Python matplotlib.colors.from_levels_and_colors() Examples
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Example #1
Source File: colors.py From pseudonetcdf with GNU Lesser General Public License v3.0 | 7 votes |
def _addusepaaqi(): aqicolors = ['green', 'yellow', 'orange', 'red', 'purple', 'maroon'] aqic = aqicolors bounds_colors = {} bounds_colors[('O3', 'mda8', 'ppbv')] = ([0, 55, 71, 86, 106, 200], aqic[:5] + ['black'], 'max') bounds_colors[('O3', 'mda8', 'ppmv')] = ([0, 0.055, 0.071, 0.086, 0.106, 0.0200], aqic[:5] + ['black'], 'max') bounds_colors[('O3', 'mda1', 'ppbv')] = ([125, 165, 205, 405, 604], ['white'] + aqic[2:] + ['black'], 'both') pmb = [0, 12.1, 35.5, 55.5, 150.5, 250.5, 500.4] pmc = aqic + ['black'] bounds_colors[('PM25', 'a24', 'micrograms/m**3')] = (pmb, pmc, 'max') for key, (bnds, colors, extend) in bounds_colors.items(): print(key) name = '_'.join(('usepa', 'aqi',) + key) cmap, norm = from_levels_and_colors(bnds, colors, extend=extend) register_cmap(name=name, cmap=cmap) register_norm(name=name, norm=norm)
Example #2
Source File: test_colors.py From neural-network-animation with MIT License | 5 votes |
def test_cmap_and_norm_from_levels_and_colors(): data = np.linspace(-2, 4, 49).reshape(7, 7) levels = [-1, 2, 2.5, 3] colors = ['red', 'green', 'blue', 'yellow', 'black'] extend = 'both' cmap, norm = mcolors.from_levels_and_colors(levels, colors, extend=extend) ax = plt.axes() m = plt.pcolormesh(data, cmap=cmap, norm=norm) plt.colorbar(m) # Hide the axes labels (but not the colorbar ones, as they are useful) for lab in ax.get_xticklabels() + ax.get_yticklabels(): lab.set_visible(False)
Example #3
Source File: test_colors.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_cmap_and_norm_from_levels_and_colors(): data = np.linspace(-2, 4, 49).reshape(7, 7) levels = [-1, 2, 2.5, 3] colors = ['red', 'green', 'blue', 'yellow', 'black'] extend = 'both' cmap, norm = mcolors.from_levels_and_colors(levels, colors, extend=extend) ax = plt.axes() m = plt.pcolormesh(data, cmap=cmap, norm=norm) plt.colorbar(m) # Hide the axes labels (but not the colorbar ones, as they are useful) ax.tick_params(labelleft=False, labelbottom=False)
Example #4
Source File: test_colors.py From ImageFusion with MIT License | 5 votes |
def test_cmap_and_norm_from_levels_and_colors(): data = np.linspace(-2, 4, 49).reshape(7, 7) levels = [-1, 2, 2.5, 3] colors = ['red', 'green', 'blue', 'yellow', 'black'] extend = 'both' cmap, norm = mcolors.from_levels_and_colors(levels, colors, extend=extend) ax = plt.axes() m = plt.pcolormesh(data, cmap=cmap, norm=norm) plt.colorbar(m) # Hide the axes labels (but not the colorbar ones, as they are useful) for lab in ax.get_xticklabels() + ax.get_yticklabels(): lab.set_visible(False)
Example #5
Source File: colorbar.py From marvin with BSD 3-Clause "New" or "Revised" License | 5 votes |
def _one_color_cmap(color): """Generate a colormap with only one color. Useful for imshow. Parameters: color (str): Color. Returns: `matplotlib.cm <http://matplotlib.org/api/cm_api.html>`_ (colormap) object """ cmap, ig = from_levels_and_colors(levels=(0, 1), colors=(color,)) return cmap
Example #6
Source File: test_colors.py From coffeegrindsize with MIT License | 5 votes |
def test_cmap_and_norm_from_levels_and_colors(): data = np.linspace(-2, 4, 49).reshape(7, 7) levels = [-1, 2, 2.5, 3] colors = ['red', 'green', 'blue', 'yellow', 'black'] extend = 'both' cmap, norm = mcolors.from_levels_and_colors(levels, colors, extend=extend) ax = plt.axes() m = plt.pcolormesh(data, cmap=cmap, norm=norm) plt.colorbar(m) # Hide the axes labels (but not the colorbar ones, as they are useful) ax.tick_params(labelleft=False, labelbottom=False)
Example #7
Source File: test_colors.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_cmap_and_norm_from_levels_and_colors(): data = np.linspace(-2, 4, 49).reshape(7, 7) levels = [-1, 2, 2.5, 3] colors = ['red', 'green', 'blue', 'yellow', 'black'] extend = 'both' cmap, norm = mcolors.from_levels_and_colors(levels, colors, extend=extend) ax = plt.axes() m = plt.pcolormesh(data, cmap=cmap, norm=norm) plt.colorbar(m) # Hide the axes labels (but not the colorbar ones, as they are useful) ax.tick_params(labelleft=False, labelbottom=False)
Example #8
Source File: test_colors.py From neural-network-animation with MIT License | 4 votes |
def test_cmap_and_norm_from_levels_and_colors2(): levels = [-1, 2, 2.5, 3] colors = ['red', (0, 1, 0), 'blue', (0.5, 0.5, 0.5), (0.0, 0.0, 0.0, 1.0)] clr = mcolors.colorConverter.to_rgba_array(colors) bad = (0.1, 0.1, 0.1, 0.1) no_color = (0.0, 0.0, 0.0, 0.0) masked_value = 'masked_value' # Define the test values which are of interest. # Note: levels are lev[i] <= v < lev[i+1] tests = [('both', None, {-2: clr[0], -1: clr[1], 2: clr[2], 2.25: clr[2], 3: clr[4], 3.5: clr[4], masked_value: bad}), ('min', -1, {-2: clr[0], -1: clr[1], 2: clr[2], 2.25: clr[2], 3: no_color, 3.5: no_color, masked_value: bad}), ('max', -1, {-2: no_color, -1: clr[0], 2: clr[1], 2.25: clr[1], 3: clr[3], 3.5: clr[3], masked_value: bad}), ('neither', -2, {-2: no_color, -1: clr[0], 2: clr[1], 2.25: clr[1], 3: no_color, 3.5: no_color, masked_value: bad}), ] for extend, i1, cases in tests: cmap, norm = mcolors.from_levels_and_colors(levels, colors[0:i1], extend=extend) cmap.set_bad(bad) for d_val, expected_color in cases.items(): if d_val == masked_value: d_val = np.ma.array([1], mask=True) else: d_val = [d_val] assert_array_equal(expected_color, cmap(norm(d_val))[0], 'Wih extend={0!r} and data ' 'value={1!r}'.format(extend, d_val)) assert_raises(ValueError, mcolors.from_levels_and_colors, levels, colors)
Example #9
Source File: test_colors.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 4 votes |
def test_cmap_and_norm_from_levels_and_colors2(): levels = [-1, 2, 2.5, 3] colors = ['red', (0, 1, 0), 'blue', (0.5, 0.5, 0.5), (0.0, 0.0, 0.0, 1.0)] clr = mcolors.to_rgba_array(colors) bad = (0.1, 0.1, 0.1, 0.1) no_color = (0.0, 0.0, 0.0, 0.0) masked_value = 'masked_value' # Define the test values which are of interest. # Note: levels are lev[i] <= v < lev[i+1] tests = [('both', None, {-2: clr[0], -1: clr[1], 2: clr[2], 2.25: clr[2], 3: clr[4], 3.5: clr[4], masked_value: bad}), ('min', -1, {-2: clr[0], -1: clr[1], 2: clr[2], 2.25: clr[2], 3: no_color, 3.5: no_color, masked_value: bad}), ('max', -1, {-2: no_color, -1: clr[0], 2: clr[1], 2.25: clr[1], 3: clr[3], 3.5: clr[3], masked_value: bad}), ('neither', -2, {-2: no_color, -1: clr[0], 2: clr[1], 2.25: clr[1], 3: no_color, 3.5: no_color, masked_value: bad}), ] for extend, i1, cases in tests: cmap, norm = mcolors.from_levels_and_colors(levels, colors[0:i1], extend=extend) cmap.set_bad(bad) for d_val, expected_color in cases.items(): if d_val == masked_value: d_val = np.ma.array([1], mask=True) else: d_val = [d_val] assert_array_equal(expected_color, cmap(norm(d_val))[0], 'Wih extend={0!r} and data ' 'value={1!r}'.format(extend, d_val)) with pytest.raises(ValueError): mcolors.from_levels_and_colors(levels, colors)
Example #10
Source File: test_colors.py From ImageFusion with MIT License | 4 votes |
def test_cmap_and_norm_from_levels_and_colors2(): levels = [-1, 2, 2.5, 3] colors = ['red', (0, 1, 0), 'blue', (0.5, 0.5, 0.5), (0.0, 0.0, 0.0, 1.0)] clr = mcolors.colorConverter.to_rgba_array(colors) bad = (0.1, 0.1, 0.1, 0.1) no_color = (0.0, 0.0, 0.0, 0.0) masked_value = 'masked_value' # Define the test values which are of interest. # Note: levels are lev[i] <= v < lev[i+1] tests = [('both', None, {-2: clr[0], -1: clr[1], 2: clr[2], 2.25: clr[2], 3: clr[4], 3.5: clr[4], masked_value: bad}), ('min', -1, {-2: clr[0], -1: clr[1], 2: clr[2], 2.25: clr[2], 3: no_color, 3.5: no_color, masked_value: bad}), ('max', -1, {-2: no_color, -1: clr[0], 2: clr[1], 2.25: clr[1], 3: clr[3], 3.5: clr[3], masked_value: bad}), ('neither', -2, {-2: no_color, -1: clr[0], 2: clr[1], 2.25: clr[1], 3: no_color, 3.5: no_color, masked_value: bad}), ] for extend, i1, cases in tests: cmap, norm = mcolors.from_levels_and_colors(levels, colors[0:i1], extend=extend) cmap.set_bad(bad) for d_val, expected_color in cases.items(): if d_val == masked_value: d_val = np.ma.array([1], mask=True) else: d_val = [d_val] assert_array_equal(expected_color, cmap(norm(d_val))[0], 'Wih extend={0!r} and data ' 'value={1!r}'.format(extend, d_val)) assert_raises(ValueError, mcolors.from_levels_and_colors, levels, colors)
Example #11
Source File: test_colors.py From coffeegrindsize with MIT License | 4 votes |
def test_cmap_and_norm_from_levels_and_colors2(): levels = [-1, 2, 2.5, 3] colors = ['red', (0, 1, 0), 'blue', (0.5, 0.5, 0.5), (0.0, 0.0, 0.0, 1.0)] clr = mcolors.to_rgba_array(colors) bad = (0.1, 0.1, 0.1, 0.1) no_color = (0.0, 0.0, 0.0, 0.0) masked_value = 'masked_value' # Define the test values which are of interest. # Note: levels are lev[i] <= v < lev[i+1] tests = [('both', None, {-2: clr[0], -1: clr[1], 2: clr[2], 2.25: clr[2], 3: clr[4], 3.5: clr[4], masked_value: bad}), ('min', -1, {-2: clr[0], -1: clr[1], 2: clr[2], 2.25: clr[2], 3: no_color, 3.5: no_color, masked_value: bad}), ('max', -1, {-2: no_color, -1: clr[0], 2: clr[1], 2.25: clr[1], 3: clr[3], 3.5: clr[3], masked_value: bad}), ('neither', -2, {-2: no_color, -1: clr[0], 2: clr[1], 2.25: clr[1], 3: no_color, 3.5: no_color, masked_value: bad}), ] for extend, i1, cases in tests: cmap, norm = mcolors.from_levels_and_colors(levels, colors[0:i1], extend=extend) cmap.set_bad(bad) for d_val, expected_color in cases.items(): if d_val == masked_value: d_val = np.ma.array([1], mask=True) else: d_val = [d_val] assert_array_equal(expected_color, cmap(norm(d_val))[0], 'Wih extend={0!r} and data ' 'value={1!r}'.format(extend, d_val)) with pytest.raises(ValueError): mcolors.from_levels_and_colors(levels, colors)
Example #12
Source File: test_colors.py From twitter-stock-recommendation with MIT License | 4 votes |
def test_cmap_and_norm_from_levels_and_colors2(): levels = [-1, 2, 2.5, 3] colors = ['red', (0, 1, 0), 'blue', (0.5, 0.5, 0.5), (0.0, 0.0, 0.0, 1.0)] clr = mcolors.to_rgba_array(colors) bad = (0.1, 0.1, 0.1, 0.1) no_color = (0.0, 0.0, 0.0, 0.0) masked_value = 'masked_value' # Define the test values which are of interest. # Note: levels are lev[i] <= v < lev[i+1] tests = [('both', None, {-2: clr[0], -1: clr[1], 2: clr[2], 2.25: clr[2], 3: clr[4], 3.5: clr[4], masked_value: bad}), ('min', -1, {-2: clr[0], -1: clr[1], 2: clr[2], 2.25: clr[2], 3: no_color, 3.5: no_color, masked_value: bad}), ('max', -1, {-2: no_color, -1: clr[0], 2: clr[1], 2.25: clr[1], 3: clr[3], 3.5: clr[3], masked_value: bad}), ('neither', -2, {-2: no_color, -1: clr[0], 2: clr[1], 2.25: clr[1], 3: no_color, 3.5: no_color, masked_value: bad}), ] for extend, i1, cases in tests: cmap, norm = mcolors.from_levels_and_colors(levels, colors[0:i1], extend=extend) cmap.set_bad(bad) for d_val, expected_color in cases.items(): if d_val == masked_value: d_val = np.ma.array([1], mask=True) else: d_val = [d_val] assert_array_equal(expected_color, cmap(norm(d_val))[0], 'Wih extend={0!r} and data ' 'value={1!r}'.format(extend, d_val)) with pytest.raises(ValueError): mcolors.from_levels_and_colors(levels, colors)