### Install amk-tools and Dependencies

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/amk_tools.ipynb

Installs the amk-tools package and its dependencies from a GitHub repository. This setup is necessary before proceeding with network generation.

```python
%%capture
!apt update
!git clone https://github.com/dgarayr/amk_tools.git
%cd /content/amk_tools
!pip install -e .
%env PATH=".:/content/amk_tools/scripts:/opt/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin"
```

--------------------------------

### Install Packages from requirements.txt

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Installs all packages listed in a requirements.txt file.

```bash
pip install -r requirements.txt
```

--------------------------------

### Clone and Install AutoMeKin

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Clones the AutoMeKin repository, configures the installation, and installs it. It also sets up environment variables for AutoMeKin and its tools.

```python
%%capture
%cd /content/
!git clone https://github.com/emartineznunez/AutoMeKin.git
%cd /content/AutoMeKin
! autoreconf -i
!./configure --prefix=/opt/AutoMeKin
!make && make install
#We make use of the total number of processors
!sed -i 's@ignore=1@ignore=0@g' /opt/AutoMeKin/bin/utils.sh
%env PATH=".:/opt/amk_tools/scripts:/opt/AutoMeKin/bin:/opt/AutoMeKin/bin/HLscripts:/opt/AutoMeKin/bin/MOPAC_DEV:/opt/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin"
%env LIBRARY_PATH="/opt/AutoMeKin/lib:/opt/AutoMeKin/bin/MOPAC_DEV:/usr/local/cuda/lib64/stubs"
%env AMK=/opt/AutoMeKin
%env inter=0
%cd /content
!rm -rf /content/AutoMeKin
```

--------------------------------

### Trajectory Generation Example

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

This snippet demonstrates generating a simple trajectory for the robot arm. It requires defining start and end points and the number of steps.

```python
import numpy as np
from automekin2.trajectory import Trajectory

# Define start and end joint angles (example)
start_angles = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
end_angles = np.array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5])
num_steps = 100

# Initialize Trajectory object
trajectory_generator = Trajectory()

# Generate trajectory
joint_trajectory = trajectory_generator.generate_trajectory(start_angles, end_angles, num_steps)

print(f"Generated trajectory with {len(joint_trajectory)} steps.")
```

--------------------------------

### Docker Compose Up

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Starts services defined in a docker-compose.yml file.

```bash
docker-compose up -d
```

--------------------------------

### Python - Model Training (Example)

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

This snippet shows a basic example of training a machine learning model after data preprocessing. It assumes X_train and y_train are available.

```python
from sklearn.linear_model import LogisticRegression

# Initialize the model
model = LogisticRegression(max_iter=1000)

# Train the model
model.fit(X_train, y_train)

print("Model trained successfully.")
```

--------------------------------

### Install Package with Pip

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Installs a Python package using pip. Ensure the virtual environment is activated.

```bash
pip install <package-name>
```

--------------------------------

### Create a Dockerfile

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

A basic Dockerfile example to build a Python application image. Customize as needed.

```dockerfile
FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["python", "your_script.py"]
```

--------------------------------

### Install amk_tools

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Installs the 'amk_tools' package, which provides additional utilities for AutoMeKin. It clones the repository, installs the package in editable mode, and updates the PATH environment variable.

```python
# @title ##### _Installing_ ```amk_tools```
%%capture
%cd /opt/
!git clone https://github.com/dgarayr/amk_tools.git
%cd /opt/amk_tools
!pip install -e .
%env PATH=".:/opt/amk_tools/scripts:/opt/AutoMeKin/bin:/opt/AutoMeKin/bin/HLscripts:/opt/AutoMeKin/bin/MOPAC_DEV:/opt/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin"
```

--------------------------------

### Python - Model Training (Example)

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

This snippet shows a basic example of training a machine learning model using scikit-learn. Ensure data is preprocessed before using this snippet.

```python
from sklearn.linear_model import LogisticRegression

# Initialize and train the model
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

# Evaluate the model (example)
accuracy = model.score(X_test, y_test)
print(f"Model accuracy: {accuracy:.4f}")
```

--------------------------------

### Clone and Install AutoMeKin

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Clones the AutoMeKin repository, configures the build, and installs it to a specified prefix. It also optimizes shell scripts for parallel processing and sets environment variables for AutoMeKin.

```python
%%capture
%cd /content/
!git clone https://github.com/emartineznunez/AutoMeKin.git
%cd /content/AutoMeKin
! autoreconf -i
!./configure --prefix=/opt/AutoMeKin
!make && make install
#We make use of the total number of processors
!sed -i 's@ignore=1@ignore=0@g' /opt/AutoMeKin/bin/utils.sh
%env PATH=".:/opt/AutoMeKin/bin:/opt/AutoMeKin/bin/HLscripts:/opt/AutoMeKin/bin/MOPAC_DEV:/opt/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin"
%env LIBRARY_PATH="/opt/AutoMeKin/lib:/opt/AutoMeKin/bin/MOPAC_DEV:/usr/local/cuda/lib64/stubs"
%env AMK=/opt/AutoMeKin
%env inter=0
%cd /content
!rm -rf /content/AutoMeKin
```

--------------------------------

### List Installed Packages

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Lists all Python packages installed in the current environment.

```bash
pip freeze
```

--------------------------------

### Install AutoMeKin Tools and Dependencies

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Installs necessary system packages and the amk_tools Python package. This is a prerequisite for using AutoMeKin.

```python
%%capture
!apt update
!pip install --upgrade x3dase
!apt install bc gawk gcc gfortran parallel sqlite3 cm-super dvipng texlive-latex-extra texlive-latex-recommended
%cd /opt/
!git clone https://github.com/dgarayr/amk_tools.git
%cd /opt/amk_tools
!pip install -e .
```

--------------------------------

### Create a Simple Web Server

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Starts a basic HTTP server in the current directory. Useful for serving static files locally.

```python
import http.server
import socketserver

PORT = 8000
Handler = http.server.SimpleHTTPRequestHandler

with socketserver.TCPServer(("", PORT), Handler) as httpd:
    print(f"Serving at port {PORT}")
    httpd.serve_forever()
```

--------------------------------

### Install AutoMeKin Dependencies

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Installs necessary libraries and system packages for AutoMeKin. This includes ASE and PubChemPy for molecular handling, and various build tools and LaTeX packages.

```python
%%capture
!pip install ase pubchempy
!sudo apt update -y
!sudo apt install -y bc gawk gcc gfortran parallel sqlite3 cm-super dvipng texlive-latex-extra texlive-latex-recommended
```

--------------------------------

### Check Python Version

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Displays the installed Python version.

```bash
python --version
```

--------------------------------

### Basic Loop Example

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Illustrates a simple 'for' loop iterating over a list. Fundamental control flow structure.

```python
my_list = [1, 2, 3, 4, 5]
for item in my_list:
    print(item * 2)
```

--------------------------------

### Check Pip Version

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Displays the installed pip version.

```bash
pip --version
```

--------------------------------

### Basic Git Commit

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Stages changes and commits them with a message. Assumes Git is installed and initialized.

```bash
git add .
git commit -m "Your commit message here"
```

--------------------------------

### Perform Sentiment Analysis (Example)

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Example of performing sentiment analysis on text data. This snippet assumes a pre-trained model or library is available.

```python
# This is a placeholder for actual sentiment analysis code.
# Example using a hypothetical library:
# from sentiment_analyzer import SentimentAnalyzer
# analyzer = SentimentAnalyzer()
# df['sentiment'] = df['text_column'].apply(analyzer.predict)
print("Sentiment analysis placeholder executed.")
```

--------------------------------

### Working with Dictionaries: Getting Keys

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Returns a view object that displays a list of all the keys in the dictionary.

```python
my_dict = {'name': 'Alice', 'age': 30}
keys = my_dict.keys()
print(list(keys))
```

--------------------------------

### Load JSON Configuration

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Loads configuration settings from a JSON file. This is typically used for application setup.

```python
import json

with open("/content/drive/MyDrive/Colab Notebooks/automekin2/config/config.json", 'r') as f:
    config = json.load(f)
```

--------------------------------

### Python - Model Evaluation (Example)

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

This snippet demonstrates how to evaluate a trained model using the test set. It calculates accuracy and prints the result.

```python
from sklearn.metrics import accuracy_score

# Make predictions on the test set
y_pred = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy:.4f}")
```

--------------------------------

### Feature Engineering

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Creates new features from existing data. This example adds a 'day_of_week' feature.

```python
df['day_of_week'] = df['timestamp'].dt.dayofweek
print(df.head())
```

--------------------------------

### Working with Dictionaries: Getting Values

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Returns a view object that displays a list of all the values in the dictionary.

```python
my_dict = {'name': 'Alice', 'age': 30}
values = my_dict.values()
print(list(values))
```

--------------------------------

### Apply a Function Element-wise

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Applies a function to each element of a Series. This example converts strings to uppercase.

```python
df['string_column'].apply(lambda x: x.upper())
```

--------------------------------

### Trajectory Generation Example

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Generates a simple linear trajectory between two points. This snippet demonstrates basic trajectory planning. Requires 'numpy'.

```python
import numpy as np

def generate_linear_trajectory(start_point, end_point, num_steps):
    return np.linspace(start_point, end_point, num_steps).tolist()

start = [0, 0]
end = [1, 1]
trajectory = generate_linear_trajectory(start, end, 10)
print(f'Generated trajectory: {trajectory}')
```

--------------------------------

### Conditional Statement Example

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Shows a basic 'if-elif-else' conditional statement. Used for decision making in code.

```python
x = 10
if x > 15:
    print("x is greater than 15")
elif x > 5:
    print("x is greater than 5")
else:
    print("x is 5 or less")
```

--------------------------------

### Initializing AutoMeKin with a configuration file

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Demonstrates how to initialize the AutoMeKin class using a YAML configuration file. This is the primary way to set up the simulation environment.

```python
config_file = "config.yaml"
autom = AutoMeKin(config_file=config_file)
```

--------------------------------

### Working with Dictionaries: Getting Items

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Returns a view object that displays a list of a dictionary's key-value tuple pairs.

```python
my_dict = {'name': 'Alice', 'age': 30}
items = my_dict.items()
print(list(items))
```

--------------------------------

### Use a Context Manager

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Demonstrates the use of a context manager (e.g., 'with open(...)') for resource management. Ensures resources are properly acquired and released.

```python
# Example already shown with file operations (e.g., pd.read_csv, open)
print("Context manager usage demonstrated in file I/O examples.")
```

--------------------------------

### Open Diels-Alder Network Visualization

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Opens the generated network.html file for the Diels-Alder reaction by starting a local HTTP server and displaying the file. It first kills any existing process on port 8000.

```python
# @title _Open the file network.html_
!fuser -k 8000/tcp
%run_local_server
```

--------------------------------

### Get Joint Acceleration

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

This code demonstrates how to retrieve the current acceleration of a joint. This allows for monitoring the acceleration of individual joints.

```python
accel = chain.get_joint_acceleration(joint_index=0)

```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, Show Index Name, Show Column Names, and Show Column Types

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, index name, column names, and column types.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True, show_column_names=True, show_column_types=True)
```

--------------------------------

### Run Local HTTP Server and Display Network

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/amk_tools.ipynb

Launches a local HTTP server to serve the network.html file and embeds it within the notebook. This allows for interactive viewing of the reaction network.

```python
# @title ##### _Open the file network.html_
import IPython
from IPython.core.magic import register_line_magic
import subprocess

@register_line_magic
def run_local_server(line):
    handle = IPython.display.display(
            IPython.display.Pretty("Launching my server..."),
            display_id=True,
    )
    subprocess.Popen(['python', '-m', 'http.server'])
    shell = """
        (async () => {
            const url = new URL(await google.colab.kernel.proxyPort(8000, {'cache': true}));
            const iframe = document.createElement('iframe');
            iframe.src = url;
            iframe.setAttribute('width', '100%');
            iframe.setAttribute('height', '800');
            iframe.setAttribute('frameborder', 0);
            document.body.appendChild(iframe);
        })();
    """
    script = IPython.display.Javascript(shell)
    handle.update(script)
!fuser -k 8000/tcp
%run_local_server
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, Show Index Name, and Show Column Names

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, index name, and column names.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True, show_column_names=True)
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, Show Index Name, Show Column Names, Show Column Types, Show Memory Usage, Show Non Null Counts, Show Null Counts, Show Duplicate Counts, Show Unique Counts, Show Missing Counts, and Show Percent Counts

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, index name, column names, column types, memory usage, non-null counts, null counts, duplicate counts, unique counts, missing counts, and percent counts.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True, show_column_names=True, show_column_types=True, show_memory_usage=True, show_non_null_counts=True, show_null_counts=True, show_duplicate_counts=True, show_unique_counts=True, show_missing_counts=True, show_percent_counts=True)
```

--------------------------------

### Prepare Input Files for Molecular System

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Sets up a working directory and fetches molecular data from PubChem. It allows users to specify a molecule by various identifiers and saves its 3D coordinates to 'mol.xyz'. It also downloads and modifies a template input file.

```python
#@title Choose the default, formic acid (FA) molecule, or a new system.
%%capture

%cd /content
!rm -rf test && mkdir test
%cd /content/test

import pubchempy as pcp

#Building the molecule

#The default is formic acid, but you might want another one https://pubchem.ncbi.nlm.nih.gov/
sel = input('This example is for \'formic acid\'.Do you want to choose your own molecule (y/n)?: ')
if sel == 'n':
  molecule = 'formic acid'
  nm = 'name'
else:
  nm = input('Identifier type: cid, name, smiles, sdf, inchi, inchikey, or formula: ')
  molecule = input('Type your molecule here: ')

query = pcp.get_compounds(molecule,nm,record_type='3d')
m = query[0]
f = open('mol.xyz','w')
f.write(str(len(m.atoms)) + '\n\n')
for i,a in enumerate(m.atoms): f.write(m.elements[i]+' '+str(a.x)+' '+str(a.y)+' '+str(a.z)+'\n')
f.close()

#Fetching input file template
!curl -L https://github.com/emartineznunez/AutoMeKin/raw/main/examples/FA.dat -o mol.dat
!sed -i 's@FA@mol@;s@imagmin 200@imagmin 50@' mol.dat
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, Show Index Name, Show Column Names, Show Column Types, Show Memory Usage, and Show Non Null Counts

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, index name, column names, column types, memory usage, and non-null counts.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True, show_column_names=True, show_column_types=True, show_memory_usage=True, show_non_null_counts=True)
```

--------------------------------

### Setting initial joint positions

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Sets the initial joint positions for the robot. This is often required before starting a simulation or performing kinematic calculations.

```python
initial_joint_positions = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
autom.set_initial_joint_positions(initial_joint_positions=initial_joint_positions)
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, Show Index Name, Show Column Names, Show Column Types, Show Memory Usage, Show Non Null Counts, Show Null Counts, Show Duplicate Counts, Show Unique Counts, and Show Missing Counts

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, index name, column names, column types, memory usage, non-null counts, null counts, duplicate counts, unique counts, and missing counts.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True, show_column_names=True, show_column_types=True, show_memory_usage=True, show_non_null_counts=True, show_null_counts=True, show_duplicate_counts=True, show_unique_counts=True, show_missing_counts=True)
```

--------------------------------

### Get Body Acceleration

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

This snippet shows how to retrieve the linear acceleration of a rigid body in the kinematic chain. This is useful for dynamic analysis.

```python
accel = chain.get_body_acceleration(body_index=0)

```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, and Show Columns

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, and column names.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True)
```

--------------------------------

### Launch Local HTTP Server with IPython Magic

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Launches a local HTTP server to serve files, typically used for viewing HTML outputs. It also embeds an iframe to display the served content within the notebook.

```python
import IPython
from IPython.core.magic import register_line_magic
import subprocess

@register_line_magic
def run_local_server(line):
    handle = IPython.display.display(
            IPython.display.Pretty("Launching my server..."),
            display_id=True,
    )
    subprocess.Popen(['python', '-m', 'http.server'])
    shell = """
        (async () => {
            const url = new URL(await google.colab.kernel.proxyPort(8000, {'cache': true}));
            const iframe = document.createElement('iframe');
            iframe.src = url;
            iframe.setAttribute('width', '100%');
            iframe.setAttribute('height', '800');
            iframe.setAttribute('frameborder', 0);
            document.body.appendChild(iframe);
        })();
    """
    script = IPython.display.Javascript(shell)
    handle.update(script)
!fuser -k 8000/tcp
%run_local_server
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, Show Index Name, Show Column Names, Show Column Types, and Show Memory Usage

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, index name, column names, column types, and memory usage.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True, show_column_names=True, show_column_types=True, show_memory_usage=True)
```

--------------------------------

### Python - Custom Function Example

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

A simple Python function to illustrate custom logic within the project. This function takes two numbers and returns their sum.

```python
def add_numbers(a, b):
    """Adds two numbers and returns the result."""
    return a + b

result = add_numbers(5, 10)
print(f"The sum is: {result}")
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, Show Index Name, Show Column Names, Show Column Types, Show Memory Usage, Show Non Null Counts, Show Null Counts, Show Duplicate Counts, Show Unique Counts, Show Missing Counts, Show Percent Counts, Show Distinct Counts, Show Range Counts, Show Type Counts, and Show Category Counts

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, index name, column names, column types, memory usage, non-null counts, null counts, duplicate counts, unique counts, missing counts, percent counts, distinct counts, range counts, type counts, and category counts.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True, show_column_names=True, show_column_types=True, show_memory_usage=True, show_non_null_counts=True, show_null_counts=True, show_duplicate_counts=True, show_unique_counts=True, show_missing_counts=True, show_percent_counts=True, show_distinct_counts=True, show_range_counts=True, show_type_counts=True, show_category_counts=True)
```

--------------------------------

### Get Unique Values in a Column

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Returns an array of unique values present in 'column_name'.

```python
df['column_name'].unique()
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, Show Index Name, Show Column Names, Show Column Types, Show Memory Usage, Show Non Null Counts, Show Null Counts, and Show Duplicate Counts

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, index name, column names, column types, memory usage, non-null counts, null counts, and duplicate counts.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True, show_column_names=True, show_column_types=True, show_memory_usage=True, show_non_null_counts=True, show_null_counts=True, show_duplicate_counts=True)
```

--------------------------------

### Python - Data Loading and Preprocessing

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

This snippet demonstrates loading and preprocessing data using Python. It includes necessary imports and common data manipulation techniques.

```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline

# Load data
df = pd.read_csv('your_data.csv')

# Separate features and target
X = df.drop('target', axis=1)
y = df['target']

# Identify categorical and numerical features
categorical_features = X.select_dtypes(include=['object', 'category']).columns
numerical_features = X.select_dtypes(include=np.number).columns

# Create preprocessing pipelines for numerical and categorical features

# Numerical pipeline
numerical_transformer = Pipeline(steps=[
    ('scaler', StandardScaler())
])

# Categorical pipeline
categorical_transformer = Pipeline(steps=[
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
])

# Combine preprocessing steps
preprocessor = ColumnTransformer(
    transformers=[
        ('num', numerical_transformer, numerical_features),
        ('cat', categorical_transformer, categorical_features)
    ])

# Apply preprocessing
X_processed = preprocessor.fit_transform(X)

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_processed, y, test_size=0.2, random_state=42)

print("Data loaded and preprocessed successfully.")
print(f"Shape of X_train: {X_train.shape}")
print(f"Shape of X_test: {X_test.shape}")
```

--------------------------------

### Get DataFrame Index

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Returns the Index object representing the row labels of the DataFrame.

```python
index_labels = df.index
print(index_labels)
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, Show Index Name, Show Column Names, Show Column Types, Show Memory Usage, Show Non Null Counts, Show Null Counts, Show Duplicate Counts, and Show Unique Counts

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, index name, column names, column types, memory usage, non-null counts, null counts, duplicate counts, and unique counts.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True, show_column_names=True, show_column_types=True, show_memory_usage=True, show_non_null_counts=True, show_null_counts=True, show_duplicate_counts=True, show_unique_counts=True)
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, Show Index Name, Show Column Names, Show Column Types, Show Memory Usage, Show Non Null Counts, Show Null Counts, Show Duplicate Counts, Show Unique Counts, Show Missing Counts, Show Percent Counts, Show Distinct Counts, Show Range Counts, Show Type Counts, Show Category Counts, Show Order Counts, and Show Index Counts

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, index name, column names, column types, memory usage, non-null counts, null counts, duplicate counts, unique counts, missing counts, percent counts, distinct counts, range counts, type counts, category counts, order counts, and index counts.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True, show_column_names=True, show_column_types=True, show_memory_usage=True, show_non_null_counts=True, show_null_counts=True, show_duplicate_counts=True, show_unique_counts=True, show_missing_counts=True, show_percent_counts=True, show_distinct_counts=True, show_range_counts=True, show_type_counts=True, show_category_counts=True, show_order_counts=True, show_index_counts=True)
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, Show Index Name, Show Column Names, Show Column Types, Show Memory Usage, Show Non Null Counts, Show Null Counts, Show Duplicate Counts, Show Unique Counts, Show Missing Counts, Show Percent Counts, Show Distinct Counts, and Show Range Counts

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, index name, column names, column types, memory usage, non-null counts, null counts, duplicate counts, unique counts, missing counts, percent counts, distinct counts, and range counts.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True, show_column_names=True, show_column_types=True, show_memory_usage=True, show_non_null_counts=True, show_null_counts=True, show_duplicate_counts=True, show_unique_counts=True, show_missing_counts=True, show_percent_counts=True, show_distinct_counts=True, show_range_counts=True)
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, Show Index Name, Show Column Names, Show Column Types, Show Memory Usage, Show Non Null Counts, and Show Null Counts

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, index name, column names, column types, memory usage, non-null counts, and null counts.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True, show_column_names=True, show_column_types=True, show_memory_usage=True, show_non_null_counts=True, show_null_counts=True)
```

--------------------------------

### Python - Data Loading and Preprocessing

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

This snippet demonstrates loading and preprocessing data using Python. It includes necessary imports and common data manipulation steps.

```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer

# Load the dataset
df = pd.read_csv('your_dataset.csv')

# Separate features and target
X = df.drop('target_column', axis=1)
y = df['target_column']

# Identify categorical and numerical features
numerical_features = X.select_dtypes(include=np.number).columns.tolist()
categorical_features = X.select_dtypes(exclude=np.number).columns.tolist()

# Create preprocessing pipelines for numerical and categorical features

# Numerical pipeline: impute missing values with mean, then scale
numerical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

# Categorical pipeline: impute missing values with a constant, then one-hot encode
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
])

# Combine preprocessing steps using ColumnTransformer
preprocessor = ColumnTransformer(
    transformers=[
        ('num', numerical_transformer, numerical_features),
        ('cat', categorical_transformer, categorical_features)
    ])

# Apply preprocessing to the features
X_processed = preprocessor.fit_transform(X)

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_processed, y, test_size=0.2, random_state=42)

print("Data loaded and preprocessed successfully.")
print(f"Shape of X_train: {X_train.shape}")
print(f"Shape of X_test: {X_test.shape}")
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, Show Index Name, Show Column Names, Show Column Types, Show Memory Usage, Show Non Null Counts, Show Null Counts, Show Duplicate Counts, Show Unique Counts, Show Missing Counts, Show Percent Counts, Show Distinct Counts, Show Range Counts, and Show Type Counts

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, index name, column names, column types, memory usage, non-null counts, null counts, duplicate counts, unique counts, missing counts, percent counts, distinct counts, range counts, and type counts.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True, show_column_names=True, show_column_types=True, show_memory_usage=True, show_non_null_counts=True, show_null_counts=True, show_duplicate_counts=True, show_unique_counts=True, show_missing_counts=True, show_percent_counts=True, show_distinct_counts=True, show_range_counts=True, show_type_counts=True)
```

--------------------------------

### Generate Visualization Files

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Creates a zip archive of the calculation results and generates visualization files, including 'network.html' for visualizing the reaction network. It also organizes these files into an 'HTML' directory.

```python
# @title ##### _Generating_ ```FINAL_LL_mol.zip``` _and_ ```network.html```
%cd /content/test
!zip -r FINAL_LL_mol.zip FINAL_LL_mol

from os import path
if path.isfile('tsdirLL_mol/KMC/starting_minimum'): tag = !awk 'NR=1{print $1}' tsdirLL_mol/KMC/starting_minimum
else: tag = [1]
!amk_gen_view.py FINAL_LL_mol RXNet.cg --b --paths MIN{tag[0]}
!mkdir HTML
!mv network.html HTML
%cd /content/test/HTML
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, Show Index, Show Columns, and Show Index Name

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, index information, column names, and index name.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True, show_columns=True, show_index_name=True)
```

--------------------------------

### Apply a Function to a Column

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Applies a lambda function to each element of 'column_name'. This example squares each value.

```python
df['column_name'].apply(lambda x: x**2)
```

--------------------------------

### Python - Basic List Manipulation

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin2.ipynb

Demonstrates basic list creation and manipulation in Python. Useful for initializing and modifying data structures.

```python
import numpy as np

def get_kinematic_chain(robot_name):
    if robot_name == "UR5e":
        # UR5e robot parameters
        a_vals = [0, -0.425, -0.392, 0, 0, 0]
        d_vals = [0.089159, 0, 0, 0.109159, 0.09465, 0.0823]
        alpha_vals = [np.pi/2, np.pi, np.pi/2, -np.pi/2, np.pi/2, 0]
        offset = [0, np.pi/2, 0, 0, 0, 0]
        
        return a_vals, d_vals, alpha_vals, offset
    else:
        raise ValueError("Unsupported robot name")

```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, Show Nulls, Show Duplicates, Show Unique, Show Missing, Show Percent, Show Distinct, Show Range, Show Type, Show Category, Show Order, and Show Index

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, null counts, duplicate counts, unique counts, missing value counts, percentages, distinct value counts, value ranges, column types, category information, order, and index information.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True, show_duplicates=True, show_unique=True, show_missing=True, show_percent=True, show_distinct=True, show_range=True, show_type=True, show_category=True, show_order=True, show_index=True)
```

--------------------------------

### Get DataFrame Info with Specific Data Types, Memory Usage, Verbose Output, Show Counts, Show Coercion, and Show Nulls

Source: https://github.com/emartineznunez/automekin/blob/main/notebooks/AutoMeKin.ipynb

Displays comprehensive information about columns of a specific data type, including memory usage, verbose details, non-null counts, coercion information, and null counts.

```python
df.info(dtype='object', memory_usage='deep', verbose=True, show_counts=True, show_coercion=True, show_nulls=True)
```