Added matplotlib to data_table.plot_all() function. Set it as default

DataModule/data_grid.py

@@ -288,7 +288,7 @@ class data_grid(data_module_base):
         df = self.df[self.x_min:self.x_max, self.y_min:self.y_max]
         df.plot.pcolormesh(self.name_x, self.name_y)
 
-    def imshow(self, colormap='magma', levels=256, zlabel_pos="right",
+    def imshow(self, colormap='magma', zlabel_pos="right",
                labelpad=0, cbar_y=0.5, **kwargs):
         """Color plot using matplotlib
 
@@ -314,9 +314,6 @@ class data_grid(data_module_base):
             Position and orientation of zlabel next to colorbar. If location is
             wrong, play with labelpad and cbar_y
         """
-        # Get colormap from matplotlib
-        #cmap = plt.cm.get_cmap(colormap, levels)
-
         # Just get select range
         df = self.df[self.x_min:self.x_max, self.y_min:self.y_max]
 

DataModule/data_table.py

@@ -12,12 +12,14 @@ from .plot_style import color_scheme, cc, check_color, color_labels
 import holoviews as hv
 import holoviews.operation.datashader as hd
 import numpy as np
-try:
-    import matplotlib.pyplot as plt
-except NotImplementedError:
-    pass
+
+# Plotting
+import matplotlib
+import matplotlib.pyplot as plt
 import bokeh.plotting as bp
 from bokeh.models import HoverTool
+
+# Data analysis
 import scipy.optimize as sp_opt
 import scipy.signal as sp_sig
 import scipy.interpolate as sp_intp
@@ -460,27 +462,74 @@ class data_table(data_module_base):
         elif engine in ['holoviews', 'h', 'hv']:
             return self.plot_hv(color=color, **kwargs)
 
-    def plot_all(self, colormap=None):
-        """Fast plot of the whole data in holoviews"""
+    def plot_all(self, engine='p', colormap=None, legend=True, **kwargs):
+        """Plot all columns
 
-        # Get default colormap
-        if colormap is None:
-            colormap = color_scheme
+        Parameters
+        ------------
+        engine : 'p', 'h'
+            Plot engine. Currently implemented are pyplot and holoviews
+        colormap : None, 'magma', 'viridis', ['#123456', '#aabbcc']
+            Colormap for plotting. None will pick a default colormap. 
+            You can specify names of default palettes 'magma', 'viridis', etc
+            or give an own array of hex colors.
+        legend : bool
+            Plot legend
+        **kwargs
+            Additional keywords for matplotlib. Like linestyle='-', etc...
+        """
+
+        # Get number of plots
+        n_plots = len(self.df.keys()) - 1
+
+        # Get colormap
+        if isinstance(colormap, str):
+            # Get colormap from default library by name
+            tmp = plt.cm.get_cmap(colormap, n_plots)
+            colormap = [matplotlib.colors.rgb2hex(tmp(i)) for 
+                        i in range(n_plots)]
+
+        elif colormap is None:
+            # Use 8 default colors if less than 8 columns to plot
+            if n_plots <= len(color_scheme):
+                colormap = color_scheme
+            # Use more colors
+            else:
+                tmp = plt.cm.get_cmap('magma', n_plots)
+                colormap = [matplotlib.colors.rgb2hex(tmp(i)) for 
+                            i in range(n_plots)]
 
         # Get columns
         cols = self.df.columns[1:]
 
-        # Create dots of the first point for legend (bug in holoviews)
-        color_points = hv.NdOverlay({cols[i]:
-                                         hv.Points([[self.x[0],
-                                                     self.df[cols[i]][0]]],
-                                                   label=str(cols[i]))
-                                    .opts(style=dict(color=colormap[i]))
-                                     for i in range(len(cols))})
-        plot = color_points
-        for col, c in zip(cols, colormap):
-            plot *= self.plot_hv(y=col, color=c)
-        return plot
+        # Holoviews
+        if engine[0].lower() == 'h':
+            # Create dots of the first point for legend (bug in holoviews)
+            color_points = hv.NdOverlay({cols[i]:
+                                             hv.Points([[self.x[0],
+                                                         self.df[cols[i]][0]]],
+                                                       label=str(cols[i]))
+                                        .opts(style=dict(color=colormap[i]))
+                                         for i in range(len(cols))})
+            plot = color_points
+            for col, c in zip(cols, colormap):
+                plot *= self.plot_hv(y=col, color=c)
+            return plot
+
+        # Matplotlib
+        else:
+            for i in range(1, len(self.df.keys())):
+               plt.plot(self.x, self.df[self.df.keys()[i]],
+                        label=self.df.keys()[i],
+                        color=colormap[i-1],
+                        **kwargs)
+            plt.grid(True)
+            if legend:
+                plt.legend(bbox_to_anchor=(1.04,0.5),
+                           loc="center left",
+                           ncol=4,
+                           borderaxespad=0)
+            plt.show()
 
     def fit(self, fitfunc, fit_p_init, boundaries = None,plot=True,
             plot_init=False, plot_params=True, labels=None, **kwargs):