2008.03.14 03:08 Mission Control
2008.05.11 03:06 UsedCars: Everything with Buying & Selling Used Cars.
2015.02.27 00:30 theflamecrow Final Fantasy Record Keeper
2024.05.15 00:31 iofhua "I am not a CAT"
On February 15, 2023, the SEC adopted rule changes to shorten the standard settlement cycle for most broker-dealer transactions from two business days after the trade date (T+2) to one business day after the trade date (T+1). The SEC set May 28, 2024, as the compliance date for the rule changes.Did DFV know about this 3 years ago? Was his post basically a coded message to the SHF's that said "I know you aren't playing by the rules, and you are going to feel pain if you keep naked shorting this stock?"
May 28, 2024, also will be the date for firms to begin to comply with updates to FINRA rules conforming to the T+1 settlement cycle.
2024.05.15 00:19 theslipperycustomer Are 90% of deaths in wars really civilians? What was the Civilian to Combatant ratio in Mosul and Raqqa?
2024.05.14 23:21 Large_Scene3565 ap statistics help Reddit Online Helper for Exam assignment quiz homework class test course takers to Hire an Expert Reddit take my AP Stat Exam Reddit do my AP statistics class Reddit write my AP statistics Assignment Homework Reddit
2024.05.14 23:08 PLLTurner Email links don't work correctly using Adobe Cloud in Edge.
2024.05.14 22:24 Fit-Hat5527 Supershortcut
2024.05.14 22:00 CURSOS-ONLINE- Curso de Hambúrguer Artesanal Vale a Pena?
Invista no seu futuro com o melhor Curso de Hambúrguer Artesanal! Com conteúdo completo, certificado de conclusão e garantia de satisfação, você terá toda a segurança para se tornar um mestre na arte dos hambúrgueres. Não espere mais e descubra o valor de investir na sua paixão pela culinária! submitted by CURSOS-ONLINE- to CursodeHamburguer [link] [comments] ✅ Para conhecer Curso de Hambúrguer Artesanal por dentro, acesse: https://hotm.art/Hamburguer-ArtesanalCurso • Você também pode acessar através do QR CODE na imagem abaixo. Curso de Hambúrguer Artesanal Vale a Pena? Curso para Hambúrguer Artesanal, curso de hambúrguer artesanal rj, curso de hamburguer em fortaleza, curso hambúrguer artesanal grátis, curso de hambúrguer artesanal grátis pdf, curso de hambúrguer artesanal grátis, curso de hambúrguer artesanal download, curso de hambúrguer artesanal em fortaleza, curso de hambúrguer artesanal recife, curso hamburguer artesanal rj, curso de hamburguer artesanal em Goiânia, curso de hamburguer artesanal reclame aqui, curso de hamburguer porto alegre, curso hamburguer artesanal bh, curso hamburguer artesanal Curitiba... |
2024.05.14 21:35 ProtoSid Python Code Review !!! PDF to PPTX. Thanks in Advance
submitted by ProtoSid to developersIndia [link] [comments]2024.05.14 20:52 No_Mixture5766 For those who want revaluation
2024.05.14 20:50 alexanderwarg The Ultimate To-Do List Test
When planning travels, many things are involved. You want to know where to go, when to do things, and what is most important. There will probably be a few PDF tickets and maybe a photo or a link to some website. For most people, it would also be a good idea to keep track of expenditures. All of this must be easy to find while in a new, often quite stressful environment. Therefore, if a to-do app can manage traveling, it can most likely also manage weekly groceries, school work, or other projects, whether personal or for work. That is why during the development of Gört, traveling was used as an example case to ensure that most aspects of what a to-do app should be able to do were covered. Traveling is thus the ultimate test of a to-do list app. submitted by alexanderwarg to ProductivityApps [link] [comments] The unique aspects that separate Gört from other to-do apps include the ability to add locations to tasks, viewable together on a map. This is not just for location-based reminders but as a new way of viewing your tasks. Additionally, just like finding all locations on a map, you can see all tasks with a priority together in a sorted list, all scheduled tasks on a timeline, and so on. This makes everything easy to find, as tasks are grouped and organized by attachment types. Although subjective to some extent, the layout provides a clear overview. For example, when you open a task, you will see all possible attachments in a list, which are easy to add with just a tap. Lastly, sharing a list is really easy. All you need to do is scan the other person's QR code or, if not in the same place, enter their email. The lists will sync in near real-time. I am currently studying at university in Sweden and have been making Gört as a side project during my studies. I thoroughly tested the app during my exchange semester in Seoul as well as a subsequent trip through Southeast Asia. The name comes from the Swedish words "gör" and "det", which translates to "do it". The app is now available on the App Store for iOS and Mac. If you have any thoughts on how it can be improved or need assistance with anything, I am happy to help. I hope you will like it! Website: https://goert.app App Store: https://apps.apple.com/us/app/gört-lists-tasks/id6475203390 https://preview.redd.it/vvuyduld4g0d1.jpg?width=3000&format=pjpg&auto=webp&s=0da368d678144b091b8b46d209673fbbf5ebd3dd https://preview.redd.it/jvnoqrld4g0d1.jpg?width=3000&format=pjpg&auto=webp&s=fae76d7c2ccbc8053caf2ede3c6d294513cc2a68 |
2024.05.14 19:52 gr33nnight Bad Religion / Social Distortion show tonight!
2024.05.14 18:34 Significant-Result14 Building an Observable arXiv RAG Chatbot with LangChain, Chainlit, and Literal AI
2024.05.14 18:33 Significant-Result14 Building an Observable arXiv RAG Chatbot with LangChain, Chainlit, and Literal AI
2024.05.14 18:32 Significant-Result14 [R] Building an Observable arXiv RAG Chatbot with LangChain, Chainlit, and Literal AI
2024.05.14 18:27 Significant-Result14 Building an Observable arXiv RAG Chatbot with LangChain, Chainlit, and Literal AI
2024.05.14 18:12 Jpwolfe99 PyMuPdf doesn't recognize every fillable element in a PDF form
from pdf_processing import ProcessPdf DATA_OBJECT = { "other": "string", "route_to_1": "string", "route_to_2": "string", "route_to_3": "string", "route_to_4": "string", "route_to_5": "string", "route_to_6": "string", "route_to_7": "string", "route_to_8": "string", "route_to_9": "string", "route_to_10": "string", "route_to_11": "string", "route_to_alt_1": "string", "route_to_alt_2": "string", "route_to_alt_3": "string", "route_to_alt_4": "string", "route_to_alt_5": "string", "dep_aerodrome": "string", "dep_elev": "string", "dep_atis_id": "string", "dep_atis_freq": "string", "dest_aerodrome": "string", "dest_elev": "string", "alt_dest": "string", "alt_elev": "string", "chan_id_1": "string", "chan_freq_1": "string", "chan_id_2": "string", "chan_freq_2": "string", "chan_id_3": "string", "chan_freq_3": "string", "chan_id_4": "string", "chan_freq_4": "string", "chan_id_5": "string", "chan_freq_5": "string", "chan_id_6": "string", "chan_freq_6": "string", "chan_id_7": "string", "chan_freq_7": "string", "chan_id_8": "string", "chan_freq_8": "string", "chan_id_9": "string", "chan_freq_9": "string", "chan_id_10": "string", "chan_freq_10": "string", "chan_id_11": "string", "chan_freq_11": "string", "chan_id_alt_1": "string", "chan_freq_alt_1": "string", "chan_id_alt_2": "string", "chan_freq_alt_2": "string", "chan_id_alt_3": "string", "chan_freq_alt_3": "string", "chan_id_alt_4": "string", "chan_freq_alt_4": "string", "chan_id_alt_5": "string", "chan_freq_alt_5": "string", "course_1": "string", "course_2": "string", "course_3": "string", "course_4": "string", "course_5": "string", "course_6": "string", "course_7": "string", "course_8": "string", "course_9": "string", "course_10": "string", "course_11": "string", "course_alt_1": "string", "course_alt_2": "string", "course_alt_3": "string", "course_alt_4": "string", "course_alt_5": "string", "dep_clearance_id": "string", "dep_clearance_freq": "string", "time_off": "string", "dep_app_cont_id": "string", "dep_app_cont_freq": "string", "dist_1": "string", "dist_2": "string", "dist_3": "string", "dist_4": "string", "dist_5": "string", "dist_6": "string", "dist_7": "string", "dist_8": "string", "dist_9": "string", "dist_10": "string", "dist_11": "string", "dist_total": "string", "alt_route": "string", "alt_app_cont_id": "string", "alt_app_cont_freq": "string", "dist_alt_1": "string", "dist_alt_2": "string", "dist_alt_3": "string", "dist_alt_4": "string", "dist_alt_5": "string", "ete_1": "string", "ete_2": "string", "ete_3": "string", "ete_4": "string", "ete_5": "string", "ete_6": "string", "ete_7": "string", "ete_8": "string", "ete_9": "string", "ete_10": "string", "ete_11": "string", "ete_total": "string", "ete_alt_1": "string", "ete_alt_2": "string", "ete_alt_3": "string", "ete_alt_4": "string", "ete_alt_5": "string", "eta_1": "string", "ata_1": "string", "eta_2": "string", "ata_2": "string", "eta_3": "string", "ata_3": "string", "eta_4": "string", "ata_4": "string", "eta_5": "string", "ata_5": "string", "eta_6": "string", "ata_6": "string", "eta_7": "string", "ata_7": "string", "eta_8": "string", "ata_8": "string", "eta_9": "string", "ata_9": "string", "eta_10": "string", "ata_10": "string", "eta_11": "string", "ata_11": "string", "eta_total": "string", "ata_total": "string", "eta_alt_1": "string", "ata_alt_1": "string", "eta_alt_2": "string", "ata_alt_2": "string", "eta_alt_3": "string", "ata_alt_3": "string", "eta_alt_4": "string", "ata_alt_4": "string", "eta_alt_5": "string", "ata_alt_5": "string", "dep_gnd_cont_id": "string", "dep_gnd_cont_freq": "string", "tas": "string", "mach": "string", "dest_tower_id": "string", "dest_tower_freq": "string", "leg_fuel_1": "string", "leg_fuel_2": "string", "leg_fuel_3": "string", "leg_fuel_4": "string", "leg_fuel_5": "string", "leg_fuel_6": "string", "leg_fuel_7": "string", "leg_fuel_8": "string", "leg_fuel_9": "string", "leg_fuel_10": "string", "leg_fuel_11": "string", "leg_fuel_total": "string", "alt_altitude": "string", "alt_tower_id": "string", "alt_tower_freq": "string", "leg_fuel_alt_1": "string", "leg_fuel_alt_2": "string", "leg_fuel_alt_3": "string", "leg_fuel_alt_4": "string", "leg_fuel_alt_5": "string", "efr_1": "string", "afr_1": "string", "efr_2": "string", "afr_2": "string", "efr_3": "string", "afr_3": "string", "efr_4": "string", "afr_4": "string", "efr_5": "string", "afr_5": "string", "efr_6": "string", "afr_6": "string", "efr_7": "string", "afr_7": "string", "efr_8": "string", "afr_8": "string", "efr_9": "string", "afr_9": "string", "efr_10": "string", "afr_10": "string", "efr_11": "string", "afr_11": "string", "efr_total": "string", "afr_total": "string", "efr_alt_1": "string", "afr_alt_1": "string", "efr_alt_2": "string", "afr_alt_2": "string", "efr_alt_3": "string", "afr_alt_3": "string", "efr_alt_4": "string", "afr_alt_4": "string", "efr_alt_5": "string", "afr_alt_5": "string", "cont_fuel": "string", "cont_fuel_1": "string", "cont_fuel_2": "string", "cont_fuel_3": "string", "cont_fuel_4": "string", "cont_fuel_5": "string", "cont_fuel_6": "string", "cont_fuel_7": "string", "cont_fuel_8": "string", "cont_fuel_9": "string", "cont_fuel_10": "string", "cont_fuel_11": "string", "alt_fuel": "string", "cont_fuel_alt_1": "string", "cont_fuel_alt_2": "string", "cont_fuel_alt_3": "string", "cont_fuel_alt_4": "string", "cont_fuel_alt_5": "string", "dep_tower_id": "string", "dep_tower_freq": "string", "lbs_ph": "string", "lbs_pm": "string", "dest_gnd_cont_id": "string", "dest_gnd_cont_freq": "string", "notes_1": "string", "notes_2": "string", "notes_3": "string", "notes_4": "string", "notes_5": "string", "notes_6": "string", "notes_7": "string", "notes_8": "string", "notes_9": "string", "notes_10": "string", "notes_11": "string", "notes_12": "string", "alt_gnd_cont_id": "string", "alt_gnd_cont_freq": "string", "notes_alt_1": "string", "notes_alt_2": "string", "notes_alt_3": "string", "notes_alt_4": "string", "notes_alt_5": "string", "alt_time": "string", "route_dest_iaf_fuel": "string", "route_alt_iaf_fuel": "string", "approaches_fuel": "string", "in_air_used_fuel": "string", "reserve_fuel": "string", "rwy_length_dest": "string", "lighting_dest": "string", "fuel_dest": "string", "ils_dest": "string", "loc_dest": "string", "asr_dest": "string", "par_mins_dest": "string", "tac_mins_dest": "string", "arr_gear_dest": "string", "pubs_dest": "string", "notams_dest": "string", "fuel_packet_dest_1": "string", "fuel_packet_dest_2": "string", "fuel_packet_dest_3": "string", "fuel_packet_dest_4": "string", "etc_dest": "string", "last_cruise_req_fuel": "string", "map_to_iaf_req_fuel": "string", "bingo_req_fuel": "string", "last_cruise_appr_fuel": "string", "map_to_iaf_appr_fuel": "string", "rwy_length_alt": "string", "lighting_alt": "string", "fuel_alt": "string", "ils_alt": "string", "loc_alt": "string", "asr_alt": "string", "par_mins_alt": "string", "tac_mins_alt": "string", "arr_gear_alt": "string", "pubs_alt": "string", "notams_alt": "string", "fuel_packet_alt_1": "string", "fuel_packet_alt_2": "string", "fuel_packet_alt_3": "string", "fuel_packet_alt_4": "string", "etc_alt": "string", "last_cruise_res_fuel": "string", "map_to_iaf_fuel": "string", "add_res_fuel": "string", "stto_fuel": "string", "total_req_fuel": "string", "total_aboard_fuel": "string", "spare_fuel": "string", "last_cruise_total_fuel": "string", "map_to_iaf_total_fuel": "string", "bingo_total": "string", "waypoint_1": "string", "waypoint_2": "string", "waypoint_3": "string", "waypoint_4": "string", "waypoint_5": "string", "waypoint_6": "string", "waypoint_7": "string", "waypoint_8": "string", "waypoint_9": "string", "waypoint_10": "string", "waypoint_11": "string", "waypoint_12": "string", "waypoint_13": "string", "waypoint_14": "string", "waypoint_15": "string", "waypoint_16": "string", "clearance_cleared_to": "string", "clearance_altitude": "string", "clearance_freq": "string", "clearance_transp": "string", "clearance_route": "string" } data = DATA_OBJECT output_file = 'final_pdf.pdf' temp_files = [] pdf = ProcessPdf('pdf_temp/', output_file) ''' PDF_TEMPLATE_PATH = path/to/your.pdf ''' data_pdf = pdf.add_data_to_pdf("Blank Jet Log Fillable.pdf", data) temp_files.append(data_pdf)pdf_processing.py
import os import re import fitz # requires fitz, PyMuPDF import pdfrw import subprocess import os.path import sys from PIL import Image ''' replace all the constants (the one in caps) with your own lists ''' ''' FORM_KEYS is a dictionary (key-value pair) that contains 1. keys - which are all the key names in the PDF form 2. values - which are the type for all the keys in the PDF form. (string, checkbox, etc.) Eg. PDF form contains 1. First Name 2. Last Name 3. Sex (Male or Female) 4. Mobile Number FORM_KEYS = { "fname": "string", "lname": "string", "sex": "checkbox", "mobile": "number" } This FORM_KEYS(key) returns the type of value for that key. I'm passing this as 2nd argument to encode_pdf_string() function. ''' FORM_KEYS = { "other": "string", "route_to_1": "string", "route_to_2": "string", "route_to_3": "string", "route_to_4": "string", "route_to_5": "string", "route_to_6": "string", "route_to_7": "string", "route_to_8": "string", "route_to_9": "string", "route_to_10": "string", "route_to_11": "string", "route_to_alt_1": "string", "route_to_alt_2": "string", "route_to_alt_3": "string", "route_to_alt_4": "string", "route_to_alt_5": "string", "dep_aerodrome": "string", "dep_elev": "string", "dep_atis_id": "string", "dep_atis_freq": "string", "dest_aerodrome": "string", "dest_elev": "string", "alt_dest": "string", "alt_elev": "string", "chan_id_1": "string", "chan_freq_1": "string", "chan_id_2": "string", "chan_freq_2": "string", "chan_id_3": "string", "chan_freq_3": "string", "chan_id_4": "string", "chan_freq_4": "string", "chan_id_5": "string", "chan_freq_5": "string", "chan_id_6": "string", "chan_freq_6": "string", "chan_id_7": "string", "chan_freq_7": "string", "chan_id_8": "string", "chan_freq_8": "string", "chan_id_9": "string", "chan_freq_9": "string", "chan_id_10": "string", "chan_freq_10": "string", "chan_id_11": "string", "chan_freq_11": "string", "chan_id_alt_1": "string", "chan_freq_alt_1": "string", "chan_id_alt_2": "string", "chan_freq_alt_2": "string", "chan_id_alt_3": "string", "chan_freq_alt_3": "string", "chan_id_alt_4": "string", "chan_freq_alt_4": "string", "chan_id_alt_5": "string", "chan_freq_alt_5": "string", "course_1": "string", "course_2": "string", "course_3": "string", "course_4": "string", "course_5": "string", "course_6": "string", "course_7": "string", "course_8": "string", "course_9": "string", "course_10": "string", "course_11": "string", "course_alt_1": "string", "course_alt_2": "string", "course_alt_3": "string", "course_alt_4": "string", "course_alt_5": "string", "dep_clearance_id": "string", "dep_clearance_freq": "string", "time_off": "string", "dep_app_cont_id": "string", "dep_app_cont_freq": "string", "dist_1": "string", "dist_2": "string", "dist_3": "string", "dist_4": "string", "dist_5": "string", "dist_6": "string", "dist_7": "string", "dist_8": "string", "dist_9": "string", "dist_10": "string", "dist_11": "string", "dist_total": "string", "alt_route": "string", "alt_app_cont_id": "string", "alt_app_cont_freq": "string", "dist_alt_1": "string", "dist_alt_2": "string", "dist_alt_3": "string", "dist_alt_4": "string", "dist_alt_5": "string", "ete_1": "string", "ete_2": "string", "ete_3": "string", "ete_4": "string", "ete_5": "string", "ete_6": "string", "ete_7": "string", "ete_8": "string", "ete_9": "string", "ete_10": "string", "ete_11": "string", "ete_total": "string", "ete_alt_1": "string", "ete_alt_2": "string", "ete_alt_3": "string", "ete_alt_4": "string", "ete_alt_5": "string", "eta_1": "string", "ata_1": "string", "eta_2": "string", "ata_2": "string", "eta_3": "string", "ata_3": "string", "eta_4": "string", "ata_4": "string", "eta_5": "string", "ata_5": "string", "eta_6": "string", "ata_6": "string", "eta_7": "string", "ata_7": "string", "eta_8": "string", "ata_8": "string", "eta_9": "string", "ata_9": "string", "eta_10": "string", "ata_10": "string", "eta_11": "string", "ata_11": "string", "eta_total": "string", "ata_total": "string", "eta_alt_1": "string", "ata_alt_1": "string", "eta_alt_2": "string", "ata_alt_2": "string", "eta_alt_3": "string", "ata_alt_3": "string", "eta_alt_4": "string", "ata_alt_4": "string", "eta_alt_5": "string", "ata_alt_5": "string", "dep_gnd_cont_id": "string", "dep_gnd_cont_freq": "string", "tas": "string", "mach": "string", "dest_tower_id": "string", "dest_tower_freq": "string", "leg_fuel_1": "string", "leg_fuel_2": "string", "leg_fuel_3": "string", "leg_fuel_4": "string", "leg_fuel_5": "string", "leg_fuel_6": "string", "leg_fuel_7": "string", "leg_fuel_8": "string", "leg_fuel_9": "string", "leg_fuel_10": "string", "leg_fuel_11": "string", "leg_fuel_total": "string", "alt_altitude": "string", "alt_tower_id": "string", "alt_tower_freq": "string", "leg_fuel_alt_1": "string", "leg_fuel_alt_2": "string", "leg_fuel_alt_3": "string", "leg_fuel_alt_4": "string", "leg_fuel_alt_5": "string", "efr_1": "string", "afr_1": "string", "efr_2": "string", "afr_2": "string", "efr_3": "string", "afr_3": "string", "efr_4": "string", "afr_4": "string", "efr_5": "string", "afr_5": "string", "efr_6": "string", "afr_6": "string", "efr_7": "string", "afr_7": "string", "efr_8": "string", "afr_8": "string", "efr_9": "string", "afr_9": "string", "efr_10": "string", "afr_10": "string", "efr_11": "string", "afr_11": "string", "efr_total": "string", "afr_total": "string", "efr_alt_1": "string", "afr_alt_1": "string", "efr_alt_2": "string", "afr_alt_2": "string", "efr_alt_3": "string", "afr_alt_3": "string", "efr_alt_4": "string", "afr_alt_4": "string", "efr_alt_5": "string", "afr_alt_5": "string", "cont_fuel": "string", "cont_fuel_1": "string", "cont_fuel_2": "string", "cont_fuel_3": "string", "cont_fuel_4": "string", "cont_fuel_5": "string", "cont_fuel_6": "string", "cont_fuel_7": "string", "cont_fuel_8": "string", "cont_fuel_9": "string", "cont_fuel_10": "string", "cont_fuel_11": "string", "alt_fuel": "string", "cont_fuel_alt_1": "string", "cont_fuel_alt_2": "string", "cont_fuel_alt_3": "string", "cont_fuel_alt_4": "string", "cont_fuel_alt_5": "string", "dep_tower_id": "string", "dep_tower_freq": "string", "lbs_ph": "string", "lbs_pm": "string", "dest_gnd_cont_id": "string", "dest_gnd_cont_freq": "string", "notes_1": "string", "notes_2": "string", "notes_3": "string", "notes_4": "string", "notes_5": "string", "notes_6": "string", "notes_7": "string", "notes_8": "string", "notes_9": "string", "notes_10": "string", "notes_11": "string", "notes_12": "string", "alt_gnd_cont_id": "string", "alt_gnd_cont_freq": "string", "notes_alt_1": "string", "notes_alt_2": "string", "notes_alt_3": "string", "notes_alt_4": "string", "notes_alt_5": "string", "alt_time": "string", "route_dest_iaf_fuel": "string", "route_alt_iaf_fuel": "string", "approaches_fuel": "string", "in_air_used_fuel": "string", "reserve_fuel": "string", "rwy_length_dest": "string", "lighting_dest": "string", "fuel_dest": "string", "ils_dest": "string", "loc_dest": "string", "asr_dest": "string", "par_mins_dest": "string", "tac_mins_dest": "string", "arr_gear_dest": "string", "pubs_dest": "string", "notams_dest": "string", "fuel_packet_dest_1": "string", "fuel_packet_dest_2": "string", "fuel_packet_dest_3": "string", "fuel_packet_dest_4": "string", "etc_dest": "string", "last_cruise_req_fuel": "string", "map_to_iaf_req_fuel": "string", "bingo_req_fuel": "string", "last_cruise_appr_fuel": "string", "map_to_iaf_appr_fuel": "string", "rwy_length_alt": "string", "lighting_alt": "string", "fuel_alt": "string", "ils_alt": "string", "loc_alt": "string", "asr_alt": "string", "par_mins_alt": "string", "tac_mins_alt": "string", "arr_gear_alt": "string", "pubs_alt": "string", "notams_alt": "string", "fuel_packet_alt_1": "string", "fuel_packet_alt_2": "string", "fuel_packet_alt_3": "string", "fuel_packet_alt_4": "string", "etc_alt": "string", "last_cruise_res_fuel": "string", "map_to_iaf_fuel": "string", "add_res_fuel": "string", "stto_fuel": "string", "total_req_fuel": "string", "total_aboard_fuel": "string", "spare_fuel": "string", "last_cruise_total_fuel": "string", "map_to_iaf_total_fuel": "string", "bingo_total": "string", "waypoint_1": "string", "waypoint_2": "string", "waypoint_3": "string", "waypoint_4": "string", "waypoint_5": "string", "waypoint_6": "string", "waypoint_7": "string", "waypoint_8": "string", "waypoint_9": "string", "waypoint_10": "string", "waypoint_11": "string", "waypoint_12": "string", "waypoint_13": "string", "waypoint_14": "string", "waypoint_15": "string", "waypoint_16": "string", "clearance_cleared_to": "string", "clearance_altitude": "string", "clearance_freq": "string", "clearance_transp": "string", "clearance_route": "string" } def encode_pdf_string(value, type): if type == 'string': if value: return pdfrw.objects.pdfstring.PdfString.encode(value.upper()) else: return pdfrw.objects.pdfstring.PdfString.encode('') elif type == 'checkbox': if value == 'True' or value == True: return pdfrw.objects.pdfname.BasePdfName('/Yes') # return pdfrw.objects.pdfstring.PdfString.encode('Y') else: return pdfrw.objects.pdfname.BasePdfName('/No') # return pdfrw.objects.pdfstring.PdfString.encode('') return '' class ProcessPdf: def __init__(self, temp_directory, output_file): print('\n########## Initiating Pdf Creation Process #########\n') print('\nDirectory for storing all temporary files is: ', temp_directory) self.temp_directory = temp_directory print("Final Pdf name will be: ", output_file) self.output_file = output_file def add_data_to_pdf(self, template_path, data): print('\nAdding data to pdf...') template = pdfrw.PdfReader(template_path) for page in template.pages: annotations = page['/Annots'] if annotations is None: continue for annotation in annotations: if annotation['/Subtype'] == '/Widget': if annotation['/T']: key = annotation['/T'][1:-1] if re.search(r'.-[0-9]+', key): key = key[:-2] if key in data: annotation.update( pdfrw.PdfDict(V=encode_pdf_string(data[key], FORM_KEYS[key])) ) annotation.update(pdfrw.PdfDict(Ff=1)) template.Root.AcroForm.update(pdfrw.PdfDict(NeedAppearances=pdfrw.PdfObject('true'))) pdfrw.PdfWriter().write(self.temp_directory + "data.pdf", template) print('Pdf saved') return self.temp_directory + "data.pdf" def convert_image_to_pdf(self, image_path, image_pdf_name): print('\nConverting image to pdf...') image = Image.open(image_path) image_rgb = image.convert('RGB') image_rgb.save(self.temp_directory + image_pdf_name) return self.temp_directory + image_pdf_name def add_image_to_pdf(self, pdf_path, images, positions): print('\nAdding images to Pdf...') file_handle = fitz.open(pdf_path) for position in positions: page = file_handle[int(position['page']) - 1] if not position['image'] in images: continue image = images[position['image']] page.insertImage( fitz.Rect(position['x0'], position['y0'], position['x1'], position['y1']), filename=image ) file_handle.save(self.temp_directory + "data_image.pdf") print('images added') return self.temp_directory + "data_image.pdf" def delete_temp_files(self, pdf_list): print('\nDeleting Temporary Files...') for path in pdf_list: try: os.remove(path) except: pass def compress_pdf(self, input_file_path, power=3): """Function to compress PDF via Ghostscript command line interface""" quality = { 0: '/default', 1: '/prepress', 2: '/printer', 3: '/ebook', 4: '/screen' } output_file_path = self.temp_directory + 'compressed.pdf' if not os.path.isfile(input_file_path): print("\nError: invalid path for input PDF file") sys.exit(1) if input_file_path.split('.')[-1].lower() != 'pdf': print("\nError: input file is not a PDF") sys.exit(1) print("\nCompressing PDF...") initial_size = os.path.getsize(input_file_path) subprocess.call(['gs', '-sDEVICE=pdfwrite', '-dCompatibilityLevel=1.4', '-dPDFSETTINGS={}'.format(quality[power]), '-dNOPAUSE', '-dQUIET', '-dBATCH', '-sOutputFile={}'.format(output_file_path), input_file_path] ) final_size = os.path.getsize(output_file_path) ratio = 1 - (final_size / initial_size) print("\nCompression by {0:.0%}.".format(ratio)) print("Final file size is {0:.1f}MB".format(final_size / 1000000)) return output_file_path
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