def testBinary(self):
'''
This tests the ability to open a file and correctly read the attributes and
variables as well as properly reshape the arrays and remove unused dimensions.
'''
# Open 2D file:
f = gitm.GitmBin(
os.path.join(self.pth, 'data', 'pybats_test', 'gitm_2D.bin'))
# Check some critical attributes/values:
self.assertEqual(self.nVars, f.attrs['nVars'])
self.assertEqual(self.nLat, f.attrs['nLat'])
self.assertEqual(self.vers, f.attrs['version'])
self.assertEqual(self.time, f['time'])
self.assertEqual(self.shape, f['Longitude'].shape)
self.assertAlmostEqual(self.lat1, f['Latitude'][0, -1], 6)
self.assertAlmostEqual(-1 * self.lat1, f['Latitude'][0, 0], 6)
def load_multiple_gitm_bin(filelist, magfile=None, *args, **kwargs):
'''
Loads a list of GITM binary files into their own GitmBin data structures.
The list may be an ascii file containing a list of files or a list object.
A list of the data structures is returned. A 3DION or 3DMAG file may
be specified for the entire list using the keyword arguement "magfile".
Input:
filelist = python list of file names or an ASCII file containing a list
of filenames
magfile = 3DMAG or 3DION file (default=None)
'''
# import local packages
from os import path
outlist = list()
if type(filelist) == str:
func_name = "load_multiple_gitm_bin"
fsize = path.getsize(filelist)
if(fsize > 2.0e9):
print func_name,"ERROR: File list size [",(fsize*1e-9),"GB > 2 GB]"
return outlist
elif(fsize == 0.0):
print func_name, "ERROR: empty file list [", filelist, "]"
return outlist
# Read in the list of files
f = open(filelist, "r")
namelist = f.readlines()
else:
namelist = filelist
for name in namelist:
name = string.strip(name)
outlist.append(gitm.GitmBin(name, magfile=magfile))
if outlist[-1].attrs['nAlt'] > 1:
outlist[-1].calc_2dion()
return(outlist)
def quickplot(infile, outfile, min, max, backfile):
'''
Takes file name, infile, and creates a PNG plot thing. Yeah.
'''
# Import shit. I needed a lot of shit this time.
import numpy as np
from spacepy.pybats import gitm
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
from matplotlib.ticker import ScalarFormatter, FormatStrFormatter
# Convert lat lon from rad to degrees.
p = 180.0 / np.pi
f = plt.figure(figsize=(6, 7)) #make a fig.
ax = f.add_subplot(111) #make an ax.
# Open file.
a = gitm.GitmBin(infile)
t = a['Temperature'][:, :, :]
sz = t.shape
c = sz[0] / 2.0
IsNotDiff = re.match(r'none', backfile)
if IsNotDiff:
diff = np.log10(a['Rho'][c, :, :])
else:
base = gitm.GitmBin(backfile)
diff = 100 * (a['Rho'][c, :, :] -
base['Rho'][c, :, :]) / base['Rho'][c, :, :]
diff[diff > max] = max
diff[diff < min] = min
# Create the contour for an altitude slice and call it 'cnt'.
# The 61 is the number of contours; you could use a vector of values to set
# levels manually if you wish. get_cmap accepts any of the color map names
# from the colormap demo pic from the Matplotlib gallery; adding '_r'
# reverses the colormap.
u = a['V!Dn!N (north)'][c, :, :]
v = a['V!Dn!N (up)'][c, :, :]
x = a['Latitude'][c, :, :] * p * 111.0
y = a['Altitude'][c, :, :] / 1000.0
if (min > -1.0e31 and max < 1.0e32):
lev = (np.arange(61)) / 60.0 * (max - min) + min
cnt = ax.contourf(x, y, diff, 61, cmap=get_cmap('RdBu_r'), levels=lev)
else:
cnt = ax.contourf(x, y, diff, 61, cmap=get_cmap('RdBu_r'))
# Configure axis.
ax.set_xlabel('Latitude (in km)')
ax.set_ylabel('Altitude (km)')
if IsNotDiff:
ax.set_title(r'Rho at longitude=%5.2f$^{\circ}$' %
(a['Longitude'][c, 0, 0] * p))
else:
ax.set_title(r'Pecent Diff of Rho at longitude=%5.2f$^{\circ}$' %
(a['Longitude'][c, 0, 0] * p))
f.suptitle('File=%s' % (a.attrs['file']))
q = plt.quiver(x, y, u, v, angles='xy', scale=5000, color='b')
# Add a colorbar and set the tick format to exponential notation.
cb = plt.colorbar(cnt)
cb.formatter = FormatStrFormatter('%7.2E')
cb.update_ticks()
# Add the quivers.
#ax.quiver(a['Longitude'][:,:,0]*p, p*a['Latitude'][:,:,0],
# a['V!Dn!N (east)'][:,:,0],a['V!Dn!N (north)'][:,:,0])
#f.savefig('filename.png')
if outfile[-4] != '.png':
outfile += '.png'
f.savefig(outfile)
# Draw to screen.
if plt.isinteractive():
plt.draw() #In interactive mode, you just "draw".
#else:
# W/o interactive mode, "show" stops the user from typing more
# at the terminal until plots are drawn.
#plt.show()
plt.close(f)
Note that as pybats.gitm is more developed, a plot like this should be made
using syntax like,
>>>a=gitm.GitmBin('filename')
>>>a.add_alt_slice(0, 'Rho', add_cbar=True)
That's how most pybats stuff works right now.
'''
# Import shit. I needed a lot of shit this time.
import numpy as np
from spacepy.pybats import gitm
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
from matplotlib.ticker import ScalarFormatter, FormatStrFormatter
# Open file.
a=gitm.GitmBin('./3DALL_t061213_000000.bin')
# Make contour of rho at lowest altitude (index 0).
# Convert lat lon from rad to degrees.
p=180.0/np.pi
f=plt.figure() #make a fig.
ax=f.add_subplot(111) #make an ax.
# Create the contour for an altitude slice and call it 'cnt' (no jokes, please.)
# The '61' is the number of contours; you could use a vector of values to set
# levels manually if you wish. get_cmap accepts any of the color map names
# from the colormap demo pic from the Matplotlib gallery; adding '_r'
# reverses the colormap.
cnt=ax.contourf(a['Longitude'][:,:,0]*p,
p*a['Latitude'][:,:,0],
a['Rho'][:,:,0], 61, cmap=get_cmap('Spectral_r'))