comparison scripts/table_compute.py @ 1:dddadbbac949 draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/table_compute commit 6820ec9431a22576f3716c40feeb27f0b8cf5e83"

0:1b0f96ed73f2

#!/usr/bin/env python3
"""
Table Compute tool - a wrapper around pandas with parameter input validation.
"""

__version__ = "0.8"

import csv
import math
from sys import argv

import numpy as np
import pandas as pd
import userconfig as uc
from safety import Safety
# This should be generated in the same directory

# Version command should not need to copy the config
if len(argv) == 2 and argv[1] == "--version":
    print(__version__)
    exit(-1)


class Utils:
    @staticmethod
    def getOneValueMathOp(op_name):
        "Returns a simple one value math operator such as log, sqrt, etc"

    @staticmethod
    def getTwoValuePandaOp(op_name, pd_obj):
        "Returns a valid two value DataFrame or Series operator"
        return getattr(type(pd_obj), "__" + op_name + "__")


# Math is imported but not directly used because users
# may specify a "math.<function>" when inserting a custom
# function. To remove linting errors, which break CI testing
# we will just use an arbitrary math statement here.
__ = math.log


# Set decimal precision
pd.options.display.precision = uc.Default["precision"]

out_table = None
params = uc.Data["params"]

if user_mode == "single":
    # Read in TSV file
    data = pd.read_csv(
        uc.Data["tables"][0]["reader_file"],
        header=uc.Data["tables"][0]["reader_header"],
        index_col=uc.Data["tables"][0]["reader_row_col"],
        keep_default_na=uc.Default["narm"],
        sep='\t'
    )
    # Fix whitespace issues in index or column names
    data.columns = [col.strip() if type(col) is str else col
                    for col in data.columns]
    data.index = [row.strip() if type(row) is str else row
                  for row in data.index]

    user_mode_single = params["user_mode_single"]

    if user_mode_single == "precision":
        # Useful for changing decimal precision on write out
        out_table = data

    elif user_mode_single == "select":
        cols_specified = params["select_cols_wanted"]
        rows_specified = params["select_rows_wanted"]

        # Select all indexes if empty array of values
        if not cols_specified:
            cols_specified = range(len(data.columns))
        if not rows_specified:
            rows_specified = range(len(data))

        # do not use duplicate indexes
        # e.g. [2,3,2,5,5,4,2] to [2,3,5,4]
        nodupes_col = not params["select_cols_unique"]

            out_table = op(data, axis)

    elif user_mode_single == "element":
        # lt, gt, ge, etc.
        operation = params["element_op"]
        if operation is not None:
            op = Utils.getTwoValuePandaOp(operation, data)
            value = params["element_value"]
            try:
                # Could be numeric
                value = float(value)
            except ValueError:
                pass
            # generate filter matrix of True/False values
            bool_mat = op(data, value)
        else:
            # implement no filtering through a filter matrix filled with
            # True values.
            bool_mat = np.full(data.shape, True)

    # Actual 0-based references "table[0]", "table[1]", etc.
    table_names_real = []

    # Read and populate tables
    for x, t_sect in enumerate(table_sections):
        tmp = pd.read_csv(
            t_sect["file"],
            header=t_sect["header"],
            index_col=t_sect["row_names"],
            keep_default_na=uc.Default["narm"],
            sep="\t"
        )
        table.append(tmp)
        table_names.append("table" + str(x + 1))
        table_names_real.append("table[" + str(x) + "]")

    custom_op = params["fulltable_customop"]

1:dddadbbac949

#!/usr/bin/env python3
"""
Table Compute tool - a wrapper around pandas with parameter input validation.
"""


__version__ = "0.9.1"

import csv
import math
from sys import argv

import numpy as np
import pandas as pd
from safety import Safety

if len(argv) == 2 and argv[1] == "--version":
    print(__version__)
    exit(-1)

# The import below should be generated in the same directory as
# the table_compute.py script.
# It is placed here so that the --version switch does not fail
import userconfig as uc  # noqa: I100,I202


class Utils:
    @staticmethod
    def getOneValueMathOp(op_name):
        "Returns a simple one value math operator such as log, sqrt, etc"

    @staticmethod
    def getTwoValuePandaOp(op_name, pd_obj):
        "Returns a valid two value DataFrame or Series operator"
        return getattr(type(pd_obj), "__" + op_name + "__")

    @staticmethod
    def readcsv(filedict, narm):
        data = pd.read_csv(
            filedict["file"],
            header=filedict["header"],
            index_col=filedict["row_names"],
            keep_default_na=narm,
            nrows=filedict["nrows"],
            skipfooter=filedict["skipfooter"],
            skip_blank_lines=filedict["skip_blank_lines"],
            sep='\t'
        )
        # Fix whitespace issues in index or column names
        data.columns = [col.strip() if type(col) is str else col
                        for col in data.columns]
        data.index = [row.strip() if type(row) is str else row
                      for row in data.index]
        return(data)

    @staticmethod
    def rangemaker(tab):
        # e.g. "1:3,2:-2" specifies "1,2,3,2,1,0,-1,-2" to give [0,1,2,1,0,-1,-2]
        # Positive indices are decremented by 1 to reference 0-base numbering
        # Negative indices are unaltered, so that -1 refers to the last column
        out = []
        err_mess = None
        for ranges in tab.split(","):
            nums = ranges.split(":")
            if len(nums) == 1:
                numb = int(nums[0])
                # Positive numbers get decremented.
                # i.e. column "3" refers to index 2
                #      column "-1" still refers to index -1
                if numb != 0:
                    out.append(numb if (numb < 0) else (numb - 1))
                else:
                    err_mess = "Please do not use 0 as an index"
            elif len(nums) == 2:
                left, right = map(int, nums)
                if 0 in (left, right):
                    err_mess = "Please do not use 0 as an index"
                elif left < right:
                    if left > 0:  # and right > 0 too
                        # 1:3 to 0,1,2
                        out.extend(range(left - 1, right))
                    elif right < 0:  # and left < 0 too
                        # -3:-1 to -3,-2,-1
                        out.extend(range(left, right + 1))
                    elif left < 0 and right > 0:
                        # -2:2 to -2,-1,0,1
                        out.extend(range(left, 0))
                        out.extend(range(0, right))
                elif right < left:
                    if right > 0:  # and left > 0
                        # 3:1 to 2,1,0
                        out.extend(range(left - 1, right - 2, -1))
                    elif left < 0:  # and right < 0
                        # -1:-3 to -1,-2,-3
                        out.extend(range(left, right - 1, -1))
                    elif right < 0 and left > 0:
                        # 2:-2 to 1,0,-1,-2
                        out.extend(range(left - 1, right - 1, -1))
                else:
                    err_mess = "%s should not be equal or contain a zero" % nums
            if err_mess:
                print(err_mess)
                return(None)
        return(out)


# Set decimal precision
pd.options.display.precision = uc.Default["precision"]

out_table = None
params = uc.Data["params"]

if user_mode == "single":
    # Read in TSV file
    data = Utils.readcsv(uc.Data["tables"][0], uc.Default["narm"])
    user_mode_single = params["user_mode_single"]

    if user_mode_single == "precision":
        # Useful for changing decimal precision on write out
        out_table = data

    elif user_mode_single == "select":
        cols_specified = params["select_cols_wanted"]
        rows_specified = params["select_rows_wanted"]

        # Select all indexes if empty array of values
        if cols_specified:
            cols_specified = Utils.rangemaker(cols_specified)
        else:
            cols_specified = range(len(data.columns))
        if rows_specified:
            rows_specified = Utils.rangemaker(rows_specified)
        else:
            rows_specified = range(len(data))

        # do not use duplicate indexes
        # e.g. [2,3,2,5,5,4,2] to [2,3,5,4]
        nodupes_col = not params["select_cols_unique"]

            out_table = op(data, axis)

    elif user_mode_single == "element":
        # lt, gt, ge, etc.
        operation = params["element_op"]
        bool_mat = None
        if operation is not None:
            if operation == "rowcol":
                # Select all indexes if empty array of values
                if "element_cols" in params:
                    cols_specified = Utils.rangemaker(params["element_cols"])
                else:
                    cols_specified = range(len(data.columns))
                if "element_rows" in params:
                    rows_specified = Utils.rangemaker(params["element_rows"])
                else:
                    rows_specified = range(len(data))

                # Inclusive selection:
                # - True: Giving a row or column will match all elements in that row or column
                # - False: Give a row or column will match only elements in both those rows or columns
                inclusive = params["element_inclusive"]

                # Create a bool matrix (intialised to False) with selected
                # rows and columns set to True
                bool_mat = data.copy()
                bool_mat[:] = False
                if inclusive:
                    bool_mat.iloc[rows_specified, :] = True
                    bool_mat.iloc[:, cols_specified] = True
                else:
                    bool_mat.iloc[rows_specified, cols_specified] = True

            else:
                op = Utils.getTwoValuePandaOp(operation, data)
                value = params["element_value"]
                try:
                    # Could be numeric
                    value = float(value)
                except ValueError:
                    pass
                # generate filter matrix of True/False values
                bool_mat = op(data, value)
        else:
            # implement no filtering through a filter matrix filled with
            # True values.
            bool_mat = np.full(data.shape, True)

    # Actual 0-based references "table[0]", "table[1]", etc.
    table_names_real = []

    # Read and populate tables
    for x, t_sect in enumerate(table_sections):
        tmp = Utils.readcsv(t_sect, uc.Default["narm"])
        table.append(tmp)
        table_names.append("table" + str(x + 1))
        table_names_real.append("table[" + str(x) + "]")

    custom_op = params["fulltable_customop"]