Sets a threshold for choosing black and white font colors load_dataset ("iris") species = iris. (if not specified explicitly in the bins parameters): [[xmin, rev2022.12.11.43106. \text{conviction}(A\rightarrow C) = \frac{1 - \text{support}(C)}{1 - \text{confidence}(A\rightarrow C)}, \;\;\; \text{range: } [0, \infty]. The Colorbar is simply an instance of plt.Axes. A-143, 9th Floor, Sovereign Corporate Tower, We use cookies to ensure you have the best browsing experience on our website. [1] Tan, Steinbach, Kumar. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Does integrating PDOS give total charge of a system? Instead, the pandas API can be used on the resulting data frame to remove individual rows. We also plot a transfer function response by using a step function. plt.colorbar() wants a mappable object, like the CircleCollection that plt.scatter() returns. E.g. The bi-dimensional histogram of samples x and y. 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. The generate_rules() function allows you to (1) specify your metric of interest and (2) the according threshold. If not None, ticks will be set to these values. 'leverage', and 'conviction' metric columns with NaNs. The leftmost and rightmost edges of the bins along each dimension count values in the return value count histogram will also be set with columns ['support', 'itemsets']. Matrix of SHAP values (# features) or (# samples x # features). Let us seen an example for convolution, 1st we take an x1 is equal to the 5 2 3 4 1 6 2 1 it is an input signal. annot_kws dict of key, value mappings, optional. What is this fallacy: Perfection is impossible, therefore imperfection should be overlooked, PSE Advent Calendar 2022 (Day 11): The other side of Christmas. Important Parameters: data: 2D dataset that can be coerced into an ndarray. The amplitude and phase of both of the LTI systems are plotted against the frequency. annot_kws dict of key, value mappings, optional. From here you can search these documents. If there are y Hello Geeks! Plot a heatmap with row and column clustering: iris = sns. The Colorbar is simply an instance of plt.Axes. This is why majorly imshow function is used. Matplotlib does not have a dedicated function for heatmap but we can build them using matplotlibs imshow function. vmin and vmax can then control the limits of your colorbar. the complete value range of the supplied data. The function will return 3 rd derivative of function x * sin (x * t), differentiated w.r.t t as below:-x^4 cos(t x) As we can notice, our function is differentiated w.r.t. Do non-Segwit nodes reject Segwit transactions with invalid signature? Method 1: Using matplotlib.patches.Circle() function. The table produced by the association rule mining algorithm contains three different support metrics: 'antecedent support', 'consequent support', and 'support'. How to change the colorbar size of a seaborn heatmap figure in Python? For some reason, the numbers along the axis are printed with a really small font, which makes them unreadable. pcolormesh method and QuadMesh Now lets see the different examples of 2D arrays in Matlab for better understanding as follows. The answer from Kabir Ahuja works because y-labels position is being used as the text.. # Creating text annotations by using for loop, "Growth of Fruits in Different Countries (in tons/year)", Agglomerative Hierarchical Clustering in Python Sklearn & Scipy, Tutorial for K Means Clustering in Python Sklearn, Sklearn Feature Scaling with StandardScaler, MinMaxScaler, RobustScaler and MaxAbsScaler, Tutorial for DBSCAN Clustering in Python Sklearn, Complete Tutorial for torch.max() in PyTorch with Examples, How to use torch.sub() to Subtract Tensors in PyTorch, How to use torch.add() to Add Tensors in PyTorch, Complete Tutorial for torch.sum() to Sum Tensor Elements in PyTorch, Split and Merge Image Color Space Channels in OpenCV and NumPy, YOLOv6 Explained with Tutorial and Example, Quick Guide for Drawing Lines in OpenCV Python using cv2.line() with, How to Scale and Resize Image in Python with OpenCV cv2.resize(), Tips and Tricks of OpenCV cv2.waitKey() Tutorial with Examples, Word2Vec in Gensim Explained for Creating Word Embedding Models (Pretrained and, Tutorial on Spacy Part of Speech (POS) Tagging, Named Entity Recognition (NER) in Spacy Library, Spacy NLP Pipeline Tutorial for Beginners, Complete Guide to Spacy Tokenizer with Examples, Beginners Guide to Policy in Reinforcement Learning, Basic Understanding of Environment and its Types in Reinforcement Learning, Top 20 Reinforcement Learning Libraries You Should Know, 16 Reinforcement Learning Environments and Platforms You Did Not Know Exist, 8 Real-World Applications of Reinforcement Learning, Tutorial of Line Plot in Base R Language with Examples, Tutorial of Violin Plot in Base R Language with Examples, Tutorial of Scatter Plot in Base R Language, Tutorial of Pie Chart in Base R Programming Language, Tutorial of Barplot in Base R Programming Language, Quick Tutorial for Python Numpy Arange Functions with Examples, Quick Tutorial for Numpy Linspace with Examples for Beginners, Using Pi in Python with Numpy, Scipy and Math Library, 7 Tips & Tricks to Rename Column in Pandas DataFrame, Matplotlib Bar Plot Complete Tutorial For Beginners, Matplotlib Scatter Plot Complete Tutorial, Matplotlib Line Plot Complete Tutorial for Beginners, Matplotlib Pie Chart Complete Tutorial for Beginners, Matplotlib Animation An Introduction for Beginners, 11 Python Data Visualization Libraries Data Scientists should know, Matplotlib Quiver Plot Tutorial for Beginners, Matplotlib Boxplot Tutorial for Beginners, Tutorial of Histogram in R Programming Language with Examples. Mining associations between sets of items in large databases. It is an error to use Why is the eastern United States green if the wind moves from west to east? figure : None or Matplotlib figure (default: None), axis : None or Matplotlib figure axis (default: None), fontcolor_threshold : Float (default: 0.5). (x_edges, y_edges = bins). Function to generate association rules from frequent itemsets, from mlxtend.frequent_patterns import association_rules. If an array-like with the same shape as data, then use this to annotate the heatmap instead of the data. As shown above, the font color threshold may not work for certain color maps. If this is a 1D array then a single force plot will be drawn, if it is a 2D array then a stacked force plot will be drawn. Leverage computes the difference between the observed frequency of A and C appearing together and the frequency that would be expected if A and C were independent. 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. tocQAQpytorch. fontcolor_threshold: Float (default: 0.5) Disconnect vertical tab connector from PCB. Should I give a brutally honest feedback on course evaluations? Given a rule "A -> C", A stands for antecedent and C stands for consequent. axis: None or Matplotlib figure axis (default: None) If None will create a new axis. Currently implemented measures are confidence and lift. Before beginning with this matplotlib bar plot tutorial, well need the Matplotlib Library. Matrix of feature values (# features) or (# samples x # features). bins None or int or [int, int] or array-like or [array, array]. data 2D array-like. Example of Matlab 2D Array. A plot is visually more powerful than normal data when we want to analyze the behavior of our function. histogrammed along the second dimension. to decide whether a candidate rule is of interest. [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(). The Colormap instance or registered colormap name used to map scalar data The normalization method used to scale scalar data to the [0, 1] range Login details for this Free course will be emailed to you Did the apostolic or early church fathers acknowledge Papal infallibility? To use 3D graphics in matplotlib, we first need to create an instance of the Axes3D class. constructor. Values in x are fmt str, optional. equal or smaller than 0.5 times the maximum cell value are converted I am trying to create a 2D plot where the 4 quadrants represent four distinct phases. [2] Michael Hahsler, http://michael.hahsler.net/research/association_rules/measures.html, [3] R. Agrawal, T. Imielinski, and A. Swami. I have a huge problem with my seaborn plots. There are a number of ways to get ticks and labels. interpreted as data[s] (unless this raises an exception): Additional parameters are passed along to the E.g., suppose we have the following rules: and we want to remove the rule "(Onion, Kidney Beans) -> (Eggs)". This is a guide to Matlab Plot Circle. Here we discuss an introduction, how to Create a circle using rectangle function, a Solid 2D Circle, a circle in MATLAB and Simple arc. For example, the confidence is computed as. 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. \text{support}(A\rightarrow C) = \text{support}(A \cup C), \;\;\; \text{range: } [0, 1]. The data for the three variables passed into the function of pcolormesh is generated using linspace function of numpy. To evaluate the "interest" of such an association rule, different metrics have been developed. If you want to change all values above to e.g., white, you can set the color threshold to a negative number. If [int, int], the number of bins in each dimension Note that DataFrames will match on position, not index. Rectangular data for clustering. Not the answer you're looking for? The confidence of a rule A->C is the probability of seeing the consequent in a transaction given that it also contains the antecedent. There are so many wrong answers suggesting to scale. A scale name, i.e. We can choose the colour from the below options. Mathematica cannot find square roots of some matrices? considered outliers and not tallied in the histogram. A leverage value of 0 indicates independence. By default, the colormap covers How can I change the font size using seaborn FacetGrid? of type frozenset, which is a Python built-in type that Hello Geeks! pivot_kws dict, Parameters for the matplotlib.collections.LineCollection that is used to plot the lines of the dendrogram tree. Python Matplotlib Seaborn . Note that the metric is not symmetric or directed; for instance, the confidence for A->C is different than the confidence for C->A. 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, association_rules: Association rules generation from frequent itemsets, Example 1 -- Generating Association Rules from Frequent Itemsets, Example 2 -- Rule Generation and Selection Criteria, Example 3 -- Frequent Itemsets with Incomplete Antecedent and Consequent Information. These metrics are computed as follows: Minimal threshold for the evaluation metric, 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). Reference Matplotlib Documentation. An array of values w_i weighing each sample (x_i, y_i). With the two different limits, you can control the range and legend of the Colorbar. (nx, ny = bins). features numpy.array. Step 5: Write unit step command. stepepoch keyword argument. name together with vmin/vmax is acceptable). Lastly, imshow function is used for plotting the final heatmap visualization.if(typeof ez_ad_units!='undefined'){ez_ad_units.push([[336,280],'machinelearningknowledge_ai-leader-1','ezslot_11',145,'0','0'])};__ez_fad_position('div-gpt-ad-machinelearningknowledge_ai-leader-1-0'); The 3rd example of the heatmap tutorial will be based on the pcolormesh function. This table layout makes clear that the information can be thought of as a two-dimensional numerical array or matrix, which we will call the features matrix.By convention, this features matrix is often stored in a variable named X.The features matrix is assumed to be two-dimensional, with shape [n_samples, n_features], and is most often contained in a NumPy not be displayed (set to NaN before passing to imshow) and these used, mapping the lowest value to 0 and the highest to 1. MLK is a knowledge sharing platform for machine learning enthusiasts, beginners, and experts. Only computes the rule support and fills the other You have entered an incorrect email address! Cmap Using this parameter, we can give colour to our graph. Harlow: Pearson Education Ltd., 2014. See the documentation for the density For instance, in the case of a perfect confidence score, the denominator becomes 0 (due to 1 - 1) for which the conviction score is defined as 'inf'. for the cells. The generate_rules takes dataframes of frequent itemsets as produced by the apriori, fpgrowth, or fpmax functions in mlxtend.association. Currently hist2d calculates its own axis limits, and any limits As already mentioned heatmap in matplotlib can be build using imshow function. I've tried to scale them with. Enter your search terms below. If given, the following parameters also accept a string s, which is If you have multiple groups in your data you may want to visualise each group in a different color. https://docs.python.org/3.6/library/stdtypes.html#frozenset). So colorlist needs to be a list of floats rather than a list of tuples as you have it now. annot: If True, write the data value How to change the figure size of a seaborn axes or figure level plot, Fine control over the font size in Seaborn plots, Changing font style in seaborn clustermaps. So, as we learned, diff command can be used in MATLAB to compute the derivative of a function. Heatmap is also used in finding the correlation between different sets of attributes.. of all rules for which behaves similarly to sets except that it is immutable assigned the correct label. sns.set(font_scale=2) from p-robot will set all the figure fonts. Display data as an image, i.e., on a 2D regular raster. To no help, this only makes the axis text larger, but not the number along the axis. Confusion matrix from evaluate.confusion matrix. that store itemsets, plus the scoring metric columns: metrics 'score', 'confidence', and 'lift', pandas DataFrame of frequent itemsets Are the S&P 500 and Dow Jones Industrial Average securities? 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. 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. and consequents. Rendering the histogram with a logarithmic color scale is From the matplotlib docs on scatter 1: cmap is only used if c is an array of floats. Why do some airports shuffle connecting passengers through security again. For usage examples, please see (For more info, see Suppose we have the following confusion matrix for a high-accuracy classifier: It can be hard to notice the cells where the models makes mistakes. Adaline: Adaptive Linear Neuron Classifier, EnsembleVoteClassifier: A majority voting classifier, MultilayerPerceptron: A simple multilayer neural network, OneRClassifier: One Rule (OneR) method for classfication, SoftmaxRegression: Multiclass version of logistic regression, StackingCVClassifier: Stacking with cross-validation, autompg_data: The Auto-MPG dataset for regression, boston_housing_data: The Boston housing dataset for regression, iris_data: The 3-class iris dataset for classification, loadlocal_mnist: A function for loading MNIST from the original ubyte files, make_multiplexer_dataset: A function for creating multiplexer data, mnist_data: A subset of the MNIST dataset for classification, three_blobs_data: The synthetic blobs for classification, wine_data: A 3-class wine dataset for classification, accuracy_score: Computing standard, balanced, and per-class accuracy, bias_variance_decomp: Bias-variance decomposition for classification and regression losses, bootstrap: The ordinary nonparametric boostrap for arbitrary parameters, bootstrap_point632_score: The .632 and .632+ boostrap for classifier evaluation, BootstrapOutOfBag: A scikit-learn compatible version of the out-of-bag bootstrap, cochrans_q: Cochran's Q test for comparing multiple classifiers, combined_ftest_5x2cv: 5x2cv combined *F* test for classifier comparisons, confusion_matrix: creating a confusion matrix for model evaluation, create_counterfactual: Interpreting models via counterfactuals. metric(rule) >= min_threshold. 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. class_names : array-like, shape = [n_classes] (default: None). center: The value at which to center the colormap when plotting divergent data. Matplotlib Heatmap is used to represent the matrix of data in the form of different colours. List of class names. import numpy as np import scipy.ndimage.filters as fi def gkern2(kernlen=21, nsig=3): """Returns a 2D Gaussian kernel array.""" import numpy as np import scipy.ndimage.filters as fi def gkern2(kernlen=21, nsig=3): """Returns a 2D Gaussian kernel array.""" How to Adjust the Position of a Matplotlib Colorbar? http://rasbt.github.io/mlxtend/user_guide/frequent_patterns/association_rules/. 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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