Merge pull request NeuroML#383 from NeuroML/feat/ruff-fixes

chore: linter fixes

pyneuroml/__init__.py

@@ -39,8 +39,8 @@ def print_v(msg):
 except KeyError:
     java_max_memory = "400M"
 
-DEFAULTS = {
+DEFAULTS: typing.Dict[str, typing.Any] = {
     "v": False,
     "default_java_max_memory": java_max_memory,
     "nogui": False,
-}  # type: dict[str, typing.Any]
+}

pyneuroml/analysis/ChannelDensityPlot.py

@@ -4,6 +4,7 @@
 #   ion channel densities in NeuroML2 cells
 #
 
+import argparse
 import logging
 import math
 import os
@@ -12,9 +13,9 @@
 from collections import OrderedDict
 
 import matplotlib
-from matplotlib.colors import LinearSegmentedColormap
 import matplotlib.pyplot as plt
 import numpy
+from matplotlib.colors import LinearSegmentedColormap
 from neuroml import (
     Cell,
     Cell2CaPools,
@@ -29,13 +30,13 @@
     ChannelDensityVShift,
     VariableParameter,
 )
+from sympy import sympify
+
 from pyneuroml.plot.Plot import generate_plot
 from pyneuroml.plot.PlotMorphology import plot_2D_cell_morphology
 from pyneuroml.pynml import get_value_in_si, read_neuroml2_file
 from pyneuroml.utils import get_ion_color
 from pyneuroml.utils.cli import build_namespace
-from sympy import sympify
-import argparse
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
@@ -495,7 +496,7 @@ def get_conductance_density_for_segments(
 
     # filter to seg_ids
     if seg_ids is not None:
-        if type(seg_ids) == str:
+        if isinstance(seg_ids, str):
             segments = [seg_ids]
         else:
             segments = list(set(seg_ids) & set(segments))
@@ -510,7 +511,7 @@ def get_conductance_density_for_segments(
                     data[seg.id] = value
     else:
         # get the inhomogeneous param/value from the channel density
-        param = channel_density.variable_parameters[0]  # type: VariableParameter
+        param: VariableParameter = channel_density.variable_parameters[0]
         inhom_val = param.inhomogeneous_value.value
         # H(x) -> Heaviside(x, 0)
         if "H" in inhom_val:
@@ -631,7 +632,7 @@ def plot_channel_densities(
         }
 
     if channel_density_ids is not None:
-        if type(channel_density_ids) == str:
+        if isinstance(channel_density_ids, str):
             channel_density_ids_list = []
             channel_density_ids_list.append(channel_density_ids)
             channel_density_ids = channel_density_ids_list
@@ -725,7 +726,7 @@ def plot_channel_densities(
                 plt.close()
 
     elif ion_channels is not None:
-        if type(ion_channels) == str:
+        if isinstance(ion_channels, str):
             ion_channel_list = []
             ion_channel_list.append(ion_channels)
             ion_channels = ion_channel_list

pyneuroml/analysis/NML2ChannelAnalysis.py

@@ -584,7 +584,7 @@ def plot_channel(channel, a, results, iv_data=None, grid=True):
 
 
 def plot_kinetics(channel, a, results, grid=True):
-    fig = plt.figure()
+    plt.figure()
     plt.get_current_fig_manager().set_window_title(
         ("Time course(s) of activation variables of " "%s from %s at %s degC")
         % (channel.id, channel.file, a.temperature)
@@ -627,7 +627,7 @@ def plot_kinetics(channel, a, results, grid=True):
 
 
 def plot_steady_state(channel, a, results, grid=True):
-    fig = plt.figure()
+    plt.figure()
     plt.get_current_fig_manager().set_window_title(
         ("Steady state(s) of activation variables of " "%s from %s at %s degC")
         % (channel.id, channel.file, a.temperature)
@@ -748,7 +748,7 @@ def plot_iv_curves(channel, a, iv_data, grid=True):
 
 def plot_iv_curve_vm(channel, a, hold_v, times, currents, grid=True):
     # Holding potentials
-    fig = plt.figure()
+    plt.figure()
     ax = plt.subplot(111)
     plt.get_current_fig_manager().set_window_title(
         ("Currents through voltage clamp for %s " "from %s at %s degC, erev: %s V")
@@ -775,7 +775,7 @@ def plot_iv_curve_vm(channel, a, hold_v, times, currents, grid=True):
 
 
 def make_iv_curve_fig(a, grid=True):
-    fig = plt.figure()
+    plt.figure()
     plt.get_current_fig_manager().set_window_title(
         "Currents vs. holding potentials at erev = %s V" % a.erev
     )
@@ -794,7 +794,7 @@ def plot_iv_curve(a, hold_v, i, *plt_args, **plt_kwargs):
         plt_kwargs["label"] = "Current"
     if not same_fig:
         make_iv_curve_fig(a, grid=grid)
-    if type(i) is dict:
+    if isinstance(i, dict):
         i = [i[v] for v in hold_v]
     plt.plot(
         [v * 1e3 for v in hold_v], [ii * 1e12 for ii in i], *plt_args, **plt_kwargs

pyneuroml/biosimulations.py

@@ -77,7 +77,7 @@ def get_simulator_versions(
     :returns: json response from API
     :rtype: str
     """
-    if type(simulators) is str:
+    if isinstance(simulators, str):
         simulators = [simulators]
     all_siminfo = {}  # type: typing.Dict[str, typing.List[str]]
     for sim in simulators:

pyneuroml/neuron/__init__.py

@@ -6,14 +6,15 @@
 
 """
 
-import os
-import logging
-import warnings
-import typing
-import pathlib
 import json
+import logging
 import math
+import os
+import pathlib
 import pprint
+import typing
+import warnings
+
 import airspeed
 import yaml
 
@@ -22,15 +23,15 @@
 except ImportError:
     from yaml import Dumper
 
-from pyneuroml.pynml import validate_neuroml1
-from pyneuroml.pynml import validate_neuroml2
+from pyneuroml.pynml import validate_neuroml1, validate_neuroml2
 
 pp = pprint.PrettyPrinter(depth=4)
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
 try:
     from neuron import h
+
     from pyneuroml.neuron.nrn_export_utils import set_erev_for_mechanism
 except ImportError:
     logger.warning("Please install optional dependencies to use NEURON features:")
@@ -468,8 +469,8 @@ def getinfo(seclist: list, doprint: str = "", listall: bool = False):
                         ms.name(pname, j)
                         totParamVal[j] += ms.get(pname)
                         if (
-                            not replace_brackets(str(sec))
-                            in paramsectiondict[rm_NML_str(pname[0])].keys()
+                            replace_brackets(str(sec))
+                            not in paramsectiondict[rm_NML_str(pname[0])].keys()
                         ):
                             paramsectiondict[rm_NML_str(pname[0])][
                                 replace_brackets(str(sec))

pyneuroml/plot/PlotMorphology.py

@@ -7,19 +7,20 @@
 Copyright 2023 NeuroML contributors
 """
 
-
 import argparse
 import logging
 import os
 import random
 import sys
 import typing
+from typing import Optional
 
 import matplotlib
 import numpy
 from matplotlib import pyplot as plt
-from neuroml import Cell, NeuroMLDocument, Segment, SegmentGroup
+from neuroml import Cell, NeuroMLDocument, SegmentGroup
 from neuroml.neuro_lex_ids import neuro_lex_ids
+
 from pyneuroml.pynml import read_neuroml2_file
 from pyneuroml.utils import extract_position_info
 from pyneuroml.utils.cli import build_namespace
@@ -34,7 +35,6 @@
     get_next_hex_color,
     load_minimal_morphplottable__model,
 )
-from typing import Optional
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)

pyneuroml/plot/PlotMorphologyVispy.py

@@ -13,11 +13,14 @@
 import math
 import random
 import typing
+from typing import Optional
 
 import numpy
 import progressbar
-from neuroml import Cell, NeuroMLDocument, SegmentGroup, Segment
+from neuroml import Cell, NeuroMLDocument, SegmentGroup
 from neuroml.neuro_lex_ids import neuro_lex_ids
+from scipy.spatial.transform import Rotation
+
 from pyneuroml.pynml import read_neuroml2_file
 from pyneuroml.utils import extract_position_info
 from pyneuroml.utils.plot import (
@@ -26,8 +29,6 @@
     get_next_hex_color,
     load_minimal_morphplottable__model,
 )
-from scipy.spatial.transform import Rotation
-from typing import Optional
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)

pyneuroml/povray/NeuroML2ToPOVRay.py

@@ -8,13 +8,12 @@
 This work has been funded by the Medical Research Council and Wellcome Trust
 """
 
-
-import typing
-import random
 import argparse
 import logging
+import random
 
 import neuroml
+
 from pyneuroml import pynml
 
 logger = logging.getLogger(__name__)
@@ -478,9 +477,9 @@ def generate_povray(
             else:
                 parent = int(segment.parent.segments)
                 proximalpoint = distpoints[parent]
-                cell_id_vs_seg_id_vs_proximal[cell.id][
-                    id
-                ] = cell_id_vs_seg_id_vs_distal[cell.id][parent]
+                cell_id_vs_seg_id_vs_proximal[cell.id][id] = (
+                    cell_id_vs_seg_id_vs_distal[cell.id][parent]
+                )
 
             proxpoints[id] = proximalpoint
 

pyneuroml/tune/NeuroMLTuner.py

@@ -15,31 +15,30 @@
 Please see the output of `pynml-tune -h` for more information on `pynml-tune`.
 """
 
-from __future__ import unicode_literals
-from __future__ import annotations
-import os
-import os.path
-import time
-import re
-from collections import OrderedDict
+from __future__ import annotations, unicode_literals
+
 import argparse
 import logging
+import os
+import os.path
 import pprint
+import time
 import typing
+from collections import OrderedDict
 
 from matplotlib import pyplot as plt
-from pyneuroml.utils.cli import build_namespace
+
 from pyneuroml import print_v
+from pyneuroml.utils.cli import build_namespace
 
 pp = pprint.PrettyPrinter(indent=4)
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
 
 try:
-    from neurotune import optimizers
-    from neurotune import evaluators
-    from neurotune import utils
+    from neurotune import evaluators, optimizers, utils
+
     from pyneuroml.tune.NeuroMLController import NeuroMLController
 except ImportError:
     logger.warning("Please install optional dependencies to use neurotune features:")

pyneuroml/utils/plot.py

@@ -18,7 +18,7 @@
 from matplotlib.lines import Line2D
 from matplotlib.patches import Rectangle
 from matplotlib_scalebar.scalebar import ScaleBar
-from neuroml import Cell, NeuroMLDocument, Segment
+from neuroml import Cell, NeuroMLDocument
 from neuroml.loaders import read_neuroml2_file
 
 logger = logging.getLogger(__name__)