| from nltk.tokenize import sent_tokenize, word_tokenize | |
| from nltk.tokenize.treebank import TreebankWordDetokenizer | |
| import argparse | |
| #python sillylib.py --input bed button kill be black open clothes also | |
| # inspired by https://towardsdatascience.com/a-i-plays-mad-libs-and-the-results-are-terrifying-78fa44e7f04e | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--input',nargs='+',default=['bed','button','kill','be','black','open','clothes','even']) | |
| parser.add_argument('--person',default='AI') |
| def cleanupPairs(pathh): | |
| import glob | |
| for i in glob.glob(pathh+'JPEGImages/'+'*png'): | |
| filename_split = os.path.splitext(i) | |
| filename_zero, fileext = filename_split | |
| basename = os.path.basename(filename_zero) | |
| if not os.path.isfile(pathh+'Annotations/'+basename+'.xml'): | |
| os.system('rm '+i) | |
| for i in glob.glob(pathh+'Annotations/'+'*xml'): | |
| filename_split = os.path.splitext(i) |
| 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): |
| 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 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) |
| 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/ |
| gdalbuildvrt foo.vrt /vsicurl/http://opendata.digitalglobe.com/cyclone-kenneth/pre-event/2017-10-28/103001007206B400/3023101.tif |
| %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) |
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!...
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(')'))