Takes data from my Kesler D3 and generate a plot for the exported data.

create_weather_plot.sh

create_weather_plot.sh  hello
% cat create_weather_plot.sh 
#!/bin/bash

sed -i '1,3d;5d' $1

echo ''
echo 'CSV formated properly.'
echo '-----------------------------------------------'
echo 'Generating plots with weather_plot_rev5.py'
echo '-----------------------------------------------'

python ~/Documents/Backcountry_Reports/Weather_Graph_Plotter.py $1

echo 'Plot has been generated!!'
echo ''

Weather_Graph_Plotter.py

''' This will take a .csv file from the Kestrel Drop 3 and manipulate it into graphing all the pertinant weather data.
Graph 1: Temperature and Dew Point
Graph 2: Head Index and Humidity 
Graph 3: Pressure

I have a script command create_weather_graph.sh which easily manipulated the .csv file to remove the unnecesssary lines.
'''

#Import python modules to be used. import sys is used to get an argument for the 
#file names and starts at index 1.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys

title = input("Enter title for weather plot: ")

df = pd.read_csv(sys.argv[1])

#I don't want the X-axis to show all the dates and times. I choose to plot 8 points and round
#them to the nearest integer. The last one needs to subtract one incase it rounds up. That is then
#appended to a list for the x lables in maplotlib
point_date = np.linspace(0,len(df['FORMATTED DATE_TIME']),8)
plot_date = np.rint(point_date)
plot_date[-1] = plot_date[-1]-1
graph_data=[]

for x in range(0,len(plot_date)):
    graph_data.append(df['FORMATTED DATE_TIME'][plot_date[x]])

#Create the figure.
fig,axes = plt.subplots(nrows=3, ncols=1,figsize=(10,7.5))

#Creation of graph 1.
#xticklabels are empty to remove those labels from the graph.
axes[0].plot(df['FORMATTED DATE_TIME'],df['Temperature'])
axes[0].plot(df['FORMATTED DATE_TIME'],df['Dew Point'])
axes[0].set_title(title,fontsize=18)
axes[0].set_ylabel('Temperature')
axes[0].set_xticks(plot_date)
axes[0].set_xticklabels([])
axes[0].grid(True)
axes[0].legend(loc='best')

#Creation of graph 2 with a shared x axis.
ax2 = axes[1].twinx()
axes[1].plot(df['FORMATTED DATE_TIME'],df['Heat Stress Index'],color='g')
ax2.plot(df['FORMATTED DATE_TIME'],df['Relative Humidity'],color='r')
axes[1].set_ylabel('Heat Index',color='g')
ax2.set_ylabel('Humidity',color='r')
axes[1].set_xticklabels([])
axes[1].set_xticks(plot_date)
axes[1].grid(True)

#Creation of graph 3, X ticks are which points to graph on the grid and the labels are
#created based on those same correspodning values.
axes[2].plot(df['FORMATTED DATE_TIME'],df['Station Pressure'])
axes[2].set_ylabel('Pressure')
axes[2].set_xticks(plot_date)
axes[2].set_xticklabels(graph_data,rotation=75)
axes[2].grid(True)

plt.tight_layout()

#df = pd.read_csv('weather.csv')

#print(df.head())
plt.savefig('/home/kyle/Documents/Backcountry_Reports/'+ title + '_Weather.pdf')
#plt.savefig(title + '_Weather.pdf')