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from .shape_dataset import ShapeDataset
from ..scaler.max_abs_scaler import MaxAbsScaler
def import_10x(
filename,
magnification=10,
batch_size=None,
shuffle=False,
val_split=0.0,
scaler=None,
sort_columns=None,
):
"""# Import a 10x Image Dataset
Pixel-to-micron: ???"""
magnification = 10
dataset = ShapeDataset(
filename,
batch_size=batch_size,
shuffle=shuffle,
pre_scale=magnification,
val_split=val_split,
scaler=scaler,
sort_columns=sort_columns,
)
return dataset
def import_20x(
filename,
magnification=10,
batch_size=None,
shuffle=False,
val_split=0.0,
scaler=None,
sort_columns=None,
):
"""# Import a 20x Image Dataset
Pixel-to-micron: ???"""
magnification = 20
dataset = ShapeDataset(
filename,
batch_size=batch_size,
shuffle=shuffle,
pre_scale=magnification,
val_split=val_split,
scaler=scaler,
sort_columns=sort_columns,
)
return dataset
def import_dataset(
filename,
magnification,
batch_size=None,
shuffle=False,
val_split=0.0,
scaler=None,
sort_columns=None,
):
"""# Import a dataset
Requires a magnificaiton to be specified"""
dataset = ShapeDataset(
filename,
pre_scale=magnification,
batch_size=batch_size,
shuffle=shuffle,
val_split=val_split,
scaler=scaler,
sort_columns=sort_columns,
)
return dataset
def import_full_dataset(fname, magnification=20, scaler=None):
"""# Import a Full Dataset
If a classification file exists(.txt with a 'Class' header and 'frame','cellnumber' headers), also import it"""
from os.path import isfile
import pandas as pd
import numpy as np
cfname = fname
tfname = cfname[:-3] + "txt"
columns = [
"frame",
"cellnumber",
"x-cent",
"y-cent",
"actinedge",
"filopodia",
"bleb",
"lamellipodia",
]
if isfile(tfname):
dataset = import_dataset(cfname, magnification=magnification, scaler=scaler)
class_df = np.loadtxt(tfname, skiprows=1)
class_df = pd.DataFrame(class_df, columns=columns)
full_df = pd.merge(
dataset.dataframe,
class_df,
left_on=["Frames", "CellNum"],
right_on=["frame", "cellnumber"],
)
full_df["Class"] = np.argmax(
class_df[["actinedge", "filopodia", "bleb", "lamellipodia"]].to_numpy(),
axis=-1,
)
dataset.labels = full_df["Class"].to_numpy()
else:
dataset = import_dataset(cfname, magnification=magnification, scaler=scaler)
full_df = dataset.dataframe
dataset.dataframe = full_df
dataset.filter_off()
return dataset
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