[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

peakdet/_version.py

@@ -84,7 +84,7 @@ def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=
                 stderr=(subprocess.PIPE if hide_stderr else None),
             )
             break
-        except EnvironmentError:
+        except OSError:
             e = sys.exc_info()[1]
             if e.errno == errno.ENOENT:
                 continue
@@ -147,7 +147,7 @@ def git_get_keywords(versionfile_abs):
     # _version.py.
     keywords = {}
     try:
-        f = open(versionfile_abs, "r")
+        f = open(versionfile_abs)
         for line in f.readlines():
             if line.strip().startswith("git_refnames ="):
                 mo = re.search(r'=\s*"(.*)"', line)
@@ -162,7 +162,7 @@ def git_get_keywords(versionfile_abs):
                 if mo:
                     keywords["date"] = mo.group(1)
         f.close()
-    except EnvironmentError:
+    except OSError:
         pass
     return keywords
 

peakdet/analytics.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 Functions and classes for generating analytics on physiological data
 """

peakdet/cli/run.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 import argparse
 import datetime
 import glob
@@ -53,7 +52,7 @@ def get_parser():
     )
 
     inp_group = parser.add_argument_group(
-        "Inputs", "Options to specify " "format of input files"
+        "Inputs", "Options to specify format of input files"
     )
     inp_group.add_argument(
         "--modality",
@@ -83,7 +82,7 @@ def get_parser():
     )
 
     out_group = parser.add_argument_group(
-        "Outputs", "Options to specify " "format of output files"
+        "Outputs", "Options to specify format of output files"
     )
     out_group.add_argument(
         "-o",
@@ -104,12 +103,12 @@ def get_parser():
         "-s",
         "--savehistory",
         action="store_true",
-        help="Whether to save history of data processing " "for each file.",
+        help="Whether to save history of data processing for each file.",
     )
 
     edit_group = parser.add_argument_group(
         "Workflow arguments (optional!)",
-        "Options to specify modifications " "to workflow",
+        "Options to specify modifications to workflow",
     )
     edit_group.add_argument(
         "-n",
@@ -255,9 +254,9 @@ def workflow(
         )
 
     # output file
-    logger.info("OUTPUT FILE:\t\t{}".format(output))
+    logger.info(f"OUTPUT FILE:\t\t{output}")
     # grab files from file template
-    logger.info("FILE TEMPLATE:\t{}".format(file_template))
+    logger.info(f"FILE TEMPLATE:\t{file_template}")
     files = glob.glob(file_template, recursive=True)
 
     # convert measurements to peakdet.HRV attribute friendly names
@@ -277,7 +276,7 @@ def workflow(
     # check if output file exists -- if so, ensure headers will match
     head = "filename," + ",".join(measurements)
     if os.path.exists(output):
-        with open(output, "r") as src:
+        with open(output) as src:
             eheader = src.readlines()[0]
         # if existing output file does not have same measurements are those
         # requested on command line, warn and use existing measurements so
@@ -300,7 +299,7 @@ def workflow(
         # iterate through all files and do peak detection with manual editing
         for fname in files:
             fname = os.path.relpath(fname)
-            logger.info("Currently processing {}".format(fname))
+            logger.info(f"Currently processing {fname}")
 
             # if we want to save history, this is the output name it would take
             outname = os.path.join(

peakdet/editor.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """Functions and class for performing interactive editing of physiological data."""
 
 import functools

peakdet/external.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 Functions for interacting with physiological data acquired by external packages
 """
@@ -37,18 +36,17 @@ def load_rtpeaks(fname, channel, fs):
     data : :class:`peakdet.Physio`
         Loaded physiological data
     """
-
     if fname.startswith("/"):
         logger.warning(
             "Provided file seems to be an absolute path. In order "
             "to ensure full reproducibility it is recommended that "
             "a relative path is provided."
         )
 
-    with open(fname, "r") as src:
+    with open(fname) as src:
         header = src.readline().strip().split(",")
 
-    col = header.index("channel{}".format(channel))
+    col = header.index(f"channel{channel}")
     data = np.loadtxt(fname, usecols=col, skiprows=1, delimiter=",")
     phys = physio.Physio(data, fs=fs)
 

peakdet/io.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 Functions for loading and saving data and analyses
 """
@@ -42,7 +41,6 @@ def load_physio(data, *, fs=None, dtype=None, history=None, allow_pickle=False):
     TypeError
         If provided `data` is unable to be loaded
     """
-
     # first check if the file was made with `save_physio`; otherwise, try to
     # load it as a plain text file and instantiate a history
     if isinstance(data, str):
@@ -53,13 +51,13 @@ def load_physio(data, *, fs=None, dtype=None, history=None, allow_pickle=False):
                     inp[attr] = inp[attr].dtype.type(inp[attr])
                 except KeyError:
                     raise ValueError(
-                        "Provided npz file {} must have all of "
-                        "the following attributes: {}".format(data, EXPECTED)
+                        f"Provided npz file {data} must have all of "
+                        f"the following attributes: {EXPECTED}"
                     )
             # fix history, which needs to be list-of-tuple
             if inp["history"] is not None:
                 inp["history"] = list(map(tuple, inp["history"]))
-        except (IOError, OSError, ValueError):
+        except (OSError, ValueError):
             inp = dict(data=np.loadtxt(data), history=[utils._get_call(exclude=[])])
         logger.debug("Instantiating Physio object from a file")
         phys = physio.Physio(**inp)
@@ -81,14 +79,14 @@ def load_physio(data, *, fs=None, dtype=None, history=None, allow_pickle=False):
         phys = utils.new_physio_like(data, data.data, fs=fs, dtype=dtype)
         phys._history += [utils._get_call()]
     else:
-        raise TypeError("Cannot load data of type {}".format(type(data)))
+        raise TypeError(f"Cannot load data of type {type(data)}")
 
     # reset sampling rate, as requested
     if fs is not None and fs != phys.fs:
         if not np.isnan(phys.fs):
             logger.warning(
                 "Provided sampling rate does not match loaded rate. "
-                "Resetting loaded sampling rate {} to provided {}".format(phys.fs, fs)
+                f"Resetting loaded sampling rate {phys.fs} to provided {fs}"
             )
         phys._fs = fs
     # coerce datatype, if needed
@@ -114,7 +112,6 @@ def save_physio(fname, data):
     fname : str
         Full filepath to saved output
     """
-
     from peakdet.utils import check_physio
 
     data = check_physio(data)
@@ -145,21 +142,20 @@ def load_history(file, verbose=False):
     file : str
         Full filepath to saved output
     """
-
     # import inside function for safety!
     # we'll likely be replaying some functions from within this module...
     import peakdet
 
     # grab history from provided JSON file
-    with open(file, "r") as src:
+    with open(file) as src:
         history = json.load(src)
 
     # replay history from beginning and return resultant Physio object
     logger.info(f"Replaying history from {file}")
     data = None
     for func, kwargs in history:
         if verbose:
-            logger.info("Rerunning {}".format(func))
+            logger.info(f"Rerunning {func}")
         # loading functions don't have `data` input because it should be the
         # first thing in `history` (when the data was originally loaded!).
         # for safety, check if `data` is None; someone could have potentially
@@ -205,7 +201,6 @@ def save_history(file, data):
     file : str
         Full filepath to saved output
     """
-
     from peakdet.utils import check_physio
 
     data = check_physio(data)

peakdet/modalities.py

@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 import numpy as np
 
 

peakdet/operations.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 Functions for processing and interpreting physiological data
 """
@@ -35,28 +34,25 @@ def filter_physio(data, cutoffs, method, *, order=3):
     filtered : :class:`peakdet.Physio`
         Filtered input `data`
     """
-
     _valid_methods = ["lowpass", "highpass", "bandpass", "bandstop"]
 
     data = utils.check_physio(data, ensure_fs=True)
     if method not in _valid_methods:
         raise ValueError(
-            "Provided method {} is not permitted; must be in {}.".format(
-                method, _valid_methods
-            )
+            f"Provided method {method} is not permitted; must be in {_valid_methods}."
         )
 
     cutoffs = np.array(cutoffs)
     if method in ["lowpass", "highpass"] and cutoffs.size != 1:
-        raise ValueError("Cutoffs must be length 1 when using {} filter".format(method))
+        raise ValueError(f"Cutoffs must be length 1 when using {method} filter")
     elif method in ["bandpass", "bandstop"] and cutoffs.size != 2:
-        raise ValueError("Cutoffs must be length 2 when using {} filter".format(method))
+        raise ValueError(f"Cutoffs must be length 2 when using {method} filter")
 
     nyq_cutoff = cutoffs / (data.fs * 0.5)
     if np.any(nyq_cutoff > 1):
         raise ValueError(
-            "Provided cutoffs {} are outside of the Nyquist "
-            "frequency for input data with sampling rate {}.".format(cutoffs, data.fs)
+            f"Provided cutoffs {cutoffs} are outside of the Nyquist "
+            f"frequency for input data with sampling rate {data.fs}."
         )
 
     if method in ["lowpass", "highpass"]:
@@ -94,7 +90,6 @@ def interpolate_physio(data, target_fs, *, kind="cubic"):
     interp : :class:`peakdet.Physio`
         Interpolated input `data`
     """
-
     data = utils.check_physio(data, ensure_fs=True)
 
     factor = target_fs / data.fs
@@ -139,7 +134,6 @@ def peakfind_physio(data, *, thresh=0.2, dist=None):
     peaks : :class:`peakdet.Physio`
         Input `data` with detected peaks and troughs
     """
-
     ensure_fs = True if dist is None else False
     data = utils.check_physio(data, ensure_fs=ensure_fs, copy=True)
     # first pass peak detection to get approximate distance between peaks
@@ -252,7 +246,6 @@ def edit_physio(data):
     edited : :class:`peakdet.Physio`
         Input `data` with manual edits
     """
-
     data = utils.check_physio(data, ensure_fs=True)
 
     # no point in manual edits if peaks/troughs aren't defined

peakdet/physio.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 Helper class for holding physiological data and associated metadata information
 """
@@ -45,27 +44,21 @@ def __init__(self, data, fs=None, history=None, metadata=None, suppdata=None):
         logger.debug("Initializing new Physio object")
         self._data = np.asarray(data).squeeze()
         if self.data.ndim > 1:
-            raise ValueError(
-                "Provided data dimensionality {} > 1.".format(self.data.ndim)
-            )
+            raise ValueError(f"Provided data dimensionality {self.data.ndim} > 1.")
         if not np.issubdtype(self.data.dtype, np.number):
-            raise ValueError(
-                "Provided data of type {} is not numeric.".format(self.data.dtype)
-            )
+            raise ValueError(f"Provided data of type {self.data.dtype} is not numeric.")
         self._fs = np.float64(fs)
         self._history = [] if history is None else history
         if not isinstance(self._history, list) or any(
             [not isinstance(f, tuple) for f in self._history]
         ):
             raise TypeError(
-                "Provided history {} must be a list-of-tuples. "
-                "Please check inputs.".format(history)
+                f"Provided history {history} must be a list-of-tuples. "
+                "Please check inputs."
             )
         if metadata is not None:
             if not isinstance(metadata, dict):
-                raise TypeError(
-                    "Provided metadata {} must be dict-like.".format(metadata)
-                )
+                raise TypeError(f"Provided metadata {metadata} must be dict-like.")
             for k in ["peaks", "troughs", "reject"]:
                 metadata.setdefault(k, np.empty(0, dtype=int))
                 if not isinstance(metadata.get(k), np.ndarray):
@@ -95,9 +88,7 @@ def __len__(self):
         return len(self.data)
 
     def __str__(self):
-        return "{name}(size={size}, fs={fs})".format(
-            name=self.__class__.__name__, size=self.data.size, fs=self.fs
-        )
+        return f"{self.__class__.__name__}(size={self.data.size}, fs={self.fs})"
 
     __repr__ = __str__
 

peakdet/tests/test_analytics.py

@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 from peakdet import analytics
 from peakdet.tests.utils import get_peak_data