def _singledata(self,data,action):
if action == 'sqrt':
return np.sqrt(data)
elif action == 'abs': #astype(np.abs(data[0]).dtype): hack to ensure right type is returned
return np.abs(data)
elif action == 'amp':
return np.abs(data)
elif action == 'power':
return np.abs(data)**2
elif action == 'phase':
if not typecomplex(data):
return np.zeros_like(data)
else:
return np.arctan2(data.imag,data.real)
elif action == 'num_nan':
return np.sum(np.isnan(data))
elif action == 'num_inf':
return np.sum(np.isinf(data))
elif action == 'num_neginf':
return np.sum(np.isneginf(data))
elif action == 'num_posinf':
return np.sum(np.isposinf(data))
elif action == 'dB':
return 10.*np.log10(data)
elif action == 'dBi':
return 10.**(data/10.)
elif action == 'ln' or action == 'log':
return np.log(data)
elif action == 'log10':
return np.log10(data)
elif action == 'exp':
return np.exp(data)
elif action == '10pow':
return 10**data
elif action == 'max':
return np.max(data)
elif action == 'min':
return np.min(data)
elif action == 'std':
return np.std(data)
elif action == 'var':
return np.var(data)
elif action == 'mean':
return np.mean(data)
elif action == 'med':
return np.median(data)
elif action == 'isnan':
return np.isnan(data).astype(data.dtype)
elif action == 'notnan':
return (-np.isnan(data)).astype(data.dtype)
else:
return data
def look2D(data, win, null=None, verbose=0):
"""
Incoherent averaging (looking) routine
Parameters
----------
data : 2D array
Input data
win : int or list
Filter window size in pixels
scalar values use a square window
list should be in order [across, down]
null : float or complex
Null value to exclude from filter window
verbose : int
1 = print line counter to screen; 0 = don't
Output
------
D : 2D array
Filtered data
"""
try:
a = len(win)
if a == 1:
d0, d1 = np.int32(win[0]), np.int32(win[0])
elif a == 2:
d0, d1 = np.int32(win[0]), np.int32(win[1])
else:
print('Incorrect window: must be list length 1 or 2')
return None
except:
d0, d1 = np.int32(win), np.int32(win)
if null:
donul = 1
else:
donul, null = 0, -9.87654321e200
shp = np.shape(data)
data = data.flatten()
dtyp = data.dtype
outlen, inlen = (shp[0] // d1) * (shp[1] // d0), shp[0] * shp[1]
if data.dtype == np.float32:
out = _looks_mod.look2d_real(data, shp[1], d1, d0, outlen, donul, null,
verbose)
elif data.dtype == np.float64:
out = _looks_mod.look2d_double(data, shp[1], d1, d0, outlen, donul,
null, verbose)
elif data.dtype == np.complex64:
out = _looks_mod.look2d_cmplx(data, shp[1], d1, d0, outlen, donul,
null, verbose)
elif data.dtype == np.complex128:
out = _looks_mod.look2d_dcmplx(data, shp[1], d1, d0, outlen, donul,
null, verbose)
else:
print('Unsupported data type...defaulting to float or complex')
dtyp = data.dtype
if typecomplex(data):
data = data.astype(np.complex64)
out = _looks_mod.look2d_cmplx(data, shp[1], d1, d0, outlen, donul,
null, verbose)
else:
data = data.astype(np.float32)
out = _looks_mod.look2d_real(data, shp[1], d1, d0, outlen, donul,
null, verbose)
out = out.astype(dtyp)
return out.reshape(-1, shp[1] // d0)
def _doubledata(self,data1,data2,action):
if action == 'add':
return data1 + data2
elif action == 'sub':
return data1 - data2
elif action == 'mult':
return data1 * data2
elif action == 'div':
return data1 / data2
elif action == 'hypot':
return np.hypot(data1,data2)
elif action == 'pow':
return data1**data2
elif action == 'mod':
return data1 % data2
elif action == 'root':
return data1**(1./data2)
elif action == 'xmult':
return data1*np.conj(data2)
elif action == 'xcor' or action == 'xcor_amp' or action == 'xcor_phase':
cor = (data1*np.conj(data2))/np.sqrt( data1*np.conj(data1) * data2*np.conj(data2))
if action == 'xcor':
return cor
elif action == 'xcor_amp':
return np.abs(cor)
elif action == 'xcor_phase':
if not typecomplex(data): return np.zeros_like(cor)
else: return np.arctan2(cor.imag,cor.real)
elif action == 'null2nan':
mask = self._genmask(data1=data1,data2=data2,tol=1.e-7)
data1[mask] = np.nan
return data1
elif action == 'nan2null':
data1[np.isnan(data1)] = data2
return data1
elif action == 'inf2null':
data1[np.isinf(data1)] = data2
return data1
elif action == 'num_null':
mask = self._genmask(data1=data1,data2=data2,tol=1.e-7)
return np.sum(mask)
elif action == 'gt':
mask = data1 > data2
return mask.astype(data1.dtype)
elif action == 'ge':
mask = data1 >= data2
return mask.astype(data1.dtype)
elif action == 'lt':
mask = data1 < data2
return mask.astype(data1.dtype)
elif action == 'le':
mask = data1 <= data2
return mask.astype(data1.dtype)
elif action == 'eq':
mask = np.abs(data1 - data2) < 1.e-7
return mask.astype(data1.dtype)
elif action == 'ne':
mask = np.abs(data1 - data2) > 1.e-7
return mask.astype(data1.dtype)
elif action == 'log_b':
return np.log(data1)/np.log(data2)
elif action == 'subphz':
return data1*np.exp(-1j*data2)
elif action == 'addphz':
return data1*np.exp(1j*data2)
else:
print('unrecognized option', action)