Modernize Python code with ruff, CI, pre-commit

.github/workflows/python-formatting.yml

@@ -0,0 +1,10 @@
+name: check format using ruff
+on: [push]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: chartboost/ruff-action@v1
+        with:
+          args: format --check

.github/workflows/python-linting.yml

@@ -0,0 +1,8 @@
+name: lint code using ruff
+on: [push]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: chartboost/ruff-action@v1

.pre-commit-config.yaml

@@ -0,0 +1,9 @@
+repos:
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  # Ruff version.
+  rev: v0.5.1
+  hooks:
+    # Run the linter.
+    - id: ruff
+    # Run the formatter.
+    - id: ruff-format

calc_fluence_dist.py

@@ -6,26 +6,27 @@
 N_SAMP = 6
 
 
-def get_fluences(filename='ACE_hourly_avg.npy'):
+def get_fluences(filename="ACE_hourly_avg.npy"):
     """
-    Get P3 cumulative fluence values at 1 hour intervals for all data in the
-    ACE mission since 1997, using 1-hr average P3 values.  Compute a fluence
-    prediction starting each 12 hours extending for 48 hours.  Store each
-    48-point fluence prediction along with the index into the global ``BINS``
-    array corresponding to the starting P3 value.
+    Get P3 cumulative fluence values at 1 hour intervals.
+
+    This returns fluences for all data in the ACE mission since 1997, using 1-hr average
+    P3 values.  Compute a fluence prediction starting each 12 hours extending for 48
+    hours.  Store each 48-point fluence prediction along with the index into the global
+    ``BINS`` array corresponding to the starting P3 value.
     """
     dat = np.load(filename)
 
     # Remove bad data points
-    ok = dat['p3'] > 1
+    ok = dat["p3"] > 1
     dat = dat[ok]
-    p3s = dat['p3']
+    p3s = dat["p3"]
 
     # Compute data times in hours
-    hrs = (dat['fp_year'] - 1997.0) * 24 * 365.25
+    hrs = (dat["fp_year"] - 1997.0) * 24 * 365.25
 
     i0s = np.arange(0, len(p3s) - N_T, N_SAMP)
-    p3_samps = np.vstack([p3s[i:i + N_T] for i in i0s])
+    p3_samps = np.vstack([p3s[i : i + N_T] for i in i0s])
     d_hrs = np.array([hrs[i + N_T] - hrs[i] - N_T for i in i0s])
     ok = np.abs(d_hrs) < 0.15
     p3_samps = p3_samps[ok]
@@ -36,8 +37,15 @@ def get_fluences(filename='ACE_hourly_avg.npy'):
     return p_fits.T, p3_samps, fluences
 
 
-def get_fluence_percentiles(p3_avg_now, p3_slope_now, p3_fits, p3_samps, fluences,
-                            min_flux_samples, max_slope_samples):
+def get_fluence_percentiles(
+    p3_avg_now,
+    p3_slope_now,
+    p3_fits,
+    p3_samps,
+    fluences,
+    min_flux_samples,
+    max_slope_samples,
+):
     """
     Compute the 10%, 50%, and 90% fluence time histories within each P3 bin.
     """
@@ -64,11 +72,12 @@ def get_fluence_percentiles(p3_avg_now, p3_slope_now, p3_fits, p3_samps, fluence
     return hrs, fl10, fl50, fl90
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     p3_fits, p3_samps, fluences = get_fluences()
-    print('p3_avg_now', end=' ')
+    print("p3_avg_now", end=" ")
     p3_avg_now = float(input())
-    print('p3_slope_now', end=' ')
+    print("p3_slope_now", end=" ")
     p3_slope_now = float(input())
     hrs, fl10, fl50, fl90 = get_fluence_percentiles(
-        p3_avg_now, p3_slope_now, p3_fits, p3_samps, fluences)
+        p3_avg_now, p3_slope_now, p3_fits, p3_samps, fluences
+    )

get_ace.py

@@ -1,25 +1,26 @@
 #!/usr/bin/env python
 
-import sys
-import urllib.request, urllib.error, urllib.parse
 import argparse
-import tables
+import sys
 import time
+import urllib.error
+import urllib.parse
+import urllib.request
 
 import numpy as np
+import tables
 from astropy.io import ascii
 from Chandra.Time import DateTime
 
-parser = argparse.ArgumentParser(description='Get ACE data')
-parser.add_argument('--h5',
-                    default='ACE.h5',
-                    help='HDF5 file name')
+parser = argparse.ArgumentParser(description="Get ACE data")
+parser.add_argument("--h5", default="ACE.h5", help="HDF5 file name")
 args = parser.parse_args()
 
-url = 'ftp://ftp.swpc.noaa.gov/pub/lists/ace/ace_epam_5m.txt'
+url = "ftp://ftp.swpc.noaa.gov/pub/lists/ace/ace_epam_5m.txt"
 
-colnames = ('year month dom  hhmm  mjd secs p1  p2  p3 '
-            'p4  p5  p6  p7  p8  p9 p10 p11').split()
+colnames = (
+    "year month dom  hhmm  mjd secs p1  p2  p3 p4  p5  p6  p7  p8  p9 p10 p11"
+).split()
 
 last_err = None
 for _ in range(3):
@@ -31,47 +32,48 @@
         last_err = err
         time.sleep(5)
 else:
-    print('Warning: failed to open URL {}: {}'.format(url, last_err))
+    print("Warning: failed to open URL {}: {}".format(url, last_err))
     sys.exit(0)
 
-colnames = ('year month dom  hhmm  mjd secs '
-            'destat de1 de4 pstat p1 p3 p5 p6 p7 anis_idx').split()
-data_colnames = ('destat de1 de4 pstat p1 p3 p5 p6 p7').split()
+colnames = (
+    "year month dom  hhmm  mjd secs destat de1 de4 pstat p1 p3 p5 p6 p7 anis_idx"
+).split()
+data_colnames = ("destat de1 de4 pstat p1 p3 p5 p6 p7").split()
 
 try:
-    dat = ascii.read(urldat, guess=False, format="no_header",
-                          data_start=3, names=colnames)
+    dat = ascii.read(
+        urldat, guess=False, format="no_header", data_start=3, names=colnames
+    )
 except Exception as err:
-    print(('Warning: malformed ACE data so table read failed: {}'
-          .format(err)))
+    print(("Warning: malformed ACE data so table read failed: {}".format(err)))
     sys.exit(0)
 
 # Strip up to two rows at the end if any values are bad (i.e. negative)
 for _ in range(2):
     if any(dat[name][-1] < 0 for name in data_colnames):
         dat = dat[:-1]
 
-mjd = dat['mjd'] + dat['secs'] / 86400.
+mjd = dat["mjd"] + dat["secs"] / 86400.0
 
-secs = DateTime(mjd, format='mjd').secs
+secs = DateTime(mjd, format="mjd").secs
 
 descrs = dat.dtype.descr
-descrs.append(('time', 'f8'))
+descrs.append(("time", "f8"))
 newdat = np.ndarray(len(dat), dtype=descrs)
 for colname in colnames:
     newdat[colname] = dat[colname]
-newdat['time'] = secs
+newdat["time"] = secs
 
-h5 = tables.open_file(args.h5, mode='a',
-                     filters=tables.Filters(complevel=5, complib='zlib'))
+h5 = tables.open_file(
+    args.h5, mode="a", filters=tables.Filters(complevel=5, complib="zlib")
+)
 try:
     table = h5.root.data
-    lasttime = table.col('time')[-1]
-    ok = newdat['time'] > lasttime
+    lasttime = table.col("time")[-1]
+    ok = newdat["time"] > lasttime
     newdat = newdat[ok]
     h5.root.data.append(newdat)
 except tables.NoSuchNodeError:
-    table = h5.create_table(h5.root, 'data', newdat,
-                           "ACE rates", expectedrows=2e7)
+    table = h5.create_table(h5.root, "data", newdat, "ACE rates", expectedrows=2e7)
 h5.root.data.flush()
 h5.close()