How To Draw Lines In Heatmap Matlab

Note

Click here to download the total case code

Creating annotated heatmaps¶

It is often desirable to show data which depends on two independent variables as a colour coded prototype plot. This is often referred to equally a heatmap. If the information is categorical, this would be chosen a chiselled heatmap.

Matplotlib'southward imshow office makes product of such plots especially easy.

The following examples show how to create a heatmap with annotations. Nosotros will starting time with an easy example and aggrandize it to exist usable every bit a universal function.

A simple categorical heatmap¶

We may start by defining some data. What nosotros demand is a 2D listing or array which defines the information to colour code. We then likewise demand ii lists or arrays of categories; of form the number of elements in those lists demand to match the data forth the respective axes. The heatmap itself is an imshow plot with the labels set to the categories we have. Note that information technology is important to set up both, the tick locations ( set_xticks ) also every bit the tick labels ( set_xticklabels ), otherwise they would become out of sync. The locations are just the ascending integer numbers, while the ticklabels are the labels to testify. Finally we can label the data itself past creating a Text within each cell showing the value of that prison cell.

                                import                numpy                as                np                import                matplotlib                import                matplotlib.pyplot                as                plt                vegetables                =                [                "cucumber"                ,                "lycopersicon esculentum"                ,                "lettuce"                ,                "asparagus"                ,                "potato"                ,                "wheat"                ,                "barley"                ]                farmers                =                [                "Farmer Joe"                ,                "Upland Bros."                ,                "Smith Gardening"                ,                "Agrifun"                ,                "Organiculture"                ,                "BioGoods Ltd."                ,                "Cornylee Corp."                ]                harvest                =                np                .                array                ([[                0.8                ,                ii.4                ,                2.5                ,                three.9                ,                0.0                ,                four.0                ,                0.0                ],                [                two.iv                ,                0.0                ,                iv.0                ,                1.0                ,                2.7                ,                0.0                ,                0.0                ],                [                i.i                ,                2.4                ,                0.eight                ,                4.3                ,                1.9                ,                iv.four                ,                0.0                ],                [                0.6                ,                0.0                ,                0.three                ,                0.0                ,                3.ane                ,                0.0                ,                0.0                ],                [                0.seven                ,                1.7                ,                0.half dozen                ,                ii.6                ,                2.2                ,                half dozen.2                ,                0.0                ],                [                one.3                ,                i.2                ,                0.0                ,                0.0                ,                0.0                ,                3.two                ,                v.ane                ],                [                0.1                ,                ii.0                ,                0.0                ,                1.4                ,                0.0                ,                1.nine                ,                half-dozen.3                ]])                fig                ,                ax                =                plt                .                subplots                ()                im                =                ax                .                imshow                (                harvest                )                # Show all ticks and characterization them with the respective list entries                ax                .                set_xticks                (                np                .                arange                (                len                (                farmers                )),                labels                =                farmers                )                ax                .                set_yticks                (                np                .                arange                (                len                (                vegetables                )),                labels                =                vegetables                )                # Rotate the tick labels and set up their alignment.                plt                .                setp                (                ax                .                get_xticklabels                (),                rotation                =                45                ,                ha                =                "right"                ,                rotation_mode                =                "ballast"                )                # Loop over data dimensions and create text annotations.                for                i                in                range                (                len                (                vegetables                )):                for                j                in                range                (                len                (                farmers                )):                text                =                ax                .                text                (                j                ,                i                ,                harvest                [                i                ,                j                ],                ha                =                "center"                ,                va                =                "center"                ,                color                =                "westward"                )                ax                .                set_title                (                "Harvest of local farmers (in tons/twelvemonth)"                )                fig                .                tight_layout                ()                plt                .                show                ()

Using the helper function code way¶

Every bit discussed in the Coding styles one might desire to reuse such lawmaking to create some kind of heatmap for different input data and/or on different axes. We create a function that takes the data and the row and column labels as input, and allows arguments that are used to customize the plot

Here, in improver to the above we as well want to create a colorbar and position the labels above of the heatmap instead of below it. The annotations shall get dissimilar colors depending on a threshold for ameliorate contrast against the pixel color. Finally, we plough the surrounding axes spines off and create a grid of white lines to separate the cells.

                                def                heatmap                (                data                ,                row_labels                ,                col_labels                ,                ax                =                None                ,                cbar_kw                =                {},                cbarlabel                =                ""                ,                **                kwargs                ):                """                                  Create a heatmap from a numpy array and two lists of labels.                                  Parameters                                  ----------                                  information                                  A 2D numpy array of shape (M, N).                                  row_labels                                  A list or assortment of length Thousand with the labels for the rows.                                  col_labels                                  A listing or array of length N with the labels for the columns.                                  ax                                  A `matplotlib.axes.Axes` instance to which the heatmap is plotted.  If                                  not provided, use current axes or create a new one.  Optional.                                  cbar_kw                                  A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.                                  cbarlabel                                  The label for the colorbar.  Optional.                                  **kwargs                                  All other arguments are forwarded to `imshow`.                                  """                if                not                ax                :                ax                =                plt                .                gca                ()                # Plot the heatmap                im                =                ax                .                imshow                (                data                ,                **                kwargs                )                # Create colorbar                cbar                =                ax                .                effigy                .                colorbar                (                im                ,                ax                =                ax                ,                **                cbar_kw                )                cbar                .                ax                .                set_ylabel                (                cbarlabel                ,                rotation                =-                xc                ,                va                =                "bottom"                )                # Show all ticks and label them with the respective listing entries.                ax                .                set_xticks                (                np                .                arange                (                data                .                shape                [                1                ]),                labels                =                col_labels                )                ax                .                set_yticks                (                np                .                arange                (                data                .                shape                [                0                ]),                labels                =                row_labels                )                # Let the horizontal axes labeling appear on top.                ax                .                tick_params                (                top                =                True                ,                bottom                =                False                ,                labeltop                =                Truthful                ,                labelbottom                =                Fake                )                # Rotate the tick labels and set their alignment.                plt                .                setp                (                ax                .                get_xticklabels                (),                rotation                =-                thirty                ,                ha                =                "right"                ,                rotation_mode                =                "anchor"                )                # Turn spines off and create white grid.                ax                .                spines                [:]                .                set_visible                (                Fake                )                ax                .                set_xticks                (                np                .                arange                (                data                .                shape                [                1                ]                +                1                )                -                .5                ,                pocket-size                =                Truthful                )                ax                .                set_yticks                (                np                .                arange                (                data                .                shape                [                0                ]                +                1                )                -                .5                ,                modest                =                Truthful                )                ax                .                filigree                (                which                =                "small-scale"                ,                color                =                "w"                ,                linestyle                =                '-'                ,                linewidth                =                3                )                ax                .                tick_params                (                which                =                "small"                ,                lesser                =                False                ,                left                =                False                )                render                im                ,                cbar                def                annotate_heatmap                (                im                ,                information                =                None                ,                valfmt                =                "                {x:.2f}                "                ,                textcolors                =                (                "black"                ,                "white"                ),                threshold                =                None                ,                **                textkw                ):                """                                  A function to annotate a heatmap.                                  Parameters                                  ----------                                  im                                  The AxesImage to be labeled.                                  data                                  Data used to comment.  If None, the image'south information is used.  Optional.                                  valfmt                                  The format of the annotations inside the heatmap.  This should either                                  employ the cord format method, e.thousand. "$ {x:.2f}", or be a                                  `matplotlib.ticker.Formatter`.  Optional.                                  textcolors                                  A pair of colors.  The first is used for values below a threshold,                                  the 2nd for those above.  Optional.                                  threshold                                  Value in data units according to which the colors from textcolors are                                  applied.  If None (the default) uses the middle of the colormap equally                                  separation.  Optional.                                  **kwargs                                  All other arguments are forwarded to each call to `text` used to create                                  the text labels.                                  """                if                non                isinstance                (                data                ,                (                list                ,                np                .                ndarray                )):                data                =                im                .                get_array                ()                # Normalize the threshold to the images color range.                if                threshold                is                not                None                :                threshold                =                im                .                norm                (                threshold                )                else                :                threshold                =                im                .                norm                (                data                .                max                ())                /                2.                # Set default alignment to center, but let it to be                # overwritten by textkw.                kw                =                dict                (                horizontalalignment                =                "center"                ,                verticalalignment                =                "center"                )                kw                .                update                (                textkw                )                # Get the formatter in case a string is supplied                if                isinstance                (                valfmt                ,                str                ):                valfmt                =                matplotlib                .                ticker                .                StrMethodFormatter                (                valfmt                )                # Loop over the data and create a `Text` for each "pixel".                # Modify the text's color depending on the information.                texts                =                []                for                i                in                range                (                data                .                shape                [                0                ]):                for                j                in                range                (                data                .                shape                [                1                ]):                kw                .                update                (                color                =                textcolors                [                int                (                im                .                norm                (                information                [                i                ,                j                ])                >                threshold                )])                text                =                im                .                axes                .                text                (                j                ,                i                ,                valfmt                (                data                [                i                ,                j                ],                None                ),                **                kw                )                texts                .                append                (                text                )                return                texts

The above now allows united states to keep the bodily plot creation pretty compact.

Some more complex heatmap examples¶

In the following we prove the versatility of the previously created functions by applying information technology in different cases and using unlike arguments.

                                np                .                random                .                seed                (                19680801                )                fig                ,                ((                ax                ,                ax2                ),                (                ax3                ,                ax4                ))                =                plt                .                subplots                (                ii                ,                2                ,                figsize                =                (                8                ,                half-dozen                ))                # Replicate the above example with a different font size and colormap.                im                ,                _                =                heatmap                (                harvest                ,                vegetables                ,                farmers                ,                ax                =                ax                ,                cmap                =                "Wistia"                ,                cbarlabel                =                "harvest [t/twelvemonth]"                )                annotate_heatmap                (                im                ,                valfmt                =                "                {x:.1f}                "                ,                size                =                vii                )                # Create some new data, give farther arguments to imshow (vmin),                # use an integer format on the annotations and provide some colors.                information                =                np                .                random                .                randint                (                2                ,                100                ,                size                =                (                7                ,                seven                ))                y                =                [                "Book                                {}                "                .                format                (                i                )                for                i                in                range                (                1                ,                8                )]                x                =                [                "Store                                {}                "                .                format                (                i                )                for                i                in                list                (                "ABCDEFG"                )]                im                ,                _                =                heatmap                (                data                ,                y                ,                x                ,                ax                =                ax2                ,                vmin                =                0                ,                cmap                =                "magma_r"                ,                cbarlabel                =                "weekly sold copies"                )                annotate_heatmap                (                im                ,                valfmt                =                "                {x:d}                "                ,                size                =                7                ,                threshold                =                20                ,                textcolors                =                (                "red"                ,                "white"                ))                # Sometimes even the data itself is categorical. Here we use a                # `matplotlib.colors.BoundaryNorm` to go the data into classes                # and use this to colorize the plot, but besides to obtain the class                # labels from an array of classes.                data                =                np                .                random                .                randn                (                6                ,                6                )                y                =                [                "Prod.                                {}                "                .                format                (                i                )                for                i                in                range                (                10                ,                70                ,                10                )]                10                =                [                "Cycle                                {}                "                .                format                (                i                )                for                i                in                range                (                1                ,                7                )]                qrates                =                list                (                "ABCDEFG"                )                norm                =                matplotlib                .                colors                .                BoundaryNorm                (                np                .                linspace                (                -                3.5                ,                iii.5                ,                8                ),                seven                )                fmt                =                matplotlib                .                ticker                .                FuncFormatter                (                lambda                10                ,                pos                :                qrates                [::                -                1                ][                norm                (                ten                )])                im                ,                _                =                heatmap                (                data                ,                y                ,                x                ,                ax                =                ax3                ,                cmap                =                plt                .                get_cmap                (                "PiYG"                ,                7                ),                norm                =                norm                ,                cbar_kw                =                dict                (                ticks                =                np                .                arange                (                -                3                ,                4                ),                format                =                fmt                ),                cbarlabel                =                "Quality Rating"                )                annotate_heatmap                (                im                ,                valfmt                =                fmt                ,                size                =                ix                ,                fontweight                =                "bold"                ,                threshold                =-                1                ,                textcolors                =                (                "red"                ,                "blackness"                ))                # We tin can nicely plot a correlation matrix. Since this is bound by -1 and i,                # we use those as vmin and vmax. We may as well remove leading zeros and hide                # the diagonal elements (which are all 1) by using a                # `matplotlib.ticker.FuncFormatter`.                corr_matrix                =                np                .                corrcoef                (                harvest                )                im                ,                _                =                heatmap                (                corr_matrix                ,                vegetables                ,                vegetables                ,                ax                =                ax4                ,                cmap                =                "PuOr"                ,                vmin                =-                1                ,                vmax                =                1                ,                cbarlabel                =                "correlation coeff."                )                def                func                (                x                ,                pos                ):                render                "                {:.2f}                "                .                format                (                x                )                .                replace                (                "0."                ,                "."                )                .                replace                (                "i.00"                ,                ""                )                annotate_heatmap                (                im                ,                valfmt                =                matplotlib                .                ticker                .                FuncFormatter                (                func                ),                size                =                7                )                plt                .                tight_layout                ()                plt                .                show                ()