a `\ @s8ddlmZddlZddlmZddlmZddlmZm Z m Z ddl Z ddl ZddlmZddlmZdd lmZdd lmZmZgd Zd d ZddZddZddZd&ddZGddde jjZGdddeZ Gddde Z!Gdd d e Z"e#ddddddd!d!f ddddded"ddddd# d$d%Z$dS)') OrderedDictN) _ImageBasePatch)BboxTransformedBbox BboxTransform) reductions)transfer_functions)Sets1to3)bypixelCanvas)ScalarDSArtistCategoricalDSArtistalpha_colormapdsshowcCs|jtjd|jdS)z5Cast a uint32 raster to a 4-channel uint8 RGBA array.dtype))viewnpZuint8reshapeshapeimgr1lib/python3.9/site-packages/datashader/mpl_ext.pyuint32_to_uint8srcCs|jtjd|jddS)z2Cast a 4-channel uint8 RGBA array to uint32 rasterrN)rrZuint32rrrrrruint8_to_uint32sr c Cs|jdkr"tjt||j|jdS|jdkr~tjt||jddt|jd|j|jdf|jd|j|jdfgdStddS) N)coordsdimsrr r)r#r"zAggregate must be 2D or 3D.)ndimtfZImager r"r#r ValueError)binnedrgbarrr to_ds_images    r*cCs<t|jtjr|}n |jjdkr.|dk}n t|}|S)Nur)rZ issubdtyperZbool_kindZisnan)r(maskrrr compute_mask.s    r.(c Cs||fD]}|dks|dkrtdqtj|\}}}tjjdd||fd||fgd||fd||fgd||fd||fgd|d|dfd|d|dfgd|dS) a Generate a transparency-based monochromatic colormap. 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If *unsampled* is True, the image will not be scaled, but an appropriate affine transformation will be returned instead. Returns ------- image : (M, N, 4) uint8 array The RGBA image, resampled unless *unsampled* is True. x, y : float The upper left corner where the image should be drawn, in pixel space. trans : Affine2D The affine transformation from image to pixel space. Nr2F) magnification unsampled) get_extentrrrbZ get_transformrr{r|r}r~rry set_ds_datarrzr*rrY set_arrayZ _make_imagerubbox)rGZrendererrrx1x2y1y2rtransZtransformed_bboxr(r)rrrr make_images>          zDSArtist.make_imagecCs ||_dS)z Set the aggregate data for the bounding box currently displayed. Should be a :class:`xarray.DataArray`. 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The plot will respond to changes in the data space bounds displayed, such as pan/zoom events. Both scalar and categorical pipelines are supported. Parameters ---------- df : pandas.DataFrame, dask.DataFrame Dataframe to apply the datashading pipeline to. glyph : Glyph The glyph to bin by. aggregator : Reduction, optional, default: :class:`~.count` The reduction to compute per-pixel. agg_hook : callable, optional A callable that takes the computed aggregate as an argument, and returns another aggregate. This can be used to do preprocessing before the aggregate is converted to an image. shade_hook : callable, optional A callable that takes the image output of the shading pipeline, and returns another :class:`~.Image` object. See :func:`~.dynspread` and :func:`~.spread` for examples. plot_width, plot_height : int, optional Grid dimensions, i.e. the width and height of the output aggregates in pixels. Default is to use the native width and height dimensions of the axes bounding box. x_range, y_range : pair of float, optional A tuple representing the initial bounds inclusive space ``[min, max]`` along the axis. If None, the initial bounds will encompass all of the data along the axis. height_scale : float, optional Factor by which to scale the height of the image in pixels relative to the height of the display space in pixels. width_scale : float, optional Factor by which to scale the width of the image in pixels relative to the width of the display space in pixels. norm : str or :class:`matplotlib.colors.Normalize`, optional For scalar aggregates, a matplotlib norm to normalize the aggregate data to [0, 1] before colormapping. The datashader arguments 'linear', 'log', 'cbrt' and 'eq_hist' are also supported and correspond to equivalent matplotlib norms. Default is the linear norm. cmap : str or list or :class:`matplotlib.cm.Colormap`, optional For scalar aggregates, a matplotlib colormap name or instance. Alternatively, an iterable of colors can be passed and will be converted to a colormap. For a single-color, transparency-based colormap, see :func:`alpha_colormap`. alpha : float For scalar aggregates, the alpha blending value, between 0 (transparent) and 1 (opaque). vmin, vmax : float, optional For scalar aggregates, the data range that the colormap covers. If vmin or vmax is None (default), the colormap autoscales to the range of data in the area displayed, unless the corresponding value is already set in the norm. color_key : dict or iterable, optional For categorical aggregates, the colors to use for blending categories. See `tf.shade`. alpha_range : pair of int, optional For categorical aggregates, the minimum and maximum alpha values in [0, 255] to use to indicate data values of non-empty pixels. The default range is (40, 255). color_baseline : float, optional For categorical aggregates, the baseline for calculating how categorical data mixes to determine the color of a pixel. See `tf.shade` for more information. Other Parameters ---------------- ax : `matplotlib.Axes`, optional Axes to draw into. If *None*, create a new figure or use ``fignum`` to draw into an existing figure. fignum : None or int or False, optional If *None* and ``ax`` is *None*, create a new figure window with automatic numbering. If a nonzero integer and ``ax`` is *None*, draw into the figure with the given number (create it if it does not exist). If 0, use the current axes (or create one if it does not exist). aspect : {'equal', 'auto'} or float, default: ``rcParams["image.aspect"]`` The aspect ratio of the axes. **kwargs All other kwargs are passed to the artist. Returns ------- :class:`ScalarDSArtist` or :class:`CategoricalDSArtist` Notes ----- If the aggregation is scalar/single-category (i.e. generates a 2D scalar mappable), the artist can be used to make a colorbar. See example. If the aggregation is multi-category (i.e. generates a 3D array with two or more components that get composited to form an image), you can use the :meth:`CategoricalDSArtist.get_legend_elements` method to obtain patch handles that can be used to make a legend. See example. Examples -------- Generate two Gaussian point clouds and (1) plot the density as a quantitative map and (2) color the points by category. .. plot:: :context: close-figs >>> import pandas as pd >>> import datashader as ds >>> import matplotlib.pyplot as plt >>> from datashader.mpl_ext import dsshow >>> n = 10000 >>> df = pd.DataFrame({ ... 'x': np.r_[np.random.randn(n) - 1, np.random.randn(n) + 1], ... 'y': np.r_[np.random.randn(n), np.random.randn(n)], ... 'c': pd.Categorical(np.r_[['cloud 1'] * n, ['cloud 2'] * n]) ... }) >>> da1 = dsshow( ... df, ... ds.Point('x', 'y'), ... aspect='equal' ... ) >>> plt.colorbar(da1); # doctest: +SKIP >>> da2 = dsshow( ... df, ... ds.Point('x', 'y'), ... ds.count_cat('c'), ... aspect='equal' ... ) >>> plt.legend(handles=da2.get_legend_elements()); # doctest: +SKIP rN)g333333?g ףp= ??r) r{r|rrr}r~rrrZ _datashaderZlinearlogZcbrtgUUUUUU?Zeq_histzX`norm` must be one of 'linear', 'log', 'cbrt', 'eq_hist', or a matplotlib norm instance.) r{r|rrr}r~rrr3z image.aspect)Zmatplotlib.pyplotZpyplotZgcaZfigureZadd_axesrr Zbyrlistr5r6r7 from_list NormalizestrZLogNormZ PowerNormr>r'rHrIrZ add_artistZrcParamsZ set_aspect)rvrwrxryrzr{r|rrr}r~rrr3rHrIrrrrrrrZpltZfigZartistrrrrs                r)r/r0r1)% collectionsrrWZmatplotlib.imagerZmatplotlib.patchesrZmatplotlib.transformsrrrZ matplotlibr5Znumpyrr r r&r6r corer r__all__rr r*r.rrr>rprrcountrrrrrsV       $U&IT