Python Scripting - Science topic

Hi HongXin,

many years ago I worte such an AU. It coverts a 2rr file into a CSV. The first three rows show point nr., ppm and Hz and the first 3 colums show the same for f1. The matrix then shows the respective intensity for each coordinate. The AU program is attached. Regards Clemens

Dear Yuhang Wu

The Fastq Manifest option is the easiest way to import data into the QIIME2 environment. Using this option, you can easily import both (R1 & R2) reads of each sample into the QIIME2 environment.

It also does not require any barcode-related information.

Try it, and if you are facing a problem, let me know. I will assist you.

Best,

Samrendra

Thank you for your answer Poorya Rahdar Poorya Rahdar

Oh, the documentation isn't clear ?

Here ( https://statannotations.readthedocs.io/en/latest/statannotations.html ) , it says,

That doesn't clear it up ?

I'm being sarcastic. But I think that's actually the answer. But I'm not sure exactly what that means.

from abaqus import *

from abaqusConstants import *

import random

def replace_material_randomly(percentage, model_name, part_name):

print('------------------replace_material_randomly-----------------')

try:

# Access the part from the model

p = mdb.models[model_name].parts[part_name]

# Define the new section

mdb.models[model_name].HomogeneousSolidSection(name='New-Section', material='MATERIAL2')

# Ensure the 'All_solids' set exists

if 'All_solids' not in p.sets:

raise ValueError("The set 'All_solids' does not exist in the part.")

elements = p.sets['All_solids'].elements

num_elements_to_replace = int(len(elements) * (percentage / 100))

if num_elements_to_replace == 0:

raise ValueError("Percentage too low; no elements selected for replacement.")

# Randomly select elements to replace

indices_to_replace = random.sample(range(len(elements)), num_elements_to_replace)

elements_to_replace = [elements[i] for i in indices_to_replace]

# Create a set of the elements to replace

region = p.Set(elements=elements_to_replace, name='Elements_to_replace')

# Remove previous section assignments

p.sectionAssignments.remove(region=region)

# Assign the new section to the selected elements

p.SectionAssignment(region=region, sectionName='New-Section', offset=0.0, offsetType=MIDDLE_SURFACE, thicknessAssignment=FROM_SECTION)

print("Material replacement completed successfully.")

except Exception as e:

print(f"An error occurred: {e}")

# Example function call

replace_material_randomly(10, 'Model-1', 'Part-1')

I explain about sensivity analysis in there:

A new version for Windows was uploaded yesterday. This version has isotope identification.

To modify the coordinate system within Abaqus via Python, one must engage with the Abaqus Scripting Interface (ASI). This process entails establishing a new coordinate system and accordingly transforming the data before its extraction. It requires the creation of the coordinate system, application of the transformation to the relevant data within the .odb file, and subsequent extraction of this data in the updated coordinate system.

Following these steps, using Python, you can effectively change the coordinate system in an Abaqus .odb file.

The starting point will be end1Edges and Ending point will be end2Edges.

Thanks Adnan Majeed ,

There are important ethic considerations as you mention.

In our use case, the digitalized clinical tests, dementia and depression test, are administered. We use Alexa devices to pass these tests so that the information is digitized, obtaining the answer of the final score automatically.

Taking into account the data collected from our users, the features to clasify them will be:

My proposal is to compare the digitized test with the classic one to check the sensitivity and specificity. And compare these two terms with the ones I get when I add the Machine Learning layer. Probably the library I use is Scikit-learn.

Do you think I'm on the right path? Are there any important suggestions to keep in mind?

Thanks in advance,

you can convert h5a to rds by using anndata library.

you can use this script to install it:

pip install anndata rpy2

EIS can be simulated computationally using tools like VASP in conjunction with Python scripts for analysis. While VASP performs the quantum mechanical calculations, Python scripts are employed to post-process the data and generate impedance spectra.

Here's a simplified example of how you might use Python to analyze VASP output for EIS:

import numpy as np

import matplotlib.pyplot as plt

# Load VASP output data (e.g., electronic structure)

# Replace the following lines with your VASP output data loading code

# For simplicity, let's generate some example data

frequencies = np.logspace(0, 5, num=100) # Frequency range

impedance_real = np.random.rand(100) # Real part of impedance

impedance_imag = np.random.rand(100) # Imaginary part of impedance

# Calculate magnitude and phase angle from real and imaginary parts

magnitude = np.sqrt(impedance_real**2 + impedance_imag**2)

phase_angle = np.arctan2(impedance_imag, impedance_real) * (180/np.pi) # Convert radians to degrees

# Plot impedance magnitude

plt.figure(figsize=(8, 6))

plt.plot(frequencies, magnitude, label='Magnitude')

plt.xlabel('Frequency (Hz)')

plt.ylabel('Impedance Magnitude (Ohms)')

plt.xscale('log')

plt.yscale('log')

plt.title('Electrochemical Impedance Spectroscopy')

plt.legend()

plt.grid(True)

plt.show()

# Plot impedance phase angle

plt.figure(figsize=(8, 6))

plt.plot(frequencies, phase_angle, label='Phase Angle')

plt.xlabel('Frequency (Hz)')

plt.ylabel('Phase Angle (degrees)')

plt.xscale('log')

plt.title('Electrochemical Impedance Spectroscopy')

plt.legend()

plt.grid(True)

plt.show()

```

This script generates random data for the impedance magnitude and phase angle, but in a real scenario, you would replace it with the actual data extracted from VASP simulations. This script then plots the impedance magnitude and phase angle as functions of frequency, which are typical outputs of an EIS experiment.

Jyri Kimari

i have attached this conversation link to the NAMD Mailing List with some details to make sure whether this method is usable or not.

Hello Saswath Ghosh,

assuming your box is a 3D cell, once you have selected the face to create a Surface, you need to input the correct keyword with a script as follows:

my_part = mdb.models['Model-1'].parts['BottomLayerB']

faces = "The face/s you have selected"

my_part.Surface(side1Faces=faces, name="My_Surface_Name")

To check the possible keyword arguments it is always good to check the abaqus scripting manual:

Take care that in the case of 2D geometries you have a top and a bottom face, hence a top and a bottom surface that can be selected with a "side1Faces" or a "side2Faces" respectively.

Note: If you look at the manual "side2Faces" also exists for 3D cells but I never used it. I do not find the exact reference but it should be related to the normal "Outward"/"Inward" with respect the volumeto .

Add equations between

*Part, name=Part-4

**

*EQUATION INPUT=90Degree_EqList.inp

*End Part

If It is under the Assembly tree I think it expects a node set.

Sir did you find the answer of this because on the same problem I am working on it.

Here you can see better the format. If you want you can send me your code to check it.

There may be an issue with the ODB (Output Database) file you're trying to read. This error typically occurs when the file is corrupted or contains invalid data that prevents it from being properly read by Abaqus or your Python script.

To avoid this error, you can implement some error handling and verification steps in your Python script. Here are a few suggestions:

1. Check the file existence: Before attempting to read the ODB file, you can use the `os.path.exists()` function to verify that the file exists. If the file doesn't exist, you can handle the error gracefully and take appropriate action.

2. Verify the file integrity: After confirming that the file exists, you can use Abaqus' built-in functionality to verify the integrity of the ODB file. This can be done using the `odbChecker` utility, which is a command-line tool provided by Abaqus. You can call this utility from your Python script using the `subprocess` module to check the integrity of the ODB file before attempting to read it. If the file fails the integrity check, you can handle the error accordingly.

3. Retry mechanism: If you encounter the error during the initial read attempt, you can implement a retry mechanism in your script. For example, you can catch the specific exception that is raised when the error occurs and then retry the operation after a short delay. This approach allows you to give Abaqus or the system some additional time to finalize the writing of the ODB file before attempting to read it again.

Here's an example code snippet that demonstrates these suggestions:

```python

import os

import subprocess

import time

odb_path = "path/to/your/odb/file.odb"

# Check if the file exists

if not os.path.exists(odb_path):

print("ODB file does not exist.")

# Handle the error or exit gracefully

exit()

# Verify file integrity using Abaqus odbChecker

odb_checker_cmd = "abaqus odbcheck integrity=high odb=" + odb_path

result = subprocess.run(odb_checker_cmd, shell=True, capture_output=True)

# Check the output of the odbChecker

if "ODB is not usable" in result.stdout.decode():

print("ODB file integrity check failed.")

# Handle the error or exit gracefully

exit()

# Retry mechanism

max_retries = 3

retry_delay = 1 # seconds

for retry in range(max_retries):

try:

# Your code to read the PEEQ data from ODB file

# ...

# If successful, break out of the retry loop

break

except Exception as e:

print("Error reading ODB file:", str(e))

# Wait for a short delay before retrying

time.sleep(retry_delay)

else:

# Retry limit exceeded, handle the error or exit gracefully

print("Exceeded maximum retries. Unable to read ODB file.")

exit()

# Continue with the rest of your script

# ...

```

By implementing these suggestions, you can handle the error gracefully, verify the integrity of the ODB file, and incorporate a retry mechanism to give the system enough time to finalize the writing of the ODB file before attempting to read it.

Hope it helps:credit AI

Learning Python, Pandas, and Jupyter Notebook is an excellent choice for anyone interested in data analysis and data science. Here's why:

So, yes, I would highly recommend learning Python, Pandas, and Jupyter Notebook if you are interested in data analysis and data science. It's a valuable investment in your career, and you'll find it worth the effort.

Hi Benjamin Kwaku Nimako

The Earth System Grid Federation (ESGF) is a platform that provides access to a vast amount of climate data from various models and experiments. If you're looking to download data programmatically, you'll usually use the `pyesgf` package, which provides a Python interface for ESGF searches and downloads.

While I cannot provide a script tailored to your exact needs without more detailed specifications, I can provide a basic example to guide you on using `pyesgf` to search for and download datasets from the ESGF:

python code:

from pyesgf.search import SearchConnection

from pyesgf.logon import LogonManager

# Logon to ESGF (replace with your OpenID credentials)

lm = LogonManager()

lm.logon(hostname="esgf-node.llnl.gov", interactive=True)

# Establish a connection to the ESGF search API

conn = SearchConnection('http://esgf-data.dkrz.de/esg-search', distrib=True)

# Search for datasets (change the query as needed)

ctx = conn.new_context(project='CORDEX', time_frequency='day', experiment='historical', variable='tas', domain='EUR-11')

# Print the number of datasets found

print(f"Number of datasets found: {ctx.hit_count}")

# Download the datasets (this is a simple example, you might want to refine the download criteria)

for result in ctx.search():

print(f"Dataset ID: {result.dataset_id}")

for file in result.file_context().search():

print(f"Downloading {file.opendap_url}")

# You can download the file using requests or another suitable library

This is a very basic example and may not be directly applicable to your requirements. The `pyesgf` library provides many more functionalities and options to refine your search and download process. You'll need to install the necessary libraries, which might include `esgf-pyclient` and others, depending on your needs.

Calvin Cornell you can use OrthoFinder to identify lineage-specific proteins within a specific genus of archaea. Here are the steps to do so:

To identify lineage-specific proteins for your specific genus of archaea:

Here's a simplified example of how you might approach this using Python and the presence/absence matrix:

pythonCopy code# Load the presence/absence matrix (tab-delimited file) # Rows are genes, columns are species/strains # 1 indicates presence, 0 indicates absence # Filter genes from your genus genus_genes = presence_absence_matrix[genus_index] # Check if a gene is present only in your genus lineage_specific_genes = [] for gene_id, presence_vector in enumerate(genus_genes): if sum(presence_vector) == 1: # Gene is present only in one species lineage_specific_genes.append(gene_id) # Retrieve the protein sequences of lineage-specific genes lineage_specific_proteins = [] for gene_id in lineage_specific_genes: protein_sequence = retrieve_protein_sequence(gene_id) # Implement this function based on your dataset lineage_specific_proteins.append(protein_sequence) # Save or analyze the lineage-specific protein sequences

Please adapt this code to your specific dataset and programming environment. The key is to filter orthogroups and genes based on your genus of interest and identify those that are unique to your genus. Then, retrieve the protein sequences associated with those genes.

Hi David, I was caught out on this recently and discovered the newer version of Abaqus has replaced "master" with "main" and "slave" with "secondary".

Talina Terrazas Monje Have you solved this problem yet?

Follow these steps:

1. Get the output database file path: Before you can extract the flux values, you need to specify the path to the Abaqus output database (.odb) file. You can obtain this path from your Abaqus simulation or specify it manually.

1. Import the necessary modules: In your Python script, import the required Abaqus modules to access the Abaqus functions and classes. The main modules you'll need are `odbAccess` and `session`.

```python

from abaqus import *

from abaqusConstants import *

from odbAccess import *

```

3. Open the output database: Use the `openOdb()` function to open the Abaqus output database.

```python

odb = openOdb(path_to_odb_file)

```

4. Get the desired nodes: Based on the node IDs and coordinates you have, you can retrieve the corresponding nodes from the output database. You can use the `rootAssembly` object to access the relevant nodes.

```python

assembly = odb.rootAssembly

nodes = assembly.nodeSets['NODESET_NAME'].nodes

```

Replace `'NODESET_NAME'` with the name of the node set that contains the desired nodes.

5. Extract the flux values: Iterate over the nodes and extract the flux values at each node using the `historyOutput` object. You can obtain the flux values by specifying the appropriate output variable name (e.g., `'RF'` for reaction forces) and the desired step and frame.

```python

for node in nodes:

flux = node.historyOutput['FLUX'].data

# Process the flux data as needed

```

Replace `'FLUX'` with the appropriate output variable name for flux in your model.

6. Close the output database: Once you have extracted the desired information, close the output database.

```python

odb.close()

```

Make sure to close the output database to release the resources.

Replace `path_to_odb_file` with the actual path to your Abaqus output database file, and `'NODESET_NAME'` with the name of your desired node set.

No, they only work for CHARMM forcefields;

The best way is to use CHARMM-GUI to obtain the .prm and .str files that can then be used with cgenff_charmm2gmx_py3_nx2.py;

If you cannot use it, you can always try MATCH.

Dear doctor

Running Alchemist on Cray XC and CS Series Supercomputers: Dask and PySpark Interfaces, Deployment Options, and Data Transfer Times

Rothauge et al.

"Abstract—Alchemist allows Apache Spark to achieve better performance by interfacing with HPC libraries for largescale distributed computations. In this paper we highlight some recent developments in Alchemist that are of interest to Cray users and the scientific community in general. We discuss our experience porting Alchemist to container images and deploying it on Cray XC (using Shifter) and CS (using Singularity) series supercomputers, on a local Kubernetes cluster, and on the cloud. Newly developed interfaces for Python, Dask and PySpark enable the use of Alchemist with additional data analysis frameworks. We also briefly discuss the combination of Alchemist with RLlib, an increasingly popular library for reinforcement learning, and consider the benefits of leveraging HPC simulations in reinforcement learning. Finally, since data transfer between the client applications and Alchemist are the main overhead Alchemist encounters, we give a qualitative assessment of these transfer times with respect to different factors.

Several recent developments have enabled more practitioners to use Alchemist to easily access HPC libraries from data analysis frameworks such as Spark, Dask and PySpark, or from single-process Python applications. The availability of Docker and other containers enables users to get started with Alchemist quickly, and we briefly discussed the potentially exciting combination of Alchemist with reinforcement learning frameworks such as RLlib. Alchemist’s main overhead comes from the data transfer between client applications and Alchemist, and we ran some experiments to better understand the behaviour of these transfer times with respect to message buffer sizes, matrix layouts, and network variability"

You're correct that restarting simulations can save considerable computational time and resources if multiple simulations share common steps initially. Abaqus provides a restart feature that allows the restart of analysis from any previously completed step or increment in a preceding analysis.

As you are facing an issue due to the change in the blade's location in the sixth step, you need a solution that allows for the modification of your model (i.e., the blade's location) between the fifth and sixth steps, and yet still enables you to restart the simulation from the sixth step.

Abaqus allows for such model changes in a restart analysis, but there are restrictions on the kind of changes that can be made. Changes like modifications to boundary conditions, load magnitudes, or material properties are allowed in a typical restart analysis. However, changes to the model geometry or topology, like moving the blade in your case, are usually not allowed.

One possible workaround would be to model all 20 blade positions in your initial simulation but activate them in sequence in subsequent simulations. Here's how you might do that:

Remember, this is just a workaround and may or may not be feasible, depending on the specifics of your simulation. You might also need to modify the workaround based on your requirements.

In conclusion, while Abaqus allows for certain modifications during a restart analysis, the changes you're trying to make might not be compatible with this feature. You might need to resort to alternative modelling techniques to achieve your goal. If you're still facing issues, I recommend contacting Abaqus support or consulting the Abaqus user manual or user community for more specific guidance.

thanks for your reply Victor.

I'm still searching for ways to do it by Abaqus subroutines since subroutines provide you with great capabilities.

Hi

I found a solution to the problem. It is:

print('\nChoosing faces stored in a set and --> getting the associated cell IDs and making a set containing those cells.')

cellsIDs = []

for face in part01.sets['Set-1'].faces:

cellsIDs.append(face.getCells()[0])

her = 1

print('cell IDs are: ' + str(cellsIDs)) # --> [0,1]

cellsForSet = part01.cells[cellsIDs[0]:cellsIDs[0]+1]

for i in range(1, len(cellsIDs)):

print('i: ' + str(i))

cellsForSet = cellsForSet + part01.cells[cellsIDs[i]:cellsIDs[i]+1]

part01.Set(cells=cellsForSet, name="set of cells1")

Pardis Nooshmehr Hello,

To apply different loads on a 2D model element in Abaqus using a Python script, you can utilize the Abaqus scripting interface called "Abaqus Scripting Interface (ASI)" or "Abaqus Python Scripting." Here is a general outline of the steps involved:

1. Import the necessary Abaqus modules in your Python script:

```python

from abaqus import *

from abaqusConstants import *

```

2. Create a new model and define the necessary parameters, material properties, and geometry. This includes defining the element type, element properties, and section assignment.

3. Create the necessary steps for each load case. You can define different types of loads, such as uniaxial, shear, or any other desired loading conditions. For example, to apply uniaxial tension in the x-direction:

```python

mdb.models['YourModel'].StaticStep(name='Step-1', nlgeom=ON)

mdb.models['YourModel'].DisplacementBC(name='BC-1', createStepName='Step-1',

region=Region(cells=elementSet),

u1=1.0, u2=0.0)

```

4. Iterate over the time increments and solve the model:

```python

for increment in range(1, numIncrements+1):

mdb.models['YourModel'].steps['Step-1'].setValues(timeIncrement=timeInc)

mdb.models['YourModel'].solution.submit()

```

5. Extract the stress and strain components at each time increment. You can use the `getValues` method to obtain the desired output quantities. For example, to extract stress components:

```python

odb = session.openOdb('YourOutputODBFile.odb')

lastFrame = odb.steps['Step-1'].frames[-1]

stressValues = lastFrame.fieldOutputs['S']

stressComponents = stressValues.getSubset(region=elementSet).values

```

6. Save the stress and strain components as a CSV file using the Python `csv` module or any other preferred method.

This is a general framework, and you may need to adapt it to your specific model and requirements. The Abaqus Scripting Reference Manual and Abaqus Python Scripting documentation provide detailed information on the available methods and functions to accomplish specific tasks.

use near tools in arcpro

To fix this error, add the following line to the custom_v6.env file in the Simulia installation folder:

Code:

cae_local_host="localhost" REF: https://www.cfd-online.com/Forums/structural-mechanics/232704-abaqus-socket-error-errno-10060-a.html

Very thanks!!!

Thank you Cristina, your work is interesting and helpful.

The error message you are receiving suggests that the 'ProteinResidue' class is not present in the 'simtk.openmm.app' module, which is causing the attribute error.

To solve this issue, you may want to check if there is an updated version of the OpenMM package that you are using, as the 'ProteinResidue' class may have been removed or renamed in the newer version.

Alternatively, you could try using a different class from the 'simtk.openmm.app' module to create your protein residue. One option might be to use the 'Residue' class, which is a more general class that can be used to represent any type of residue in a molecular system. You could then specify the residue type as 'protein' when creating your 'Modeller' object, which should ensure that the appropriate parameters are used when simulating your system.

Here's an example of how you could create a protein residue using the 'Residue' class:

python:

Krzysztof S Stopka Thank you for the suggestion! I'll definitely look into it!

Oceanus Lingose To obtain the node label of fieldOutputs in an Abaqus odb file, utilize the mesh attribute of the FieldOutput object. The mesh property returns a Mesh object that contains information about the mesh's nodes and elements.

Here's how you can use it to obtain the node label for each field output value:

odb = session.openOdb(path='example.odb')

step = odb.steps['step1']

frame = step.frames[1]

field_output = frame.fieldOutputs['PEEQ']

mesh = field_output.mesh

# Iterate over each field output value

for value in field_output.values:

# Get the element label and integration point index

element_label = value['elementLabel']

integration_point = value['integrationPoint']

# Get the corresponding element

element = mesh.getElementFromLabel(element_label)

# Get the node labels for the element

node_labels = element.connectivity

# Get the node label for the integration point

node_label = node_labels[integration_point-1]

# Do something with the node label

print(node_label)

This code will display the node label for each field output value that corresponds to the element's integration point with the specified element label.

Md. Ibrahim Kholil It is determined by the program used for the analysis. If you're working with commercial finite element analysis software, you should be able to export results like volume data to Excel. Before exporting volume data for only the display group, make sure that only the desired items are chosen.

If you're working with a more general-purpose program like MATLAB, you may develop a script to extract the necessary data and export it to Excel. The script would have to access the data in the display group, extract the volume data for the selected items, and save it to an Excel file.

The following is a general summary of the actions you would need to do in MATLAB:

1. Load the display group's data into MATLAB.

2. Only the necessary elements should be chosen from the data.

3. Using the xlswrite function, save the specified data to an Excel file.

Here's an example of how the script may look:

% Load the data from the display group

data = load('display_group_data.mat');

% Select only the required elements

selected_data = data(data.element_id == desired_element_id);

% Write the selected data to an Excel file

xlswrite('volume_data.xlsx', selected_data);

Please keep in mind that the preceding code is only an example and may need to be updated depending on the unique data structure and format.

Oceanus Lingose The error message indicates that the keyword input "nodeLabel" is not acceptable for the "FieldOutput" object's "getSubset" function. You appear to be attempting to extract a subset of the field output data for specified nodes, and it appears that the "getSubset" function lacks the "nodeLabel" input.

Check the documentation for the program you're using (Abaqus, for example) to see whether there is another method or parameter that may be used to retrieve field output data for certain nodes. You might also try extracting the whole field output data and then manually picking the data for the exact nodes that interest you.

Oceanus Lingose The openpyxl library, which is a python library for reading and writing excel files, is one technique to save an excel file in Abaqus using python script. Here's an example of how it may be used to save an excel file:

import pandas as pd

data = pd.read_csv('data.csv')

for r in data_range(1,len(data)):

for c in data_range(1,len(data.columns)):

ws.cell(row = r,column = c,value = data.iloc[r-1,c-1])

You can alter the place where the file will be saved by using the save feature.

Make sure you have the openpyxl library installed; if not, install it in your command prompt or terminal by typing!pip install openpyxl or!conda install openpyxl.

Chinedu Nwaigwe Great, Interested, In fact, 'm pleased to learn you're working on open-source programs to solve integral equations. Publish your code to make your study more publicly available and to allow others to build on your work.

A partner with skills in code development and numerical analysis would be ideal. As you indicated, knowledge of Git and Python would be a must. However, further knowledge of numerical techniques for solving integral equations would be advantageous.

I am also interested. Will check. Thanks

Naveen Venkat Ram Kumar SWDOWN stands for surface downward shortwave radiation, which is the quantity of solar radiation received by the earth's surface. In Python, you can visualize this data from WRF output by using a library like xarray to read in the WRF output data and then a library like Matplotlib or Cartopy to construct the visualizations.

Here's some Python code that shows how to use xarray and Matplotlib to view SWDOWN data from WRF output:

import xarray as xr

import matplotlib.pyplot as plt

# Open the WRF output file using xarray

ds = xr.open_dataset('wrfout.nc')

# Extract the SWDOWN data from the dataset

swdown = ds['SWDOWN']

# Select a single time step to plot

swdown_slice = swdown.isel(time=0)

# Plot the SWDOWN data using Matplotlib

plt.figure()

swdown_slice.plot()

plt.show()

This code will use xarray to load the WRF output file, extract the SWDOWN data from the dataset, choose a single time step to plot, and finally plot the data with Matplotlib. You can change the code to plot additional time steps or to tweak the plot's look as desired.

I hope this was helpful! Please let me know if you have any queries or need any further information.

To troubleshoot this issue, you may want to try the following:

import numpy as np

from sklearn.neural_network import MLPRegressor

# Load the data from a file or other source

X = ... # input features (e.g., thickness of steel layer, concrete strength, etc.)

y = ... # target strength values

# Split the data into training and test sets

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Standardize the data (optional)

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)

X_test = scaler.transform(X_test)

# Define the model

model = MLPRegressor(hidden_layer_sizes=(100, 50, 25), max_iter=1000)

# Train the model

model.fit(X_train, y_train)

# Evaluate the model on the test set

score = model.score(X_test, y_test)

# Print the R^2 score

print(f'Test R^2 score: {score:.3f}')

Related to you query, I suggest you follow https://github.com/suyashtrivedi99/Concrete-Strength---Neural-Networks

You can't just put 'line' into your path string because it treats that as the literal string "line" and does not get the value of your variable line. You need to build the path string using an expression like (assuming the start and end portions are the same for each):

path = '/mnt/wwn-0x5000039a52203bb1-part1/run/backup_PD_swedd/PD_total/derivatives/nipype-1.8.0/' + line + '/ses-1/diffusion_pipeline/connectome_stage/compute_matrice'

@Qamar UI IsIam

Hi. can anybody help mi?

my abaqus error in optimization process is: Hook after chek point "Design cycle responses" faild. return code=1

Hello, I want to extract the same data during the simulation. Did you find any helpful sources for this? Any help would be appreciated!

Take a look at gmx traj tool

with the flag -com you will get the center of mass of the selected group. It most likely won't fall on top of another atom anyway, but you can look for the nearest one, if needed.

Best

Nicola

OpenMM sounds optimal for that, you can just load a pdb define a forcefield and do a minimization. Afterwardas the system has an energy. Basically the quickstart code that they have listed here (bottom of the page):

I hope this is what you were looking for.

Best regards,

Ben

Hi Amal,

Assuming that the affinity scores always begin from the 23rd row and are always in the second column.

So, here it goes

Hi Md. Ibrahim Kholil

You can read the data by a text file;

import numpy as np

x = np.loadtxt(x.txt, dtype = float)

The text file can include a vector (write the numbers then enter).

Then, you can extract value from the x and insert it into your materials properties.

Example -----------------------------------------------------------------------------------------------------

For instance, for an isotropic and linearly elastic material, you may use this function:

def MaterialIsotropic(MaterialName, E, v):

mdb.models['Model-1'].Material(name=MaterialName)

mdb.models['Model-1'].materials[MaterialName].Elastic(table=((E, v), ))

# Then execute it:

E=x[0];

v=x[1];

MaterialName = AmazingMetal

MaterialIsotropic(MaterialName, E, v)

# It is assumed that you have already imported all Abaqus modulus in your code