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 )