%pylab inline

import ee
import geemap

# import the cartoee functionality from geemap
from geemap import cartoee
import cartopy.io.img_tiles as cimgt

geemap.ee_initialize()

# get a landsat image to visualize
image = ee.Image("LANDSAT/LC08/C01/T1_SR/LC08_044034_20140318")

# define the visualization parameters to view
vis = {"bands": ["B5", "B4", "B3"], "min": 0, "max": 5000, "gamma": 1.3}

fig = plt.figure(figsize=(15, 10))

# use cartoee to get a map
ax = cartoee.get_map(image, vis_params=vis, basemap="ROADMAP", zoom_level=8)

# pad the view for some visual appeal
cartoee.pad_view(ax)

# add the gridlines and specify that the xtick labels be rotated 45 degrees
cartoee.add_gridlines(ax, interval=0.5, xtick_rotation=0, linestyle=":")

# add the coastline
ax.coastlines(color="yellow")

plt.show()

basemap = cimgt.Stamen("terrain-background")

fig = plt.figure(figsize=(15, 10))

# use cartoee to get a map
ax = cartoee.get_map(image, vis_params=vis, basemap=basemap, zoom_level=8)

# pad the view for some visual appeal
cartoee.pad_view(ax)

# add the gridlines and specify that the xtick labels be rotated 45 degrees
cartoee.add_gridlines(ax, interval=0.5, xtick_rotation=0, linestyle=":")

# add the coastline
ax.coastlines(color="yellow")

plt.show()

basemap = cimgt.OSM()

fig = plt.figure(figsize=(15, 10))

# use cartoee to get a map
ax = cartoee.get_map(image, vis_params=vis, basemap=basemap, zoom_level=8)

# pad the view for some visual appeal
cartoee.pad_view(ax)

# add the gridlines and specify that the xtick labels be rotated 45 degrees
cartoee.add_gridlines(ax, interval=0.5, xtick_rotation=0, linestyle=":")

# add the coastline
ax.coastlines(color="yellow")

plt.show()

from matplotlib.transforms import offset_copy
import cartopy.crs as ccrs

fig = plt.figure(figsize=(15, 10))

# use cartoee to get a map
ax = cartoee.get_map(image, vis_params=vis, basemap="SATELLITE", zoom_level=8)

# pad the view for some visual appeal
cartoee.pad_view(ax)

# add the gridlines and specify that the xtick labels be rotated 45 degrees
cartoee.add_gridlines(ax, interval=0.5, xtick_rotation=0, linestyle=":")

# add the coastline
ax.coastlines(color="yellow")

plt.plot(
    -122.4457,
    37.7574,
    marker="o",
    color="blue",
    markersize=10,
    alpha=0.7,
    transform=ccrs.Geodetic(),
)

# Use the cartopy interface to create a matplotlib transform object
# for the Geodetic coordinate system. We will use this along with
# matplotlib's offset_copy function to define a coordinate system which
# translates the text by 25 pixels to the left.
geodetic_transform = ccrs.Geodetic()._as_mpl_transform(ax)
text_transform = offset_copy(geodetic_transform, units="dots", x=-25)

plt.text(
    -122.4457,
    37.7574,
    "San Francisco",
    verticalalignment="center",
    horizontalalignment="right",
    transform=text_transform,
    fontsize="large",
    fontweight="bold",
    color="white",
    bbox=dict(facecolor="sandybrown", alpha=0.5, boxstyle="round"),
)

# Save the plot by calling plt.savefig() BEFORE plt.show()
plt.savefig("sfo.pdf")
plt.savefig("sfo.jpg", dpi=300)

plt.show()

# get an earth engine image of ocean data for Jan-Mar 2018
ocean = (
    ee.ImageCollection("NASA/OCEANDATA/MODIS-Terra/L3SMI")
    .filter(ee.Filter.date("2018-01-01", "2018-03-01"))
    .median()
    .select(["sst"], ["SST"])
)

# set parameters for plotting
# will plot the Sea Surface Temp with specific range and colormap
visualization = {"bands": "SST", "min": -2, "max": 30}
# specify region to focus on
bbox = [180, -88, -180, 88]

fig = plt.figure(figsize=(15, 10))

# plot the result with cartoee using a PlateCarre projection (default)
ax = cartoee.get_map(
    ocean,
    cmap="plasma",
    vis_params=visualization,
    region=bbox,
    basemap=cimgt.Stamen("terrain"),
    zoom_level=2,
)
cb = cartoee.add_colorbar(ax, vis_params=visualization, loc="right", cmap="plasma")

ax.set_title(label="Sea Surface Temperature", fontsize=15)

ax.coastlines()

# Save the plot by calling plt.savefig() BEFORE plt.show()
plt.savefig("sst.pdf")
plt.savefig("sst.jpg", dpi=300)

plt.show()