body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1 value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
69f243bb4b6db0410ad1e1b3f05619a2fde8a5917a5580db7eb04ba90df755be | def open_image_from_url(url):
'Loads an image from the specified URL.\n\n Args:\n url (str): URL of the image.\n\n Returns:\n object: Image object.\n '
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) | Loads an image from the specified URL.
Args:
url (str): URL of the image.
Returns:
object: Image object. | geemap/common.py | open_image_from_url | arheem/geemap | 1 | python | def open_image_from_url(url):
'Loads an image from the specified URL.\n\n Args:\n url (str): URL of the image.\n\n Returns:\n object: Image object.\n '
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) | def open_image_from_url(url):
'Loads an image from the specified URL.\n\n Args:\n url (str): URL of the image.\n\n Returns:\n object: Image object.\n '
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)<|docstring|>Loads an image from the specified URL.
Args:
url (str): URL of the image.
Returns:
object: Image object.<|endoftext|> |
640a8b70c64d0fe3daeb5852496fd806e65a5a8a1d421af38ca112ef38f24388 | def show_image(img_path, width=None, height=None):
'Shows an image within Jupyter notebook.\n\n Args:\n img_path (str): The image file path.\n width (int, optional): Width of the image in pixels. Defaults to None.\n height (int, optional): Height of the image in pixels. Defaults to None.\n\n '
from IPython.display import display
try:
out = widgets.Output()
out.clear_output(wait=True)
display(out)
with out:
file = open(img_path, 'rb')
image = file.read()
if ((width is None) and (height is None)):
display(widgets.Image(value=image))
elif ((width is not None) and (height is not None)):
display(widgets.Image(value=image, width=width, height=height))
else:
print('You need set both width and height.')
return
except Exception as e:
print(e) | Shows an image within Jupyter notebook.
Args:
img_path (str): The image file path.
width (int, optional): Width of the image in pixels. Defaults to None.
height (int, optional): Height of the image in pixels. Defaults to None. | geemap/common.py | show_image | arheem/geemap | 1 | python | def show_image(img_path, width=None, height=None):
'Shows an image within Jupyter notebook.\n\n Args:\n img_path (str): The image file path.\n width (int, optional): Width of the image in pixels. Defaults to None.\n height (int, optional): Height of the image in pixels. Defaults to None.\n\n '
from IPython.display import display
try:
out = widgets.Output()
out.clear_output(wait=True)
display(out)
with out:
file = open(img_path, 'rb')
image = file.read()
if ((width is None) and (height is None)):
display(widgets.Image(value=image))
elif ((width is not None) and (height is not None)):
display(widgets.Image(value=image, width=width, height=height))
else:
print('You need set both width and height.')
return
except Exception as e:
print(e) | def show_image(img_path, width=None, height=None):
'Shows an image within Jupyter notebook.\n\n Args:\n img_path (str): The image file path.\n width (int, optional): Width of the image in pixels. Defaults to None.\n height (int, optional): Height of the image in pixels. Defaults to None.\n\n '
from IPython.display import display
try:
out = widgets.Output()
out.clear_output(wait=True)
display(out)
with out:
file = open(img_path, 'rb')
image = file.read()
if ((width is None) and (height is None)):
display(widgets.Image(value=image))
elif ((width is not None) and (height is not None)):
display(widgets.Image(value=image, width=width, height=height))
else:
print('You need set both width and height.')
return
except Exception as e:
print(e)<|docstring|>Shows an image within Jupyter notebook.
Args:
img_path (str): The image file path.
width (int, optional): Width of the image in pixels. Defaults to None.
height (int, optional): Height of the image in pixels. Defaults to None.<|endoftext|> |
c8423880a8f5d7f2e6db1364b6ce2a994357f11297aefde0cf2c9e14a74329af | def has_transparency(img):
'Checks whether an image has transparency.\n\n Args:\n img (object): a PIL Image object.\n\n Returns:\n bool: True if it has transparency, False otherwise.\n '
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 | Checks whether an image has transparency.
Args:
img (object): a PIL Image object.
Returns:
bool: True if it has transparency, False otherwise. | geemap/common.py | has_transparency | arheem/geemap | 1 | python | def has_transparency(img):
'Checks whether an image has transparency.\n\n Args:\n img (object): a PIL Image object.\n\n Returns:\n bool: True if it has transparency, False otherwise.\n '
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 | def has_transparency(img):
'Checks whether an image has transparency.\n\n Args:\n img (object): a PIL Image object.\n\n Returns:\n bool: True if it has transparency, False otherwise.\n '
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<|docstring|>Checks whether an image has transparency.
Args:
img (object): a PIL Image object.
Returns:
bool: True if it has transparency, False otherwise.<|endoftext|> |
8336a93cc5d8a72ba4123e31e3cbeaf838001190baa9f7274b2fa14157ebe314 | def upload_to_imgur(in_gif):
'Uploads an image to imgur.com\n\n Args:\n in_gif (str): The file path to the image.\n '
import subprocess
pkg_name = 'imgur-uploader'
if (not is_tool(pkg_name)):
check_install(pkg_name)
try:
IMGUR_API_ID = os.environ.get('IMGUR_API_ID', None)
IMGUR_API_SECRET = os.environ.get('IMGUR_API_SECRET', None)
credentials_path = os.path.join(os.path.expanduser('~'), '.config/imgur_uploader/uploader.cfg')
if (((IMGUR_API_ID is not None) and (IMGUR_API_SECRET is not None)) or os.path.exists(credentials_path)):
proc = subprocess.Popen(['imgur-uploader', in_gif], stdout=subprocess.PIPE)
for _ in range(0, 2):
line = proc.stdout.readline()
print(line.rstrip().decode('utf-8'))
else:
print('Imgur API credentials could not be found. Please check https://pypi.org/project/imgur-uploader/ for instructions on how to get Imgur API credentials')
return
except Exception as e:
print(e) | Uploads an image to imgur.com
Args:
in_gif (str): The file path to the image. | geemap/common.py | upload_to_imgur | arheem/geemap | 1 | python | def upload_to_imgur(in_gif):
'Uploads an image to imgur.com\n\n Args:\n in_gif (str): The file path to the image.\n '
import subprocess
pkg_name = 'imgur-uploader'
if (not is_tool(pkg_name)):
check_install(pkg_name)
try:
IMGUR_API_ID = os.environ.get('IMGUR_API_ID', None)
IMGUR_API_SECRET = os.environ.get('IMGUR_API_SECRET', None)
credentials_path = os.path.join(os.path.expanduser('~'), '.config/imgur_uploader/uploader.cfg')
if (((IMGUR_API_ID is not None) and (IMGUR_API_SECRET is not None)) or os.path.exists(credentials_path)):
proc = subprocess.Popen(['imgur-uploader', in_gif], stdout=subprocess.PIPE)
for _ in range(0, 2):
line = proc.stdout.readline()
print(line.rstrip().decode('utf-8'))
else:
print('Imgur API credentials could not be found. Please check https://pypi.org/project/imgur-uploader/ for instructions on how to get Imgur API credentials')
return
except Exception as e:
print(e) | def upload_to_imgur(in_gif):
'Uploads an image to imgur.com\n\n Args:\n in_gif (str): The file path to the image.\n '
import subprocess
pkg_name = 'imgur-uploader'
if (not is_tool(pkg_name)):
check_install(pkg_name)
try:
IMGUR_API_ID = os.environ.get('IMGUR_API_ID', None)
IMGUR_API_SECRET = os.environ.get('IMGUR_API_SECRET', None)
credentials_path = os.path.join(os.path.expanduser('~'), '.config/imgur_uploader/uploader.cfg')
if (((IMGUR_API_ID is not None) and (IMGUR_API_SECRET is not None)) or os.path.exists(credentials_path)):
proc = subprocess.Popen(['imgur-uploader', in_gif], stdout=subprocess.PIPE)
for _ in range(0, 2):
line = proc.stdout.readline()
print(line.rstrip().decode('utf-8'))
else:
print('Imgur API credentials could not be found. Please check https://pypi.org/project/imgur-uploader/ for instructions on how to get Imgur API credentials')
return
except Exception as e:
print(e)<|docstring|>Uploads an image to imgur.com
Args:
in_gif (str): The file path to the image.<|endoftext|> |
a8e30dd42d61d40e09bb1dd5c839e75ce14af5460fc8202eb1d3de3c9b207343 | def rgb_to_hex(rgb=(255, 255, 255)):
'Converts RGB to hex color. In RGB color R stands for Red, G stands for Green, and B stands for Blue, and it ranges from the decimal value of 0 – 255.\n\n Args:\n rgb (tuple, optional): RGB color code as a tuple of (red, green, blue). Defaults to (255, 255, 255).\n\n Returns:\n str: hex color code\n '
return ('%02x%02x%02x' % rgb) | Converts RGB to hex color. In RGB color R stands for Red, G stands for Green, and B stands for Blue, and it ranges from the decimal value of 0 – 255.
Args:
rgb (tuple, optional): RGB color code as a tuple of (red, green, blue). Defaults to (255, 255, 255).
Returns:
str: hex color code | geemap/common.py | rgb_to_hex | arheem/geemap | 1 | python | def rgb_to_hex(rgb=(255, 255, 255)):
'Converts RGB to hex color. In RGB color R stands for Red, G stands for Green, and B stands for Blue, and it ranges from the decimal value of 0 – 255.\n\n Args:\n rgb (tuple, optional): RGB color code as a tuple of (red, green, blue). Defaults to (255, 255, 255).\n\n Returns:\n str: hex color code\n '
return ('%02x%02x%02x' % rgb) | def rgb_to_hex(rgb=(255, 255, 255)):
'Converts RGB to hex color. In RGB color R stands for Red, G stands for Green, and B stands for Blue, and it ranges from the decimal value of 0 – 255.\n\n Args:\n rgb (tuple, optional): RGB color code as a tuple of (red, green, blue). Defaults to (255, 255, 255).\n\n Returns:\n str: hex color code\n '
return ('%02x%02x%02x' % rgb)<|docstring|>Converts RGB to hex color. In RGB color R stands for Red, G stands for Green, and B stands for Blue, and it ranges from the decimal value of 0 – 255.
Args:
rgb (tuple, optional): RGB color code as a tuple of (red, green, blue). Defaults to (255, 255, 255).
Returns:
str: hex color code<|endoftext|> |
14fb086e3bdb9619fc6bbc3eaef9a5f948673b2e271ece749d58d80f6f706bac | def hex_to_rgb(value='FFFFFF'):
"Converts hex color to RGB color. \n\n Args:\n value (str, optional): Hex color code as a string. Defaults to 'FFFFFF'.\n\n Returns:\n tuple: RGB color as a tuple.\n "
value = value.lstrip('#')
lv = len(value)
return tuple((int(value[i:(i + (lv // 3))], 16) for i in range(0, lv, (lv // 3)))) | 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. | geemap/common.py | hex_to_rgb | arheem/geemap | 1 | python | def hex_to_rgb(value='FFFFFF'):
"Converts hex color to RGB color. \n\n Args:\n value (str, optional): Hex color code as a string. Defaults to 'FFFFFF'.\n\n Returns:\n tuple: RGB color as a tuple.\n "
value = value.lstrip('#')
lv = len(value)
return tuple((int(value[i:(i + (lv // 3))], 16) for i in range(0, lv, (lv // 3)))) | def hex_to_rgb(value='FFFFFF'):
"Converts hex color to RGB color. \n\n Args:\n value (str, optional): Hex color code as a string. Defaults to 'FFFFFF'.\n\n Returns:\n tuple: RGB color as a tuple.\n "
value = value.lstrip('#')
lv = len(value)
return tuple((int(value[i:(i + (lv // 3))], 16) for i in range(0, lv, (lv // 3))))<|docstring|>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.<|endoftext|> |
0e99961709d5f7457e1fdb0496a7625bedcdb26ae52afe679035d67e1a628020 | def check_color(in_color):
"Checks the input color and returns the corresponding hex color code.\n\n Args:\n in_color (str or tuple): It can be a string (e.g., 'red', '#ffff00') or tuple (e.g., (255, 127, 0)).\n\n Returns:\n str: A hex color code.\n "
import colour
out_color = '#000000'
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 | 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. | geemap/common.py | check_color | arheem/geemap | 1 | python | def check_color(in_color):
"Checks the input color and returns the corresponding hex color code.\n\n Args:\n in_color (str or tuple): It can be a string (e.g., 'red', '#ffff00') or tuple (e.g., (255, 127, 0)).\n\n Returns:\n str: A hex color code.\n "
import colour
out_color = '#000000'
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 | def check_color(in_color):
"Checks the input color and returns the corresponding hex color code.\n\n Args:\n in_color (str or tuple): It can be a string (e.g., 'red', '#ffff00') or tuple (e.g., (255, 127, 0)).\n\n Returns:\n str: A hex color code.\n "
import colour
out_color = '#000000'
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<|docstring|>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.<|endoftext|> |
cde97d694e349284756e89477dfaae6094bac651cc2b1f390c7351d92ab3a25d | def system_fonts(show_full_path=False):
'Gets a list of system fonts\n\n # Common font locations:\n # Linux: /usr/share/fonts/TTF/\n # Windows: C:\\Windows\\Fonts\n # macOS: System > Library > Fonts\n\n Args:\n show_full_path (bool, optional): Whether to show the full path of each system font. Defaults to False.\n\n Returns:\n list: A list of system fonts.\n '
try:
import matplotlib.font_manager
font_list = matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf')
font_list.sort()
font_names = [os.path.basename(f) for f in font_list]
font_names.sort()
if show_full_path:
return font_list
else:
return font_names
except Exception as e:
print(e) | Gets a list of system fonts
# Common font locations:
# Linux: /usr/share/fonts/TTF/
# Windows: C:\Windows\Fonts
# macOS: System > Library > Fonts
Args:
show_full_path (bool, optional): Whether to show the full path of each system font. Defaults to False.
Returns:
list: A list of system fonts. | geemap/common.py | system_fonts | arheem/geemap | 1 | python | def system_fonts(show_full_path=False):
'Gets a list of system fonts\n\n # Common font locations:\n # Linux: /usr/share/fonts/TTF/\n # Windows: C:\\Windows\\Fonts\n # macOS: System > Library > Fonts\n\n Args:\n show_full_path (bool, optional): Whether to show the full path of each system font. Defaults to False.\n\n Returns:\n list: A list of system fonts.\n '
try:
import matplotlib.font_manager
font_list = matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf')
font_list.sort()
font_names = [os.path.basename(f) for f in font_list]
font_names.sort()
if show_full_path:
return font_list
else:
return font_names
except Exception as e:
print(e) | def system_fonts(show_full_path=False):
'Gets a list of system fonts\n\n # Common font locations:\n # Linux: /usr/share/fonts/TTF/\n # Windows: C:\\Windows\\Fonts\n # macOS: System > Library > Fonts\n\n Args:\n show_full_path (bool, optional): Whether to show the full path of each system font. Defaults to False.\n\n Returns:\n list: A list of system fonts.\n '
try:
import matplotlib.font_manager
font_list = matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf')
font_list.sort()
font_names = [os.path.basename(f) for f in font_list]
font_names.sort()
if show_full_path:
return font_list
else:
return font_names
except Exception as e:
print(e)<|docstring|>Gets a list of system fonts
# Common font locations:
# Linux: /usr/share/fonts/TTF/
# Windows: C:\Windows\Fonts
# macOS: System > Library > Fonts
Args:
show_full_path (bool, optional): Whether to show the full path of each system font. Defaults to False.
Returns:
list: A list of system fonts.<|endoftext|> |
ee9ef305b6b3354dc81928f401a2838971d83c1541948e62e99461621e3099d6 | 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)\n\n Args:\n url (str): The HTTP URL to download.\n out_file_name (str, optional): The output file name to use. Defaults to None.\n out_dir (str, optional): The output directory to use. Defaults to '.'.\n unzip (bool, optional): Whether to unzip the downloaded file if it is a zip file. Defaults to True.\n verbose (bool, optional): Whether to display or not the output of the function\n "
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:
print('The URL is invalid. Please double check the URL.')
return
final_path = out_file_path
if unzip:
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 ('.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 | 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 | geemap/common.py | download_from_url | arheem/geemap | 1 | python | 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)\n\n Args:\n url (str): The HTTP URL to download.\n out_file_name (str, optional): The output file name to use. Defaults to None.\n out_dir (str, optional): The output directory to use. Defaults to '.'.\n unzip (bool, optional): Whether to unzip the downloaded file if it is a zip file. Defaults to True.\n verbose (bool, optional): Whether to display or not the output of the function\n "
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:
print('The URL is invalid. Please double check the URL.')
return
final_path = out_file_path
if unzip:
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 ('.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 | 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)\n\n Args:\n url (str): The HTTP URL to download.\n out_file_name (str, optional): The output file name to use. Defaults to None.\n out_dir (str, optional): The output directory to use. Defaults to '.'.\n unzip (bool, optional): Whether to unzip the downloaded file if it is a zip file. Defaults to True.\n verbose (bool, optional): Whether to display or not the output of the function\n "
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:
print('The URL is invalid. Please double check the URL.')
return
final_path = out_file_path
if unzip:
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 ('.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<|docstring|>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<|endoftext|> |
59afa63fda95b259e31b831e25ac0de26a8ce1bc4da8d7790e8f5a05d312a241 | def download_from_gdrive(gfile_url, file_name, out_dir='.', unzip=True, verbose=True):
"Download a file shared via Google Drive \n (e.g., https://drive.google.com/file/d/18SUo_HcDGltuWYZs1s7PpOmOq_FvFn04/view?usp=sharing)\n\n Args:\n gfile_url (str): The Google Drive shared file URL\n file_name (str): The output file name to use.\n out_dir (str, optional): The output directory. Defaults to '.'.\n unzip (bool, optional): Whether to unzip the output file if it is a zip file. Defaults to True.\n verbose (bool, optional): Whether to display or not the output of the function\n "
try:
from google_drive_downloader import GoogleDriveDownloader as gdd
except ImportError:
print('GoogleDriveDownloader package not installed. Installing ...')
subprocess.check_call(['python', '-m', 'pip', 'install', 'googledrivedownloader'])
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 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 | geemap/common.py | download_from_gdrive | arheem/geemap | 1 | python | def download_from_gdrive(gfile_url, file_name, out_dir='.', unzip=True, verbose=True):
"Download a file shared via Google Drive \n (e.g., https://drive.google.com/file/d/18SUo_HcDGltuWYZs1s7PpOmOq_FvFn04/view?usp=sharing)\n\n Args:\n gfile_url (str): The Google Drive shared file URL\n file_name (str): The output file name to use.\n out_dir (str, optional): The output directory. Defaults to '.'.\n unzip (bool, optional): Whether to unzip the output file if it is a zip file. Defaults to True.\n verbose (bool, optional): Whether to display or not the output of the function\n "
try:
from google_drive_downloader import GoogleDriveDownloader as gdd
except ImportError:
print('GoogleDriveDownloader package not installed. Installing ...')
subprocess.check_call(['python', '-m', 'pip', 'install', 'googledrivedownloader'])
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 | def download_from_gdrive(gfile_url, file_name, out_dir='.', unzip=True, verbose=True):
"Download a file shared via Google Drive \n (e.g., https://drive.google.com/file/d/18SUo_HcDGltuWYZs1s7PpOmOq_FvFn04/view?usp=sharing)\n\n Args:\n gfile_url (str): The Google Drive shared file URL\n file_name (str): The output file name to use.\n out_dir (str, optional): The output directory. Defaults to '.'.\n unzip (bool, optional): Whether to unzip the output file if it is a zip file. Defaults to True.\n verbose (bool, optional): Whether to display or not the output of the function\n "
try:
from google_drive_downloader import GoogleDriveDownloader as gdd
except ImportError:
print('GoogleDriveDownloader package not installed. Installing ...')
subprocess.check_call(['python', '-m', 'pip', 'install', 'googledrivedownloader'])
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<|docstring|>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<|endoftext|> |
341903b3ad9d4b186b8888eb87283a04fc4fcc46227bcbad558130eb81b88255 | 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\n\n Args:\n filename (str): The file path to the file to download\n title (str, optional): str. Defaults to "Click here to download: ".\n\n Returns:\n str: HTML download URL.\n '
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) | 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. | geemap/common.py | create_download_link | arheem/geemap | 1 | python | 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\n\n Args:\n filename (str): The file path to the file to download\n title (str, optional): str. Defaults to "Click here to download: ".\n\n Returns:\n str: HTML download URL.\n '
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) | 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\n\n Args:\n filename (str): The file path to the file to download\n title (str, optional): str. Defaults to "Click here to download: ".\n\n Returns:\n str: HTML download URL.\n '
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)<|docstring|>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.<|endoftext|> |
e65f63d809ed24d72bd510454a27a8dd5974314b46c0e4af87d02245eb9ca73d | 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\n\n Args:\n htmlWidget (object): The HTML widget to display the URL.\n filename (str): File path to download. \n title (str, optional): Download description. Defaults to "Click here to download: ".\n '
from IPython.display import HTML
import ipywidgets as widgets
import base64
htmlWidget.value = '<i class="fa fa-spinner fa-spin fa-2x fa-fw"></i><span class="sr-only">Loading...</span>'
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}" target="_blank">{title}</a>'
htmlWidget.value = html.format(payload=payload, title=(title + basename), filename=basename) | 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: ". | geemap/common.py | edit_download_html | arheem/geemap | 1 | python | 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\n\n Args:\n htmlWidget (object): The HTML widget to display the URL.\n filename (str): File path to download. \n title (str, optional): Download description. Defaults to "Click here to download: ".\n '
from IPython.display import HTML
import ipywidgets as widgets
import base64
htmlWidget.value = '<i class="fa fa-spinner fa-spin fa-2x fa-fw"></i><span class="sr-only">Loading...</span>'
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}" target="_blank">{title}</a>'
htmlWidget.value = html.format(payload=payload, title=(title + basename), filename=basename) | 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\n\n Args:\n htmlWidget (object): The HTML widget to display the URL.\n filename (str): File path to download. \n title (str, optional): Download description. Defaults to "Click here to download: ".\n '
from IPython.display import HTML
import ipywidgets as widgets
import base64
htmlWidget.value = '<i class="fa fa-spinner fa-spin fa-2x fa-fw"></i><span class="sr-only">Loading...</span>'
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}" target="_blank">{title}</a>'
htmlWidget.value = html.format(payload=payload, title=(title + basename), filename=basename)<|docstring|>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: ".<|endoftext|> |
cc243521f5a3e9e46196b3a7a738ec5d1fb905125ff22c121039eed736e7a016 | def xy_to_points(in_csv, latitude='latitude', longitude='longitude'):
"Converts a csv containing points (latitude and longitude) into an ee.FeatureCollection.\n\n Args:\n 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\n latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.\n longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.\n\n Returns:\n ee.FeatureCollection: The ee.FeatureCollection containing the points converted from the input csv.\n "
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)
in_csv = os.path.abspath(in_csv)
if (not os.path.exists(in_csv)):
raise Exception('The provided csv file does not exist.')
points = []
with open(in_csv) as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
(lat, lon) = (float(row[latitude]), float(row[longitude]))
points.append([lon, lat])
ee_list = ee.List(points)
ee_points = ee_list.map((lambda xy: ee.Feature(ee.Geometry.Point(xy))))
return ee.FeatureCollection(ee_points) | Converts a csv containing points (latitude and longitude) into an ee.FeatureCollection.
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
latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.
longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.
Returns:
ee.FeatureCollection: The ee.FeatureCollection containing the points converted from the input csv. | geemap/common.py | xy_to_points | arheem/geemap | 1 | python | def xy_to_points(in_csv, latitude='latitude', longitude='longitude'):
"Converts a csv containing points (latitude and longitude) into an ee.FeatureCollection.\n\n Args:\n 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\n latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.\n longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.\n\n Returns:\n ee.FeatureCollection: The ee.FeatureCollection containing the points converted from the input csv.\n "
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)
in_csv = os.path.abspath(in_csv)
if (not os.path.exists(in_csv)):
raise Exception('The provided csv file does not exist.')
points = []
with open(in_csv) as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
(lat, lon) = (float(row[latitude]), float(row[longitude]))
points.append([lon, lat])
ee_list = ee.List(points)
ee_points = ee_list.map((lambda xy: ee.Feature(ee.Geometry.Point(xy))))
return ee.FeatureCollection(ee_points) | def xy_to_points(in_csv, latitude='latitude', longitude='longitude'):
"Converts a csv containing points (latitude and longitude) into an ee.FeatureCollection.\n\n Args:\n 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\n latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.\n longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.\n\n Returns:\n ee.FeatureCollection: The ee.FeatureCollection containing the points converted from the input csv.\n "
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)
in_csv = os.path.abspath(in_csv)
if (not os.path.exists(in_csv)):
raise Exception('The provided csv file does not exist.')
points = []
with open(in_csv) as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
(lat, lon) = (float(row[latitude]), float(row[longitude]))
points.append([lon, lat])
ee_list = ee.List(points)
ee_points = ee_list.map((lambda xy: ee.Feature(ee.Geometry.Point(xy))))
return ee.FeatureCollection(ee_points)<|docstring|>Converts a csv containing points (latitude and longitude) into an ee.FeatureCollection.
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
latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.
longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.
Returns:
ee.FeatureCollection: The ee.FeatureCollection containing the points converted from the input csv.<|endoftext|> |
ab260ed9470adc02e1eb5e0c84c2faab6f3a439f99aa39cd402c7b5fffacac68 | def csv_points_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude'):
"Converts a csv file containing points (latitude, longitude) into a shapefile.\n\n Args:\n 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\n out_shp (str): File path to the output shapefile.\n latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.\n longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.\n\n "
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) 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) | 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'. | geemap/common.py | csv_points_to_shp | arheem/geemap | 1 | python | def csv_points_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude'):
"Converts a csv file containing points (latitude, longitude) into a shapefile.\n\n Args:\n 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\n out_shp (str): File path to the output shapefile.\n latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.\n longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.\n\n "
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) 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) | def csv_points_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude'):
"Converts a csv file containing points (latitude, longitude) into a shapefile.\n\n Args:\n 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\n out_shp (str): File path to the output shapefile.\n latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.\n longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.\n\n "
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) 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)<|docstring|>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'.<|endoftext|> |
13cc5cac2497dfc805d008555652f8b5e45973a5e3f4f443301cdb1300465de5 | def csv_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude'):
"Converts a csv file with latlon info to a point shapefile.\n\n Args:\n in_csv (str): The input csv file containing longitude and latitude columns.\n out_shp (str): The file path to the output shapefile.\n latitude (str, optional): The column name of the latitude column. Defaults to 'latitude'.\n longitude (str, optional): The column name of the longitude column. Defaults to 'longitude'.\n "
import csv
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) 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) | 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'. | geemap/common.py | csv_to_shp | arheem/geemap | 1 | python | def csv_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude'):
"Converts a csv file with latlon info to a point shapefile.\n\n Args:\n in_csv (str): The input csv file containing longitude and latitude columns.\n out_shp (str): The file path to the output shapefile.\n latitude (str, optional): The column name of the latitude column. Defaults to 'latitude'.\n longitude (str, optional): The column name of the longitude column. Defaults to 'longitude'.\n "
import csv
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) 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) | def csv_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude'):
"Converts a csv file with latlon info to a point shapefile.\n\n Args:\n in_csv (str): The input csv file containing longitude and latitude columns.\n out_shp (str): The file path to the output shapefile.\n latitude (str, optional): The column name of the latitude column. Defaults to 'latitude'.\n longitude (str, optional): The column name of the longitude column. Defaults to 'longitude'.\n "
import csv
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) 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)<|docstring|>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'.<|endoftext|> |
98d43f23c0ef5c3687d8cddabbd6fe2078a862ec4b90b72ef88b90d37dae24b3 | def geojson_to_ee(geo_json, geodesic=True):
'Converts a geojson to ee.Geometry()\n\n Args:\n geo_json (dict): A geojson geometry dictionary or file path.\n\n Returns:\n ee_object: An ee.Geometry object\n '
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):
geom = ee.Geometry(geo_json['geometry'])
radius = geo_json['properties']['style']['radius']
geom = geom.buffer(radius)
elif (geo_json['geometry']['type'] == '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:
print('Could not convert the geojson to ee.Geometry()')
except Exception as e:
print('Could not convert the geojson to ee.Geometry()')
print(e) | Converts a geojson to ee.Geometry()
Args:
geo_json (dict): A geojson geometry dictionary or file path.
Returns:
ee_object: An ee.Geometry object | geemap/common.py | geojson_to_ee | arheem/geemap | 1 | python | def geojson_to_ee(geo_json, geodesic=True):
'Converts a geojson to ee.Geometry()\n\n Args:\n geo_json (dict): A geojson geometry dictionary or file path.\n\n Returns:\n ee_object: An ee.Geometry object\n '
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):
geom = ee.Geometry(geo_json['geometry'])
radius = geo_json['properties']['style']['radius']
geom = geom.buffer(radius)
elif (geo_json['geometry']['type'] == '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:
print('Could not convert the geojson to ee.Geometry()')
except Exception as e:
print('Could not convert the geojson to ee.Geometry()')
print(e) | def geojson_to_ee(geo_json, geodesic=True):
'Converts a geojson to ee.Geometry()\n\n Args:\n geo_json (dict): A geojson geometry dictionary or file path.\n\n Returns:\n ee_object: An ee.Geometry object\n '
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):
geom = ee.Geometry(geo_json['geometry'])
radius = geo_json['properties']['style']['radius']
geom = geom.buffer(radius)
elif (geo_json['geometry']['type'] == '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:
print('Could not convert the geojson to ee.Geometry()')
except Exception as e:
print('Could not convert the geojson to ee.Geometry()')
print(e)<|docstring|>Converts a geojson to ee.Geometry()
Args:
geo_json (dict): A geojson geometry dictionary or file path.
Returns:
ee_object: An ee.Geometry object<|endoftext|> |
a460299137be789cd52d4dc519d703b3f0560132e93fd8c3ccdfd86772cbc5d3 | def ee_to_geojson(ee_object, out_json=None):
'Converts Earth Engine object to geojson.\n\n Args:\n ee_object (object): An Earth Engine object.\n\n Returns:\n object: GeoJSON object.\n '
from json import dumps
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) | Converts Earth Engine object to geojson.
Args:
ee_object (object): An Earth Engine object.
Returns:
object: GeoJSON object. | geemap/common.py | ee_to_geojson | arheem/geemap | 1 | python | def ee_to_geojson(ee_object, out_json=None):
'Converts Earth Engine object to geojson.\n\n Args:\n ee_object (object): An Earth Engine object.\n\n Returns:\n object: GeoJSON object.\n '
from json import dumps
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) | def ee_to_geojson(ee_object, out_json=None):
'Converts Earth Engine object to geojson.\n\n Args:\n ee_object (object): An Earth Engine object.\n\n Returns:\n object: GeoJSON object.\n '
from json import dumps
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)<|docstring|>Converts Earth Engine object to geojson.
Args:
ee_object (object): An Earth Engine object.
Returns:
object: GeoJSON object.<|endoftext|> |
ab39b72ffdd91410d6468b689ea640384ccdbf5667f4e489b0b3e25044d818dc | def shp_to_geojson(in_shp, out_json=None):
'Converts a shapefile to GeoJSON.\n\n Args:\n in_shp (str): File path of the input shapefile.\n out_json (str, optional): File path of the output GeoJSON. Defaults to None.\n\n Returns:\n object: The json object representing the shapefile.\n '
try:
import json
import shapefile
from datetime import date
in_shp = os.path.abspath(in_shp)
if (out_json is None):
out_json = (os.path.splitext(in_shp)[0] + '.json')
if os.path.exists(out_json):
out_json = out_json.replace('.json', '_bk.json')
elif (not os.path.exists(os.path.dirname(out_json))):
os.makedirs(os.path.dirname(out_json))
reader = shapefile.Reader(in_shp)
fields = reader.fields[1:]
field_names = [field[0] for field in fields]
date_fields_names = [field[0] for field in fields if (field[1] == 'D')]
buffer = []
for sr in reader.shapeRecords():
atr = dict(zip(field_names, sr.record))
for date_field in date_fields_names:
value = atr[date_field]
if isinstance(value, date):
value = '{:04d}{:02d}{:02d}'.format(value.year, value.month, value.day)
elif (not value):
value = ('0' * 8)
atr[date_field] = value
geom = sr.shape.__geo_interface__
buffer.append(dict(type='Feature', geometry=geom, properties=atr))
from json import dumps
geojson = open(out_json, 'w')
geojson.write((dumps({'type': 'FeatureCollection', 'features': buffer}, indent=2) + '\n'))
geojson.close()
with open(out_json) as f:
json_data = json.load(f)
return json_data
except Exception as e:
print(e) | Converts a shapefile to GeoJSON.
Args:
in_shp (str): File path of the input shapefile.
out_json (str, optional): File path of the output GeoJSON. Defaults to None.
Returns:
object: The json object representing the shapefile. | geemap/common.py | shp_to_geojson | arheem/geemap | 1 | python | def shp_to_geojson(in_shp, out_json=None):
'Converts a shapefile to GeoJSON.\n\n Args:\n in_shp (str): File path of the input shapefile.\n out_json (str, optional): File path of the output GeoJSON. Defaults to None.\n\n Returns:\n object: The json object representing the shapefile.\n '
try:
import json
import shapefile
from datetime import date
in_shp = os.path.abspath(in_shp)
if (out_json is None):
out_json = (os.path.splitext(in_shp)[0] + '.json')
if os.path.exists(out_json):
out_json = out_json.replace('.json', '_bk.json')
elif (not os.path.exists(os.path.dirname(out_json))):
os.makedirs(os.path.dirname(out_json))
reader = shapefile.Reader(in_shp)
fields = reader.fields[1:]
field_names = [field[0] for field in fields]
date_fields_names = [field[0] for field in fields if (field[1] == 'D')]
buffer = []
for sr in reader.shapeRecords():
atr = dict(zip(field_names, sr.record))
for date_field in date_fields_names:
value = atr[date_field]
if isinstance(value, date):
value = '{:04d}{:02d}{:02d}'.format(value.year, value.month, value.day)
elif (not value):
value = ('0' * 8)
atr[date_field] = value
geom = sr.shape.__geo_interface__
buffer.append(dict(type='Feature', geometry=geom, properties=atr))
from json import dumps
geojson = open(out_json, 'w')
geojson.write((dumps({'type': 'FeatureCollection', 'features': buffer}, indent=2) + '\n'))
geojson.close()
with open(out_json) as f:
json_data = json.load(f)
return json_data
except Exception as e:
print(e) | def shp_to_geojson(in_shp, out_json=None):
'Converts a shapefile to GeoJSON.\n\n Args:\n in_shp (str): File path of the input shapefile.\n out_json (str, optional): File path of the output GeoJSON. Defaults to None.\n\n Returns:\n object: The json object representing the shapefile.\n '
try:
import json
import shapefile
from datetime import date
in_shp = os.path.abspath(in_shp)
if (out_json is None):
out_json = (os.path.splitext(in_shp)[0] + '.json')
if os.path.exists(out_json):
out_json = out_json.replace('.json', '_bk.json')
elif (not os.path.exists(os.path.dirname(out_json))):
os.makedirs(os.path.dirname(out_json))
reader = shapefile.Reader(in_shp)
fields = reader.fields[1:]
field_names = [field[0] for field in fields]
date_fields_names = [field[0] for field in fields if (field[1] == 'D')]
buffer = []
for sr in reader.shapeRecords():
atr = dict(zip(field_names, sr.record))
for date_field in date_fields_names:
value = atr[date_field]
if isinstance(value, date):
value = '{:04d}{:02d}{:02d}'.format(value.year, value.month, value.day)
elif (not value):
value = ('0' * 8)
atr[date_field] = value
geom = sr.shape.__geo_interface__
buffer.append(dict(type='Feature', geometry=geom, properties=atr))
from json import dumps
geojson = open(out_json, 'w')
geojson.write((dumps({'type': 'FeatureCollection', 'features': buffer}, indent=2) + '\n'))
geojson.close()
with open(out_json) as f:
json_data = json.load(f)
return json_data
except Exception as e:
print(e)<|docstring|>Converts a shapefile to GeoJSON.
Args:
in_shp (str): File path of the input shapefile.
out_json (str, optional): File path of the output GeoJSON. Defaults to None.
Returns:
object: The json object representing the shapefile.<|endoftext|> |
24635867ca0da823571b3c739d62d4b1b85f994ca354f30c8bef46fafdb2665d | def shp_to_ee(in_shp):
'Converts a shapefile to Earth Engine objects. Note that the CRS of the shapefile must be EPSG:4326\n\n Args:\n in_shp (str): File path to a shapefile.\n\n Returns:\n object: Earth Engine objects representing the shapefile.\n '
try:
json_data = shp_to_geojson(in_shp)
ee_object = geojson_to_ee(json_data)
return ee_object
except Exception as e:
print(e) | Converts a shapefile to Earth Engine objects. Note that the CRS of the shapefile must be EPSG:4326
Args:
in_shp (str): File path to a shapefile.
Returns:
object: Earth Engine objects representing the shapefile. | geemap/common.py | shp_to_ee | arheem/geemap | 1 | python | def shp_to_ee(in_shp):
'Converts a shapefile to Earth Engine objects. Note that the CRS of the shapefile must be EPSG:4326\n\n Args:\n in_shp (str): File path to a shapefile.\n\n Returns:\n object: Earth Engine objects representing the shapefile.\n '
try:
json_data = shp_to_geojson(in_shp)
ee_object = geojson_to_ee(json_data)
return ee_object
except Exception as e:
print(e) | def shp_to_ee(in_shp):
'Converts a shapefile to Earth Engine objects. Note that the CRS of the shapefile must be EPSG:4326\n\n Args:\n in_shp (str): File path to a shapefile.\n\n Returns:\n object: Earth Engine objects representing the shapefile.\n '
try:
json_data = shp_to_geojson(in_shp)
ee_object = geojson_to_ee(json_data)
return ee_object
except Exception as e:
print(e)<|docstring|>Converts a shapefile to Earth Engine objects. Note that the CRS of the shapefile must be EPSG:4326
Args:
in_shp (str): File path to a shapefile.
Returns:
object: Earth Engine objects representing the shapefile.<|endoftext|> |
92b6c4a06fca3708f96e608019ad07be6a4e20e6ff4197fe64eea71ee714ad78 | def filter_polygons(ftr):
'Converts GeometryCollection to Polygon/MultiPolygon\n\n Args:\n ftr (object): ee.Feature\n\n Returns:\n object: ee.Feature\n '
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) | Converts GeometryCollection to Polygon/MultiPolygon
Args:
ftr (object): ee.Feature
Returns:
object: ee.Feature | geemap/common.py | filter_polygons | arheem/geemap | 1 | python | def filter_polygons(ftr):
'Converts GeometryCollection to Polygon/MultiPolygon\n\n Args:\n ftr (object): ee.Feature\n\n Returns:\n object: ee.Feature\n '
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) | def filter_polygons(ftr):
'Converts GeometryCollection to Polygon/MultiPolygon\n\n Args:\n ftr (object): ee.Feature\n\n Returns:\n object: ee.Feature\n '
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)<|docstring|>Converts GeometryCollection to Polygon/MultiPolygon
Args:
ftr (object): ee.Feature
Returns:
object: ee.Feature<|endoftext|> |
5ead7e1930a5654bef626f345a4ed6d0f2d23c5c18e1518601cbf5042f38cf78 | def ee_export_vector(ee_object, filename, selectors=None):
'Exports Earth Engine FeatureCollection to other formats, including shp, csv, json, kml, and kmz.\n\n Args:\n ee_object (object): ee.FeatureCollection to export.\n filename (str): Output file name.\n selectors (list, optional): A list of attributes to export. Defaults to None.\n '
import requests
import zipfile
if (not isinstance(ee_object, ee.FeatureCollection)):
raise ValueError('ee_object must be an ee.FeatureCollection')
allowed_formats = ['csv', 'geojson', 'kml', 'kmz', 'shp']
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'):
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:
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)
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()
os.remove(filename)
filename = filename.replace('.zip', '.shp')
print('Data downloaded to {}'.format(filename))
except Exception as e:
raise ValueError(e) | 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. | geemap/common.py | ee_export_vector | arheem/geemap | 1 | python | def ee_export_vector(ee_object, filename, selectors=None):
'Exports Earth Engine FeatureCollection to other formats, including shp, csv, json, kml, and kmz.\n\n Args:\n ee_object (object): ee.FeatureCollection to export.\n filename (str): Output file name.\n selectors (list, optional): A list of attributes to export. Defaults to None.\n '
import requests
import zipfile
if (not isinstance(ee_object, ee.FeatureCollection)):
raise ValueError('ee_object must be an ee.FeatureCollection')
allowed_formats = ['csv', 'geojson', 'kml', 'kmz', 'shp']
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'):
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:
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)
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()
os.remove(filename)
filename = filename.replace('.zip', '.shp')
print('Data downloaded to {}'.format(filename))
except Exception as e:
raise ValueError(e) | def ee_export_vector(ee_object, filename, selectors=None):
'Exports Earth Engine FeatureCollection to other formats, including shp, csv, json, kml, and kmz.\n\n Args:\n ee_object (object): ee.FeatureCollection to export.\n filename (str): Output file name.\n selectors (list, optional): A list of attributes to export. Defaults to None.\n '
import requests
import zipfile
if (not isinstance(ee_object, ee.FeatureCollection)):
raise ValueError('ee_object must be an ee.FeatureCollection')
allowed_formats = ['csv', 'geojson', 'kml', 'kmz', 'shp']
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'):
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:
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)
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()
os.remove(filename)
filename = filename.replace('.zip', '.shp')
print('Data downloaded to {}'.format(filename))
except Exception as e:
raise ValueError(e)<|docstring|>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.<|endoftext|> |
d35adeec278a9c4de21b96c8e1abe29761b44144a323e3ba79427f8da9202b30 | 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.\n\n Args:\n ee_object (object): ee.FeatureCollection to export.\n description (str): File name of the output file.\n folder (str): Folder name within Google Drive to save the exported file.\n file_format (str, optional): The supported file format include shp, csv, json, kml, kmz, and TFRecord. Defaults to 'shp'.\n selectors (list, optional): The list of attributes to export. Defaults to None.\n "
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')):
ee_object = ee_object.select(['.*'], None, False)
print('Exporting {}...'.format(description))
task = ee.batch.Export.table.toDrive(ee_object, description, **task_config)
task.start() | 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. | geemap/common.py | ee_export_vector_to_drive | arheem/geemap | 1 | python | 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.\n\n Args:\n ee_object (object): ee.FeatureCollection to export.\n description (str): File name of the output file.\n folder (str): Folder name within Google Drive to save the exported file.\n file_format (str, optional): The supported file format include shp, csv, json, kml, kmz, and TFRecord. Defaults to 'shp'.\n selectors (list, optional): The list of attributes to export. Defaults to None.\n "
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')):
ee_object = ee_object.select(['.*'], None, False)
print('Exporting {}...'.format(description))
task = ee.batch.Export.table.toDrive(ee_object, description, **task_config)
task.start() | 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.\n\n Args:\n ee_object (object): ee.FeatureCollection to export.\n description (str): File name of the output file.\n folder (str): Folder name within Google Drive to save the exported file.\n file_format (str, optional): The supported file format include shp, csv, json, kml, kmz, and TFRecord. Defaults to 'shp'.\n selectors (list, optional): The list of attributes to export. Defaults to None.\n "
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')):
ee_object = ee_object.select(['.*'], None, False)
print('Exporting {}...'.format(description))
task = ee.batch.Export.table.toDrive(ee_object, description, **task_config)
task.start()<|docstring|>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.<|endoftext|> |
93109a49465024f60b6e0de640613763f770d86c86d6be816f01d78aad64fe98 | def ee_export_geojson(ee_object, filename=None, selectors=None):
'Exports Earth Engine FeatureCollection to geojson.\n\n Args:\n ee_object (object): ee.FeatureCollection to export.\n filename (str): Output file name. Defaults to None.\n selectors (list, optional): A list of attributes to export. Defaults to None.\n '
import requests
import zipfile
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:
url = ee_object.getDownloadURL(filetype=filetype, selectors=selectors, filename=name)
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 | 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. | geemap/common.py | ee_export_geojson | arheem/geemap | 1 | python | def ee_export_geojson(ee_object, filename=None, selectors=None):
'Exports Earth Engine FeatureCollection to geojson.\n\n Args:\n ee_object (object): ee.FeatureCollection to export.\n filename (str): Output file name. Defaults to None.\n selectors (list, optional): A list of attributes to export. Defaults to None.\n '
import requests
import zipfile
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:
url = ee_object.getDownloadURL(filetype=filetype, selectors=selectors, filename=name)
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 | def ee_export_geojson(ee_object, filename=None, selectors=None):
'Exports Earth Engine FeatureCollection to geojson.\n\n Args:\n ee_object (object): ee.FeatureCollection to export.\n filename (str): Output file name. Defaults to None.\n selectors (list, optional): A list of attributes to export. Defaults to None.\n '
import requests
import zipfile
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:
url = ee_object.getDownloadURL(filetype=filetype, selectors=selectors, filename=name)
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<|docstring|>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.<|endoftext|> |
692c860dd903f8bf77f5e2cfc210f5022ddc543945039a37fece0a27f06dafc2 | def ee_to_shp(ee_object, filename, selectors=None):
'Downloads an ee.FeatureCollection as a shapefile.\n\n Args:\n ee_object (object): ee.FeatureCollection\n filename (str): The output filepath of the shapefile.\n selectors (list, optional): A list of attributes to export. Defaults to None.\n '
try:
if filename.lower().endswith('.shp'):
ee_export_vector(ee_object=ee_object, filename=filename, selectors=selectors)
else:
print('The filename must end with .shp')
except Exception as e:
print(e) | 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. | geemap/common.py | ee_to_shp | arheem/geemap | 1 | python | def ee_to_shp(ee_object, filename, selectors=None):
'Downloads an ee.FeatureCollection as a shapefile.\n\n Args:\n ee_object (object): ee.FeatureCollection\n filename (str): The output filepath of the shapefile.\n selectors (list, optional): A list of attributes to export. Defaults to None.\n '
try:
if filename.lower().endswith('.shp'):
ee_export_vector(ee_object=ee_object, filename=filename, selectors=selectors)
else:
print('The filename must end with .shp')
except Exception as e:
print(e) | def ee_to_shp(ee_object, filename, selectors=None):
'Downloads an ee.FeatureCollection as a shapefile.\n\n Args:\n ee_object (object): ee.FeatureCollection\n filename (str): The output filepath of the shapefile.\n selectors (list, optional): A list of attributes to export. Defaults to None.\n '
try:
if filename.lower().endswith('.shp'):
ee_export_vector(ee_object=ee_object, filename=filename, selectors=selectors)
else:
print('The filename must end with .shp')
except Exception as e:
print(e)<|docstring|>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.<|endoftext|> |
189c6898abb27f5fa0279c27cb2b735942f496b8e85211c843d2867d2eb0224e | def ee_to_csv(ee_object, filename, selectors=None):
'Downloads an ee.FeatureCollection as a CSV file.\n\n Args:\n ee_object (object): ee.FeatureCollection\n filename (str): The output filepath of the CSV file.\n selectors (list, optional): A list of attributes to export. Defaults to None.\n '
try:
if filename.lower().endswith('.csv'):
ee_export_vector(ee_object=ee_object, filename=filename, selectors=selectors)
else:
print('The filename must end with .csv')
except Exception as e:
print(e) | 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. | geemap/common.py | ee_to_csv | arheem/geemap | 1 | python | def ee_to_csv(ee_object, filename, selectors=None):
'Downloads an ee.FeatureCollection as a CSV file.\n\n Args:\n ee_object (object): ee.FeatureCollection\n filename (str): The output filepath of the CSV file.\n selectors (list, optional): A list of attributes to export. Defaults to None.\n '
try:
if filename.lower().endswith('.csv'):
ee_export_vector(ee_object=ee_object, filename=filename, selectors=selectors)
else:
print('The filename must end with .csv')
except Exception as e:
print(e) | def ee_to_csv(ee_object, filename, selectors=None):
'Downloads an ee.FeatureCollection as a CSV file.\n\n Args:\n ee_object (object): ee.FeatureCollection\n filename (str): The output filepath of the CSV file.\n selectors (list, optional): A list of attributes to export. Defaults to None.\n '
try:
if filename.lower().endswith('.csv'):
ee_export_vector(ee_object=ee_object, filename=filename, selectors=selectors)
else:
print('The filename must end with .csv')
except Exception as e:
print(e)<|docstring|>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.<|endoftext|> |
f4286b1472a9e194ffc670dbf8dbee7fb166e8dd6e583b3dc4ce369b2c6e60ad | def dict_to_csv(data_dict, out_csv, by_row=False):
'Downloads an ee.Dictionary as a CSV file.\n\n Args:\n data_dict (ee.Dictionary): The input ee.Dictionary.\n out_csv (str): The output file path to the CSV file.\n by_row (bool, optional): Whether to use by row or by column. Defaults to False.\n '
import geemap
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) | 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. | geemap/common.py | dict_to_csv | arheem/geemap | 1 | python | def dict_to_csv(data_dict, out_csv, by_row=False):
'Downloads an ee.Dictionary as a CSV file.\n\n Args:\n data_dict (ee.Dictionary): The input ee.Dictionary.\n out_csv (str): The output file path to the CSV file.\n by_row (bool, optional): Whether to use by row or by column. Defaults to False.\n '
import geemap
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) | def dict_to_csv(data_dict, out_csv, by_row=False):
'Downloads an ee.Dictionary as a CSV file.\n\n Args:\n data_dict (ee.Dictionary): The input ee.Dictionary.\n out_csv (str): The output file path to the CSV file.\n by_row (bool, optional): Whether to use by row or by column. Defaults to False.\n '
import geemap
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)<|docstring|>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.<|endoftext|> |
9430822fbbb8221df75a940f8128a6f8bdbdfee9600c2fe6a01ebbd62bd94182 | def ee_export_image(ee_object, filename, scale=None, crs=None, region=None, file_per_band=False):
'Exports an ee.Image as a GeoTIFF.\n\n Args:\n ee_object (object): The ee.Image to download.\n filename (str): Output filename for the exported image.\n 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.\n crs (str, optional): A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.\n region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.\n file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.\n '
import requests
import zipfile
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) | 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. | geemap/common.py | ee_export_image | arheem/geemap | 1 | python | def ee_export_image(ee_object, filename, scale=None, crs=None, region=None, file_per_band=False):
'Exports an ee.Image as a GeoTIFF.\n\n Args:\n ee_object (object): The ee.Image to download.\n filename (str): Output filename for the exported image.\n 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.\n crs (str, optional): A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.\n region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.\n file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.\n '
import requests
import zipfile
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) | def ee_export_image(ee_object, filename, scale=None, crs=None, region=None, file_per_band=False):
'Exports an ee.Image as a GeoTIFF.\n\n Args:\n ee_object (object): The ee.Image to download.\n filename (str): Output filename for the exported image.\n 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.\n crs (str, optional): A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.\n region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.\n file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.\n '
import requests
import zipfile
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)<|docstring|>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.<|endoftext|> |
5f0c030903d2e68ad2b9fdd74e1e5c7daf0207d8bf9e985135795ee9eb7aa42c | def ee_export_image_collection(ee_object, out_dir, scale=None, crs=None, region=None, file_per_band=False):
'Exports an ImageCollection as GeoTIFFs.\n\n Args:\n ee_object (object): The ee.Image to download.\n out_dir (str): The output directory for the exported images.\n 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.\n crs (str, optional): A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.\n region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.\n file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.\n '
import requests
import zipfile
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) | 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. | geemap/common.py | ee_export_image_collection | arheem/geemap | 1 | python | def ee_export_image_collection(ee_object, out_dir, scale=None, crs=None, region=None, file_per_band=False):
'Exports an ImageCollection as GeoTIFFs.\n\n Args:\n ee_object (object): The ee.Image to download.\n out_dir (str): The output directory for the exported images.\n 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.\n crs (str, optional): A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.\n region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.\n file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.\n '
import requests
import zipfile
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) | def ee_export_image_collection(ee_object, out_dir, scale=None, crs=None, region=None, file_per_band=False):
'Exports an ImageCollection as GeoTIFFs.\n\n Args:\n ee_object (object): The ee.Image to download.\n out_dir (str): The output directory for the exported images.\n 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.\n crs (str, optional): A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.\n region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.\n file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.\n '
import requests
import zipfile
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)<|docstring|>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.<|endoftext|> |
5c83686f25b222b229c00baa3ae8adb52ef19a8fb5896f45b858621f1fd135bd | def 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.\n\n Args:\n ee_object (object): The image to export.\n description (str): A human-readable name of the task. \n folder (str, optional): The Google Drive Folder that the export will reside in. Defaults to None.\n 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.\n scale (float, optional): Resolution in meters per pixel. Defaults to 10 times of the image resolution.\n crs (str, optional): CRS to use for the exported image.. Defaults to None.\n max_pixels (int, optional): Restrict the number of pixels in the export. Defaults to 1.0E13.\n file_format (str, optional): The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'.\n format_options (dict, optional): A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True}\n "
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) | 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} | geemap/common.py | ee_export_image_to_drive | arheem/geemap | 1 | python | def 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.\n\n Args:\n ee_object (object): The image to export.\n description (str): A human-readable name of the task. \n folder (str, optional): The Google Drive Folder that the export will reside in. Defaults to None.\n 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.\n scale (float, optional): Resolution in meters per pixel. Defaults to 10 times of the image resolution.\n crs (str, optional): CRS to use for the exported image.. Defaults to None.\n max_pixels (int, optional): Restrict the number of pixels in the export. Defaults to 1.0E13.\n file_format (str, optional): The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'.\n format_options (dict, optional): A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True}\n "
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) | def 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.\n\n Args:\n ee_object (object): The image to export.\n description (str): A human-readable name of the task. \n folder (str, optional): The Google Drive Folder that the export will reside in. Defaults to None.\n 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.\n scale (float, optional): Resolution in meters per pixel. Defaults to 10 times of the image resolution.\n crs (str, optional): CRS to use for the exported image.. Defaults to None.\n max_pixels (int, optional): Restrict the number of pixels in the export. Defaults to 1.0E13.\n file_format (str, optional): The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'.\n format_options (dict, optional): A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True}\n "
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)<|docstring|>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}<|endoftext|> |
18f36688a4f6739059c615d462b8c623aa555f8f22860835dcb01aa75a074f7a | def 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.\n\n Args:\n ee_object (object): The image to export.\n descriptions (list): A list of human-readable names of the tasks. \n folder (str, optional): The Google Drive Folder that the export will reside in. Defaults to None.\n 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.\n scale (float, optional): Resolution in meters per pixel. Defaults to 10 times of the image resolution.\n crs (str, optional): CRS to use for the exported image.. Defaults to None.\n max_pixels (int, optional): Restrict the number of pixels in the export. Defaults to 1.0E13.\n file_format (str, optional): The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'.\n format_options (dict, optional): A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True}\n "
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) | 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} | geemap/common.py | ee_export_image_collection_to_drive | arheem/geemap | 1 | python | def 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.\n\n Args:\n ee_object (object): The image to export.\n descriptions (list): A list of human-readable names of the tasks. \n folder (str, optional): The Google Drive Folder that the export will reside in. Defaults to None.\n 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.\n scale (float, optional): Resolution in meters per pixel. Defaults to 10 times of the image resolution.\n crs (str, optional): CRS to use for the exported image.. Defaults to None.\n max_pixels (int, optional): Restrict the number of pixels in the export. Defaults to 1.0E13.\n file_format (str, optional): The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'.\n format_options (dict, optional): A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True}\n "
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) | def 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.\n\n Args:\n ee_object (object): The image to export.\n descriptions (list): A list of human-readable names of the tasks. \n folder (str, optional): The Google Drive Folder that the export will reside in. Defaults to None.\n 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.\n scale (float, optional): Resolution in meters per pixel. Defaults to 10 times of the image resolution.\n crs (str, optional): CRS to use for the exported image.. Defaults to None.\n max_pixels (int, optional): Restrict the number of pixels in the export. Defaults to 1.0E13.\n file_format (str, optional): The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'.\n format_options (dict, optional): A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True}\n "
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)<|docstring|>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}<|endoftext|> |
d00865e0adfd3ae1821ba20e06a154a85efc68a4f5b03943a93a9e042d5b9905 | def get_image_thumbnail(ee_object, out_img, vis_params, dimensions=500, region=None, format='png'):
"Download a thumbnail for an ee.Image.\n\n Args:\n ee_object (object): The ee.Image instance.\n out_png (str): The output file path to the png thumbnail.\n vis_params (dict): The visualization parameters. \n 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.\n 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.\n format (str, optional): Either 'png' or 'jpg'. Default to 'png'.\n "
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) | Download a thumbnail for an ee.Image.
Args:
ee_object (object): The ee.Image instance.
out_png (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'. | geemap/common.py | get_image_thumbnail | arheem/geemap | 1 | python | def get_image_thumbnail(ee_object, out_img, vis_params, dimensions=500, region=None, format='png'):
"Download a thumbnail for an ee.Image.\n\n Args:\n ee_object (object): The ee.Image instance.\n out_png (str): The output file path to the png thumbnail.\n vis_params (dict): The visualization parameters. \n 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.\n 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.\n format (str, optional): Either 'png' or 'jpg'. Default to 'png'.\n "
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) | def get_image_thumbnail(ee_object, out_img, vis_params, dimensions=500, region=None, format='png'):
"Download a thumbnail for an ee.Image.\n\n Args:\n ee_object (object): The ee.Image instance.\n out_png (str): The output file path to the png thumbnail.\n vis_params (dict): The visualization parameters. \n 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.\n 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.\n format (str, optional): Either 'png' or 'jpg'. Default to 'png'.\n "
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)<|docstring|>Download a thumbnail for an ee.Image.
Args:
ee_object (object): The ee.Image instance.
out_png (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'.<|endoftext|> |
d5d58146579c58437f38851b872452f12f2e685b86cb932251ea3c404bf515ec | 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.\n\n Args:\n ee_object (object): The ee.ImageCollection instance.\n out_dir ([str): The output directory to store thumbnails.\n vis_params (dict): The visualization parameters.\n 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.\n 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.\n format (str, optional): Either 'png' or 'jpg'. Default to 'png'.\n names (list, optional): The list of output file names. Defaults to None.\n verbose (bool, optional): Whether or not to print hints. Defaults to True.\n "
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) | 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. | geemap/common.py | get_image_collection_thumbnails | arheem/geemap | 1 | python | 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.\n\n Args:\n ee_object (object): The ee.ImageCollection instance.\n out_dir ([str): The output directory to store thumbnails.\n vis_params (dict): The visualization parameters.\n 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.\n 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.\n format (str, optional): Either 'png' or 'jpg'. Default to 'png'.\n names (list, optional): The list of output file names. Defaults to None.\n verbose (bool, optional): Whether or not to print hints. Defaults to True.\n "
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) | 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.\n\n Args:\n ee_object (object): The ee.ImageCollection instance.\n out_dir ([str): The output directory to store thumbnails.\n vis_params (dict): The visualization parameters.\n 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.\n 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.\n format (str, optional): Either 'png' or 'jpg'. Default to 'png'.\n names (list, optional): The list of output file names. Defaults to None.\n verbose (bool, optional): Whether or not to print hints. Defaults to True.\n "
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)<|docstring|>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.<|endoftext|> |
7ff4b221be12d9b98cdb4705b5ffbbb8908683c03032f43de3e899c6696f1d6f | 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.\n\n Args:\n ee_object (object): The image to sample.\n bands (list, optional): The list of band names to extract. Please make sure that all bands have the same spatial resolution. Defaults to None. \n 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.\n properties (list, optional): The properties to copy over from the sampled image. Defaults to all non-system properties.\n default_value (float, optional): A default value used when a sampled pixel is masked or outside a band's footprint. Defaults to None.\n\n Returns:\n array: A 3D numpy array.\n "
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) | 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. | geemap/common.py | ee_to_numpy | arheem/geemap | 1 | python | 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.\n\n Args:\n ee_object (object): The image to sample.\n bands (list, optional): The list of band names to extract. Please make sure that all bands have the same spatial resolution. Defaults to None. \n 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.\n properties (list, optional): The properties to copy over from the sampled image. Defaults to all non-system properties.\n default_value (float, optional): A default value used when a sampled pixel is masked or outside a band's footprint. Defaults to None.\n\n Returns:\n array: A 3D numpy array.\n "
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) | 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.\n\n Args:\n ee_object (object): The image to sample.\n bands (list, optional): The list of band names to extract. Please make sure that all bands have the same spatial resolution. Defaults to None. \n 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.\n properties (list, optional): The properties to copy over from the sampled image. Defaults to all non-system properties.\n default_value (float, optional): A default value used when a sampled pixel is masked or outside a band's footprint. Defaults to None.\n\n Returns:\n array: A 3D numpy array.\n "
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)<|docstring|>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.<|endoftext|> |
64c118a199672c9b7866def905c56c1796577a9d9ef64a66a3329659d8f85172 | def download_ee_video(collection, video_args, out_gif):
'Downloads a video thumbnail as a GIF image from Earth Engine.\n\n Args:\n collection (object): An ee.ImageCollection.\n video_args (object): Parameters for expring the video thumbnail.\n out_gif (str): File path to the output GIF.\n '
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) | 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. | geemap/common.py | download_ee_video | arheem/geemap | 1 | python | def download_ee_video(collection, video_args, out_gif):
'Downloads a video thumbnail as a GIF image from Earth Engine.\n\n Args:\n collection (object): An ee.ImageCollection.\n video_args (object): Parameters for expring the video thumbnail.\n out_gif (str): File path to the output GIF.\n '
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) | def download_ee_video(collection, video_args, out_gif):
'Downloads a video thumbnail as a GIF image from Earth Engine.\n\n Args:\n collection (object): An ee.ImageCollection.\n video_args (object): Parameters for expring the video thumbnail.\n out_gif (str): File path to the output GIF.\n '
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)<|docstring|>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.<|endoftext|> |
b1f3310d1838958b0ee804a91c37cc667e8b718ab84e82b8ad3f81d2f8845fd9 | def screen_capture(outfile, monitor=1):
'Takes a full screenshot of the selected monitor.\n\n Args:\n outfile (str): The output file path to the screenshot.\n monitor (int, optional): The monitor to take the screenshot. Defaults to 1.\n '
from mss import mss
out_dir = os.path.dirname(outfile)
if (not os.path.exists(out_dir)):
os.makedirs(out_dir)
if (not isinstance(monitor, int)):
print('The monitor number must be an integer.')
return
try:
with mss() as sct:
sct.shot(output=outfile, mon=monitor)
return outfile
except Exception as e:
print(e) | Takes a full screenshot of the selected monitor.
Args:
outfile (str): The output file path to the screenshot.
monitor (int, optional): The monitor to take the screenshot. Defaults to 1. | geemap/common.py | screen_capture | arheem/geemap | 1 | python | def screen_capture(outfile, monitor=1):
'Takes a full screenshot of the selected monitor.\n\n Args:\n outfile (str): The output file path to the screenshot.\n monitor (int, optional): The monitor to take the screenshot. Defaults to 1.\n '
from mss import mss
out_dir = os.path.dirname(outfile)
if (not os.path.exists(out_dir)):
os.makedirs(out_dir)
if (not isinstance(monitor, int)):
print('The monitor number must be an integer.')
return
try:
with mss() as sct:
sct.shot(output=outfile, mon=monitor)
return outfile
except Exception as e:
print(e) | def screen_capture(outfile, monitor=1):
'Takes a full screenshot of the selected monitor.\n\n Args:\n outfile (str): The output file path to the screenshot.\n monitor (int, optional): The monitor to take the screenshot. Defaults to 1.\n '
from mss import mss
out_dir = os.path.dirname(outfile)
if (not os.path.exists(out_dir)):
os.makedirs(out_dir)
if (not isinstance(monitor, int)):
print('The monitor number must be an integer.')
return
try:
with mss() as sct:
sct.shot(output=outfile, mon=monitor)
return outfile
except Exception as e:
print(e)<|docstring|>Takes a full screenshot of the selected monitor.
Args:
outfile (str): The output file path to the screenshot.
monitor (int, optional): The monitor to take the screenshot. Defaults to 1.<|endoftext|> |
3c75eb3f3d3e0273e9f1d6743e68a653bbda29d7421c3ec015aad885caa143d6 | def naip_timeseries(roi=None, start_year=2009, end_year=2018):
'Creates NAIP annual timeseries\n\n Args:\n roi (object, optional): An ee.Geometry representing the region of interest. Defaults to None.\n start_year (int, optional): Starting year for the timeseries. Defaults to2009.\n end_year (int, optional): Ending year for the timeseries. Defaults to 2018.\n\n Returns:\n object: An ee.ImageCollection representing annual NAIP imagery.\n '
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) | 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. | geemap/common.py | naip_timeseries | arheem/geemap | 1 | python | def naip_timeseries(roi=None, start_year=2009, end_year=2018):
'Creates NAIP annual timeseries\n\n Args:\n roi (object, optional): An ee.Geometry representing the region of interest. Defaults to None.\n start_year (int, optional): Starting year for the timeseries. Defaults to2009.\n end_year (int, optional): Ending year for the timeseries. Defaults to 2018.\n\n Returns:\n object: An ee.ImageCollection representing annual NAIP imagery.\n '
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) | def naip_timeseries(roi=None, start_year=2009, end_year=2018):
'Creates NAIP annual timeseries\n\n Args:\n roi (object, optional): An ee.Geometry representing the region of interest. Defaults to None.\n start_year (int, optional): Starting year for the timeseries. Defaults to2009.\n end_year (int, optional): Ending year for the timeseries. Defaults to 2018.\n\n Returns:\n object: An ee.ImageCollection representing annual NAIP imagery.\n '
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)<|docstring|>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.<|endoftext|> |
12178c4f943c1278bc33774da194a02cd689f4acca2195db57fd7bbb2ae4129d | def sentinel2_timeseries(roi=None, start_year=2015, end_year=2019, start_date='01-01', end_date='12-31'):
"Generates an annual Sentinel 2 ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work.\n Images include both level 1C and level 2A imagery.\n Args:\n\n roi (object, optional): Region of interest to create the timelapse. Defaults to None.\n start_year (int, optional): Starting year for the timelapse. Defaults to 2015.\n end_year (int, optional): Ending year for the timelapse. Defaults to 2019.\n start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '01-01'.\n end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.\n Returns:\n object: Returns an ImageCollection containing annual Sentinel 2 images.\n "
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
geojson = ee_to_geojson(roi)
geojson = adjust_longitude(geojson)
roi = ee.Geometry(geojson)
if (isinstance(start_year, int) and (start_year >= 2015) and (start_year <= 2020)):
pass
else:
print('The start year must be an integer >= 2015.')
return
if (isinstance(end_year, int) and (end_year >= 2015) 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 data 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.')
print(e)
return
try:
start_test = datetime.datetime(int(start_year), int(start_date[:2]), int(start_date[3:5]))
end_test = datetime.datetime(int(end_year), int(end_date[:2]), int(end_date[3:5]))
if (start_test > end_test):
raise ValueError('Start date must be prior to end date')
except Exception as e:
print(e)
return
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)
MSILCcol = ee.ImageCollection('COPERNICUS/S2')
MSI2Acol = ee.ImageCollection('COPERNICUS/S2_SR')
def colFilter(col, roi, start_date, end_date):
return col.filterBounds(roi).filterDate(start_date, end_date)
def renameMSI(img):
return img.select(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12', 'QA60'], ['Blue', 'Green', 'Red', 'Red Edge 1', 'Red Edge 2', 'Red Edge 3', 'NIR', 'Red Edge 4', 'SWIR1', 'SWIR2', 'QA60'])
def calcNbr(img):
return img.addBands(img.normalizedDifference(['NIR', 'SWIR2']).multiply((- 10000)).rename('NBR')).int16()
def fmask(img):
cloudOpaqueBitMask = (1 << 10)
cloudCirrusBitMask = (1 << 11)
qa = img.select('QA60')
mask = qa.bitwiseAnd(cloudOpaqueBitMask).eq(0).And(qa.bitwiseAnd(cloudCirrusBitMask).eq(0))
return img.updateMask(mask)
def prepMSI(img):
orig = img
img = renameMSI(img)
img = fmask(img)
return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample('bicubic')
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')
MSILCcoly = colFilter(MSILCcol, roi, startDate, endDate).map(prepMSI)
MSI2Acoly = colFilter(MSI2Acol, roi, startDate, endDate).map(prepMSI)
col = MSILCcoly.merge(MSI2Acoly)
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})
bandNames = ee.List(['Blue', 'Green', 'Red', 'Red Edge 1', 'Red Edge 2', 'Red Edge 3', 'NIR', 'Red Edge 4', 'SWIR1', 'SWIR2', 'QA60'])
fillerValues = ee.List.repeat(0, bandNames.size())
dummyImg = ee.Image.constant(fillerValues).rename(bandNames).selfMask().int16()
years = ee.List.sequence(start_year, end_year)
imgList = years.map(getAnnualComp)
imgCol = ee.ImageCollection.fromImages(imgList)
imgCol = imgCol.map((lambda img: img.clip(roi)))
return imgCol | Generates an annual Sentinel 2 ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work.
Images include both level 1C and level 2A imagery.
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 2015.
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 '01-01'.
end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.
Returns:
object: Returns an ImageCollection containing annual Sentinel 2 images. | geemap/common.py | sentinel2_timeseries | arheem/geemap | 1 | python | def sentinel2_timeseries(roi=None, start_year=2015, end_year=2019, start_date='01-01', end_date='12-31'):
"Generates an annual Sentinel 2 ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work.\n Images include both level 1C and level 2A imagery.\n Args:\n\n roi (object, optional): Region of interest to create the timelapse. Defaults to None.\n start_year (int, optional): Starting year for the timelapse. Defaults to 2015.\n end_year (int, optional): Ending year for the timelapse. Defaults to 2019.\n start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '01-01'.\n end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.\n Returns:\n object: Returns an ImageCollection containing annual Sentinel 2 images.\n "
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
geojson = ee_to_geojson(roi)
geojson = adjust_longitude(geojson)
roi = ee.Geometry(geojson)
if (isinstance(start_year, int) and (start_year >= 2015) and (start_year <= 2020)):
pass
else:
print('The start year must be an integer >= 2015.')
return
if (isinstance(end_year, int) and (end_year >= 2015) 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 data 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.')
print(e)
return
try:
start_test = datetime.datetime(int(start_year), int(start_date[:2]), int(start_date[3:5]))
end_test = datetime.datetime(int(end_year), int(end_date[:2]), int(end_date[3:5]))
if (start_test > end_test):
raise ValueError('Start date must be prior to end date')
except Exception as e:
print(e)
return
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)
MSILCcol = ee.ImageCollection('COPERNICUS/S2')
MSI2Acol = ee.ImageCollection('COPERNICUS/S2_SR')
def colFilter(col, roi, start_date, end_date):
return col.filterBounds(roi).filterDate(start_date, end_date)
def renameMSI(img):
return img.select(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12', 'QA60'], ['Blue', 'Green', 'Red', 'Red Edge 1', 'Red Edge 2', 'Red Edge 3', 'NIR', 'Red Edge 4', 'SWIR1', 'SWIR2', 'QA60'])
def calcNbr(img):
return img.addBands(img.normalizedDifference(['NIR', 'SWIR2']).multiply((- 10000)).rename('NBR')).int16()
def fmask(img):
cloudOpaqueBitMask = (1 << 10)
cloudCirrusBitMask = (1 << 11)
qa = img.select('QA60')
mask = qa.bitwiseAnd(cloudOpaqueBitMask).eq(0).And(qa.bitwiseAnd(cloudCirrusBitMask).eq(0))
return img.updateMask(mask)
def prepMSI(img):
orig = img
img = renameMSI(img)
img = fmask(img)
return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample('bicubic')
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')
MSILCcoly = colFilter(MSILCcol, roi, startDate, endDate).map(prepMSI)
MSI2Acoly = colFilter(MSI2Acol, roi, startDate, endDate).map(prepMSI)
col = MSILCcoly.merge(MSI2Acoly)
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})
bandNames = ee.List(['Blue', 'Green', 'Red', 'Red Edge 1', 'Red Edge 2', 'Red Edge 3', 'NIR', 'Red Edge 4', 'SWIR1', 'SWIR2', 'QA60'])
fillerValues = ee.List.repeat(0, bandNames.size())
dummyImg = ee.Image.constant(fillerValues).rename(bandNames).selfMask().int16()
years = ee.List.sequence(start_year, end_year)
imgList = years.map(getAnnualComp)
imgCol = ee.ImageCollection.fromImages(imgList)
imgCol = imgCol.map((lambda img: img.clip(roi)))
return imgCol | def sentinel2_timeseries(roi=None, start_year=2015, end_year=2019, start_date='01-01', end_date='12-31'):
"Generates an annual Sentinel 2 ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work.\n Images include both level 1C and level 2A imagery.\n Args:\n\n roi (object, optional): Region of interest to create the timelapse. Defaults to None.\n start_year (int, optional): Starting year for the timelapse. Defaults to 2015.\n end_year (int, optional): Ending year for the timelapse. Defaults to 2019.\n start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '01-01'.\n end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.\n Returns:\n object: Returns an ImageCollection containing annual Sentinel 2 images.\n "
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
geojson = ee_to_geojson(roi)
geojson = adjust_longitude(geojson)
roi = ee.Geometry(geojson)
if (isinstance(start_year, int) and (start_year >= 2015) and (start_year <= 2020)):
pass
else:
print('The start year must be an integer >= 2015.')
return
if (isinstance(end_year, int) and (end_year >= 2015) 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 data 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.')
print(e)
return
try:
start_test = datetime.datetime(int(start_year), int(start_date[:2]), int(start_date[3:5]))
end_test = datetime.datetime(int(end_year), int(end_date[:2]), int(end_date[3:5]))
if (start_test > end_test):
raise ValueError('Start date must be prior to end date')
except Exception as e:
print(e)
return
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)
MSILCcol = ee.ImageCollection('COPERNICUS/S2')
MSI2Acol = ee.ImageCollection('COPERNICUS/S2_SR')
def colFilter(col, roi, start_date, end_date):
return col.filterBounds(roi).filterDate(start_date, end_date)
def renameMSI(img):
return img.select(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12', 'QA60'], ['Blue', 'Green', 'Red', 'Red Edge 1', 'Red Edge 2', 'Red Edge 3', 'NIR', 'Red Edge 4', 'SWIR1', 'SWIR2', 'QA60'])
def calcNbr(img):
return img.addBands(img.normalizedDifference(['NIR', 'SWIR2']).multiply((- 10000)).rename('NBR')).int16()
def fmask(img):
cloudOpaqueBitMask = (1 << 10)
cloudCirrusBitMask = (1 << 11)
qa = img.select('QA60')
mask = qa.bitwiseAnd(cloudOpaqueBitMask).eq(0).And(qa.bitwiseAnd(cloudCirrusBitMask).eq(0))
return img.updateMask(mask)
def prepMSI(img):
orig = img
img = renameMSI(img)
img = fmask(img)
return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample('bicubic')
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')
MSILCcoly = colFilter(MSILCcol, roi, startDate, endDate).map(prepMSI)
MSI2Acoly = colFilter(MSI2Acol, roi, startDate, endDate).map(prepMSI)
col = MSILCcoly.merge(MSI2Acoly)
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})
bandNames = ee.List(['Blue', 'Green', 'Red', 'Red Edge 1', 'Red Edge 2', 'Red Edge 3', 'NIR', 'Red Edge 4', 'SWIR1', 'SWIR2', 'QA60'])
fillerValues = ee.List.repeat(0, bandNames.size())
dummyImg = ee.Image.constant(fillerValues).rename(bandNames).selfMask().int16()
years = ee.List.sequence(start_year, end_year)
imgList = years.map(getAnnualComp)
imgCol = ee.ImageCollection.fromImages(imgList)
imgCol = imgCol.map((lambda img: img.clip(roi)))
return imgCol<|docstring|>Generates an annual Sentinel 2 ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work.
Images include both level 1C and level 2A imagery.
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 2015.
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 '01-01'.
end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.
Returns:
object: Returns an ImageCollection containing annual Sentinel 2 images.<|endoftext|> |
8235dd9c0a6829d5d1ba3bff744da54574d58639769e0f96f2ca4d86b3aa2eab | 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.\n\n Args:\n roi (object, optional): Region of interest to create the timelapse. Defaults to None.\n start_year (int, optional): Starting year for the timelapse. Defaults to 1984.\n end_year (int, optional): Ending year for the timelapse. Defaults to 2020.\n start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.\n end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.\n apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.\n Returns:\n object: Returns an ImageCollection containing annual Landsat images.\n "
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
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.')
return
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)
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')
def colFilter(col, roi, start_date, end_date):
return col.filterBounds(roi).filterDate(start_date, end_date)
def renameOli(img):
return img.select(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'pixel_qa'], ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa'])
def renameEtm(img):
return img.select(['B1', 'B2', 'B3', 'B4', 'B5', 'B7', 'pixel_qa'], ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa'])
def calcNbr(img):
return img.addBands(img.normalizedDifference(['NIR', 'SWIR2']).multiply((- 10000)).rename('NBR')).int16()
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)
def prepOli(img):
orig = img
img = renameOli(img)
if apply_fmask:
img = fmask(img)
return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample('bicubic')
def prepEtm(img):
orig = img
img = renameEtm(img)
if apply_fmask:
img = fmask(img)
return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample('bicubic')
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')
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)
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})
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()
years = ee.List.sequence(start_year, end_year)
imgList = years.map(getAnnualComp)
imgCol = ee.ImageCollection.fromImages(imgList)
imgCol = imgCol.map((lambda img: img.clip(roi).set({'coordinates': roi.coordinates()})))
return imgCol | 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. | geemap/common.py | landsat_timeseries | arheem/geemap | 1 | python | 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.\n\n Args:\n roi (object, optional): Region of interest to create the timelapse. Defaults to None.\n start_year (int, optional): Starting year for the timelapse. Defaults to 1984.\n end_year (int, optional): Ending year for the timelapse. Defaults to 2020.\n start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.\n end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.\n apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.\n Returns:\n object: Returns an ImageCollection containing annual Landsat images.\n "
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
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.')
return
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)
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')
def colFilter(col, roi, start_date, end_date):
return col.filterBounds(roi).filterDate(start_date, end_date)
def renameOli(img):
return img.select(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'pixel_qa'], ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa'])
def renameEtm(img):
return img.select(['B1', 'B2', 'B3', 'B4', 'B5', 'B7', 'pixel_qa'], ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa'])
def calcNbr(img):
return img.addBands(img.normalizedDifference(['NIR', 'SWIR2']).multiply((- 10000)).rename('NBR')).int16()
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)
def prepOli(img):
orig = img
img = renameOli(img)
if apply_fmask:
img = fmask(img)
return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample('bicubic')
def prepEtm(img):
orig = img
img = renameEtm(img)
if apply_fmask:
img = fmask(img)
return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample('bicubic')
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')
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)
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})
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()
years = ee.List.sequence(start_year, end_year)
imgList = years.map(getAnnualComp)
imgCol = ee.ImageCollection.fromImages(imgList)
imgCol = imgCol.map((lambda img: img.clip(roi).set({'coordinates': roi.coordinates()})))
return imgCol | 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.\n\n Args:\n roi (object, optional): Region of interest to create the timelapse. Defaults to None.\n start_year (int, optional): Starting year for the timelapse. Defaults to 1984.\n end_year (int, optional): Ending year for the timelapse. Defaults to 2020.\n start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.\n end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.\n apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.\n Returns:\n object: Returns an ImageCollection containing annual Landsat images.\n "
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
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.')
return
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)
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')
def colFilter(col, roi, start_date, end_date):
return col.filterBounds(roi).filterDate(start_date, end_date)
def renameOli(img):
return img.select(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'pixel_qa'], ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa'])
def renameEtm(img):
return img.select(['B1', 'B2', 'B3', 'B4', 'B5', 'B7', 'pixel_qa'], ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa'])
def calcNbr(img):
return img.addBands(img.normalizedDifference(['NIR', 'SWIR2']).multiply((- 10000)).rename('NBR')).int16()
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)
def prepOli(img):
orig = img
img = renameOli(img)
if apply_fmask:
img = fmask(img)
return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample('bicubic')
def prepEtm(img):
orig = img
img = renameEtm(img)
if apply_fmask:
img = fmask(img)
return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample('bicubic')
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')
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)
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})
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()
years = ee.List.sequence(start_year, end_year)
imgList = years.map(getAnnualComp)
imgCol = ee.ImageCollection.fromImages(imgList)
imgCol = imgCol.map((lambda img: img.clip(roi).set({'coordinates': roi.coordinates()})))
return imgCol<|docstring|>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.<|endoftext|> |
c6f274f9e26e2c189a8950e8581058ecf7d55fe2598801b9992ab8b18ec29748 | 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.\n\n Args:\n roi (object, optional): Region of interest to create the timelapse. Defaults to None.\n out_gif (str, optional): File path to the output animated GIF. Defaults to None.\n start_year (int, optional): Starting year for the timelapse. Defaults to 1984.\n end_year (int, optional): Ending year for the timelapse. Defaults to 2019.\n start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.\n end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.\n bands (list, optional): Three bands selected from ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa']. Defaults to ['NIR', 'Red', 'Green'].\n vis_params (dict, optional): Visualization parameters. Defaults to None.\n 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.\n frames_per_second (int, optional): Animation speed. Defaults to 10.\n apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.\n 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). \n nd_threshold (float, optional): The threshold for extacting pixels from the normalized difference band. \n nd_palette (list, optional): The color palette to use for displaying the normalized difference band. \n\n Returns:\n str: File path to the output GIF image.\n "
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
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) | 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. | geemap/common.py | landsat_ts_gif | arheem/geemap | 1 | python | 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.\n\n Args:\n roi (object, optional): Region of interest to create the timelapse. Defaults to None.\n out_gif (str, optional): File path to the output animated GIF. Defaults to None.\n start_year (int, optional): Starting year for the timelapse. Defaults to 1984.\n end_year (int, optional): Ending year for the timelapse. Defaults to 2019.\n start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.\n end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.\n bands (list, optional): Three bands selected from ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa']. Defaults to ['NIR', 'Red', 'Green'].\n vis_params (dict, optional): Visualization parameters. Defaults to None.\n 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.\n frames_per_second (int, optional): Animation speed. Defaults to 10.\n apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.\n 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). \n nd_threshold (float, optional): The threshold for extacting pixels from the normalized difference band. \n nd_palette (list, optional): The color palette to use for displaying the normalized difference band. \n\n Returns:\n str: File path to the output GIF image.\n "
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
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) | 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.\n\n Args:\n roi (object, optional): Region of interest to create the timelapse. Defaults to None.\n out_gif (str, optional): File path to the output animated GIF. Defaults to None.\n start_year (int, optional): Starting year for the timelapse. Defaults to 1984.\n end_year (int, optional): Ending year for the timelapse. Defaults to 2019.\n start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.\n end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.\n bands (list, optional): Three bands selected from ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa']. Defaults to ['NIR', 'Red', 'Green'].\n vis_params (dict, optional): Visualization parameters. Defaults to None.\n 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.\n frames_per_second (int, optional): Animation speed. Defaults to 10.\n apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.\n 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). \n nd_threshold (float, optional): The threshold for extacting pixels from the normalized difference band. \n nd_palette (list, optional): The color palette to use for displaying the normalized difference band. \n\n Returns:\n str: File path to the output GIF image.\n "
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
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)<|docstring|>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.<|endoftext|> |
1ff1ea2b776e4ab9465b5f8102142fb835f42d4eba01e63fe6bcd2a6aebf15c9 | 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.\n\n Args:\n in_gif (str): The file path to the input GIF image.\n out_gif (str): The file path to the output GIF image.\n xy (tuple, optional): Top left corner of the text. It can be formatted like this: (10, 10) or (\'15%\', \'25%\'). Defaults to None.\n 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.\n font_type (str, optional): Font type. Defaults to "arial.ttf".\n font_size (int, optional): Font size. Defaults to 20.\n 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\'.\n add_progress_bar (bool, optional): Whether to add a progress bar at the bottom of the GIF. Defaults to True.\n progress_bar_color (str, optional): Color for the progress bar. Defaults to \'white\'.\n progress_bar_height (int, optional): Height of the progress bar. Defaults to 5.\n 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.\n 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.\n\n '
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):
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 as e:
print("The specified xy is invalid. It must be formatted like this ('10%', '10%')")
return
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 as e:
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 = []
for (index, frame) in enumerate(ImageSequence.Iterator(image)):
frame = frame.convert('RGB')
draw = ImageDraw.Draw(frame)
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)
frames[0].save(out_gif, save_all=True, append_images=frames[1:], duration=duration, loop=loop, optimize=True)
except Exception as e:
print(e) | 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. | geemap/common.py | add_text_to_gif | arheem/geemap | 1 | python | 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.\n\n Args:\n in_gif (str): The file path to the input GIF image.\n out_gif (str): The file path to the output GIF image.\n xy (tuple, optional): Top left corner of the text. It can be formatted like this: (10, 10) or (\'15%\', \'25%\'). Defaults to None.\n 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.\n font_type (str, optional): Font type. Defaults to "arial.ttf".\n font_size (int, optional): Font size. Defaults to 20.\n 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\'.\n add_progress_bar (bool, optional): Whether to add a progress bar at the bottom of the GIF. Defaults to True.\n progress_bar_color (str, optional): Color for the progress bar. Defaults to \'white\'.\n progress_bar_height (int, optional): Height of the progress bar. Defaults to 5.\n 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.\n 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.\n\n '
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):
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 as e:
print("The specified xy is invalid. It must be formatted like this ('10%', '10%')")
return
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 as e:
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 = []
for (index, frame) in enumerate(ImageSequence.Iterator(image)):
frame = frame.convert('RGB')
draw = ImageDraw.Draw(frame)
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)
frames[0].save(out_gif, save_all=True, append_images=frames[1:], duration=duration, loop=loop, optimize=True)
except Exception as e:
print(e) | 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.\n\n Args:\n in_gif (str): The file path to the input GIF image.\n out_gif (str): The file path to the output GIF image.\n xy (tuple, optional): Top left corner of the text. It can be formatted like this: (10, 10) or (\'15%\', \'25%\'). Defaults to None.\n 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.\n font_type (str, optional): Font type. Defaults to "arial.ttf".\n font_size (int, optional): Font size. Defaults to 20.\n 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\'.\n add_progress_bar (bool, optional): Whether to add a progress bar at the bottom of the GIF. Defaults to True.\n progress_bar_color (str, optional): Color for the progress bar. Defaults to \'white\'.\n progress_bar_height (int, optional): Height of the progress bar. Defaults to 5.\n 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.\n 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.\n\n '
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):
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 as e:
print("The specified xy is invalid. It must be formatted like this ('10%', '10%')")
return
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 as e:
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 = []
for (index, frame) in enumerate(ImageSequence.Iterator(image)):
frame = frame.convert('RGB')
draw = ImageDraw.Draw(frame)
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)
frames[0].save(out_gif, save_all=True, append_images=frames[1:], duration=duration, loop=loop, optimize=True)
except Exception as e:
print(e)<|docstring|>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.<|endoftext|> |
cb58640638e7f9fe529aa3bbe91b9fec7025f58e79b823875bab719ca8fa803e | 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.\n\n Args:\n in_gif (str): Input file path to the GIF image.\n out_gif (str): Output file path to the GIF image.\n in_image (str): Input file path to the image.\n xy (tuple, optional): Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.\n image_size (tuple, optional): Resize image. Defaults to (80, 80).\n circle_mask (bool, optional): Whether to apply a circle mask to the image. This only works with non-png images. Defaults to False.\n "
import io
import warnings
from PIL import Image, ImageDraw, ImageSequence, ImageFilter
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):
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 as e:
print("The specified xy is invalid. It must be formatted like this ('10%', '10%')")
return
else:
print("The specified xy is invalid. It must be formatted like this: (10, 10) or ('10%', '10%')")
return
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) | 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. | geemap/common.py | add_image_to_gif | arheem/geemap | 1 | python | 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.\n\n Args:\n in_gif (str): Input file path to the GIF image.\n out_gif (str): Output file path to the GIF image.\n in_image (str): Input file path to the image.\n xy (tuple, optional): Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.\n image_size (tuple, optional): Resize image. Defaults to (80, 80).\n circle_mask (bool, optional): Whether to apply a circle mask to the image. This only works with non-png images. Defaults to False.\n "
import io
import warnings
from PIL import Image, ImageDraw, ImageSequence, ImageFilter
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):
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 as e:
print("The specified xy is invalid. It must be formatted like this ('10%', '10%')")
return
else:
print("The specified xy is invalid. It must be formatted like this: (10, 10) or ('10%', '10%')")
return
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) | 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.\n\n Args:\n in_gif (str): Input file path to the GIF image.\n out_gif (str): Output file path to the GIF image.\n in_image (str): Input file path to the image.\n xy (tuple, optional): Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.\n image_size (tuple, optional): Resize image. Defaults to (80, 80).\n circle_mask (bool, optional): Whether to apply a circle mask to the image. This only works with non-png images. Defaults to False.\n "
import io
import warnings
from PIL import Image, ImageDraw, ImageSequence, ImageFilter
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):
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 as e:
print("The specified xy is invalid. It must be formatted like this ('10%', '10%')")
return
else:
print("The specified xy is invalid. It must be formatted like this: (10, 10) or ('10%', '10%')")
return
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)<|docstring|>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.<|endoftext|> |
445ac5fdb9b8edf9da3ad3b2c11e7743e41f28cca7a8f157f5ada5f0d00db294 | def reduce_gif_size(in_gif, out_gif=None):
'Reduces a GIF image using ffmpeg.\n\n Args:\n in_gif (str): The input file path to the GIF image.\n out_gif (str, optional): The output file path to the GIF image. Defaults to None.\n '
import ffmpeg
import shutil
if (not is_tool('ffmpeg')):
print('ffmpeg is not installed on your computer.')
return
if (not os.path.exists(in_gif)):
print('The input gif file does not exist.')
return
if (out_gif is None):
out_gif = in_gif
elif (not os.path.exists(os.path.dirname(out_gif))):
os.makedirs(os.path.dirname(out_gif))
if (in_gif == out_gif):
tmp_gif = in_gif.replace('.gif', '_tmp.gif')
shutil.copyfile(in_gif, tmp_gif)
stream = ffmpeg.input(tmp_gif)
stream = ffmpeg.output(stream, in_gif).overwrite_output()
ffmpeg.run(stream)
os.remove(tmp_gif)
else:
stream = ffmpeg.input(in_gif)
stream = ffmpeg.output(stream, out_gif).overwrite_output()
ffmpeg.run(stream) | Reduces a GIF image using ffmpeg.
Args:
in_gif (str): The input file path to the GIF image.
out_gif (str, optional): The output file path to the GIF image. Defaults to None. | geemap/common.py | reduce_gif_size | arheem/geemap | 1 | python | def reduce_gif_size(in_gif, out_gif=None):
'Reduces a GIF image using ffmpeg.\n\n Args:\n in_gif (str): The input file path to the GIF image.\n out_gif (str, optional): The output file path to the GIF image. Defaults to None.\n '
import ffmpeg
import shutil
if (not is_tool('ffmpeg')):
print('ffmpeg is not installed on your computer.')
return
if (not os.path.exists(in_gif)):
print('The input gif file does not exist.')
return
if (out_gif is None):
out_gif = in_gif
elif (not os.path.exists(os.path.dirname(out_gif))):
os.makedirs(os.path.dirname(out_gif))
if (in_gif == out_gif):
tmp_gif = in_gif.replace('.gif', '_tmp.gif')
shutil.copyfile(in_gif, tmp_gif)
stream = ffmpeg.input(tmp_gif)
stream = ffmpeg.output(stream, in_gif).overwrite_output()
ffmpeg.run(stream)
os.remove(tmp_gif)
else:
stream = ffmpeg.input(in_gif)
stream = ffmpeg.output(stream, out_gif).overwrite_output()
ffmpeg.run(stream) | def reduce_gif_size(in_gif, out_gif=None):
'Reduces a GIF image using ffmpeg.\n\n Args:\n in_gif (str): The input file path to the GIF image.\n out_gif (str, optional): The output file path to the GIF image. Defaults to None.\n '
import ffmpeg
import shutil
if (not is_tool('ffmpeg')):
print('ffmpeg is not installed on your computer.')
return
if (not os.path.exists(in_gif)):
print('The input gif file does not exist.')
return
if (out_gif is None):
out_gif = in_gif
elif (not os.path.exists(os.path.dirname(out_gif))):
os.makedirs(os.path.dirname(out_gif))
if (in_gif == out_gif):
tmp_gif = in_gif.replace('.gif', '_tmp.gif')
shutil.copyfile(in_gif, tmp_gif)
stream = ffmpeg.input(tmp_gif)
stream = ffmpeg.output(stream, in_gif).overwrite_output()
ffmpeg.run(stream)
os.remove(tmp_gif)
else:
stream = ffmpeg.input(in_gif)
stream = ffmpeg.output(stream, out_gif).overwrite_output()
ffmpeg.run(stream)<|docstring|>Reduces a GIF image using ffmpeg.
Args:
in_gif (str): The input file path to the GIF image.
out_gif (str, optional): The output file path to the GIF image. Defaults to None.<|endoftext|> |
108ca35e9875f0b6420197eb9e460048af421c87afd029b62c4c9e92532a35ce | def landsat_ts_norm_diff(collection, bands=['Green', 'SWIR1'], threshold=0):
"Computes a normalized difference index based on a Landsat timeseries.\n\n Args:\n collection (ee.ImageCollection): A Landsat timeseries.\n bands (list, optional): The bands to use for computing normalized difference. Defaults to ['Green', 'SWIR1'].\n threshold (float, optional): The threshold to extract features. Defaults to 0.\n\n Returns:\n ee.ImageCollection: An ImageCollection containing images with values greater than the specified threshold. \n "
nd_images = collection.map((lambda img: img.normalizedDifference(bands).gt(threshold).copyProperties(img, img.propertyNames())))
return nd_images | 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. | geemap/common.py | landsat_ts_norm_diff | arheem/geemap | 1 | python | def landsat_ts_norm_diff(collection, bands=['Green', 'SWIR1'], threshold=0):
"Computes a normalized difference index based on a Landsat timeseries.\n\n Args:\n collection (ee.ImageCollection): A Landsat timeseries.\n bands (list, optional): The bands to use for computing normalized difference. Defaults to ['Green', 'SWIR1'].\n threshold (float, optional): The threshold to extract features. Defaults to 0.\n\n Returns:\n ee.ImageCollection: An ImageCollection containing images with values greater than the specified threshold. \n "
nd_images = collection.map((lambda img: img.normalizedDifference(bands).gt(threshold).copyProperties(img, img.propertyNames())))
return nd_images | def landsat_ts_norm_diff(collection, bands=['Green', 'SWIR1'], threshold=0):
"Computes a normalized difference index based on a Landsat timeseries.\n\n Args:\n collection (ee.ImageCollection): A Landsat timeseries.\n bands (list, optional): The bands to use for computing normalized difference. Defaults to ['Green', 'SWIR1'].\n threshold (float, optional): The threshold to extract features. Defaults to 0.\n\n Returns:\n ee.ImageCollection: An ImageCollection containing images with values greater than the specified threshold. \n "
nd_images = collection.map((lambda img: img.normalizedDifference(bands).gt(threshold).copyProperties(img, img.propertyNames())))
return nd_images<|docstring|>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.<|endoftext|> |
d672531b6777303ae0cb26a1d322ae0c6059c0ded6e34a7504678597b628217d | def landsat_ts_norm_diff_gif(collection, out_gif=None, vis_params=None, palette=['black', 'blue'], dimensions=768, frames_per_second=10):
"[summary]\n\n Args:\n collection (ee.ImageCollection): The normalized difference Landsat timeseires.\n out_gif (str, optional): File path to the output animated GIF. Defaults to None.\n vis_params (dict, optional): Visualization parameters. Defaults to None.\n 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.\n 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.\n frames_per_second (int, optional): Animation speed. Defaults to 10.\n\n Returns:\n str: File path to the output animated GIF.\n "
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 | [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. | geemap/common.py | landsat_ts_norm_diff_gif | arheem/geemap | 1 | python | def landsat_ts_norm_diff_gif(collection, out_gif=None, vis_params=None, palette=['black', 'blue'], dimensions=768, frames_per_second=10):
"[summary]\n\n Args:\n collection (ee.ImageCollection): The normalized difference Landsat timeseires.\n out_gif (str, optional): File path to the output animated GIF. Defaults to None.\n vis_params (dict, optional): Visualization parameters. Defaults to None.\n 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.\n 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.\n frames_per_second (int, optional): Animation speed. Defaults to 10.\n\n Returns:\n str: File path to the output animated GIF.\n "
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 | def landsat_ts_norm_diff_gif(collection, out_gif=None, vis_params=None, palette=['black', 'blue'], dimensions=768, frames_per_second=10):
"[summary]\n\n Args:\n collection (ee.ImageCollection): The normalized difference Landsat timeseires.\n out_gif (str, optional): File path to the output animated GIF. Defaults to None.\n vis_params (dict, optional): Visualization parameters. Defaults to None.\n 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.\n 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.\n frames_per_second (int, optional): Animation speed. Defaults to 10.\n\n Returns:\n str: File path to the output animated GIF.\n "
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<|docstring|>[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.<|endoftext|> |
eca7cba811b816fc685ad59b8abcf408ce747b0bdd6fe9d3c5e035596150b0ed | def api_docs():
'Open a browser and navigate to the geemap API documentation.\n '
import webbrowser
url = 'https://geemap.org/geemap'
webbrowser.open_new_tab(url) | Open a browser and navigate to the geemap API documentation. | geemap/common.py | api_docs | arheem/geemap | 1 | python | def api_docs():
'\n '
import webbrowser
url = 'https://geemap.org/geemap'
webbrowser.open_new_tab(url) | def api_docs():
'\n '
import webbrowser
url = 'https://geemap.org/geemap'
webbrowser.open_new_tab(url)<|docstring|>Open a browser and navigate to the geemap API documentation.<|endoftext|> |
fb5f61c630ae16678814c16b57dc3cdd88f34c4fbb12facc519b98ea0e51e392 | def show_youtube(id='h0pz3S6Tvx0'):
"Displays a YouTube video within Jupyter notebooks.\n\n Args:\n id (str, optional): Unique ID of the video. Defaults to 'h0pz3S6Tvx0'.\n\n "
from IPython.display import YouTubeVideo, display
try:
out = widgets.Output(layout={'width': '815px'})
out.clear_output(wait=True)
display(out)
with out:
display(YouTubeVideo(id, width=800, height=450))
except Exception as e:
print(e) | Displays a YouTube video within Jupyter notebooks.
Args:
id (str, optional): Unique ID of the video. Defaults to 'h0pz3S6Tvx0'. | geemap/common.py | show_youtube | arheem/geemap | 1 | python | def show_youtube(id='h0pz3S6Tvx0'):
"Displays a YouTube video within Jupyter notebooks.\n\n Args:\n id (str, optional): Unique ID of the video. Defaults to 'h0pz3S6Tvx0'.\n\n "
from IPython.display import YouTubeVideo, display
try:
out = widgets.Output(layout={'width': '815px'})
out.clear_output(wait=True)
display(out)
with out:
display(YouTubeVideo(id, width=800, height=450))
except Exception as e:
print(e) | def show_youtube(id='h0pz3S6Tvx0'):
"Displays a YouTube video within Jupyter notebooks.\n\n Args:\n id (str, optional): Unique ID of the video. Defaults to 'h0pz3S6Tvx0'.\n\n "
from IPython.display import YouTubeVideo, display
try:
out = widgets.Output(layout={'width': '815px'})
out.clear_output(wait=True)
display(out)
with out:
display(YouTubeVideo(id, width=800, height=450))
except Exception as e:
print(e)<|docstring|>Displays a YouTube video within Jupyter notebooks.
Args:
id (str, optional): Unique ID of the video. Defaults to 'h0pz3S6Tvx0'.<|endoftext|> |
c9f4eb5874c02b8ed235ed0c1553b0141afcd89bca9544168c5ccf87a536e411 | 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.\n\n Args:\n width (int, optional): Width of the colorbar in pixels. Defaults to 150.\n height (int, optional): Height of the colorbar in pixels. Defaults to 30.\n 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'].\n add_ticks (bool, optional): Whether to add tick markers to the colorbar. Defaults to True.\n add_labels (bool, optional): Whether to add labels to the colorbar. Defaults to True.\n labels (list, optional): A list of labels to add to the colorbar. Defaults to None.\n vertical (bool, optional): Whether to rotate the colorbar vertically. Defaults to False.\n out_file (str, optional): File path to the output colorbar in png format. Defaults to None.\n font_type (str, optional): Font type to use for labels. Defaults to 'arial.ttf'.\n font_size (int, optional): Font size to use for labels. Defaults to 12.\n font_color (str, optional): Font color to use for labels. Defaults to 'black'.\n add_outline (bool, optional): Whether to add an outline to the colorbar. Defaults to True.\n outline_color (str, optional): Color for the outline of the colorbar. Defaults to 'black'.\n\n Returns:\n str: File path of the output colorbar in png format.\n\n "
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 | 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. | geemap/common.py | create_colorbar | arheem/geemap | 1 | python | 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.\n\n Args:\n width (int, optional): Width of the colorbar in pixels. Defaults to 150.\n height (int, optional): Height of the colorbar in pixels. Defaults to 30.\n 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'].\n add_ticks (bool, optional): Whether to add tick markers to the colorbar. Defaults to True.\n add_labels (bool, optional): Whether to add labels to the colorbar. Defaults to True.\n labels (list, optional): A list of labels to add to the colorbar. Defaults to None.\n vertical (bool, optional): Whether to rotate the colorbar vertically. Defaults to False.\n out_file (str, optional): File path to the output colorbar in png format. Defaults to None.\n font_type (str, optional): Font type to use for labels. Defaults to 'arial.ttf'.\n font_size (int, optional): Font size to use for labels. Defaults to 12.\n font_color (str, optional): Font color to use for labels. Defaults to 'black'.\n add_outline (bool, optional): Whether to add an outline to the colorbar. Defaults to True.\n outline_color (str, optional): Color for the outline of the colorbar. Defaults to 'black'.\n\n Returns:\n str: File path of the output colorbar in png format.\n\n "
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 | 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.\n\n Args:\n width (int, optional): Width of the colorbar in pixels. Defaults to 150.\n height (int, optional): Height of the colorbar in pixels. Defaults to 30.\n 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'].\n add_ticks (bool, optional): Whether to add tick markers to the colorbar. Defaults to True.\n add_labels (bool, optional): Whether to add labels to the colorbar. Defaults to True.\n labels (list, optional): A list of labels to add to the colorbar. Defaults to None.\n vertical (bool, optional): Whether to rotate the colorbar vertically. Defaults to False.\n out_file (str, optional): File path to the output colorbar in png format. Defaults to None.\n font_type (str, optional): Font type to use for labels. Defaults to 'arial.ttf'.\n font_size (int, optional): Font size to use for labels. Defaults to 12.\n font_color (str, optional): Font color to use for labels. Defaults to 'black'.\n add_outline (bool, optional): Whether to add an outline to the colorbar. Defaults to True.\n outline_color (str, optional): Color for the outline of the colorbar. Defaults to 'black'.\n\n Returns:\n str: File path of the output colorbar in png format.\n\n "
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<|docstring|>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.<|endoftext|> |
7c4962964a691d1de3d42debf2dbfb3e71e2e565d33fd09b4aa741742ca60870 | def minimum_bounding_box(geojson):
'Gets the minimum bounding box for a geojson polygon.\n\n Args:\n geojson (dict): A geojson dictionary.\n\n Returns:\n 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)).\n '
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]))
upper_right = (max([x[1] for x in coordinates]), max([x[0] for x in coordinates]))
bounds = (lower_left, upper_right)
return bounds
except Exception as e:
raise Exception(e) | 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)). | geemap/common.py | minimum_bounding_box | arheem/geemap | 1 | python | def minimum_bounding_box(geojson):
'Gets the minimum bounding box for a geojson polygon.\n\n Args:\n geojson (dict): A geojson dictionary.\n\n Returns:\n 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)).\n '
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]))
upper_right = (max([x[1] for x in coordinates]), max([x[0] for x in coordinates]))
bounds = (lower_left, upper_right)
return bounds
except Exception as e:
raise Exception(e) | def minimum_bounding_box(geojson):
'Gets the minimum bounding box for a geojson polygon.\n\n Args:\n geojson (dict): A geojson dictionary.\n\n Returns:\n 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)).\n '
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]))
upper_right = (max([x[1] for x in coordinates]), max([x[0] for x in coordinates]))
bounds = (lower_left, upper_right)
return bounds
except Exception as e:
raise Exception(e)<|docstring|>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)).<|endoftext|> |
3451a9847c872d25447bf9fc74eb149ce60d8af18c70d5c77ee0a1cbfa826477 | def geocode(location, max_rows=10, reverse=False):
'Search location by address and lat/lon coordinates.\n\n Args:\n location (str): Place name or address\n max_rows (int, optional): Maximum number of records to return. Defaults to 10.\n reverse (bool, optional): Search place based on coordinates. Defaults to False.\n\n Returns:\n list: Returns a list of locations.\n '
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 (not (address 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 (not (address 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 | 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. | geemap/common.py | geocode | arheem/geemap | 1 | python | def geocode(location, max_rows=10, reverse=False):
'Search location by address and lat/lon coordinates.\n\n Args:\n location (str): Place name or address\n max_rows (int, optional): Maximum number of records to return. Defaults to 10.\n reverse (bool, optional): Search place based on coordinates. Defaults to False.\n\n Returns:\n list: Returns a list of locations.\n '
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 (not (address 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 (not (address 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 | def geocode(location, max_rows=10, reverse=False):
'Search location by address and lat/lon coordinates.\n\n Args:\n location (str): Place name or address\n max_rows (int, optional): Maximum number of records to return. Defaults to 10.\n reverse (bool, optional): Search place based on coordinates. Defaults to False.\n\n Returns:\n list: Returns a list of locations.\n '
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 (not (address 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 (not (address 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<|docstring|>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.<|endoftext|> |
9569513ad7f84fda3205b7ec365251e17ca14963214a588ff7612888f4a3c374 | def is_latlon_valid(location):
'Checks whether a pair of coordinates is valid.\n\n Args:\n location (str): A pair of latlon coordinates separated by comma or space.\n\n Returns:\n bool: Returns True if valid.\n '
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 (lat <= 180)):
return True
else:
return False
except Exception as e:
print(e)
return False | 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. | geemap/common.py | is_latlon_valid | arheem/geemap | 1 | python | def is_latlon_valid(location):
'Checks whether a pair of coordinates is valid.\n\n Args:\n location (str): A pair of latlon coordinates separated by comma or space.\n\n Returns:\n bool: Returns True if valid.\n '
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 (lat <= 180)):
return True
else:
return False
except Exception as e:
print(e)
return False | def is_latlon_valid(location):
'Checks whether a pair of coordinates is valid.\n\n Args:\n location (str): A pair of latlon coordinates separated by comma or space.\n\n Returns:\n bool: Returns True if valid.\n '
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 (lat <= 180)):
return True
else:
return False
except Exception as e:
print(e)
return False<|docstring|>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.<|endoftext|> |
a543a3f6e5642af8fc49faa0a9d32284170d67c1e2653778b74031014d189d15 | def latlon_from_text(location):
'Extracts latlon from text.\n\n Args:\n location (str): A pair of latlon coordinates separated by comma or space.\n\n Returns:\n bool: Returns (lat, lon) if valid.\n '
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 (lat <= 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 | Extracts latlon from text.
Args:
location (str): A pair of latlon coordinates separated by comma or space.
Returns:
bool: Returns (lat, lon) if valid. | geemap/common.py | latlon_from_text | arheem/geemap | 1 | python | def latlon_from_text(location):
'Extracts latlon from text.\n\n Args:\n location (str): A pair of latlon coordinates separated by comma or space.\n\n Returns:\n bool: Returns (lat, lon) if valid.\n '
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 (lat <= 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 | def latlon_from_text(location):
'Extracts latlon from text.\n\n Args:\n location (str): A pair of latlon coordinates separated by comma or space.\n\n Returns:\n bool: Returns (lat, lon) if valid.\n '
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 (lat <= 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<|docstring|>Extracts latlon from text.
Args:
location (str): A pair of latlon coordinates separated by comma or space.
Returns:
bool: Returns (lat, lon) if valid.<|endoftext|> |
0eaf768bfd00778e832cdcd8353ea3ca995dd0be9e164a99c37ef9cea3736d1e | def search_ee_data(keywords):
'Searches Earth Engine data catalog.\n\n Args:\n keywords (str): Keywords to search for can be id, provider, tag and so on\n\n Returns:\n list: Returns a lit of assets.\n '
try:
cmd = 'geeadd search --keywords "{}"'.format(str(keywords))
output = os.popen(cmd).read()
start_index = output.index('[')
assets = eval(output[start_index:])
results = []
for asset in assets:
asset_dates = ((asset['start_date'] + ' - ') + asset['end_date'])
asset_snippet = asset['ee_id_snippet']
start_index = (asset_snippet.index("'") + 1)
end_index = asset_snippet.index("'", start_index)
asset_id = asset_snippet[start_index:end_index]
asset['dates'] = asset_dates
asset['id'] = asset_id
asset['uid'] = asset_id.replace('/', '_')
results.append(asset)
return results
except Exception as e:
print(e) | Searches Earth Engine data catalog.
Args:
keywords (str): Keywords to search for can be id, provider, tag and so on
Returns:
list: Returns a lit of assets. | geemap/common.py | search_ee_data | arheem/geemap | 1 | python | def search_ee_data(keywords):
'Searches Earth Engine data catalog.\n\n Args:\n keywords (str): Keywords to search for can be id, provider, tag and so on\n\n Returns:\n list: Returns a lit of assets.\n '
try:
cmd = 'geeadd search --keywords "{}"'.format(str(keywords))
output = os.popen(cmd).read()
start_index = output.index('[')
assets = eval(output[start_index:])
results = []
for asset in assets:
asset_dates = ((asset['start_date'] + ' - ') + asset['end_date'])
asset_snippet = asset['ee_id_snippet']
start_index = (asset_snippet.index("'") + 1)
end_index = asset_snippet.index("'", start_index)
asset_id = asset_snippet[start_index:end_index]
asset['dates'] = asset_dates
asset['id'] = asset_id
asset['uid'] = asset_id.replace('/', '_')
results.append(asset)
return results
except Exception as e:
print(e) | def search_ee_data(keywords):
'Searches Earth Engine data catalog.\n\n Args:\n keywords (str): Keywords to search for can be id, provider, tag and so on\n\n Returns:\n list: Returns a lit of assets.\n '
try:
cmd = 'geeadd search --keywords "{}"'.format(str(keywords))
output = os.popen(cmd).read()
start_index = output.index('[')
assets = eval(output[start_index:])
results = []
for asset in assets:
asset_dates = ((asset['start_date'] + ' - ') + asset['end_date'])
asset_snippet = asset['ee_id_snippet']
start_index = (asset_snippet.index("'") + 1)
end_index = asset_snippet.index("'", start_index)
asset_id = asset_snippet[start_index:end_index]
asset['dates'] = asset_dates
asset['id'] = asset_id
asset['uid'] = asset_id.replace('/', '_')
results.append(asset)
return results
except Exception as e:
print(e)<|docstring|>Searches Earth Engine data catalog.
Args:
keywords (str): Keywords to search for can be id, provider, tag and so on
Returns:
list: Returns a lit of assets.<|endoftext|> |
b77af3e5c4e307c90a1011181cbed82210fadf4aed88d96234242b5c5b722bac | def ee_data_thumbnail(asset_id):
'Retrieves the thumbnail URL of an Earth Engine asset.\n\n Args:\n asset_id (str): An Earth Engine asset id.\n\n Returns:\n str: An http url of the thumbnail.\n '
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) | 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. | geemap/common.py | ee_data_thumbnail | arheem/geemap | 1 | python | def ee_data_thumbnail(asset_id):
'Retrieves the thumbnail URL of an Earth Engine asset.\n\n Args:\n asset_id (str): An Earth Engine asset id.\n\n Returns:\n str: An http url of the thumbnail.\n '
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) | def ee_data_thumbnail(asset_id):
'Retrieves the thumbnail URL of an Earth Engine asset.\n\n Args:\n asset_id (str): An Earth Engine asset id.\n\n Returns:\n str: An http url of the thumbnail.\n '
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)<|docstring|>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.<|endoftext|> |
f857e12de1910ca85e728696c8c4d282d2e380aedc69b561e8c41db43a8dd41d | def ee_data_html(asset):
'Generates HTML from an asset to be used in the HTML widget.\n\n Args:\n asset (dict): A dictionary containing an Earth Engine asset.\n\n Returns:\n str: A string containing HTML.\n '
template = '\n <html>\n <body>\n <h3>asset_title</h3>\n <h4>Dataset Availability</h4>\n <p style="margin-left: 40px">asset_dates</p>\n <h4>Earth Engine Snippet</h4>\n <p style="margin-left: 40px">ee_id_snippet</p>\n <h4>Earth Engine Data Catalog</h4>\n <p style="margin-left: 40px"><a href="asset_url" target="_blank">asset_id</a></p>\n <h4>Dataset Thumbnail</h4>\n <img src="thumbnail_url">\n </body>\n </html>\n '
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'])
text = text.replace('thumbnail_url', asset['thumbnail_url'])
return text
except Exception as e:
print(e) | 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. | geemap/common.py | ee_data_html | arheem/geemap | 1 | python | def ee_data_html(asset):
'Generates HTML from an asset to be used in the HTML widget.\n\n Args:\n asset (dict): A dictionary containing an Earth Engine asset.\n\n Returns:\n str: A string containing HTML.\n '
template = '\n <html>\n <body>\n <h3>asset_title</h3>\n <h4>Dataset Availability</h4>\n <p style="margin-left: 40px">asset_dates</p>\n <h4>Earth Engine Snippet</h4>\n <p style="margin-left: 40px">ee_id_snippet</p>\n <h4>Earth Engine Data Catalog</h4>\n <p style="margin-left: 40px"><a href="asset_url" target="_blank">asset_id</a></p>\n <h4>Dataset Thumbnail</h4>\n <img src="thumbnail_url">\n </body>\n </html>\n '
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'])
text = text.replace('thumbnail_url', asset['thumbnail_url'])
return text
except Exception as e:
print(e) | def ee_data_html(asset):
'Generates HTML from an asset to be used in the HTML widget.\n\n Args:\n asset (dict): A dictionary containing an Earth Engine asset.\n\n Returns:\n str: A string containing HTML.\n '
template = '\n <html>\n <body>\n <h3>asset_title</h3>\n <h4>Dataset Availability</h4>\n <p style="margin-left: 40px">asset_dates</p>\n <h4>Earth Engine Snippet</h4>\n <p style="margin-left: 40px">ee_id_snippet</p>\n <h4>Earth Engine Data Catalog</h4>\n <p style="margin-left: 40px"><a href="asset_url" target="_blank">asset_id</a></p>\n <h4>Dataset Thumbnail</h4>\n <img src="thumbnail_url">\n </body>\n </html>\n '
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'])
text = text.replace('thumbnail_url', asset['thumbnail_url'])
return text
except Exception as e:
print(e)<|docstring|>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.<|endoftext|> |
119015169e7e01302ef3dc856a3bec656ddd917a24eb9b61e6992958796e124a | def create_code_cell(code='', where='below'):
"Creates a code cell in the IPython Notebook.\n\n Args:\n code (str, optional): Code to fill the new code cell with. Defaults to ''.\n where (str, optional): Where to add the new code cell. It can be one of the following: above, below, at_bottom. Defaults to 'below'.\n "
import base64
from IPython.display import Javascript, display
encoded_code = base64.b64encode(str.encode(code)).decode()
display(Javascript('\n var code = IPython.notebook.insert_cell_{0}(\'code\');\n code.set_text(atob("{1}"));\n '.format(where, encoded_code))) | 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'. | geemap/common.py | create_code_cell | arheem/geemap | 1 | python | def create_code_cell(code=, where='below'):
"Creates a code cell in the IPython Notebook.\n\n Args:\n code (str, optional): Code to fill the new code cell with. Defaults to .\n where (str, optional): Where to add the new code cell. It can be one of the following: above, below, at_bottom. Defaults to 'below'.\n "
import base64
from IPython.display import Javascript, display
encoded_code = base64.b64encode(str.encode(code)).decode()
display(Javascript('\n var code = IPython.notebook.insert_cell_{0}(\'code\');\n code.set_text(atob("{1}"));\n '.format(where, encoded_code))) | def create_code_cell(code=, where='below'):
"Creates a code cell in the IPython Notebook.\n\n Args:\n code (str, optional): Code to fill the new code cell with. Defaults to .\n where (str, optional): Where to add the new code cell. It can be one of the following: above, below, at_bottom. Defaults to 'below'.\n "
import base64
from IPython.display import Javascript, display
encoded_code = base64.b64encode(str.encode(code)).decode()
display(Javascript('\n var code = IPython.notebook.insert_cell_{0}(\'code\');\n code.set_text(atob("{1}"));\n '.format(where, encoded_code)))<|docstring|>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'.<|endoftext|> |
69db9bb4837a18589b564ebfc6dfb1c2bac70664dc85118701e5b6bc4a1054ea | 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.\n\n Args:\n outfile (str, optional): The output file path to a csv file. Defaults to None.\n '
import csv
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
elif (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) | 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. | geemap/common.py | ee_api_to_csv | arheem/geemap | 1 | python | 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.\n\n Args:\n outfile (str, optional): The output file path to a csv file. Defaults to None.\n '
import csv
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
elif (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) | 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.\n\n Args:\n outfile (str, optional): The output file path to a csv file. Defaults to None.\n '
import csv
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
elif (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)<|docstring|>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.<|endoftext|> |
4872f3fc0e6c7776d7395e18a3ca02fece98401aa994e7377faf20dc930e615b | def read_api_csv():
'Extracts Earth Engine API from a csv file and returns a dictionary containing information about each function.\n\n Returns:\n dict: The dictionary containing information about each function, including name, description, function form, return type, arguments, html. \n '
import copy
import csv
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')
csv_file = os.path.join(template_dir, 'ee_api_docs.csv')
html_file = os.path.join(template_dir, 'ee_api_docs.html')
with open(html_file) as f:
in_html_lines = f.readlines()
api_dict = {}
with open(csv_file, 'r', encoding='utf-8') as f:
csv_reader = csv.DictReader(f, delimiter='\t')
for line in csv_reader:
out_html_lines = copy.copy(in_html_lines)
out_html_lines[65] = in_html_lines[65].replace('function_name', line['name'])
out_html_lines[66] = in_html_lines[66].replace('function_description', line.get('description'))
out_html_lines[74] = in_html_lines[74].replace('function_usage', line.get('function'))
out_html_lines[75] = in_html_lines[75].replace('function_returns', line.get('returns'))
arguments = line.get('argument')
types = line.get('type')
details = line.get('details')
if ('|' in arguments):
argument_items = arguments.split('|')
else:
argument_items = [arguments]
if ('|' in types):
types_items = types.split('|')
else:
types_items = [types]
if ('|' in details):
details_items = details.split('|')
else:
details_items = [details]
out_argument_lines = []
for index in range(len(argument_items)):
in_argument_lines = in_html_lines[87:92]
in_argument_lines[1] = in_argument_lines[1].replace('function_argument', argument_items[index])
in_argument_lines[2] = in_argument_lines[2].replace('function_type', types_items[index])
in_argument_lines[3] = in_argument_lines[3].replace('function_details', details_items[index])
out_argument_lines.append(''.join(in_argument_lines))
out_html_lines = ((out_html_lines[:87] + out_argument_lines) + out_html_lines[92:])
contents = ''.join(out_html_lines)
api_dict[line['name']] = {'description': line.get('description'), 'function': line.get('function'), 'returns': line.get('returns'), 'argument': line.get('argument'), 'type': line.get('type'), 'details': line.get('details'), 'html': contents}
return api_dict | Extracts Earth Engine API from a csv file and returns a dictionary containing information about each function.
Returns:
dict: The dictionary containing information about each function, including name, description, function form, return type, arguments, html. | geemap/common.py | read_api_csv | arheem/geemap | 1 | python | def read_api_csv():
'Extracts Earth Engine API from a csv file and returns a dictionary containing information about each function.\n\n Returns:\n dict: The dictionary containing information about each function, including name, description, function form, return type, arguments, html. \n '
import copy
import csv
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')
csv_file = os.path.join(template_dir, 'ee_api_docs.csv')
html_file = os.path.join(template_dir, 'ee_api_docs.html')
with open(html_file) as f:
in_html_lines = f.readlines()
api_dict = {}
with open(csv_file, 'r', encoding='utf-8') as f:
csv_reader = csv.DictReader(f, delimiter='\t')
for line in csv_reader:
out_html_lines = copy.copy(in_html_lines)
out_html_lines[65] = in_html_lines[65].replace('function_name', line['name'])
out_html_lines[66] = in_html_lines[66].replace('function_description', line.get('description'))
out_html_lines[74] = in_html_lines[74].replace('function_usage', line.get('function'))
out_html_lines[75] = in_html_lines[75].replace('function_returns', line.get('returns'))
arguments = line.get('argument')
types = line.get('type')
details = line.get('details')
if ('|' in arguments):
argument_items = arguments.split('|')
else:
argument_items = [arguments]
if ('|' in types):
types_items = types.split('|')
else:
types_items = [types]
if ('|' in details):
details_items = details.split('|')
else:
details_items = [details]
out_argument_lines = []
for index in range(len(argument_items)):
in_argument_lines = in_html_lines[87:92]
in_argument_lines[1] = in_argument_lines[1].replace('function_argument', argument_items[index])
in_argument_lines[2] = in_argument_lines[2].replace('function_type', types_items[index])
in_argument_lines[3] = in_argument_lines[3].replace('function_details', details_items[index])
out_argument_lines.append(.join(in_argument_lines))
out_html_lines = ((out_html_lines[:87] + out_argument_lines) + out_html_lines[92:])
contents = .join(out_html_lines)
api_dict[line['name']] = {'description': line.get('description'), 'function': line.get('function'), 'returns': line.get('returns'), 'argument': line.get('argument'), 'type': line.get('type'), 'details': line.get('details'), 'html': contents}
return api_dict | def read_api_csv():
'Extracts Earth Engine API from a csv file and returns a dictionary containing information about each function.\n\n Returns:\n dict: The dictionary containing information about each function, including name, description, function form, return type, arguments, html. \n '
import copy
import csv
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')
csv_file = os.path.join(template_dir, 'ee_api_docs.csv')
html_file = os.path.join(template_dir, 'ee_api_docs.html')
with open(html_file) as f:
in_html_lines = f.readlines()
api_dict = {}
with open(csv_file, 'r', encoding='utf-8') as f:
csv_reader = csv.DictReader(f, delimiter='\t')
for line in csv_reader:
out_html_lines = copy.copy(in_html_lines)
out_html_lines[65] = in_html_lines[65].replace('function_name', line['name'])
out_html_lines[66] = in_html_lines[66].replace('function_description', line.get('description'))
out_html_lines[74] = in_html_lines[74].replace('function_usage', line.get('function'))
out_html_lines[75] = in_html_lines[75].replace('function_returns', line.get('returns'))
arguments = line.get('argument')
types = line.get('type')
details = line.get('details')
if ('|' in arguments):
argument_items = arguments.split('|')
else:
argument_items = [arguments]
if ('|' in types):
types_items = types.split('|')
else:
types_items = [types]
if ('|' in details):
details_items = details.split('|')
else:
details_items = [details]
out_argument_lines = []
for index in range(len(argument_items)):
in_argument_lines = in_html_lines[87:92]
in_argument_lines[1] = in_argument_lines[1].replace('function_argument', argument_items[index])
in_argument_lines[2] = in_argument_lines[2].replace('function_type', types_items[index])
in_argument_lines[3] = in_argument_lines[3].replace('function_details', details_items[index])
out_argument_lines.append(.join(in_argument_lines))
out_html_lines = ((out_html_lines[:87] + out_argument_lines) + out_html_lines[92:])
contents = .join(out_html_lines)
api_dict[line['name']] = {'description': line.get('description'), 'function': line.get('function'), 'returns': line.get('returns'), 'argument': line.get('argument'), 'type': line.get('type'), 'details': line.get('details'), 'html': contents}
return api_dict<|docstring|>Extracts Earth Engine API from a csv file and returns a dictionary containing information about each function.
Returns:
dict: The dictionary containing information about each function, including name, description, function form, return type, arguments, html.<|endoftext|> |
bffaafd1ef75101919499c44f8f4eb2b4d4d595560a6cf68ae22ea7c837c3466 | 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"]\n\n Args:\n name (str): The name of the Earth Engine function\n\n Returns:\n list: The list for parent functions.\n '
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.') | 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. | geemap/common.py | ee_function_tree | arheem/geemap | 1 | python | 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"]\n\n Args:\n name (str): The name of the Earth Engine function\n\n Returns:\n list: The list for parent functions.\n '
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.') | 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"]\n\n Args:\n name (str): The name of the Earth Engine function\n\n Returns:\n list: The list for parent functions.\n '
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.')<|docstring|>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.<|endoftext|> |
6f47b9f506bf9530cd7462ed81692f8065e603186dc972a75f3a3fbcb269b6ce | def build_api_tree(api_dict, output_widget, layout_width='100%'):
"Builds an Earth Engine API tree view.\n\n Args:\n api_dict (dict): The dictionary containing information about each Earth Engine API function.\n output_widget (object): An Output widget.\n layout_width (str, optional): The percentage width of the widget. Defaults to '100%'.\n\n Returns:\n tuple: Returns a tuple containing two items: a tree Output widget and a tree dictionary.\n "
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) | 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. | geemap/common.py | build_api_tree | arheem/geemap | 1 | python | def build_api_tree(api_dict, output_widget, layout_width='100%'):
"Builds an Earth Engine API tree view.\n\n Args:\n api_dict (dict): The dictionary containing information about each Earth Engine API function.\n output_widget (object): An Output widget.\n layout_width (str, optional): The percentage width of the widget. Defaults to '100%'.\n\n Returns:\n tuple: Returns a tuple containing two items: a tree Output widget and a tree dictionary.\n "
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) | def build_api_tree(api_dict, output_widget, layout_width='100%'):
"Builds an Earth Engine API tree view.\n\n Args:\n api_dict (dict): The dictionary containing information about each Earth Engine API function.\n output_widget (object): An Output widget.\n layout_width (str, optional): The percentage width of the widget. Defaults to '100%'.\n\n Returns:\n tuple: Returns a tuple containing two items: a tree Output widget and a tree dictionary.\n "
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)<|docstring|>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.<|endoftext|> |
5702824d441af1436f3f6e40085b604e346f460944c755eef9269144c080ab13 | def search_api_tree(keywords, api_tree):
'Search Earth Engine API and return functions containing the specified keywords\n\n Args:\n keywords (str): The keywords to search for.\n api_tree (dict): The dictionary containing the Earth Engine API tree.\n\n Returns:\n object: An ipytree object/widget.\n '
import warnings
warnings.filterwarnings('ignore')
sub_tree = Tree()
for key in api_tree.keys():
if (keywords in key):
sub_tree.add_node(api_tree[key])
return sub_tree | Search Earth Engine API and return functions containing the specified keywords
Args:
keywords (str): The keywords to search for.
api_tree (dict): The dictionary containing the Earth Engine API tree.
Returns:
object: An ipytree object/widget. | geemap/common.py | search_api_tree | arheem/geemap | 1 | python | def search_api_tree(keywords, api_tree):
'Search Earth Engine API and return functions containing the specified keywords\n\n Args:\n keywords (str): The keywords to search for.\n api_tree (dict): The dictionary containing the Earth Engine API tree.\n\n Returns:\n object: An ipytree object/widget.\n '
import warnings
warnings.filterwarnings('ignore')
sub_tree = Tree()
for key in api_tree.keys():
if (keywords in key):
sub_tree.add_node(api_tree[key])
return sub_tree | def search_api_tree(keywords, api_tree):
'Search Earth Engine API and return functions containing the specified keywords\n\n Args:\n keywords (str): The keywords to search for.\n api_tree (dict): The dictionary containing the Earth Engine API tree.\n\n Returns:\n object: An ipytree object/widget.\n '
import warnings
warnings.filterwarnings('ignore')
sub_tree = Tree()
for key in api_tree.keys():
if (keywords in key):
sub_tree.add_node(api_tree[key])
return sub_tree<|docstring|>Search Earth Engine API and return functions containing the specified keywords
Args:
keywords (str): The keywords to search for.
api_tree (dict): The dictionary containing the Earth Engine API tree.
Returns:
object: An ipytree object/widget.<|endoftext|> |
e9a1310c263babf9cdbcf8489d7507b1568c83beb8a85e6edf3f547dfebfa863 | 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:\n jupyter notebook --NotebookApp.iopub_msg_rate_limit=10000\n\n Args:\n asset_limit (int, optional): The number of assets to display for each asset type, i.e., Image, ImageCollection, and FeatureCollection. Defaults to 100.\n '
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) | 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. | geemap/common.py | ee_search | arheem/geemap | 1 | python | 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:\n jupyter notebook --NotebookApp.iopub_msg_rate_limit=10000\n\n Args:\n asset_limit (int, optional): The number of assets to display for each asset type, i.e., Image, ImageCollection, and FeatureCollection. Defaults to 100.\n '
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) | 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:\n jupyter notebook --NotebookApp.iopub_msg_rate_limit=10000\n\n Args:\n asset_limit (int, optional): The number of assets to display for each asset type, i.e., Image, ImageCollection, and FeatureCollection. Defaults to 100.\n '
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)<|docstring|>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.<|endoftext|> |
680e1b41b3be604f12896b138c8c867c69f22ec7d12b88d9e6f9d98e64c090aa | def ee_user_id():
'Gets Earth Engine account user id.\n\n Returns:\n str: A string containing the user id.\n '
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 | Gets Earth Engine account user id.
Returns:
str: A string containing the user id. | geemap/common.py | ee_user_id | arheem/geemap | 1 | python | def ee_user_id():
'Gets Earth Engine account user id.\n\n Returns:\n str: A string containing the user id.\n '
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 | def ee_user_id():
'Gets Earth Engine account user id.\n\n Returns:\n str: A string containing the user id.\n '
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<|docstring|>Gets Earth Engine account user id.
Returns:
str: A string containing the user id.<|endoftext|> |
f0590d1280a90b9157a2d2feb598536e8dba4b7d88e65f6502dfb8e137e9ffc6 | def build_repo_tree(out_dir=None, name='gee_repos'):
"Builds a repo tree for GEE account.\n\n Args:\n out_dir (str): The output directory for the repos. Defaults to None.\n name (str, optional): The output name for the repo directory. Defaults to 'gee_repos'.\n\n Returns:\n tuple: Returns a tuple containing a tree widget, an output widget, and a tree dictionary containing nodes.\n "
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 = {'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 ('github.com' in url):
clone_github_repo(url, out_dir=clone_dir)
elif ('googlesource' in url):
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) | 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. | geemap/common.py | build_repo_tree | arheem/geemap | 1 | python | def build_repo_tree(out_dir=None, name='gee_repos'):
"Builds a repo tree for GEE account.\n\n Args:\n out_dir (str): The output directory for the repos. Defaults to None.\n name (str, optional): The output name for the repo directory. Defaults to 'gee_repos'.\n\n Returns:\n tuple: Returns a tuple containing a tree widget, an output widget, and a tree dictionary containing nodes.\n "
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 = {'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 ('github.com' in url):
clone_github_repo(url, out_dir=clone_dir)
elif ('googlesource' in url):
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) | def build_repo_tree(out_dir=None, name='gee_repos'):
"Builds a repo tree for GEE account.\n\n Args:\n out_dir (str): The output directory for the repos. Defaults to None.\n name (str, optional): The output name for the repo directory. Defaults to 'gee_repos'.\n\n Returns:\n tuple: Returns a tuple containing a tree widget, an output widget, and a tree dictionary containing nodes.\n "
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 = {'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 ('github.com' in url):
clone_github_repo(url, out_dir=clone_dir)
elif ('googlesource' in url):
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)<|docstring|>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.<|endoftext|> |
f7cf76a658192718e8c987556a0f44ac3c373357a430d2bc52e84507ac007f53 | def file_browser(in_dir=None, show_hidden=False, add_root_node=True, search_description=None, use_import=False, return_sep_widgets=False):
'Creates a simple file browser and text editor.\n\n Args:\n in_dir (str, optional): The input directory. Defaults to None, which will use the current working directory.\n show_hidden (bool, optional): Whether to show hidden files/folders. Defaults to False.\n add_root_node (bool, optional): Whether to add the input directory as a root node. Defaults to True.\n search_description (str, optional): The description of the search box. Defaults to None.\n use_import (bool, optional): Whether to show the import button. Defaults to False.\n return_sep_widgets (bool, optional): Whether to return the results as separate widgets. Defaults to False.\n\n Returns:\n object: An ipywidget.\n '
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'
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 = 'file'
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 | 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. | geemap/common.py | file_browser | arheem/geemap | 1 | python | def file_browser(in_dir=None, show_hidden=False, add_root_node=True, search_description=None, use_import=False, return_sep_widgets=False):
'Creates a simple file browser and text editor.\n\n Args:\n in_dir (str, optional): The input directory. Defaults to None, which will use the current working directory.\n show_hidden (bool, optional): Whether to show hidden files/folders. Defaults to False.\n add_root_node (bool, optional): Whether to add the input directory as a root node. Defaults to True.\n search_description (str, optional): The description of the search box. Defaults to None.\n use_import (bool, optional): Whether to show the import button. Defaults to False.\n return_sep_widgets (bool, optional): Whether to return the results as separate widgets. Defaults to False.\n\n Returns:\n object: An ipywidget.\n '
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'
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 = 'file'
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 | def file_browser(in_dir=None, show_hidden=False, add_root_node=True, search_description=None, use_import=False, return_sep_widgets=False):
'Creates a simple file browser and text editor.\n\n Args:\n in_dir (str, optional): The input directory. Defaults to None, which will use the current working directory.\n show_hidden (bool, optional): Whether to show hidden files/folders. Defaults to False.\n add_root_node (bool, optional): Whether to add the input directory as a root node. Defaults to True.\n search_description (str, optional): The description of the search box. Defaults to None.\n use_import (bool, optional): Whether to show the import button. Defaults to False.\n return_sep_widgets (bool, optional): Whether to return the results as separate widgets. Defaults to False.\n\n Returns:\n object: An ipywidget.\n '
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'
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 = 'file'
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<|docstring|>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.<|endoftext|> |
9ea01e1c8d5232db51717eed784d5545836bddc99a4976c9a8f5def1fa30a51b | def date_sequence(start, end, unit, date_format='YYYY-MM-dd'):
"Creates a date sequence.\n\n Args:\n start (str): The start date, e.g., '2000-01-01'.\n end (str): The end date, e.g., '2000-12-31'.\n unit (str): One of 'year', 'month' 'week', 'day', 'hour', 'minute', or 'second'.\n 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'.\n\n Returns:\n ee.List: A list of date sequence.\n "
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 | 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. | geemap/common.py | date_sequence | arheem/geemap | 1 | python | def date_sequence(start, end, unit, date_format='YYYY-MM-dd'):
"Creates a date sequence.\n\n Args:\n start (str): The start date, e.g., '2000-01-01'.\n end (str): The end date, e.g., '2000-12-31'.\n unit (str): One of 'year', 'month' 'week', 'day', 'hour', 'minute', or 'second'.\n 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'.\n\n Returns:\n ee.List: A list of date sequence.\n "
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 | def date_sequence(start, end, unit, date_format='YYYY-MM-dd'):
"Creates a date sequence.\n\n Args:\n start (str): The start date, e.g., '2000-01-01'.\n end (str): The end date, e.g., '2000-12-31'.\n unit (str): One of 'year', 'month' 'week', 'day', 'hour', 'minute', or 'second'.\n 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'.\n\n Returns:\n ee.List: A list of date sequence.\n "
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<|docstring|>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.<|endoftext|> |
3e5fdf94ae1ef9e1cdcc1464a8fcd61ba42021184225db718e481eee41dfa778 | def legend_from_ee(ee_class_table):
'Extract legend from an Earth Engine class table on the Earth Engine Data Catalog page\n such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1\n\n Value\tColor\tDescription\n 0\t1c0dff\tWater\n 1\t05450a\tEvergreen needleleaf forest\n 2\t086a10\tEvergreen broadleaf forest\n 3\t54a708\tDeciduous needleleaf forest\n 4\t78d203\tDeciduous broadleaf forest\n 5\t009900\tMixed forest\n 6\tc6b044\tClosed shrublands\n 7\tdcd159\tOpen shrublands\n 8\tdade48\tWoody savannas\n 9\tfbff13\tSavannas\n 10\tb6ff05\tGrasslands\n 11\t27ff87\tPermanent wetlands\n 12\tc24f44\tCroplands\n 13\ta5a5a5\tUrban and built-up\n 14\tff6d4c\tCropland/natural vegetation mosaic\n 15\t69fff8\tSnow and ice\n 16\tf9ffa4\tBarren or sparsely vegetated\n 254\tffffff\tUnclassified\n\n Args:\n ee_class_table (str): An Earth Engine class table with triple quotes.\n\n Returns:\n dict: Returns a legend dictionary that can be used to create a legend.\n '
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) | 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
Value Color Description
0 1c0dff Water
1 05450a Evergreen needleleaf forest
2 086a10 Evergreen broadleaf forest
3 54a708 Deciduous needleleaf forest
4 78d203 Deciduous broadleaf forest
5 009900 Mixed forest
6 c6b044 Closed shrublands
7 dcd159 Open shrublands
8 dade48 Woody savannas
9 fbff13 Savannas
10 b6ff05 Grasslands
11 27ff87 Permanent wetlands
12 c24f44 Croplands
13 a5a5a5 Urban and built-up
14 ff6d4c Cropland/natural vegetation mosaic
15 69fff8 Snow and ice
16 f9ffa4 Barren or sparsely vegetated
254 ffffff Unclassified
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. | geemap/common.py | legend_from_ee | arheem/geemap | 1 | python | def legend_from_ee(ee_class_table):
'Extract legend from an Earth Engine class table on the Earth Engine Data Catalog page\n such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1\n\n Value\tColor\tDescription\n 0\t1c0dff\tWater\n 1\t05450a\tEvergreen needleleaf forest\n 2\t086a10\tEvergreen broadleaf forest\n 3\t54a708\tDeciduous needleleaf forest\n 4\t78d203\tDeciduous broadleaf forest\n 5\t009900\tMixed forest\n 6\tc6b044\tClosed shrublands\n 7\tdcd159\tOpen shrublands\n 8\tdade48\tWoody savannas\n 9\tfbff13\tSavannas\n 10\tb6ff05\tGrasslands\n 11\t27ff87\tPermanent wetlands\n 12\tc24f44\tCroplands\n 13\ta5a5a5\tUrban and built-up\n 14\tff6d4c\tCropland/natural vegetation mosaic\n 15\t69fff8\tSnow and ice\n 16\tf9ffa4\tBarren or sparsely vegetated\n 254\tffffff\tUnclassified\n\n Args:\n ee_class_table (str): An Earth Engine class table with triple quotes.\n\n Returns:\n dict: Returns a legend dictionary that can be used to create a legend.\n '
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) | def legend_from_ee(ee_class_table):
'Extract legend from an Earth Engine class table on the Earth Engine Data Catalog page\n such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1\n\n Value\tColor\tDescription\n 0\t1c0dff\tWater\n 1\t05450a\tEvergreen needleleaf forest\n 2\t086a10\tEvergreen broadleaf forest\n 3\t54a708\tDeciduous needleleaf forest\n 4\t78d203\tDeciduous broadleaf forest\n 5\t009900\tMixed forest\n 6\tc6b044\tClosed shrublands\n 7\tdcd159\tOpen shrublands\n 8\tdade48\tWoody savannas\n 9\tfbff13\tSavannas\n 10\tb6ff05\tGrasslands\n 11\t27ff87\tPermanent wetlands\n 12\tc24f44\tCroplands\n 13\ta5a5a5\tUrban and built-up\n 14\tff6d4c\tCropland/natural vegetation mosaic\n 15\t69fff8\tSnow and ice\n 16\tf9ffa4\tBarren or sparsely vegetated\n 254\tffffff\tUnclassified\n\n Args:\n ee_class_table (str): An Earth Engine class table with triple quotes.\n\n Returns:\n dict: Returns a legend dictionary that can be used to create a legend.\n '
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)<|docstring|>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
Value Color Description
0 1c0dff Water
1 05450a Evergreen needleleaf forest
2 086a10 Evergreen broadleaf forest
3 54a708 Deciduous needleleaf forest
4 78d203 Deciduous broadleaf forest
5 009900 Mixed forest
6 c6b044 Closed shrublands
7 dcd159 Open shrublands
8 dade48 Woody savannas
9 fbff13 Savannas
10 b6ff05 Grasslands
11 27ff87 Permanent wetlands
12 c24f44 Croplands
13 a5a5a5 Urban and built-up
14 ff6d4c Cropland/natural vegetation mosaic
15 69fff8 Snow and ice
16 f9ffa4 Barren or sparsely vegetated
254 ffffff Unclassified
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.<|endoftext|> |
07f7d30fe3213bee6bf9b9842da62ad93acfb77e3252956fa9143d098313f005 | def vis_to_qml(ee_class_table, out_qml):
'Create a QGIS Layer Style (.qml) based on an Earth Engine class table from the Earth Engine Data Catalog page\n such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1\n\n Value\tColor\tDescription\n 0\t1c0dff\tWater\n 1\t05450a\tEvergreen needleleaf forest\n 2\t086a10\tEvergreen broadleaf forest\n 3\t54a708\tDeciduous needleleaf forest\n 4\t78d203\tDeciduous broadleaf forest\n 5\t009900\tMixed forest\n 6\tc6b044\tClosed shrublands\n 7\tdcd159\tOpen shrublands\n 8\tdade48\tWoody savannas\n 9\tfbff13\tSavannas\n 10\tb6ff05\tGrasslands\n 11\t27ff87\tPermanent wetlands\n 12\tc24f44\tCroplands\n 13\ta5a5a5\tUrban and built-up\n 14\tff6d4c\tCropland/natural vegetation mosaic\n 15\t69fff8\tSnow and ice\n 16\tf9ffa4\tBarren or sparsely vegetated\n 254\tffffff\tUnclassified\n\n Args:\n ee_class_table (str): An Earth Engine class table with triple quotes.\n out_qml (str): File path to the output QGIS Layer Style (.qml).\n '
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)
with open(qml_template) as f:
lines = f.readlines()
header = lines[:31]
footer = lines[51:]
entries = []
try:
ee_class_table = ee_class_table.strip()
lines = ee_class_table.split('\n')[1:]
if (lines[0] == 'Value\tColor\tDescription'):
lines = lines[1:]
for line in lines:
items = line.split('\t')
items = [item.strip() for item in items]
value = items[0]
color = items[1]
label = items[2]
entry = ' <paletteEntry alpha="255" color="#{}" value="{}" label="{}"/>\n'.format(color, value, label)
entries.append(entry)
out_lines = ((header + entries) + footer)
with open(out_qml, 'w') as f:
f.writelines(out_lines)
except Exception as e:
print(e) | Create a QGIS Layer Style (.qml) based on an Earth Engine class table from the Earth Engine Data Catalog page
such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1
Value Color Description
0 1c0dff Water
1 05450a Evergreen needleleaf forest
2 086a10 Evergreen broadleaf forest
3 54a708 Deciduous needleleaf forest
4 78d203 Deciduous broadleaf forest
5 009900 Mixed forest
6 c6b044 Closed shrublands
7 dcd159 Open shrublands
8 dade48 Woody savannas
9 fbff13 Savannas
10 b6ff05 Grasslands
11 27ff87 Permanent wetlands
12 c24f44 Croplands
13 a5a5a5 Urban and built-up
14 ff6d4c Cropland/natural vegetation mosaic
15 69fff8 Snow and ice
16 f9ffa4 Barren or sparsely vegetated
254 ffffff Unclassified
Args:
ee_class_table (str): An Earth Engine class table with triple quotes.
out_qml (str): File path to the output QGIS Layer Style (.qml). | geemap/common.py | vis_to_qml | arheem/geemap | 1 | python | def vis_to_qml(ee_class_table, out_qml):
'Create a QGIS Layer Style (.qml) based on an Earth Engine class table from the Earth Engine Data Catalog page\n such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1\n\n Value\tColor\tDescription\n 0\t1c0dff\tWater\n 1\t05450a\tEvergreen needleleaf forest\n 2\t086a10\tEvergreen broadleaf forest\n 3\t54a708\tDeciduous needleleaf forest\n 4\t78d203\tDeciduous broadleaf forest\n 5\t009900\tMixed forest\n 6\tc6b044\tClosed shrublands\n 7\tdcd159\tOpen shrublands\n 8\tdade48\tWoody savannas\n 9\tfbff13\tSavannas\n 10\tb6ff05\tGrasslands\n 11\t27ff87\tPermanent wetlands\n 12\tc24f44\tCroplands\n 13\ta5a5a5\tUrban and built-up\n 14\tff6d4c\tCropland/natural vegetation mosaic\n 15\t69fff8\tSnow and ice\n 16\tf9ffa4\tBarren or sparsely vegetated\n 254\tffffff\tUnclassified\n\n Args:\n ee_class_table (str): An Earth Engine class table with triple quotes.\n out_qml (str): File path to the output QGIS Layer Style (.qml).\n '
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)
with open(qml_template) as f:
lines = f.readlines()
header = lines[:31]
footer = lines[51:]
entries = []
try:
ee_class_table = ee_class_table.strip()
lines = ee_class_table.split('\n')[1:]
if (lines[0] == 'Value\tColor\tDescription'):
lines = lines[1:]
for line in lines:
items = line.split('\t')
items = [item.strip() for item in items]
value = items[0]
color = items[1]
label = items[2]
entry = ' <paletteEntry alpha="255" color="#{}" value="{}" label="{}"/>\n'.format(color, value, label)
entries.append(entry)
out_lines = ((header + entries) + footer)
with open(out_qml, 'w') as f:
f.writelines(out_lines)
except Exception as e:
print(e) | def vis_to_qml(ee_class_table, out_qml):
'Create a QGIS Layer Style (.qml) based on an Earth Engine class table from the Earth Engine Data Catalog page\n such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1\n\n Value\tColor\tDescription\n 0\t1c0dff\tWater\n 1\t05450a\tEvergreen needleleaf forest\n 2\t086a10\tEvergreen broadleaf forest\n 3\t54a708\tDeciduous needleleaf forest\n 4\t78d203\tDeciduous broadleaf forest\n 5\t009900\tMixed forest\n 6\tc6b044\tClosed shrublands\n 7\tdcd159\tOpen shrublands\n 8\tdade48\tWoody savannas\n 9\tfbff13\tSavannas\n 10\tb6ff05\tGrasslands\n 11\t27ff87\tPermanent wetlands\n 12\tc24f44\tCroplands\n 13\ta5a5a5\tUrban and built-up\n 14\tff6d4c\tCropland/natural vegetation mosaic\n 15\t69fff8\tSnow and ice\n 16\tf9ffa4\tBarren or sparsely vegetated\n 254\tffffff\tUnclassified\n\n Args:\n ee_class_table (str): An Earth Engine class table with triple quotes.\n out_qml (str): File path to the output QGIS Layer Style (.qml).\n '
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)
with open(qml_template) as f:
lines = f.readlines()
header = lines[:31]
footer = lines[51:]
entries = []
try:
ee_class_table = ee_class_table.strip()
lines = ee_class_table.split('\n')[1:]
if (lines[0] == 'Value\tColor\tDescription'):
lines = lines[1:]
for line in lines:
items = line.split('\t')
items = [item.strip() for item in items]
value = items[0]
color = items[1]
label = items[2]
entry = ' <paletteEntry alpha="255" color="#{}" value="{}" label="{}"/>\n'.format(color, value, label)
entries.append(entry)
out_lines = ((header + entries) + footer)
with open(out_qml, 'w') as f:
f.writelines(out_lines)
except Exception as e:
print(e)<|docstring|>Create a QGIS Layer Style (.qml) based on an Earth Engine class table from the Earth Engine Data Catalog page
such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1
Value Color Description
0 1c0dff Water
1 05450a Evergreen needleleaf forest
2 086a10 Evergreen broadleaf forest
3 54a708 Deciduous needleleaf forest
4 78d203 Deciduous broadleaf forest
5 009900 Mixed forest
6 c6b044 Closed shrublands
7 dcd159 Open shrublands
8 dade48 Woody savannas
9 fbff13 Savannas
10 b6ff05 Grasslands
11 27ff87 Permanent wetlands
12 c24f44 Croplands
13 a5a5a5 Urban and built-up
14 ff6d4c Cropland/natural vegetation mosaic
15 69fff8 Snow and ice
16 f9ffa4 Barren or sparsely vegetated
254 ffffff Unclassified
Args:
ee_class_table (str): An Earth Engine class table with triple quotes.
out_qml (str): File path to the output QGIS Layer Style (.qml).<|endoftext|> |
67e457d678eb8881207554e5b8fad4e54243716deedd9d173f759a0b71933757 | def create_nlcd_qml(out_qml):
'Create a QGIS Layer Style (.qml) for NLCD data\n\n Args:\n out_qml (str): File path to the ouput qml. \n '
import pkg_resources
import shutil
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) | Create a QGIS Layer Style (.qml) for NLCD data
Args:
out_qml (str): File path to the ouput qml. | geemap/common.py | create_nlcd_qml | arheem/geemap | 1 | python | def create_nlcd_qml(out_qml):
'Create a QGIS Layer Style (.qml) for NLCD data\n\n Args:\n out_qml (str): File path to the ouput qml. \n '
import pkg_resources
import shutil
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) | def create_nlcd_qml(out_qml):
'Create a QGIS Layer Style (.qml) for NLCD data\n\n Args:\n out_qml (str): File path to the ouput qml. \n '
import pkg_resources
import shutil
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)<|docstring|>Create a QGIS Layer Style (.qml) for NLCD data
Args:
out_qml (str): File path to the ouput qml.<|endoftext|> |
1afb922560f51660debd472e64be716b86fed5abd176e7f36640eced313be42f | 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:\n Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif\n Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif\n 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\n\n Args:\n URL (str): The Cloud Storage URL of the GeoTIFF to load.\n\n Returns:\n ee.Image: an Earth Engine image.\n '
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 | 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. | geemap/common.py | load_GeoTIFF | arheem/geemap | 1 | python | 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:\n Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif\n Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif\n 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\n\n Args:\n URL (str): The Cloud Storage URL of the GeoTIFF to load.\n\n Returns:\n ee.Image: an Earth Engine image.\n '
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 | 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:\n Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif\n Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif\n 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\n\n Args:\n URL (str): The Cloud Storage URL of the GeoTIFF to load.\n\n Returns:\n ee.Image: an Earth Engine image.\n '
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<|docstring|>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.<|endoftext|> |
668f29a56cf11a3cbdc904702466c8a815161b8a5f1fe8723be10bb2c8b7163e | 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:\n Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif\n Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif\n 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\n\n Args:\n URLs (list): A list of Cloud Storage URL of the GeoTIFF to load.\n\n Returns:\n ee.ImageCollection: An Earth Engine ImageCollection.\n '
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) | 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. | geemap/common.py | load_GeoTIFFs | arheem/geemap | 1 | python | 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:\n Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif\n Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif\n 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\n\n Args:\n URLs (list): A list of Cloud Storage URL of the GeoTIFF to load.\n\n Returns:\n ee.ImageCollection: An Earth Engine ImageCollection.\n '
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) | 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:\n Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif\n Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif\n 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\n\n Args:\n URLs (list): A list of Cloud Storage URL of the GeoTIFF to load.\n\n Returns:\n ee.ImageCollection: An Earth Engine ImageCollection.\n '
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)<|docstring|>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.<|endoftext|> |
7ca478f381d73df7938bf74c45131feddb4adc5cec50f259e307780c64208356 | def get_COG_tile(url, titiler_endpoint='https://api.cogeo.xyz/', **kwargs):
'Get a tile layer from a Cloud Optimized GeoTIFF (COG).\n Source code adapted from https://developmentseed.org/titiler/examples/Working_with_CloudOptimizedGeoTIFF_simple/\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n tuple: Returns the COG Tile layer URL and bounds. \n '
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 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. | geemap/common.py | get_COG_tile | arheem/geemap | 1 | python | def get_COG_tile(url, titiler_endpoint='https://api.cogeo.xyz/', **kwargs):
'Get a tile layer from a Cloud Optimized GeoTIFF (COG).\n Source code adapted from https://developmentseed.org/titiler/examples/Working_with_CloudOptimizedGeoTIFF_simple/\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n tuple: Returns the COG Tile layer URL and bounds. \n '
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] | def get_COG_tile(url, titiler_endpoint='https://api.cogeo.xyz/', **kwargs):
'Get a tile layer from a Cloud Optimized GeoTIFF (COG).\n Source code adapted from https://developmentseed.org/titiler/examples/Working_with_CloudOptimizedGeoTIFF_simple/\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n tuple: Returns the COG Tile layer URL and bounds. \n '
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]<|docstring|>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.<|endoftext|> |
7ff548951bbf46336e106ea7a2aa1f50fb4acff28c8347fdccaa0a12983917f2 | def get_COG_bounds(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get the bounding box of a Cloud Optimized GeoTIFF (COG).\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n list: A list of values representing [left, bottom, right, top]\n '
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 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] | geemap/common.py | get_COG_bounds | arheem/geemap | 1 | python | def get_COG_bounds(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get the bounding box of a Cloud Optimized GeoTIFF (COG).\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n list: A list of values representing [left, bottom, right, top]\n '
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 | def get_COG_bounds(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get the bounding box of a Cloud Optimized GeoTIFF (COG).\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n list: A list of values representing [left, bottom, right, top]\n '
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<|docstring|>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]<|endoftext|> |
6cc05331ab7c7a27268337a6d8e650598e5235b6af0cfc14a404c01a426976c6 | def get_COG_center(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get the centroid of a Cloud Optimized GeoTIFF (COG).\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n tuple: A tuple representing (longitude, latitude)\n '
bounds = get_COG_bounds(url, titiler_endpoint)
center = (((bounds[0] + bounds[2]) / 2), ((bounds[1] + bounds[3]) / 2))
return center | 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) | geemap/common.py | get_COG_center | arheem/geemap | 1 | python | def get_COG_center(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get the centroid of a Cloud Optimized GeoTIFF (COG).\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n tuple: A tuple representing (longitude, latitude)\n '
bounds = get_COG_bounds(url, titiler_endpoint)
center = (((bounds[0] + bounds[2]) / 2), ((bounds[1] + bounds[3]) / 2))
return center | def get_COG_center(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get the centroid of a Cloud Optimized GeoTIFF (COG).\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n tuple: A tuple representing (longitude, latitude)\n '
bounds = get_COG_bounds(url, titiler_endpoint)
center = (((bounds[0] + bounds[2]) / 2), ((bounds[1] + bounds[3]) / 2))
return center<|docstring|>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)<|endoftext|> |
e1bc7a2503edfecd557060b57d06fc8f7745e0f98c0c8342937d32260f362a88 | def get_COG_bands(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get band names of a Cloud Optimized GeoTIFF (COG).\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n list: A list of band names\n '
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 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 | geemap/common.py | get_COG_bands | arheem/geemap | 1 | python | def get_COG_bands(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get band names of a Cloud Optimized GeoTIFF (COG).\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n list: A list of band names\n '
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 | def get_COG_bands(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get band names of a Cloud Optimized GeoTIFF (COG).\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n list: A list of band names\n '
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<|docstring|>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<|endoftext|> |
c554a10a6fb47a3cbe0b967a25be206bec801daa0478909aaa8f3cebe004e82e | 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.\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n tuple: Returns the COG Tile layer URL and bounds. \n '
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] | 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. | geemap/common.py | get_STAC_tile | arheem/geemap | 1 | python | 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.\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n tuple: Returns the COG Tile layer URL and bounds. \n '
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] | 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.\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n tuple: Returns the COG Tile layer URL and bounds. \n '
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]<|docstring|>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.<|endoftext|> |
e7dd595b1fa36270fe475339aca1f76b132f8757bcaa8e93a2ed09d7ccdf944e | def get_STAC_bounds(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get the bounding box of a single SpatialTemporal Asset Catalog (STAC) item.\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n list: A list of values representing [left, bottom, right, top]\n '
import requests
r = requests.get(f'{titiler_endpoint}/stac/bounds', params={'url': url}).json()
bounds = r['bounds']
return bounds | 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] | geemap/common.py | get_STAC_bounds | arheem/geemap | 1 | python | def get_STAC_bounds(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get the bounding box of a single SpatialTemporal Asset Catalog (STAC) item.\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n list: A list of values representing [left, bottom, right, top]\n '
import requests
r = requests.get(f'{titiler_endpoint}/stac/bounds', params={'url': url}).json()
bounds = r['bounds']
return bounds | def get_STAC_bounds(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get the bounding box of a single SpatialTemporal Asset Catalog (STAC) item.\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n list: A list of values representing [left, bottom, right, top]\n '
import requests
r = requests.get(f'{titiler_endpoint}/stac/bounds', params={'url': url}).json()
bounds = r['bounds']
return bounds<|docstring|>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]<|endoftext|> |
69e48270ece92706a571c49e8868c058dba22b210bf0b562a71d9367747dbef9 | def get_STAC_center(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get the centroid of a single SpatialTemporal Asset Catalog (STAC) item.\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n tuple: A tuple representing (longitude, latitude)\n '
bounds = get_STAC_bounds(url, titiler_endpoint)
center = (((bounds[0] + bounds[2]) / 2), ((bounds[1] + bounds[3]) / 2))
return center | 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) | geemap/common.py | get_STAC_center | arheem/geemap | 1 | python | def get_STAC_center(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get the centroid of a single SpatialTemporal Asset Catalog (STAC) item.\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n tuple: A tuple representing (longitude, latitude)\n '
bounds = get_STAC_bounds(url, titiler_endpoint)
center = (((bounds[0] + bounds[2]) / 2), ((bounds[1] + bounds[3]) / 2))
return center | def get_STAC_center(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get the centroid of a single SpatialTemporal Asset Catalog (STAC) item.\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n tuple: A tuple representing (longitude, latitude)\n '
bounds = get_STAC_bounds(url, titiler_endpoint)
center = (((bounds[0] + bounds[2]) / 2), ((bounds[1] + bounds[3]) / 2))
return center<|docstring|>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)<|endoftext|> |
85f72c91097112612081ae77257e3d9b57fdd9f902b5bc401d783d0e39b7abf7 | def get_STAC_bands(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get band names of a single SpatialTemporal Asset Catalog (STAC) item.\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n list: A list of band names\n '
import requests
r = requests.get(f'{titiler_endpoint}/stac/info', params={'url': url}).json()
return r | 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 | geemap/common.py | get_STAC_bands | arheem/geemap | 1 | python | def get_STAC_bands(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get band names of a single SpatialTemporal Asset Catalog (STAC) item.\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n list: A list of band names\n '
import requests
r = requests.get(f'{titiler_endpoint}/stac/info', params={'url': url}).json()
return r | def get_STAC_bands(url, titiler_endpoint='https://api.cogeo.xyz/'):
'Get band names of a single SpatialTemporal Asset Catalog (STAC) item.\n\n Args:\n 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\n titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".\n\n Returns:\n list: A list of band names\n '
import requests
r = requests.get(f'{titiler_endpoint}/stac/info', params={'url': url}).json()
return r<|docstring|>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<|endoftext|> |
2d769e732aeea67bae8abfd6ed9e5a2ef96ee8e27bf3ff911e38439cb4d8dd8a | def bbox_to_geojson(bounds):
'Convert coordinates of a bounding box to a geojson.\n\n Args:\n bounds (list): A list of coordinates representing [left, bottom, right, top].\n\n Returns:\n dict: A geojson feature.\n '
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'} | 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. | geemap/common.py | bbox_to_geojson | arheem/geemap | 1 | python | def bbox_to_geojson(bounds):
'Convert coordinates of a bounding box to a geojson.\n\n Args:\n bounds (list): A list of coordinates representing [left, bottom, right, top].\n\n Returns:\n dict: A geojson feature.\n '
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'} | def bbox_to_geojson(bounds):
'Convert coordinates of a bounding box to a geojson.\n\n Args:\n bounds (list): A list of coordinates representing [left, bottom, right, top].\n\n Returns:\n dict: A geojson feature.\n '
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'}<|docstring|>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.<|endoftext|> |
9f70c0445d689cfa5e0f3aa0e8c1550239ccaca3c9e654929e397440712d3e42 | def coords_to_geojson(coords):
'Convert a list of bbox coordinates representing [left, bottom, right, top] to geojson FeatureCollection.\n\n Args:\n coords (list): A list of bbox coordinates representing [left, bottom, right, top].\n\n Returns:\n dict: A geojson FeatureCollection.\n '
features = []
for bbox in coords:
features.append(bbox_to_geojson(bbox))
return {'type': 'FeatureCollection', 'features': features} | 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. | geemap/common.py | coords_to_geojson | arheem/geemap | 1 | python | def coords_to_geojson(coords):
'Convert a list of bbox coordinates representing [left, bottom, right, top] to geojson FeatureCollection.\n\n Args:\n coords (list): A list of bbox coordinates representing [left, bottom, right, top].\n\n Returns:\n dict: A geojson FeatureCollection.\n '
features = []
for bbox in coords:
features.append(bbox_to_geojson(bbox))
return {'type': 'FeatureCollection', 'features': features} | def coords_to_geojson(coords):
'Convert a list of bbox coordinates representing [left, bottom, right, top] to geojson FeatureCollection.\n\n Args:\n coords (list): A list of bbox coordinates representing [left, bottom, right, top].\n\n Returns:\n dict: A geojson FeatureCollection.\n '
features = []
for bbox in coords:
features.append(bbox_to_geojson(bbox))
return {'type': 'FeatureCollection', 'features': features}<|docstring|>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.<|endoftext|> |
7a185c8ef5f433f7b2c4875c224f0d76833fe8d1b21cc2ae4a1d1c26333de426 | def explode(coords):
"Explode a GeoJSON geometry's coordinates object and yield\n coordinate tuples. As long as the input is conforming, the type of\n the geometry doesn't matter. From Fiona 1.4.8\n\n Args:\n coords (list): A list of coordinates.\n\n Yields:\n [type]: [description]\n "
for e in coords:
if isinstance(e, (float, int)):
(yield coords)
break
else:
for f in explode(e):
(yield f) | 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] | geemap/common.py | explode | arheem/geemap | 1 | python | def explode(coords):
"Explode a GeoJSON geometry's coordinates object and yield\n coordinate tuples. As long as the input is conforming, the type of\n the geometry doesn't matter. From Fiona 1.4.8\n\n Args:\n coords (list): A list of coordinates.\n\n Yields:\n [type]: [description]\n "
for e in coords:
if isinstance(e, (float, int)):
(yield coords)
break
else:
for f in explode(e):
(yield f) | def explode(coords):
"Explode a GeoJSON geometry's coordinates object and yield\n coordinate tuples. As long as the input is conforming, the type of\n the geometry doesn't matter. From Fiona 1.4.8\n\n Args:\n coords (list): A list of coordinates.\n\n Yields:\n [type]: [description]\n "
for e in coords:
if isinstance(e, (float, int)):
(yield coords)
break
else:
for f in explode(e):
(yield f)<|docstring|>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]<|endoftext|> |
93c39647ff5d60144a9c4d235383727d07de1171a96de83883fabce805d119eb | def get_bounds(geometry, north_up=True, transform=None):
'Bounding box of a GeoJSON geometry, GeometryCollection, or FeatureCollection.\n left, bottom, right, top\n *not* xmin, ymin, xmax, ymax\n If not north_up, y will be switched to guarantee the above.\n Source code adapted from https://github.com/mapbox/rasterio/blob/master/rasterio/features.py#L361\n\n Args:\n geometry (dict): A GeoJSON dict.\n north_up (bool, optional): . Defaults to True.\n transform ([type], optional): . Defaults to None.\n\n Returns:\n list: A list of coordinates representing [left, bottom, right, top]\n '
if ('bbox' in geometry):
return tuple(geometry['bbox'])
geometry = (geometry.get('geometry') or geometry)
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):
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):
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):
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]))
raise ValueError('geometry must be a GeoJSON-like geometry, GeometryCollection, or FeatureCollection') | 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] | geemap/common.py | get_bounds | arheem/geemap | 1 | python | def get_bounds(geometry, north_up=True, transform=None):
'Bounding box of a GeoJSON geometry, GeometryCollection, or FeatureCollection.\n left, bottom, right, top\n *not* xmin, ymin, xmax, ymax\n If not north_up, y will be switched to guarantee the above.\n Source code adapted from https://github.com/mapbox/rasterio/blob/master/rasterio/features.py#L361\n\n Args:\n geometry (dict): A GeoJSON dict.\n north_up (bool, optional): . Defaults to True.\n transform ([type], optional): . Defaults to None.\n\n Returns:\n list: A list of coordinates representing [left, bottom, right, top]\n '
if ('bbox' in geometry):
return tuple(geometry['bbox'])
geometry = (geometry.get('geometry') or geometry)
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):
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):
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):
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]))
raise ValueError('geometry must be a GeoJSON-like geometry, GeometryCollection, or FeatureCollection') | def get_bounds(geometry, north_up=True, transform=None):
'Bounding box of a GeoJSON geometry, GeometryCollection, or FeatureCollection.\n left, bottom, right, top\n *not* xmin, ymin, xmax, ymax\n If not north_up, y will be switched to guarantee the above.\n Source code adapted from https://github.com/mapbox/rasterio/blob/master/rasterio/features.py#L361\n\n Args:\n geometry (dict): A GeoJSON dict.\n north_up (bool, optional): . Defaults to True.\n transform ([type], optional): . Defaults to None.\n\n Returns:\n list: A list of coordinates representing [left, bottom, right, top]\n '
if ('bbox' in geometry):
return tuple(geometry['bbox'])
geometry = (geometry.get('geometry') or geometry)
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):
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):
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):
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]))
raise ValueError('geometry must be a GeoJSON-like geometry, GeometryCollection, or FeatureCollection')<|docstring|>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]<|endoftext|> |
37d13956b56a8d7416b2a36732e8009207788697f16a3674aa8a25ef03108383 | def get_center(geometry, north_up=True, transform=None):
'Get the centroid of a GeoJSON.\n\n Args:\n geometry (dict): A GeoJSON dict.\n north_up (bool, optional): . Defaults to True.\n transform ([type], optional): . Defaults to None.\n\n Returns:\n list: [lon, lat]\n '
bounds = get_bounds(geometry, north_up, transform)
center = (((bounds[0] + bounds[2]) / 2), ((bounds[1] + bounds[3]) / 2))
return center | 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] | geemap/common.py | get_center | arheem/geemap | 1 | python | def get_center(geometry, north_up=True, transform=None):
'Get the centroid of a GeoJSON.\n\n Args:\n geometry (dict): A GeoJSON dict.\n north_up (bool, optional): . Defaults to True.\n transform ([type], optional): . Defaults to None.\n\n Returns:\n list: [lon, lat]\n '
bounds = get_bounds(geometry, north_up, transform)
center = (((bounds[0] + bounds[2]) / 2), ((bounds[1] + bounds[3]) / 2))
return center | def get_center(geometry, north_up=True, transform=None):
'Get the centroid of a GeoJSON.\n\n Args:\n geometry (dict): A GeoJSON dict.\n north_up (bool, optional): . Defaults to True.\n transform ([type], optional): . Defaults to None.\n\n Returns:\n list: [lon, lat]\n '
bounds = get_bounds(geometry, north_up, transform)
center = (((bounds[0] + bounds[2]) / 2), ((bounds[1] + bounds[3]) / 2))
return center<|docstring|>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]<|endoftext|> |
ca10a78c082e426d2fe1da3d0abbe202a5fff5ed00d6b8c3c2899ef8a70b7244 | def image_props(img, date_format='YYYY-MM-dd'):
"Gets image properties.\n\n Args:\n img (ee.Image): The input image.\n date_format (str, optional): The output date format. Defaults to 'YYYY-MM-dd HH:mm:ss'.\n\n Returns:\n dd.Dictionary: The dictionary containing image properties.\n "
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.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(1000000.0).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 | 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. | geemap/common.py | image_props | arheem/geemap | 1 | python | def image_props(img, date_format='YYYY-MM-dd'):
"Gets image properties.\n\n Args:\n img (ee.Image): The input image.\n date_format (str, optional): The output date format. Defaults to 'YYYY-MM-dd HH:mm:ss'.\n\n Returns:\n dd.Dictionary: The dictionary containing image properties.\n "
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.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(1000000.0).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 | def image_props(img, date_format='YYYY-MM-dd'):
"Gets image properties.\n\n Args:\n img (ee.Image): The input image.\n date_format (str, optional): The output date format. Defaults to 'YYYY-MM-dd HH:mm:ss'.\n\n Returns:\n dd.Dictionary: The dictionary containing image properties.\n "
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.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(1000000.0).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<|docstring|>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.<|endoftext|> |
d443a67b732deefea558641b19ae90ea843347beec706e6d6f6f5265d51710ca | def image_stats(img, region=None, scale=None):
"Gets image descriptive statistics.\n\n Args:\n img (ee.Image): The input image to calculate descriptive statistics.\n region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n\n Returns:\n ee.Dictionary: A dictionary containing the description statistics of the input image.\n "
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 | 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. | geemap/common.py | image_stats | arheem/geemap | 1 | python | def image_stats(img, region=None, scale=None):
"Gets image descriptive statistics.\n\n Args:\n img (ee.Image): The input image to calculate descriptive statistics.\n region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n\n Returns:\n ee.Dictionary: A dictionary containing the description statistics of the input image.\n "
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 | def image_stats(img, region=None, scale=None):
"Gets image descriptive statistics.\n\n Args:\n img (ee.Image): The input image to calculate descriptive statistics.\n region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n\n Returns:\n ee.Dictionary: A dictionary containing the description statistics of the input image.\n "
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<|docstring|>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.<|endoftext|> |
cbf729f15ae355a504e304c9a0e51a54ea6d365ba6dfaf1219cf1b7f6f72e629 | def adjust_longitude(in_fc):
'Adjusts longitude if it is less than -180 or greater than 180.\n\n Args:\n in_fc (dict): The input dictionary containing coordinates.\n\n Returns:\n dict: A dictionary containing the converted longitudes\n '
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 | 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 | geemap/common.py | adjust_longitude | arheem/geemap | 1 | python | def adjust_longitude(in_fc):
'Adjusts longitude if it is less than -180 or greater than 180.\n\n Args:\n in_fc (dict): The input dictionary containing coordinates.\n\n Returns:\n dict: A dictionary containing the converted longitudes\n '
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 | def adjust_longitude(in_fc):
'Adjusts longitude if it is less than -180 or greater than 180.\n\n Args:\n in_fc (dict): The input dictionary containing coordinates.\n\n Returns:\n dict: A dictionary containing the converted longitudes\n '
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<|docstring|>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<|endoftext|> |
15d7b7df6628441a2458e85358e05285172f1e7b1329e16c32acf9f954e43347 | def zonal_statistics(in_value_raster, in_zone_vector, out_file_path, statistics_type='MEAN', scale=None, crs=None, tile_scale=1.0, **kwargs):
"Summarizes the values of a raster within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.\n\n Args:\n in_value_raster (object): An ee.Image that contains the values on which to calculate a statistic.\n in_zone_vector (object): An ee.FeatureCollection that defines the zones.\n out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz\n statistics_type (str, optional): Statistic type to be calculated. Defaults to 'MEAN'. For 'HIST', you can provide three parameters: max_buckets, min_bucket_width, and max_raw. For 'FIXED_HIST', you must provide three parameters: hist_min, hist_max, and hist_steps.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n crs (str, optional): The projection to work in. 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.\n 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. Defaults to 1.0.\n "
if (not isinstance(in_value_raster, ee.Image)):
print('The input raster must be an ee.Image.')
return
if (not isinstance(in_zone_vector, ee.FeatureCollection)):
print('The input zone data must be an ee.FeatureCollection.')
return
allowed_formats = ['csv', 'json', 'kml', 'kmz', 'shp']
filename = os.path.abspath(out_file_path)
basename = os.path.basename(filename)
filetype = os.path.splitext(basename)[1][1:].lower()
if (not (filetype in allowed_formats)):
print('The file type must be one of the following: {}'.format(', '.join(allowed_formats)))
return
max_buckets = None
min_bucket_width = None
max_raw = None
hist_min = 1.0
hist_max = 100.0
hist_steps = 10
if ('max_buckets' in kwargs.keys()):
max_buckets = kwargs['max_buckets']
if ('min_bucket_width' in kwargs.keys()):
min_bucket_width = kwargs['min_bucket']
if ('max_raw' in kwargs.keys()):
max_raw = kwargs['max_raw']
if ((statistics_type.upper() == 'FIXED_HIST') and ('hist_min' in kwargs.keys()) and ('hist_max' in kwargs.keys()) and ('hist_steps' in kwargs.keys())):
hist_min = kwargs['hist_min']
hist_max = kwargs['hist_max']
hist_steps = kwargs['hist_steps']
elif (statistics_type.upper() == 'FIXED_HIST'):
print('To use fixedHistogram, please provide these three parameters: hist_min, hist_max, and hist_steps.')
return
allowed_statistics = {'MEAN': ee.Reducer.mean(), 'MAXIMUM': ee.Reducer.max(), 'MEDIAN': ee.Reducer.median(), 'MINIMUM': ee.Reducer.min(), 'STD': ee.Reducer.stdDev(), 'MIN_MAX': ee.Reducer.minMax(), 'SUM': ee.Reducer.sum(), 'VARIANCE': ee.Reducer.variance(), 'HIST': ee.Reducer.histogram(maxBuckets=max_buckets, minBucketWidth=min_bucket_width, maxRaw=max_raw), 'FIXED_HIST': ee.Reducer.fixedHistogram(hist_min, hist_max, hist_steps)}
if (not (statistics_type.upper() in allowed_statistics.keys())):
print('The statistics type must be one of the following: {}'.format(', '.join(list(allowed_statistics.keys()))))
return
if (scale is None):
scale = in_value_raster.projection().nominalScale().multiply(10)
try:
print('Computing statistics ...')
result = in_value_raster.reduceRegions(collection=in_zone_vector, reducer=allowed_statistics[statistics_type], scale=scale, crs=crs, tileScale=tile_scale)
ee_export_vector(result, filename)
except Exception as e:
print(e) | Summarizes the values of a raster within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.
Args:
in_value_raster (object): An ee.Image that contains the values on which to calculate a statistic.
in_zone_vector (object): An ee.FeatureCollection that defines the zones.
out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz
statistics_type (str, optional): Statistic type to be calculated. Defaults to 'MEAN'. For 'HIST', you can provide three parameters: max_buckets, min_bucket_width, and max_raw. For 'FIXED_HIST', you must provide three parameters: hist_min, hist_max, and hist_steps.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
crs (str, optional): The projection to work in. 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.
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. Defaults to 1.0. | geemap/common.py | zonal_statistics | arheem/geemap | 1 | python | def zonal_statistics(in_value_raster, in_zone_vector, out_file_path, statistics_type='MEAN', scale=None, crs=None, tile_scale=1.0, **kwargs):
"Summarizes the values of a raster within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.\n\n Args:\n in_value_raster (object): An ee.Image that contains the values on which to calculate a statistic.\n in_zone_vector (object): An ee.FeatureCollection that defines the zones.\n out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz\n statistics_type (str, optional): Statistic type to be calculated. Defaults to 'MEAN'. For 'HIST', you can provide three parameters: max_buckets, min_bucket_width, and max_raw. For 'FIXED_HIST', you must provide three parameters: hist_min, hist_max, and hist_steps.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n crs (str, optional): The projection to work in. 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.\n 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. Defaults to 1.0.\n "
if (not isinstance(in_value_raster, ee.Image)):
print('The input raster must be an ee.Image.')
return
if (not isinstance(in_zone_vector, ee.FeatureCollection)):
print('The input zone data must be an ee.FeatureCollection.')
return
allowed_formats = ['csv', 'json', 'kml', 'kmz', 'shp']
filename = os.path.abspath(out_file_path)
basename = os.path.basename(filename)
filetype = os.path.splitext(basename)[1][1:].lower()
if (not (filetype in allowed_formats)):
print('The file type must be one of the following: {}'.format(', '.join(allowed_formats)))
return
max_buckets = None
min_bucket_width = None
max_raw = None
hist_min = 1.0
hist_max = 100.0
hist_steps = 10
if ('max_buckets' in kwargs.keys()):
max_buckets = kwargs['max_buckets']
if ('min_bucket_width' in kwargs.keys()):
min_bucket_width = kwargs['min_bucket']
if ('max_raw' in kwargs.keys()):
max_raw = kwargs['max_raw']
if ((statistics_type.upper() == 'FIXED_HIST') and ('hist_min' in kwargs.keys()) and ('hist_max' in kwargs.keys()) and ('hist_steps' in kwargs.keys())):
hist_min = kwargs['hist_min']
hist_max = kwargs['hist_max']
hist_steps = kwargs['hist_steps']
elif (statistics_type.upper() == 'FIXED_HIST'):
print('To use fixedHistogram, please provide these three parameters: hist_min, hist_max, and hist_steps.')
return
allowed_statistics = {'MEAN': ee.Reducer.mean(), 'MAXIMUM': ee.Reducer.max(), 'MEDIAN': ee.Reducer.median(), 'MINIMUM': ee.Reducer.min(), 'STD': ee.Reducer.stdDev(), 'MIN_MAX': ee.Reducer.minMax(), 'SUM': ee.Reducer.sum(), 'VARIANCE': ee.Reducer.variance(), 'HIST': ee.Reducer.histogram(maxBuckets=max_buckets, minBucketWidth=min_bucket_width, maxRaw=max_raw), 'FIXED_HIST': ee.Reducer.fixedHistogram(hist_min, hist_max, hist_steps)}
if (not (statistics_type.upper() in allowed_statistics.keys())):
print('The statistics type must be one of the following: {}'.format(', '.join(list(allowed_statistics.keys()))))
return
if (scale is None):
scale = in_value_raster.projection().nominalScale().multiply(10)
try:
print('Computing statistics ...')
result = in_value_raster.reduceRegions(collection=in_zone_vector, reducer=allowed_statistics[statistics_type], scale=scale, crs=crs, tileScale=tile_scale)
ee_export_vector(result, filename)
except Exception as e:
print(e) | def zonal_statistics(in_value_raster, in_zone_vector, out_file_path, statistics_type='MEAN', scale=None, crs=None, tile_scale=1.0, **kwargs):
"Summarizes the values of a raster within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.\n\n Args:\n in_value_raster (object): An ee.Image that contains the values on which to calculate a statistic.\n in_zone_vector (object): An ee.FeatureCollection that defines the zones.\n out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz\n statistics_type (str, optional): Statistic type to be calculated. Defaults to 'MEAN'. For 'HIST', you can provide three parameters: max_buckets, min_bucket_width, and max_raw. For 'FIXED_HIST', you must provide three parameters: hist_min, hist_max, and hist_steps.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n crs (str, optional): The projection to work in. 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.\n 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. Defaults to 1.0.\n "
if (not isinstance(in_value_raster, ee.Image)):
print('The input raster must be an ee.Image.')
return
if (not isinstance(in_zone_vector, ee.FeatureCollection)):
print('The input zone data must be an ee.FeatureCollection.')
return
allowed_formats = ['csv', 'json', 'kml', 'kmz', 'shp']
filename = os.path.abspath(out_file_path)
basename = os.path.basename(filename)
filetype = os.path.splitext(basename)[1][1:].lower()
if (not (filetype in allowed_formats)):
print('The file type must be one of the following: {}'.format(', '.join(allowed_formats)))
return
max_buckets = None
min_bucket_width = None
max_raw = None
hist_min = 1.0
hist_max = 100.0
hist_steps = 10
if ('max_buckets' in kwargs.keys()):
max_buckets = kwargs['max_buckets']
if ('min_bucket_width' in kwargs.keys()):
min_bucket_width = kwargs['min_bucket']
if ('max_raw' in kwargs.keys()):
max_raw = kwargs['max_raw']
if ((statistics_type.upper() == 'FIXED_HIST') and ('hist_min' in kwargs.keys()) and ('hist_max' in kwargs.keys()) and ('hist_steps' in kwargs.keys())):
hist_min = kwargs['hist_min']
hist_max = kwargs['hist_max']
hist_steps = kwargs['hist_steps']
elif (statistics_type.upper() == 'FIXED_HIST'):
print('To use fixedHistogram, please provide these three parameters: hist_min, hist_max, and hist_steps.')
return
allowed_statistics = {'MEAN': ee.Reducer.mean(), 'MAXIMUM': ee.Reducer.max(), 'MEDIAN': ee.Reducer.median(), 'MINIMUM': ee.Reducer.min(), 'STD': ee.Reducer.stdDev(), 'MIN_MAX': ee.Reducer.minMax(), 'SUM': ee.Reducer.sum(), 'VARIANCE': ee.Reducer.variance(), 'HIST': ee.Reducer.histogram(maxBuckets=max_buckets, minBucketWidth=min_bucket_width, maxRaw=max_raw), 'FIXED_HIST': ee.Reducer.fixedHistogram(hist_min, hist_max, hist_steps)}
if (not (statistics_type.upper() in allowed_statistics.keys())):
print('The statistics type must be one of the following: {}'.format(', '.join(list(allowed_statistics.keys()))))
return
if (scale is None):
scale = in_value_raster.projection().nominalScale().multiply(10)
try:
print('Computing statistics ...')
result = in_value_raster.reduceRegions(collection=in_zone_vector, reducer=allowed_statistics[statistics_type], scale=scale, crs=crs, tileScale=tile_scale)
ee_export_vector(result, filename)
except Exception as e:
print(e)<|docstring|>Summarizes the values of a raster within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.
Args:
in_value_raster (object): An ee.Image that contains the values on which to calculate a statistic.
in_zone_vector (object): An ee.FeatureCollection that defines the zones.
out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz
statistics_type (str, optional): Statistic type to be calculated. Defaults to 'MEAN'. For 'HIST', you can provide three parameters: max_buckets, min_bucket_width, and max_raw. For 'FIXED_HIST', you must provide three parameters: hist_min, hist_max, and hist_steps.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
crs (str, optional): The projection to work in. 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.
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. Defaults to 1.0.<|endoftext|> |
378578b0e075295b0490145293974696801100923d63febf9e1456aed8d7a2b1 | def zonal_statistics_by_group(in_value_raster, in_zone_vector, out_file_path, statistics_type='SUM', decimal_places=0, denominator=1.0, scale=None, crs=None, tile_scale=1.0):
"Summarizes the area or percentage of a raster by group within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.\n\n Args:\n in_value_raster (object): An integer Image that contains the values on which to calculate area/percentage.\n in_zone_vector (object): An ee.FeatureCollection that defines the zones.\n out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz\n statistics_type (str, optional): Can be either 'SUM' or 'PERCENTAGE' . Defaults to 'SUM'.\n decimal_places (int, optional): The number of decimal places to use. Defaults to 0.\n denominator (float, optional): To covert area units (e.g., from square meters to square kilometers). Defaults to 1.0.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n crs (str, optional): The projection to work in. 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.\n 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. Defaults to 1.0.\n\n "
if (not isinstance(in_value_raster, ee.Image)):
print('The input raster must be an ee.Image.')
return
band_count = in_value_raster.bandNames().size().getInfo()
band_name = ''
if (band_count == 1):
band_name = in_value_raster.bandNames().get(0)
else:
print('The input image can only have one band.')
return
band_types = in_value_raster.bandTypes().get(band_name).getInfo()
band_type = band_types.get('precision')
if (band_type != 'int'):
print('The input image band must be integer type.')
return
if (not isinstance(in_zone_vector, ee.FeatureCollection)):
print('The input zone data must be an ee.FeatureCollection.')
return
allowed_formats = ['csv', 'json', 'kml', 'kmz', 'shp']
filename = os.path.abspath(out_file_path)
basename = os.path.basename(filename)
filetype = os.path.splitext(basename)[1][1:]
if (not (filetype.lower() in allowed_formats)):
print('The file type must be one of the following: {}'.format(', '.join(allowed_formats)))
return
out_dir = os.path.dirname(filename)
if (not os.path.exists(out_dir)):
os.makedirs(out_dir)
allowed_statistics = ['SUM', 'PERCENTAGE']
if (not (statistics_type.upper() in allowed_statistics)):
print('The statistics type can only be one of {}'.format(', '.join(allowed_statistics)))
return
if (scale is None):
scale = in_value_raster.projection().nominalScale().multiply(10)
try:
print('Computing ... ')
geometry = in_zone_vector.geometry()
hist = in_value_raster.reduceRegion(ee.Reducer.frequencyHistogram(), geometry=geometry, bestEffort=True, scale=scale)
class_values = ee.Dictionary(hist.get(band_name)).keys().map((lambda v: ee.Number.parse(v))).sort()
class_names = class_values.map((lambda c: ee.String('Class_').cat(ee.Number(c).format())))
dataset = ee.Image.pixelArea().divide(denominator).addBands(in_value_raster)
init_result = dataset.reduceRegions(**{'collection': in_zone_vector, 'reducer': ee.Reducer.sum().group(**{'groupField': 1, 'groupName': 'group'}), 'scale': scale})
def get_keys(input_list):
return input_list.map((lambda x: ee.String('Class_').cat(ee.Number(ee.Dictionary(x).get('group')).format())))
def get_values(input_list):
decimal_format = '%.{}f'.format(decimal_places)
return input_list.map((lambda x: ee.Number.parse(ee.Number(ee.Dictionary(x).get('sum')).format(decimal_format))))
def set_attribute(f):
groups = ee.List(f.get('groups'))
keys = get_keys(groups)
values = get_values(groups)
total_area = ee.List(values).reduce(ee.Reducer.sum())
def get_class_values(x):
cls_value = ee.Algorithms.If(keys.contains(x), values.get(keys.indexOf(x)), 0)
cls_value = ee.Algorithms.If(ee.String(statistics_type).compareTo(ee.String('SUM')), ee.Number(cls_value).divide(ee.Number(total_area)), cls_value)
return cls_value
full_values = class_names.map((lambda x: get_class_values(x)))
attr_dict = ee.Dictionary.fromLists(class_names, full_values)
attr_dict = attr_dict.set('Class_sum', total_area)
return f.set(attr_dict).set('groups', None)
final_result = init_result.map(set_attribute)
ee_export_vector(final_result, filename)
except Exception as e:
print(e) | Summarizes the area or percentage of a raster by group within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.
Args:
in_value_raster (object): An integer Image that contains the values on which to calculate area/percentage.
in_zone_vector (object): An ee.FeatureCollection that defines the zones.
out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz
statistics_type (str, optional): Can be either 'SUM' or 'PERCENTAGE' . Defaults to 'SUM'.
decimal_places (int, optional): The number of decimal places to use. Defaults to 0.
denominator (float, optional): To covert area units (e.g., from square meters to square kilometers). Defaults to 1.0.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
crs (str, optional): The projection to work in. 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.
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. Defaults to 1.0. | geemap/common.py | zonal_statistics_by_group | arheem/geemap | 1 | python | def zonal_statistics_by_group(in_value_raster, in_zone_vector, out_file_path, statistics_type='SUM', decimal_places=0, denominator=1.0, scale=None, crs=None, tile_scale=1.0):
"Summarizes the area or percentage of a raster by group within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.\n\n Args:\n in_value_raster (object): An integer Image that contains the values on which to calculate area/percentage.\n in_zone_vector (object): An ee.FeatureCollection that defines the zones.\n out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz\n statistics_type (str, optional): Can be either 'SUM' or 'PERCENTAGE' . Defaults to 'SUM'.\n decimal_places (int, optional): The number of decimal places to use. Defaults to 0.\n denominator (float, optional): To covert area units (e.g., from square meters to square kilometers). Defaults to 1.0.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n crs (str, optional): The projection to work in. 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.\n 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. Defaults to 1.0.\n\n "
if (not isinstance(in_value_raster, ee.Image)):
print('The input raster must be an ee.Image.')
return
band_count = in_value_raster.bandNames().size().getInfo()
band_name =
if (band_count == 1):
band_name = in_value_raster.bandNames().get(0)
else:
print('The input image can only have one band.')
return
band_types = in_value_raster.bandTypes().get(band_name).getInfo()
band_type = band_types.get('precision')
if (band_type != 'int'):
print('The input image band must be integer type.')
return
if (not isinstance(in_zone_vector, ee.FeatureCollection)):
print('The input zone data must be an ee.FeatureCollection.')
return
allowed_formats = ['csv', 'json', 'kml', 'kmz', 'shp']
filename = os.path.abspath(out_file_path)
basename = os.path.basename(filename)
filetype = os.path.splitext(basename)[1][1:]
if (not (filetype.lower() in allowed_formats)):
print('The file type must be one of the following: {}'.format(', '.join(allowed_formats)))
return
out_dir = os.path.dirname(filename)
if (not os.path.exists(out_dir)):
os.makedirs(out_dir)
allowed_statistics = ['SUM', 'PERCENTAGE']
if (not (statistics_type.upper() in allowed_statistics)):
print('The statistics type can only be one of {}'.format(', '.join(allowed_statistics)))
return
if (scale is None):
scale = in_value_raster.projection().nominalScale().multiply(10)
try:
print('Computing ... ')
geometry = in_zone_vector.geometry()
hist = in_value_raster.reduceRegion(ee.Reducer.frequencyHistogram(), geometry=geometry, bestEffort=True, scale=scale)
class_values = ee.Dictionary(hist.get(band_name)).keys().map((lambda v: ee.Number.parse(v))).sort()
class_names = class_values.map((lambda c: ee.String('Class_').cat(ee.Number(c).format())))
dataset = ee.Image.pixelArea().divide(denominator).addBands(in_value_raster)
init_result = dataset.reduceRegions(**{'collection': in_zone_vector, 'reducer': ee.Reducer.sum().group(**{'groupField': 1, 'groupName': 'group'}), 'scale': scale})
def get_keys(input_list):
return input_list.map((lambda x: ee.String('Class_').cat(ee.Number(ee.Dictionary(x).get('group')).format())))
def get_values(input_list):
decimal_format = '%.{}f'.format(decimal_places)
return input_list.map((lambda x: ee.Number.parse(ee.Number(ee.Dictionary(x).get('sum')).format(decimal_format))))
def set_attribute(f):
groups = ee.List(f.get('groups'))
keys = get_keys(groups)
values = get_values(groups)
total_area = ee.List(values).reduce(ee.Reducer.sum())
def get_class_values(x):
cls_value = ee.Algorithms.If(keys.contains(x), values.get(keys.indexOf(x)), 0)
cls_value = ee.Algorithms.If(ee.String(statistics_type).compareTo(ee.String('SUM')), ee.Number(cls_value).divide(ee.Number(total_area)), cls_value)
return cls_value
full_values = class_names.map((lambda x: get_class_values(x)))
attr_dict = ee.Dictionary.fromLists(class_names, full_values)
attr_dict = attr_dict.set('Class_sum', total_area)
return f.set(attr_dict).set('groups', None)
final_result = init_result.map(set_attribute)
ee_export_vector(final_result, filename)
except Exception as e:
print(e) | def zonal_statistics_by_group(in_value_raster, in_zone_vector, out_file_path, statistics_type='SUM', decimal_places=0, denominator=1.0, scale=None, crs=None, tile_scale=1.0):
"Summarizes the area or percentage of a raster by group within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.\n\n Args:\n in_value_raster (object): An integer Image that contains the values on which to calculate area/percentage.\n in_zone_vector (object): An ee.FeatureCollection that defines the zones.\n out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz\n statistics_type (str, optional): Can be either 'SUM' or 'PERCENTAGE' . Defaults to 'SUM'.\n decimal_places (int, optional): The number of decimal places to use. Defaults to 0.\n denominator (float, optional): To covert area units (e.g., from square meters to square kilometers). Defaults to 1.0.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n crs (str, optional): The projection to work in. 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.\n 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. Defaults to 1.0.\n\n "
if (not isinstance(in_value_raster, ee.Image)):
print('The input raster must be an ee.Image.')
return
band_count = in_value_raster.bandNames().size().getInfo()
band_name =
if (band_count == 1):
band_name = in_value_raster.bandNames().get(0)
else:
print('The input image can only have one band.')
return
band_types = in_value_raster.bandTypes().get(band_name).getInfo()
band_type = band_types.get('precision')
if (band_type != 'int'):
print('The input image band must be integer type.')
return
if (not isinstance(in_zone_vector, ee.FeatureCollection)):
print('The input zone data must be an ee.FeatureCollection.')
return
allowed_formats = ['csv', 'json', 'kml', 'kmz', 'shp']
filename = os.path.abspath(out_file_path)
basename = os.path.basename(filename)
filetype = os.path.splitext(basename)[1][1:]
if (not (filetype.lower() in allowed_formats)):
print('The file type must be one of the following: {}'.format(', '.join(allowed_formats)))
return
out_dir = os.path.dirname(filename)
if (not os.path.exists(out_dir)):
os.makedirs(out_dir)
allowed_statistics = ['SUM', 'PERCENTAGE']
if (not (statistics_type.upper() in allowed_statistics)):
print('The statistics type can only be one of {}'.format(', '.join(allowed_statistics)))
return
if (scale is None):
scale = in_value_raster.projection().nominalScale().multiply(10)
try:
print('Computing ... ')
geometry = in_zone_vector.geometry()
hist = in_value_raster.reduceRegion(ee.Reducer.frequencyHistogram(), geometry=geometry, bestEffort=True, scale=scale)
class_values = ee.Dictionary(hist.get(band_name)).keys().map((lambda v: ee.Number.parse(v))).sort()
class_names = class_values.map((lambda c: ee.String('Class_').cat(ee.Number(c).format())))
dataset = ee.Image.pixelArea().divide(denominator).addBands(in_value_raster)
init_result = dataset.reduceRegions(**{'collection': in_zone_vector, 'reducer': ee.Reducer.sum().group(**{'groupField': 1, 'groupName': 'group'}), 'scale': scale})
def get_keys(input_list):
return input_list.map((lambda x: ee.String('Class_').cat(ee.Number(ee.Dictionary(x).get('group')).format())))
def get_values(input_list):
decimal_format = '%.{}f'.format(decimal_places)
return input_list.map((lambda x: ee.Number.parse(ee.Number(ee.Dictionary(x).get('sum')).format(decimal_format))))
def set_attribute(f):
groups = ee.List(f.get('groups'))
keys = get_keys(groups)
values = get_values(groups)
total_area = ee.List(values).reduce(ee.Reducer.sum())
def get_class_values(x):
cls_value = ee.Algorithms.If(keys.contains(x), values.get(keys.indexOf(x)), 0)
cls_value = ee.Algorithms.If(ee.String(statistics_type).compareTo(ee.String('SUM')), ee.Number(cls_value).divide(ee.Number(total_area)), cls_value)
return cls_value
full_values = class_names.map((lambda x: get_class_values(x)))
attr_dict = ee.Dictionary.fromLists(class_names, full_values)
attr_dict = attr_dict.set('Class_sum', total_area)
return f.set(attr_dict).set('groups', None)
final_result = init_result.map(set_attribute)
ee_export_vector(final_result, filename)
except Exception as e:
print(e)<|docstring|>Summarizes the area or percentage of a raster by group within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.
Args:
in_value_raster (object): An integer Image that contains the values on which to calculate area/percentage.
in_zone_vector (object): An ee.FeatureCollection that defines the zones.
out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz
statistics_type (str, optional): Can be either 'SUM' or 'PERCENTAGE' . Defaults to 'SUM'.
decimal_places (int, optional): The number of decimal places to use. Defaults to 0.
denominator (float, optional): To covert area units (e.g., from square meters to square kilometers). Defaults to 1.0.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
crs (str, optional): The projection to work in. 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.
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. Defaults to 1.0.<|endoftext|> |
af19a4b8c5432f9b665d0bfff518e17c1d2f4347557a6f89fc4538ffc90808cc | def vec_area(fc):
'Calculate the area (m2) of each each feature in a feature collection.\n\n Args:\n fc (object): The feature collection to compute the area.\n\n Returns:\n object: ee.FeatureCollection\n '
return fc.map((lambda f: f.set({'area_m2': f.area(1).round()}))) | Calculate the area (m2) of each each feature in a feature collection.
Args:
fc (object): The feature collection to compute the area.
Returns:
object: ee.FeatureCollection | geemap/common.py | vec_area | arheem/geemap | 1 | python | def vec_area(fc):
'Calculate the area (m2) of each each feature in a feature collection.\n\n Args:\n fc (object): The feature collection to compute the area.\n\n Returns:\n object: ee.FeatureCollection\n '
return fc.map((lambda f: f.set({'area_m2': f.area(1).round()}))) | def vec_area(fc):
'Calculate the area (m2) of each each feature in a feature collection.\n\n Args:\n fc (object): The feature collection to compute the area.\n\n Returns:\n object: ee.FeatureCollection\n '
return fc.map((lambda f: f.set({'area_m2': f.area(1).round()})))<|docstring|>Calculate the area (m2) of each each feature in a feature collection.
Args:
fc (object): The feature collection to compute the area.
Returns:
object: ee.FeatureCollection<|endoftext|> |
9c3b35429ce71379e485e6cac3c94038bf6c50ba3374ac213d8ac5f3da99f243 | def vec_area_km2(fc):
'Calculate the area (km2) of each each feature in a feature collection.\n\n Args:\n fc (object): The feature collection to compute the area.\n\n Returns:\n object: ee.FeatureCollection\n '
return fc.map((lambda f: f.set({'area_km2': f.area(1).divide(1000000.0).round()}))) | Calculate the area (km2) of each each feature in a feature collection.
Args:
fc (object): The feature collection to compute the area.
Returns:
object: ee.FeatureCollection | geemap/common.py | vec_area_km2 | arheem/geemap | 1 | python | def vec_area_km2(fc):
'Calculate the area (km2) of each each feature in a feature collection.\n\n Args:\n fc (object): The feature collection to compute the area.\n\n Returns:\n object: ee.FeatureCollection\n '
return fc.map((lambda f: f.set({'area_km2': f.area(1).divide(1000000.0).round()}))) | def vec_area_km2(fc):
'Calculate the area (km2) of each each feature in a feature collection.\n\n Args:\n fc (object): The feature collection to compute the area.\n\n Returns:\n object: ee.FeatureCollection\n '
return fc.map((lambda f: f.set({'area_km2': f.area(1).divide(1000000.0).round()})))<|docstring|>Calculate the area (km2) of each each feature in a feature collection.
Args:
fc (object): The feature collection to compute the area.
Returns:
object: ee.FeatureCollection<|endoftext|> |
bc4234a180a8cad22fd25bb6755077d60b38adc9b9967cfb8b7215830c88d831 | def vec_area_mi2(fc):
'Calculate the area (square mile) of each each feature in a feature collection.\n\n Args:\n fc (object): The feature collection to compute the area.\n\n Returns:\n object: ee.FeatureCollection\n '
return fc.map((lambda f: f.set({'area_mi2': f.area(1).divide(2590000.0).round()}))) | Calculate the area (square mile) of each each feature in a feature collection.
Args:
fc (object): The feature collection to compute the area.
Returns:
object: ee.FeatureCollection | geemap/common.py | vec_area_mi2 | arheem/geemap | 1 | python | def vec_area_mi2(fc):
'Calculate the area (square mile) of each each feature in a feature collection.\n\n Args:\n fc (object): The feature collection to compute the area.\n\n Returns:\n object: ee.FeatureCollection\n '
return fc.map((lambda f: f.set({'area_mi2': f.area(1).divide(2590000.0).round()}))) | def vec_area_mi2(fc):
'Calculate the area (square mile) of each each feature in a feature collection.\n\n Args:\n fc (object): The feature collection to compute the area.\n\n Returns:\n object: ee.FeatureCollection\n '
return fc.map((lambda f: f.set({'area_mi2': f.area(1).divide(2590000.0).round()})))<|docstring|>Calculate the area (square mile) of each each feature in a feature collection.
Args:
fc (object): The feature collection to compute the area.
Returns:
object: ee.FeatureCollection<|endoftext|> |
01af229549124c9b41e63ee7f3d32c22552ee7676c456b03bb1002eb49002ba3 | def vec_area_ha(fc):
'Calculate the area (hectare) of each each feature in a feature collection.\n\n Args:\n fc (object): The feature collection to compute the area.\n\n Returns:\n object: ee.FeatureCollection\n '
return fc.map((lambda f: f.set({'area_ha': f.area(1).divide(10000.0).round()}))) | Calculate the area (hectare) of each each feature in a feature collection.
Args:
fc (object): The feature collection to compute the area.
Returns:
object: ee.FeatureCollection | geemap/common.py | vec_area_ha | arheem/geemap | 1 | python | def vec_area_ha(fc):
'Calculate the area (hectare) of each each feature in a feature collection.\n\n Args:\n fc (object): The feature collection to compute the area.\n\n Returns:\n object: ee.FeatureCollection\n '
return fc.map((lambda f: f.set({'area_ha': f.area(1).divide(10000.0).round()}))) | def vec_area_ha(fc):
'Calculate the area (hectare) of each each feature in a feature collection.\n\n Args:\n fc (object): The feature collection to compute the area.\n\n Returns:\n object: ee.FeatureCollection\n '
return fc.map((lambda f: f.set({'area_ha': f.area(1).divide(10000.0).round()})))<|docstring|>Calculate the area (hectare) of each each feature in a feature collection.
Args:
fc (object): The feature collection to compute the area.
Returns:
object: ee.FeatureCollection<|endoftext|> |
c3aaeef907816c2c2f670dc57bc495517c67d1d56ce6eca62ee9f620c5dba721 | def remove_geometry(fc):
'Remove .geo coordinate field from a FeatureCollection\n\n Args:\n fc (object): The input FeatureCollection.\n\n Returns:\n object: The output FeatureCollection without the geometry field.\n '
return fc.select(['.*'], None, False) | Remove .geo coordinate field from a FeatureCollection
Args:
fc (object): The input FeatureCollection.
Returns:
object: The output FeatureCollection without the geometry field. | geemap/common.py | remove_geometry | arheem/geemap | 1 | python | def remove_geometry(fc):
'Remove .geo coordinate field from a FeatureCollection\n\n Args:\n fc (object): The input FeatureCollection.\n\n Returns:\n object: The output FeatureCollection without the geometry field.\n '
return fc.select(['.*'], None, False) | def remove_geometry(fc):
'Remove .geo coordinate field from a FeatureCollection\n\n Args:\n fc (object): The input FeatureCollection.\n\n Returns:\n object: The output FeatureCollection without the geometry field.\n '
return fc.select(['.*'], None, False)<|docstring|>Remove .geo coordinate field from a FeatureCollection
Args:
fc (object): The input FeatureCollection.
Returns:
object: The output FeatureCollection without the geometry field.<|endoftext|> |
eb1d33a0fb814f7cf6359aacc52fb72433e2202ffca9c4198c1f04bfc4af0f88 | def image_cell_size(img):
'Retrieves the image cell size (e.g., spatial resolution)\n\n Args:\n img (object): ee.Image\n\n Returns:\n float: The nominal scale in meters.\n '
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 | Retrieves the image cell size (e.g., spatial resolution)
Args:
img (object): ee.Image
Returns:
float: The nominal scale in meters. | geemap/common.py | image_cell_size | arheem/geemap | 1 | python | def image_cell_size(img):
'Retrieves the image cell size (e.g., spatial resolution)\n\n Args:\n img (object): ee.Image\n\n Returns:\n float: The nominal scale in meters.\n '
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 | def image_cell_size(img):
'Retrieves the image cell size (e.g., spatial resolution)\n\n Args:\n img (object): ee.Image\n\n Returns:\n float: The nominal scale in meters.\n '
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<|docstring|>Retrieves the image cell size (e.g., spatial resolution)
Args:
img (object): ee.Image
Returns:
float: The nominal scale in meters.<|endoftext|> |
cb4dbce0afb25974099f407fba7827eb61f5042d33d8cf144dfaa9efeec1c544 | def image_scale(img):
'Retrieves the image cell size (e.g., spatial resolution)\n\n Args:\n img (object): ee.Image\n\n Returns:\n float: The nominal scale in meters.\n '
return img.select(0).projection().nominalScale() | Retrieves the image cell size (e.g., spatial resolution)
Args:
img (object): ee.Image
Returns:
float: The nominal scale in meters. | geemap/common.py | image_scale | arheem/geemap | 1 | python | def image_scale(img):
'Retrieves the image cell size (e.g., spatial resolution)\n\n Args:\n img (object): ee.Image\n\n Returns:\n float: The nominal scale in meters.\n '
return img.select(0).projection().nominalScale() | def image_scale(img):
'Retrieves the image cell size (e.g., spatial resolution)\n\n Args:\n img (object): ee.Image\n\n Returns:\n float: The nominal scale in meters.\n '
return img.select(0).projection().nominalScale()<|docstring|>Retrieves the image cell size (e.g., spatial resolution)
Args:
img (object): ee.Image
Returns:
float: The nominal scale in meters.<|endoftext|> |
de4acde04173fc7dc39a6b9dabb7c65312d241b0d6004b089b34edfa45aaed72 | def image_band_names(img):
'Gets image band names.\n\n Args:\n img (ee.Image): The input image.\n\n Returns:\n ee.List: The returned list of image band names.\n '
return img.bandNames() | Gets image band names.
Args:
img (ee.Image): The input image.
Returns:
ee.List: The returned list of image band names. | geemap/common.py | image_band_names | arheem/geemap | 1 | python | def image_band_names(img):
'Gets image band names.\n\n Args:\n img (ee.Image): The input image.\n\n Returns:\n ee.List: The returned list of image band names.\n '
return img.bandNames() | def image_band_names(img):
'Gets image band names.\n\n Args:\n img (ee.Image): The input image.\n\n Returns:\n ee.List: The returned list of image band names.\n '
return img.bandNames()<|docstring|>Gets image band names.
Args:
img (ee.Image): The input image.
Returns:
ee.List: The returned list of image band names.<|endoftext|> |
1bfc923d049919e14b44a97c8e2684a3842189b37e289477a9fde82fcd976998 | def image_date(img, date_format='YYYY-MM-dd'):
"Retrieves the image acquisition date.\n\n Args:\n img (object): ee.Image\n date_format (str, optional): The date format to use. Defaults to 'YYYY-MM-dd'.\n\n Returns:\n str: A string representing the acquisition of the image.\n "
return ee.Date(img.get('system:time_start')).format(date_format) | 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. | geemap/common.py | image_date | arheem/geemap | 1 | python | def image_date(img, date_format='YYYY-MM-dd'):
"Retrieves the image acquisition date.\n\n Args:\n img (object): ee.Image\n date_format (str, optional): The date format to use. Defaults to 'YYYY-MM-dd'.\n\n Returns:\n str: A string representing the acquisition of the image.\n "
return ee.Date(img.get('system:time_start')).format(date_format) | def image_date(img, date_format='YYYY-MM-dd'):
"Retrieves the image acquisition date.\n\n Args:\n img (object): ee.Image\n date_format (str, optional): The date format to use. Defaults to 'YYYY-MM-dd'.\n\n Returns:\n str: A string representing the acquisition of the image.\n "
return ee.Date(img.get('system:time_start')).format(date_format)<|docstring|>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.<|endoftext|> |
a9cd618ddcec8b34b3a851c1438955d04313a6acabb764bbb17d3dafd837795c | def image_dates(img_col, date_format='YYYY-MM-dd'):
"Get image dates of all images in an ImageCollection.\n\n Args:\n img_col (object): ee.ImageCollection\n 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'.\n\n Returns:\n object: ee.List\n "
dates = img_col.aggregate_array('system:time_start')
new_dates = dates.map((lambda d: ee.Date(d).format(date_format)))
return new_dates | 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 | geemap/common.py | image_dates | arheem/geemap | 1 | python | def image_dates(img_col, date_format='YYYY-MM-dd'):
"Get image dates of all images in an ImageCollection.\n\n Args:\n img_col (object): ee.ImageCollection\n 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'.\n\n Returns:\n object: ee.List\n "
dates = img_col.aggregate_array('system:time_start')
new_dates = dates.map((lambda d: ee.Date(d).format(date_format)))
return new_dates | def image_dates(img_col, date_format='YYYY-MM-dd'):
"Get image dates of all images in an ImageCollection.\n\n Args:\n img_col (object): ee.ImageCollection\n 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'.\n\n Returns:\n object: ee.List\n "
dates = img_col.aggregate_array('system:time_start')
new_dates = dates.map((lambda d: ee.Date(d).format(date_format)))
return new_dates<|docstring|>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<|endoftext|> |
88ed8e54ad7acd999949a02c529c8150f9b00dfdc8262571df5110bf469c09ac | def image_area(img, region=None, scale=None, denominator=1.0):
"Calculates the the area of an image.\n\n Args:\n img (object): ee.Image\n region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n denominator (float, optional): The denominator to use for converting size from square meters to other units. Defaults to 1.0.\n\n Returns:\n object: ee.Dictionary\n "
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': 1000000000000.0})
return img_area | 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 | geemap/common.py | image_area | arheem/geemap | 1 | python | def image_area(img, region=None, scale=None, denominator=1.0):
"Calculates the the area of an image.\n\n Args:\n img (object): ee.Image\n region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n denominator (float, optional): The denominator to use for converting size from square meters to other units. Defaults to 1.0.\n\n Returns:\n object: ee.Dictionary\n "
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': 1000000000000.0})
return img_area | def image_area(img, region=None, scale=None, denominator=1.0):
"Calculates the the area of an image.\n\n Args:\n img (object): ee.Image\n region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n denominator (float, optional): The denominator to use for converting size from square meters to other units. Defaults to 1.0.\n\n Returns:\n object: ee.Dictionary\n "
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': 1000000000000.0})
return img_area<|docstring|>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<|endoftext|> |
1d4c86f143e07a123ee75d81e90097efca619959cfec678ba5f9e87d95ab49e4 | def image_max_value(img, region=None, scale=None):
"Retrieves the maximum value of an image.\n\n Args:\n img (object): The image to calculate the maximum value.\n region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n\n Returns:\n object: ee.Number\n "
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': 1000000000000.0})
return max_value | 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 | geemap/common.py | image_max_value | arheem/geemap | 1 | python | def image_max_value(img, region=None, scale=None):
"Retrieves the maximum value of an image.\n\n Args:\n img (object): The image to calculate the maximum value.\n region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n\n Returns:\n object: ee.Number\n "
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': 1000000000000.0})
return max_value | def image_max_value(img, region=None, scale=None):
"Retrieves the maximum value of an image.\n\n Args:\n img (object): The image to calculate the maximum value.\n region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.\n scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.\n\n Returns:\n object: ee.Number\n "
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': 1000000000000.0})
return max_value<|docstring|>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<|endoftext|> |
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