How can I plot a custom result which is based on a condition?

Javier Vique

My custom result must be based on a condition applied to scaled Von Mises stresses. If these scaled Von Mises stresses are higher than a threshold (e.g. 2.5), the custom result will follow a specific formulae (e.g. 10^(x/2.5)). If they are lower, the custom result will be got applying another formulae (e.g. 10^(7.5log10(x100)-x*5)).
Please be aware that this formulae are dummy.

Javier Vique

This can be done using Python Result object. Below an example of script based on the formulae given above. Depending on the complexity of the formulae, we can make use of dpf operators or not. In this example, it is not used because log10 is not available in math operators.

def define_dpf_workflow(analysis):
    import mech_dpf
    import Ans.DataProcessing as dpf
    mech_dpf.setExtAPI(ExtAPI)
    dataSource = dpf.DataSources(analysis.ResultFileName)

    import math

    ScaleRatio = 28300./210000.
    MyThreshold = 2.5

    op_seqv = dpf.operators.result.stress_eqv_as_mechanical()
    op_seqv.inputs.data_sources.Connect(dataSource)
    seqv = op_seqv.outputs.fields_container.GetData()

    op_scale = dpf.operators.math.scale_fc()
    op_scale.inputs.fields_container.Connect(seqv)
    op_scale.inputs.ponderation.Connect(ScaleRatio)
    seqv_scaled = op_scale.outputs.fields_container.GetData()

    op_hp = dpf.operators.filter.field_high_pass_fc()
    op_hp.inputs.fields_container.Connect(seqv_scaled)
    op_hp.inputs.threshold.Connect(MyThreshold)
    seqv_scaled_h = op_hp.outputs.fields_container.GetData()

    op_lp = dpf.operators.filter.field_low_pass_fc()
    op_lp.inputs.fields_container.Connect(seqv_scaled)
    op_lp.inputs.threshold.Connect(MyThreshold)
    seqv_scaled_l = op_lp.outputs.fields_container.GetData()

    num_h = seqv_scaled_h[0].ScopingIds.Count
    num_l = seqv_scaled_l[0].ScopingIds.Count
    N_h = []
    N_l = []
    for i in range(num_h):
        x = seqv_scaled_h[0].Data[i]
        data = 10**(x/2.5)
        N_h.append(data)
    for i in range(num_l):
        x = seqv_scaled_l[0].Data[i]
        data = 10**(7.5*math.log10(x*100)-x*5)
        N_l.append(data)

    N_h_field = dpf.FieldsFactory.CreateScalarField(numEntities=num_h)
    N_h_field.MeshedRegionSupport = seqv_scaled_h[0].MeshedRegionSupport
    N_h_field.ScopingIds = seqv_scaled_h[0].ScopingIds
    N_h_field.Data = N_h

    N_l_field = dpf.FieldsFactory.CreateScalarField(numEntities=num_l)
    N_l_field.MeshedRegionSupport = seqv_scaled_l[0].MeshedRegionSupport
    N_l_field.ScopingIds = seqv_scaled_l[0].ScopingIds
    N_l_field.Data = N_l

    op_merge = dpf.operators.utility.merge_fields()
    op_merge.inputs.fields1.Connect(N_h_field)
    op_merge.inputs.fields2.Connect(N_l_field)

    dpf_workflow = dpf.Workflow()
    dpf_workflow.Add(op_merge)
    # dpf_workflow.SetInputName(u, 0, 'time')
    # dpf_workflow.Connect('time', timeScop)
    dpf_workflow.SetOutputContour(op_merge)
    dpf_workflow.Record('wf_id', False)
    this.WorkflowId = dpf_workflow.GetRecordedId()