Regions

regions of interest stuff

import p4tools as p4t

from p4tools.io import get_region_names
from matplotlib import pyplot as plt, rcParams

rcParams['image.origin'] = 'lower'

regions = get_region_names()

sorted(regions.roi_name.unique())

['Albany',
 'Atka',
 'Bilbao',
 'Binghamton',
 'BuenosAires',
 'Caterpillar',
 'Cortland',
 'Geneseo',
 'Giza',
 'Halifax',
 'Inca_City',
 'Inca_City_Ridges',
 'Ithaca',
 'Macclesfield',
 'Manhattan2',
 'Manhattan_Classic',
 'Manhattan_Cracks',
 'Manhattan_Frontinella',
 'Oswego_Edge',
 'Pisaq',
 'Portsmouth',
 'Potsdam',
 'Rochester',
 'Schenectady',
 'Starburst',
 'Troy',
 'Wellington',
 'unknown']

regions.query("roi_name=='Manhattan_Cracks'").describe().loc['mean', 'lat_IND':'lon_IND'].values

array([-86.25696154,  98.77399231])

regions.query("roi_name=='Halifax'").describe()

	lat_IND	lon_IND	minimal_distance	lat_WORD	lon_WORD	MY
count	3.000000	3.000000	3.000000	3.0	3.0	3.0
mean	-87.042933	72.417667	4.849353	-87.0	72.3	29.0
std	0.001858	0.054604	0.245513	0.0	0.0	0.0
min	-87.044200	72.356400	4.568739	-87.0	72.3	29.0
25%	-87.044000	72.395900	4.761747	-87.0	72.3	29.0
50%	-87.043800	72.435400	4.954755	-87.0	72.3	29.0
75%	-87.042300	72.448300	4.989660	-87.0	72.3	29.0
max	-87.040800	72.461200	5.024566	-87.0	72.3	29.0

lon = 103.901
lat = -85.401

west = lon - 2
east = lon + 2

north = lat + 0.1
south = lat - 0.1

import math

image_size = (600, 800, 3)

lat, lon

(-85.401, 103.901)

import folium

tiles='https://astro.arcgis.com/arcgis/rest/services/OnMars/MColorDEM/MapServer/tile/{z}/{y}/{x}'

crs = 'EPSG3857'
# crs = 'Simple'
# crs = 'EPSG3031'
m = folium.Map(location=[lat, lon], zoom_start=3, tiles=tiles, attr='usgs/esri', crs=crs)

m

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