hypercoast module¶
Main module.
Map (Map)
¶
A class that extends leafmap.Map to provide additional functionality for hypercoast.
Methods
Any methods inherited from leafmap.Map.
Source code in hypercoast/hypercoast.py
class Map(leafmap.Map):
"""
A class that extends leafmap.Map to provide additional functionality for
hypercoast.
Attributes:
Any attributes inherited from leafmap.Map.
Methods:
Any methods inherited from leafmap.Map.
"""
def __init__(self, **kwargs):
"""
Initializes a new instance of the Map class.
Args:
**kwargs: Arbitrary keyword arguments that are passed to the parent
class's constructor.
"""
super().__init__(**kwargs)
self._spectral_data = {}
self._plot_options = None
self._plot_marker_cluster = ipyleaflet.MarkerCluster(name="Marker Cluster")
def add(self, obj, position="topright", xlim=None, ylim=None, **kwargs):
"""Add a layer to the map.
Args:
obj (str or object): The name of the layer or a layer object.
position (str, optional): The position of the layer widget. Can be
'topright', 'topleft', 'bottomright', or 'bottomleft'. Defaults
to 'topright'.
xlim (tuple, optional): The x-axis limits of the plot. Defaults to None.
ylim (tuple, optional): The y-axis limits of the plot. Defaults to None.
**kwargs: Arbitrary keyword arguments that are passed to the parent
class's add_layer method.
"""
if isinstance(obj, str):
if obj == "spectral":
SpectralWidget(self, position=position, xlim=xlim, ylim=ylim, **kwargs)
self.set_plot_options(add_marker_cluster=True)
else:
super().add(obj, **kwargs)
else:
super().add(obj, **kwargs)
def search_emit(self, default_dataset="EMITL2ARFL"):
"""
Adds a NASA Earth Data search tool to the map with a default dataset for
EMIT.
Args:
default_dataset (str, optional): The default dataset to search for.
Defaults to "EMITL2ARFL".
"""
self.add("nasa_earth_data", default_dataset=default_dataset)
def search_pace(self, default_dataset="PACE_OCI_L2_AOP_NRT"):
"""
Adds a NASA Earth Data search tool to the map with a default dataset for
PACE.
Args:
default_dataset (str, optional): The default dataset to search for.
Defaults to "PACE_OCI_L2_AOP_NRT".
"""
self.add("nasa_earth_data", default_dataset=default_dataset)
def search_ecostress(self, default_dataset="ECO_L2T_LSTE"):
"""
Adds a NASA Earth Data search tool to the map with a default dataset for
ECOSTRESS.
Args:
default_dataset (str, optional): The default dataset to search for.
Defaults to "ECO_L2T_LSTE".
"""
self.add("nasa_earth_data", default_dataset=default_dataset)
def add_raster(
self,
source,
indexes=None,
colormap=None,
vmin=None,
vmax=None,
nodata=None,
attribution=None,
layer_name="Raster",
layer_index=None,
zoom_to_layer=True,
visible=True,
opacity=1.0,
array_args=None,
client_args={"cors_all": False},
open_args=None,
**kwargs,
):
"""Add a local raster dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using `pip install jupyter-server-proxy`,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'
Args:
source (str): The path to the GeoTIFF file or the URL of the Cloud
Optimized GeoTIFF.
indexes (int, optional): The band(s) to use. Band indexing starts
at 1. Defaults to None.
colormap (str, optional): The name of the colormap from `matplotlib`
to use when plotting a single band. See
https://matplotlib.org/stable/gallery/color/colormap_reference.html.
Default is greyscale.
vmin (float, optional): The minimum value to use when colormapping
the palette when plotting a single band. Defaults to None.
vmax (float, optional): The maximum value to use when colormapping
the palette when plotting a single band. Defaults to None.
nodata (float, optional): The value from the band to use to interpret
as not valid data. Defaults to None.
attribution (str, optional): Attribution for the source raster. This
defaults to a message about it being a local file.. Defaults to None.
layer_name (str, optional): The layer name to use. Defaults to 'Raster'.
layer_index (int, optional): The index of the layer. Defaults to None.
zoom_to_layer (bool, optional): Whether to zoom to the extent of the
layer. Defaults to True.
visible (bool, optional): Whether the layer is visible. Defaults to
True.
opacity (float, optional): The opacity of the layer. Defaults to 1.0.
array_args (dict, optional): Additional arguments to pass to
`array_to_memory_file` when reading the raster. Defaults to {}.
client_args (dict, optional): Additional arguments to pass to
localtileserver.TileClient. Defaults to { "cors_all": False }.
open_args (dict, optional): Additional arguments to pass to
rioxarray.open_rasterio.
"""
import rioxarray as rxr
if array_args is None:
array_args = {}
if open_args is None:
open_args = {}
if nodata is None:
nodata = np.nan
super().add_raster(
source,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
layer_name=layer_name,
layer_index=layer_index,
zoom_to_layer=zoom_to_layer,
visible=visible,
opacity=opacity,
array_args=array_args,
client_args=client_args,
**kwargs,
)
if isinstance(source, str):
da = rxr.open_rasterio(source, **open_args)
dims = da.dims
da = da.transpose(dims[1], dims[2], dims[0])
xds = da.to_dataset(name="data")
self.cog_layer_dict[layer_name]["xds"] = xds
# if self.cog_layer_dict[layer_name].get("hyper") is None:
# self.cog_layer_dict[layer_name]["hyper"] = "COG"
def add_dataset(
self,
source,
indexes=None,
colormap=None,
vmin=None,
vmax=None,
nodata=None,
attribution=None,
layer_name="Raster",
zoom_to_layer=True,
visible=True,
array_args=None,
open_args=None,
**kwargs,
):
import rioxarray as rxr
from leafmap import array_to_image
if array_args is None:
array_args = {}
if open_args is None:
open_args = {}
if isinstance(source, str):
da = rxr.open_rasterio(source, **open_args)
dims = da.dims
da = da.transpose(dims[1], dims[2], dims[0])
xds = da.to_dataset(name="data")
elif not isinstance(source, xr.Dataset):
raise ValueError(
"source must be a path to a raster file or an xarray.Dataset object."
)
else:
xds = source
if indexes is None:
if xds.sizes[dims[2]] < 3:
indexes = [1]
elif xds.sizes[dims[2]] < 4:
indexes = [1, 2, 3]
else:
indexes = [3, 2, 1]
bands = [i - 1 for i in indexes]
da = xds.isel(band=bands)["data"]
image = array_to_image(da, transpose=False)
self.add_raster(
image,
indexes=None,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
layer_name=layer_name,
zoom_to_layer=zoom_to_layer,
visible=visible,
array_args=array_args,
**kwargs,
)
self.cog_layer_dict[layer_name]["xds"] = xds
self.cog_layer_dict[layer_name]["type"] = "XARRAY"
self.cog_layer_dict[layer_name]["hyper"] = "XARRAY"
self.cog_layer_dict[layer_name]["band_names"] = [
"b" + str(i) for i in xds.coords["band"].values.tolist()
]
self.cog_layer_dict[layer_name]["indexes"] = indexes
self.cog_layer_dict[layer_name]["vis_bands"] = ["b" + str(i) for i in indexes]
def add_emit(
self,
source,
wavelengths=None,
indexes=None,
colormap=None,
vmin=None,
vmax=None,
nodata=np.nan,
attribution=None,
layer_name="EMIT",
zoom_to_layer=True,
visible=True,
array_args=None,
**kwargs,
):
"""Add an EMIT dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using `pip install jupyter-server-proxy`,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'
Args:
source (str): The path to the GeoTIFF file or the URL of the Cloud
Optimized GeoTIFF.
indexes (int, optional): The band(s) to use. Band indexing starts
at 1. Defaults to None.
colormap (str, optional): The name of the colormap from `matplotlib`
to use when plotting a single band.
See https://matplotlib.org/stable/gallery/color/colormap_reference.html.
Default is greyscale.
vmin (float, optional): The minimum value to use when colormapping
the palette when plotting a single band. Defaults to None.
vmax (float, optional): The maximum value to use when colormapping
the palette when plotting a single band. Defaults to None.
nodata (float, optional): The value from the band to use to
interpret as not valid data. Defaults to None.
attribution (str, optional): Attribution for the source raster. This
defaults to a message about it being a local file.. Defaults to None.
layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.
zoom_to_layer (bool, optional): Whether to zoom to the extent of the
layer. Defaults to True.
visible (bool, optional): Whether the layer is visible. Defaults to
True.
array_args (dict, optional): Additional arguments to pass to
`array_to_memory_file` when reading the raster. Defaults to {}.
"""
if array_args is None:
array_args = {}
xds = None
if isinstance(source, str):
xds = read_emit(source)
source = emit_to_image(xds, wavelengths=wavelengths)
elif isinstance(source, xr.Dataset):
xds = source
source = emit_to_image(xds, wavelengths=wavelengths)
self.add_raster(
source,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
layer_name=layer_name,
zoom_to_layer=zoom_to_layer,
visible=visible,
array_args=array_args,
**kwargs,
)
self.cog_layer_dict[layer_name]["xds"] = xds
self.cog_layer_dict[layer_name]["hyper"] = "EMIT"
self._update_band_names(layer_name, wavelengths)
def add_pace(
self,
source,
wavelengths=None,
indexes=None,
colormap="jet",
vmin=None,
vmax=None,
nodata=np.nan,
attribution=None,
layer_name="PACE",
zoom_to_layer=True,
visible=True,
method="nearest",
gridded=False,
array_args=None,
**kwargs,
):
"""Add a PACE dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using `pip install jupyter-server-proxy`,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'
Args:
source (str): The path to the GeoTIFF file or the URL of the Cloud
Optimized GeoTIFF.
indexes (int, optional): The band(s) to use. Band indexing starts
at 1. Defaults to None.
colormap (str, optional): The name of the colormap from `matplotlib`
to use when plotting a single band. See
https://matplotlib.org/stable/gallery/color/colormap_reference.html.
Default is greyscale.
vmin (float, optional): The minimum value to use when colormapping
the palette when plotting a single band. Defaults to None.
vmax (float, optional): The maximum value to use when colormapping
the palette when plotting a single band. Defaults to None.
nodata (float, optional): The value from the band to use to interpret
as not valid data. Defaults to None.
attribution (str, optional): Attribution for the source raster. This
defaults to a message about it being a local file.. Defaults to None.
layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.
zoom_to_layer (bool, optional): Whether to zoom to the extent of the
layer. Defaults to True.
visible (bool, optional): Whether the layer is visible. Defaults to True.
array_args (dict, optional): Additional arguments to pass to
`array_to_memory_file` when reading the raster. Defaults to {}.
"""
if array_args is None:
array_args = {}
if isinstance(source, str):
source = read_pace(source)
try:
image = pace_to_image(
source, wavelengths=wavelengths, method=method, gridded=gridded
)
if isinstance(wavelengths, list) and len(wavelengths) > 1:
colormap = None
self.add_raster(
image,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
layer_name=layer_name,
zoom_to_layer=zoom_to_layer,
visible=visible,
array_args=array_args,
**kwargs,
)
self.cog_layer_dict[layer_name]["xds"] = source
self.cog_layer_dict[layer_name]["hyper"] = "PACE"
self._update_band_names(layer_name, wavelengths)
except Exception as e:
print(e)
def add_desis(
self,
source,
wavelengths=[900, 650, 525],
indexes=None,
colormap="jet",
vmin=None,
vmax=None,
nodata=np.nan,
attribution=None,
layer_name="DESIS",
zoom_to_layer=True,
visible=True,
method="nearest",
array_args=None,
**kwargs,
):
"""Add a DESIS dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using `pip install jupyter-server-proxy`,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'
Args:
source (str): The path to the GeoTIFF file or the URL of the Cloud
Optimized GeoTIFF.
indexes (int, optional): The band(s) to use. Band indexing starts
at 1. Defaults to None.
colormap (str, optional): The name of the colormap from `matplotlib`
to use when plotting a single band. See
https://matplotlib.org/stable/gallery/color/colormap_reference.html.
Default is 'jet'.
vmin (float, optional): The minimum value to use when colormapping
the palette when plotting a single band. Defaults to None.
vmax (float, optional): The maximum value to use when colormapping
the palette when plotting a single band. Defaults to None.
nodata (float, optional): The value from the band to use to interpret
as not valid data. Defaults to None.
attribution (str, optional): Attribution for the source raster. This
defaults to a message about it being a local file.. Defaults to None.
layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.
zoom_to_layer (bool, optional): Whether to zoom to the extent of the
layer. Defaults to True.
visible (bool, optional): Whether the layer is visible. Defaults to True.
array_args (dict, optional): Additional arguments to pass to
`array_to_memory_file` when reading the raster. Defaults to {}.
"""
if array_args is None:
array_args = {}
if isinstance(source, str):
source = read_desis(source)
image = desis_to_image(source, wavelengths=wavelengths, method=method)
if isinstance(wavelengths, list) and len(wavelengths) > 1:
colormap = None
if isinstance(wavelengths, int):
wavelengths = [wavelengths]
if indexes is None:
if isinstance(wavelengths, list) and len(wavelengths) == 1:
indexes = [1]
else:
indexes = [1, 2, 3]
self.add_raster(
image,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
layer_name=layer_name,
zoom_to_layer=zoom_to_layer,
visible=visible,
array_args=array_args,
**kwargs,
)
self.cog_layer_dict[layer_name]["xds"] = source
self.cog_layer_dict[layer_name]["hyper"] = "DESIS"
self._update_band_names(layer_name, wavelengths)
def add_neon(
self,
source,
wavelengths=None,
indexes=None,
colormap=None,
vmin=0,
vmax=0.5,
nodata=np.nan,
attribution=None,
layer_name="NEON",
zoom_to_layer=True,
visible=True,
array_args=None,
method="nearest",
**kwargs,
):
"""Add an NEON AOP dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using `pip install jupyter-server-proxy`,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'
Args:
source (str): The path to the NEON AOP HDF5 file.
indexes (int, optional): The band(s) to use. Band indexing starts
at 1. Defaults to None.
colormap (str, optional): The name of the colormap from `matplotlib`
to use when plotting a single band. See
https://matplotlib.org/stable/gallery/color/colormap_reference.html.
Default is greyscale.
vmin (float, optional): The minimum value to use when colormapping
the palette when plotting a single band. Defaults to 0.
vmax (float, optional): The maximum value to use when colormapping
the palette when plotting a single band. Defaults to 0.5.
nodata (float, optional): The value from the band to use to
interpret as not valid data. Defaults to np.nan.
attribution (str, optional): Attribution for the source raster. This
defaults to a message about it being a local file.. Defaults to None.
layer_name (str, optional): The layer name to use. Defaults to 'NEON'.
zoom_to_layer (bool, optional): Whether to zoom to the extent of the
layer. Defaults to True.
visible (bool, optional): Whether the layer is visible. Defaults
to True.
array_args (dict, optional): Additional arguments to pass to
`array_to_memory_file` when reading the raster. Defaults to {}.
method (str, optional): The method to use for data interpolation.
Defaults to "nearest".
"""
if array_args is None:
array_args = {}
xds = None
if isinstance(source, str):
xds = read_neon(source)
source = neon_to_image(xds, wavelengths=wavelengths, method=method)
elif isinstance(source, xr.Dataset):
xds = source
source = neon_to_image(xds, wavelengths=wavelengths, method=method)
self.add_raster(
source,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
layer_name=layer_name,
zoom_to_layer=zoom_to_layer,
visible=visible,
array_args=array_args,
**kwargs,
)
self.cog_layer_dict[layer_name]["xds"] = xds
self.cog_layer_dict[layer_name]["hyper"] = "NEON"
self._update_band_names(layer_name, wavelengths)
def add_aviris(
self,
source,
wavelengths=None,
indexes=None,
colormap=None,
vmin=0,
vmax=0.5,
nodata=np.nan,
attribution=None,
layer_name="AVIRIS",
zoom_to_layer=True,
visible=True,
array_args=None,
method="nearest",
**kwargs,
):
"""Add an AVIRIS dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using `pip install jupyter-server-proxy`,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'
Args:
source (str): The path to the AVIRIS file.
indexes (int, optional): The band(s) to use. Band indexing starts
at 1. Defaults to None.
colormap (str, optional): The name of the colormap from `matplotlib`
to use when plotting a single band. See
https://matplotlib.org/stable/gallery/color/colormap_reference.html.
Default is greyscale.
vmin (float, optional): The minimum value to use when colormapping
the palette when plotting a single band. Defaults to 0.
vmax (float, optional): The maximum value to use when colormapping
the palette when plotting a single band. Defaults to 0.5.
nodata (float, optional): The value from the band to use to
interpret as not valid data. Defaults to np.nan.
attribution (str, optional): Attribution for the source raster. This
defaults to a message about it being a local file.. Defaults to None.
layer_name (str, optional): The layer name to use. Defaults to 'NEON'.
zoom_to_layer (bool, optional): Whether to zoom to the extent of the
layer. Defaults to True.
visible (bool, optional): Whether the layer is visible. Defaults
to True.
array_args (dict, optional): Additional arguments to pass to
`array_to_memory_file` when reading the raster. Defaults to {}.
method (str, optional): The method to use for data interpolation.
Defaults to "nearest".
"""
if array_args is None:
array_args = {}
xds = None
if isinstance(source, str):
xds = read_aviris(source)
source = neon_to_image(xds, wavelengths=wavelengths, method=method)
elif isinstance(source, xr.Dataset):
xds = source
source = aviris_to_image(xds, wavelengths=wavelengths, method=method)
self.add_raster(
source,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
layer_name=layer_name,
zoom_to_layer=zoom_to_layer,
visible=visible,
array_args=array_args,
**kwargs,
)
self.cog_layer_dict[layer_name]["xds"] = xds
self.cog_layer_dict[layer_name]["hyper"] = "AVIRIS"
self._update_band_names(layer_name, wavelengths)
def add_hyper(self, xds, dtype, wvl_indexes=None, **kwargs):
"""Add a hyperspectral dataset to the map.
Args:
xds (str): The Xarray dataset containing the hyperspectral data.
dtype (str): The type of the hyperspectral dataset. Can be one of
"EMIT", "PACE", "DESIS", "NEON", "AVIRIS".
**kwargs: Additional keyword arguments to pass to the corresponding
add function.
"""
if wvl_indexes is not None:
if dtype == "XARRAY":
kwargs["indexes"] = [i + 1 for i in wvl_indexes]
else:
kwargs["wavelengths"] = (
xds.isel(wavelength=wvl_indexes)
.coords["wavelength"]
.values.tolist()
)
if dtype == "EMIT":
self.add_emit(xds, **kwargs)
elif dtype == "PACE":
self.add_pace(xds, **kwargs)
elif dtype == "DESIS":
self.add_desis(xds, **kwargs)
elif dtype == "NEON":
self.add_neon(xds, **kwargs)
elif dtype == "AVIRIS":
self.add_aviris(xds, **kwargs)
elif dtype == "XARRAY":
kwargs.pop("wavelengths", None)
self.add_dataset(xds, **kwargs)
def set_plot_options(
self,
add_marker_cluster=False,
plot_type=None,
overlay=False,
position="bottomright",
min_width=None,
max_width=None,
min_height=None,
max_height=None,
**kwargs,
):
"""Sets plotting options.
Args:
add_marker_cluster (bool, optional): Whether to add a marker cluster.
Defaults to False.
sample_scale (float, optional): A nominal scale in meters of the
projection to sample in . Defaults to None.
plot_type (str, optional): The plot type can be one of "None", "bar",
"scatter" or "hist". Defaults to None.
overlay (bool, optional): Whether to overlay plotted lines on the
figure. Defaults to False.
position (str, optional): Position of the control, can be
‘bottomleft’, ‘bottomright’, ‘topleft’, or ‘topright’. Defaults
to 'bottomright'.
min_width (int, optional): Min width of the widget (in pixels), if
None it will respect the content size. Defaults to None.
max_width (int, optional): Max width of the widget (in pixels), if
None it will respect the content size. Defaults to None.
min_height (int, optional): Min height of the widget (in pixels), if
None it will respect the content size. Defaults to None.
max_height (int, optional): Max height of the widget (in pixels), if
None it will respect the content size. Defaults to None.
"""
plot_options_dict = {}
plot_options_dict["add_marker_cluster"] = add_marker_cluster
plot_options_dict["plot_type"] = plot_type
plot_options_dict["overlay"] = overlay
plot_options_dict["position"] = position
plot_options_dict["min_width"] = min_width
plot_options_dict["max_width"] = max_width
plot_options_dict["min_height"] = min_height
plot_options_dict["max_height"] = max_height
for key in kwargs:
plot_options_dict[key] = kwargs[key]
self._plot_options = plot_options_dict
if not hasattr(self, "_plot_marker_cluster"):
self._plot_marker_cluster = ipyleaflet.MarkerCluster(name="Marker Cluster")
if add_marker_cluster and (self._plot_marker_cluster not in self.layers):
self.add(self._plot_marker_cluster)
def spectral_to_df(self, **kwargs):
"""Converts the spectral data to a pandas DataFrame.
Returns:
pd.DataFrame: The spectral data as a pandas DataFrame.
"""
import pandas as pd
df = pd.DataFrame(self._spectral_data, **kwargs)
return df
def spectral_to_gdf(self, **kwargs):
"""Converts the spectral data to a GeoPandas GeoDataFrame.
Returns:
gpd.DataFrame: The spectral data as a pandas DataFrame.
"""
import geopandas as gpd
from shapely.geometry import Point
df = self.spectral_to_df()
if len(df) == 0:
return df
# Step 1: Extract the coordinates from the columns
if "wavelength" in df.columns:
df = df.rename(columns={"wavelength": "latlon"})
elif "wavelengths" in df.columns:
df = df.rename(columns={"wavelengths": "latlon"})
coordinates = [col.strip("()").split() for col in df.columns[1:]]
coords = [(float(lat), float(lon)) for lat, lon in coordinates]
# Step 2: Create Point geometries for each coordinate
points = [Point(lon, lat) for lat, lon in coords]
# Step 3: Create a GeoDataFrame
df_transposed = df.set_index("latlon").T
# Convert the column names to strings to ensure compatibility with GeoJSON
df_transposed.columns = df_transposed.columns.astype(str)
# Create the GeoDataFrame
gdf = gpd.GeoDataFrame(df_transposed, geometry=points, **kwargs)
# Set the coordinate reference system (CRS)
gdf = gdf.set_geometry("geometry").set_crs("EPSG:4326")
return gdf
def spectral_to_csv(self, filename, index=True, **kwargs):
"""Saves the spectral data to a CSV file.
Args:
filename (str): The output CSV file.
index (bool, optional): Whether to write the index. Defaults to True.
"""
df = self.spectral_to_df()
df = df.rename_axis("band")
df.to_csv(filename, index=index, **kwargs)
def _update_band_names(self, layer_name, wavelengths):
# Function to find the nearest indices
def find_nearest_indices(
dataarray, selected_wavelengths, dim_name="wavelength"
):
indices = []
for wavelength in selected_wavelengths:
if dim_name == "band":
nearest_wavelength = dataarray.sel(
band=wavelength, method="nearest"
)
else:
nearest_wavelength = dataarray.sel(
wavelength=wavelength, method="nearest"
)
nearest_wavelength_index = nearest_wavelength[dim_name].item()
nearest_index = (
dataarray[dim_name].values.tolist().index(nearest_wavelength_index)
)
indices.append(nearest_index + 1)
return indices
if "xds" in self.cog_layer_dict[layer_name]:
xds = self.cog_layer_dict[layer_name]["xds"]
dim_name = "wavelength"
if "band" in xds:
dim_name = "band"
band_count = xds.sizes[dim_name]
band_names = ["b" + str(band) for band in range(1, band_count + 1)]
self.cog_layer_dict[layer_name]["band_names"] = band_names
try:
indexes = find_nearest_indices(xds, wavelengths, dim_name=dim_name)
vis_bands = ["b" + str(index) for index in indexes]
self.cog_layer_dict[layer_name]["indexes"] = indexes
self.cog_layer_dict[layer_name]["vis_bands"] = vis_bands
except Exception as e:
print(e)
def add_field_data(
self,
data: Union[str],
x_col: str = "wavelength",
y_col_prefix: str = "(",
x_label: str = "Wavelengths (nm)",
y_label: str = "Reflectance",
use_marker_cluster: bool = True,
min_width: int = 400,
max_width: int = 600,
min_height: int = 200,
max_height: int = 250,
layer_name: str = "Marker Cluster",
**kwargs,
):
"""
Displays field data on a map with interactive markers and popups showing time series data.
Args:
data (Union[str, pd.DataFrame]): Path to the CSV file or a pandas DataFrame containing the data.
x_col (str): Column name to use for the x-axis of the charts. Default is "wavelength".
y_col_prefix (str): Prefix to identify the columns that contain the location-specific data. Default is "(".
x_label (str): Label for the x-axis of the charts. Default is "Wavelengths (nm)".
y_label (str): Label for the y-axis of the charts. Default is "Reflectance".
use_marker_cluster (bool): Whether to use marker clustering. Default is True.
min_width (int): Minimum width of the popup. Default is 400.
max_width (int): Maximum width of the popup. Default is 600.
min_height (int): Minimum height of the popup. Default is 200.
max_height (int): Maximum height of the popup. Default is 250.
layer_name (str): Name of the marker cluster layer. Default is "Marker Cluster".
Returns:
Map: An ipyleaflet Map with the added markers and popups.
"""
show_field_data(
data,
x_col,
y_col_prefix,
x_label=x_label,
y_label=y_label,
use_marker_cluster=use_marker_cluster,
min_width=min_width,
max_width=max_width,
min_height=min_height,
max_height=max_height,
layer_name=layer_name,
m=self,
**kwargs,
)
__init__(self, **kwargs)
special
¶
Initializes a new instance of the Map class.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
**kwargs |
Arbitrary keyword arguments that are passed to the parent class's constructor. |
{} |
Source code in hypercoast/hypercoast.py
def __init__(self, **kwargs):
"""
Initializes a new instance of the Map class.
Args:
**kwargs: Arbitrary keyword arguments that are passed to the parent
class's constructor.
"""
super().__init__(**kwargs)
self._spectral_data = {}
self._plot_options = None
self._plot_marker_cluster = ipyleaflet.MarkerCluster(name="Marker Cluster")
add(self, obj, position='topright', xlim=None, ylim=None, **kwargs)
¶
Add a layer to the map.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
obj |
str or object |
The name of the layer or a layer object. |
required |
position |
str |
The position of the layer widget. Can be 'topright', 'topleft', 'bottomright', or 'bottomleft'. Defaults to 'topright'. |
'topright' |
xlim |
tuple |
The x-axis limits of the plot. Defaults to None. |
None |
ylim |
tuple |
The y-axis limits of the plot. Defaults to None. |
None |
**kwargs |
Arbitrary keyword arguments that are passed to the parent class's add_layer method. |
{} |
Source code in hypercoast/hypercoast.py
def add(self, obj, position="topright", xlim=None, ylim=None, **kwargs):
"""Add a layer to the map.
Args:
obj (str or object): The name of the layer or a layer object.
position (str, optional): The position of the layer widget. Can be
'topright', 'topleft', 'bottomright', or 'bottomleft'. Defaults
to 'topright'.
xlim (tuple, optional): The x-axis limits of the plot. Defaults to None.
ylim (tuple, optional): The y-axis limits of the plot. Defaults to None.
**kwargs: Arbitrary keyword arguments that are passed to the parent
class's add_layer method.
"""
if isinstance(obj, str):
if obj == "spectral":
SpectralWidget(self, position=position, xlim=xlim, ylim=ylim, **kwargs)
self.set_plot_options(add_marker_cluster=True)
else:
super().add(obj, **kwargs)
else:
super().add(obj, **kwargs)
add_aviris(self, source, wavelengths=None, indexes=None, colormap=None, vmin=0, vmax=0.5, nodata=nan, attribution=None, layer_name='AVIRIS', zoom_to_layer=True, visible=True, array_args=None, method='nearest', **kwargs)
¶
Add an AVIRIS dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using pip install jupyter-server-proxy,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
1 2 | |
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
str |
The path to the AVIRIS file. |
required |
indexes |
int |
The band(s) to use. Band indexing starts at 1. Defaults to None. |
None |
colormap |
str |
The name of the colormap from |
None |
vmin |
float |
The minimum value to use when colormapping the palette when plotting a single band. Defaults to 0. |
0 |
vmax |
float |
The maximum value to use when colormapping the palette when plotting a single band. Defaults to 0.5. |
0.5 |
nodata |
float |
The value from the band to use to interpret as not valid data. Defaults to np.nan. |
nan |
attribution |
str |
Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None. |
None |
layer_name |
str |
The layer name to use. Defaults to 'NEON'. |
'AVIRIS' |
zoom_to_layer |
bool |
Whether to zoom to the extent of the layer. Defaults to True. |
True |
visible |
bool |
Whether the layer is visible. Defaults to True. |
True |
array_args |
dict |
Additional arguments to pass to
|
None |
method |
str |
The method to use for data interpolation. Defaults to "nearest". |
'nearest' |
Source code in hypercoast/hypercoast.py
def add_aviris(
self,
source,
wavelengths=None,
indexes=None,
colormap=None,
vmin=0,
vmax=0.5,
nodata=np.nan,
attribution=None,
layer_name="AVIRIS",
zoom_to_layer=True,
visible=True,
array_args=None,
method="nearest",
**kwargs,
):
"""Add an AVIRIS dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using `pip install jupyter-server-proxy`,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'
Args:
source (str): The path to the AVIRIS file.
indexes (int, optional): The band(s) to use. Band indexing starts
at 1. Defaults to None.
colormap (str, optional): The name of the colormap from `matplotlib`
to use when plotting a single band. See
https://matplotlib.org/stable/gallery/color/colormap_reference.html.
Default is greyscale.
vmin (float, optional): The minimum value to use when colormapping
the palette when plotting a single band. Defaults to 0.
vmax (float, optional): The maximum value to use when colormapping
the palette when plotting a single band. Defaults to 0.5.
nodata (float, optional): The value from the band to use to
interpret as not valid data. Defaults to np.nan.
attribution (str, optional): Attribution for the source raster. This
defaults to a message about it being a local file.. Defaults to None.
layer_name (str, optional): The layer name to use. Defaults to 'NEON'.
zoom_to_layer (bool, optional): Whether to zoom to the extent of the
layer. Defaults to True.
visible (bool, optional): Whether the layer is visible. Defaults
to True.
array_args (dict, optional): Additional arguments to pass to
`array_to_memory_file` when reading the raster. Defaults to {}.
method (str, optional): The method to use for data interpolation.
Defaults to "nearest".
"""
if array_args is None:
array_args = {}
xds = None
if isinstance(source, str):
xds = read_aviris(source)
source = neon_to_image(xds, wavelengths=wavelengths, method=method)
elif isinstance(source, xr.Dataset):
xds = source
source = aviris_to_image(xds, wavelengths=wavelengths, method=method)
self.add_raster(
source,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
layer_name=layer_name,
zoom_to_layer=zoom_to_layer,
visible=visible,
array_args=array_args,
**kwargs,
)
self.cog_layer_dict[layer_name]["xds"] = xds
self.cog_layer_dict[layer_name]["hyper"] = "AVIRIS"
self._update_band_names(layer_name, wavelengths)
add_desis(self, source, wavelengths=[900, 650, 525], indexes=None, colormap='jet', vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='DESIS', zoom_to_layer=True, visible=True, method='nearest', array_args=None, **kwargs)
¶
Add a DESIS dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using pip install jupyter-server-proxy,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
1 2 | |
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
str |
The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF. |
required |
indexes |
int |
The band(s) to use. Band indexing starts at 1. Defaults to None. |
None |
colormap |
str |
The name of the colormap from |
'jet' |
vmin |
float |
The minimum value to use when colormapping the palette when plotting a single band. Defaults to None. |
None |
vmax |
float |
The maximum value to use when colormapping the palette when plotting a single band. Defaults to None. |
None |
nodata |
float |
The value from the band to use to interpret as not valid data. Defaults to None. |
nan |
attribution |
str |
Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None. |
None |
layer_name |
str |
The layer name to use. Defaults to 'EMIT'. |
'DESIS' |
zoom_to_layer |
bool |
Whether to zoom to the extent of the layer. Defaults to True. |
True |
visible |
bool |
Whether the layer is visible. Defaults to True. |
True |
array_args |
dict |
Additional arguments to pass to
|
None |
Source code in hypercoast/hypercoast.py
def add_desis(
self,
source,
wavelengths=[900, 650, 525],
indexes=None,
colormap="jet",
vmin=None,
vmax=None,
nodata=np.nan,
attribution=None,
layer_name="DESIS",
zoom_to_layer=True,
visible=True,
method="nearest",
array_args=None,
**kwargs,
):
"""Add a DESIS dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using `pip install jupyter-server-proxy`,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'
Args:
source (str): The path to the GeoTIFF file or the URL of the Cloud
Optimized GeoTIFF.
indexes (int, optional): The band(s) to use. Band indexing starts
at 1. Defaults to None.
colormap (str, optional): The name of the colormap from `matplotlib`
to use when plotting a single band. See
https://matplotlib.org/stable/gallery/color/colormap_reference.html.
Default is 'jet'.
vmin (float, optional): The minimum value to use when colormapping
the palette when plotting a single band. Defaults to None.
vmax (float, optional): The maximum value to use when colormapping
the palette when plotting a single band. Defaults to None.
nodata (float, optional): The value from the band to use to interpret
as not valid data. Defaults to None.
attribution (str, optional): Attribution for the source raster. This
defaults to a message about it being a local file.. Defaults to None.
layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.
zoom_to_layer (bool, optional): Whether to zoom to the extent of the
layer. Defaults to True.
visible (bool, optional): Whether the layer is visible. Defaults to True.
array_args (dict, optional): Additional arguments to pass to
`array_to_memory_file` when reading the raster. Defaults to {}.
"""
if array_args is None:
array_args = {}
if isinstance(source, str):
source = read_desis(source)
image = desis_to_image(source, wavelengths=wavelengths, method=method)
if isinstance(wavelengths, list) and len(wavelengths) > 1:
colormap = None
if isinstance(wavelengths, int):
wavelengths = [wavelengths]
if indexes is None:
if isinstance(wavelengths, list) and len(wavelengths) == 1:
indexes = [1]
else:
indexes = [1, 2, 3]
self.add_raster(
image,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
layer_name=layer_name,
zoom_to_layer=zoom_to_layer,
visible=visible,
array_args=array_args,
**kwargs,
)
self.cog_layer_dict[layer_name]["xds"] = source
self.cog_layer_dict[layer_name]["hyper"] = "DESIS"
self._update_band_names(layer_name, wavelengths)
add_emit(self, source, wavelengths=None, indexes=None, colormap=None, vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='EMIT', zoom_to_layer=True, visible=True, array_args=None, **kwargs)
¶
Add an EMIT dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using pip install jupyter-server-proxy,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
1 2 | |
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
str |
The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF. |
required |
indexes |
int |
The band(s) to use. Band indexing starts at 1. Defaults to None. |
None |
colormap |
str |
The name of the colormap from |
None |
vmin |
float |
The minimum value to use when colormapping the palette when plotting a single band. Defaults to None. |
None |
vmax |
float |
The maximum value to use when colormapping the palette when plotting a single band. Defaults to None. |
None |
nodata |
float |
The value from the band to use to interpret as not valid data. Defaults to None. |
nan |
attribution |
str |
Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None. |
None |
layer_name |
str |
The layer name to use. Defaults to 'EMIT'. |
'EMIT' |
zoom_to_layer |
bool |
Whether to zoom to the extent of the layer. Defaults to True. |
True |
visible |
bool |
Whether the layer is visible. Defaults to True. |
True |
array_args |
dict |
Additional arguments to pass to
|
None |
Source code in hypercoast/hypercoast.py
def add_emit(
self,
source,
wavelengths=None,
indexes=None,
colormap=None,
vmin=None,
vmax=None,
nodata=np.nan,
attribution=None,
layer_name="EMIT",
zoom_to_layer=True,
visible=True,
array_args=None,
**kwargs,
):
"""Add an EMIT dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using `pip install jupyter-server-proxy`,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'
Args:
source (str): The path to the GeoTIFF file or the URL of the Cloud
Optimized GeoTIFF.
indexes (int, optional): The band(s) to use. Band indexing starts
at 1. Defaults to None.
colormap (str, optional): The name of the colormap from `matplotlib`
to use when plotting a single band.
See https://matplotlib.org/stable/gallery/color/colormap_reference.html.
Default is greyscale.
vmin (float, optional): The minimum value to use when colormapping
the palette when plotting a single band. Defaults to None.
vmax (float, optional): The maximum value to use when colormapping
the palette when plotting a single band. Defaults to None.
nodata (float, optional): The value from the band to use to
interpret as not valid data. Defaults to None.
attribution (str, optional): Attribution for the source raster. This
defaults to a message about it being a local file.. Defaults to None.
layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.
zoom_to_layer (bool, optional): Whether to zoom to the extent of the
layer. Defaults to True.
visible (bool, optional): Whether the layer is visible. Defaults to
True.
array_args (dict, optional): Additional arguments to pass to
`array_to_memory_file` when reading the raster. Defaults to {}.
"""
if array_args is None:
array_args = {}
xds = None
if isinstance(source, str):
xds = read_emit(source)
source = emit_to_image(xds, wavelengths=wavelengths)
elif isinstance(source, xr.Dataset):
xds = source
source = emit_to_image(xds, wavelengths=wavelengths)
self.add_raster(
source,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
layer_name=layer_name,
zoom_to_layer=zoom_to_layer,
visible=visible,
array_args=array_args,
**kwargs,
)
self.cog_layer_dict[layer_name]["xds"] = xds
self.cog_layer_dict[layer_name]["hyper"] = "EMIT"
self._update_band_names(layer_name, wavelengths)
add_field_data(self, data, x_col='wavelength', y_col_prefix='(', x_label='Wavelengths (nm)', y_label='Reflectance', use_marker_cluster=True, min_width=400, max_width=600, min_height=200, max_height=250, layer_name='Marker Cluster', **kwargs)
¶
Displays field data on a map with interactive markers and popups showing time series data.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data |
Union[str, pd.DataFrame] |
Path to the CSV file or a pandas DataFrame containing the data. |
required |
x_col |
str |
Column name to use for the x-axis of the charts. Default is "wavelength". |
'wavelength' |
y_col_prefix |
str |
Prefix to identify the columns that contain the location-specific data. Default is "(". |
'(' |
x_label |
str |
Label for the x-axis of the charts. Default is "Wavelengths (nm)". |
'Wavelengths (nm)' |
y_label |
str |
Label for the y-axis of the charts. Default is "Reflectance". |
'Reflectance' |
use_marker_cluster |
bool |
Whether to use marker clustering. Default is True. |
True |
min_width |
int |
Minimum width of the popup. Default is 400. |
400 |
max_width |
int |
Maximum width of the popup. Default is 600. |
600 |
min_height |
int |
Minimum height of the popup. Default is 200. |
200 |
max_height |
int |
Maximum height of the popup. Default is 250. |
250 |
layer_name |
str |
Name of the marker cluster layer. Default is "Marker Cluster". |
'Marker Cluster' |
Returns:
| Type | Description |
|---|---|
Map |
An ipyleaflet Map with the added markers and popups. |
Source code in hypercoast/hypercoast.py
def add_field_data(
self,
data: Union[str],
x_col: str = "wavelength",
y_col_prefix: str = "(",
x_label: str = "Wavelengths (nm)",
y_label: str = "Reflectance",
use_marker_cluster: bool = True,
min_width: int = 400,
max_width: int = 600,
min_height: int = 200,
max_height: int = 250,
layer_name: str = "Marker Cluster",
**kwargs,
):
"""
Displays field data on a map with interactive markers and popups showing time series data.
Args:
data (Union[str, pd.DataFrame]): Path to the CSV file or a pandas DataFrame containing the data.
x_col (str): Column name to use for the x-axis of the charts. Default is "wavelength".
y_col_prefix (str): Prefix to identify the columns that contain the location-specific data. Default is "(".
x_label (str): Label for the x-axis of the charts. Default is "Wavelengths (nm)".
y_label (str): Label for the y-axis of the charts. Default is "Reflectance".
use_marker_cluster (bool): Whether to use marker clustering. Default is True.
min_width (int): Minimum width of the popup. Default is 400.
max_width (int): Maximum width of the popup. Default is 600.
min_height (int): Minimum height of the popup. Default is 200.
max_height (int): Maximum height of the popup. Default is 250.
layer_name (str): Name of the marker cluster layer. Default is "Marker Cluster".
Returns:
Map: An ipyleaflet Map with the added markers and popups.
"""
show_field_data(
data,
x_col,
y_col_prefix,
x_label=x_label,
y_label=y_label,
use_marker_cluster=use_marker_cluster,
min_width=min_width,
max_width=max_width,
min_height=min_height,
max_height=max_height,
layer_name=layer_name,
m=self,
**kwargs,
)
add_hyper(self, xds, dtype, wvl_indexes=None, **kwargs)
¶
Add a hyperspectral dataset to the map.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
xds |
str |
The Xarray dataset containing the hyperspectral data. |
required |
dtype |
str |
The type of the hyperspectral dataset. Can be one of "EMIT", "PACE", "DESIS", "NEON", "AVIRIS". |
required |
**kwargs |
Additional keyword arguments to pass to the corresponding add function. |
{} |
Source code in hypercoast/hypercoast.py
def add_hyper(self, xds, dtype, wvl_indexes=None, **kwargs):
"""Add a hyperspectral dataset to the map.
Args:
xds (str): The Xarray dataset containing the hyperspectral data.
dtype (str): The type of the hyperspectral dataset. Can be one of
"EMIT", "PACE", "DESIS", "NEON", "AVIRIS".
**kwargs: Additional keyword arguments to pass to the corresponding
add function.
"""
if wvl_indexes is not None:
if dtype == "XARRAY":
kwargs["indexes"] = [i + 1 for i in wvl_indexes]
else:
kwargs["wavelengths"] = (
xds.isel(wavelength=wvl_indexes)
.coords["wavelength"]
.values.tolist()
)
if dtype == "EMIT":
self.add_emit(xds, **kwargs)
elif dtype == "PACE":
self.add_pace(xds, **kwargs)
elif dtype == "DESIS":
self.add_desis(xds, **kwargs)
elif dtype == "NEON":
self.add_neon(xds, **kwargs)
elif dtype == "AVIRIS":
self.add_aviris(xds, **kwargs)
elif dtype == "XARRAY":
kwargs.pop("wavelengths", None)
self.add_dataset(xds, **kwargs)
add_neon(self, source, wavelengths=None, indexes=None, colormap=None, vmin=0, vmax=0.5, nodata=nan, attribution=None, layer_name='NEON', zoom_to_layer=True, visible=True, array_args=None, method='nearest', **kwargs)
¶
Add an NEON AOP dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using pip install jupyter-server-proxy,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
1 2 | |
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
str |
The path to the NEON AOP HDF5 file. |
required |
indexes |
int |
The band(s) to use. Band indexing starts at 1. Defaults to None. |
None |
colormap |
str |
The name of the colormap from |
None |
vmin |
float |
The minimum value to use when colormapping the palette when plotting a single band. Defaults to 0. |
0 |
vmax |
float |
The maximum value to use when colormapping the palette when plotting a single band. Defaults to 0.5. |
0.5 |
nodata |
float |
The value from the band to use to interpret as not valid data. Defaults to np.nan. |
nan |
attribution |
str |
Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None. |
None |
layer_name |
str |
The layer name to use. Defaults to 'NEON'. |
'NEON' |
zoom_to_layer |
bool |
Whether to zoom to the extent of the layer. Defaults to True. |
True |
visible |
bool |
Whether the layer is visible. Defaults to True. |
True |
array_args |
dict |
Additional arguments to pass to
|
None |
method |
str |
The method to use for data interpolation. Defaults to "nearest". |
'nearest' |
Source code in hypercoast/hypercoast.py
def add_neon(
self,
source,
wavelengths=None,
indexes=None,
colormap=None,
vmin=0,
vmax=0.5,
nodata=np.nan,
attribution=None,
layer_name="NEON",
zoom_to_layer=True,
visible=True,
array_args=None,
method="nearest",
**kwargs,
):
"""Add an NEON AOP dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using `pip install jupyter-server-proxy`,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'
Args:
source (str): The path to the NEON AOP HDF5 file.
indexes (int, optional): The band(s) to use. Band indexing starts
at 1. Defaults to None.
colormap (str, optional): The name of the colormap from `matplotlib`
to use when plotting a single band. See
https://matplotlib.org/stable/gallery/color/colormap_reference.html.
Default is greyscale.
vmin (float, optional): The minimum value to use when colormapping
the palette when plotting a single band. Defaults to 0.
vmax (float, optional): The maximum value to use when colormapping
the palette when plotting a single band. Defaults to 0.5.
nodata (float, optional): The value from the band to use to
interpret as not valid data. Defaults to np.nan.
attribution (str, optional): Attribution for the source raster. This
defaults to a message about it being a local file.. Defaults to None.
layer_name (str, optional): The layer name to use. Defaults to 'NEON'.
zoom_to_layer (bool, optional): Whether to zoom to the extent of the
layer. Defaults to True.
visible (bool, optional): Whether the layer is visible. Defaults
to True.
array_args (dict, optional): Additional arguments to pass to
`array_to_memory_file` when reading the raster. Defaults to {}.
method (str, optional): The method to use for data interpolation.
Defaults to "nearest".
"""
if array_args is None:
array_args = {}
xds = None
if isinstance(source, str):
xds = read_neon(source)
source = neon_to_image(xds, wavelengths=wavelengths, method=method)
elif isinstance(source, xr.Dataset):
xds = source
source = neon_to_image(xds, wavelengths=wavelengths, method=method)
self.add_raster(
source,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
layer_name=layer_name,
zoom_to_layer=zoom_to_layer,
visible=visible,
array_args=array_args,
**kwargs,
)
self.cog_layer_dict[layer_name]["xds"] = xds
self.cog_layer_dict[layer_name]["hyper"] = "NEON"
self._update_band_names(layer_name, wavelengths)
add_pace(self, source, wavelengths=None, indexes=None, colormap='jet', vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='PACE', zoom_to_layer=True, visible=True, method='nearest', gridded=False, array_args=None, **kwargs)
¶
Add a PACE dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using pip install jupyter-server-proxy,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
1 2 | |
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
str |
The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF. |
required |
indexes |
int |
The band(s) to use. Band indexing starts at 1. Defaults to None. |
None |
colormap |
str |
The name of the colormap from |
'jet' |
vmin |
float |
The minimum value to use when colormapping the palette when plotting a single band. Defaults to None. |
None |
vmax |
float |
The maximum value to use when colormapping the palette when plotting a single band. Defaults to None. |
None |
nodata |
float |
The value from the band to use to interpret as not valid data. Defaults to None. |
nan |
attribution |
str |
Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None. |
None |
layer_name |
str |
The layer name to use. Defaults to 'EMIT'. |
'PACE' |
zoom_to_layer |
bool |
Whether to zoom to the extent of the layer. Defaults to True. |
True |
visible |
bool |
Whether the layer is visible. Defaults to True. |
True |
array_args |
dict |
Additional arguments to pass to
|
None |
Source code in hypercoast/hypercoast.py
def add_pace(
self,
source,
wavelengths=None,
indexes=None,
colormap="jet",
vmin=None,
vmax=None,
nodata=np.nan,
attribution=None,
layer_name="PACE",
zoom_to_layer=True,
visible=True,
method="nearest",
gridded=False,
array_args=None,
**kwargs,
):
"""Add a PACE dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using `pip install jupyter-server-proxy`,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'
Args:
source (str): The path to the GeoTIFF file or the URL of the Cloud
Optimized GeoTIFF.
indexes (int, optional): The band(s) to use. Band indexing starts
at 1. Defaults to None.
colormap (str, optional): The name of the colormap from `matplotlib`
to use when plotting a single band. See
https://matplotlib.org/stable/gallery/color/colormap_reference.html.
Default is greyscale.
vmin (float, optional): The minimum value to use when colormapping
the palette when plotting a single band. Defaults to None.
vmax (float, optional): The maximum value to use when colormapping
the palette when plotting a single band. Defaults to None.
nodata (float, optional): The value from the band to use to interpret
as not valid data. Defaults to None.
attribution (str, optional): Attribution for the source raster. This
defaults to a message about it being a local file.. Defaults to None.
layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.
zoom_to_layer (bool, optional): Whether to zoom to the extent of the
layer. Defaults to True.
visible (bool, optional): Whether the layer is visible. Defaults to True.
array_args (dict, optional): Additional arguments to pass to
`array_to_memory_file` when reading the raster. Defaults to {}.
"""
if array_args is None:
array_args = {}
if isinstance(source, str):
source = read_pace(source)
try:
image = pace_to_image(
source, wavelengths=wavelengths, method=method, gridded=gridded
)
if isinstance(wavelengths, list) and len(wavelengths) > 1:
colormap = None
self.add_raster(
image,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
layer_name=layer_name,
zoom_to_layer=zoom_to_layer,
visible=visible,
array_args=array_args,
**kwargs,
)
self.cog_layer_dict[layer_name]["xds"] = source
self.cog_layer_dict[layer_name]["hyper"] = "PACE"
self._update_band_names(layer_name, wavelengths)
except Exception as e:
print(e)
add_raster(self, source, indexes=None, colormap=None, vmin=None, vmax=None, nodata=None, attribution=None, layer_name='Raster', layer_index=None, zoom_to_layer=True, visible=True, opacity=1.0, array_args=None, client_args={'cors_all': False}, open_args=None, **kwargs)
¶
Add a local raster dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using pip install jupyter-server-proxy,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
1 2 | |
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
str |
The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF. |
required |
indexes |
int |
The band(s) to use. Band indexing starts at 1. Defaults to None. |
None |
colormap |
str |
The name of the colormap from |
None |
vmin |
float |
The minimum value to use when colormapping the palette when plotting a single band. Defaults to None. |
None |
vmax |
float |
The maximum value to use when colormapping the palette when plotting a single band. Defaults to None. |
None |
nodata |
float |
The value from the band to use to interpret as not valid data. Defaults to None. |
None |
attribution |
str |
Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None. |
None |
layer_name |
str |
The layer name to use. Defaults to 'Raster'. |
'Raster' |
layer_index |
int |
The index of the layer. Defaults to None. |
None |
zoom_to_layer |
bool |
Whether to zoom to the extent of the layer. Defaults to True. |
True |
visible |
bool |
Whether the layer is visible. Defaults to True. |
True |
opacity |
float |
The opacity of the layer. Defaults to 1.0. |
1.0 |
array_args |
dict |
Additional arguments to pass to
|
None |
client_args |
dict |
Additional arguments to pass to localtileserver.TileClient. Defaults to { "cors_all": False }. |
{'cors_all': False} |
open_args |
dict |
Additional arguments to pass to rioxarray.open_rasterio. |
None |
Source code in hypercoast/hypercoast.py
def add_raster(
self,
source,
indexes=None,
colormap=None,
vmin=None,
vmax=None,
nodata=None,
attribution=None,
layer_name="Raster",
layer_index=None,
zoom_to_layer=True,
visible=True,
opacity=1.0,
array_args=None,
client_args={"cors_all": False},
open_args=None,
**kwargs,
):
"""Add a local raster dataset to the map.
If you are using this function in JupyterHub on a remote server
(e.g., Binder, Microsoft Planetary Computer) and
if the raster does not render properly, try installing
jupyter-server-proxy using `pip install jupyter-server-proxy`,
then running the following code before calling this function. For
more info, see https://bit.ly/3JbmF93.
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'
Args:
source (str): The path to the GeoTIFF file or the URL of the Cloud
Optimized GeoTIFF.
indexes (int, optional): The band(s) to use. Band indexing starts
at 1. Defaults to None.
colormap (str, optional): The name of the colormap from `matplotlib`
to use when plotting a single band. See
https://matplotlib.org/stable/gallery/color/colormap_reference.html.
Default is greyscale.
vmin (float, optional): The minimum value to use when colormapping
the palette when plotting a single band. Defaults to None.
vmax (float, optional): The maximum value to use when colormapping
the palette when plotting a single band. Defaults to None.
nodata (float, optional): The value from the band to use to interpret
as not valid data. Defaults to None.
attribution (str, optional): Attribution for the source raster. This
defaults to a message about it being a local file.. Defaults to None.
layer_name (str, optional): The layer name to use. Defaults to 'Raster'.
layer_index (int, optional): The index of the layer. Defaults to None.
zoom_to_layer (bool, optional): Whether to zoom to the extent of the
layer. Defaults to True.
visible (bool, optional): Whether the layer is visible. Defaults to
True.
opacity (float, optional): The opacity of the layer. Defaults to 1.0.
array_args (dict, optional): Additional arguments to pass to
`array_to_memory_file` when reading the raster. Defaults to {}.
client_args (dict, optional): Additional arguments to pass to
localtileserver.TileClient. Defaults to { "cors_all": False }.
open_args (dict, optional): Additional arguments to pass to
rioxarray.open_rasterio.
"""
import rioxarray as rxr
if array_args is None:
array_args = {}
if open_args is None:
open_args = {}
if nodata is None:
nodata = np.nan
super().add_raster(
source,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
layer_name=layer_name,
layer_index=layer_index,
zoom_to_layer=zoom_to_layer,
visible=visible,
opacity=opacity,
array_args=array_args,
client_args=client_args,
**kwargs,
)
if isinstance(source, str):
da = rxr.open_rasterio(source, **open_args)
dims = da.dims
da = da.transpose(dims[1], dims[2], dims[0])
xds = da.to_dataset(name="data")
self.cog_layer_dict[layer_name]["xds"] = xds
# if self.cog_layer_dict[layer_name].get("hyper") is None:
# self.cog_layer_dict[layer_name]["hyper"] = "COG"
search_ecostress(self, default_dataset='ECO_L2T_LSTE')
¶
Adds a NASA Earth Data search tool to the map with a default dataset for ECOSTRESS.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
default_dataset |
str |
The default dataset to search for. Defaults to "ECO_L2T_LSTE". |
'ECO_L2T_LSTE' |
Source code in hypercoast/hypercoast.py
def search_ecostress(self, default_dataset="ECO_L2T_LSTE"):
"""
Adds a NASA Earth Data search tool to the map with a default dataset for
ECOSTRESS.
Args:
default_dataset (str, optional): The default dataset to search for.
Defaults to "ECO_L2T_LSTE".
"""
self.add("nasa_earth_data", default_dataset=default_dataset)
search_emit(self, default_dataset='EMITL2ARFL')
¶
Adds a NASA Earth Data search tool to the map with a default dataset for EMIT.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
default_dataset |
str |
The default dataset to search for. Defaults to "EMITL2ARFL". |
'EMITL2ARFL' |
Source code in hypercoast/hypercoast.py
def search_emit(self, default_dataset="EMITL2ARFL"):
"""
Adds a NASA Earth Data search tool to the map with a default dataset for
EMIT.
Args:
default_dataset (str, optional): The default dataset to search for.
Defaults to "EMITL2ARFL".
"""
self.add("nasa_earth_data", default_dataset=default_dataset)
search_pace(self, default_dataset='PACE_OCI_L2_AOP_NRT')
¶
Adds a NASA Earth Data search tool to the map with a default dataset for PACE.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
default_dataset |
str |
The default dataset to search for. Defaults to "PACE_OCI_L2_AOP_NRT". |
'PACE_OCI_L2_AOP_NRT' |
Source code in hypercoast/hypercoast.py
def search_pace(self, default_dataset="PACE_OCI_L2_AOP_NRT"):
"""
Adds a NASA Earth Data search tool to the map with a default dataset for
PACE.
Args:
default_dataset (str, optional): The default dataset to search for.
Defaults to "PACE_OCI_L2_AOP_NRT".
"""
self.add("nasa_earth_data", default_dataset=default_dataset)
set_plot_options(self, add_marker_cluster=False, plot_type=None, overlay=False, position='bottomright', min_width=None, max_width=None, min_height=None, max_height=None, **kwargs)
¶
Sets plotting options.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
add_marker_cluster |
bool |
Whether to add a marker cluster. Defaults to False. |
False |
sample_scale |
float |
A nominal scale in meters of the projection to sample in . Defaults to None. |
required |
plot_type |
str |
The plot type can be one of "None", "bar", "scatter" or "hist". Defaults to None. |
None |
overlay |
bool |
Whether to overlay plotted lines on the figure. Defaults to False. |
False |
position |
str |
Position of the control, can be ‘bottomleft’, ‘bottomright’, ‘topleft’, or ‘topright’. Defaults to 'bottomright'. |
'bottomright' |
min_width |
int |
Min width of the widget (in pixels), if None it will respect the content size. Defaults to None. |
None |
max_width |
int |
Max width of the widget (in pixels), if None it will respect the content size. Defaults to None. |
None |
min_height |
int |
Min height of the widget (in pixels), if None it will respect the content size. Defaults to None. |
None |
max_height |
int |
Max height of the widget (in pixels), if None it will respect the content size. Defaults to None. |
None |
Source code in hypercoast/hypercoast.py
def set_plot_options(
self,
add_marker_cluster=False,
plot_type=None,
overlay=False,
position="bottomright",
min_width=None,
max_width=None,
min_height=None,
max_height=None,
**kwargs,
):
"""Sets plotting options.
Args:
add_marker_cluster (bool, optional): Whether to add a marker cluster.
Defaults to False.
sample_scale (float, optional): A nominal scale in meters of the
projection to sample in . Defaults to None.
plot_type (str, optional): The plot type can be one of "None", "bar",
"scatter" or "hist". Defaults to None.
overlay (bool, optional): Whether to overlay plotted lines on the
figure. Defaults to False.
position (str, optional): Position of the control, can be
‘bottomleft’, ‘bottomright’, ‘topleft’, or ‘topright’. Defaults
to 'bottomright'.
min_width (int, optional): Min width of the widget (in pixels), if
None it will respect the content size. Defaults to None.
max_width (int, optional): Max width of the widget (in pixels), if
None it will respect the content size. Defaults to None.
min_height (int, optional): Min height of the widget (in pixels), if
None it will respect the content size. Defaults to None.
max_height (int, optional): Max height of the widget (in pixels), if
None it will respect the content size. Defaults to None.
"""
plot_options_dict = {}
plot_options_dict["add_marker_cluster"] = add_marker_cluster
plot_options_dict["plot_type"] = plot_type
plot_options_dict["overlay"] = overlay
plot_options_dict["position"] = position
plot_options_dict["min_width"] = min_width
plot_options_dict["max_width"] = max_width
plot_options_dict["min_height"] = min_height
plot_options_dict["max_height"] = max_height
for key in kwargs:
plot_options_dict[key] = kwargs[key]
self._plot_options = plot_options_dict
if not hasattr(self, "_plot_marker_cluster"):
self._plot_marker_cluster = ipyleaflet.MarkerCluster(name="Marker Cluster")
if add_marker_cluster and (self._plot_marker_cluster not in self.layers):
self.add(self._plot_marker_cluster)
spectral_to_csv(self, filename, index=True, **kwargs)
¶
Saves the spectral data to a CSV file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
The output CSV file. |
required |
index |
bool |
Whether to write the index. Defaults to True. |
True |
Source code in hypercoast/hypercoast.py
def spectral_to_csv(self, filename, index=True, **kwargs):
"""Saves the spectral data to a CSV file.
Args:
filename (str): The output CSV file.
index (bool, optional): Whether to write the index. Defaults to True.
"""
df = self.spectral_to_df()
df = df.rename_axis("band")
df.to_csv(filename, index=index, **kwargs)
spectral_to_df(self, **kwargs)
¶
Converts the spectral data to a pandas DataFrame.
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
The spectral data as a pandas DataFrame. |
Source code in hypercoast/hypercoast.py
def spectral_to_df(self, **kwargs):
"""Converts the spectral data to a pandas DataFrame.
Returns:
pd.DataFrame: The spectral data as a pandas DataFrame.
"""
import pandas as pd
df = pd.DataFrame(self._spectral_data, **kwargs)
return df
spectral_to_gdf(self, **kwargs)
¶
Converts the spectral data to a GeoPandas GeoDataFrame.
Returns:
| Type | Description |
|---|---|
gpd.DataFrame |
The spectral data as a pandas DataFrame. |
Source code in hypercoast/hypercoast.py
def spectral_to_gdf(self, **kwargs):
"""Converts the spectral data to a GeoPandas GeoDataFrame.
Returns:
gpd.DataFrame: The spectral data as a pandas DataFrame.
"""
import geopandas as gpd
from shapely.geometry import Point
df = self.spectral_to_df()
if len(df) == 0:
return df
# Step 1: Extract the coordinates from the columns
if "wavelength" in df.columns:
df = df.rename(columns={"wavelength": "latlon"})
elif "wavelengths" in df.columns:
df = df.rename(columns={"wavelengths": "latlon"})
coordinates = [col.strip("()").split() for col in df.columns[1:]]
coords = [(float(lat), float(lon)) for lat, lon in coordinates]
# Step 2: Create Point geometries for each coordinate
points = [Point(lon, lat) for lat, lon in coords]
# Step 3: Create a GeoDataFrame
df_transposed = df.set_index("latlon").T
# Convert the column names to strings to ensure compatibility with GeoJSON
df_transposed.columns = df_transposed.columns.astype(str)
# Create the GeoDataFrame
gdf = gpd.GeoDataFrame(df_transposed, geometry=points, **kwargs)
# Set the coordinate reference system (CRS)
gdf = gdf.set_geometry("geometry").set_crs("EPSG:4326")
return gdf