##Using Jupyter Notebook with AWS EC2 Thanks to http://blog.impiyush.me/2015/02/running-ipython-notebook-server-on-aws.html #Ensure your EC2 aws security settings are set appropriately: SSH TCP 22 Anywhere HTTPS TCP 443 Anywhere CustomTCPRule TCP 8888 Anywhere
#Create a sha1 key for ipython:
$ ipython
Image download from ftp --> upload to AWS s3 bucket
Pull to AWS EC2 machine. Save off SWIR band ratios as tif with appropriate contrast. The SWIR data is an 8-band image. You need to first grab onnly the bands you need for visualization (8-3-1 or 6-3-1 make good combos)
import rasterio
import numpy as np
raster='18NOV09185526-A2AS-058669488010_01_P001.TIF'. #your file
import os
import geopandas as gpd
import pandas as pd
import boto3
os.system('cat my_roi_4326.geojson | supermercado burn 18 | mercantile shapes | fio collect > my_roi_4326_outtiles.geojson')
tiles = gpd.read_file('my_roi_4326_outtiles.geojson')
tiles.id=tiles.id.map(lambda x: x.lstrip('(').rstrip(')'))
I have this lovely ‘Keep Pursuing’ backpack with the new EV logo (retail $295). I wanted to give it to the person in our team that has a proven track record of (1) collaborating with others, (2) being helpful, & (3) adding true technical value to our team. So maybe we start by looking at the tacos people on our team have recieved.
But wait!...
| %matplotlib inline | |
| import osmnx as ox | |
| import matplotlib.pyplot as plt | |
| place_name = "Nzwani Centre, Comores" | |
| graph = ox.graph_from_place(place_name, which_result=2) | |
| fig, ax = ox.plot_graph(graph) | |
| area = ox.gdf_from_place(place_name) | |
| buildings = ox.buildings_from_place(place_name) |
| gdalbuildvrt foo.vrt /vsicurl/http://opendata.digitalglobe.com/cyclone-kenneth/pre-event/2017-10-28/103001007206B400/3023101.tif |
| gdal_translate -of MBTiles nzwani_dg_wv3_05032019_104001004A465900.tif nzwani_dg_wv3_05032019_104001004A465900.mbtiles | |
| mb-util nzwani_dg_wv3_05032019_104001004A465900.mbtiles tiles/ |
| def flattenTMS(tilepath,zoom,voc): | |
| """flatten the TMS tile structure to put all images in one directory | |
| e.g. tilepath='tiles', zoom='18'""" | |
| import glob,os | |
| for i in glob.glob(tilepath+'/'+zoom+'/*/*png'): | |
| filename_split = os.path.splitext(i) | |
| filename_zero, fileext = filename_split | |
| basename = os.path.basename(filename_zero) | |
| strn=i.split('/')[-3]+'_'+i.split('/')[-2]+'_'+i.split('/')[-1] | |
| #write_xml_annotation(strn, boxCoords) |
| def footprint2box(vector): | |
| df = vector | |
| df['geometry']=df.envelope #round to nearest (axis-aligned) rectangle | |
| df['class']='building' #all buildings we will just call class=buildings | |
| return df | |
| df=footprint2box(buildings) |
| def ll2subpix(lat,long): | |
| import numpy as np | |
| n = 2 ** zoom | |
| xtile = n * ((long + 180) / 360) | |
| ytile = n * (1 - (np.log(np.tan(lat*np.pi/180) + 1/(np.cos(lat*np.pi/180))) / np.pi)) / 2 | |
| fracy,integery=np.modf(ytile) | |
| fracx,integerx=np.modf(xtile) | |
| return fracy,integery,fracx,integerx | |
| def tmsVOCxml(dirr,label): |
