Data Analysis¶

analysis/
├── get_clmf_data.py    # Load and process CLMF data
├── plot_clmf.py        # Generate CLMF figures
└── plot_clnf.py        # Generate CLNF figures

import helper as clh

# Load configuration from session
cfgDict = clh.GetConfigValues('/path/to/session/')

dff = (image - runningAvg) / runningAvg
dffCorrected = dffGreen - dffBlue  # Ratiometric correction

# Plot ROI activity over time
clh.plt_roi_activity(mouse_id, data_dir)

# Plot threshold crossings
clh.plt_threshold_crossings(mouse_id, data_dir)

# Plot spontaneous thresholds
clh.plt_spontaneous_thresholds(mouse_id, data_dir)

frame   time    roi_dff freq    rew_threshold   reward  trial   audio   lick

import pandas as pd

# Load trial log
df = pd.read_csv('/path/to/session/log.txt', sep='\t')

# Access pose data
pose_cols = [col for col in df.columns if '_x' in col or '_y' in col]

import math

# Calculate speed between frames
speed = math.dist(prev_pos, curr_pos)

# In plot_clmf.py
import matplotlib.pyplot as plt

# Plot trial outcomes
plt.figure()
plt.plot(df['trial'], df['reward'])

# Plot speed over time
plt.figure()
plt.plot(df['cj_speed'])

frame   time    cj_speed    freq    reward  trial   audio   lick    pose_x  pose_y  likelihood...

processed_data/
├── clmf_kld_df.csv
├── clmf_sessions_df.csv
├── clmf_trials_df.csv
└── clnf_sessions_df.csv

import pandas as pd
import matplotlib.pyplot as plt

# Load summary data
df = pd.read_csv('processed_data/clnf_sessions_df.csv')

# Plot reward rate over sessions
plt.figure(figsize=(10, 6))
for mouse in df['mouse_id'].unique():
    mouse_data = df[df['mouse_id'] == mouse]
    plt.plot(mouse_data['session'], mouse_data['rewards'], label=mouse)

plt.xlabel('Session')
plt.ylabel('Total Rewards')
plt.legend()
plt.title('Learning Curve')
plt.show()

# Calculate rewards per minute
sess_dur_min = duration / 60
reward_rate = total_rewards / sess_dur_min

# Find threshold for target reward rate
def find_threshold(dff_values, target_rate):
    for thresh in np.arange(0, 0.2, 0.001):
        rate = sum(dff_values > thresh) / sess_dur_min
        if rate <= target_rate:
            return thresh
    return None

df.to_csv('output.csv', index=False)

import tables

with tables.open_file('output.hdf5', mode='w') as f:
    f.create_earray(f.root, 'data', obj=array)