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driver.py
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driver.py
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#among many resources, http://scipy-lectures.github.io/advanced/image_processing/ seems like a useful introduction to image processing
# for a list of scipy.ndimage functions: http://docs.scipy.org/doc/scipy/reference/ndimage.html
import numpy as np
import netCDF4
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
from scipy import ndimage, stats
from mpl_toolkits.basemap import Basemap
Cp = 1004.5; Rd = 287.04; Rd_Cp = Rd/Cp; p0 = 1.e5; grav = 9.81
def calc_potentialTemperature(t,p):
theta = t*(p0/p)**Rd_Cp
return theta
def calc_refFields(fieldsIn, windowSpacing=50., gridSpacing=1.):
#Input list of 2d fields.
#calc regional values as reference "environmental" values, where convolutions define the regional values.
#return list of 2d reference fields
windowLen = int(windowSpacing/gridSpacing)
wts = np.ones((windowLen,windowLen),dtype=float)
countVal = np.ones(fieldsIn[0].shape, dtype=int)
nVals = ndimage.filters.convolve(countVal, wts, output=None, mode='reflect')
nFields = len(fieldsIn)
refVals = []
for iField in xrange(nFields):
sumVals = ndimage.filters.convolve(fieldsIn[iField], wts, output=None, mode='reflect')
refVals.append(sumVals/nVals)
return refVals
def readTimeLevel(data):
#return t,p,q,u,v from file and convert to useful units
#first model level variables
t = data.variables['T'][:,:]+273.15 #to K\
p = data.variables['PSFC'][:,:]*100. #to Pa
q = data.variables['Q'][:,:] #kg/kg
u = data.variables['U0'][:,:] #m/s
v = data.variables['V'][:,:] #m/s
slp = data.variables['SLP'][:,:]*100. #Pa
return (t,p,q,u,v,slp)
class MidpointNormalize(Normalize):
#taken from http://stackoverflow.com/questions/20144529/shifted-colorbar-matplotlib
def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
self.midpoint = midpoint
Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
# I'm ignoring masked values and all kinds of edge cases to make a
# simple example...
x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
return np.ma.masked_array(np.interp(value, x, y))
def plot_field_recentered(var, norm, title=' ', showFig=False):
plt.figure()
plt.pcolormesh(var,norm=norm,cmap=plt.cm.RdBu_r)
plt.colorbar()
plt.title(title)
if (showFig):
plt.show()
else:
return plt
def makeMapCoords(lat,lon):
#return x,y coordinates on a map from lat/lon in degrees
m = Basemap(projection='ortho',lon_0=260.,lat_0=35.5, resolution='l')
return m(lon,lat)
def demo():
#read in data --------------------
fDir = '/data01/densityCurrents/cases/'
f = fDir+'2014071015.nc'
dxGrid = 1.e3
data = netCDF4.Dataset(f,'r')
t,p,q,u,v,slp = readTimeLevel(data)
ny,nx = t.shape
#calculate some variables --------------
theta = calc_potentialTemperature(t,p)
thetav = theta*(1+.61*q)
fieldsRef = calc_refFields([thetav,slp], windowSpacing=50*dxGrid, gridSpacing=dxGrid)
thetav_ref = fieldsRef[0]; slp_ref = fieldsRef[1]
buoy = grav*(thetav-thetav_ref)/thetav_ref
#gradb_dir = np.gradient(buoy);
#gradb = np.maximum(gradb_dir[0],gradb_dir[1])
#gradb = ndimage.morphological_gradient(buoy, size=(5,5))
pPerturb = slp-slp_ref
#gradP = ndimage.morphological_gradient(pPerturb, size=(5,5))
du_dxy = np.gradient(u/dxGrid)
dv_dxy = np.gradient(v/dxGrid)
div = du_dxy[1]+dv_dxy[0]
#gradDiv = ndimage.morphological_gradient(div, size=(5,5))
maxDiv = ndimage.filters.maximum_filter(div, size=(7,7))
minDiv = ndimage.filters.minimum_filter(div, size=(7,7))
divDiff = maxDiv-minDiv
#plot some stuff --------------
if (False):
normBuoy = MidpointNormalize(midpoint=0)
plot_field_recentered(buoy, normBuoy, title='buoyancy', showFig=False)
plt.figure()
plt.pcolormesh(thetav)
plt.colorbar()
normGrad = MidpointNormalize(midpoint=0)
plot_field_recentered(gradb, normGrad, title='grad(buoyancy)',showFig=False)
normP = MidpointNormalize(midpoint=0)
plot_field_recentered(pPerturb, normP, title='perturbation pressure',showFig=False)
normDiv = MidpointNormalize(midpoint=0)
plot_field_recentered(div, normDiv,title='divergence', showFig=False)
norm = MidpointNormalize(midpoint=0)
plot_field_recentered(gradDiv, norm,title='grad(divergence)', showFig=False)
plt.show()
#signals should persist across different variables -> correlations
signalThresh = (grav*1./300.)*50.
signalFlow = -buoy*pPerturb
norm = MidpointNormalize(midpoint=0)
plot_field_recentered(signalFlow, norm, title='-buoy*pPerturb',showFig=False)
if (False):
signalFront = gradb*gradP
norm = MidpointNormalize(midpoint=0)
plot_field_recentered(signalFront, norm, title='gradb*gradp',showFig=False)
candidates = (signalFlow>0)*(signalFront>signalThresh)
if (False):
candidates = (signalFlow>signalThresh)*(divDiff>1.e-3)
else:
candidates = signalFlow>signalThresh
candidates = ndimage.morphology.binary_closing(candidates, structure=np.ones((5,5),dtype=int), iterations=1)
objs, nObjs = ndimage.measurements.label(candidates)
if (True):
norm1 = MidpointNormalize(midpoint=0)
plot_field_recentered(divDiff, norm1,title='ddiv', showFig=False)
divInObject = divDiff.copy(); divInObject[objs==0] = 0.
norm = MidpointNormalize(midpoint=0)
plot_field_recentered(divInObject, norm,title='divObject', showFig=False)
#i think background is always 0?
plt.figure()
plt.pcolormesh(objs, cmap=plt.cm.flag)
plt.colorbar()
plt.show()
if __name__=='__main__':
demo()