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map_plotting_full_himalaya.py
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#scatter
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot as plt
import pandas as pd
import sys
import matplotlib.cm as cm
target = '/exports/csce/datastore/geos/users/s1134744/LSDTopoTools/Topographic_projects/full_himalaya_5000/'
#target = '/exports/csce/datastore/geos/users/s1134744/LSDTopoTools/Topographic_projects/figures_to_keep/full/'
source_list = ['0_1_ex_MChiSegmented_burned.csv','0_15_ex_MChiSegmented_burned.csv','0_2_ex_MChiSegmented_burned.csv',
'0_25_ex_MChiSegmented_burned.csv','0_3_ex_MChiSegmented_burned.csv','0_35_ex_MChiSegmented_burned.csv',
'0_4_ex_MChiSegmented_burned.csv','0_45_ex_MChiSegmented_burned.csv','0_5_ex_MChiSegmented_burned.csv',
'0_55_ex_MChiSegmented_burned.csv','0_6_ex_MChiSegmented_burned.csv','0_65_ex_MChiSegmented_burned.csv',
'0_7_ex_MChiSegmented_burned.csv','0_75_ex_MChiSegmented_burned.csv','0_8_ex_MChiSegmented_burned.csv',
'0_85_ex_MChiSegmented_burned.csv','0_9_ex_MChiSegmented_burned.csv','0_95_ex_MChiSegmented_burned.csv']
#def openPandas(source):
# df = pd.read_csv(target+source)
# return df
#for source in source_list:
# df = openPandas(source)
#print pandasDF
# df.to_csv(target+'full_data.csv',mode='a',index=False,header=False)
weights = ['ksn']#'quaternary_burned_data','monsoon','burned_data','secondary_burned_data','tertiary_burned_data','segmented_elevation']
#weights = ['m_chi']
with open(target+'0_35_ex_MChiSegmented_burned.csv','r') as csvfile:
#with open(target+'full_data.csv','r') as csvfile:
pandasDF = pd.read_csv(csvfile,delimiter=',')
#pandasDF = pandasDF[pandasDF['m_chi'] > 0]
#pandasDF = pandasDF[pandasDF['strain_ezz'] >= 0]
#pandasDF = pandasDF[pandasDF['longitude'] > 85]
#pandasDF = pandasDF[pandasDF['longitude'] < 90]
#pandasDF = pandasDF[pandasDF['distance_along'] < 1000]
#pandasDF = pandasDF[pandasDF['distance_along'] > 150]
x_Series = pandasDF['longitude']
y_Series = pandasDF['latitude']
for x in weights:
#weight = pandasDF[x]
#reducing nodata
#pandasDF = pandasDF[pandasDF["second_inv"] < 150]
#lister = weight.tolist()
#color = [str(item/255.) for item in lister]
#x_list = x_Series.tolist()
#y_list = y_Series.tolist()
#DF = pd.concat([x_Series,y_Series],axis=1)
#print x_list,y_list
try:
fig = plt.figure(1, figsize=(30,9))
ax = fig.add_subplot(111)
weight_series = pandasDF["m_chi"]
weight = weight_series.tolist()
plt.scatter(x_Series,y_Series,marker='.', c=weight, cmap=cm.Reds)
cb = plt.colorbar()
#plt.gca().invert_xaxis()
#matplotlib.axes.Axes.invert_xaxis
#ax.hist2d(x_Series,y_Series,bins=(40,40),range=((150,1000),(0,3000)))
#plt.ylim(0,200)
fig.savefig(target+'lat_lon_%s_full_data_map_plot.png'%(x), bbox_inches='tight')
#plt.clf()
#plt.cla()
#plt.close()
#pandasDF = pandasDF[pandasDF["glaciated"] == 1]
#x_Series = pandasDF['longitude']
#y_Series = pandasDF['latitude']
#plt.scatter(x_Series,y_Series,marker='.', c='k')
#fig.savefig(target+'lat_lon_elevation_glaciated_full_data_map_plot.png', bbox_inches='tight')
except Exception as e:
print e
print("error in %s"%(x))