IndexH_Cons=FSdata.index(close) + 1 # For temperatures lower than the constraint T, indexL_Cons is the index of the one that is closest to the constraint TįracSolid =(FSdata-temp)/(FSdata-FSdata)*(FSdata-FSdata)+FSdata
The Python Calculator allows a user to apply calculations that are available in Python. These are the Python Calculator and the Programmable Filter.
#Paraview python programmable filter code#
While this filter can take only one input, it is based on the same code used by the Python Calculator (described in Section 5.9. In the example above, the expression inputs0 refers to the first input to the filter. IndexH_Cons=FSdata.index(close) # For temperatures higher than the constraint T, indexH_Cons is the index of the one that is closest to the constraint T ParaView has two filters that give a user access to python math functions as well as the underlying VTK library. Note that the arrays in the input are accessed in the above example using their original array names. # Calculate the fraction solid by linearly interpolating data suppliedĬlose = min(FSdata, key=lambda x:abs(x-temp)) #Confirm temperature within solidus liquidus range Processing data entails transforming input data by applying defined operations to generate a new output. Sources > Sphere or read a file from disk. To generate data, the module may use a mathematical model e.g. # This function returns the fraction liquid at a given temperature A pipeline module in ParaView does one of two things: it either generates data or processes input data. I’m stuck where the code is trying to evaluate the data in the existing array against > 0. However, this firstly requires sending the converted point data arrays to the output and then to call them again in order to perform further calculations with these arrays. It converts some arrays from cell data to point data and then it performs some calculations with the new point-based data (see code below). Specifically if a nodal value is greater than zero, I perform a different calculation than if it is negative. Hello, I am using a programmable filter capable of reading cell centered data. Why does the min() function produce this error, and how do I get around this issue?īelow is the python from my Programmable Filter Window, import osįSdata.append(float(s1)) # The dataList in dataList stores the time column of the model output fileįSdata.append(float(s1)) # dataList contains the temperature evolution of Point nodeNum I’m trying to implement at programmable filter in which I read in an existing array from an imported solution, then calculate a new array based on an if else statement. However, if it is a value requiring interpolation (temp=600), then the min function is used and the script fails with the error. I pass in float with a value not requiring interpolation then the script works (temp = 700, temp = 300). The function getFracLiq requires a float value. TypeError: min() got an unexpected keyword argument ‘key’ I am trying to use a programmable filter to read in the fraction solid data, then for each cell interpolate the Fraction Solid based on the temperature data. csv file with the fraction solid Vs temperature data. Question: Paraview Data Science Python Programmable Filter I want to create a script in Paraview that extracts all of the attributes of the Source and saves. I have a a model with a temperature field.
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