Merge pull request #13 from ryanharvey1/remove-warning-suppressors

Remove warning suppressors in submodules

neuro_py/ensemble/explained_variance.py

@@ -1,5 +1,3 @@
-import warnings
-
 import numpy as np
 
 from nelpy.core._eventarray import SpikeTrainArray
@@ -8,10 +6,6 @@
 from numba import jit
 
 
-warnings.simplefilter(action="ignore", category=FutureWarning)
-warnings.simplefilter(action="ignore", category=RuntimeWarning)
-
-
 class ExplainedVariance(object):
     """Explained variance measure for assessing reactivation of neuronal activity using pairwise correlations.
 

neuro_py/io/loading.py

@@ -6,7 +6,6 @@
 import warnings
 from itertools import chain
 from typing import List, Union
-from warnings import simplefilter
 from xml.dom import minidom
 
 import nelpy as nel
@@ -20,8 +19,6 @@
 from neuro_py.process.intervals import find_interval, in_intervals
 from neuro_py.process.peri_event import get_participation
 
-simplefilter(action="ignore", category=pd.errors.PerformanceWarning)
-
 
 def loadXML(basepath: str):
     """

neuro_py/plotting/events.py

@@ -9,6 +9,7 @@
 
 from neuro_py.stats.stats import confidence_intervals
 
+
 def plot_events(events, labels, cmap="tab20", gridlines=True, alpha=0.75, ax=None):
     """
     events: nested list of nelpy EpochArrays

neuro_py/spikes/spike_tools.py

@@ -1,4 +1,3 @@
-
 from typing import Union
 
 import numpy as np

neuro_py/tuning/maps.py

@@ -13,8 +13,6 @@
 from neuro_py.stats.stats import get_significant_events
 from neuro_py.tuning import fields
 
-np.seterr(divide="ignore", invalid="ignore")
-
 
 class SpatialMap(object):
     """
@@ -212,10 +210,8 @@ def compute_ratemap_1d(
                     ),
                     bins=self.x_edges,
                 )
-            # divide by occupancy
-            ratemap = ratemap / occupancy
 
-        # if data to map is analog signal (continous)
+        # if data to map is analog signal (continuous)
         elif isinstance(st_run, nel.core._analogsignalarray.AnalogSignalArray):
             # get x location for every bin center
             x = np.interp(
@@ -228,8 +224,9 @@ def compute_ratemap_1d(
                 ratemap[:, bidx - 1] += st_run.data[:, tt]
             # divide by sampling rate
             ratemap = ratemap * st_run.fs
-            # divide by occupancy
-            ratemap = ratemap / occupancy
+
+        # divide by occupancy
+        np.divide(ratemap, occupancy, where=occupancy != 0, out=ratemap)
 
         # remove nans and infs
         bad_idx = np.isnan(ratemap) | np.isinf(ratemap)
@@ -323,7 +320,6 @@ def compute_ratemap_2d(
                     np.interp(st_run.data[i], pos_run.abscissa_vals, pos_run.data[1, :]),
                     bins=(self.x_edges, self.y_edges),
                 )
-            ratemap = ratemap / occupancy
 
         elif isinstance(st_run, nel.core._analogsignalarray.AnalogSignalArray):
             x = np.interp(
@@ -337,7 +333,8 @@ def compute_ratemap_2d(
             for tt, (bidxx, bidxy) in enumerate(zip(ext_bin_idx_x, ext_bin_idx_y)):
                 ratemap[:, bidxx - 1, bidxy - 1] += st_run.data[:, tt]
             ratemap = ratemap * st_run.fs
-            ratemap = ratemap / occupancy
+
+        np.divide(ratemap, occupancy, where=occupancy != 0, out=ratemap)
 
         bad_idx = np.isnan(ratemap) | np.isinf(ratemap)
         ratemap[bad_idx] = 0