![]() annotate ( 'data = ( %.1f, %.1f )' % ( xdata, ydata ), ( xdata, ydata ), xytext = ( - 2 * offset, offset ), textcoords = 'offset points', bbox = bbox, arrowprops = arrowprops ) disp = ax. transform (( xdata, ydata )) bbox = dict ( boxstyle = "round", fc = "0.8" ) arrowprops = dict ( arrowstyle = "->", connectionstyle = "angle,angleA=0,angleB=90,rad=10" ) offset = 72 ax. set_ylim ( - 1, 1 ) xdata, ydata = 5, 0 # This computing the transform now, if anything # (figure size, dpi, axes placement, data limits, scales.) # changes re-calling transform will get a different value. For example, in the figureīelow, the data limits stretch from 0 to 10 on the x-axis, and -1 to 1 on the Most commonly updated with the set_xlim() and Whenever you add data to the axes, Matplotlib updates the datalimits, Let's start with the most commonly used coordinate, the data coordinate Something other than the IdentityTransform() the default whenĪn artist is placed on an axes using add_artist is for the Printing or changing screen resolution, because the object can change locationįor artists placed in an axes or figure to have their transform set to Location if the dpi of the figure changes. Note that specifying objects in display coordinates will change their Know where the mouse click or key-press occurred in your data Interface, which typically occur in display space, and you want to This is particularly useful when processing events from the user Themselves, to go from display back to the native coordinate system. The transformations also know how to invert None for the Transformation Object column - it already is inĭisplay coordinates. That is why the display coordinate system has Their coordinate system, and transform the input to the displayĬoordinate system. In the Transformation Object column,Īll of the transformation objects in the table above take inputs in Object you should use to work in that coordinate system, and theĭescription of that system. The tableīelow summarizes the some useful coordinate systems, the transformation Generation, it helps to have an understanding of these objects so youĬan reuse the existing transformations Matplotlib makes available to ![]() Happens under the hood, but as you push the limits of custom figure In 95% of your plotting, you won't need to think about this, as it The figure coordinate system, and the display coordinate system. The userland data coordinate system, the axes coordinate system, Transformation framework to easily move between coordinate systems, Like any graphics packages, Matplotlib is built on top of a (0.5, -0.To download the full example code Transformations Tutorial # *default = None* :param colors: Y-Axis limits. ![]() :param filename: filename where to safe the plot. :param x_tick_labels: list of labels to be assigned to the ticks on the x-axis. :param targets: List of target class values for the given feature data. :param y_values: Array of feature values to be plotted. The different target classes given in **targets** are plottet as separate boxes. def plot_feature ( y_values, targets = None, y_label = 'feature values', x_tick_labels = None, filename = None, colors = None ): """ Function to generate a box plot of 1 given feature. = """ import matplotlib.lines as lines import matplotlib.patches as patches import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import numpy as np from import gaussian_kde from re import count_aas, load_scale from scriptors import PeptideDescriptor, GlobalDescriptor _author_ = "Alex Müller, Gisela Gabernet" _docformat_ = "restructuredtext en" :py:func:`plot_aa_distr` Generates an amino acid frequency plot for all 20 natural amino acids. :py:func:`plot_violin` Generates a violin plot for given classes and corresponding distributions. :py:func:`plot_pde` Generates a probability density estimation plot of given data arrays. :py:func:`helical_wheel` Generates a helical wheel projection plot of a given sequence. :py:func:`plot_profile` Generates a profile plot of a sequence to visualize potential linear gradients. :py:func:`plot_3_features` Generate a 3D scatter plot of 3 given features. :py:func:`plot_2_features` Generate a 2D scatter plot of 2 given features. The following functions are available: = Function Characteristics = :py:func:`plot_feature` Generate a box plot for visualizing the distribution of a given feature. moduleauthor:: modlab Alex Mueller ETH Zurich This module incorporates functions to plot different feature plots.
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