Messages

Hi,

do you have a structure model where you want to add surface roughness? That can be done with GrainGeo - Roughen Surface (https://geodict-userguide.math2market.de/2025/graingeo_roughensurface.html).
Or do you have a segmented scan where the solid surface is rough and you want to somehow calculate the roughness? Is this to create a statistical digital twin? There is no direct solution for this, but using Python scripting you can optimize the structure according to the desired properties, e.g. pore size distribution or permeability. 

Best regards,
Janine

[Settings > Edit Expert Settings]

Hi Jack Davis,

Good news: using the following expert settings (Settings > Edit Expert Settings) and a threshold of 0.7 we could improve the result significantly:

[attachimg=1]
   
Short explanation for the chosen expert settings :
   

• Reconnection Threshold: we strongly relaxed it because the fibers are very isolated and it does not connect through the pore space.
• Max Distance: this parameter is important because otherwise only the local fiber radius is considered.
• Remove Crossing Radius: this removes crossing artefacts at crossings, preventing branched fibers
• Cleanse Centerlines: this cleans the result improving the centerlines. This is needed because the fibers have such a rough surface. For this reason also a higher threshold is needed. It is easier to connect through gaps, than to resolve crossings.

 

Another thing that improves the result a little bit more is to apply a "morphological closing" before using FiberFind. For this, use ProcessGeo > Morphological Operations > Dilate.
First reassign the non-fiber material to pore material (ProcessGeo - Reassign Materials & Material IDs).
Then, dilate the fiber material with the same ID.
Afterwards dilate the pore material with pore material.
Then, the fibers are little bit smoothed without losing to much of their shape.
This helps FiberFind to identify the fibers afterwards. 

Best regards,
Janine

[FiberGeo > Create]

Dear Jack Davis,

I'm sorry for the late reply. I think it would be great if you could send the structure file. If you don't want to share it here, send it to support@math2market.de. Then, we can have a look at it.

In case the current networks cannot handle this structure, it could help if you train your own custom network using GeoDict-AI specialized on your structures.
https://geodict-userguide.math2market.de/2025/geodictai.html

There is also a tutorial for training a neural network for identifying binder. I will send you the password for unzipping the tutorial via e-mail.
https://www.gddownload.de/Tutorials/Training-Neural-Network-for-binder-identification-in-NMC-cathodes-with-GeoDictAI.zip

If you want to train your own network, I think the training data generation macro should then (at least) contain the following steps:
  * use FiberGeo > Create to create the fibers https://geodict-userguide.math2market.de/2025/fibergeo_create.html
    * create the fibers as close as possible to the original regarding fiber curvature, diameters, etc.
  * save the structure as *.gad file (File > Save Structure as)
  * use GrainGeo > Roughen Surface to roughen the surface https://geodict-userguide.math2market.de/2025/graingeo_roughensurface.html
  * use GadGeo > Add & Import > Add GAD Objects from File to add the previously saved gad file to the current structure to have the gad data and the roughened data in memory at the same time https://geodict-userguide.math2market.de/2025/gadgeo_addgadfile.html

Best regards,
Janine

Hi,

your code worked more or less for me. In your plot it seems, as if no solid material ID was found. Did you load the segmented structure file with material ID 1 and 2?

By the way, it would be great if you would copy paste your code here instead of using screenshots. Then, it would be easier for me to reproduce the problem Use the "#" symbol above the editor to enter the code beginning and ending markers

However I did not understand, why you want to divide the X-values by 2? Then the histogram gets moved to the left. I assume, that you wanted to account for the problem of the different lengths of the arrays count and bin_edges. For that I think it would be better to just remove the last entry of bin_edges. Even better would be to use matplotlib instead to plot the histogram. You can find an example for a very basic plot below. Matplotlib offers many options to change the plotting style as desired.

Another improvement you find in my code example below is to get the gray values with numpy.where instead of a for loop. This is much faster for large gray value images.

Code Select Expand


import numpy as np
import matplotlib.pyplot as plt

VolumeInfo  = gd.getVolumeFieldsInfo() # get list of dictionaries of loaded Volume Fields and assign it to variable VolumeInfo
nx, ny, nz  = gd.getVolDimensions()    # get the number of voxels in X-, Y- and Z-direction and assign the numbers to variables
Structure   = gd.getStructure()        # assign 3D numpy array describing the loaded structure to the variable Structure

Name        = VolumeInfo[0]['name']    # assign the name of the volume field to the variable Name.
VolumeField = gd.getVolumeField(Name)  # assign the numpy array describing the volume field to the variable VolumeField
Statistic   = []                       # Create empty list to store the statistical values

grayvalues = np.empty((nx,ny,nz),dtype=np.float32) # create 3D numpy array with the same shape as the gray value image
material_ID = np.where(Structure[:,:,:]==1)        # find out where the structure has ID 1
grayvalues[material_ID]=VolumeField[material_ID]   # take the gray values where the structure has ID 1 and save them in the new field

flat      = grayvalues.flatten()

# ---------- plot with graph dialog GeoPy API ------------------
gDlg      = gd.makeGraphDialog()                   
graph     = gDlg.addGraph('Histogram', Name, "Voxel Count")
bin_count = 256
counts, bin_edges = np.histogram(flat,bins = bin_count, range=(0,255))

x_values = bin_edges[1:-1]   # crop the first value, because the number of zeros is not interesting, crop the last value to match the number of entries in counts
y_values = counts[1:]        # crop the first entry, because the number of zeros is not interesting
x_list   = x_values.tolist()
y_list   = y_values.tolist()

graph.addData(x_list,y_list, Name)     
gDlg.run()                                   

# ---------- plot with matplotlib ------------------
plt.hist(flat, bins=bin_edges)
plt.title('Histogram')
plt.xlabel('Gray Values')
plt.ylabel('Voxel Count')
plt.savefig('HistogramPlot.png')


Hi TP,

following the topic in the User Guide I mentioned earlier, you can just record the execution of several macros into one. Just start Macro Recording and then in the Macro Execution Control select one macro after the other and click Record Only for each of them in the order you want to run them. Afterwards, end macro recording. Finally, you can run the new macro which then executes all the other macros automatically.

If you just want to run the same permeability simulation on different structures, it would be more elegant to just use one macro and the Python code shown in the tutorial I linked you in my earlier post. There, different structures are saved in one folder and the goal is to analyze Open and Closed Porosity on each of them automatically. You only have to replace the command for the open and closed porosity by the corresponding permeability simulation command.

Best regards,
Janine

Hello,

yes, the job queue server is not included in your license. For a job queue server you would need a floating license to access GeoDict from different machines in your internal network. Then you and your colleagues could send jobs to a defined job queue server. With a node-locked license you only have access to GeoDict from one machine. Thus, you don't have a job server machine.

However, if you know the macros to run beforehand, you can create a macro, that runs the other macros one after the other. The macro execution can be recorded in a macro the same way as all other commands. Find an example in the User Guide:
https://geodict-userguide.math2market.de/2025/automation_varymacrofromgeopymacro.html

Also, if you want to run the same commands with varying parameters on different structures, you only need one macro. Learn how to create a parameter macro here:
https://geodict-userguide.math2market.de/2025/automation_createparametermacro.html

If you model the structures with the GeoDict structure generators, this can just be part of the macro. If you have a stack of structures, you can automatically run the study on all structure files in one folder as described in the tutorial "Automating the handling of multiple datasets with GeoPy". Download the tutorial from our website and request the password via e-mail.
https://www.math2market.com/service-support/learning-opportunities/tutorials.html

Does this help you?

Best regards,
Janine

[Color By]

Hi,

I assume, your gray value image does not have index 1. If only one image is loaded it has always index 0. If more images are loaded, have a look at the Color By combo-box in the Volume Field tab. Counting starts with 0. The fields have the order in which they are loaded to GeoDict.
[attachimg=1]

Another thing I noted in your code example, is that the indentation does not seem to make sense within the loop. In the example in the User Guide it was meant like the following. I marked the significant differences to your code with "!!!".

Code Select Expand


VolumeInfo  = gd.getVolumeFieldsInfo() # get list of dictionaries of loaded Volume Fields and assign it to variable VolumeInfo
nx, ny, nz  = gd.getVolDimensions()    # get the number of voxels in X-, Y- and Z-direction and assign the numbers to variables
Structure   = gd.getStructure()        # assign 3D numpy array describing the loaded structure to the variable Structure

# !!! In the following command ensure to have the right index for your gray value field !!!
Name        = VolumeInfo[0]['name']    # assign the name of the volume field to the variable Name.
VolumeField = gd.getVolumeField(Name)  # assign the numpy array describing the volume field to the variable VolumeField
Statistic   = []                       # Create empty list to store the statistical values

for k in range(nz): # loop over all Z-layers
value_sum   = 0    # creating variable value_sum to sum up the result values
value_count = 0    # creating variable value_count to count the summands

for j in range(ny):  # loop over all Y-layers

  for i in range(nx): # loop over all X-layers

    if Structure[i][j][k] == 0:                       # condition: if the kth Z-value in the jth Y-column of the ith X-layeris pore space (ID 0), the following indented section is executed
       value_sum   = value_sum + VolumeField[i][j][k] # add all pore space result values of the kth Z-layer to the sum value_sum
       value_count = value_count + 1                  # count the summands of value_sum

# !!! The following section should have the right indentation according to what should be plotted. Since we want to have one value for each Z-layer it belongs to the outer loop (for k in range nz) and thus needs to have the same indentation level as the head of the second loop, here 1 space. !!!
meanVal = value_sum / value_count # compute mean value of all pore space result values in the kth Z-layer and assign it to the variable meanVal
Statistic.append(meanVal)         # append the mean value of the Z-layer to the Statistic list

# !!! The following section needs to be outside of the loop to plot all data in one plot !!!
gDlg  = gd.makeGraphDialog()                  # create a graph dialog object
graph = gDlg.addGraph(Name, "Z layers", Name) # add a graph object with the name of the volume field as title and Y-axis legend and Z-layers as X-axis legend 
Z_layers = list(range(1, nz + 1))             # writes the Z-layer numbers 1, 2, ..., nz-1, nz into a list named Z-layers
graph.addData(Z_layers, Statistic, Name)      # add a single dataset with the Z-layers as X-values, the mean result values as Y-values and the name of the volume field as legend to this graph
gDlg.run()                                    # show graph dialog


This will output something like this:
[attachimg=2]

Hello Caroline,

you can reduce the number of data points in the Electrolyte Model Parameters. Then, less computations are done for the meso parameters.

We have a tutorial for creating the txt files. This tutorial also has txt files for many of the database materials including NMC622. You can download it here: https://www.gddownload.de/Tutorials/Create-txt-file-for-concentration-dependent-parameters-in-active-material-expert-settings.zip
The password can be requested here: https://www.math2market.com/service-support/technical-support.html
But I will just send you the password via e-mail.

Best regards,
Janine

Hello Caroline,

do you run a homogenized simulation? For the homogenized simulation currently the meso parameters are computed for each data point from the concentration dependent parameter tables, which takes much time. With the upcoming Service Pack 3 for GeoDict 2025 it is possible to reduce this time. For this, set the Separator and  Binder & Carbon Black model parameters to porous and the input mode of the effective values to Tortuosity Factors. Then, the meso parameter can be computed from the tortuosity, which is much faster.

For the active material you can use concentration dependent parameters by using an expert setting as described in this YouTube tutorial: https://www.youtube.com/watch?v=wEhk8YZtQSI

Best regards,
Janine

Hello Jack,

there are many options to analyze volume fields with the Compute Metrics command, as described here: https://geodict-userguide.math2market.de/2025/geodict_computemetrics.html

For example, you can compute the mean value and standard deviation for a specific material with it. However, the option to compute the histogram for only one material is not included yet.

But if needed, you can use Python to create your own custom histogram GeoApp.

For example, you get a 3D numpy array of your structure using the command gd.getStructure() as described here:
https://geodict-userguide.math2market.de/2025/automation_api_structure.html?anchor=getstructure

and for your volume field using gd.getVolumefield(volume field index), as described here:
https://geodict-userguide.math2market.de/2025/automation_api-results.html?anchor=getvolumefield

Then, you can for example use numpy methods to get a histogram for only one material ID.
https://numpy.org/doc/stable/index.html

Best regards,
Janine

Hi Mark,

reassigning only a part of a material should be possible with GeoPy, but not directly from the GUI. But did you try to crop each material and embed it again, after having them split by the app or an own GeoPy script? Then adding them together with LayerGeo Add, as suggested before? You could even use a table variable to set the cropping values as needed. I think, this is the easiest way for the task at the moment. So maybe step after step:

1. split the structure in it's materials, e.g. for a structure with 2 solid materials, split the structure in two structures, one only containing material 1 and one only containing material 2. This can be done by reassigning the other materials to the pore material and saving the structure afterwards.
2. If for example only material 1 should be clipped, crop the structure only containing material 1 and embed it afterwards with pore material to obtain the same domain size as before.
3. Use the module LayerGeo Add to add the differently cropped materials together.
[attachimg=1]

You can script all these steps in a GeoPy script and set a variable of type table, as explained in the GeoPy scripting User Guide on page 46. Then, you can define the cropping values for each material.

Best regards,
Janine

[Easy Image]

Hi Mark,

right away from the GUI it is currently not possible. But there could be other possibilities.

The Easy Image GeoApp can do a similar thing. There is an option called Split Structure by Materials. This splits the materials into different structures and adds them together again but shifted. This app can be found under GeoApp -> General -> Report & Image ->Easy Image. The parameter options open after clicking Run. For Image Presets select Split Structure by Material. There is also a YouTube tutorial for this feature https://www.youtube.com/watch?v=dVCApDddQPo. The tutorial is done with GeoDict 2020. Thus, the app is located in another place, but the functionality did not change much.

You could use the resulting structure parts and crop them as needed and add them again afterwards with LayerGeo - Add. If you need this often, you can write a GeoPy script for this task.

Best regards,
Janine

Hi,

yes, there is. The values can be found in the Resultmap. Thus, you can access them from Python via the stringmap package. Find a detailed description for the stringmap options in the GeoPy Scripting User Guide on page 81 (https://www.math2market.com/fileadmin/UserGuide/GeoDict2024/Automation2024.pdf). Below find an example how the file with the saturation closest to 0.5 can be found.

Code Select Expand


import stringmap

gdr = stringmap.parseGDR("CapillaryPressureResult.gdr")
gdr.push("ResultMap")
saturationlist = gdr.getList("SaturationInvading")
print(saturationlist)

def getSaturationIndex(saturationvalue):
  saturationdifference = 1
  for saturation in saturationlist:
    diff = abs(saturationvalue-float(saturation))
    print(diff)
    if diff < saturationdifference:
      saturationdifference=diff
    else:
      saturationindex = saturationlist.index(saturation)
      break
  return saturationindex
 
file = f"CapillaryPressureResult/Step_{getSaturationIndex(0.5):06}.gdt"


[stringmap]

Hello Mark,

you can change the result file from Python after the generation using the stringmap package. This package is described in the GeoPy scripting User Guide in the chapter "Access to GeoDict result files (*.gdr)" on page 81.
https://www.math2market.com/fileadmin/UserGuide/GeoDict2024/Automation2024.pdf

I give you a short example code to change the description in the metadata tab of a FlowDict result viewer:

Code Select Expand


import stringmap
gdr=stringmap.parseGDR('StokesResult.gdr')
GDR_Description=gdr['Description']
gdr['Description']='some text\n'+GDR_Description
gdr.toFile('new.gdr')


Find the needed keys for the result part to change by printing the gdr in the console

Code Select Expand

print(gdr)

Best regards,
Janine

[Macro - Execute Macro/Script]

Hi Erik,

• It is possible by using a GeoPy script. Attached find a script that labels the given gray value ranges with the according IDs. To run the script, the gray value image should be loaded in the Image Processing dialog. From the main GeoDict window select Macro - Execute Macro/Script and browse for the script. Click on Parameters to assign material IDs to value ranges in a table. Afterwards click Run to generate an AI-label file (*.gld). Load this file in the Image Processing dialog by clicking Load Labels and browsing for the file. To learn more about automation by scripting in GeoDict, refer to the GeoPy scripting User Guide: https://www.geodict.com/fileadmin/UserGuide/GeoDict2023/Automation2023.pdf.
• Good news for you: With the new GeoDict 2024 release coming soon, a new feature is available. The "Magic Brush" analyzes the scan on clusters recognizing edges. Then, labels can be created by simply selecting these clusters.

Best regards,
Janine