matplotlib gallery

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(1)
x = np.random.randn(1000)
x_mesh = np.linspace(start = -4, stop = 4, num = 100)
y = np.exp(-0.5 * x_mesh ** 2) / np.sqrt(2 * np.pi)

fig = plt.figure()
ax = fig.add_axes([0.1,0.1,0.8,0.8])
ax.hist(x, bins = 20, density=True, color="#67a9cf")
ax.plot(x_mesh, y, color="#ef8a62", linewidth=3)
ax.set_xlabel("X")
ax.set_ylabel("density")

fig.savefig("fig01.png", dpi=300)

import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats

np.random.seed(1)
num_points = 50
x = np.random.randn(num_points)
x.sort()

unit_mesh = np.linspace(
    start = num_points / (num_points + 1),
    stop = 1 / (num_points + 1),
    num = num_points)
quantile_vals = stats.norm.isf(unit_mesh)

line_lims = [min(quantile_vals) * 1.1,max(quantile_vals) * 1.1]

fig = plt.figure()
ax = fig.add_axes([0.1,0.1,0.8,0.8])
ax.scatter(quantile_vals, x, color="#67a9cf")
ax.plot(line_lims, line_lims, color="#ef8a62")
ax.set_title("Normal Q-Q plot")
ax.set_xlabel("Theoretical quantiles")
ax.set_ylabel("Sample quantiles")

fig.savefig("fig02.png", dpi=300)

import matplotlib.pyplot as plt

data = {'apple': 10, 'orange': 15, 'lemon': 5}
names = list(data.keys())
values = list(data.values())

fig = plt.figure()
ax = fig.add_axes([0.1,0.1,0.8,0.8])
ax.bar(names,
       values,
       color = "#cccccc",
       edgecolor = "#252525")

fig.savefig("fig03.png", dpi=300)

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 8, 1000)
y1 = np.sin(x)
y2 = np.cos(x)

fig = plt.figure()
ax = fig.add_axes([0.1,0.1,0.8,0.8])
ax.plot(x, y1, color="#67a9cf", label="sine")
ax.plot(x, y2, color = "#ef8a62", label="cosine")
ax.legend(loc="upper left")
ax.set_ylim([-1.5, 2])

fig.savefig("fig04.png", dpi=300)

import numpy as np
import matplotlib.pyplot as plt

fig, axs = plt.subplots(1, 3, figsize=(9, 3))

var_and_col = list(zip(['a', 'b'], ["#67a9cf", "#ef8a62"]))

for ix in range(3):
    for v, c in var_and_col:
        xs = range(7)
        ys = np.random.uniform(size=7)
        axs[ix].set_title("panel {ix}".format(ix=ix))
        axs[ix].scatter(xs, ys, color = c, label = v)

axs[2].legend(loc = "upper right")
fig.savefig("fig05.png", dpi=300)

import matplotlib.pyplot as plt
import pandas as pd

iris = pd.read_csv("iris.csv")
unique_species = iris.species.unique()
grouped_sepal_lengths = [iris[iris.species == species].sepal_length
                         for species in unique_species]

plt.figure()
plt.boxplot(x = grouped_sepal_lengths,
            labels = unique_species)
plt.savefig("fig06.png", dpi=300)

import matplotlib.pyplot as plt
import matplotlib as matplotlib
import pandas as pd

iris = pd.read_csv("iris.csv")
numeric_cols = iris.columns.to_list()[0:4]
iris_corrs = iris[numeric_cols].corr().to_numpy()

fig, ax = plt.subplots()
im = ax.imshow(iris_corrs)
cbar = ax.figure.colorbar(im)
cbar.ax.set_ylabel("Correlation")
ax.set_xticks(range(iris_corrs.shape[1]))
ax.set_yticks(range(iris_corrs.shape[0]))
ax.set_xticklabels(numeric_cols)
ax.set_yticklabels(numeric_cols)
ax.tick_params(top=False, bottom=False,
               labeltop=True, labelbottom=False)

kw = dict(horizontalalignment="center",
          verticalalignment="center",
          color="black")
valfmt = matplotlib.ticker.StrMethodFormatter("{x:.1f}")

for i in range(iris_corrs.shape[0]):
    for j in range(iris_corrs.shape[1]):
        im.axes.text(j, i, valfmt(iris_corrs[i, j], None), **kw)

plt.tick_params(left=False)
plt.savefig("fig07.png", dpi=300)

from Bio import Phylo
from io import StringIO
import matplotlib.pyplot as plt
import numpy as np


def ltt_data(tree):
    node_times = []
    def traverse(clade, current_time):
        bl = clade.branch_length if clade.branch_length else 0
        if clade.clades:
            node_times.append(('branch', current_time))
        for child in clade.clades:
            if child.is_terminal():
                node_times.append(('tip', current_time + child.branch_length))
            else:
                traverse(child, current_time + child.branch_length)
    traverse(tree.root, 0)
    times = []
    num_lines = []
    curr_ltt = 1
    for (node_type, time) in node_times:
        times.append(time)
        if node_type == 'branch':
            curr_ltt += 1
            num_lines.append(curr_ltt)
        else:
            curr_ltt -= 1
            num_lines.append(curr_ltt)
    return times, num_lines


newick_tree = StringIO("((A:0.1, B:0.2):0.3, (C:0.4, D:0.5):0.6);")
tree = Phylo.read(newick_tree, "newick")

fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, gridspec_kw={'height_ratios': [3, 2]})

Phylo.draw(tree, do_show = False, axes = ax1)
ax1.yaxis.set_ticks([])
ax1.yaxis.set_ticklabels([])
ax1.set_ylabel('')
ax1.set_xlabel('')

ltt_x, ltt_y = ltt_data(tree)
ax2.plot(ltt_x, ltt_y, 'bo')
ax2.step(ltt_x, ltt_y, 'b-', where='post', label='Sine Wave')
ax2.set_ylabel('Lineage count', color='b')
ax2.set_xlabel('Time (branch length)')
ax2.tick_params(axis='y', labelcolor='b')

plt.subplots_adjust(right=0.85)
fig.savefig("fig08.png", dpi=300)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


mtcars = pd.read_csv("mtcars.csv")


plot_df = (
    mtcars.groupby("cyl")
    .agg(
        mean_mpg=("mpg", "mean"),
        sd_mpg=("mpg", "std"),
        n=("mpg", "size"),  # Count the number of occurrences for each group
    )
    .reset_index()
)

plot_df["lower_mpg"] = plot_df["mean_mpg"] - 1.96 * plot_df["sd_mpg"] / np.sqrt(
    plot_df["n"]
)
plot_df["upper_mpg"] = plot_df["mean_mpg"] + 1.96 * plot_df["sd_mpg"] / np.sqrt(
    plot_df["n"]
)


fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
ax.fill_between(
    plot_df["cyl"], plot_df["lower_mpg"], plot_df["upper_mpg"], color="blue", alpha=0.2
)
ax.plot(plot_df["cyl"], plot_df["lower_mpg"], "--", color="blue")
ax.plot(plot_df["cyl"], plot_df["upper_mpg"], "--", color="blue")
ax.plot(plot_df["cyl"], plot_df["mean_mpg"], "-", color="red", label="Mean")
ax.set_xlabel("Number of Cylinders")
ax.set_ylabel("Miles per Gallon (MPG)")
ax.set_xticks(range(4, 9))
ax.set_yticks(range(15, 35, 5))

ax.grid(True, which="major", linestyle="-", linewidth=0.25, color="grey", zorder=0)
# Ensure the grid lines are in the background by setting the z-order of the plots
ax.set_axisbelow(True)

fig.savefig("fig09.png", dpi=300)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


mtcars = pd.read_csv("mtcars.csv")

plot_df = mtcars[["mpg", "hp", "wt", "cyl"]]


fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")

scatter = ax.scatter(
    plot_df["mpg"],
    plot_df["hp"],
    plot_df["wt"],
    c=plot_df["cyl"],
    cmap="viridis",
    marker="o",
)
colorbar = fig.colorbar(scatter, ax=ax, fraction=0.025, pad=0.25)
colorbar.set_label("Number of Cylinders")

ax.set_xlabel("Horsepower (HP)")
ax.set_ylabel("Weight (1000 lbs)")
ax.set_zlabel("Miles per Gallon (MPG)")

# For an interactive plot, uncomment the following line.
# plt.show()

fig.savefig("fig10.png", dpi=300)