cosmoplots 0.4.0

Routines to get a sane default configuration for production quality plots.

cosmoplots

Routines to get a sane default configuration for production quality plots. Used by complex systems modelling group at UiT.

Installation

The package is published to PyPI and can be installed with

pip install cosmoplots

If you want the development version you must first clone the repo to your local machine, then install the project and its dependencies with poetry:

git clone https://github.com/uit-cosmo/cosmoplots.git
cd cosmoplots
poetry install

Usage

Set your rcparams before plotting in your code, for example:

import cosmoplots
import matplotlib as mpl

# If you only want the default style
mpl.style.use("cosmoplots.default")
# If you want the two column style, combine it with the default. Setting it after is
# important, since values from the default is overridden.
mpl.style.use(["cosmoplots.default", "cosmoplots.two_columns"])

change_log_axis_base

import matplotlib.pyplot as plt
import numpy as np
import cosmoplots
import matplotlib as mpl

# Setup
mpl.style.use("cosmoplots.default")
a = np.exp(np.linspace(-3, 1, 100))

# Plotting
fig = plt.figure()
ax1 = plt.gca()
ax1.set_xlabel("X Axis")
ax1.set_ylabel("Y Axis")
base = 2  # Default is 10, but 2 works equally well
# Do plotting ...
ax1.semilogx(a)
# It is recommended to call the change_log_axis_base function after doing all the
# plotting. By default, it will try to infer the scaling used for the axis and only
# adjust accordingly.
cosmoplots.change_log_axis_base(ax1, base=base)
# Plotting
fig = plt.figure()
ax2 = plt.gca()
ax2.set_xlabel("X Axis")
ax2.set_ylabel("Y Axis")
base = 2  # Default is 10, but 2 works equally well
cosmoplots.change_log_axis_base(ax2, "x", base=base)
# Do plotting ...
# If you use "plot", the change_log_axis_base can be called at the top (along with add_axes
# etc.), but using loglog, semilogx, semilogy will re-set, and the change_log_axis_base
# function must be called again.
ax2.plot(a)
plt.show()

matplotlib vs. cosmoplots defaults

import cosmoplots
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

def plot() -> None:
    a = np.exp(np.linspace(-3, 5, 100))
    fig = plt.figure()
    ax = fig.add_subplot()
    ax.set_xlabel("X Axis")
    ax.set_ylabel("Y Axis")
    ax.semilogy(a)

# Matplotlib ------------------------------------------------------------------------- #
with mpl.style.context("default"):
    plot()
    # plt.savefig("assets/matplotlib.png")
    plt.show()

# Cosmoplots ------------------------------------------------------------------------- #
with mpl.style.context("cosmoplots.default"):
    plot()
    # plt.savefig("assets/cosmoplots.png")
    plt.show()

matplotlib	cosmoplots

generate_hex_colors

This function generates the hex numbers for the colours extracted from a matplotlib colour map based on the number of points of interest. The colors change gradually from bright to dark or vice versa.

import matplotlib.pyplot as plt
import cosmoplots

axes_size = cosmoplots.set_rcparams_dynamo(plt.rcParams, num_cols=1, ls="thin")


color_list = cosmoplots.generate_hex_colors(5, 'viridis', show_swatch=True, ascending=True)
plt.savefig("./assets/hex_colors.png")

# Print color_list to retrieve the hex numbers
print(color_list) #['#fde725', '#5ec962', '#21918c', '#3b528b', '#440154']

fig = plt.figure()
ax = fig.add_axes(axes_size)
for i, color in enumerate(color_list):
    ax.plot([1,2],[i,i+1], c = color)

plt.savefig("./assets/hex_colors_example.png")
plt.show()

hex_colors.png	hex_colors_example.png

combine

Sometimes, plots might be related and better placed as subfigures in a larger figure. If combining the plots using the subfigure environment in latex or similar is not an option, this is easily done with imagemagick in a systematic way.

The Combine class within the concat module implements such procedures, and is also conveniently available from the combine function in cosmoplots.

An example is shown below. Also see the tests directory for more examples. A help method that prints the imagemagick commands that are used under the hood is also available.

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

import cosmoplots

mpl.style.use("cosmoplots.default")


def plot(i) -> None:
    """Create a simple plot."""
    a = np.exp(np.linspace(-3, 5, 100))
    fig = plt.figure()
    ax = fig.add_subplot()
    ax.set_xlabel("X Axis")
    ax.set_ylabel("Y Axis")
    ax.semilogy(a)
    plt.savefig(f"./assets/{i}.png")
    plt.close(fig)

plot(1)
plot(2)
plot(3)
plot(4)
plot(5)
plot(6)
plot(7)
plot(8)
plot(9)
plot(10)
# See `convert -list font` for all available fonts.
figs = [f"./assets/{i}.png" for i in range(1, 11)]
cosmoplots.combine(*figs).using(
    font="JetBrainsMonoNL-NFM-Medium",
    fontsize=60,
    gravity="southeast",
    pos=(100, 200),
    color="green",
).in_grid(w=3, h=4).with_labels(  # Specifying labels is optional
    "one", "four", "three", "two", "eight", "six", "seven", "five", "nine", "ten"
).save("./assets/concat.png")

# Note that cosmoplots.combine() == cosmoplots.Combine().combine()
cosmoplots.combine().help()
# Or equivalently
cosmoplots.Combine().help()