Fixed some linting warnings

Author: meson800

docs/index.rst

@@ -13,4 +13,4 @@
    Tutorials <tutorial/index>
    API <api/rushd.rst>
 
-* :ref:`search`
+* :ref:`search`

docs/tutorial/ddpcr/index.rst

@@ -5,5 +5,5 @@ Droplet digital PCR (ddPCR)
 .. toctree::
    :maxdepth: 1
    :glob:
-   
-   *
+
+   *

docs/tutorial/flow/index.rst

@@ -5,5 +5,5 @@ Flow cytometry
 .. toctree::
    :maxdepth: 1
    :glob:
-   
-   *
+
+   *

docs/tutorial/index.rst

@@ -4,11 +4,11 @@ Tutorials
 .. toctree::
    :maxdepth: 2
    :glob:
-   
+
    overview/index
    flow/index
    qpcr/index
    ddpcr/index
    plotting/index
 
-   */index
+   */index

docs/tutorial/overview/index.rst

@@ -5,5 +5,5 @@ Overview of ``rushd``
 .. toctree::
    :maxdepth: 1
    :glob:
-   
-   *
+
+   *

docs/tutorial/plot/index.rst

@@ -5,5 +5,5 @@ Plotting
 .. toctree::
    :maxdepth: 1
    :glob:
-   
-   *
+
+   *

docs/tutorial/qpcr/index.rst

@@ -5,5 +5,5 @@ qPCR
 .. toctree::
    :maxdepth: 1
    :glob:
-   
-   *
+
+   *

src/rushd/ddpcr.py

@@ -5,30 +5,24 @@
 Allows users to specify custom metadata applied via well mapping.
 """
 
+import json
 import re
+import shutil
+import tempfile
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, Optional, Union
 
-# Support Python 3.7 by importing Literal from typing_extensions
-try:
-    from typing import Literal  # type: ignore
-except ImportError:
-    from typing_extensions import Literal
-
-import json
 import numpy as np
 import pandas as pd
 import py7zr
-import shutil
-import tempfile
-import yaml
 
-from . import well_mapper, flow
+from . import flow
 
 
 class YamlError(RuntimeError):
     """Error raised when there is an issue with the provided .yaml file."""
 
+
 class DataPathError(RuntimeError):
     """Error raised when the path to the data is not specified correctly."""
 
@@ -38,10 +32,10 @@ def load_ddpcr_metadata(unzipped_path: Path) -> Dict[Any, Any]:
     Load well metadata from an unzipped .ddpcr file.
 
     Generates a metadata dict in the same format as the YAML well mapping,
-    i.e., key -> {well -> value}. The columns are a subset of the 
-    metadata associated with each well in the BioRad software, namely 
-    sample names (numbered 'Sample description' fields, returned as 
-    numbered 'sample_description' keys) and targets for each channel/dye 
+    i.e., key -> {well -> value}. The columns are a subset of the
+    metadata associated with each well in the BioRad software, namely
+    sample names (numbered 'Sample description' fields, returned as
+    numbered 'sample_description' keys) and targets for each channel/dye
     (returned as '[channel]_target' keys).
 
     Parameters
@@ -54,36 +48,39 @@ def load_ddpcr_metadata(unzipped_path: Path) -> Dict[Any, Any]:
     A dictionary that contains a well mapping for metadata extracted from
     the .ddpcr experiment.
     """
-
     filename_regex = r"^.*[\\/](?P<well>[A-P]\d+)\.dd.*json"
-        
-    # Create map of well index -> ID 
+
+    # Create map of well index -> ID
     well_id_map = {}
-    for f in (unzipped_path/'PeakMetaData').glob("*.ddmetajson"):
-        with open(f, 'r') as file:
+    for f in (unzipped_path / "PeakMetaData").glob("*.ddmetajson"):
+        with open(f, "r") as file:
             d = json.load(file)
-            well_id_map[d['WellIndex']] = re.compile(filename_regex).match(file.name).group("well")
-    
+            well_id_map[d["WellIndex"]] = re.compile(filename_regex).match(file.name).group("well")
+
     # Get plate file name from last modified .ddplt file
-    plate_file = ''
+    plate_file = ""
     last_mod_time = 0
     for f in unzipped_path.glob("*.ddplt"):
         mtime = f.stat().st_mtime
         if mtime > last_mod_time:
             last_mod_time = mtime
             plate_file = f.name
-    
+
     # Load metadata from plate file
     metadata_from_plt = {}
-    with open(unzipped_path/plate_file, 'r') as file:
+    with open(unzipped_path / plate_file, "r") as file:
         f = json.load(file)
-        for w in f['WellSamples']:
-            well = well_id_map[w['WellIndex']]
-            condition_map = {f'sample_description_{i+1}': val for i,val in enumerate(w['SampleIds'])}
-            target_map = {p['Dye']['DyeName']+'_target': p['TargetName'] for p in w['Panel']['Targets']}
+        for w in f["WellSamples"]:
+            well = well_id_map[w["WellIndex"]]
+            condition_map = {
+                f"sample_description_{i+1}": val for i, val in enumerate(w["SampleIds"])
+            }
+            target_map = {
+                p["Dye"]["DyeName"] + "_target": p["TargetName"] for p in w["Panel"]["Targets"]
+            }
             metadata_from_plt[well] = condition_map | target_map
-    
-    metadata_map = pd.DataFrame.from_dict(metadata_from_plt, orient='index').to_dict()
+
+    metadata_map = pd.DataFrame.from_dict(metadata_from_plt, orient="index").to_dict()
     return metadata_map
 
 
@@ -98,7 +95,7 @@ def load_ddpcr(
 
     Generates a pandas DataFrame from a .ddpcr file, which is the
     file type for experiments on the BioRad QX100/QX200 machines.
-    Adds columns for metadata encoded by a given .yaml file. 
+    Adds columns for metadata encoded by a given .yaml file.
     Metadata is associated with the data based on well IDs extracted
     from the experiment data.
 
@@ -111,9 +108,9 @@ def load_ddpcr(
         All metadata must be contained under the header 'metadata'.
     extract_metadata: bool, default True
         Whether to extract metadata from the .ddpcr file. If True,
-        adds a subset of the metadata associated with each well in the 
+        adds a subset of the metadata associated with each well in the
         BioRad software, namely sample names (numbered 'Sample description' fields,
-        returned as numbered 'condition' keys) and targets for each channel/dye 
+        returned as numbered 'condition' keys) and targets for each channel/dye
         (returned as '[channel]_target' keys).
 
     Returns
@@ -123,12 +120,14 @@ def load_ddpcr(
     if not isinstance(data_path, Path):
         data_path = Path(data_path)
 
-    if data_path.suffix != '.ddpcr':
+    if data_path.suffix != ".ddpcr":
         raise DataPathError("'data_path' must be a .ddpcr file.")
-    
+
     # Unzip .ddpcr file
     tmp_data_path = Path(tempfile.mkdtemp())
-    with py7zr.SevenZipFile(data_path, 'r', password='1b53402e-503a-4303-bf86-71af1f3178dd') as experiment:
+    with py7zr.SevenZipFile(
+        data_path, "r", password="1b53402e-503a-4303-bf86-71af1f3178dd"
+    ) as experiment:
         experiment.extractall(path=tmp_data_path)
 
     metadata_map = {}
@@ -139,26 +138,29 @@ def load_ddpcr(
             metadata_map = flow.load_well_metadata(yaml_path)
         except FileNotFoundError as err:
             raise YamlError("Specified metadata YAML file does not exist!") from err
-    
+
     # Load metadata from .ddpcr file
     if extract_metadata:
         metadata_map = metadata_map | load_ddpcr_metadata(tmp_data_path)
 
     # Load data for each well
     data_list = []
-    for f in (tmp_data_path/'PeakData').glob("*.ddpeakjson"):
-        with open(f, 'r') as file:
+    for f in (tmp_data_path / "PeakData").glob("*.ddpeakjson"):
+        with open(f, "r") as file:
             d = json.load(file)
 
             # Ignore wells for which no data was collected
-            if not d["DataAcquisitionInfo"]['WasAcquired']: continue
+            if not d["DataAcquisitionInfo"]["WasAcquired"]:
+                continue
 
             # Extract raw data (channel amplitude) and channel names
-            channel_map = {c['Channel']-1: c['Dye'] for c in d["DataAcquisitionInfo"]['ChannelMap']}
-            df = pd.DataFrame(np.transpose(d['PeakInfo']['Amplitudes'])).rename(columns=channel_map)
+            channel_map = {
+                c["Channel"] - 1: c["Dye"] for c in d["DataAcquisitionInfo"]["ChannelMap"]
+            }
+            df = pd.DataFrame(np.transpose(d["PeakInfo"]["Amplitudes"])).rename(columns=channel_map)
 
             well = f.stem
-            df.insert(0, 'well', [well]*len(df))
+            df.insert(0, "well", [well] * len(df))
 
             # Add metadata to DataFrame
             index = 0
@@ -170,10 +172,13 @@ def load_ddpcr(
             data_list.append(df)
 
     # Fill empty values with <NA> and drop empty columns
-    data = pd.concat(data_list, ignore_index=True).replace([float('nan'), np.nan, ''], pd.NA).dropna(axis='columns', how='all')
+    data = (
+        pd.concat(data_list, ignore_index=True)
+        .replace([float("nan"), np.nan, ""], pd.NA)
+        .dropna(axis="columns", how="all")
+    )
 
     # Delete unzipped files
     shutil.rmtree(tmp_data_path)
-    
-    return data
 
+    return data

src/rushd/flow.py

@@ -175,7 +175,7 @@ def load_csv_with_metadata(
     if len(data_list) == 0:
         raise RegexError(f"No data files match the regular expression '{filename_regex}'")
     else:
-        data = pd.concat(data_list, ignore_index=True).replace([float('nan'),np.nan], pd.NA)  # type: ignore
+        data = pd.concat(data_list, ignore_index=True).replace([float("nan"), np.nan], pd.NA)  # type: ignore
 
     return data
 
@@ -243,7 +243,9 @@ def load_groups_with_metadata(
         yaml_path = base_path / Path(group["yaml_path"])
         if "filename_regex" in groups_df.columns:
             filename_regex = group["filename_regex"]
-        group_data = load_csv_with_metadata(data_path, yaml_path, filename_regex, columns=columns, csv_kwargs=csv_kwargs)
+        group_data = load_csv_with_metadata(
+            data_path, yaml_path, filename_regex, columns=columns, csv_kwargs=csv_kwargs
+        )
 
         # Add associated metadata (not paths)
         for k, v in group.items():
@@ -253,7 +255,7 @@ def load_groups_with_metadata(
         group_list.append(group_data)
 
     # Concatenate all the data into a single DataFrame
-    data = pd.concat(group_list, ignore_index=True).replace([float('nan'),np.nan], pd.NA)
+    data = pd.concat(group_list, ignore_index=True).replace([float("nan"), np.nan], pd.NA)
     return data
 
 
@@ -327,7 +329,7 @@ def load_csv(
     if len(data_list) == 0:
         raise RegexError(f"No data files match the regular expression '{filename_regex}'")
     else:
-        data = pd.concat(data_list, ignore_index=True).replace([float('nan'),np.nan], pd.NA)  # type: ignore
+        data = pd.concat(data_list, ignore_index=True).replace([float("nan"), np.nan], pd.NA)  # type: ignore
 
     return data