In [1]:

    

%matplotlib inline
import betterplotlib as bpl
import numpy as np
import matplotlib.pyplot as plt


    

In [2]:

    

xs1 = np.random.normal(0, 1, 500)
ys1 = np.random.normal(0, 1, 500)

xs2 = np.random.normal(1, 1, 500)
ys2 = np.random.normal(1, 1, 500)


    

In [3]:

    

fig, [ax0, ax1] = plt.subplots(figsize=[7, 10], nrows=2, tight_layout=True)
ax0.hist(xs1)
ax0.set_xlabel("Data Values")
ax0.set_ylabel("Number")
ax0.set_xlim(-4, 4)
ax1.scatter(xs1, ys1, label="Data 1")
ax1.scatter(xs2, ys2, label="Data 2")
ax1.set_xlabel("X Values")
ax1.set_ylabel("Y Values")
ax1.legend(loc=2)
ax1.set_xlim(-4, 6)
ax1.set_ylim(-4, 6)
ax0.set_title("matplotlib defaults", fontsize=24)
fig.savefig("plt.png", dpi=300)


    

    




/Users/gillenb/miniconda3/envs/general/lib/python3.5/site-packages/matplotlib/figure.py:1744: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.
  warnings.warn("This figure includes Axes that are not "






    

In [4]:

    

bpl.presentation_style()


    

In [5]:

    

fig, [ax0, ax1] = bpl.subplots(figsize=[7, 10], nrows=2, tight_layout=True)
ax0.hist(xs1, bin_size=0.5)
ax0.set_xlabel("Data Values")
ax0.set_ylabel("Number")
ax0.set_xlim(-4, 4)
ax1.scatter(xs1, ys1, label="Data 1")
ax1.scatter(xs2, ys2, label="Data 2")
ax1.set_xlabel("X Values")
ax1.set_ylabel("Y Values")
ax1.legend(loc=2)
ax1.set_xlim(-4, 6)
ax1.set_ylim(-4, 6)
ax0.set_title("betterplotlib defaults", fontsize=24)
fig.savefig("bpl.png", dpi=300)


    

    




/Users/gillenb/miniconda3/envs/general/lib/python3.5/site-packages/matplotlib/figure.py:1744: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.
  warnings.warn("This figure includes Axes that are not "






    

In [12]:

    

x1 = np.random.normal(-1, 2, 5000)
x2 = np.random.normal(1, 2, 5000)

contour_xs = np.concatenate([np.random.normal(0, 1, 10000),
                             np.random.normal(3, 1, 10000),
                             np.random.normal(0, 1, 10000)])
contour_ys = np.concatenate([np.random.normal(0, 1, 10000),
                             np.random.normal(3, 1, 10000),
                             np.random.normal(3, 1, 10000)])


    

In [33]:

    

x3 = np.random.normal(0, 1, 500)
y3 = np.random.normal(0, 1, 500)

x4 = np.random.normal(2, 1, 500)
y4 = np.random.normal(0, 1, 500)

x5 = np.random.normal(2, 1, 500)
y5 = np.random.normal(2, 1, 500)


    

In [23]:

    

sc_x1 = np.random.uniform(-2, 3, 200)
sc_y1 = sc_x1 + np.random.normal(0, 0.5, 200)

sc_x2 = np.random.normal(1, 1, 200)
sc_y2 = np.random.normal(1, 1, 200)


    

In [39]:

    

fig, [ax1, ax2] = bpl.subplots(ncols=2, nrows=1, figsize=[15, 7])

# [ax1, ax2], [ax3, ax4] = axs

ax1.hist(x1, rel_freq=True, histtype="step", bin_size=0.5, lw=3, hatch="\\", label="Data 1")
ax1.hist(x2, rel_freq=True, histtype="step", bin_size=0.5, lw=3, hatch= "/", label="Data 2")

ax1.remove_spines(["top", "right"])
ax1.add_labels("X Value", "Relative Frequency")
ax1.set_limits(-10, 10, 0, 0.12)
ax1.legend()

ax2.contour_scatter(contour_xs, contour_ys, bin_size=0.3, scatter_kwargs={"label":"Outliers"})
ax2.equal_scale()
ax2.make_ax_dark()
ax2.set_limits(-4, 8, -4, 8)
ax2.legend("light")
ax2.add_labels("X Value", "Y Value")

# ax3.scatter(x3, y3)
# ax3.scatter(x4, y4)
# ax3.scatter(x5, y5)
# ax3.add_labels("X Value", "Y Value")

# ax4.scatter(sc_x1, sc_y1, label="Data 1")
# ax4.scatter(sc_x2, sc_y2, label="Data 2")
# ax4.remove_spines(["top", "right"])
# ax4.legend(loc=2)
# ax4.add_labels("X Value", "Y Value")

fig.savefig("bpl_demo.png")


    

In [84]:

    

fig, ax = bpl.subplots()
xs = [0, 1]
ax.fill_between(xs, 0, 1, color=bpl.almost_black)
ax.fill_between([0.75, 1.0], 0, 1, color="k")
ax.fill_between(xs, 1, 2, color=bpl.light_gray)
ax.fill_between(xs, 2, 3, color=bpl.steel_blue)
ax.fill_between(xs, 3, 4, color=bpl.parks_canada_heritage_green)
for idx, col in enumerate(bpl.color_cycle[::-1]):
    ax.fill_between(xs, 4 + 2 * idx / len(bpl.color_cycle), 4 + 2 * (idx + 1) / len(bpl.color_cycle), color=col)

ax.remove_spines(["all"])
ax.remove_labels("both")

ax.add_text(0.5, 0.5, "almost_black", ha="center", va="center", color="w")
ax.add_text((1.0 + 0.75) / 2.0, 0.5, "black", ha="center", va="center", color="w")
ax.add_text(0.5, 1.5, "light_gray", ha="center", va="center", color=bpl.almost_black)
ax.add_text(0.5, 2.5, "steel_blue", ha="center", va="center", color="w")
ax.add_text(0.5, 3.5, "parks_canada_heritage_green", ha="center", va="center", color="w")
ax.text(0.25, 5.0, "Color Cycle", rotation=45, ha="center", va="center", color="w", fontsize=28)
ax.add_text(0.75, 4 + 1.0 / len(bpl.color_cycle), "9", ha="center", va="center", color="w", fontsize=14)
ax.add_text(0.75, 4 + 3.0 / len(bpl.color_cycle), "8", ha="center", va="center", color="w", fontsize=14)
ax.add_text(0.75, 4 + 5.0 / len(bpl.color_cycle), "7", ha="center", va="center", color="w", fontsize=14)
ax.add_text(0.75, 4 + 7.0 / len(bpl.color_cycle), "6", ha="center", va="center", color="w", fontsize=14)
ax.add_text(0.75, 4 + 9.0 / len(bpl.color_cycle), "5", ha="center", va="center", color="w", fontsize=14)
ax.add_text(0.75, 4 + 11.0 / len(bpl.color_cycle), "4", ha="center", va="center", color="w", fontsize=14)
ax.add_text(0.75, 4 + 13.0 / len(bpl.color_cycle), "3", ha="center", va="center", color="w", fontsize=14)
ax.add_text(0.75, 4 + 15.0 / len(bpl.color_cycle), "2", ha="center", va="center", color="w", fontsize=14)
ax.add_text(0.75, 4 + 17.0 / len(bpl.color_cycle), "1", ha="center", va="center", color="w", fontsize=14)
ax.add_text(0.75, 4 + 19.0 / len(bpl.color_cycle), "0", ha="center", va="center", color="w", fontsize=14)
fig.savefig("colors.png")


    

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