is_file()
result = process_folder(dir_for_caching)
assert len(result) == 100
assert cache_path.is_file()
def test_process_folder_reads_from_cache(dir_for_caching, example_file):
from sensovation_data_parser.parser import process_folder
process_folder(dir_for_caching)
csv_file = dir_for_caching / example_file.name
csv_file.unlink()
result = process_folder(dir_for_caching)
assert len(result) == 100
def test_process_folder_read_cache_fails_silently(
dir_for_caching, exposure_df
):
from sensovation_data_parser.parser import (
process_folder,
CACHE_FILE_NAME,
)
cache_path = dir_for_caching / CACHE_FILE_NAME
exposure_df.to_hdf(cache_path, "unknown table")
result = process_folder(dir_for_caching)
assert result["Field.Row"][0] == "A"
def test_get_cache_table_name():
from sensovation_data_parser.parser import _get_cache_table_name
from sensovation_data_parser import __version__
result = _get_cache_table_name()
assert result.startswith("v")
assert result[1:] == __version__
def test_process_folder_read_cache_no_cache_arg(dir_for_caching, exposure_df):
from sensovation_data_parser.parser import (
process_folder,
_get_cache_table_name,
CACHE_FILE_NAME,
)
cache_path = dir_for_caching / CACHE_FILE_NAME
exposure_df.to_hdf(cache_path, _get_cache_table_name())
result = process_folder(dir_for_caching, use_cache=False)
assert result["Field.Row"][0] == "A"
def test_process_folder_writes_cache(dir_for_caching):
from sensovation_data_parser.parser import (
process_folder,
CACHE_FILE_NAME,
)
process_folder(dir_for_caching, use_cache=True)
cache_path = dir_for_caching / CACHE_FILE_NAME
assert cache_path.is_file()
def test_process_folder_writes_cache_no_cache_arg(dir_for_caching):
from sensovation_data_parser.parser import process_folder, CACHE_FILE_NAME
process_folder(dir_for_caching, use_cache=False)
cache_path = dir_for_caching / CACHE_FILE_NAME
assert not cache_path.is_file()

534
tests/test_sensovation_data_parser.py
Unescape View File

@ -1,529 +1,9 @@ @@ -1,529 +1,9 @@
""" Stub file for testing the project """
""" testing the __ini__ file """
from pathlib import Path
import numpy
import pytest
EXAMPLE_DIR_WO_PARAMS = "mtp_wo_parameters"
EXAMPLE_DIR_WITH_PARAMS = "mtp_with_parameters"
@pytest.fixture
def example_dir(request):
root_dir = Path(request.config.rootdir)
yield root_dir / "example_data"
@pytest.fixture
def example_file(example_dir):
data_dir = example_dir / EXAMPLE_DIR_WO_PARAMS
yield data_dir / "160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv"
@pytest.fixture
def exposure_df():
from pandas import DataFrame
yield DataFrame(data={"Exposure.Id": [1, 2, 3]})
@pytest.fixture
def dir_for_caching(tmpdir, example_file):
import shutil
temp_path = Path(tmpdir)
dest = temp_path / example_file.name
shutil.copy(example_file, dest)
yield temp_path
@pytest.mark.parametrize(
"sub_dir, file_name",
[
(
EXAMPLE_DIR_WO_PARAMS,
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv",
),
(
EXAMPLE_DIR_WITH_PARAMS,
"160210_SG2-010-001_Regen_cy3100_1_A1_1.csv",
),
],
)
def test_parse_csv(example_dir, sub_dir, file_name):
from sensovation_data_parser import _parse_csv
result = _parse_csv(example_dir / sub_dir / file_name)
columns = {
" ID ": numpy.int64,
"Pos.X": numpy.int64,
"Pos.Y": numpy.int64,
"Bkg.Mean": float,
"Spot.Mean": float,
"Bkg.Median": float,
"Spot.Median": float,
"Bkg.StdDev": float,
"Spot.StdDev": float,
"Bkg.Sum": numpy.int64,
"Spot.Sum": numpy.int64,
"Bkg.Area": numpy.int64,
"Spot.Area": numpy.int64,
"Spot.Sat. (%)": numpy.int64,
"Found": numpy.bool_,
"Pos.Nom.X": numpy.int64,
"Pos.Nom.Y": numpy.int64,
"Dia.": numpy.int64,
"Rect.": str,
"Contour": object, # ignore the type of contour
}
assert set(result.columns) == set(columns.keys())
assert len(result[" ID "].unique()) == 100
assert len(result) == 100
for column, value_type in columns.items():
assert isinstance(result[column][0], value_type)
def test_parse_csv_no_array(example_dir):
from sensovation_data_parser import _parse_csv
result = _parse_csv(example_dir / "no_array_A1_1.csv")
assert len(result) == 1
assert result[" ID "][0] == 0
@pytest.mark.parametrize(
"input, expected", [("", "."), ("..,", "."), (".,,", ","), ("..,,", "."),]
)
def test_guess_decimal_separator_returns_correct_separator(input, expected):
from sensovation_data_parser import _guess_decimal_separator
from io import StringIO
handle = StringIO(f"header\n{input}\n")
result = _guess_decimal_separator(handle)
assert result == expected
def test_guess_decimal_separator_rewinds_handle():
from sensovation_data_parser import _guess_decimal_separator
from io import StringIO
handle = StringIO(f"header\n{input}\n")
_guess_decimal_separator(handle)
assert next(handle) == "header\n"
def test_well_regex_ok():
from sensovation_data_parser import REGEX_WELL
result = REGEX_WELL.match("AbC123")
assert result["row"] == "AbC"
assert result["column"] == "123"
@pytest.mark.parametrize("input", ["", "A", "1", "1A", "-1", "A-"])
def test_well_regex_no_match(input):
from sensovation_data_parser import REGEX_WELL
result = REGEX_WELL.match(input)
assert result is None
@pytest.mark.parametrize(
"filename, expected",
[("A1_1.csv", ("A", 1, 1)), ("test/measurement_1_H12_2", ("H", 12, 2)),],
)
def test_extract_measurement_info_ok(filename, expected):
from sensovation_data_parser import _extract_measurement_info
result = _extract_measurement_info(filename)
assert result == expected
@pytest.mark.parametrize("filename", ["wrong_exposure_A1_B", "no_well_XX_1"])
def test_extract_measurement_info_raises_error(filename):
from sensovation_data_parser import _extract_measurement_info
with pytest.raises(ValueError):
_extract_measurement_info(filename)
def test_cleanup_data_columns():
from sensovation_data_parser import _cleanup_data_columns
from pandas import DataFrame
columns = ["Rect.", "Contour", " ID ", "Found", "Dia."]
data = {col: [i] for i, col in enumerate(columns)}
data_frame = DataFrame(data=data)
result = _cleanup_data_columns(data_frame)
assert set(result.columns) == {"Pos.Id", "Spot.Found", "Spot.Diameter"}
assert result["Pos.Id"][0] == 2
assert result["Spot.Found"][0] == 3
assert result["Spot.Diameter"][0] == 4
def test_parse_file(example_file):
from sensovation_data_parser import parse_file
result = parse_file(example_file)
columns = {
"Pos.Id",
"Pos.X",
"Pos.Y",
"Bkg.Mean",
"Spot.Mean",
"Bkg.Median",
"Spot.Median",
"Bkg.StdDev",
"Spot.StdDev",
"Bkg.Sum",
"Spot.Sum",
"Bkg.Area",
"Spot.Area",
"Spot.Sat. (%)",
"Spot.Found",
"Pos.Nom.X",
"Pos.Nom.Y",
"Spot.Diameter",
"Field.Row",
"Field.Column",
"Exposure.Id",
}
assert set(result.columns) == columns
assert result["Field.Row"][0] == "A"
assert result["Field.Column"][0] == 1
assert result["Exposure.Id"][0] == 1
@pytest.mark.parametrize(
"file_list",
[
[
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv",
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_2.csv",
],
["160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv"],
],
)
def testparse_multiple_files_ok(example_dir, file_list):
from sensovation_data_parser import parse_multiple_files
sub_dir = example_dir / EXAMPLE_DIR_WO_PARAMS
files = [sub_dir / file for file in file_list]
data_frame = parse_multiple_files(files)
print(data_frame["Exposure.Id"].unique())
assert len(data_frame) == 100 * len(files)
assert len(data_frame["Exposure.Id"].unique()) == len(files)
def testparse_multiple_files_empty_file_list():
from sensovation_data_parser import parse_multiple_files
with pytest.raises(ValueError):
parse_multiple_files([])
def testparse_multiple_files_empty_array(example_dir):
from sensovation_data_parser import parse_multiple_files
files = [example_dir / "no_array_A1_1.csv"]
data_frame = parse_multiple_files(files)
print(data_frame["Exposure.Id"].unique())
assert len(data_frame) == 1
def test_list_csv_files(example_dir):
from sensovation_data_parser import _list_csv_files
result = list(_list_csv_files(example_dir / EXAMPLE_DIR_WITH_PARAMS))
assert len(result) == 36 * 3
assert all(str(item).endswith(".csv") for item in result)
assert all(not item.stem.startswith(".") for item in result)
def test_parse_folder(example_dir):
from sensovation_data_parser import parse_folder
data_frame = parse_folder(example_dir / EXAMPLE_DIR_WITH_PARAMS)
assert len(data_frame) == 36 * 3 * 100
assert len(data_frame["Field.Row"].unique()) == 3
assert len(data_frame["Field.Column"].unique()) == 12
assert len(data_frame["Exposure.Id"].unique()) == 3
assert len(data_frame["Pos.Id"].unique()) == 100
def test_sanity_check_ok(example_dir):
from sensovation_data_parser import _sanity_check, parse_multiple_files
sub_dir = example_dir / EXAMPLE_DIR_WO_PARAMS
file_list = [
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv",
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_2.csv",
]
files = [sub_dir / file for file in file_list]
data_frame = parse_multiple_files(files)
result = _sanity_check(data_frame)
assert len(result) == len(data_frame)
def test_sanity_check_raises_value_error(example_dir):
from sensovation_data_parser import _sanity_check, parse_multiple_files
sub_dir = example_dir / EXAMPLE_DIR_WO_PARAMS
file_list = [
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv",
"160218_SG2-013-001_Regen1_Cy3-100_1_A1_2.csv",
]
files = [sub_dir / file for file in file_list]
data_frame = parse_multiple_files(files)
data_frame = data_frame.drop(data_frame.index[1])
with pytest.raises(ValueError):
_sanity_check(data_frame)
def test_search_channel_info_file_ok(example_dir):
from sensovation_data_parser import _search_channel_info_file
result = _search_channel_info_file(example_dir / EXAMPLE_DIR_WITH_PARAMS)
assert result.suffix == ".svexp"
def test_search_channel_info_file_no_parameters_folder(example_dir):
from sensovation_data_parser import _search_channel_info_file
result = _search_channel_info_file(example_dir / EXAMPLE_DIR_WO_PARAMS)
assert result is None
def test_search_channel_info_file_no_parameters_file(tmpdir):
from sensovation_data_parser import _search_channel_info_file
params_dir = tmpdir / "Parameters"
params_dir.mkdir()
result = _search_channel_info_file(tmpdir)
assert result is None
def test_parse_channel_info(example_dir):
from sensovation_data_parser import (
_search_channel_info_file,
_parse_channel_info,
)
params = _search_channel_info_file(example_dir / EXAMPLE_DIR_WITH_PARAMS)
result = _parse_channel_info(params)
assert set(result.keys()) == {1, 2, 3}
assert result[1] == ("green", 100)
assert result[2] == ("red", 150)
assert result[3] == ("red", 15)
def test_get_valid_exposure_info_provided_ok(exposure_df):
from sensovation_data_parser import _get_valid_exposure_info
exposure_info = {1: None, 2: None, 3: None}
result = _get_valid_exposure_info(
"/nonexistent", exposure_df, exposure_info=exposure_info
)
assert result == exposure_info
def test_get_valid_exposure_info_provided_not_ok(exposure_df):
from sensovation_data_parser import _get_valid_exposure_info
exposure_info = {1: None, 2: None}
result = _get_valid_exposure_info(
"/nonexistent", exposure_df, exposure_info=exposure_info
)
assert set(result.keys()) == {1, 2, 3}
assert all(v == (None, None) for v in result.values())
def test_get_valid_exposure_info_info_from_file_ok(example_dir, exposure_df):
from sensovation_data_parser import _get_valid_exposure_info
result = _get_valid_exposure_info(
example_dir / EXAMPLE_DIR_WITH_PARAMS, exposure_df, exposure_info=None
)
assert set(result.keys()) == {1, 2, 3}
assert result[1] == ("green", 100)
assert result[2] == ("red", 150)
assert result[3] == ("red", 15)
def test_get_valid_exposure_info_info_from_file_not_ok(
example_dir, exposure_df
):
from sensovation_data_parser import _get_valid_exposure_info
data_frame = exposure_df.drop(exposure_df.index[1])
result = _get_valid_exposure_info(
example_dir / EXAMPLE_DIR_WITH_PARAMS, data_frame, exposure_info=None
)
assert set(result.keys()) == {1, 3}
assert all(v == (None, None) for v in result.values())
def test_augment_exposure_info(exposure_df):
from sensovation_data_parser import _augment_exposure_info, ExposureInfo
exposure_info = {
1: ExposureInfo("red", 10),
2: ExposureInfo("green", 20),
3: ExposureInfo("blue", 50),
}
result = _augment_exposure_info(exposure_df, exposure_info)
assert result["Exposure.Id"][0] == 1
assert result["Exposure.Channel"][0] == "red"
assert result["Exposure.Time"][0] == 10
assert result["Exposure.Id"][1] == 2
assert result["Exposure.Channel"][1] == "green"
assert result["Exposure.Time"][1] == 20
assert result["Exposure.Id"][2] == 3
assert result["Exposure.Channel"][2] == "blue"
assert result["Exposure.Time"][2] == 50
def test_process_folder_with_exposure_info(example_dir):
from sensovation_data_parser import _process_folder
result = _process_folder(example_dir / EXAMPLE_DIR_WITH_PARAMS)
assert len(result) == 36 * 100 * 3
expected = [(1, "green", 100), (2, "red", 150), (3, "red", 15)]
for exposure_id, channel, time in expected:
mask = result["Exposure.Id"] == exposure_id
example_row = result.loc[mask].iloc[1]
assert example_row["Exposure.Channel"] == channel
assert example_row["Exposure.Time"] == time
def test_process_folder_without_exposure_info(example_dir):
from sensovation_data_parser import _process_folder
from pandas import isnull
result = _process_folder(example_dir / EXAMPLE_DIR_WO_PARAMS)
assert len(result) == 96 * 100 * 3
for exposure_id in range(1, 4):
mask = result["Exposure.Id"] == exposure_id
example_row = result.loc[mask].iloc[1]
print(type(example_row["Exposure.Channel"]))
assert isnull(example_row["Exposure.Channel"])
assert isnull(example_row["Exposure.Time"])
def test_process_folder_creates_cache(dir_for_caching):
from sensovation_data_parser import (
process_folder,
CACHE_FILE_NAME,
)
cache_path = dir_for_caching / CACHE_FILE_NAME
assert not cache_path.is_file()
result = process_folder(dir_for_caching)
assert len(result) == 100
assert cache_path.is_file()
def test_process_folder_reads_from_cache(dir_for_caching, example_file):
from sensovation_data_parser import process_folder
process_folder(dir_for_caching)
csv_file = dir_for_caching / example_file.name
csv_file.unlink()
result = process_folder(dir_for_caching)
assert len(result) == 100
def test_process_folder_read_cache_fails_silently(
dir_for_caching, exposure_df
):
from sensovation_data_parser import (
process_folder,
CACHE_FILE_NAME,
)
cache_path = dir_for_caching / CACHE_FILE_NAME
exposure_df.to_hdf(cache_path, "unknown table")
result = process_folder(dir_for_caching)
assert result["Field.Row"][0] == "A"
def test_process_folder_read_cache_no_cache_arg(dir_for_caching, exposure_df):
from sensovation_data_parser import (
process_folder,
CACHE_FILE_NAME,
CACHE_TABLE_NAME,
)
cache_path = dir_for_caching / CACHE_FILE_NAME
exposure_df.to_hdf(cache_path, CACHE_TABLE_NAME)
result = process_folder(dir_for_caching, use_cache=False)
assert result["Field.Row"][0] == "A"
def test_process_folder_writes_cache(dir_for_caching):
from sensovation_data_parser import (
process_folder,
CACHE_FILE_NAME,
)
process_folder(dir_for_caching, use_cache=True)
cache_path = dir_for_caching / CACHE_FILE_NAME
assert cache_path.is_file()
def test_process_folder_writes_cache_no_cache_arg(dir_for_caching):
from sensovation_data_parser import process_folder, CACHE_FILE_NAME
process_folder(dir_for_caching, use_cache=False)
cache_path = dir_for_caching / CACHE_FILE_NAME
assert not cache_path.is_file()
def test_import_api():
from sensovation_data_parser import ExposureInfo # noqa: F401
from sensovation_data_parser import parse_file # noqa: F401
from sensovation_data_parser import parse_multiple_files # noqa: F401
from sensovation_data_parser import parse_folder # noqa: F401
from sensovation_data_parser import process_folder # noqa: F401

Write Preview
Loading…
Cancel
Save