Fixed clearing of background, many more plots
This commit is contained in:
MikeTheWatchGuy
parent
28a38dcf86
commit
23bdd2664c
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@ -35,6 +35,307 @@ def PyplotSimple():
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fig = plt.gcf() # get the figure to show
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return fig
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def PyplotGGPlotSytleSheet():
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import numpy as np
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import matplotlib.pyplot as plt
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plt.style.use('ggplot')
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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fig, axes = plt.subplots(ncols=2, nrows=2)
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ax1, ax2, ax3, ax4 = axes.ravel()
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# scatter plot (Note: `plt.scatter` doesn't use default colors)
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x, y = np.random.normal(size=(2, 200))
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ax1.plot(x, y, 'o')
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# sinusoidal lines with colors from default color cycle
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L = 2 * np.pi
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x = np.linspace(0, L)
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ncolors = len(plt.rcParams['axes.prop_cycle'])
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shift = np.linspace(0, L, ncolors, endpoint=False)
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for s in shift:
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ax2.plot(x, np.sin(x + s), '-')
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ax2.margins(0)
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# bar graphs
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x = np.arange(5)
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y1, y2 = np.random.randint(1, 25, size=(2, 5))
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width = 0.25
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ax3.bar(x, y1, width)
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ax3.bar(x + width, y2, width,
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color=list(plt.rcParams['axes.prop_cycle'])[2]['color'])
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ax3.set_xticks(x + width)
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ax3.set_xticklabels(['a', 'b', 'c', 'd', 'e'])
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# circles with colors from default color cycle
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for i, color in enumerate(plt.rcParams['axes.prop_cycle']):
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xy = np.random.normal(size=2)
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ax4.add_patch(plt.Circle(xy, radius=0.3, color=color['color']))
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ax4.axis('equal')
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ax4.margins(0)
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fig = plt.gcf() # get the figure to show
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return fig
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def PyplotBoxPlot():
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import numpy as np
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import matplotlib.pyplot as plt
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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# fake up some data
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spread = np.random.rand(50) * 100
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center = np.ones(25) * 50
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flier_high = np.random.rand(10) * 100 + 100
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flier_low = np.random.rand(10) * -100
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data = np.concatenate((spread, center, flier_high, flier_low), 0)
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fig1, ax1 = plt.subplots()
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ax1.set_title('Basic Plot')
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ax1.boxplot(data)
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return fig1
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def PyplotRadarChart():
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.path import Path
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from matplotlib.spines import Spine
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from matplotlib.projections.polar import PolarAxes
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from matplotlib.projections import register_projection
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def radar_factory(num_vars, frame='circle'):
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"""Create a radar chart with `num_vars` axes.
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This function creates a RadarAxes projection and registers it.
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Parameters
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----------
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num_vars : int
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Number of variables for radar chart.
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frame : {'circle' | 'polygon'}
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Shape of frame surrounding axes.
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"""
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# calculate evenly-spaced axis angles
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theta = np.linspace(0, 2 * np.pi, num_vars, endpoint=False)
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def draw_poly_patch(self):
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# rotate theta such that the first axis is at the top
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verts = unit_poly_verts(theta + np.pi / 2)
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return plt.Polygon(verts, closed=True, edgecolor='k')
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def draw_circle_patch(self):
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# unit circle centered on (0.5, 0.5)
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return plt.Circle((0.5, 0.5), 0.5)
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patch_dict = {'polygon': draw_poly_patch, 'circle': draw_circle_patch}
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if frame not in patch_dict:
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raise ValueError('unknown value for `frame`: %s' % frame)
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class RadarAxes(PolarAxes):
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name = 'radar'
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# use 1 line segment to connect specified points
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RESOLUTION = 1
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# define draw_frame method
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draw_patch = patch_dict[frame]
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def __init__(self, *args, **kwargs):
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super(RadarAxes, self).__init__(*args, **kwargs)
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# rotate plot such that the first axis is at the top
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self.set_theta_zero_location('N')
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def fill(self, *args, **kwargs):
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"""Override fill so that line is closed by default"""
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closed = kwargs.pop('closed', True)
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return super(RadarAxes, self).fill(closed=closed, *args, **kwargs)
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def plot(self, *args, **kwargs):
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"""Override plot so that line is closed by default"""
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lines = super(RadarAxes, self).plot(*args, **kwargs)
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for line in lines:
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self._close_line(line)
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def _close_line(self, line):
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x, y = line.get_data()
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# FIXME: markers at x[0], y[0] get doubled-up
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if x[0] != x[-1]:
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x = np.concatenate((x, [x[0]]))
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y = np.concatenate((y, [y[0]]))
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line.set_data(x, y)
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def set_varlabels(self, labels):
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self.set_thetagrids(np.degrees(theta), labels)
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def _gen_axes_patch(self):
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return self.draw_patch()
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def _gen_axes_spines(self):
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if frame == 'circle':
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return PolarAxes._gen_axes_spines(self)
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# The following is a hack to get the spines (i.e. the axes frame)
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# to draw correctly for a polygon frame.
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# spine_type must be 'left', 'right', 'top', 'bottom', or `circle`.
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spine_type = 'circle'
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verts = unit_poly_verts(theta + np.pi / 2)
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# close off polygon by repeating first vertex
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verts.append(verts[0])
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path = Path(verts)
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spine = Spine(self, spine_type, path)
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spine.set_transform(self.transAxes)
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return {'polar': spine}
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register_projection(RadarAxes)
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return theta
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def unit_poly_verts(theta):
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"""Return vertices of polygon for subplot axes.
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This polygon is circumscribed by a unit circle centered at (0.5, 0.5)
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"""
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x0, y0, r = [0.5] * 3
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verts = [(r * np.cos(t) + x0, r * np.sin(t) + y0) for t in theta]
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return verts
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def example_data():
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# The following data is from the Denver Aerosol Sources and Health study.
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# See doi:10.1016/j.atmosenv.2008.12.017
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#
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# The data are pollution source profile estimates for five modeled
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# pollution sources (e.g., cars, wood-burning, etc) that emit 7-9 chemical
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# species. The radar charts are experimented with here to see if we can
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# nicely visualize how the modeled source profiles change across four
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# scenarios:
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# 1) No gas-phase species present, just seven particulate counts on
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# Sulfate
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# Nitrate
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# Elemental Carbon (EC)
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# Organic Carbon fraction 1 (OC)
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# Organic Carbon fraction 2 (OC2)
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# Organic Carbon fraction 3 (OC3)
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# Pyrolized Organic Carbon (OP)
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# 2)Inclusion of gas-phase specie carbon monoxide (CO)
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# 3)Inclusion of gas-phase specie ozone (O3).
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# 4)Inclusion of both gas-phase species is present...
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data = [
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['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO', 'O3'],
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('Basecase', [
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[0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00],
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[0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00],
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[0.01, 0.02, 0.85, 0.19, 0.05, 0.10, 0.00, 0.00, 0.00],
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[0.02, 0.01, 0.07, 0.01, 0.21, 0.12, 0.98, 0.00, 0.00],
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[0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00]]),
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('With CO', [
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[0.88, 0.02, 0.02, 0.02, 0.00, 0.05, 0.00, 0.05, 0.00],
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[0.08, 0.94, 0.04, 0.02, 0.00, 0.01, 0.12, 0.04, 0.00],
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[0.01, 0.01, 0.79, 0.10, 0.00, 0.05, 0.00, 0.31, 0.00],
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[0.00, 0.02, 0.03, 0.38, 0.31, 0.31, 0.00, 0.59, 0.00],
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[0.02, 0.02, 0.11, 0.47, 0.69, 0.58, 0.88, 0.00, 0.00]]),
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('With O3', [
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[0.89, 0.01, 0.07, 0.00, 0.00, 0.05, 0.00, 0.00, 0.03],
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[0.07, 0.95, 0.05, 0.04, 0.00, 0.02, 0.12, 0.00, 0.00],
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[0.01, 0.02, 0.86, 0.27, 0.16, 0.19, 0.00, 0.00, 0.00],
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[0.01, 0.03, 0.00, 0.32, 0.29, 0.27, 0.00, 0.00, 0.95],
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[0.02, 0.00, 0.03, 0.37, 0.56, 0.47, 0.87, 0.00, 0.00]]),
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('CO & O3', [
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[0.87, 0.01, 0.08, 0.00, 0.00, 0.04, 0.00, 0.00, 0.01],
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[0.09, 0.95, 0.02, 0.03, 0.00, 0.01, 0.13, 0.06, 0.00],
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[0.01, 0.02, 0.71, 0.24, 0.13, 0.16, 0.00, 0.50, 0.00],
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[0.01, 0.03, 0.00, 0.28, 0.24, 0.23, 0.00, 0.44, 0.88],
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[0.02, 0.00, 0.18, 0.45, 0.64, 0.55, 0.86, 0.00, 0.16]])
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]
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return data
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N = 9
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theta = radar_factory(N, frame='polygon')
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data = example_data()
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spoke_labels = data.pop(0)
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fig, axes = plt.subplots(figsize=(9, 9), nrows=2, ncols=2,
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subplot_kw=dict(projection='radar'))
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fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
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colors = ['b', 'r', 'g', 'm', 'y']
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# Plot the four cases from the example data on separate axes
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for ax, (title, case_data) in zip(axes.flatten(), data):
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ax.set_rgrids([0.2, 0.4, 0.6, 0.8])
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ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
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horizontalalignment='center', verticalalignment='center')
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for d, color in zip(case_data, colors):
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ax.plot(theta, d, color=color)
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ax.fill(theta, d, facecolor=color, alpha=0.25)
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ax.set_varlabels(spoke_labels)
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# add legend relative to top-left plot
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ax = axes[0, 0]
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labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
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legend = ax.legend(labels, loc=(0.9, .95),
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labelspacing=0.1, fontsize='small')
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fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
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horizontalalignment='center', color='black', weight='bold',
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size='large')
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return fig
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def DifferentScales():
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import numpy as np
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import matplotlib.pyplot as plt
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# Create some mock data
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t = np.arange(0.01, 10.0, 0.01)
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data1 = np.exp(t)
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data2 = np.sin(2 * np.pi * t)
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fig, ax1 = plt.subplots()
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color = 'tab:red'
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ax1.set_xlabel('time (s)')
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ax1.set_ylabel('exp', color=color)
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ax1.plot(t, data1, color=color)
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ax1.tick_params(axis='y', labelcolor=color)
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ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
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color = 'tab:blue'
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ax2.set_ylabel('sin', color=color) # we already handled the x-label with ax1
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ax2.plot(t, data2, color=color)
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ax2.tick_params(axis='y', labelcolor=color)
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fig.tight_layout() # otherwise the right y-label is slightly clipped
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return fig
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def ExploringNormalizations():
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import matplotlib.pyplot as plt
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import matplotlib.colors as mcolors
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import numpy as np
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from numpy.random import multivariate_normal
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data = np.vstack([
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multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
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multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000)
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])
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gammas = [0.8, 0.5, 0.3]
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fig, axes = plt.subplots(nrows=2, ncols=2)
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axes[0, 0].set_title('Linear normalization')
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axes[0, 0].hist2d(data[:, 0], data[:, 1], bins=100)
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for ax, gamma in zip(axes.flat[1:], gammas):
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ax.set_title(r'Power law $(\gamma=%1.1f)$' % gamma)
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ax.hist2d(data[:, 0], data[:, 1],
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bins=100, norm=mcolors.PowerNorm(gamma))
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fig.tight_layout()
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return fig
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def PyplotFormatstr():
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def f(t):
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@ -195,6 +496,7 @@ def AxesGrid():
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plt.draw()
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return plt.gcf()
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# The magic function that makes it possible.... glues together tkinter and pyplot using Canvas Widget
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def draw_figure(canvas, figure, loc=(0, 0)):
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""" Draw a matplotlib figure onto a Tk canvas
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@ -218,8 +520,6 @@ def draw_figure(canvas, figure, loc=(0, 0)):
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# which must be kept live or else the picture disappears
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return photo
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#------------------------------- PASTE YOUR MATPLOTLIB CODE HERE -------------------------------
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# -------------------------------- GUI Starts Here -------------------------------#
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# fig = your figure you want to display. Assumption is that 'fig' holds the #
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@ -227,35 +527,41 @@ def draw_figure(canvas, figure, loc=(0, 0)):
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# --------------------------------------------------------------------------------#
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fig_dict = {'Pyplot Simple':PyplotSimple, 'Pyplot Formatstr':PyplotFormatstr,'PyPlot Three':Subplot3d,
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'Unicode Minus': UnicodeMinus, 'Pyplot Scales' : PyplotScales, 'Axes Grid' : AxesGrid}
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'Unicode Minus': UnicodeMinus, 'Pyplot Scales' : PyplotScales, 'Axes Grid' : AxesGrid,
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'Exploring Normalizations' : ExploringNormalizations, 'Different Scales' : DifferentScales,
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'Pyplot Box Plot' : PyplotBoxPlot, 'Pyplot ggplot Style Sheet' : PyplotGGPlotSytleSheet}
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figure_w, figure_h = 640,480
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canvas_elem = g.Canvas(size=(figure_w, figure_h)) # get the canvas we'll be drawing on
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# define the form layout
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listbox_values = [key for key in fig_dict.keys()]
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col_listbox = [[g.Listbox(values=listbox_values,size=(20,8), key='func')],
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[g.ReadFormButton('Plot', pad=((50,0), 3))]]
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col_listbox = [[g.Listbox(values=listbox_values,size=(25,len(listbox_values)), key='func')],
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[g.T(' '), g.ReadFormButton('Plot', size=(5,2)), g.Exit(size=(5,2))]]
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layout = [[g.Text('Matplotlib Plot Test', font=('current 18'))],
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[g.Column(col_listbox), canvas_elem],
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[g.Exit(pad=((50,0), 3), size=(4,2))]]
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[g.Column(col_listbox), canvas_elem]]
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# create the form and show it without the plot
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form = g.FlexForm('Demo Application - Embedding Matplotlib In PySimpleGUI')
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form.Layout(layout)
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form.Show(non_blocking=True)
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form.NonBlocking = False
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# add the plot to the window
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while True:
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button, values = form.Read()
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# show it all again and get buttons
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if button is None or button is 'Exit':
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break
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if button is 'Clear':
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canvas_elem.TKCanvas.delete(Tk.ALL)
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continue
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choice = values['func'][0]
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try:
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func = fig_dict[choice]
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except:
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func = fig_dict['Pyplot Simple']
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plt.clf()
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fig = func()
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fig_photo = draw_figure(canvas_elem.TKCanvas, fig)
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