delvendahl / miniml Goto Github PK

Hi dear sir/madam,

Thanke you for your work and i am trying to use miniML to analyze mEPSC data.
I have got problem in installation miniML. It goes well when i use pip to install miniML. But jupyterlab failed to import this module as :

I have checked the installed document in my conda package, it show as :

Do you have any idea on this issue?

Thank you very much!

Jinming Zhang

Hello, I am wondering if there is a way to run a portion of a file and not the whole file? Some of my recordings have weird noise at the beginning or I'll start to loose a cell at the end and only want to use the beginning half of the recording. It would be nice to either be able to crop a recording or tell miniML to only look at a portion of the recording. Do you know of a way to do this?

Thanks for developing such a useful model. We recorded sIPSCs of pyramidal neurons at a holding potential of 0 mV, so the currents are positive. Can I use miniML to detect sIPSCs, or should I create a transfer learning model? Thanks.

Hi,
I was able to smoothly run the code and save the file with the template method. However, when the same input file is processed with the gradient method, I am unable to save it. This is the error message I get:

ValueError Traceback (most recent call last)
Cell In[22], line 2
1 # Save the result
----> 2 x = np.array(events)
3 y = np.array(scores)
4 indices = np.array(idx)

ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (303,) + inhomogeneous part.

Can you please help me solve this issue?

I am currently following your tutorial and trying it out on one of my recordings. I successfully loaded the .abf file, but when I try to initialize the EventDetection object, I get the following error:

TypeError: Could not locate class 'LSTM'. Make sure custom classes are decorated with `@keras.saving.register_keras_serializable()`. Full object config: {'class_name': 'LSTM', 'config': {'name': 'lstm', 'trainable': True, 'dtype': 'float32', 'return_sequences': False, 'return_state': False, 'go_backwards': False, 'stateful': False, 'unroll': False, 'time_major': False, 'units': 96, 'activation': 'tanh', 'recurrent_activation': 'sigmoid', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'recurrent_initializer': {'class_name': 'Orthogonal', 'config': {'gain': 1.0, 'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'unit_forget_bias': True, 'kernel_regularizer': None, 'recurrent_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'recurrent_constraint': None, 'bias_constraint': None, 'dropout': 0.2, 'recurrent_dropout': 0.0, 'implementation': 2}

Here is the code I am using:

import sys
sys.path.append('./core/')
from miniML import MiniTrace, EventDetection

filepath = '/Users/ss/Desktop/50-59_PhD/53_Data_analyis/peter test/abf_files/24510005.abf'
scaling = 1e12
unit = 'pA'

# Load data from .abf file
trace = MiniTrace.from_axon_file(filepath=filepath, scaling=scaling, unit=unit)

win_size = 600
stride = int(win_size / 30)
direction = 'negative'

detection = EventDetection(
    data=trace,
    model_path='/Users/ss/Desktop/50-59_PhD/53_Data_analyis/miniML/model/GC_lstm_model.h5',
    window_size=win_size,
    model_threshold=0.5,
    batch_size=512,
    event_direction=direction,
    compile_model=True
)

Could you please help me understand what might be causing this error and how to resolve it?

Thank you!