How to use diff_tables method in dbt-osmosis

Best Python code snippet using dbt-osmosis_python

overlap.py

Source:overlap.py

1import pandas as pd2import yaml3from glob import glob4from os import path5from functools import reduce6def get_tables_array(tool_selection, output_dir, comparison):7    tables = []8    for t in tool_selection:9        if t == "miso":10            # TODO: Implement11            pass12        if t == "rmats":13            diff_tables = glob(14                path.join(15                    output_dir, "rmats", "results", comparison, "*.merged.w_coord.tsv"16                )17            )18            tables += diff_tables19        elif t == "whippet":20            diff_tables = glob(21                path.join(output_dir, "whippet", "delta", comparison, "*.diff.gz")22            )23            tables += diff_tables24    return tables25def format_cols(df_array, comparison):26    new_df_array = []27    condition_A, condition_B = comparison.split("_vs_")28    for df in df_array:29        if "bayes_factor" in df.columns:30            # TODO: Implement31            pass32        elif "FDR" in df.columns:33            rmats_cols = "coord event_type strand flank IncLevel1 IncLevel2 IncLevelDifference FDR".split()34            rmats_cols_rename = (35                "coord event_type strand rmats_flank rmats_psi_{} rmats_psi_{} rmats_dpsi rmats_fdr"36                .format(condition_A,condition_B).split()37            )38            df_subset = df[rmats_cols]39            df_subset.columns = rmats_cols_rename40            new_df_array.append(df_subset)41        elif "Probability" in df.columns:42            whippet_cols = "Coord Type Strand Psi_A Psi_B DeltaPsi Probability".split()43            whippet_cols_rename = (44                "coord whippet_type strand whippet_psi_{} whippet_psi_{} whippet_dpsi whippet_prob"45                .format(condition_A,condition_B).split()46            )47            df_subset = df[whippet_cols]48            df_subset.columns = whippet_cols_rename49            new_df_array.append(df_subset)50    return new_df_array51def significant_miso(x):52    if ((x.miso_dpsi >= 0.1) or (x.miso_dpsi <= -0.1)) and (x.miso_bf > 5):53        return True54    else:55        return False56    return None57def significant_rmats(x):58    if ((x.rmats_dpsi >= 0.1) or (x.rmats_dpsi <= -0.1)) and (x.rmats_fdr <= 0.1):59        return True60    else:61        return False62    return None63def significant_whippet(x):64    if ((x.whippet_dpsi >= 0.1) or (x.whippet_dpsi <= -0.1)) and (x.whippet_prob >= 0.9):65        return True66    else:67        return False68    return None69def assign_significance(tool_selection, dataframe):70    if "miso" in tool_selection:71        pass72    if "rmats" in tool_selection:73        dataframe["rmats_significant"] = dataframe.apply(74            lambda x: significant_rmats(x), axis=175        )76    if "whippet" in tool_selection:77        dataframe["whippet_significant"] = dataframe.apply(78            lambda x: significant_whippet(x), axis=179        )80    return dataframe81def assign_group(tool_selection, entry):82    group = ""83    for t in tool_selection:84        if t == "miso":85            if entry.miso_significant:86                group += "m"87        if t == "rmats":88            if entry.rmats_significant:89                group += "r"90        if t == "whippet":91            if entry.whippet_significant:92                group += "w"93    if group != "":94        entry["group"] = group95    elif group == "":96        entry["group"] = "none"97    return entry98tool_selection = snakemake.config["parameters"]["general"]["tools"]99output_dir = snakemake.config["locations"]["output_dir"]100comparison = snakemake.wildcards.comparison101tables = get_tables_array(tool_selection, output_dir, comparison)102df_array = [pd.read_csv(x, sep="\t", index_col=False) for x in tables]103df_array_formated = format_cols(df_array, comparison)104df_merged = reduce(lambda x,y: pd.merge(105    left=x, right=y, how="outer", on=["coord", "strand"]), df_array_formated106)107df_w_significance = assign_significance(tool_selection, df_merged)108df_w_group = (109    df_w_significance.apply(lambda x: assign_group(tool_selection, x), axis=1)110    .drop_duplicates()111)...

solution_1.py

Source:solution_1.py

1import sys2def print_diff_table(n, values):3    diff_tables = [values]4    while len(diff_tables[-1]) > 1:5        diff_tables.append([])6        for i in range(len(diff_tables[-2]) - 1):7            diff_tables[-1].append(diff_tables[-2][i + 1] - diff_tables[-2][i])8    for i in range(len(diff_tables)):9        print("\t".join(map(str, diff_tables[i])))10    print()11def main():12    n = int(sys.stdin.readline().strip())13    v = list(map(int, sys.stdin.readline().strip().split()))14    print_diff_table(n, v)15if __name__ == '__main__':...

solution_2.py

Source:solution_2.py

1import sys2def print_diff_table(n, values):3    diff_tables = [values]4    while len(diff_tables[-1]) > 1:5        diff_tables.append([])6        for i in range(len(diff_tables[-2]) - 1):7            diff_tables[-1].append(diff_tables[-2][i + 1] - diff_tables[-2][i])8    for i in range(len(diff_tables)):9        print("\t".join(map(str, diff_tables[i])))10    print()11def main():12    n = int(sys.stdin.readline().strip())13    v = list(map(int, sys.stdin.readline().strip().split()))14    print_diff_table(n, v)15if __name__ == '__main__':...

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