common module¶
This module contains some common functions for both folium and ipyleaflet to interact with the Earth Engine Python API.
add_image_to_gif(in_gif, out_gif, in_image, xy=None, image_size=(80, 80), circle_mask=False)
¶
Adds an image logo to a GIF image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_gif |
str |
Input file path to the GIF image. |
required |
out_gif |
str |
Output file path to the GIF image. |
required |
in_image |
str |
Input file path to the image. |
required |
xy |
tuple |
Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None. |
None |
image_size |
tuple |
Resize image. Defaults to (80, 80). |
(80, 80) |
circle_mask |
bool |
Whether to apply a circle mask to the image. This only works with non-png images. Defaults to False. |
False |
Source code in geemap/common.py
def add_image_to_gif(
in_gif, out_gif, in_image, xy=None, image_size=(80, 80), circle_mask=False
):
"""Adds an image logo to a GIF image.
Args:
in_gif (str): Input file path to the GIF image.
out_gif (str): Output file path to the GIF image.
in_image (str): Input file path to the image.
xy (tuple, optional): Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
image_size (tuple, optional): Resize image. Defaults to (80, 80).
circle_mask (bool, optional): Whether to apply a circle mask to the image. This only works with non-png images. Defaults to False.
"""
import io
import warnings
from PIL import Image, ImageDraw, ImageSequence
warnings.simplefilter("ignore")
in_gif = os.path.abspath(in_gif)
is_url = False
if in_image.startswith("http"):
is_url = True
if not os.path.exists(in_gif):
print("The input gif file does not exist.")
return
if (not is_url) and (not os.path.exists(in_image)):
print("The provided logo file does not exist.")
return
if not os.path.exists(os.path.dirname(out_gif)):
os.makedirs(os.path.dirname(out_gif))
try:
image = Image.open(in_gif)
except Exception as e:
print("An error occurred while opening the image.")
print(e)
return
logo_raw_image = None
try:
if in_image.startswith("http"):
logo_raw_image = open_image_from_url(in_image)
else:
in_image = os.path.abspath(in_image)
logo_raw_image = Image.open(in_image)
except Exception as e:
print(e)
logo_raw_size = logo_raw_image.size
image_size = min(logo_raw_size[0], image_size[0]), min(
logo_raw_size[1], image_size[1]
)
logo_image = logo_raw_image.convert("RGBA")
logo_image.thumbnail(image_size, Image.ANTIALIAS)
W, H = image.size
mask_im = None
if circle_mask:
mask_im = Image.new("L", image_size, 0)
draw = ImageDraw.Draw(mask_im)
draw.ellipse((0, 0, image_size[0], image_size[1]), fill=255)
if has_transparency(logo_raw_image):
mask_im = logo_image.copy()
if xy is None:
# default logo location is 5% width and 5% height of the image.
xy = (int(0.05 * W), int(0.05 * H))
elif (xy is not None) and (not isinstance(xy, tuple)) and (len(xy) == 2):
print("xy must be a tuple, e.g., (10, 10), ('10%', '10%')")
return
elif all(isinstance(item, int) for item in xy) and (len(xy) == 2):
x, y = xy
if (x > 0) and (x < W) and (y > 0) and (y < H):
pass
else:
print(
"xy is out of bounds. x must be within [0, {}], and y must be within [0, {}]".format(
W, H
)
)
return
elif all(isinstance(item, str) for item in xy) and (len(xy) == 2):
x, y = xy
if ("%" in x) and ("%" in y):
try:
x = int(float(x.replace("%", "")) / 100.0 * W)
y = int(float(y.replace("%", "")) / 100.0 * H)
xy = (x, y)
except Exception:
raise Exception(
"The specified xy is invalid. It must be formatted like this ('10%', '10%')"
)
else:
raise Exception(
"The specified xy is invalid. It must be formatted like this: (10, 10) or ('10%', '10%')"
)
try:
frames = []
for _, frame in enumerate(ImageSequence.Iterator(image)):
frame = frame.convert("RGBA")
frame.paste(logo_image, xy, mask_im)
b = io.BytesIO()
frame.save(b, format="GIF")
frame = Image.open(b)
frames.append(frame)
frames[0].save(out_gif, save_all=True, append_images=frames[1:])
except Exception as e:
print(e)
add_text_to_gif(in_gif, out_gif, xy=None, text_sequence=None, font_type='arial.ttf', font_size=20, font_color='#000000', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, duration=100, loop=0)
¶
Adds animated text to a GIF image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_gif |
str |
The file path to the input GIF image. |
required |
out_gif |
str |
The file path to the output GIF image. |
required |
xy |
tuple |
Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None. |
None |
text_sequence |
int, str, list |
Text to be drawn. It can be an integer number, a string, or a list of strings. Defaults to None. |
None |
font_type |
str |
Font type. Defaults to "arial.ttf". |
'arial.ttf' |
font_size |
int |
Font size. Defaults to 20. |
20 |
font_color |
str |
Font color. It can be a string (e.g., 'red'), rgb tuple (e.g., (255, 127, 0)), or hex code (e.g., '#ff00ff'). Defaults to '#000000'. |
'#000000' |
add_progress_bar |
bool |
Whether to add a progress bar at the bottom of the GIF. Defaults to True. |
True |
progress_bar_color |
str |
Color for the progress bar. Defaults to 'white'. |
'white' |
progress_bar_height |
int |
Height of the progress bar. Defaults to 5. |
5 |
duration |
int |
controls how long each frame will be displayed for, in milliseconds. It is the inverse of the frame rate. Setting it to 100 milliseconds gives 10 frames per second. You can decrease the duration to give a smoother animation.. Defaults to 100. |
100 |
loop |
int |
controls how many times the animation repeats. The default, 1, means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0. |
0 |
Source code in geemap/common.py
def add_text_to_gif(
in_gif,
out_gif,
xy=None,
text_sequence=None,
font_type="arial.ttf",
font_size=20,
font_color="#000000",
add_progress_bar=True,
progress_bar_color="white",
progress_bar_height=5,
duration=100,
loop=0,
):
"""Adds animated text to a GIF image.
Args:
in_gif (str): The file path to the input GIF image.
out_gif (str): The file path to the output GIF image.
xy (tuple, optional): Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
text_sequence (int, str, list, optional): Text to be drawn. It can be an integer number, a string, or a list of strings. Defaults to None.
font_type (str, optional): Font type. Defaults to "arial.ttf".
font_size (int, optional): Font size. Defaults to 20.
font_color (str, optional): Font color. It can be a string (e.g., 'red'), rgb tuple (e.g., (255, 127, 0)), or hex code (e.g., '#ff00ff'). Defaults to '#000000'.
add_progress_bar (bool, optional): Whether to add a progress bar at the bottom of the GIF. Defaults to True.
progress_bar_color (str, optional): Color for the progress bar. Defaults to 'white'.
progress_bar_height (int, optional): Height of the progress bar. Defaults to 5.
duration (int, optional): controls how long each frame will be displayed for, in milliseconds. It is the inverse of the frame rate. Setting it to 100 milliseconds gives 10 frames per second. You can decrease the duration to give a smoother animation.. Defaults to 100.
loop (int, optional): controls how many times the animation repeats. The default, 1, means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0.
"""
import io
import pkg_resources
import warnings
from PIL import Image, ImageDraw, ImageSequence, ImageFont
warnings.simplefilter("ignore")
pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
default_font = os.path.join(pkg_dir, "data/fonts/arial.ttf")
in_gif = os.path.abspath(in_gif)
out_gif = os.path.abspath(out_gif)
if not os.path.exists(in_gif):
print("The input gif file does not exist.")
return
if not os.path.exists(os.path.dirname(out_gif)):
os.makedirs(os.path.dirname(out_gif))
if font_type == "arial.ttf":
font = ImageFont.truetype(default_font, font_size)
else:
try:
font_list = system_fonts(show_full_path=True)
font_names = [os.path.basename(f) for f in font_list]
if (font_type in font_list) or (font_type in font_names):
font = ImageFont.truetype(font_type, font_size)
else:
print(
"The specified font type could not be found on your system. Using the default font instead."
)
font = ImageFont.truetype(default_font, font_size)
except Exception as e:
print(e)
font = ImageFont.truetype(default_font, font_size)
color = check_color(font_color)
progress_bar_color = check_color(progress_bar_color)
try:
image = Image.open(in_gif)
except Exception as e:
print("An error occurred while opening the gif.")
print(e)
return
count = image.n_frames
W, H = image.size
progress_bar_widths = [i * 1.0 / count * W for i in range(1, count + 1)]
progress_bar_shapes = [
[(0, H - progress_bar_height), (x, H)] for x in progress_bar_widths
]
if xy is None:
# default text location is 5% width and 5% height of the image.
xy = (int(0.05 * W), int(0.05 * H))
elif (xy is not None) and (not isinstance(xy, tuple)) and (len(xy) == 2):
print("xy must be a tuple, e.g., (10, 10), ('10%', '10%')")
return
elif all(isinstance(item, int) for item in xy) and (len(xy) == 2):
x, y = xy
if (x > 0) and (x < W) and (y > 0) and (y < H):
pass
else:
print(
"xy is out of bounds. x must be within [0, {}], and y must be within [0, {}]".format(
W, H
)
)
return
elif all(isinstance(item, str) for item in xy) and (len(xy) == 2):
x, y = xy
if ("%" in x) and ("%" in y):
try:
x = int(float(x.replace("%", "")) / 100.0 * W)
y = int(float(y.replace("%", "")) / 100.0 * H)
xy = (x, y)
except Exception:
raise Exception(
"The specified xy is invalid. It must be formatted like this ('10%', '10%')"
)
else:
print(
"The specified xy is invalid. It must be formatted like this: (10, 10) or ('10%', '10%')"
)
return
if text_sequence is None:
text = [str(x) for x in range(1, count + 1)]
elif isinstance(text_sequence, int):
text = [str(x) for x in range(text_sequence, text_sequence + count + 1)]
elif isinstance(text_sequence, str):
try:
text_sequence = int(text_sequence)
text = [str(x) for x in range(text_sequence, text_sequence + count + 1)]
except Exception:
text = [text_sequence] * count
elif isinstance(text_sequence, list) and len(text_sequence) != count:
print(
"The length of the text sequence must be equal to the number ({}) of frames in the gif.".format(
count
)
)
return
else:
text = [str(x) for x in text_sequence]
try:
frames = []
# Loop over each frame in the animated image
for index, frame in enumerate(ImageSequence.Iterator(image)):
# Draw the text on the frame
frame = frame.convert("RGB")
draw = ImageDraw.Draw(frame)
# w, h = draw.textsize(text[index])
draw.text(xy, text[index], font=font, fill=color)
if add_progress_bar:
draw.rectangle(progress_bar_shapes[index], fill=progress_bar_color)
del draw
b = io.BytesIO()
frame.save(b, format="GIF")
frame = Image.open(b)
frames.append(frame)
# https://www.pythoninformer.com/python-libraries/pillow/creating-animated-gif/
# Save the frames as a new image
frames[0].save(
out_gif,
save_all=True,
append_images=frames[1:],
duration=duration,
loop=loop,
optimize=True,
)
except Exception as e:
print(e)
adjust_longitude(in_fc)
¶
Adjusts longitude if it is less than -180 or greater than 180.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_fc |
dict |
The input dictionary containing coordinates. |
required |
Returns:
| Type | Description |
|---|---|
dict |
A dictionary containing the converted longitudes |
Source code in geemap/common.py
def adjust_longitude(in_fc):
"""Adjusts longitude if it is less than -180 or greater than 180.
Args:
in_fc (dict): The input dictionary containing coordinates.
Returns:
dict: A dictionary containing the converted longitudes
"""
try:
keys = in_fc.keys()
if "geometry" in keys:
coordinates = in_fc["geometry"]["coordinates"]
if in_fc["geometry"]["type"] == "Point":
longitude = coordinates[0]
if longitude < -180:
longitude = 360 + longitude
elif longitude > 180:
longitude = longitude - 360
in_fc["geometry"]["coordinates"][0] = longitude
elif in_fc["geometry"]["type"] == "Polygon":
for index1, item in enumerate(coordinates):
for index2, element in enumerate(item):
longitude = element[0]
if longitude < -180:
longitude = 360 + longitude
elif longitude > 180:
longitude = longitude - 360
in_fc["geometry"]["coordinates"][index1][index2][0] = longitude
elif in_fc["geometry"]["type"] == "LineString":
for index, element in enumerate(coordinates):
longitude = element[0]
if longitude < -180:
longitude = 360 + longitude
elif longitude > 180:
longitude = longitude - 360
in_fc["geometry"]["coordinates"][index][0] = longitude
elif "type" in keys:
coordinates = in_fc["coordinates"]
if in_fc["type"] == "Point":
longitude = coordinates[0]
if longitude < -180:
longitude = 360 + longitude
elif longitude > 180:
longitude = longitude - 360
in_fc["coordinates"][0] = longitude
elif in_fc["type"] == "Polygon":
for index1, item in enumerate(coordinates):
for index2, element in enumerate(item):
longitude = element[0]
if longitude < -180:
longitude = 360 + longitude
elif longitude > 180:
longitude = longitude - 360
in_fc["coordinates"][index1][index2][0] = longitude
elif in_fc["type"] == "LineString":
for index, element in enumerate(coordinates):
longitude = element[0]
if longitude < -180:
longitude = 360 + longitude
elif longitude > 180:
longitude = longitude - 360
in_fc["coordinates"][index][0] = longitude
return in_fc
except Exception as e:
print(e)
return None
annual_NAIP(year, region)
¶
Create an NAIP mosaic of a specified year for a specified region.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
year |
int |
The specified year to create the mosaic for. |
required |
region |
object |
ee.Geometry |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Image |
Source code in geemap/common.py
def annual_NAIP(year, region):
"""Create an NAIP mosaic of a specified year for a specified region.
Args:
year (int): The specified year to create the mosaic for.
region (object): ee.Geometry
Returns:
object: ee.Image
"""
start_date = ee.Date.fromYMD(year, 1, 1)
end_date = ee.Date.fromYMD(year, 12, 31)
collection = (
ee.ImageCollection("USDA/NAIP/DOQQ")
.filterDate(start_date, end_date)
.filterBounds(region)
)
time_start = ee.Date(
ee.List(collection.aggregate_array("system:time_start")).sort().get(0)
)
time_end = ee.Date(
ee.List(collection.aggregate_array("system:time_end")).sort().get(-1)
)
image = ee.Image(collection.mosaic().clip(region))
NDWI = ee.Image(image).normalizedDifference(["G", "N"]).select(["nd"], ["ndwi"])
NDVI = ee.Image(image).normalizedDifference(["N", "R"]).select(["nd"], ["ndvi"])
image = image.addBands(NDWI)
image = image.addBands(NDVI)
return image.set({"system:time_start": time_start, "system:time_end": time_end})
api_docs()
¶
Open a browser and navigate to the geemap API documentation.
Source code in geemap/common.py
def api_docs():
"""Open a browser and navigate to the geemap API documentation."""
import webbrowser
url = "https://geemap.org/geemap"
webbrowser.open_new_tab(url)
array_mean(arr)
¶
Calculates the mean of an array along the given axis.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
arr |
object |
Array to calculate mean. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def array_mean(arr):
"""Calculates the mean of an array along the given axis.
Args:
arr (object): Array to calculate mean.
Returns:
object: ee.Number
"""
total = ee.Array(arr).accum(0).get([-1])
size = arr.length()
return ee.Number(total.divide(size))
array_sum(arr)
¶
Accumulates elements of an array along the given axis.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
arr |
object |
Array to accumulate. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def array_sum(arr):
"""Accumulates elements of an array along the given axis.
Args:
arr (object): Array to accumulate.
Returns:
object: ee.Number
"""
return ee.Array(arr).accum(0).get([-1])
bands_to_image_collection(img)
¶
Converts all bands in an image to an image collection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to convert. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.ImageCollection |
Source code in geemap/common.py
def bands_to_image_collection(img):
"""Converts all bands in an image to an image collection.
Args:
img (object): The image to convert.
Returns:
object: ee.ImageCollection
"""
collection = ee.ImageCollection(img.bandNames().map(lambda b: img.select([b])))
return collection
bbox_to_geojson(bounds)
¶
Convert coordinates of a bounding box to a geojson.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
bounds |
list |
A list of coordinates representing [left, bottom, right, top]. |
required |
Returns:
| Type | Description |
|---|---|
dict |
A geojson feature. |
Source code in geemap/common.py
def bbox_to_geojson(bounds):
"""Convert coordinates of a bounding box to a geojson.
Args:
bounds (list): A list of coordinates representing [left, bottom, right, top].
Returns:
dict: A geojson feature.
"""
return {
"geometry": {
"type": "Polygon",
"coordinates": [
[
[bounds[0], bounds[3]],
[bounds[0], bounds[1]],
[bounds[2], bounds[1]],
[bounds[2], bounds[3]],
[bounds[0], bounds[3]],
]
],
},
"type": "Feature",
}
build_api_tree(api_dict, output_widget, layout_width='100%')
¶
Builds an Earth Engine API tree view.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_dict |
dict |
The dictionary containing information about each Earth Engine API function. |
required |
output_widget |
object |
An Output widget. |
required |
layout_width |
str |
The percentage width of the widget. Defaults to '100%'. |
'100%' |
Returns:
| Type | Description |
|---|---|
tuple |
Returns a tuple containing two items: a tree Output widget and a tree dictionary. |
Source code in geemap/common.py
def build_api_tree(api_dict, output_widget, layout_width="100%"):
"""Builds an Earth Engine API tree view.
Args:
api_dict (dict): The dictionary containing information about each Earth Engine API function.
output_widget (object): An Output widget.
layout_width (str, optional): The percentage width of the widget. Defaults to '100%'.
Returns:
tuple: Returns a tuple containing two items: a tree Output widget and a tree dictionary.
"""
import warnings
warnings.filterwarnings("ignore")
tree = Tree()
tree_dict = {}
names = api_dict.keys()
def handle_click(event):
if event["new"]:
name = event["owner"].name
values = api_dict[name]
with output_widget:
output_widget.clear_output()
html_widget = widgets.HTML(value=values["html"])
display(html_widget)
for name in names:
func_list = ee_function_tree(name)
first = func_list[0]
if first not in tree_dict.keys():
tree_dict[first] = Node(first)
tree_dict[first].opened = False
tree.add_node(tree_dict[first])
for index, func in enumerate(func_list):
if index > 0:
if func not in tree_dict.keys():
node = tree_dict[func_list[index - 1]]
node.opened = False
tree_dict[func] = Node(func)
node.add_node(tree_dict[func])
if index == len(func_list) - 1:
node = tree_dict[func_list[index]]
node.icon = "file"
node.observe(handle_click, "selected")
return tree, tree_dict
build_repo_tree(out_dir=None, name='gee_repos')
¶
Builds a repo tree for GEE account.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
out_dir |
str |
The output directory for the repos. Defaults to None. |
None |
name |
str |
The output name for the repo directory. Defaults to 'gee_repos'. |
'gee_repos' |
Returns:
| Type | Description |
|---|---|
tuple |
Returns a tuple containing a tree widget, an output widget, and a tree dictionary containing nodes. |
Source code in geemap/common.py
def build_repo_tree(out_dir=None, name="gee_repos"):
"""Builds a repo tree for GEE account.
Args:
out_dir (str): The output directory for the repos. Defaults to None.
name (str, optional): The output name for the repo directory. Defaults to 'gee_repos'.
Returns:
tuple: Returns a tuple containing a tree widget, an output widget, and a tree dictionary containing nodes.
"""
import warnings
warnings.filterwarnings("ignore")
if out_dir is None:
out_dir = os.path.join(os.path.expanduser("~"))
repo_dir = os.path.join(out_dir, name)
if not os.path.exists(repo_dir):
os.makedirs(repo_dir)
URLs = {
# 'Owner': 'https://earthengine.googlesource.com/{}/default'.format(ee_user_id()),
"Writer": "",
"Reader": "https://github.com/giswqs/geemap",
"Examples": "https://github.com/giswqs/earthengine-py-examples",
"Archive": "https://earthengine.googlesource.com/EGU2017-EE101",
}
user_id = ee_user_id()
if user_id is not None:
URLs["Owner"] = "https://earthengine.googlesource.com/{}/default".format(
ee_user_id()
)
path_widget = widgets.Text(placeholder="Enter the link to a Git repository here...")
path_widget.layout.width = "475px"
clone_widget = widgets.Button(
description="Clone",
button_style="primary",
tooltip="Clone the repository to folder.",
)
info_widget = widgets.HBox()
groups = ["Owner", "Writer", "Reader", "Examples", "Archive"]
for group in groups:
group_dir = os.path.join(repo_dir, group)
if not os.path.exists(group_dir):
os.makedirs(group_dir)
example_dir = os.path.join(repo_dir, "Examples/earthengine-py-examples")
if not os.path.exists(example_dir):
clone_github_repo(URLs["Examples"], out_dir=example_dir)
left_widget, right_widget, tree_dict = file_browser(
in_dir=repo_dir,
add_root_node=False,
search_description="Filter scripts...",
use_import=True,
return_sep_widgets=True,
)
info_widget.children = [right_widget]
def handle_folder_click(event):
if event["new"]:
url = ""
selected = event["owner"]
if selected.name in URLs.keys():
url = URLs[selected.name]
path_widget.value = url
clone_widget.disabled = False
info_widget.children = [path_widget, clone_widget]
else:
info_widget.children = [right_widget]
for group in groups:
dirname = os.path.join(repo_dir, group)
node = tree_dict[dirname]
node.observe(handle_folder_click, "selected")
def handle_clone_click(b):
url = path_widget.value
default_dir = os.path.join(repo_dir, "Examples")
if url == "":
path_widget.value = "Please enter a valid URL to the repository."
else:
for group in groups:
key = os.path.join(repo_dir, group)
node = tree_dict[key]
if node.selected:
default_dir = key
try:
path_widget.value = "Cloning..."
clone_dir = os.path.join(default_dir, os.path.basename(url))
if url.find("github.com") != -1:
clone_github_repo(url, out_dir=clone_dir)
elif url.find("googlesource") != -1:
clone_google_repo(url, out_dir=clone_dir)
path_widget.value = "Cloned to {}".format(clone_dir)
clone_widget.disabled = True
except Exception as e:
path_widget.value = (
"An error occurred when trying to clone the repository " + str(e)
)
clone_widget.disabled = True
clone_widget.on_click(handle_clone_click)
return left_widget, info_widget, tree_dict
check_color(in_color)
¶
Checks the input color and returns the corresponding hex color code.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_color |
str or tuple |
It can be a string (e.g., 'red', '#ffff00') or tuple (e.g., (255, 127, 0)). |
required |
Returns:
| Type | Description |
|---|---|
str |
A hex color code. |
Source code in geemap/common.py
def check_color(in_color):
"""Checks the input color and returns the corresponding hex color code.
Args:
in_color (str or tuple): It can be a string (e.g., 'red', '#ffff00') or tuple (e.g., (255, 127, 0)).
Returns:
str: A hex color code.
"""
import colour
out_color = "#000000" # default black color
if isinstance(in_color, tuple) and len(in_color) == 3:
if all(isinstance(item, int) for item in in_color):
rescaled_color = [x / 255.0 for x in in_color]
out_color = colour.Color(rgb=tuple(rescaled_color))
return out_color.hex_l
else:
print(
"RGB color must be a tuple with three integer values ranging from 0 to 255."
)
return
else:
try:
out_color = colour.Color(in_color)
return out_color.hex_l
except Exception as e:
print("The provided color is invalid. Using the default black color.")
print(e)
return out_color
check_git_install()
¶
Checks if Git is installed.
Returns:
| Type | Description |
|---|---|
bool |
Returns True if Git is installed, otherwise returns False. |
Source code in geemap/common.py
def check_git_install():
"""Checks if Git is installed.
Returns:
bool: Returns True if Git is installed, otherwise returns False.
"""
import webbrowser
cmd = "git --version"
output = os.popen(cmd).read()
if "git version" in output:
return True
else:
url = "https://git-scm.com/downloads"
print(
"Git is not installed. Please download Git from {} and install it.".format(
url
)
)
webbrowser.open_new_tab(url)
return False
check_install(package)
¶
Checks whether a package is installed. If not, it will install the package.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
package |
str |
The name of the package to check. |
required |
Source code in geemap/common.py
def check_install(package):
"""Checks whether a package is installed. If not, it will install the package.
Args:
package (str): The name of the package to check.
"""
import subprocess
try:
__import__(package)
# print('{} is already installed.'.format(package))
except ImportError:
print("{} is not installed. Installing ...".format(package))
try:
subprocess.check_call(["python", "-m", "pip", "install", package])
except Exception as e:
print("Failed to install {}".format(package))
print(e)
print("{} has been installed successfully.".format(package))
clone_github_repo(url, out_dir)
¶
Clones a GitHub repository.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The link to the GitHub repository |
required |
out_dir |
str |
The output directory for the cloned repository. |
required |
Source code in geemap/common.py
def clone_github_repo(url, out_dir):
"""Clones a GitHub repository.
Args:
url (str): The link to the GitHub repository
out_dir (str): The output directory for the cloned repository.
"""
repo_name = os.path.basename(url)
# url_zip = os.path.join(url, 'archive/master.zip')
url_zip = url + "/archive/master.zip"
if os.path.exists(out_dir):
print(
"The specified output directory already exists. Please choose a new directory."
)
return
parent_dir = os.path.dirname(out_dir)
out_file_path = os.path.join(parent_dir, repo_name + ".zip")
try:
urllib.request.urlretrieve(url_zip, out_file_path)
except Exception:
print("The provided URL is invalid. Please double check the URL.")
return
with zipfile.ZipFile(out_file_path, "r") as zip_ref:
zip_ref.extractall(parent_dir)
src = out_file_path.replace(".zip", "-master")
os.rename(src, out_dir)
os.remove(out_file_path)
clone_google_repo(url, out_dir=None)
¶
Clones an Earth Engine repository from https://earthengine.googlesource.com, such as https://earthengine.googlesource.com/users/google/datasets
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The link to the Earth Engine repository |
required |
out_dir |
str |
The output directory for the cloned repository. Defaults to None. |
None |
Source code in geemap/common.py
def clone_google_repo(url, out_dir=None):
"""Clones an Earth Engine repository from https://earthengine.googlesource.com, such as https://earthengine.googlesource.com/users/google/datasets
Args:
url (str): The link to the Earth Engine repository
out_dir (str, optional): The output directory for the cloned repository. Defaults to None.
"""
repo_name = os.path.basename(url)
if out_dir is None:
out_dir = os.path.join(os.getcwd(), repo_name)
if not os.path.exists(os.path.dirname(out_dir)):
os.makedirs(os.path.dirname(out_dir))
if os.path.exists(out_dir):
print(
"The specified output directory already exists. Please choose a new directory."
)
return
if check_git_install():
cmd = 'git clone "{}" "{}"'.format(url, out_dir)
os.popen(cmd).read()
clone_repo(out_dir='.', unzip=True)
¶
Clones the geemap GitHub repository.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
out_dir |
str |
Output folder for the repo. Defaults to '.'. |
'.' |
unzip |
bool |
Whether to unzip the repository. Defaults to True. |
True |
Source code in geemap/common.py
def clone_repo(out_dir=".", unzip=True):
"""Clones the geemap GitHub repository.
Args:
out_dir (str, optional): Output folder for the repo. Defaults to '.'.
unzip (bool, optional): Whether to unzip the repository. Defaults to True.
"""
url = "https://github.com/giswqs/geemap/archive/master.zip"
filename = "geemap-master.zip"
download_from_url(url, out_file_name=filename, out_dir=out_dir, unzip=unzip)
column_stats(collection, column, stats_type)
¶
Aggregates over a given property of the objects in a collection, calculating the sum, min, max, mean, sample standard deviation, sample variance, total standard deviation and total variance of the selected property.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
FeatureCollection |
The input feature collection to calculate statistics. |
required |
column |
str |
The name of the column to calculate statistics. |
required |
stats_type |
str |
The type of statistics to calculate. |
required |
Returns:
| Type | Description |
|---|---|
dict |
The dictionary containing information about the requested statistics. |
Source code in geemap/common.py
def column_stats(collection, column, stats_type):
"""Aggregates over a given property of the objects in a collection, calculating the sum, min, max, mean,
sample standard deviation, sample variance, total standard deviation and total variance of the selected property.
Args:
collection (FeatureCollection): The input feature collection to calculate statistics.
column (str): The name of the column to calculate statistics.
stats_type (str): The type of statistics to calculate.
Returns:
dict: The dictionary containing information about the requested statistics.
"""
stats_type = stats_type.lower()
allowed_stats = ["min", "max", "mean", "median", "sum", "stdDev", "variance"]
if stats_type not in allowed_stats:
print(
"The stats type must be one of the following: {}".format(
",".join(allowed_stats)
)
)
return
stats_dict = {
"min": ee.Reducer.min(),
"max": ee.Reducer.max(),
"mean": ee.Reducer.mean(),
"median": ee.Reducer.median(),
"sum": ee.Reducer.sum(),
"stdDev": ee.Reducer.stdDev(),
"variance": ee.Reducer.variance(),
}
selectors = [column]
stats = collection.reduceColumns(
**{"selectors": selectors, "reducer": stats_dict[stats_type]}
)
return stats
coords_to_geojson(coords)
¶
Convert a list of bbox coordinates representing [left, bottom, right, top] to geojson FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
coords |
list |
A list of bbox coordinates representing [left, bottom, right, top]. |
required |
Returns:
| Type | Description |
|---|---|
dict |
A geojson FeatureCollection. |
Source code in geemap/common.py
def coords_to_geojson(coords):
"""Convert a list of bbox coordinates representing [left, bottom, right, top] to geojson FeatureCollection.
Args:
coords (list): A list of bbox coordinates representing [left, bottom, right, top].
Returns:
dict: A geojson FeatureCollection.
"""
features = []
for bbox in coords:
features.append(bbox_to_geojson(bbox))
return {"type": "FeatureCollection", "features": features}
copy_credentials_to_colab()
¶
Copies ee credentials from Google Drive to Google Colab.
Source code in geemap/common.py
def copy_credentials_to_colab():
"""Copies ee credentials from Google Drive to Google Colab."""
src = "/content/drive/My Drive/.config/earthengine/credentials"
dst = "/root/.config/earthengine/credentials"
wd = os.path.dirname(dst)
if not os.path.exists(wd):
os.makedirs(wd)
shutil.copyfile(src, dst)
copy_credentials_to_drive()
¶
Copies ee credentials from Google Colab to Google Drive.
Source code in geemap/common.py
def copy_credentials_to_drive():
"""Copies ee credentials from Google Colab to Google Drive."""
src = "/root/.config/earthengine/credentials"
dst = "/content/drive/My Drive/.config/earthengine/credentials"
wd = os.path.dirname(dst)
if not os.path.exists(wd):
os.makedirs(wd)
shutil.copyfile(src, dst)
create_code_cell(code='', where='below')
¶
Creates a code cell in the IPython Notebook.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
code |
str |
Code to fill the new code cell with. Defaults to ''. |
'' |
where |
str |
Where to add the new code cell. It can be one of the following: above, below, at_bottom. Defaults to 'below'. |
'below' |
Source code in geemap/common.py
def create_code_cell(code="", where="below"):
"""Creates a code cell in the IPython Notebook.
Args:
code (str, optional): Code to fill the new code cell with. Defaults to ''.
where (str, optional): Where to add the new code cell. It can be one of the following: above, below, at_bottom. Defaults to 'below'.
"""
import base64
from IPython.display import Javascript, display
encoded_code = (base64.b64encode(str.encode(code))).decode()
display(
Javascript(
"""
var code = IPython.notebook.insert_cell_{0}('code');
code.set_text(atob("{1}"));
""".format(
where, encoded_code
)
)
)
create_colorbar(width=150, height=30, palette=['blue', 'green', 'red'], add_ticks=True, add_labels=True, labels=None, vertical=False, out_file=None, font_type='arial.ttf', font_size=12, font_color='black', add_outline=True, outline_color='black')
¶
Creates a colorbar based on the provided palette.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
width |
int |
Width of the colorbar in pixels. Defaults to 150. |
150 |
height |
int |
Height of the colorbar in pixels. Defaults to 30. |
30 |
palette |
list |
Palette for the colorbar. Each color can be provided as a string (e.g., 'red'), a hex string (e.g., '#ff0000'), or an RGB tuple (255, 0, 255). Defaults to ['blue', 'green', 'red']. |
['blue', 'green', 'red'] |
add_ticks |
bool |
Whether to add tick markers to the colorbar. Defaults to True. |
True |
add_labels |
bool |
Whether to add labels to the colorbar. Defaults to True. |
True |
labels |
list |
A list of labels to add to the colorbar. Defaults to None. |
None |
vertical |
bool |
Whether to rotate the colorbar vertically. Defaults to False. |
False |
out_file |
str |
File path to the output colorbar in png format. Defaults to None. |
None |
font_type |
str |
Font type to use for labels. Defaults to 'arial.ttf'. |
'arial.ttf' |
font_size |
int |
Font size to use for labels. Defaults to 12. |
12 |
font_color |
str |
Font color to use for labels. Defaults to 'black'. |
'black' |
add_outline |
bool |
Whether to add an outline to the colorbar. Defaults to True. |
True |
outline_color |
str |
Color for the outline of the colorbar. Defaults to 'black'. |
'black' |
Returns:
| Type | Description |
|---|---|
str |
File path of the output colorbar in png format. |
Source code in geemap/common.py
def create_colorbar(
width=150,
height=30,
palette=["blue", "green", "red"],
add_ticks=True,
add_labels=True,
labels=None,
vertical=False,
out_file=None,
font_type="arial.ttf",
font_size=12,
font_color="black",
add_outline=True,
outline_color="black",
):
"""Creates a colorbar based on the provided palette.
Args:
width (int, optional): Width of the colorbar in pixels. Defaults to 150.
height (int, optional): Height of the colorbar in pixels. Defaults to 30.
palette (list, optional): Palette for the colorbar. Each color can be provided as a string (e.g., 'red'), a hex string (e.g., '#ff0000'), or an RGB tuple (255, 0, 255). Defaults to ['blue', 'green', 'red'].
add_ticks (bool, optional): Whether to add tick markers to the colorbar. Defaults to True.
add_labels (bool, optional): Whether to add labels to the colorbar. Defaults to True.
labels (list, optional): A list of labels to add to the colorbar. Defaults to None.
vertical (bool, optional): Whether to rotate the colorbar vertically. Defaults to False.
out_file (str, optional): File path to the output colorbar in png format. Defaults to None.
font_type (str, optional): Font type to use for labels. Defaults to 'arial.ttf'.
font_size (int, optional): Font size to use for labels. Defaults to 12.
font_color (str, optional): Font color to use for labels. Defaults to 'black'.
add_outline (bool, optional): Whether to add an outline to the colorbar. Defaults to True.
outline_color (str, optional): Color for the outline of the colorbar. Defaults to 'black'.
Returns:
str: File path of the output colorbar in png format.
"""
import decimal
# import io
import pkg_resources
import warnings
from colour import Color
from PIL import Image, ImageDraw, ImageFont
warnings.simplefilter("ignore")
pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
if out_file is None:
filename = "colorbar_" + random_string() + ".png"
out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
out_file = os.path.join(out_dir, filename)
elif not out_file.endswith(".png"):
print("The output file must end with .png")
return
else:
out_file = os.path.abspath(out_file)
if not os.path.exists(os.path.dirname(out_file)):
os.makedirs(os.path.dirname(out_file))
im = Image.new("RGBA", (width, height))
ld = im.load()
def float_range(start, stop, step):
while start < stop:
yield float(start)
start += decimal.Decimal(step)
n_colors = len(palette)
decimal_places = 2
rgb_colors = [Color(check_color(c)).rgb for c in palette]
keys = [
round(c, decimal_places)
for c in list(float_range(0, 1.0001, 1.0 / (n_colors - 1)))
]
heatmap = []
for index, item in enumerate(keys):
pair = [item, rgb_colors[index]]
heatmap.append(pair)
def gaussian(x, a, b, c, d=0):
return a * math.exp(-((x - b) ** 2) / (2 * c ** 2)) + d
def pixel(x, width=100, map=[], spread=1):
width = float(width)
r = sum(
[
gaussian(x, p[1][0], p[0] * width, width / (spread * len(map)))
for p in map
]
)
g = sum(
[
gaussian(x, p[1][1], p[0] * width, width / (spread * len(map)))
for p in map
]
)
b = sum(
[
gaussian(x, p[1][2], p[0] * width, width / (spread * len(map)))
for p in map
]
)
return min(1.0, r), min(1.0, g), min(1.0, b)
for x in range(im.size[0]):
r, g, b = pixel(x, width=width, map=heatmap)
r, g, b = [int(256 * v) for v in (r, g, b)]
for y in range(im.size[1]):
ld[x, y] = r, g, b
if add_outline:
draw = ImageDraw.Draw(im)
draw.rectangle(
[(0, 0), (width - 1, height - 1)], outline=check_color(outline_color)
)
del draw
if add_ticks:
tick_length = height * 0.1
x = [key * width for key in keys]
y_top = height - tick_length
y_bottom = height
draw = ImageDraw.Draw(im)
for i in x:
shape = [(i, y_top), (i, y_bottom)]
draw.line(shape, fill="black", width=0)
del draw
if vertical:
im = im.transpose(Image.ROTATE_90)
width, height = im.size
if labels is None:
labels = [str(c) for c in keys]
elif len(labels) == 2:
try:
lowerbound = float(labels[0])
upperbound = float(labels[1])
step = (upperbound - lowerbound) / (len(palette) - 1)
labels = [str(lowerbound + c * step) for c in range(0, len(palette))]
except Exception as e:
print(e)
print("The labels are invalid.")
return
elif len(labels) == len(palette):
labels = [str(c) for c in labels]
else:
print("The labels must have the same length as the palette.")
return
if add_labels:
default_font = os.path.join(pkg_dir, "data/fonts/arial.ttf")
if font_type == "arial.ttf":
font = ImageFont.truetype(default_font, font_size)
else:
try:
font_list = system_fonts(show_full_path=True)
font_names = [os.path.basename(f) for f in font_list]
if (font_type in font_list) or (font_type in font_names):
font = ImageFont.truetype(font_type, font_size)
else:
print(
"The specified font type could not be found on your system. Using the default font instead."
)
font = ImageFont.truetype(default_font, font_size)
except Exception as e:
print(e)
font = ImageFont.truetype(default_font, font_size)
font_color = check_color(font_color)
draw = ImageDraw.Draw(im)
w, h = draw.textsize(labels[0], font=font)
for label in labels:
w_tmp, h_tmp = draw.textsize(label, font)
if w_tmp > w:
w = w_tmp
if h_tmp > h:
h = h_tmp
W, H = width + w * 2, height + h * 2
background = Image.new("RGBA", (W, H))
draw = ImageDraw.Draw(background)
if vertical:
xy = (0, h)
else:
xy = (w, 0)
background.paste(im, xy, im)
for index, label in enumerate(labels):
w_tmp, h_tmp = draw.textsize(label, font)
if vertical:
spacing = 5
x = width + spacing
y = int(height + h - keys[index] * height - h_tmp / 2 - 1)
draw.text((x, y), label, font=font, fill=font_color)
else:
x = int(keys[index] * width + w - w_tmp / 2)
spacing = int(h * 0.05)
y = height + spacing
draw.text((x, y), label, font=font, fill=font_color)
im = background.copy()
im.save(out_file)
return out_file
create_download_link(filename, title='Click here to download: ')
¶
Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
The file path to the file to download |
required |
title |
str |
str. Defaults to "Click here to download: ". |
'Click here to download: ' |
Returns:
| Type | Description |
|---|---|
str |
HTML download URL. |
Source code in geemap/common.py
def create_download_link(filename, title="Click here to download: "):
"""Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578
Args:
filename (str): The file path to the file to download
title (str, optional): str. Defaults to "Click here to download: ".
Returns:
str: HTML download URL.
"""
import base64
from IPython.display import HTML
data = open(filename, "rb").read()
b64 = base64.b64encode(data)
payload = b64.decode()
basename = os.path.basename(filename)
html = '<a download="{filename}" href="data:text/csv;base64,{payload}" style="color:#0000FF;" target="_blank">{title}</a>'
html = html.format(payload=payload, title=title + f" {basename}", filename=basename)
return HTML(html)
create_nlcd_qml(out_qml)
¶
Create a QGIS Layer Style (.qml) for NLCD data
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
out_qml |
str |
File path to the ouput qml. |
required |
Source code in geemap/common.py
def create_nlcd_qml(out_qml):
"""Create a QGIS Layer Style (.qml) for NLCD data
Args:
out_qml (str): File path to the ouput qml.
"""
import pkg_resources
pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
data_dir = os.path.join(pkg_dir, "data")
template_dir = os.path.join(data_dir, "template")
qml_template = os.path.join(template_dir, "NLCD.qml")
out_dir = os.path.dirname(out_qml)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
shutil.copyfile(qml_template, out_qml)
credentials_in_colab()
¶
Checks if the ee credentials file exists in Google Colab.
Returns:
| Type | Description |
|---|---|
bool |
Returns True if Google Drive is mounted, False otherwise. |
Source code in geemap/common.py
def credentials_in_colab():
"""Checks if the ee credentials file exists in Google Colab.
Returns:
bool: Returns True if Google Drive is mounted, False otherwise.
"""
credentials_path = "/root/.config/earthengine/credentials"
if os.path.exists(credentials_path):
return True
else:
return False
credentials_in_drive()
¶
Checks if the ee credentials file exists in Google Drive.
Returns:
| Type | Description |
|---|---|
bool |
Returns True if Google Drive is mounted, False otherwise. |
Source code in geemap/common.py
def credentials_in_drive():
"""Checks if the ee credentials file exists in Google Drive.
Returns:
bool: Returns True if Google Drive is mounted, False otherwise.
"""
credentials_path = "/content/drive/My Drive/.config/earthengine/credentials"
if os.path.exists(credentials_path):
return True
else:
return False
csv_points_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude')
¶
Converts a csv file containing points (latitude, longitude) into a shapefile.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_csv |
str |
File path or HTTP URL to the input csv file. For example, https://raw.githubusercontent.com/giswqs/data/main/world/world_cities.csv |
required |
out_shp |
str |
File path to the output shapefile. |
required |
latitude |
str |
Column name for the latitude column. Defaults to 'latitude'. |
'latitude' |
longitude |
str |
Column name for the longitude column. Defaults to 'longitude'. |
'longitude' |
Source code in geemap/common.py
def csv_points_to_shp(in_csv, out_shp, latitude="latitude", longitude="longitude"):
"""Converts a csv file containing points (latitude, longitude) into a shapefile.
Args:
in_csv (str): File path or HTTP URL to the input csv file. For example, https://raw.githubusercontent.com/giswqs/data/main/world/world_cities.csv
out_shp (str): File path to the output shapefile.
latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.
longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.
"""
import whitebox
if in_csv.startswith("http") and in_csv.endswith(".csv"):
out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
out_name = os.path.basename(in_csv)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
download_from_url(in_csv, out_dir=out_dir)
in_csv = os.path.join(out_dir, out_name)
wbt = whitebox.WhiteboxTools()
in_csv = os.path.abspath(in_csv)
out_shp = os.path.abspath(out_shp)
if not os.path.exists(in_csv):
raise Exception("The provided csv file does not exist.")
with open(in_csv, encoding="utf-8") as csv_file:
reader = csv.DictReader(csv_file)
fields = reader.fieldnames
xfield = fields.index(longitude)
yfield = fields.index(latitude)
wbt.csv_points_to_vector(in_csv, out_shp, xfield=xfield, yfield=yfield, epsg=4326)
csv_to_pandas(in_csv, **kwargs)
¶
Converts a CSV file to pandas dataframe.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_csv |
str |
File path to the input CSV. |
required |
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
pandas DataFrame |
Source code in geemap/common.py
def csv_to_pandas(in_csv, **kwargs):
"""Converts a CSV file to pandas dataframe.
Args:
in_csv (str): File path to the input CSV.
Returns:
pd.DataFrame: pandas DataFrame
"""
import pandas as pd
try:
return pd.read_csv(in_csv, **kwargs)
except Exception as e:
raise Exception(e)
csv_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude')
¶
Converts a csv file with latlon info to a point shapefile.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_csv |
str |
The input csv file containing longitude and latitude columns. |
required |
out_shp |
str |
The file path to the output shapefile. |
required |
latitude |
str |
The column name of the latitude column. Defaults to 'latitude'. |
'latitude' |
longitude |
str |
The column name of the longitude column. Defaults to 'longitude'. |
'longitude' |
Source code in geemap/common.py
def csv_to_shp(in_csv, out_shp, latitude="latitude", longitude="longitude"):
"""Converts a csv file with latlon info to a point shapefile.
Args:
in_csv (str): The input csv file containing longitude and latitude columns.
out_shp (str): The file path to the output shapefile.
latitude (str, optional): The column name of the latitude column. Defaults to 'latitude'.
longitude (str, optional): The column name of the longitude column. Defaults to 'longitude'.
"""
import shapefile as shp
if in_csv.startswith("http") and in_csv.endswith(".csv"):
out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
out_name = os.path.basename(in_csv)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
download_from_url(in_csv, out_dir=out_dir)
in_csv = os.path.join(out_dir, out_name)
out_dir = os.path.dirname(out_shp)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
try:
points = shp.Writer(out_shp, shapeType=shp.POINT)
with open(in_csv, encoding="utf-8") as csvfile:
csvreader = csv.DictReader(csvfile)
header = csvreader.fieldnames
[points.field(field) for field in header]
for row in csvreader:
points.point((float(row[longitude])), (float(row[latitude])))
points.record(*tuple([row[f] for f in header]))
out_prj = out_shp.replace(".shp", ".prj")
with open(out_prj, "w") as f:
prj_str = 'GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.0174532925199433]] '
f.write(prj_str)
except Exception as e:
print(e)
date_sequence(start, end, unit, date_format='YYYY-MM-dd')
¶
Creates a date sequence.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
start |
str |
The start date, e.g., '2000-01-01'. |
required |
end |
str |
The end date, e.g., '2000-12-31'. |
required |
unit |
str |
One of 'year', 'month' 'week', 'day', 'hour', 'minute', or 'second'. |
required |
date_format |
str |
A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'. |
'YYYY-MM-dd' |
Returns:
| Type | Description |
|---|---|
ee.List |
A list of date sequence. |
Source code in geemap/common.py
def date_sequence(start, end, unit, date_format="YYYY-MM-dd"):
"""Creates a date sequence.
Args:
start (str): The start date, e.g., '2000-01-01'.
end (str): The end date, e.g., '2000-12-31'.
unit (str): One of 'year', 'month' 'week', 'day', 'hour', 'minute', or 'second'.
date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.
Returns:
ee.List: A list of date sequence.
"""
start_date = ee.Date(start)
end_date = ee.Date(end)
count = ee.Number(end_date.difference(start_date, unit)).toInt()
num_seq = ee.List.sequence(0, count)
date_seq = num_seq.map(lambda d: start_date.advance(d, unit).format(date_format))
return date_seq
delete_shp(in_shp, verbose=True)
¶
Deletes a shapefile.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_shp |
str |
The input shapefile to delete. |
required |
verbose |
bool |
Whether to print out descriptive text. Defaults to True. |
True |
Source code in geemap/common.py
def delete_shp(in_shp, verbose=True):
"""Deletes a shapefile.
Args:
in_shp (str): The input shapefile to delete.
verbose (bool, optional): Whether to print out descriptive text. Defaults to True.
"""
from pathlib import Path
in_shp = os.path.abspath(in_shp)
in_dir = os.path.dirname(in_shp)
basename = os.path.basename(in_shp).replace(".shp", "")
files = Path(in_dir).rglob(basename + ".*")
for file in files:
filepath = os.path.join(in_dir, str(file))
os.remove(filepath)
if verbose:
print(f"Deleted {filepath}")
dict_to_csv(data_dict, out_csv, by_row=False)
¶
Downloads an ee.Dictionary as a CSV file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data_dict |
ee.Dictionary |
The input ee.Dictionary. |
required |
out_csv |
str |
The output file path to the CSV file. |
required |
by_row |
bool |
Whether to use by row or by column. Defaults to False. |
False |
Source code in geemap/common.py
def dict_to_csv(data_dict, out_csv, by_row=False):
"""Downloads an ee.Dictionary as a CSV file.
Args:
data_dict (ee.Dictionary): The input ee.Dictionary.
out_csv (str): The output file path to the CSV file.
by_row (bool, optional): Whether to use by row or by column. Defaults to False.
"""
out_dir = os.path.dirname(out_csv)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if not by_row:
csv_feature = ee.Feature(None, data_dict)
csv_feat_col = ee.FeatureCollection([csv_feature])
else:
keys = data_dict.keys()
data = keys.map(lambda k: ee.Dictionary({"name": k, "value": data_dict.get(k)}))
csv_feature = data.map(lambda f: ee.Feature(None, f))
csv_feat_col = ee.FeatureCollection(csv_feature)
ee_export_vector(csv_feat_col, out_csv)
download_ee_video(collection, video_args, out_gif)
¶
Downloads a video thumbnail as a GIF image from Earth Engine.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
object |
An ee.ImageCollection. |
required |
video_args |
object |
Parameters for expring the video thumbnail. |
required |
out_gif |
str |
File path to the output GIF. |
required |
Source code in geemap/common.py
def download_ee_video(collection, video_args, out_gif):
"""Downloads a video thumbnail as a GIF image from Earth Engine.
Args:
collection (object): An ee.ImageCollection.
video_args (object): Parameters for expring the video thumbnail.
out_gif (str): File path to the output GIF.
"""
import requests
out_gif = os.path.abspath(out_gif)
if not out_gif.endswith(".gif"):
print("The output file must have an extension of .gif.")
return
if not os.path.exists(os.path.dirname(out_gif)):
os.makedirs(os.path.dirname(out_gif))
if "region" in video_args.keys():
roi = video_args["region"]
if not isinstance(roi, ee.Geometry):
try:
roi = roi.geometry()
except Exception as e:
print("Could not convert the provided roi to ee.Geometry")
print(e)
return
video_args["region"] = roi
try:
print("Generating URL...")
url = collection.getVideoThumbURL(video_args)
print("Downloading GIF image from {}\nPlease wait ...".format(url))
r = requests.get(url, stream=True)
if r.status_code != 200:
print("An error occurred while downloading.")
return
else:
with open(out_gif, "wb") as fd:
for chunk in r.iter_content(chunk_size=1024):
fd.write(chunk)
print("The GIF image has been saved to: {}".format(out_gif))
except Exception as e:
print(e)
download_from_gdrive(gfile_url, file_name, out_dir='.', unzip=True, verbose=True)
¶
Download a file shared via Google Drive (e.g., https://drive.google.com/file/d/18SUo_HcDGltuWYZs1s7PpOmOq_FvFn04/view?usp=sharing)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
gfile_url |
str |
The Google Drive shared file URL |
required |
file_name |
str |
The output file name to use. |
required |
out_dir |
str |
The output directory. Defaults to '.'. |
'.' |
unzip |
bool |
Whether to unzip the output file if it is a zip file. Defaults to True. |
True |
verbose |
bool |
Whether to display or not the output of the function |
True |
Source code in geemap/common.py
def download_from_gdrive(gfile_url, file_name, out_dir=".", unzip=True, verbose=True):
"""Download a file shared via Google Drive
(e.g., https://drive.google.com/file/d/18SUo_HcDGltuWYZs1s7PpOmOq_FvFn04/view?usp=sharing)
Args:
gfile_url (str): The Google Drive shared file URL
file_name (str): The output file name to use.
out_dir (str, optional): The output directory. Defaults to '.'.
unzip (bool, optional): Whether to unzip the output file if it is a zip file. Defaults to True.
verbose (bool, optional): Whether to display or not the output of the function
"""
from google_drive_downloader import GoogleDriveDownloader as gdd
file_id = gfile_url.split("/")[5]
if verbose:
print("Google Drive file id: {}".format(file_id))
dest_path = os.path.join(out_dir, file_name)
gdd.download_file_from_google_drive(file_id, dest_path, True, unzip)
return
download_from_url(url, out_file_name=None, out_dir='.', unzip=True, verbose=True)
¶
Download a file from a URL (e.g., https://github.com/giswqs/whitebox/raw/master/examples/testdata.zip)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The HTTP URL to download. |
required |
out_file_name |
str |
The output file name to use. Defaults to None. |
None |
out_dir |
str |
The output directory to use. Defaults to '.'. |
'.' |
unzip |
bool |
Whether to unzip the downloaded file if it is a zip file. Defaults to True. |
True |
verbose |
bool |
Whether to display or not the output of the function |
True |
Source code in geemap/common.py
def download_from_url(url, out_file_name=None, out_dir=".", unzip=True, verbose=True):
"""Download a file from a URL (e.g., https://github.com/giswqs/whitebox/raw/master/examples/testdata.zip)
Args:
url (str): The HTTP URL to download.
out_file_name (str, optional): The output file name to use. Defaults to None.
out_dir (str, optional): The output directory to use. Defaults to '.'.
unzip (bool, optional): Whether to unzip the downloaded file if it is a zip file. Defaults to True.
verbose (bool, optional): Whether to display or not the output of the function
"""
in_file_name = os.path.basename(url)
if out_file_name is None:
out_file_name = in_file_name
out_file_path = os.path.join(os.path.abspath(out_dir), out_file_name)
if verbose:
print("Downloading {} ...".format(url))
try:
urllib.request.urlretrieve(url, out_file_path)
except Exception:
raise Exception("The URL is invalid. Please double check the URL.")
final_path = out_file_path
if unzip:
# if it is a zip file
if ".zip" in out_file_name:
if verbose:
print("Unzipping {} ...".format(out_file_name))
with zipfile.ZipFile(out_file_path, "r") as zip_ref:
zip_ref.extractall(out_dir)
final_path = os.path.join(
os.path.abspath(out_dir), out_file_name.replace(".zip", "")
)
# if it is a tar file
if ".tar" in out_file_name:
if verbose:
print("Unzipping {} ...".format(out_file_name))
with tarfile.open(out_file_path, "r") as tar_ref:
tar_ref.extractall(out_dir)
final_path = os.path.join(
os.path.abspath(out_dir), out_file_name.replace(".tart", "")
)
if verbose:
print("Data downloaded to: {}".format(final_path))
return
edit_download_html(htmlWidget, filename, title='Click here to download: ')
¶
Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578#issuecomment-617668058
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
htmlWidget |
object |
The HTML widget to display the URL. |
required |
filename |
str |
File path to download. |
required |
title |
str |
Download description. Defaults to "Click here to download: ". |
'Click here to download: ' |
Source code in geemap/common.py
def edit_download_html(htmlWidget, filename, title="Click here to download: "):
"""Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578#issuecomment-617668058
Args:
htmlWidget (object): The HTML widget to display the URL.
filename (str): File path to download.
title (str, optional): Download description. Defaults to "Click here to download: ".
"""
# from IPython.display import HTML
# import ipywidgets as widgets
import base64
# Change widget html temporarily to a font-awesome spinner
htmlWidget.value = '<i class="fa fa-spinner fa-spin fa-2x fa-fw"></i><span class="sr-only">Loading...</span>'
# Process raw data
data = open(filename, "rb").read()
b64 = base64.b64encode(data)
payload = b64.decode()
basename = os.path.basename(filename)
# Create and assign html to widget
html = '<a download="{filename}" href="data:text/csv;base64,{payload}" target="_blank">{title}</a>'
htmlWidget.value = html.format(
payload=payload, title=title + basename, filename=basename
)
# htmlWidget = widgets.HTML(value = '')
# htmlWidget
ee_api_to_csv(outfile=None)
¶
Extracts Earth Engine API documentation from https://developers.google.com/earth-engine/api_docs as a csv file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
outfile |
str |
The output file path to a csv file. Defaults to None. |
None |
Source code in geemap/common.py
def ee_api_to_csv(outfile=None):
"""Extracts Earth Engine API documentation from https://developers.google.com/earth-engine/api_docs as a csv file.
Args:
outfile (str, optional): The output file path to a csv file. Defaults to None.
"""
import pkg_resources
import requests
from bs4 import BeautifulSoup
pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
data_dir = os.path.join(pkg_dir, "data")
template_dir = os.path.join(data_dir, "template")
csv_file = os.path.join(template_dir, "ee_api_docs.csv")
if outfile is None:
outfile = csv_file
else:
if not outfile.endswith(".csv"):
print("The output file must end with .csv")
return
else:
out_dir = os.path.dirname(outfile)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
url = "https://developers.google.com/earth-engine/api_docs"
try:
r = requests.get(url)
soup = BeautifulSoup(r.content, "html.parser")
names = []
descriptions = []
functions = []
returns = []
arguments = []
types = []
details = []
names = [h2.text for h2 in soup.find_all("h2")]
descriptions = [h2.next_sibling.next_sibling.text for h2 in soup.find_all("h2")]
func_tables = soup.find_all("table", class_="blue")
functions = [func_table.find("code").text for func_table in func_tables]
returns = [func_table.find_all("td")[1].text for func_table in func_tables]
detail_tables = []
tables = soup.find_all("table", class_="blue")
for table in tables:
item = table.next_sibling
if item.attrs == {"class": ["details"]}:
detail_tables.append(item)
else:
detail_tables.append("")
for detail_table in detail_tables:
if detail_table != "":
items = [item.text for item in detail_table.find_all("code")]
else:
items = ""
arguments.append(items)
for detail_table in detail_tables:
if detail_table != "":
items = [item.text for item in detail_table.find_all("td")]
items = items[1::3]
else:
items = ""
types.append(items)
for detail_table in detail_tables:
if detail_table != "":
items = [item.text for item in detail_table.find_all("p")]
else:
items = ""
details.append(items)
csv_file = open(outfile, "w", encoding="utf-8")
csv_writer = csv.writer(csv_file, delimiter="\t")
csv_writer.writerow(
[
"name",
"description",
"function",
"returns",
"argument",
"type",
"details",
]
)
for i in range(len(names)):
name = names[i]
description = descriptions[i]
function = functions[i]
return_type = returns[i]
argument = "|".join(arguments[i])
argu_type = "|".join(types[i])
detail = "|".join(details[i])
csv_writer.writerow(
[name, description, function, return_type, argument, argu_type, detail]
)
csv_file.close()
except Exception as e:
print(e)
ee_data_html(asset)
¶
Generates HTML from an asset to be used in the HTML widget.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
asset |
dict |
A dictionary containing an Earth Engine asset. |
required |
Returns:
| Type | Description |
|---|---|
str |
A string containing HTML. |
Source code in geemap/common.py
def ee_data_html(asset):
"""Generates HTML from an asset to be used in the HTML widget.
Args:
asset (dict): A dictionary containing an Earth Engine asset.
Returns:
str: A string containing HTML.
"""
template = """
<html>
<body>
<h3>asset_title</h3>
<h4>Dataset Availability</h4>
<p style="margin-left: 40px">asset_dates</p>
<h4>Earth Engine Snippet</h4>
<p style="margin-left: 40px">ee_id_snippet</p>
<h4>Earth Engine Data Catalog</h4>
<p style="margin-left: 40px"><a href="asset_url" target="_blank">asset_id</a></p>
<h4>Dataset Thumbnail</h4>
<img src="thumbnail_url">
</body>
</html>
"""
try:
text = template.replace("asset_title", asset["title"])
text = text.replace("asset_dates", asset["dates"])
text = text.replace("ee_id_snippet", asset["ee_id_snippet"])
text = text.replace("asset_id", asset["id"])
text = text.replace("asset_url", asset["asset_url"])
# asset['thumbnail'] = ee_data_thumbnail(asset['id'])
text = text.replace("thumbnail_url", asset["thumbnail_url"])
return text
except Exception as e:
print(e)
ee_data_thumbnail(asset_id)
¶
Retrieves the thumbnail URL of an Earth Engine asset.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
asset_id |
str |
An Earth Engine asset id. |
required |
Returns:
| Type | Description |
|---|---|
str |
An http url of the thumbnail. |
Source code in geemap/common.py
def ee_data_thumbnail(asset_id):
"""Retrieves the thumbnail URL of an Earth Engine asset.
Args:
asset_id (str): An Earth Engine asset id.
Returns:
str: An http url of the thumbnail.
"""
import requests
import urllib
from bs4 import BeautifulSoup
asset_uid = asset_id.replace("/", "_")
asset_url = "https://developers.google.com/earth-engine/datasets/catalog/{}".format(
asset_uid
)
thumbnail_url = "https://mw1.google.com/ges/dd/images/{}_sample.png".format(
asset_uid
)
r = requests.get(thumbnail_url)
try:
if r.status_code != 200:
html_page = urllib.request.urlopen(asset_url)
soup = BeautifulSoup(html_page, features="html.parser")
for img in soup.findAll("img"):
if "sample.png" in img.get("src"):
thumbnail_url = img.get("src")
return thumbnail_url
return thumbnail_url
except Exception as e:
print(e)
ee_export_geojson(ee_object, filename=None, selectors=None)
¶
Exports Earth Engine FeatureCollection to geojson.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
ee.FeatureCollection to export. |
required |
filename |
str |
Output file name. Defaults to None. |
None |
selectors |
list |
A list of attributes to export. Defaults to None. |
None |
Source code in geemap/common.py
def ee_export_geojson(ee_object, filename=None, selectors=None):
"""Exports Earth Engine FeatureCollection to geojson.
Args:
ee_object (object): ee.FeatureCollection to export.
filename (str): Output file name. Defaults to None.
selectors (list, optional): A list of attributes to export. Defaults to None.
"""
import requests
if not isinstance(ee_object, ee.FeatureCollection):
print("The ee_object must be an ee.FeatureCollection.")
return
if filename is None:
out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
filename = os.path.join(out_dir, random_string(6) + ".geojson")
allowed_formats = ["geojson"]
filename = os.path.abspath(filename)
basename = os.path.basename(filename)
name = os.path.splitext(basename)[0]
filetype = os.path.splitext(basename)[1][1:].lower()
if not (filetype.lower() in allowed_formats):
print("The output file type must be geojson.")
return
if selectors is None:
selectors = ee_object.first().propertyNames().getInfo()
selectors = [".geo"] + selectors
elif not isinstance(selectors, list):
print("selectors must be a list, such as ['attribute1', 'attribute2']")
return
else:
allowed_attributes = ee_object.first().propertyNames().getInfo()
for attribute in selectors:
if not (attribute in allowed_attributes):
print(
"Attributes must be one chosen from: {} ".format(
", ".join(allowed_attributes)
)
)
return
try:
# print('Generating URL ...')
url = ee_object.getDownloadURL(
filetype=filetype, selectors=selectors, filename=name
)
# print('Downloading data from {}\nPlease wait ...'.format(url))
r = requests.get(url, stream=True)
if r.status_code != 200:
print("An error occurred while downloading. \n Retrying ...")
try:
new_ee_object = ee_object.map(filter_polygons)
print("Generating URL ...")
url = new_ee_object.getDownloadURL(
filetype=filetype, selectors=selectors, filename=name
)
print("Downloading data from {}\nPlease wait ...".format(url))
r = requests.get(url, stream=True)
except Exception as e:
print(e)
with open(filename, "wb") as fd:
for chunk in r.iter_content(chunk_size=1024):
fd.write(chunk)
except Exception as e:
print("An error occurred while downloading.")
print(e)
return
with open(filename) as f:
geojson = f.read()
return geojson
ee_export_image(ee_object, filename, scale=None, crs=None, region=None, file_per_band=False)
¶
Exports an ee.Image as a GeoTIFF.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
The ee.Image to download. |
required |
filename |
str |
Output filename for the exported image. |
required |
scale |
float |
A default scale to use for any bands that do not specify one; ignored if crs and crs_transform is specified. Defaults to None. |
None |
crs |
str |
A default CRS string to use for any bands that do not explicitly specify one. Defaults to None. |
None |
region |
object |
A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None. |
None |
file_per_band |
bool |
Whether to produce a different GeoTIFF per band. Defaults to False. |
False |
Source code in geemap/common.py
def ee_export_image(
ee_object, filename, scale=None, crs=None, region=None, file_per_band=False
):
"""Exports an ee.Image as a GeoTIFF.
Args:
ee_object (object): The ee.Image to download.
filename (str): Output filename for the exported image.
scale (float, optional): A default scale to use for any bands that do not specify one; ignored if crs and crs_transform is specified. Defaults to None.
crs (str, optional): A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.
region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.
file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.
"""
import requests
if not isinstance(ee_object, ee.Image):
print("The ee_object must be an ee.Image.")
return
filename = os.path.abspath(filename)
basename = os.path.basename(filename)
name = os.path.splitext(basename)[0]
filetype = os.path.splitext(basename)[1][1:].lower()
filename_zip = filename.replace(".tif", ".zip")
if filetype != "tif":
print("The filename must end with .tif")
return
try:
print("Generating URL ...")
params = {"name": name, "filePerBand": file_per_band}
if scale is None:
scale = ee_object.projection().nominalScale().multiply(10)
params["scale"] = scale
if region is None:
region = ee_object.geometry()
params["region"] = region
if crs is not None:
params["crs"] = crs
url = ee_object.getDownloadURL(params)
print("Downloading data from {}\nPlease wait ...".format(url))
r = requests.get(url, stream=True)
if r.status_code != 200:
print("An error occurred while downloading.")
return
with open(filename_zip, "wb") as fd:
for chunk in r.iter_content(chunk_size=1024):
fd.write(chunk)
except Exception as e:
print("An error occurred while downloading.")
print(e)
return
try:
z = zipfile.ZipFile(filename_zip)
z.extractall(os.path.dirname(filename))
z.close()
os.remove(filename_zip)
if file_per_band:
print("Data downloaded to {}".format(os.path.dirname(filename)))
else:
print("Data downloaded to {}".format(filename))
except Exception as e:
print(e)
ee_export_image_collection(ee_object, out_dir, scale=None, crs=None, region=None, file_per_band=False)
¶
Exports an ImageCollection as GeoTIFFs.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
The ee.Image to download. |
required |
out_dir |
str |
The output directory for the exported images. |
required |
scale |
float |
A default scale to use for any bands that do not specify one; ignored if crs and crs_transform is specified. Defaults to None. |
None |
crs |
str |
A default CRS string to use for any bands that do not explicitly specify one. Defaults to None. |
None |
region |
object |
A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None. |
None |
file_per_band |
bool |
Whether to produce a different GeoTIFF per band. Defaults to False. |
False |
Source code in geemap/common.py
def ee_export_image_collection(
ee_object, out_dir, scale=None, crs=None, region=None, file_per_band=False
):
"""Exports an ImageCollection as GeoTIFFs.
Args:
ee_object (object): The ee.Image to download.
out_dir (str): The output directory for the exported images.
scale (float, optional): A default scale to use for any bands that do not specify one; ignored if crs and crs_transform is specified. Defaults to None.
crs (str, optional): A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.
region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.
file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.
"""
if not isinstance(ee_object, ee.ImageCollection):
print("The ee_object must be an ee.ImageCollection.")
return
if not os.path.exists(out_dir):
os.makedirs(out_dir)
try:
count = int(ee_object.size().getInfo())
print("Total number of images: {}\n".format(count))
for i in range(0, count):
image = ee.Image(ee_object.toList(count).get(i))
name = image.get("system:index").getInfo() + ".tif"
filename = os.path.join(os.path.abspath(out_dir), name)
print("Exporting {}/{}: {}".format(i + 1, count, name))
ee_export_image(
image,
filename=filename,
scale=scale,
crs=crs,
region=region,
file_per_band=file_per_band,
)
print("\n")
except Exception as e:
print(e)
ee_export_image_collection_to_drive(ee_object, descriptions=None, folder=None, region=None, scale=None, crs=None, max_pixels=10000000000000.0, file_format='GeoTIFF', format_options={})
¶
Creates a batch task to export an ImageCollection as raster images to Google Drive.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
The image to export. |
required |
descriptions |
list |
A list of human-readable names of the tasks. |
None |
folder |
str |
The Google Drive Folder that the export will reside in. Defaults to None. |
None |
region |
object |
A LinearRing, Polygon, or coordinates representing region to export. These may be specified as the Geometry objects or coordinates serialized as a string. If not specified, the region defaults to the viewport at the time of invocation. Defaults to None. |
None |
scale |
float |
Resolution in meters per pixel. Defaults to 10 times of the image resolution. |
None |
crs |
str |
CRS to use for the exported image.. Defaults to None. |
None |
max_pixels |
int |
Restrict the number of pixels in the export. Defaults to 1.0E13. |
10000000000000.0 |
file_format |
str |
The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'. |
'GeoTIFF' |
format_options |
dict |
A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True} |
{} |
Source code in geemap/common.py
def ee_export_image_collection_to_drive(
ee_object,
descriptions=None,
folder=None,
region=None,
scale=None,
crs=None,
max_pixels=1.0e13,
file_format="GeoTIFF",
format_options={},
):
"""Creates a batch task to export an ImageCollection as raster images to Google Drive.
Args:
ee_object (object): The image to export.
descriptions (list): A list of human-readable names of the tasks.
folder (str, optional): The Google Drive Folder that the export will reside in. Defaults to None.
region (object, optional): A LinearRing, Polygon, or coordinates representing region to export. These may be specified as the Geometry objects or coordinates serialized as a string. If not specified, the region defaults to the viewport at the time of invocation. Defaults to None.
scale (float, optional): Resolution in meters per pixel. Defaults to 10 times of the image resolution.
crs (str, optional): CRS to use for the exported image.. Defaults to None.
max_pixels (int, optional): Restrict the number of pixels in the export. Defaults to 1.0E13.
file_format (str, optional): The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'.
format_options (dict, optional): A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True}
"""
# ee_initialize()
if not isinstance(ee_object, ee.ImageCollection):
print("The ee_object must be an ee.ImageCollection.")
return
try:
count = int(ee_object.size().getInfo())
print("Total number of images: {}\n".format(count))
if (descriptions is not None) and (len(descriptions) != count):
print("The number of descriptions is not equal to the number of images.")
return
if descriptions is None:
descriptions = ee_object.aggregate_array("system:index").getInfo()
images = ee_object.toList(count)
for i in range(0, count):
image = ee.Image(images.get(i))
name = descriptions[i]
ee_export_image_to_drive(
image,
name,
folder,
region,
scale,
crs,
max_pixels,
file_format,
format_options,
)
except Exception as e:
print(e)
ee_export_image_to_drive(ee_object, description, folder=None, region=None, scale=None, crs=None, max_pixels=10000000000000.0, file_format='GeoTIFF', format_options={})
¶
Creates a batch task to export an Image as a raster to Google Drive.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
The image to export. |
required |
description |
str |
A human-readable name of the task. |
required |
folder |
str |
The Google Drive Folder that the export will reside in. Defaults to None. |
None |
region |
object |
A LinearRing, Polygon, or coordinates representing region to export. These may be specified as the Geometry objects or coordinates serialized as a string. If not specified, the region defaults to the viewport at the time of invocation. Defaults to None. |
None |
scale |
float |
Resolution in meters per pixel. Defaults to 10 times of the image resolution. |
None |
crs |
str |
CRS to use for the exported image.. Defaults to None. |
None |
max_pixels |
int |
Restrict the number of pixels in the export. Defaults to 1.0E13. |
10000000000000.0 |
file_format |
str |
The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'. |
'GeoTIFF' |
format_options |
dict |
A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True} |
{} |
Source code in geemap/common.py
def ee_export_image_to_drive(
ee_object,
description,
folder=None,
region=None,
scale=None,
crs=None,
max_pixels=1.0e13,
file_format="GeoTIFF",
format_options={},
):
"""Creates a batch task to export an Image as a raster to Google Drive.
Args:
ee_object (object): The image to export.
description (str): A human-readable name of the task.
folder (str, optional): The Google Drive Folder that the export will reside in. Defaults to None.
region (object, optional): A LinearRing, Polygon, or coordinates representing region to export. These may be specified as the Geometry objects or coordinates serialized as a string. If not specified, the region defaults to the viewport at the time of invocation. Defaults to None.
scale (float, optional): Resolution in meters per pixel. Defaults to 10 times of the image resolution.
crs (str, optional): CRS to use for the exported image.. Defaults to None.
max_pixels (int, optional): Restrict the number of pixels in the export. Defaults to 1.0E13.
file_format (str, optional): The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'.
format_options (dict, optional): A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True}
"""
# ee_initialize()
if not isinstance(ee_object, ee.Image):
print("The ee_object must be an ee.Image.")
return
try:
params = {}
if folder is not None:
params["driveFolder"] = folder
if region is not None:
params["region"] = region
if scale is None:
scale = ee_object.projection().nominalScale().multiply(10)
params["scale"] = scale
if crs is not None:
params["crs"] = crs
params["maxPixels"] = max_pixels
params["fileFormat"] = file_format
params["formatOptions"] = format_options
task = ee.batch.Export.image(ee_object, description, params)
task.start()
print("Exporting {} ...".format(description))
except Exception as e:
print(e)
ee_export_vector(ee_object, filename, selectors=None, verbose=True, keep_zip=False)
¶
Exports Earth Engine FeatureCollection to other formats, including shp, csv, json, kml, and kmz.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
ee.FeatureCollection to export. |
required |
filename |
str |
Output file name. |
required |
selectors |
list |
A list of attributes to export. Defaults to None. |
None |
verbose |
bool |
Whether to print out descriptive text. |
True |
keep_zip |
bool |
Whether to keep the downloaded shapefile as a zip file. |
False |
Source code in geemap/common.py
def ee_export_vector(ee_object, filename, selectors=None, verbose=True, keep_zip=False):
"""Exports Earth Engine FeatureCollection to other formats, including shp, csv, json, kml, and kmz.
Args:
ee_object (object): ee.FeatureCollection to export.
filename (str): Output file name.
selectors (list, optional): A list of attributes to export. Defaults to None.
verbose (bool, optional): Whether to print out descriptive text.
keep_zip (bool, optional): Whether to keep the downloaded shapefile as a zip file.
"""
import requests
if not isinstance(ee_object, ee.FeatureCollection):
raise ValueError("ee_object must be an ee.FeatureCollection")
allowed_formats = ["csv", "geojson", "kml", "kmz", "shp"]
# allowed_formats = ['csv', 'kml', 'kmz']
filename = os.path.abspath(filename)
basename = os.path.basename(filename)
name = os.path.splitext(basename)[0]
filetype = os.path.splitext(basename)[1][1:].lower()
if filetype == "shp":
filename = filename.replace(".shp", ".zip")
if not (filetype.lower() in allowed_formats):
print(
"The file type must be one of the following: {}".format(
", ".join(allowed_formats)
)
)
print(
"Earth Engine no longer supports downloading featureCollection as shapefile or json. \nPlease use geemap.ee_export_vector_to_drive() to export featureCollection to Google Drive."
)
raise ValueError
if selectors is None:
selectors = ee_object.first().propertyNames().getInfo()
if filetype == "csv":
# remove .geo coordinate field
ee_object = ee_object.select([".*"], None, False)
if filetype == "geojson":
selectors = [".geo"] + selectors
elif not isinstance(selectors, list):
raise ValueError(
"selectors must be a list, such as ['attribute1', 'attribute2']"
)
else:
allowed_attributes = ee_object.first().propertyNames().getInfo()
for attribute in selectors:
if not (attribute in allowed_attributes):
raise ValueError(
"Attributes must be one chosen from: {} ".format(
", ".join(allowed_attributes)
)
)
try:
if verbose:
print("Generating URL ...")
url = ee_object.getDownloadURL(
filetype=filetype, selectors=selectors, filename=name
)
if verbose:
print("Downloading data from {}\nPlease wait ...".format(url))
r = requests.get(url, stream=True)
if r.status_code != 200:
print("An error occurred while downloading. \n Retrying ...")
try:
new_ee_object = ee_object.map(filter_polygons)
print("Generating URL ...")
url = new_ee_object.getDownloadURL(
filetype=filetype, selectors=selectors, filename=name
)
print("Downloading data from {}\nPlease wait ...".format(url))
r = requests.get(url, stream=True)
except Exception as e:
print(e)
raise ValueError
with open(filename, "wb") as fd:
for chunk in r.iter_content(chunk_size=1024):
fd.write(chunk)
except Exception as e:
print("An error occurred while downloading.")
raise ValueError(e)
try:
if filetype == "shp":
z = zipfile.ZipFile(filename)
z.extractall(os.path.dirname(filename))
z.close()
if not keep_zip:
os.remove(filename)
filename = filename.replace(".zip", ".shp")
if verbose:
print("Data downloaded to {}".format(filename))
except Exception as e:
raise ValueError(e)
ee_export_vector_to_drive(ee_object, description, folder, file_format='shp', selectors=None)
¶
Exports Earth Engine FeatureCollection to Google Drive. other formats, including shp, csv, json, kml, and kmz.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
ee.FeatureCollection to export. |
required |
description |
str |
File name of the output file. |
required |
folder |
str |
Folder name within Google Drive to save the exported file. |
required |
file_format |
str |
The supported file format include shp, csv, json, kml, kmz, and TFRecord. Defaults to 'shp'. |
'shp' |
selectors |
list |
The list of attributes to export. Defaults to None. |
None |
Source code in geemap/common.py
def ee_export_vector_to_drive(
ee_object, description, folder, file_format="shp", selectors=None
):
"""Exports Earth Engine FeatureCollection to Google Drive. other formats, including shp, csv, json, kml, and kmz.
Args:
ee_object (object): ee.FeatureCollection to export.
description (str): File name of the output file.
folder (str): Folder name within Google Drive to save the exported file.
file_format (str, optional): The supported file format include shp, csv, json, kml, kmz, and TFRecord. Defaults to 'shp'.
selectors (list, optional): The list of attributes to export. Defaults to None.
"""
if not isinstance(ee_object, ee.FeatureCollection):
print("The ee_object must be an ee.FeatureCollection.")
return
allowed_formats = ["csv", "json", "kml", "kmz", "shp", "tfrecord"]
if not (file_format.lower() in allowed_formats):
print(
"The file type must be one of the following: {}".format(
", ".join(allowed_formats)
)
)
return
task_config = {
"folder": folder,
"fileFormat": file_format,
}
if selectors is not None:
task_config["selectors"] = selectors
elif (selectors is None) and (file_format.lower() == "csv"):
# remove .geo coordinate field
ee_object = ee_object.select([".*"], None, False)
print("Exporting {}...".format(description))
task = ee.batch.Export.table.toDrive(ee_object, description, **task_config)
task.start()
ee_function_tree(name)
¶
Construct the tree structure based on an Earth Engine function. For example, the function "ee.Algorithms.FMask.matchClouds" will return a list ["ee.Algorithms", "ee.Algorithms.FMask", "ee.Algorithms.FMask.matchClouds"]
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
name |
str |
The name of the Earth Engine function |
required |
Returns:
| Type | Description |
|---|---|
list |
The list for parent functions. |
Source code in geemap/common.py
def ee_function_tree(name):
"""Construct the tree structure based on an Earth Engine function. For example, the function "ee.Algorithms.FMask.matchClouds" will return a list ["ee.Algorithms", "ee.Algorithms.FMask", "ee.Algorithms.FMask.matchClouds"]
Args:
name (str): The name of the Earth Engine function
Returns:
list: The list for parent functions.
"""
func_list = []
try:
items = name.split(".")
if items[0] == "ee":
for i in range(2, len(items) + 1):
func_list.append(".".join(items[0:i]))
else:
for i in range(1, len(items) + 1):
func_list.append(".".join(items[0:i]))
return func_list
except Exception as e:
print(e)
print("The provided function name is invalid.")
ee_initialize(token_name='EARTHENGINE_TOKEN')
¶
Authenticates Earth Engine and initialize an Earth Engine session
Source code in geemap/common.py
def ee_initialize(token_name="EARTHENGINE_TOKEN"):
"""Authenticates Earth Engine and initialize an Earth Engine session"""
if ee.data._credentials is None:
try:
ee_token = os.environ.get(token_name)
if ee_token is not None:
credential_file_path = os.path.expanduser("~/.config/earthengine/")
if not os.path.exists(credential_file_path):
credential = '{"refresh_token":"%s"}' % ee_token
os.makedirs(credential_file_path, exist_ok=True)
with open(credential_file_path + "credentials", "w") as file:
file.write(credential)
elif in_colab_shell():
if credentials_in_drive() and (not credentials_in_colab()):
copy_credentials_to_colab()
elif not credentials_in_colab:
ee.Authenticate()
if is_drive_mounted() and (not credentials_in_drive()):
copy_credentials_to_drive()
else:
if is_drive_mounted():
copy_credentials_to_drive()
ee.Initialize()
except Exception:
ee.Authenticate()
ee.Initialize()
ee_num_round(num, decimal=2)
¶
Rounds a number to a specified number of decimal places.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
num |
ee.Number |
The number to round. |
required |
decimal |
int |
The number of decimal places to round. Defaults to 2. |
2 |
Returns:
| Type | Description |
|---|---|
ee.Number |
The number with the specified decimal places rounded. |
Source code in geemap/common.py
def ee_num_round(num, decimal=2):
"""Rounds a number to a specified number of decimal places.
Args:
num (ee.Number): The number to round.
decimal (int, optional): The number of decimal places to round. Defaults to 2.
Returns:
ee.Number: The number with the specified decimal places rounded.
"""
format_str = "%.{}f".format(decimal)
return ee.Number.parse(ee.Number(num).format(format_str))
ee_search(asset_limit=100)
¶
Search Earth Engine API and user assets. If you received a warning (IOPub message rate exceeded) in Jupyter notebook, you can relaunch Jupyter notebook using the following command: jupyter notebook --NotebookApp.iopub_msg_rate_limit=10000
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
asset_limit |
int |
The number of assets to display for each asset type, i.e., Image, ImageCollection, and FeatureCollection. Defaults to 100. |
100 |
Source code in geemap/common.py
def ee_search(asset_limit=100):
"""Search Earth Engine API and user assets. If you received a warning (IOPub message rate exceeded) in Jupyter notebook, you can relaunch Jupyter notebook using the following command:
jupyter notebook --NotebookApp.iopub_msg_rate_limit=10000
Args:
asset_limit (int, optional): The number of assets to display for each asset type, i.e., Image, ImageCollection, and FeatureCollection. Defaults to 100.
"""
import warnings
warnings.filterwarnings("ignore")
class Flags:
def __init__(
self,
repos=None,
docs=None,
assets=None,
docs_dict=None,
asset_dict=None,
asset_import=None,
):
self.repos = repos
self.docs = docs
self.assets = assets
self.docs_dict = docs_dict
self.asset_dict = asset_dict
self.asset_import = asset_import
flags = Flags()
search_type = widgets.ToggleButtons(
options=["Scripts", "Docs", "Assets"],
tooltips=[
"Search Earth Engine Scripts",
"Search Earth Engine API",
"Search Earth Engine Assets",
],
button_style="primary",
)
search_type.style.button_width = "100px"
search_box = widgets.Text(placeholder="Filter scripts...", value="Loading...")
search_box.layout.width = "310px"
tree_widget = widgets.Output()
left_widget = widgets.VBox()
right_widget = widgets.VBox()
output_widget = widgets.Output()
output_widget.layout.max_width = "650px"
search_widget = widgets.HBox()
search_widget.children = [left_widget, right_widget]
display(search_widget)
repo_tree, repo_output, _ = build_repo_tree()
left_widget.children = [search_type, repo_tree]
right_widget.children = [repo_output]
flags.repos = repo_tree
search_box.value = ""
def search_type_changed(change):
search_box.value = ""
output_widget.clear_output()
tree_widget.clear_output()
if change["new"] == "Scripts":
search_box.placeholder = "Filter scripts..."
left_widget.children = [search_type, repo_tree]
right_widget.children = [repo_output]
elif change["new"] == "Docs":
search_box.placeholder = "Filter methods..."
search_box.value = "Loading..."
left_widget.children = [search_type, search_box, tree_widget]
right_widget.children = [output_widget]
if flags.docs is None:
api_dict = read_api_csv()
ee_api_tree, tree_dict = build_api_tree(api_dict, output_widget)
flags.docs = ee_api_tree
flags.docs_dict = tree_dict
else:
ee_api_tree = flags.docs
with tree_widget:
tree_widget.clear_output()
display(ee_api_tree)
right_widget.children = [output_widget]
search_box.value = ""
elif change["new"] == "Assets":
search_box.placeholder = "Filter assets..."
left_widget.children = [search_type, search_box, tree_widget]
right_widget.children = [output_widget]
search_box.value = "Loading..."
if flags.assets is None:
asset_tree, asset_widget, asset_dict = build_asset_tree(
limit=asset_limit
)
flags.assets = asset_tree
flags.asset_dict = asset_dict
flags.asset_import = asset_widget
with tree_widget:
tree_widget.clear_output()
display(flags.assets)
right_widget.children = [flags.asset_import]
search_box.value = ""
search_type.observe(search_type_changed, names="value")
def search_box_callback(text):
if search_type.value == "Docs":
with tree_widget:
if text.value == "":
print("Loading...")
tree_widget.clear_output(wait=True)
display(flags.docs)
else:
tree_widget.clear_output()
print("Searching...")
tree_widget.clear_output(wait=True)
sub_tree = search_api_tree(text.value, flags.docs_dict)
display(sub_tree)
elif search_type.value == "Assets":
with tree_widget:
if text.value == "":
print("Loading...")
tree_widget.clear_output(wait=True)
display(flags.assets)
else:
tree_widget.clear_output()
print("Searching...")
tree_widget.clear_output(wait=True)
sub_tree = search_api_tree(text.value, flags.asset_dict)
display(sub_tree)
search_box.on_submit(search_box_callback)
ee_to_csv(ee_object, filename, selectors=None, verbose=True)
¶
Downloads an ee.FeatureCollection as a CSV file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
ee.FeatureCollection |
required |
filename |
str |
The output filepath of the CSV file. |
required |
selectors |
list |
A list of attributes to export. Defaults to None. |
None |
verbose |
bool |
Whether to print out descriptive text. |
True |
Source code in geemap/common.py
def ee_to_csv(ee_object, filename, selectors=None, verbose=True):
"""Downloads an ee.FeatureCollection as a CSV file.
Args:
ee_object (object): ee.FeatureCollection
filename (str): The output filepath of the CSV file.
selectors (list, optional): A list of attributes to export. Defaults to None.
verbose (bool, optional): Whether to print out descriptive text.
"""
# ee_initialize()
try:
if filename.lower().endswith(".csv"):
ee_export_vector(
ee_object=ee_object,
filename=filename,
selectors=selectors,
verbose=verbose,
)
else:
print("The filename must end with .csv")
except Exception as e:
print(e)
ee_to_geojson(ee_object, out_json=None)
¶
Converts Earth Engine object to geojson.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
An Earth Engine object. |
required |
Returns:
| Type | Description |
|---|---|
object |
GeoJSON object. |
Source code in geemap/common.py
def ee_to_geojson(ee_object, out_json=None):
"""Converts Earth Engine object to geojson.
Args:
ee_object (object): An Earth Engine object.
Returns:
object: GeoJSON object.
"""
from json import dumps
# ee_initialize()
try:
if (
isinstance(ee_object, ee.geometry.Geometry)
or isinstance(ee_object, ee.feature.Feature)
or isinstance(ee_object, ee.featurecollection.FeatureCollection)
):
json_object = ee_object.getInfo()
if out_json is not None:
out_json = os.path.abspath(out_json)
if not os.path.exists(os.path.dirname(out_json)):
os.makedirs(os.path.dirname(out_json))
geojson = open(out_json, "w")
geojson.write(
dumps(
{"type": "FeatureCollection", "features": json_object}, indent=2
)
+ "\n"
)
geojson.close()
return json_object
else:
print("Could not convert the Earth Engine object to geojson")
except Exception as e:
print(e)
ee_to_geopandas(ee_object, selectors=None, verbose=False)
¶
Converts an ee.FeatureCollection to Geopandas dataframe.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
ee.FeatureCollection |
ee.FeatureCollection. |
required |
selectors |
list |
A list of attributes to export. Defaults to None. |
None |
verbose |
bool |
Whether to print out descriptive text. Defaults to False. |
False |
Exceptions:
| Type | Description |
|---|---|
TypeError |
ee_object must be an ee.FeatureCollection. |
Returns:
| Type | Description |
|---|---|
gpd.GeoDataFrame |
geopandas.GeoDataFrame |
Source code in geemap/common.py
def ee_to_geopandas(ee_object, selectors=None, verbose=False):
"""Converts an ee.FeatureCollection to Geopandas dataframe.
Args:
ee_object (ee.FeatureCollection): ee.FeatureCollection.
selectors (list, optional): A list of attributes to export. Defaults to None.
verbose (bool, optional): Whether to print out descriptive text. Defaults to False.
Raises:
TypeError: ee_object must be an ee.FeatureCollection.
Returns:
gpd.GeoDataFrame: geopandas.GeoDataFrame
"""
from pathlib import Path
if not isinstance(ee_object, ee.FeatureCollection):
raise TypeError("ee_object must be an ee.FeatureCollection")
out_shp = os.path.join(os.getcwd(), random_string(6) + ".shp")
ee_to_shp(ee_object, out_shp, selectors=selectors, verbose=verbose)
df = shp_to_geopandas(out_shp)
files = Path(os.getcwd()).rglob(os.path.basename(out_shp)[:-4] + "*")
for file in files:
os.remove(os.path.join(os.getcwd(), str(file)))
return df
ee_to_numpy(ee_object, bands=None, region=None, properties=None, default_value=None)
¶
Extracts a rectangular region of pixels from an image into a 2D numpy array per band.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
The image to sample. |
required |
bands |
list |
The list of band names to extract. Please make sure that all bands have the same spatial resolution. Defaults to None. |
None |
region |
object |
The region whose projected bounding box is used to sample the image. The maximum number of pixels you can export is 262,144. Resampling and reprojecting all bands to a fixed scale can be useful. Defaults to the footprint in each band. |
None |
properties |
list |
The properties to copy over from the sampled image. Defaults to all non-system properties. |
None |
default_value |
float |
A default value used when a sampled pixel is masked or outside a band's footprint. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
array |
A 3D numpy array. |
Source code in geemap/common.py
def ee_to_numpy(
ee_object, bands=None, region=None, properties=None, default_value=None
):
"""Extracts a rectangular region of pixels from an image into a 2D numpy array per band.
Args:
ee_object (object): The image to sample.
bands (list, optional): The list of band names to extract. Please make sure that all bands have the same spatial resolution. Defaults to None.
region (object, optional): The region whose projected bounding box is used to sample the image. The maximum number of pixels you can export is 262,144. Resampling and reprojecting all bands to a fixed scale can be useful. Defaults to the footprint in each band.
properties (list, optional): The properties to copy over from the sampled image. Defaults to all non-system properties.
default_value (float, optional): A default value used when a sampled pixel is masked or outside a band's footprint. Defaults to None.
Returns:
array: A 3D numpy array.
"""
import numpy as np
if not isinstance(ee_object, ee.Image):
print("The input must be an ee.Image.")
return
if region is None:
region = ee_object.geometry()
try:
if bands is not None:
ee_object = ee_object.select(bands)
else:
bands = ee_object.bandNames().getInfo()
band_arrs = ee_object.sampleRectangle(
region=region, properties=properties, defaultValue=default_value
)
band_values = []
for band in bands:
band_arr = band_arrs.get(band).getInfo()
band_value = np.array(band_arr)
band_values.append(band_value)
image = np.dstack(band_values)
return image
except Exception as e:
print(e)
ee_to_pandas(ee_object, selectors=None, verbose=False, **kwargs)
¶
Converts an ee.FeatureCollection to pandas dataframe.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
ee.FeatureCollection |
ee.FeatureCollection. |
required |
selectors |
list |
A list of attributes to export. Defaults to None. |
None |
verbose |
bool |
Whether to print out descriptive text. Defaults to False. |
False |
Exceptions:
| Type | Description |
|---|---|
TypeError |
ee_object must be an ee.FeatureCollection |
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
pandas DataFrame |
Source code in geemap/common.py
def ee_to_pandas(ee_object, selectors=None, verbose=False, **kwargs):
"""Converts an ee.FeatureCollection to pandas dataframe.
Args:
ee_object (ee.FeatureCollection): ee.FeatureCollection.
selectors (list, optional): A list of attributes to export. Defaults to None.
verbose (bool, optional): Whether to print out descriptive text. Defaults to False.
Raises:
TypeError: ee_object must be an ee.FeatureCollection
Returns:
pd.DataFrame: pandas DataFrame
"""
import pandas as pd
if not isinstance(ee_object, ee.FeatureCollection):
raise TypeError("ee_object must be an ee.FeatureCollection")
out_csv = os.path.join(os.getcwd(), random_string(6) + ".csv")
try:
ee_to_csv(ee_object, out_csv, selectors=selectors, verbose=verbose)
df = csv_to_pandas(out_csv, **kwargs)
os.remove(out_csv)
return df
except Exception as e:
raise Exception(e)
ee_to_shp(ee_object, filename, selectors=None, verbose=True, keep_zip=False)
¶
Downloads an ee.FeatureCollection as a shapefile.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
ee.FeatureCollection |
required |
filename |
str |
The output filepath of the shapefile. |
required |
selectors |
list |
A list of attributes to export. Defaults to None. |
None |
verbose |
bool |
Whether to print out descriptive text. |
True |
keep_zip |
bool |
Whether to keep the downloaded shapefile as a zip file. |
False |
Source code in geemap/common.py
def ee_to_shp(ee_object, filename, selectors=None, verbose=True, keep_zip=False):
"""Downloads an ee.FeatureCollection as a shapefile.
Args:
ee_object (object): ee.FeatureCollection
filename (str): The output filepath of the shapefile.
selectors (list, optional): A list of attributes to export. Defaults to None.
verbose (bool, optional): Whether to print out descriptive text.
keep_zip (bool, optional): Whether to keep the downloaded shapefile as a zip file.
"""
# ee_initialize()
try:
if filename.lower().endswith(".shp"):
ee_export_vector(
ee_object=ee_object,
filename=filename,
selectors=selectors,
verbose=verbose,
keep_zip=keep_zip,
)
else:
print("The filename must end with .shp")
except Exception as e:
print(e)
ee_user_id()
¶
Gets Earth Engine account user id.
Returns:
| Type | Description |
|---|---|
str |
A string containing the user id. |
Source code in geemap/common.py
def ee_user_id():
"""Gets Earth Engine account user id.
Returns:
str: A string containing the user id.
"""
# ee_initialize()
roots = ee.data.getAssetRoots()
if len(roots) == 0:
return None
else:
root = ee.data.getAssetRoots()[0]
user_id = root["id"].replace("projects/earthengine-legacy/assets/", "")
return user_id
explode(coords)
¶
Explode a GeoJSON geometry's coordinates object and yield coordinate tuples. As long as the input is conforming, the type of the geometry doesn't matter. From Fiona 1.4.8
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
coords |
list |
A list of coordinates. |
required |
!!! yields [type]: [description]
Source code in geemap/common.py
def explode(coords):
"""Explode a GeoJSON geometry's coordinates object and yield
coordinate tuples. As long as the input is conforming, the type of
the geometry doesn't matter. From Fiona 1.4.8
Args:
coords (list): A list of coordinates.
Yields:
[type]: [description]
"""
for e in coords:
if isinstance(e, (float, int)):
yield coords
break
else:
for f in explode(e):
yield f
extract_pixel_values(ee_object, region, scale=None, projection=None, tileScale=1, getInfo=False)
¶
Samples the pixels of an image, returning them as a ee.Dictionary.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
ee.Image | ee.ImageCollection |
The ee.Image or ee.ImageCollection to sample. |
required |
region |
ee.Geometry |
The region to sample from. If unspecified, uses the image's whole footprint. |
required |
scale |
float |
A nominal scale in meters of the projection to sample in. Defaults to None. |
None |
projection |
str |
The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None. |
None |
tileScale |
int |
A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1. |
1 |
getInfo |
bool |
Whether to use getInfo with the results, i.e., returning the values a list. Default to False. |
False |
Exceptions:
| Type | Description |
|---|---|
TypeError |
The image must be an instance of ee.Image. |
TypeError |
Region must be an instance of ee.Geometry. |
Returns:
| Type | Description |
|---|---|
ee.Dictionary |
The dictionary containing band names and pixel values. |
Source code in geemap/common.py
def extract_pixel_values(
ee_object, region, scale=None, projection=None, tileScale=1, getInfo=False
):
"""Samples the pixels of an image, returning them as a ee.Dictionary.
Args:
ee_object (ee.Image | ee.ImageCollection): The ee.Image or ee.ImageCollection to sample.
region (ee.Geometry): The region to sample from. If unspecified, uses the image's whole footprint.
scale (float, optional): A nominal scale in meters of the projection to sample in. Defaults to None.
projection (str, optional): The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.
tileScale (int, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.
getInfo (bool, optional): Whether to use getInfo with the results, i.e., returning the values a list. Default to False.
Raises:
TypeError: The image must be an instance of ee.Image.
TypeError: Region must be an instance of ee.Geometry.
Returns:
ee.Dictionary: The dictionary containing band names and pixel values.
"""
if isinstance(ee_object, ee.ImageCollection):
ee_object = ee_object.toBands()
if not isinstance(ee_object, ee.Image):
raise TypeError("The image must be an instance of ee.Image.")
if not isinstance(region, ee.Geometry):
raise TypeError("Region must be an instance of ee.Geometry.")
dict_values = (
ee_object.sample(region, scale, projection, tileScale=tileScale)
.first()
.toDictionary()
)
if getInfo:
return list(dict_values.getInfo().values())
else:
return dict_values
extract_values_to_points(in_fc, image, out_fc=None, properties=None, scale=None, projection=None, tile_scale=1, geometries=True)
¶
Extracts image values to points.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_fc |
object |
ee.FeatureCollection |
required |
image |
object |
The ee.Image to extract pixel values |
required |
properties |
list |
The list of properties to copy from each input feature. Defaults to all non-system properties. |
None |
scale |
float |
A nominal scale in meters of the projection to sample in. If unspecified,the scale of the image's first band is used. |
None |
projection |
str |
The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. |
None |
tile_scale |
float |
A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. |
1 |
geometries |
bool |
If true, the results will include a geometry per sampled pixel. Otherwise, geometries will be omitted (saving memory). |
True |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def extract_values_to_points(
in_fc,
image,
out_fc=None,
properties=None,
scale=None,
projection=None,
tile_scale=1,
geometries=True,
):
"""Extracts image values to points.
Args:
in_fc (object): ee.FeatureCollection
image (object): The ee.Image to extract pixel values
properties (list, optional): The list of properties to copy from each input feature. Defaults to all non-system properties.
scale (float, optional): A nominal scale in meters of the projection to sample in. If unspecified,the scale of the image's first band is used.
projection (str, optional): The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.
tile_scale (float, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default.
geometries (bool, optional): If true, the results will include a geometry per sampled pixel. Otherwise, geometries will be omitted (saving memory).
Returns:
object: ee.FeatureCollection
"""
if not isinstance(in_fc, ee.FeatureCollection):
try:
in_fc = shp_to_ee(in_fc)
except Exception as e:
print(e)
return
if not isinstance(image, ee.Image):
print("The image must be an instance of ee.Image.")
return
result = image.sampleRegions(
**{
"collection": in_fc,
"properties": properties,
"scale": scale,
"projection": projection,
"tileScale": tile_scale,
"geometries": geometries,
}
)
if out_fc is not None:
ee_export_vector(result, out_fc)
else:
return result
file_browser(in_dir=None, show_hidden=False, add_root_node=True, search_description=None, use_import=False, return_sep_widgets=False, node_icon='file')
¶
Creates a simple file browser and text editor.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_dir |
str |
The input directory. Defaults to None, which will use the current working directory. |
None |
show_hidden |
bool |
Whether to show hidden files/folders. Defaults to False. |
False |
add_root_node |
bool |
Whether to add the input directory as a root node. Defaults to True. |
True |
search_description |
str |
The description of the search box. Defaults to None. |
None |
use_import |
bool |
Whether to show the import button. Defaults to False. |
False |
return_sep_widgets |
bool |
Whether to return the results as separate widgets. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
object |
An ipywidget. |
Source code in geemap/common.py
def file_browser(
in_dir=None,
show_hidden=False,
add_root_node=True,
search_description=None,
use_import=False,
return_sep_widgets=False,
node_icon="file",
):
"""Creates a simple file browser and text editor.
Args:
in_dir (str, optional): The input directory. Defaults to None, which will use the current working directory.
show_hidden (bool, optional): Whether to show hidden files/folders. Defaults to False.
add_root_node (bool, optional): Whether to add the input directory as a root node. Defaults to True.
search_description (str, optional): The description of the search box. Defaults to None.
use_import (bool, optional): Whether to show the import button. Defaults to False.
return_sep_widgets (bool, optional): Whether to return the results as separate widgets. Defaults to False.
Returns:
object: An ipywidget.
"""
import platform
if in_dir is None:
in_dir = os.getcwd()
if not os.path.exists(in_dir):
print("The provided directory does not exist.")
return
elif not os.path.isdir(in_dir):
print("The provided path is not a valid directory.")
return
sep = "/"
if platform.system() == "Windows":
sep = "\\"
if in_dir.endswith(sep):
in_dir = in_dir[:-1]
full_widget = widgets.HBox()
left_widget = widgets.VBox()
right_widget = widgets.VBox()
import_btn = widgets.Button(
description="import",
button_style="primary",
tooltip="import the content to a new cell",
disabled=True,
)
import_btn.layout.width = "70px"
path_widget = widgets.Text()
path_widget.layout.min_width = "400px"
# path_widget.layout.max_width = '400px'
save_widget = widgets.Button(
description="Save",
button_style="primary",
tooltip="Save edits to file.",
disabled=True,
)
info_widget = widgets.HBox()
info_widget.children = [path_widget, save_widget]
if use_import:
info_widget.children = [import_btn, path_widget, save_widget]
text_widget = widgets.Textarea()
text_widget.layout.width = "630px"
text_widget.layout.height = "600px"
right_widget.children = [info_widget, text_widget]
full_widget.children = [left_widget]
if search_description is None:
search_description = "Search files/folders..."
search_box = widgets.Text(placeholder=search_description)
search_box.layout.width = "310px"
tree_widget = widgets.Output()
tree_widget.layout.max_width = "310px"
tree_widget.overflow = "auto"
left_widget.children = [search_box, tree_widget]
tree = Tree(multiple_selection=False)
tree_dict = {}
def on_button_clicked(b):
content = text_widget.value
out_file = path_widget.value
out_dir = os.path.dirname(out_file)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
with open(out_file, "w") as f:
f.write(content)
text_widget.disabled = True
text_widget.value = "The content has been saved successfully."
save_widget.disabled = True
path_widget.disabled = True
if (out_file not in tree_dict.keys()) and (out_dir in tree_dict.keys()):
node = Node(os.path.basename(out_file))
tree_dict[out_file] = node
parent_node = tree_dict[out_dir]
parent_node.add_node(node)
save_widget.on_click(on_button_clicked)
def import_btn_clicked(b):
if (text_widget.value != "") and (path_widget.value.endswith(".py")):
create_code_cell(text_widget.value)
import_btn.on_click(import_btn_clicked)
def search_box_callback(text):
with tree_widget:
if text.value == "":
print("Loading...")
tree_widget.clear_output(wait=True)
display(tree)
else:
tree_widget.clear_output()
print("Searching...")
tree_widget.clear_output(wait=True)
sub_tree = search_api_tree(text.value, tree_dict)
display(sub_tree)
search_box.on_submit(search_box_callback)
def handle_file_click(event):
if event["new"]:
cur_node = event["owner"]
for key in tree_dict.keys():
if (cur_node is tree_dict[key]) and (os.path.isfile(key)):
if key.endswith(".py"):
import_btn.disabled = False
else:
import_btn.disabled = True
try:
with open(key) as f:
content = f.read()
text_widget.value = content
text_widget.disabled = False
path_widget.value = key
path_widget.disabled = False
save_widget.disabled = False
full_widget.children = [left_widget, right_widget]
except Exception as e:
path_widget.value = key
path_widget.disabled = True
save_widget.disabled = True
text_widget.disabled = True
text_widget.value = (
"Failed to open {}.".format(cur_node.name) + "\n\n" + str(e)
)
full_widget.children = [left_widget, right_widget]
return
break
def handle_folder_click(event):
if event["new"]:
full_widget.children = [left_widget]
text_widget.value = ""
if add_root_node:
root_name = in_dir.split(sep)[-1]
root_node = Node(root_name)
tree_dict[in_dir] = root_node
tree.add_node(root_node)
root_node.observe(handle_folder_click, "selected")
for root, d_names, f_names in os.walk(in_dir):
if not show_hidden:
folders = root.split(sep)
for folder in folders:
if folder.startswith("."):
continue
for d_name in d_names:
if d_name.startswith("."):
d_names.remove(d_name)
for f_name in f_names:
if f_name.startswith("."):
f_names.remove(f_name)
d_names.sort()
f_names.sort()
if (not add_root_node) and (root == in_dir):
for d_name in d_names:
node = Node(d_name)
tree_dict[os.path.join(in_dir, d_name)] = node
tree.add_node(node)
node.opened = False
node.observe(handle_folder_click, "selected")
if (root != in_dir) and (root not in tree_dict.keys()):
name = root.split(sep)[-1]
dir_name = os.path.dirname(root)
parent_node = tree_dict[dir_name]
node = Node(name)
tree_dict[root] = node
parent_node.add_node(node)
node.observe(handle_folder_click, "selected")
if len(f_names) > 0:
parent_node = tree_dict[root]
parent_node.opened = False
for f_name in f_names:
node = Node(f_name)
node.icon = node_icon
full_path = os.path.join(root, f_name)
tree_dict[full_path] = node
parent_node.add_node(node)
node.observe(handle_file_click, "selected")
with tree_widget:
tree_widget.clear_output()
display(tree)
if return_sep_widgets:
return left_widget, right_widget, tree_dict
else:
return full_widget
filter_HUC08(region)
¶
Filters HUC08 watersheds intersecting a given region.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
region |
object |
ee.Geometry |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def filter_HUC08(region):
"""Filters HUC08 watersheds intersecting a given region.
Args:
region (object): ee.Geometry
Returns:
object: ee.FeatureCollection
"""
USGS_HUC08 = ee.FeatureCollection("USGS/WBD/2017/HUC08") # Subbasins
HUC08 = USGS_HUC08.filterBounds(region)
return HUC08
filter_HUC10(region)
¶
Filters HUC10 watersheds intersecting a given region.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
region |
object |
ee.Geometry |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def filter_HUC10(region):
"""Filters HUC10 watersheds intersecting a given region.
Args:
region (object): ee.Geometry
Returns:
object: ee.FeatureCollection
"""
USGS_HUC10 = ee.FeatureCollection("USGS/WBD/2017/HUC10") # Watersheds
HUC10 = USGS_HUC10.filterBounds(region)
return HUC10
filter_NWI(HUC08_Id, region, exclude_riverine=True)
¶
Retrives NWI dataset for a given HUC8 watershed.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
HUC08_Id |
str |
The HUC8 watershed id. |
required |
region |
object |
ee.Geometry |
required |
exclude_riverine |
bool |
Whether to exclude riverine wetlands. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def filter_NWI(HUC08_Id, region, exclude_riverine=True):
"""Retrives NWI dataset for a given HUC8 watershed.
Args:
HUC08_Id (str): The HUC8 watershed id.
region (object): ee.Geometry
exclude_riverine (bool, optional): Whether to exclude riverine wetlands. Defaults to True.
Returns:
object: ee.FeatureCollection
"""
nwi_asset_prefix = "users/wqs/NWI-HU8/HU8_"
nwi_asset_suffix = "_Wetlands"
nwi_asset_path = nwi_asset_prefix + HUC08_Id + nwi_asset_suffix
nwi_huc = ee.FeatureCollection(nwi_asset_path).filterBounds(region)
if exclude_riverine:
nwi_huc = nwi_huc.filter(
ee.Filter.notEquals(**{"leftField": "WETLAND_TY", "rightValue": "Riverine"})
)
return nwi_huc
filter_polygons(ftr)
¶
Converts GeometryCollection to Polygon/MultiPolygon
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ftr |
object |
ee.Feature |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Feature |
Source code in geemap/common.py
def filter_polygons(ftr):
"""Converts GeometryCollection to Polygon/MultiPolygon
Args:
ftr (object): ee.Feature
Returns:
object: ee.Feature
"""
# ee_initialize()
geometries = ftr.geometry().geometries()
geometries = geometries.map(
lambda geo: ee.Feature(ee.Geometry(geo)).set("geoType", ee.Geometry(geo).type())
)
polygons = (
ee.FeatureCollection(geometries)
.filter(ee.Filter.eq("geoType", "Polygon"))
.geometry()
)
return ee.Feature(polygons).copyProperties(ftr)
find_HUC08(HUC08_Id)
¶
Finds a HUC08 watershed based on a given HUC08 ID
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
HUC08_Id |
str |
The HUC08 ID. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def find_HUC08(HUC08_Id):
"""Finds a HUC08 watershed based on a given HUC08 ID
Args:
HUC08_Id (str): The HUC08 ID.
Returns:
object: ee.FeatureCollection
"""
USGS_HUC08 = ee.FeatureCollection("USGS/WBD/2017/HUC08") # Subbasins
HUC08 = USGS_HUC08.filter(ee.Filter.eq("huc8", HUC08_Id))
return HUC08
find_HUC10(HUC10_Id)
¶
Finds a HUC10 watershed based on a given HUC08 ID
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
HUC10_Id |
str |
The HUC10 ID. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def find_HUC10(HUC10_Id):
"""Finds a HUC10 watershed based on a given HUC08 ID
Args:
HUC10_Id (str): The HUC10 ID.
Returns:
object: ee.FeatureCollection
"""
USGS_HUC10 = ee.FeatureCollection("USGS/WBD/2017/HUC10") # Watersheds
HUC10 = USGS_HUC10.filter(ee.Filter.eq("huc10", HUC10_Id))
return HUC10
find_landsat_by_path_row(landsat_col, path_num, row_num)
¶
Finds Landsat images by WRS path number and row number.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
landsat_col |
str |
The image collection id of Landsat. |
required |
path_num |
int |
The WRS path number. |
required |
row_num |
int |
the WRS row number. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.ImageCollection |
Source code in geemap/common.py
def find_landsat_by_path_row(landsat_col, path_num, row_num):
"""Finds Landsat images by WRS path number and row number.
Args:
landsat_col (str): The image collection id of Landsat.
path_num (int): The WRS path number.
row_num (int): the WRS row number.
Returns:
object: ee.ImageCollection
"""
try:
if isinstance(landsat_col, str):
landsat_col = ee.ImageCollection(landsat_col)
collection = landsat_col.filter(ee.Filter.eq("WRS_PATH", path_num)).filter(
ee.Filter.eq("WRS_ROW", row_num)
)
return collection
except Exception as e:
print(e)
find_NAIP(region, add_NDVI=True, add_NDWI=True)
¶
Create annual NAIP mosaic for a given region.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
region |
object |
ee.Geometry |
required |
add_NDVI |
bool |
Whether to add the NDVI band. Defaults to True. |
True |
add_NDWI |
bool |
Whether to add the NDWI band. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
object |
ee.ImageCollection |
Source code in geemap/common.py
def find_NAIP(region, add_NDVI=True, add_NDWI=True):
"""Create annual NAIP mosaic for a given region.
Args:
region (object): ee.Geometry
add_NDVI (bool, optional): Whether to add the NDVI band. Defaults to True.
add_NDWI (bool, optional): Whether to add the NDWI band. Defaults to True.
Returns:
object: ee.ImageCollection
"""
init_collection = (
ee.ImageCollection("USDA/NAIP/DOQQ")
.filterBounds(region)
.filterDate("2009-01-01", "2019-12-31")
.filter(ee.Filter.listContains("system:band_names", "N"))
)
yearList = ee.List(
init_collection.distinct(["system:time_start"]).aggregate_array(
"system:time_start"
)
)
init_years = yearList.map(lambda y: ee.Date(y).get("year"))
# remove duplicates
init_years = ee.Dictionary(
init_years.reduce(ee.Reducer.frequencyHistogram())
).keys()
years = init_years.map(lambda x: ee.Number.parse(x))
# years = init_years.map(lambda x: x)
# Available NAIP years with NIR band
def NAIPAnnual(year):
start_date = ee.Date.fromYMD(year, 1, 1)
end_date = ee.Date.fromYMD(year, 12, 31)
collection = init_collection.filterDate(start_date, end_date)
# .filterBounds(geometry)
# .filter(ee.Filter.listContains("system:band_names", "N"))
time_start = ee.Date(
ee.List(collection.aggregate_array("system:time_start")).sort().get(0)
).format("YYYY-MM-dd")
time_end = ee.Date(
ee.List(collection.aggregate_array("system:time_end")).sort().get(-1)
).format("YYYY-MM-dd")
col_size = collection.size()
image = ee.Image(collection.mosaic().clip(region))
if add_NDVI:
NDVI = (
ee.Image(image)
.normalizedDifference(["N", "R"])
.select(["nd"], ["ndvi"])
)
image = image.addBands(NDVI)
if add_NDWI:
NDWI = (
ee.Image(image)
.normalizedDifference(["G", "N"])
.select(["nd"], ["ndwi"])
)
image = image.addBands(NDWI)
return image.set(
{
"system:time_start": time_start,
"system:time_end": time_end,
"tiles": col_size,
}
)
# remove years with incomplete coverage
naip = ee.ImageCollection(years.map(NAIPAnnual))
mean_size = ee.Number(naip.aggregate_mean("tiles"))
total_sd = ee.Number(naip.aggregate_total_sd("tiles"))
threshold = mean_size.subtract(total_sd.multiply(1))
naip = naip.filter(
ee.Filter.Or(ee.Filter.gte("tiles", threshold), ee.Filter.gte("tiles", 15))
)
naip = naip.filter(ee.Filter.gte("tiles", 7))
naip_count = naip.size()
naip_seq = ee.List.sequence(0, naip_count.subtract(1))
def set_index(index):
img = ee.Image(naip.toList(naip_count).get(index))
return img.set({"system:uid": ee.Number(index).toUint8()})
naip = naip_seq.map(set_index)
return ee.ImageCollection(naip)
find_NWI(HUC08_Id, exclude_riverine=True)
¶
Finds NWI dataset for a given HUC08 watershed.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
HUC08_Id |
str |
The HUC08 watershed ID. |
required |
exclude_riverine |
bool |
Whether to exclude riverine wetlands. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def find_NWI(HUC08_Id, exclude_riverine=True):
"""Finds NWI dataset for a given HUC08 watershed.
Args:
HUC08_Id (str): The HUC08 watershed ID.
exclude_riverine (bool, optional): Whether to exclude riverine wetlands. Defaults to True.
Returns:
object: ee.FeatureCollection
"""
nwi_asset_prefix = "users/wqs/NWI-HU8/HU8_"
nwi_asset_suffix = "_Wetlands"
nwi_asset_path = nwi_asset_prefix + HUC08_Id + nwi_asset_suffix
nwi_huc = ee.FeatureCollection(nwi_asset_path)
if exclude_riverine:
nwi_huc = nwi_huc.filter(
ee.Filter.notEquals(**{"leftField": "WETLAND_TY", "rightValue": "Riverine"})
)
return nwi_huc
geocode(location, max_rows=10, reverse=False)
¶
Search location by address and lat/lon coordinates.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
location |
str |
Place name or address |
required |
max_rows |
int |
Maximum number of records to return. Defaults to 10. |
10 |
reverse |
bool |
Search place based on coordinates. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
list |
Returns a list of locations. |
Source code in geemap/common.py
def geocode(location, max_rows=10, reverse=False):
"""Search location by address and lat/lon coordinates.
Args:
location (str): Place name or address
max_rows (int, optional): Maximum number of records to return. Defaults to 10.
reverse (bool, optional): Search place based on coordinates. Defaults to False.
Returns:
list: Returns a list of locations.
"""
import geocoder
if not isinstance(location, str):
print("The location must be a string.")
return None
if not reverse:
locations = []
addresses = set()
g = geocoder.arcgis(location, maxRows=max_rows)
for result in g:
address = result.address
if address not in addresses:
addresses.add(address)
locations.append(result)
if len(locations) > 0:
return locations
else:
return None
else:
try:
if "," in location:
latlon = [float(x) for x in location.split(",")]
elif " " in location:
latlon = [float(x) for x in location.split(" ")]
else:
print(
"The lat-lon coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
)
return
g = geocoder.arcgis(latlon, method="reverse")
locations = []
addresses = set()
for result in g:
address = result.address
if address not in addresses:
addresses.add(address)
locations.append(result)
if len(locations) > 0:
return locations
else:
return None
except Exception as e:
print(e)
return None
geojson_to_ee(geo_json, geodesic=True)
¶
Converts a geojson to ee.Geometry()
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geo_json |
dict |
A geojson geometry dictionary or file path. |
required |
geodesic |
bool |
Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected. |
True |
Returns:
| Type | Description |
|---|---|
ee_object |
An ee.Geometry object |
Source code in geemap/common.py
def geojson_to_ee(geo_json, geodesic=True):
"""Converts a geojson to ee.Geometry()
Args:
geo_json (dict): A geojson geometry dictionary or file path.
geodesic (bool, optional): Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected.
Returns:
ee_object: An ee.Geometry object
"""
try:
import json
if not isinstance(geo_json, dict) and os.path.isfile(geo_json):
with open(os.path.abspath(geo_json)) as f:
geo_json = json.load(f)
if geo_json["type"] == "FeatureCollection":
features = ee.FeatureCollection(geo_json["features"])
return features
elif geo_json["type"] == "Feature":
geom = None
keys = geo_json["properties"]["style"].keys()
if "radius" in keys: # Checks whether it is a circle
geom = ee.Geometry(geo_json["geometry"])
radius = geo_json["properties"]["style"]["radius"]
geom = geom.buffer(radius)
elif (
geo_json["geometry"]["type"] == "Point"
): # Checks whether it is a point
coordinates = geo_json["geometry"]["coordinates"]
longitude = coordinates[0]
latitude = coordinates[1]
geom = ee.Geometry.Point(longitude, latitude)
else:
geom = ee.Geometry(geo_json["geometry"], "", geodesic)
return geom
else:
raise Exception("Could not convert the geojson to ee.Geometry()")
except Exception as e:
print("Could not convert the geojson to ee.Geometry()")
raise Exception(e)
geometry_type(ee_object)
¶
Get geometry type of an Earth Engine object.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
An Earth Engine object. |
required |
Returns:
| Type | Description |
|---|---|
str |
Returns geometry type. One of Point, MultiPoint, LineString, LinearRing, MultiLineString, BBox, Rectangle, Polygon, MultiPolygon. |
Source code in geemap/common.py
def geometry_type(ee_object):
"""Get geometry type of an Earth Engine object.
Args:
ee_object (object): An Earth Engine object.
Returns:
str: Returns geometry type. One of Point, MultiPoint, LineString, LinearRing, MultiLineString, BBox, Rectangle, Polygon, MultiPolygon.
"""
if isinstance(ee_object, ee.Geometry):
return ee_object.type().getInfo()
elif isinstance(ee_object, ee.Feature):
return ee_object.geometry().type().getInfo()
elif isinstance(ee_object, ee.FeatureCollection):
return ee.Feature(ee_object.first()).geometry().type().getInfo()
else:
raise TypeError(
"The ee_object must be one of ee.Geometry, ee.Feature, ee.FeatureCollection."
)
geopandas_to_ee(gdf, geodesic=True)
¶
Converts a GeoPandas GeoDataFrame to ee.FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
gdf |
geopandas.GeoDataFrame |
The input geopandas.GeoDataFrame to be converted ee.FeatureCollection. |
required |
geodesic |
bool |
Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected.. Defaults to True. |
True |
Exceptions:
| Type | Description |
|---|---|
TypeError |
The input data type must be geopandas.GeoDataFrame. |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
The output ee.FeatureCollection converted from the input geopandas.GeoDataFrame. |
Source code in geemap/common.py
def geopandas_to_ee(gdf, geodesic=True):
"""Converts a GeoPandas GeoDataFrame to ee.FeatureCollection.
Args:
gdf (geopandas.GeoDataFrame): The input geopandas.GeoDataFrame to be converted ee.FeatureCollection.
geodesic (bool, optional): Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected.. Defaults to True.
Raises:
TypeError: The input data type must be geopandas.GeoDataFrame.
Returns:
ee.FeatureCollection: The output ee.FeatureCollection converted from the input geopandas.GeoDataFrame.
"""
check_package(name="geopandas", URL="https://geopandas.org")
import geopandas as gpd
if not isinstance(gdf, gpd.GeoDataFrame):
raise TypeError("The input data type must be geopandas.GeoDataFrame.")
out_json = os.path.join(os.getcwd(), random_string(6) + ".geojson")
gdf.to_file(out_json, driver="GeoJSON")
fc = geojson_to_ee(out_json, geodesic=geodesic)
os.remove(out_json)
return fc
get_all_NAIP(start_year=2009, end_year=2019)
¶
Creates annual NAIP imagery mosaic.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
start_year |
int |
The starting year. Defaults to 2009. |
2009 |
end_year |
int |
The ending year. Defaults to 2019. |
2019 |
Returns:
| Type | Description |
|---|---|
object |
ee.ImageCollection |
Source code in geemap/common.py
def get_all_NAIP(start_year=2009, end_year=2019):
"""Creates annual NAIP imagery mosaic.
Args:
start_year (int, optional): The starting year. Defaults to 2009.
end_year (int, optional): The ending year. Defaults to 2019.
Returns:
object: ee.ImageCollection
"""
try:
def get_annual_NAIP(year):
try:
collection = ee.ImageCollection("USDA/NAIP/DOQQ")
start_date = ee.Date.fromYMD(year, 1, 1)
end_date = ee.Date.fromYMD(year, 12, 31)
naip = collection.filterDate(start_date, end_date).filter(
ee.Filter.listContains("system:band_names", "N")
)
return ee.ImageCollection(naip)
except Exception as e:
print(e)
years = ee.List.sequence(start_year, end_year)
collection = years.map(get_annual_NAIP)
return collection
except Exception as e:
print(e)
get_annual_NAIP(year, RGBN=True)
¶
Filters NAIP ImageCollection by year.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
year |
int |
The year to filter the NAIP ImageCollection. |
required |
RGBN |
bool |
Whether to retrieve 4-band NAIP imagery only. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
object |
ee.ImageCollection |
Source code in geemap/common.py
def get_annual_NAIP(year, RGBN=True):
"""Filters NAIP ImageCollection by year.
Args:
year (int): The year to filter the NAIP ImageCollection.
RGBN (bool, optional): Whether to retrieve 4-band NAIP imagery only. Defaults to True.
Returns:
object: ee.ImageCollection
"""
try:
collection = ee.ImageCollection("USDA/NAIP/DOQQ")
start_date = str(year) + "-01-01"
end_date = str(year) + "-12-31"
naip = collection.filterDate(start_date, end_date)
if RGBN:
naip = naip.filter(ee.Filter.listContains("system:band_names", "N"))
return naip
except Exception as e:
print(e)
get_bounds(geometry, north_up=True, transform=None)
¶
Bounding box of a GeoJSON geometry, GeometryCollection, or FeatureCollection. left, bottom, right, top not xmin, ymin, xmax, ymax If not north_up, y will be switched to guarantee the above. Source code adapted from https://github.com/mapbox/rasterio/blob/master/rasterio/features.py#L361
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geometry |
dict |
A GeoJSON dict. |
required |
north_up |
bool |
. Defaults to True. |
True |
transform |
[type] |
. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
list |
A list of coordinates representing [left, bottom, right, top] |
Source code in geemap/common.py
def get_bounds(geometry, north_up=True, transform=None):
"""Bounding box of a GeoJSON geometry, GeometryCollection, or FeatureCollection.
left, bottom, right, top
*not* xmin, ymin, xmax, ymax
If not north_up, y will be switched to guarantee the above.
Source code adapted from https://github.com/mapbox/rasterio/blob/master/rasterio/features.py#L361
Args:
geometry (dict): A GeoJSON dict.
north_up (bool, optional): . Defaults to True.
transform ([type], optional): . Defaults to None.
Returns:
list: A list of coordinates representing [left, bottom, right, top]
"""
if "bbox" in geometry:
return tuple(geometry["bbox"])
geometry = geometry.get("geometry") or geometry
# geometry must be a geometry, GeometryCollection, or FeatureCollection
if not (
"coordinates" in geometry or "geometries" in geometry or "features" in geometry
):
raise ValueError(
"geometry must be a GeoJSON-like geometry, GeometryCollection, "
"or FeatureCollection"
)
if "features" in geometry:
# Input is a FeatureCollection
xmins = []
ymins = []
xmaxs = []
ymaxs = []
for feature in geometry["features"]:
xmin, ymin, xmax, ymax = get_bounds(feature["geometry"])
xmins.append(xmin)
ymins.append(ymin)
xmaxs.append(xmax)
ymaxs.append(ymax)
if north_up:
return min(xmins), min(ymins), max(xmaxs), max(ymaxs)
else:
return min(xmins), max(ymaxs), max(xmaxs), min(ymins)
elif "geometries" in geometry:
# Input is a geometry collection
xmins = []
ymins = []
xmaxs = []
ymaxs = []
for geometry in geometry["geometries"]:
xmin, ymin, xmax, ymax = get_bounds(geometry)
xmins.append(xmin)
ymins.append(ymin)
xmaxs.append(xmax)
ymaxs.append(ymax)
if north_up:
return min(xmins), min(ymins), max(xmaxs), max(ymaxs)
else:
return min(xmins), max(ymaxs), max(xmaxs), min(ymins)
elif "coordinates" in geometry:
# Input is a singular geometry object
if transform is not None:
xyz = list(explode(geometry["coordinates"]))
xyz_px = [transform * point for point in xyz]
xyz = tuple(zip(*xyz_px))
return min(xyz[0]), max(xyz[1]), max(xyz[0]), min(xyz[1])
else:
xyz = tuple(zip(*list(explode(geometry["coordinates"]))))
if north_up:
return min(xyz[0]), min(xyz[1]), max(xyz[0]), max(xyz[1])
else:
return min(xyz[0]), max(xyz[1]), max(xyz[0]), min(xyz[1])
# all valid inputs returned above, so whatever falls through is an error
raise ValueError(
"geometry must be a GeoJSON-like geometry, GeometryCollection, "
"or FeatureCollection"
)
get_center(geometry, north_up=True, transform=None)
¶
Get the centroid of a GeoJSON.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geometry |
dict |
A GeoJSON dict. |
required |
north_up |
bool |
. Defaults to True. |
True |
transform |
[type] |
. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
list |
[lon, lat] |
Source code in geemap/common.py
def get_center(geometry, north_up=True, transform=None):
"""Get the centroid of a GeoJSON.
Args:
geometry (dict): A GeoJSON dict.
north_up (bool, optional): . Defaults to True.
transform ([type], optional): . Defaults to None.
Returns:
list: [lon, lat]
"""
bounds = get_bounds(geometry, north_up, transform)
center = ((bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2) # (lat, lon)
return center
get_COG_bands(url, titiler_endpoint='https://api.cogeo.xyz/')
¶
Get band names of a Cloud Optimized GeoTIFF (COG).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://api.cogeo.xyz/". |
'https://api.cogeo.xyz/' |
Returns:
| Type | Description |
|---|---|
list |
A list of band names |
Source code in geemap/common.py
def get_COG_bands(url, titiler_endpoint="https://api.cogeo.xyz/"):
"""Get band names of a Cloud Optimized GeoTIFF (COG).
Args:
url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".
Returns:
list: A list of band names
"""
import requests
r = requests.get(
f"{titiler_endpoint}/cog/info",
params={
"url": url,
},
).json()
bands = [b[1] for b in r["band_descriptions"]]
return bands
get_COG_bounds(url, titiler_endpoint='https://api.cogeo.xyz/')
¶
Get the bounding box of a Cloud Optimized GeoTIFF (COG).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://api.cogeo.xyz/". |
'https://api.cogeo.xyz/' |
Returns:
| Type | Description |
|---|---|
list |
A list of values representing [left, bottom, right, top] |
Source code in geemap/common.py
def get_COG_bounds(url, titiler_endpoint="https://api.cogeo.xyz/"):
"""Get the bounding box of a Cloud Optimized GeoTIFF (COG).
Args:
url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".
Returns:
list: A list of values representing [left, bottom, right, top]
"""
import requests
r = requests.get(f"{titiler_endpoint}/cog/bounds", params={"url": url}).json()
if "bounds" in r.keys():
bounds = r["bounds"]
else:
bounds = None
return bounds
get_COG_center(url, titiler_endpoint='https://api.cogeo.xyz/')
¶
Get the centroid of a Cloud Optimized GeoTIFF (COG).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://api.cogeo.xyz/". |
'https://api.cogeo.xyz/' |
Returns:
| Type | Description |
|---|---|
tuple |
A tuple representing (longitude, latitude) |
Source code in geemap/common.py
def get_COG_center(url, titiler_endpoint="https://api.cogeo.xyz/"):
"""Get the centroid of a Cloud Optimized GeoTIFF (COG).
Args:
url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".
Returns:
tuple: A tuple representing (longitude, latitude)
"""
bounds = get_COG_bounds(url, titiler_endpoint)
center = ((bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2) # (lat, lon)
return center
get_COG_tile(url, titiler_endpoint='https://api.cogeo.xyz/', **kwargs)
¶
Get a tile layer from a Cloud Optimized GeoTIFF (COG). Source code adapted from https://developmentseed.org/titiler/examples/Working_with_CloudOptimizedGeoTIFF_simple/
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://api.cogeo.xyz/". |
'https://api.cogeo.xyz/' |
Returns:
| Type | Description |
|---|---|
tuple |
Returns the COG Tile layer URL and bounds. |
Source code in geemap/common.py
def get_COG_tile(url, titiler_endpoint="https://api.cogeo.xyz/", **kwargs):
"""Get a tile layer from a Cloud Optimized GeoTIFF (COG).
Source code adapted from https://developmentseed.org/titiler/examples/Working_with_CloudOptimizedGeoTIFF_simple/
Args:
url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".
Returns:
tuple: Returns the COG Tile layer URL and bounds.
"""
import requests
params = {"url": url}
TileMatrixSetId = "WebMercatorQuad"
if "TileMatrixSetId" in kwargs.keys():
TileMatrixSetId = kwargs["TileMatrixSetId"]
if "tile_format" in kwargs.keys():
params["tile_format"] = kwargs["tile_format"]
if "tile_scale" in kwargs.keys():
params["tile_scale"] = kwargs["tile_scale"]
if "minzoom" in kwargs.keys():
params["minzoom"] = kwargs["minzoom"]
if "maxzoom" in kwargs.keys():
params["maxzoom"] = kwargs["maxzoom"]
r = requests.get(
f"{titiler_endpoint}/cog/{TileMatrixSetId}/tilejson.json", params=params
).json()
return r["tiles"][0]
get_image_collection_thumbnails(ee_object, out_dir, vis_params, dimensions=500, region=None, format='png', names=None, verbose=True)
¶
Download thumbnails for all images in an ImageCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
The ee.ImageCollection instance. |
required |
out_dir |
[str |
The output directory to store thumbnails. |
required |
vis_params |
dict |
The visualization parameters. |
required |
dimensions |
int |
(a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 500. |
500 |
region |
object |
Geospatial region of the image to render, it may be an ee.Geometry, GeoJSON, or an array of lat/lon points (E,S,W,N). If not set the default is the bounds image. Defaults to None. |
None |
format |
str |
Either 'png' or 'jpg'. Default to 'png'. |
'png' |
names |
list |
The list of output file names. Defaults to None. |
None |
verbose |
bool |
Whether or not to print hints. Defaults to True. |
True |
Source code in geemap/common.py
def get_image_collection_thumbnails(
ee_object,
out_dir,
vis_params,
dimensions=500,
region=None,
format="png",
names=None,
verbose=True,
):
"""Download thumbnails for all images in an ImageCollection.
Args:
ee_object (object): The ee.ImageCollection instance.
out_dir ([str): The output directory to store thumbnails.
vis_params (dict): The visualization parameters.
dimensions (int, optional):(a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 500.
region (object, optional): Geospatial region of the image to render, it may be an ee.Geometry, GeoJSON, or an array of lat/lon points (E,S,W,N). If not set the default is the bounds image. Defaults to None.
format (str, optional): Either 'png' or 'jpg'. Default to 'png'.
names (list, optional): The list of output file names. Defaults to None.
verbose (bool, optional): Whether or not to print hints. Defaults to True.
"""
if not isinstance(ee_object, ee.ImageCollection):
print("The ee_object must be an ee.ImageCollection.")
raise TypeError("The ee_object must be an ee.Image.")
if format not in ["png", "jpg"]:
raise ValueError("The output image format must be png or jpg.")
if not os.path.exists(out_dir):
os.makedirs(out_dir)
try:
count = int(ee_object.size().getInfo())
if verbose:
print("Total number of images: {}\n".format(count))
if (names is not None) and (len(names) != count):
print("The number of names is not equal to the number of images.")
return
if names is None:
names = ee_object.aggregate_array("system:index").getInfo()
images = ee_object.toList(count)
for i in range(0, count):
image = ee.Image(images.get(i))
name = str(names[i])
ext = os.path.splitext(name)[0][1:]
if ext != format:
name = name + "." + format
out_img = os.path.join(out_dir, name)
if verbose:
print(f"Downloading {i+1}/{count}: {name} ...")
get_image_thumbnail(image, out_img, vis_params, dimensions, region, format)
except Exception as e:
print(e)
get_image_thumbnail(ee_object, out_img, vis_params, dimensions=500, region=None, format='png')
¶
Download a thumbnail for an ee.Image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
The ee.Image instance. |
required |
out_img |
str |
The output file path to the png thumbnail. |
required |
vis_params |
dict |
The visualization parameters. |
required |
dimensions |
int |
(a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 500. |
500 |
region |
object |
Geospatial region of the image to render, it may be an ee.Geometry, GeoJSON, or an array of lat/lon points (E,S,W,N). If not set the default is the bounds image. Defaults to None. |
None |
format |
str |
Either 'png' or 'jpg'. Default to 'png'. |
'png' |
Source code in geemap/common.py
def get_image_thumbnail(
ee_object, out_img, vis_params, dimensions=500, region=None, format="png"
):
"""Download a thumbnail for an ee.Image.
Args:
ee_object (object): The ee.Image instance.
out_img (str): The output file path to the png thumbnail.
vis_params (dict): The visualization parameters.
dimensions (int, optional):(a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 500.
region (object, optional): Geospatial region of the image to render, it may be an ee.Geometry, GeoJSON, or an array of lat/lon points (E,S,W,N). If not set the default is the bounds image. Defaults to None.
format (str, optional): Either 'png' or 'jpg'. Default to 'png'.
"""
import requests
if not isinstance(ee_object, ee.Image):
raise TypeError("The ee_object must be an ee.Image.")
ext = os.path.splitext(out_img)[1][1:]
if ext not in ["png", "jpg"]:
raise ValueError("The output image format must be png or jpg.")
else:
format = ext
out_image = os.path.abspath(out_img)
out_dir = os.path.dirname(out_image)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if region is not None:
vis_params["region"] = region
vis_params["dimensions"] = dimensions
vis_params["format"] = format
url = ee_object.getThumbURL(vis_params)
r = requests.get(url, stream=True)
if r.status_code != 200:
print("An error occurred while downloading.")
else:
with open(out_img, "wb") as fd:
for chunk in r.iter_content(chunk_size=1024):
fd.write(chunk)
get_STAC_bands(url, titiler_endpoint='https://api.cogeo.xyz/')
¶
Get band names of a single SpatialTemporal Asset Catalog (STAC) item.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://api.cogeo.xyz/". |
'https://api.cogeo.xyz/' |
Returns:
| Type | Description |
|---|---|
list |
A list of band names |
Source code in geemap/common.py
def get_STAC_bands(url, titiler_endpoint="https://api.cogeo.xyz/"):
"""Get band names of a single SpatialTemporal Asset Catalog (STAC) item.
Args:
url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".
Returns:
list: A list of band names
"""
import requests
r = requests.get(
f"{titiler_endpoint}/stac/info",
params={
"url": url,
},
).json()
return r
get_STAC_bounds(url, titiler_endpoint='https://api.cogeo.xyz/')
¶
Get the bounding box of a single SpatialTemporal Asset Catalog (STAC) item.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://api.cogeo.xyz/". |
'https://api.cogeo.xyz/' |
Returns:
| Type | Description |
|---|---|
list |
A list of values representing [left, bottom, right, top] |
Source code in geemap/common.py
def get_STAC_bounds(url, titiler_endpoint="https://api.cogeo.xyz/"):
"""Get the bounding box of a single SpatialTemporal Asset Catalog (STAC) item.
Args:
url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".
Returns:
list: A list of values representing [left, bottom, right, top]
"""
import requests
r = requests.get(f"{titiler_endpoint}/stac/bounds", params={"url": url}).json()
bounds = r["bounds"]
return bounds
get_STAC_center(url, titiler_endpoint='https://api.cogeo.xyz/')
¶
Get the centroid of a single SpatialTemporal Asset Catalog (STAC) item.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://api.cogeo.xyz/". |
'https://api.cogeo.xyz/' |
Returns:
| Type | Description |
|---|---|
tuple |
A tuple representing (longitude, latitude) |
Source code in geemap/common.py
def get_STAC_center(url, titiler_endpoint="https://api.cogeo.xyz/"):
"""Get the centroid of a single SpatialTemporal Asset Catalog (STAC) item.
Args:
url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".
Returns:
tuple: A tuple representing (longitude, latitude)
"""
bounds = get_STAC_bounds(url, titiler_endpoint)
center = ((bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2) # (lat, lon)
return center
get_STAC_tile(url, bands=None, titiler_endpoint='https://api.cogeo.xyz/', **kwargs)
¶
Get a tile layer from a single SpatialTemporal Asset Catalog (STAC) item.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://api.cogeo.xyz/". |
'https://api.cogeo.xyz/' |
Returns:
| Type | Description |
|---|---|
tuple |
Returns the COG Tile layer URL and bounds. |
Source code in geemap/common.py
def get_STAC_tile(url, bands=None, titiler_endpoint="https://api.cogeo.xyz/", **kwargs):
"""Get a tile layer from a single SpatialTemporal Asset Catalog (STAC) item.
Args:
url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".
Returns:
tuple: Returns the COG Tile layer URL and bounds.
"""
import requests
params = {"url": url}
TileMatrixSetId = "WebMercatorQuad"
if "TileMatrixSetId" in kwargs.keys():
TileMatrixSetId = kwargs["TileMatrixSetId"]
if "expression" in kwargs.keys():
params["expression"] = kwargs["expression"]
if "tile_format" in kwargs.keys():
params["tile_format"] = kwargs["tile_format"]
if "tile_scale" in kwargs.keys():
params["tile_scale"] = kwargs["tile_scale"]
if "minzoom" in kwargs.keys():
params["minzoom"] = kwargs["minzoom"]
if "maxzoom" in kwargs.keys():
params["maxzoom"] = kwargs["maxzoom"]
allowed_bands = get_STAC_bands(url, titiler_endpoint)
if bands is None:
bands = [allowed_bands[0]]
elif len(bands) <= 3 and all(x in allowed_bands for x in bands):
pass
else:
raise Exception(
"You can only select 3 bands from the following: {}".format(
", ".join(allowed_bands)
)
)
assets = ",".join(bands)
params["assets"] = assets
r = requests.get(
f"{titiler_endpoint}/stac/{TileMatrixSetId}/tilejson.json", params=params
).json()
return r["tiles"][0]
has_transparency(img)
¶
Checks whether an image has transparency.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
a PIL Image object. |
required |
Returns:
| Type | Description |
|---|---|
bool |
True if it has transparency, False otherwise. |
Source code in geemap/common.py
def has_transparency(img):
"""Checks whether an image has transparency.
Args:
img (object): a PIL Image object.
Returns:
bool: True if it has transparency, False otherwise.
"""
if img.mode == "P":
transparent = img.info.get("transparency", -1)
for _, index in img.getcolors():
if index == transparent:
return True
elif img.mode == "RGBA":
extrema = img.getextrema()
if extrema[3][0] < 255:
return True
return False
hex_to_rgb(value='FFFFFF')
¶
Converts hex color to RGB color.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
value |
str |
Hex color code as a string. Defaults to 'FFFFFF'. |
'FFFFFF' |
Returns:
| Type | Description |
|---|---|
tuple |
RGB color as a tuple. |
Source code in geemap/common.py
def hex_to_rgb(value="FFFFFF"):
"""Converts hex color to RGB color.
Args:
value (str, optional): Hex color code as a string. Defaults to 'FFFFFF'.
Returns:
tuple: RGB color as a tuple.
"""
value = value.lstrip("#")
lv = len(value)
return tuple(int(value[i : i + lv // 3], 16) for i in range(0, lv, lv // 3))
image_area(img, region=None, scale=None, denominator=1.0)
¶
Calculates the the area of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
ee.Image |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
denominator |
float |
The denominator to use for converting size from square meters to other units. Defaults to 1.0. |
1.0 |
Returns:
| Type | Description |
|---|---|
object |
ee.Dictionary |
Source code in geemap/common.py
def image_area(img, region=None, scale=None, denominator=1.0):
"""Calculates the the area of an image.
Args:
img (object): ee.Image
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
denominator (float, optional): The denominator to use for converting size from square meters to other units. Defaults to 1.0.
Returns:
object: ee.Dictionary
"""
if region is None:
region = img.geometry()
if scale is None:
scale = image_scale(img)
pixel_area = (
img.unmask().neq(ee.Image(0)).multiply(ee.Image.pixelArea()).divide(denominator)
)
img_area = pixel_area.reduceRegion(
**{
"geometry": region,
"reducer": ee.Reducer.sum(),
"scale": scale,
"maxPixels": 1e12,
}
)
return img_area
image_band_names(img)
¶
Gets image band names.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
ee.Image |
The input image. |
required |
Returns:
| Type | Description |
|---|---|
ee.List |
The returned list of image band names. |
Source code in geemap/common.py
def image_band_names(img):
"""Gets image band names.
Args:
img (ee.Image): The input image.
Returns:
ee.List: The returned list of image band names.
"""
return img.bandNames()
image_cell_size(img)
¶
Retrieves the image cell size (e.g., spatial resolution)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
ee.Image |
required |
Returns:
| Type | Description |
|---|---|
float |
The nominal scale in meters. |
Source code in geemap/common.py
def image_cell_size(img):
"""Retrieves the image cell size (e.g., spatial resolution)
Args:
img (object): ee.Image
Returns:
float: The nominal scale in meters.
"""
bands = img.bandNames()
scales = bands.map(lambda b: img.select([b]).projection().nominalScale())
scale = ee.Algorithms.If(
scales.distinct().size().gt(1),
ee.Dictionary.fromLists(bands.getInfo(), scales),
scales.get(0),
)
return scale
image_date(img, date_format='YYYY-MM-dd')
¶
Retrieves the image acquisition date.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
ee.Image |
required |
date_format |
str |
The date format to use. Defaults to 'YYYY-MM-dd'. |
'YYYY-MM-dd' |
Returns:
| Type | Description |
|---|---|
str |
A string representing the acquisition of the image. |
Source code in geemap/common.py
def image_date(img, date_format="YYYY-MM-dd"):
"""Retrieves the image acquisition date.
Args:
img (object): ee.Image
date_format (str, optional): The date format to use. Defaults to 'YYYY-MM-dd'.
Returns:
str: A string representing the acquisition of the image.
"""
return ee.Date(img.get("system:time_start")).format(date_format)
image_dates(img_col, date_format='YYYY-MM-dd')
¶
Get image dates of all images in an ImageCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img_col |
object |
ee.ImageCollection |
required |
date_format |
str |
A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html; if omitted will use ISO standard date formatting. Defaults to 'YYYY-MM-dd'. |
'YYYY-MM-dd' |
Returns:
| Type | Description |
|---|---|
object |
ee.List |
Source code in geemap/common.py
def image_dates(img_col, date_format="YYYY-MM-dd"):
"""Get image dates of all images in an ImageCollection.
Args:
img_col (object): ee.ImageCollection
date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html; if omitted will use ISO standard date formatting. Defaults to 'YYYY-MM-dd'.
Returns:
object: ee.List
"""
dates = img_col.aggregate_array("system:time_start")
new_dates = dates.map(lambda d: ee.Date(d).format(date_format))
return new_dates
image_max_value(img, region=None, scale=None)
¶
Retrieves the maximum value of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to calculate the maximum value. |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def image_max_value(img, region=None, scale=None):
"""Retrieves the maximum value of an image.
Args:
img (object): The image to calculate the maximum value.
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
Returns:
object: ee.Number
"""
if region is None:
region = img.geometry()
if scale is None:
scale = image_scale(img)
max_value = img.reduceRegion(
**{
"reducer": ee.Reducer.max(),
"geometry": region,
"scale": scale,
"maxPixels": 1e12,
"bestEffort": True,
}
)
return max_value
image_mean_value(img, region=None, scale=None)
¶
Retrieves the mean value of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to calculate the mean value. |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def image_mean_value(img, region=None, scale=None):
"""Retrieves the mean value of an image.
Args:
img (object): The image to calculate the mean value.
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
Returns:
object: ee.Number
"""
if region is None:
region = img.geometry()
if scale is None:
scale = image_scale(img)
mean_value = img.reduceRegion(
**{
"reducer": ee.Reducer.mean(),
"geometry": region,
"scale": scale,
"maxPixels": 1e12,
"bestEffort": True,
}
)
return mean_value
image_min_value(img, region=None, scale=None)
¶
Retrieves the minimum value of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to calculate the minimum value. |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def image_min_value(img, region=None, scale=None):
"""Retrieves the minimum value of an image.
Args:
img (object): The image to calculate the minimum value.
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
Returns:
object: ee.Number
"""
if region is None:
region = img.geometry()
if scale is None:
scale = image_scale(img)
min_value = img.reduceRegion(
**{
"reducer": ee.Reducer.min(),
"geometry": region,
"scale": scale,
"maxPixels": 1e12,
"bestEffort": True,
}
)
return min_value
image_props(img, date_format='YYYY-MM-dd')
¶
Gets image properties.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
ee.Image |
The input image. |
required |
date_format |
str |
The output date format. Defaults to 'YYYY-MM-dd HH:mm:ss'. |
'YYYY-MM-dd' |
Returns:
| Type | Description |
|---|---|
dd.Dictionary |
The dictionary containing image properties. |
Source code in geemap/common.py
def image_props(img, date_format="YYYY-MM-dd"):
"""Gets image properties.
Args:
img (ee.Image): The input image.
date_format (str, optional): The output date format. Defaults to 'YYYY-MM-dd HH:mm:ss'.
Returns:
dd.Dictionary: The dictionary containing image properties.
"""
if not isinstance(img, ee.Image):
print("The input object must be an ee.Image")
return
keys = img.propertyNames().remove("system:footprint").remove("system:bands")
values = keys.map(lambda p: img.get(p))
bands = img.bandNames()
scales = bands.map(lambda b: img.select([b]).projection().nominalScale())
scale = ee.Algorithms.If(
scales.distinct().size().gt(1),
ee.Dictionary.fromLists(bands.getInfo(), scales),
scales.get(0),
)
image_date = ee.Date(img.get("system:time_start")).format(date_format)
time_start = ee.Date(img.get("system:time_start")).format("YYYY-MM-dd HH:mm:ss")
# time_end = ee.Date(img.get('system:time_end')).format('YYYY-MM-dd HH:mm:ss')
time_end = ee.Algorithms.If(
ee.List(img.propertyNames()).contains("system:time_end"),
ee.Date(img.get("system:time_end")).format("YYYY-MM-dd HH:mm:ss"),
time_start,
)
asset_size = (
ee.Number(img.get("system:asset_size"))
.divide(1e6)
.format()
.cat(ee.String(" MB"))
)
props = ee.Dictionary.fromLists(keys, values)
props = props.set("system:time_start", time_start)
props = props.set("system:time_end", time_end)
props = props.set("system:asset_size", asset_size)
props = props.set("NOMINAL_SCALE", scale)
props = props.set("IMAGE_DATE", image_date)
return props
image_reclassify(img, in_list, out_list)
¶
Reclassify an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to which the remapping is applied. |
required |
in_list |
list |
The source values (numbers or EEArrays). All values in this list will be mapped to the corresponding value in 'out_list'. |
required |
out_list |
list |
The destination values (numbers or EEArrays). These are used to replace the corresponding values in 'from'. Must have the same number of values as 'in_list'. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Image |
Source code in geemap/common.py
def image_reclassify(img, in_list, out_list):
"""Reclassify an image.
Args:
img (object): The image to which the remapping is applied.
in_list (list): The source values (numbers or EEArrays). All values in this list will be mapped to the corresponding value in 'out_list'.
out_list (list): The destination values (numbers or EEArrays). These are used to replace the corresponding values in 'from'. Must have the same number of values as 'in_list'.
Returns:
object: ee.Image
"""
image = img.remap(in_list, out_list)
return image
image_scale(img)
¶
Retrieves the image cell size (e.g., spatial resolution)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
ee.Image |
required |
Returns:
| Type | Description |
|---|---|
float |
The nominal scale in meters. |
Source code in geemap/common.py
def image_scale(img):
"""Retrieves the image cell size (e.g., spatial resolution)
Args:
img (object): ee.Image
Returns:
float: The nominal scale in meters.
"""
# bands = img.bandNames()
# scales = bands.map(lambda b: img.select([b]).projection().nominalScale())
# scale = ee.Algorithms.If(scales.distinct().size().gt(1), ee.Dictionary.fromLists(bands.getInfo(), scales), scales.get(0))
return img.select(0).projection().nominalScale()
image_smoothing(img, reducer, kernel)
¶
Smooths an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to be smoothed. |
required |
reducer |
object |
ee.Reducer |
required |
kernel |
object |
ee.Kernel |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Image |
Source code in geemap/common.py
def image_smoothing(img, reducer, kernel):
"""Smooths an image.
Args:
img (object): The image to be smoothed.
reducer (object): ee.Reducer
kernel (object): ee.Kernel
Returns:
object: ee.Image
"""
image = img.reduceNeighborhood(
**{
"reducer": reducer,
"kernel": kernel,
}
)
return image
image_stats(img, region=None, scale=None)
¶
Gets image descriptive statistics.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
ee.Image |
The input image to calculate descriptive statistics. |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
ee.Dictionary |
A dictionary containing the description statistics of the input image. |
Source code in geemap/common.py
def image_stats(img, region=None, scale=None):
"""Gets image descriptive statistics.
Args:
img (ee.Image): The input image to calculate descriptive statistics.
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
Returns:
ee.Dictionary: A dictionary containing the description statistics of the input image.
"""
if not isinstance(img, ee.Image):
print("The input object must be an ee.Image")
return
stat_types = ["min", "max", "mean", "std", "sum"]
image_min = image_min_value(img, region, scale)
image_max = image_max_value(img, region, scale)
image_mean = image_mean_value(img, region, scale)
image_std = image_std_value(img, region, scale)
image_sum = image_sum_value(img, region, scale)
stat_results = ee.List([image_min, image_max, image_mean, image_std, image_sum])
stats = ee.Dictionary.fromLists(stat_types, stat_results)
return stats
image_std_value(img, region=None, scale=None)
¶
Retrieves the standard deviation of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to calculate the standard deviation. |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def image_std_value(img, region=None, scale=None):
"""Retrieves the standard deviation of an image.
Args:
img (object): The image to calculate the standard deviation.
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
Returns:
object: ee.Number
"""
if region is None:
region = img.geometry()
if scale is None:
scale = image_scale(img)
std_value = img.reduceRegion(
**{
"reducer": ee.Reducer.stdDev(),
"geometry": region,
"scale": scale,
"maxPixels": 1e12,
"bestEffort": True,
}
)
return std_value
image_sum_value(img, region=None, scale=None)
¶
Retrieves the sum of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to calculate the standard deviation. |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def image_sum_value(img, region=None, scale=None):
"""Retrieves the sum of an image.
Args:
img (object): The image to calculate the standard deviation.
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
Returns:
object: ee.Number
"""
if region is None:
region = img.geometry()
if scale is None:
scale = image_scale(img)
sum_value = img.reduceRegion(
**{
"reducer": ee.Reducer.sum(),
"geometry": region,
"scale": scale,
"maxPixels": 1e12,
"bestEffort": True,
}
)
return sum_value
in_colab_shell()
¶
Tests if the code is being executed within Google Colab.
Source code in geemap/common.py
def in_colab_shell():
"""Tests if the code is being executed within Google Colab."""
import sys
if "google.colab" in sys.modules:
return True
else:
return False
install_from_github(url)
¶
Install a package from a GitHub repository.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The URL of the GitHub repository. |
required |
Source code in geemap/common.py
def install_from_github(url):
"""Install a package from a GitHub repository.
Args:
url (str): The URL of the GitHub repository.
"""
try:
download_dir = os.path.join(os.path.expanduser("~"), "Downloads")
if not os.path.exists(download_dir):
os.makedirs(download_dir)
repo_name = os.path.basename(url)
zip_url = os.path.join(url, "archive/master.zip")
filename = repo_name + "-master.zip"
download_from_url(
url=zip_url, out_file_name=filename, out_dir=download_dir, unzip=True
)
pkg_dir = os.path.join(download_dir, repo_name + "-master")
pkg_name = os.path.basename(url)
work_dir = os.getcwd()
os.chdir(pkg_dir)
print("Installing {}...".format(pkg_name))
cmd = "pip install ."
os.system(cmd)
os.chdir(work_dir)
print("{} has been installed successfully.".format(pkg_name))
# print("\nPlease comment out 'install_from_github()' and restart the kernel to take effect:\nJupyter menu -> Kernel -> Restart & Clear Output")
except Exception as e:
print(e)
is_drive_mounted()
¶
Checks whether Google Drive is mounted in Google Colab.
Returns:
| Type | Description |
|---|---|
bool |
Returns True if Google Drive is mounted, False otherwise. |
Source code in geemap/common.py
def is_drive_mounted():
"""Checks whether Google Drive is mounted in Google Colab.
Returns:
bool: Returns True if Google Drive is mounted, False otherwise.
"""
drive_path = "/content/drive/My Drive"
if os.path.exists(drive_path):
return True
else:
return False
is_latlon_valid(location)
¶
Checks whether a pair of coordinates is valid.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
location |
str |
A pair of latlon coordinates separated by comma or space. |
required |
Returns:
| Type | Description |
|---|---|
bool |
Returns True if valid. |
Source code in geemap/common.py
def is_latlon_valid(location):
"""Checks whether a pair of coordinates is valid.
Args:
location (str): A pair of latlon coordinates separated by comma or space.
Returns:
bool: Returns True if valid.
"""
latlon = []
if "," in location:
latlon = [float(x) for x in location.split(",")]
elif " " in location:
latlon = [float(x) for x in location.split(" ")]
else:
print(
"The coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
)
return False
try:
lat, lon = float(latlon[0]), float(latlon[1])
if lat >= -90 and lat <= 90 and lon >= -180 and lon <= 180:
return True
else:
return False
except Exception as e:
print(e)
return False
is_tool(name)
¶
Check whether name is on PATH and marked as executable.
Source code in geemap/common.py
def is_tool(name):
"""Check whether `name` is on PATH and marked as executable."""
from shutil import which
return which(name) is not None
kml_to_ee(in_kml)
¶
Converts a KML to ee.FeatureColleciton.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_kml |
str |
The file path to the input KML. |
required |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
The input KML could not be found. |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def kml_to_ee(in_kml):
"""Converts a KML to ee.FeatureColleciton.
Args:
in_kml (str): The file path to the input KML.
Raises:
FileNotFoundError: The input KML could not be found.
Returns:
object: ee.FeatureCollection
"""
import warnings
warnings.filterwarnings("ignore")
in_kml = os.path.abspath(in_kml)
if not os.path.exists(in_kml):
raise FileNotFoundError("The input KML could not be found.")
out_json = os.path.join(os.getcwd(), "tmp.geojson")
check_package(name="geopandas", URL="https://geopandas.org")
kml_to_geojson(in_kml, out_json)
ee_object = geojson_to_ee(out_json)
os.remove(out_json)
return ee_object
kml_to_geojson(in_kml, out_json)
¶
Converts a KML to GeoJSON.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_kml |
str |
The file path to the input KML. |
required |
out_json |
str |
The file path to the output GeoJSON. |
required |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
The input KML could not be found. |
TypeError |
The output must be a GeoJSON. |
Source code in geemap/common.py
def kml_to_geojson(in_kml, out_json):
"""Converts a KML to GeoJSON.
Args:
in_kml (str): The file path to the input KML.
out_json (str): The file path to the output GeoJSON.
Raises:
FileNotFoundError: The input KML could not be found.
TypeError: The output must be a GeoJSON.
"""
import warnings
warnings.filterwarnings("ignore")
in_kml = os.path.abspath(in_kml)
if not os.path.exists(in_kml):
raise FileNotFoundError("The input KML could not be found.")
out_json = os.path.abspath(out_json)
ext = os.path.splitext(out_json)[1].lower()
if ext not in [".json", ".geojson"]:
raise TypeError("The output file must be a GeoJSON.")
out_dir = os.path.dirname(out_json)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
check_package(name="geopandas", URL="https://geopandas.org")
import geopandas as gpd
# import fiona
# print(fiona.supported_drivers)
gpd.io.file.fiona.drvsupport.supported_drivers["KML"] = "rw"
df = gpd.read_file(in_kml, driver="KML")
df.to_file(out_json, driver="GeoJSON")
kml_to_shp(in_kml, out_shp)
¶
Converts a KML to shapefile.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_kml |
str |
The file path to the input KML. |
required |
out_shp |
str |
The file path to the output shapefile. |
required |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
The input KML could not be found. |
TypeError |
The output must be a shapefile. |
Source code in geemap/common.py
def kml_to_shp(in_kml, out_shp):
"""Converts a KML to shapefile.
Args:
in_kml (str): The file path to the input KML.
out_shp (str): The file path to the output shapefile.
Raises:
FileNotFoundError: The input KML could not be found.
TypeError: The output must be a shapefile.
"""
import warnings
warnings.filterwarnings("ignore")
in_kml = os.path.abspath(in_kml)
if not os.path.exists(in_kml):
raise FileNotFoundError("The input KML could not be found.")
out_shp = os.path.abspath(out_shp)
if not out_shp.endswith(".shp"):
raise TypeError("The output must be a shapefile.")
out_dir = os.path.dirname(out_shp)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
check_package(name="geopandas", URL="https://geopandas.org")
import geopandas as gpd
# import fiona
# print(fiona.supported_drivers)
gpd.io.file.fiona.drvsupport.supported_drivers["KML"] = "rw"
df = gpd.read_file(in_kml, driver="KML")
df.to_file(out_shp)
kmz_to_ee(in_kmz)
¶
Converts a KMZ to ee.FeatureColleciton.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_kmz |
str |
The file path to the input KMZ. |
required |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
The input KMZ could not be found. |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def kmz_to_ee(in_kmz):
"""Converts a KMZ to ee.FeatureColleciton.
Args:
in_kmz (str): The file path to the input KMZ.
Raises:
FileNotFoundError: The input KMZ could not be found.
Returns:
object: ee.FeatureCollection
"""
in_kmz = os.path.abspath(in_kmz)
if not os.path.exists(in_kmz):
raise FileNotFoundError("The input KMZ could not be found.")
out_dir = os.path.dirname(in_kmz)
out_kml = os.path.join(out_dir, "doc.kml")
with zipfile.ZipFile(in_kmz, "r") as zip_ref:
zip_ref.extractall(out_dir)
fc = kml_to_ee(out_kml)
os.remove(out_kml)
return fc
landsat_timeseries(roi=None, start_year=1984, end_year=2020, start_date='06-10', end_date='09-20', apply_fmask=True)
¶
Generates an annual Landsat ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
roi |
object |
Region of interest to create the timelapse. Defaults to None. |
None |
start_year |
int |
Starting year for the timelapse. Defaults to 1984. |
1984 |
end_year |
int |
Ending year for the timelapse. Defaults to 2020. |
2020 |
start_date |
str |
Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'. |
'06-10' |
end_date |
str |
Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'. |
'09-20' |
apply_fmask |
bool |
Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking. |
True |
Returns:
| Type | Description |
|---|---|
object |
Returns an ImageCollection containing annual Landsat images. |
Source code in geemap/common.py
def landsat_timeseries(
roi=None,
start_year=1984,
end_year=2020,
start_date="06-10",
end_date="09-20",
apply_fmask=True,
):
"""Generates an annual Landsat ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work.
Args:
roi (object, optional): Region of interest to create the timelapse. Defaults to None.
start_year (int, optional): Starting year for the timelapse. Defaults to 1984.
end_year (int, optional): Ending year for the timelapse. Defaults to 2020.
start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.
end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.
apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.
Returns:
object: Returns an ImageCollection containing annual Landsat images.
"""
################################################################################
# Input and output parameters.
import re
import datetime
if roi is None:
roi = ee.Geometry.Polygon(
[
[
[-115.471773, 35.892718],
[-115.471773, 36.409454],
[-114.271283, 36.409454],
[-114.271283, 35.892718],
[-115.471773, 35.892718],
]
],
None,
False,
)
if not isinstance(roi, ee.Geometry):
try:
roi = roi.geometry()
except Exception as e:
print("Could not convert the provided roi to ee.Geometry")
print(e)
return
################################################################################
# Setup vars to get dates.
if isinstance(start_year, int) and (start_year >= 1984) and (start_year < 2020):
pass
else:
print("The start year must be an integer >= 1984.")
return
if isinstance(end_year, int) and (end_year > 1984) and (end_year <= 2020):
pass
else:
print("The end year must be an integer <= 2020.")
return
if re.match("[0-9]{2}\-[0-9]{2}", start_date) and re.match(
"[0-9]{2}\-[0-9]{2}", end_date
):
pass
else:
print("The start date and end date must be month-day, such as 06-10, 09-20")
return
try:
datetime.datetime(int(start_year), int(start_date[:2]), int(start_date[3:5]))
datetime.datetime(int(end_year), int(end_date[:2]), int(end_date[3:5]))
except Exception as e:
print("The input dates are invalid.")
raise Exception(e)
def days_between(d1, d2):
d1 = datetime.datetime.strptime(d1, "%Y-%m-%d")
d2 = datetime.datetime.strptime(d2, "%Y-%m-%d")
return abs((d2 - d1).days)
n_days = days_between(
str(start_year) + "-" + start_date, str(start_year) + "-" + end_date
)
start_month = int(start_date[:2])
start_day = int(start_date[3:5])
# start_date = str(start_year) + "-" + start_date
# end_date = str(end_year) + "-" + end_date
# # Define a collection filter by date, bounds, and quality.
# def colFilter(col, aoi): # , startDate, endDate):
# return(col.filterBounds(aoi))
# Landsat collection preprocessingEnabled
# Get Landsat surface reflectance collections for OLI, ETM+ and TM sensors.
LC08col = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
LE07col = ee.ImageCollection("LANDSAT/LE07/C01/T1_SR")
LT05col = ee.ImageCollection("LANDSAT/LT05/C01/T1_SR")
LT04col = ee.ImageCollection("LANDSAT/LT04/C01/T1_SR")
# Define a collection filter by date, bounds, and quality.
def colFilter(col, roi, start_date, end_date):
return col.filterBounds(roi).filterDate(start_date, end_date)
# .filter('CLOUD_COVER < 5')
# .filter('GEOMETRIC_RMSE_MODEL < 15')
# .filter('IMAGE_QUALITY == 9 || IMAGE_QUALITY_OLI == 9'))
# Function to get and rename bands of interest from OLI.
def renameOli(img):
return img.select(
["B2", "B3", "B4", "B5", "B6", "B7", "pixel_qa"],
["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2", "pixel_qa"],
)
# Function to get and rename bands of interest from ETM+.
def renameEtm(img):
return img.select(
["B1", "B2", "B3", "B4", "B5", "B7", "pixel_qa"],
["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2", "pixel_qa"],
)
# Add NBR for LandTrendr segmentation.
def calcNbr(img):
return img.addBands(
img.normalizedDifference(["NIR", "SWIR2"]).multiply(-10000).rename("NBR")
).int16()
# Define function to mask out clouds and cloud shadows in images.
# Use CFmask band included in USGS Landsat SR image product.
def fmask(img):
cloudShadowBitMask = 1 << 3
cloudsBitMask = 1 << 5
qa = img.select("pixel_qa")
mask = (
qa.bitwiseAnd(cloudShadowBitMask)
.eq(0)
.And(qa.bitwiseAnd(cloudsBitMask).eq(0))
)
return img.updateMask(mask)
# Define function to prepare OLI images.
def prepOli(img):
orig = img
img = renameOli(img)
if apply_fmask:
img = fmask(img)
return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample(
"bicubic"
)
# Define function to prepare ETM+ images.
def prepEtm(img):
orig = img
img = renameEtm(img)
if apply_fmask:
img = fmask(img)
return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample(
"bicubic"
)
# Get annual median collection.
def getAnnualComp(y):
startDate = ee.Date.fromYMD(
ee.Number(y), ee.Number(start_month), ee.Number(start_day)
)
endDate = startDate.advance(ee.Number(n_days), "day")
# Filter collections and prepare them for merging.
LC08coly = colFilter(LC08col, roi, startDate, endDate).map(prepOli)
LE07coly = colFilter(LE07col, roi, startDate, endDate).map(prepEtm)
LT05coly = colFilter(LT05col, roi, startDate, endDate).map(prepEtm)
LT04coly = colFilter(LT04col, roi, startDate, endDate).map(prepEtm)
# Merge the collections.
col = LC08coly.merge(LE07coly).merge(LT05coly).merge(LT04coly)
yearImg = col.median()
nBands = yearImg.bandNames().size()
yearImg = ee.Image(ee.Algorithms.If(nBands, yearImg, dummyImg))
return calcNbr(yearImg).set(
{"year": y, "system:time_start": startDate.millis(), "nBands": nBands}
)
################################################################################
# Make a dummy image for missing years.
bandNames = ee.List(["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2", "pixel_qa"])
fillerValues = ee.List.repeat(0, bandNames.size())
dummyImg = ee.Image.constant(fillerValues).rename(bandNames).selfMask().int16()
################################################################################
# Get a list of years
years = ee.List.sequence(start_year, end_year)
################################################################################
# Make list of annual image composites.
imgList = years.map(getAnnualComp)
# Convert image composite list to collection
imgCol = ee.ImageCollection.fromImages(imgList)
imgCol = imgCol.map(
lambda img: img.clip(roi).set({"coordinates": roi.coordinates()})
)
return imgCol
# ################################################################################
# # Run LandTrendr.
# lt = ee.Algorithms.TemporalSegmentation.LandTrendr(
# timeSeries=imgCol.select(['NBR', 'SWIR1', 'NIR', 'Green']),
# maxSegments=10,
# spikeThreshold=0.7,
# vertexCountOvershoot=3,
# preventOneYearRecovery=True,
# recoveryThreshold=0.5,
# pvalThreshold=0.05,
# bestModelProportion=0.75,
# minObservationsNeeded=6)
# ################################################################################
# # Get fitted imagery. This starts export tasks.
# def getYearStr(year):
# return(ee.String('yr_').cat(ee.Algorithms.String(year).slice(0,4)))
# yearsStr = years.map(getYearStr)
# r = lt.select(['SWIR1_fit']).arrayFlatten([yearsStr]).toShort()
# g = lt.select(['NIR_fit']).arrayFlatten([yearsStr]).toShort()
# b = lt.select(['Green_fit']).arrayFlatten([yearsStr]).toShort()
# for i, c in zip([r, g, b], ['r', 'g', 'b']):
# descr = 'mamore-river-'+c
# name = 'users/user/'+descr
# print(name)
# task = ee.batch.Export.image.toAsset(
# image=i,
# region=roi.getInfo()['coordinates'],
# assetId=name,
# description=descr,
# scale=30,
# crs='EPSG:3857',
# maxPixels=1e13)
# task.start()
landsat_ts_gif(roi=None, out_gif=None, start_year=1984, end_year=2019, start_date='06-10', end_date='09-20', bands=['NIR', 'Red', 'Green'], vis_params=None, dimensions=768, frames_per_second=10, apply_fmask=True, nd_bands=None, nd_threshold=0, nd_palette=['black', 'blue'])
¶
Generates a Landsat timelapse GIF image. This function is adapted from https://emaprlab.users.earthengine.app/view/lt-gee-time-series-animator. A huge thank you to Justin Braaten for sharing his fantastic work.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
roi |
object |
Region of interest to create the timelapse. Defaults to None. |
None |
out_gif |
str |
File path to the output animated GIF. Defaults to None. |
None |
start_year |
int |
Starting year for the timelapse. Defaults to 1984. |
1984 |
end_year |
int |
Ending year for the timelapse. Defaults to 2019. |
2019 |
start_date |
str |
Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'. |
'06-10' |
end_date |
str |
Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'. |
'09-20' |
bands |
list |
Three bands selected from ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa']. Defaults to ['NIR', 'Red', 'Green']. |
['NIR', 'Red', 'Green'] |
vis_params |
dict |
Visualization parameters. Defaults to None. |
None |
dimensions |
int |
a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768. |
768 |
frames_per_second |
int |
Animation speed. Defaults to 10. |
10 |
apply_fmask |
bool |
Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking. |
True |
nd_bands |
list |
A list of names specifying the bands to use, e.g., ['Green', 'SWIR1']. The normalized difference is computed as (first − second) / (first + second). Note that negative input values are forced to 0 so that the result is confined to the range (-1, 1). |
None |
nd_threshold |
float |
The threshold for extacting pixels from the normalized difference band. |
0 |
nd_palette |
list |
The color palette to use for displaying the normalized difference band. |
['black', 'blue'] |
Returns:
| Type | Description |
|---|---|
str |
File path to the output GIF image. |
Source code in geemap/common.py
def landsat_ts_gif(
roi=None,
out_gif=None,
start_year=1984,
end_year=2019,
start_date="06-10",
end_date="09-20",
bands=["NIR", "Red", "Green"],
vis_params=None,
dimensions=768,
frames_per_second=10,
apply_fmask=True,
nd_bands=None,
nd_threshold=0,
nd_palette=["black", "blue"],
):
"""Generates a Landsat timelapse GIF image. This function is adapted from https://emaprlab.users.earthengine.app/view/lt-gee-time-series-animator. A huge thank you to Justin Braaten for sharing his fantastic work.
Args:
roi (object, optional): Region of interest to create the timelapse. Defaults to None.
out_gif (str, optional): File path to the output animated GIF. Defaults to None.
start_year (int, optional): Starting year for the timelapse. Defaults to 1984.
end_year (int, optional): Ending year for the timelapse. Defaults to 2019.
start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.
end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.
bands (list, optional): Three bands selected from ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa']. Defaults to ['NIR', 'Red', 'Green'].
vis_params (dict, optional): Visualization parameters. Defaults to None.
dimensions (int, optional): a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
frames_per_second (int, optional): Animation speed. Defaults to 10.
apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.
nd_bands (list, optional): A list of names specifying the bands to use, e.g., ['Green', 'SWIR1']. The normalized difference is computed as (first − second) / (first + second). Note that negative input values are forced to 0 so that the result is confined to the range (-1, 1).
nd_threshold (float, optional): The threshold for extacting pixels from the normalized difference band.
nd_palette (list, optional): The color palette to use for displaying the normalized difference band.
Returns:
str: File path to the output GIF image.
"""
# ee_initialize()
if roi is None:
roi = ee.Geometry.Polygon(
[
[
[-115.471773, 35.892718],
[-115.471773, 36.409454],
[-114.271283, 36.409454],
[-114.271283, 35.892718],
[-115.471773, 35.892718],
]
],
None,
False,
)
elif isinstance(roi, ee.Feature) or isinstance(roi, ee.FeatureCollection):
roi = roi.geometry()
elif isinstance(roi, ee.Geometry):
pass
else:
print("The provided roi is invalid. It must be an ee.Geometry")
return
if out_gif is None:
out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
filename = "landsat_ts_" + random_string() + ".gif"
out_gif = os.path.join(out_dir, filename)
elif not out_gif.endswith(".gif"):
print("The output file must end with .gif")
return
# elif not os.path.isfile(out_gif):
# print('The output file must be a file')
# return
else:
out_gif = os.path.abspath(out_gif)
out_dir = os.path.dirname(out_gif)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
allowed_bands = ["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2", "pixel_qa"]
if len(bands) == 3 and all(x in allowed_bands for x in bands):
pass
else:
raise Exception(
"You can only select 3 bands from the following: {}".format(
", ".join(allowed_bands)
)
)
if nd_bands is not None:
if len(nd_bands) == 2 and all(x in allowed_bands[:-1] for x in nd_bands):
pass
else:
raise Exception(
"You can only select two bands from the following: {}".format(
", ".join(allowed_bands[:-1])
)
)
try:
col = landsat_timeseries(
roi, start_year, end_year, start_date, end_date, apply_fmask
)
if vis_params is None:
vis_params = {}
vis_params["bands"] = bands
vis_params["min"] = 0
vis_params["max"] = 4000
vis_params["gamma"] = [1, 1, 1]
video_args = vis_params.copy()
video_args["dimensions"] = dimensions
video_args["region"] = roi
video_args["framesPerSecond"] = frames_per_second
video_args["crs"] = "EPSG:3857"
if "bands" not in video_args.keys():
video_args["bands"] = bands
if "min" not in video_args.keys():
video_args["min"] = 0
if "max" not in video_args.keys():
video_args["max"] = 4000
if "gamma" not in video_args.keys():
video_args["gamma"] = [1, 1, 1]
download_ee_video(col, video_args, out_gif)
if nd_bands is not None:
nd_images = landsat_ts_norm_diff(
col, bands=nd_bands, threshold=nd_threshold
)
out_nd_gif = out_gif.replace(".gif", "_nd.gif")
landsat_ts_norm_diff_gif(
nd_images,
out_gif=out_nd_gif,
vis_params=None,
palette=nd_palette,
dimensions=dimensions,
frames_per_second=frames_per_second,
)
return out_gif
except Exception as e:
print(e)
landsat_ts_norm_diff(collection, bands=['Green', 'SWIR1'], threshold=0)
¶
Computes a normalized difference index based on a Landsat timeseries.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
ee.ImageCollection |
A Landsat timeseries. |
required |
bands |
list |
The bands to use for computing normalized difference. Defaults to ['Green', 'SWIR1']. |
['Green', 'SWIR1'] |
threshold |
float |
The threshold to extract features. Defaults to 0. |
0 |
Returns:
| Type | Description |
|---|---|
ee.ImageCollection |
An ImageCollection containing images with values greater than the specified threshold. |
Source code in geemap/common.py
def landsat_ts_norm_diff(collection, bands=["Green", "SWIR1"], threshold=0):
"""Computes a normalized difference index based on a Landsat timeseries.
Args:
collection (ee.ImageCollection): A Landsat timeseries.
bands (list, optional): The bands to use for computing normalized difference. Defaults to ['Green', 'SWIR1'].
threshold (float, optional): The threshold to extract features. Defaults to 0.
Returns:
ee.ImageCollection: An ImageCollection containing images with values greater than the specified threshold.
"""
nd_images = collection.map(
lambda img: img.normalizedDifference(bands)
.gt(threshold)
.copyProperties(img, img.propertyNames())
)
return nd_images
landsat_ts_norm_diff_gif(collection, out_gif=None, vis_params=None, palette=['black', 'blue'], dimensions=768, frames_per_second=10)
¶
[summary]
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
ee.ImageCollection |
The normalized difference Landsat timeseires. |
required |
out_gif |
str |
File path to the output animated GIF. Defaults to None. |
None |
vis_params |
dict |
Visualization parameters. Defaults to None. |
None |
palette |
list |
The palette to use for visualizing the timelapse. Defaults to ['black', 'blue']. The first color in the list is the background color. |
['black', 'blue'] |
dimensions |
int |
a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768. |
768 |
frames_per_second |
int |
Animation speed. Defaults to 10. |
10 |
Returns:
| Type | Description |
|---|---|
str |
File path to the output animated GIF. |
Source code in geemap/common.py
def landsat_ts_norm_diff_gif(
collection,
out_gif=None,
vis_params=None,
palette=["black", "blue"],
dimensions=768,
frames_per_second=10,
):
"""[summary]
Args:
collection (ee.ImageCollection): The normalized difference Landsat timeseires.
out_gif (str, optional): File path to the output animated GIF. Defaults to None.
vis_params (dict, optional): Visualization parameters. Defaults to None.
palette (list, optional): The palette to use for visualizing the timelapse. Defaults to ['black', 'blue']. The first color in the list is the background color.
dimensions (int, optional): a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
frames_per_second (int, optional): Animation speed. Defaults to 10.
Returns:
str: File path to the output animated GIF.
"""
coordinates = ee.Image(collection.first()).get("coordinates")
roi = ee.Geometry.Polygon(coordinates, None, False)
if out_gif is None:
out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
filename = "landsat_ts_nd_" + random_string() + ".gif"
out_gif = os.path.join(out_dir, filename)
elif not out_gif.endswith(".gif"):
raise Exception("The output file must end with .gif")
bands = ["nd"]
if vis_params is None:
vis_params = {}
vis_params["bands"] = bands
vis_params["palette"] = palette
video_args = vis_params.copy()
video_args["dimensions"] = dimensions
video_args["region"] = roi
video_args["framesPerSecond"] = frames_per_second
video_args["crs"] = "EPSG:3857"
if "bands" not in video_args.keys():
video_args["bands"] = bands
download_ee_video(collection, video_args, out_gif)
return out_gif
latlon_from_text(location)
¶
Extracts latlon from text.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
location |
str |
A pair of latlon coordinates separated by comma or space. |
required |
Returns:
| Type | Description |
|---|---|
bool |
Returns (lat, lon) if valid. |
Source code in geemap/common.py
def latlon_from_text(location):
"""Extracts latlon from text.
Args:
location (str): A pair of latlon coordinates separated by comma or space.
Returns:
bool: Returns (lat, lon) if valid.
"""
latlon = []
try:
if "," in location:
latlon = [float(x) for x in location.split(",")]
elif " " in location:
latlon = [float(x) for x in location.split(" ")]
else:
print(
"The lat-lon coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
)
return None
lat, lon = latlon[0], latlon[1]
if lat >= -90 and lat <= 90 and lon >= -180 and lon <= 180:
return lat, lon
else:
return None
except Exception as e:
print(e)
print(
"The lat-lon coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
)
return None
legend_from_ee(ee_class_table)
¶
Extract legend from an Earth Engine class table on the Earth Engine Data Catalog page such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_class_table |
str |
An Earth Engine class table with triple quotes. |
required |
Returns:
| Type | Description |
|---|---|
dict |
Returns a legend dictionary that can be used to create a legend. |
Source code in geemap/common.py
def legend_from_ee(ee_class_table):
"""Extract legend from an Earth Engine class table on the Earth Engine Data Catalog page
such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1
Args:
ee_class_table (str): An Earth Engine class table with triple quotes.
Returns:
dict: Returns a legend dictionary that can be used to create a legend.
"""
try:
ee_class_table = ee_class_table.strip()
lines = ee_class_table.split("\n")[1:]
if lines[0] == "Value\tColor\tDescription":
lines = lines[1:]
legend_dict = {}
for _, line in enumerate(lines):
items = line.split("\t")
items = [item.strip() for item in items]
color = items[1]
key = items[0] + " " + items[2]
legend_dict[key] = color
return legend_dict
except Exception as e:
print(e)
list_vars(var_type=None)
¶
Lists all defined avariables.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
var_type |
object |
The object type of variables to list. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
list |
A list of all defined variables. |
Source code in geemap/common.py
def list_vars(var_type=None):
"""Lists all defined avariables.
Args:
var_type (object, optional): The object type of variables to list. Defaults to None.
Returns:
list: A list of all defined variables.
"""
result = []
for var in globals():
reserved_vars = [
"In",
"Out",
"get_ipython",
"exit",
"quit",
"json",
"getsizeof",
"NamespaceMagics",
"np",
"var_dic_list",
"list_vars",
"ee",
"geemap",
]
if (not var.startswith("_")) and (var not in reserved_vars):
if var_type is not None and isinstance(eval(var), var_type):
result.append(var)
elif var_type is None:
result.append(var)
return result
load_GeoTIFF(URL)
¶
Loads a Cloud Optimized GeoTIFF (COG) as an Image. Only Google Cloud Storage is supported. The URL can be one of the following formats: Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif Option 3: https://storage.cloud.google.com/gcp-public-data-landsat/LC08/01/044/034/LC08_L1TP_044034_20131228_20170307_01_T1/LC08_L1TP_044034_20131228_20170307_01_T1_B5.TIF
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
URL |
str |
The Cloud Storage URL of the GeoTIFF to load. |
required |
Returns:
| Type | Description |
|---|---|
ee.Image |
an Earth Engine image. |
Source code in geemap/common.py
def load_GeoTIFF(URL):
"""Loads a Cloud Optimized GeoTIFF (COG) as an Image. Only Google Cloud Storage is supported. The URL can be one of the following formats:
Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif
Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif
Option 3: https://storage.cloud.google.com/gcp-public-data-landsat/LC08/01/044/034/LC08_L1TP_044034_20131228_20170307_01_T1/LC08_L1TP_044034_20131228_20170307_01_T1_B5.TIF
Args:
URL (str): The Cloud Storage URL of the GeoTIFF to load.
Returns:
ee.Image: an Earth Engine image.
"""
uri = URL.strip()
if uri.startswith("https://storage.googleapis.com/"):
uri = uri.replace("https://storage.googleapis.com/", "gs://")
elif uri.startswith("https://storage.cloud.google.com/"):
uri = uri.replace("https://storage.cloud.google.com/", "gs://")
if not uri.startswith("gs://"):
raise Exception(
'Invalid GCS URL: {}. Expected something of the form "gs://bucket/path/to/object.tif".'.format(
uri
)
)
if not uri.lower().endswith(".tif"):
raise Exception(
'Invalid GCS URL: {}. Expected something of the form "gs://bucket/path/to/object.tif".'.format(
uri
)
)
cloud_image = ee.Image.loadGeoTIFF(uri)
return cloud_image
load_GeoTIFFs(URLs)
¶
Loads a list of Cloud Optimized GeoTIFFs (COG) as an ImageCollection. URLs is a list of URL, which can be one of the following formats: Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif Option 3: https://storage.cloud.google.com/gcp-public-data-landsat/LC08/01/044/034/LC08_L1TP_044034_20131228_20170307_01_T1/LC08_L1TP_044034_20131228_20170307_01_T1_B5.TIF
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
URLs |
list |
A list of Cloud Storage URL of the GeoTIFF to load. |
required |
Returns:
| Type | Description |
|---|---|
ee.ImageCollection |
An Earth Engine ImageCollection. |
Source code in geemap/common.py
def load_GeoTIFFs(URLs):
"""Loads a list of Cloud Optimized GeoTIFFs (COG) as an ImageCollection. URLs is a list of URL, which can be one of the following formats:
Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif
Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif
Option 3: https://storage.cloud.google.com/gcp-public-data-landsat/LC08/01/044/034/LC08_L1TP_044034_20131228_20170307_01_T1/LC08_L1TP_044034_20131228_20170307_01_T1_B5.TIF
Args:
URLs (list): A list of Cloud Storage URL of the GeoTIFF to load.
Returns:
ee.ImageCollection: An Earth Engine ImageCollection.
"""
if not isinstance(URLs, list):
raise Exception("The URLs argument must be a list.")
URIs = []
for URL in URLs:
uri = URL.strip()
if uri.startswith("https://storage.googleapis.com/"):
uri = uri.replace("https://storage.googleapis.com/", "gs://")
elif uri.startswith("https://storage.cloud.google.com/"):
uri = uri.replace("https://storage.cloud.google.com/", "gs://")
if not uri.startswith("gs://"):
raise Exception(
'Invalid GCS URL: {}. Expected something of the form "gs://bucket/path/to/object.tif".'.format(
uri
)
)
if not uri.lower().endswith(".tif"):
raise Exception(
'Invalid GCS URL: {}. Expected something of the form "gs://bucket/path/to/object.tif".'.format(
uri
)
)
URIs.append(uri)
URIs = ee.List(URIs)
collection = URIs.map(lambda uri: ee.Image.loadGeoTIFF(uri))
return ee.ImageCollection(collection)
minimum_bounding_box(geojson)
¶
Gets the minimum bounding box for a geojson polygon.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geojson |
dict |
A geojson dictionary. |
required |
Returns:
| Type | Description |
|---|---|
tuple |
Returns a tuple containing the minimum bounding box in the format of (lower_left(lat, lon), upper_right(lat, lon)), such as ((13, -130), (32, -120)). |
Source code in geemap/common.py
def minimum_bounding_box(geojson):
"""Gets the minimum bounding box for a geojson polygon.
Args:
geojson (dict): A geojson dictionary.
Returns:
tuple: Returns a tuple containing the minimum bounding box in the format of (lower_left(lat, lon), upper_right(lat, lon)), such as ((13, -130), (32, -120)).
"""
coordinates = []
try:
if "geometry" in geojson.keys():
coordinates = geojson["geometry"]["coordinates"][0]
else:
coordinates = geojson["coordinates"][0]
lower_left = min([x[1] for x in coordinates]), min(
[x[0] for x in coordinates]
) # (lat, lon)
upper_right = max([x[1] for x in coordinates]), max(
[x[0] for x in coordinates]
) # (lat, lon)
bounds = (lower_left, upper_right)
return bounds
except Exception as e:
raise Exception(e)
naip_timeseries(roi=None, start_year=2009, end_year=2018)
¶
Creates NAIP annual timeseries
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
roi |
object |
An ee.Geometry representing the region of interest. Defaults to None. |
None |
start_year |
int |
Starting year for the timeseries. Defaults to2009. |
2009 |
end_year |
int |
Ending year for the timeseries. Defaults to 2018. |
2018 |
Returns:
| Type | Description |
|---|---|
object |
An ee.ImageCollection representing annual NAIP imagery. |
Source code in geemap/common.py
def naip_timeseries(roi=None, start_year=2009, end_year=2018):
"""Creates NAIP annual timeseries
Args:
roi (object, optional): An ee.Geometry representing the region of interest. Defaults to None.
start_year (int, optional): Starting year for the timeseries. Defaults to2009.
end_year (int, optional): Ending year for the timeseries. Defaults to 2018.
Returns:
object: An ee.ImageCollection representing annual NAIP imagery.
"""
# ee_initialize()
try:
def get_annual_NAIP(year):
try:
collection = ee.ImageCollection("USDA/NAIP/DOQQ")
if roi is not None:
collection = collection.filterBounds(roi)
start_date = ee.Date.fromYMD(year, 1, 1)
end_date = ee.Date.fromYMD(year, 12, 31)
naip = collection.filterDate(start_date, end_date).filter(
ee.Filter.listContains("system:band_names", "N")
)
naip = ee.Image(ee.ImageCollection(naip).mosaic())
return naip
except Exception as e:
print(e)
years = ee.List.sequence(start_year, end_year)
collection = years.map(get_annual_NAIP)
return collection
except Exception as e:
print(e)
num_round(num, decimal=2)
¶
Rounds a number to a specified number of decimal places.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
num |
float |
The number to round. |
required |
decimal |
int |
The number of decimal places to round. Defaults to 2. |
2 |
Returns:
| Type | Description |
|---|---|
float |
The number with the specified decimal places rounded. |
Source code in geemap/common.py
def num_round(num, decimal=2):
"""Rounds a number to a specified number of decimal places.
Args:
num (float): The number to round.
decimal (int, optional): The number of decimal places to round. Defaults to 2.
Returns:
float: The number with the specified decimal places rounded.
"""
return round(num, decimal)
numpy_to_ee(np_array, crs=None, transform=None, transformWkt=None, band_names=None)
¶
Creates an ee.Image from a 3D numpy array where each 2D numpy slice is added to a band, and a geospatial transform that indicates where to put the data. If the np_array is already 2D only, then it is only a one-band image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
np_array |
|
the 3D (or 2D) numpy array to add to an image |
required |
crs(str) |
|
The base coordinate reference system of this Projection, given as a well-known authority code (e.g. 'EPSG:4326') or a WKT string. |
required |
transform(list) |
|
The transform between projected coordinates and the base coordinate system, specified as a 2x3 affine transform matrix in row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. May not specify both this and 'transformWkt'. |
required |
transformWkt(str) |
|
The transform between projected coordinates and the base coordinate system, specified as a WKT string. May not specify both this and 'transform'. |
required |
band_names(str |
or list |
The list of names for the bands. The default names are 'constant', and 'constant_1', 'constant_2', etc. |
required |
Returns |
|
image: An ee.Image |
required |
Source code in geemap/common.py
def numpy_to_ee(np_array, crs=None, transform=None, transformWkt=None, band_names=None
):
"""
Creates an ee.Image from a 3D numpy array where each 2D numpy slice is added to a band, and a geospatial transform that indicates where to put the data. If the np_array is already 2D only, then it is only a one-band image.
Args:
np_array: the 3D (or 2D) numpy array to add to an image
crs(str): The base coordinate reference system of this Projection, given as a well-known authority code (e.g. 'EPSG:4326') or a WKT string.
transform(list): The transform between projected coordinates and the base coordinate system, specified as a 2x3 affine transform matrix in row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. May not specify both this and 'transformWkt'.
transformWkt(str): The transform between projected coordinates and the base coordinate system, specified as a WKT string. May not specify both this and 'transform'.
band_names(str or list, optional): The list of names for the bands. The default names are 'constant', and 'constant_1', 'constant_2', etc.
Returns:
image: An ee.Image
"""
import numpy as np
if not isinstance(np_array, np.ndarray):
print("The input must be a numpy.ndarray.")
return
if not len(np_array.shape) in [2,3]:
print("The input must have 2 or 3 dimensions")
return
if band_names and not isinstance(band_names, (list,str)):
print("Band names must be a str or list")
return
try:
projection = ee.Projection(crs, transform, transformWkt);
coords = ee.Image.pixelCoordinates(projection).floor().int32();
x = coords.select('x');
y = coords.select('y');
s = np_array.shape
if len(s)<3:
dimx = s[0]
dimy = s[1]
else:
dimx = s[1]
dimy = s[2]
dimz = s[0]
coord_mask = x.gte(0).And(y.gte(0)).And(x.lt(dimx)).And(y.lt(dimy))
coords = coords.updateMask(coord_mask);
def list_to_ee(a_list):
ee_data = ee.Array(a_list)
image = ee.Image(ee_data).arrayGet(coords)
return image
if len(s)<3:
image = list_to_ee(np_array.tolist())
else:
image = list_to_ee(np_array[0].tolist())
for z in np.arange(1,dimz):
image = image.addBands(list_to_ee(np_array[z].tolist()))
if band_names:
image = image.rename(band_names)
return image
except Exception as e:
print(e)
nwi_add_color(fc)
¶
Converts NWI vector dataset to image and add color to it.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
fc |
object |
ee.FeatureCollection |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Image |
Source code in geemap/common.py
def nwi_add_color(fc):
"""Converts NWI vector dataset to image and add color to it.
Args:
fc (object): ee.FeatureCollection
Returns:
object: ee.Image
"""
emergent = ee.FeatureCollection(
fc.filter(ee.Filter.eq("WETLAND_TY", "Freshwater Emergent Wetland"))
)
emergent = emergent.map(lambda f: f.set("R", 127).set("G", 195).set("B", 28))
# print(emergent.first())
forested = fc.filter(
ee.Filter.eq("WETLAND_TY", "Freshwater Forested/Shrub Wetland")
)
forested = forested.map(lambda f: f.set("R", 0).set("G", 136).set("B", 55))
pond = fc.filter(ee.Filter.eq("WETLAND_TY", "Freshwater Pond"))
pond = pond.map(lambda f: f.set("R", 104).set("G", 140).set("B", 192))
lake = fc.filter(ee.Filter.eq("WETLAND_TY", "Lake"))
lake = lake.map(lambda f: f.set("R", 19).set("G", 0).set("B", 124))
riverine = fc.filter(ee.Filter.eq("WETLAND_TY", "Riverine"))
riverine = riverine.map(lambda f: f.set("R", 1).set("G", 144).set("B", 191))
fc = ee.FeatureCollection(
emergent.merge(forested).merge(pond).merge(lake).merge(riverine)
)
# base = ee.Image(0).mask(0).toInt8()
base = ee.Image().byte()
img = base.paint(fc, "R").addBands(
base.paint(fc, "G").addBands(base.paint(fc, "B"))
)
return img
open_github(subdir=None)
¶
Opens the GitHub repository for this package.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
subdir |
str |
Sub-directory of the repository. Defaults to None. |
None |
Source code in geemap/common.py
def open_github(subdir=None):
"""Opens the GitHub repository for this package.
Args:
subdir (str, optional): Sub-directory of the repository. Defaults to None.
"""
import webbrowser
url = "https://github.com/giswqs/geemap"
if subdir == "source":
url += "/tree/master/geemap/"
elif subdir == "examples":
url += "/tree/master/examples"
elif subdir == "tutorials":
url += "/tree/master/tutorials"
webbrowser.open_new_tab(url)
open_image_from_url(url)
¶
Loads an image from the specified URL.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
URL of the image. |
required |
Returns:
| Type | Description |
|---|---|
object |
Image object. |
Source code in geemap/common.py
def open_image_from_url(url):
"""Loads an image from the specified URL.
Args:
url (str): URL of the image.
Returns:
object: Image object.
"""
from PIL import Image
import requests
from io import BytesIO
# from urllib.parse import urlparse
try:
response = requests.get(url)
img = Image.open(BytesIO(response.content))
return img
except Exception as e:
print(e)
open_youtube()
¶
Opens the YouTube tutorials for geemap.
Source code in geemap/common.py
def open_youtube():
"""Opens the YouTube tutorials for geemap."""
import webbrowser
url = "https://www.youtube.com/playlist?list=PLAxJ4-o7ZoPccOFv1dCwvGI6TYnirRTg3"
webbrowser.open_new_tab(url)
pandas_to_ee(df, latitude='latitude', longitude='longitude', **kwargs)
¶
Converts a pandas DataFrame to ee.FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df |
pandas.DataFrame |
An input pandas.DataFrame. |
required |
latitude |
str |
Column name for the latitude column. Defaults to 'latitude'. |
'latitude' |
longitude |
str |
Column name for the longitude column. Defaults to 'longitude'. |
'longitude' |
Exceptions:
| Type | Description |
|---|---|
TypeError |
The input data type must be pandas.DataFrame. |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
The ee.FeatureCollection converted from the input pandas DataFrame. |
Source code in geemap/common.py
def pandas_to_ee(df, latitude="latitude", longitude="longitude", **kwargs):
"""Converts a pandas DataFrame to ee.FeatureCollection.
Args:
df (pandas.DataFrame): An input pandas.DataFrame.
latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.
longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.
Raises:
TypeError: The input data type must be pandas.DataFrame.
Returns:
ee.FeatureCollection: The ee.FeatureCollection converted from the input pandas DataFrame.
"""
import pandas as pd
if not isinstance(df, pd.DataFrame):
raise TypeError("The input data type must be pandas.DataFrame.")
out_csv = os.path.join(os.getcwd(), random_string(6) + ".csv")
df.to_csv(out_csv, **kwargs)
fc = xy_to_points(out_csv, latitude=latitude, longitude=longitude)
os.remove(out_csv)
return fc
png_to_gif(in_dir, out_gif, fps=10, loop=0)
¶
Convert a list of png images to gif.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_dir |
str |
The input directory containing png images. |
required |
out_gif |
str |
The output file path to the gif. |
required |
fps |
int |
Frames per second. Defaults to 10. |
10 |
loop |
bool |
controls how many times the animation repeats. 1 means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0. |
0 |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
No png images could be found. |
Source code in geemap/common.py
def png_to_gif(in_dir, out_gif, fps=10, loop=0):
"""Convert a list of png images to gif.
Args:
in_dir (str): The input directory containing png images.