Source code for yt.visualization.image_writer

"""


"""
from __future__ import print_function
from __future__ import absolute_import

#-----------------------------------------------------------------------------
# Copyright (c) 2013, yt Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------

import numpy as np

from yt.config import \
    ytcfg
from yt.funcs import \
    mylog, \
    get_image_suffix, \
    get_brewer_cmap
from yt.units.yt_array import YTQuantity
from yt.utilities.exceptions import YTNotInsideNotebook
from .color_maps import mcm
from . import _colormap_data as cmd
import yt.utilities.lib.image_utilities as au
import yt.utilities.png_writer as pw
from yt.extern.six.moves import builtins


[docs]def scale_image(image, mi=None, ma=None): r"""Scale an image ([NxNxM] where M = 1-4) to be uint8 and values scaled from [0,255]. Parameters ---------- image : array_like or tuple of image info Examples -------- >>> image = scale_image(image) >>> image = scale_image(image, min=0, max=1000) """ if isinstance(image, np.ndarray) and image.dtype == np.uint8: return image if isinstance(image, (tuple, list)): image, mi, ma = image if mi is None: mi = image.min() if ma is None: ma = image.max() image = (np.clip((image-mi)/(ma-mi) * 255, 0, 255)).astype('uint8') return image
[docs]def multi_image_composite(fn, red_channel, blue_channel, green_channel = None, alpha_channel = None): r"""Write an image with different color channels corresponding to different quantities. Accepts at least a red and a blue array, of shape (N,N) each, that are optionally scaled and composited into a final image, written into `fn`. Can also accept green and alpha. Parameters ---------- fn : string Filename to save red_channel : array_like or tuple of image info Array, of shape (N,N), to be written into the red channel of the output image. If not already uint8, will be converted (and scaled) into uint8. Optionally, you can also specify a tuple that includes scaling information, in the form of (array_to_plot, min_value_to_scale, max_value_to_scale). blue_channel : array_like or tuple of image info Array, of shape (N,N), to be written into the blue channel of the output image. If not already uint8, will be converted (and scaled) into uint8. Optionally, you can also specify a tuple that includes scaling information, in the form of (array_to_plot, min_value_to_scale, max_value_to_scale). green_channel : array_like or tuple of image info, optional Array, of shape (N,N), to be written into the green channel of the output image. If not already uint8, will be converted (and scaled) into uint8. If not supplied, will be left empty. Optionally, you can also specify a tuple that includes scaling information, in the form of (array_to_plot, min_value_to_scale, max_value_to_scale). alpha_channel : array_like or tuple of image info, optional Array, of shape (N,N), to be written into the alpha channel of the output image. If not already uint8, will be converted (and scaled) into uint8. If not supplied, will be made fully opaque. Optionally, you can also specify a tuple that includes scaling information, in the form of (array_to_plot, min_value_to_scale, max_value_to_scale). Examples -------- >>> red_channel = np.log10(frb["Temperature"]) >>> blue_channel = np.log10(frb["Density"]) >>> multi_image_composite("multi_channel1.png", red_channel, blue_channel) """ red_channel = scale_image(red_channel) blue_channel = scale_image(blue_channel) if green_channel is None: green_channel = np.zeros(red_channel.shape, dtype='uint8') else: green_channel = scale_image(green_channel) if alpha_channel is None: alpha_channel = np.zeros(red_channel.shape, dtype='uint8') + 255 else: alpha_channel = scale_image(alpha_channel) image = np.array([red_channel, green_channel, blue_channel, alpha_channel]) image = image.transpose().copy() # Have to make sure it's contiguous pw.write_png(image, fn)
[docs]def write_bitmap(bitmap_array, filename, max_val = None, transpose=False): r"""Write out a bitmapped image directly to a PNG file. This accepts a three- or four-channel `bitmap_array`. If the image is not already uint8, it will be scaled and converted. If it is four channel, only the first three channels will be scaled, while the fourth channel is assumed to be in the range of [0,1]. If it is not four channel, a fourth alpha channel will be added and set to fully opaque. The resultant image will be directly written to `filename` as a PNG with no colormap applied. `max_val` is a value used if the array is passed in as anything other than uint8; it will be the value used for scaling and clipping in the first three channels when the array is converted. Additionally, the minimum is assumed to be zero; this makes it primarily suited for the results of volume rendered images, rather than misaligned projections. Parameters ---------- bitmap_array : array_like Array of shape (N,M,3) or (N,M,4), to be written. If it is not already a uint8 array, it will be scaled and converted to uint8. filename : string Filename to save to. If None, PNG contents will be returned as a string. max_val : float, optional The upper limit to clip values to in the output, if converting to uint8. If `bitmap_array` is already uint8, this will be ignore. transpose : boolean, optional If transpose is False, we assume that the incoming bitmap_array is such that the first element resides in the upper-left corner. If True, the first element will be placed in the lower-left corner. """ if len(bitmap_array.shape) != 3 or bitmap_array.shape[-1] not in (3,4): raise RuntimeError if bitmap_array.dtype != np.uint8: s1, s2 = bitmap_array.shape[:2] if bitmap_array.shape[-1] == 3: alpha_channel = 255*np.ones((s1,s2,1), dtype='uint8') else: alpha_channel = (255*bitmap_array[:,:,3]).astype('uint8') alpha_channel.shape = s1, s2, 1 if max_val is None: max_val = bitmap_array[:,:,:3].max() bitmap_array = np.clip(bitmap_array[:,:,:3] / max_val, 0.0, 1.0) * 255 bitmap_array = np.concatenate([bitmap_array.astype('uint8'), alpha_channel], axis=-1) if transpose: bitmap_array = bitmap_array.swapaxes(0,1).copy(order="C") if filename is not None: pw.write_png(bitmap_array, filename) else: return pw.write_png_to_string(bitmap_array.copy()) return bitmap_array
[docs]def write_image(image, filename, color_bounds = None, cmap_name = None, func = lambda x: x): r"""Write out a floating point array directly to a PNG file, scaling it and applying a colormap. This function will scale an image and directly call libpng to write out a colormapped version of that image. It is designed for rapid-fire saving of image buffers generated using `yt.visualization.api.FixedResolutionBuffers` and the like. Parameters ---------- image : array_like This is an (unscaled) array of floating point values, shape (N,N,) to save in a PNG file. filename : string Filename to save as. color_bounds : tuple of floats, optional The min and max to scale between. Outlying values will be clipped. cmap_name : string, optional An acceptable colormap. See either yt.visualization.color_maps or http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps . func : function, optional A function to transform the buffer before applying a colormap. Returns ------- scaled_image : uint8 image that has been saved Examples -------- >>> sl = ds.slice(0, 0.5, "Density") >>> frb1 = FixedResolutionBuffer(sl, (0.2, 0.3, 0.4, 0.5), (1024, 1024)) >>> write_image(frb1["Density"], "saved.png") """ if cmap_name is None: cmap_name = ytcfg.get("yt", "default_colormap") if len(image.shape) == 3: mylog.info("Using only channel 1 of supplied image") image = image[:,:,0] to_plot = apply_colormap(image, color_bounds = color_bounds, cmap_name = cmap_name) pw.write_png(to_plot, filename) return to_plot
[docs]def apply_colormap(image, color_bounds = None, cmap_name = None, func=lambda x: x): r"""Apply a colormap to a floating point image, scaling to uint8. This function will scale an image and directly call libpng to write out a colormapped version of that image. It is designed for rapid-fire saving of image buffers generated using `yt.visualization.api.FixedResolutionBuffers` and the like. Parameters ---------- image : array_like This is an (unscaled) array of floating point values, shape (N,N,) to save in a PNG file. color_bounds : tuple of floats, optional The min and max to scale between. Outlying values will be clipped. cmap_name : string, optional An acceptable colormap. See either yt.visualization.color_maps or http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps . func : function, optional A function to transform the buffer before applying a colormap. Returns ------- to_plot : uint8 image with colorbar applied. """ if cmap_name is None: cmap_name = ytcfg.get("yt", "default_colormap") from yt.data_objects.image_array import ImageArray image = ImageArray(func(image)) if color_bounds is None: mi = np.nanmin(image[~np.isinf(image)])*image.uq ma = np.nanmax(image[~np.isinf(image)])*image.uq color_bounds = mi, ma else: color_bounds = [YTQuantity(func(c), image.units) for c in color_bounds] image = (image - color_bounds[0])/(color_bounds[1] - color_bounds[0]) to_plot = map_to_colors(image, cmap_name) to_plot = np.clip(to_plot, 0, 255) return to_plot
[docs]def map_to_colors(buff, cmap_name): try: lut = cmd.color_map_luts[cmap_name] except KeyError: try: # if cmap is tuple, then we're using palettable or brewer2mpl cmaps if isinstance(cmap_name, tuple): cmap = get_brewer_cmap(cmap_name) else: cmap = mcm.get_cmap(cmap_name) cmap(0.0) lut = cmap._lut.T except ValueError: raise KeyError( "Your color map (%s) was not found in either the extracted" " colormap file or matplotlib colormaps" % cmap_name) if isinstance(cmap_name, tuple): # If we are using the colorbrewer maps, don't interpolate shape = buff.shape # We add float_eps so that digitize doesn't go out of bounds x = np.mgrid[0.0:1.0+np.finfo(np.float32).eps:lut[0].shape[0]*1j] inds = np.digitize(buff.ravel(), x) inds.shape = (shape[0], shape[1]) mapped = np.dstack([(v[inds]*255).astype('uint8') for v in lut]) del inds else: x = np.mgrid[0.0:1.0:lut[0].shape[0]*1j] mapped = np.dstack( [(np.interp(buff, x, v)*255).astype('uint8') for v in lut ]) return mapped.copy("C")
[docs]def strip_colormap_data(fn = "color_map_data.py", cmaps = ("jet", "algae", "hot", "gist_stern", "RdBu", "kamae", "kelp", "arbre", "octarine", "dusk")): import pprint from . import color_maps as rcm f = open(fn, "w") f.write("### Auto-generated colormap tables, taken from Matplotlib ###\n\n") f.write("from numpy import array\n") f.write("color_map_luts = {}\n\n\n") if cmaps is None: cmaps = rcm.ColorMaps for cmap_name in sorted(cmaps): print("Stripping", cmap_name) vals = rcm._extract_lookup_table(cmap_name) f.write("### %s ###\n\n" % (cmap_name)) f.write("color_map_luts['%s'] = \\\n" % (cmap_name)) f.write(" (\n") for v in vals: f.write(pprint.pformat(v, indent=3)) f.write(",\n") f.write(" )\n\n") f.close()
[docs]def splat_points(image, points_x, points_y, contribution = None, transposed = False): if contribution is None: contribution = 100.0 val = contribution * 1.0/points_x.size if transposed: points_y = 1.0 - points_y points_x = 1.0 - points_x im = image.copy() au.add_points_to_image(im, points_x, points_y, val) return im
[docs]def write_projection(data, filename, colorbar=True, colorbar_label=None, title=None, limits=None, take_log=True, figsize=(8,6), dpi=100, cmap_name=None, extent=None, xlabel=None, ylabel=None): r"""Write a projection or volume rendering to disk with a variety of pretty parameters such as limits, title, colorbar, etc. write_projection uses the standard matplotlib interface to create the figure. N.B. This code only works *after* you have created the projection using the standard framework (i.e. the Camera interface or off_axis_projection). Accepts an NxM sized array representing the projection itself as well as the filename to which you will save this figure. Note that the final resolution of your image will be a product of dpi/100 * figsize. Parameters ---------- data : array_like image array as output by off_axis_projection or camera.snapshot() filename : string the filename where the data will be saved colorbar : boolean do you want a colorbar generated to the right of the image? colorbar_label : string the label associated with your colorbar title : string the label at the top of the figure limits : 2-element array_like the lower limit and the upper limit to be plotted in the figure of the data array take_log : boolean plot the log of the data array (and take the log of the limits if set)? figsize : array_like width, height in inches of final image dpi : int final image resolution in pixels / inch cmap_name : string The name of the colormap. Examples -------- >>> image = off_axis_projection(ds, c, L, W, N, "Density", no_ghost=False) >>> write_projection(image, 'test.png', colorbar_label="Column Density (cm$^{-2}$)", title="Offaxis Projection", limits=(1e-5,1e-3), take_log=True) """ if cmap_name is None: cmap_name = ytcfg.get("yt", "default_colormap") import matplotlib.figure import matplotlib.colors from ._mpl_imports import FigureCanvasAgg, FigureCanvasPdf, FigureCanvasPS # If this is rendered as log, then apply now. if take_log: norm = matplotlib.colors.LogNorm() else: norm = matplotlib.colors.Normalize() if limits is None: limits = [None, None] # Create the figure and paint the data on fig = matplotlib.figure.Figure(figsize=figsize) ax = fig.add_subplot(111) cax = ax.imshow(data.to_ndarray(), vmin=limits[0], vmax=limits[1], norm=norm, extent=extent, cmap=cmap_name) if title: ax.set_title(title) if xlabel: ax.set_xlabel(xlabel) if ylabel: ax.set_ylabel(ylabel) # Suppress the x and y pixel counts if extent is None: ax.set_xticks(()) ax.set_yticks(()) # Add a color bar and label if requested if colorbar: cbar = fig.colorbar(cax) if colorbar_label: cbar.ax.set_ylabel(colorbar_label) suffix = get_image_suffix(filename) if suffix == '': suffix = '.png' filename = "%s%s" % (filename, suffix) mylog.info("Saving plot %s", filename) if suffix == ".png": canvas = FigureCanvasAgg(fig) elif suffix == ".pdf": canvas = FigureCanvasPdf(fig) elif suffix in (".eps", ".ps"): canvas = FigureCanvasPS(fig) else: mylog.warning("Unknown suffix %s, defaulting to Agg", suffix) canvas = FigureCanvasAgg(fig) fig.tight_layout() canvas.print_figure(filename, dpi=dpi) return filename
[docs]def display_in_notebook(image, max_val=None): """ A helper function to display images in an IPython notebook Must be run from within an IPython notebook, or else it will raise a YTNotInsideNotebook exception. Parameters ---------- image : array_like This is an (unscaled) array of floating point values, shape (N,N,3) or (N,N,4) to display in the notebook. The first three channels will be scaled automatically. max_val : float, optional The upper limit to clip values of the image. Only applies to the first three channels. """ if "__IPYTHON__" in dir(builtins): from IPython.core.displaypub import publish_display_data data = write_bitmap(image, None, max_val=max_val) publish_display_data( data={'image/png': data}, source='yt.visualization.image_writer.display_in_notebook', ) else: raise YTNotInsideNotebook