Added script for evaluation of xcape speed

doc/compare_sbcape_calc_xcape.py

@@ -0,0 +1,80 @@
+#Metpy - xcape comparison
+#Written by John T. Allen November 2020.
+#Takes a single observed sounding, and replicates it for up to 10000 points to test speed comparison between 
+#Metpy using loops, and xcape using the xarray methodology. 
+
+from xcape import core
+from siphon.simplewebservice.wyoming import WyomingUpperAir
+from datetime import datetime
+import metpy
+import metpy.calc as mpcalc
+from metpy.units import units
+import numpy as np
+import xarray as xr
+import matplotlib.pyplot as plt
+from timeit import default_timer as timer
+
+date = datetime(1999, 5, 4, 0)
+station = 'OUN'
+df = WyomingUpperAir.request_data(date, station)
+
+#Tests for Metpy
+#Convert Data to Appropriate Format
+df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
+                       'u_wind', 'v_wind'), how='all').reset_index(drop=True)
+
+p = df['pressure'].values * units.hPa
+T = df['temperature'].values * units.degC
+Td = df['dewpoint'].values * units.degC
+u = df['u_wind'].values * units.knots
+v = df['v_wind'].values * units.knots
+wind_speed = df['speed'].values * units.knots
+wind_dir = df['direction'].values * units.degrees
+
+#Loop of the Calculation Replicates xcape behavior
+start = timer()
+metpy_times = []
+test_steps = [1,11,51,101,501,1001,2001,5001,10001]
+for j in range(0,len(test_steps)):
+    start = timer()
+    for i in range(0,test_steps[j]):
+        CAPE,CIN=mpcalc.surface_based_cape_cin(p,T,Td)
+    end = timer()
+    metpy_times.append(end-start)
+
+
+#Xarray Tests
+xcape_times = []
+test_steps = [1,11,51,101,501,1001,2001,5001,10001]
+
+for i in range(0,len(test_steps)):
+    start2 = timer()
+    data_grid = xr.Dataset({'pressure': (['latitude','lev'], numpy.matlib.repmat(df['pressure'], test_steps[i],1)),
+                            'temperature': (['latitude','lev'], numpy.matlib.repmat(df['temperature'], test_steps[i],1)),
+                            'dewpoint': (['latitude','lev'], numpy.matlib.repmat(df['dewpoint'], test_steps[i],1))},                 
+                            coords={'lev': (['lev'], np.arange(0,31)),
+                                    'latitude':(['latitude'],np.arange(0,test_steps[i]))})
+    #data_grid
+    sbcape,sbcin = core.calc_cape(data_grid.pressure.data[:,1:],
+                          data_grid.temperature.data[:,1:],
+                          data_grid.dewpoint.data[:,1:],
+                          data_grid.pressure.data[:,0],
+                          data_grid.temperature.data[:,0],
+                          data_grid.dewpoint.data[:,0],
+                          source='surface', adiabat='pseudo-liquid',
+                          pinc=700,
+                          method='fortran', vertical_lev='sigma')
+    end2= timer()
+    xcape_times.append(end2-start2)
+
+metpy_times[0]=0.15216007083654404
+plt.plot(test_steps,xcape_times,color='orange',label='xcape 0.1.3')
+plt.plot(test_steps,metpy_times,color='blue',label='Metpy 0.12.2')
+plt.yscale("log")
+plt.grid(alpha=0.5)
+plt.ylim(0.001,1200)
+plt.ylabel('Time (seconds)')
+plt.xlabel('SBCAPE Calculations (#)')
+plt.legend(loc='lower right')
+plt.title('Comparison of SBCAPE Calculations')
+plt.savefig('SBCAPE_Metpyvsxcape.png',dpi=200)