| PyPI version |
|
| conda-forge version |
|
| DOI |
|
It can be cascaded with other styles to fine-tune the appearance. For example, the ``science`` + ``notebook`` styles (intended for Jupyter notebooks):
Please see [the project Wiki](https://github.com/garrettj403/SciencePlots/wiki/Gallery) for a full list of available styles.
Specific Styles for Academic Journals
-------------------------------------
The ``science`` + ``ieee`` styles for IEEE papers:
- IEEE requires figures to be readable when printed in black and white. The ``ieee`` style also sets the figure width to fit within one column of an IEEE paper.
The ``science`` + ``nature`` styles for Nature articles:
- Nature recommends sans-serif fonts.
Other languages
---------------
SciencePlots currently supports:
* [Traditional Chinese](https://github.com/garrettj403/SciencePlots/wiki/Gallery#traditional-chinese)
* [Simplified Chinese](https://github.com/garrettj403/SciencePlots/wiki/Gallery#simplified-chinese)
* [Japanese](https://github.com/garrettj403/SciencePlots/wiki/Gallery#japanese)
* [Korean](https://github.com/garrettj403/SciencePlots/wiki/Gallery#korean)
* [Russian](https://github.com/garrettj403/SciencePlots/wiki/Gallery#russian)
* [Turkish](https://github.com/garrettj403/SciencePlots/wiki/Gallery#turkish)
Example: Traditional Chinese (`science` + `no-latex` + `cjk-tc-font`):
See the [FAQ](https://github.com/garrettj403/SciencePlots/wiki/FAQ#installing-cjk-fonts) for information on installing CJK fonts.
Other color cycles
------------------
SciencePlots comes with a variety of different color cycles. For a full list, [see the project Wiki](https://github.com/garrettj403/SciencePlots/wiki/Gallery#color-cycles). Two examples are shown below.
The ``bright`` color cycle (color blind safe):
The ``high-vis`` color cycle:
Paul Tol's discrete rainbow color sets are available as well, with the style identifier ``discrete-rainbow-
Help and Contributing
---------------------
Please feel free to contribute to the SciencePlots repo! For example, it would be good to add new styles for different journals and add new color cycles. Before starting a new style or making any changes, please create an issue through the [GitHub issue tracker](https://github.com/garrettj403/SciencePlots/issues). That way we can discuss if the changes are necessary and the best approach.
If you need any help with SciencePlots, please first check the [FAQ](https://github.com/garrettj403/SciencePlots/wiki/FAQ) and search through the [previous GitHub issues](https://github.com/garrettj403/SciencePlots/issues). If you can't find an answer, create a new issue through the [GitHub issue tracker](https://github.com/garrettj403/SciencePlots/issues).
You can checkout [Matplotlib's documentation](https://matplotlib.org/tutorials/introductory/customizing.html) for more information on plotting settings.
FAQ
---
You can find [the FAQ in the project Wiki.](https://github.com/garrettj403/SciencePlots/wiki/FAQ)
SciencePlots in Academic Papers
-------------------------------
The following papers use ``SciencePlots``:
- C.-S. Li, S.-P. Ma, and T.-H. Lin, ["FMSA: A Universal Microservice Architecture Based on FHIR Medical Informatics Standard,"](https://ieeexplore.ieee.org/document/11326732) 2025 Second International Conference on Artificial Intelligence for Medicine, Health and Care (AIxMHC), pp. 42–49, Oct. 2025.
- Keçeci, Mehmet. "Scalable Complexity: Mathematical Analysis and Potential for Physical Applications of the Keçeci Circle Fractal", 13 May 2025. https://doi.org/10.5281/zenodo.15396198. (Reference has been made to the module "SciencePlots".)
- J. D. Garrett, C.-Y. E. Tong, L. Zeng, T.-J. Chen and M.-J. Wang, ["A 345 GHz Sideband-Separating Receiver Prototype with Ultra-Wide Instantaneous Bandwidth,"](https://ieeexplore.ieee.org/document/10089556) *IEEE Trans. THz Sci. Technol.*, vol. 13, no. 3, pp. 237-245, Mar. 2023.
- J. Garrett, B.-K. Tan, C. Chaumont, F. Boussaha, and G. Yassin, ["A 230-GHz Endfire SIS Mixer With Near Quantum-Limited Performance,"](https://ieeexplore.ieee.org/document/9833810) *IEEE Microw. Wirel. Compon. Lett.*, Jul. 2022. ([open access](https://ora.ox.ac.uk/objects/uuid:59a100bf-c997-499a-be20-01fc66fffe2b))
- J. Garrett, and E. Tong, ["Measuring Cryogenic Waveguide Loss in the Terahertz Regime,"](https://ieeexplore.ieee.org/document/9727077) *IEEE Trans. THz Sci. Technol.*, vol. 12, no. 3, pp. 293-299, May 2022.
- Y. Liu, X. Liu, and Y. Sun, ["QGrain: An open-source and easy-to-use software for the comprehensive analysis of grain size distributions"](https://doi.org/10.1016/j.sedgeo.2021.105980), *Sedimentary Geology*, vol. 423, 105980, Aug. 2021.
- M. Gasanov, *et al.*, ["A New Multi-objective Approach to Optimize Irrigation Using a Crop Simulation Model and Weather History"](https://link.springer.com/chapter/10.1007/978-3-030-77970-2_7) in *Computational Science–ICCS 2021*, Krakow, Poland, Jun. 2021, pp. 75-88. ([open access](https://www.researchgate.net/profile/Sergey-Matveev-5/publication/352285985_A_New_Multi-objective_Approach_to_Optimize_Irrigation_Using_a_Crop_Simulation_Model_and_Weather_History/links/60e74b88b8c0d5588ce2da07/A-New-Multi-objective-Approach-to-Optimize-Irrigation-Using-a-Crop-Simulation-Model-and-Weather-History.pdf))
- J. Garrett, and E. Tong, ["A Dispersion-Compensated Algorithm for the Analysis of Electromagnetic Waveguides,"](https://ieeexplore.ieee.org/document/9447194) *IEEE Signal Process. Lett.*, vol. 28, pp. 1175-1179, Jun. 2021.
- G. Jegannathan, *et al.*, ["Current-Assisted SPAD with Improved p-n Junction and Enhanced NIR Performance"](https://www.mdpi.com/1424-8220/20/24/7105), *Sensors*, Dec 2020. ([open access](https://www.mdpi.com/1424-8220/20/24/7105))
- H. Tian, *et al.*, ["ivis Dimensionality Reduction Framework for Biomacromolecular Simulations"](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.0c00485), *J. Chem. Inf. Model.*, Aug 2020. ([open access](https://arxiv.org/pdf/2004.10718.pdf))
- P. Stoltz, *et al.*, ["A new simple algorithm for space charge limited emission,"](https://aip.scitation.org/doi/10.1063/5.0020781) *Phys. Plasmas*, vol. 27, no. 9, pp. 093103, Sep. 2020. ([open access](https://aip.scitation.org/doi/10.1063/5.0020781))
- J. Garrett, *et al.*, ["A Nonlinear Transmission Line Model for Simulating Distributed SIS Frequency Multipliers,"](https://ieeexplore.ieee.org/abstract/document/9050728) *IEEE Trans. THz Sci. Technol.*, vol. 10, no. 3, pp. 246-255, May 2020. ([open access](https://ora.ox.ac.uk/objects/uuid:5ca31c2c-a984-462c-b21a-3fe16eee0d9b/download_file?safe_filename=XXXX_final_JohnGarrett.pdf&type_of_work=Journal+article))
- J. Garrett, *et al.*, ["Simulating the Behavior of a 230 GHz SIS Mixer Using Multi-Tone Spectral Domain Analysis,"](https://ieeexplore.ieee.org/document/8822760/) *IEEE Trans. THz Sci. Technol.*, vol. 9, no. 9, pp. 540-548, Nov. 2019. ([open access](https://ora.ox.ac.uk/objects/uuid:0fd4537d-258c-454a-bbfb-09b1bcd88d49/download_file?file_format=pdf&safe_filename=XXXX_final.pdf&type_of_work=Journal+article))
- J. Garrett, *et al.*, ["A Compact and Easy to Fabricate E-plane Waveguide Bend,"](https://ieeexplore.ieee.org/document/8760521) *IEEE Microw. Wireless Compon. Lett.*, vol. 29, no. 8, pp. 529-531, Aug. 2019. ([open access](https://ora.ox.ac.uk/objects/uuid:496855f9-be2a-47cd-b498-1753d8033f50/download_file?file_format=pdf&safe_filename=Waveguide_Bend__IEEE_MWCL_.pdf&type_of_work=Journal+article))
- J. Garrett, ["A 230 GHz Focal Plane Array Using a Wide IF Bandwidth SIS Receiver,"](https://ora.ox.ac.uk/objects/uuid:d47fbf3b-1cf3-4e58-be97-767b9893066e/download_file?file_format=pdf&safe_filename=GarrettJ_DPhilThesis.pdf&type_of_work=Thesis) DPhil thesis, University of Oxford, Oxford, UK, 2018. ([open access](https://ora.ox.ac.uk/objects/uuid:d47fbf3b-1cf3-4e58-be97-767b9893066e/download_file?file_format=pdf&safe_filename=GarrettJ_DPhilThesis.pdf&type_of_work=Thesis))
If you use ``SciencePlots`` in your paper/thesis, feel free to add it to the list!
Citing SciencePlots
-------------------
You don't have to cite SciencePlots if you use it but it's nice if you do:
@article{SciencePlots,
author = {John D. Garrett},
title = {{garrettj403/SciencePlots}},
month = sep,
year = 2021,
publisher = {Zenodo},
version = {1.0.9},
doi = {10.5281/zenodo.4106649},
url = {http://doi.org/10.5281/zenodo.4106649}
}
================================================
FILE: examples/plot-examples.py
================================================
"""Plot examples of SciencePlot styles."""
# %%
import numpy as np
import matplotlib.pyplot as plt
import scienceplots # noqa: F401
import os
# Check we are in examples dir
current_dir = os.getcwd().lower()
if current_dir.endswith("scienceplots"):
os.chdir("./examples")
# Create 'figures' folder if it does not exist
if not os.path.exists("./figures"):
os.makedirs("figures")
def model(x, p):
return x ** (2 * p + 1) / (1 + x ** (2 * p))
pparam = dict(xlabel="Voltage (mV)", ylabel=r"Current ($\mu$A)")
x = np.linspace(0.75, 1.25, 201)
# %%
# Style 'science'
with plt.style.context(["science"]):
fig, ax = plt.subplots()
for p in [10, 15, 20, 30, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig01a.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'no-latex'
with plt.style.context(["science", "no-latex"]):
fig, ax = plt.subplots()
for p in [10, 15, 20, 30, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig01b.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'ieee'
with plt.style.context(["science", "ieee"]):
fig, ax = plt.subplots()
for p in [10, 20, 40, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig02a.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'ieee', 'std-colors'
with plt.style.context(["science", "ieee", "std-colors"]):
fig, ax = plt.subplots()
for p in [10, 15, 20, 30, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig02b.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'nature'
with plt.style.context(["science", "nature"]):
fig, ax = plt.subplots()
for p in [10, 15, 20, 30, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig02c.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'scatter'
with plt.style.context(["science", "scatter"]):
fig, ax = plt.subplots(figsize=(4, 4))
ax.plot([-2, 2], [-2, 2], "k--")
ax.fill_between(
[-2, 2], [-2.2, 1.8], [-1.8, 2.2], color="dodgerblue", alpha=0.2, lw=0
)
for i in range(7):
x1 = np.random.normal(0, 0.5, 10)
y1 = x1 + np.random.normal(0, 0.2, 10)
ax.plot(x1, y1, label=r"$^\#${}".format(i + 1))
lgd = r"$\mathring{P}=\begin{cases}1&\text{if $\nu\geq0$}\\0&\text{if $\nu<0$}\end{cases}$"
ax.legend(title=lgd, loc=2, ncol=2)
xlbl = r"$\log_{10}\left(\frac{L_\mathrm{IR}}{\mathrm{L}_\odot}\right)$"
ylbl = r"$\log_{10}\left(\frac{L_\circledast}{\mathrm{L}_\odot}\right)$"
ax.set_xlabel(xlbl)
ax.set_ylabel(ylbl)
ax.set_xlim([-2, 2])
ax.set_ylim([-2, 2])
fig.savefig("figures/fig03.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'high-vis'
with plt.style.context(["science", "high-vis"]):
fig, ax = plt.subplots()
for p in [10, 15, 20, 30, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig04.jpg", dpi=300)
plt.close()
# %%
# Styles 'dark_background', 'science', 'high-vis'
with plt.style.context(["dark_background", "science", "high-vis"]):
fig, ax = plt.subplots()
for p in [10, 15, 20, 30, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig05.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'notebook'
with plt.style.context(["science", "notebook"]):
fig, ax = plt.subplots()
for p in [10, 15, 20, 30, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig10.jpg", dpi=300)
plt.close()
# %%
# Plot different color cycles
# %%
# Styles 'science', 'bright'
with plt.style.context(["science", "bright"]):
fig, ax = plt.subplots()
for p in [5, 10, 15, 20, 30, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig06.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'vibrant'
with plt.style.context(["science", "vibrant"]):
fig, ax = plt.subplots()
for p in [5, 10, 15, 20, 30, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig07.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'muted'
with plt.style.context(["science", "muted"]):
fig, ax = plt.subplots()
for p in [5, 7, 10, 15, 20, 30, 38, 50, 100, 500]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order", fontsize=7)
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig08.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'retro'
with plt.style.context(["science", "retro"]):
fig, ax = plt.subplots()
for p in [10, 15, 20, 30, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig09.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'grid'
with plt.style.context(["science", "grid"]):
fig, ax = plt.subplots()
for p in [10, 15, 20, 30, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig11.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'high-contrast'
with plt.style.context(["science", "high-contrast"]):
fig, ax = plt.subplots()
for p in [10, 20, 50]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig12.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'light'
with plt.style.context(["science", "light"]):
fig, ax = plt.subplots()
for p in [5, 7, 10, 15, 20, 30, 38, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order", fontsize=7)
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig("figures/fig13.jpg", dpi=300)
plt.close()
# %%
# Styles for CJK languages
# Note: You need to install the Noto Serif CJK Fonts before running
# examples 14 and 15. See FAQ in README.
# %%
# Styles 'science', 'no-latex', 'cjk-*-font'
with plt.style.context(["science", "no-latex", "cjk-tc-font"]):
fig, ax = plt.subplots()
for p in [5, 7, 10, 15, 20, 30, 38, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order", fontsize=7)
ax.set(xlabel=r"電壓 (mV)")
ax.set(ylabel=r"電流 ($\mu$A)")
ax.autoscale(tight=True)
fig.savefig("figures/fig14a.jpg", dpi=300)
plt.close()
with plt.style.context(["science", "no-latex", "cjk-sc-font"]):
fig, ax = plt.subplots()
for p in [5, 7, 10, 15, 20, 30, 38, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order", fontsize=7)
ax.set(xlabel=r"电压 (mV)")
ax.set(ylabel=r"电流 ($\mu$A)")
ax.autoscale(tight=True)
fig.savefig("figures/fig14b.jpg", dpi=300)
plt.close()
with plt.style.context(["science", "no-latex", "cjk-jp-font"]):
fig, ax = plt.subplots()
for p in [5, 7, 10, 15, 20, 30, 38, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order", fontsize=7)
ax.set(xlabel=r"電圧 (mV)")
ax.set(ylabel=r"電気 ($\mu$A)")
ax.autoscale(tight=True)
fig.savefig("figures/fig14c.jpg", dpi=300)
plt.close()
with plt.style.context(["science", "no-latex", "cjk-kr-font"]):
fig, ax = plt.subplots()
for p in [5, 7, 10, 15, 20, 30, 38, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title="Order", fontsize=7)
ax.set(xlabel=r"전압 (mV)")
ax.set(ylabel=r"전류 ($\mu$A)")
ax.autoscale(tight=True)
fig.savefig("figures/fig14d.jpg", dpi=300)
plt.close()
# import matplotlib
# matplotlib.use('pgf') # stwich backend to pgf
# matplotlib.rcParams.update({
# "pgf.preamble": [
# "\\usepackage{fontspec}",
# '\\usepackage{xeCJK}',
# r'\setmainfont{Times New Roman}', # EN fonts Romans
# r'\setCJKmainfont{SimHei}', # set CJK fonts as SimSun
# r'\setCJKsansfont{SimHei}',
# r'\newCJKfontfamily{\Song}{SimSun}',
# ]
# })
# with plt.style.context(['science', 'cjk-tc-font']):
# fig, ax = plt.subplots()
# for p in [5, 7, 10, 15, 20, 30, 38, 50, 100]:
# ax.plot(x, model(x, p), label=p)
# ax.legend(title='Order', fontsize=7)
# ax.set(xlabel=r'電壓 (mV)')
# ax.set(ylabel=r'電流 ($\mu$A)')
# ax.autoscale(tight=True)
# fig.savefig('figures/fig15.pdf', backend='pgf')
# plt.close()
# %%
# Styles 'science', 'russian-font'
with plt.style.context(["science", "russian-font"]):
fig, ax = plt.subplots()
for p in [5, 7, 10, 15, 20, 30, 38, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title=r"Число", fontsize=7)
ax.set(xlabel=r"Напряжение (mV)")
ax.set(ylabel=r"Сила тока ($\mu$A)")
ax.autoscale(tight=True)
fig.savefig("figures/fig16.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'turkish-font'
with plt.style.context(["science", "turkish-font"]):
fig, ax = plt.subplots()
for p in [5, 7, 10, 15, 20, 30, 38, 50, 100]:
ax.plot(x, model(x, p), label=p)
ax.legend(title=r"Düzen", fontsize=7)
ax.set(xlabel=r"Gerilim/Volt (mV)")
ax.set(ylabel=r"Mevcut Güç/Akım ($\mu$A)")
ax.autoscale(tight=True)
fig.savefig("figures/fig17.jpg", dpi=300)
plt.close()
# %%
# Styles 'science', 'no-latex', 'discrete-rainbow-*'
# Plot all discrete-rainbow styles
p_list = np.logspace(0, 2, 23)
for index in range(1, 23 + 1):
style_name = f"discrete-rainbow-{index}"
figure_name = f"fig_dr_{index}.jpg"
with plt.style.context(["science", "no-latex", style_name]):
fig, ax = plt.subplots()
for p in p_list[: index]:
ax.plot(x, model(x, p))
ax.autoscale(tight=True)
ax.set(**pparam)
fig.savefig(f"figures/{figure_name}", dpi=300)
plt.close()
# %%
================================================
FILE: pyproject.toml
================================================
[build-system]
requires = ["setuptools>=77", "setuptools_scm>=8"]
build-backend = "setuptools.build_meta"
[project]
name = "SciencePlots"
authors = [{ name = "John Garrett", email = "garrettj403@gmail.com" }]
maintainers = [{ name = "Echedey Luis", email = "echelual@gmail.com" }]
description = "Format Matplotlib for scientific plotting"
readme = { file = "README.md", content-type = "text/markdown" }
license = "MIT"
license-files = ["LICENSE"]
keywords = [
"matplotlib-style-sheets",
"matplotlib-figures",
"scientific-papers",
"thesis-template",
"matplotlib-styles",
"python",
]
classifiers = [
"Development Status :: 6 - Mature",
"Intended Audience :: Science/Research",
"Programming Language :: Python :: 3",
"Topic :: Scientific/Engineering :: Visualization",
"Framework :: Matplotlib",
]
dynamic = ["version"]
requires-python = ">=3.8"
dependencies = ["matplotlib"]
[project.optional-dependencies]
test = ["pytest", "numpy"]
all = ["SciencePlots[test]"]
[project.urls]
Homepage = "https://github.com/garrettj403/SciencePlots/wiki"
Documentation = "https://github.com/garrettj403/SciencePlots/wiki"
Repository = "https://github.com/garrettj403/SciencePlots"
Issues = "https://github.com/garrettj403/SciencePlots/issues"
Changelog = "https://github.com/garrettj403/SciencePlots/blob/master/CHANGES.md"
[tool.setuptools_scm]
# blank section to avoid warning
[tool.pytest.ini_options]
addopts = "-ra"
testpaths = ["scienceplots/tests"]
[tool.ruff]
# Same as Black.
line-length = 88
indent-width = 4
# Exclude annotations and type comments
lint.ignore = ["ANN"]
================================================
FILE: src/scienceplots/__init__.py
================================================
import os # pathlib.Path.walk not available in Python <3.12
import matplotlib.pyplot as plt
import scienceplots
from .styles_discovery import read_styles_in_folders
# register the bundled stylesheets in the matplotlib style library
scienceplots_path = scienceplots.__path__[0]
styles_path = os.path.join(scienceplots_path, "styles")
# Reads styles in /styles folder and all subfolders
stylesheets = read_styles_in_folders(styles_path)
# Update dictionary of styles - plt.style.library
plt.style.core.update_nested_dict(plt.style.library, stylesheets)
# Update `plt.style.available`, copy-paste from:
# https://github.com/matplotlib/matplotlib/blob/a170539a421623bb2967a45a24bb7926e2feb542/lib/matplotlib/style/core.py#L266 # noqa: E501
plt.style.core.available[:] = sorted(plt.style.library.keys())
================================================
FILE: src/scienceplots/styles/color/bright.mplstyle
================================================
# Bright color scheme
# color-blind safe
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['4477AA', 'EE6677', '228833', 'CCBB44', '66CCEE', 'AA3377', 'BBBBBB'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-1.mplstyle
================================================
# Discrete rainbow color scheme with 1 color
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['1965B0'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-10.mplstyle
================================================
# Discrete rainbow color scheme with 10 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['882E72', '1965B0', '7BAFDE', '4EB265', 'CAE0AB', 'F7F056', 'F4A736', 'E8601C', 'DC050C', '72190E'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-11.mplstyle
================================================
# Discrete rainbow color scheme with 11 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['882E72', '1965B0', '5289C7', '7BAFDE', '4EB265', 'CAE0AB', 'F7F056', 'F4A736', 'E8601C', 'DC050C', '72190E'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-12.mplstyle
================================================
# Discrete rainbow color scheme with 12 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['D1BBD7', 'AE76A3', '882E72', '1965B0', '5289C7', '7BAFDE', '4EB265', 'CAE0AB', 'F7F056', 'F4A736', 'E8601C', 'DC050C'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-13.mplstyle
================================================
# Discrete rainbow color scheme with 13 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['D1BBD7', 'AE76A3', '882E72', '1965B0', '5289C7', '7BAFDE', '4EB265', '90C987', 'CAE0AB', 'F7F056', 'F4A736', 'E8601C', 'DC050C'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-14.mplstyle
================================================
# Discrete rainbow color scheme with 14 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['D1BBD7', 'AE76A3', '882E72', '1965B0', '5289C7', '7BAFDE', '4EB265', '90C987', 'CAE0AB', 'F7F056', 'F6C141', 'F1932D', 'E8601C', 'DC050C'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-15.mplstyle
================================================
# Discrete rainbow color scheme with 15 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['D1BBD7', 'AE76A3', '882E72', '1965B0', '5289C7', '7BAFDE', '4EB265', '90C987', 'CAE0AB', 'F7F056', 'F6C141', 'F1932D', 'E8601C', 'DC050C', '72190E'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-16.mplstyle
================================================
# Discrete rainbow color scheme with 16 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['D1BBD7', 'BA8DB4', 'AA6F9E', '882E72', '1965B0', '5289C7', '7BAFDE', '4EB265', '90C987', 'CAE0AB', 'F7F056', 'F6C141', 'F1932D', 'E8601C', 'DC050C', '72190E'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-17.mplstyle
================================================
# Discrete rainbow color scheme with 17 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['D1BBD7', 'BA8DB4', 'AA6F9E', '994F88', '882E72', '1965B0', '5289C7', '7BAFDE', '4EB265', '90C987', 'CAE0AB', 'F7F056', 'F6C141', 'F1932D', 'E8601C', 'DC050C', '72190E'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-18.mplstyle
================================================
# Discrete rainbow color scheme with 18 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['D1BBD7', 'BA8DB4', 'AA6F9E', '994F88', '882E72', '1965B0', '5289C7', '7BAFDE', '4EB265', '90C987', 'CAE0AB', 'F7F056', 'F6C141', 'F1932D', 'E8601C', 'DC050C', 'A5170E', '72190E'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-19.mplstyle
================================================
# Discrete rainbow color scheme with 19 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['D9CCE3', 'CAACCB', 'BA8DB4', 'AA6F9E', '994F88', '882E72', '1965B0', '5289C7', '7BAFDE', '4EB265', '90C987', 'CAE0AB', 'F7F056', 'F6C141', 'F1932D', 'E8601C', 'DC050C', 'A5170E', '72190E'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-2.mplstyle
================================================
# Discrete rainbow color scheme with 2 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['1965B0', 'DC050C'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-20.mplstyle
================================================
# Discrete rainbow color scheme with 20 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['D9CCE3', 'CAACCB', 'BA8DB4', 'AA6F9E', '994F88', '882E72', '1965B0', '437DBF', '6195CF', '7BAFDE', '4EB265', '90C987', 'CAE0AB', 'F7F056', 'F6C141', 'F1932D', 'E8601C', 'DC050C', 'A5170E', '72190E'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-21.mplstyle
================================================
# Discrete rainbow color scheme with 21 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['D9CCE3', 'CAACCB', 'BA8DB4', 'AA6F9E', '994F88', '882E72', '1965B0', '437DBF', '6195CF', '7BAFDE', '4EB265', '90C987', 'CAE0AB', 'F7F056', 'F7CB45', 'F4A736', 'EE8026', 'E65518', 'DC050C', 'A5170E', '72190E'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-22.mplstyle
================================================
# Discrete rainbow color scheme with 22 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['D9CCE3', 'CAACCB', 'BA8DB4', 'AA6F9E', '994F88', '882E72', '1965B0', '437DBF', '6195CF', '7BAFDE', '4EB265', '90C987', 'CAE0AB', 'F7F056', 'F7CB45', 'F4A736', 'EE8026', 'E65518', 'DC050C', 'A5170E', '72190E', '42150A'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-23.mplstyle
================================================
# Discrete rainbow color scheme with 23 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['E8ECFB', 'D9CCE3', 'CAACCB', 'BA8DB4', 'AA6F9E', '994F88', '882E72', '1965B0', '437DBF', '6195CF', '7BAFDE', '4EB265', '90C987', 'CAE0AB', 'F7F056', 'F7CB45', 'F4A736', 'EE8026', 'E65518', 'DC050C', 'A5170E', '72190E', '42150A'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-3.mplstyle
================================================
# Discrete rainbow color scheme with 3 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['1965B0', 'F7F056', 'DC050C'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-4.mplstyle
================================================
# Discrete rainbow color scheme with 4 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['1965B0', '4EB265', 'F7F056', 'DC050C'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-5.mplstyle
================================================
# Discrete rainbow color scheme with 5 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['1965B0', '7BAFDE', '4EB265', 'F7F056', 'DC050C'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-6.mplstyle
================================================
# Discrete rainbow color scheme with 6 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['1965B0', '7BAFDE', '4EB265', 'CAE0AB', 'F7F056', 'DC050C'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-7.mplstyle
================================================
# Discrete rainbow color scheme with 7 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['882E72', '1965B0', '7BAFDE', '4EB265', 'CAE0AB', 'F7F056', 'DC050C'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-8.mplstyle
================================================
# Discrete rainbow color scheme with 8 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['882E72', '1965B0', '7BAFDE', '4EB265', 'CAE0AB', 'F7F056', 'EE8026', 'DC050C'])
================================================
FILE: src/scienceplots/styles/color/discrete-rainbow/discrete-rainbow-9.mplstyle
================================================
# Discrete rainbow color scheme with 9 colors
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['882E72', '1965B0', '7BAFDE', '4EB265', 'CAE0AB', 'F7F056', 'EE8026', 'DC050C', '72190E'])
================================================
FILE: src/scienceplots/styles/color/high-contrast.mplstyle
================================================
# High-contrast color scheme
# color-blind safe
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['004488', 'DDAA33', 'BB5566'])
================================================
FILE: src/scienceplots/styles/color/high-vis.mplstyle
================================================
# Matplotlib style for high visability plots (i.e., bright colors!!!)
# Set color cycle
axes.prop_cycle : (cycler('color', ["0d49fb", "e6091c", "26eb47", "8936df", "fec32d", "25d7fd"]) + cycler('ls', ['-', '--', '-.', ':', '-', '--']))
================================================
FILE: src/scienceplots/styles/color/light.mplstyle
================================================
# Light color scheme
# color-blind safe
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['77AADD', 'EE8866', 'EEDD88', 'FFAABB', '99DDFF', '44BB99', 'BBCC33', 'AAAA00', 'DDDDDD'])
================================================
FILE: src/scienceplots/styles/color/muted.mplstyle
================================================
# Muted color scheme
# color-blind safe
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['CC6677', '332288', 'DDCC77', '117733', '88CCEE', '882255', '44AA99', '999933', 'AA4499', 'DDDDDD'])
================================================
FILE: src/scienceplots/styles/color/retro.mplstyle
================================================
# Retro color style
# Set color cycle
axes.prop_cycle : cycler('color', ['4165c0', 'e770a2', '5ac3be', '696969', 'f79a1e', 'ba7dcd'])
================================================
FILE: src/scienceplots/styles/color/std-colors.mplstyle
================================================
# Standard SciencePlots color cycle
# Set color cycle: blue, green, yellow, red, violet, gray
axes.prop_cycle : cycler('color', ['0C5DA5', '00B945', 'FF9500', 'FF2C00', '845B97', '474747', '9e9e9e'])
================================================
FILE: src/scienceplots/styles/color/vibrant.mplstyle
================================================
# Vibrant color scheme
# color-blind safe
# from Paul Tol's website: https://sronpersonalpages.nl/~pault/
# Set color cycle
axes.prop_cycle : cycler('color', ['EE7733', '0077BB', '33BBEE', 'EE3377', 'CC3311', '009988', 'BBBBBB'])
================================================
FILE: src/scienceplots/styles/journals/ieee.mplstyle
================================================
# Matplotlib style for IEEE plots
# This style should work for most two-column journals
# Set color cycle
# Set line style as well for black and white graphs
axes.prop_cycle : (cycler('color', ['k', 'r', 'b', 'g']) + cycler('ls', ['-', '--', ':', '-.']))
# Set default figure size
figure.figsize : 3.3, 2.5
figure.dpi : 600
# Font sizes
font.size : 8
font.family : serif
font.serif : Times
================================================
FILE: src/scienceplots/styles/journals/nature.mplstyle
================================================
# Matplotlib style for Nature journal figures.
# In general, they advocate for all fonts to be panel labels to be sans serif
# and all font sizes in a figure to be 7 pt and panel labels to be 8 pt bold.
# Figure size
figure.figsize : 3.3, 2.5 # max width is 3.5 for single column
# Font sizes
axes.labelsize: 7
xtick.labelsize: 7
ytick.labelsize: 7
legend.fontsize: 7
font.size: 7
# Font Family
font.family: sans-serif
font.sans-serif: DejaVu Sans, Arial, Helvetica, Lucida Grande, Verdana, Geneva, Lucid, Avant Garde, sans-serif
mathtext.fontset : dejavusans
# Set line widths
axes.linewidth : 0.5
grid.linewidth : 0.5
lines.linewidth : 1.
lines.markersize: 3
# Always save as 'tight'
# savefig.bbox : tight
# savefig.pad_inches : 0.01 # Use virtually all space when we specify figure dimensions
# LaTeX packages
text.latex.preamble : \usepackage{amsmath} \usepackage{amssymb} \usepackage{sfmath}
================================================
FILE: src/scienceplots/styles/languages/cjk-jp-font.mplstyle
================================================
# Add fonts for CJK characters (Japanese)
# See FAQ in README for installation instructions
font.serif : Noto Serif CJK JP
font.family : serif
================================================
FILE: src/scienceplots/styles/languages/cjk-kr-font.mplstyle
================================================
# Add fonts for CJK characters (Korean)
# See FAQ in README for installation instructions
font.serif : Noto Serif CJK KR
font.family : serif
================================================
FILE: src/scienceplots/styles/languages/cjk-sc-font.mplstyle
================================================
# Add fonts for CJK characters (simplified Chinese)
# See FAQ in README for installation instructions
font.serif : Noto Serif CJK SC
font.family : serif
================================================
FILE: src/scienceplots/styles/languages/cjk-tc-font.mplstyle
================================================
# Add fonts for CJK characters (traditional Chinese)
# See FAQ in README for installation instructions
font.serif : Noto Serif CJK TC
font.family : serif
================================================
FILE: src/scienceplots/styles/languages/russian-font.mplstyle
================================================
# Add Latex packages for Russian (cyrillic) fonts
text.latex.preamble : \usepackage[russian]{babel} \usepackage[utf8]{inputenc} \usepackage[T2A]{fontenc} \usepackage{amsmath} \usepackage{amssymb}
================================================
FILE: src/scienceplots/styles/languages/turkish-font.mplstyle
================================================
# Add Latex packages for Turkish (Türkçe) fonts
text.latex.preamble: \usepackage[turkish]{babel} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath} \usepackage{amssymb}
================================================
FILE: src/scienceplots/styles/misc/grid.mplstyle
================================================
# Add grid lines and turn the legend frame on
# Grid lines
axes.grid : True
axes.axisbelow : True
grid.linestyle : --
grid.color : k
grid.alpha : 0.5
grid.linewidth : 0.5
# Legend
legend.frameon : True
legend.framealpha : 1.0
legend.fancybox : True
legend.numpoints : 1
================================================
FILE: src/scienceplots/styles/misc/latex-sans.mplstyle
================================================
# Latex with sans-serif fonts
mathtext.fontset : dejavusans
# Use LaTeX for math formatting
text.usetex : True
# In general, we need to be careful with the preamble. A common font for
# sans serif LaTeX is cmbright. However, it does not support bold fontweights
# so we use sfmath instead.
text.latex.preamble : \usepackage{amsmath} \usepackage{amssymb} \usepackage{sfmath}
================================================
FILE: src/scienceplots/styles/misc/no-latex.mplstyle
================================================
# Deactivate LaTeX
# and use mathtext
text.usetex : False
# Set fonts to be LaTeX defaults
font.serif : STIXGeneral
font.family : serif
axes.formatter.use_mathtext : True
mathtext.fontset : cm
================================================
FILE: src/scienceplots/styles/misc/pgf.mplstyle
================================================
# Add LaTeX package to allow PGF plots
text.latex.preamble : \usepackage{amsmath} \usepackage[T1]{fontenc} \usepackage{pgfplots}
================================================
FILE: src/scienceplots/styles/misc/sans.mplstyle
================================================
# Sans-serif
font.family : sans-serif
# Latex with sans-serif fonts
mathtext.fontset : dejavusans
# In general, we need to be careful with the preamble. A common font for
# sans serif LaTeX is cmbright. However, it does not support bold fontweights
# so we use sfmath instead.
text.latex.preamble : \usepackage{amsmath} \usepackage{amssymb} \usepackage{sfmath}
================================================
FILE: src/scienceplots/styles/notebook.mplstyle
================================================
# Matplotlib style for Jupyter notebooks
# Set default figure size
figure.figsize : 8, 6
# Set x axis
xtick.major.size : 6
xtick.major.width : 1
xtick.minor.size : 3
xtick.minor.width : 1
# Set y axis
ytick.major.size : 6
ytick.major.width : 1
ytick.minor.size : 3
ytick.minor.width : 1
# Fontsizes
xtick.labelsize : 16
ytick.labelsize : 16
legend.fontsize : 16
legend.title_fontsize : 16
axes.titlesize : 16
axes.labelsize : 16
# Set line widths
axes.linewidth : 1
grid.linewidth : 1
lines.linewidth : 2.
# Use sans-serif fonts
font.family : sans-serif
mathtext.fontset : dejavusans
# Use LaTeX for math formatting
text.usetex : False
================================================
FILE: src/scienceplots/styles/scatter.mplstyle
================================================
# Matplotlib style for scatter plots
# Set markers (style, color, no lines)
axes.prop_cycle : (cycler('marker', ['o', 's', '^', 'v', '<', '>', 'd']) + cycler('color', ['0C5DA5', '00B945', 'FF9500', 'FF2C00', '845B97', '474747', '9e9e9e']) + cycler('ls', [' ', ' ', ' ', ' ', ' ', ' ', ' ']))
# Set marker size
lines.markersize : 3
================================================
FILE: src/scienceplots/styles/science.mplstyle
================================================
# Matplotlib style for scientific plotting
# This is the base style for "SciencePlots"
# see: https://github.com/garrettj403/SciencePlots
# Set color cycle: blue, green, yellow, red, violet, gray
axes.prop_cycle : cycler('color', ['0C5DA5', '00B945', 'FF9500', 'FF2C00', '845B97', '474747', '9e9e9e'])
# Set default figure size
figure.figsize : 3.5, 2.625
# Set x axis
xtick.direction : in
xtick.major.size : 3
xtick.major.width : 0.5
xtick.minor.size : 1.5
xtick.minor.width : 0.5
xtick.minor.visible : True
xtick.top : True
# Set y axis
ytick.direction : in
ytick.major.size : 3
ytick.major.width : 0.5
ytick.minor.size : 1.5
ytick.minor.width : 0.5
ytick.minor.visible : True
ytick.right : True
# Set line widths
axes.linewidth : 0.5
grid.linewidth : 0.5
lines.linewidth : 1.
# Remove legend frame
legend.frameon : False
# Always save as 'tight'
savefig.bbox : tight
savefig.pad_inches : 0.05
# Use serif fonts
# font.serif : Times
font.family : serif
mathtext.fontset : dejavuserif
# Use LaTeX for math formatting
text.usetex : True
text.latex.preamble : \usepackage{amsmath} \usepackage{amssymb}
================================================
FILE: src/scienceplots/styles_discovery.py
================================================
import os
import matplotlib.pyplot as plt
def read_styles_in_folders(root_path):
"""
Reads all stylesheets in the given path and its subfolders.
Parameters
----------
root_path : str
Path to the root folder containing the stylesheets and other subfolders
with stylesheets.
Returns
-------
stylesheets : dict
Dictionary of stylesheets in the form of {style_name: rcParams}.
Should be compatible with matplotlib's plt.style.library dictionary.
"""
stylesheets = {} # plt.style.library is a dictionary
for folder, _, _ in os.walk(root_path):
new_stylesheets = plt.style.core.read_style_directory(folder)
stylesheets.update(new_stylesheets)
return stylesheets
================================================
FILE: src/scienceplots/tests/__init__.py
================================================
================================================
FILE: src/scienceplots/tests/conftest.py
================================================
"""
Configuration of SciencePlots tests
"""
import pytest
import scienceplots
import numpy as np
import os
from pathlib import Path
STYLES_PATH = Path(scienceplots.__path__[0], "styles")
def get_styles_in_dir(dir):
"""
Input: directory path
Output: set of matplotlib styles filenames (without trailing '.mplstyle')
"""
styles_paths = Path(dir).glob("*.mplstyle")
return set(fn.stem for fn in styles_paths)
@pytest.fixture(scope="session")
def styles_in_scienceplots_per_folder():
"""
Output: dictionary of styles per folder in SciencePlots
"""
styles_per_folder = {}
for folder, _, _ in os.walk(STYLES_PATH): # 1st _ : subdirs, 2nd _ : files
folder = Path(folder)
styles_per_folder[folder.name] = get_styles_in_dir(folder)
return styles_per_folder
@pytest.fixture(scope="session")
def xy_example_values():
"""
Output: "x" 1D values, "y" 2D values, and 1D "p" values for the example plot
"""
def model(x, p): # from examples/plot-examples.py
return x ** (2 * p + 1) / (1 + x ** (2 * p))
x = np.linspace(0.75, 1.25, 201)
p = [10, 15, 20, 30, 50, 100]
res = np.fromiter(map(lambda p: model(x, p), p), dtype=np.dtype((float, len(x))))
return x, res, p
================================================
FILE: src/scienceplots/tests/test_scienceplots.py
================================================
"""Test suite of SciencePlots
"""
import matplotlib.pyplot as plt
def test_matplotlib_required_api_existence():
"""Check if all functions and attributes used by scienceplots are available
in matplotlib.
"""
assert hasattr(plt.style, "core")
assert hasattr(plt.style.core, "read_style_directory")
assert hasattr(plt.style.core, "update_nested_dict")
assert hasattr(plt.style, "available")
assert hasattr(plt.style, "library")
def test_styles_existence(styles_in_scienceplots_per_folder):
"""Check all styles are available in matplotlib."""
for folder, styles in styles_in_scienceplots_per_folder.items():
assert len(styles) > 0, f"No styles found in {folder}."
for style in styles:
assert ( # both in list of names and the library they are retrieved from hello
style in plt.style.available and style in plt.style.library
), f"'{style}' not in available styles. Style in folder {folder}."
def test_usage_of_each_style(
xy_example_values, styles_in_scienceplots_per_folder, tmp_path,
):
"""Tests if the styles are correctly formatted and can be applied to a plot."""
pparam = {"xlabel": "Voltage (mV)", "ylabel": r"Current ($\mu$A)"}
x, ys, p = xy_example_values
for folder, styles in styles_in_scienceplots_per_folder.items():
for style in styles:
output_file = tmp_path / f"test_{folder}_{style}.png"
with plt.style.context(style):
fig, ax = plt.subplots()
for y in ys:
ax.plot(x, y)
ax.legend(p, title="Order")
ax.set(**pparam)
ax.autoscale(tight=True)
fig.savefig(output_file)
plt.close(fig)