"""
Register custom color maps.
Custom color maps for standard meteorological variables with the necessary
bounds, ticks, and labels for building the colorbar.
National Weather Service standard color maps:
- Source: Joseph Moore <joseph.moore@noaa.gov>
- Document: references/NWS Standard Color Curve Summary.pdf
"""
try:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
except ImportError:
raise ImportError("herbie.paint requires matplotlib.")
def make_custom_cmaps(name, colors, bounds: list = None, N: int = None):
if N is None:
N = len(colors)
linear_cmap = mcolors.LinearSegmentedColormap.from_list(name, colors)
segment_cmap = mcolors.LinearSegmentedColormap.from_list(name + "2", colors, N=N)
# When data is NaN, set color to transparent
linear_cmap.set_bad("#ffffff00")
segment_cmap.set_bad("#ffffff00")
for cm in [linear_cmap, segment_cmap]:
mpl.colormaps.register(cmap=cm, force=True)
mpl.colormaps.register(cmap=cm.reversed(), force=True)
if bounds is not None:
return (
mcolors.Normalize(bounds.min(), bounds.max()),
mcolors.BoundaryNorm(bounds, linear_cmap.N),
)
# ======================================================================
# National Weather Service proposed standard color curves (2018)
# ======================================================================
[docs]
class NWSTemperature:
name = "nws.tmp"
units = r"$\degree$C"
variable = "Temperature"
colors = np.array(
[
"#91003f",
"#ce1256",
"#e7298a",
"#df65b0",
"#ff73df",
"#ffbee8",
"#ffffff",
"#dadaeb",
"#bcbddc",
"#9e9ac8",
"#756bb1",
"#54278f",
"#0d007d",
"#0d3d9c",
"#0066c2",
"#299eff",
"#4ac7ff",
"#73d7ff",
"#adffff",
"#30cfc2",
"#009996",
"#125757",
"#066d2c",
"#31a354",
"#74c476",
"#a1d99b",
"#d3ffbe",
"#ffffb3",
"#ffeda0",
"#fed176",
"#feae2a",
"#fd8d3c",
"#fc4e2a",
"#e31a1c",
"#b10026",
"#800026",
"#590042",
"#280028",
]
)
# NWS bounds in Fahrenheit
bounds = np.linspace(-65, 125, len(colors) + 1)
# Convert to Celsius (approximate)
bounds = (bounds - 30) / 2
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds[1::2])
[docs]
class NWSWindChill:
"""National Weather Service Wind Chill colormap properties.
Identical to NWSHeatIndex.
"""
name = "nws.wind_chill"
units = r"$\degree$C"
variable = "Wind Chill"
colors = np.array(
[
"#91003f",
"#ce1256",
"#e7298a",
"#df65b0",
"#ff73df",
"#ffbee8",
"#ffffff",
"#dadaeb",
"#bcbddc",
"#9e9ac8",
"#756bb1",
"#54278f",
"#0d007d",
"#0d3d9c",
"#0066c2",
"#299eff",
"#4ac7ff",
"#73d7ff",
"#adffff",
"#30cfc2",
"#009996",
"#dcdcdc", # grey
"#dcdcdc", # grey
"#dcdcdc", # grey
"#dcdcdc", # grey
"#dcdcdc", # grey
"#dcdcdc", # grey
"#dcdcdc", # grey
"#ffeda0",
"#fed176",
"#feae2a",
"#fd8d3c",
"#fc4e2a",
"#e31a1c",
"#b10026",
"#800026",
"#590042",
"#280028",
]
)
# NWS bounds in Fahrenheit
bounds = np.linspace(-65, 125, len(colors) + 1)
# Convert to Celsius (approximate)
bounds = (bounds - 30) / 2
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds[1::2])
[docs]
class NWSHeatIndex:
"""Identical to NWSWindChill."""
name = "nws.heat_index"
units = r"$\degree$C"
variable = "Heat Index"
colors = np.array(
[
"#91003f",
"#ce1256",
"#e7298a",
"#df65b0",
"#ff73df",
"#ffbee8",
"#ffffff",
"#dadaeb",
"#bcbddc",
"#9e9ac8",
"#756bb1",
"#54278f",
"#0d007d",
"#0d3d9c",
"#0066c2",
"#299eff",
"#4ac7ff",
"#73d7ff",
"#adffff",
"#30cfc2",
"#009996",
"#dcdcdc", # grey
"#dcdcdc", # grey
"#dcdcdc", # grey
"#dcdcdc", # grey
"#dcdcdc", # grey
"#dcdcdc", # grey
"#dcdcdc", # grey
"#ffeda0",
"#fed176",
"#feae2a",
"#fd8d3c",
"#fc4e2a",
"#e31a1c",
"#b10026",
"#800026",
"#590042",
"#280028",
]
)
# NWS bounds in Fahrenheit
bounds = np.linspace(-65, 125, len(colors) + 1)
# Convert to Celsius (approximate)
bounds = (bounds - 30) / 2
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds[1::2])
[docs]
class NWSDewPointTemperature:
name = "nws.dpt"
units = r"$\degree$C"
variable = "Dew Point"
colors = np.array(
[
"#3b2204",
"#543005",
"#8c520a",
"#bf812d",
"#cca854",
"#dfc27d",
"#e6d9b5",
"#d3ebe7",
"#a9dbd3",
"#72b8ad",
"#318c85",
"#01665f",
"#003c30",
"#002921",
]
)
# NWS bounds in F
bounds = np.array([-10, 0, 10, 20, 30, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85])
# convert to C (approximate)
bounds = (bounds - 30) / 2
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
[docs]
class NWSRelativeHumidity:
name = "nws.rh"
units = "%"
variable = "Relative Humidity"
colors = np.array(
[
"#910022",
"#a61122",
"#bd2e24",
"#d44e33",
"#e36d42",
"#fa8f43",
"#fcad58",
"#fed884",
"#fff2aa",
"#e6f49d",
"#bce378",
"#71b55c",
"#26914b",
"#00572e",
]
)
bounds = np.array([0, 5, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, 100])
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
[docs]
class NWSWindSpeed:
name = "nws.wind"
units = r"m s$\mathregular{^{-1}}$"
variable = "Wind Speed"
colors = np.array(
[
"#103f78",
"#225ea8",
"#1d91c0",
"#41b6c4",
"#7fcdbb",
"#b4d79e",
"#dfff9e",
"#ffffa6",
"#ffe873",
"#ffc400",
"#ffaa00",
"#ff5900",
"#ff0000",
"#a80000",
"#6e0000",
"#ffbee8",
"#ff73df",
]
)
# MPH
bounds = np.array(
[0.0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 100, 120, 140, 160]
)
# Convert to m/s (approximate)
bounds *= 0.5
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
[docs]
class NWSCloudCover:
name = "nws.cloud"
units = "%"
variable = "Cloud Cover"
colors = np.array(
[
"#24a0f2",
"#4eb0f2",
"#80b7f8",
"#a0c8ff",
"#d2e1ff",
"#e1e1e1",
"#c9c9c9",
"#a5a5a5",
"#6e6e6e",
"#505050",
]
)
bounds = np.linspace(0, 100, len(colors) + 1)
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
[docs]
class NWSProbabilityOfPrecipitation:
name = "nws.pop"
units = "%"
variable = "Probability of Precipitation"
colors = np.array(
[
"#f5f5f5",
"#e2f6da",
"#d5f2ca",
"#c0ebaf",
"#98df7b",
"#6fd349",
"#43c634",
"#23b70b",
"#139e07",
"#0b8403",
]
)
bounds = np.linspace(0, 100, len(colors) + 1)
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
[docs]
class NWSProbabilityOfPrecipitationSnow:
name = "nws.pop_snow"
units = "%"
variable = "Probability of Snow"
colors = np.array(
[
"#f5f5f5",
"#e3ebff",
"#bdd6ff",
"#94b8ff",
"#66a3ff",
"#3690ff",
"#0a7afa",
"#006bd6",
"#004ead",
"#002487",
]
)
bounds = np.linspace(0, 100, len(colors) + 1)
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
[docs]
class NWSProbabilityOfPrecipitationIce:
name = "nws.pop_ice"
units = "%"
variable = "Probability of Ice"
colors = np.array(
[
"#f5f5f5",
"#ffd9ed",
"#ffaafa",
"#ff83f9",
"#ff57f7",
"#ff37f5",
"#e619f9",
"#d500fd",
"#a200ad",
"#640087",
]
)
bounds = np.linspace(0, 100, len(colors) + 1)
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds[1::2])
[docs]
class NWSPrecipitation:
"""National Weather Service precipitation amount colorbar properties.
Also known as Qualitative Precipitation Forecast/Estimate (QPF/QPE).
"""
name = "nws.pcp"
units = "mm"
variable = "Precipitation"
colors = np.array(
[
"#ffffff",
"#c7e9c0",
"#a1d99b",
"#74c476",
"#31a353",
"#006d2c",
"#fffa8a",
"#ffcc4f",
"#fe8d3c",
"#fc4e2a",
"#d61a1c",
"#ad0026",
"#700026",
"#3b0030",
"#4c0073",
"#ffdbff",
]
)
# NWS bounds in inches
bounds = np.array(
[0, 0.01, 0.1, 0.25, 0.5, 1, 1.5, 2, 3, 4, 6, 8, 10, 15, 20, 30, 50]
)
# Convert to mm (approximate)
bounds *= 25
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="uniform", ticks=bounds)
[docs]
class NWSPrecipitationSnow:
name = "nws.pcp_snow"
units = "mm"
variable = "Snow"
colors = np.array(
[
"#ffffff",
"#bdd7e7",
"#6baed6",
"#3182bd",
"#08519c",
"#082694",
"#ffff96",
"#ffc400",
"#ff8700",
"#db1400",
"#9e0000",
"#690000",
"#360000",
]
)
# NWS bounds in inches
bounds = np.array([0, 0.1, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36, 42])
# Convert to mm (approximate)
bounds *= 25
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="uniform", ticks=bounds)
[docs]
class NWSPrecipitationIce:
name = "nws.pcp_ice"
units = "mm"
variable = "Ice"
colors = np.array(
[
"#f3ea3b",
"#ffc000",
"#ff0000",
"#c00000",
"#9966ff",
"#720ac8",
"#24055b",
]
)
# NWS bounds in inches
bounds = np.array([0.01, 0.1, 0.25, 0.5, 0.75, 1.0, 2.0, 3.0])
# convert to mm (approximate)
bounds *= 25
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="uniform", ticks=bounds)
[docs]
class NWSWaveHeight:
name = "nws.wave"
units = "m"
variable = "Wave Height"
colors = np.array(
[
"#ebfdff",
"#abedf5",
"#78cdd6",
"#4bb8c4",
"#55b59f",
"#86d483",
"#b0e890",
"#ddff99",
"#fed976",
"#feb24c",
"#fd8d3c",
"#fc4e2a",
"#e31a1c",
"#bd0026",
"#800026",
"#5c002f",
"#330023",
]
)
# NWS bounds in feet
bounds = np.array(
[0.0, 1, 2, 3, 4, 5, 7, 10, 12, 15, 20, 25, 30, 35, 40, 50, 60, 70], dtype=float
)
# convert to meters (approximate)
bounds *= 0.3
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
# ======================================================================
# NCAR
# ======================================================================
class NCARProbability:
"""Ensemble probability.
Based on the NCAR Ensemble probability colorscale.
https://www2.mmm.ucar.edu/projects/ncar_ensemble/legacy/
"""
name = "ncar.probability"
units = "%"
variable = "Probability"
colors = np.array(
[
"#ffffff",
"#d7e3ee",
"#b5caff",
"#8fb3ff",
"#7f97ff",
"#abcf63",
"#e8f59e",
"#fffa14",
"#ffd121",
"#ffa30a",
"#ff4c00",
]
)
bounds = np.linspace(0, 100, len(colors) + 1)
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
# ======================================================================
# Air Quality Index
# ======================================================================
class AQIPm25:
"""Colormap for PM2.5 air quality index.
Based on AirNow `Air Quality Index <https://www.airnow.gov/aqi/aqi-basics/>`_
and `Utah Division of Air Quality AQI <https://air.utah.gov/currentconditions.php>`_.
"""
name = "aqi.pm25"
units = r"$\mu$g m$\mathregular{^{-3}}$"
variable = "Particular Mater"
colors = np.array(
[
"#00e400", # Green - Good
"#ffff00", # Yellow - Moderate
"#ff7e00", # Orange - Sensitive Groups
"#ff0000", # Red - Unhealthy
"#99004c", # Purple - Hazardous
"#4c0026", # Maroon - Very Unhealthy
]
)
bounds = np.array([0, 12.1, 35.5, 55.5, 150.5, 250.5, 300])
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
class AQIOzone:
"""Colormap for ozone air quality index.
Based on AirNow `Air Quality Index <https://www.airnow.gov/aqi/aqi-basics/>`_
and `Utah Division of Air Quality AQI <https://air.utah.gov/currentconditions.php>`_.
"""
name = "aqi.ozone"
units = "ppb"
variable = "Ozone"
colors = np.array(
[
"#00e400", # Green - Good
"#ffff00", # Yellow - Moderate
"#ff7e00", # Orange - Sensitive Groups
"#ff0000", # Red - Unhealthy
"#99004c", # Purple - Hazardous
"#4c0026", # Maroon - Very Unhealthy
]
)
bounds = np.array([0, 55, 71, 86, 106, 201, 300])
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
# ======================================================================
# Radar Colormaps
# ======================================================================
# I can't remember where I go these, but I didn't make them myself.
# Maybe I bsed these on PyArt?? I can't remember.
class RadarReflectivity:
name = "radar.reflectivity"
units = "dBZ"
variable = "Reflectivity"
colors = np.array(
[
"#00ecec",
"#01b5f3",
"#0021f6",
"#00de20",
"#00cb00",
"#079300",
"#fdf900",
"#ebb700",
"#fd9500",
"#ff0400",
"#d50000",
"#c80021",
"#ea11f4",
"#6e3d90",
"#000000",
]
)
# NWS bounds in feet
bounds = np.linspace(0, 80, len(colors) + 1)
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
class RadarRadialVelocity:
name = "radar.velocity"
units = r"m s$\mathregular{^{-1}}$"
variable = "Radial Velocity"
colors = np.array(
[
"#90009f",
"#29b72d",
"#00ed00",
"#00cc00",
"#00b100",
"#008f00",
"#0c740c",
"#7d9177",
"#947a77",
"#810303",
"#a10000",
"#bc0000",
"#dd0000",
"#f30000",
"#ff0000",
]
)
# NWS bounds in feet
bounds = np.linspace(0, 80, len(colors) + 1)
norm, norm2 = make_custom_cmaps(name, colors, bounds)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
kwargs = dict(cmap=cmap, norm=norm)
kwargs2 = dict(cmap=cmap, norm=norm2)
cbar_kwargs = dict(label=f"{variable} ({units})")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
# ======================================================================
# Terrain height and land cover
# ======================================================================
[docs]
class LandTan:
name = "land.tan"
cmap = mcolors.LinearSegmentedColormap.from_list(
name,
[
(0, "#f8b893"),
(0.4, "#c0784f"),
(0.6, "#97674c"),
(0.85, "#6b3d22"),
(1, "#dadada"),
],
)
cmap.set_bad("#97b6e1")
plt.colormaps.register(cmap, force=True)
[docs]
class LandBrown:
name = "land.brown"
cmap = mcolors.LinearSegmentedColormap.from_list(
name,
[
(0, "#ffad7d"),
(0.5, "#b46f46"),
(1, "#6b3d22"),
],
)
cmap.set_bad("#97b6e1")
plt.colormaps.register(cmap, force=True)
[docs]
class LandGreen:
name = "land.green"
cmap = mcolors.LinearSegmentedColormap.from_list(
name,
[
(0.0, "yellowgreen"),
(0.03, "darkgreen"),
(0.08, "forestgreen"),
(0.45, "wheat"),
(0.60, "tan"),
(0.95, "sienna"),
(1.00, "snow"),
],
)
cmap.set_bad("#97b6e1")
plt.colormaps.register(cmap, force=True)
[docs]
class Water:
name = "water"
cmap = mcolors.LinearSegmentedColormap.from_list(
name,
[
(0.0, "mediumblue"),
(0.8, "deepskyblue"),
(1, "#97b6e1"),
],
)
plt.colormaps.register(cmap, force=True)
plt.colormaps.register(cmap.reversed(), force=True)
class MODIS21:
name = "modis21"
landuse = {
1: {"color": "#1a5e1a", "label": "Evergreen Needleleaf Forest"},
2: {"color": "#2d7a3a", "label": "Evergreen Broadleaf Forest"},
3: {"color": "#6aaa4e", "label": "Deciduous Needleleaf Forest"},
4: {"color": "#8db85a", "label": "Deciduous Broadleaf Forest"},
5: {"color": "#4e8c3a", "label": "Mixed Forests"},
6: {"color": "#b5874a", "label": "Closed Shrublands"},
7: {"color": "#cca96e", "label": "Open Shrublands"},
8: {"color": "#c4b37a", "label": "Woody Savannas"},
9: {"color": "#d9c46a", "label": "Savannas"},
10: {"color": "#b8d48a", "label": "Grasslands"},
11: {"color": "#5ba8a0", "label": "Permanent Wetlands"},
12: {"color": "#e8d48a", "label": "Croplands"},
13: {"color": "#c1553a", "label": "Urban and Built-Up"},
14: {"color": "#d4c47a", "label": "Cropland/Natural Vegetation Mosaic"},
15: {"color": "#e8e8e8", "label": "Snow and Ice"},
16: {"color": "#c8b89a", "label": "Barren or Sparsely Vegetated"},
17: {"color": "#4a7ab5", "label": "Water"},
18: {"color": "#7a9e6e", "label": "Wooded Tundra"},
19: {"color": "#a89e7a", "label": "Mixed Tundra"},
20: {"color": "#c8bea8", "label": "Barren Tundra"},
21: {"color": "#2362af", "label": "Lake"},
}
colors = np.array([v["color"] for k, v in landuse.items()])
bounds = np.array(list(landuse) + [22])
labels = np.array([f"{k} " + v["label"] for k, v in landuse.items()])
cmap = mcolors.LinearSegmentedColormap.from_list(name, colors, N=len(bounds) - 1)
norm = mcolors.BoundaryNorm(boundaries=bounds, ncolors=len(bounds))
plt.colormaps.register(cmap, force=True)
cmap = plt.get_cmap(name)
kwargs = dict(cmap=cmap, norm=norm)
cbar_kwargs = dict(label="MODIS Landuse")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
class USGS24:
"""Colormap for 24-category USGS Landuse."""
name = "usgs24"
landuse = {
1: {"color": "#c1553a", "label": "Urban and Built-up Land"},
2: {"color": "#e8d48a", "label": "Dryland Cropland and Pasture"},
3: {"color": "#eddc9f", "label": "Irrigated Cropland and Pasture"},
4: {
"color": "#d9c97f",
"label": "Mixed Dryland/Irrigated Cropland and Pasture",
},
5: {"color": "#d4c47a", "label": "Cropland/Grassland Mosaic"},
6: {"color": "#cdbb6c", "label": "Cropland/Woodland Mosaic"},
7: {"color": "#b8d48a", "label": "Grassland"},
8: {"color": "#cca96e", "label": "Shrubland"},
9: {"color": "#e0cc7f", "label": "Mixed Shrubland/Grassland"},
10: {"color": "#d9c46a", "label": "Savanna"},
11: {"color": "#8db85a", "label": "Deciduous Broadleaf Forest"},
12: {"color": "#6aaa4e", "label": "Deciduous Needleleaf Forest"},
13: {"color": "#2d7a3a", "label": "Evergreen Broadleaf Forest"},
14: {"color": "#1a5e1a", "label": "Evergreen Needleleaf Forest"},
15: {"color": "#4e8c3a", "label": "Mixed Forests"},
16: {"color": "#4a7ab5", "label": "Water Bodies"},
17: {"color": "#5ba8a0", "label": "Herbaceous Wetlands"},
18: {"color": "#6fb8af", "label": "Wooden Wetlands"},
19: {"color": "#c8b89a", "label": "Barren or Sparsely Vegetated"},
20: {"color": "#b5ab85", "label": "Herbaceous Tundraa"},
21: {"color": "#7a9e6e", "label": "Wooded Tundra"},
22: {"color": "#9c9270", "label": "Mixed Tundra"},
23: {"color": "#c8bea8", "label": "Barren Tundra"},
24: {"color": "#e8e8e8", "label": "Snow and Ice"},
}
colors = np.array([v["color"] for k, v in landuse.items()])
bounds = np.array(list(landuse) + [22])
labels = np.array([f"{k} " + v["label"] for k, v in landuse.items()])
cmap = mcolors.LinearSegmentedColormap.from_list(name, colors, N=len(bounds) - 1)
norm = mcolors.BoundaryNorm(boundaries=bounds, ncolors=len(bounds))
plt.colormaps.register(cmap, force=True)
cmap = plt.get_cmap(name)
kwargs = dict(cmap=cmap, norm=norm)
cbar_kwargs = dict(label="USGS Landuse")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
class SOIL16:
"""Colormap for 16-category Soil Dataset."""
name = "soil16"
landuse = {
1: {"color": "#d4b483", "label": "Sand"},
2: {"color": "#c8a46e", "label": "Loamy Sand"},
3: {"color": "#b89458", "label": "Sandy Loam"},
4: {"color": "#a07840", "label": "Silt Loam"},
5: {"color": "#c8b89a", "label": "Silt"},
6: {"color": "#8c6430", "label": "Loam"},
7: {"color": "#7a5020", "label": "Sandy Clay Loam"},
8: {"color": "#6e4818", "label": "Silty Clay Loam"},
9: {"color": "#603c10", "label": "Clay Loam"},
10: {"color": "#543008", "label": "Sandy Clay"},
11: {"color": "#482800", "label": "Silty Clay"},
12: {"color": "#3c2000", "label": "Clay"},
13: {"color": "#5ba8a0", "label": "Organic Material"},
14: {"color": "#4a7ab5", "label": "Water"},
15: {"color": "#e8e8e8", "label": "Bedrock"},
16: {"color": "#c8b89a", "label": "Other/Ice"},
}
colors = np.array([v["color"] for k, v in landuse.items()])
bounds = np.array(list(landuse) + [22])
labels = np.array([f"{k} " + v["label"] for k, v in landuse.items()])
cmap = mcolors.LinearSegmentedColormap.from_list(name, colors, N=len(bounds) - 1)
norm = mcolors.BoundaryNorm(boundaries=bounds, ncolors=len(bounds))
plt.colormaps.register(cmap, force=True)
cmap = plt.get_cmap(name)
kwargs = dict(cmap=cmap, norm=norm)
cbar_kwargs = dict(label="Soild Categories")
cbar_kwargs2 = cbar_kwargs | dict(spacing="proportional", ticks=bounds)
# ======================================================================
# Other
# ======================================================================
red = "#88211b"
blue = "#0c3576"
tan = "#f0ead2"
white = "#ffffff"
black = "#111111"
[docs]
class HerbieWhite:
name = "herbie.white"
colors = [blue, white, red]
make_custom_cmaps(name, colors, N=7)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
[docs]
class HerbieTan:
name = "herbie.tan"
colors = [blue, tan, red]
make_custom_cmaps(name, colors, N=7)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
[docs]
class HerbieBlack:
name = "herbie.black"
colors = [blue, black, red]
make_custom_cmaps(name, colors, N=7)
cmap = plt.get_cmap(name)
cmap2 = plt.get_cmap(name + "2")
