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plot_snow.py
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plot_snow.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Oct 4 18:55:53 2016
@author: tyler
"""
import os
import pandas as pd
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import griddata
from matplotlib.colors import LinearSegmentedColormap
import datetime
from matplotlib import rc
#rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
### for Palatino and other serif fonts use:
##rc('font',**{'family':'serif','serif':['Palatino']})
#rc('text', usetex=False)
import pdb
#make map
#%%
def makeMap(proj='merc'):
plt.cla()
coords = {'lower_lat':25,'upper_lat':45,'lower_long':65,'upper_long':105} #set as lower and upper bounds for lat and long
center = ((coords['upper_long']-coords['lower_long'])/2+coords['lower_long'],(coords['upper_lat']-coords['lower_lat'])/2+coords['lower_lat'])
if proj == 'ortho':
#Due to a document error, you have to set m in terms of llcrnrx, llcrnry, etc.
m = Basemap(projection=proj,
resolution='c',
lat_0 = center[1],
lon_0 = center[0])
#pdb.set_trace()
#llcrnrx, llcrnry = m(coords['lower_lat'],coords['lower_long'])
#urcrnrx, urcrnry = m(coords['upper_lat'],coords['upper_long'])
#m = Basemap(projection=proj,
#llcrnrx=llcrnrx,
#llcrnry=llcrnry,
#urcrnrx=urcrnrx,
#urcrnry=urcrnry,
#resolution='c',
#lat_0 = center[1],
#lon_0 = center[0])
elif proj == 'geos':
#lat_0 must be zero
m = Basemap(projection=proj,
llcrnrlat=coords['lower_lat'],
urcrnrlat=coords['upper_lat'],
llcrnrlon=coords['lower_long'],
urcrnrlon=coords['upper_long'],
lat_ts=20,
resolution='c',
lat_0 = 0,
lon_0 = center[0])
else:
m = Basemap(projection=proj,
llcrnrlat=coords['lower_lat'],
urcrnrlat=coords['upper_lat'],
llcrnrlon=coords['lower_long'],
urcrnrlon=coords['upper_long'],
lat_ts=20,
resolution='c',
lat_0 = center[1],
lon_0 = center[0])
#m.drawcoastlines()
# draw a boundary around the map, fill the background.
# this background will end up being the ocean color, since
# the continents will be drawn on top.
#m.drawmapboundary(fill_color='None')
# fill continents, set lake color same as ocean color.
#m.fillcontinents(color='None',lake_color='aqua')
#m.bluemarble()
#m.shadedrelief()
m.etopo()
parallels = np.arange(0., 81, 10)
meridians = np.arange(10, 351, 10)
m.drawparallels(parallels, labels =[False, True, True, False])
m.drawmeridians(meridians, labels =[True, False, False, True])
return m
#%%
fig = plt.figure()
# global ortho map centered on lon_0,lat_0
lat_0=10.; lon_0=57.
# resolution = None means don't process the boundary datasets.
m1 = Basemap(projection='ortho',lon_0=lon_0,lat_0=lat_0,resolution=None)
# add an axes with a black background
ax = fig.add_axes([0.1,0.1,0.8,0.8],axisbg='k')
# plot just upper right quadrant (corners determined from global map).
# keywords llcrnrx,llcrnry,urcrnrx,urcrnry used to define the lower
# left and upper right corners in map projection coordinates.
# llcrnrlat,llcrnrlon,ucrnrlon,urcrnrlat could be used to define
# lat/lon values of corners - but this won't work in cases such as this
# where one of the corners does not lie on the earth.
m = Basemap(projection='ortho',lon_0=lon_0,lat_0=lat_0,resolution='l',\
llcrnrx=0.,llcrnry=0.,urcrnrx=m1.urcrnrx/2.,urcrnry=m1.urcrnry/2.)
m.drawcoastlines()
m.drawmapboundary(fill_color='aqua')
m.fillcontinents(color='coral',lake_color='aqua')
m.drawcountries()
# draw parallels and meridians.
m.drawparallels(np.arange(-90.,120.,30.))
m.drawmeridians(np.arange(0.,360.,60.))
m.drawmapboundary()
plt.title('Orthographic Map Showing A Quadrant of the Globe')
plt.show()
#%%
#Needs work:
#Lambert Azimuthal Equal Area Projection: laea
#must specify lat_0 and long_0
plt.figure(0)
m_laea= makeMap('laea')
#Mercator Projection: merc
plt.figure(1)
m_merc= makeMap('merc')
#Azimuthal Equidistant Projection
plt.figure(2)
m_aeqd=makeMap('aeqd')
#Gnomic projection
plt.figure(3)
m_aeqd = makeMap('gnom')
#Needs work, make a global option?:
#Orthographic projection
plt.figure(4)
m_orth = makeMap('ortho')
#Geostationary projection
plt.figure(5)
m_geos = makeMap('geos')
#Equidistant Cylindrical Projection
plt.figure(6)
m_cyl = makeMap('cyl')
#Cassini
plt.figure(7)
m_cass = makeMap('cass')
#Transverse Mercator Projection
plt.figure(8)
m_tmerc = makeMap('tmerc')
#Polyconic Projection
plt.figure(9)
m_poly = makeMap('poly')
#Gall Stereographic Projection
plt.figure(10)
m_gall = makeMap('gall')
#Miller Cylindrical Projection
plt.figure(11)
m_mill = makeMap('mill')
#Lambert Conformal Projection
plt.figure(12)
m_lcc = makeMap('lcc')
#Stereographic Projection
plt.figure(13)
m_stere = makeMap('stere')
#Equidistant Conic Projection
plt.figure(14)
m_eqdc = makeMap('eqdc')
#Albers Equal Area Projection
plt.figure(15)
m_aea = makeMap('aea')
#%%
def snow_and_ice(x):
if x==4 or x==3:
x=1
else:
x=0
return x
snow = '#FEFEFE'
terra = '#00ff7f'
sea = '#007fff'
ice = '#A5F2F3'
firmament = '#26211f'
#%%
def make_plot_from_col(df, col, plot_dir, show = True, save = False):
year, day = df[col].name.split("_")
date = datetime.datetime(int(year), 1, 1) + datetime.timedelta(int(day))
title_string = date.strftime('%Y-%m-%d')
m=makeMap('merc')
data = df[col].apply(snow_and_ice)
grid_z0 = griddata(points, data.values, (grid_x, grid_y), method='linear') #can be nearest, linear, or cubic interpolation
grid_z0[ grid_z0 != 1 ] = np.nan
m.contourf(grid_x, grid_y,grid_z0, latlon=False, cmap = cmap1, alpha=1)
plt.title(title_string,fontsize=16, color = "black")
if show:
plt.show()
if save:
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
plt.savefig(plot_dir+col+'.png')
plt.close()
#%%
#Setup grid
os.chdir('/home/tyler/Desktop/tibet_snowpack/Tibet Project/snowCode/')
direc = '/home/tyler/Desktop/tibet_snowpack/Tibet Project/snowCode/data/'
df_lat_long = pd.read_csv(direc+'lat_long_centroids_area_24km.csv')
lat = df_lat_long['lat'].values
lon = df_lat_long['long'].values
m=makeMap()
x,y = m(lon,lat)
points = np.transpose(np.array([x,y]))
grid_x, grid_y = np.mgrid[min(x):max(x):3000j, min(y):max(y):3000j]
cmap1 = LinearSegmentedColormap.from_list("my_colormap", (snow, snow), N=6, gamma=1)
plt.close()
points = np.transpose(np.array([x,y]))
grid_x, grid_y = np.mgrid[min(x):max(x):3000j, min(y):max(y):3000j]
#%%
csv_to_plot_dir = 'output/24km_test/'
filename = '1997.csv'
plot_dir = csv_to_plot_dir+'plots_fun/'
df = pd.read_csv(csv_to_plot_dir+filename)
#make_plot_from_col(df, df.columns[1], plot_dir, show = True, save = False) #TEST
plt.ioff()
for col in df.columns[1:]:
make_plot_from_col(df, col, plot_dir, show = False, save = True)
#%%
#run thru loop without function
plt.ioff()
csv_to_plot_dir = 'output/24km_test/'
filename = '1997.csv'
output_dir = os.getcwd()+'/output/'
for col in df.columns[1:]:
year, day = df[col].name.split("_")
date = datetime.datetime(int(year), 1, 1) + datetime.timedelta(int(day))
title_string = date.strftime('%Y-%m-%d')
m=makeMap()
data = df[col].apply(snow_and_ice)
grid_z0 = griddata(points, data.values, (grid_x, grid_y), method='linear') #can be nearest, linear, or cubic interpolation
grid_z0[ grid_z0 != 1 ] = np.nan
m.contourf(grid_x, grid_y,grid_z0, latlon=False, cmap = cmap1, alpha=1)
plt.title(title_string,fontsize=16, color = "black")
plt.savefig('output/24km_test/plots/'+col+'.png')
#pdb.set_trace()
plt.close()