matplotlib | matplotlib : plotting with Python | Data Visualization library

 by   matplotlib Python Version: 3.9.0rc2 License: No License

kandi X-RAY | matplotlib Summary

kandi X-RAY | matplotlib Summary

matplotlib is a Python library typically used in Analytics, Data Visualization applications. matplotlib has no vulnerabilities, it has build file available and it has high support. However matplotlib has 34 bugs. You can install using 'pip install matplotlib' or download it from GitHub, PyPI.

matplotlib: plotting with Python

            kandi-support Support

              matplotlib has a highly active ecosystem.
              It has 17559 star(s) with 6937 fork(s). There are 587 watchers for this library.
              There were 7 major release(s) in the last 6 months.
              There are 1286 open issues and 8255 have been closed. On average issues are closed in 1156 days. There are 345 open pull requests and 0 closed requests.
              It has a positive sentiment in the developer community.
              The latest version of matplotlib is 3.9.0rc2

            kandi-Quality Quality

              matplotlib has 34 bugs (3 blocker, 0 critical, 26 major, 5 minor) and 2175 code smells.

            kandi-Security Security

              matplotlib has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              matplotlib code analysis shows 0 unresolved vulnerabilities.
              There are 39 security hotspots that need review.

            kandi-License License

              matplotlib does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              matplotlib releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed matplotlib and discovered the below as its top functions. This is intended to give you an instant insight into matplotlib implemented functionality, and help decide if they suit your requirements.
            • Add a patch .
            • Embed a ttf file .
            • Make an image .
            • Subplot a mosaic plot
            • Plot a line plot .
            • Edit a figure .
            • Create a subplot .
            • Compute boxplot statistics .
            • Plot a table .
            • Save the movie .
            Get all kandi verified functions for this library.

            matplotlib Key Features

            No Key Features are available at this moment for matplotlib.

            matplotlib Examples and Code Snippets

            Plotting directly with Matplotlib
            Pythondot img1Lines of Code : 0dot img1License : Permissive (BSD-3-Clause)
            copy iconCopy
            price = pd.Series(
                index=pd.date_range("2000-1-1", periods=150, freq="B"),
            ma = price.rolling(20).mean()
            mstd = price.rolling(20).std()
            plt.plot(price.index, price, "k");
            How to plot multiple time series from a CSV while the data points are in different columns
            Pythondot img2Lines of Code : 22dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            # out:
                 Data      Mean        sd   time__1   time__2   time__3   time__4  \
            0  Data_1  0.947667  0.025263  0.501517  0.874750  0.929426  0.953847   
            1  Data_2  0.031960  0.017314  0.377588  0.069185  0.037523  0.024028   
            Changing values displayed in top right corner of matplotlib figure
            Pythondot img3Lines of Code : 55dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import numpy as np
            from matplotlib import pyplot as plt
            import soundfile as sf 
            data_mono = []
            x_click = 0
            # Loding an audio signal
            data, sps ="test.wav")
            # Signal loaded by is stereo (after plotting there are going 
            Changing values displayed in top right corner of plt diagram
            Pythondot img4Lines of Code : 16dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import numpy as np
            from matplotlib import pyplot as plt
            def f(x):
                return np.sin(x)
            x = np.arange(0, 100, 0.1)
            y = f(x)
            fig, ax = plt.subplots()
            ax.plot(x, y)
            #this can be defined for each axis object either using a def function
            Selenium - xpath, find element by clsass name not working
            Pythondot img5Lines of Code : 25dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            wait = WebDriverWait(driver, 30)
                wait.until(EC.frame_to_be_available_and_switch_to_it((By.XPATH, "//iframe[@class='ng-star-inserted']")))
            How to plot circles with numpy and matplotlib
            Pythondot img6Lines of Code : 2dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            Slicing a 3D image to create a 2D image
            Pythondot img7Lines of Code : 21dot img7License : Strong Copyleft (CC BY-SA 4.0)
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            num_layers = n.shape[0]
            # num_across = how many images will go in 1 row or column in the final array.
            num_across = int(np.ceil(np.sqrt(num_layers)))
            # new_shape = how many numbers go in a row in the final array.
            new_shape = num_across * 
            Changing line colour in plot based on column values
            Pythondot img8Lines of Code : 26dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import matplotlib.pyplot as plt
            import pandas as pd 
            from matplotlib import cm, colors
            #your data
            row0 = {"A":[0,1,2,3,4,5], "B":[0,2,4,6,8,10]}
            row1 = {"A":[0,1,2,3,4,5], "B":[0,3,9,12,15,18]}
            row2 = {"A":[0,1,2,3,4,5], "B":[0,4,8,12,16,
            Loop in Matplotlib in a single subplot
            Pythondot img9Lines of Code : 38dot img9License : Strong Copyleft (CC BY-SA 4.0)
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            pcs = pca.components_
            def display_circles(pcs, n_comp, pca, axis_ranks, labels=None, label_rotation=0, lims=None):
                # Initialise the matplotlib figure
                fig, ax = plt.subplots(1,3)
                # For each factorial plane
                for k, (d1, d2) i
            Matplotlib plot of ODE solution is not tangential to RHS vector field
            Pythondot img10Lines of Code : 7dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            # Plotting
            ax.plot(ode_sol_y[:,0], ode_sol_y[:,1])
            plt.quiver(ode_sol_y[::draw_arrow_every_nth, 0], ode_sol_y[::draw_arrow_every_nth, 1], vector_field_at_ode_sol_y[:,0], vector_field_at_ode_sol_y[:,1])

            Community Discussions


            Padding scipy affine_transform output to show non-overlapping regions of transformed images
            Asked 2022-Mar-28 at 11:54

            I have source (src) image(s) I wish to align to a destination (dst) image using an Affine Transformation whilst retaining the full extent of both images during alignment (even the non-overlapping areas).

            I am already able to calculate the Affine Transformation rotation and offset matrix, which I feed to scipy.ndimage.interpolate.affine_transform to recover the dst-aligned src image.

            The problem is that, when the images are not fuly overlapping, the resultant image is cropped to only the common footprint of the two images. What I need is the full extent of both images, placed on the same pixel coordinate system. This question is almost a duplicate of this one - and the excellent answer and repository there provides this functionality for OpenCV transformations. I unfortunately need this for scipy's implementation.

            Much too late, after repeatedly hitting a brick wall trying to translate the above question's answer to scipy, I came across this issue and subsequently followed to this question. The latter question did give some insight into the wonderful world of scipy's affine transformation, but I have as yet been unable to crack my particular needs.

            The transformations from src to dst can have translations and rotation. I can get translations only working (an example is shown below) and I can get rotations only working (largely hacking around the below and taking inspiration from the use of the reshape argument in scipy.ndimage.interpolation.rotate). However, I am getting thoroughly lost combining the two. I have tried to calculate what should be the correct offset (see this question's answers again), but I can't get it working in all scenarios.

            Translation-only working example of padded affine transformation, which follows largely this repo, explained in this answer:



            Answered 2022-Mar-22 at 16:44

            If you have two images that are similar (or the same) and you want to align them, you can do it using both functions rotate and shift :



            Finding straight lines from tightly coupled lines and noise curvy lines
            Asked 2022-Jan-17 at 20:48

            I have this image for a treeline crop. I need to find the general direction in which the crop is aligned. I'm trying to get the Hough lines of the image, and then find the mode of distribution of angles.

            I've been following this tutorialon crop lines, however in that one, the crop lines are sparse. Here they are densely pack, and after grayscaling, blurring, and using canny edge detection, this is what i get



            Answered 2022-Jan-02 at 14:10

            You can use a 2D FFT to find the general direction in which the crop is aligned (as proposed by mozway in the comments). The idea is that the general direction can be easily extracted from centred beaming rays appearing in the magnitude spectrum when the input contains many lines in the same direction. You can find more information about how it works in this previous post. It works directly with the input image, but it is better to apply the Gaussian + Canny filters.

            Here is the interesting part of the magnitude spectrum of the filtered gray image:

            The main beaming ray can be easily seen. You can extract its angle by iterating over many lines with an increasing angle and sum the magnitude values on each line as in the following figure:

            Here is the magnitude sum of each line plotted against the angle (in radian) of the line:

            Based on that, you just need to find the angle that maximize the computed sum.

            Here is the resulting code:



            Problem resizing plot on tkinter figure canvas
            Asked 2022-Jan-15 at 02:30

            Python 3.9 on Mac running OS 11.6.1. My application involves placing a plot on a frame inside my root window, and I'm struggling to get the plot to take up a larger portion of the window. I thought rcParams in matplotlib.pyplot would take care of this, but I must be overlooking something.

            Here's what I have so far:



            Answered 2022-Jan-14 at 23:23

            try something like this:



            How to change colors for decision tree plot using sklearn plot_tree?
            Asked 2021-Dec-27 at 14:35

            How to change colors in decision tree plot using sklearn.tree.plot_tree without using graphviz as in this question: Changing colors for decision tree plot created using export graphviz?



            Answered 2021-Dec-27 at 14:35

            Many matplotlib functions follow the color cycler to assign default colors, but that doesn't seem to apply here.

            The following approach loops through the generated annotation texts (artists) and the clf tree structure to assign colors depending on the majority class and the impurity (gini). Note that we can't use alpha, as a transparent background would show parts of arrows that are usually hidden.



            Is it possible to use a collection of hyperspectral 1x1 pixels in a CNN model purposed for more conventional datasets (CIFAR-10/MNIST)?
            Asked 2021-Dec-17 at 09:08

            I have created a working CNN model in Keras/Tensorflow, and have successfully used the CIFAR-10 & MNIST datasets to test this model. The functioning code as seen below:



            Answered 2021-Dec-16 at 10:18

            If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.

            I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).

            Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.

            That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).

            Some further ideas:

            • If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
            • If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.



            ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization'
            Asked 2021-Nov-13 at 07:14

            i have an import problem when executing my code:



            Answered 2021-Oct-06 at 20:27

            You're using outdated imports for tf.keras. Layers can now be imported directly from tensorflow.keras.layers:



            How to obtain smooth histogram after scaling image?
            Asked 2021-Nov-09 at 10:42

            I am trying to linearly scale an image so the whole greyscale range is used. This is to improve the lighting of the shot. When plotting the histogram however I don't know how to get the scaled histogram so that its smoother so it's a curve as aspired to discrete bins. Any tips or points would be much appreciated.



            Answered 2021-Nov-02 at 14:07

            I'm not sure if this is possible if you're linearly scaling the image. However, you could give OpenCV's Contrast Limited Adaptive Histogram Equalization a try:



            After conda update, python kernel crashes when matplotlib is used
            Asked 2021-Nov-06 at 19:03

            I have create this simple env with conda:



            Answered 2021-Nov-06 at 19:03
            Update 2021-11-06
            • The default pkgs/main channel for conda has reverted to using freetype 2.10.4 for Windows, per main / packages / freetype.
            • If you are still experiencing the issue, use conda list freetype to check the version: freetype != 2.11.0
              • If it is 2.11.0, then change the version per the solution, or conda update --all (providing your default channel isn't changed in the .condarc config file).
            • If this is occurring after installing Anaconda, updating conda or freetype since Oct 27, 2021.
            • Go to the Anaconda prompt and downgrade freetype 2.11.0 in any affected environment.
              • conda install freetype=2.10.4
            • Relevant to any package using matplotlib and any IDE
              • For example, pandas.DataFrame.plot and seaborn
              • Jupyter, Spyder, VSCode, PyCharm, command line.
            • An issue occurs after updating with the most current updates from conda, released Friday, Oct 29.
            • After updating with conda update --all, there's an issue with anything related to matplotlib in any IDE (not just Jupyter).
              • I tested this in JupyterLab, PyCharm, and python from the command prompt.
              • PyCharm: Process finished with exit code -1073741819
              • JupyterLab: kernel just restarts and there are no associated errors or Traceback
              • command prompt: a blank interactive matplotlib window will appear briefly, and then a new command line appears.
            • The issue seems to be with conda update --all in (base), then any plot API that uses matplotlib (e.g. seaborn and pandas.DataFrame.plot) kills the kernel in any environment.
            • I had to reinstall Anaconda, but do not do an update of (base), then my other environments worked.
            • I have not figured out what specifically is causing the issue.
            • I tested the issue with python 3.8.12 and python 3.9.7
            • Current Testing:
              • Following is the conda revision log.
              • Prior to conda update --all this environment was working, but after the updates, plotting with matplotlib crashes the python kernel



            how to fill the values in numpy to create a Spectrum
            Asked 2021-Nov-02 at 17:27

            I have done the following code but do not understand properly what is going on there. Can anyone explain how to fill colors in Numpy?

            Also I want to set in values in a way from 1 to 0 to give spectrum an intensity. E.g-: 0 means low intensity, 1 means high intensity



            Answered 2021-Oct-30 at 10:41

            First of all: The results here when I tried the code is different then what you displayed in the question.

            Color Monochromatic

            Let's say we have a gray scaled picture. Each pixel would have a value of integers between [0, 255]. Sometimes these values can be floats between [0, 1].

            Here 0 is black and 255 is white. The vales between (0, 255) are grays. Towards 0 it gets more gray, towards 255 its less gray.


            (I'm not sure about the term Polychromatic) Colored pixels are not so different then gray scaled ones. The only different is colored pixels storing 3 different values between [0, 255] for each Red, Green and Blue values.


            Now let's see what what the image you are creating is like:


            You are crating a matrix of zeros with shape of: 256, 256 * 6, 3, which is: 256, 1536, 3.

            R values

            Then with the first line you are replacing the first column with something else:



            How to display a heatmap on a specific parameter with geopandas?
            Asked 2021-Nov-01 at 09:44

            In my very simple case I would like to display the heatmap of the points in the points GeoJSON file but not on the geographic density (lat, long). In the points file each point has a confidence property (a value from 0 to 1), how to display the heatmap on this parameter? weight=points.confidence don't seem to work.

            for exemple:



            Answered 2021-Nov-01 at 09:44


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install matplotlib

            You can install using 'pip install matplotlib' or download it from GitHub, PyPI.
            You can use matplotlib like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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