# -*- coding: utf-8 -*-
""" texturizer.texturizer: provides entry point main()."""
import pandas as pd
import sys
import os
from .process import load_complete_dataframe
from .process import process_file_in_chunks
from .process import initialise_profile
from .process import print_profiles
from .process import print_output
from .featurize import generate_feature_function
from .config import max_filesize
[docs]def main():
"""Main texturizer application entry point.
parses out CL options and determine the size of the file.
Then process the file for the requested features
"""
if len(sys.argv) < 2:
print("ERROR: MISSING ARGUMENTS")
print_usage(sys.argv)
exit(1)
else:
params = get_cmd_line_params(sys.argv)
if not os.path.exists(params["dataset"]):
print("ERROR: Dataset does not exist")
print_usage(sys.argv)
exit(1)
initialise_profile()
feature_func = generate_feature_function(params)
filesize = os.stat(params["dataset"]).st_size
if filesize<max_filesize:
df = load_complete_dataframe( params["dataset"] )
simple = feature_func(df)
print_output( simple )
else:
process_file_in_chunks(params["dataset"], feature_func)
print_profiles()
#############################################################
[docs]def get_cmd_line_params(argv):
""" parse out the option from an array of command line arguments """
data = argv[-1]
options = argv[1:-1]
result = {"dataset":data,
"columns":[],
"profanity":False,
"sentiment":False,
"emoticons":False,
"topics":False,
"count_matches":False,
"traits":False,
"rhetoric":False,
"pos":False,
"literacy":False,
"scarcity":False,
"comparison":False,
"embedding":False,
"normalize_embedding":False,
"normalize_topics":False,
}
for o in options:
parts = o.split("=")
if parts[0] == "-literacy":
result["literacy"]=True
if parts[0] == "-profanity":
result["profanity"]=True
if parts[0] == "-scarcity":
result["scarcity"]=True
if parts[0] == "-sentiment":
result["sentiment"]=True
if parts[0] == "-topics":
result["topics"]=True
if len(parts)>1:
if parts[1] == 'count':
result["count_matches"]=True
if parts[1] == 'normalize':
result["count_matches"]=True
result["normalize_topics"]=True
if parts[0] == "-traits":
result["traits"]=True
if parts[0] == "-rhetoric":
result["rhetoric"]=True
if parts[0] == "-pos":
result["pos"]=True
if parts[0] == "-emoticons":
result["emoticons"]=True
if parts[0] == "-embedding":
result["embedding"]=True
if len(parts)>1:
if parts[1] == 'normalize':
result["normalize_embedding"]=True
if parts[0] == "-comparison":
result["comparison"]=True
if parts[0] == "-columns":
cols = parts[1].split(",")
result["columns"]=cols
return result
#############################################################
[docs]def print_usage(args):
""" Command line application usage instrutions. """
print("USAGE ")
print(args[0], " [ARGS] <PATH TO DATASET>")
print(" <PATH TO DATASET> - Supported file types: csv, tsv, xls, xlsx, odf")
print(" [ARGS] In most cases these are switches that turn on the feature type")
print(" -columns=<COMMA SEPARATED LIST>. REQUIRED")
print(" -topics Default: False. Indicators for words from common topics.")
print(" -topics=count Count matching words from common topics.")
print(" -topics=normalize Count matching topic key words and normalize over topics.")
print(" -traits Default: False. Word usage for personality traits.")
print(" -rhetoric Default: False. Word usage for rhetorical devices.")
print(" -pos Default: False. Part of speech proportions.")
print(" -literacy Default: False. Checks for common literacy markers.")
print(" -profanity Default: False. Profanity check flags.")
print(" -sentiment Default: False. Words counts for positive and negative sentiment.")
print(" -scarcity Default: False. Word scarcity scores.")
print(" -emoticons Default: False. Emoticon check flags.")
print(" -embedding Default: False. Aggregate of Word Embedding Vectors.")
print(" -embedding=normalize Normalised Aggregate of Word Embedding Vectors.")
print(" -comparison Default: False. Cross-column comparisons.")
print("")