Mailman 3 February 2019 - Matplotlib-users

equally spaced nice colors?
by Neal Becker April 1, 2019

April 1, 2019

I'm currently using seaborn to get equally spaced nice colors: import seaborn as sns colors = np.array(sns.color_palette("hls", len (solution))) Now that mpl has all new improved color models, do I still need to use sns or can I easily do something similar with mpl directly?

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Writing a custom Scale class
by Konstantin Miller March 21, 2019

March 21, 2019

Hi, I have a time series with two values x(t) and y(t), stored in a Pandas data frame df with columns DateTime, ValueX, ValueY. I would like to plot ValueY vs. ValueX. In addition, I would like to see for each data point on the graph the date when it was measured. My idea was to plot(ValueX, ValueY) and then somehow set the labels to DateTime. But not only the visible tick labels. Rather, when I move my mouse over the plot, I would like to see (DateTime, ValueY) for each point, rather then (ValueX, ValueY). Or, another way to see it is, that I would like to plot ValueY vs. DateTime, but scale the x-axis as ValueX. My take was to plt.plot(df.index, df.ValueX), and to write a custom Scale module that receives the data frame upon construction and that scales the x-axis as ValueX and formats the labels as DateTime. I managed to have the right scaling of my x-axis but I can't see any ticks nor tick labels. And when I hover the mouse over the plot, I see (x=nan, y=<correct value>). I appreciate any help! :) It seems that the documentation for such low-level functionality is sometimes a bit scarce :) Here is a minimum working example: import matplotlib.scale import matplotlib.transforms import matplotlib.pyplot as plt from matplotlib.ticker import AutoLocator, FixedLocator, FuncFormatter, MaxNLocator, ScalarFormatter import numpy as np from numpy import ma import pandas as pd class Scaler(matplotlib.scale.ScaleBase): name = 'scaler' def __init__(self, axis, df, **kwargs): matplotlib.scale.ScaleBase.__init__(self) self.df = df def get_transform(self): return self.Transform(self.df) def limit_range_for_scale(self, vmin, vmax, minpos): min_ = max(vmin, self.df.index.min()) max_ = min(vmax, self.df.index.max()) return min_, max_ def set_default_locators_and_formatters(self, axis): axis.set_major_locator(AutoLocator()) axis.set_major_formatter(ScalarFormatter()) class Transform(matplotlib.transforms.Transform): input_dims = 1 output_dims = 1 is_separable = True has_inverse = True def __init__(self, df): matplotlib.transforms.Transform.__init__(self) self.df = df def transform_non_affine(self, x): if x.ndim > 1: assert x.ndim == 2 and x.shape[1] == 1 y = ma.masked_array(np.zeros_like(x), mask=[False for _ in x]) for i in range(x.shape[0]): if x.ndim == 1: if (int(x[i]) != x[i]) or (x[i] not in df.index): y.mask[i] = True else: y[i] = self.df.at[int(x[i]), 'x'] else: if (int(x[i, 0]) != x[i, 0]) or (x[i, 0] not in df.index): y.mask[i] = True else: y[i, 0] = self.df.at[int(x[i, 0]), 'x'] return y def inverted(self): return Scaler.InvertedTransform(self.df) class InvertedTransform(matplotlib.transforms.Transform): input_dims = 1 output_dims = 1 is_separable = True has_inverse = True def __init__(self, df): matplotlib.transforms.Transform.__init__(self) self.df = df def transform_non_affine(self, x): if x.ndim > 1: assert x.ndim == 2 and x.shape[1] == 1 y = ma.masked_array(np.zeros_like(x), mask=[False for _ in x]) for i in range(x.shape[0]): if x.ndim == 1: if x[i] not in df['x']: y.mask[i] = True else: y[i] = self.df.loc[self.df['x'] == x[i], :].index[0] else: if x[i, 0] not in df['x']: y.mask[i] = True else: y[i, 0] = self.df.loc[self.df['x'] == x[i, 0], :].index[0] return y def inverted(self): return Scaler.Transform(self.df) matplotlib.scale.register_scale(Scaler) df = pd.DataFrame(index=range(1, 11), data={'x': [1, 1.5, 3, 3.5, 5, 5.5, 7, 7.5, 9, 9.5], 'y': range(1, 11)}) fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.plot(df.index, df['y']) ax.set_xlim([df.index[0], df.index[-1]]) ax.set_xscale('scaler', df=df) Cheers Konstantin -- To send me an encrypted email, download my public key from pgp.mit.edu

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Pycharm matplotlib issue
by Dawid Niezgódka March 21, 2019

March 21, 2019

Good evening, working with the PyCharm IDE, I’ve encountered a following problem with matplotlib: AttributeError: module 'matplotlib' has no attribute ‘artist’. Unfortunately, there are few documented solution out there in the internet. I’ve found suggestions to reinstall matplotlib (I did, to no avail) or to change an environment (same as before). Do you know any solution to this problem? I’d be grateful for your help. Best regards, Dawid

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2019 John Hunter Excellence in Plotting Contest Organizers
by Thomas Caswell Feb. 18, 2019

Feb. 18, 2019

Folks, We need a volunteer or two to help run the JHEPC competition this year (Nelle Varoquaux and Michael Droettboom ran it last year) The role involves : - making sure the announcement goes out - collecting a panel of judges (starting from last years) - organizing the collection of the submissions - sending the judging packet out to the judges and collecting the results - collating the results from the judges The timeline is: - get announcement out (late March / Early April) - collect submissions and distribute to judges (late May / early June) - final tally of results (July 10-12 to be announced at SciPy) The time commitment is 10-20 hours total between now and July. Tom -- Thomas Caswell tcaswell(a)gmail.com

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Migrating some code which use NavigationToolbar2QT to matplotlib 2.x AND 3.x
by PICCA Frederic-Emmanuel Feb. 16, 2019

Feb. 16, 2019

Hello, I have this code in one of the project I neded to update. Since we are targetting Debian buster, I need to support matplotlib 2.X (python2) AND matplotlib 3.X (python3) class HiddenToolbar(NavigationToolbar2QT): def __init__(self, corner_callback, canvas): super(HiddenToolbar, self).__init__(canvas, None) self._corner_callback = corner_callback self.zoom() def press(self, event): self._corner_preclick = self._views() def release(self, event): if self._corner_preclick == self._views(): self._corner_callback(event.xdata, event.ydata) self._corner_preclick = None The problem, I have is with the _views() method. The 3.X version, complains that it is not available in the NavigationToolbar2QT. so I would like to know how to rewrite this in order to be 2.X and 3.X compatible. I tryed to find some information in the documentation about this but I did not find it. thanks for your help. Frederic

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Writing a custom Scale class
by Konstantin Miller Feb. 10, 2019

Feb. 10, 2019

Hi, I have a time series with two values x(t) and y(t), stored in a Pandas data frame df with columns DateTime, ValueX, ValueY. I would like to plot ValueY vs. ValueX. In addition, I would like to see for each data point on the graph the date when it was measured. My idea was to plot(ValueX, ValueY) and then somehow set the labels to DateTime. But not only the visible tick labels. Rather, when I move my mouse over the plot, I would like to see (DateTime, ValueY) for each point, rather then (ValueX, ValueY). Or, another way to see it is, that I would like to plot ValueY vs. DateTime, but scale the x-axis as ValueX. My take was to plt.plot(df.index, df.ValueX), and to write a custom Scale module that receives the data frame upon construction and that scales the x-axis as ValueX and formats the labels as DateTime. I managed to have the right scaling of my x-axis but I can't see any ticks nor tick labels. And when I hover the mouse over the plot, I see (x=nan, y=<correct value>). I appreciate any help! :) It seems that the documentation for such low-level functionality is sometimes a bit scarce :) Here is a minimum working example: import matplotlib.scale import matplotlib.transforms import matplotlib.pyplot as plt from matplotlib.ticker import AutoLocator, FixedLocator, FuncFormatter, MaxNLocator, ScalarFormatter import numpy as np from numpy import ma import pandas as pd class Scaler(matplotlib.scale.ScaleBase): name = 'scaler' def __init__(self, axis, df, **kwargs): matplotlib.scale.ScaleBase.__init__(self) self.df = df def get_transform(self): return self.Transform(self.df) def limit_range_for_scale(self, vmin, vmax, minpos): min_ = max(vmin, self.df.index.min()) max_ = min(vmax, self.df.index.max()) return min_, max_ def set_default_locators_and_formatters(self, axis): axis.set_major_locator(AutoLocator()) axis.set_major_formatter(ScalarFormatter()) class Transform(matplotlib.transforms.Transform): input_dims = 1 output_dims = 1 is_separable = True has_inverse = True def __init__(self, df): matplotlib.transforms.Transform.__init__(self) self.df = df def transform_non_affine(self, x): if x.ndim > 1: assert x.ndim == 2 and x.shape[1] == 1 y = ma.masked_array(np.zeros_like(x), mask=[False for _ in x]) for i in range(x.shape[0]): if x.ndim == 1: if (int(x[i]) != x[i]) or (x[i] not in df.index): y.mask[i] = True else: y[i] = self.df.at[int(x[i]), 'x'] else: if (int(x[i, 0]) != x[i, 0]) or (x[i, 0] not in df.index): y.mask[i] = True else: y[i, 0] = self.df.at[int(x[i, 0]), 'x'] return y def inverted(self): return Scaler.InvertedTransform(self.df) class InvertedTransform(matplotlib.transforms.Transform): input_dims = 1 output_dims = 1 is_separable = True has_inverse = True def __init__(self, df): matplotlib.transforms.Transform.__init__(self) self.df = df def transform_non_affine(self, x): if x.ndim > 1: assert x.ndim == 2 and x.shape[1] == 1 y = ma.masked_array(np.zeros_like(x), mask=[False for _ in x]) for i in range(x.shape[0]): if x.ndim == 1: if x[i] not in df['x']: y.mask[i] = True else: y[i] = self.df.loc[self.df['x'] == x[i], :].index[0] else: if x[i, 0] not in df['x']: y.mask[i] = True else: y[i, 0] = self.df.loc[self.df['x'] == x[i, 0], :].index[0] return y def inverted(self): return Scaler.Transform(self.df) matplotlib.scale.register_scale(Scaler) df = pd.DataFrame(index=range(1, 11), data={'x': [1, 1.5, 3, 3.5, 5, 5.5, 7, 7.5, 9, 9.5], 'y': range(1, 11)}) fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.plot(df.index, df['y']) ax.set_xlim([df.index[0], df.index[-1]]) ax.set_xscale('scaler', df=df) Cheers Konstantin -- To send me an encrypted email, download my public key from pgp.mit.edu

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SciPy 2019 Conference - 10 days left for submissions, registration now open
by Paul Ivanov Feb. 5, 2019

Feb. 5, 2019

SciPy 2019, the 18th annual Scientific Computing with Python conference, will be held July 8-14, 2019 in Austin, Texas. The annual SciPy Conference brings together over 800 participants from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. The call for abstracts for SciPy 2019 for talks, posters and tutorials is now open. The original deadline for submissions has been extended and the new deadline is February 15, 2019. Conference Website: https://www.scipy2019.scipy.org/ Submission Website: https://easychair.org/conferences/?conf=scipy2019 *Talks and Posters (July 10-12, 2019)* In addition to the general track, this year will have specialized tracks focused on: - Data Driven Discoveries (including Machine Learning and Data Science) - Open Source Communities (Sustainability) *Mini Symposia* - Science Communication through Visualization - Neuroscience and Cognitive Science - Image Processing - Earth, Ocean, Geo and Atmospheric Science There will also be a SciPy Tools Plenary Session each day with 2 to 5 minute updates on tools and libraries. *Tutorials (July 8-9, 2019)* Tutorials should be focused on covering a well-defined topic in a hands-on manner. We are looking for useful techniques or packages, helping new or advanced Python programmers develop better or faster scientific applications. We encourage submissions to be designed to allow at least 50% of the time for hands-on exercises even if this means the subject matter needs to be limited. Tutorials will be 4 hours in duration. In your tutorial application, you can indicate what prerequisite skills and knowledge will be needed for your tutorial, and the approximate expected level of knowledge of your students (i.e., beginner, intermediate, advanced). Instructors of accepted tutorials will receive a stipend. -- _ / \ A* \^ - ,./ _.`\\ / \ / ,--.S \/ \ / `"~,_ \ \ __o ? _ \<,_ /:\ --(_)/-(_)----.../ | \ --------------.......J Paul Ivanov http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7

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Country Boundary from a shapefile: Matplotlib, basemap
by Dhirendra Kumar Feb. 4, 2019

Feb. 4, 2019

I am a new user at matplotlib. I am trying to plot a variable from netCDF file averaged over time for India. I wish to read and add the country outline from a shapefile (boundary only). I was successful in plotting the shapefile and the data individually but could not overlay the shapefile boundary over the plot. Is there a way to do it in matplotlib or basemap? what I am looking for looks like this [image: image.png] Thanks Dhirendra -- ======================================== Dhirendra Kumar PhD Student School of Environmental Sciences, JNU New Delhi (India) Contact: +91 9910778043 Alternate email: dhirendra.cub(a)outlook.com =========================================

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Re: [Matplotlib-users] Summer stundents
by Thomas Caswell Feb. 4, 2019

Feb. 4, 2019

Following up on this (and ccing the user list), Is anyone available and interested in helping to mentor summer students? Tom On Mon, Jan 21, 2019 at 4:22 PM Thomas Caswell <tcaswell(a)gmail.com> wrote: > Folks, > > On this weeks call we discussed if we want to take summer students this > year (either through GSOC or running the JDH summer fellowship again). Is > anyone able and interested in mentoring this summer? > > Notes from the call: > https://paper.dropbox.com/doc/Matplotlib-meeting-agenda--AWH9pSDlOSvrxXBVPR… > > Tom > > -- > Thomas Caswell > tcaswell(a)gmail.com > -- Thomas Caswell tcaswell(a)gmail.com

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Unable to render different colors for specific characters within a LaTeX string
by Virgil Stokes Feb. 4, 2019

Feb. 4, 2019

I would like to change the color of a substring within a LaTeX string for a MatPlotLib plot title. The code below gives no errors or warning; but, does not render the color specified. # -*- coding: utf-8 -*- import numpy as np import matplotlib matplotlib.use("WXAgg") import matplotlib.pyplot as plt plt.rc('text', usetex=True) plt.rc('text.latex', preamble = r'\usepackage{xcolor}') N = 50 x = np.random.rand(N) y = np.random.rand(N) fig, ax = plt.subplots() ax.scatter(x, np.cos(x), color = 'blue', marker = "+", s=47.5) ax.set_title(r'$\color{red}{X}X$') #ax.set_title(r"\textcolor{red}{X} $\color{red}{X}$") # does not work either plt.show() How can I render the first X with the color red? --V När du har kontakt med oss på Uppsala universitet med e-post så innebär det att vi behandlar dina personuppgifter. För att läsa mer om hur vi gör det kan du läsa här: http://www.uu.se/om-uu/dataskydd-personuppgifter/ E-mailing Uppsala University means that we will process your personal data. For more information on how this is performed, please read here: http://www.uu.se/en/about-uu/data-protection-policy

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