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refined_box.py
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import pandas as pd
import matplotlib
matplotlib.use("Agg")
from scipy import stats
from matplotlib import pyplot as plt
import numpy as np
import matplotlib.cm as cm
target = '/exports/csce/datastore/geos/users/s1134744/LSDTopoTools/Topographic_projects/full_himalaya/raw/'
source = '0_35_ex_MChiSegmented_burned.csv'
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']
#
source_list = ['full_data.csv']
#source_list=[1,2,3,4]
hist2d = False
box = False
glacial = False
pandas_hist = True
def toSeries(DF,target,values,tectonics=False):
if not tectonics:
selectedDF = DF[DF[target].isin(range(values[0],values[1]))]
if tectonics:
selectedDF = DF[DF[target] == values]
selectedDF = selectedDF[selectedDF['m_chi'] > 0]
series = selectedDF['m_chi']
if hist2d:
return selectedDF
return series
def ks_2sample_test(pandasSeries_A,pandasSeries_B):
list_A = pandasSeries_A.tolist()
list_B = pandasSeries_B.tolist()
statistic,p_value = stats.ks_2samp(list_A,list_B)
return statistic,p_value
if glacial:
for x in source_list:
with open(target+x,'r') as csvfile:
pandasDF = pd.read_csv(csvfile,delimiter=',')
#print x
glacial_1 = toSeries(pandasDF,'glaciated',0,tectonics=True)
glacial_2 = toSeries(pandasDF,'glaciated',1,tectonics=True)
list_1 = glacial_1.tolist()
list_2 = glacial_2.tolist()
list_1 = len(list_1)
list_2 = len(list_2)
proportion = float(list_2)/float(list_1)
#except:
# print "error"
# proportion = 0
print proportion
with open(target+source,'r') as csvfile:
pandasDF = pd.read_csv(csvfile,delimiter=',')
#pandasDF = pandasDF[pandasDF['segmented_elevation'] > 1000]
#pandasDF = pandasDF[pandasDF['segmented_elevation'] < 4000]
box_1 = toSeries(pandasDF,'secondary_burned_data',[0,500])
box_2 = toSeries(pandasDF,'secondary_burned_data',[500,1000])
box_3 = toSeries(pandasDF,'secondary_burned_data',[1000,1500])
box_4 = toSeries(pandasDF,'secondary_burned_data',[1500,2000])
box_5 = toSeries(pandasDF,'secondary_burned_data',[2000,2500])
box_6 = toSeries(pandasDF,'secondary_burned_data',[2500,3000])
box_7 = toSeries(pandasDF,'secondary_burned_data',[3000,3500])
box_8 = toSeries(pandasDF,'secondary_burned_data',[3500,4000])
box_9 = toSeries(pandasDF,'secondary_burned_data',[4000,4500])
box_10 = toSeries(pandasDF,'secondary_burned_data',[4500,5000])
box_11 = toSeries(pandasDF,'secondary_burned_data',[5000,5500])
box_12 = toSeries(pandasDF,'secondary_burned_data',[5500,6000])
box_13 = toSeries(pandasDF,'secondary_burned_data',[6000,6500])
box_14 = toSeries(pandasDF,'secondary_burned_data',[6500,7000])
#tect_1 = toSeries(pandasDF,'tectonics',1,tectonics=True)
#tect_2 = toSeries(pandasDF,'tectonics',2,tectonics=True)
#tect_3 = toSeries(pandasDF,'tectonics',3,tectonics=True)
#tect_4 = toSeries(pandasDF,'tectonics',4,tectonics=True)
litho_1 = toSeries(pandasDF,'burned_data',[10000,20000])
litho_2 = toSeries(pandasDF,'burned_data',[20000,30000])
litho_3 = toSeries(pandasDF,'burned_data',[30000,40000])
litho_4 = toSeries(pandasDF,'burned_data',[40000,50000])
litho_5 = toSeries(pandasDF,'burned_data',[50000,60000])
litho_6 = toSeries(pandasDF,'burned_data',[60000,70000])
litho_7 = toSeries(pandasDF,'burned_data',[70000,80000])
litho_8 = toSeries(pandasDF,'burned_data',[80000,90000])
litho_9 = toSeries(pandasDF,'burned_data',[90000,100000])
litho_10 = toSeries(pandasDF,'burned_data',[100000,110000])
litho_11 = toSeries(pandasDF,'burned_data',[110000,120000])
litho_12 = toSeries(pandasDF,'burned_data',[120000,130000])
litho_13 = toSeries(pandasDF,'burned_data',[130000,140000])
litho_14 = toSeries(pandasDF,'burned_data',[140000,150000])
litho_15 = toSeries(pandasDF,'burned_data',[150000,160000])
litho_16 = toSeries(pandasDF,'burned_data',[160000,170000])
glacial_1 = toSeries(pandasDF,'glaciated',0,tectonics=True)
glacial_2 = toSeries(pandasDF,'glaciated',1,tectonics=True)
if box:
fig = plt.figure(1, figsize=(20, 20))
# Create an axes
ax = fig.add_subplot(111)
# Create the boxplot
#bp = ax.boxplot([litho_1,litho_2,litho_3,litho_4,litho_5,litho_6,litho_7,litho_8,litho_9,litho_10,litho_11,litho_12,litho_13,litho_14,litho_15,litho_16])
bp = ax.boxplot([box_1,box_2,box_3,box_4,box_5,box_6,box_7,box_8,box_9,box_10,box_11,box_12,box_13,box_14])
#bp = ax.boxplot([glacial_1,glacial_2])
#bp = ax.scatter(df_x,df_y,marker='.')
# Save the figure
fig.savefig(target+'0.35_precip_box_elevation_clipped_low.png', bbox_inches='tight')
#print ks_2sample_test(litho_9,litho_10)
print ks_2sample_test(tect_1,tect_4)
print ks_2sample_test(tect_1,tect_3)
print ks_2sample_test(tect_1,tect_2)
print ks_2sample_test(tect_2,tect_4)
print ks_2sample_test(tect_2,tect_3)
print ks_2sample_test(tect_3,tect_4)
if hist2d:
#lith_mins = [10000,20000,30000,40000,50000,60000,70000,80000,90000,100000,110000,120000,130000,140000,150000,160000]
#lith_maxs = [20000,30000,40000,50000,60000,70000,80000,90000,100000,110000,120000,130000,140000,150000,160000,170000]
lith_range = [litho_1,litho_2,litho_3,litho_4,litho_5,litho_6,litho_7,litho_8,litho_9,litho_10,litho_11,litho_12,litho_13,litho_14,litho_15,litho_16]
precip_range = [box_1,box_2,box_3,box_4,box_5,box_6,box_7,box_8,box_9,box_10,box_11,box_12,box_13,box_14]
tecto_range = [tect_1,tect_2,tect_3,tect_4]
iterator = 1
for data in tecto_range:
x_Series = data['tectonics']
y_Series = data['m_chi']
print x_Series
print y_Series
#x_list = x_Series.tolist()
#y_list = y_Series.tolist()
#DF = pd.concat([x_Series,y_Series],axis=1)
#print x_list,y_list
fig = plt.figure(1, figsize=(18,18))
ax = fig.add_subplot(111)
ax.hist2d(x_Series,y_Series,bins=(5,50),range=((0,5),(0,200)))
#plt.ylim(0,200)
fig.savefig(target+'tect_%s_ksn_hist2d_0.35.png'%(str(iterator)), bbox_inches='tight')
#required to clear the axes. Each call of this function wouldn't do that otherwise.
plt.cla()
iterator += 1
bin_range_lower = range(0,140,10)
bin_range_upper = range(10,150,10)
#bin_range_lower = range(0,160000,10000)
#bin_range_upper = range(10000,170000,10000)
#bin_range_lower = [10000,20000,30000,40000,50000,60000,70000,80000,90000,100000,110000,120000,130000,140000,150000,160000]
#bin_range_upper = [20000,30000,40000,50000,60000,70000,80000,90000,100000,110000,120000,130000,140000,150000,160000,170000]
#bin_range_lower = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2,1.3]
#bin_range_upper = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2,1.3,1.4]
if pandas_hist:
median_full = []
value_full = []
#source_list = ['segmented_elevation','quaternary_burned_data','secondary_burned_data','monsoon']
for x in source_list:
#with open(target+x,'r') as csvfile:
with open(target+'0_35_ex_MChiSegmented_burned.csv','r') as csvfile:
medians = []
values = []
weights = []
list_of_series = []
pandasDF = pd.read_csv(csvfile,delimiter=',')
#for y,z in zip(bin_range_lower,bin_range_upper):
for y in [1,2,3,4]:
#
selectedDF = pandasDF[pandasDF['second_inv'] > 0]
#selectedDF = selectedDF[selectedDF['second_inv'] <= 100]
#selectedDF = selectedDF[selectedDF['segmented_elevation'] > y]
#selectedDF = selectedDF[selectedDF['segmented_elevation'] < z]
selectedDF = selectedDF[selectedDF['quaternary_burned_data'] == y]
#selectedDF = selectedDF[selectedDF['tectonics'] == y]
#selectedDF = selectedDF[selectedDF['strain_ezz'] >= y]
#selectedDF = selectedDF[selectedDF['strain_ezz'] < z]
series = selectedDF['second_inv']
#series = selectedDF['secondary_burned_data']
lister = series.tolist()
list_of_series.append(lister)
value=1
#value = z+((z-y)/2)
values.append(value)
medians.append(series.median())
#weighting
weighter = selectedDF['secondary_burned_data'].median()
weights.append(weighter)
median_full.append(medians)
value_full.append(values)
fig = plt.figure(1, figsize=(24,9))
ax = fig.add_subplot(111)
#plt.yscale('log')
#plt.xlabel("segmented elevation in 50m bins")
#plt.ylabel("median Ksn - log scale")
plt.ylabel("Strain")
plt.xlabel("Tectonic zones")
#plt.scatter(values,medians,c=weights,cmap=cm.Blues)
#plt.colorbar()
plt.boxplot(list_of_series)
# hs = series.hist(bins=200)
write_name = x.replace('_ex_MChiSegmented_burned.csv','')
#plt.title("concavity "+write_name)
fig.savefig(target+'0.35_strain_2nd_tectonics_box.png', bbox_inches='tight')
#fig.savefig(target+'elevation_concavity_scatter_%s.png'%(x), bbox_inches='tight')
#fig.savefig(target+'longitude_scatter_precip_%s.png'%(write_name), bbox_inches='tight')
plt.cla()
x = np.arange(18)
ys = [i+x+(i*x)**2 for i in range(18)]
colors = cm.Reds(np.linspace(0, 1, len(ys)))
#median_full = median_full[:9]
#value_full = value_full[:9]
for median,value,colour in zip(median_full,value_full,colors):
plt.scatter(value,median,color=colour)
plt.yscale('log')
plt.xlabel("segmented elevation in 50m bins")
plt.ylabel("median Ksn - log scale")
#fig.savefig(target+'full_scatter.png', bbox_inches='tight')
#plt.cla()
#selectDF = pandasDF[pandasDF['m_chi'] > 0]
#selectedDF = selectDF[selectDF['segmented_elevation'] > 350]
#selectedDF = selectedDF[selectedDF['segmented_elevation'] < 600]
#elevation_series_1 = selectedDF['m_chi']
#selectedDF = selectDF[selectDF['segmented_elevation'] > 600]
#selectedDF = selectedDF[selectedDF['segmented_elevation'] < 850]
#elevation_series_2 = selectedDF['m_chi']
#selectedDF = selectDF[selectDF['segmented_elevation'] > 850]
#selectedDF = selectedDF[selectedDF['segmented_elevation'] < 1100]
#elevation_series_3 = selectedDF['m_chi']
#selectedDF = selectDF[selectDF['segmented_elevation'] > 1100]
#selectedDF = selectedDF[selectedDF['segmented_elevation'] < 1350]
#elevation_series_4 = selectedDF['m_chi']
#print elevation_series_1.median()
#print elevation_series_2.median()
#print elevation_series_3.median()
#print elevation_series_4.median()
#print ks_2sample_test(elevation_series_1,elevation_series_2)
#print ks_2sample_test(elevation_series_1,elevation_series_3)
#print ks_2sample_test(elevation_series_1,elevation_series_4)
#print ks_2sample_test(elevation_series_2,elevation_series_3)
#print ks_2sample_test(elevation_series_2,elevation_series_4)
#print ks_2sample_test(elevation_series_3,elevation_series_4)