Remove restriction pylint==2.4.4 (#147)

* Removed pylint==2.4.4 version pin * Fixed linter warnings Co-authored-by: Gianluca Ficarelli <26835404+GianlucaFicarelli@users.noreply.github.com>
BlueBrain · Oct 5, 2021 · 6ba92b2 · 6ba92b2
1 parent f152dfa
commit 6ba92b2
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Showing 15 changed files with 114 additions and 122 deletions.
diff --git a/bluepysnap/_plotting.py b/bluepysnap/_plotting.py
@@ -31,12 +31,11 @@ def _get_pyplot():
     try:
         import matplotlib.pyplot as plt
     except ImportError as e:
-        msg = (
+        raise ImportError(
             "Bluepysnap requirements are not installed.\n"
             "Please pip install as follows:\n"
             "  pip install bluepysnap[plots] --upgrade"
-        )
-        raise ImportError(str(e) + "\n\n" + msg)
+        ) from e
     return plt
 
 
@@ -60,7 +59,7 @@ def spikes_firing_rate_histogram(filtered_report, time_binsize=None, ax=None):
     # pylint: disable=too-many-locals
     plt = _get_pyplot()
     if time_binsize is not None and time_binsize <= 0:
-        raise BluepySnapError("Invalid time_binsize = {}. Should be > 0.".format(time_binsize))
+        raise BluepySnapError(f"Invalid time_binsize = {time_binsize}. Should be > 0.")
 
     spike_report = filtered_report.spike_report
 
@@ -69,7 +68,7 @@ def spikes_firing_rate_histogram(filtered_report, time_binsize=None, ax=None):
 
     if len(times) == 0:
         raise BluepySnapError("No data to display. You should check your "
-                              "'group' query: {}.".format(spike_report.group))
+                              f"'group' query: {spike_report.group}.")
 
     time_start = np.min(times)
     time_stop = np.max(times)
@@ -174,7 +173,7 @@ def _update_raster_properties():
                 ax.set_yticklabels(labels)
                 if len(labels) > 1:
                     ax.set_ylim(-0.5, len(labels) - 0.5)
-            ax.set_ylabel("{}".format(y_axis))
+            ax.set_ylabel(y_axis)
 
     ax.scatter(data.index.to_numpy(), data.to_numpy(), s=10, marker='|')
     if len(props["pop_separators"]) > 1:
@@ -204,14 +203,14 @@ def spikes_isi(filtered_report, use_frequency=False, binsize=None, ax=None):  #
     """
     plt = _get_pyplot()
     if binsize is not None and binsize <= 0:
-        raise BluepySnapError("Invalid binsize = {}. Should be > 0.".format(binsize))
+        raise BluepySnapError(f"Invalid binsize = {binsize}. Should be > 0.")
 
     gb = filtered_report.report.groupby(["ids", "population"])
     values = np.concatenate([np.diff(node_spikes.index.to_numpy()) for _, node_spikes in gb])
 
     if len(values) == 0:
         raise BluepySnapError("No data to display. You should check your "
-                              "'group' query: {}.".format(filtered_report.spike_report.group))
+                              f"'group' query: {filtered_report.spike_report.group}.")
     if use_frequency:
         values = values[values > 0]  # filter out zero intervals
         values = 1000.0 / values
@@ -275,7 +274,7 @@ def _check_axis(axis):
         """Verifies axes values."""
         axes = {Node.X, Node.Y, Node.Z}
         if axis not in axes:
-            raise BluepySnapError('{} is not a valid axis'.format(axis))
+            raise BluepySnapError(f'{axis} is not a valid axis')
 
     _check_axis(x_axis)
     _check_axis(y_axis)
@@ -302,13 +301,13 @@ def _check_axis(axis):
     if ax is None:
         fig = plt.figure()
         ax = plt.gca()
-        ax.set_title('time = {}ms'.format(np.min(data.index)))
+        ax.set_title(f'time = {np.min(data.index)}ms')
         x_limits = [data[x_axis].min(), data[x_axis].max()]
         y_limits = [data[y_axis].min(), data[y_axis].max()]
         ax.set_xlim(*x_limits)
         ax.set_ylim(*y_limits)
-        ax.set_xlabel(r'{} $\mu$m'.format(x_axis))  # noqa
-        ax.set_ylabel(r'{} $\mu$m'.format(y_axis))  # noqa
+        ax.set_xlabel(rf'{x_axis} $\mu$m')  # noqa
+        ax.set_ylabel(rf'{y_axis} $\mu$m')  # noqa
 
     else:
         fig = ax.figure
@@ -349,10 +348,10 @@ def frame_trace(filtered_report, plot_type='mean', ax=None):  # pragma: no cover
         ax = plt.gca()
         data_units = filtered_report.frame_report.data_units
         if plot_type == "mean":
-            ax.set_ylabel('Avg volt. [{}]'.format(data_units))
+            ax.set_ylabel(f'Avg volt. [{data_units}]')
         elif plot_type == "all":
-            ax.set_ylabel('Voltage [{}]'.format(data_units))
-        ax.set_xlabel("Time [{}]".format(filtered_report.frame_report.time_units))
+            ax.set_ylabel(f'Voltage [{data_units}]')
+        ax.set_xlabel(f"Time [{filtered_report.frame_report.time_units}]")
         ax.set_xlim([filtered_report.report.index.min(), filtered_report.report.index.max()])
 
     if plot_type == "mean":
@@ -374,5 +373,5 @@ def frame_trace(filtered_report, plot_type='mean', ax=None):  # pragma: no cover
         for _, row in data.loc[kept_ids].iterrows():
             ax.plot(row)
     else:
-        raise BluepySnapError("Unknown plot_type {}. Should be 'mean or 'all'.".format(plot_type))
+        raise BluepySnapError(f"Unknown plot_type {plot_type}. Should be 'mean or 'all'.")
     return ax
diff --git a/bluepysnap/circuit_ids.py b/bluepysnap/circuit_ids.py
@@ -80,7 +80,7 @@ def from_arrays(cls, populations, population_ids, sort_index=True):
 
         if len(populations) != len(population_ids):
             raise BluepySnapError("populations and population_ids must have the same size. "
-                                  "{} != {}".format(len(populations), len(population_ids)))
+                                  f"{len(populations)} != {len(population_ids)}")
 
         index = pd.MultiIndex.from_arrays([populations, population_ids])
         return cls(index, sort_index=sort_index)