From 57ab7d2e0fff9227392c61e6f628326be0b8d075 Mon Sep 17 00:00:00 2001 From: MikeTheWatchGuy Date: Sat, 15 Dec 2018 15:18:11 -0500 Subject: [PATCH] New demo program that demonstrates using Panes --- DemoPrograms/Demo_Matplotlib_Browser_Paned.py | 909 ++++++++++++++++++ 1 file changed, 909 insertions(+) create mode 100644 DemoPrograms/Demo_Matplotlib_Browser_Paned.py diff --git a/DemoPrograms/Demo_Matplotlib_Browser_Paned.py b/DemoPrograms/Demo_Matplotlib_Browser_Paned.py new file mode 100644 index 00000000..756857fb --- /dev/null +++ b/DemoPrograms/Demo_Matplotlib_Browser_Paned.py @@ -0,0 +1,909 @@ +#!/usr/bin/env python +import sys +if sys.version_info[0] >= 3: + import PySimpleGUI as sg +else: + import PySimpleGUI27 as sg +import matplotlib +matplotlib.use('TkAgg') +from matplotlib.backends.backend_tkagg import FigureCanvasAgg +import matplotlib.backends.tkagg as tkagg +import tkinter as Tk +import inspect + +""" +Demonstrates one way of embedding Matplotlib figures into a PySimpleGUI window. + +Basic steps are: + * Create a Canvas Element + * Layout form + * Display form (NON BLOCKING) + * Draw plots onto convas + * Display form (BLOCKING) +""" + + + +import numpy as np +import matplotlib.pyplot as plt + + +def PyplotSimple(): + import numpy as np + import matplotlib.pyplot as plt + + # evenly sampled time at 200ms intervals + t = np.arange(0., 5., 0.2) + + # red dashes, blue squares and green triangles + plt.plot(t, t, 'r--', t, t ** 2, 'bs', t, t ** 3, 'g^') + + fig = plt.gcf() # get the figure to show + return fig + +def PyplotHistogram(): + """ + ============================================================= + Demo of the histogram (hist) function with multiple data sets + ============================================================= + + Plot histogram with multiple sample sets and demonstrate: + + * Use of legend with multiple sample sets + * Stacked bars + * Step curve with no fill + * Data sets of different sample sizes + + Selecting different bin counts and sizes can significantly affect the + shape of a histogram. The Astropy docs have a great section on how to + select these parameters: + http://docs.astropy.org/en/stable/visualization/histogram.html + """ + + import numpy as np + import matplotlib.pyplot as plt + + np.random.seed(0) + + n_bins = 10 + x = np.random.randn(1000, 3) + + fig, axes = plt.subplots(nrows=2, ncols=2) + ax0, ax1, ax2, ax3 = axes.flatten() + + colors = ['red', 'tan', 'lime'] + ax0.hist(x, n_bins, normed=1, histtype='bar', color=colors, label=colors) + ax0.legend(prop={'size': 10}) + ax0.set_title('bars with legend') + + ax1.hist(x, n_bins, normed=1, histtype='bar', stacked=True) + ax1.set_title('stacked bar') + + ax2.hist(x, n_bins, histtype='step', stacked=True, fill=False) + ax2.set_title('stack step (unfilled)') + + # Make a multiple-histogram of data-sets with different length. + x_multi = [np.random.randn(n) for n in [10000, 5000, 2000]] + ax3.hist(x_multi, n_bins, histtype='bar') + ax3.set_title('different sample sizes') + + fig.tight_layout() + return fig + +def PyplotArtistBoxPlots(): + """ + ========================================= + Demo of artist customization in box plots + ========================================= + + This example demonstrates how to use the various kwargs + to fully customize box plots. The first figure demonstrates + how to remove and add individual components (note that the + mean is the only value not shown by default). The second + figure demonstrates how the styles of the artists can + be customized. It also demonstrates how to set the limit + of the whiskers to specific percentiles (lower right axes) + + A good general reference on boxplots and their history can be found + here: http://vita.had.co.nz/papers/boxplots.pdf + + """ + + import numpy as np + import matplotlib.pyplot as plt + + # fake data + np.random.seed(937) + data = np.random.lognormal(size=(37, 4), mean=1.5, sigma=1.75) + labels = list('ABCD') + fs = 10 # fontsize + + # demonstrate how to toggle the display of different elements: + fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(6, 6), sharey=True) + axes[0, 0].boxplot(data, labels=labels) + axes[0, 0].set_title('Default', fontsize=fs) + + axes[0, 1].boxplot(data, labels=labels, showmeans=True) + axes[0, 1].set_title('showmeans=True', fontsize=fs) + + axes[0, 2].boxplot(data, labels=labels, showmeans=True, meanline=True) + axes[0, 2].set_title('showmeans=True,\nmeanline=True', fontsize=fs) + + axes[1, 0].boxplot(data, labels=labels, showbox=False, showcaps=False) + tufte_title = 'Tufte Style \n(showbox=False,\nshowcaps=False)' + axes[1, 0].set_title(tufte_title, fontsize=fs) + + axes[1, 1].boxplot(data, labels=labels, notch=True, bootstrap=10000) + axes[1, 1].set_title('notch=True,\nbootstrap=10000', fontsize=fs) + + axes[1, 2].boxplot(data, labels=labels, showfliers=False) + axes[1, 2].set_title('showfliers=False', fontsize=fs) + + for ax in axes.flatten(): + ax.set_yscale('log') + ax.set_yticklabels([]) + + fig.subplots_adjust(hspace=0.4) + return fig + +def ArtistBoxplot2(): + + # fake data + np.random.seed(937) + data = np.random.lognormal(size=(37, 4), mean=1.5, sigma=1.75) + labels = list('ABCD') + fs = 10 # fontsize + + # demonstrate how to customize the display different elements: + boxprops = dict(linestyle='--', linewidth=3, color='darkgoldenrod') + flierprops = dict(marker='o', markerfacecolor='green', markersize=12, + linestyle='none') + medianprops = dict(linestyle='-.', linewidth=2.5, color='firebrick') + meanpointprops = dict(marker='D', markeredgecolor='black', + markerfacecolor='firebrick') + meanlineprops = dict(linestyle='--', linewidth=2.5, color='purple') + + fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(6, 6), sharey=True) + axes[0, 0].boxplot(data, boxprops=boxprops) + axes[0, 0].set_title('Custom boxprops', fontsize=fs) + + axes[0, 1].boxplot(data, flierprops=flierprops, medianprops=medianprops) + axes[0, 1].set_title('Custom medianprops\nand flierprops', fontsize=fs) + + axes[0, 2].boxplot(data, whis='range') + axes[0, 2].set_title('whis="range"', fontsize=fs) + + axes[1, 0].boxplot(data, meanprops=meanpointprops, meanline=False, + showmeans=True) + axes[1, 0].set_title('Custom mean\nas point', fontsize=fs) + + axes[1, 1].boxplot(data, meanprops=meanlineprops, meanline=True, + showmeans=True) + axes[1, 1].set_title('Custom mean\nas line', fontsize=fs) + + axes[1, 2].boxplot(data, whis=[15, 85]) + axes[1, 2].set_title('whis=[15, 85]\n#percentiles', fontsize=fs) + + for ax in axes.flatten(): + ax.set_yscale('log') + ax.set_yticklabels([]) + + fig.suptitle("I never said they'd be pretty") + fig.subplots_adjust(hspace=0.4) + return fig + +def PyplotScatterWithLegend(): + import matplotlib.pyplot as plt + from numpy.random import rand + + fig, ax = plt.subplots() + for color in ['red', 'green', 'blue']: + n = 750 + x, y = rand(2, n) + scale = 200.0 * rand(n) + ax.scatter(x, y, c=color, s=scale, label=color, + alpha=0.3, edgecolors='none') + + ax.legend() + ax.grid(True) + return fig + +def PyplotLineStyles(): + """ + ========== + Linestyles + ========== + + This examples showcases different linestyles copying those of Tikz/PGF. + """ + import numpy as np + import matplotlib.pyplot as plt + from collections import OrderedDict + from matplotlib.transforms import blended_transform_factory + + linestyles = OrderedDict( + [('solid', (0, ())), + ('loosely dotted', (0, (1, 10))), + ('dotted', (0, (1, 5))), + ('densely dotted', (0, (1, 1))), + + ('loosely dashed', (0, (5, 10))), + ('dashed', (0, (5, 5))), + ('densely dashed', (0, (5, 1))), + + ('loosely dashdotted', (0, (3, 10, 1, 10))), + ('dashdotted', (0, (3, 5, 1, 5))), + ('densely dashdotted', (0, (3, 1, 1, 1))), + + ('loosely dashdotdotted', (0, (3, 10, 1, 10, 1, 10))), + ('dashdotdotted', (0, (3, 5, 1, 5, 1, 5))), + ('densely dashdotdotted', (0, (3, 1, 1, 1, 1, 1)))]) + + plt.figure(figsize=(10, 6)) + ax = plt.subplot(1, 1, 1) + + X, Y = np.linspace(0, 100, 10), np.zeros(10) + for i, (name, linestyle) in enumerate(linestyles.items()): + ax.plot(X, Y + i, linestyle=linestyle, linewidth=1.5, color='black') + + ax.set_ylim(-0.5, len(linestyles) - 0.5) + plt.yticks(np.arange(len(linestyles)), linestyles.keys()) + plt.xticks([]) + + # For each line style, add a text annotation with a small offset from + # the reference point (0 in Axes coords, y tick value in Data coords). + reference_transform = blended_transform_factory(ax.transAxes, ax.transData) + for i, (name, linestyle) in enumerate(linestyles.items()): + ax.annotate(str(linestyle), xy=(0.0, i), xycoords=reference_transform, + xytext=(-6, -12), textcoords='offset points', color="blue", + fontsize=8, ha="right", family="monospace") + + plt.tight_layout() + return plt.gcf() + +def PyplotLinePolyCollection(): + import matplotlib.pyplot as plt + from matplotlib import collections, colors, transforms + import numpy as np + + nverts = 50 + npts = 100 + + # Make some spirals + r = np.arange(nverts) + theta = np.linspace(0, 2 * np.pi, nverts) + xx = r * np.sin(theta) + yy = r * np.cos(theta) + spiral = np.column_stack([xx, yy]) + + # Fixing random state for reproducibility + rs = np.random.RandomState(19680801) + + # Make some offsets + xyo = rs.randn(npts, 2) + + # Make a list of colors cycling through the default series. + colors = [colors.to_rgba(c) + for c in plt.rcParams['axes.prop_cycle'].by_key()['color']] + + fig, axes = plt.subplots(2, 2) + fig.subplots_adjust(top=0.92, left=0.07, right=0.97, + hspace=0.3, wspace=0.3) + ((ax1, ax2), (ax3, ax4)) = axes # unpack the axes + + col = collections.LineCollection([spiral], offsets=xyo, + transOffset=ax1.transData) + trans = fig.dpi_scale_trans + transforms.Affine2D().scale(1.0 / 72.0) + col.set_transform(trans) # the points to pixels transform + # Note: the first argument to the collection initializer + # must be a list of sequences of x,y tuples; we have only + # one sequence, but we still have to put it in a list. + ax1.add_collection(col, autolim=True) + # autolim=True enables autoscaling. For collections with + # offsets like this, it is neither efficient nor accurate, + # but it is good enough to generate a plot that you can use + # as a starting point. If you know beforehand the range of + # x and y that you want to show, it is better to set them + # explicitly, leave out the autolim kwarg (or set it to False), + # and omit the 'ax1.autoscale_view()' call below. + + # Make a transform for the line segments such that their size is + # given in points: + col.set_color(colors) + + ax1.autoscale_view() # See comment above, after ax1.add_collection. + ax1.set_title('LineCollection using offsets') + + # The same data as above, but fill the curves. + col = collections.PolyCollection([spiral], offsets=xyo, + transOffset=ax2.transData) + trans = transforms.Affine2D().scale(fig.dpi / 72.0) + col.set_transform(trans) # the points to pixels transform + ax2.add_collection(col, autolim=True) + col.set_color(colors) + + ax2.autoscale_view() + ax2.set_title('PolyCollection using offsets') + + # 7-sided regular polygons + + col = collections.RegularPolyCollection( + 7, sizes=np.abs(xx) * 10.0, offsets=xyo, transOffset=ax3.transData) + trans = transforms.Affine2D().scale(fig.dpi / 72.0) + col.set_transform(trans) # the points to pixels transform + ax3.add_collection(col, autolim=True) + col.set_color(colors) + ax3.autoscale_view() + ax3.set_title('RegularPolyCollection using offsets') + + # Simulate a series of ocean current profiles, successively + # offset by 0.1 m/s so that they form what is sometimes called + # a "waterfall" plot or a "stagger" plot. + + nverts = 60 + ncurves = 20 + offs = (0.1, 0.0) + + yy = np.linspace(0, 2 * np.pi, nverts) + ym = np.max(yy) + xx = (0.2 + (ym - yy) / ym) ** 2 * np.cos(yy - 0.4) * 0.5 + segs = [] + for i in range(ncurves): + xxx = xx + 0.02 * rs.randn(nverts) + curve = np.column_stack([xxx, yy * 100]) + segs.append(curve) + + col = collections.LineCollection(segs, offsets=offs) + ax4.add_collection(col, autolim=True) + col.set_color(colors) + ax4.autoscale_view() + ax4.set_title('Successive data offsets') + ax4.set_xlabel('Zonal velocity component (m/s)') + ax4.set_ylabel('Depth (m)') + # Reverse the y-axis so depth increases downward + ax4.set_ylim(ax4.get_ylim()[::-1]) + return fig + +def PyplotGGPlotSytleSheet(): + import numpy as np + import matplotlib.pyplot as plt + + plt.style.use('ggplot') + + # Fixing random state for reproducibility + np.random.seed(19680801) + + fig, axes = plt.subplots(ncols=2, nrows=2) + ax1, ax2, ax3, ax4 = axes.ravel() + + # scatter plot (Note: `plt.scatter` doesn't use default colors) + x, y = np.random.normal(size=(2, 200)) + ax1.plot(x, y, 'o') + + # sinusoidal lines with colors from default color cycle + L = 2 * np.pi + x = np.linspace(0, L) + ncolors = len(plt.rcParams['axes.prop_cycle']) + shift = np.linspace(0, L, ncolors, endpoint=False) + for s in shift: + ax2.plot(x, np.sin(x + s), '-') + ax2.margins(0) + + # bar graphs + x = np.arange(5) + y1, y2 = np.random.randint(1, 25, size=(2, 5)) + width = 0.25 + ax3.bar(x, y1, width) + ax3.bar(x + width, y2, width, + color=list(plt.rcParams['axes.prop_cycle'])[2]['color']) + ax3.set_xticks(x + width) + ax3.set_xticklabels(['a', 'b', 'c', 'd', 'e']) + + # circles with colors from default color cycle + for i, color in enumerate(plt.rcParams['axes.prop_cycle']): + xy = np.random.normal(size=2) + ax4.add_patch(plt.Circle(xy, radius=0.3, color=color['color'])) + ax4.axis('equal') + ax4.margins(0) + fig = plt.gcf() # get the figure to show + return fig + +def PyplotBoxPlot(): + import numpy as np + import matplotlib.pyplot as plt + + # Fixing random state for reproducibility + np.random.seed(19680801) + + # fake up some data + spread = np.random.rand(50) * 100 + center = np.ones(25) * 50 + flier_high = np.random.rand(10) * 100 + 100 + flier_low = np.random.rand(10) * -100 + data = np.concatenate((spread, center, flier_high, flier_low), 0) + fig1, ax1 = plt.subplots() + ax1.set_title('Basic Plot') + ax1.boxplot(data) + return fig1 + +def PyplotRadarChart(): + import numpy as np + + import matplotlib.pyplot as plt + from matplotlib.path import Path + from matplotlib.spines import Spine + from matplotlib.projections.polar import PolarAxes + from matplotlib.projections import register_projection + + def radar_factory(num_vars, frame='circle'): + """Create a radar chart with `num_vars` axes. + + This function creates a RadarAxes projection and registers it. + + Parameters + ---------- + num_vars : int + Number of variables for radar chart. + frame : {'circle' | 'polygon'} + Shape of frame surrounding axes. + + """ + # calculate evenly-spaced axis angles + theta = np.linspace(0, 2 * np.pi, num_vars, endpoint=False) + + def draw_poly_patch(self): + # rotate theta such that the first axis is at the top + verts = unit_poly_verts(theta + np.pi / 2) + return plt.Polygon(verts, closed=True, edgecolor='k') + + def draw_circle_patch(self): + # unit circle centered on (0.5, 0.5) + return plt.Circle((0.5, 0.5), 0.5) + + patch_dict = {'polygon': draw_poly_patch, 'circle': draw_circle_patch} + if frame not in patch_dict: + raise ValueError('unknown value for `frame`: %s' % frame) + + class RadarAxes(PolarAxes): + + name = 'radar' + # use 1 line segment to connect specified points + RESOLUTION = 1 + # define draw_frame method + draw_patch = patch_dict[frame] + + def __init__(self, *args, **kwargs): + super(RadarAxes, self).__init__(*args, **kwargs) + # rotate plot such that the first axis is at the top + self.set_theta_zero_location('N') + + def fill(self, *args, **kwargs): + """Override fill so that line is closed by default""" + closed = kwargs.pop('closed', True) + return super(RadarAxes, self).fill(closed=closed, *args, **kwargs) + + def plot(self, *args, **kwargs): + """Override plot so that line is closed by default""" + lines = super(RadarAxes, self).plot(*args, **kwargs) + for line in lines: + self._close_line(line) + + def _close_line(self, line): + x, y = line.get_data() + # FIXME: markers at x[0], y[0] get doubled-up + if x[0] != x[-1]: + x = np.concatenate((x, [x[0]])) + y = np.concatenate((y, [y[0]])) + line.set_data(x, y) + + def set_varlabels(self, labels): + self.set_thetagrids(np.degrees(theta), labels) + + def _gen_axes_patch(self): + return self.draw_patch() + + def _gen_axes_spines(self): + if frame == 'circle': + return PolarAxes._gen_axes_spines(self) + # The following is a hack to get the spines (i.e. the axes frame) + # to draw correctly for a polygon frame. + + # spine_type must be 'left', 'right', 'top', 'bottom', or `circle`. + spine_type = 'circle' + verts = unit_poly_verts(theta + np.pi / 2) + # close off polygon by repeating first vertex + verts.append(verts[0]) + path = Path(verts) + + spine = Spine(self, spine_type, path) + spine.set_transform(self.transAxes) + return {'polar': spine} + + register_projection(RadarAxes) + return theta + + def unit_poly_verts(theta): + """Return vertices of polygon for subplot axes. + + This polygon is circumscribed by a unit circle centered at (0.5, 0.5) + """ + x0, y0, r = [0.5] * 3 + verts = [(r * np.cos(t) + x0, r * np.sin(t) + y0) for t in theta] + return verts + + def example_data(): + # The following data is from the Denver Aerosol Sources and Health study. + # See doi:10.1016/j.atmosenv.2008.12.017 + # + # The data are pollution source profile estimates for five modeled + # pollution sources (e.g., cars, wood-burning, etc) that emit 7-9 chemical + # species. The radar charts are experimented with here to see if we can + # nicely visualize how the modeled source profiles change across four + # scenarios: + # 1) No gas-phase species present, just seven particulate counts on + # Sulfate + # Nitrate + # Elemental Carbon (EC) + # Organic Carbon fraction 1 (OC) + # Organic Carbon fraction 2 (OC2) + # Organic Carbon fraction 3 (OC3) + # Pyrolized Organic Carbon (OP) + # 2)Inclusion of gas-phase specie carbon monoxide (CO) + # 3)Inclusion of gas-phase specie ozone (O3). + # 4)Inclusion of both gas-phase species is present... + data = [ + ['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO', 'O3'], + ('Basecase', [ + [0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00], + [0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00], + [0.01, 0.02, 0.85, 0.19, 0.05, 0.10, 0.00, 0.00, 0.00], + [0.02, 0.01, 0.07, 0.01, 0.21, 0.12, 0.98, 0.00, 0.00], + [0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00]]), + ('With CO', [ + [0.88, 0.02, 0.02, 0.02, 0.00, 0.05, 0.00, 0.05, 0.00], + [0.08, 0.94, 0.04, 0.02, 0.00, 0.01, 0.12, 0.04, 0.00], + [0.01, 0.01, 0.79, 0.10, 0.00, 0.05, 0.00, 0.31, 0.00], + [0.00, 0.02, 0.03, 0.38, 0.31, 0.31, 0.00, 0.59, 0.00], + [0.02, 0.02, 0.11, 0.47, 0.69, 0.58, 0.88, 0.00, 0.00]]), + ('With O3', [ + [0.89, 0.01, 0.07, 0.00, 0.00, 0.05, 0.00, 0.00, 0.03], + [0.07, 0.95, 0.05, 0.04, 0.00, 0.02, 0.12, 0.00, 0.00], + [0.01, 0.02, 0.86, 0.27, 0.16, 0.19, 0.00, 0.00, 0.00], + [0.01, 0.03, 0.00, 0.32, 0.29, 0.27, 0.00, 0.00, 0.95], + [0.02, 0.00, 0.03, 0.37, 0.56, 0.47, 0.87, 0.00, 0.00]]), + ('CO & O3', [ + [0.87, 0.01, 0.08, 0.00, 0.00, 0.04, 0.00, 0.00, 0.01], + [0.09, 0.95, 0.02, 0.03, 0.00, 0.01, 0.13, 0.06, 0.00], + [0.01, 0.02, 0.71, 0.24, 0.13, 0.16, 0.00, 0.50, 0.00], + [0.01, 0.03, 0.00, 0.28, 0.24, 0.23, 0.00, 0.44, 0.88], + [0.02, 0.00, 0.18, 0.45, 0.64, 0.55, 0.86, 0.00, 0.16]]) + ] + return data + + N = 9 + theta = radar_factory(N, frame='polygon') + + data = example_data() + spoke_labels = data.pop(0) + + fig, axes = plt.subplots(figsize=(9, 9), nrows=2, ncols=2, + subplot_kw=dict(projection='radar')) + fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05) + + colors = ['b', 'r', 'g', 'm', 'y'] + # Plot the four cases from the example data on separate axes + for ax, (title, case_data) in zip(axes.flatten(), data): + ax.set_rgrids([0.2, 0.4, 0.6, 0.8]) + ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1), + horizontalalignment='center', verticalalignment='center') + for d, color in zip(case_data, colors): + ax.plot(theta, d, color=color) + ax.fill(theta, d, facecolor=color, alpha=0.25) + ax.set_varlabels(spoke_labels) + + # add legend relative to top-left plot + ax = axes[0, 0] + labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5') + legend = ax.legend(labels, loc=(0.9, .95), + labelspacing=0.1, fontsize='small') + + fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios', + horizontalalignment='center', color='black', weight='bold', + size='large') + return fig + +def DifferentScales(): + import numpy as np + import matplotlib.pyplot as plt + + # Create some mock data + t = np.arange(0.01, 10.0, 0.01) + data1 = np.exp(t) + data2 = np.sin(2 * np.pi * t) + + fig, ax1 = plt.subplots() + + color = 'tab:red' + ax1.set_xlabel('time (s)') + ax1.set_ylabel('exp', color=color) + ax1.plot(t, data1, color=color) + ax1.tick_params(axis='y', labelcolor=color) + + ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis + + color = 'tab:blue' + ax2.set_ylabel('sin', color=color) # we already handled the x-label with ax1 + ax2.plot(t, data2, color=color) + ax2.tick_params(axis='y', labelcolor=color) + + fig.tight_layout() # otherwise the right y-label is slightly clipped + return fig + +def ExploringNormalizations(): + import matplotlib.pyplot as plt + import matplotlib.colors as mcolors + import numpy as np + from numpy.random import multivariate_normal + + data = np.vstack([ + multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000), + multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000) + ]) + + gammas = [0.8, 0.5, 0.3] + + fig, axes = plt.subplots(nrows=2, ncols=2) + + axes[0, 0].set_title('Linear normalization') + axes[0, 0].hist2d(data[:, 0], data[:, 1], bins=100) + + for ax, gamma in zip(axes.flat[1:], gammas): + ax.set_title(r'Power law $(\gamma=%1.1f)$' % gamma) + ax.hist2d(data[:, 0], data[:, 1], + bins=100, norm=mcolors.PowerNorm(gamma)) + + fig.tight_layout() + return fig + +def PyplotFormatstr(): + + def f(t): + return np.exp(-t) * np.cos(2*np.pi*t) + + t1 = np.arange(0.0, 5.0, 0.1) + t2 = np.arange(0.0, 5.0, 0.02) + + plt.figure(1) + plt.subplot(211) + plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k') + + plt.subplot(212) + plt.plot(t2, np.cos(2*np.pi*t2), 'r--') + fig = plt.gcf() # get the figure to show + return fig + +def UnicodeMinus(): + import numpy as np + import matplotlib + import matplotlib.pyplot as plt + + # Fixing random state for reproducibility + np.random.seed(19680801) + + matplotlib.rcParams['axes.unicode_minus'] = False + fig, ax = plt.subplots() + ax.plot(10 * np.random.randn(100), 10 * np.random.randn(100), 'o') + ax.set_title('Using hyphen instead of Unicode minus') + return fig + +def Subplot3d(): + from mpl_toolkits.mplot3d.axes3d import Axes3D + from matplotlib import cm + # from matplotlib.ticker import LinearLocator, FixedLocator, FormatStrFormatter + import matplotlib.pyplot as plt + import numpy as np + + fig = plt.figure() + + ax = fig.add_subplot(1, 2, 1, projection='3d') + X = np.arange(-5, 5, 0.25) + Y = np.arange(-5, 5, 0.25) + X, Y = np.meshgrid(X, Y) + R = np.sqrt(X ** 2 + Y ** 2) + Z = np.sin(R) + surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, + linewidth=0, antialiased=False) + ax.set_zlim3d(-1.01, 1.01) + + # ax.w_zaxis.set_major_locator(LinearLocator(10)) + # ax.w_zaxis.set_major_formatter(FormatStrFormatter('%.03f')) + + fig.colorbar(surf, shrink=0.5, aspect=5) + + from mpl_toolkits.mplot3d.axes3d import get_test_data + ax = fig.add_subplot(1, 2, 2, projection='3d') + X, Y, Z = get_test_data(0.05) + ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10) + return fig + +def PyplotScales(): + import numpy as np + import matplotlib.pyplot as plt + + from matplotlib.ticker import NullFormatter # useful for `logit` scale + + # Fixing random state for reproducibility + np.random.seed(19680801) + + # make up some data in the interval ]0, 1[ + y = np.random.normal(loc=0.5, scale=0.4, size=1000) + y = y[(y > 0) & (y < 1)] + y.sort() + x = np.arange(len(y)) + + # plot with various axes scales + plt.figure(1) + + # linear + plt.subplot(221) + plt.plot(x, y) + plt.yscale('linear') + plt.title('linear') + plt.grid(True) + + # log + plt.subplot(222) + plt.plot(x, y) + plt.yscale('log') + plt.title('log') + plt.grid(True) + + # symmetric log + plt.subplot(223) + plt.plot(x, y - y.mean()) + plt.yscale('symlog', linthreshy=0.01) + plt.title('symlog') + plt.grid(True) + + # logit + plt.subplot(224) + plt.plot(x, y) + plt.yscale('logit') + plt.title('logit') + plt.grid(True) + # Format the minor tick labels of the y-axis into empty strings with + # `NullFormatter`, to avoid cumbering the axis with too many labels. + plt.gca().yaxis.set_minor_formatter(NullFormatter()) + # Adjust the subplot layout, because the logit one may take more space + # than usual, due to y-tick labels like "1 - 10^{-3}" + plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25, + wspace=0.35) + return plt.gcf() + + +def AxesGrid(): + import numpy as np + import matplotlib.pyplot as plt + from mpl_toolkits.axes_grid1.axes_rgb import RGBAxes + + def get_demo_image(): + # prepare image + delta = 0.5 + + extent = (-3, 4, -4, 3) + x = np.arange(-3.0, 4.001, delta) + y = np.arange(-4.0, 3.001, delta) + X, Y = np.meshgrid(x, y) + Z1 = np.exp(-X ** 2 - Y ** 2) + Z2 = np.exp(-(X - 1) ** 2 - (Y - 1) ** 2) + Z = (Z1 - Z2) * 2 + + return Z, extent + + def get_rgb(): + Z, extent = get_demo_image() + + Z[Z < 0] = 0. + Z = Z / Z.max() + + R = Z[:13, :13] + G = Z[2:, 2:] + B = Z[:13, 2:] + + return R, G, B + + fig = plt.figure(1) + ax = RGBAxes(fig, [0.1, 0.1, 0.8, 0.8]) + + r, g, b = get_rgb() + kwargs = dict(origin="lower", interpolation="nearest") + ax.imshow_rgb(r, g, b, **kwargs) + + ax.RGB.set_xlim(0., 9.5) + ax.RGB.set_ylim(0.9, 10.6) + + plt.draw() + return plt.gcf() + +# The magic function that makes it possible.... glues together tkinter and pyplot using Canvas Widget +def draw_figure(canvas, figure, loc=(0, 0)): + """ Draw a matplotlib figure onto a Tk canvas + + loc: location of top-left corner of figure on canvas in pixels. + + Inspired by matplotlib source: lib/matplotlib/backends/backend_tkagg.py + """ + figure_canvas_agg = FigureCanvasAgg(figure) + figure_canvas_agg.draw() + figure_x, figure_y, figure_w, figure_h = figure.bbox.bounds + figure_w, figure_h = int(figure_w), int(figure_h) + photo = Tk.PhotoImage(master=canvas, width=figure_w, height=figure_h) + + # Position: convert from top-left anchor to center anchor + canvas.create_image(loc[0] + figure_w/2, loc[1] + figure_h/2, image=photo) + + # Unfortunately, there's no accessor for the pointer to the native renderer + tkagg.blit(photo, figure_canvas_agg.get_renderer()._renderer, colormode=2) + + # Return a handle which contains a reference to the photo object + # which must be kept live or else the picture disappears + return photo + + +# -------------------------------- GUI Starts Here -------------------------------# +# fig = your figure you want to display. Assumption is that 'fig' holds the # +# information to display. # +# --------------------------------------------------------------------------------# + +# print(inspect.getsource(PyplotSimple)) + + +fig_dict = {'Pyplot Simple':PyplotSimple, 'Pyplot Formatstr':PyplotFormatstr,'PyPlot Three':Subplot3d, + 'Unicode Minus': UnicodeMinus, 'Pyplot Scales' : PyplotScales, 'Axes Grid' : AxesGrid, + 'Exploring Normalizations' : ExploringNormalizations, 'Different Scales' : DifferentScales, + 'Pyplot Box Plot' : PyplotBoxPlot, 'Pyplot ggplot Style Sheet' : PyplotGGPlotSytleSheet, + 'Pyplot Line Poly Collection' : PyplotLinePolyCollection, 'Pyplot Line Styles' : PyplotLineStyles, + 'Pyplot Scatter With Legend' :PyplotScatterWithLegend, 'Artist Customized Box Plots' : PyplotArtistBoxPlots, + 'Artist Customized Box Plots 2' : ArtistBoxplot2, 'Pyplot Histogram' : PyplotHistogram} + + +sg.ChangeLookAndFeel('LightGreen') +figure_w, figure_h = 650, 650 +# define the form layout +listbox_values = [key for key in fig_dict.keys()] +col_listbox = [[sg.Listbox(values=listbox_values, change_submits=True, size=(28, len(listbox_values)), key='func')], + [sg.T(' ' * 12), sg.Exit(size=(5, 2))]] + +col_multiline = sg.Column([[sg.Multiline(size=(70, 35), key='multiline')]]) +col_canvas = sg.Column([[ sg.Canvas(size=(figure_w, figure_h), key='canvas')]]) + +layout = [[sg.Text('Matplotlib Plot Test', font=('current 18'))], + [sg.Column(col_listbox), sg.Pane([col_canvas, col_multiline], size=(800,600))], + ] + +# create the form and show it without the plot +window = sg.Window('Demo Application - Embedding Matplotlib In PySimpleGUI',resizable=True, grab_anywhere=False).Layout(layout) +window.Finalize() + +canvas_elem = window.FindElement('canvas') +multiline_elem= window.FindElement('multiline') + +while True: + event, values = window.Read() + # print(event) + # show it all again and get buttons + if event is None or event is 'Exit': + break + + try: + choice = values['func'][0] + func = fig_dict[choice] + except: + pass + + multiline_elem.Update(inspect.getsource(func)) + plt.clf() + fig = func() + fig_photo = draw_figure(canvas_elem.TKCanvas, fig) + +