{
    "info": {
        "author": "Brett Chapman",
        "author_email": "brett.chapman@ecu.edu.au, brett.chapman78@gmail.com",
        "bugtrack_url": null,
        "classifiers": [],
        "description": "# MultiVis\nThe MultiVis package contains the necessary tools for visualisation of multivariate data.\n\n## Installation\n\n### Dependencies\nmultivis requires:\n- Python (>=3.5)\n- NumPy (>=1.12)\n- Pandas\n- Matplotlib\n- Seaborn\n- Networkx\n- SciPy\n- Scikit-learn\n- tqdm\n- xlrd\n\n### User installation\nThe recommend way to install multivis and dependencies is to using ``conda``:\n```console\nconda install -c brett.chapman multivis\n```\nor ``pip``:\n```console\npip install multivis\n```\nAlternatively, to install directly from github:\n```console\npip install https://github.com/brettChapman/multivis/archive/master.zip\n```\n\n### API\nFor further detail on the usage refer to the docstring.\n\n#### multivis\n- [Edge](https://github.com/brettChapman/multivis/blob/master/multivis/Edge.py): Generates dataframe of nodes and edges.\n\t- [init_parameters](https://github.com/brettChapman/multivis/blob/master/multivis/Edge.py#L34-L43)\n\t\t- [peaktable] : Pandas dataframe containing peak data. Must contain 'Name' and 'Label'.\n\t\t- [similarities] : Pandas dataframe matrix containing similarity scores.\n\t\t- [pvalues] : Pandas dataframe matrix containing similarity pvalues.\n\t- [methods](https://github.com/brettChapman/multivis/blob/master/multivis/Edge.py#L45-L122)\n\t\t- [set_params] : Set parameters\n\t\t\t- [filterScoreType] : Value type to filter similarities on (default: 'pvalue')\n\t\t\t- [hard_threshold] : Value to filter similarities on (default: 0.005)\n\t\t\t- [internalSimilarities] : Include similarities within blocks if building multi-block network (default: False)\n\t\t\t- [sign] : The sign of the similarity score to filter on ('pos', 'neg' or 'both') (default: 'both')\n\t\t\t- [node_color_column] : The Peak Table column to use for node colours (default: None sets to black)\n\t\t\t- [node_cmap] : The CMAP colour palette to use for nodes (default: 'brg')\n\n\t\t- [run] : Builds the nodes and edges.\n\t\t- [getNodes] : Returns a Pandas dataframe of all nodes.\n\t\t- [getEdges] : Returns a Pandas dataframe of all edges.        \t\n\n- [Network](https://github.com/brettChapman/multivis/blob/master/multivis/Network.py): Inherits from Edge and generates dataframe of nodes, edges, and a NetworkX graph.\n\t- [init_parameters](https://github.com/brettChapman/multivis/blob/master/multivis/Network.py#L33-L37)\n\t\t- [peaktable] : Pandas dataframe containing peak data. Must contain 'Name' and 'Label'.\n\t\t- [similarities] : Pandas dataframe matrix containing similarity scores.\n\t\t- [pvalues] : Pandas dataframe matrix containing similarity pvalues.\n\t- [methods](https://github.com/brettChapman/multivis/blob/master/multivis/Network.py#L39-L59)\n\t\t- [set_params] : Set parameters\n\t\t\t- [filterScoreType] : Value type to filter similarities on (default: 'pvalue')\n\t\t\t- [hard_threshold] : Value to filter similarities on (default: 0.005)\n\t\t\t- [link_type] : The value type to represent links in the network (default: 'score')\n\t\t\t- [internalSimilarities] : Include similarities within blocks if building multi-block network (default: False)\n\t\t\t- [sign] : The sign of the similarity score to filter on ('pos', 'neg' or 'both') (default: 'both')\n\t\t\t- [node_color_column] : The Peak Table column to use for node colours (default: None sets to black)\n\t\t\t- [node_cmap] : The CMAP colour palette to use for nodes (default: 'brg')\n\n\t\t- [run] : Builds nodes, edges and NetworkX graph.\n\t\t- [getNetworkx] : Returns a NetworkX graph.\n\t\t- [getLinkType] : Returns the link type parameter used in building the network.\n\n- [edgeBundle](https://github.com/brettChapman/multivis/blob/master/multivis/edgeBundle.py): Generates and displays a Hierarchical edge bundle plot.\n\t- [init_parameters](https://github.com/brettChapman/multivis/blob/master/multivis/edgeBundle.py#L33-37)\n\t\t- [edges] : Pandas dataframe containing edges generated from Edge.\n\t- [methods](https://github.com/brettChapman/multivis/blob/master/multivis/edgeBundle.py#L39-L104)\n\t\t- [set_params] : Set parameters\n\t\t\t- [html_file] : Name to save the HTML file as (default: 'hEdgeBundle.html')\n\t\t\t- [innerRadiusOffset] : Sets the inner radius based on the offset value from the canvas width/diameter (default: 120)\n\t\t\t- [groupSeparation] : Value to set the distance between different segmented groups (default: 1)\n\t\t\t- [linkFadeOpacity] : The link fade opacity when hovering over/clicking nodes (default: 0.05)\n\t\t\t- [mouseOver] : Setting to 'True' swaps from clicking to hovering over nodes to select them (default: True)\n\t\t\t- [fontSize] : The font size set to each node (default: 1)\n\t\t\t- [backgroundColor] : Set the background colour of the plot (default: 'white')\n\t\t\t- [foregroundColor] : Set the foreground colour of the plot (default: 'black')\n\t\t\t- [edge_color_scale] : Set the values to colour the edges by. Either 'score or 'pvalue' (default: 'score')\n\t\t\t- [edge_cmap] : Set the CMAP colour palette to use for colouring the edges (default: 'brg')\n\n\t\t- [run] : Generates the JavaScript embedded HTML code and writes to a HTML file\n\n- [plotNetwork](https://github.com/brettChapman/multivis/blob/master/multivis/plotNetwork.py): Generates and displays a static NetworkX graph given a user defined layout.\n\t- [init_parameters](https://github.com/brettChapman/multivis/blob/master/multivis/plotNetwork.py#L41-L45)\n\t\t- [g] : NetworkX graph.\n\t- [methods](https://github.com/brettChapman/multivis/blob/master/multivis/plotNetwork.py#L47-L211)\n\t\t- [set_params] : Set parameters\n\t\t\t- [imageFileName] : The image file name to save to (default: 'networkPlot.jpg')\n\t\t\t- [edgeLabels] : Setting to 'True' labels all edges with the similarity score (default: True)\n\t\t\t- [saveImage] : Setting to 'True' will save the image to file (default: True)\n\t\t\t- [layout] : Set the NetworkX layout type ('circular', 'kamada_kawai', 'random', 'spring', 'spectral') (default: 'spring')\n\t\t\t- [dpi] : The number of Dots Per Inch (DPI) for the image (default: 200)\n\t\t\t- [figSize] : The figure size as a tuple (width,height) (default: (30,20))\n\t\t\t- [sizing_column] : The node sizing colomn to use (default: sizes all nodes to 1)\n\t\t\t- [sizeScale] : The node size scale to apply (\"linear\", \"reverse_linear\", \"log\", \"reverse_log\", \"square\", \"reverse_square\", \"area\", \"reverse_area\", \"volume\", \"reverse_volume\") (default: 'reverse_linear')\n\t\t\t- [size_range] : The node size scale range to apply. Tuple of length 2. Minimum size to maximum size (default: (150,2000))\n\t\t\t- [alpha] :  Node opacity value (default: 0.5)\n\t\t\t- [nodeLabels] : Setting to 'True' will label the nodes (default: True)\n\t\t\t- [fontSize] : The font size set for each node (default: 15)\n\t\t\t- [keepSingletons] : Setting to 'True' will keep any single nodes not connected by edges in the NetworkX graph) (default: True)\n\t\t\t- [column] : Column from Peak Table to filter on (default: no filtering)\n\t\t\t- [threshold] : Value to filter on (default: no filtering)\n\t\t\t- [operator] : The comparison operator to use when filtering (default: '>')\n\t\t\t- [sign] : The sign of the similarity score to filter on ('pos', 'neg' or 'both') (default: 'pos')\n\n\t\t- [run] : Generates and displays the NetworkX graph.\n\n- [springNetwork](https://github.com/brettChapman/multivis/blob/master/multivis/springNetwork.py): Interactive spring-embedded network which inherits data from the NetworkX graph.\n\t- [init_parameters](https://github.com/brettChapman/multivis/blob/master/multivis/springNetwork.py#L38-L42)\n\t\t- [g] : NetworkX graph.\n\t- [methods](https://github.com/brettChapman/multivis/blob/master/multivis/springNetwork.py#L44-L113)\n\t\t- [set_params] : Set parameters\n\t\t\t- [node_size_scale]: dictionary(Peak Table column name as index: dictionary('scale': (\"linear\", \"reverse_linear\", \"log\", \"reverse_log\", \"square\", \"reverse_square\", \"area\", \"reverse_area\", \"volume\", \"reverse_volume\")\n                \t                                                                            'range': a number array of length 2 - minimum size to maximum size)) (default: sizes all nodes to 10 with no dropdown menu)\n\t\t\t- [html_file] : Name to save the HTML file as (default: 'springNetwork.html')\n\t\t\t- [backgroundColor] : Set the background colour of the plot (default: 'white')\n\t\t\t- [foregroundColor] : Set the foreground colour of the plot (default: 'black')\n\t\t\t- [chargeStrength] : The charge strength of the spring-embedded network (force between springs) (default: -120)\n\t\t\t- [node_text_size] : The text size for each node (default: 15)\n\t\t\t- [fix_nodes] : Setting to 'True' will fix nodes in place when manually moved (default: False)\n\t\t\t- [displayLabel] : Setting to 'True' will set the node labels to the 'Label' column, otherwise it will set the labels to the 'Name' column from the Peak Table (default: False)\n\t\t\t- [node_data] : Peak Table column names to include in the mouse over information (default: 'Name' and 'Label')\n\t\t\t- [link_type] : The link type used in building the network (default: 'score')\n\t\t\t- [link_width] : The width of the links (default: 0.5)\n\t\t\t- [pos_score_color] : Colour value for positive similarity scores. Can be HTML/CSS name, hex code, and (R,G,B) tuples (default: 'red')\n\t\t\t- [neg_score_color] : Colour value for negative similarity scores. Can be HTML/CSS name, hex code, and (R,G,B) tuples (default: 'black')\n\n\t\t- [run] : Generates the JavaScript embedded HTML code and writes to a HTML file\n\n- [clustermap](https://github.com/brettChapman//multivis/blob/master/multivis/clustermap.py): Hierarchical Clustered Heatmap.\n\t- [init_parameters](https://github.com/brettChapman//multivis/blob/master/multivis/clustermap.py#L42-L50)\n\t\t- [similarities] : Pandas dataframe containing similarity scores.\n        \t- [row_linkage] : Precomputed linkage matrix for the rows from a linkage clustered similarities matrix\n        \t- [col_linkage] : Precomputed linkage matrix for the columns from a linkage clustered similarities matrix\n\t- [methods](https://github.com/brettChapman//multivis/blob/master/multivis/clustermap.py#L52-L350)\n\t\t- [set_params] : Set parameters\n\t\t\t- [xLabels] : A Pandas Series for labelling the X axis of the HCH\n\t\t\t- [yLabels] : A Pandas Series for labelling the Y axis of the HCH\n\t\t\t- [imageFileName] : The image file name to save to (default: 'clusterMap.png')\n\t\t\t- [saveImage] : Setting to 'True' will save the image to file (default: True)\n\t\t\t- [dpi] : The number of Dots Per Inch (DPI) for the image (default: 200)\n\t\t\t- [figSize] : The figure size as a tuple (width,height) (default: (80,70))\n\t\t\t- [dendrogram_ratio_shift] : The ratio to shift the position of the dendrogram in relation to the heatmap (default: 0.0)\n\t\t\t- [fontSize] : The font size set for each node (default: 30)\n\t\t\t- [heatmap_cmap] : The CMAP colour palette to use for the heatmap (default: 'RdYlGn')\n\t\t\t- [cluster_cmap] : The CMAP colour palette to use for the branch seperation of clusters in the dendrogram (default: 'Set1')\n\t\t\t- [rowColorCluster] : Setting to 'True' will display a colour bar for the clustered rows (default: False)\n\t\t\t- [colColorCluster] : Setting to 'True' will display a colour bar for the clustered columns (default: False)\n\t\t\t- [row_color_threshold] : The colouring threshold for the row dendrogram (default: 10)\n\t\t\t- [col_color_threshold] : The colouring threshold for the column dendrogram (default: 10)\n\n\t\t- [run] : : Generates and displays the Hierarchical Clustered Heatmap (HCH).\n\n- [polarDendrogram](https://github.com/brettChapman/multivis/blob/master/multivis/polarDendrogram.py): Polar dendrogram\n\t- [init_parameters](https://github.com/brettChapman/multivis/blob/master/multivis/polarDendrogram.py#L36-L40)\n\t\t- [dn] : Dendrogram dictionary labelled by Peak Table index\n\t- [methods](https://github.com/brettChapman/multivis/blob/master/multivis/polarDendrogram.py#42-L159)\n\t\t- set_params : Set parameters\n\t\t\t- [imageFileName] : The image file name to save to (default: 'polarDendrogram.png')\n\t\t\t- [saveImage] : Setting to 'True' will save the image to file (default: True)\n\t\t\t- [branch_scale] : The branch distance scale to apply ('linear', 'log', 'square') (default: 'linear')\n\t\t\t- [gap] : The gap size within the polar dendrogram (default: 0.1)\n\t\t\t- [grid] : Setting to 'True' will overlay a grid over the polar dendrogram (default: False)\n\t\t\t- [style_sheet] : Setting the Seaborn style-sheet (see https://python-graph-gallery.com/104-seaborn-themes/) (default: 'seaborn-white')\n\t\t\t- [dpi] : The number of Dots Per Inch (DPI) for the image (default: 200)\n\t\t\t- [figSize] : The figure size as a tuple (width,height) (default: (10,10))\n\t\t\t- [fontSize] : The font size for all text (default: 15)\n\t\t\t- [PeakTable] : The Peak Table Pandas dataframe (default: empty dataframe)\n\t\t\t- [Color_column] : The colour column to use from Peak Table (Can be colour or numerical values such as 'pvalue') (default: 'black')\n\t\t\t- [Label_column] : The label column to use from Peak Table (default: use original Peak Table index from cartesian dendrogram)\n\t\t\t- [text_cmap] : The CMAP colour palette to use (default: 'brg')\n\n\t\t- [getClusterPlots] : Generates plots of mean peak area over the 'Class' variable for each cluster from the polar dendrogram\n\t\t\t- [column_numbers] : The number of columns to display in the plots (default: 2)\n\t\t\t- [log] : Setting to 'True' will log the data (default: True)\n\t\t\t- [autoscale] :  Setting to 'True' will scale the data to unit variance (Default: True)\n\t\t\t- [figSize] : The figure size as a tuple (width,height) (default: (10,20))\n\t\t\t- [saveImage] : Setting to 'True' will save the image to file (default: True)\n\t\t\t- [imageFileName] : The image file name to save to (default: 'clusterPlots.png')\n\t\t\t- [dpi] : The number of Dots Per Inch (DPI) for the image (default: 200)\n\n\t\t- [run] : Generates and displays the Polar dendrogram.\n\n- [pca](https://github.com/brettChapman/multivis/blob/master/multivis/pca.py): Principle Component Analysis (PCA) plot\n\t- [parameters](https://github.com/brettChapman/multivis/blob/master/multivis/pca.py#L7)\n\t\t- [data] : array-like matrix, shape (n_samples, n_features)\n\t\t- [imageFileName] : The image file name to save to (default: 'PCA.png')\n\t\t- [saveImage] : Setting to 'True' will save the image to file (default: True)\n\t\t- [dpi] : The number of Dots Per Inch (DPI) for the image (default: 200)\n\t\t- [pcx] : The first component (default: 1)\n\t\t- [pcy] : The second component (default: 2)\n\t\t- [group_label] : Labels to assign to each group/class in the PCA plot (default: None)\n\t\t- [sample_label] : Labels to assign to each sample in the PCA plot (default: None)\n\t\t- [peak_label] : Labels to assign to each peak in the loadings biplot (default: None)\n\t\t- [markerSize] : The size of each marker (default: 100)\n\t\t- [fontSize] : The font size set for each node (default: 12)\n\t\t- [figSize] : The figure size as a tuple (width,height) (default: (20,10))\n\n- [pcoa](https://github.com/brettChapman/multivis/blob/master/multivis/pcoa.py): Principle Coordinates Analysis (PCoA) plot\n\t- [parameters](https://github.com/brettChapman/multivis/blob/master/multivis/pcoa.py#L8)\n\t\t- [similarities] : array-like matrix, shape (n_samples, n_features)\n\t\t- [imageFileName] : The image file name to save to (default: 'PCOA.png')\n\t\t- [saveImage] : Setting to 'True' will save the image to file (default: True)\n\t\t- [dpi] : The number of Dots Per Inch (DPI) for the image (default: 200)\n\t\t- [n_components] : Number of components (default: 2)\n\t\t- [max_iter] : Maximum number of iterations of the SMACOF algorithm (default: 300)\n\t\t- [eps] : Relative tolerance with respect to stress at which to declare convergence (default: 1e-3)\n\t\t- [seed] : Seed number used by the random number generator for the RandomState instance (default: 3)\n\t\t- [group_label] : Labels to assign to each group/class (default: None)\n\t\t- [peak_label] : Labels to assign to each peak (default: None)\n\t\t- [markerSize] : The size of each marker (default: 100)\n\t\t- [fontSize] : The font size set for each node (default: 12)\n\t\t- [figSize] : The figure size as a tuple (width,height) (default: (20,10))   \n\n#### multivis.utils\n\n- [loadData](https://github.com/brettChapman/multivis/blob/master/multivis/utils/loadData.py): Loads and validates the Data and Peak sheet from an excel file.\n\t- [parameters](https://github.com/brettChapman/multivis/blob/master/multivis/utils/loadData.py#L6)\n\t\t- [filename] : The name of the excel file (.xlsx file) e.g. 'Data.xlsx'.\n\t\t- [DataSheet] : The name of the data sheet in the file e.g. 'Data'. The data sheet must contain an 'Idx', 'SampleID', and 'Class' column.\n\t\t- [PeakSheet] : The name of the peak sheet in the file e.g. 'Peak'. The peak sheet must contain an 'Idx', 'Name', and 'Label' column.\n\t- [Returns](https://github.com/brettChapman/multivis/blob/master/multivis/utils/loadData.py#L52)\n\t\t- DataTable: Pandas dataFrame\n\t\t- PeakTable: Pandas dataFrame\n\n- [mergeBlocks](https://github.com/brettChapman/multivis/blob/master/multivis/utils/mergeBlocks.py): Merges multiply different Data Tables and Peak Tables into a single Peak Table and Data Table (used for multi-block/multi-omics data preparation). The 'Name' column needs to be unique across all blocks. Automatically annotates the merged Peak Table with a 'Block' column using the dictionary keys.\n\t- [parameters](https://github.com/brettChapman/multivis/blob/master/multivis/utils/mergeBlocks.py#L5)\n\t\t- [peak_blocks] : A dictionary of Pandas Peak Table dataframes from different datasets indexed by dataset type.\n\t\t- [data_blocks] : A dictionary of Pandas Data Table dataframes from different datasets indexed by dataset type.\n\t- [Returns](https://github.com/brettChapman/multivis/blob/master/multivis/utils/mergeBlocks.p#77)\n\t\t- [DataTable] : Merged Pandas dataFrame\n\t\t- [PeakTable] : Merged Pandas dataFrame\n\n- [range_scale](https://github.com/brettChapman/multivis/blob/master/multivis/utils/range_scale.py): Scales a range of values in a numpy array between a minimum and maximum value.\n\t- [parameters](https://github.com/brettChapman/multivis/blob/master/multivis/utils/range_scale.py#L4)\n\t\t- [data] : A 1D numpy array of values\n\t\t- [newMin] : The minimum value to scale the numpy array to\n\t\t- [newMax] : The maximum value to scale the number array to\n\t- [Returns](https://github.com/brettChapman/multivis/blob/master/multivis/utils/range_scale.py#L25)\n\t\t- [scaled_data] : A scaled numpy array\n\n- [corrAnalysis](https://github.com/brettChapman/multivis/blob/master/multivis/utils/corrAnalysis.py): Correlation analysis with Pearson, Spearman or Kendall's Tau.\n\t- [parameters](https://github.com/brettChapman/multivis/blob/master/multivis/utils/corrAnalysis.py#L7)\n\t\t- [df_data] : A Pandas dataframe matrix of values\n\t\t- [correlationType] : The correlation type to apply. Either 'Pearson', 'Spearman' or 'KendallTau'\n\t- [Returns](https://github.com/brettChapman/multivis/blob/master/multivis/utils/corrAnalysis.py#L60)\n\t\t- [df_corr] : Pandas dataframe matrix of all correlation coefficients\n\t\t- [df_pval] : Pandas dataframe matrix of all correlation pvalues\n\n- [cluster](https://github.com/brettChapman/multivis/blob/master/multivis/utils/cluster.py): Clusters data using a linkage cluster method. If the data is correlated the correlations are first preprocessed, then clustered, otherwise a distance metric is applied to non-correlated data before clustering.\n\t- [parameters](https://github.com/brettChapman/multivis/blob/master/multivis/utils/cluster.py#L7)\n\t\t- [matrix] : A Pandas dataframe matrix of values (may or may not be a matrix of correlation coefficients)\n\t\t- [transpose_non_correlated] : Setting to 'True' will transpose the matrix if it is not correlated data\n\t\t- [is_correlated] : Setting to 'True' will treat the matrix as if it contains correlation coefficients\n\t\t- [distance_metric] : Set the distance metric. Used if the matrix does not contain correlation coefficients.\n\t\t- [linkage_method] : Set the linkage method for the clustering.\n\t- [Returns](https://github.com/brettChapman/multivis/blob/master/multivis/utils/cluster.py#L47)\n\t\t- [matrix] : The original matrix, transposed if transpose_non_correlated is 'True' and is_correlated is 'False'.\n\t\t- [row_linkage] : linkage matrix for the rows from a linkage clustered similarities matrix\n\t\t- [col_linkage] : linkage matrix for the columns from a linkage clustered similarities matrix\n\n### License\nMultivis is licensed under the MIT license.\n\n### Authors\n- Brett Chapman\n- https://scholar.google.com.au/citations?user=A_wYNAQAAAAJ&hl=en\n\n### Correspondence\nDr. Brett Chapman, Post-doctoral Research Fellow at the Centre for Integrative Metabolomics & Computational Biology at Edith Cowan University.\nE-mail: brett.chapman@ecu.edu.au, brett.chapman78@gmail.com\n\n### Citation\nIf you would cite multivis in a scientific publication, you can use the following: [currently pending publication submission\n\n\n",
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