Aug 27, 2017 · Dec 5, 2016 · Dec 5, 2016 · Dec 5, 2016 · Dec 5, 2016 · Dec 5, 2016
diff --git a/doc/api/axis_api.rst b/doc/api/axis_api.rst
   Axis.axis_date


 Data and viewinternvals
 ------------------------
 Data and viewintervals
 -----------------------

 .. autosummary::
   :toctree: _as_gen
diff --git a/examples/animation/unchained.py b/examples/animation/unchained.py
 # Generate random data
 data=np.random.uniform(0,1, (64,75))
 X=np.linspace(-1,1,data.shape[-1])
 G=1.5*np.exp(-4*X*X)
 G=1.5*np.exp(-4*X**2)

 # Generate line plots
 lines= []
diff --git a/examples/api/custom_projection_example.py b/examples/api/custom_projection_example.py
            self._resolution = resolution

        def transform_non_affine(self, xy):
            x = xy[:, 0:1]
            y = xy[:, 1:2]

            quarter_x = 0.25 * x
            half_y = 0.5 * y
            z = np.sqrt(1.0 - quarter_x*quarter_x - half_y*half_y)
            longitude = 2 * np.arctan((z*x) / (2.0 * (2.0*z*z - 1.0)))
            x, y = xy.T
            z = np.sqrt(1 - (x / 4) ** 2 - (y / 2) ** 2)
            longitude = 2 * np.arctan((z * x) / (2 * (2 * z ** 2 - 1)))
            latitude = np.arcsin(y*z)
            return np.concatenate((longitude, latitude), 1)
            return np.column_stack([longitude, latitude])
        transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__

        def inverted(self):
diff --git a/examples/api/scatter_piecharts.py b/examples/api/scatter_piecharts.py

 Thanks to Manuel Metz for the example
 """
 import math

 import numpy as np
 import matplotlib.pyplot as plt

 r2 = r1 + 0.4  # 40%

 # define some sizes of the scatter marker
 sizes = [60, 80, 120]
 sizes =np.array([60, 80, 120])

 # calculate the points of the first pie marker
 #
 # these are just the origin (0,0) +
 # some points on a circle cos,sin
 x = [0] + np.cos(np.linspace(0, 2*math.pi*r1, 10)).tolist()
 y = [0] + np.sin(np.linspace(0, 2*math.pi*r1, 10)).tolist()

 x = [0] + np.cos(np.linspace(0, 2 * np.pi * r1, 10)).tolist()
 y = [0] + np.sin(np.linspace(0, 2 * np.pi * r1, 10)).tolist()
 xy1 = list(zip(x, y))
 s1 = max(max(x), max(y))
 s1 =np.max(xy1)

 # ...
 x = [0] + np.cos(np.linspace(2*math.pi*r1, 2*math.pi*r2, 10)).tolist()
 y = [0] + np.sin(np.linspace(2*math.pi*r1, 2*math.pi*r2, 10)).tolist()
 x = [0] + np.cos(np.linspace(2 * np.pi * r1, 2 * np.pi * r2, 10)).tolist()
 y = [0] + np.sin(np.linspace(2 * np.pi * r1, 2 * np.pi * r2, 10)).tolist()
 xy2 = list(zip(x, y))
 s2 = max(max(x), max(y))
 s2 =np.max(xy2)

 x = [0] + np.cos(np.linspace(2*math.pi*r2, 2*math.pi, 10)).tolist()
 y = [0] + np.sin(np.linspace(2*math.pi*r2, 2*math.pi, 10)).tolist()
 x = [0] + np.cos(np.linspace(2 * np.pi *r2, 2 * np.pi, 10)).tolist()
 y = [0] + np.sin(np.linspace(2 * np.pi *r2, 2 * np.pi, 10)).tolist()
 xy3 = list(zip(x, y))
 s3 = max(max(x), max(y))
 s3 =np.max(xy3)

 fig, ax = plt.subplots()
 ax.scatter(np.arange(3),np.arange(3), marker=(xy1, 0),
           s=[s1*s1*_ for _ insizes], facecolor='blue')
 ax.scatter(np.arange(3),np.arange(3), marker=(xy2, 0),
           s=[s2*s2*_ for _ insizes], facecolor='green')
 ax.scatter(np.arange(3),np.arange(3), marker=(xy3, 0),
           s=[s3*s3*_ for _ insizes], facecolor='red')
 ax.scatter(range(3),range(3), marker=(xy1, 0),
           s=s1 ** 2 *sizes, facecolor='blue')
 ax.scatter(range(3),range(3), marker=(xy2, 0),
           s=s2 ** 2 *sizes, facecolor='green')
 ax.scatter(range(3),range(3), marker=(xy3, 0),
           s=s3 ** 2 *sizes, facecolor='red')

 plt.show()
diff --git a/examples/event_handling/timers.py b/examples/event_handling/timers.py
 fig, ax = plt.subplots()

 x = np.linspace(-3, 3)
 ax.plot(x, x*x)
 ax.plot(x, x ** 2)

 # Create a new timer object. Set the interval to 100 milliseconds
 # (1000 is default) and tell the timer what function should be called.
diff --git a/examples/event_handling/trifinder_event_demo.py b/examples/event_handling/trifinder_event_demo.py
 from matplotlib.tri import Triangulation
 from matplotlib.patches import Polygon
 import numpy as np
 import math


 def update_polygon(tri):
    if tri == -1:
        points = [0, 0, 0]
    else:
        points =triangulation.triangles[tri]
    xs =triangulation.x[points]
    ys =triangulation.y[points]
        points =triang.triangles[tri]
    xs =triang.x[points]
    ys =triang.y[points]
    polygon.set_xy(list(zip(xs, ys)))


 n_radii = 5
 min_radius = 0.25
 radii = np.linspace(min_radius, 0.95, n_radii)
 angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
 angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
 angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
 angles[:, 1::2] +=math.pi / n_angles
 angles[:, 1::2] +=np.pi / n_angles
 x = (radii*np.cos(angles)).flatten()
 y = (radii*np.sin(angles)).flatten()
 triangulation = Triangulation(x, y)
 xmid = x[triangulation.triangles].mean(axis=1)
 ymid = y[triangulation.triangles].mean(axis=1)
 mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
 triangulation.set_mask(mask)
 triang = Triangulation(x, y)
 triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
                         y[triang.triangles].mean(axis=1))
                < min_radius)

 # Use the triangulation's default TriFinder object.
 trifinder =triangulation.get_trifinder()
 trifinder =triang.get_trifinder()

 # Setup plot and callbacks.
 plt.subplot(111, aspect='equal')
 plt.triplot(triangulation, 'bo-')
 plt.triplot(triang, 'bo-')
 polygon = Polygon([[0, 0], [0, 0]], facecolor='y')  # dummy data for xs,ys
 update_polygon(-1)
 plt.gca().add_patch(polygon)
diff --git a/examples/images_contours_and_fields/tricontour_demo.py b/examples/images_contours_and_fields/tricontour_demo.py
 import matplotlib.pyplot as plt
 import matplotlib.tri as tri
 import numpy as np
 import math

 ###############################################################################
 # Creating a Triangulation without specifying the triangles results in the
 min_radius = 0.25
 radii = np.linspace(min_radius, 0.95, n_radii)

 angles = np.linspace(0, 2 *math.pi, n_angles, endpoint=False)
 angles = np.linspace(0, 2 *np.pi, n_angles, endpoint=False)
 angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
 angles[:, 1::2] +=math.pi / n_angles
 angles[:, 1::2] +=np.pi / n_angles

 x = (radii * np.cos(angles)).flatten()
 y = (radii * np.sin(angles)).flatten()
 z = (np.cos(radii) * np.cos(angles *3.0)).flatten()
 z = (np.cos(radii) * np.cos(3 *angles)).flatten()

 # Create the Triangulation; no triangles so Delaunay triangulation created.
 triang = tri.Triangulation(x, y)

 # Mask off unwanted triangles.
 xmid = x[triang.triangles].mean(axis=1)
 ymid = y[triang.triangles].mean(axis=1)
 mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
 triang.set_mask(mask)
 triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
                         y[triang.triangles].mean(axis=1))
                < min_radius)

 ###############################################################################
 # pcolor plot.
diff --git a/examples/images_contours_and_fields/tricontour_smooth_user.py b/examples/images_contours_and_fields/tricontour_smooth_user.py
 import matplotlib.pyplot as plt
 import matplotlib.cm as cm
 import numpy as np
 import math


 #-----------------------------------------------------------------------------
 min_radius = 0.15
 radii = np.linspace(min_radius, 0.95, n_radii)

 angles = np.linspace(0, 2 *math.pi, n_angles, endpoint=False)
 angles = np.linspace(0, 2 *np.pi, n_angles, endpoint=False)
 angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
 angles[:, 1::2] +=math.pi / n_angles
 angles[:, 1::2] +=np.pi / n_angles

 x = (radii * np.cos(angles)).flatten()
 y = (radii * np.sin(angles)).flatten()
 triang = tri.Triangulation(x, y)

 # Mask off unwanted triangles.
 xmid = x[triang.triangles].mean(axis=1)
 ymid = y[triang.triangles].mean(axis=1)
 mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
 triang.set_mask(mask)
 triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
                         y[triang.triangles].mean(axis=1))
                < min_radius)

 #-----------------------------------------------------------------------------
 # Refine data
diff --git a/examples/images_contours_and_fields/trigradient_demo.py b/examples/images_contours_and_fields/trigradient_demo.py

 Demonstrates computation of gradient with matplotlib.tri.CubicTriInterpolator.
 """
 from matplotlib.tri importTriangulation, UniformTriRefiner,\
    CubicTriInterpolator
 from matplotlib.tri import(
 Triangulation, UniformTriRefiner,CubicTriInterpolator)
 import matplotlib.pyplot as plt
 import matplotlib.cm as cm
 import numpy as np
 import math


 #-----------------------------------------------------------------------------
 min_radius = 0.2
 radii = np.linspace(min_radius, 0.95, n_radii)

 angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
 angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
 angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
 angles[:, 1::2] +=math.pi/n_angles
 angles[:, 1::2] +=np.pi /n_angles

 x = (radii*np.cos(angles)).flatten()
 y = (radii*np.sin(angles)).flatten()
 triang = Triangulation(x, y)

 # Mask off unwanted triangles.
 xmid = x[triang.triangles].mean(axis=1)
 ymid = y[triang.triangles].mean(axis=1)
 mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
 triang.set_mask(mask)
 triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
                         y[triang.triangles].mean(axis=1))
                < min_radius)

 #-----------------------------------------------------------------------------
 # Refine data - interpolates the electrical potential V
diff --git a/examples/images_contours_and_fields/tripcolor_demo.py b/examples/images_contours_and_fields/tripcolor_demo.py
 import matplotlib.pyplot as plt
 import matplotlib.tri as tri
 import numpy as np
 import math

 ###############################################################################
 # Creating a Triangulation without specifying the triangles results in the
 min_radius = 0.25
 radii = np.linspace(min_radius, 0.95, n_radii)

 angles = np.linspace(0, 2 *math.pi, n_angles, endpoint=False)
 angles = np.linspace(0, 2 *np.pi, n_angles, endpoint=False)
 angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
 angles[:, 1::2] +=math.pi / n_angles
 angles[:, 1::2] +=np.pi / n_angles

 x = (radii * np.cos(angles)).flatten()
 y = (radii * np.sin(angles)).flatten()
 z = (np.cos(radii) * np.cos(angles *3.0)).flatten()
 z = (np.cos(radii) * np.cos(3 *angles)).flatten()

 # Create the Triangulation; no triangles so Delaunay triangulation created.
 triang = tri.Triangulation(x, y)

 # Mask off unwanted triangles.
 xmid = x[triang.triangles].mean(axis=1)
 ymid = y[triang.triangles].mean(axis=1)
 mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
 triang.set_mask(mask)
 triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
                         y[triang.triangles].mean(axis=1))
                < min_radius)

 ###############################################################################
 # tripcolor plot.
diff --git a/examples/images_contours_and_fields/triplot_demo.py b/examples/images_contours_and_fields/triplot_demo.py
 import matplotlib.pyplot as plt
 import matplotlib.tri as tri
 import numpy as np
 import math

 ###############################################################################
 # Creating a Triangulation without specifying the triangles results in the
 min_radius = 0.25
 radii = np.linspace(min_radius, 0.95, n_radii)

 angles = np.linspace(0, 2 *math.pi, n_angles, endpoint=False)
 angles = np.linspace(0, 2 *np.pi, n_angles, endpoint=False)
 angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
 angles[:, 1::2] +=math.pi / n_angles
 angles[:, 1::2] +=np.pi / n_angles

 x = (radii * np.cos(angles)).flatten()
 y = (radii * np.sin(angles)).flatten()
 triang = tri.Triangulation(x, y)

 # Mask off unwanted triangles.
 xmid = x[triang.triangles].mean(axis=1)
 ymid = y[triang.triangles].mean(axis=1)
 mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
 triang.set_mask(mask)
 triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
                         y[triang.triangles].mean(axis=1))
                < min_radius)

 ###############################################################################
 # Plot the triangulation.
diff --git a/examples/lines_bars_and_markers/nan_test.py b/examples/lines_bars_and_markers/nan_test.py
 importmatplotlib.pyplotasplt

 t=np.arange(0.0,1.0+0.01,0.01)
 s=np.cos(2*2*np.pi*t)
 s=np.cos(2*2*np.pi*t)
 t[41:60]=np.nan

 plt.subplot(2,1,1)
diff --git a/examples/misc/multipage_pdf.py b/examples/misc/multipage_pdf.py

    plt.rc('text', usetex=False)
    fig = plt.figure(figsize=(4, 5))
    plt.plot(x, x*x, 'ko')
    plt.plot(x, x ** 2, 'ko')
    plt.title('Page Three')
    pdf.savefig(fig)  # or you can pass a Figure object to pdf.savefig
    plt.close()
diff --git a/examples/misc/pythonic_matplotlib.py b/examples/misc/pythonic_matplotlib.py
 fig = figure(1)

 ax1 = fig.add_subplot(211)
 ax1.plot(t, sin(2*pi*t))
 ax1.plot(t, sin(2*pi *t))
 ax1.grid(True)
 ax1.set_ylim((-2, 2))
 ax1.set_ylabel('1 Hz')


 ax2 = fig.add_subplot(212)
 ax2.plot(t, sin(2*2*pi*t))
 ax2.plot(t, sin(2 *2*pi *t))
 ax2.grid(True)
 ax2.set_ylim((-2, 2))
 l = ax2.set_xlabel('Hi mom')
diff --git a/examples/misc/table_demo.py b/examples/misc/table_demo.py
 bar_width = 0.4

 # Initialize the vertical-offset for the stacked bar chart.
 y_offset = np.array([0.0] *len(columns))
 y_offset = np.zeros(len(columns))

 # Plot bars and create text labels for the table
 cell_text = []
Original file line number	Diff line number	Diff line change
Expand Up		@@ -109,8 +109,8 @@ Ticks, tick labels and Offset text
		Axis.axis_date


		Data and viewinternvals
		------------------------
		Data and viewintervals
		-----------------------

		.. autosummary::
		:toctree: _as_gen
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -25,7 +25,7 @@
		# Generate random data
		data=np.random.uniform(0,1, (64,75))
		X=np.linspace(-1,1,data.shape[-1])
		G=1.5np.exp(-4X*X)
		G=1.5np.exp(-4X**2)
Copy link Member tacaswellDec 5, 2016 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. In most cases I think this construct is marginally faster. Copy link ContributorAuthor anntzerDec 5, 2016 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. `# float is probably the most common caseIn [8]: x = np.arange(100000).astype(float)In [9]: %timeit x * x10000 loops, best of 3: 65.5 µs per loopIn [10]: %timeit x 210000 loops, best of 3: 66.4 µs per loop` (and you can easily get them reversed on another run). Copy link Member tacaswellDec 5, 2016 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. interesting. That is an apparently wrong rule of thumb I have been using for a while. Copy link ContributorAuthor anntzerDec 5, 2016 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. Why would you think this would be the case? (I am honestly puzzled.) Copy link Member efiringDec 5, 2016 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. It dates back to the early days of computers and compilers, when computers were slow and compilers were not so bright. Multiplication is faster than taking a power, in general. But now compilers recognize things like this and find the best algorithm. In the case of numpy, I'm pretty sure there is explicit internal optimization of small integer powers so that we wouldn't have to use that ancient manual optimization. Copy link Member tacaswellDec 29, 2016 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. I respectfully request the children remain off of my lawn. 😈 ivanov reacted with laugh emoji Copy link Contributor eric-wieser**Dec 15, 2017• edited Loading Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. As@efiring says, numpy hard-codes`power(x, 2)` to fall back on`square(x)`, along with a handful of other constants (I think`(-1, 0, 0.5, 1, 2)`)

		# Generate line plots
		lines= []
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Original file line number	Diff line number	Diff line change
Expand Up		@@ -434,15 +434,11 @@ def __init__(self, resolution):
		self._resolution = resolution

		def transform_non_affine(self, xy):
		x = xy[:, 0:1]
		y = xy[:, 1:2]

		quarter_x = 0.25 * x
		half_y = 0.5 * y
		z = np.sqrt(1.0 - quarter_xquarter_x - half_yhalf_y)
		longitude = 2 * np.arctan((zx) / (2.0 (2.0zz - 1.0)))
		x, y = xy.T
		z = np.sqrt(1 - (x / 4) 2 - (y / 2) 2)
		longitude = 2 * np.arctan((z * x) / (2 * (2 * z ** 2 - 1)))
		latitude = np.arcsin(y*z)
		return np.concatenate((longitude, latitude), 1)
		return np.column_stack([longitude, latitude])
		transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__

		def inverted(self):
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -7,7 +7,7 @@

		Thanks to Manuel Metz for the example
		"""
		import math

		import numpy as np
		import matplotlib.pyplot as plt

Expand All		@@ -16,35 +16,33 @@
		r2 = r1 + 0.4 # 40%

		# define some sizes of the scatter marker
		sizes = [60, 80, 120]
		sizes =np.array([60, 80, 120])

		# calculate the points of the first pie marker
		#
		# these are just the origin (0,0) +
		# some points on a circle cos,sin
		x = [0] + np.cos(np.linspace(0, 2math.pir1, 10)).tolist()
		y = [0] + np.sin(np.linspace(0, 2math.pir1, 10)).tolist()

		x = [0] + np.cos(np.linspace(0, 2 * np.pi * r1, 10)).tolist()
		y = [0] + np.sin(np.linspace(0, 2 * np.pi * r1, 10)).tolist()
		xy1 = list(zip(x, y))
		s1 = max(max(x), max(y))
		s1 =np.max(xy1)

		# ...
		x = [0] + np.cos(np.linspace(2math.pir1, 2math.pir2, 10)).tolist()
		y = [0] + np.sin(np.linspace(2math.pir1, 2math.pir2, 10)).tolist()
		x = [0] + np.cos(np.linspace(2 * np.pi * r1, 2 * np.pi * r2, 10)).tolist()
		y = [0] + np.sin(np.linspace(2 * np.pi * r1, 2 * np.pi * r2, 10)).tolist()
		xy2 = list(zip(x, y))
		s2 = max(max(x), max(y))
		s2 =np.max(xy2)

		x = [0] + np.cos(np.linspace(2math.pir2, 2*math.pi, 10)).tolist()
		y = [0] + np.sin(np.linspace(2math.pir2, 2*math.pi, 10)).tolist()
		x = [0] + np.cos(np.linspace(2 * np.pi r2, 2 np.pi, 10)).tolist()
		y = [0] + np.sin(np.linspace(2 * np.pi r2, 2 np.pi, 10)).tolist()
		xy3 = list(zip(x, y))
		s3 = max(max(x), max(y))
		s3 =np.max(xy3)

		fig, ax = plt.subplots()
		ax.scatter(np.arange(3),np.arange(3), marker=(xy1, 0),
		s=[s1s1_ for _ insizes], facecolor='blue')
		ax.scatter(np.arange(3),np.arange(3), marker=(xy2, 0),
		s=[s2s2_ for _ insizes], facecolor='green')
		ax.scatter(np.arange(3),np.arange(3), marker=(xy3, 0),
		s=[s3s3_ for _ insizes], facecolor='red')
		ax.scatter(range(3),range(3), marker=(xy1, 0),
		s=s1 ** 2 *sizes, facecolor='blue')
		ax.scatter(range(3),range(3), marker=(xy2, 0),
		s=s2 ** 2 *sizes, facecolor='green')
		ax.scatter(range(3),range(3), marker=(xy3, 0),
		s=s3 ** 2 *sizes, facecolor='red')

		plt.show()
Original file line number	Diff line number	Diff line change
Expand Up		@@ -18,7 +18,7 @@ def update_title(axes):
		fig, ax = plt.subplots()

		x = np.linspace(-3, 3)
		ax.plot(x, x*x)
		ax.plot(x, x ** 2)

		# Create a new timer object. Set the interval to 100 milliseconds
		# (1000 is default) and tell the timer what function should be called.
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -11,16 +11,15 @@
		from matplotlib.tri import Triangulation
		from matplotlib.patches import Polygon
		import numpy as np
		import math


		def update_polygon(tri):
		if tri == -1:
		points = [0, 0, 0]
		else:
		points =triangulation.triangles[tri]
		xs =triangulation.x[points]
		ys =triangulation.y[points]
		points =triang.triangles[tri]
		xs =triang.x[points]
		ys =triang.y[points]
		polygon.set_xy(list(zip(xs, ys)))


Expand All		@@ -39,23 +38,22 @@ def motion_notify(event):
		n_radii = 5
		min_radius = 0.25
		radii = np.linspace(min_radius, 0.95, n_radii)
		angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
		angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
		angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
		angles[:, 1::2] +=math.pi / n_angles
		angles[:, 1::2] +=np.pi / n_angles
		x = (radii*np.cos(angles)).flatten()
		y = (radii*np.sin(angles)).flatten()
		triangulation = Triangulation(x, y)
		xmid = x[triangulation.triangles].mean(axis=1)
		ymid = y[triangulation.triangles].mean(axis=1)
		mask = np.where(xmidxmid + ymidymid < min_radius*min_radius, 1, 0)
		triangulation.set_mask(mask)
		triang = Triangulation(x, y)
		triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
		y[triang.triangles].mean(axis=1))
		< min_radius)

		# Use the triangulation's default TriFinder object.
		trifinder =triangulation.get_trifinder()
		trifinder =triang.get_trifinder()

		# Setup plot and callbacks.
		plt.subplot(111, aspect='equal')
		plt.triplot(triangulation, 'bo-')
		plt.triplot(triang, 'bo-')
		polygon = Polygon([[0, 0], [0, 0]], facecolor='y') # dummy data for xs,ys
		update_polygon(-1)
		plt.gca().add_patch(polygon)
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Original file line number	Diff line number	Diff line change
Expand Up		@@ -8,7 +8,6 @@
		import matplotlib.pyplot as plt
		import matplotlib.tri as tri
		import numpy as np
		import math

		###############################################################################
		# Creating a Triangulation without specifying the triangles results in the
Expand All		@@ -20,22 +19,21 @@
		min_radius = 0.25
		radii = np.linspace(min_radius, 0.95, n_radii)

		angles = np.linspace(0, 2 *math.pi, n_angles, endpoint=False)
		angles = np.linspace(0, 2 *np.pi, n_angles, endpoint=False)
		angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
		angles[:, 1::2] +=math.pi / n_angles
		angles[:, 1::2] +=np.pi / n_angles

		x = (radii * np.cos(angles)).flatten()
		y = (radii * np.sin(angles)).flatten()
		z = (np.cos(radii) * np.cos(angles *3.0)).flatten()
		z = (np.cos(radii) * np.cos(3 *angles)).flatten()

		# Create the Triangulation; no triangles so Delaunay triangulation created.
		triang = tri.Triangulation(x, y)

		# Mask off unwanted triangles.
		xmid = x[triang.triangles].mean(axis=1)
		ymid = y[triang.triangles].mean(axis=1)
		mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
		triang.set_mask(mask)
		triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
		y[triang.triangles].mean(axis=1))
		< min_radius)

		###############################################################################
		# pcolor plot.
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Original file line number	Diff line number	Diff line change
Expand Up		@@ -10,7 +10,6 @@
		import matplotlib.pyplot as plt
		import matplotlib.cm as cm
		import numpy as np
		import math


		#-----------------------------------------------------------------------------
Expand All		@@ -36,9 +35,9 @@ def function_z(x, y):
		min_radius = 0.15
		radii = np.linspace(min_radius, 0.95, n_radii)

		angles = np.linspace(0, 2 *math.pi, n_angles, endpoint=False)
		angles = np.linspace(0, 2 *np.pi, n_angles, endpoint=False)
		angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
		angles[:, 1::2] +=math.pi / n_angles
		angles[:, 1::2] +=np.pi / n_angles

		x = (radii * np.cos(angles)).flatten()
		y = (radii * np.sin(angles)).flatten()
Expand All		@@ -50,10 +49,9 @@ def function_z(x, y):
		triang = tri.Triangulation(x, y)

		# Mask off unwanted triangles.
		xmid = x[triang.triangles].mean(axis=1)
		ymid = y[triang.triangles].mean(axis=1)
		mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
		triang.set_mask(mask)
		triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
		y[triang.triangles].mean(axis=1))
		< min_radius)

		#-----------------------------------------------------------------------------
		# Refine data
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Original file line number	Diff line number	Diff line change
Expand Up		@@ -5,12 +5,11 @@

		Demonstrates computation of gradient with matplotlib.tri.CubicTriInterpolator.
		"""
		from matplotlib.tri importTriangulation, UniformTriRefiner,\
		CubicTriInterpolator
		from matplotlib.tri import(
		Triangulation, UniformTriRefiner,CubicTriInterpolator)
		import matplotlib.pyplot as plt
		import matplotlib.cm as cm
		import numpy as np
		import math


		#-----------------------------------------------------------------------------
Expand All		@@ -33,9 +32,9 @@ def dipole_potential(x, y):
		min_radius = 0.2
		radii = np.linspace(min_radius, 0.95, n_radii)

		angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
		angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
		angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
		angles[:, 1::2] +=math.pi/n_angles
		angles[:, 1::2] +=np.pi /n_angles

		x = (radii*np.cos(angles)).flatten()
		y = (radii*np.sin(angles)).flatten()
Expand All		@@ -46,10 +45,9 @@ def dipole_potential(x, y):
		triang = Triangulation(x, y)

		# Mask off unwanted triangles.
		xmid = x[triang.triangles].mean(axis=1)
		ymid = y[triang.triangles].mean(axis=1)
		mask = np.where(xmidxmid + ymidymid < min_radius*min_radius, 1, 0)
		triang.set_mask(mask)
		triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
		y[triang.triangles].mean(axis=1))
		< min_radius)

		#-----------------------------------------------------------------------------
		# Refine data - interpolates the electrical potential V
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Original file line number	Diff line number	Diff line change
Expand Up		@@ -9,7 +9,7 @@
		importmatplotlib.pyplotasplt

		t=np.arange(0.0,1.0+0.01,0.01)
		s=np.cos(22np.pi*t)
		s=np.cos(22np.pi*t)
Copy link Contributor dopplershiftAug 24, 2017 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. Just curious: why isthis the one where you don't have spaces around it? Copy link Contributor dopplershiftAug 24, 2017 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. nm...I guess I see it now.
		t[41:60]=np.nan

		plt.subplot(2,1,1)
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