Chapter 2: Figures
Robert Johansson
Source code listings for Numerical Python - Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib (ISBN 979-8-8688-0412-0).
%matplotlib inline%config InlineBackend.figure_format='retina'import matplotlib as mpl
import matplotlib.pyplot as pltimport numpy as npdef show_array(shape, sel, filename=None):
"""Visualize indexing of arrays"""
data = np.zeros(shape)
exec("data[%s] = 1" % sel)
fig, ax = plt.subplots(1, 1, figsize=shape)
ax.patch.set_facecolor("black")
ax.set_aspect("equal", "box")
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_locator(plt.NullLocator())
size = 0.97
for (m, n), w in np.ndenumerate(data):
color = "#1199ff" if w > 0 else "#eeeeee"
rect = plt.Rectangle(
[n - size / 2, m - size / 2], size, size, facecolor=color, edgecolor=color
)
ax.add_patch(rect)
ax.text(m, n, "(%d, %d)" % (n, m), ha="center", va="center", fontsize=12)
ax.autoscale_view()
ax.set_xlim(-0.5, 3.5)
ax.set_ylim(-0.5, 3.5)
ax.invert_yaxis()
if sel == ":, :":
ax.set_title("data\n", fontsize=12)
else:
ax.set_title("data[%s]\n" % sel, fontsize=12)
fig.tight_layout()
if filename:
fig.savefig(filename + ".png", dpi=200)
fig.savefig(filename + ".svg")
fig.savefig(filename + ".pdf")show_array((4, 4), ":, :", "array_indexing_1")
show_array((4, 4), "0", "array_indexing_2")
show_array((4, 4), "1, :", "array_indexing_3")
show_array((4, 4), ":, 2", "array_indexing_4")
show_array((4, 4), "0:2, 0:2", "array_indexing_5")
show_array((4, 4), "0:2, 2:4", "array_indexing_6")
show_array((4, 4), "::2, ::2", "array_indexing_7")
show_array((4, 4), "1::2, 1::2", "array_indexing_8")
show_array((4, 4), ":,[0,3]", "array_indexing_9")
show_array((4, 4), "[1,3],[0,3]", "array_indexing_10")
show_array((4, 4), ":,np.array([False, True, True, False])", "array_indexing_11")
show_array((4, 4), ":,np.array([False, True, True, False])", "array_indexing_12")show_array((4, 4), "1:3,np.array([False, True, True, False])", "array_indexing_12")
Summary¶
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Aggregation¶
data = np.arange(9).reshape(3, 3)dataarray([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])data.sum()np.int64(36)data.sum(axis=0)array([ 9, 12, 15])data.sum(axis=1)array([ 3, 12, 21])def show_array_aggregation(data, axis, filename=None):
"""Visualize indexing of arrays"""
fig, axes = plt.subplots(2, 1, figsize=(4, 8))
ax = axes[0]
ax.patch.set_facecolor("black")
# ax.set_aspect('equal', 'box')
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_locator(plt.NullLocator())
colors = ["#1199ff", "#ee3311", "#66ff22"]
for (m, n), w in np.ndenumerate(data):
size = 0.97
color = "#1199ff" if w > 0 else "#eeeeee"
color = "#eeeeee"
rect = plt.Rectangle(
[n - size / 2, m - size / 2], size, size, facecolor=color, edgecolor=color
)
ax.add_patch(rect)
if axis is None:
color = "#1199ff"
elif axis == 1:
color = colors[m]
else:
color = colors[n]
size = 0.8
rect = plt.Rectangle(
[n - size / 2, m - size / 2], size, size, facecolor=color, edgecolor=color
)
ax.add_patch(rect)
ax.text(n, m, "%d" % data[m, n], ha="center", va="center", fontsize=12)
ax.autoscale_view()
ax.set_xlim(-0.5, 2.5)
ax.set_ylim(-0.5, 2.5)
ax.invert_yaxis()
ax.set_title("data", fontsize=12)
if False:
ax = axes[1]
ax.set_frame_on(False)
ax.patch.set_facecolor("white")
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_locator(plt.NullLocator())
ax.set_xlim(0, 3)
ax.set_ylim(0, 3)
if axis is not None:
ax.text(
1.5,
1.5,
"data.sum(axis=%d)" % axis,
ha="center",
va="center",
fontsize=18,
)
else:
ax.text(1.5, 1.5, "data.sum()", ha="center", va="center", fontsize=18)
ax.arrow(0.3, 1.25, 2.1, 0.0, head_width=0.1, head_length=0.2, fc="k", ec="k")
if axis == 0:
adata = data.sum(axis=axis)[:, np.newaxis]
elif axis == 1:
adata = data.sum(axis=axis)[:, np.newaxis]
else:
adata = np.atleast_2d(data.sum(axis=axis))
ax = axes[1]
ax.set_frame_on(False)
ax.patch.set_facecolor("white")
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_locator(plt.NullLocator())
colors = ["#1199ff", "#ee3311", "#66ff22"]
for (m, n), w in np.ndenumerate(data):
size = 1.0
color = "#ffffff"
rect = plt.Rectangle(
[n - size / 2, m - size / 2], size, size, facecolor=color, edgecolor=color
)
ax.add_patch(rect)
for (m, n), w in np.ndenumerate(adata):
if axis is None:
size = 1.0
color = "#000000"
rect = plt.Rectangle(
[1 + m - size / 2, 0 + n - size / 2],
size,
size,
facecolor=color,
edgecolor=color,
)
ax.add_patch(rect)
size = 0.97
color = "#eeeeee"
rect = plt.Rectangle(
[1 + m - size / 2, 0 + n - size / 2],
size,
size,
facecolor=color,
edgecolor=color,
)
ax.add_patch(rect)
if axis is None:
color = "#1199ff"
elif axis == 1:
color = colors[n]
else:
color = colors[m]
size = 0.8
rect = plt.Rectangle(
[1 + m - size / 2, 0 + n - size / 2],
size,
size,
facecolor=color,
edgecolor=color,
)
ax.add_patch(rect)
if n == 0:
ax.text(
1 + m,
n + 0,
"%d" % adata[m, n],
ha="center",
va="center",
fontsize=10,
)
if axis == 0:
size = 1.0
color = "#000000"
rect = plt.Rectangle(
[m - size / 2, 0 + n - size / 2],
size,
size,
facecolor=color,
edgecolor=color,
)
ax.add_patch(rect)
size = 0.97
color = "#eeeeee"
rect = plt.Rectangle(
[m - size / 2, 0 + n - size / 2],
size,
size,
facecolor=color,
edgecolor=color,
)
ax.add_patch(rect)
if axis is None:
color = "#1199ff"
elif axis == 1:
color = colors[n]
else:
color = colors[m]
size = 0.8
rect = plt.Rectangle(
[m - size / 2, 0 + n - size / 2],
size,
size,
facecolor=color,
edgecolor=color,
)
ax.add_patch(rect)
if n == 0:
ax.text(
m, n + 0, "%d" % adata[m, n], ha="center", va="center", fontsize=10
)
if axis == 1:
size = 1.0
color = "#000000"
rect = plt.Rectangle(
[0 + m - size / 2, n - size / 2],
size,
size,
facecolor=color,
edgecolor=color,
)
ax.add_patch(rect)
size = 0.97
color = "#eeeeee"
rect = plt.Rectangle(
[0 + m - size / 2, n - size / 2],
size,
size,
facecolor=color,
edgecolor=color,
)
ax.add_patch(rect)
if axis is None:
color = "#1199ff"
elif axis == 1:
color = colors[m]
else:
color = colors[m]
size = 0.8
rect = plt.Rectangle(
[0 + m - size / 2, n - size / 2],
size,
size,
facecolor=color,
edgecolor=color,
)
ax.add_patch(rect)
if axis == 0 and m == 0:
ax.text(
m + 0, n, "%d" % adata[m, n], ha="center", va="center", fontsize=12
)
if axis == 1 and n == 0:
ax.text(
m, n + 0, "%d" % adata[m, n], ha="center", va="center", fontsize=10
)
ax.autoscale_view()
ax.set_xlim(-0.5, 2.5)
ax.set_ylim(-0.5, 2.5)
ax.invert_yaxis()
if axis is not None:
ax.set_title("data.sum(axis=%d)" % axis, fontsize=12)
else:
ax.set_title("data.sum()", fontsize=12)
fig.tight_layout()
if filename:
fig.savefig(filename + ".png", dpi=200)
fig.savefig(filename + ".svg")
fig.savefig(filename + ".pdf")data = np.arange(1, 10).reshape(3, 3)
dataarray([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])data.sum(axis=0)array([12, 15, 18])show_array_aggregation(data, None, filename="array_aggregation_1")
show_array_aggregation(data, 0, filename="array_aggregation_2")
show_array_aggregation(data, 1, filename="array_aggregation_3")
Broadcasting¶
def show_array_broadcasting(a, b, filename=None):
"""Visualize broadcasting of arrays"""
c = a + b
fig, axes = plt.subplots(1, 3, figsize=(12, 4))
data = a
ax = axes[0]
ax.patch.set_facecolor("black")
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_locator(plt.NullLocator())
colors = ["#1199ff", "#ee3311", "#66ff22"]
for (m, n), w in np.ndenumerate(data):
size = 0.97
color = "#1199ff"
rect = plt.Rectangle(
[n - size / 2, m - size / 2], size, size, facecolor=color, edgecolor=color
)
ax.add_patch(rect)
ax.text(m, n, "%d" % data[n, m], ha="center", va="center", fontsize=12)
ax.text(2.8, 1, "+", ha="center", va="center", fontsize=22)
ax.autoscale_view()
ax.set_xlim(-0.5, 2.5)
ax.set_ylim(-0.5, 2.5)
ax.invert_yaxis()
data = np.zeros_like(a) + b
ax = axes[1]
ax.patch.set_facecolor("black")
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_locator(plt.NullLocator())
colors = ["#1199ff", "#ee3311", "#66ff22"]
for (m, n), w in np.ndenumerate(data):
size = 0.97
color = "#eeeeee"
rect = plt.Rectangle(
[n - size / 2, m - size / 2], size, size, facecolor=color, edgecolor=color
)
ax.add_patch(rect)
if (np.argmax(b.T.shape) == 0 and m == 0) or (
np.argmax(b.T.shape) == 1 and n == 0
):
color = "#1199ff"
# size = 0.8
rect = plt.Rectangle(
[n - size / 2, m - size / 2],
size,
size,
facecolor=color,
edgecolor=color,
)
ax.add_patch(rect)
ax.text(m, n, "%d" % data[n, m], ha="center", va="center", fontsize=12)
ax.text(2.8, 1, "=", ha="center", va="center", fontsize=22)
ax.autoscale_view()
ax.set_xlim(-0.5, 2.5)
ax.set_ylim(-0.5, 2.5)
ax.invert_yaxis()
data = c
ax = axes[2]
ax.patch.set_facecolor("black")
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_locator(plt.NullLocator())
colors = ["#1199ff", "#ee3311", "#66ff22"]
for (m, n), w in np.ndenumerate(data):
size = 0.97
color = "#1199ff" if w > 0 else "#eeeeee"
color = "#eeeeee"
rect = plt.Rectangle(
[n - size / 2, m - size / 2], size, size, facecolor=color, edgecolor=color
)
ax.add_patch(rect)
color = "#1199ff"
# size = 0.8
rect = plt.Rectangle(
[n - size / 2, m - size / 2], size, size, facecolor=color, edgecolor=color
)
ax.add_patch(rect)
ax.text(m, n, "%d" % data[n, m], ha="center", va="center", fontsize=12)
ax.autoscale_view()
ax.set_xlim(-0.5, 2.5)
ax.set_ylim(-0.5, 2.5)
ax.invert_yaxis()
# fig.tight_layout()
if filename:
fig.savefig(filename + ".png", dpi=200)
fig.savefig(filename + ".svg")
fig.savefig(filename + ".pdf")a = np.array([[11, 12, 13], [21, 22, 23], [31, 32, 33]])b = np.array([[1, 2, 3]])show_array_broadcasting(a, b, filename="array_broadcasting_1")
show_array_broadcasting(a, b.T, filename="array_broadcasting_2")
- Johansson, R. (2024). Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib. Apress. 10.1007/979-8-8688-0413-7