def generate_bill(a_family, a_cost_per_person):
a_title = DIR_UBERSICHT + "Vorauszahlung_Ubersicht_"+a_family+".pdf"
pdf = PDFDocument(a_title)
pdf.init_report()
pdf.p_markup("Automatisch generiert am: "+ str(date.today()), pdf.style.right)
pdf.spacer(20)
pdf.h1("Übersicht Vorauszahlung für Familie " + a_family)
pdf.spacer(20)
pdf.h2("Kostenaufstellung")
pdf.hr()
pdf.spacer(10)
my_list = []
q = db.query(Members).join(Familys).filter_by(f_name=a_family)
total = 0
for result in db.execute(q):
my_list.append([result[2] + " " + result[3], str(a_cost_per_person) + " €"])
total += a_cost_per_person
try:
pdf.table(my_list, 220)
except Exception:
print("No members")
pdf.spacer(10)
pdf.hr()
pdf.spacer(20)
pdf.p_markup("Total: " + str(total) + " €", pdf.style.bold)
pdf.spacer(20)
pdf.p(UBERWEISUNG_FOOTER)
pdf.generate()
return os.path.abspath(a_title)
def pdf():
results = g.conn.execute('select * from reports')
first = results.fetchall()[0]
primary_key, data = first
data_json = json.loads(data)
org_name = data_json['organization']
report_at = data_json['reported_at']
created_at = data_json['created_at']
# returns a file object - that stays in the memory and isn't saved
f = BytesIO()
# pass file object 'f' to pdf object
pdf = PDFDocument(f)
#initialise
pdf.init_report()
# convert dictionary to string and add h1 & p tags to pdf
pdf.h1('The Report')
pdf.p(f'Organisation: {org_name}')
pdf.p(f'Reported at: {report_at}')
pdf.p(f'Created at: {created_at}')
pdf.generate()
return f.getvalue(), 200, {'Content-Type': 'application/pdf', 'Content-Disposition':'attachment; filename=report.pdf'}
def say_hello():
f = BytesIO()
pdf = PDFDocument(f)
pdf.init_report()
pdf.h1('Hello World')
pdf.p('Creating PDFs made easy.')
pdf.generate()
return f.getvalue()
def say_hello():
f = BytesIO()
pdf = PDFDocument(f)
pdf.init_report()
pdf.h1('Hello World')
pdf.p('Creating PDFs made easy.')
pdf.generate()
return f.getvalue()
def export_to_pdf(hotel_data, request):
response = HttpResponse(mimetype='application/pdf')
response['Content-Disposition'] = 'attachment; filename=hotel_data.pdf'
pdf = PDFDocument(response)
pdf.init_report()
description, star_rating, facilities = get_description_from_filter(request)
description = str(len(hotel_data)) + " " + description
pdf.h1(description)
if star_rating:
pdf.p(" Star rating:" + star_rating)
if facilities:
pdf.p("Facilities: " + facilities)
from reportlab.lib.styles import getSampleStyleSheet
styles = getSampleStyleSheet()
styleN = styles["BodyText"]
styleN.alignment = TA_LEFT
styleBH = styles["Normal"]
data = []
# Defile header
data.append([
'', 'Hotel Name', 'Location',
Paragraph('Lowest Price (USD)', styleN),
Paragraph('Star rating', styleN),
Paragraph('User rating', styleN)
])
number = 1
# define row in table
for hotel in hotel_data:
name = Paragraph(hotel.name.encode('ascii', 'ignore'), styleN)
address = Paragraph(hotel.address.encode('ascii', 'ignore'), styleN)
arr = [
number, name, address, hotel.lowest_price, hotel.star_rating,
hotel.user_rating
]
data.append(arr)
number += 1
t = Table(data,
colWidths=[30, 135, 290, 40, 32, 32],
style=[
('GRID', (0, 0), (-1, -1), 1, colors.black),
('BACKGROUND', (0, 0), (5, 0), colors.limegreen),
])
# append table to page
pdf.story.append(t)
# pdf.table(data, 100)
pdf.generate()
return response
def generatePDF(title='No title', subtitle='', data=[]):
f = BytesIO()
pdf = PDFDocument(f)
pdf.init_report()
pdf.h1('Hello World')
pdf.p('Creating PDFs made easy.')
pdf.generate()
return f.getvalue()
def create_assignment_pdf(name, assignments):
pdf = PDFDocument(name)
pdf.init_report()
addr = {"first_name": "A.I.T", "last_name": "(c) A.I.T 2016"}
pdf.h1("Generated by A.I.T (Your A.I Tutor)\n")
pdf.h2("Your Assignment (Please solve this)\n")
counter = 1
for i in assignments:
pdf.p("Q" + str(counter) + ": " + i + "\n")
counter += 1
pdf.generate()
def generate_pdf(video_seen_id,user_id):
last_video_seen = DUSerVideoSeen.objects.filter(id=video_seen_id).first()
latest_video_seen = DUSerVideoSeen.objects.filter(seen_by_id=last_video_seen.asked_by_id).order_by('-seen_at').first()
if last_video_seen == latest_video_seen:
catalog_question = CatalogQuestion.objects.filter(id=last_video_seen.catalog_question_id).first()
question_json = catalog_question.similar_question
f = BytesIO()
pdf = PDFDocument(f)
pdf.init_report()
pdf.p(question_json)
pdf.generate()
return f.getvalue()
def export_to_pdf(hotel_data, request):
response = HttpResponse(mimetype='application/pdf')
response['Content-Disposition'] = 'attachment; filename=hotel_data.pdf'
pdf = PDFDocument(response)
pdf.init_report()
description, star_rating, facilities = get_description_from_filter(request)
description = str(len(hotel_data)) + " " + description
pdf.h1(description)
if star_rating:
pdf.p(" Star rating:" + star_rating )
if facilities:
pdf.p("Facilities: " + facilities)
from reportlab.lib.styles import getSampleStyleSheet
styles = getSampleStyleSheet()
styleN = styles["BodyText"]
styleN.alignment = TA_LEFT
styleBH = styles["Normal"]
data = []
# Defile header
data.append(['', 'Hotel Name', 'Location', Paragraph('Lowest Price (USD)', styleN) , Paragraph('Star rating', styleN), Paragraph('User rating', styleN)])
number = 1
# define row in table
for hotel in hotel_data:
name = Paragraph(hotel.name.encode('ascii', 'ignore'), styleN)
address = Paragraph(hotel.address.encode('ascii', 'ignore'), styleN)
arr = [number, name, address, hotel.lowest_price, hotel.star_rating, hotel.user_rating]
data.append(arr)
number += 1
t=Table(data,colWidths=[30, 135, 290, 40, 32, 32], style=[
('GRID',(0,0),(-1,-1),1,colors.black),
('BACKGROUND',(0,0),(5,0),colors.limegreen),
])
# append table to page
pdf.story.append(t)
# pdf.table(data, 100)
pdf.generate()
return response
def createPDF(path):
"""
Create a simple PDF
"""
pdf = PDFDocument(path)
pdf.init_report()
addr = {
'first_name': "John",
'last_name': "Hanes",
'address': "123 Ding Dong Lane",
'zip_code': "75002",
'city': "Dakota"
}
pdf.address(addr)
pdf.p("This is a paragraph!")
pdf.generate()
def generate_pdf_files(self, fname, num_of_files,seed_text):
"""Usage: gnerate_pdf_files"""
now = datetime.datetime.now()
f = BytesIO()
pdf = PDFDocument(f)
pdf.init_letter()
pdf.h1('{}'.format(seed_text))
pdf.p(str(now))
pdf.generate()
try:
for x in range(0, num_of_files):
with open("data/{}_{}.pdf".format(fname, x), 'w') as pdfout:
pdfout.write(f.getvalue())
pdfout.close()
except Exception as e:
print('There was a problem saving your pdf file: {}'.format(e))
def generate_teilnehmerliste():
a_title = DIR_TEILNEHMERLISTE + "Teilnehmer.pdf"
pdf = PDFDocument(a_title)
pdf.init_report()
pdf.p_markup(date.today(), pdf.style.right)
pdf.spacer(20)
pdf.h1("Teilnehmer Skifahren 2017")
pdf.hr_mini()
pdf.spacer(20)
familys = Familys.query.all()
for family in familys:
pdf.h3(family.f_name)
q = db.query(Members).join(Familys).filter_by(f_name=family.f_name)
members = []
for result in db.execute(q):
members.append(result[2] + " " + result[3])
pdf.ul(members)
pdf.spacer(10)
pdf.p("Info:")
pdf.p(family.f_info)
pdf.spacer(20)
pdf.hr()
pdf.generate()
return os.path.abspath(a_title)
def generate_pdf(self, topic_obj):
print 'Generating PDF..'
path = "./PDFDocuments/"+topic_obj.topic+".pdf"
pdf = PDFDocument(path)
pdf.init_report()
# pdf.h2("San Jose State University",style=pdf.style.bold)
# pdf.h1("College of Science, Department of Computer Science",style=pdf.style.bold)
pdf.h2("Topic Recommendation:\n")
pdf.h1("\nTopics in "+topic_obj.topic)
pdf.h2('\nCourse Description')
techs = ""
for tech in topic_obj.technologies:
techs += tech + ", "
pdf.p("\nIntroduction to topics in " + topic_obj.topic + " such as, "+techs)
pdf.p(str(topic_obj.listedTech))
pdf.h2("\nCourse Learning Outcomes:\n\n")
pdf.p(topic_obj.actionList)
pdf.h2("\nSummary from top job descriptions:\n")
pdf.p(topic_obj.summary)
pdf.generate()
print 'PDF generated..'
subprocess.Popen(path, shell=True)
def toPdf(path, text):
pdf = PDFDocument(path)
pdf.init_report()
pdf.p(text)
pdf.generate()
def writeToPDF(self, path, data):
pdf = PDFDocument(path)
pdf.init_report()
pdf.generate_style()
pdf.style.heading1.alignment = TA_CENTER
pdf.style.heading1.fontSize = 18
heading_style = pdf.style.heading1
pdf.h1('Fahrtenbuch Firma RAC', style=heading_style)
pdf.p('\n\n\n')
content = [[
'Name', 'Datum', 'Anfangskilometerstand', 'Endkilometerstand',
'gefahrene Kilometer', 'Art der Fahrt', 'Zweck der Fahrt',
'Fahrtanfang', 'Fahrtziel'
]]
for p in data['rides']:
row = []
for d in p:
if (d != 'Bestaetigt'):
row.append(self.adjustStringLength(p.get(d)))
content.append(row)
# ReportLab formatting
table_style = (
("FONT", (0, 0), (-1, -1), "%s" % pdf.style.fontName, 4),
("TOPPADDING", (0, 0), (-1, -1), 2),
("BOTTOMPADDING", (0, 0), (-1, -1), 2),
("LEFTPADDING", (0, 0), (-1, -1), 2),
("RIGHTPADDING", (0, 0), (-1, -1), 2),
("VALIGN", (0, 0), (-1, -1), "MIDDLE"),
("ALIGN", (0, 0), (-1, -1), "CENTER"),
('GRID', (0, 0), (-1, -1), 0.5, colors.black),
(
"FONT",
(0, 0),
(-1, 0),
"%s-Bold" % pdf.style.fontName,
4.5,
),
)
header = [['KFZ-Kennzeichen', 'Anfangsdatum', 'Enddatum'],
[
data['header'][0]['KFZ-Kennzeichen'],
data['header'][0]['Anfangsdatum'],
data['header'][0]['Enddatum']
]]
header2 = [['Anfangskilometerstand', 'Endkilometerstand'],
[
data['header'][0]['Anfangskilometerstand'],
data['header'][0]['Endkilometerstand']
]]
pdf.table(header, 80, style=table_style)
pdf.table(header2, 80, style=table_style)
pdf.p('\n\n\n')
pdf.table(content, 60, style=table_style)
pdf.generate()
#!/usr/bin/python
from http_analyzer import http_analyzer
from login import search_brute_force
from alert import searcher
from pdfdocument.document import PDFDocument
from dns_analyzer import search_dns
from malicious_file_scanner import scan_files
from corelation import corelation
c = PDFDocument("THREAT_HUNT_REPORT_DON.pdf")
c.init_report()
c.h1(" THREAT-HUNT-REPORT \n\n")
c.p("autogenerated by python\n\nauthor: Nishan Maharjan\n\n")
http_analyzer(c)
search_brute_force(c)
search_dns(c)
scan_files(c)
corelation(c)
L = []
searcher(L, c)
c.generate()
def Run(self, pipeline, result_folder, store_folder):
# Data Description
data_description_text = " "
all_training_result_csv_path = os.path.join(result_folder, 'all_train_result.csv')
if not os.path.exists(all_training_result_csv_path):
print('There is no result here')
return False
all_training_result_df = pd.read_csv(all_training_result_csv_path, index_col=0, header=0)
sample_number = all_training_result_df.iloc[0, :]['sample_number']
positive_number = all_training_result_df.iloc[0, :]['positive_number']
negative_number = all_training_result_df.iloc[0, :]['negative_number']
data_description_text += "We selected {:d} cases as the training data set ({:d}/{:d} = positive/negative)). ".\
format(sample_number, positive_number, negative_number)
testing_result_csv_path = os.path.join(result_folder, 'test_result.csv')
if not os.path.exists(testing_result_csv_path):
data_description_text += "Since the number of the samples were limited, there was no independent testing data. "
else:
testing_result_df = pd.read_csv(testing_result_csv_path, index_col=0, header=0)
sample_number = testing_result_df.iloc[0, :]['sample_number']
positive_number = testing_result_df.iloc[0, :]['positive_number']
negative_number = testing_result_df.iloc[0, :]['negative_number']
data_description_text += "We also selected another {:d} cases as the independent testing data " \
"set ({:d}/{:d} = positive/negative). \n" \
"".format(sample_number, positive_number, negative_number)
# Method Description
method_description_text = " "
method_description_text += pipeline.GetNormalizer().GetDescription()
method_description_text += pipeline.GetDimensionReduction().GetDescription()
method_description_text += pipeline.GetFeatureSelector().GetDescription()
method_description_text += pipeline.GetClassifier().GetDescription()
method_description_text += pipeline.GetCrossValidatiaon().GetDescription()
method_description_text += "\n"
statistic_description_text = " The performance of the model was evaluated using receiver operating characteristic " \
"(ROC) curve analysis. The area under the ROC curve (AUC) was calculated for quantification. " \
"The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative " \
"predictive value (NPV) were also calculated at a cutoff value that maximized the " \
"value of the Yorden index. We also boosted estimation 1000 times and applied paired " \
"t-test to give the 95% confidence interval. All above processes were implemented with " \
"FeAture Explorer (FAE, v0.2.5, https://github.com/salan668/FAE) on Python (3.6.8, https://www.python.org/). \n"
# Result Description
result_folder = os.path.join(result_folder, pipeline.GetStoreName())
result = pd.read_csv(os.path.join(result_folder, 'result.csv'), index_col=0)
if os.path.exists(testing_result_csv_path):
result_description_text = "We found that the model based on {:d} features can get the highest AUC on the " \
"validation data set. The AUC and the accuracy could achieve {:.3f} and {:.3f}, respectively. In this point, " \
"The AUC and the accuracy of the model achieve {:.3f} and {:.3f} on testing data set. " \
"The clinical statistics in the diagonsis and the selected features were shown in Table 1 and Table 2. " \
"The ROC curve was shown in Figure 1. \n" \
"".format(pipeline.GetFeatureSelector().GetSelectedFeatureNumber(),
float(result.loc['val_auc'].values),
float(result.loc['val_accuracy'].values),
float(result.loc['test_auc'].values),
float(result.loc['test_accuracy'].values)
)
else:
result_description_text = "We found that the model based on {:d} features can get the highest AUC on the " \
"validation data set. The AUC and the accuracy could achieve {:.3f} and {:.3f}, respectively. " \
"The clinical statistics in the diagonsis and the selected features were shown in Table 1 and Table 2. " \
"The ROC curve was shown in Figure 1. \n" \
"".format(pipeline.GetFeatureSelector().GetSelectedFeatureNumber(),
float(result.loc['val_auc'].values), float(result.loc['val_accuracy'].values))
from reportlab.lib import colors
table_stype = (
('FONT', (0, 0), (-1, -1), '%s' % 'Helvetica', 9),
('LINEABOVE', (0, 0), (-1, 0), 1, colors.black),
('LINEABOVE', (0, 1), (-1, 1), 1, colors.black),
('LINEBELOW', (0, -1), (-1, -1), 1, colors.black),
('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),
('ALIGN', (0, 0), (-1, -1), 'CENTER')
)
table_1_header = "Table 1. Clinical statistics in the diagnosis. "
if not os.path.exists(testing_result_csv_path):
table_1 = [['Statistics', 'Value'],
['Accuracy', str(result.loc['val_accuracy'].values[0])],
['AUC', str(result.loc['val_auc'].values[0])],
['AUC 95% CIs', str(result.loc['val_auc 95% CIs'].values[0])],
['NPV', str(result.loc['val_negative predictive value'].values[0])],
['PPV', str(result.loc['val_positive predictive value'].values[0])],
['Sensitivity', str(result.loc['val_sensitivity'].values[0])],
['Specificity', str(result.loc['val_specificity'].values[0])]]
else:
table_1 = [['Statistics', 'Value'],
['Accuracy', str(result.loc['test_accuracy'].values[0])],
['AUC', str(result.loc['test_auc'].values[0])],
['AUC 95% CIs', str(result.loc['test_auc 95% CIs'].values[0])],
['NPV', str(result.loc['test_negative predictive value'].values[0])],
['PPV', str(result.loc['test_positive predictive value'].values[0])],
['Sensitivity', str(result.loc['test_sensitivity'].values[0])],
['Specificity', str(result.loc['test_specificity'].values[0])]]
candidate_file = glob.glob(os.path.join(result_folder, '*coef.csv'))
if len(candidate_file) > 0:
coef = pd.read_csv(candidate_file[0], index_col=0, header=0)
table_2_header = 'Table 2. The coefficients of features in the model. '
table_2 = [['Features', 'Coef in model']]
for index in coef.index:
table_2.append([str(index), "{:.3f}".format(coef.loc[index].values[0])])
else:
with open(os.path.join(result_folder, 'feature_select_info.csv'), 'r', newline='') as file:
reader = csv.reader(file)
for row in reader:
if row[0] == 'selected_feature':
features = row[1:]
table_2_header = 'Table 2. The selected of features. '
table_2 = [['Features', 'Rank']]
for index in range(len(features)):
table_2.append([features[index], str(index + 1)])
figure_title = "Figure 1. The ROC curve. "
# Build PDF
pdf = PDFDocument(os.path.join(store_folder, 'description.pdf'))
pdf.init_report()
pdf.h1("Materials and Methods")
pdf.p(data_description_text)
pdf.p(method_description_text)
pdf.p(statistic_description_text)
pdf.h1("Result")
pdf.p(result_description_text)
pdf.table_header(table_1_header)
pdf.table(table_1, 130, style=table_stype)
pdf.table_header(table_2_header)
pdf.table(table_2, 200, style=table_stype)
pdf.p("\n\n")
pdf.image(os.path.join(store_folder, 'ROC.jpg'))
pdf.table_header(figure_title)
pdf.end_connect("Thanks for using FAE v.0.2.5. If you need a specific description, please connect to Yang Song ([email protected]) or Guang Yang "
"([email protected]). Welcome any co-operation and discussion. ")
pdf.generate()
# Must be UTF-16 with correct BOM and MD-DOS line endings
importFile = open("importFile", mode="w", newline="\r\n", encoding='utf-16')
# PaperCut needs this header line in this format
importFile.write(
"CardNumber,BatchID,ValueNumber,Value,ExpDate,ExpDateDisplay\n")
# Modify to as needed
expDate = date.today() + timedelta(
days=14) # expire all the cards -- default to 14 days
displayExpDate = expDate.strftime("%x")
isoExpDate = expDate.isoformat()
displayValue = locale.currency(cardValue, grouping=True)
for cardNumber in sample(range(100000000), numberOfCards):
importFile.write(
f'"{batchID}-{cardNumber:09d}","{batchID}","{cardValue}","{displayValue}","{isoExpDate}","{displayExpDate}"\n'
)
pdf.start_keeptogether()
# Make sure cards are not split across page boundaries
pdf.h1("PaperCut NG Top Up Card")
pdf.p(template.format(displayValue, displayExpDate, batchID, cardNumber))
pdf.end_keeptogether()
pdf.generate()
importFile.close()
import json
from io import BytesIO
from pdfdocument.document import PDFDocument
if __name__ == "__main__":
with open('elpais_mvm.json') as json_file:
articles = json.load(json_file)
f = BytesIO()
pdf = PDFDocument(f)
pdf.init_report()
for article in articles:
pdf.h1(article['title'])
pdf.smaller(article['date'])
pdf.p(article['content'])
pdf.h3('Tags')
pdf.smaller(', '.join(article['tags']))
pdf.next_frame()
pdf.generate()
with open('mvm_articles.pdf', 'w') as file:
file.write(f.getvalue())