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elevation_ksn_scatter_tectonics.py
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#Ksn by elevation
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot as plt
import pandas as pd
import sys
import numpy as np
from numpy import median
import matplotlib.cm as cm
import matplotlib.patches as mpatches
from mpl_toolkits.axes_grid.inset_locator import inset_axes
target = '/exports/csce/datastore/geos/users/s1134744/LSDTopoTools/Topographic_projects/full_himalaya_5000/'
source= '0_4_ex_MChiSegmented_burned_outlier_removed.csv'
def getAxis(column):
with open(target+source,'r') as csvfile:
df = pd.read_csv(csvfile,delimiter=',')
distance = []
full_data = []
for lower,upper in zip(range(350,5000,50),range(400,5050,50)):
selectedDF_c = df[df[column] >= lower]
selectedDF_c = selectedDF_c[selectedDF_c[column] < upper]
series = selectedDF_c["m_chi"].tolist()
value = median(series)
full_data.append(value)
distance.append(full_data)
for x in [1,2,3,4]:
selectedDF = df[df["tectonics"] == x]
#selectedDF = selectedDF[selectedDF["distance_a"] < y]
elevation = []
for lower,upper in zip(range(350,5000,50),range(400,5050,50)):
selectedDF_b = selectedDF[selectedDF[column] >= lower]
selectedDF_b = selectedDF_b[selectedDF_b[column] < upper]
series = selectedDF_b["m_chi"].tolist()
value = median(series)
#print value
elevation.append(value)
distance.append(elevation)
return distance
def getAxisHist(column):
with open(target+source,'r') as csvfile:
df = pd.read_csv(csvfile,delimiter=',')
distance = []
full_data = []
#for lower,upper in zip(range(350,5000,50),range(400,5050,50)):
# selectedDF_c = df[df[column] >= lower]
# selectedDF_c = selectedDF_c[selectedDF_c[column] < upper]
series = df[column].tolist()
#value = median(series)
distance.append(series)
#distance.append(full_data)
for x in [1,2,3,4]:
selectedDF = df[df["tectonics"] == x]
#selectedDF = selectedDF[selectedDF["distance_a"] < y]
series = selectedDF[column].tolist()
distance.append(series)
return distance
data = getAxis("segmented_")
x_series = range(375,5025,50)
fig = plt.figure(1, figsize=(18,9))
legends = []
x = np.arange(len(range(0,2000,500)))
ys = [i+x+(i*x)**2 for i in range(len(range(0,2500,500)))]
colours = cm.rainbow(np.linspace(0, 1, len(ys)))
#bins = [str(y)+'-'+str(z) for y,z in zip(range(0,2000,500),range(500,2500,500))]
bins = ['Sub Himalaya','Lesser Himalaya','Greater Himalaya','Tethyan Himalaya']
ax = fig.add_subplot(111)
for item,colour,name in zip(data[1:],colours,bins):
ax.scatter(x_series,item,marker=',',s=5,c=colour)
legend = mpatches.Patch(color=colour, label=name)
legends.append(legend)
ax.scatter(x_series,data[0],marker='o',c='k')
legend = mpatches.Patch(color='k', label='Full Dataset')
#legend = mpatches.Circle((0.5, 0.5), 1, facecolor='k', linewidth=3)
legends.append(legend)
leg = plt.legend(handles=legends, title='Distance along mountain front (500km bins)',ncol=6,loc='upper center', bbox_to_anchor=(0.5, 1.05))
plt.ylabel(r"Median $K_{sn}$")
plt.xlabel("Elevation (50m bins)")
leg.get_frame().set_alpha(1)
#adding inset histogram
new_axes = inset_axes(ax, width="40%",height="40%",bbox_to_anchor=(0.5,0.5,1200,300))
data_2 = getAxisHist("segmented_")
new_colour = []
new_colour.append('k')
for x in colours:
new_colour.append(x)
small = plt.hist(data_2,bins=x_series,stacked=True,color=new_colour[:5],alpha=0.5)
plt.title("Histogram of Data Points in Elevation Bins")
plt.ylabel("Frequency")
plt.xlabel("Elevation (50m bins)")
fig.savefig('../elevation_scatter_tectonics.png', bbox_inches='tight')