def from_h5(src, debug=False):
"""
Read the HDF5 file in the given path, build the metAry accordingly.
dest can be string or h5py.Group object.
If src is a string, it is interpret as the destination file path.
If src is a h5py.Group object, the content will be read from the given group.
"""
# Reading from existing group
if isinstance(src, h5py.Group):
ary = src['ndarray'][()] # Read the array
info = __read_info(src['info']) # Read the meta info
# Reading from file path
path = filePath(src)
if not path.exist:
raise IOError('File ' + str(path.full) + ' does not exist')
if not path.read:
raise IOError("Unable to read from: " + str(path.full))
with h5py.File(path.full, 'r') as f:
ary = f['ndarray'][()] # Read the array
info = __read_info(f['info']) # Read the meta info
return metaArray(ary, info=info)
def __call__(self):
"""
Return a metaArray when called
"""
metainfo = self.metainfo
index = array(self.getcolumn(3), dtype=float)
data = array(self.getcolumn(4), dtype=float)
if linearChk(index, debug=self.debug) is not True:
raise ValueError("The index array is not linear")
# Write the data array as metaArray
ary = metaArray(data)
# Update the basic metaArray info
ary['name'] = self.name
ary['unit'] = metainfo['Vertical Units']
# Update the scaling info
ary['range']['begin'][0] = index[0]
ary['range']['end'][0] = index[-1]
ary['range']['unit'][0] = metainfo['Horizontal Units']
ary['range']['label'][0] = metainfo['Source']
# Include the rest of the metainfo into metaArray
for field, value in metainfo.items():
ary["TDS2.csv."+field] = value
ary.update_range()
return ary
def __call__(self):
"""
Return a metaArray when called
"""
metainfo = self.metainfo
index = array(self.getcolumn(3), dtype=float)
data = array(self.getcolumn(4), dtype=float)
if linearChk(index, debug=self.debug) is not True:
raise ValueError("The index array is not linear")
# Write the data array as metaArray
ary = metaArray(data)
# Update the basic metaArray info
ary['name'] = self.name
ary['unit'] = metainfo['Vertical Units']
# Update the scaling info
ary['range']['begin'][0] = index[0]
ary['range']['end'][0] = index[-1]
ary['range']['unit'][0] = metainfo['Horizontal Units']
ary['range']['label'][0] = metainfo['Source']
# Include the rest of the metainfo into metaArray
for field, value in metainfo.items():
ary["TDS2.csv." + field] = value
ary.update_range()
return ary
def __call__(self):
"""
Return a metaArray when called
"""
metainfo = self.metainfo
rcd_len = metainfo['Record Length']
index = csv_file.getcolumn(self, 0)[self.label_row:]
index_name = index[0]
index = array(index[1:rcd_len+1], dtype=float)
try:
data = csv_file.getcolumn(self, self.data_col)[self.label_row:]
metainfo['Source'] = data[0]
data = array(data[1:rcd_len+1], dtype=float)
except IndexError as err:
print("Data column doesn't exist:", err)
print("Defaulting to first data column")
data = csv_file.getcolumn(self, 1)[self.label_row:]
metainfo['Source'] = data[0]
data = array(data[1:rcd_len+1], dtype=float)
if linearChk(index, debug=self.debug) is not True:
raise ValueError("The index array is not linear")
# Write the data array as metaArray
ary = metaArray(data)
# Update the basic metaArray info
ary['unit'] = metainfo['Vertical Units']
if metainfo['Label'] is '':
ary['name'] = self.name
else:
ary['name'] = metainfo['Label']
# Update the scaling info
ary['range']['begin'][0] = index[0]
ary['range']['end'][0] = index[-1]
ary['range']['unit'][0] = metainfo['Horizontal Units']
ary['range']['label'][0] = index_name
# Include the rest of the metainfo into metaArray
for field, value in metainfo.items():
ary["DPO2.csv."+field] = value
ary.update_range()
return ary
def __call__(self):
"""
Return a metaArray when called
"""
metainfo = self.metainfo
rcd_len = metainfo['Record Length']
index = csv_file.getcolumn(self, 0)[self.label_row:]
index_name = index[0]
index = array(index[1:rcd_len + 1], dtype=float)
try:
data = csv_file.getcolumn(self, self.data_col)[self.label_row:]
metainfo['Source'] = data[0]
data = array(data[1:rcd_len + 1], dtype=float)
except IndexError as err:
print("Data column doesn't exist:", err)
print("Defaulting to first data column")
data = csv_file.getcolumn(self, 1)[self.label_row:]
metainfo['Source'] = data[0]
data = array(data[1:rcd_len + 1], dtype=float)
if linearChk(index, debug=self.debug) is not True:
raise ValueError("The index array is not linear")
# Write the data array as metaArray
ary = metaArray(data)
# Update the basic metaArray info
ary['unit'] = metainfo['Vertical Units']
if metainfo['Label'] is '':
ary['name'] = self.name
else:
ary['name'] = metainfo['Label']
# Update the scaling info
ary['range']['begin'][0] = index[0]
ary['range']['end'][0] = index[-1]
ary['range']['unit'][0] = metainfo['Horizontal Units']
ary['range']['label'][0] = index_name
# Include the rest of the metainfo into metaArray
for field, value in metainfo.items():
ary["DPO2.csv." + field] = value
ary.update_range()
return ary
def meta_histogram(metAry, bins=False):
"""
Compute a histogram of the given 1D metaArray.
It will try to work out the maximum number of bins (i.e. minimum
quantisation from the data) by default.
Will raise QuantsationError if unable to determin number of bins.
"""
assert metAry.ndim is 1, "Only 1D metaArray accepted, there are {0:d} dimemsions in the given data.".format(
metAry.ndim)
# Flatten the data to 1D array
data = metAry.data.ravel()
if bins is not False:
quanter = data.ptp() / bins
else:
# Try to quantise the array data
quanter = quantise(data)
# Quantise the data, and offset to the +ve side of value, bincount requires +ve
# int arrays
quantum = np.round(data / quanter).astype(int)
quantum -= quantum.min()
# Do the bincount for histogram
hist = np.bincount(quantum)
# Update the metaInfo
hist = metaArray(hist)
hist.set_range(0, 'begin', metAry.min())
hist.set_range(0, 'end', metAry.max())
hist.set_range(0, 'unit', metAry['unit'])
hist.set_range(0, 'label', metAry['label'])
hist['name'] = 'Histogram of ' + metAry['name']
hist['unit'] = ''
hist['label'] = 'Counts'
return hist
def meta_histogram(metAry, bins=False):
"""
Compute a histogram of the given 1D metaArray.
It will try to work out the maximum number of bins (i.e. minimum
quantisation from the data) by default.
Will raise QuantsationError if unable to determin number of bins.
"""
assert metAry.ndim is 1, "Only 1D metaArray accepted, there are {0:d} dimemsions in the given data.".format(metAry.ndim)
# Flatten the data to 1D array
data = metAry.data.ravel()
if bins is not False:
quanter = data.ptp() / bins
else:
# Try to quantise the array data
quanter = quantise(data)
# Quantise the data, and offset to the +ve side of value, bincount requires +ve
# int arrays
quantum = np.round(data / quanter).astype(int)
quantum -= quantum.min()
# Do the bincount for histogram
hist = np.bincount(quantum)
# Update the metaInfo
hist = metaArray(hist)
hist.set_range(0, 'begin', metAry.min())
hist.set_range(0, 'end', metAry.max())
hist.set_range(0, 'unit', metAry['unit'])
hist.set_range(0, 'label', metAry['label'])
hist['name'] = 'Histogram of ' + metAry['name']
hist['unit'] = ''
hist['label'] = 'Counts'
return hist
def rfft(metAry, n=None, axes=-1):
"""
RFFT function wraper for numpy.fft to be used on metaArray objects, returns
scaled metaArray object.
"""
nyquist = len(metAry) * 0.5 / (metAry.get_range(axes, 'end') - metAry.get_range(axes, 'begin'))
fary = metaArray(np.fft.rfft(metAry.data, n, axes))
fary.set_range(0, 'begin', 0)
fary.set_range(0, 'end', nyquist)
fary.set_range(0, 'unit', 'Hz')
fary.set_range(0, 'label', 'Frequency')
fary['unit'] = ''
fary['label'] = 'Amplitude'
try:
fary['name'] = 'FFT{ ' + metAry['name'] + ' }'
except TypeError:
fary['name'] = 'FFT{ }'
return fary
def rfft(metAry, n=None, axes=-1):
"""
RFFT function wraper for numpy.fft to be used on metaArray objects, returns
scaled metaArray object.
"""
nyquist = len(metAry) * 0.5 / (metAry.get_range(axes, 'end') -
metAry.get_range(axes, 'begin'))
fary = metaArray(np.fft.rfft(metAry.data, n, axes))
fary.set_range(0, 'begin', 0)
fary.set_range(0, 'end', nyquist)
fary.set_range(0, 'unit', 'Hz')
fary.set_range(0, 'label', 'Frequency')
fary['unit'] = ''
fary['label'] = 'Amplitude'
try:
fary['name'] = 'FFT{ ' + metAry['name'] + ' }'
except TypeError:
fary['name'] = 'FFT{ }'
return fary
def stfft(metAry, tres=100, fres=None, window='blackmanharris', \
fmin=None, fmax=None, mag=True, debug=False):
"""
Simple implementation of short time fast Fourier transform on metaArray
object.
metAry Input metaArray
tres Temporal resolution
fres Frequency resolution
window Window function or None
fmin Cut-off frequency for the return data, default to the 0
fmax Cut-off frequency for the return data, default to the Nyquist
mag The default is to return the abs() value, return complex array if false
Each window will overlap 50% with its immediate neighbours. e.g.:
|_____________________________________________|
| | 1 | 3 | 5 | 7 | 9 | 11| 13| 15| 17| 19| 21|
| 0 | 2 | 4 | 6 | 8 | 10| 12| 14| 16| 18| 20| |
"""
f1 = fmax
# Length of the (short) time window
l = int(round(2 * len(metAry) / float(tres)))
# List of (short) time window starting points
winlst = sp.linspace(0, len(metAry) - l, tres).round().astype(int)
# Native RFFT frequency resolution to Nyquist
lfres = int(np.floor(l/2.0)+1)
Nyquist = 0.5 * len(metAry) / (metAry.get_range(0, 'end') - metAry.get_range(0, 'begin'))
# RFFT len, use native resolution by default
n = None
# Generate the (short) time window function
if window is None:
win = 1
else:
win = sp.signal.get_window(window, l)
# Decide where to slice the rfft output as a ratio to Nyquist
# Make fmax < 1 or None
if fmax is not None:
if fmax < Nyquist:
fmax = fmax / Nyquist
elif fmax >= Nyquist:
fmax = None
if debug:
print("*** Warning, spec frequency range beyond Nyquist limit")
# Check whether padding is needed
# If fres is not specified, use the native resolution
if fres is None:
if fmax is None:
# No freq limit, use native resolution
fres = lfres
else:
# Still on native resolution, but truncated to fmax
fres = int(round(fmax * lfres))
else:
# fres is specified
if fmax is not None:
# freq limit specified, work out global freq resolution
gfres = int(round(fres / fmax))
else:
# No freq limit, global freq resolution is same as fres
gfres = fres
# Global freq resolution is greater than native freq resolution
# Need padding for rfft
if gfres > lfres:
n = (gfres - 1) * 2
elif gfres < lfres:
# No need for padding, but throwing away freq resolution for nothing
if debug:
print("*** Warning, frequency resolution is artificially limited")
# else gfres = lfres, no need for padding, native fres is just right
# Convert fmax to array length if specified
if fmax is not None:
# If rfft is padded
if n is not None:
fmax = int(round(int(np.floor(n/2.0)+1) * fmax))
else:
# Otherwise just truncate from native output
fmax = int(round(lfres * fmax))
if debug:
src_len = len(metAry.data[:l]*win)
rfft_len = len(np.fft.rfft(metAry.data[:l]*win, n=n))
print("*** l: " + str(l))
print("*** lfres: " + str(lfres))
print("*** Nyquist: " + str(Nyquist))
print("*** n: " + str(n))
print("*** fmax: " + str(fmax))
print("*** fres: " + str(fres))
print("*** src_len: " + str(src_len))
print("*** rfft_len: " + str(rfft_len))
if mag:
# Construct a place holder of the 2D time-freq output
tfary = np.zeros((tres, fres)).astype(float)
for i in range(len(winlst)):
t = winlst[i] # Where the (short) time window starts
# Do the rfft to length n, and slice to fmax, then take abs()
tfary[i] = spline_resize(abs(np.fft.rfft(metAry.data[t:t+l]*win, n=n)[:fmax]), fres)
else:
# Construct a place holder of the 2D time-freq output
tfary = np.zeros((tres, fres)).astype(complex)
for i in range(len(winlst)):
t = winlst[i]
# Do the rfft to length n, and slice to fmax
tfary[i] = spline_resize(np.fft.rfft(metAry.data[t:t+l]*win, n=n)[:fmax], fres)
tfary = metaArray(tfary)
try:
tfary['name'] = 'STFFT{ ' + metAry['name'] + ' }'
except:
tfary['name'] = 'STFFT{ }'
tfary['unit'] = metAry['unit']
tfary['label'] = metAry['label']
# Per axis definitions
tfary.set_range(0, 'begin', metAry.get_range(0, 'begin'))
tfary.set_range(0, 'end', metAry.get_range(0, 'end'))
tfary.set_range(0, 'unit', metAry.get_range(0, 'unit'))
tfary.set_range(0, 'label', metAry.get_range(0, 'label'))
tfary.set_range(1, 'begin', 0)
if f1 is None:
tfary.set_range(1, 'end', Nyquist)
else:
tfary.set_range(1, 'end', f1)
tfary.set_range(1, 'unit', 'Hz')
tfary.set_range(1, 'label', 'Frequency')
return tfary
def cwt(x, wavelet, scale0, scale1, res, scale=10, tres=None, debug=False):
"""
This function will return continous wavelet transform of x,
calculated by the convolution method.
x is a 1-D metaArray
Inputs:
x The data as an metaArray object.
wavelet Mother wavelet function (e.g. wavelet(scale0) should
provide the largest scale daughter wavelet)
scale0 Starting scale length
scale1 Stoping scale length
res Resolution in the scale space (i.e. number of daughter wavelets)
tres Resolution in the time space (Default to be same as x)
Options:
scale [int|float|True|'linscal'|'linfreq']
int float True
Logarithmic scale (based 10) is used by default for the
production of daughter wavelets. If a number is given
the log space will be generated with that base.
'linscal'
Scale length is step linearly (i.e. freq in 1/x space)
'linfreq'
Frequency is step linearly (i.e. scale length in 1/x space)
Output:
A 2D metaArray in the time-sacle space.
"""
data = x.data
d_len = len(data)
if tres is None:
tres = len(x)
flag_resample = False
else:
# Sanity check
flag_resample = True
resmp_time = np.arange(len(x)) # i.e. pretend 1Hz sample
resmp_rate = float(tres) / len(x)
# Generate a blank time-sacle space array
page = np.zeros((tres, res)).astype('complex128')
page = metaArray(page)
prange = page['range']
x_range = x['range']
prange['label'][0] = x_range['label'][0]
prange['begin'][0] = x_range['begin'][0]
prange['end'][0] = x_range['end'][0]
prange['unit'][0] = x_range['unit'][0]
prange['label'][1] = "Scale"
prange['begin'][1] = scale0
prange['end'][1] = scale1
try:
prange['unit'][1] = wavelet.unit
except:
pass
if x['name'] is not None:
page['name'] = 'cwt{' + x['name'] + '}'
else:
page['name'] = 'cwt'
# Generate a list of scales
if isinstance(scale, int) or isinstance(scale, float):
# Log scale applied, given log base.
scale0 = np.log(scale0)/np.log(scale)
scale1 = np.log(scale1)/np.log(scale)
# print "*** num", scale0, scale1, res, scale
scl_lst = sp.logspace(scale0, scale1, res, base=scale)
prange['log'][1] = scale
elif scale == True:
# Log scale applied, use default base
scale0 = np.log(scale0)/np.log(10)
scale1 = np.log(scale1)/np.log(10)
# print "*** log", scale0, scale1, res, scale
scl_lst = sp.logspace(scale0, scale1, res, base=10)
prange['log'][1] = 10
elif scale == 'linscal':
# print "*** lin", scale0, scale1, res, scale
# Log scale is not applied, everything is linear
scl_lst = sp.linspace(scale0, scale1, res)
elif scale == 'linfreq':
scale0 = 1 / scale0
scale1 = 1 / scale1
scl_lst = sp.linspace(scale0, scale1, res)
scl_lst = 1 / scl_lst
else:
raise ValueError("Log scale descriptor can only be int,\
float, True, False or None, given: " + str(scale))
# return scl_lst, page
if debug:
print("There are a total number of " + str(len(scl_lst)) + " scales to be processed:")
for i in range(len(scl_lst)):
d_wavelet = wavelet(scl_lst[i])
###if debug:
### print "\t line number: " + str(i) + "\t scale: " + str(scl_lst[i]) + "\t x.data: " + str(len(x.data)) + "\t wavelet: " + str(len(d_wavelet)
if len(d_wavelet) > d_len:
# It probably shouldnt happen, because the daughter wavelet is longer than
# the signal itself now
print("\t line number: " + str(i) + "\t scale: " + str(scl_lst[i]) + "\t data length: " + str(len(x.data)) + "\t wavelet length: " + str(len(d_wavelet)))
raise ValueError("Warning - Daughter wavelet is longer than itself!!")
else:
line = sp.signal.convolve(data, d_wavelet, mode='same')
if flag_resample:
###if debug:
### print "\t resmp_time: " + str(len(resmp_time)) + "\t line: " + str(len(line)) + "\t resmp_rate: " + str(resmp_rate)
line = resample(resmp_time, line, resmp_rate)[1]
if debug:
print("\t line number: " + str(i) + "\t scale: " + str(scl_lst[i]))
page.data[:len(line), i] = line
return page
def stfft(metAry, tres=100, fres=None, window='blackmanharris', \
fmin=None, fmax=None, mag=True, debug=False):
"""
Simple implementation of short time fast Fourier transform on metaArray
object.
metAry Input metaArray
tres Temporal resolution
fres Frequency resolution
window Window function or None
fmin Cut-off frequency for the return data, default to the 0
fmax Cut-off frequency for the return data, default to the Nyquist
mag The default is to return the abs() value, return complex array if false
Each window will overlap 50% with its immediate neighbours. e.g.:
|_____________________________________________|
| | 1 | 3 | 5 | 7 | 9 | 11| 13| 15| 17| 19| 21|
| 0 | 2 | 4 | 6 | 8 | 10| 12| 14| 16| 18| 20| |
"""
f1 = fmax
# Length of the (short) time window
l = int(round(2 * len(metAry) / float(tres)))
# List of (short) time window starting points
winlst = sp.linspace(0, len(metAry) - l, tres).round().astype(int)
# Native RFFT frequency resolution to Nyquist
lfres = int(np.floor(l / 2.0) + 1)
Nyquist = 0.5 * len(metAry) / (metAry.get_range(0, 'end') -
metAry.get_range(0, 'begin'))
# RFFT len, use native resolution by default
n = None
# Generate the (short) time window function
if window is None:
win = 1
else:
win = sp.signal.get_window(window, l)
# Decide where to slice the rfft output as a ratio to Nyquist
# Make fmax < 1 or None
if fmax is not None:
if fmax < Nyquist:
fmax = fmax / Nyquist
elif fmax >= Nyquist:
fmax = None
if debug:
print("*** Warning, spec frequency range beyond Nyquist limit")
# Check whether padding is needed
# If fres is not specified, use the native resolution
if fres is None:
if fmax is None:
# No freq limit, use native resolution
fres = lfres
else:
# Still on native resolution, but truncated to fmax
fres = int(round(fmax * lfres))
else:
# fres is specified
if fmax is not None:
# freq limit specified, work out global freq resolution
gfres = int(round(fres / fmax))
else:
# No freq limit, global freq resolution is same as fres
gfres = fres
# Global freq resolution is greater than native freq resolution
# Need padding for rfft
if gfres > lfres:
n = (gfres - 1) * 2
elif gfres < lfres:
# No need for padding, but throwing away freq resolution for nothing
if debug:
print(
"*** Warning, frequency resolution is artificially limited"
)
# else gfres = lfres, no need for padding, native fres is just right
# Convert fmax to array length if specified
if fmax is not None:
# If rfft is padded
if n is not None:
fmax = int(round(int(np.floor(n / 2.0) + 1) * fmax))
else:
# Otherwise just truncate from native output
fmax = int(round(lfres * fmax))
if debug:
src_len = len(metAry.data[:l] * win)
rfft_len = len(np.fft.rfft(metAry.data[:l] * win, n=n))
print("*** l: " + str(l))
print("*** lfres: " + str(lfres))
print("*** Nyquist: " + str(Nyquist))
print("*** n: " + str(n))
print("*** fmax: " + str(fmax))
print("*** fres: " + str(fres))
print("*** src_len: " + str(src_len))
print("*** rfft_len: " + str(rfft_len))
if mag:
# Construct a place holder of the 2D time-freq output
tfary = np.zeros((tres, fres)).astype(float)
for i in range(len(winlst)):
t = winlst[i] # Where the (short) time window starts
# Do the rfft to length n, and slice to fmax, then take abs()
tfary[i] = spline_resize(
abs(np.fft.rfft(metAry.data[t:t + l] * win, n=n)[:fmax]), fres)
else:
# Construct a place holder of the 2D time-freq output
tfary = np.zeros((tres, fres)).astype(complex)
for i in range(len(winlst)):
t = winlst[i]
# Do the rfft to length n, and slice to fmax
tfary[i] = spline_resize(
np.fft.rfft(metAry.data[t:t + l] * win, n=n)[:fmax], fres)
tfary = metaArray(tfary)
try:
tfary['name'] = 'STFFT{ ' + metAry['name'] + ' }'
except:
tfary['name'] = 'STFFT{ }'
tfary['unit'] = metAry['unit']
tfary['label'] = metAry['label']
# Per axis definitions
tfary.set_range(0, 'begin', metAry.get_range(0, 'begin'))
tfary.set_range(0, 'end', metAry.get_range(0, 'end'))
tfary.set_range(0, 'unit', metAry.get_range(0, 'unit'))
tfary.set_range(0, 'label', metAry.get_range(0, 'label'))
tfary.set_range(1, 'begin', 0)
if f1 is None:
tfary.set_range(1, 'end', Nyquist)
else:
tfary.set_range(1, 'end', f1)
tfary.set_range(1, 'unit', 'Hz')
tfary.set_range(1, 'label', 'Frequency')
return tfary
def cwt(x, wavelet, scale0, scale1, res, scale=10, tres=None, debug=False):
"""
This function will return continous wavelet transform of x,
calculated by the convolution method.
x is a 1-D metaArray
Inputs:
x The data as an metaArray object.
wavelet Mother wavelet function (e.g. wavelet(scale0) should
provide the largest scale daughter wavelet)
scale0 Starting scale length
scale1 Stoping scale length
res Resolution in the scale space (i.e. number of daughter wavelets)
tres Resolution in the time space (Default to be same as x)
Options:
scale [int|float|True|'linscal'|'linfreq']
int float True
Logarithmic scale (based 10) is used by default for the
production of daughter wavelets. If a number is given
the log space will be generated with that base.
'linscal'
Scale length is step linearly (i.e. freq in 1/x space)
'linfreq'
Frequency is step linearly (i.e. scale length in 1/x space)
Output:
A 2D metaArray in the time-sacle space.
"""
data = x.data
d_len = len(data)
if tres is None:
tres = len(x)
flag_resample = False
else:
# Sanity check
flag_resample = True
resmp_time = np.arange(len(x)) # i.e. pretend 1Hz sample
resmp_rate = float(tres) / len(x)
# Generate a blank time-sacle space array
page = np.zeros((tres, res)).astype('complex128')
page = metaArray(page)
prange = page['range']
x_range = x['range']
prange['label'][0] = x_range['label'][0]
prange['begin'][0] = x_range['begin'][0]
prange['end'][0] = x_range['end'][0]
prange['unit'][0] = x_range['unit'][0]
prange['label'][1] = "Scale"
prange['begin'][1] = scale0
prange['end'][1] = scale1
try:
prange['unit'][1] = wavelet.unit
except:
pass
if x['name'] is not None:
page['name'] = 'cwt{' + x['name'] + '}'
else:
page['name'] = 'cwt'
# Generate a list of scales
if isinstance(scale, int) or isinstance(scale, float):
# Log scale applied, given log base.
scale0 = np.log(scale0) / np.log(scale)
scale1 = np.log(scale1) / np.log(scale)
# print "*** num", scale0, scale1, res, scale
scl_lst = sp.logspace(scale0, scale1, res, base=scale)
prange['log'][1] = scale
elif scale == True:
# Log scale applied, use default base
scale0 = np.log(scale0) / np.log(10)
scale1 = np.log(scale1) / np.log(10)
# print "*** log", scale0, scale1, res, scale
scl_lst = sp.logspace(scale0, scale1, res, base=10)
prange['log'][1] = 10
elif scale == 'linscal':
# print "*** lin", scale0, scale1, res, scale
# Log scale is not applied, everything is linear
scl_lst = sp.linspace(scale0, scale1, res)
elif scale == 'linfreq':
scale0 = 1 / scale0
scale1 = 1 / scale1
scl_lst = sp.linspace(scale0, scale1, res)
scl_lst = 1 / scl_lst
else:
raise ValueError("Log scale descriptor can only be int,\
float, True, False or None, given: " + str(scale))
# return scl_lst, page
if debug:
print("There are a total number of " + str(len(scl_lst)) +
" scales to be processed:")
for i in range(len(scl_lst)):
d_wavelet = wavelet(scl_lst[i])
###if debug:
### print "\t line number: " + str(i) + "\t scale: " + str(scl_lst[i]) + "\t x.data: " + str(len(x.data)) + "\t wavelet: " + str(len(d_wavelet)
if len(d_wavelet) > d_len:
# It probably shouldnt happen, because the daughter wavelet is longer than
# the signal itself now
print("\t line number: " + str(i) + "\t scale: " +
str(scl_lst[i]) + "\t data length: " + str(len(x.data)) +
"\t wavelet length: " + str(len(d_wavelet)))
raise ValueError(
"Warning - Daughter wavelet is longer than itself!!")
else:
line = sp.signal.convolve(data, d_wavelet, mode='same')
if flag_resample:
###if debug:
### print "\t resmp_time: " + str(len(resmp_time)) + "\t line: " + str(len(line)) + "\t resmp_rate: " + str(resmp_rate)
line = resample(resmp_time, line, resmp_rate)[1]
if debug:
print("\t line number: " + str(i) + "\t scale: " +
str(scl_lst[i]))
page.data[:len(line), i] = line
return page
def __getitem__(self, index):
"""
Return metaArray object of the given index item in the file
"""
# sanity check
assert type(index) is int, "Given index is not int type: %r" % index
idx = self.idx
#if index < 0 or index >= len(idx):
# raise IndexError, "Requested index outside range (" + str(len(idx)) + ")."
# [hdr_pos, hdr_len, data_pos, data_len, hdr_dict, unpack_str]
hdr_pos, hdr_len, data_pos, data_len, hdr_dict, unpack_str = idx[index]
if unpack_str is None:
raise ValueError("Do not know how to decode the data byte stream.")
# Read in the binary
f = self.open()
f.seek(data_pos)
data = f.read(data_len)
f.close()
data = array(unpack(unpack_str, data))
# Attempt to scale the data
# YMU
if hdr_dict.has_key('YMU'):
YMU = hdr_dict['YMU']
elif hdr_dict.has_key('YMULT'):
YMU = hdr_dict['YMULT']
else:
YMU = 1
# YOF
if hdr_dict.has_key('YOF'):
YOF = hdr_dict['YOF']
elif hdr_dict.has_key('YOFF'):
YOF = hdr_dict['YOFF']
else:
YOF = 0
# YZE
if hdr_dict.has_key('YZE'):
YZE = hdr_dict['YZE']
elif hdr_dict.has_key('YZERO'):
YZE = hdr_dict['YZERO']
else:
YZE = 0
data = YZE + YMU * (data - YOF)
# Attempt to label the data
data = metaArray(data)
# data['unit']
if hdr_dict.has_key('YUN'):
data['unit'] = hdr_dict['YUN']
elif hdr_dict.has_key('YUNIT'):
data['unit'] = hdr_dict['YUNIT']
# data['label']
if hdr_dict.has_key('COMP'):
data['label'] = hdr_dict['COMP']
elif hdr_dict.has_key('PT_F'):
data['label'] = hdr_dict['PT_F']
elif hdr_dict.has_key('PT_FMT'):
data['label'] = hdr_dict['PT_FMT']
# XUN
if hdr_dict.has_key('XUN'):
data.set_range(0, 'unit', hdr_dict['XUN'])
elif hdr_dict.has_key('XUNIT'):
data.set_range(0, 'unit', hdr_dict['XUNIT'])
# WFI
WFI = None
if hdr_dict.has_key('WFI'):
WFI = hdr_dict['WFI']
elif hdr_dict.has_key('WFID'):
WFI = hdr_dict['WFID']
else:
for key in hdr_dict.keys():
if key.find(':WFI') != -1:
WFI = hdr_dict[key]
break
# data['name']
data['name'] = self.file_path.name + '[' + str(index) + ']'
if WFI is not None:
chansep = WFI.find(',')
data.set_range(0, 'label', WFI[:chansep])
# data['name'] += WFI[chansep:]
# scale the x-axis
# XIN
if hdr_dict.has_key('XIN'):
XIN = hdr_dict['XIN']
elif hdr_dict.has_key('XINCR'):
XIN = hdr_dict['XINCR']
else:
XIN = 1
# PT_O
if hdr_dict.has_key('PT_O'):
PT_O = hdr_dict['PT_O']
elif hdr_dict.has_key('PT_OFF'):
PT_O = hdr_dict['PT_OFF']
else:
PT_O = 0
# XZE
if hdr_dict.has_key('XZE'):
XZE = hdr_dict['XZE']
else:
XZE = PT_O * -XIN
#elif hdr_dict.has_key('PT_OFF'):
# XZE = PT_O * -XIN
#else:
# XZE = 0
data.set_range(0, 'begin', XZE)
data.set_range(0, 'end', XZE + XIN * len(data))
# Include the rest of the metainfo into metaArray
for field, value in hdr_dict.items():
data["isf."+field] = value
data.update_range()
return data
def __getitem__(self, index):
"""
Return metaArray object of the given index item in the file
"""
# sanity check
assert type(index) is int, "Given index is not int type: %r" % index
idx = self.idx
#if index < 0 or index >= len(idx):
# raise IndexError, "Requested index outside range (" + str(len(idx)) + ")."
# [hdr_pos, hdr_len, data_pos, data_len, hdr_dict, unpack_str]
hdr_pos, hdr_len, data_pos, data_len, hdr_dict, unpack_str = idx[index]
if unpack_str is None:
raise ValueError("Do not know how to decode the data byte stream.")
# Read in the binary
f = self.open()
f.seek(data_pos)
data = f.read(data_len)
f.close()
data = array(unpack(unpack_str, data))
# Attempt to scale the data
# YMU
if hdr_dict.has_key('YMU'):
YMU = hdr_dict['YMU']
elif hdr_dict.has_key('YMULT'):
YMU = hdr_dict['YMULT']
else:
YMU = 1
# YOF
if hdr_dict.has_key('YOF'):
YOF = hdr_dict['YOF']
elif hdr_dict.has_key('YOFF'):
YOF = hdr_dict['YOFF']
else:
YOF = 0
# YZE
if hdr_dict.has_key('YZE'):
YZE = hdr_dict['YZE']
elif hdr_dict.has_key('YZERO'):
YZE = hdr_dict['YZERO']
else:
YZE = 0
data = YZE + YMU * (data - YOF)
# Attempt to label the data
data = metaArray(data)
# data['unit']
if hdr_dict.has_key('YUN'):
data['unit'] = hdr_dict['YUN']
elif hdr_dict.has_key('YUNIT'):
data['unit'] = hdr_dict['YUNIT']
# data['label']
if hdr_dict.has_key('COMP'):
data['label'] = hdr_dict['COMP']
elif hdr_dict.has_key('PT_F'):
data['label'] = hdr_dict['PT_F']
elif hdr_dict.has_key('PT_FMT'):
data['label'] = hdr_dict['PT_FMT']
# XUN
if hdr_dict.has_key('XUN'):
data.set_range(0, 'unit', hdr_dict['XUN'])
elif hdr_dict.has_key('XUNIT'):
data.set_range(0, 'unit', hdr_dict['XUNIT'])
# WFI
WFI = None
if hdr_dict.has_key('WFI'):
WFI = hdr_dict['WFI']
elif hdr_dict.has_key('WFID'):
WFI = hdr_dict['WFID']
else:
for key in hdr_dict.keys():
if key.find(':WFI') != -1:
WFI = hdr_dict[key]
break
# data['name']
data['name'] = self.file_path.name + '[' + str(index) + ']'
if WFI is not None:
chansep = WFI.find(',')
data.set_range(0, 'label', WFI[:chansep])
# data['name'] += WFI[chansep:]
# scale the x-axis
# XIN
if hdr_dict.has_key('XIN'):
XIN = hdr_dict['XIN']
elif hdr_dict.has_key('XINCR'):
XIN = hdr_dict['XINCR']
else:
XIN = 1
# PT_O
if hdr_dict.has_key('PT_O'):
PT_O = hdr_dict['PT_O']
elif hdr_dict.has_key('PT_OFF'):
PT_O = hdr_dict['PT_OFF']
else:
PT_O = 0
# XZE
if hdr_dict.has_key('XZE'):
XZE = hdr_dict['XZE']
else:
XZE = PT_O * -XIN
#elif hdr_dict.has_key('PT_OFF'):
# XZE = PT_O * -XIN
#else:
# XZE = 0
data.set_range(0, 'begin', XZE)
data.set_range(0, 'end', XZE + XIN * len(data))
# Include the rest of the metainfo into metaArray
for field, value in hdr_dict.items():
data["isf." + field] = value
data.update_range()
return data
