comparison scripts/table_compute.py @ 2:02c3e335a695 draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/table_compute commit d00a518202228b990aeeea2ec3f842501fd2ec09"

1:dddadbbac949

"""
Table Compute tool - a wrapper around pandas with parameter input validation.
"""


__version__ = "0.9.1"

import csv
import math
from sys import argv

        # Get the main processing mode
        mode = params["element_mode"]
        if mode == "replace":
            replacement_val = params["element_replace"]
            out_table = data.mask(bool_mat, replacement_val)
        elif mode == "modify":
            mod_op = Utils.getOneValueMathOp(params["element_modify_op"])
            out_table = data.mask(
                bool_mat, data.where(bool_mat).applymap(mod_op)
            )

            exit(-1)

    elif user_mode_single == "fulltable":
        general_mode = params["mode"]

        if general_mode == "melt":
            melt_ids = params["MELT"]["melt_ids"]
            melt_values = params["MELT"]["melt_values"]

            out_table = pd.melt(data, id_vars=melt_ids, value_vars=melt_values)
        elif general_mode == "pivot":

2:02c3e335a695

"""
Table Compute tool - a wrapper around pandas with parameter input validation.
"""


__version__ = "0.9.2"

import csv
import math
from sys import argv

        # Get the main processing mode
        mode = params["element_mode"]
        if mode == "replace":
            replacement_val = params["element_replace"]
            out_table = data.mask(
                bool_mat,
                data.where(bool_mat).applymap(
                    lambda x: replacement_val.format(elem=x)
                )
            )
        elif mode == "modify":
            mod_op = Utils.getOneValueMathOp(params["element_modify_op"])
            out_table = data.mask(
                bool_mat, data.where(bool_mat).applymap(mod_op)
            )

            exit(-1)

    elif user_mode_single == "fulltable":
        general_mode = params["mode"]

        if general_mode == "transpose":
            out_table = data.T
        elif general_mode == "melt":
            melt_ids = params["MELT"]["melt_ids"]
            melt_values = params["MELT"]["melt_values"]

            out_table = pd.melt(data, id_vars=melt_ids, value_vars=melt_values)
        elif general_mode == "pivot":