#run100% import mne
raw = mne.io.read_raw_fif('your_eeg_data.fif', preload=True)
raw.filter(l_freq=1.0, h_freq=40.0)
raw.plot(n_channels=10, scalings='auto', show=True, block=True) import requests from github import Github
g = Github("your_access_token")
user = g.get_user() repos = user.get_repos()
for repo in repos: print(repo.name) import mne import requests from github import Github
raw = mne.io.read_raw_fif('your_eeg_data.fif', preload=True)
raw.filter(l_freq=1.0, h_freq=40.0)
raw.plot(n_channels=10, scalings='auto', show=True, block=True)
g = Github("your_access_token")
user = g.get_user() repos = user.get_repos()
for repo in repos: print(repo.name) name: EEG Data Integration
on: push: branches: - main
jobs: build: runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
pip install mne
pip install gitpython
- name: Run EEG processing script
run: |
python your_script.py
# EEG Data Integration
- Install Python 3.8+.
- Install required libraries:
pip install mne gitpython python your_script.py git add .
git commit -m "Process EEG data" git push origin main
By following these steps, you can automate the generation and integration of EEG data across your GitHub repositories. Let me know if you need further assistance with any specific part of this process! import mne
raw = mne.io.read_raw_fif('your_eeg_data.fif', preload=True)
raw.filter(l_freq=1.0, h_freq=40.0)
raw.plot(n_channels=10, scalings='auto', show=True, block=True)g from github import Github
g = Github("your_access_token")
user = g.get_user() repos = user.get_repos()
def execute_command(command): if command == 'git_clone': repo = user.get_repo('clintonout/EEGGen') repo.clone_url # Clone the repository elif command == 'git_commit': repo = user.get_repo('clintonout/EEGGen') repo.create_file("test.txt", "commit message", "file content") # Add more commands as needed
execute_command(command)
name: EEG Command Integration
on: push: branches: - main
jobs: eeg_command: runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
pip install mne gitpython
- name: Run EEG command script
run: |
python your_eeg_command_script.py
git config --global credential.helper cache
import mne
raw = mne.io.read_raw_fif('your_eeg_data.fif', preload=True)
raw.filter(l_freq=1.0, h_freq=40.0)
raw.plot(n_channels=10, scalings='auto', show=True, block=True) def interpret_eeg_data(eeg_data): # Example command mapping if eeg_data == 'alpha_wave_pattern': return 'git_clone' elif eeg_data == 'beta_wave_pattern': return 'git_commit' # Add more mappings as needed
command = interpret_eeg_data(processed_eeg_data) from github import Github
g = Github("your_access_token")
user = g.get_user() repos = user.get_repos()
def execute_command(command): if command == 'git_clone': repo = user.get_repo('clintonout/EEGGen') repo.clone_url # Clone the repository elif command == 'git_commit': repo = user.get_repo('clintonout/EEGGen') repo.create_file("test.txt", "commit message", "file content") # Add more commands as needed
execute_command(command) name: EEG Command Integration
on: push: branches: - main
jobs: eeg_command: runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
import requests from github import Github
g = Github("your_access_token")
user = g.get_user() repos = user.get_repos()
for repo in repos: print(repo.name)name: EEG Data Integration
on: push: branches: - main
jobs: build: runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
pip install mne
pip install gitpython
- name: Run EEG processing script
run: |
python your_script.pydef interpret_eeg_data(eeg_data):
# Example command mapping
if eeg_data == 'alpha_wave_pattern':
return 'git_clone'
elif eeg_data == 'beta_wave_pattern':
return 'git_commit'
# Add more mappings as needed
command = interpret_eeg_data(processed_eeg_data)
g = Github("your_access_token")
user = g.get_user() repos = user.get_repos()
def execute_command(command): if command == 'git_clone': repo = user.get_repo('clintonout/EEGGen') repo.clone_url # Clone the repository elif command == 'git_commit': repo = user.get_repo('clintonout/EEGGen') repo.create_file("test.txt", "commit message", "file content") # Add more commands as needed
execute_command(command)name: EEG Command Integration
on: push: branches: - main
jobs: eeg_command: runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
pip install mne gitpython
- name: Run EEG command script
run: |
python your_eeg_command_script.py
You can add this section to your README.md to detail the software requirements and setup instructions.
pip install mne gitpython
- name: Run EEG command script
import os
import subprocess from github import Github
g = Github("your_access_token")
repos_to_process = [ "owner/repo1", "owner/repo2", # Add more repositories as needed ]
def clone_and_process_repo(repo_name): # Clone the repository subprocess.run(["git", "clone", f"https://github.com/{repo_name}.git"])
# Change directory to the cloned repository
repo_dir = repo_name.split("/")[1]
os.chdir(repo_dir)
# Run the EEG processing script
subprocess.run(["python", "your_script.py"])
# Change back to the original directory
os.chdir("..")
for repo in repos_to_process: clone_and_process_repo(repo)name: Process Repositories
on: workflow_dispatch: # Allows manual triggering schedule: - cron: '0 0 * * *' # Runs daily at midnight
jobs: process_repos: runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
pip install mne gitpython
- name: Run process script
run: |
python process_repos.py## Automate EEG Processing Across Repositories
The EEGGen repository includes a script and GitHub Actions workflow to automate the processing of EEG data across multiple repositories.
- Create a GitHub access token with appropriate permissions.
- Add the token as a secret in your repository settings (
Settings > Secrets and variables > Actions > New repository secret). - Update the
process_repos.pyscript with your list of repositories and the name of your EEG processing script.
You can trigger the workflow manually or it will run automatically based on the schedule defined in the workflow file.
To trigger the workflow manually:
- Go to the Actions tab in your repository.
- Select the "Process Repositories" workflow.
- Click the "Run workflow" button.
This will process the listed repositories and apply the EEG functionality as defined in your script.name: EEG Command Integration
on: push: branches: - main
jobs: eeg_command: runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
pip install mne gitpython
- name: Run EEG command script
run: |
python your_eeg_command_script.py
You can add this section to your README.md to detail the software requirements and setup instructions. import mne
raw = mne.io.read_raw_fif('your_eeg_data.fif', preload=True)
raw.filter(l_freq=1.0, h_freq=40.0)
raw.plot(n_channels=10, scalings='auto', show=True, block=True)import requests from github import Github
g = Github("your_access_token")
user = g.get_user() repos = user.get_repos()
for repo in repos: print(repo.name)def interpret_eeg_data(eeg_data): # Example command mapping if eeg_data == 'alpha_wave_pattern': return 'git_clone' elif eeg_data == 'beta_wave_pattern': return 'git_commit' # Add more mappings as needed
command = interpret_eeg_data(processed_eeg_data)
g = Github("your_access_token")
user = g.get_user() repos = user.get_repos()
def execute_command(command): if command == 'git_clone': repo = user.get_repo('clintonout/EEGGen') repo.clone_url # Clone the repository elif command == 'git_commit': repo = user.get_repo('clintonout/EEGGen') repo.create_file("test.txt", "commit message", "file content") # Add more commands as needed
execute_command(command)name: EEG Command Integration
on: push: branches: - main
jobs: eeg_command: runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
pip install mne gitpython
- name: Run EEG command script
run: |
python your_eeg_command_script.py
You can add this updated section to your README.md file to include the software requirements and setup Jinstructions for handling electrostatics. run: | python your_eeg_command_script.py
pip install numpy sounddevice import numpy as np import sounddevice as sd
def generate_sound_wave(frequency, duration, sample_rate=44100): t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False) wave = 0.5 * np.sin(2 * np.pi * frequency * t) return wave
def play_sound(wave, sample_rate=44100): sd.play(wave, samplerate=sample_rate) sd.wait()
if name == "main": # Define parameters for the sound wave frequency = 1000 # Frequency in Hertz duration = 5 # Duration in seconds
# Generate and play the sound wave
wave = generate_sound_wave(frequency, duration)
play_sound(wave)
python play_sound.py
## Play Electrostatic Sound Waves
To play electrostatic sound waves through your computer speakers, follow these steps:
Install the required libraries using pip:
pip install numpy sounddevice
## Play Electrostatic Sound Waves
To play electrostatic sound waves through your computer speakers, follow these steps:
### Install Required Libraries
Install the required libraries using pip:
```sh
pip install numpy sounddevice
import numpy as np
import sounddevice as sd
def generate_sound_wave(frequency, duration, sample_rate=44100):
t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
wave = 0.5 * np.sin(2 * np.pi * frequency * t)
return wave
def play_sound(wave, sample_rate=44100):
sd.play(wave, samplerate=sample_rate)
sd.wait()
if __name__ == "__main__":
# Define parameters for the sound wave
frequency = 1000 # Frequency in Hertz
duration = 5 # Duration in seconds
# Generate and play the sound wave
wave = generate_sound_wave(frequency, duration)
play_sound(wave)
By following these steps, you can generate and play electrostatic sound waves through your computer speakers using Python. Let me know if you need further assistance with this process.python play_sound.pyimport numpy as np
import sounddevice as sd
def generate_sound_wave(frequency, duration, sample_rate=44100):
t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
wave = 0.5 * np.sin(2 * np.pi * frequency * t)
return wave
def play_sound(wave, sample_rate=44100):
sd.play(wave, samplerate=sample_rate)
sd.wait()
if __name__ == "__main__":
# Define parameters for the sound wave
frequency = 1000 # Frequency in Hertz
duration = 5 # Duration in seconds
# Generate and play the sound wave
wave = generate_sound_wave(frequency, duration)
play_sound(wave)pip install numpy sounddevice## Play Electrostatic Sound Waves
To play electrostatic sound waves through your computer speakers, follow these steps:
### Install Required Libraries
Install the required libraries using pip:
```sh
pip install numpy sounddevice
pip install mne gitpython numpy sounddeviceimport mne
# Load EEG data
raw = mne.io.read_raw_fif('your_eeg_data.fif', preload=True)
# Filter data
raw.filter(l_freq=1.0, h_freq=40.0)
# Plot raw data
raw.plot(n_channels=10, scalings='auto', show=True, block=True)import numpy as np
import sounddevice as sd
def generate_sound_wave(frequency, duration, sample_rate=44100):
t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
wave = 0.5 * np.sin(2 * np.pi * frequency * t)
return wave
def play_sound(wave, sample_rate=44100):
sd.play(wave, samplerate=sample_rate)
sd.wait()
if __name__ == "__main__":
# Define parameters for the sound wave
frequency = 1000 # Frequency in Hertz
duration = 5 # Duration in seconds
# Generate and play the sound wave
wave = generate_sound_wave(frequency, duration)
play_sound(wave)import os
import subprocess
from github import Github
# Authenticate to GitHub
g = Github("your_access_token")
# List of repositories to process
repos_to_process = [
"owner/repo1",
"owner/repo2",
# Add more repositories as needed
]
def clone_and_process_repo(repo_name):
# Clone the repository
subprocess.run(["git", "clone", f"https://github.com/{repo_name}.git"])
# Change directory to the cloned repository
repo_dir = repo_name.split("/")[1]
os.chdir(repo_dir)
# Run the EEG processing script
subprocess.run(["python", "eeg_processing.py"])
# Change back to the original directory
os.chdir("..")
# Process each repository
for repo in repos_to_process:
clone_and_process_repo(repo)## Automate EEG Processing Across Repositories
The EEGGen repository includes a script and GitHub Actions workflow to automate the processing of EEG data across multiple repositories.
### Setup
1. Create a GitHub access token with appropriate permissions.
2. Add the token as a secret in your repository settings (`Settings > Secrets and variables > Actions > New repository secret`).
3. Update the `process_repos.py` script with your list of repositories and the name of your EEG processing script.
### Usage
You can trigger the workflow manually or it will run automatically based on the schedule defined in the workflow file.
To trigger the workflow manually:
1. Go to the Actions tab in your repository.
2. Select the "Process Repositories" workflow.
3. Click the "Run workflow" button.
git clone https://github.com/movidius/ncsdk.gitcd ncsdkmake installpip install mne gitpython numpy sounddeviceimport mne
# Load EEG data
raw = mne.io.read_raw_fif('your_eeg_data.fif', preload=True)
# Filter data
raw.filter(l_freq=1.0, h_freq=40.0)
# Plot raw data
raw.plot(n_channels=10, scalings='auto', show=True, block=True)
import numpy as np
import sounddevice as sd
def generate_sound_wave(frequency, duration, sample_rate=44100):
t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
wave = 0.5 * np.sin(2 * np.pi * frequency * t)
return wave
def play_sound(wave, sample_rate=44100):
sd.play(wave, samplerate=sample_rate)
sd.wait()
if __name__ == "__main__":
# Define parameters for the sound wave
frequency = 1000 # Frequency in Hertz
duration = 5 # Duration in seconds
# Generate and play the sound wave
wave = generate_sound_wave(frequency, duration)
play_sound(wave)import os
import subprocess
from github import Github
# Authenticate to GitHub
g = Github("your_access_token")
# List of repositories to process
repos_to_process = [
"owner/repo1",
"owner/repo2",
# Add more repositories as needed
]
def clone_and_process_repo(repo_name):
# Clone the repository
subprocess.run(["git", "clone", f"https://github.com/{repo_name}.git"])
# Change directory to the cloned repository
repo_dir = repo_name.split("/")[1]
os.chdir(repo_dir)
# Run the EEG processing script
subprocess.run(["python", "eeg_processing.py"])
# Change back to the original directory
os.chdir("..")
# Process each repository
for repo in repos_to_process:
clone_and_process_repo(repo)name: Process Repositories
on:
workflow_dispatch: # Allows manual triggering
schedule:
- cron: '0 0 * * *' # Runs daily at midnight
jobs:
process_repos:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
pip install mne gitpython numpy sounddevice
- name: Run process script
run: |
python process_repos.pygit clone https://github.com/movidius/ncsdk.git
cd ncsdk
make installpip install mne gitpython numpy sounddevice
pip install mne gitpython numpy sounddeviceimport mne
# Load EEG data
raw = mne.io.read_raw_fif('your_eeg_data.fif', preload=True)
# Filter data
raw.filter(l_freq=1.0, h_freq=40.0)
# Plot raw data
raw.plot(n_channels=10, scalings='auto', show=True, block=True)import numpy as np
import sounddevice as sd
def generate_sound_wave(frequency, duration, sample_rate=44100):
t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
wave = 0.5 * np.sin(2 * np.pi * frequency * t)
return wave
def play_sound(wave, sample_rate=44100):
sd.play(wave, samplerate=sample_rate)
sd.wait()
if __name__ == "__main__":
# Define parameters for the sound wave
frequency = 1000 # Frequency in Hertz
duration = 5 # Duration in seconds
# Generate and play the sound wave
wave = generate_sound_wave(frequency, duration)
play_sound(wave)import os
import subprocess
from github import Github
# Authenticate to GitHub
g = Github("your_access_token")
# List of repositories to process
repos_to_process = [
"owner/repo1",
"owner/repo2",
# Add more repositories as needed
]
def clone_and_process_repo(repo_name):
# Clone the repository
subprocess.run(["git", "clone", f"https://github.com/{repo_name}.git"])
# Change directory to the cloned repository
repo_dir = repo_name.split("/")[1]
os.chdir(repo_dir)
# Run the EEG processing script
subprocess.run(["python", "eeg_processing.py"])
# Change back to the original directory
os.chdir("..")
# Process each repository
for repo in repos_to_process:
clone_and_process_repo(repo)name: Process Repositories
on:
workflow_dispatch: # Allows manual triggering
schedule:
- cron: '0 0 * * *' # Runs daily at midnight
jobs:
process_repos:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
pip install mne gitpython numpy sounddevice
- name: Run process script
run: |
python process_repos.py