def data_from_image(im, classes, mask, limits=[0., 1.]):
""" This function extracts the data from a image, based on the classes and a mask.
Once the data is masked, a look-up-table is used to set values to the classes.
"""
import time
N = int(basic.nonan(classes).max() + 1)
# zero is a class...
classes[mask] = np.nan
classLimits = np.linspace(np.min(limits), np.max(limits), N)
cmax = 2**np.round(np.log2(im.max()))
imSingle = im[:, :, 0] * (cmax**0) + im[:, :, 1] * (
cmax**1) + im[:, :, 2] * (cmax**2.)
data = np.zeros(imSingle.shape)
t0 = 0
for k in xrange(N - 1):
#for each class do a linear interpolation
if np.any(classes == k):
data[classes == k] = basic.rescale(imSingle[classes == k],
classLimits[k:k + 2],
quiet=True)
if basic.progresstime(t0):
basic.progress(k, N)
t0 = time.time()
data[mask] = np.nan
return data
def onclick(event):
P.figure(fh.number)
P.clf()
#ax = P.gca()
#inv = ax.transData.inverted()
#A=inv.transform((event.x, event.y))
#A[1]=np.int(np.round((1-A[1])*array2d.shape[1]))
#A[0]=np.int(np.round((A[0])*array2d.shape[0]))
try:
y = np.round(event.xdata)
except:
return
x = np.round(event.ydata)
#ARRAY MAPPING IS first axis y(rows) and second axis is cols (x)
if all(np.isnan(array3d[x, y, :])):
#if there are no points to plot (all nan) then return
return
#Plot second scatter data.
if array3d2 is not None:
if isinstance(array3d2, list):
if yerror3d is None:
w = np.ones(array3d[x, y, :].shape)
else:
w = basic.rescale(1. / yerror3d[x, y, :], [1, 2])
markers = ['*', '+', 's', 'd', 'x', 'v', '<', '>', '^']
m = 0
for arr in array3d2:
print("%d, %d, %d" % (x, y, m))
P.scatter(xScat, arr[x, y, :], marker=markers[m])
idx = ~(np.isnan(arr[x, y, :])
| np.isnan(array3d[x, y, :]))
#c=insar.crosscorrelate(basic.nonan(w[idx]*arr[x, y,idx]),basic.nonan(w[idx]*array3d[x, y,idx]))
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(
basic.nonan(w[idx] * arr[x, y, idx]),
basic.nonan(w[idx] * array3d[x, y, idx]))
P.annotate(str("r2[%s]: %0.2f" % (markers[m], r_value)),
(0, 0.9 - m * 0.05),
xycoords='axes fraction')
m = m + 1
else:
if xerror3d2 is None:
xerr = None
else:
xerr = xerror3d2[x, y, :]
if yerror3d2 is None:
yerr = None
else:
yerr = yerror3d2[x, y, :]
P.errorbar(xScat,
array3d2[x, y, :],
xerr=xerr,
yerr=yerr,
marker='*',
fmt='o')
#Plot function result as scatter data.
p = None
if fn is not None:
if fn == 'linear_amplitude_annual':
dataMask = ~np.isnan(array3d[x, y, :])
p0 = np.array([1, 0, 0, basic.nonan(array3d[x, y, :]).mean()])
fitfun = lambda p: (p[0] + p[1] * xScat[dataMask] / 365.
) * np.cos(2 * np.pi * xScat[dataMask] /
365. + p[2]) + p[3]
xScat2 = np.linspace(xScat.min(), xScat.max())
fitfun2 = lambda p: (p[0] + p[1] * xScat2 / 365.) * np.cos(
2 * np.pi * xScat2 / 365. + p[2]) + p[3]
#errfun=lambda p: sum(abs(basic.nonan(array3d[x, y,:])-fitfun(p)));
if yerror3d is None:
w = np.ones(array3d[x, y, :].shape)
else:
w = basic.rescale(1. / yerror3d[x, y, :], [1, 2])
errfun = lambda p: basic.nonan(w * array3d[x, y, :]) - w[
dataMask] * fitfun(p)
#p=scipy.optimize.fmin_powell(errfun, p0)
p = scipy.optimize.leastsq(errfun, p0)
p = p[0]
P.scatter(xScat[dataMask], fitfun(p), marker='^')
sortedxy = np.squeeze(np.dstack([xScat2, fitfun2(p)]))
sortedxy = sortedxy[sortedxy[:, 0].argsort(), :]
P.plot(sortedxy[:, 0], sortedxy[:, 1])
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(
basic.nonan(w * array3d[x, y, :]), w[dataMask] * fitfun(p))
P.annotate(
str("a0:%0.2f\na1:%0.2f\npha:%0.2f\nbias:%0.2f\nr2:%0.2f" %
(p[0], p[1], p[2], p[3], r_value**2.)), (0.8, 0.8),
xycoords='axes fraction')
elif fn == 'quadratic_amplitude_annual':
dataMask = ~np.isnan(array3d[x, y, :])
p0 = np.array(
[1, 0, 0, 0,
basic.nonan(array3d[x, y, :]).mean()])
fitfun = lambda p: (p[0] + p[1] * xScat[dataMask] / 365. + p[
2] * (xScat[dataMask] / 365.)**2.) * np.cos(
2 * np.pi * xScat[dataMask] / 365. + p[3]) + p[4]
xScat2 = np.linspace(xScat.min(), xScat.max())
fitfun2 = lambda p: (p[0] + p[1] * xScat2 / 365. + p[2] * (
xScat2 / 365.)**2.) * np.cos(2 * np.pi * xScat2 / 365. + p[
3]) + p[4]
#errfun=lambda p: sum(abs(basic.nonan(array3d[x, y,:])-fitfun(p)));
if yerror3d is None:
w = np.ones(array3d[x, y, :].shape)
else:
w = basic.rescale(1. / yerror3d[x, y, :], [1, 2])
errfun = lambda p: basic.nonan(w * array3d[x, y, :]) - w[
dataMask] * fitfun(p)
#p=scipy.optimize.fmin_powell(errfun, p0)
p = scipy.optimize.leastsq(errfun, p0)
p = p[0]
P.scatter(xScat[dataMask], fitfun(p), marker='^')
sortedxy = np.squeeze(np.dstack([xScat2, fitfun2(p)]))
sortedxy = sortedxy[sortedxy[:, 0].argsort(), :]
P.plot(sortedxy[:, 0], sortedxy[:, 1])
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(
basic.nonan(w * array3d[x, y, :]), w[dataMask] * fitfun(p))
P.annotate(str(
"a0:%0.2f\na1:%0.2f\na2:%0.2f\npha:%0.2f\nbias:%0.2f\nr2:%0.2f"
% (p[0], p[1], p[2], p[3], p[4], r_value**2.)), (0.8, 0.8),
xycoords='axes fraction')
elif fn == 'annual':
dataMask = ~np.isnan(array3d[x, y, :])
p0 = np.array([1, 1, basic.nonan(array3d[x, y, :]).mean()])
fitfun = lambda p: p[0] * np.cos(2 * np.pi * xScat[dataMask] /
365. + p[1]) + p[2]
xScat2 = np.linspace(xScat.min(), xScat.max())
fitfun2 = lambda p: p[0] * np.cos(2 * np.pi * xScat2 / 365. +
p[1]) + p[2]
#errfun=lambda p: sum(abs(basic.nonan(array3d[x, y,:])-fitfun(p)));
if yerror3d is None:
w = np.ones(array3d[x, y, :].shape)
else:
w = basic.rescale(1. / yerror3d[x, y, :], [1, 2])
errfun = lambda p: basic.nonan(w * array3d[x, y, :]) - w[
dataMask] * fitfun(p)
#p=scipy.optimize.fmin_powell(errfun, p0)
p = scipy.optimize.leastsq(errfun, p0)
p = p[0]
P.scatter(xScat[dataMask], fitfun(p), marker='^')
sortedxy = np.squeeze(np.dstack([xScat2, fitfun2(p)]))
sortedxy = sortedxy[sortedxy[:, 0].argsort(), :]
P.plot(sortedxy[:, 0], sortedxy[:, 1])
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(
basic.nonan(w * array3d[x, y, :]), w[dataMask] * fitfun(p))
P.annotate(str("amp:%0.2f\npha:%0.2f\nbias:%0.2f\nr2:%0.2f" %
(p[0], p[1], p[2], r_value**2.)), (0.8, 0.8),
xycoords='axes fraction')
else:
p = None
P.scatter(xScat, fn(xScat), marker='^')
#convert axis to date...
if dateaxis:
try:
P.figure(fh.number).axes[0].xaxis_date(tz=None)
P.figure(fh.number).autofmt_xdate()
except:
pass
#change x y to xMap, yMap
if yMap is not None:
xM = ya * x + yb
else:
xM = x
if xMap is not None:
yM = xa * (y) + xb
else:
yM = y
#x and y are flipped in the try/except block above. So Flip again.
#if p is not None:
# P.title("x,y,[]: " + str(yM) + ", " + str(xM) + ', ' + str(p) )
#else:
P.title("x,y,z,z.std: " + str(yM) + ", " + str(xM) + ', ' +
str(array2d[x, y]) + ', ' +
str(np.std(basic.nonan(array3d[x, y, :]))))
# rotate and align the tick labels so they look better
#P.figure(fh.number).autofmt_xdate()
# use a more precise date string for the x axis locations in the
# toolbar
#P.gca().fmt_xdata = mdates.DateFormatter('%Y-%m-%d')
if xerror3d is None:
xerr = None
else:
xerr = xerror3d[x, y, :]
if yerror3d is None:
yerr = None
else:
yerr = yerror3d[x, y, :]
if modelError:
yerr = yerror3d[x, y, :]
yerr[dataMask] = errfun(p)
P.errorbar(xScat, array3d[x, y, :], xerr=xerr, yerr=yerr, fmt='ro')
if ylimScat is not None:
P.ylim(ylimScat)
def onclick(event):
P.figure(fh.number);
P.clf();
#ax = P.gca()
#inv = ax.transData.inverted()
#A=inv.transform((event.x, event.y))
#A[1]=np.int(np.round((1-A[1])*array2d.shape[1]))
#A[0]=np.int(np.round((A[0])*array2d.shape[0]))
try:
y=np.round(event.xdata);
except:
return
x=np.round(event.ydata);
#ARRAY MAPPING IS first axis y(rows) and second axis is cols (x)
if all(np.isnan(array3d[x, y,:])):
#if there are no points to plot (all nan) then return
return
#Plot second scatter data.
if array3d2 is not None:
if isinstance(array3d2, list):
if yerror3d is None:
w=np.ones(array3d[x, y,:].shape);
else:
w=basic.rescale(1./yerror3d[x,y,:], [1,2])
markers=['*','+','s','d','x','v','<','>','^']
m=0;
for arr in array3d2:
print ("%d, %d, %d" % (x,y,m))
P.scatter(xScat, arr[x, y,:], marker=markers[m]);
idx=~( np.isnan(arr[x, y,:]) | np.isnan(array3d[x, y,:]))
#c=insar.crosscorrelate(basic.nonan(w[idx]*arr[x, y,idx]),basic.nonan(w[idx]*array3d[x, y,idx]))
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(basic.nonan(w[idx]*arr[x, y,idx]), basic.nonan(w[idx]*array3d[x, y,idx]))
P.annotate(str("r2[%s]: %0.2f" % (markers[m],r_value)), (0,0.9-m*0.05), xycoords='axes fraction')
m=m+1;
else:
if xerror3d2 is None:
xerr=None;
else:
xerr=xerror3d2[x,y,:]
if yerror3d2 is None:
yerr=None;
else:
yerr=yerror3d2[x, y,:]
P.errorbar(xScat,array3d2[x, y,:], xerr=xerr, yerr=yerr, marker='*', fmt='o');
#Plot function result as scatter data.
p=None
if fn is not None:
if fn=='linear_amplitude_annual':
dataMask=~np.isnan(array3d[x, y,:])
p0=np.array([1,0,0,basic.nonan(array3d[x, y,:]).mean() ])
fitfun=lambda p: (p[0]+p[1]*xScat[dataMask]/365. )* np.cos(2*np.pi*xScat[dataMask]/365.+p[2]) + p[3]
xScat2=np.linspace(xScat.min(),xScat.max())
fitfun2=lambda p: (p[0]+p[1]*xScat2/365.) * np.cos(2*np.pi*xScat2/365.+p[2]) + p[3]
#errfun=lambda p: sum(abs(basic.nonan(array3d[x, y,:])-fitfun(p)));
if yerror3d is None:
w=np.ones(array3d[x, y,:].shape);
else:
w=basic.rescale(1./yerror3d[x,y,:], [1,2])
errfun=lambda p: basic.nonan(w*array3d[x, y,:])-w[dataMask]*fitfun(p);
#p=scipy.optimize.fmin_powell(errfun, p0)
p=scipy.optimize.leastsq(errfun, p0);
p=p[0];
P.scatter(xScat[dataMask], fitfun(p), marker='^');
sortedxy= np.squeeze(np.dstack([xScat2, fitfun2(p)]));
sortedxy=sortedxy[sortedxy[:,0].argsort(),:]
P.plot(sortedxy[:,0], sortedxy[:,1]);
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(basic.nonan(w*array3d[x, y,:]),w[dataMask]*fitfun(p))
P.annotate(str("a0:%0.2f\na1:%0.2f\npha:%0.2f\nbias:%0.2f\nr2:%0.2f" % (p[0], p[1], p[2], p[3], r_value**2.)), (0.8,0.8), xycoords='axes fraction')
elif fn=='quadratic_amplitude_annual':
dataMask=~np.isnan(array3d[x, y,:])
p0=np.array([1,0,0,0,basic.nonan(array3d[x, y,:]).mean() ])
fitfun=lambda p: (p[0]+p[1]*xScat[dataMask]/365.+p[2]*(xScat[dataMask]/365.)**2. )* np.cos(2*np.pi*xScat[dataMask]/365.+p[3]) + p[4]
xScat2=np.linspace(xScat.min(),xScat.max())
fitfun2=lambda p: (p[0]+p[1]*xScat2/365.+p[2]*(xScat2/365.)**2.) * np.cos(2*np.pi*xScat2/365.+p[3]) + p[4]
#errfun=lambda p: sum(abs(basic.nonan(array3d[x, y,:])-fitfun(p)));
if yerror3d is None:
w=np.ones(array3d[x, y,:].shape);
else:
w=basic.rescale(1./yerror3d[x,y,:], [1,2])
errfun=lambda p: basic.nonan(w*array3d[x, y,:])-w[dataMask]*fitfun(p);
#p=scipy.optimize.fmin_powell(errfun, p0)
p=scipy.optimize.leastsq(errfun, p0);
p=p[0];
P.scatter(xScat[dataMask], fitfun(p), marker='^');
sortedxy= np.squeeze(np.dstack([xScat2, fitfun2(p)]));
sortedxy=sortedxy[sortedxy[:,0].argsort(),:]
P.plot(sortedxy[:,0], sortedxy[:,1]);
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(basic.nonan(w*array3d[x, y,:]),w[dataMask]*fitfun(p))
P.annotate(str("a0:%0.2f\na1:%0.2f\na2:%0.2f\npha:%0.2f\nbias:%0.2f\nr2:%0.2f" % (p[0], p[1], p[2], p[3], p[4], r_value**2.)), (0.8,0.8), xycoords='axes fraction')
elif fn=='annual':
dataMask=~np.isnan(array3d[x, y,:])
p0=np.array([1,1,basic.nonan(array3d[x, y,:]).mean() ])
fitfun=lambda p: p[0]* np.cos(2*np.pi*xScat[dataMask]/365.+p[1]) + p[2]
xScat2=np.linspace(xScat.min(),xScat.max())
fitfun2=lambda p: p[0]* np.cos(2*np.pi*xScat2/365.+p[1]) + p[2]
#errfun=lambda p: sum(abs(basic.nonan(array3d[x, y,:])-fitfun(p)));
if yerror3d is None:
w=np.ones(array3d[x, y,:].shape);
else:
w=basic.rescale(1./yerror3d[x,y,:], [1,2])
errfun=lambda p: basic.nonan(w*array3d[x, y,:])-w[dataMask]*fitfun(p);
#p=scipy.optimize.fmin_powell(errfun, p0)
p=scipy.optimize.leastsq(errfun, p0);
p=p[0];
P.scatter(xScat[dataMask], fitfun(p), marker='^');
sortedxy= np.squeeze(np.dstack([xScat2, fitfun2(p)]));
sortedxy=sortedxy[sortedxy[:,0].argsort(),:]
P.plot(sortedxy[:,0], sortedxy[:,1]);
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(basic.nonan(w*array3d[x, y,:]),w[dataMask]*fitfun(p))
P.annotate(str("amp:%0.2f\npha:%0.2f\nbias:%0.2f\nr2:%0.2f" % (p[0], p[1], p[2], r_value**2.)), (0.8,0.8), xycoords='axes fraction')
else:
p=None
P.scatter(xScat, fn(xScat), marker='^');
#convert axis to date...
if dateaxis:
try:
P.figure(fh.number).axes[0].xaxis_date(tz=None)
P.figure(fh.number).autofmt_xdate()
except:
pass
#change x y to xMap, yMap
if yMap is not None:
xM=ya*x+yb;
else:
xM=x;
if xMap is not None:
yM=xa*(y)+xb;
else:
yM=y;
#x and y are flipped in the try/except block above. So Flip again.
#if p is not None:
# P.title("x,y,[]: " + str(yM) + ", " + str(xM) + ', ' + str(p) )
#else:
P.title("x,y,z,z.std: " + str(yM) + ", " + str(xM) + ', ' + str(array2d[x,y]) +', ' + str(np.std(basic.nonan(array3d[x, y,:]))) )
# rotate and align the tick labels so they look better
#P.figure(fh.number).autofmt_xdate()
# use a more precise date string for the x axis locations in the
# toolbar
#P.gca().fmt_xdata = mdates.DateFormatter('%Y-%m-%d')
if xerror3d is None:
xerr=None;
else:
xerr=xerror3d[x,y,:]
if yerror3d is None:
yerr=None;
else:
yerr=yerror3d[x, y,:]
if modelError:
yerr=yerror3d[x, y,:]
yerr[dataMask]=errfun(p)
P.errorbar(xScat,array3d[x, y,:], xerr=xerr, yerr=yerr, fmt='ro');
if ylimScat is not None:
P.ylim(ylimScat);
import cv2
import carve, basic, merg
if __name__ == '__main__':
img = cv2.imread('testdata/p1.jpg')
overlay = carve.circular(img)
overlay = basic.rescale(overlay, 0.1)
frame = basic.rescale(cv2.imread('testdata/p1.jpg'), .3)
output = merg.place(overlay, frame, 570, 100)
basic.imshow(output)
def writeCSV(ary,
filename='kgiAlos.tif',
rescale=None,
dataformat="%f",
lon=None,
lat=None,
nodata=None,
grid=False,
srs_proj4='+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs'):
'''writeAny(ary, geoTransform, format="GTiff", filename='kgiAlos.tif', rescale=None, format=gdal.GDT_Float64 ,lon=None, lat=None):
ary: 2D array.
geoTransform: [top left x, w-e pixel resolution, rotation, top left y, rotation, n-s pixel resolution]
format: "GTiff"
rescale: [min max]: If given rescale ary values between min and max.
If lon lat is specified set coord to None
'''
if coord is None:
import scipy
import scipy.linalg
s = [sk / 10 for sk in ary.shape]
ary10 = ary[::s[0], ::s[1]]
lon10 = lon[::s[0], ::s[1]]
lat10 = lat[::s[0], ::s[1]]
#P.figure()
#P.scatter(lon10.ravel(), lat10.ravel(), 5, ary10.ravel(), edgecolors='none')
A = N.ones([N.multiply(*ary10.shape), 3])
line, pixel = N.meshgrid(N.r_[0:ary.shape[0]:s[0]],
N.r_[0:ary.shape[1]:s[1]])
A[:, 1] = pixel.ravel()
A[:, 2] = line.ravel()
xlon = N.dot(scipy.linalg.pinv(A), lon10.ravel())
xlat = N.dot(scipy.linalg.pinv(A), lat10.ravel())
##check flip flop
#if xlon[1]<0: #flip lr
# ary=N.fliplr(ary)
#
#if xlat[2]>0: #flip ud
# ary=N.flipud(ary)
coord = [xlon[0], xlon[2], xlon[1], xlat[0], xlat[2], xlat[1]]
print coord
#x=lon[0,0]
#y=lat[0,0]
#dx=lon[0,1]-lon[0,0]
#dy=lat[1,0]-lat[0,0]
#xrot=0.
#yrot=0.
#coord=[x,dx, xrot, y,yrot, dy]
if grid:
import scipy.interpolate
LON, LAT = N.meshgrid(N.r_[lon.min():lon.max():abs(coord[1])],
N.r_[lat.max():lat.min():-abs(coord[5])])
#ary=P.griddata(lon.ravel(),lat.ravel(),ary.ravel(),LON,LAT);
ary = scipy.interpolate.griddata(N.array(
[lon.ravel(), lat.ravel()]).T,
ary.ravel(), (LON, LAT),
method='cubic')
coord = [
LON[0, 0],
abs(coord[1]), 0, LAT[0, 0], 0, -abs(coord[5])
]
print coord
if rescale:
import basic
ary = basic.rescale(ary, rescale)
# data exists in 'ary' with values range 0 - 255
# Uncomment next line if ary[0][0] is upper-left corner
#ary = numpy.flipup(ary)
Ny, Nx = ary.shape
item_length = Ny * Nx
import csv
lol = [lon.ravel(), lat.ravel(), ary.ravel()]
with open(filename, 'wb') as test_file:
file_writer = csv.writer(test_file)
for i in range(item_length):
file_writer.writerow([x[i] for x in lol])
print "File written to: " + filename
def writeTiff(ary,
coord,
filename='kgiAlos.tif',
rescale=None,
dataformat=gdal.GDT_Float64,
lon=None,
lat=None,
nodata=None,
grid=None,
srs_proj4='+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs'):
'''writeTiff(ary, geoTransform, filename='kgiAlos.tif', rescale=None, format=gdal.GDT_Float64 ,lon=None, lat=None):
ary: 2D array.
geoTransform: [top left x, w-e pixel resolution, rotation, top left y, rotation, n-s pixel resolution]
rescale: [min max]: If given rescale ary values between min and max.
grid: Gridding (interpolation) method None, 'nearest', 'linear', or 'cubic'. Default None.
If lon lat is specified set coord to None
'''
if coord is None:
import scipy
import scipy.linalg
s = [sk / 10 for sk in ary.shape]
ary10 = ary[::s[0], ::s[1]]
lon10 = lon[::s[0], ::s[1]]
lat10 = lat[::s[0], ::s[1]]
#P.figure()
#P.scatter(lon10.ravel(), lat10.ravel(), 5, ary10.ravel(), edgecolors='none')
A = N.ones([N.multiply(*ary10.shape), 3])
line, pixel = N.meshgrid(N.r_[0:ary.shape[0]:s[0]],
N.r_[0:ary.shape[1]:s[1]])
A[:, 1] = pixel.ravel()
A[:, 2] = line.ravel()
xlon = N.dot(scipy.linalg.pinv(A), lon10.ravel())
xlat = N.dot(scipy.linalg.pinv(A), lat10.ravel())
##check flip flop
#if xlon[1]<0: #flip lr
# ary=N.fliplr(ary)
#
#if xlat[2]>0: #flip ud
# ary=N.flipud(ary)
coord = [xlon[0], xlon[2], xlon[1], xlat[0], xlat[2], xlat[1]]
print coord
#x=lon[0,0]
#y=lat[0,0]
#dx=lon[0,1]-lon[0,0]
#dy=lat[1,0]-lat[0,0]
#xrot=0.
#yrot=0.
#coord=[x,dx, xrot, y,yrot, dy]
if grid is not None:
import scipy.interpolate
LON, LAT = N.meshgrid(N.r_[lon.min():lon.max():abs(coord[1])],
N.r_[lat.max():lat.min():-abs(coord[5])])
#ary=P.griddata(lon.ravel(),lat.ravel(),ary.ravel(),LON,LAT);
ary = scipy.interpolate.griddata(N.array(
[lon.ravel(), lat.ravel()]).T,
ary.ravel(), (LON, LAT),
method=grid,
fill_value=nodata)
coord = [
LON[0, 0],
abs(coord[1]), 0, LAT[0, 0], 0, -abs(coord[5])
]
print coord
if rescale:
import basic
ary = basic.rescale(ary, rescale)
# data exists in 'ary' with values range 0 - 255
# Uncomment next line if ary[0][0] is upper-left corner
#ary = numpy.flipup(ary)
Ny, Nx = ary.shape
driver = gdal.GetDriverByName("GTiff")
ds = driver.Create(filename, Nx, Ny, 1, gdal.GDT_Float64)
#ds.SetGeoTransform( ... ) # define GeoTransform tuple
# top left x, w-e pixel resolution, rotation, top left y, rotation, n-s pixel resolution
ds.SetGeoTransform(coord)
srs = osr.SpatialReference()
if os.path.isfile(srs_proj4):
srs.ImportFromWkt(open(srs_proj4).read())
else:
srs.ImportFromProj4(srs_proj4)
ds.SetProjection(srs.ExportToWkt())
if nodata is not None:
ds.GetRasterBand(1).SetNoDataValue(nodata)
ds.GetRasterBand(1).WriteArray(ary)
ds = None
print "File written to: " + filename
