Pulse
/opt/hostedtoolcache/Python/3.12.13/x64/lib/python3.12/site-packages/fastcore/docscrape.py:259: UserWarning: potentially wrong underline length...
Example
-------- in
Function to create a temporary table in a database.
...
else: warn(msg)
/opt/hostedtoolcache/Python/3.12.13/x64/lib/python3.12/site-packages/fastcore/docscrape.py:259: UserWarning: Unknown section Example
else: warn(msg)create_table_from_csv
def create_table_from_csv(
cursor, connection, table_name:str, # Name of the table
table_query:str, # Table creation query
csv_path:str, # CSV path for table
):
Function to create a temporary table in a database.
connect_to_postgres
def connect_to_postgres(
postgres_connection_path:str='/hpc/users/foxb02/.ecg_postgres.yaml', # Path of yaml file with configurations for postgres database
):
Function to connect to a postgres database
generate_plot
def generate_plot(
waveform_array, figsize, dpi, save:bool=False, save_file_path:NoneType=None, return_figure:bool=False
):
Function to plot a
process_ecg
def process_ecg(
waveform_array:list, # Array/list of waveform integer values,
cutoff, # Length of array subset,
random_index:bool=True, # Indicator of whether to randomly subset the waveform at a random start index
random_cutoff:bool=False, # Indicator of whether to randomly generator a cutoff value
)->list: # Processed waveform array
High level function to process ecg data
subset_waveform
def subset_waveform(
waveform_array:list, # Array/list of waveform integer values
cutoff:int=5000, # Length of array subset
random_index:bool=False, # Indicator of whether to randomly subset the waveform at a random start index
random_cutoff:bool=False, # Indicator of whether to randomly generator a cutoff value
)->list: # subsetted waveform array
Function to take a subset of a waveform array
decode_waveform
def decode_waveform(
waveform_string:str, # Waveform string
)->list: # List of int16 waveform values
Function to convert a waveform string from a buffer
ECG_Model_TST
def ECG_Model_TST(
c_in, c_out, seq_len, n_layers:int=3, d_model:int=128, n_heads:int=16, d_ff:int=256, dropout:float=0.1,
act:str='gelu', compression_channels:int=32, compression_rate:int=100
):
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.
.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.
:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool
ECG_Model_TSAI
def ECG_Model_TSAI(
c_in, d_model, c_out, n_head, dim_ff, n_layers, compression_channels:int=32, compression_rate:int=100,
dropout:float=0.1, max_len:int=5000
):
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.
.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.
:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool
ECG_Model
def ECG_Model(
c_in, d_model, c_out, nhead, dim_ff, num_layers, compression_rate:int=100, dropout:float=0.1, max_len:int=5000
):
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.
.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.
:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool
PositionalEncoding
def PositionalEncoding(
d_model:int, dropout:float=0.1, max_len:int=5000
):
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.
.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.
:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool