def make_Histogram(self):
self.read_table()
functions.process(self.dispData, self.dicData)
self.make_CorrFigs()
self.make_TMSFig()
on = self.dicData['hdf5_on'] # this one contains all the histogram axis
res = self.dicData['res'] # this contains the calculation results
fig1 = Figure(facecolor='white', edgecolor='white')
ax1 = fig1.add_subplot(2, 2, 1)
ax2 = fig1.add_subplot(2, 2, 2)
ax3 = fig1.add_subplot(2, 2, 3)
ax4 = fig1.add_subplot(2, 2, 4)
ax1.imshow(res.IQmapM_avg[0], interpolation='nearest', origin='low',
extent=[on.xII[0], on.xII[-1], on.yII[0], on.yII[-1]], aspect='auto')
ax2.imshow(res.IQmapM_avg[1], interpolation='nearest', origin='low',
extent=[on.xQQ[0], on.xQQ[-1], on.yQQ[0], on.yQQ[-1]], aspect='auto')
ax3.imshow(res.IQmapM_avg[2], interpolation='nearest', origin='low',
extent=[on.xIQ[0], on.xIQ[-1], on.yIQ[0], on.yIQ[-1]], aspect='auto')
ax4.imshow(res.IQmapM_avg[3], interpolation='nearest', origin='low',
extent=[on.xQI[0], on.xQI[-1], on.yQI[0], on.yQI[-1]], aspect='auto')
fig1.tight_layout()
ax1.set_title('IIc')
ax2.set_title('QQc')
ax3.set_title('IQc')
ax4.set_title('QIc')
self.update_page_1(fig1) # send figure to the show_figure terminal
self.read_table()
def test_one_file_processing(conn, dmp_file_path, date_stamp):
# get station details
[aws_id, scm] = functions.get_station_details(conn, dmp_file_path)
# process the file
functions.process(dmp_file_path)
# compare the processed file's results to the test results
a = get_results_from_tbls(conn, aws_id, 'tbl_15min_test', date_stamp)
b = get_results_from_tbls(conn, aws_id, 'tbl_data_minutes', date_stamp)
return results_are_same(compare_table_results(a, b))
def main():
"""Display HTML template and retrieve user input from locally hosted
site, before disaggregating values at arguments for process() function"""
url = request.args.get('url')
text_type = request.args.get('text_type')
choice = f"{url}, {text_type}"
choice_list = [x.split() for x in choice.split(',')]
if not url:
print("ERROR: No URL entered")
else:
process(str(choice_list[0][0]), str(choice_list[1][0]))
return render_template("form.html", choice=choice)
def main(dictionary, tfidf_model, tfidf_corpus, matrix_sim, lsi_matrix,
lsi_model, paragraphs):
# Processes the query with the same function as used in task 1, and convert to BOW representation.
#q = "What is the function of money?"
q = "How taxes influence Economics?"
q = functions.process(q)
q = dictionary.doc2bow(q)
# Converts BOW to TF-IDF representation.
tfidf_index = tfidf_model[q]
#print(r)
# 3 most relevant paragraph for q according to TF-IDF model
doc2similarity = enumerate(matrix_sim[tfidf_index])
docs = sorted(doc2similarity, key=lambda kv: -kv[1])[:3]
print("# 3 most relevant paragraph for q according to TF-IDF model for q:")
print("\n" + "[paragraph " + str(docs[0][0]) + "]")
print(paragraphs[docs[0][0]])
print("\n" + "[paragraph " + str(docs[1][0]) + "]")
print(paragraphs[docs[1][0]])
print("\n" + "[paragraph " + str(docs[2][0]) + "]")
print(paragraphs[docs[2][0]])
#Compare TF-IDF representation for the query with LSI-topic representation
print(
"Comparing TF-IDF representation for the query with LSI-topic representation:\n"
)
lsi_query = lsi_model[q]
topics = sorted(lsi_query, key=lambda kv: -abs(kv[1]))[:3]
for topic in enumerate(topics):
top = topic[1][0]
print("\n[Topic #" + str(top) + "]")
print(lsi_model.show_topics()[top])
doc2similarity = enumerate(lsi_matrix[lsi_query])
docs = sorted(doc2similarity, key=lambda kv: -kv[1])[:3]
for document in docs:
d = document[0]
print("\n[Paragraph #" + str(d) + "]")
print(paragraphs[d])
def upload():
target = os.path.join(APP_ROOT, 'images/')
# target = os.path.join(APP_ROOT, 'static/')
print(target)
if not os.path.isdir(target):
os.mkdir(target)
else:
print("Couldn't create upload directory: {}".format(target))
print(request.files.getlist("file"))
for upload in request.files.getlist("file"):
print(upload)
print("{} is the file name".format(upload.filename))
filename = upload.filename
destination = "/".join([target, filename])
print ("Accept incoming file:", filename)
print ("Save it to:", destination)
upload.save(destination)
global List
target2 = os.path.join(APP_ROOT, 'images/',filename)
List = functions.process(target2)
Output = functions.input_leaf(List)
if (Output == 0):
result = 'apple scabe'
elif (Output == 'a'):
result = 'Angel man Syndrome founded'
elif (Output == 'aa'):
result = 'Williams Syndrome founded'
else:
result = 'No Syndrome founded'
if (Output == 'aaa'):
description = '======='
frec = '========='
elif (Output == 'aaaa'):
description = '=============='
frec = '=================='
elif (Output == 'aaaaa'):
description = '===========.'
frec = '===================='
else:
description = '====== '
frec = '==========='
# return send_from_directory("images", filename, as_attachment=True)
return render_template("final.html", name=result, desc = description, desc2 = frec, image_name=filename)
def main(dictionary, tfidf_model, tfidf_corpus, matrix_sim, lsi_matrix,
lsi_model, paragraphs):
# Task 4.1
query = "How taxes influence economics".lower()
query = functions.process(query)
query = dictionary.doc2bow(query)
# Task 4.2
tfidf_index = tfidf_model[query]
# Task 4.3
docsim = enumerate(matrix_sim[tfidf_index])
docs = sorted(docsim, key=lambda kv: -kv[1])[:3]
print(docs)
# Task 4.4
lsi_query = lsi_model[query]
topics = sorted(lsi_query, key=lambda kv: -abs(kv[1]))[:3]
functions.show_topics(topics, lsi_model)
docsim = enumerate(lsi_matrix[lsi_query])
docs = sorted(docsim, key=lambda kv: -kv[1])[:3]
functions.show_docs(docs, paragraphs)
import pandas as pd
from tkinter import filedialog
from functions import process
from tqdm import tqdm
from tkinter import ttk
import time
# Initialize window
root = Tk()
root.title("DrugPred-NN")
root.iconbitmap("icon.ico")
root.configure(bg='#FFFFFF')
#root.geometry("400x475")
# Initialize object
obj = process()
# Initialize frames
frame1 = LabelFrame(root,
text="Step 1: Import file",
padx=20,
pady=20,
bg='#FFFFFF')
frame1.grid(row=0, column=0, padx=10, pady=10, sticky="NSEW")
frame2 = LabelFrame(root,
text="Step 2: Feature Extraction",
padx=20,
pady=20,
bg='#FFFFFF')
frame2.grid(row=1, column=0, padx=10, pady=10, sticky="NSEW")
import functions as app
if __name__ == '__main__':
app.init()
app.process()
app.end()
def main():
ex7_0 = get_input_by_lines(7)
ex7 = process(ex7_0)
print(get_weight(ex7, where_to_go(ex7, find_wrong(ex7_0)[0])[0]))
import os
import settings
if __name__ == "__main__":
# set up test logging
logging.basicConfig(filename=settings.HOME_DIR + 'test/test.log',
format='%(asctime)s %(levelname)s\n%(message)s\n\n',
level=logging.DEBUG)
# get test data files
dmp_files = []
dmp_files_dir = settings.HOME_DIR + 'test/DMP_files/'
for f in os.listdir(dmp_files_dir):
dmp_files.append(dmp_files_dir + f)
functions.process(dmp_files_dir + f)
#functions.process(dmp_files_dir + 'NENANDI150306-0000.DMP')
#functions.process(dmp_files_dir + 'ALDINGA150306-0100.DMP')
#functions.process(dmp_files_dir + 'MTCMPASS150306-0000.DMP')
#functions.process(dmp_files_dir + 'JOYCE150306-0000.DMP')
#functions.process(dmp_files_dir + 'CADELL150306-0000.DMP')
#functions.process()
#for var in reading_vars:
# print var
#print '-----------------------------------------------------'
#print('Number of features:', len(data['features']))
with open(path.join(output_dir, file_name), 'w') as data_file:
json.dump(data, data_file)
# return file path to saved data
return path.join(output_dir, file_name)
def import_file(file_name):
"""
Convert a .geojson file, or similar, into a geo-dataframe
"""
df = gpd.read_file(file_name, encoding='UTF-8')
return df
def create_df(json_data):
"""
Convert raw geojson into a geo-dataframe
"""
return gpd.read_geojson(json_data)
##############################################
# fetch data and save to file
data_path = get_data()
# run path width calculations
df_1, df_2 = process(import_file(data_path))
from functions import process
# Prompt user input for URL and HTML type to parse, then call process()
# function
print('''\nPlease enter the URL of the site you'd like to process\n''')
url_raw = input('> ')
print('''\nPlease select type of words to rank:
all, bold or italic text\n''')
text_pref = input('> ')
process(url_raw, text_pref)
