How to use trollimage - 10 common examples

for i in range(int(num)):
            cmap.append((i / num, (data[i, 0] / 255., data[i, 1] / 255.,
                                   data[i, 2] / 255.)))
        return Colormap(*cmap)

    colors = palette.get('colors', None)
    if isinstance(colors, (tuple, list)):
        cmap = []
        values = palette.get('values', None)
        for idx, color in enumerate(colors):
            if values is not None:
                value = values[idx]
            else:
                value = idx / float(len(colors) - 1)
            cmap.append((value, tuple(color)))
        return Colormap(*cmap)

    if isinstance(colors, str):
        from trollimage import colormap
        import copy
        return copy.copy(getattr(colormap, colors))

    return None

from trollimage.colormap import Colormap
    ct = band.GetRasterColorTable()
    data = band.ReadAsArray()
    max_val = np.iinfo(data.dtype).max
    # if we have an alpha band then include the entire colormap
    # otherwise assume it is using 0 as a fill value
    start_idx = 1 if band_count == 1 else 0
    if ct is None:
        # NOTE: the comma is needed to make this a tuple
        color_iter = (
            (idx / float(max_val), (int(idx / float(max_val)),) * 3 + (1.0,)) for idx in range(max_val))
    else:
        color_iter = ((idx / float(max_val), ct.GetColorEntry(idx))
                      for idx in range(start_idx, ct.GetCount()))
        color_iter = ((idx, tuple(x / float(max_val) for x in color)) for idx, color in color_iter)
    cmap = Colormap(*color_iter)
    return cmap

# You should have received a copy of the GNU General Public License
# along with this program.  If not, see .

"""This script adds synope data for visibility to given geolocated image"""

import fogpy
import numpy as np
import os
from .import_synop import read_synop
from datetime import datetime
from trollimage.image import Image
from trollimage.colormap import Colormap


# Define custom fog colormap
fogcol = Colormap((0., (250 / 255.0, 200 / 255.0, 40 / 255.0)),
                  (1., (1.0, 1.0, 229 / 255.0)))

maskcol = Colormap((1., (250 / 255.0, 200 / 255.0, 40 / 255.0)))

viscol = Colormap((0., (1.0, 0.0, 0.0)),
                  (5000, (0.7, 0.7, 0.7)))
# Red - Violet - Blue - Green
vis_colset = Colormap((0, (228 / 255.0, 26 / 255.0, 28 / 255.0)),
                      (1000, (152 / 255.0, 78 / 255.0, 163 / 255.0)),
                      (5000, (55 / 255.0, 126 / 255.0, 184 / 255.0)),
                      (10000, (77 / 255.0, 175 / 255.0, 74 / 255.0)))


def add_to_array(arr, area, time, bufr, savedir='/tmp', name=None, mode='L',
                 resize=None, ptsize=None, save=False):
    """Add synoptical reports from stations to provided geolocated image array

"""This script adds synope data for visibility to given geolocated image"""

import fogpy
import numpy as np
import os
from .import_synop import read_synop
from datetime import datetime
from trollimage.image import Image
from trollimage.colormap import Colormap


# Define custom fog colormap
fogcol = Colormap((0., (250 / 255.0, 200 / 255.0, 40 / 255.0)),
                  (1., (1.0, 1.0, 229 / 255.0)))

maskcol = Colormap((1., (250 / 255.0, 200 / 255.0, 40 / 255.0)))

viscol = Colormap((0., (1.0, 0.0, 0.0)),
                  (5000, (0.7, 0.7, 0.7)))
# Red - Violet - Blue - Green
vis_colset = Colormap((0, (228 / 255.0, 26 / 255.0, 28 / 255.0)),
                      (1000, (152 / 255.0, 78 / 255.0, 163 / 255.0)),
                      (5000, (55 / 255.0, 126 / 255.0, 184 / 255.0)),
                      (10000, (77 / 255.0, 175 / 255.0, 74 / 255.0)))


def add_to_array(arr, area, time, bufr, savedir='/tmp', name=None, mode='L',
                 resize=None, ptsize=None, save=False):
    """Add synoptical reports from stations to provided geolocated image array
    """
    # Create array image
    arrshp = arr.shape[:2]

bg_img.stretch("crude")
        bg_img.convert("RGB")
#         bg_img.invert()
        image.merge(bg_img)
    # Import bufr
    stations = read_synop(bufr, 'visibility')
    currentstations = stations[time.strftime("%Y%m%d%H0000")]
    lats = [i[2] for i in currentstations]
    lons = [i[3] for i in currentstations]
    vis = [i[4] for i in currentstations]

    # Create array for synop parameter
    visarr = np.empty(arrshp)
    visarr.fill(np.nan)
    # Red - Violet - Blue - Green
    vis_colset = Colormap((0, (228 / 255.0, 26 / 255.0, 28 / 255.0)),
                          (1000, (152 / 255.0, 78 / 255.0, 163 / 255.0)),
                          (5000, (55 / 255.0, 126 / 255.0, 184 / 255.0)),
                          (10000, (77 / 255.0, 175 / 255.0, 74 / 255.0)))
    x, y = (area.get_xy_from_lonlat(lons, lats))
    vis_ma = np.ma.array(vis, mask=x.mask)
    if ptsize:
        xpt = np.array([])
        ypt = np.array([])
        for i, j in zip(x, y):
            xmesh, ymesh = np.meshgrid(np.linspace(i - ptsize, i + ptsize,
                                                   ptsize * 2 + 1),
                                       np.linspace(j - ptsize, j + ptsize,
                                                   ptsize * 2 + 1))
            xpt = np.append(xpt, xmesh.ravel())
            ypt = np.append(ypt, ymesh.ravel())
        vispt = np.ma.array([np.full(((ptsize * 2 + 1,

def total_precipitable_water(img, **kwargs):
    """Palettizes images from VIIRS flood data.

    This modifies the image's data so the correct colors
    can be applied to it, and then palettizes the image.
    """
    palette = kwargs['palettes']
    palette['colors'] = tuple(map(tuple, palette['colors']))

    cm = Colormap(*palette['colors'])
    img.palettize(cm)

area=None):
        """Plotting function for additional filter attributes.

        .. Note:: Masks should be correctly setup to be plotted:
                  **True** (1) mask values are not shown, **False** (0) mask
                  values are displayed.
        """
        raise NotImplementedError(
                "plotting is disabled until fogpy supports dask, "
                "see https://github.com/gerritholl/fogpy/issues/6")
        from trollimage.image import Image
        from trollimage.colormap import Colormap
        # Define custom fog colormap
        fogcol = Colormap((0., (250 / 255.0, 200 / 255.0, 40 / 255.0)),
                          (1., (1.0, 1.0, 229 / 255.0)))
        maskcol = Colormap((1., (250 / 255.0, 200 / 255.0, 40 / 255.0)))
        # Get save directory
        attrdir = os.path.join(dir, self.name + '_' + name + '_' +
                               datetime.strftime(self.time,
                                                 '%Y%m%d%H%M') + '.' + type)
        logger.info("Plotting filter attribute {} to {}".format(name, attrdir))
        # Generate image
        attr = getattr(self, name)
        if attr.dtype == 'bool':
            attr = np.ma.masked_where(attr == 1, attr)
        attr_img = Image(attr.squeeze(), mode='L', fill_value=None)
        attr_img.colorize(fogcol)
        # Get background image
        bg_img = Image(self.bg_img.squeeze(), mode='L', fill_value=None)
        bg_img.stretch("crude")
        bg_img.convert("RGB")
        bg_img.invert()

def build_colormap(palette, info):
        """Create the colormap from the `raw_palette` and the valid_range."""

        from trollimage.colormap import Colormap
        if 'palette_meanings' in palette.attrs:
            palette_indices = palette.attrs['palette_meanings']
        else:
            palette_indices = range(len(palette))

        sqpalette = np.asanyarray(palette).squeeze() / 255.0
        tups = [(val, tuple(tup))
                for (val, tup) in zip(palette_indices, sqpalette)]
        colormap = Colormap(*tups)
        if 'palette_meanings' not in palette.attrs:
            sf = info.get('scale_factor', np.array(1))
            colormap.set_range(
                *(np.array(info['valid_range']) * sf + info.get('add_offset', 0)))

        return colormap, sqpalette

def _merge_colormaps(kwargs):
    """Merge colormaps listed in kwargs."""
    from trollimage.colormap import Colormap
    full_cmap = None

    palette = kwargs['palettes']
    if isinstance(palette, Colormap):
        full_cmap = palette
    else:
        for itm in palette:
            cmap = create_colormap(itm)
            cmap.set_range(itm["min_value"], itm["max_value"])
            if full_cmap is None:
                full_cmap = cmap
            else:
                full_cmap = full_cmap + cmap

    return full_cmap

height = np.ma.masked_where(height <= 0, height, copy=False) * 1000.

    if False:
        from trollimage.image import Image as trollimage
        from trollimage.colormap import rainbow
        from copy import deepcopy 
        # cloud top height
        prop = height
        min_data = prop.min()
        max_data = prop.max()
        print " estimated CTH(meter) (min/max): ", min_data, max_data
        min_data =     0
        max_data = 12000    
        colormap = deepcopy(rainbow)
        colormap.set_range(min_data, max_data)
        img = trollimage(prop, mode="L") #, fill_value=[0,0,0]
        img.colorize(colormap)
        img.show()

    # return cloud top height in meter
    return height