Python update Examples

Example #1

File: cm.py Project: btree1970/PriceForecasting

    # Generate the colormap object.
    if 'red' in spec:
        return colors.LinearSegmentedColormap(name, spec, lutsize)
    elif 'listed' in spec:
        return colors.ListedColormap(spec['listed'], name)
    else:
        return colors.LinearSegmentedColormap.from_list(name, spec, lutsize)


LUTSIZE = mpl.rcParams['image.lut']

# Generate the reversed specifications (all at once, to avoid
# modify-when-iterating).
datad.update({
    cmapname + '_r': _reverse_cmap_spec(spec)
    for cmapname, spec in datad.items()
})

# Precache the cmaps with ``lutsize = LUTSIZE``.
# Also add the reversed ones added in the section above:
for cmapname in datad:
    cmap_d[cmapname] = _generate_cmap(cmapname, LUTSIZE)

cmap_d.update(cmaps_listed)

locals().update(cmap_d)

# Continue with definitions ...


def register_cmap(name=None, cmap=None, data=None, lut=None):

Example #2

File: cm.py Project: QuLogic/matplotlib

    spec = datad[name]

    # Generate the colormap object.
    if 'red' in spec:
        return colors.LinearSegmentedColormap(name, spec, lutsize)
    elif 'listed' in spec:
        return colors.ListedColormap(spec['listed'], name)
    else:
        return colors.LinearSegmentedColormap.from_list(name, spec, lutsize)


LUTSIZE = mpl.rcParams['image.lut']

# Generate the reversed specifications (all at once, to avoid
# modify-when-iterating).
datad.update({cmapname + '_r': _reverse_cmap_spec(spec)
              for cmapname, spec in datad.items()})

# Precache the cmaps with ``lutsize = LUTSIZE``.
# Also add the reversed ones added in the section above:
for cmapname in datad:
    cmap_d[cmapname] = _generate_cmap(cmapname, LUTSIZE)

cmap_d.update(cmaps_listed)

locals().update(cmap_d)


# Continue with definitions ...


def register_cmap(name=None, cmap=None, data=None, lut=None):

Example #3

    # Generate the colormap object.
    if 'red' in spec:
        return colors.LinearSegmentedColormap(name, spec, lutsize)
    elif 'listed' in spec:
        return colors.ListedColormap(spec['listed'], name)
    else:
        return colors.LinearSegmentedColormap.from_list(name, spec, lutsize)


LUTSIZE = mpl.rcParams['image.lut']

# Generate the reversed specifications (all at once, to avoid
# modify-when-iterating).
datad.update({
    cmapname + '_r': _reverse_cmap_spec(spec)
    for cmapname, spec in six.iteritems(datad)
})

# Precache the cmaps with ``lutsize = LUTSIZE``.
# Also add the reversed ones added in the section above:
for cmapname in datad:
    cmap_d[cmapname] = _generate_cmap(cmapname, LUTSIZE)

cmap_d.update(cmaps_listed)

locals().update(cmap_d)

# Continue with definitions ...


def register_cmap(name=None, cmap=None, data=None, lut=None):

Example #4

File: cm.py Project: JIE2016G/matplotlib

    # Use superclass method to avoid deprecation warnings during initial load.
    spec = dict.__getitem__(datad, name)

    # Generate the colormap object.
    if 'red' in spec:
        return colors.LinearSegmentedColormap(name, spec, lutsize)
    elif 'listed' in spec:
        return colors.ListedColormap(spec['listed'], name)
    else:
        return colors.LinearSegmentedColormap.from_list(name, spec, lutsize)

LUTSIZE = mpl.rcParams['image.lut']

# Generate the reversed specifications (all at once, to avoid
# modify-when-iterating).
datad.update({cmapname + '_r': _reverse_cmap_spec(spec)
              for cmapname, spec in six.iteritems(datad)})

# Precache the cmaps with ``lutsize = LUTSIZE``.
# Also add the reversed ones added in the section above:
for cmapname in datad:
    cmap_d[cmapname] = _generate_cmap(cmapname, LUTSIZE)

cmap_d.update(cmaps_listed)

locals().update(cmap_d)


# Continue with definitions ...


def register_cmap(name=None, cmap=None, data=None, lut=None):