Remove unnecessary calls to np.array in examples.

anntzer · anntzer · commit b5155b7bf1b0 · 2020-02-07T00:27:59.000+01:00
There's many places where explicit conversion to np.array is not necessary
-- because matplotlib or numpy already handles the conversion, or
because the variable is already an array.  Removing these calls makes
the examples concentrate more on the plotting-relevant parts.
diff --git a/examples/animation/animated_histogram.py b/examples/animation/animated_histogram.py
@@ -21,8 +21,8 @@
 n, bins = np.histogram(data, 100)
 
 # get the corners of the rectangles for the histogram
-left = np.array(bins[:-1])
-right = np.array(bins[1:])
+left = bins[:-1]
+right = bins[1:]
 bottom = np.zeros(len(left))
 top = bottom + n
 nrects = len(left)
diff --git a/examples/images_contours_and_fields/image_annotated_heatmap.py b/examples/images_contours_and_fields/image_annotated_heatmap.py
@@ -271,7 +271,7 @@ def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
 y = ["Prod. {}".format(i) for i in range(10, 70, 10)]
 x = ["Cycle {}".format(i) for i in range(1, 7)]
 
-qrates = np.array(list("ABCDEFG"))
+qrates = list("ABCDEFG")
 norm = matplotlib.colors.BoundaryNorm(np.linspace(-3.5, 3.5, 8), 7)
 fmt = matplotlib.ticker.FuncFormatter(lambda x, pos: qrates[::-1][norm(x)])
 
diff --git a/examples/lines_bars_and_markers/eventplot_demo.py b/examples/lines_bars_and_markers/eventplot_demo.py
@@ -24,7 +24,7 @@
 
 # set different line properties for each set of positions
 # note that some overlap
-lineoffsets1 = np.array([-15, -3, 1, 1.5, 6, 10])
+lineoffsets1 = [-15, -3, 1, 1.5, 6, 10]
 linelengths1 = [5, 2, 1, 1, 3, 1.5]
 
 fig, axs = plt.subplots(2, 2)
diff --git a/examples/lines_bars_and_markers/scatter_star_poly.py b/examples/lines_bars_and_markers/scatter_star_poly.py
@@ -28,7 +28,7 @@
 axs[0, 1].set_title(r"marker=r'\$\alpha\$'")
 
 # marker from path
-verts = np.array([[-1, -1], [1, -1], [1, 1], [-1, -1]])
+verts = [[-1, -1], [1, -1], [1, 1], [-1, -1]]
 axs[0, 2].scatter(x, y, s=80, c=z, marker=verts)
 axs[0, 2].set_title("marker=verts")
 
diff --git a/examples/lines_bars_and_markers/timeline.py b/examples/lines_bars_and_markers/timeline.py
@@ -81,10 +81,11 @@
 markerline.set_ydata(np.zeros(len(dates)))
 
 # annotate lines
-vert = np.array(['top', 'bottom'])[(levels > 0).astype(int)]
-for d, l, r, va in zip(dates, levels, names, vert):
-    ax.annotate(r, xy=(d, l), xytext=(-3, np.sign(l)*3),
-                textcoords="offset points", va=va, ha="right")
+for d, l, r in zip(dates, levels, names):
+    ax.annotate(r, xy=(d, l),
+                xytext=(-3, np.sign(l)*3), textcoords="offset points",
+                horizontalalignment="right",
+                verticalalignment="bottom" if l > 0 else "top")
 
 # format xaxis with 4 month intervals
 ax.get_xaxis().set_major_locator(mdates.MonthLocator(interval=4))
diff --git a/examples/misc/histogram_path.py b/examples/misc/histogram_path.py
@@ -31,8 +31,8 @@
 n, bins = np.histogram(data, 50)
 
 # get the corners of the rectangles for the histogram
-left = np.array(bins[:-1])
-right = np.array(bins[1:])
+left = bins[:-1]
+right = bins[1:]
 bottom = np.zeros(len(left))
 top = bottom + n
 
diff --git a/examples/pie_and_polar_charts/nested_pie.py b/examples/pie_and_polar_charts/nested_pie.py
@@ -32,7 +32,7 @@
 
 cmap = plt.get_cmap("tab20c")
 outer_colors = cmap(np.arange(3)*4)
-inner_colors = cmap(np.array([1, 2, 5, 6, 9, 10]))
+inner_colors = cmap([1, 2, 5, 6, 9, 10])
 
 ax.pie(vals.sum(axis=1), radius=1, colors=outer_colors,
        wedgeprops=dict(width=size, edgecolor='w'))
@@ -63,7 +63,7 @@
 
 cmap = plt.get_cmap("tab20c")
 outer_colors = cmap(np.arange(3)*4)
-inner_colors = cmap(np.array([1, 2, 5, 6, 9, 10]))
+inner_colors = cmap([1, 2, 5, 6, 9, 10])
 
 ax.bar(x=valsleft[:, 0],
        width=valsnorm.sum(axis=1), bottom=1-size, height=size,
diff --git a/examples/scales/logit_demo.py b/examples/scales/logit_demo.py
@@ -6,16 +6,17 @@
 Examples of plots with logit axes.
 """
 
+import math
+
 import numpy as np
 import matplotlib.pyplot as plt
 
 xmax = 10
 x = np.linspace(-xmax, xmax, 10000)
-cdf_norm = np.array([np.math.erf(w / np.sqrt(2)) / 2 + 1 / 2 for w in x])
-cdf_laplacian = np.array(
-    [1 / 2 * np.exp(w) if w < 0 else 1 - 1 / 2 * np.exp(-w) for w in x]
-)
-cdf_cauchy = 1 / np.pi * np.arctan(x) + 1 / 2
+cdf_norm = [math.erf(w / np.sqrt(2)) / 2 + 1 / 2 for w in x]
+cdf_laplacian = [1 / 2 * np.exp(w) if w < 0 else 1 - 1 / 2 * np.exp(-w)
+                 for w in x]
+cdf_cauchy = np.arctan(x) / np.pi + 1 / 2
 
 fig, axs = plt.subplots(nrows=3, ncols=2, figsize=(6.4, 8.5))
 
diff --git a/examples/shapes_and_collections/artist_reference.py b/examples/shapes_and_collections/artist_reference.py
@@ -86,13 +86,13 @@ def label(xy, text):
 label(grid[7], "FancyBboxPatch")
 
 # add a line
-x, y = np.array([[-0.06, 0.0, 0.1], [0.05, -0.05, 0.05]])
+x, y = ([-0.06, 0.0, 0.1], [0.05, -0.05, 0.05])
 line = mlines.Line2D(x + grid[8, 0], y + grid[8, 1], lw=5., alpha=0.3)
 label(grid[8], "Line2D")
 
 colors = np.linspace(0, 1, len(patches))
 collection = PatchCollection(patches, cmap=plt.cm.hsv, alpha=0.3)
-collection.set_array(np.array(colors))
+collection.set_array(colors)
 ax.add_collection(collection)
 ax.add_line(line)
 
diff --git a/examples/shapes_and_collections/compound_path.py b/examples/shapes_and_collections/compound_path.py
@@ -7,12 +7,11 @@
 and a triangle.  Use ``CLOSEPOLY`` and ``MOVETO`` for the different parts of
 the compound path
 """
-import numpy as np
+
 from matplotlib.path import Path
 from matplotlib.patches import PathPatch
 import matplotlib.pyplot as plt
 
-
 vertices = []
 codes = []
 
@@ -22,7 +21,6 @@
 codes += [Path.MOVETO] + [Path.LINETO]*2 + [Path.CLOSEPOLY]
 vertices += [(4, 4), (5, 5), (5, 4), (0, 0)]
 
-vertices = np.array(vertices, float)
 path = Path(vertices, codes)
 
 pathpatch = PathPatch(path, facecolor='None', edgecolor='green')
diff --git a/examples/shapes_and_collections/patch_collection.py b/examples/shapes_and_collections/patch_collection.py
@@ -48,9 +48,9 @@
     polygon = Polygon(np.random.rand(N, 2), True)
     patches.append(polygon)
 
-colors = 100*np.random.rand(len(patches))
+colors = 100 * np.random.rand(len(patches))
 p = PatchCollection(patches, alpha=0.4)
-p.set_array(np.array(colors))
+p.set_array(colors)
 ax.add_collection(p)
 fig.colorbar(p, ax=ax)
 
diff --git a/examples/specialty_plots/leftventricle_bulleye.py b/examples/specialty_plots/leftventricle_bulleye.py
@@ -46,7 +46,7 @@ def bullseye_plot(ax, data, seg_bold=None, cmap=None, norm=None):
         seg_bold = []
 
     linewidth = 2
-    data = np.array(data).ravel()
+    data = np.ravel(data)
 
     if cmap is None:
         cmap = plt.cm.viridis
@@ -129,7 +129,7 @@ def bullseye_plot(ax, data, seg_bold=None, cmap=None, norm=None):
 
 
 # Create the fake data
-data = np.array(range(17)) + 1
+data = np.arange(17) + 1
 
 
 # Make a figure and axes with dimensions as desired.
diff --git a/examples/statistics/boxplot_demo.py b/examples/statistics/boxplot_demo.py
@@ -221,7 +221,7 @@ def fake_bootstrapper(n):
 conf_intervals = [None, None, ci1, ci2]
 
 fig, ax = plt.subplots()
-pos = np.array(range(len(treatments))) + 1
+pos = np.arange(len(treatments)) + 1
 bp = ax.boxplot(treatments, sym='k+', positions=pos,
                 notch=1, bootstrap=5000,
                 usermedians=medians,
diff --git a/examples/statistics/confidence_ellipse.py b/examples/statistics/confidence_ellipse.py
@@ -128,12 +128,12 @@ def get_correlated_dataset(n, dependency, mu, scale):
 np.random.seed(0)
 
 PARAMETERS = {
-    'Positive correlation': np.array([[0.85, 0.35],
-                                      [0.15, -0.65]]),
-    'Negative correlation': np.array([[0.9, -0.4],
-                                      [0.1, -0.6]]),
-    'Weak correlation': np.array([[1, 0],
-                                  [0, 1]]),
+    'Positive correlation': [[0.85, 0.35],
+                             [0.15, -0.65]],
+    'Negative correlation': [[0.9, -0.4],
+                             [0.1, -0.6]],
+    'Weak correlation': [[1, 0],
+                         [0, 1]],
 }
 
 mu = 2, 4
@@ -164,10 +164,8 @@ def get_correlated_dataset(n, dependency, mu, scale):
 
 fig, ax_nstd = plt.subplots(figsize=(6, 6))
 
-dependency_nstd = np.array([
-    [0.8, 0.75],
-    [-0.2, 0.35]
-])
+dependency_nstd = [[0.8, 0.75],
+                   [-0.2, 0.35]]
 mu = 0, 0
 scale = 8, 5
 
@@ -199,10 +197,8 @@ def get_correlated_dataset(n, dependency, mu, scale):
 # to have the ellipse rendered in different ways.
 
 fig, ax_kwargs = plt.subplots(figsize=(6, 6))
-dependency_kwargs = np.array([
-    [-0.8, 0.5],
-    [-0.2, 0.5]
-])
+dependency_kwargs = [[-0.8, 0.5],
+                     [-0.2, 0.5]]
 mu = 2, -3
 scale = 6, 5
 
diff --git a/examples/statistics/errorbar_limits.py b/examples/statistics/errorbar_limits.py
@@ -58,8 +58,8 @@
 # mock up some limits by modifying previous data
 xlolims = lolims
 xuplims = uplims
-lolims = np.zeros(x.shape)
-uplims = np.zeros(x.shape)
+lolims = np.zeros_like(x)
+uplims = np.zeros_like(x)
 lolims[[6]] = True  # only limited at this index
 uplims[[3]] = True  # only limited at this index
 
diff --git a/examples/subplots_axes_and_figures/axes_box_aspect.py b/examples/subplots_axes_and_figures/axes_box_aspect.py
@@ -106,7 +106,7 @@
 axs[1, 0].set_box_aspect(1)
 axs[1, 1].set_box_aspect(3/1)
 
-x, y = np.random.randn(2, 400) * np.array([[.5], [180]])
+x, y = np.random.randn(2, 400) * [[.5], [180]]
 axs[1, 0].scatter(x, y)
 axs[0, 0].hist(x)
 axs[1, 1].hist(y, orientation="horizontal")
diff --git a/examples/text_labels_and_annotations/custom_legends.py b/examples/text_labels_and_annotations/custom_legends.py
@@ -27,8 +27,8 @@
 np.random.seed(19680801)
 
 N = 10
-data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]
-data = np.array(data).T
+data = np.transpose([np.logspace(0, 1, 100) + np.random.randn(100) + ii
+                     for ii in range(N)])
 cmap = plt.cm.coolwarm
 rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))
 
diff --git a/examples/text_labels_and_annotations/text_rotation_relative_to_line.py b/examples/text_labels_and_annotations/text_rotation_relative_to_line.py
@@ -17,25 +17,26 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
+fig, ax = plt.subplots()
+
 # Plot diagonal line (45 degrees)
-h = plt.plot(np.arange(0, 10), np.arange(0, 10))
+h = ax.plot(range(0, 10), range(0, 10))
 
 # set limits so that it no longer looks on screen to be 45 degrees
-plt.xlim([-10, 20])
+ax.set_xlim([-10, 20])
 
 # Locations to plot text
 l1 = np.array((1, 1))
 l2 = np.array((5, 5))
 
 # Rotate angle
 angle = 45
-trans_angle = plt.gca().transData.transform_angles(np.array((45,)),
-                                                   l2.reshape((1, 2)))[0]
+trans_angle = ax.transData.transform_angles([45], l2.reshape((1, 2)))[0]
 
 # Plot text
-th1 = plt.text(l1[0], l1[1], 'text not rotated correctly', fontsize=16,
-               rotation=angle, rotation_mode='anchor')
-th2 = plt.text(l2[0], l2[1], 'text rotated correctly', fontsize=16,
-               rotation=trans_angle, rotation_mode='anchor')
+th1 = ax.text(*l1, 'text not rotated correctly', fontsize=16,
+              rotation=angle, rotation_mode='anchor')
+th2 = ax.text(*l2, 'text rotated correctly', fontsize=16,
+              rotation=trans_angle, rotation_mode='anchor')
 
 plt.show()
diff --git a/examples/ticks_and_spines/date_concise_formatter.py b/examples/ticks_and_spines/date_concise_formatter.py
@@ -24,8 +24,7 @@
 # First, the default formatter.
 
 base = datetime.datetime(2005, 2, 1)
-dates = np.array([base + datetime.timedelta(hours=(2 * i))
-                  for i in range(732)])
+dates = [base + datetime.timedelta(hours=(2 * i)) for i in range(732)]
 N = len(dates)
 np.random.seed(19680801)
 y = np.cumsum(np.random.randn(N))
diff --git a/examples/units/ellipse_with_units.py b/examples/units/ellipse_with_units.py
@@ -30,7 +30,7 @@
     ])
 
 
-x, y = np.dot(R, np.array([x, y]))
+x, y = np.dot(R, [x, y])
 x += xcenter
 y += ycenter
 
