Add EE charting functions

[giswqs]

This PR adds more charting functions to the chart module.

Reference: https://developers.google.com/earth-engine/guides/charts_image

chart.image_byRegion

ecoregions = ee.FeatureCollection('projects/google/charts_feature_example')
normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm81m').toBands().select('[0-9][0-9]_tmean')
labels = list(calendar.month_abbr)[1:] # a list of month labels, e.g. ['Jan', 'Feb', ...]

chart.image_byRegion(
    image=normClim, 
    regions=ecoregions, 
    reducer="mean", 
    scale=500, 
    xProperty='label', 
    xlabel='Ecoregion',
    ylabel='Temperature',
    labels=labels,
    # colors=['red', 'green', 'blue'],
    )

[github-actions]

🚀 Deployed on https://666a63104ac3e67a7fb72dd7--opengeos.netlify.app

[giswqs]

chart.image_byClass

Reference: https://developers.google.com/earth-engine/guides/charts_image#uichartimagebyclass

bqplot example with line smoothing

import numpy as np
import bqplot.pyplot as plt

fig = plt.figure(title="Random Walks")
x = np.arange(100)
# three random walks
y = np.random.randn(3, 100).cumsum(axis=1)

lines = plt.plot(
    x, y,
    labels=["Line1", "Line2", "Line 3"],
    display_legend=True
)

# Plot the lines with interpolation
line1 = plt.plot(x, y[0], 'b-', label='Line 1', interpolation='basis')
line2 = plt.plot(x, y[1], 'r-', label='Line 2', interpolation='step-after')
line3 = plt.plot(x, y[2], 'g-', label='Line 3', interpolation='cardinal')

plt.show()

[jdbcode]

Hi @giswqs, just a quick note to say I see the review request and will follow up shortly.

[giswqs]

No rush. I will be adding more commit over the next few days.

[jdbcode]

It looks like you're maybe still working on chart.image_byClass?
I saw the new Image_byClass class but the image_byClass function is unchanged from todo. Do you want to add it to this PR, or would you like to merge the current changes?

[giswqs]

@jdbcode Thanks fort testing it. I will be adding more classes and functions. Don't merge this PR yet.

[jdbcode]

Sounds good

[smith-kyle]

I'm a little late to the party, but I noticed you all aren't using a notebook review tool and wanted to invite you to review this pull request with GitNotebooks: https://gitnotebooks.com/gee-community/geemap/pull/1855

It lets you do things like comment on rendered markdown and code cells, so might be an easy win for your PR reviews.

[giswqs]

chart.image_byClass

Reference: https://developers.google.com/earth-engine/guides/charts_image#uichartimagebyclass

import ee
import geemap
from geemap.chart import BaseChart
import geemap.chart as chart
from bqplot import Figure, Lines, Axis, LinearScale, ColorScale
from IPython.display import display

geemap.ee_initialize()

ecoregions = ee.FeatureCollection('projects/google/charts_feature_example')

image = ee.ImageCollection('MODIS/061/MOD09A1') \
    .filter(ee.Filter.date('2018-06-01', '2018-09-01')) \
    .select('sur_refl_b0[0-7]') \
    .mean() \
    .select([2, 3, 0, 1, 4, 5, 6])

wavelengths = [469, 555, 655, 858, 1240, 1640, 2130]

fc = geemap.zonal_stats(
    image, ecoregions, stat_type="MEAN", scale=500, verbose=False, return_fc=True
)
bands = image.bandNames().getInfo()
df = geemap.ee_to_df(fc)[bands + ["label"]]

# Define the scales
x_sc = LinearScale()
y_sc = LinearScale()
color_sc = ColorScale()

lines = []
colors =  ['#f0af07', '#0f8755', '#76b349'] 

x_values = list(range(len(df.columns) - 1))
x_values = [469, 555, 655, 858, 1240, 1640, 2130]
band_names = ['B03', 'B04', 'B01', 'B02', 'B05', 'B06', 'B07']

for i, label in enumerate(df['label']):
    line = Lines(
        x=x_values, 
        y=df.iloc[i, :-1].values.astype(float),  # Ensure y-values are float
        scales={'x': x_sc, 'y': y_sc},
        colors=[colors[i]],
        labels=[label],
        display_legend=True,
        # interpolation='basis',
        stroke_width=3
    )
    lines.append(line)

x_ax = Axis(scale=x_sc, label='Wavelength (nm)', tick_values=x_values)
y_ax = Axis(scale=y_sc, orientation='vertical', label='Reflectance')

fig = Figure(marks=lines, axes=[x_ax, y_ax], title='Ecoregion Spectral Reflectance', legend_location='top-right')

display(fig)

no interpolation

basis interpolation

[giswqs]

image_byClass

import ee
import geemap
import geemap.chart as chart

geemap.ee_initialize()

ecoregions = ee.FeatureCollection("projects/google/charts_feature_example")

image = (
    ee.ImageCollection("MODIS/061/MOD09A1")
    .filter(ee.Filter.date("2018-06-01", "2018-09-01"))
    .select("sur_refl_b0[0-7]")
    .mean()
    .select([2, 3, 0, 1, 4, 5, 6])
)

wavelengths = [469, 555, 655, 858, 1240, 1640, 2130]

fig = chart.image_byClass(
    image,
    classBand="label",
    region=ecoregions,
    reducer="MEAN",
    scale=500,
    xLabels=wavelengths,
    title="Ecoregion Spectral Signatures",
    x_label="Wavelength (nm)",
    y_label="Reflectance",
    interpolation="basis",
)
fig

[giswqs]

chart.image_histogram()
https://developers.google.com/earth-engine/guides/charts_image#uichartimagehistogram

import ee
import geemap.chart as chart
geemap.ee_initialize()

modis_sr = ee.ImageCollection('MODIS/061/MOD09A1') \
    .filter(ee.Filter.date('2018-06-01', '2018-09-01')) \
    .select(['sur_refl_b01', 'sur_refl_b02', 'sur_refl_b06']) \
    .mean()

hist_region = ee.Geometry.Rectangle([-112.60, 40.60, -111.18, 41.22])

hist_fig = chart.image_histogram(
    image=modis_sr, 
    region=hist_region, 
    scale=500, 
    maxBuckets=200, 
    minBucketWidth=1.0, 
    maxRaw=1000, 
    maxPixels=int(1e6),
    title='MODIS SR Reflectance Histogram',
    labels=['Red', 'NIR', 'SWIR'],
    colors=['#cf513e', '#1d6b99', '#f0af07']
)

hist_fig

[giswqs]

Added chart.image_series() function
Reference: https://developers.google.com/earth-engine/guides/charts_image_collection#uichartimageseries

import ee
import geemap
import geemap.chart as chart

geemap.ee_initialize()

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

# Load MODIS vegetation indices data and subset a decade of images.
veg_indices = (
    ee.ImageCollection("MODIS/061/MOD13A1")
    .filter(ee.Filter.date("2010-01-01", "2020-01-01"))
    .select(["NDVI", "EVI"])
)

# Set the chart properties.
title = "Average Vegetation Index Value by Date for Forest"
xlabel = "Year"
ylabel = "Vegetation index (x1e4)"
colors = ["#e37d05", "#1d6b99"]
axes_options = {
    "x": {"label_offset": "30px"},
    "y": {"label_offset": "50px"},
}

# Create the chart.
fig = chart.image_series(
    veg_indices,
    region=forest,
    reducer=ee.Reducer.mean(),
    scale=500,
    xProperty="system:time_start",
    chart_type="LineChart",
    x_cols="date",
    y_cols=["NDVI", "EVI"],
    title=title,
    xlabel=xlabel,
    ylabel=ylabel,
    colors=colors,
    axes_options=axes_options,
)
fig

[giswqs]

Add chart.image_seriesByRegion() function.
Reference: https://developers.google.com/earth-engine/guides/charts_image_collection#uichartimageseriesbyregion

import ee
import geemap
import geemap.chart as chart

geemap.ee_initialize()

# Import the example feature collection.
ecoregions = ee.FeatureCollection('projects/google/charts_feature_example')

# Load MODIS vegetation indices data and subset a decade of images.
veg_indices = ee.ImageCollection('MODIS/061/MOD13A1') \
    .filter(ee.Filter.date('2010-01-01', '2020-01-01')) \
    .select(['NDVI'])

# Set the chart properties.
title = "Average NDVI Value by Date"
xlabel = "Date"
ylabel = "NDVI (x1e4)"
x_cols='index'
y_cols=["Desert", "Forest", "Grassland"]
colors = ['#f0af07', '#0f8755', '#76b349']
axes_options = {
    "x": {"label_offset": "30px"},
    "y": {"label_offset": "50px"},
}

# Create the chart.
fig = chart.image_seriesByRegion(
    veg_indices,
    regions=ecoregions,
    reducer=ee.Reducer.mean(),
    band="NDVI",
    scale=500,
    xProperty="system:time_start",
    seriesProperty="label",
    chart_type="LineChart",
    x_cols=x_cols,
    y_cols=y_cols,
    title=title,
    xlabel=xlabel,
    ylabel=ylabel,
    colors=colors,
    axes_options=axes_options,
    stroke_width=3,
    legend_location="bottom-left"
)
fig