147 chart array list

Array and List Charts

Uncomment the following line to install geemap if needed.

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# %pip install -U geemap

# %pip install -U geemap

Import libraries¶

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import ee
import geemap
from geemap import chart

import ee import geemap from geemap import chart

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geemap.ee_initialize()

geemap.ee_initialize()

Scatter plot¶

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# Import the example feature collection and subset the forest feature.
forest = ee.FeatureCollection("projects/google/charts_feature_example").filter(
    ee.Filter.eq("label", "Forest")
)

# Define a MODIS surface reflectance composite.
modisSr = (
    ee.ImageCollection("MODIS/061/MOD09A1")
    .filter(ee.Filter.date("2018-06-01", "2018-09-01"))
    .select("sur_refl_b0[0-7]")
    .mean()
)

# Reduce MODIS reflectance bands by forest region; get a dictionary with
# band names as keys, pixel values as lists.
pixel_vals = modisSr.reduceRegion(
    **{"reducer": ee.Reducer.toList(), "geometry": forest.geometry(), "scale": 2000}
)

# Convert NIR and SWIR value lists to an array to be plotted along the y-axis.
y_values = pixel_vals.toArray(["sur_refl_b02", "sur_refl_b06"])


# Get the red band value list; to be plotted along the x-axis.
x_values = ee.List(pixel_vals.get("sur_refl_b01"))

# Import the example feature collection and subset the forest feature. forest = ee.FeatureCollection("projects/google/charts_feature_example").filter( ee.Filter.eq("label", "Forest") ) # Define a MODIS surface reflectance composite. modisSr = ( ee.ImageCollection("MODIS/061/MOD09A1") .filter(ee.Filter.date("2018-06-01", "2018-09-01")) .select("sur_refl_b0[0-7]") .mean() ) # Reduce MODIS reflectance bands by forest region; get a dictionary with # band names as keys, pixel values as lists. pixel_vals = modisSr.reduceRegion( **{"reducer": ee.Reducer.toList(), "geometry": forest.geometry(), "scale": 2000} ) # Convert NIR and SWIR value lists to an array to be plotted along the y-axis. y_values = pixel_vals.toArray(["sur_refl_b02", "sur_refl_b06"]) # Get the red band value list; to be plotted along the x-axis. x_values = ee.List(pixel_vals.get("sur_refl_b01"))

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title = "Relationship Among Spectral Bands for Forest Pixels"
colors = ["rgba(29,107,153,0.4)", "rgba(207,81,62,0.4)"]

title = "Relationship Among Spectral Bands for Forest Pixels" colors = ["rgba(29,107,153,0.4)", "rgba(207,81,62,0.4)"]

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fig = chart.array_values(
    y_values,
    axis=1,
    x_labels=x_values,
    series_names=["NIR", "SWIR"],
    chart_type="ScatterChart",
    colors=colors,
    title=title,
    x_label="Red reflectance (x1e4)",
    y_label="NIR & SWIR reflectance (x1e4)",
    default_size=15,
    xlim=(0, 800),
)
fig

fig = chart.array_values( y_values, axis=1, x_labels=x_values, series_names=["NIR", "SWIR"], chart_type="ScatterChart", colors=colors, title=title, x_label="Red reflectance (x1e4)", y_label="NIR & SWIR reflectance (x1e4)", default_size=15, xlim=(0, 800), ) fig

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x = ee.List(pixel_vals.get("sur_refl_b01"))
y = ee.List(pixel_vals.get("sur_refl_b06"))

x = ee.List(pixel_vals.get("sur_refl_b01")) y = ee.List(pixel_vals.get("sur_refl_b06"))

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fig = chart.array_values(
    y,
    x_labels=x,
    series_names=["SWIR"],
    chart_type="ScatterChart",
    colors=["rgba(207,81,62,0.4)"],
    title=title,
    x_label="Red reflectance (x1e4)",
    y_label="SWIR reflectance (x1e4)",
    default_size=15,
    xlim=(0, 800),
)
fig

fig = chart.array_values( y, x_labels=x, series_names=["SWIR"], chart_type="ScatterChart", colors=["rgba(207,81,62,0.4)"], title=title, x_label="Red reflectance (x1e4)", y_label="SWIR reflectance (x1e4)", default_size=15, xlim=(0, 800), ) fig

Transect line plot¶

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# Define a line across the Olympic Peninsula, USA.
transect = ee.Geometry.LineString([[-122.8, 47.8], [-124.5, 47.8]])

# Define a pixel coordinate image.
lat_lon_img = ee.Image.pixelLonLat()

# Import a digital surface model and add latitude and longitude bands.
elev_img = ee.Image("USGS/SRTMGL1_003").select("elevation").addBands(lat_lon_img)

# Reduce elevation and coordinate bands by transect line; get a dictionary with
# band names as keys, pixel values as lists.
elev_transect = elev_img.reduceRegion(
    reducer=ee.Reducer.toList(),
    geometry=transect,
    scale=1000,
)

# Get longitude and elevation value lists from the reduction dictionary.
lon = ee.List(elev_transect.get("longitude"))
elev = ee.List(elev_transect.get("elevation"))

# Sort the longitude and elevation values by ascending longitude.
lon_sort = lon.sort(lon)
elev_sort = elev.sort(lon)

# Define a line across the Olympic Peninsula, USA. transect = ee.Geometry.LineString([[-122.8, 47.8], [-124.5, 47.8]]) # Define a pixel coordinate image. lat_lon_img = ee.Image.pixelLonLat() # Import a digital surface model and add latitude and longitude bands. elev_img = ee.Image("USGS/SRTMGL1_003").select("elevation").addBands(lat_lon_img) # Reduce elevation and coordinate bands by transect line; get a dictionary with # band names as keys, pixel values as lists. elev_transect = elev_img.reduceRegion( reducer=ee.Reducer.toList(), geometry=transect, scale=1000, ) # Get longitude and elevation value lists from the reduction dictionary. lon = ee.List(elev_transect.get("longitude")) elev = ee.List(elev_transect.get("elevation")) # Sort the longitude and elevation values by ascending longitude. lon_sort = lon.sort(lon) elev_sort = elev.sort(lon)

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fig = chart.array_values(
    elev_sort,
    x_labels=lon_sort,
    series_names=["Elevation"],
    chart_type="AreaChart",
    colors=["#1d6b99"],
    title="Elevation Profile Across Longitude",
    x_label="Longitude",
    y_label="Elevation (m)",
    stroke_width=5,
    fill="bottom",
    fill_opacities=[0.4],
    ylim=(0, 2500),
)
fig

fig = chart.array_values( elev_sort, x_labels=lon_sort, series_names=["Elevation"], chart_type="AreaChart", colors=["#1d6b99"], title="Elevation Profile Across Longitude", x_label="Longitude", y_label="Elevation (m)", stroke_width=5, fill="bottom", fill_opacities=[0.4], ylim=(0, 2500), ) fig

Metadata scatter plot¶

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# Import a Landsat 8 collection and filter to a single path/row.
col = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2").filter(
    ee.Filter.expression("WRS_PATH ==  45 && WRS_ROW == 30")
)

# Reduce image properties to a series of lists; one for each selected property.
propVals = col.reduceColumns(
    reducer=ee.Reducer.toList().repeat(2),
    selectors=["CLOUD_COVER", "GEOMETRIC_RMSE_MODEL"],
).get("list")

# Get selected image property value lists; to be plotted along x and y axes.
x = ee.List(ee.List(propVals).get(0))
y = ee.List(ee.List(propVals).get(1))

# Import a Landsat 8 collection and filter to a single path/row. col = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2").filter( ee.Filter.expression("WRS_PATH == 45 && WRS_ROW == 30") ) # Reduce image properties to a series of lists; one for each selected property. propVals = col.reduceColumns( reducer=ee.Reducer.toList().repeat(2), selectors=["CLOUD_COVER", "GEOMETRIC_RMSE_MODEL"], ).get("list") # Get selected image property value lists; to be plotted along x and y axes. x = ee.List(ee.List(propVals).get(0)) y = ee.List(ee.List(propVals).get(1))

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colors = [geemap.hex_to_rgba("#96356f", 0.4)]
print(colors)

colors = [geemap.hex_to_rgba("#96356f", 0.4)] print(colors)

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fig = chart.array_values(
    y,
    x_labels=x,
    series_names=["RMSE"],
    chart_type="ScatterChart",
    colors=colors,
    title="Landsat 8 Image Collection Metadata (045030)",
    x_label="Cloud cover (%)",
    y_label="Geometric RMSE (m)",
    default_size=15,
)
fig

fig = chart.array_values( y, x_labels=x, series_names=["RMSE"], chart_type="ScatterChart", colors=colors, title="Landsat 8 Image Collection Metadata (045030)", x_label="Cloud cover (%)", y_label="Geometric RMSE (m)", default_size=15, ) fig

Mapped function scatter & line plot¶

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import math

start = -2 * math.pi
end = 2 * math.pi
points = ee.List.sequence(start, end, None, 50)


def sin_func(val):
    return ee.Number(val).sin()


values = points.map(sin_func)

import math start = -2 * math.pi end = 2 * math.pi points = ee.List.sequence(start, end, None, 50) def sin_func(val): return ee.Number(val).sin() values = points.map(sin_func)

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fig = chart.array_values(
    values,
    points,
    chart_type="LineChart",
    colors=["#39a8a7"],
    title="Sine Function",
    x_label="radians",
    y_label="sin(x)",
    marker="circle",
)
fig

fig = chart.array_values( values, points, chart_type="LineChart", colors=["#39a8a7"], title="Sine Function", x_label="radians", y_label="sin(x)", marker="circle", ) fig