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Update prediction_tools.py #169

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14 changes: 6 additions & 8 deletions maxatac/utilities/prediction_tools.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
import logging

import numpy as np
import pandas as pd
import pybedtools
Expand Down Expand Up @@ -31,6 +30,7 @@ def write_predictions_to_bigwig(df: pd.DataFrame,
output_filename: str,
chrom_sizes_dictionary: dict,
chromosomes: list,
max_zooms: int,
agg_mean: bool = True
):
"""Write the predictions dataframe into a bigwig file
Expand All @@ -40,10 +40,11 @@ def write_predictions_to_bigwig(df: pd.DataFrame,
output_filename (str): The output bigwig filename
chrom_sizes_dictionary (dict): A dictionary of chromosome sizes used to form the bigwig file
chromosomes (list): A list of chromosomes that you are predicting in
agg_mean (bool, optional): use aggregation method of mean. Defaults to True.
max_zooms (int, optional): The desired number of zoom levels to be retained in the output bigWig file. Defaults to 5.
agg_mean (bool, optional): Use aggregation method of mean. Defaults to True.

Returns:
Writes a bigwig file
Writes a bigWig file

Example:

Expand All @@ -63,7 +64,7 @@ def write_predictions_to_bigwig(df: pd.DataFrame,
header = [(x, chrom_sizes_dictionary[x]) for x in chromosomes]

# Add header to bigwig
data_stream.addHeader(header)
data_stream.addHeader(header, maxZooms = max_zooms)

for chromosome in chromosomes:
# Create a tmp df from the predictions dataframe for each chrom
Expand All @@ -76,10 +77,9 @@ def write_predictions_to_bigwig(df: pd.DataFrame,
data_stream.addEntries(chroms=tmp_chrom_df["chr"].tolist(),
starts=tmp_chrom_df["start"].tolist(),
ends=tmp_chrom_df["stop"].tolist(),
values=tmp_chrom_df["score"].tolist()
values=tmp_chrom_df["score"].astype(np.float16).tolist()
)


def import_prediction_regions(bed_file: str,
chromosomes: list,
chrom_sizes_dictionary: dict,
Expand Down Expand Up @@ -220,7 +220,6 @@ def create_prediction_regions(chromosomes: list,

return df


class PredictionDataGenerator(tf.keras.utils.Sequence):
def __init__(self,
signal,
Expand Down Expand Up @@ -327,7 +326,6 @@ def __get_region_values__(self, roi_pool):

return np.array(inputs_batch)


def make_stranded_predictions(roi_pool: pd.DataFrame,
signal: str,
sequence: str,
Expand Down