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QuLogic merged 1 commit into from
Jun 29, 2021
Merged

Cleanup plot types #20509

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8 changes: 4 additions & 4 deletions plot_types/arrays/contourf.py
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Expand Up @@ -12,14 +12,14 @@

# make data
X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
Z = (1 - X/2. + X**5 + Y**3)*np.exp(-X**2-Y**2)
Z = (1 - X/2. + X**5 + Y**3) * np.exp(-X**2 - Y**2)
Z = Z - Z.min()
levs = np.linspace(np.min(Z), np.max(Z), 7)
levels = np.linspace(np.min(Z), np.max(Z), 7)

# plot
fig, ax = plt.subplots()

plt.contourf(X, Y, Z, levels=levs)
plt.contour(X, Y, Z, levels=levs, colors="white", linewidths=0.5)
ax.contourf(X, Y, Z, levels=levels)
ax.contour(X, Y, Z, levels=levels, colors="white", linewidths=0.5)

plt.show()
6 changes: 2 additions & 4 deletions plot_types/arrays/imshow.py
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Expand Up @@ -19,10 +19,8 @@

# plot
fig, ax = plt.subplots()
ax.grid(False)

ax.imshow(Z, extent=[0, 8, 0, 8], interpolation="nearest",
cmap=plt.get_cmap('Blues'), vmin=0, vmax=1.6)

ax.set(xticks=[], yticks=[])
ax.imshow(Z)

plt.show()
3 changes: 2 additions & 1 deletion plot_types/arrays/pcolormesh.py
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Expand Up @@ -27,7 +27,8 @@

# plot
fig, ax = plt.subplots()
ax.grid(False)

ax.pcolormesh(X, Y, Z, vmin=0, vmax=1.5, shading='nearest')
ax.pcolormesh(X, Y, Z, vmin=0, vmax=1.5)

plt.show()
10 changes: 5 additions & 5 deletions plot_types/arrays/quiver.py
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Expand Up @@ -11,15 +11,15 @@
plt.style.use('mpl_plot_gallery')

# make data
T = np.linspace(0, 2*np.pi, 8)
X, Y = 4 + 1 * np.cos(T), 4 + 1 * np.sin(T)
U, V = 1.5 * np.cos(T), 1.5 * np.sin(T)
phi = np.linspace(0, 2 * np.pi, 8)
x, y = 4 + 1 * np.cos(phi), 4 + 1 * np.sin(phi)
u, v = 1.5 * np.cos(phi), 1.5 * np.sin(phi)

# plot
fig, ax = plt.subplots()

plt.quiver(X, Y, U, V, color="C0", angles='xy',
scale_units='xy', scale=0.5, width=.05)
ax.quiver(x, y, u, v, color="C0", angles='xy',
scale_units='xy', scale=0.5, width=.05)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
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6 changes: 3 additions & 3 deletions plot_types/basic/bar.py
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Expand Up @@ -11,13 +11,13 @@

# make data:
np.random.seed(3)
X = 0.5 + np.arange(8)
Y = np.random.uniform(2, 7, len(X))
x = 0.5 + np.arange(8)
y = np.random.uniform(2, 7, len(x))

# plot
fig, ax = plt.subplots()

ax.bar(X, Y, width=1, edgecolor="white", linewidth=0.7)
ax.bar(x, y, width=1, edgecolor="white", linewidth=0.7)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
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6 changes: 3 additions & 3 deletions plot_types/basic/pie.py
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Expand Up @@ -12,12 +12,12 @@


# make data
X = [1, 2, 3, 4]
colors = plt.get_cmap('Blues')(np.linspace(0.2, 0.7, len(X)))
x = [1, 2, 3, 4]
colors = plt.get_cmap('Blues')(np.linspace(0.2, 0.7, len(x)))

# plot
fig, ax = plt.subplots()
ax.pie(X, colors=colors, radius=3, center=(4, 4),
ax.pie(x, colors=colors, radius=3, center=(4, 4),
wedgeprops={"linewidth": 1, "edgecolor": "white"}, frame=True)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
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6 changes: 3 additions & 3 deletions plot_types/basic/plot.py
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Expand Up @@ -12,13 +12,13 @@
plt.style.use('mpl_plot_gallery')

# make data
X = np.linspace(0, 10, 100)
Y = 4 + 2 * np.sin(2 * X)
x = np.linspace(0, 10, 100)
y = 4 + 2 * np.sin(2 * x)

# plot
fig, ax = plt.subplots()

ax.plot(X, Y, linewidth=2.0)
ax.plot(x, y, linewidth=2.0)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
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9 changes: 5 additions & 4 deletions plot_types/basic/scatter_plot.py
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Expand Up @@ -12,15 +12,16 @@

# make the data
np.random.seed(3)
X = 4 + np.random.normal(0, 2, 24)
Y = 4 + np.random.normal(0, 2, len(X))
x = 4 + np.random.normal(0, 2, 24)
y = 4 + np.random.normal(0, 2, len(x))
# size and color:
S = np.random.uniform(15, 80, len(X))
sizes = np.random.uniform(15, 80, len(x))
colors = np.random.uniform(15, 80, len(x))

# plot
fig, ax = plt.subplots()

ax.scatter(X, Y, s=S, c=-S, cmap=plt.get_cmap('Blues'), vmin=-100, vmax=0)
ax.scatter(x, y, s=sizes, c=colors, vmin=-100, vmax=0)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
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6 changes: 3 additions & 3 deletions plot_types/basic/stem.py
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Expand Up @@ -12,13 +12,13 @@

# make data
np.random.seed(3)
X = 0.5 + np.arange(8)
Y = np.random.uniform(2, 7, len(X))
x = 0.5 + np.arange(8)
y = np.random.uniform(2, 7, len(x))

# plot
fig, ax = plt.subplots()

ax.stem(X, Y, bottom=0, linefmt="-", markerfmt="d")
ax.stem(x, y)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
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6 changes: 3 additions & 3 deletions plot_types/basic/step.py
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Expand Up @@ -12,13 +12,13 @@

# make data
np.random.seed(3)
X = 0.5 + np.arange(8)
Y = np.random.uniform(2, 7, len(X))
x = 0.5 + np.arange(8)
y = np.random.uniform(2, 7, len(x))

# plot
fig, ax = plt.subplots()

ax.step(X, Y, linewidth=2.5)
ax.step(x, y, linewidth=2.5)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
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7 changes: 3 additions & 4 deletions plot_types/stats/barbs.py
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Expand Up @@ -14,14 +14,13 @@
np.random.seed(1)
X = [[2, 4, 6]]
Y = [[1.5, 3, 2]]
U = -np.ones((1, 3)) * 0
V = -np.ones((1, 3)) * np.linspace(50, 100, 3)
U = np.zeros_like(X)
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@story645 story645 Jun 24, 2021

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This was made lowercase everywhere else. Why uppercase here?

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@timhoffm timhoffm Jun 25, 2021

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I mostly follow the capitalization in the signature of the respective plot function, some of which are lowercase, some are uppercase.

There's a loose convention that 1D arrays have regular lowercase names and 2D arrays from np.meshgrid() have uppercase names (e.g. X, Y = np.meshgrid(x, y)). I suspect that the signature roughly follows this pattern: If we expect 2D input, we use an uppercase parameter name. For barbs this is a bit ambiguous as parameters can be 1D or 2D, so I followed the signature style.

V = -np.ones_like(X) * np.linspace(50, 100, 3)
Comment on lines 15 to +18
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@QuLogic QuLogic Jun 24, 2021

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Lower case?

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@timhoffm timhoffm Jun 25, 2021

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See comment in #20509 (review)


# plot:
fig, ax = plt.subplots()

ax.barbs(X, Y, U, V, barbcolor="C0", flagcolor="C0",
length=10, linewidth=1.5)
ax.barbs(X, Y, U, V, barbcolor="C0", flagcolor="C0", length=10, linewidth=1.5)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
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10 changes: 5 additions & 5 deletions plot_types/stats/errorbar_plot.py
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@@ -1,6 +1,6 @@
"""
==========================
errorbar(X, Y, xerr, yerr)
errorbar(x, y, yerr, xerr)
==========================

See `~matplotlib.axes.Axes.errorbar`.
Expand All @@ -12,14 +12,14 @@

# make data:
np.random.seed(1)
X = [2, 4, 6]
Y = [4, 5, 4]
E = np.random.uniform(0.5, 1.5, 3)
x = [2, 4, 6]
y = [4, 5, 4]
yerr = np.random.uniform(0.5, 1.5, 3)

# plot:
fig, ax = plt.subplots()

ax.errorbar(X, Y, E, linewidth=2, capsize=6)
ax.errorbar(x, y, yerr, linewidth=2, capsize=6)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
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4 changes: 2 additions & 2 deletions plot_types/stats/eventplot.py
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Expand Up @@ -12,13 +12,13 @@

# make data:
np.random.seed(1)
X = [2, 4, 6]
x = [2, 4, 6]
D = np.random.gamma(4, size=(3, 50))

# plot:
fig, ax = plt.subplots()

ax.eventplot(D, orientation="vertical", lineoffsets=X, linewidth=0.75)
ax.eventplot(D, orientation="vertical", lineoffsets=x, linewidth=0.75)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
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2 changes: 1 addition & 1 deletion plot_types/stats/hexbin.py
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Expand Up @@ -13,7 +13,7 @@
# make data: correlated + noise
np.random.seed(1)
x = np.random.randn(5000)
y = 1.2 * x + np.random.randn(5000)/3
y = 1.2 * x + np.random.randn(5000) / 3

# plot:
fig, ax = plt.subplots()
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2 changes: 1 addition & 1 deletion plot_types/stats/hist2d.py
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Expand Up @@ -13,7 +13,7 @@
# make data: correlated + noise
np.random.seed(1)
x = np.random.randn(5000)
y = 1.2 * x + np.random.randn(5000)/3
y = 1.2 * x + np.random.randn(5000) / 3

# plot:
fig, ax = plt.subplots()
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6 changes: 3 additions & 3 deletions plot_types/stats/hist_plot.py
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@@ -1,6 +1,6 @@
"""
=======
hist(X)
hist(x)
=======

See `~matplotlib.axes.Axes.hist`.
Expand All @@ -12,12 +12,12 @@

# make data
np.random.seed(1)
X = 4 + np.random.normal(0, 1.5, 200)
x = 4 + np.random.normal(0, 1.5, 200)

# plot:
fig, ax = plt.subplots()

ax.hist(X, bins=8, linewidth=0.5, edgecolor="white")
ax.hist(x, bins=8, linewidth=0.5, edgecolor="white")

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
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6 changes: 3 additions & 3 deletions plot_types/stats/violin.py
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Expand Up @@ -17,10 +17,10 @@
# plot:
fig, ax = plt.subplots()

VP = ax.violinplot(D, [2, 4, 6], widths=2,
showmeans=False, showmedians=False, showextrema=False)
vp = ax.violinplot(D, [2, 4, 6], widths=2,
showmeans=False, showmedians=False, showextrema=False)
# styling:
for body in VP['bodies']:
for body in vp['bodies']:
body.set_alpha(0.9)
ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
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10 changes: 5 additions & 5 deletions plot_types/unstructured/tricontour.py
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Expand Up @@ -12,13 +12,13 @@

# make structured data
X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
Z = (1 - X/2. + X**5 + Y**3)*np.exp(-X**2-Y**2)
Z = (1 - X/2. + X**5 + Y**3) * np.exp(-X**2 - Y**2)
Z = Z - Z.min()

# sample it to make unstructured x, y, z
np.random.seed(1)
ysamp = np.random.randint(0, high=256, size=250)
xsamp = np.random.randint(0, high=256, size=250)
ysamp = np.random.randint(0, 256, size=250)
xsamp = np.random.randint(0, 256, size=250)
y = Y[:, 0][ysamp]
x = X[0, :][xsamp]
z = Z[ysamp, xsamp]
Expand All @@ -27,8 +27,8 @@
fig, ax = plt.subplots()

ax.plot(x, y, '.k', alpha=0.5)
levs = np.linspace(np.min(Z), np.max(Z), 7)
ax.tricontourf(x, y, z, levels=levs)
levels = np.linspace(np.min(Z), np.max(Z), 7)
ax.tricontourf(x, y, z, levels=levels)

ax.set(xlim=(-3, 3), ylim=(-3, 3))

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4 changes: 2 additions & 2 deletions plot_types/unstructured/tripcolor.py
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Expand Up @@ -17,8 +17,8 @@

# sample it to make unstructured x, y, z
np.random.seed(1)
ysamp = np.random.randint(0, high=256, size=250)
xsamp = np.random.randint(0, high=256, size=250)
ysamp = np.random.randint(0, 256, size=250)
xsamp = np.random.randint(0, 256, size=250)
y = Y[:, 0][ysamp]
x = X[0, :][xsamp]
z = Z[ysamp, xsamp]
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4 changes: 2 additions & 2 deletions plot_types/unstructured/triplot.py
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Expand Up @@ -15,8 +15,8 @@

# sample it to make x, y, z
np.random.seed(1)
ysamp = np.random.randint(0, high=256, size=250)
xsamp = np.random.randint(0, high=256, size=250)
ysamp = np.random.randint(0, 256, size=250)
xsamp = np.random.randint(0, 256, size=250)
y = Y[:, 0][ysamp]
x = X[0, :][xsamp]

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