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sipy.py
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"""!
SiPy: Statistics in Python (GUI Version)
Date created: 9th September 2022
License: GNU General Public License version 3 for academic or
not-for-profit use only
SiPy package is free software: you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import datetime
import os
import subprocess
import sys
import traceback
import warnings
warnings.filterwarnings("ignore")
import numpy
import pandas as pd
import PySimpleGUI as sg
import libsipy
import sipy_info
class SiPy_Shell(object):
"""!
Command-line shell for SiPy.
"""
def __init__(self):
"""!
Initialization method.
"""
self.count = 1
self.data = {}
self.environment = {"cwd": os.getcwd(),
"prompt": ">>>",
"separator": ",",
"verbosity": 0}
self.history = {}
self.modules = [m for m in dir(libsipy)
if m not in ['__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__']]
self.result = {}
def formatExceptionInfo(self, maxTBlevel=10):
"""!
Method to gather information about an exception raised. It is used to readout the exception messages and type of exception. This method takes a parameter, maxTBlevel, which is set to 10, which defines the maximum level of tracebacks to recall.
This method is obtained from http://www.linuxjournal.com/article.php?sid=5821
"""
cla, exc, trbk = sys.exc_info()
excName = cla.__name__
try: excArgs = exc.__dict__["args"]
except KeyError: excArgs = "<no args>"
excTb = traceback.format_tb(trbk, maxTBlevel)
return (excName, excArgs, excTb)
def header(self):
"""!
Prints header, which is displayed at the start of the shell.
"""
print(sipy_info.header)
return None
def do_copyright(self):
"""!
Prints copyright statement.
"""
print(sipy_info.copyright)
return sipy_info.copyright
def do_credits(self):
"""!
Prints list of credits for SiPy development.
"""
print(sipy_info.credits)
print(sipy_info.citations)
return sipy_info.credits + sipy_info.citations
def do_license(self):
"""!
Prints license statement.
"""
print("")
license = open("LICENSE", "r").readlines()
license = [x[:-1] for x in license]
for line in license: print(line)
print("")
return license
def intercept_processor(self, statement):
"""!
Method to intercept non-bytecode statements and channel to the appropriate handlers.
@param statement String: command-line statement
"""
if statement == "copyright": return self.do_copyright()
if statement == "credits": return self.do_credits()
if statement == "exit": return "exit"
if statement == "license": return self.do_license()
if statement == "quit": return "exit"
def error_message(self, code, msg):
"""!
Generic error code/message printer to display error/warning messages.
@param code String: error/warning code to display
@param msg String: error/warning message to display
"""
print("%s: %s" % (str(code), str(msg)))
def do_anova(self, operand):
"""!
Performs comparison of means for 2 or more samples.
Commands:
anova 1way {list|series|tuple|vector} <variable name 1> <variable name 2> ... <variable name N>
anova 1way {dataframe|df|frame|table} wide <variable name>
#anova kruskal {list|series|tuple|vector} <variable name 1> <variable name 2> ... <variable name N>
#anova kruskal {dataframe|df|frame|table} wide <variable name>
@return: String containing results of command execution
"""
data_type = operand[1].lower()
if operand[0].lower() in ["1way"]:
if data_type in ["list", "series", "tuple", "vector"]:
# anova 1way {list|series|tuple|vector} <variable name 1> <variable name 2> ... <variable name N>
data_values = [self.data[operand[i]] for i in range(2, len(operand))]
result = libsipy.base.anova1way(data_values)
retR = "F = %.3f; p-value = %s" % (result.statistic, result.pvalue)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# anova 1way {dataframe|df|frame|table} wide <variable name>
data_values = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns="all", rtype="list")
result = libsipy.base.anova1way(data_values)
retR = "F = %.3f; p-value = %s" % (result.statistic, result.pvalue)
# Not working - to be done later
elif operand[0].lower() in ["kruskal"]:
if data_type in ["list", "series", "tuple", "vector"]:
# anova 1way {list|series|tuple|vector} <variable name 1> <variable name 2> ... <variable name N>
data_values = [self.data[operand[i]] for i in range(2, len(operand))]
result = libsipy.base.anovakruskal(data_values)
retR = "F = %.3f; p-value = %s" % (result.statistic, result.pvalue)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# anova 1way {dataframe|df|frame|table} wide <variable name>
data_values = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns="all", rtype="list")
result = libsipy.base.anovakruskal(data_values)
retR = "F = %.3f; p-value = %s" % (result.statistic, result.pvalue)
#####
else:
retR = "Unknown sub-operation: %s" % operand[0].lower()
print(retR)
return retR
def do_compute_effsize(self, operand):
"""!
Performs Student's t-test(s) on values.
Commands:
compute_effsize none {list|series|tuple|vector} <variable name A> <variable name B>
compute_effsize none {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
compute_effsize cohen {list|series|tuple|vector} <variable name A> <variable name B>
compute_effsize cohen {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
compute_effsize hedges {list|series|tuple|vector} <variable name A> <variable name B>
compute_effsize hedges {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
compute_effsize r {list|series|tuple|vector} <variable name A> <variable name B>
compute_effsize r {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
####not working compute_effsize pointbiserialr {list|series|tuple|vector} <variable name A> <variable name B>
compute_effsize pointbiserialr {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>####
compute_effsize eta-square {list|series|tuple|vector} <variable name A> <variable name B>
compute_effsize etasquare {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
compute_effsize odds-ratio {list|series|tuple|vector} <variable name A> <variable name B>
compute_effsize odds-ratio {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
compute_effsize AUC {list|series|tuple|vector} <variable name A> <variable name B>
compute_effsize AUC {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
compute_effsize CLES {list|series|tuple|vector} <variable name A> <variable name B>
compute_effsize CLES {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
@return: String containing results of command execution
"""
data_type = operand[1].lower()
if operand[0].lower() in ["none"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate none {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.compute_effsize_none(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate none {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.compute_effsize_none(data_valuesA, data_valuesB)
elif operand[0].lower() in ["cohen"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate cohen {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.compute_effsize_cohen(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate cohen {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.compute_effsize_cohen(data_valuesA, data_valuesB)
elif operand[0].lower() in ["hedges"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate hedges {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.compute_effsize_hedges(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate hedges {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.compute_effsize_hedges(data_valuesA, data_valuesB)
elif operand[0].lower() in ["r"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate r {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.compute_effsize_r(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate r {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.compute_effsize_r(data_valuesA, data_valuesB)
elif operand[0].lower() in ["pointbiserialr"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate pointbiserialr {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.compute_effsize_pointbiserialr(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate pointbiserialr {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.compute_effsize_pointbiserialr(data_valuesA, data_valuesB)
elif operand[0].lower() in ["eta-square"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate eta-square {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.compute_effsize_etasquare(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate eta-square {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.compute_effsize_etasquare(data_valuesA, data_valuesB)
elif operand[0].lower() in ["odds-ratio"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate odds-ratio {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.compute_effsize_oddsratio(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate odds-ratio {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.compute_effsize_oddsratio(data_valuesA, data_valuesB)
elif operand[0].lower() in ["AUC", "auc"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate AUC {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.compute_effsize_AUC(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate AUC {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.compute_effsize_AUC(data_valuesA, data_valuesB)
elif operand[0].lower() in ["CLES", "cles"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate CLES {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.compute_effsize_CLES(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate CLES {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.compute_effsize_CLES(data_valuesA, data_valuesB)
else:
retR = "Unknown sub-operation: %s" % operand[0].lower()
print(retR)
return retR
def do_correlate(self, operand):
"""!
Performs Student's t-test(s) on values.
Commands:
correlate pearson {list|series|tuple|vector} <variable name A> <variable name B>
correlate pearson {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
correlate spearman {list|series|tuple|vector} <variable name A> <variable name B>
correlate spearman {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
correlate kendall {list|series|tuple|vector} <variable name A> <variable name B>
correlate kendall {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
####### not working ##### correlate bicor {list|series|tuple|vector} <variable name A> <variable name B>
correlate bicor {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
correlate percbend {list|series|tuple|vector} <variable name A> <variable name B>
correlate percbend {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
####### warning ####### correlate skipped {list|series|tuple|vector} <variable name A> <variable name B>
####### warning ####### correlate skipped {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
##### shepherd not working #####
correlate kendall {list|series|tuple|vector} <variable name A> <variable name B>
correlate kendall {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
correlate distance {list|series|tuple|vector} <variable name A> <variable name B>
correlate distance {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
##### not working correlate cv1 {list|series|tuple|vector} <variable name A> <variable name B>
correlate cv1 {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
correlate cv2 {list|series|tuple|vector} <variable name A> <variable name B>
correlate cv2 {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>####
@return: String containing results of command execution
"""
data_type = operand[1].lower()
if operand[0].lower() in ["pearson", "r"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate pearson {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.correlatePearson(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate pearson {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.correlatePearson(data_valuesA, data_valuesB)
elif operand[0].lower() in ["spearman"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate spearman {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.correlateSpearman(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate spearman {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.correlateSpearman(data_valuesA, data_valuesB)
elif operand[0].lower() in ["kendall"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate kendall {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.correlateKendall(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate kendall {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.correlateKendall(data_valuesA, data_valuesB)
elif operand[0].lower() in ["bicor"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate bicor {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.correlateBicor(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate bicor {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.correlateBicor(data_valuesA, data_valuesB)
elif operand[0].lower() in ["percbend"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate percbend {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.correlatePercbend(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate percbend {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.correlatePercbend(data_valuesA, data_valuesB)
elif operand[0].lower() in ["skipped"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate skipped {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.correlateSkipped(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate skipped {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.correlateSkipped(data_valuesA, data_valuesB)
#######elif operand[0].lower() in ["shepherd"]:
##if data_type in ["list", "series", "tuple", "vector"]:
# correlate skipped {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.correlateShepherd(data_valuesA, data_valuesB)
##elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate skipped {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.correlateShepherd(data_valuesA, data_valuesB)####
elif operand[0].lower() in ["distance"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate distance {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.correlateDistance(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate distance {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.correlateDistance(data_valuesA, data_valuesB)
elif operand[0].lower() in ["2cv"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate skipped {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.correlate2cv(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate skipped {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.correlate2cv(data_valuesA, data_valuesB)
elif operand[0].lower() in ["1cv"]:
if data_type in ["list", "series", "tuple", "vector"]:
# correlate skipped {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.correlate1cv(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# correlate skipped {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.correlate1cv(data_valuesA, data_valuesB)
### Shepherd is not working ####
else:
retR = "Unknown sub-operation: %s" % operand[0].lower()
print(retR.to_string())
return retR
def do_describe(self, operand):
"""!
Calculating various descriptions (standard deviation, variance, standarad error) of the values.
Commands:
describe {kurtosis|kurt} <variable_name>
describe {skew|sk} <variable_name>
describe {stdev|stdev.s|s|sd} <variable_name>
describe se <variable_name>
describe {var|var.s} <variable_name>
@return: String containing results of command execution
"""
variable_name = operand[1]
data_values = self.data[variable_name]
if operand[0].lower() in ["kurtosis" , "kurt"]:
# describe {kurtosis|kurt} <variable_name>
result = libsipy.base.kurtosis(data_values)
retR = "Kurtosis = %s" % result
elif operand[0].lower() in ["skew" , "sk"]:
# describe {skew|sk} <variable_name>
result = libsipy.base.skew(data_values)
retR = "Skew = %s" % result
elif operand[0].lower() in ["stdev", "stdev.s", "s", "sd"]:
# describe {stdev|stdev.s|s|sd} <variable_name>
result = libsipy.base.standardDeviation(data_values)
retR = "Standard deviation = %s" % result
elif operand[0].lower() in ["se"]:
# describe se <variable_name>
result = libsipy.base.standardError(data_values)
retR = "Standard error = %s" % result
elif operand[0].lower() in ["var", "var.s"]:
# describe {var|var.s} <variable_name>
result = libsipy.base.variance(data_values)
retR = "Variance = %s" % result
else:
retR = "Unknown sub-operation: %s" % operand[0].lower()
print(retR)
return retR
def do_let(self, operand):
"""!
Assign a value or list of values to a variable.
Commands:
let <variable_name> be {numeric|number|num|integer|int|float|value} <value>
let <variable_name> be {list|series|tuple|vector} <comma-separated values>
let <variable_name> be {dataframe|df|frame|table} <data descriptor>
let <new_variable_name> from {dataframe|df|frame|table} <existing_variable_name> <series name>
@return: String containing results of command execution
"""
variable_name = operand[0]
if operand[1].lower() == "be":
data_type = operand[2]
if data_type.lower() in ["numeric", "number", "num", "integer", "int", "float", "value"]:
# let <variable_name> be {numeric|number|num|integer|int|float|value} <value>
data_values = operand[3]
self.data[variable_name] = float(data_values)
retR = "%s = %s" % (variable_name, str(data_values))
elif data_type.lower() in ["list", "series", "tuple", "vector"]:
# let <variable_name> be {list|series|tuple|vector} <comma-separated values>
data_values = "".join(operand[3:])
data_values = [float(x) for x in data_values.split(self.environment["separator"])]
self.data[variable_name] = pd.Series(data_values)
retR = "%s = %s" % (variable_name, str(data_values))
elif data_type.lower() in ["dataframe", "df", "frame", "table"]:
# let <variable_name> be {dataframe|df|frame|table} <data descriptor>
data_values = operand[3:]
source_descriptors = [x.split(":") for x in data_values]
source_data = {}
for d in source_descriptors:
source_data[d[0]] = self.data[d[1]]
self.data[variable_name] = pd.concat(source_data, axis=1)
retR = "%s = %s" % (variable_name, str(data_values))
elif operand[1].lower() == "from" and operand[2].lower() in ["dataframe", "df", "frame", "table"]:
if len(operand) == 5:
# let <new_variable_name> from {dataframe|df|frame|table} <existing_variable_name> <series name>
data_values = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
self.data[variable_name] = data_values
retR = "%s from %s.%s; %s = %s" % (variable_name, operand[3], operand[4], variable_name, str(data_values))
print(retR)
return retR
def do_mean(self, operand):
"""!
Calculating various means (arithmetic mean, geometric mean, harmonic mean) of the values.
Commands:
mean {arithmetic|amean|average|avg|mean} <variable_name>
mean {geometric|gmean|geo} <variable_name>
mean {harmonic|hmean|harm} <variable_name>
@return: String containing results of command execution
"""
variable_name = operand[1]
data_values = self.data[variable_name]
if operand[0].lower() in ["arithmetic", "amean", "average", "avg", "mean"]:
# mean {arithmetic|amean|average|avg|mean} <variable_name>
result = libsipy.base.arithmeticMean(data_values)
retR = "Arimethic mean = %s" % result
elif operand[0].lower() in ["geometric", "gmean", "geo"]:
# mean {geometric|gmean|geo} <variable_name>
result = libsipy.base.geometricMean(data_values)
retR = "Geometric mean = %s" % result
elif operand[0].lower() in ["harmonic", "hmean", "harm"]:
# mean {harmonic|hmean|harm} <variable_name>
result = libsipy.base.harmonicMean(data_values)
retR = "Harmonic mean = %s" % result
else:
retR = "Unknown sub-operation: %s" % operand[0].lower()
print(retR)
return retR
def do_normality(self, operand):
"""!
Perform normality test(s) on the values.
Commands:
normality kurtosis <variable_name>
normality {jarquebera|jb} <variable_name>
normality {shapirowilk|sw} <variable_name>
normality {skewtest|sk} <variable_name>
@return: String containing results of command execution
"""
variable_name = operand[1]
data_values = self.data[variable_name]
if operand[0].lower() == "kurtosis":
# normality kurtosis <variable_name>
result = libsipy.base.kurtosisNormalityTest(data_values)
retR = "Z-score = %s; p-value = %s" % (str(result[0]), str(result[1]))
elif operand[0].lower() in ["jb" , "jarquebera" , "jarqueBera"]:
# normality {jarquebera|jb} <variable_name>
result = libsipy.base.jarqueBeraNormalityTest(data_values)
retR = "Statistic = %s; p-value = %s" % (str(result[0]), str(result[1]))
elif operand[0].lower() in ["shapirowilk" , "sw" , "shapiroWilk"]:
# normality {shapirowilk|sw} <variable_name>
result = libsipy.base.shapiroWilkNormalityTest(data_values)
retR = "Statistic = %s; p-value = %s" % (str(result[0]), str(result[1]))
elif operand[0].lower() in ["skewtest" , "sk"]:
# normality {skewtest|sk} <variable_name>
try:
result = libsipy.base.skewNormalityTest(data_values)
except ValueError:
# caters for data frame variable
data_values = libsipy.data_wrangler.df_extract(data_values, columns="all", rtype="list")
data_values = libsipy.data_wrangler.flatten(data_values)
result = libsipy.base.skewNormalityTest(data_values)
if type(result[0]) is numpy.ndarray:
retR = "Z-score, p-value \n"
temp = [[str(result[0][i]), str(result[1][i])]
for i in range(len(result[0]))]
temp = [", ".join(x) for x in temp]
retR = retR + " \n".join(temp)
else:
retR = "Z-score = %f; p-value = %f" % (result[0], result[1])
else:
retR = "Unknown sub-operation: %s" % operand[0].lower()
print(retR)
return retR
def do_read(self, operand):
"""!
Read external data files into SiPy.
Commands:
read excel <variable_name> from <file_name> <sheet_name>
@return: String containing results of command execution
"""
variable_name = operand[1]
if operand[0].lower() == "excel":
# read excel <variable_name> from <file_name> <sheet_name>
df = pd.read_excel(operand[3], sheet_name=operand[4])
self.data[variable_name] = df
retR = "Read Excel: %s.%s into %s" % (operand[3], operand[4], operand[1])
else:
retR = "Unknown sub-operation: %s" % operand[0].lower()
print(retR)
return retR
def do_regression(self, operand):
"""!
Performs regression(s).
Commands:
regress linear <dependent variable name> <independent variable name> [False for intercept=0]
regress logistic <dependent variable name> <independent variable name>
@return: String containing results of command execution
"""
if operand[0].lower() in ["linear", "lin"]:
# regress linear <dependent variable name> <independent variable name> [False for intercept=0]
try:
if operand[3] in [True, "True", "TRUE", "true", "T", "t", "Yes", "YES", "Y", "y"]:
add_intercept = True
elif operand[3] in [False, "False", "FALSE", "false", "F", "f", "No", "NO", "N", "n"]:
add_intercept = False
else:
add_intercept = True
except IndexError: add_intercept = True
y = self.data[operand[1]]
X = self.data[operand[2]]
retR = libsipy.base.regressionLinear(X, y, add_intercept)
elif operand[0].lower() in ["logistic", "log"]:
# regress logistic <dependent variable name> <independent variable name>
y = self.data[operand[1]]
X = self.data[operand[2]]
retR = libsipy.base.regressionLogistic(X, y)
else:
retR = "Unknown sub-operation: %s" % operand[0].lower()
print(retR.to_string())
return retR
def do_show(self, operand):
"""!
Show various status of the SiPy.
Commands:
show data [variable name]
show {history|environment|modules}
show item <history number>
show result
@return: String containing results of command execution
"""
if operand[0].lower() in ["data", "d"]:
# show data [variable name]
if len(operand) == 1:
# show data
for x in self.data:
retR = self.data
print("%s: %s" % (str(x), str(self.data[x])))
else:
# show data <variable name>
item = str(operand[1])
retR = {item: self.data[item]}
print("%s: %s" % (item, str(retR[item])))
elif operand[0].lower() in ["history", "hist", "h"]:
# show history
for x in self.history:
retR = self.history
print("%s: %s" % (str(x), str(self.history[x])))
elif operand[0].lower() in ["environment", "env", "e"]:
# # show environment
for x in self.environment:
retR = self.environment
print("%s: %s" % (str(x), str(self.environment[x])))
elif operand[0].lower() in ["item", "i"]:
# show item <history number>
item = str(int(operand[1]))
retR = ["Command: %s" % (str(self.history[item])),
"Result: %s" % (str(self.result[item]))]
print(retR[0])
print(retR[1])
retR = "\n".join(retR)
elif operand[0].lower() in ["modules", "mod", "m"]:
# show modules
print("List of Available Modules:")
retR = self.modules
for module in self.modules: print(module)
elif operand[0].lower() in ["result", "r"]:
# show result
retR = self.result
for x in self.result:
print("%s: %s" % (str(x), str(self.result[x])))
else:
retR = "Unknown sub-operation: %s" % operand[0].lower()
return retR
def do_ttest(self, operand):
"""!
Performs Student's t-test(s) on values.
Commands:
ttest 1s {list|series|tuple|vector} <variable name> <population mean>
ttest 1s {dataframe|df|frame|table} wide <variable name> <series name> <population mean>
ttest 2se {list|series|tuple|vector} <variable name A> <variable name B>
ttest 2se {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
ttest 2su {list|series|tuple|vector} <variable name A> <variable name B>
ttest 2su {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
ttest mwu {list|series|tuple|vector} <variable name A> <variable name B>
ttest mwu {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
ttest paired {list|series|tuple|vector} <variable name A> <variable name B>
ttest paired {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
ttest wilcoxon {list|series|tuple|vector} <variable name A> <variable name B>
ttest wilcoxon {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
@return: String containing results of command execution
"""
data_type = operand[1].lower()
if operand[0].lower() in ["1s", "1sample"]:
if data_type in ["list", "series", "tuple", "vector"]:
# ttest 1s {list|series|tuple|vector} <variable name> <population mean>
data_values = self.data[operand[2]]
mu = float(operand[3])
retR = libsipy.base.tTest1Sample(data_values, mu)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# ttest 1s {dataframe|df|frame|table} wide <variable name> <series name> <population mean>
data_values = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
mu = float(operand[5])
retR = libsipy.base.tTest1Sample(data_values, mu)
elif operand[0].lower() in ["2se", "2sample_equal"]:
if data_type in ["list", "series", "tuple", "vector"]:
# ttest 2se {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.tTest2SampleEqual(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# ttest 2se {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.tTest2SampleEqual(data_valuesA, data_valuesB)
elif operand[0].lower() in ["2su", "2sample_unequal"]:
if data_type in ["list", "series", "tuple", "vector"]:
# ttest 2su {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.tTest2SampleUnequal(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# ttest 2su {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.tTest2SampleUnequal(data_valuesA, data_valuesB)
elif operand[0].lower() in ["mwu", "mannwhitney"]:
if data_type in ["list", "series", "tuple", "vector"]:
# ttest mwu {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.mannWhitneyU(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# ttest mwu {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.mannWhitneyU(data_valuesA, data_valuesB)
elif operand[0].lower() in ["paired", "dependent"]:
if data_type in ["list", "series", "tuple", "vector"]:
# ttest paired {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.tTest2SamplePaired(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# ttest paired {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.tTest2SamplePaired(data_valuesA, data_valuesB)
elif operand[0].lower() in ["wilcoxon"]:
if data_type in ["list", "series", "tuple", "vector"]:
# ttest mwu {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.wilcoxon(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# ttest mwu {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.wilcoxon(data_valuesA, data_valuesB)
elif operand[0].lower() in ["tost"]:
if data_type in ["list", "series", "tuple", "vector"]:
# ttest mwu {list|series|tuple|vector} <variable name A> <variable name B>
data_valuesA = self.data[operand[2]]
data_valuesB = self.data[operand[3]]
retR = libsipy.base.TOST(data_valuesA, data_valuesB)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# ttest mwu {dataframe|df|frame|table} wide <variable name> <series name A> <series name B>
data_valuesA = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[4], rtype="list")
data_valuesB = libsipy.data_wrangler.df_extract(df=self.data[operand[3]], columns=operand[5], rtype="list")
retR = libsipy.base.TOST(data_valuesA, data_valuesB)
else:
retR = "Unknown sub-operation: %s" % operand[0].lower()
print(retR.to_string())
return retR
def do_variance(self, operand):
"""!
Performs test for equality of variances of samples.
Commands:
variance bartlett {list|series|tuple|vector} <variable name 1> <variable name 2> ... <variable name N>
variance bartlett {dataframe|df|frame|table} wide <variable name>
variance fligner {list|series|tuple|vector} <variable name 1> <variable name 2> ... <variable name N>
variance fligner {dataframe|df|frame|table} wide <variable name>
variance levene {list|series|tuple|vector} <variable name 1> <variable name 2> ... <variable name N>
variance levene {dataframe|df|frame|table} wide <variable name>
@return: String containing results of command execution
"""
data_type = operand[1].lower()
if operand[0].lower() in ["bartlett"]:
if data_type in ["list", "series", "tuple", "vector"]:
# variance bartlett {list|series|tuple|vector} <variable name 1> <variable name 2> ... <variable name N>
data_values = [self.data[operand[i]] for i in range(2, len(operand))]
result = libsipy.base.BartlettTest(data_values)
retR = "Statistic = %.3f; p-value = %s" % (result.statistic, result.pvalue)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# variance bartlett {dataframe|df|frame|table} wide <variable name>
data_values = libsipy.data_wrangler.df_extract(self.data[operand[3]], columns="all", rtype="list")
result = libsipy.base.BartlettTest(data_values)
retR = "Statistic = %.3f; p-value = %s" % (result.statistic, result.pvalue)
elif operand[0].lower() in ["fligner"]:
if data_type in ["list", "series", "tuple", "vector"]:
# variance fligner {list|series|tuple|vector} <variable name 1> <variable name 2> ... <variable name N>
data_values = [self.data[operand[i]] for i in range(2, len(operand))]
result = libsipy.base.FlignerTest(data_values)
retR = "Statistic = %.3f; p-value = %s" % (result.statistic, result.pvalue)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# variance fligner {dataframe|df|frame|table} wide <variable name>
data_values = libsipy.data_wrangler.df_extract(self.data[operand[3]], columns="all", rtype="list")
result = libsipy.base.FlignerTest(data_values)
retR = "Statistic = %.3f; p-value = %s" % (result.statistic, result.pvalue)
elif operand[0].lower() in ["levene"]:
if data_type in ["list", "series", "tuple", "vector"]:
# variance levene {list|series|tuple|vector} <variable name 1> <variable name 2> ... <variable name N>
data_values = [self.data[operand[i]] for i in range(2, len(operand))]
result = libsipy.base.LeveneTest(data_values)
retR = "Statistic = %.3f; p-value = %s" % (result.statistic, result.pvalue)
elif data_type in ["dataframe", "df", "frame", "table"] and operand[2].lower() == "wide":
# variance levene {dataframe|df|frame|table} wide <variable name>
data_values = libsipy.data_wrangler.df_extract(self.data[operand[3]], columns="all", rtype="list")
result = libsipy.base.LeveneTest(data_values)
retR = "Statistic = %.3f; p-value = %s" % (result.statistic, result.pvalue)
else:
retR = "Unknown sub-operation: %s" % operand[0].lower()
print(retR)
return retR
def command_processor(self, operator, operand, kwargs):
"""
Method to channel bytecodes operand(s), if any, into the respective bytecode processors.
@param operator String: bytecode operator
@param operand list: bytecode operand(s), if any
@return: String containing results of command execution
"""
if operator == "anova": return self.do_anova(operand)
elif operator == "compute_effsize": return self.do_compute_effsize(operand)
elif operator == "correlate": return self.do_correlate(operand)
elif operator == "describe": return self.do_describe(operand)
elif operator == "let": return self.do_let(operand)
elif operator == "mean": return self.do_mean(operand)
elif operator == "normality": return self.do_normality(operand)
elif operator == "read": return self.do_read(operand)
elif operator == "regress": return self.do_regression(operand)
elif operator == "show": return self.do_show(operand)
elif operator == "ttest": return self.do_ttest(operand)
elif operator == "variance": return self.do_variance(operand)
else: print("Unknown command / operation: %s" % operator)
def interpret(self, statement):
"""!
Method to process an input statement by either sending it to SiPy_Shell.intercept_processor() or SiPy_Shell.command_processor().
@param statement String: command-line statement
@return: String containing results of command execution
"""
def dictionize(operand):
op_list = []
op_dict = {}
for op in operand:
op = op.strip()
if "=" not in op: op_list.append(op)
else:
op = [x.strip() for x in op.split("=")]
op_dict[op[0]] = op[1]
return (op_list, op_dict)
try:
self.history[str(self.count)] = statement
if statement.lower() in ["copyright", "copyright;", "credits", "credits;", "exit", "exit;",
"license", "license;", "quit", "quit;"]:
retR = self.intercept_processor(statement)
if retR == "exit": return "exit"
else:
statement = statement.strip()
statement = [x.strip() for x in statement.split()]
operator = statement[0].lower()
(operand, kwargs) = dictionize(statement[1:])
retR = self.command_processor(operator, operand, kwargs)
self.result[str(self.count)] = retR
self.count = self.count + 1
return retR
except:
error_message = list(self.formatExceptionInfo())
for line in error_message:
if (type(line) == list):
for l in line:
print(l)
def cmdLoop(self):
"""!
Command-line loop executor. This runs the shell like a command-line interpreter and calls SiPy_Shell.interpret() method to process the statement/command from the command-line.
"""
self.header()
while True:
statement = input("SiPy: %s %s " % (str(self.count), self.environment["prompt"])).strip()
if statement == "exit": return 0
elif statement.startswith("#"): continue
else: _ = self.interpret(statement)
print("")
def cmdScript(self, script):
"""!
Script executor. This method is used to execute a script file that is passed into SiPy and calls SiPy_Shell.interpret() method to process the ordered list of commands in script.
@param script List: ordered list of commands in script to execute.
"""
for statement in script:
statement = statement.strip()
print('Command #%s: %s' % (str(self.count), statement))
if statement == 'exit': return 0
_ = self.interpret(statement)
def runScript(self, scriptfile, operation="script_execute"):
"""!
Function to execute script file written in SiPy language.
@param scriptfile String: absolute path to SiPy script file
@param operation String: type of operation. Allowable types are "script_execute" (execute the script), and "script_merge" (merge the scripts without execution)
"""
dirname = os.path.dirname(scriptfile)
if operation == "script_execute":
print("")
print("Executing script file: %s" % scriptfile)
print("")
def process_script(scriptfile):
if operation == "script_execute":
print("")
print("Reading script file: %s" % scriptfile)
print("")
script = open(scriptfile, "r").readlines()