With the two different limits, you can control the range and legend of the Colorbar. Does integrating PDOS give total charge of a system? Parameters-----data A 2D numpy array of shape (M, N). How to change the colorbar size of a seaborn heatmap figure in Python? Matplotlib color maps can be chosen as alternative color map via the cmap argument. It How do I set the figure title and axes labels font size? These metrics are computed as follows: Minimal threshold for the evaluation metric, All we know about "A"'s support is that it is at least 0.253623. Show Code The generate_rules() function allows you to (1) specify your metric of interest and (2) the according threshold. E.g., suppose we have the following rules: and we want to remove the rule "(Onion, Kidney Beans) -> (Eggs)". of type frozenset, which is a Python built-in type that If not None, ticks will be set to these values. Mathematica cannot find square roots of some matrices? The amplitude and phase of both of the LTI systems are plotted against the frequency. When using scalar data and no explicit norm, vmin and vmax define If [array, array], the bin edges in each dimension figure : None or Matplotlib figure (default: None), axis : None or Matplotlib figure axis (default: None), fontcolor_threshold : Float (default: 0.5). count values in the return value count histogram will also be set Now lets see the different examples of 2D arrays in Matlab for better understanding as follows. (nx, ny = bins). Some links in our website may be affiliate links which means if you make any purchase through them we earn a little commission on it, This helps us to sustain the operation of our website and continue to bring new and quality Machine Learning contents for you. (if not specified explicitly in the bins parameters): [[xmin, fig, ax : matplotlib.pyplot subplot objects, For usage examples, please see The data for heatmap is passed as an array, with the help of subplots function and imshow function, we can plot labeled heatmap. with columns ['support', 'itemsets']. Here we discuss an introduction, how to Create a circle using rectangle function, a Solid 2D Circle, a circle in MATLAB and Simple arc. Recommended Articles. b) you simply want to speed up the computation because Let's say you are interested in rules derived from the frequent itemsets only if the level of confidence is above the 70 percent threshold (min_threshold=0.7): If you are interested in rules according to a different metric of interest, you can simply adjust the metric and min_threshold arguments . 'leverage', and 'conviction' Seaborn Matplotlib . First, well generate random data, then the data is passed to histogram2d function of numpy library. For better understanding, we will cover different types of examples of heatmap plot with matplotlib/. vmin and vmax can then control the limits of your colorbar. Copyright 20022012 John Hunter, Darren Dale, Eric Firing, Michael Droettboom and the Matplotlib development team; 20122022 The Matplotlib development team. Similar to lift, if items are independent, the conviction is 1. For some reason, the numbers along the axis are printed with a really small font, which makes them unreadable. In Proc. previously set are ignored. Where is it documented? Then we take impulse response in h1, h1 equals to 2 4 -1 3, then we perform a convolution using a conv function, we take conv(x1, h1, same), it perform convolution of x1 and h1 signal and stored it in the y1 and y1 has a length of 7 because we use a shape as I have a huge problem with my seaborn plots. For usage examples, please see This answer will address setting x or y ticklabel size independently. At that time we can use the above statement to create the 2D array. By default, the colormap covers The currently supported metrics for evaluating association rules and setting selection thresholds are listed below. import numpy as np # import pandas as pd # import matplotlib.pyplot as plt import seaborn as sns Plot both positive and negative values between +/- 1.2, Python Programming Foundation -Self Paced Course, Data Structures & Algorithms- Self Paced Course, Set Matplotlib colorbar size to match graph, Matplotlib.figure.Figure.colorbar() in Python, Matplotlib.pyplot.colorbar() function in Python, Rotation of colorbar tick labels in Matplotlib. class_names: array-like, shape = [n_classes] (default: None) List of class names. cmap : matplotlib colormap (default: None). to black. features numpy.array. A leverage value of 0 indicates independence. \text{confidence}(A\rightarrow C) = \frac{\text{support}(A\rightarrow C)}{\text{support}(A)}, \;\;\; \text{range: } [0, 1]. Ready to optimize your JavaScript with Rust? To no help, this only makes the axis text larger, but not the number along the axis. Dynamic itemset counting and implication rules for market basket data. Pearson New International Edition. axis: None or Matplotlib figure axis (default: None) If None will create a new axis. We can choose the colour from the below options. The current implementation make use of the confidence and lift metrics. \text{conviction}(A\rightarrow C) = \frac{1 - \text{support}(C)}{1 - \text{confidence}(A\rightarrow C)}, \;\;\; \text{range: } [0, \infty]. Copyright 2014-2022 Sebastian Raschka The lift metric is commonly used to measure how much more often the antecedent and consequent of a rule A->C occur together than we would expect if they were statistically independent. [2022] 6 Jupyter Notebook Cloud Platforms with GPUs One Click Tutorial Pandas Concat, Pandas Append, Pandas Merge, Pandas Join, Pandas Tutorial describe(), head(), unique() and count(). List of class names. All bins that has count less than cmin or more than cmax will I am trying to create a 2D plot where the 4 quadrants represent four distinct phases. pandas DataFrame with columns "antecedents" and "consequents" It provides a scale for number-to shap_values numpy.array. MATLAB provides us with a convenient environment that can be used to integrate tasks like manipulations on matrix, plotting data and functions, implementing algorithms, keyword argument. To evaluate the "interest" of such an association rule, different metrics have been developed. pivot_kws dict, Parameters for the matplotlib.collections.LineCollection that is used to plot the lines of the dendrogram tree. This answer will address setting x or y ticklabel size independently. E.g. In the United States, must state courts follow rulings by federal courts of appeals? (pp. The result of this function is a histogram with desired features. Now as per our requirement, we can train this data and get a response plot, residual plot, min MSE plot using the options available. Majorly we discuss imshow and pcolormesh functions. . It provides a scale for number-to-color ratio based on the data in a graph. A Circle is a mathematical figure formed by joining all points lying on the same plane and are at equal distance from a given point. colors.PowerNorm. A-143, 9th Floor, Sovereign Corporate Tower, We use cookies to ensure you have the best browsing experience on our website. MATLAB 2D Array; MATLAB? Display data as an image, i.e., on a 2D regular raster. There are so many wrong answers suggesting to scale. The table produced by the association rule mining algorithm contains three different support metrics: 'antecedent support', 'consequent support', and 'support'. Matrix of feature values (# features) or (# samples x # features). This is useful if: a) the input DataFrame is incomplete, e.g., does By default all values larger than 0.5 times the maximum cell value are converted to white, and everything equal or smaller than 0.5 times the maximum cell value are converted to black. If you want to change all values above to e.g., white, you can set the color threshold to a negative number. Scatter plot. feature_importance_permutation: Estimate feature importance via feature permutation. A list of colormaps can be found here: https://matplotlib.org/stable/tutorials/colors/colormaps.html. Most metrics computed by association_rules depends on the consequent and antecedent support score of a given rule provided in the frequent itemset input DataFrame. See the documentation for the density String formatting code to use when adding annotations. The Colormap instance or registered colormap name used to map scalar data Heatmap is also used in finding the correlation between different sets of attributes.if(typeof ez_ad_units!='undefined'){ez_ad_units.push([[300,250],'machinelearningknowledge_ai-box-4','ezslot_3',124,'0','0'])};__ez_fad_position('div-gpt-ad-machinelearningknowledge_ai-box-4-0'); NOTE There isnt any dedicated function in Matplotlib for building Heatmaps. Using Matplotlib, I want to plot a 2D heat map. proportion of training examples per class that are We can also format our circle as per our requirement. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, Yes, thank you for this answer! Concentration bounds for martingales with adaptive Gaussian steps. plt.colorbar() wants a mappable object, like the CircleCollection that plt.scatter() returns. 327-414). load_dataset ("iris") species = iris. Currently implemented measures are confidence and lift. import numpy as np import scipy.ndimage.filters as fi def gkern2(kernlen=21, nsig=3): """Returns a 2D Gaussian kernel array.""" Example #3. The generate_rules takes dataframes of frequent itemsets as produced by the apriori, fpgrowth, or fpmax functions in mlxtend.association. From here you can search these documents. This is why majorly imshow function is used. we have to pass a 2D array as an input. Should I give a brutally honest feedback on course evaluations? So colorlist needs to be a list of floats rather than a list of tuples as you have it now. to decide whether a candidate rule is of interest. How I can increase the x, y tick label font size in seaborn heatmap subplots? Step 6: Finally plot the function. and instantiated. GroupTimeSeriesSplit: A scikit-learn compatible version of the time series validation with groups, lift_score: Lift score for classification and association rule mining, mcnemar_table: Ccontingency table for McNemar's test, mcnemar_tables: contingency tables for McNemar's test and Cochran's Q test, mcnemar: McNemar's test for classifier comparisons, paired_ttest_5x2cv: 5x2cv paired *t* test for classifier comparisons, paired_ttest_kfold_cv: K-fold cross-validated paired *t* test, paired_ttest_resample: Resampled paired *t* test, permutation_test: Permutation test for hypothesis testing, PredefinedHoldoutSplit: Utility for the holdout method compatible with scikit-learn, RandomHoldoutSplit: split a dataset into a train and validation subset for validation, scoring: computing various performance metrics, LinearDiscriminantAnalysis: Linear discriminant analysis for dimensionality reduction, PrincipalComponentAnalysis: Principal component analysis (PCA) for dimensionality reduction, ColumnSelector: Scikit-learn utility function to select specific columns in a pipeline, ExhaustiveFeatureSelector: Optimal feature sets by considering all possible feature combinations, SequentialFeatureSelector: The popular forward and backward feature selection approaches (including floating variants), find_filegroups: Find files that only differ via their file extensions, find_files: Find files based on substring matches, extract_face_landmarks: extract 68 landmark features from face images, EyepadAlign: align face images based on eye location, num_combinations: combinations for creating subsequences of *k* elements, num_permutations: number of permutations for creating subsequences of *k* elements, vectorspace_dimensionality: compute the number of dimensions that a set of vectors spans, vectorspace_orthonormalization: Converts a set of linearly independent vectors to a set of orthonormal basis vectors, Scategory_scatter: Create a scatterplot with categories in different colors, checkerboard_plot: Create a checkerboard plot in matplotlib, plot_pca_correlation_graph: plot correlations between original features and principal components, ecdf: Create an empirical cumulative distribution function plot, enrichment_plot: create an enrichment plot for cumulative counts, plot_confusion_matrix: Visualize confusion matrices, plot_decision_regions: Visualize the decision regions of a classifier, plot_learning_curves: Plot learning curves from training and test sets, plot_linear_regression: A quick way for plotting linear regression fits, plot_sequential_feature_selection: Visualize selected feature subset performances from the SequentialFeatureSelector, scatterplotmatrix: visualize datasets via a scatter plot matrix, scatter_hist: create a scatter histogram plot, stacked_barplot: Plot stacked bar plots in matplotlib, CopyTransformer: A function that creates a copy of the input array in a scikit-learn pipeline, DenseTransformer: Transforms a sparse into a dense NumPy array, e.g., in a scikit-learn pipeline, MeanCenterer: column-based mean centering on a NumPy array, MinMaxScaling: Min-max scaling fpr pandas DataFrames and NumPy arrays, shuffle_arrays_unison: shuffle arrays in a consistent fashion, standardize: A function to standardize columns in a 2D NumPy array, LinearRegression: An implementation of ordinary least-squares linear regression, StackingCVRegressor: stacking with cross-validation for regression, StackingRegressor: a simple stacking implementation for regression, generalize_names: convert names into a generalized format, generalize_names_duplcheck: Generalize names while preventing duplicates among different names, tokenizer_emoticons: tokenizers for emoticons, Example 2 - Binary absolute and relative with colorbar, Example 5 - Changing Color Maps and Font Color, Example 6 - Normalizing Colormaps to Highlight Off-Diagonals. "support", "confidence", "lift", (x_edges, y_edges = bins). Python Matplotlib Seaborn . to nan upon return. histogrammed along the first dimension and values in y are vmin, vmax: Values to anchor the colormap, otherwise they are inferred from the data and other keyword arguments. equal or smaller than 0.5 times the maximum cell value are converted Method 1: Using matplotlib.patches.Circle() function. Utility function for visualizing confusion matrices via matplotlib, from mlxtend.plotting import plot_confusion_matrix. My data is an n-by-n Numpy array, each with a value between 0 and 1. We do this by creating a mesh-grid with np.meshgrid our inputs to this function are an array of x-values and y-values to repeat in the grid, which we not contain support values for all rule antecedents (nx=ny=bins). An array of values w_i weighing each sample (x_i, y_i). [6] Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, and Shalom Turk. Consider the following example: Note that this is a "cropped" DataFrame that doesn't contain the support values of the item subsets. The feature matrix contains the values of all 30 features in the dataset. It is an amazing visualization library in Python for 2D plots of arrays, It is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. MLK is a knowledge sharing platform for machine learning enthusiasts, beginners, and experts. Of the many, matplotlib and seaborn seems to be very widely used for basic to intermediate level of visualizations. Instead, the pandas API can be used on the resulting data frame to remove individual rows. If given, the following parameters also accept a string s, which is How can I change the font size using seaborn FacetGrid? via the metric parameter, Hebrews 1:3 What is the Relationship Between Jesus and The Word of His Power? Hello Geeks! Parameters: x, y array-like, shape (n, ). Bode plot graphs the frequency response of a linear time-invariant (LTI) system. Step 5: Write unit step command. MATLAB 2D Array; MATLAB? of the ACM SIGMOD Int'l Conference on Management of Data, pages 207-216, Washington D.C., May 1993, [4] S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Note that in general, due to the downward closure property, all subsets of a frequent itemset are also frequent. must be True. col_labels A list or array of length N with the labels for the columns. not be displayed (set to NaN before passing to imshow) and these If an array-like with the same shape as data, then use this to annotate the heatmap instead of the data. Heatmap is an interesting visualization that helps in knowing the data intensity.It conveys this information by using different colors and gradients. A plot is visually more powerful than normal data when we want to analyze the behavior of our function. Matplotlib allows us a large range of Colorbar customization. The Colorbar is simply an instance of plt.Axes. annot: If True, write the data value MATLAB 2D Array; MATLAB? (see Colormap Normalization). if you are only interested in rules that have a lift score of >= 1.2, you would do the following: Pandas DataFrames make it easy to filter the results further. Step 4: Create zero th row vector to avoid from garbage value. Plot a heatmap with row and column clustering: iris = sns. Matplotlib Heatmap Tutorial. In an attempt to this, I created a color mixer: The normed confusion matrix coefficients give the http://rasbt.github.io/mlxtend/user_guide/plotting/plot_confusion_matrix/. Save my name, email, and website in this browser for the next time I comment. For the 2nd example, we will be learning how to build 2-D histogram with the help of numpy and matplotlibs imshow function. This will allow us to visualize the data on a 2d or 3d plot (if we choose the number of principal components as 2 or 3). If int, the number of bins for the two dimensions The next step is to perform some mathematical operatins for finding the minimum and maximum values for the plot.if(typeof ez_ad_units!='undefined'){ez_ad_units.push([[250,250],'machinelearningknowledge_ai-large-mobile-banner-1','ezslot_4',127,'0','0'])};__ez_fad_position('div-gpt-ad-machinelearningknowledge_ai-large-mobile-banner-1-0'); We use the subplots function for plotting heatmap using pcolormesh function. Why is the eastern United States green if the wind moves from west to east? Change the label size and tick label size of colorbar using Matplotlib in Python. zero padding; MATLAB sort matrix; MATLAB Plot Function; 2D Plots in MATLAB; 3D Plots in MATLAB; Let us now learn how can we plot an exponential function. constructor. Step 2: Take user or programmer choice either advanced or delayed function. Are the S&P 500 and Dow Jones Industrial Average securities? How to change the font size on a matplotlib plot, Matplotlib make tick labels font size smaller. The normalization method used to scale scalar data to the [0, 1] range The support metric is defined for itemsets, not assocication rules. pcolormesh method and QuadMesh What's the \synctex primitive? Steps are as follows: Step 1: Take interval from user or decide by programmer. A plot is visually more powerful than normal data when we want to analyze the behavior of our function. It conveys this information by using different colors and gradients. MATLAB or Matrix Laboratory is a programming language that was developed by MathWorks. Enter your search terms below. figure: None or Matplotlib figure (default: None) If None will create a new figure. Connect and share knowledge within a single location that is structured and easy to search. In this article, we will try to set the color range using the matplotlib Python module. the maximum cell value are converted to white, and everything To demonstrate the usage of the generate_rules method, we first create a pandas DataFrame of frequent itemsets as generated by the fpgrowth function: The generate_rules() function allows you to (1) specify your metric of interest and (2) the according threshold. [1] Tan, Steinbach, Kumar. I.e., the query, rules[rules['antecedents'] == {'Eggs', 'Kidney Beans'}], is equivalent to any of the following three. Automatically set to 'support' if support_only=True. Hello Geeks! The confidence of a rule A->C is the probability of seeing the consequent in a transaction given that it also contains the antecedent. fontcolor_threshold: Float (default: 0.5) How could my characters be tricked into thinking they are on Mars? For a If there are y assigned the correct label. To use 3D graphics in matplotlib, we first need to create an instance of the Axes3D class. Function to generate association rules from frequent itemsets, from mlxtend.frequent_patterns import association_rules. You have entered an incorrect email address! Help us identify new roles for community members, Proposing a Community-Specific Closure Reason for non-English content, Adjust font size of x-axis and y-axis labels in Seaborn Matplotlib PyQT5, Python Seaborn: reducing the size of x-axis labels only, having different font sizes for label and numbers in Seaborn plots. So for the (i, j) element of this array, I want to plot a square at the (i, j) coordinate in my heat map, whose color is proportional to the element's value in the array. used, mapping the lowest value to 0 and the highest to 1. hist2d (x, y, bins = 10, range = None, density = False, weights = None, cmin = None, cmax = None, *, data = None, ** kwargs) [source] # Make a 2D histogram plot. We refer to an itemset as a "frequent itemset" if you support is larger than a specified minimum-support threshold. behaves similarly to sets except that it is immutable Enter your search terms below. for the cells. In SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, pages 255-264, Tucson, Arizona, USA, May 1997. A = [2 4; 5 -2; 4 8] Explanation: Suppose we need to create a 2D array that is size 2 by 2. annot_kws dict of key, value mappings, optional. This can create problems if we want to compute the association rule metrics for, e.g., 176 => 177. By default, a linear scaling is In that case, a suitable Normalize subclass is dynamically generated xmax], [ymin, ymax]]. ; cmap: The mapping from data values to color space. If you have multiple groups in your data you may want to visualise each group in a different color. After this imshow function is called where we pass the data, colormap value and interpolation method (this method basically helps in improving the image quality if used). 3D axes can be added to a matplotlib figure canvas in exactly the same way as 2D axes; or, more conveniently, by passing a projection='3d' keyword argument rev2022.12.11.43106. String formatting code to use when adding annotations. None or int or [int, int] or array-like or [array, array], Animated image using a precomputed list of images, matplotlib.animation.ImageMagickFileWriter, matplotlib.artist.Artist.format_cursor_data, matplotlib.artist.Artist.set_sketch_params, matplotlib.artist.Artist.get_sketch_params, matplotlib.artist.Artist.set_path_effects, matplotlib.artist.Artist.get_path_effects, matplotlib.artist.Artist.get_window_extent, matplotlib.artist.Artist.get_transformed_clip_path_and_affine, matplotlib.artist.Artist.is_transform_set, matplotlib.axes.Axes.get_legend_handles_labels, matplotlib.axes.Axes.get_xmajorticklabels, matplotlib.axes.Axes.get_xminorticklabels, matplotlib.axes.Axes.get_ymajorticklabels, matplotlib.axes.Axes.get_yminorticklabels, matplotlib.axes.Axes.get_rasterization_zorder, matplotlib.axes.Axes.set_rasterization_zorder, matplotlib.axes.Axes.get_xaxis_text1_transform, matplotlib.axes.Axes.get_xaxis_text2_transform, matplotlib.axes.Axes.get_yaxis_text1_transform, matplotlib.axes.Axes.get_yaxis_text2_transform, matplotlib.axes.Axes.get_default_bbox_extra_artists, matplotlib.axes.Axes.get_transformed_clip_path_and_affine, matplotlib.axis.Axis.remove_overlapping_locs, matplotlib.axis.Axis.get_remove_overlapping_locs, matplotlib.axis.Axis.set_remove_overlapping_locs, matplotlib.axis.Axis.get_ticklabel_extents, matplotlib.axis.YAxis.set_offset_position, matplotlib.axis.Axis.limit_range_for_scale, matplotlib.axis.Axis.set_default_intervals, matplotlib.colors.LinearSegmentedColormap, matplotlib.colors.get_named_colors_mapping, matplotlib.gridspec.GridSpecFromSubplotSpec, matplotlib.pyplot.install_repl_displayhook, matplotlib.pyplot.uninstall_repl_displayhook, matplotlib.pyplot.get_current_fig_manager, mpl_toolkits.mplot3d.art3d.Line3DCollection, mpl_toolkits.mplot3d.art3d.Patch3DCollection, mpl_toolkits.mplot3d.art3d.Path3DCollection, mpl_toolkits.mplot3d.art3d.Poly3DCollection, mpl_toolkits.mplot3d.art3d.get_dir_vector, mpl_toolkits.mplot3d.art3d.line_collection_2d_to_3d, mpl_toolkits.mplot3d.art3d.patch_2d_to_3d, mpl_toolkits.mplot3d.art3d.patch_collection_2d_to_3d, mpl_toolkits.mplot3d.art3d.pathpatch_2d_to_3d, mpl_toolkits.mplot3d.art3d.poly_collection_2d_to_3d, mpl_toolkits.mplot3d.proj3d.inv_transform, mpl_toolkits.mplot3d.proj3d.persp_transformation, mpl_toolkits.mplot3d.proj3d.proj_trans_points, mpl_toolkits.mplot3d.proj3d.proj_transform, mpl_toolkits.mplot3d.proj3d.proj_transform_clip, mpl_toolkits.mplot3d.proj3d.view_transformation, mpl_toolkits.mplot3d.proj3d.world_transformation, mpl_toolkits.axes_grid1.anchored_artists.AnchoredAuxTransformBox, mpl_toolkits.axes_grid1.anchored_artists.AnchoredDirectionArrows, mpl_toolkits.axes_grid1.anchored_artists.AnchoredDrawingArea, mpl_toolkits.axes_grid1.anchored_artists.AnchoredEllipse, mpl_toolkits.axes_grid1.anchored_artists.AnchoredSizeBar, mpl_toolkits.axes_grid1.axes_divider.AxesDivider, mpl_toolkits.axes_grid1.axes_divider.AxesLocator, mpl_toolkits.axes_grid1.axes_divider.Divider, mpl_toolkits.axes_grid1.axes_divider.HBoxDivider, mpl_toolkits.axes_grid1.axes_divider.SubplotDivider, mpl_toolkits.axes_grid1.axes_divider.VBoxDivider, mpl_toolkits.axes_grid1.axes_divider.make_axes_area_auto_adjustable, mpl_toolkits.axes_grid1.axes_divider.make_axes_locatable, mpl_toolkits.axes_grid1.axes_grid.AxesGrid, mpl_toolkits.axes_grid1.axes_grid.CbarAxes, mpl_toolkits.axes_grid1.axes_grid.CbarAxesBase, mpl_toolkits.axes_grid1.axes_grid.ImageGrid, mpl_toolkits.axes_grid1.axes_rgb.make_rgb_axes, mpl_toolkits.axes_grid1.axes_size.AddList, mpl_toolkits.axes_grid1.axes_size.Fraction, mpl_toolkits.axes_grid1.axes_size.GetExtentHelper, mpl_toolkits.axes_grid1.axes_size.MaxExtent, mpl_toolkits.axes_grid1.axes_size.MaxHeight, mpl_toolkits.axes_grid1.axes_size.MaxWidth, mpl_toolkits.axes_grid1.axes_size.Scalable, mpl_toolkits.axes_grid1.axes_size.SizeFromFunc, mpl_toolkits.axes_grid1.axes_size.from_any, mpl_toolkits.axes_grid1.inset_locator.AnchoredLocatorBase, mpl_toolkits.axes_grid1.inset_locator.AnchoredSizeLocator, mpl_toolkits.axes_grid1.inset_locator.AnchoredZoomLocator, mpl_toolkits.axes_grid1.inset_locator.BboxConnector, mpl_toolkits.axes_grid1.inset_locator.BboxConnectorPatch, mpl_toolkits.axes_grid1.inset_locator.BboxPatch, mpl_toolkits.axes_grid1.inset_locator.InsetPosition, mpl_toolkits.axes_grid1.inset_locator.inset_axes, mpl_toolkits.axes_grid1.inset_locator.mark_inset, mpl_toolkits.axes_grid1.inset_locator.zoomed_inset_axes, mpl_toolkits.axes_grid1.mpl_axes.SimpleAxisArtist, mpl_toolkits.axes_grid1.mpl_axes.SimpleChainedObjects, mpl_toolkits.axes_grid1.parasite_axes.HostAxes, mpl_toolkits.axes_grid1.parasite_axes.HostAxesBase, mpl_toolkits.axes_grid1.parasite_axes.ParasiteAxes, mpl_toolkits.axes_grid1.parasite_axes.ParasiteAxesBase, mpl_toolkits.axes_grid1.parasite_axes.host_axes, mpl_toolkits.axes_grid1.parasite_axes.host_axes_class_factory, mpl_toolkits.axes_grid1.parasite_axes.host_subplot, mpl_toolkits.axes_grid1.parasite_axes.host_subplot_class_factory, mpl_toolkits.axes_grid1.parasite_axes.parasite_axes_class_factory, mpl_toolkits.axisartist.angle_helper.ExtremeFinderCycle, mpl_toolkits.axisartist.angle_helper.FormatterDMS, mpl_toolkits.axisartist.angle_helper.FormatterHMS, mpl_toolkits.axisartist.angle_helper.LocatorBase, mpl_toolkits.axisartist.angle_helper.LocatorD, mpl_toolkits.axisartist.angle_helper.LocatorDM, mpl_toolkits.axisartist.angle_helper.LocatorDMS, mpl_toolkits.axisartist.angle_helper.LocatorH, mpl_toolkits.axisartist.angle_helper.LocatorHM, mpl_toolkits.axisartist.angle_helper.LocatorHMS, mpl_toolkits.axisartist.angle_helper.select_step, mpl_toolkits.axisartist.angle_helper.select_step24, mpl_toolkits.axisartist.angle_helper.select_step360, mpl_toolkits.axisartist.angle_helper.select_step_degree, mpl_toolkits.axisartist.angle_helper.select_step_hour, mpl_toolkits.axisartist.angle_helper.select_step_sub, mpl_toolkits.axisartist.axes_grid.AxesGrid, mpl_toolkits.axisartist.axes_grid.CbarAxes, mpl_toolkits.axisartist.axes_grid.ImageGrid, mpl_toolkits.axisartist.axis_artist.AttributeCopier, mpl_toolkits.axisartist.axis_artist.AxisArtist, mpl_toolkits.axisartist.axis_artist.AxisLabel, mpl_toolkits.axisartist.axis_artist.GridlinesCollection, mpl_toolkits.axisartist.axis_artist.LabelBase, mpl_toolkits.axisartist.axis_artist.TickLabels, mpl_toolkits.axisartist.axis_artist.Ticks, mpl_toolkits.axisartist.axisline_style.AxislineStyle, mpl_toolkits.axisartist.axislines.AxesZero, mpl_toolkits.axisartist.axislines.AxisArtistHelper, mpl_toolkits.axisartist.axislines.AxisArtistHelperRectlinear, mpl_toolkits.axisartist.axislines.GridHelperBase, mpl_toolkits.axisartist.axislines.GridHelperRectlinear, mpl_toolkits.axisartist.clip_path.clip_line_to_rect, mpl_toolkits.axisartist.floating_axes.ExtremeFinderFixed, mpl_toolkits.axisartist.floating_axes.FixedAxisArtistHelper, mpl_toolkits.axisartist.floating_axes.FloatingAxes, mpl_toolkits.axisartist.floating_axes.FloatingAxesBase, mpl_toolkits.axisartist.floating_axes.FloatingAxisArtistHelper, mpl_toolkits.axisartist.floating_axes.GridHelperCurveLinear, mpl_toolkits.axisartist.floating_axes.floatingaxes_class_factory, mpl_toolkits.axisartist.grid_finder.DictFormatter, mpl_toolkits.axisartist.grid_finder.ExtremeFinderSimple, mpl_toolkits.axisartist.grid_finder.FixedLocator, mpl_toolkits.axisartist.grid_finder.FormatterPrettyPrint, mpl_toolkits.axisartist.grid_finder.GridFinder, mpl_toolkits.axisartist.grid_finder.MaxNLocator, mpl_toolkits.axisartist.grid_helper_curvelinear, mpl_toolkits.axisartist.grid_helper_curvelinear.FixedAxisArtistHelper, mpl_toolkits.axisartist.grid_helper_curvelinear.FloatingAxisArtistHelper, mpl_toolkits.axisartist.grid_helper_curvelinear.GridHelperCurveLinear.

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