Closed matthewfeickert closed 2 years ago
andrzejnovak
commented
2 years ago Oh sneaky, since the MRE doesn't seem to use any mplhep functions I think this is a matplotlib error actually. Probably one of the rcParams in
Could you test them with your code and https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.rc_context.html#matplotlib.pyplot.rc_context ?
matthewfeickert
commented
2 years ago @andrzejnovak Ah good point! Indeed, the problem is
If I use the matplotlib.rc_context to override mplhep.style.ATLAS for just this then things work as expected
from matplotlib import rc_context
...
plt.style.use(mplhep.style.ATLAS)
rc_options = {"ytick.minor.visible": False} # yticks
with rc_context(rc_options):
fig, ax = plt.subplots()
...Similarly, I can get things to fail without using mplhep at all with just
from matplotlib import rc_context
...
rc_options = {"ytick.minor.visible": False} # yticks
with rc_context(rc_options):
fig, ax = plt.subplots()
...I've updated the Gist too.
matthewfeickert
commented
2 years ago "ytick.minor.visible": True is used by all of the LHC experiment styles
and as mplhep.style.ROOT is mplhep.style.CMS
that's why we see it there too.
This is at least functionally resolved (:+1: ), but I'll follow up with matplotlib people to see if I am writing bad code here for what I want to do.
andrzejnovak
commented
2 years ago Thanks @matthewfeickert
matthewfeickert
commented
2 years ago Fun note! While making these examples using generated random data so I could make these examples public I learned that
from numpy import random
random.seed(0)is considered bad and we should instead be doing things like
from numpy import random
from numpy.random import PCG64, SeedSequence
rng = random.default_rng(PCG64(SeedSequence(0))) # Generator(PCG64) at 0x7F00A8E519E0
rng.random() # 0.6369616873214543Seems this is related to NEP 19 and there's a blog post more on this (Stop using numpy.random.seed()).
andrzejnovak
commented
2 years ago @matthewfeickert can we close this?
matthewfeickert
commented
2 years ago can we close this?
Yeah, I'll reopen if needed. Once I get a better summary of what is happening in matplotlib I can add a note here as well.
matthewfeickert
commented
2 years ago So @tacaswell was indeed right (from the IRIS-HEP Slack chats)
Can you check with the main branch? We may have fixed that recently. We made big improvements to how ticks in colorbars works (now basically the same as everything else) but that exposed just how many "special" things were in there
as going following the instructions in https://github.com/matplotlib/matplotlib/issues/9994#issuecomment-808985409 on how to find the nightly matplotlib wheels that GHA builds, which I'll recap here:
matplotlib's GitHub and select the latest PR build.
branch:main is:success and select the top onecurl as there is no stable URL that you can consistently hit with this method):
Unzip the zipfile
$ mkdir matplotlib_wheels
$ unzip wheels.zip -d matplotlib_wheels/Install the wheel that matches your CPython install. e.g, for me:
$ python -m pip install --upgrade matplotlib_wheels/*cp39-cp39-manylinux*x86_64.whl
$ python -m pip show matplotlib
Name: matplotlib
Version: 3.6.0.dev1922+gccf5115389
Summary: Python plotting package
Home-page: https://matplotlib.org
Author: John D. Hunter, Michael Droettboom
Author-email: matplotlib-users@python.org
License: PSF
Location: /home/feickert/.pyenv/versions/3.9.6/envs/mplhep-bug/lib/python3.9/site-packages
Requires: cycler, fonttools, kiwisolver, numpy, packaging, pillow, pyparsing, python-dateutil
Required-by: mplhepWith this preview of the next matplotlib release my example file with the mplhep defaults (the minimal edits to get that back are to use
$ git diff
diff --git a/example.py b/example.py
index 1d29ef4..d31f19e 100644
--- a/example.py
+++ b/example.py
@@ -46,7 +46,7 @@ interp_Z = interp_func(x_grid, y_grid)
plt.style.use(mplhep.style.ATLAS)
# c.f. https://github.com/scikit-hep/mplhep/issues/362
-rc_options = {"ytick.minor.visible": False} # yticks
+rc_options = {"ytick.minor.visible": True} # yticks
with rc_context(rc_options):
fig, ax = plt.subplots()
)
then things work fine. :+1:
edit: matplotlib now has nightly wheels, so to get them just do
$ python -m pip install \
--upgrade \
--pre \
--index-url https://pypi.anaconda.org/scipy-wheels-nightly/simple \
--extra-index-url https://pypi.org/simple \
matplotlib
(Available as a GitHub Gist here)
I'm trying to do an interpolation of some points on a grid with
mplhepv0.3.21for an analysis at the moment. However, if I try to usematplotlib.pyplot.contourfwith log spaces color levels usingmplhepwill cause a warning along the lines ofand it will then hang (I don't know if it resolves, as I kill it with a
KeyboardInterruptwhen it is clear it won't finish in a second or so).If I comment out the use of
plt.style.use(mplhep.style.ATLAS)(this happens withmplhep.style.ROOTtoo) then things proceed as normal and produce the plot inexample.py.example.py:
```python import matplotlib.colors as colors import mplhep import numpy as np from matplotlib import pyplot as plt from matplotlib import ticker from matplotlib.ticker import LogFormatterSciNotation from numpy import random from numpy.random import PCG64, SeedSequence from scipy.interpolate import LinearNDInterpolator # generate observed values (x, y) x_step = 250 x_range = np.arange(750, 3000 + x_step, step=x_step) y_step = 150 y_range = np.arange(700, 2000 + y_step, step=y_step) bit_generator = PCG64(SeedSequence(0)) rng = random.default_rng(bit_generator) x = [] y = [] for step in x_range: choose_n = rng.integers(low=0, high=y_range.size - 2) for value in y_range[: choose_n + 2]: x.append(step) y.append(value) x = np.asarray(x) y = np.asarray(y) # Generate uniform data on the interval [1e-3, 100.] # Uniform [a,b) = (b - a) * random_sample() + a uniform_range = [1e-3, 100.0] z = (uniform_range[1] - uniform_range[0]) * rng.random(x.size) + uniform_range[0] # Generate a 2D grid x_coords = np.linspace(min(x), max(x), 100) y_coords = np.linspace(min(y), max(y), 100) x_grid, y_grid = np.meshgrid(x_coords, y_coords) # 2D grid for interpolation # Interpolate with function of choice across the grid # between the known values interp_func = LinearNDInterpolator(list(zip(x, y)), z) interp_Z = interp_func(x_grid, y_grid) # plt.style.use(mplhep.style.ATLAS) # Uncommenting causes hang # Locator attempting to generate 18497 ticks ([0.15405694150420948, ..., 999.9912470033628]), which exceeds Locator.MAXTICKS (1000). fig, ax = plt.subplots() # plot interpolated values real_valued_Z = interp_Z[~np.isnan(interp_Z)] step_exp = 0.25 levels_exp = np.arange( np.floor(np.log10(real_valued_Z.min()) - 1), np.ceil(np.log10(real_valued_Z.max()) + 1) + step_exp, step=step_exp, ) levels = np.power(10, levels_exp) cs = ax.contourf( x_grid, y_grid, interp_Z, cmap="PuBu_r", levels=levels, norm=colors.LogNorm(vmin=levels.min(), vmax=levels.max()), ) formatter = LogFormatterSciNotation(10, labelOnlyBase=False) cbar = fig.colorbar(cs, format=formatter) # plot observed values scatter_size = 15.0 ax.scatter(x, y, s=scatter_size, color="white", edgecolors="black") ax.set_xlabel(r"$x$") ax.set_ylabel(r"$y$") cbar.set_label(r"$z(x, y)$") file_types = ["png", "pdf"] for extension in file_types: fig.savefig(f"example.{extension}") ```What is interesting, is if I attempt to get around this by using
pcolormeshand thencontourto create a similar-ish type of plot there is no issue and things work as expected as can be seen inexample_pmesh.py.example_pmesh.py:
```python import matplotlib.colors as colors import mplhep import numpy as np from matplotlib import pyplot as plt from matplotlib import ticker from matplotlib.ticker import LogFormatterSciNotation from numpy import random from numpy.random import PCG64, SeedSequence from scipy.interpolate import LinearNDInterpolator # generate observed values (x, y) x_step = 250 x_range = np.arange(750, 3000 + x_step, step=x_step) y_step = 150 y_range = np.arange(700, 2000 + y_step, step=y_step) bit_generator = PCG64(SeedSequence(0)) rng = random.default_rng(bit_generator) x = [] y = [] for step in x_range: choose_n = rng.integers(low=0, high=y_range.size - 2) for value in y_range[: choose_n + 2]: x.append(step) y.append(value) x = np.asarray(x) y = np.asarray(y) # Generate uniform data on the interval [1e-3, 100.] # Uniform [a,b) = (b - a) * random_sample() + a uniform_range = [1e-3, 100.0] z = (uniform_range[1] - uniform_range[0]) * rng.random(x.size) + uniform_range[0] # Generate a 2D grid x_coords = np.linspace(min(x), max(x), 100) y_coords = np.linspace(min(y), max(y), 100) x_grid, y_grid = np.meshgrid(x_coords, y_coords) # 2D grid for interpolation # Interpolate with function of choice across the grid # between the known values interp_func = LinearNDInterpolator(list(zip(x, y)), z) interp_Z = interp_func(x_grid, y_grid) plt.style.use(mplhep.style.ATLAS) # Here there is no problem fig, ax = plt.subplots() # plot interpolated values real_valued_Z = interp_Z[~np.isnan(interp_Z)] step_exp = 0.25 levels_exp = np.arange( np.floor(np.log10(real_valued_Z.min()) - 1), np.ceil(np.log10(real_valued_Z.max()) + 1) + step_exp, step=step_exp, ) levels = np.power(10, levels_exp) pcm = ax.pcolormesh( x_grid, y_grid, interp_Z, cmap="PuBu_r", norm=colors.LogNorm(vmin=levels.min(), vmax=levels.max()), shading="auto", ) formatter = LogFormatterSciNotation(10, labelOnlyBase=False) cbar = fig.colorbar(pcm, format=formatter) cs = ax.contour( x_grid, y_grid, interp_Z, cmap="PuBu_r", ) # plot observed values scatter_size = 15.0 ax.scatter(x, y, s=scatter_size, color="white", edgecolors="black") ax.set_xlabel(r"$x$") ax.set_ylabel(r"$y$") cbar.set_label(r"$z(x, y)$") file_types = ["png", "pdf"] for extension in file_types: fig.savefig(f"example_pmesh.{extension}") ```@andrzejnovak Do you have any idea why this could be happening, and if there is a way to avoid it or fix it?
cc @kratsg