mdsumner

"https://elevation-direct-downloads.s3-ap-southeast-2.amazonaws.com/5m-dem/national_utm_mosaics/cocosislandz47.zip" 

"https://elevation-direct-downloads.s3-ap-southeast-2.amazonaws.com/5m-dem/national_utm_mosaics/christmasislandz48.zip"

"https://elevation-direct-downloads.s3-ap-southeast-2.amazonaws.com/5m-dem/national_utm_mosaics/waz50.zip"

"https://elevation-direct-downloads.s3-ap-southeast-2.amazonaws.com/5m-dem/national_utm_mosaics/waz51.zip"

"https://elevation-direct-downloads.s3-ap-southeast-2.amazonaws.com/5m-dem/national_utm_mosaics/ntz52.zip"

robbibt

metadata:
  generateName: dea-coastlines-
  namespace: processing
  labels:
    workflows.argoproj.io/workflow-template: dea-coastlines
spec:
  arguments:
    parameters:
      - name: image-tag
        value: 1.2.3

jscarto

blue_fluorite = ['#291b32', '#2a1b34', '#2b1b34', '#2d1c36', '#2f1c38', '#301c39', '#301d3a', '#321d3b', '#331d3d', '#351d3f', '#351e40', '#371e41', '#381e43', '#3a1e45', '#3b1f45', '#3c1f46', '#3e1f48', '#3f1f4a', '#401f4c', '#42204d', '#43204e', '#44204f', '#462051', '#472052', '#482054', '#4a2056', '#4a2157', '#4c2158', '#4e215a', '#4f215b', '#50215d', '#52215e', '#532160', '#552162', '#552263', '#562264', '#582265', '#592267', '#5b2268', '#5c226b', '#5e226c', '#5f226e', '#60226f', '#622271', '#632272', '#642274', '#662276', '#672277', '#692278', '#6a227a', '#6c227b', '#6e227d', '#6e237e', '#6f247f', '#702480', '#712581', '#722681', '#732683', '#742783', '#752884', '#762985', '#772987', '#792a87', '#792b88', '#7a2c89', '#7b2c8a', '#7c2d8a', '#7d2d8c', '#7e2e8d', '#7f2f8d', '#80308e', '#813190', '#823191', '#833292', '#843292', '#863393', '#863494', '#873595', '#893596', '#8a3697', '#8b3798', '#8b3899', '#8c389a', '#8e399b', '#8e3a9c', '#8f3b9c', '#8f3d9d', '#8f3e9e', '#903f9e', '#90419e', '#90439f', '#9044

KMarkert

import os
import fire
import h5py
import glob
import tqdm
import numpy as np
import pandas as pd
# requires h5py, tqdm, fire, numpy, and pandas to run

Kirill888

mhweber

import geopandas as gpd
import rasterio
import fiona
from rasterio import features
import os
from datetime import datetime as dt

def Rasterize(shapefile, inras, outras, meta, field):
    with rasterio.open(inras) as src:
        kwargs = src.meta.copy()

rabernat

import xarray as xr
import numpy as np

# create an example dataset
da = xr.DataArray(np.random.rand(10,30,40), dims=['time', 'lat', 'lon'])

# define a function to compute a linear trend of a timeseries
def linear_trend(x):
    pf = np.polyfit(x.time, x, 1)
    # we need to return a dataarray or else xarray's groupby won't be happy

lukas-h

Markdown License badges

Collection of License badges for your Project's README file.
This list includes the most common open source and open data licenses.
Easily copy and paste the code under the badges into your Markdown files.

Notes

• The badges do not fully replace the license informations for your projects, they are only emblems for the README, that the user can see the License at first glance.

Translations: (No guarantee that the translations are up-to-date)

• 🇫🇷 Cette liste en français

lizecillie

from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, img_as_float


def median_filter(A, filter_size):
    rows = np.shape(A)[0]
    columns = np.shape(A)[1]
    half_length = int((filter_size-1)/2)