def MakeFrameMask(data,frame):
pixelSize = data['pixelSize']
scalex = pixelSize[0]/1000.
scaley = pixelSize[1]/1000.
blkSize = 512
Nx,Ny = data['size']
nXBlks = (Nx-1)/blkSize+1
nYBlks = (Ny-1)/blkSize+1
tam = ma.make_mask_none(data['size'])
for iBlk in range(nXBlks):
iBeg = iBlk*blkSize
iFin = min(iBeg+blkSize,Nx)
for jBlk in range(nYBlks):
jBeg = jBlk*blkSize
jFin = min(jBeg+blkSize,Ny)
nI = iFin-iBeg
nJ = jFin-jBeg
tax,tay = np.mgrid[iBeg+0.5:iFin+.5,jBeg+.5:jFin+.5] #bin centers not corners
tax = np.asfarray(tax*scalex,dtype=np.float32)
tay = np.asfarray(tay*scaley,dtype=np.float32)
tamp = ma.make_mask_none((1024*1024))
tamp = ma.make_mask(pm.polymask(nI*nJ,tax.flatten(),
tay.flatten(),len(frame),frame,tamp)[:nI*nJ])-True #switch to exclude around frame
if tamp.shape:
tamp = np.reshape(tamp[:nI*nJ],(nI,nJ))
tam[iBeg:iFin,jBeg:jFin] = ma.mask_or(tamp[0:nI,0:nJ],tam[iBeg:iFin,jBeg:jFin])
else:
tam[iBeg:iFin,jBeg:jFin] = True
return tam.T
def MakeFrameMask(data, frame):
pixelSize = data['pixelSize']
scalex = pixelSize[0] / 1000.
scaley = pixelSize[1] / 1000.
blkSize = 512
Nx, Ny = data['size']
nXBlks = (Nx - 1) / blkSize + 1
nYBlks = (Ny - 1) / blkSize + 1
tam = ma.make_mask_none(data['size'])
for iBlk in range(nXBlks):
iBeg = iBlk * blkSize
iFin = min(iBeg + blkSize, Nx)
for jBlk in range(nYBlks):
jBeg = jBlk * blkSize
jFin = min(jBeg + blkSize, Ny)
nI = iFin - iBeg
nJ = jFin - jBeg
tax, tay = np.mgrid[iBeg + 0.5:iFin + .5,
jBeg + .5:jFin + .5] #bin centers not corners
tax = np.asfarray(tax * scalex, dtype=np.float32)
tay = np.asfarray(tay * scaley, dtype=np.float32)
tamp = ma.make_mask_none((1024 * 1024))
tamp = ma.make_mask(
pm.polymask(
nI * nJ, tax.flatten(), tay.flatten(), len(frame), frame,
tamp)[:nI * nJ]) - True #switch to exclude around frame
if tamp.shape:
tamp = np.reshape(tamp[:nI * nJ], (nI, nJ))
tam[iBeg:iFin,
jBeg:jFin] = ma.mask_or(tamp[0:nI, 0:nJ], tam[iBeg:iFin,
jBeg:jFin])
else:
tam[iBeg:iFin, jBeg:jFin] = True
return tam.T
def Make2ThetaAzimuthMap(data, masks, iLim, jLim,
times): #most expensive part of integration!
'Needs a doc string'
#transforms 2D image from x,y space to 2-theta,azimuth space based on detector orientation
pixelSize = data['pixelSize']
scalex = pixelSize[0] / 1000.
scaley = pixelSize[1] / 1000.
tay, tax = np.mgrid[iLim[0] + 0.5:iLim[1] + .5,
jLim[0] + .5:jLim[1] + .5] #bin centers not corners
tax = np.asfarray(tax * scalex, dtype=np.float32)
tay = np.asfarray(tay * scaley, dtype=np.float32)
nI = iLim[1] - iLim[0]
nJ = jLim[1] - jLim[0]
t0 = time.time()
#make position masks here
frame = masks['Frames']
tam = ma.make_mask_none((nI, nJ))
if frame:
tamp = ma.make_mask_none((1024 * 1024))
tamp = ma.make_mask(
pm.polymask(
nI * nJ, tax.flatten(), tay.flatten(), len(frame), frame,
tamp)[:nI * nJ]) - True #switch to exclude around frame
tam = ma.mask_or(tam.flatten(), tamp)
polygons = masks['Polygons']
for polygon in polygons:
if polygon:
tamp = ma.make_mask_none((1024 * 1024))
tamp = ma.make_mask(
pm.polymask(nI * nJ, tax.flatten(), tay.flatten(),
len(polygon), polygon, tamp)[:nI * nJ])
tam = ma.mask_or(tam.flatten(), tamp)
if tam.shape: tam = np.reshape(tam, (nI, nJ))
spots = masks['Points']
for X, Y, diam in spots:
tamp = ma.getmask(
ma.masked_less((tax - X)**2 + (tay - Y)**2, (diam / 2.)**2))
tam = ma.mask_or(tam, tamp)
times[0] += time.time() - t0
t0 = time.time()
TA = np.array(GetTthAzmG(
tax, tay,
data)) #includes geom. corr. as dist**2/d0**2 - most expensive step
times[1] += time.time() - t0
TA[1] = np.where(TA[1] < 0, TA[1] + 360, TA[1])
return np.array(
TA), tam #2-theta, azimuth & geom. corr. arrays & position mask
def Make2ThetaAzimuthMap(data,masks,iLim,jLim,times): #most expensive part of integration!
'Needs a doc string'
#transforms 2D image from x,y space to 2-theta,azimuth space based on detector orientation
pixelSize = data['pixelSize']
scalex = pixelSize[0]/1000.
scaley = pixelSize[1]/1000.
tay,tax = np.mgrid[iLim[0]+0.5:iLim[1]+.5,jLim[0]+.5:jLim[1]+.5] #bin centers not corners
tax = np.asfarray(tax*scalex,dtype=np.float32)
tay = np.asfarray(tay*scaley,dtype=np.float32)
nI = iLim[1]-iLim[0]
nJ = jLim[1]-jLim[0]
t0 = time.time()
#make position masks here
frame = masks['Frames']
tam = ma.make_mask_none((nI,nJ))
if frame:
tamp = ma.make_mask_none((1024*1024))
tamp = ma.make_mask(pm.polymask(nI*nJ,tax.flatten(),
tay.flatten(),len(frame),frame,tamp)[:nI*nJ])-True #switch to exclude around frame
tam = ma.mask_or(tam.flatten(),tamp)
polygons = masks['Polygons']
for polygon in polygons:
if polygon:
tamp = ma.make_mask_none((1024*1024))
tamp = ma.make_mask(pm.polymask(nI*nJ,tax.flatten(),
tay.flatten(),len(polygon),polygon,tamp)[:nI*nJ])
tam = ma.mask_or(tam.flatten(),tamp)
if tam.shape: tam = np.reshape(tam,(nI,nJ))
spots = masks['Points']
for X,Y,diam in spots:
tamp = ma.getmask(ma.masked_less((tax-X)**2+(tay-Y)**2,(diam/2.)**2))
tam = ma.mask_or(tam,tamp)
times[0] += time.time()-t0
t0 = time.time()
TA = np.array(GetTthAzmG(tax,tay,data)) #includes geom. corr. as dist**2/d0**2 - most expensive step
times[1] += time.time()-t0
TA[1] = np.where(TA[1]<0,TA[1]+360,TA[1])
return np.array(TA),tam #2-theta, azimuth & geom. corr. arrays & position mask
def test_hardmask(self):
# Test hardmask
base = self.base.copy()
mbase = base.view(mrecarray)
mbase.harden_mask()
self.assertTrue(mbase._hardmask)
mbase.mask = nomask
assert_equal_records(mbase._mask, base._mask)
mbase.soften_mask()
self.assertTrue(not mbase._hardmask)
mbase.mask = nomask
# So, the mask of a field is no longer set to nomask...
assert_equal_records(mbase._mask, ma.make_mask_none(base.shape, base.dtype))
self.assertTrue(ma.make_mask(mbase["b"]._mask) is nomask)
assert_equal(mbase["a"]._mask, mbase["b"]._mask)
def test_hardmask(self):
# Test hardmask
base = self.base.copy()
mbase = base.view(mrecarray)
mbase.harden_mask()
assert_(mbase._hardmask)
mbase.mask = nomask
assert_equal_records(mbase._mask, base._mask)
mbase.soften_mask()
assert_(not mbase._hardmask)
mbase.mask = nomask
# So, the mask of a field is no longer set to nomask...
assert_equal_records(mbase._mask, ma.make_mask_none(base.shape, base.dtype))
assert_(ma.make_mask(mbase["b"]._mask) is nomask)
assert_equal(mbase["a"]._mask, mbase["b"]._mask)
def getWeightedAvg(a, neighbourArray): kernSize = 2 kernel = utils.gauss_kern(kernSize) pixVal = np.zeros((a.shape)) #initialising, pixel value for inpainting weightSum = np.zeros((a.shape)) #initialising, value for later normalisation #for pixels further away from the edges: maxNeighbours = np.max(neighbourArray) print np.max(neighbourArray) neighbours = ((-2, -2), (-2, -1), (-2, 0), (-2, 1), (-2, 2), (-1, -2), (-1, -1), (-1, 0), (-1, 1), (-1, 2), (0, -2), (0, -1), (0, 1), (0, 2), (1, -2), (1, -1), (1, 0), (1, 1), (1, 2), (2, -2), (2, -1), (2, 0), (2, 1), (2, 2)) a_reduced = a[2:-2, 2:-2].copy() maxNMask = ma.make_mask_none((a.shape)) maxNMask[np.where(neighbourArray == maxNeighbours)] = 1 print a_reduced.shape for hor_shift,vert_shift in neighbours: #if not np.any(a.mask): break a_shifted = np.roll(a_reduced, shift = hor_shift, axis = 1) a_shifted = np.roll(a_shifted, shift = vert_shift, axis = 0) idx=~a_shifted.mask*maxNMask[2:-2, 2:-2] weightSum = weightSum + kernel[2-vert_shift, 2-hor_shift] pixVal[idx] = pixVal[idx]+ a_shifted[idx]*kernel[2-vert_shift, 2-hor_shift] b = a.copy() b[idx] = np.divide(pixVal[idx], weightSum[idx]) edgesTop = a[0:2, :] edgesLeft = a[:, 0:2] edgesRight = a[:, -1:-3:-1] edgesBottom = a[-1:-3:-1, :] for i in range(0, 3): for j in range(0, a.shape[1]): if neighbourArray[i, j] == maxNeighbours: b[i, j] = getWeightedAvgEdges(inputArray, i, j) for i in range(a.shape[0] - 2, a.shape[0]): for j in range(0, a.shape[1]): if neighbourArray[i, j] == maxNeighbours: b[i, j] = getWeightedAvgEdges(inputArray, i, j) for i in range(0, a.shape[0]): for j in range(0, 3): if neighbourArray[i, j] == maxNeighbours: b[i, j] = getWeightedAvgEdges(inputArray, i, j) for i in range(0, a.shape[0]): for j in range(a.shape[1] - 2, a.shape[1]): if neighbourArray[i, j] == maxNeighbours: b[i, j] = getWeightedAvgEdges(inputArray, i, j) return b
def __array_finalize__(self, obj):
# Make sure we have a _fieldmask by default ..
_mask = getattr(obj, "_mask", None)
if _mask is None:
objmask = getattr(obj, "_mask", nomask)
_dtype = ndarray.__getattribute__(self, "dtype")
if objmask is nomask:
_mask = ma.make_mask_none(self.shape, dtype=_dtype)
else:
mdescr = ma.make_mask_descr(_dtype)
_mask = narray([tuple([m] * len(mdescr)) for m in objmask], dtype=mdescr).view(recarray)
# Update some of the attributes
_dict = self.__dict__
_dict.update(_mask=_mask, _fieldmask=_mask)
self._update_from(obj)
if _dict["_baseclass"] == ndarray:
_dict["_baseclass"] = recarray
return
def __array_finalize__(self, obj):
# Make sure we have a _fieldmask by default
_mask = getattr(obj, '_mask', None)
if _mask is None:
objmask = getattr(obj, '_mask', nomask)
_dtype = ndarray.__getattribute__(self, 'dtype')
if objmask is nomask:
_mask = ma.make_mask_none(self.shape, dtype=_dtype)
else:
mdescr = ma.make_mask_descr(_dtype)
_mask = narray([tuple([m] * len(mdescr)) for m in objmask],
dtype=mdescr).view(recarray)
# Update some of the attributes
_dict = self.__dict__
_dict.update(_mask=_mask)
self._update_from(obj)
if _dict['_baseclass'] == ndarray:
_dict['_baseclass'] = recarray
return
def ImageRecalibrate(self, data, masks):
'Needs a doc string'
import ImageCalibrants as calFile
print 'Image recalibration:'
time0 = time.time()
pixelSize = data['pixelSize']
scalex = 1000. / pixelSize[0]
scaley = 1000. / pixelSize[1]
pixLimit = data['pixLimit']
cutoff = data['cutoff']
data['rings'] = []
data['ellipses'] = []
if not data['calibrant']:
print 'no calibration material selected'
return True
skip = data['calibskip']
dmin = data['calibdmin']
Bravais, SGs, Cells = calFile.Calibrants[data['calibrant']][:3]
HKL = []
for bravais, sg, cell in zip(Bravais, SGs, Cells):
A = G2lat.cell2A(cell)
if sg:
SGData = G2spc.SpcGroup(sg)[1]
hkl = G2pwd.getHKLpeak(dmin, SGData, A)
HKL += hkl
else:
hkl = G2lat.GenHBravais(dmin, bravais, A)
HKL += hkl
HKL = G2lat.sortHKLd(HKL, True, False)
varyList = [item for item in data['varyList'] if data['varyList'][item]]
parmDict = {
'dist': data['distance'],
'det-X': data['center'][0],
'det-Y': data['center'][1],
'tilt': data['tilt'],
'phi': data['rotation'],
'wave': data['wavelength'],
'dep': data['DetDepth']
}
Found = False
wave = data['wavelength']
frame = masks['Frames']
tam = ma.make_mask_none(self.ImageZ.shape)
if frame:
tam = ma.mask_or(tam, MakeFrameMask(data, frame))
for iH, H in enumerate(HKL):
if debug: print H
dsp = H[3]
tth = 2.0 * asind(wave / (2. * dsp))
if tth + abs(data['tilt']) > 90.:
print 'next line is a hyperbola - search stopped'
break
ellipse = GetEllipse(dsp, data)
Ring = makeRing(dsp, ellipse, pixLimit, cutoff, scalex, scaley,
ma.array(self.ImageZ, mask=tam))
if Ring:
if iH >= skip:
data['rings'].append(np.array(Ring))
data['ellipses'].append(copy.deepcopy(ellipse + ('r', )))
Found = True
elif not Found: #skipping inner rings, keep looking until ring found
continue
else: #no more rings beyond edge of detector
data['ellipses'].append([])
continue
# break
rings = np.concatenate((data['rings']), axis=0)
chisq = FitDetector(rings, varyList, parmDict)
data['wavelength'] = parmDict['wave']
data['distance'] = parmDict['dist']
data['center'] = [parmDict['det-X'], parmDict['det-Y']]
data['rotation'] = np.mod(parmDict['phi'], 360.0)
data['tilt'] = parmDict['tilt']
data['DetDepth'] = parmDict['dep']
data['chisq'] = chisq
N = len(data['ellipses'])
data['ellipses'] = [] #clear away individual ellipse fits
for H in HKL[:N]:
ellipse = GetEllipse(H[3], data)
data['ellipses'].append(copy.deepcopy(ellipse + ('b', )))
print 'calibration time = ', time.time() - time0
G2plt.PlotImage(self, newImage=True)
return True
def ImageRecalibrate(self,data,masks):
'Needs a doc string'
import ImageCalibrants as calFile
print 'Image recalibration:'
time0 = time.time()
pixelSize = data['pixelSize']
scalex = 1000./pixelSize[0]
scaley = 1000./pixelSize[1]
pixLimit = data['pixLimit']
cutoff = data['cutoff']
data['rings'] = []
data['ellipses'] = []
if not data['calibrant']:
print 'no calibration material selected'
return True
skip = data['calibskip']
dmin = data['calibdmin']
Bravais,SGs,Cells = calFile.Calibrants[data['calibrant']][:3]
HKL = []
for bravais,sg,cell in zip(Bravais,SGs,Cells):
A = G2lat.cell2A(cell)
if sg:
SGData = G2spc.SpcGroup(sg)[1]
hkl = G2pwd.getHKLpeak(dmin,SGData,A)
HKL += hkl
else:
hkl = G2lat.GenHBravais(dmin,bravais,A)
HKL += hkl
HKL = G2lat.sortHKLd(HKL,True,False)
varyList = [item for item in data['varyList'] if data['varyList'][item]]
parmDict = {'dist':data['distance'],'det-X':data['center'][0],'det-Y':data['center'][1],
'tilt':data['tilt'],'phi':data['rotation'],'wave':data['wavelength'],'dep':data['DetDepth']}
Found = False
wave = data['wavelength']
frame = masks['Frames']
tam = ma.make_mask_none(self.ImageZ.shape)
if frame:
tam = ma.mask_or(tam,MakeFrameMask(data,frame))
for iH,H in enumerate(HKL):
if debug: print H
dsp = H[3]
tth = 2.0*asind(wave/(2.*dsp))
if tth+abs(data['tilt']) > 90.:
print 'next line is a hyperbola - search stopped'
break
ellipse = GetEllipse(dsp,data)
Ring = makeRing(dsp,ellipse,pixLimit,cutoff,scalex,scaley,ma.array(self.ImageZ,mask=tam))
if Ring:
if iH >= skip:
data['rings'].append(np.array(Ring))
data['ellipses'].append(copy.deepcopy(ellipse+('r',)))
Found = True
elif not Found: #skipping inner rings, keep looking until ring found
continue
else: #no more rings beyond edge of detector
data['ellipses'].append([])
continue
# break
rings = np.concatenate((data['rings']),axis=0)
chisq = FitDetector(rings,varyList,parmDict)
data['wavelength'] = parmDict['wave']
data['distance'] = parmDict['dist']
data['center'] = [parmDict['det-X'],parmDict['det-Y']]
data['rotation'] = np.mod(parmDict['phi'],360.0)
data['tilt'] = parmDict['tilt']
data['DetDepth'] = parmDict['dep']
data['chisq'] = chisq
N = len(data['ellipses'])
data['ellipses'] = [] #clear away individual ellipse fits
for H in HKL[:N]:
ellipse = GetEllipse(H[3],data)
data['ellipses'].append(copy.deepcopy(ellipse+('b',)))
print 'calibration time = ',time.time()-time0
G2plt.PlotImage(self,newImage=True)
return True
def find_bad_pixels(files, hot_threshold=np.inf, var_factor=5.0, chipgaps=False, read_fun=read_cbf):
"""Return a mask where pixels determined as dead or hot are masked."""
# Reject pixel if the sum of this pixel in all frames is less than this
dead_threshold = 1
# Reject if ratio of pixel value to median filtered value exceeds this
# in all frames
medfilt_var_const = 3.0
# Reject if ratio of pixel value to median filtered value exceeds this
# even in a single frame
medfilt_var_gross = 6.0
# Curvature threshold above which deviates from median are not rejected
laplace_threshold = 500.0
# Laplace operator with diagonals, see
# http://en.wikipedia.org/wiki/Discrete_Laplace_operator
laplace = np.ones((3,3), dtype=np.float64)
laplace[1,1] = -8.0
f0 = read_fun(files[0])
s = f0.im.shape
modules = match_shape_to_pilatus(s)
if modules is None:
warnings.warn("Frame shape does not match any Pilatus. Not using a gap mask.")
a_gaps = np.zeros(s, dtype=np.bool)
else:
a_gaps = np.logical_not(pilatus_gapmask(modules, chipgaps=chipgaps))
# Boolean arrays of various invalid pixels (logical_not of a mask!)
# Pixels above hot_threshold
a_hots = ma.make_mask_none((s[0], s[1]))
# Pixels always deviating from median filtered
a_medf = np.ones((s[0], s[1]), dtype=np.bool)
# Pixels grossly deviating from median filtered, even in a single frame
a_gross = np.zeros((s[0], s[1]), dtype=np.bool)
Asum = np.zeros((s[0], s[1]), dtype=np.float64)
Asumsq = np.zeros((s[0], s[1]), dtype=np.float64)
for i in range(0,len(files)):
print(files[i])
tim = (read_fun(files[i])).im.astype(np.float64)
Asum += tim
Asumsq += tim**2.0
a_hots = bool_add(a_hots, (tim > hot_threshold))
mfilt = scipy.signal.medfilt2d(tim, kernel_size=5)
# Pixels where curvature is less than threshold
lapm = (np.abs(scipy.signal.convolve2d(mfilt, laplace, mode='same',
boundary='symm')) < laplace_threshold)
absdev = np.abs(tim - mfilt)
# Pixels deviating somewhat from median, not in curved regions
mrej = (medfilt_var_const < (absdev/(mfilt+1))) * lapm
a_medf = a_medf * mrej
# Pixels deviating grossly from median filtered
a_gross = bool_add(a_gross, (medfilt_var_gross < (absdev/(mfilt+1))) * lapm)
sA = Asum
mA = Asum / float(len(files))
Esq = Asumsq / float(len(files))
vA = Esq - mA**2
a_consts = bool_sub((vA == 0), a_gaps)
# a_randoms = ((var_factor*vA) > mA)
a_randoms = False # Something strange with variance, disabling for now
a_deads = bool_sub((sA < dead_threshold), a_gaps)
# total bads outside gaps
a_bads = reduce(bool_add, [a_consts, a_randoms, a_hots, a_deads, a_medf, a_gross])
print("Frame has %dx%d = %d pixels" % (s[0], s[1], s[0]*s[1]))
print("%d invalid pixels in module gaps" % (np.sum(a_gaps)))
nconsts = np.sum(a_consts)
print("%d constant pixels outside gaps, %d are >= %d" % \
(nconsts, (nconsts - np.sum(a_deads)), dead_threshold))
# print("Found %d pixels with variance larger than %f * mean" % (np.sum(m_randoms), var_factor))
print("%d hot (count > %e) pixels" % (np.sum(a_hots), hot_threshold))
print("%d pixels consistently deviating from median" % (np.sum(a_medf)))
print("%d pixels grossly deviating from median" % (np.sum(a_gross)))
print("Total of %d bad pixels outside of gaps" % (np.sum(a_bads)))
mask = np.ones((s[0], s[1]), dtype=np.bool)
mask = bool_sub(mask, bool_add(a_bads, a_gaps))
print("Mask has a total of %d bad pixels" % (np.sum(mask == False)))
return mask
def press2alt(arg, P0=None, T0=None, missing=1e+20, invert=0):
"""Calculate elevation given pressure (or vice versa).
Calculations are made assuming that the temperature distribution
follows the 1976 Standard Atmosphere. Technically the standard
atmosphere defines temperature distribution as a function of
geopotential altitude, and this routine actually calculates geo-
potential altitude rather than geometric altitude.
Method Positional Argument:
* arg: Numeric floating point vector of any shape and size, or a
Numeric floating point scalar. If invert=0 (the default), arg
is air pressure [hPa]. If invert=1, arg is elevation [m].
Method Keyword Arguments:
* P0: Pressure [hPa] at the surface (altitude equals 0). Numeric
floating point vector of same size and shape as arg or a scalar.
Default of keyword is set to None, in which case the routine
uses the value of instance attribute sea_level_press (converted
to hPa) from the AtmConst class. Keyword value is used if the
keyword is set in the function call. This keyword cannot have
any missing values.
* T0: Temperature [K] at the surface (altitude equals 0). Numeric
floating point vector of same size and shape as arg or a scalar.
Default of keyword is set to None, in which case the routine uses
the value of instance attribute sea_level_temp from the AtmConst
class. Keyword value is used if the keyword is set in the func-
tion call. This keyword cannot have any missing values.
* missing: If arg has missing values, this is the missing value
value. Floating point scalar. Default is 1e+20.
* invert: If set to 1, function calculates pressure [hPa] from
altitude [m]. In that case, positional input variable arg is
altitude [m] and the output is pressure [hPa]. Default value of
invert=0, which means the function calculates altitude given
pressure.
Output:
* If invert=0 (the default), output is elevation [m] at each
element of arg, relative to the surface. If invert=1, output
is the air pressure [hPa]. Numeric floating point array of
the same size and shape as arg.
If there are any missing values in output, those values are set
to the value in argument missing from the input. If there are
missing values in the output due to math errors and missing is
set to None, output will fill those missing values with the MA
default value of 1e+20.
References:
* Carmichael, Ralph (2003): "Definition of the 1976 Standard Atmo-
sphere to 86 km," Public Domain Aeronautical Software (PDAS).
URL: http://www.pdas.com/coesa.htm.
* Wallace, J. M., and P. V. Hobbs (1977): Atmospheric Science:
An Introductory Survey. San Diego, CA: Academic Press, ISBN
0-12-732950-1, pp. 60-61.
Examples:
(1) Calculating altitude given pressure:
>>> from press2alt import press2alt
>>> import Numeric as N
>>> press = N.array([200., 350., 850., 1e+20, 50.])
>>> alt = press2alt(press, missing=1e+20)
>>> ['%.7g' % alt[i] for i in range(5)]
['11783.94', '8117.19', '1457.285', '1e+20', '20575.96']
(2) Calculating pressure given altitude:
>>> alt = N.array([0., 10000., 15000., 20000., 50000.])
>>> press = press2alt(alt, missing=1e+20, invert=1)
>>> ['%.7g' % press[i] for i in range(5)]
['1013.25', '264.3589', '120.443', '54.74718', '0.7593892']
(3) Input is a Numeric floating point scalar, and using a keyword
set surface pressure to a different scalar:
>>> alt = press2alt(N.array(850.), P0=1000.)
>>> ['%.7g' % alt[0]]
['1349.778']
"""
import numpy.ma as MA
import numpy as N
from atmconst import AtmConst
#from is_numeric_float import is_numeric_float
#- Check input is of the correct type:
#if is_numeric_float(arg) != 1:
# raise TypeError, "press2alt: Arg not Numeric floating"
#- Import general constants and set additional constants. h1_std
# is the lower limit of the Standard Atmosphere layer geopoten-
# tial altitude [m], h2_std is the upper limit [m] of the layer,
# and dT/dh is the temperature gradient (i.e. negative of the
# lapse rate) [K/m]:
const = AtmConst()
h1_std = N.array([0., 11., 20., 32., 47., 51., 71.]) * 1000.
h2_std = N.array(MA.concatenate([h1_std[1:], [84.852 * 1000.]]))
dTdh_std = N.array([-6.5, 0.0, 1.0, 2.8, 0.0, -2.8, -2.0]) / 1000.
#- Prep arrays for masked array calculation and set conditions
# at sea-level. Pressures are in hPa and temperatures in K.
# Sea-level conditions arrays are same shape/size as P_or_z.
# If input argument is a scalar, make the local variable used
# for calculations a 1-element vector:
if missing == None: P_or_z = MA.masked_array(arg)
else: P_or_z = MA.masked_values(arg, missing, copy=0)
if P_or_z.shape == ():
P_or_z = MA.reshape(P_or_z, (1, ))
if P0 == None:
P0_use = MA.zeros(P_or_z.shape) \
+ (const.sea_level_press / 100.)
else:
P0_use = MA.zeros(P_or_z.shape) \
+ MA.masked_array(P0)
if T0 == None:
T0_use = MA.zeros(P_or_z.shape) \
+ const.sea_level_temp
else:
T0_use = MA.zeros(P_or_z.shape) \
+ MA.masked_array(T0)
#- Calculate P and T for the boundaries of the 7 layers of the
# Standard Atmosphere for the given P0 and T0 (layer 0 goes from
# P0 to P1, layer 1 from P1 to P2, etc.). These are given as
# 8 element dictionaries P_std and T_std where the key is the
# location (P_std[0] is at the bottom of layer 0, P_std[1] is the
# top of layer 0 and bottom of layer 1, ... and P_std[7] is the
# top of layer 6). Remember P_std and T_std are dictionaries but
# dTdh_std, h1_std, and h2_std are vectors:
P_std = {0: P0_use}
T_std = {0: T0_use}
for i in range(len(h1_std)):
P_std[i+1] = _pfromz_MA( h2_std[i], -dTdh_std[i] \
, P_std[i], T_std[i], h1_std[i] )
T_std[i + 1] = T_std[i] + (dTdh_std[i] * (h2_std[i] - h1_std[i]))
#- Test input is within Standard Atmosphere limits:
if invert == 0:
tmp = MA.where(P_or_z < P_std[len(h1_std)], 1, 0)
if MA.sum(MA.ravel(tmp)) > 0:
raise ValueError, "press2alt: Pressure out-of-range"
else:
tmp = MA.where(P_or_z > MA.maximum(h2_std), 1, 0)
if MA.sum(MA.ravel(tmp)) > 0:
raise ValueError, "press2alt: Altitude out-of-range"
#- What layer number is each element of P_or_z in?
P_or_z_layer = 0 #MA.zeros(P_or_z.shape)
#if invert == 0:
# for i in range(len(h1_std)):
# tmp = MA.where( MA.logical_and( (P_or_z <= P_std[i]) \
# , (P_or_z > P_std[i+1]) ) \
# , i, 0 )
# P_or_z_layer += tmp
#else:
# for i in range(len(h1_std)):
# tmp = MA.where( MA.logical_and( (P_or_z >= h1_std[i]) \
# , (P_or_z < h2_std[i]) ) \
# , i, 0 )
# P_or_z_layer += tmp
#- Fill in the bottom-of-the-layer variables and the lapse rate
# for the layers that the levels are in. The *_actual variables
# are the values of dTdh, P_bott, etc. for each element in the
# P_or_z_flat array:
P_or_z_flat = MA.ravel(P_or_z)
if P_or_z_flat.mask() == None:
P_or_z_flat_mask = MA.make_mask_none(P_or_z_flat.shape)
else:
P_or_z_flat_mask = P_or_z_flat.mask()
P_or_z_layer_flat = MA.ravel(P_or_z_layer)
dTdh_actual = MA.zeros(P_or_z_flat.shape)
P_bott_actual = MA.zeros(P_or_z_flat.shape)
T_bott_actual = MA.zeros(P_or_z_flat.shape)
z_bott_actual = MA.zeros(P_or_z_flat.shape)
for i in xrange(MA.size(P_or_z_flat)):
if P_or_z_flat_mask[i] != 1:
layer_number = P_or_z_layer_flat[i]
dTdh_actual[i] = dTdh_std[layer_number]
P_bott_actual[i] = MA.ravel(P_std[layer_number])[i]
T_bott_actual[i] = MA.ravel(T_std[layer_number])[i]
z_bott_actual[i] = h1_std[layer_number]
else:
dTdh_actual[i] = MA.masked
P_bott_actual[i] = MA.masked
T_bott_actual[i] = MA.masked
z_bott_actual[i] = MA.masked
#- Calculate pressure/altitude from altitude/pressure (output is
# a flat array):
if invert == 0:
output = _zfromp_MA( P_or_z_flat, -dTdh_actual \
, P_bott_actual, T_bott_actual, z_bott_actual )
else:
output = _pfromz_MA( P_or_z_flat, -dTdh_actual \
, P_bott_actual, T_bott_actual, z_bott_actual )
#- Return output as same shape as input positional argument:
return MA.filled(MA.reshape(output, arg.shape), missing)
import numpy as np
import numpy.ma as ma
ma.make_mask_none((3, ))
dtype = np.dtype({'names': ['foo', 'bar'], 'formats': [np.float32, np.int]})
ma.make_mask_none((3, ), dtype=dtype)
def press2alt(arg, P0=None, T0=None, missing=1e+20, invert=0):
"""Calculate elevation given pressure (or vice versa).
Calculations are made assuming that the temperature distribution
follows the 1976 Standard Atmosphere. Technically the standard
atmosphere defines temperature distribution as a function of
geopotential altitude, and this routine actually calculates geo-
potential altitude rather than geometric altitude.
Method Positional Argument:
* arg: Numeric floating point vector of any shape and size, or a
Numeric floating point scalar. If invert=0 (the default), arg
is air pressure [hPa]. If invert=1, arg is elevation [m].
Method Keyword Arguments:
* P0: Pressure [hPa] at the surface (altitude equals 0). Numeric
floating point vector of same size and shape as arg or a scalar.
Default of keyword is set to None, in which case the routine
uses the value of instance attribute sea_level_press (converted
to hPa) from the AtmConst class. Keyword value is used if the
keyword is set in the function call. This keyword cannot have
any missing values.
* T0: Temperature [K] at the surface (altitude equals 0). Numeric
floating point vector of same size and shape as arg or a scalar.
Default of keyword is set to None, in which case the routine uses
the value of instance attribute sea_level_temp from the AtmConst
class. Keyword value is used if the keyword is set in the func-
tion call. This keyword cannot have any missing values.
* missing: If arg has missing values, this is the missing value
value. Floating point scalar. Default is 1e+20.
* invert: If set to 1, function calculates pressure [hPa] from
altitude [m]. In that case, positional input variable arg is
altitude [m] and the output is pressure [hPa]. Default value of
invert=0, which means the function calculates altitude given
pressure.
Output:
* If invert=0 (the default), output is elevation [m] at each
element of arg, relative to the surface. If invert=1, output
is the air pressure [hPa]. Numeric floating point array of
the same size and shape as arg.
If there are any missing values in output, those values are set
to the value in argument missing from the input. If there are
missing values in the output due to math errors and missing is
set to None, output will fill those missing values with the MA
default value of 1e+20.
References:
* Carmichael, Ralph (2003): "Definition of the 1976 Standard Atmo-
sphere to 86 km," Public Domain Aeronautical Software (PDAS).
URL: http://www.pdas.com/coesa.htm.
* Wallace, J. M., and P. V. Hobbs (1977): Atmospheric Science:
An Introductory Survey. San Diego, CA: Academic Press, ISBN
0-12-732950-1, pp. 60-61.
Examples:
(1) Calculating altitude given pressure:
>>> from press2alt import press2alt
>>> import Numeric as N
>>> press = N.array([200., 350., 850., 1e+20, 50.])
>>> alt = press2alt(press, missing=1e+20)
>>> ['%.7g' % alt[i] for i in range(5)]
['11783.94', '8117.19', '1457.285', '1e+20', '20575.96']
(2) Calculating pressure given altitude:
>>> alt = N.array([0., 10000., 15000., 20000., 50000.])
>>> press = press2alt(alt, missing=1e+20, invert=1)
>>> ['%.7g' % press[i] for i in range(5)]
['1013.25', '264.3589', '120.443', '54.74718', '0.7593892']
(3) Input is a Numeric floating point scalar, and using a keyword
set surface pressure to a different scalar:
>>> alt = press2alt(N.array(850.), P0=1000.)
>>> ['%.7g' % alt[0]]
['1349.778']
"""
import numpy as N
import numpy.ma as MA
#jfp was import MA
#jfp was import Numeric as N
from atmconst import AtmConst
from is_numeric_float import is_numeric_float
#- Check input is of the correct type:
if is_numeric_float(arg) != 1:
raise TypeError, "press2alt: Arg not Numeric floating"
#- Import general constants and set additional constants. h1_std
# is the lower limit of the Standard Atmosphere layer geopoten-
# tial altitude [m], h2_std is the upper limit [m] of the layer,
# and dT/dh is the temperature gradient (i.e. negative of the
# lapse rate) [K/m]:
const = AtmConst()
h1_std = N.array([0., 11., 20., 32., 47., 51., 71.]) * 1000.
h2_std = N.array( MA.concatenate([h1_std[1:], [84.852*1000.]]) )
dTdh_std = N.array([-6.5, 0.0, 1.0, 2.8, 0.0, -2.8, -2.0]) / 1000.
#- Prep arrays for masked array calculation and set conditions
# at sea-level. Pressures are in hPa and temperatures in K.
# Sea-level conditions arrays are same shape/size as P_or_z.
# If input argument is a scalar, make the local variable used
# for calculations a 1-element vector:
if missing == None: P_or_z = MA.masked_array(arg)
else: P_or_z = MA.masked_values(arg, missing, copy=0)
if P_or_z.shape == ():
P_or_z = MA.reshape(P_or_z, (1,))
if P0 == None:
#jfp was P0_use = MA.zeros(P_or_z.shape, typecode=MA.Float) \
P0_use = MA.zeros(P_or_z.shape) \
+ (const.sea_level_press / 100.)
else:
#jfp was P0_use = MA.zeros(P_or_z.shape, typecode=MA.Float) \
P0_use = MA.zeros(P_or_z.shape) \
+ MA.masked_array(P0)
if T0 == None:
#jfp was T0_use = MA.zeros(P_or_z.shape, typecode=MA.Float) \
T0_use = MA.zeros(P_or_z.shape) \
+ const.sea_level_temp
else:
#jfp was T0_use = MA.zeros(P_or_z.shape, typecode=MA.Float) \
T0_use = MA.zeros(P_or_z.shape) \
+ MA.masked_array(T0)
#- Calculate P and T for the boundaries of the 7 layers of the
# Standard Atmosphere for the given P0 and T0 (layer 0 goes from
# P0 to P1, layer 1 from P1 to P2, etc.). These are given as
# 8 element dictionaries P_std and T_std where the key is the
# location (P_std[0] is at the bottom of layer 0, P_std[1] is the
# top of layer 0 and bottom of layer 1, ... and P_std[7] is the
# top of layer 6). Remember P_std and T_std are dictionaries but
# dTdh_std, h1_std, and h2_std are vectors:
P_std = {0:P0_use}
T_std = {0:T0_use}
for i in range(len(h1_std)):
P_std[i+1] = _pfromz_MA( h2_std[i], -dTdh_std[i] \
, P_std[i], T_std[i], h1_std[i] )
T_std[i+1] = T_std[i] + ( dTdh_std[i] * (h2_std[i]-h1_std[i]) )
#- Test input is within Standard Atmosphere limits:
if invert == 0:
tmp = MA.where(P_or_z < P_std[len(h1_std)], 1, 0)
if MA.sum(MA.ravel(tmp)) > 0:
raise ValueError, "press2alt: Pressure out-of-range"
else:
tmp = MA.where(P_or_z > MA.maximum(h2_std), 1, 0)
if MA.sum(MA.ravel(tmp)) > 0:
raise ValueError, "press2alt: Altitude out-of-range"
#- What layer number is each element of P_or_z in?
P_or_z_layer = MA.zeros(P_or_z.shape)
if invert == 0:
for i in range(len(h1_std)):
tmp = MA.where( MA.logical_and( (P_or_z <= P_std[i]) \
, (P_or_z > P_std[i+1]) ) \
, i, 0 )
P_or_z_layer += tmp
else:
for i in range(len(h1_std)):
tmp = MA.where( MA.logical_and( (P_or_z >= h1_std[i]) \
, (P_or_z < h2_std[i]) ) \
, i, 0 )
P_or_z_layer += tmp
#- Fill in the bottom-of-the-layer variables and the lapse rate
# for the layers that the levels are in. The *_actual variables
# are the values of dTdh, P_bott, etc. for each element in the
# P_or_z_flat array:
P_or_z_flat = MA.ravel(P_or_z)
P_or_z_flat_mask = P_or_z_flat.mask
if P_or_z_flat.mask==False:
P_or_z_flat_mask = MA.make_mask_none(P_or_z_flat.shape)
#jfp was:
#if P_or_z_flat.mask() == None:
# P_or_z_flat_mask = MA.make_mask_none(P_or_z_flat.shape)
#else:
# P_or_z_flat_mask = P_or_z_flat.mask()
P_or_z_layer_flat = MA.ravel(P_or_z_layer)
#jfp was dTdh_actual = MA.zeros(P_or_z_flat.shape, typecode=MA.Float)
#jfp was P_bott_actual = MA.zeros(P_or_z_flat.shape, typecode=MA.Float)
#jfp was T_bott_actual = MA.zeros(P_or_z_flat.shape, typecode=MA.Float)
#jfp was z_bott_actual = MA.zeros(P_or_z_flat.shape, typecode=MA.Float)
dTdh_actual = MA.zeros(P_or_z_flat.shape)
P_bott_actual = MA.zeros(P_or_z_flat.shape)
T_bott_actual = MA.zeros(P_or_z_flat.shape)
z_bott_actual = MA.zeros(P_or_z_flat.shape)
for i in xrange(MA.size(P_or_z_flat)):
if P_or_z_flat_mask[i] != 1:
layer_number = P_or_z_layer_flat[i]
dTdh_actual[i] = dTdh_std[layer_number]
P_bott_actual[i] = MA.ravel(P_std[layer_number])[i]
T_bott_actual[i] = MA.ravel(T_std[layer_number])[i]
z_bott_actual[i] = h1_std[layer_number]
else:
dTdh_actual[i] = MA.masked
P_bott_actual[i] = MA.masked
T_bott_actual[i] = MA.masked
z_bott_actual[i] = MA.masked
#- Calculate pressure/altitude from altitude/pressure (output is
# a flat array):
if invert == 0:
output = _zfromp_MA( P_or_z_flat, -dTdh_actual \
, P_bott_actual, T_bott_actual, z_bott_actual )
else:
output = _pfromz_MA( P_or_z_flat, -dTdh_actual \
, P_bott_actual, T_bott_actual, z_bott_actual )
#- Return output as same shape as input positional argument:
return MA.filled( MA.reshape(output, arg.shape), missing )
