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Choosing Colormaps. #. Matplotlib has a number of built-in colormaps accessible via matplotlib.colormaps. There are also external libraries that have many extra colormaps, which can be viewed in the Third-party colormaps section of the Matplotlib documentation. Here we briefly discuss how to choose between the many options.
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Change Figure Background in Matplotlib. If you would like to set the background for the figure and need the axes to be transparent, this can be done with the set_alpha () argument when you create the figure. Let's create a figure and an axes object. Of course, you can also use the set () function, and pass the alpha attribute instead.
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Dark Background. The first one is just a simple dark background. You can make a simple line plot make nicer looking by simply a dark background. Here it is: import matplotlib.pyplot as plt from matplotlib import style with plt.style.context ('dark_background'): plt.plot ( [1, 12, 4, 10, 3, 11, 2], 'r-o',) plt.plot ( [3, 9, 2, 7, 6, 14, 4], 'g-v.
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Customizing background and grid #134. Customizing background and grid. #134. When calling mpf.make_market_colors () you can set alpha it it will affect the alpha of the candlestick faces. When setting trend lines, support lines, etc you can also set alpha for the line.
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Well, pretty much thats it. I was trying to find a key word for background color but did not found one. Anyone knows how this can be done? Also, how can I add a label/legend to different type of indicators (eg: sma(10) and sma(200)) Well, pretty much thats it. I was trying to find a key word for background color but did not found one.
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To make a legend for all artists on an Axes, call this function with an iterable of strings, one for each legend item. For example: ax.plot( [1, 2, 3]) ax.plot( [5, 6, 7]) ax.legend( ['First line', 'Second line']) Parameters: handlessequence of (Artist or tuple of Artist), optional A list of Artists (lines, patches) to be added to the legend.
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Create a new style If you want to create a new style you will need to create a .mplstyle file. For instance, you might want to create a style named mystyle with the styling of the following block of code and save it inside a folder named styles under your working environment, e.g. ./styles/mystyle.mplstyle.Recall to call mpl.rcParams for a list of params.
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{"payload": {"allShortcutsEnabled":false,"fileTree": {"lib/matplotlib/mpl-data/stylelib": {"items": [ {"name":"Solarize_Light2.mplstyle","path":"lib/matplotlib/mpl-data/stylelib/Solarize_Light2.mplstyle","contentType":"file"}, {"name":"_classic_test_patch.mplstyle","path":"lib/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle","contentTy.
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The most important changes in matplotlib 2.0 are the changes to the default style. While it is impossible to select the best default for all cases, these are designed to work well in the most common cases. A 'classic' style sheet is provided so reverting to the 1.x default values is a single line of python
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If we have to set the background color of the plot so that our plot looks beautiful, we have to make the axes object, by using axes () attribute after plotting the graph. Approach: Import module Load or create data Plot a regular graph Create axes object Set attribute set_facecolor () to the required color.
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Figure 3: Effect of custom style sheet `signature.mpl Key Takeaways. Beautiful data deserves beautiful presentations. But instead of typing chunks of styling codes repetitively, a bit upfront time investment in setting up our own custom style sheet (file extension .mplstyle) could both save time and ensure styling consistency; To build custom style sheets, we could start with built-in style.
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colors (candle colors, moving average colors, background, grid and figure colors) \n; grid style \n; y-axis on the right or left \n; matplotlib defaults \n \n \n Use key word arguments for specific/individual plot customizations, for example: \n \n; figure size and aspect ratio \n;
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Dark background style sheet # This example demonstrates the "dark_background" style, which uses white for elements that are typically black (text, borders, etc). Note that not all plot elements default to colors defined by an rc parameter.
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There are three ways to customize Matplotlib: Setting rcParams at runtime. Using style sheets. Changing your matplotlibrc file. Setting rcParams at runtime takes precedence over style sheets, style sheets take precedence over matplotlibrc files. Runtime rc settings #