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conversion to new python3

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This commit is contained in:
Markus Rosenstihl
2026-02-27 22:09:44 +01:00
parent 0b152b31fd
commit 29f003c99e
6 changed files with 78 additions and 73 deletions
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src/data/ADC_Result.py
+14 -13
View File
@@ -75,7 +75,7 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
for i in range(channels):
self.y.append(numpy.zeros((samples,), dtype="Int16"))
self.x = numpy.zeros((samples,), dtype="Float64")
self.x = numpy.zeros((samples,), dtype="float64")
self.index.append((0, samples-1))
self.cont_data = True
@@ -231,7 +231,8 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
if self.description is not None:
for (key,value) in self.description.items():
accu_group._v_attrs.__setattr__("description_"+key,str(value))
if key != None:
accu_group._v_attrs.__setattr__("description_"+key, str(value))
accu_group._v_attrs.__setattr__("sampling_rate",self.sampling_rate)
# save interval information
@@ -267,7 +268,7 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
# prepare saving data
channel_no=len(self.y)
timedata=numpy.empty((len(self.y[0]),channel_no),
dtype = "Int32")
dtype = "int16")
for ch in range(channel_no):
timedata[:,ch]=self.get_ydata(ch)
@@ -283,7 +284,7 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
time_slice_data=hdffile.create_carray(accu_group,
name="adc_data",
shape=timedata.shape,
atom=tables.Int32Atom(shape=chunkshape,
atom=tables.Int16Atom(shape=chunkshape,
flavor="numpy"),
filters=filter,
title="adc data")
@@ -292,7 +293,7 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
name="adc_data",
shape=timedata.shape,
chunkshape=chunkshape,
atom=tables.Int32Atom(),
atom=tables.Int16Atom(),
filters=filter,
title="adc data")
time_slice_data[:]=timedata
@@ -340,7 +341,7 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
tmp_y = []
for i in range(self.get_number_of_channels()):
tmp_y.append(numpy.array(self.y[i], dtype="Float64") + other)
tmp_y.append(numpy.array(self.y[i], dtype="float64") + other)
r = ADC_Result(x = self.x[:], y = tmp_y, index = self.index[:], sampl_freq = self.sampling_rate, desc = self.description, job_id = self.job_id, job_date = self.job_date)
self.lock.release()
@@ -361,7 +362,7 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
tmp_y = []
for i in range(self.get_number_of_channels()):
tmp_y.append(numpy.array(self.y[i], dtype="Float64") - other)
tmp_y.append(numpy.array(self.y[i], dtype="float64") - other)
r = ADC_Result(x = self.x[:], y = tmp_y, index = self.index[:], sampl_freq = self.sampling_rate, desc = self.description, job_id = self.job_id, job_date = self.job_date)
self.lock.release()
@@ -377,7 +378,7 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
tmp_y = []
for i in range(self.get_number_of_channels()):
tmp_y.append(other - numpy.array(self.y[i], dtype="Float64"))
tmp_y.append(other - numpy.array(self.y[i], dtype="float64"))
r = ADC_Result(x = self.x[:], y = tmp_y, index = self.index[:], sampl_freq = self.sampling_rate, desc = self.description, job_id = self.job_id, job_date = self.job_date)
self.lock.release()
@@ -394,7 +395,7 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
tmp_y = []
for i in range(self.get_number_of_channels()):
tmp_y.append(numpy.array(self.y[i], dtype="Float64") * other)
tmp_y.append(numpy.array(self.y[i], dtype="float64") * other)
r = ADC_Result(x = self.x[:], y = tmp_y, index = self.index[:], sampl_freq = self.sampling_rate, desc = self.description, job_id = self.job_id, job_date = self.job_date)
self.lock.release()
@@ -414,7 +415,7 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
tmp_y = []
for i in range(self.get_number_of_channels()):
tmp_y.append(numpy.array(self.y[i], dtype="Float64") ** other)
tmp_y.append(numpy.array(self.y[i], dtype="float64") ** other)
r = ADC_Result(x = self.x[:], y = tmp_y, index = self.index[:], sampl_freq = self.sampling_rate, desc = self.description, job_id = self.job_id, job_date = self.job_date)
self.lock.release()
@@ -430,7 +431,7 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
tmp_y = []
for i in range(self.get_number_of_channels()):
tmp_y.append(numpy.array(self.y[i], dtype="Float64") / other)
tmp_y.append(numpy.array(self.y[i], dtype="float64") / other)
r = ADC_Result(x = self.x[:], y = tmp_y, index = self.index[:], sampl_freq = self.sampling_rate, desc = self.description, job_id = self.job_id, job_date = self.job_date)
self.lock.release()
@@ -446,7 +447,7 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
tmp_y = []
for i in range(self.get_number_of_channels()):
tmp_y.append(other / numpy.array(self.y[i], dtype="Float64"))
tmp_y.append(other / numpy.array(self.y[i], dtype="float64"))
r = ADC_Result(x = self.x[:], y = tmp_y, index = self.index[:], sampl_freq = self.sampling_rate, desc = self.description, job_id = self.job_id, job_date = self.job_date)
self.lock.release()
@@ -516,7 +517,7 @@ def read_from_hdf(hdf_node):
# now do the real data
adc_data=hdf_node.adc_data.read()
adc.x=numpy.arange(adc_data.shape[0], dtype="Float64")/adc.sampling_rate
adc.x=numpy.arange(adc_data.shape[0], dtype="float64")/adc.sampling_rate
for ch in range(adc_data.shape[1]):
adc.y.append(adc_data[:,ch])
src/data/Accumulation.py
+28 -28
View File
@@ -131,19 +131,19 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
return error (std.dev/sqrt(n)) of mean
"""
if not self.uses_statistics(): return numpy.zeros((len(self.y[0]),),dtype="Float64")
if not self.uses_statistics(): return numpy.zeros((len(self.y[0]),),dtype="float64")
if not self.contains_data(): return []
self.lock.acquire()
if self.n < 2:
retval=numpy.zeros((len(self.y[0]),),dtype="Float64")
retval=numpy.zeros((len(self.y[0]),),dtype="float64")
self.lock.release()
return retval
try:
variance_over_n = (self.y_square[channel] - (self.y[channel]**2 / float(self.n)))/float((self.n-1)*self.n)
except IndexError:
print("Warning Accumulation.get_ydata(channel): Channel index does not exist.")
variance_over_n = numpy.zeros((len(self.y[0]),), dtype="Float64")
variance_over_n = numpy.zeros((len(self.y[0]),), dtype="float64")
self.lock.release()
# sample standard deviation / sqrt(n)
return numpy.nan_to_num(numpy.sqrt(variance_over_n))
@@ -160,7 +160,7 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
tmp_y = self.y[channel] / self.n
except IndexError:
print("Warning Accumulation.get_ydata(channel): Channel index does not exist.")
tmp_y = numpy.zeros((len(self.y[0]),), dtype="Float64")
tmp_y = numpy.zeros((len(self.y[0]),), dtype="float64")
self.lock.release()
return tmp_y
@@ -466,13 +466,13 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
# prepare saving data
channel_no=len(self.y)
timedata=numpy.empty((len(self.y[0]),channel_no*2), dtype = "Float64")
timedata=numpy.empty((len(self.y[0]),channel_no*2), dtype = "float64")
for ch in range(channel_no):
timedata[:,ch*2]=self.get_ydata(ch)
if self.uses_statistics():
timedata[:,ch*2+1]=self.get_yerr(ch)
else:
timedata[:,ch*2+1]=numpy.zeros((len(self.y[0]),),dtype = "Float64")
timedata[:,ch*2+1]=numpy.zeros((len(self.y[0]),),dtype = "float64")
# save data
time_slice_data=None
@@ -564,9 +564,9 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
tmp_y.append(self.y[i] + (other*self.n))
if self.uses_statistics():
r = Accumulation(x = numpy.array(self.x, dtype="Float64"), y = tmp_y, y_2 = tmp_ysquare, n = self.n, index = self.index, sampl_freq = self.sampling_rate, error = self.use_error)
r = Accumulation(x = numpy.array(self.x, dtype="float64"), y = tmp_y, y_2 = tmp_ysquare, n = self.n, index = self.index, sampl_freq = self.sampling_rate, error = self.use_error)
else:
r = Accumulation(x = numpy.array(self.x, dtype="Float64"), y = tmp_y, n = self.n, index = self.index, sampl_freq = self.sampling_rate, error = self.use_error)
r = Accumulation(x = numpy.array(self.x, dtype="float64"), y = tmp_y, n = self.n, index = self.index, sampl_freq = self.sampling_rate, error = self.use_error)
r.job_id = self.job_id # added by Oleg Petrov
self.lock.release()
return r
@@ -587,14 +587,14 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
self.lock.acquire()
for i in range(other.get_number_of_channels()):
tmp_y.append(numpy.array(other.y[i], dtype="Float64"))
tmp_y.append(numpy.array(other.y[i], dtype="float64"))
if self.uses_statistics(): tmp_ysquare.append(tmp_y[i] ** 2)
if self.uses_statistics():
r = Accumulation(x = numpy.array(other.x, dtype="Float64"), y = tmp_y, y_2 = tmp_ysquare, n = 1, index = other.index, sampl_freq = other.sampling_rate, error = True)
r = Accumulation(x = numpy.array(other.x, dtype="float64"), y = tmp_y, y_2 = tmp_ysquare, n = 1, index = other.index, sampl_freq = other.sampling_rate, error = True)
else:
r = Accumulation(x = numpy.array(other.x, dtype="Float64"), y = tmp_y, index = other.index, sampl_freq = other.sampling_rate, n = 1, error = False)
r = Accumulation(x = numpy.array(other.x, dtype="float64"), y = tmp_y, index = other.index, sampl_freq = other.sampling_rate, n = 1, error = False)
r.time_period=[other.job_date,other.job_date]
r.job_id = other.job_id # added by Oleg Petrov
r.common_descriptions=other.description.copy()
@@ -616,12 +616,12 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
for i in range(self.get_number_of_channels()):
tmp_y.append(self.y[i] + other.y[i])
if self.uses_statistics(): tmp_ysquare.append(self.y_square[i] + (numpy.array(other.y[i], dtype="Float64") ** 2))
if self.uses_statistics(): tmp_ysquare.append(self.y_square[i] + (numpy.array(other.y[i], dtype="float64") ** 2))
if self.uses_statistics():
r = Accumulation(x = numpy.array(self.x, dtype="Float64"), y = tmp_y, y_2 = tmp_ysquare, n = self.n + 1, index = self.index, sampl_freq = self.sampling_rate, error = True)
r = Accumulation(x = numpy.array(self.x, dtype="float64"), y = tmp_y, y_2 = tmp_ysquare, n = self.n + 1, index = self.index, sampl_freq = self.sampling_rate, error = True)
else:
r = Accumulation(x = numpy.array(self.x, dtype="Float64"), y = tmp_y, n = self.n + 1, index = self.index, sampl_freq = self.sampling_rate, error = False)
r = Accumulation(x = numpy.array(self.x, dtype="float64"), y = tmp_y, n = self.n + 1, index = self.index, sampl_freq = self.sampling_rate, error = False)
r.time_period=[min(self.time_period[0],other.job_date),
max(self.time_period[1],other.job_date)]
r.job_id = other.job_id # added by Oleg Petrov
@@ -649,9 +649,9 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
self.lock.acquire()
if self.uses_statistics():
r = Accumulation(x = numpy.array(other.x, dtype="Float64"), y = tmp_y, y_2 = tmp_ysquare, n = other.n, index = other.index, sampl_freq = other.sampling_rate, error = True)
r = Accumulation(x = numpy.array(other.x, dtype="float64"), y = tmp_y, y_2 = tmp_ysquare, n = other.n, index = other.index, sampl_freq = other.sampling_rate, error = True)
else:
r = Accumulation(x = numpy.array(other.x, dtype="Float64"), y = tmp_y, n = other.n, index = other.index, sampl_freq = other.sampling_rate, error = False)
r = Accumulation(x = numpy.array(other.x, dtype="float64"), y = tmp_y, n = other.n, index = other.index, sampl_freq = other.sampling_rate, error = False)
for i in range(other.get_number_of_channels()):
tmp_y.append(other.y[i])
tmp_ysquare.append(other.y_square[i])
@@ -684,9 +684,9 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
tmp_ysquare.append(self.y_square[i] + other.y_square[i])
if self.uses_statistics():
r = Accumulation(x = numpy.array(self.x, dtype="Float64"), y = tmp_y, y_2 = tmp_ysquare, n = other.n + self.n, index = self.index, sampl_freq = self.sampling_rate, error = True)
r = Accumulation(x = numpy.array(self.x, dtype="float64"), y = tmp_y, y_2 = tmp_ysquare, n = other.n + self.n, index = self.index, sampl_freq = self.sampling_rate, error = True)
else:
r = Accumulation(x = numpy.array(self.x, dtype="Float64"), y = tmp_y, n = other.n + self.n, index = self.index, sampl_freq = self.sampling_rate, error = False)
r = Accumulation(x = numpy.array(self.x, dtype="float64"), y = tmp_y, n = other.n + self.n, index = self.index, sampl_freq = self.sampling_rate, error = False)
r.time_period=[min(self.time_period[0],other.time_period[0]),
max(self.time_period[1],other.time_period[1])]
@@ -745,11 +745,11 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
self.n += 1
self.index = other.index[0:]
self.sampling_rate = other.sampling_rate
self.x = numpy.array(other.x, dtype="Float64")
self.x = numpy.array(other.x, dtype="float64")
self.cont_data = True
for i in range(other.get_number_of_channels()):
self.y.append(numpy.array(other.y[i], dtype="Float64"))
self.y.append(numpy.array(other.y[i], dtype="float64"))
if self.uses_statistics(): self.y_square.append(self.y[i] ** 2)
self.set_title(self.__title_pattern % self.n)
@@ -774,7 +774,7 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
for i in range(self.get_number_of_channels()):
self.y[i] += other.y[i]
if self.uses_statistics(): self.y_square[i] += numpy.array(other.y[i], dtype="Float64") ** 2
if self.uses_statistics(): self.y_square[i] += numpy.array(other.y[i], dtype="float64") ** 2
self.n += 1
self.time_period=[min(self.time_period[0],other.job_date),
@@ -804,11 +804,11 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
self.n += other.n
self.index = other.index[0:]
self.sampling_rate = other.sampling_rate
self.x = numpy.array(other.x, dtype="Float64")
self.x = numpy.array(other.x, dtype="float64")
self.cont_data = True
for i in range(other.get_number_of_channels()):
self.y.append(numpy.array(other.y[i], dtype="Float64"))
self.y.append(numpy.array(other.y[i], dtype="float64"))
if self.uses_statistics(): self.y_square.append(self.y[i] ** 2)
self.set_title(self.__title_pattern % self.n)
@@ -869,12 +869,12 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
self.lock.acquire()
for i in range(self.get_number_of_channels()):
tmp_y.append(numpy.array(-self.y[i], dtype="Float64"))
tmp_y.append(numpy.array(-self.y[i], dtype="float64"))
if self.uses_statistics():
r = Accumulation(x = numpy.array(self.x, dtype="Float64"), y = tmp_y, y_2 = numpy.array(self.y_square), n = self.n, index = self.index, sampl_freq = self.sampling_rate, error = True)
r = Accumulation(x = numpy.array(self.x, dtype="float64"), y = tmp_y, y_2 = numpy.array(self.y_square), n = self.n, index = self.index, sampl_freq = self.sampling_rate, error = True)
else:
r = Accumulation(x = numpy.array(self.x, dtype="Float64"), y = tmp_y, n = self.n, index = self.index, sampl_freq = self.sampling_rate, error = False)
r = Accumulation(x = numpy.array(self.x, dtype="float64"), y = tmp_y, n = self.n, index = self.index, sampl_freq = self.sampling_rate, error = False)
self.lock.release()
return r
@@ -951,7 +951,7 @@ def read_from_hdf(hdf_node):
# now do the real data
accu_data=hdf_node.accu_data.read()
accu.x=numpy.arange(accu_data.shape[0], dtype="Float64")/accu.sampling_rate
accu.x=numpy.arange(accu_data.shape[0], dtype="float64")/accu.sampling_rate
# assume error information, todo: save this information explicitly
accu.y_square=[]
accu.use_error=False
@@ -959,7 +959,7 @@ def read_from_hdf(hdf_node):
for ch in range(accu_data.shape[1]/2):
accu.y.append(accu_data[:,ch*2]*accu.n)
if accu.n<2 or numpy.all(accu_data[:,ch*2+1]==0.0):
accu.y_square.append(numpy.zeros((accu_data.shape[0]) ,dtype="Float64"))
accu.y_square.append(numpy.zeros((accu_data.shape[0]) ,dtype="float64"))
else:
accu.use_error=True
accu.y_square.append((accu_data[:,ch*2+1]**2)*float((accu.n-1.0)*accu.n)+(accu_data[:,ch*2]**2)*accu.n)
src/data/MeasurementResult.py
+5 -5
View File
@@ -168,7 +168,7 @@ class MeasurementResult(Drawable.Drawable, collections.UserDict):
sorted array of all dictionary entries without Accumulated Value objects with n==0
"""
keys=numpy.array([k for k in list(self.data.keys()) if not (isinstance(self.data[k], AccumulatedValue) and self.data[k].n==0)],
dtype="Float64")
dtype="float64")
keys.sort()
return keys
@@ -177,18 +177,18 @@ class MeasurementResult(Drawable.Drawable, collections.UserDict):
def get_xydata(self):
k=self.get_xdata()
v=numpy.array([self.data[key].mean() for key in k], dtype="Float64")
v=numpy.array([self.data[key].mean() for key in k], dtype="float64")
return [k,v]
def get_errorplotdata(self):
k=self.get_xdata()
v=numpy.array([self.data[key].mean() for key in k], dtype="Float64")
e=numpy.array([self.data[key].mean_error() for key in k], dtype="Float64")
v=numpy.array([self.data[key].mean() for key in k], dtype="float64")
e=numpy.array([self.data[key].mean_error() for key in k], dtype="float64")
return [k,v,e]
def get_lineplotdata(self):
k=self.get_xdata()
v=numpy.array(self.y, dtype="Float64")
v=numpy.array(self.y, dtype="float64")
return [k, v]
def uses_statistics(self):