Routines to get a sane default configuration for production quality plots.
Project description
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 matplotlib.pyplot as plt
import cosmoplots
# If you only want the default style
plt.style.use(["cosmoplots.default"])
Muliple subfigures
To make a figure with multiple rows or columns, use cosmoplots.figure_multiple_rows_columns
.
By default, the labels are $\mathrm{(a)}$, $\mathrm{(b)}$, $\mathrm{(c)}$, ..., but they may be replaced using the labels
argument.
import matplotlib.pyplot as plt
import cosmoplots
plt.style.use(["cosmoplots.default"])
import numpy as np
rows = 1
columns = 2
fig, ax = cosmoplots.figure_multiple_rows_columns(rows, columns)
a = np.linspace(-3,3,100)
for i in range(rows*columns):
ax[i].set_xlabel("X Axis")
ax[i].set_ylabel("Y Axis")
ax[i].plot(i*a)
plt.show()
change_log_axis_base
import matplotlib.pyplot as plt
import cosmoplots
plt.style.use(["cosmoplots.default"])
import numpy as np
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 matplotlib.pyplot as plt
import cosmoplots
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 plt.style.context("default"):
plot()
# plt.savefig("assets/matplotlib.png")
plt.show()
# Cosmoplots ------------------------------------------------------------------------- #
with plt.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
plt.style.use(["cosmoplots.default"])
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 = plt.gca()
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.pyplot as plt
import cosmoplots
plt.style.use("cosmoplots.default")
import numpy as np
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()
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