In the first example, we create an acquisition point condition, but only added it to the conditions inside the data file object. Conditions can also be, and in many cases, should be associated directly with the data set(s) they are representing. It is often the case that the same condition is shared between several data sets. For instance, the condition describing the instrument (Instrument) is usually applicable to all collected data sets. So, how to add conditions to data sets?
Let’s assume we have a data set named datum and a condition named acq (as in the first example):
# Condition
from pyhmsa.spec.condition.specimenposition import SpecimenPosition
from pyhmsa.spec.condition.acquisition import AcquisitionPoint
position = SpecimenPosition(x=0.0, y=1.0, z=2.0)
acq = AcquisitionPoint(position=position)
acq.set_dwell_time(5.0, 's')
# Dataset
import random
import numpy as np
from pyhmsa.spec.datum.analysis import Analysis1D
channels = 1000
datum = Analysis1D(channels, np.int64)
datum[:] = [random.randint(0, 5000) for _ in range(channels)]
In the current state, the data set nor the condition are added to the data file object. Each data set object has a conditions attribute that acts exactly as the conditions attribute of the DataFile object. We can then simply add the condition as we did before.
datum.conditions['Acq0'] = acq
Now let’s add the datum to the data file object.
datafile.data['Spectrum'] = datum
That’s all, but there is some magic that also happened when adding the data set to the data file object. If we list the data file’s conditions, we will see that our condition, added to the data set, is also present.
print(list(datafile.conditions)) # Returns: ['Acq0']
The conditions attribute of the data file object contains all conditions of the whole data file object. We can technically retrieve and modified the same condition from two locations: the data file or the data set.
assert datafile.conditions['Acq0'] is datafile.data['Spectrum'].conditions['Acq0']
Note that the order of the operation is not important. The conditions added to a data set will always be added to the overall conditions of the data file.
Some precisions must be made regarding removing conditions. If a condition is removed from the data file, it will also be removed from all data sets having this condition.
del datafile.conditions['Acq0']
print(list(datafile.conditions)) # Returns: []
print(list(datafile.data['Spectrum'].conditions)) # Returns: []
However, if a condition is removed from a data set, it will only be removed from this data set and not from the data file.
datafile.data['Spectrum'].conditions['Acq0'] = acq # Reset
del datafile.data['Spectrum'].conditions['Acq0']
print(list(datafile.conditions)) # Returns: ['Acq0']
print(list(datafile.data['Spectrum'].conditions)) # Returns: []
This behavior may appear counter-intuiative but it is not possible to know if that condition was added first to a data set or directly to the data file’s conditions.
#!/usr/bin/env python
from pyhmsa.datafile import DataFile
datafile = DataFile()
# Condition
from pyhmsa.spec.condition.specimenposition import SpecimenPosition
from pyhmsa.spec.condition.acquisition import AcquisitionPoint
position = SpecimenPosition(x=0.0, y=1.0, z=2.0)
acq = AcquisitionPoint(position=position)
acq.set_dwell_time(5.0, 's')
# Dataset
import random
import numpy as np
from pyhmsa.spec.datum.analysis import Analysis1D
channels = 1000
datum = Analysis1D(channels, np.int64)
datum[:] = [random.randint(0, 5000) for _ in range(channels)]
# Assign condition
datum.conditions['Acq0'] = acq
# Add dataset
datafile.data['Spectrum'] = datum
# Check
print(list(datafile.conditions)) # Returns: ['Acq0']
assert datafile.conditions['Acq0'] is datafile.data['Spectrum'].conditions['Acq0']
# Removing globally
del datafile.conditions['Acq0']
print(list(datafile.conditions)) # Returns: []
print(list(datafile.data['Spectrum'].conditions)) # Returns: []
# Removing locally
datafile.data['Spectrum'].conditions['Acq0'] = acq # Reset
del datafile.data['Spectrum'].conditions['Acq0']
print(list(datafile.conditions)) # Returns: ['Acq0']
print(list(datafile.data['Spectrum'].conditions)) # Returns: []