Remove pylab imports

caiman/base/movies.py

@@ -13,11 +13,11 @@
 import ipywidgets as widgets
 import logging
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import os
 import pathlib
 import pims
-import pylab as pl
 import scipy
 import skimage
 import sklearn
@@ -893,8 +893,8 @@ def local_correlations(self,
                                             order_mean=order_mean)
                 Cn = np.maximum(Cn, rho)
                 if do_plot:
-                    pl.imshow(Cn, cmap='gray')
-                    pl.pause(.1)
+                    plt.imshow(Cn, cmap='gray')
+                    plt.pause(.1)
 
             logging.debug('number of chunks:' + str(n_chunks - 1) + ' frames: ' +
                           str([(n_chunks - 1) * frames_per_chunk, T]))
@@ -904,8 +904,8 @@ def local_correlations(self,
                                         order_mean=order_mean)
             Cn = np.maximum(Cn, rho)
             if do_plot:
-                pl.imshow(Cn, cmap='gray')
-                pl.pause(.1)
+                plt.imshow(Cn, cmap='gray')
+                plt.pause(.1)
 
         return Cn
 
@@ -1117,7 +1117,7 @@ def to_2D(self, order='F') -> np.ndarray:
         T = self.shape[0]
         return np.reshape(self, (T, -1), order=order)
 
-    def zproject(self, method: str = 'mean', cmap=pl.cm.gray, aspect='auto', **kwargs) -> np.ndarray:
+    def zproject(self, method: str = 'mean', cmap=matplotlib.cm.gray, aspect='auto', **kwargs) -> np.ndarray:
         """
         Compute and plot projection across time:
 
@@ -1141,7 +1141,7 @@ def zproject(self, method: str = 'mean', cmap=pl.cm.gray, aspect='auto', **kwarg
             zp = np.std(self, axis=0)
         else:
             raise Exception('Method not implemented')
-        pl.imshow(zp, cmap=cmap, aspect=aspect, **kwargs)
+        plt.imshow(zp, cmap=cmap, aspect=aspect, **kwargs)
         return zp
 
     def play(self,
@@ -1175,7 +1175,7 @@ def play(self,
             interpolation:
                 interpolation method for 'opencv' and 'embed_opencv' backends
 
-            backend: 'pylab', 'notebook', 'opencv' or 'embed_opencv'; the latter 2 are much faster
+            backend: 'opencv', 'embed_opencv', 'pyplot', 'notebook': the first two are much faster
 
             do_loop: Whether to loop the video
 
@@ -2363,7 +2363,7 @@ def play_movie(movie,
         interpolation:
             interpolation method for 'opencv' and 'embed_opencv' backends
 
-        backend: 'pylab', 'notebook', 'opencv' or 'embed_opencv'; the latter 2 are much faster
+        backend: 'opencv', 'embed_opencv', 'pyplot', or 'notebook': the first two are much faster
 
         do_loop: Whether to loop the video
 
@@ -2399,8 +2399,8 @@ def play_movie(movie,
     """
     # todo: todocument
     it = True if (isinstance(movie, list) or isinstance(movie, tuple) or isinstance(movie, str)) else False
-    if backend == 'pylab':
-        logging.warning('*** Using pylab. This might be slow. If you can use the opencv backend it may be faster')
+    if backend == 'pyplot':
+        logging.warning('Using pyplot back end: not recommended. Using opencv will yield faster, higher-quality results.')
 
     gain = float(gain)     # convert to float in case we were passed an int
     if q_max < 100:
@@ -2444,26 +2444,26 @@ def process_frame(iddxx, frame, bord_px, magnification, interpolation, minmov, m
                         thickness=1)
         return frame
 
-    if backend == 'pylab':
-        pl.ion()
-        fig = pl.figure(1)
+    if backend == 'pyplot':
+        plt.ion()
+        fig = plt.figure(1)
         ax = fig.add_subplot(111)
         ax.set_title("Play Movie")
         im = ax.imshow((offset + (load(movie, subindices=slice(0,2), var_name_hdf5=var_name_hdf5) if it else movie)[0] - minmov) * gain / (maxmov - minmov + offset),
-                       cmap=pl.cm.gray,
+                       cmap=plt.cm.gray,
                        vmin=0,
                        vmax=1,
-                       interpolation='none')                                            # Blank starting image
+                       interpolation='none')  # Blank starting image
         fig.show()
         im.axes.figure.canvas.draw()
-        pl.pause(1)
+        plt.pause(1)
 
     elif backend == 'notebook':
         # First set up the figure, the axis, and the plot element we want to animate
-        fig = pl.figure()
-        im = pl.imshow(next(load_iter(movie, subindices=slice(0,1), var_name_hdf5=var_name_hdf5))\
-                    if it else movie[0], interpolation='None', cmap=pl.cm.gray)
-        pl.axis('off')
+        fig = plt.figure()
+        im = plt.imshow(next(load_iter(movie, subindices=slice(0,1), var_name_hdf5=var_name_hdf5))\
+                    if it else movie[0], interpolation='None', cmap=matplotlib.cm.gray)
+        plt.axis('off')
 
         if it:
             m_iter = load_iter(movie, subindices, var_name_hdf5)
@@ -2508,7 +2508,7 @@ def view(button):
                     frame_sum = 0
                     display_handle.update(Image(data=cv2.imencode(
                             '.jpg', np.clip((frame * 255.), 0, 255).astype('u1'))[1].tobytes()))
-                    pl.pause(1. / fr)
+                    plt.pause(1. / fr)
                 if stopButton.value==True:
                     break
         display(stopButton)
@@ -2556,17 +2556,17 @@ def view(button):
                 elif backend == 'embed_opencv' and not save_movie:
                     break
 
-                elif backend == 'pylab':
+                elif backend == 'pyplot':
                     if bord_px is not None and np.sum(bord_px) > 0:
                         frame = frame[bord_px:-bord_px, bord_px:-bord_px]
                     im.set_data((offset + frame) * gain / maxmov)
                     ax.set_title(str(iddxx))
-                    pl.axis('off')
+                    plt.axis('off')
                     fig.canvas.draw()
-                    pl.pause(1. / fr * .5)
-                    ev = pl.waitforbuttonpress(1. / fr * .5)
+                    plt.pause(1. / fr * .5)
+                    ev = plt.waitforbuttonpress(1. / fr * .5)
                     if ev is not None:
-                        pl.close()
+                        plt.close()
                         break
 
                 elif backend == 'notebook':

caiman/base/timeseries.py

@@ -9,9 +9,9 @@
 from dateutil.tz import tzlocal
 import h5py
 import logging
+import matplotlib.pyplot as plt
 import numpy as np
 import os
-import pylab as plt
 from pynwb import NWBHDF5IO, NWBFile
 from pynwb.ophys import TwoPhotonSeries, OpticalChannel
 from pynwb.device import Device

caiman/base/traces.py

@@ -3,8 +3,9 @@
 import cv2
 import logging
 import numpy as np
-import pylab as pl
-pl.ion()
+import matplotlib
+import matplotlib.pyplot as plt
+plt.ion()
 
 import caiman.base.timeseries
 
@@ -80,7 +81,7 @@ def computeDFF(self, window_sec=5, minQuantile=20):
     def resample(self, fx=1, fy=1, fz=1, interpolation=cv2.INTER_AREA):
         raise Exception('Not Implemented. Look at movie resize')
 
-    def plot(self, stacked=True, subtract_minimum=False, cmap=pl.cm.jet, **kwargs):
+    def plot(self, stacked=True, subtract_minimum=False, cmap=matplotlib.cm.jet, **kwargs):
         """Plot the data
 
         author: ben deverett
@@ -91,7 +92,7 @@ def plot(self, stacked=True, subtract_minimum=False, cmap=pl.cm.jet, **kwargs):
             subtract_minimum : bool
                 subtract minimum from each individual trace
             cmap : matplotlib.LinearSegmentedColormap
-                color map for display. Options are found in pl.colormaps(), and are accessed as pl.cm.my_favourite_map
+                color map for display. Options are found in matplotlib.colormaps(), and are accessed as matplotlib.cm.my_favourite_map
             kwargs : dict
                 any arguments accepted by matplotlib.plot
 
@@ -104,7 +105,7 @@ def plot(self, stacked=True, subtract_minimum=False, cmap=pl.cm.jet, **kwargs):
         if len(d.shape) > 1:
             n = d.shape[1]
 
-        ax = pl.gca()
+        ax = plt.gca()
 
         colors = cmap(np.linspace(0, 1, n))
         ax.set_color_cycle(colors)
@@ -122,7 +123,7 @@ def plot(self, stacked=True, subtract_minimum=False, cmap=pl.cm.jet, **kwargs):
         ax2.set_yticklabels([str(i) for i in range(n)], weight='bold')
         [l.set_color(c) for l, c in zip(ax2.get_yticklabels(), colors)]
 
-        pl.gcf().canvas.draw()
+        plt.gcf().canvas.draw()
         return ax
 
     def extract_epochs(self, trigs=None, tb=1, ta=1):

caiman/behavior/behavior.py

@@ -5,8 +5,9 @@
 """
 
 import cv2
+import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
-import pylab as pl
 import scipy
 from scipy.sparse import coo_matrix
 from scipy.io import loadmat
@@ -38,14 +39,14 @@ def select_roi(img: np.ndarray, n_rois: int = 1) -> list:
 
     masks = []
     for _ in range(n_rois):
-        fig = pl.figure()
-        pl.imshow(img, cmap=pl.cm.gray)
+        fig = plt.figure()
+        plt.imshow(img, cmap=matplotlib.cm.gray)
         pts = fig.ginput(0, timeout=0)
         mask = np.zeros(np.shape(img), dtype=np.int32)
         pts = np.asarray(pts, dtype=np.int32)
         cv2.fillConvexPoly(mask, pts, (1, 1, 1), lineType=cv2.LINE_AA)
         masks.append(mask)
-        pl.close()
+        plt.close()
 
     return masks
 
@@ -353,12 +354,12 @@ def extract_components(mov_tot,
 
 def plot_components(sp_filt, t_trace) -> None:
     # todo: todocument
-    pl.figure()
+    plt.figure()
     count = 0
     for comp, tr in zip(sp_filt, t_trace):
         count += 1
-        pl.subplot(6, 2, count)
-        pl.imshow(comp)
+        plt.subplot(6, 2, count)
+        plt.imshow(comp)
         count += 1
-        pl.subplot(6, 2, count)
-        pl.plot(tr)
+        plt.subplot(6, 2, count)
+        plt.plot(tr)

caiman/motion_correction.py

@@ -41,11 +41,11 @@
 import gc
 import itertools
 import logging
+import matplotlib.pyplot as plt
 import numpy as np
 from numpy.fft import ifftshift
 import os
 import sys
-import pylab as pl
 import tifffile
 from typing import Optional
 from skimage.transform import resize as resize_sk
@@ -362,8 +362,8 @@ def motion_correct_pwrigid(self, save_movie:bool=True, template:np.ndarray=None,
                     indices=self.indices)
             if not self.is3D:
                 if show_template:
-                    pl.imshow(new_template_els)
-                    pl.pause(.5)
+                    plt.imshow(new_template_els)
+                    plt.pause(.5)  #TODO: figure out if pausing half-second is necessary
             if np.isnan(np.sum(new_template_els)):
                 raise Exception(
                     'Template contains NaNs, something went wrong. Reconsider the parameters')
@@ -877,10 +877,10 @@ def motion_correct_online(movie_iterable, add_to_movie, max_shift_w=25, max_shif
                     template = np.median(buffer_templates, 0)
 
                 if show_template:
-                    pl.cla()
-                    pl.imshow(template, cmap='gray', vmin=250,
+                    plt.cla()
+                    plt.imshow(template, cmap='gray', vmin=250,
                               vmax=350, interpolation='none')
-                    pl.pause(.001)
+                    plt.pause(.001)
 
                 logging.debug('Relative change in template:' + str(
                     np.sum(np.abs(template - template_old)) / np.sum(np.abs(template))))
@@ -2687,23 +2687,23 @@ def compute_metrics_motion_correction(fname, final_size_x, final_size_y, swap_di
                 cv2.putText(vid_frame, 'x_flow', (2 * dims[0] + 10, 20), fontFace=5, fontScale=0.8, color=(
                     0, 255, 0), thickness=1)
                 cv2.imshow('frame', vid_frame)
-                pl.pause(1 / fr_play)
+                cv2.waitKey(1 / fr_play) # to pause between frames
                 if cv2.waitKey(1) & 0xFF == ord('q'):
                     break
             else:
-                pl.subplot(1, 3, 1)
-                pl.cla()
-                pl.imshow(fr, vmin=m_min, vmax=m_max, cmap='gray')
-                pl.title('movie')
-                pl.subplot(1, 3, 3)
-                pl.cla()
-                pl.imshow(flow[:, :, 1], vmin=vmin, vmax=vmax)
-                pl.title('y_flow')
-                pl.subplot(1, 3, 2)
-                pl.cla()
-                pl.imshow(flow[:, :, 0], vmin=vmin, vmax=vmax)
-                pl.title('x_flow')
-                pl.pause(1 / fr_play)
+                plt.subplot(1, 3, 1)
+                plt.cla()
+                plt.imshow(fr, vmin=m_min, vmax=m_max, cmap='gray')
+                plt.title('movie')
+                plt.subplot(1, 3, 3)
+                plt.cla()
+                plt.imshow(flow[:, :, 1], vmin=vmin, vmax=vmax)
+                plt.title('y_flow')
+                plt.subplot(1, 3, 2)
+                plt.cla()
+                plt.imshow(flow[:, :, 0], vmin=vmin, vmax=vmax)
+                plt.title('x_flow')
+                plt.pause(1 / fr_play)
 
         n = np.linalg.norm(flow)
         flows.append(flow)

caiman/source_extraction/cnmf/utilities.py

@@ -13,7 +13,7 @@
 import numpy as np
 import os
 import pathlib
-import pylab as pl
+import matplotlib.pyplot as plt
 import scipy
 from scipy.sparse import spdiags, issparse, csc_matrix, csr_matrix
 import scipy.ndimage as ndi
@@ -749,7 +749,7 @@ def manually_refine_components(Y, xxx_todo_changeme, A, C, Cn, thr=0.9, display_
 
     x, y = np.mgrid[0:d1:1, 0:d2:1]
 
-    pl.imshow(Cn, interpolation=None, cmap=cmap)
+    plt.imshow(Cn, interpolation=None, cmap=cmap)
     cm = caiman.base.rois.com(A, d1, d2)
 
     Bmat = np.zeros((np.minimum(nr, max_number), d1, d2))
@@ -763,13 +763,13 @@ def manually_refine_components(Y, xxx_todo_changeme, A, C, Cn, thr=0.9, display_
 
     T = np.shape(Y)[-1]
 
-    pl.close()
-    fig = pl.figure()
-    ax = pl.gca()
+    plt.close()
+    fig = plt.figure()
+    ax = plt.gca()
     ax.imshow(Cn, interpolation=None, cmap=cmap,
               vmin=np.percentile(Cn[~np.isnan(Cn)], 1), vmax=np.percentile(Cn[~np.isnan(Cn)], 99))
     for i in range(np.minimum(nr, max_number)):
-        pl.contour(y, x, Bmat[i], [thr])
+        plt.contour(y, x, Bmat[i], [thr])
 
     if display_numbers:
         for i in range(np.minimum(nr, max_number)):
@@ -812,8 +812,8 @@ def manually_refine_components(Y, xxx_todo_changeme, A, C, Cn, thr=0.9, display_
             Bvec = np.zeros(d)
             Bvec[indx] = cumEn
             bmat = np.reshape(Bvec, np.shape(Cn), order='F')
-            pl.contour(y, x, bmat, [thr])
-            pl.pause(.01)
+            plt.contour(y, x, bmat, [thr])
+            plt.pause(.01)
 
         elif pts == []:
             break