def getmap(ifile, resolution = 'i'):
from mpl_toolkits.basemap import Basemap
from .conventions.ioapi import get_ioapi_sphere
if getattr(ifile, 'GDTYP', 0) in (2, 7) and all([hasattr(ifile, k) for k in 'P_GAM P_ALP P_BET XORIG YORIG XCELL YCELL'.split()]):
try:
NROWS = len(ifile.dimensions['ROW'])
NCOLS = len(ifile.dimensions['COL'])
except KeyError:
NROWS = ifile.NROWS
NCOLS = ifile.NCOLS
llcrnrx = ifile.XORIG
urcrnrx = ifile.XORIG + NCOLS * ifile.XCELL
llcrnry = ifile.YORIG
urcrnry = ifile.YORIG + NROWS * ifile.YCELL
semi_major_axis, semi_minor_axis = get_ioapi_sphere()
if ifile.GDTYP == 2:
from mpl_toolkits.basemap.pyproj import Proj
p = Proj(proj='lcc',rsphere = (semi_major_axis, semi_major_axis), lon_0 = ifile.P_GAM, lat_1 = ifile.P_ALP, lat_2 = ifile.P_BET, lat_0 = ifile.YCENT)
llcrnrlon, llcrnrlat = p(llcrnrx, llcrnry, inverse = True)
urcrnrlon, urcrnrlat = p(urcrnrx, urcrnry, inverse = True)
m = Basemap(projection = 'lcc', rsphere = (semi_major_axis, semi_major_axis), lon_0=ifile.P_GAM, lat_1 = ifile.P_ALP, lat_2 = ifile.P_BET, lat_0 = ifile.YCENT, llcrnrlon = llcrnrlon, llcrnrlat = llcrnrlat, urcrnrlat = urcrnrlat, urcrnrlon = urcrnrlon, resolution = resolution, suppress_ticks = False)
elif ifile.GDTYP == 7:
from mpl_toolkits.basemap.pyproj import Proj
mapstr = '+proj=merc +a=%s +b=%s +lat_ts=0 +lon_0=%s' % (semi_major_axis, semi_major_axis, ifile.XCENT)
p = Proj(mapstr)
#p = Proj(proj='merc',rsphere = (semi_major_axis, semi_major_axis), lat_ts = ifile.P_ALP, lat_0 = ifile.YCENT, lon_0 = ifile.XCENT)
llcrnrlon, llcrnrlat = p(llcrnrx, llcrnry, inverse = True)
urcrnrlon, urcrnrlat = p(urcrnrx, urcrnry, inverse = True)
m = Basemap(projection = 'merc', rsphere = (semi_major_axis, semi_major_axis), lon_0=ifile.XCENT, lat_ts = 0, llcrnrlon = llcrnrlon, llcrnrlat = llcrnrlat, urcrnrlat = urcrnrlat, urcrnrlon = urcrnrlon, resolution = resolution, suppress_ticks = False)
print('Found IO/API Mapping parameters')
else:
kwds = dict(suppress_ticks = False)
try:
lat, latunit = getlatbnds(ifile)
lon, lonunit = getlonbnds(ifile)
kwds['resolution'] = 'i'
kwds['llcrnrlat'] = lat[:].min()
kwds['urcrnrlat'] = lat[:].max()
kwds['llcrnrlon'] = lon[:].min()
kwds['urcrnrlon'] = lon[:].max()
kwds['resolution'] = resolution
except:
pass
m = Basemap(**kwds)
return m
def _define_proj(self, utm_zone):
'''
Define the UTM projection from specified UTM zone. Store it for later use.
'''
self.projs[utm_zone] = Proj(proj='utm',
zone=utm_zone,
ellps='WGS84',
datum='WGS84',
units='m')
def make_proj_grids(proj_dict, grid_dict):
map_proj = Proj(proj_dict)
sw_x, sw_y = map_proj(grid_dict['sw_lon'], grid_dict['sw_lat'])
ne_x, ne_y = map_proj(grid_dict['ne_lon'], grid_dict['ne_lat'])
dx = grid_dict['dx']
dy = grid_dict['dy']
x = np.arange(np.round(sw_x), np.round(ne_x) + dx, dx)
y = np.arange(np.round(sw_y), np.round(ne_y) + dy, dy)
x_grid, y_grid = np.meshgrid(x, y)
lon_grid, lat_grid = map_proj(x_grid, y_grid, inverse=True)
mapping_data = {'lon': lon_grid, 'lat': lat_grid, 'x': x_grid, 'y': y_grid}
return mapping_data
def _set_proj(self):
'''
Define the projection
'''
if self.proj_name in self.proj_dict:
for k, v in self.proj_dict[self.proj_name].iteritems():
setattr(self, k, v)
if self.gdtyp == 6:
self.proj = Proj(proj='stere',
lat_ts=self.p_bet,
lat_0=self.ycent,
lon_0=self.p_gam)
elif self.gdtyp == 2:
self.proj = Proj(proj='lcc',
lat_1=self.p_alp,
lat_2=self.p_bet,
lon_0=self.p_gam,
lat_0=self.ycent,
a=6370000,
b=6370000,
no_defs=True)
def get_proj_obj(proj_dict):
return Proj(proj_dict)
#import matplotlib.pyplot as pylab
#
from numpy import sqrt, arange, pi, array, cos, sin, dot, matrix, mat, rint, linspace, average, median, std
#from statlib import stats
#import scipy.stats as ss
#from scipy import interpolate
#
#from pylab import *
from mpl_toolkits.basemap.pyproj import Proj
from mpl_toolkits.basemap.pyproj import Geod
#from mpl_toolkits.basemap import Basemap as Basemap
#from matplotlib.mlab import griddata
#from waveloc_funcs import *
#from flexwin_funcs import *
proj_default = Proj(proj='utm', zone='10', ellps='WGS84')
geod_default = Geod(ellps='WGS84')
class GeoPoint(object):
"""
A point in 3D space, as general as can be.
Abstract class - do not instantiate.
Contains methods common to daughter classes GeoPointXY and GeoPointLL.
"""
def __init__(self):
raise NotImplemented
def dist_km_from(self, alat, alon):
"""
import os
from mpl_toolkits.basemap.pyproj import Proj
import matplotlib.pyplot as plt
import numpy as np
import json
# Project data to New York Long Island SRS (Spatial Reference System)
# http://www.spatialreference.org/ref/epsg/2263/
p = Proj(init="epsg:2263")
# Load GPS data from url or file
#f = urllib2.urlopen("https://feeds.citibikenyc.com/stations/stations.json")
#jsonData = json.load(f)
with open('../data/citibike.json', 'r') as f:
jsonData = json.load(f)
X = []
for i in range(len(jsonData['stationBeanList'])):
lon = jsonData['stationBeanList'][i]['longitude']
lat = jsonData['stationBeanList'][i]['latitude']
X.append((lat, lon))
X = np.array(X)
x, y = p(X[:, 1], X[:, 0]) # Projection of coordinates
# Get underlying nyc mapping data
nyc_neighborhoods = "../nyc_data/nyc_neighborhoods.json"
polygons = []
with open(nyc_neighborhoods, 'r') as f:
jsonData = json.load(f)
for feature in jsonData['features']:
for polygon in feature['geometry']['coordinates']:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap.pyproj import Proj
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from osgeo import ogr
import json
# Project data to New York Long Island SRS (Spatial Reference System)
# http://www.spatialreference.org/ref/epsg/2263/
p = Proj(init="epsg:2263")
# Load neighborhood histogram
histogram = np.load('neighborhood_histogram.npy').item()
# Load neighborhood polygons into dictionary
neighborhoods = {}
with open("../nyc_data/nyc_neighborhoods.json", 'r') as f:
jsonData = json.load(f)
for feature in jsonData['features']:
polygon = np.array(feature['geometry']['coordinates']).squeeze()
x, y = p(polygon[:, 0], polygon[:, 1]) # Projection of coordinates
neighborhoods[feature['properties']['neighborhood']] = np.array(
zip(x, y))
# Create polygon patches
patches = []
colors = []
for key in neighborhoods:
polygon = Polygon(neighborhoods[key], True)
patches.append(polygon)
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap.pyproj import Proj
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from osgeo import ogr
import json
# Project data to New York Long Island SRS (Spatial Reference System)
# http://www.spatialreference.org/ref/epsg/2263/
p = Proj(init="epsg:2263")
# Load neighborhood histogram
histogram = np.load('neighborhood_histogram.npy').item()
# Load neighborhood polygons into dictionary
neighborhoods = {}
with open("../nyc_data/nyc_neighborhoods.json", 'r') as f:
jsonData = json.load(f)
for feature in jsonData['features']:
polygon = np.array(feature['geometry']['coordinates']).squeeze()
x, y = p(polygon[:, 0], polygon[:, 1]) # Projection of coordinates
neighborhoods[feature['properties']['neighborhood']] = np.array(zip(x, y))
# Create polygon patches
patches = []
colors = []