ruff

Author: antgonza

.github/workflows/qiita-plugin-ci.yml

@@ -156,7 +156,7 @@ jobs:
   lint:
     runs-on: ubuntu-latest
     steps:
-      - name: flake8
+      - name: ruff
         uses: actions/setup-python@v2
         with:
           python-version: 3.9
@@ -166,5 +166,5 @@ jobs:
         uses: actions/checkout@v2
       - name: lint
         run: |
-          pip install -q flake8
-          flake8 .
+          pip install -q ruff
+          ruff check .

README.md

@@ -14,7 +14,7 @@ git clone https://github.com/biocore/mg-scripts.git
 Create a Python3 Conda environment in which to run the notebook:
 
 ```bash
-conda create --yes -n spp python='python=3.9' scikit-learn pandas numpy nose pep8 flake8 matplotlib jupyter notebook 'seaborn>=0.7.1' pip openpyxl 'seqtk>=1.4' click scipy fastq-pair
+conda create --yes -n spp python='python=3.9' scikit-learn pandas numpy nose ruff matplotlib jupyter notebook 'seaborn>=0.7.1' pip openpyxl 'seqtk>=1.4' click scipy fastq-pair
 ```
 
 Activate the Conda environment:

pyproject.toml

@@ -43,7 +43,7 @@ dependencies = [
     "pandas",
     "lxml",
     'requests',
-    'flake8',
+    'ruff',
     'nose',
     'coverage',
     'pgzip',

src/sequence_processing_pipeline/contrib/create_picklist.py

@@ -1,72 +1,87 @@
 import os
+
 # from metapool.metapool import *
 from sys import argv
-import pandas as pd
+
 import matplotlib.pyplot as plt
-from metapool.metapool import (read_survival, make_2D_array,
-                               calculate_iseqnorm_pooling_volumes,
-                               format_pooling_echo_pick_list)
+import pandas as pd
 import seaborn as sns
+from metapool.metapool import (
+    calculate_iseqnorm_pooling_volumes,
+    format_pooling_echo_pick_list,
+    make_2D_array,
+    read_survival,
+)
 
 input_sheet_filename = argv[1]
 
-plate_df_w_reads = pd.read_csv(input_sheet_filename, sep='\t')
-plate_df_w_reads['Blank'] = [True if 'blank' in s.lower() else False
-                             for s in plate_df_w_reads['Sample_Name']]
-reads_column = 'read_counts'
+plate_df_w_reads = pd.read_csv(input_sheet_filename, sep="\t")
+plate_df_w_reads["Blank"] = [
+    True if "blank" in s.lower() else False for s in plate_df_w_reads["Sample_Name"]
+]
+reads_column = "read_counts"
 
-well_col = 'Sample_Well'
+well_col = "Sample_Well"
 assert reads_column in plate_df_w_reads.columns
 
-f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(8, 8))
+_, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(8, 8))
 # evenness plot
 rmax = int(round(plate_df_w_reads[reads_column].max(), -2))
 
-foo = read_survival(plate_df_w_reads.loc[plate_df_w_reads['Blank'] is True,
-                                         reads_column],
-                    label='Blanks',
-                    rmax=rmax)
+foo = read_survival(
+    plate_df_w_reads.loc[plate_df_w_reads["Blank"] is True, reads_column],
+    label="Blanks",
+    rmax=rmax,
+)
 
-bar = read_survival(plate_df_w_reads.loc[plate_df_w_reads['Blank'] is False,
-                                         reads_column],
-                    label='Samples',
-                    rmax=rmax)
+bar = read_survival(
+    plate_df_w_reads.loc[plate_df_w_reads["Blank"] is False, reads_column],
+    label="Samples",
+    rmax=rmax,
+)
 
 survival_df = pd.concat([foo, bar])
 
 ax3.set_xlabel(reads_column)
-ax3.set_ylabel('Samples')
-survival_df.plot(color=['coral', 'steelblue'], ax=ax1)
+ax3.set_ylabel("Samples")
+survival_df.plot(color=["coral", "steelblue"], ax=ax1)
 ax1.set_xlabel(reads_column)
-ax1.set_ylabel('Samples')
+ax1.set_ylabel("Samples")
 
 # Histogram
 sns.histplot(plate_df_w_reads[reads_column], ax=ax3)
 
 # Boxplot
 sns.boxplot(x="Blank", y=reads_column, data=plate_df_w_reads, ax=ax4)
-sns.stripplot(x="Blank", y=reads_column, data=plate_df_w_reads, ax=ax4,
-              size=3, color='black', alpha=0.5)
+sns.stripplot(
+    x="Blank",
+    y=reads_column,
+    data=plate_df_w_reads,
+    ax=ax4,
+    size=3,
+    color="black",
+    alpha=0.5,
+)
 
 plt.tight_layout()
-plt.savefig(input_sheet_filename + '.comboplot.pdf')
+plt.savefig(input_sheet_filename + ".comboplot.pdf")
 
-pdfn = calculate_iseqnorm_pooling_volumes(plate_df_w_reads,
-                                          dynamic_range=20,
-                                          normalization_column=reads_column)
-plt.savefig(input_sheet_filename + '.normalizedplot.pdf')
+pdfn = calculate_iseqnorm_pooling_volumes(
+    plate_df_w_reads, dynamic_range=20, normalization_column=reads_column
+)
+plt.savefig(input_sheet_filename + ".normalizedplot.pdf")
 
-vols = make_2D_array(pdfn,
-                     data_col='iSeq normpool volume',
-                     well_col=well_col).astype(float)
+vols = make_2D_array(pdfn, data_col="iSeq normpool volume", well_col=well_col).astype(
+    float
+)
 
 # Write the picklist as .csv
-picklist_fp = input_sheet_filename + '.picklist.csv'
+picklist_fp = input_sheet_filename + ".picklist.csv"
 
 if os.path.isfile(picklist_fp):
     print("Warning! This file exists already.")
 
 picklist = format_pooling_echo_pick_list(vols, max_vol_per_well=30000)
 
-with open(picklist_fp, 'w') as f:
+with open(picklist_fp, "w") as f:
     f.write(picklist)