def plot_grids(x, y, g1, g2, g3, g4):
"""
Plot the 4 calculated grids: dh, dAGC, n_ad, n_da.
Notes
-----
`pcolor` cannot have NaN, use `masked array` instead.
`pcolor` does not preserve the lon,lat aspect ratio like `imshow`.
"""
try:
apy.rcparams()
except:
pass
#sys.path.append('/Users/fpaolo/code/misc')
#import viz
#cmap = viz.colormap('rgb')
g1 = np.ma.masked_invalid(g1)
g2 = np.ma.masked_invalid(g2)
g3 = np.ma.masked_invalid(g3)
g4 = np.ma.masked_invalid(g4)
x = np.ma.masked_invalid(x)
y = np.ma.masked_invalid(y)
xx, yy = np.meshgrid(x, y)
fig = plt.figure()
plt.subplot(211)
plt.pcolor(xx, yy, g1)
plt.colorbar()
plt.subplot(212)
plt.pcolor(xx, yy, g2)
plt.colorbar()
#viz.colorbar(fig, cmap, (-2,2))
fig = plt.figure()
plt.subplot(211)
plt.pcolor(xx, yy, g3, cmap=plt.cm.copper_r)
plt.colorbar()
plt.subplot(212)
plt.pcolor(xx, yy, g4, cmap=plt.cm.copper_r)
plt.colorbar()
def plot_grids(x, y, g1, g2, g3, g4):
"""
Plot the 4 calculated grids: dh, dAGC, n_ad, n_da.
Notes
-----
`pcolor` cannot have NaN, use `masked array` instead.
`pcolor` does not preserve the lon,lat aspect ratio like `imshow`.
"""
try:
apy.rcparams()
except:
pass
#sys.path.append('/Users/fpaolo/code/misc')
#import viz
#cmap = viz.colormap('rgb')
g1 = np.ma.masked_invalid(g1)
g2 = np.ma.masked_invalid(g2)
g3 = np.ma.masked_invalid(g3)
g4 = np.ma.masked_invalid(g4)
x = np.ma.masked_invalid(x)
y = np.ma.masked_invalid(y)
xx, yy = np.meshgrid(x, y)
fig = plt.figure()
plt.subplot(211)
plt.pcolor(xx, yy, g1)
plt.colorbar()
plt.subplot(212)
plt.pcolor(xx, yy, g2)
plt.colorbar()
#viz.colorbar(fig, cmap, (-2,2))
fig = plt.figure()
plt.subplot(211)
plt.pcolor(xx, yy, g3, cmap=plt.cm.copper_r)
plt.colorbar()
plt.subplot(212)
plt.pcolor(xx, yy, g4, cmap=plt.cm.copper_r)
plt.colorbar()
lat = f2.root.y_edges[:]
'''
# maps grid indexes to coordinates
ii, jj = xrange(len(lat)), xrange(len(lon))
ij2ll = dict([((i,j),(la,lo)) for i,j,la,lo in zip(ii,jj,lat,lon)])
df_h0 = pd.Panel(h0, items=t0, major_axis=ii, minor_axis=jj
).to_frame(filter_observations=False).T
del h0, t0, lon, lat
print df_h0
sys.exit()
'''
#------------------------- FIGURES --------------------------
ap.rcparams()
i1 = np.where( t < 1997)
i2 = np.where((t > 1997) & ( t < 2000))
i3 = np.where((t > 2000) & ( t < 2002))
i4 = np.where((t > 2002) & ( t < 2004))
i5 = np.where(t > 2004)
plt.imshow(h0[10,...], extent=(lon[0], lon[-1], lat[0], lat[-1]), origin='lower', interpolation='nearest')
# plot GPS locations
plt.plot(x[i1], y[i1], 'b.', mec='blue', label='1995')
plt.plot(x[i2], y[i2], 'c.', mec='cyan', label='1999')
plt.plot(x[i3], y[i3], 'g.', mec='green', label='2001')
plt.plot(x[i4], y[i4], 'y.', mec='yellow', label='2003')
plt.plot(x[i5], y[i5], 'r.', mec='red', label='2006')
plt.legend(loc=3).draw_frame(False)
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import altimpy as ap
ap.rcparams()
fname = '/Users/fpaolo/data/shelves/amundsen/amundsen_seasonal_mat.txt'
#fname = 'gisst.dat'
# plot PCs
'''
d = pd.read_table(fname+'_spc.tmp', header=None, sep=' ', skipinitialspace=True)
title = '%d Leading PCs from original data' % len(d.columns)
d.plot(subplots=True, linewidth=2, legend=False, title=title, yticks=[])
### 1) plot variance
d = pd.read_table(fname+'_spc.tmp_mssa_eigvar.tmp', header=None, sep=' ',
skipinitialspace=True,
names=['rank', '%-var']
)
plt.figure()
plt.title('Variance per Mode')
d.plot(x='rank', y='%-var', logy=True, marker='o')
plt.ylabel('MSSA % Variance')
plt.xlabel('Rank K')
'''
### 2) plot singular spectrum (eigenval^1/2)
d = pd.read_table(fname + '_spc.tmp_mcmssa.tmp',
