How to use safeformat method in Pytest

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tables.py

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1#!/usr/bin/python2# -*- coding: utf-8 -*-3from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle4from reportlab.platypus import Paragraph5from reportlab.lib.enums import TA_CENTER6import copy7import pandas as pd8import pdb9import numbers10import re11style = getSampleStyleSheet()12whiteParaStyle = ParagraphStyle('whiteParaStyle',parent=style['BodyText'],textColor="white",alignment=TA_CENTER)13greyParaStyle = ParagraphStyle('greyParaStyle',parent=style['BodyText'],textColor="#443e42")14greyCenterParaStyle = ParagraphStyle('greyParaStyle',parent=style['BodyText'],textColor="#443e42",alignment=TA_CENTER)15# offCourseStyle = ParagraphStyle('offCourseStyle',parent=style['BodyText'],textColor="white",alignment=TA_CENTER,backColor="#d83110")16# progressStyle = ParagraphStyle('progressStyle',parent=style['BodyText'],textColor="#443e42",alignment=TA_CENTER,backColor="#f39000")17# onCourseStyle = ParagraphStyle('onCourseStyle',parent=style['BodyText'],textColor="#443e42",alignment=TA_CENTER,backColor="#d8da00")18offCourseStyle = ParagraphStyle('offCourseStyle',parent=style['BodyText'],textColor="#d83110",alignment=TA_CENTER)19progressStyle = ParagraphStyle('progressStyle',parent=style['BodyText'],textColor="#f39000",alignment=TA_CENTER)20onCourseStyle = ParagraphStyle('onCourseStyle',parent=style['BodyText'],textColor="#d8da00",alignment=TA_CENTER)21def condStyle(progress):22 if progress=="On course":23 return onCourseStyle24 elif progress=="No progress or worsening":25 return offCourseStyle26 elif progress=="Some progress":27 return progressStyle28 elif progress=="Off course":29 return offCourseStyle30 else:31 return greyCenterParaStyle32 33#Read a CSV to make this data?34dataDictionary = {"Mozambique":{}}35dataDictionary["Mozambique"]["country"] = "Mozambique"36dataDictionary["Mozambique"]["table1"] = [["Gini index score*",Paragraph("Gini index rank**",style=whiteParaStyle),"Year"],[51,125,2011]]37dataDictionary["Mozambique"]["table2"] = [38 ["Population (thousands)",format(12428,",d"),2015]39 ,["Under-5 population (thousands)",format(1935,",d"),2015]40 ,["Urban (%)",format(20,",d"),2015]41 ,[">65 years (%)",format(5,",d"),2015]42 ]43dataDictionary["Mozambique"]["table3"] = [44 ["Number of children under 5 affected (thousands)","",""]45 ,[Paragraph("Stunting<super>1</super>",style=greyParaStyle),format(733,",d"),2015]46 ,[Paragraph("Wasting<super>1</super>",style=greyParaStyle),format(43,",d"),2015]47 ,[Paragraph("Overweight<super>1</super>",style=greyParaStyle),format(149,",d"),2015]48 ,["% of children under 5 affected","",""]49 ,[Paragraph("Wasting<super>1</super>",style=greyParaStyle),format(2,"d"),2015]50 ,[Paragraph("Severe wasting<super>1</super>",style=greyParaStyle),format(1,"d"),2015]51 ,[Paragraph("Overweight<super>1</super>",style=greyParaStyle),format(8,"d"),2015]52 ,[Paragraph("Low birth weight<super>2</super>",style=greyParaStyle),format(7,"d"),2015]53 ]54dataDictionary["Mozambique"]["table4"] = [55 [Paragraph("Adolescent overweight<super>1</super>",style=greyParaStyle),"NA","NA"]56 ,[Paragraph("Adolescent obesity<super>1</super>",style=greyParaStyle),"NA","NA"]57 ,[Paragraph("Women of reproductive age, thinness<super>2</super>",style=greyParaStyle),format(5,"d"),2010]58 ,[Paragraph("Women of reproductive age, short stature<super>2</super>",style=greyParaStyle),format(2,"d"),2010]59]60dataDictionary["Mozambique"]["table5"] = [61 [Paragraph("Women of reproductive age with anaemia<super>1</super>",style=greyParaStyle),"",""]62 ,["Total population affected (thousands of people)",format(467,",d"),2011]63 ,["Total population affected (%)",format(17,"d"),2011]64 ,[Paragraph(u"Vitamin A deficiency in children 6\u201359 months old (%)<super>2</super>",style=greyParaStyle),format(39,"d"),2013]65 ,[Paragraph(u"Population classification of iodine nutrition (age group 5\u201319 years old)<super>3</super>",style=greyParaStyle),Paragraph("Risk of iodine-induced hyperthyroidism (IIH) within 5-10 years following introduction of iodized salt in susceptible groups)",style=greyParaStyle),1996]66]67dataDictionary["Mozambique"]["table6"] = [68 [69 Paragraph("<b>Under-5 stunting</b>",style=whiteParaStyle)70 ,Paragraph("<b>Under-5 wasting</b>",style=whiteParaStyle)71 ,Paragraph("<b>Under-5 overweight</b>",style=whiteParaStyle)72 ,Paragraph("<b>WRA anaemia</b>",style=whiteParaStyle)73 ,Paragraph("<b>EBF</b>",style=whiteParaStyle)74 ]75 ,["Off course, some progress","On course","Off course, no progress","Off course","On course"]76]77dataDictionary["Mozambique"]["table6a"] = [78 [79 Paragraph("<b>Adult female obesity</b>",style=whiteParaStyle)80 ,Paragraph("<b>Adult male obesity</b>",style=whiteParaStyle)81 ,Paragraph("<b>Adult female diabetes</b>",style=whiteParaStyle)82 ,Paragraph("<b>Adult male diabetes</b>",style=whiteParaStyle)83 ]84 ,["Off course, some progress","On course","Off course, no progress","Off course"]85]86dataDictionary["Mozambique"]["table7"] = [87 [Paragraph("Severe acute malnutrition, geographic coverage<super>1</super>",style=greyParaStyle),"9","2012"]88 ,[Paragraph("Vitamin A supplementation, full coverage<super>2</super>",style=greyParaStyle),"65","2013"]89 ,[Paragraph("Children under 5 with diarrhoea receiving ORS<super>2</super>",style=greyParaStyle),"44","2011"]90 ,[Paragraph("Immunisation coverage, DTP3<super>3</super>",style=greyParaStyle),"78","2016"]91 ,[Paragraph("Iodised salt consumption<super>2</super>",style=greyParaStyle),"87","2006"]92]93dataDictionary["Mozambique"]["table8"] = [94 ["Minimum acceptable diet","6","2011"]95 ,["Minimum dietary diversity","13","2011"]96]97dataDictionary["Mozambique"]["table9"] = [98 [Paragraph("Early childbearing: births by age 18 (%)<super>1</super>",style=greyParaStyle),"33","2011"]99 ,[Paragraph("Gender Inequality Index (score*)<super>2</super>",style=greyParaStyle),"0.529","2013"]100 ,[Paragraph("Gender Inequality Index (country rank)<super>2</super>",style=greyParaStyle),"155","2013"]101]102dataDictionary["Mozambique"]["table10"] = [103 ["Physicians","0.117","2005"]104 ,["Nurses and midwives","1.306","2005"]105 ,["Community health workers","0.188","2005"]106]107dataDictionary["Mozambique"]["table11"] = [108 [Paragraph("National implementation of the International Code of Marketing of Breast-milk Substitutes<super>1</super>",style=greyParaStyle),"Law","2014"]109 ,[Paragraph("Extent of constitutional right to food<super>2</super>",style=greyParaStyle),"High","2003"]110 ,[Paragraph("Maternity Protection Convention 183<super>3</super>",style=greyParaStyle),"No","2011"]111 ,[Paragraph("Wheat fortification legislation<super>4</super>",style=greyParaStyle),"Mandatory","2015"]112 ,[Paragraph("Undernutrition mentioned in national development plans and economic growth strategies<super>5</super>",style=greyParaStyle),"Rank: 39/126","2010-2015"]113 ,[Paragraph("Overnutrition mentioned in national development plans and economic growth strategies<super>5</super>",style=greyParaStyle),"Rank: 57/116","2010-2015"]114]115dataDictionary["Mozambique"]["table12"] = [116 [Paragraph("All major NCDs",style=greyParaStyle),Paragraph("Available, partially implemented",style=greyParaStyle),"2010"]117]118dataDictionary["Afghanistan"] = copy.deepcopy(dataDictionary["Mozambique"])119dataDictionary["Albania"] = copy.deepcopy(dataDictionary["Mozambique"])120dataDictionary["Algeria"] = copy.deepcopy(dataDictionary["Mozambique"])121dataDictionary["Andorra"] = copy.deepcopy(dataDictionary["Mozambique"])122dataDictionary["Angola"] = copy.deepcopy(dataDictionary["Mozambique"])123dataDictionary["Antigua and Barbuda"] = copy.deepcopy(dataDictionary["Mozambique"])124dataDictionary["Argentina"] = copy.deepcopy(dataDictionary["Mozambique"])125dataDictionary["Armenia"] = copy.deepcopy(dataDictionary["Mozambique"])126dataDictionary["Australia"] = copy.deepcopy(dataDictionary["Mozambique"])127dataDictionary["Austria"] = copy.deepcopy(dataDictionary["Mozambique"])128dataDictionary["Azerbaijan"] = copy.deepcopy(dataDictionary["Mozambique"])129dataDictionary["Bahamas"] = copy.deepcopy(dataDictionary["Mozambique"])130dataDictionary["Bahrain"] = copy.deepcopy(dataDictionary["Mozambique"])131dataDictionary["Bangladesh"] = copy.deepcopy(dataDictionary["Mozambique"])132dataDictionary["Barbados"] = copy.deepcopy(dataDictionary["Mozambique"])133dataDictionary["Belarus"] = copy.deepcopy(dataDictionary["Mozambique"])134dataDictionary["Belgium"] = copy.deepcopy(dataDictionary["Mozambique"])135dataDictionary["Belize"] = copy.deepcopy(dataDictionary["Mozambique"])136dataDictionary["Benin"] = copy.deepcopy(dataDictionary["Mozambique"])137dataDictionary["Bhutan"] = copy.deepcopy(dataDictionary["Mozambique"])138dataDictionary["Bolivia"] = copy.deepcopy(dataDictionary["Mozambique"])139dataDictionary["Bosnia and Herzegovina"] = copy.deepcopy(dataDictionary["Mozambique"])140dataDictionary["Botswana"] = copy.deepcopy(dataDictionary["Mozambique"])141dataDictionary["Brazil"] = copy.deepcopy(dataDictionary["Mozambique"])142dataDictionary["Brunei Darussalam"] = copy.deepcopy(dataDictionary["Mozambique"])143dataDictionary["Bulgaria"] = copy.deepcopy(dataDictionary["Mozambique"])144dataDictionary["Burkina Faso"] = copy.deepcopy(dataDictionary["Mozambique"])145dataDictionary["Burundi"] = copy.deepcopy(dataDictionary["Mozambique"])146dataDictionary["Cambodia"] = copy.deepcopy(dataDictionary["Mozambique"])147dataDictionary["Cameroon"] = copy.deepcopy(dataDictionary["Mozambique"])148dataDictionary["Canada"] = copy.deepcopy(dataDictionary["Mozambique"])149dataDictionary["Cape Verde"] = copy.deepcopy(dataDictionary["Mozambique"])150dataDictionary["Central African Republic"] = copy.deepcopy(dataDictionary["Mozambique"])151dataDictionary["Chad"] = copy.deepcopy(dataDictionary["Mozambique"])152dataDictionary["Chile"] = copy.deepcopy(dataDictionary["Mozambique"])153dataDictionary["China"] = copy.deepcopy(dataDictionary["Mozambique"])154dataDictionary["Colombia"] = copy.deepcopy(dataDictionary["Mozambique"])155dataDictionary["Comoros"] = copy.deepcopy(dataDictionary["Mozambique"])156dataDictionary["Congo (Republic of the)"] = copy.deepcopy(dataDictionary["Mozambique"])157dataDictionary["Costa Rica"] = copy.deepcopy(dataDictionary["Mozambique"])158dataDictionary["Cote d'Ivoire"] = copy.deepcopy(dataDictionary["Mozambique"])159dataDictionary["Croatia"] = copy.deepcopy(dataDictionary["Mozambique"])160dataDictionary["Cuba"] = copy.deepcopy(dataDictionary["Mozambique"])161dataDictionary["Cyprus"] = copy.deepcopy(dataDictionary["Mozambique"])162dataDictionary["Czech Republic"] = copy.deepcopy(dataDictionary["Mozambique"])163dataDictionary["Democratic People's Republic of Korea"] = copy.deepcopy(dataDictionary["Mozambique"])164dataDictionary["Democratic Republic of the Congo"] = copy.deepcopy(dataDictionary["Mozambique"])165dataDictionary["Denmark"] = copy.deepcopy(dataDictionary["Mozambique"])166dataDictionary["Djibouti"] = copy.deepcopy(dataDictionary["Mozambique"])167dataDictionary["Dominica"] = copy.deepcopy(dataDictionary["Mozambique"])168dataDictionary["Dominican Republic"] = copy.deepcopy(dataDictionary["Mozambique"])169dataDictionary["Ecuador"] = copy.deepcopy(dataDictionary["Mozambique"])170dataDictionary["Egypt"] = copy.deepcopy(dataDictionary["Mozambique"])171dataDictionary["El Salvador"] = copy.deepcopy(dataDictionary["Mozambique"])172dataDictionary["Equatorial Guinea"] = copy.deepcopy(dataDictionary["Mozambique"])173dataDictionary["Eritrea"] = copy.deepcopy(dataDictionary["Mozambique"])174dataDictionary["Estonia"] = copy.deepcopy(dataDictionary["Mozambique"])175dataDictionary["Ethiopia"] = copy.deepcopy(dataDictionary["Mozambique"])176dataDictionary["Fiji"] = copy.deepcopy(dataDictionary["Mozambique"])177dataDictionary["Finland"] = copy.deepcopy(dataDictionary["Mozambique"])178dataDictionary["France"] = copy.deepcopy(dataDictionary["Mozambique"])179dataDictionary["Gabon"] = copy.deepcopy(dataDictionary["Mozambique"])180dataDictionary["Gambia"] = copy.deepcopy(dataDictionary["Mozambique"])181dataDictionary["Georgia"] = copy.deepcopy(dataDictionary["Mozambique"])182dataDictionary["Germany"] = copy.deepcopy(dataDictionary["Mozambique"])183dataDictionary["Ghana"] = copy.deepcopy(dataDictionary["Mozambique"])184dataDictionary["Greece"] = copy.deepcopy(dataDictionary["Mozambique"])185dataDictionary["Grenada"] = copy.deepcopy(dataDictionary["Mozambique"])186dataDictionary["Guatemala"] = copy.deepcopy(dataDictionary["Mozambique"])187dataDictionary["Guinea"] = copy.deepcopy(dataDictionary["Mozambique"])188dataDictionary["Guinea-Bissau"] = copy.deepcopy(dataDictionary["Mozambique"])189dataDictionary["Guyana"] = copy.deepcopy(dataDictionary["Mozambique"])190dataDictionary["Haiti"] = copy.deepcopy(dataDictionary["Mozambique"])191dataDictionary["Honduras"] = copy.deepcopy(dataDictionary["Mozambique"])192dataDictionary["Hungary"] = copy.deepcopy(dataDictionary["Mozambique"])193dataDictionary["Iceland"] = copy.deepcopy(dataDictionary["Mozambique"])194dataDictionary["India"] = copy.deepcopy(dataDictionary["Mozambique"])195dataDictionary["Indonesia"] = copy.deepcopy(dataDictionary["Mozambique"])196dataDictionary["Iran"] = copy.deepcopy(dataDictionary["Mozambique"])197dataDictionary["Iraq"] = copy.deepcopy(dataDictionary["Mozambique"])198dataDictionary["Ireland"] = copy.deepcopy(dataDictionary["Mozambique"])199dataDictionary["Israel"] = copy.deepcopy(dataDictionary["Mozambique"])200dataDictionary["Italy"] = copy.deepcopy(dataDictionary["Mozambique"])201dataDictionary["Jamaica"] = copy.deepcopy(dataDictionary["Mozambique"])202dataDictionary["Japan"] = copy.deepcopy(dataDictionary["Mozambique"])203dataDictionary["Jordan"] = copy.deepcopy(dataDictionary["Mozambique"])204dataDictionary["Kazakhstan"] = copy.deepcopy(dataDictionary["Mozambique"])205dataDictionary["Kenya"] = copy.deepcopy(dataDictionary["Mozambique"])206dataDictionary["Kiribati"] = copy.deepcopy(dataDictionary["Mozambique"])207dataDictionary["Kuwait"] = copy.deepcopy(dataDictionary["Mozambique"])208dataDictionary["Kyrgyzstan"] = copy.deepcopy(dataDictionary["Mozambique"])209dataDictionary["Lao People's Democratic Republic"] = copy.deepcopy(dataDictionary["Mozambique"])210dataDictionary["Latvia"] = copy.deepcopy(dataDictionary["Mozambique"])211dataDictionary["Lebanon"] = copy.deepcopy(dataDictionary["Mozambique"])212dataDictionary["Lesotho"] = copy.deepcopy(dataDictionary["Mozambique"])213dataDictionary["Liberia"] = copy.deepcopy(dataDictionary["Mozambique"])214dataDictionary["Libya"] = copy.deepcopy(dataDictionary["Mozambique"])215dataDictionary["Liechtenstein"] = copy.deepcopy(dataDictionary["Mozambique"])216dataDictionary["Lithuania"] = copy.deepcopy(dataDictionary["Mozambique"])217dataDictionary["Luxembourg"] = copy.deepcopy(dataDictionary["Mozambique"])218dataDictionary["Madagascar"] = copy.deepcopy(dataDictionary["Mozambique"])219dataDictionary["Malawi"] = copy.deepcopy(dataDictionary["Mozambique"])220dataDictionary["Malaysia"] = copy.deepcopy(dataDictionary["Mozambique"])221dataDictionary["Maldives"] = copy.deepcopy(dataDictionary["Mozambique"])222dataDictionary["Mali"] = copy.deepcopy(dataDictionary["Mozambique"])223dataDictionary["Malta"] = copy.deepcopy(dataDictionary["Mozambique"])224dataDictionary["Marshall Islands"] = copy.deepcopy(dataDictionary["Mozambique"])225dataDictionary["Mauritania"] = copy.deepcopy(dataDictionary["Mozambique"])226dataDictionary["Mauritius"] = copy.deepcopy(dataDictionary["Mozambique"])227dataDictionary["Mexico"] = copy.deepcopy(dataDictionary["Mozambique"])228dataDictionary["Micronesia (Federated States of)"] = copy.deepcopy(dataDictionary["Mozambique"])229dataDictionary["Monaco"] = copy.deepcopy(dataDictionary["Mozambique"])230dataDictionary["Mongolia"] = copy.deepcopy(dataDictionary["Mozambique"])231dataDictionary["Montenegro"] = copy.deepcopy(dataDictionary["Mozambique"])232dataDictionary["Morocco"] = copy.deepcopy(dataDictionary["Mozambique"])233# dataDictionary["Mozambique"] = copy.deepcopy(dataDictionary["Mozambique"])234dataDictionary["Myanmar"] = copy.deepcopy(dataDictionary["Mozambique"])235dataDictionary["Namibia"] = copy.deepcopy(dataDictionary["Mozambique"])236dataDictionary["Nauru"] = copy.deepcopy(dataDictionary["Mozambique"])237dataDictionary["Nepal"] = copy.deepcopy(dataDictionary["Mozambique"])238dataDictionary["Netherlands"] = copy.deepcopy(dataDictionary["Mozambique"])239dataDictionary["New Zealand"] = copy.deepcopy(dataDictionary["Mozambique"])240dataDictionary["Nicaragua"] = copy.deepcopy(dataDictionary["Mozambique"])241dataDictionary["Niger"] = copy.deepcopy(dataDictionary["Mozambique"])242dataDictionary["Nigeria"] = copy.deepcopy(dataDictionary["Mozambique"])243dataDictionary["Norway"] = copy.deepcopy(dataDictionary["Mozambique"])244dataDictionary["Oman"] = copy.deepcopy(dataDictionary["Mozambique"])245dataDictionary["Pakistan"] = copy.deepcopy(dataDictionary["Mozambique"])246dataDictionary["Palau"] = copy.deepcopy(dataDictionary["Mozambique"])247dataDictionary["Panama"] = copy.deepcopy(dataDictionary["Mozambique"])248dataDictionary["Papua New Guinea"] = copy.deepcopy(dataDictionary["Mozambique"])249dataDictionary["Paraguay"] = copy.deepcopy(dataDictionary["Mozambique"])250dataDictionary["Peru"] = copy.deepcopy(dataDictionary["Mozambique"])251dataDictionary["Philippines"] = copy.deepcopy(dataDictionary["Mozambique"])252dataDictionary["Poland"] = copy.deepcopy(dataDictionary["Mozambique"])253dataDictionary["Portugal"] = copy.deepcopy(dataDictionary["Mozambique"])254dataDictionary["Qatar"] = copy.deepcopy(dataDictionary["Mozambique"])255dataDictionary["Republic of Korea"] = copy.deepcopy(dataDictionary["Mozambique"])256dataDictionary["Republic of Moldova"] = copy.deepcopy(dataDictionary["Mozambique"])257dataDictionary["Romania"] = copy.deepcopy(dataDictionary["Mozambique"])258dataDictionary["Russian Federation"] = copy.deepcopy(dataDictionary["Mozambique"])259dataDictionary["Rwanda"] = copy.deepcopy(dataDictionary["Mozambique"])260dataDictionary["Saint Kitts and Nevis"] = copy.deepcopy(dataDictionary["Mozambique"])261dataDictionary["Saint Lucia"] = copy.deepcopy(dataDictionary["Mozambique"])262dataDictionary["Saint Vincent and the Grenadines"] = copy.deepcopy(dataDictionary["Mozambique"])263dataDictionary["Samoa"] = copy.deepcopy(dataDictionary["Mozambique"])264dataDictionary["San Marino"] = copy.deepcopy(dataDictionary["Mozambique"])265dataDictionary["Sao Tome and Principe"] = copy.deepcopy(dataDictionary["Mozambique"])266dataDictionary["Saudi Arabia"] = copy.deepcopy(dataDictionary["Mozambique"])267dataDictionary["Senegal"] = copy.deepcopy(dataDictionary["Mozambique"])268dataDictionary["Serbia"] = copy.deepcopy(dataDictionary["Mozambique"])269dataDictionary["Seychelles"] = copy.deepcopy(dataDictionary["Mozambique"])270dataDictionary["Sierra Leone"] = copy.deepcopy(dataDictionary["Mozambique"])271dataDictionary["Singapore"] = copy.deepcopy(dataDictionary["Mozambique"])272dataDictionary["Slovakia"] = copy.deepcopy(dataDictionary["Mozambique"])273dataDictionary["Slovenia"] = copy.deepcopy(dataDictionary["Mozambique"])274dataDictionary["Solomon Islands"] = copy.deepcopy(dataDictionary["Mozambique"])275dataDictionary["Somalia"] = copy.deepcopy(dataDictionary["Mozambique"])276dataDictionary["South Africa"] = copy.deepcopy(dataDictionary["Mozambique"])277dataDictionary["South Sudan"] = copy.deepcopy(dataDictionary["Mozambique"])278dataDictionary["Spain"] = copy.deepcopy(dataDictionary["Mozambique"])279dataDictionary["Sri Lanka"] = copy.deepcopy(dataDictionary["Mozambique"])280dataDictionary["Sudan"] = copy.deepcopy(dataDictionary["Mozambique"])281dataDictionary["Suriname"] = copy.deepcopy(dataDictionary["Mozambique"])282dataDictionary["Swaziland"] = copy.deepcopy(dataDictionary["Mozambique"])283dataDictionary["Sweden"] = copy.deepcopy(dataDictionary["Mozambique"])284dataDictionary["Switzerland"] = copy.deepcopy(dataDictionary["Mozambique"])285dataDictionary["Syria"] = copy.deepcopy(dataDictionary["Mozambique"])286dataDictionary["Tajikistan"] = copy.deepcopy(dataDictionary["Mozambique"])287dataDictionary["Thailand"] = copy.deepcopy(dataDictionary["Mozambique"])288dataDictionary["The former Yugoslav Republic of Macedonia"] = copy.deepcopy(dataDictionary["Mozambique"])289dataDictionary["Timor-Leste"] = copy.deepcopy(dataDictionary["Mozambique"])290dataDictionary["Togo"] = copy.deepcopy(dataDictionary["Mozambique"])291dataDictionary["Tonga"] = copy.deepcopy(dataDictionary["Mozambique"])292dataDictionary["Trinidad and Tobago"] = copy.deepcopy(dataDictionary["Mozambique"])293dataDictionary["Tunisia"] = copy.deepcopy(dataDictionary["Mozambique"])294dataDictionary["Turkey"] = copy.deepcopy(dataDictionary["Mozambique"])295dataDictionary["Turkmenistan"] = copy.deepcopy(dataDictionary["Mozambique"])296dataDictionary["Tuvalu"] = copy.deepcopy(dataDictionary["Mozambique"])297dataDictionary["Uganda"] = copy.deepcopy(dataDictionary["Mozambique"])298dataDictionary["Ukraine"] = copy.deepcopy(dataDictionary["Mozambique"])299dataDictionary["United Arab Emirates"] = copy.deepcopy(dataDictionary["Mozambique"])300dataDictionary["United Kingdom"] = copy.deepcopy(dataDictionary["Mozambique"])301dataDictionary["United Republic of Tanzania"] = copy.deepcopy(dataDictionary["Mozambique"])302dataDictionary["United States of America"] = copy.deepcopy(dataDictionary["Mozambique"])303dataDictionary["Uruguay"] = copy.deepcopy(dataDictionary["Mozambique"])304dataDictionary["Uzbekistan"] = copy.deepcopy(dataDictionary["Mozambique"])305dataDictionary["Vanuatu"] = copy.deepcopy(dataDictionary["Mozambique"])306dataDictionary["Venezuela"] = copy.deepcopy(dataDictionary["Mozambique"])307dataDictionary["Viet Nam"] = copy.deepcopy(dataDictionary["Mozambique"])308dataDictionary["Yemen"] = copy.deepcopy(dataDictionary["Mozambique"])309dataDictionary["Zambia"] = copy.deepcopy(dataDictionary["Mozambique"])310dataDictionary["Zimbabwe"] = copy.deepcopy(dataDictionary["Mozambique"])311def replaceDash(x):312 x = str(x)313 y = re.sub(r"((?:^|[^{])\d+)-(\d+[^}])",u"\\1\u2013\\2", x)314 return y315missingVals = [" ",".","","Insufficient data to make assessment"]316def safeFormat(x,commas=False,precision=0):317 if pd.isnull(x):318 return "NA"319 elif x in missingVals:320 return "NA"321 else:322 if not isinstance(x,numbers.Number):323 return replaceDash(x)324 if precision == 0:325 x = int(round(x,precision))326 else:327 x = round(x,precision)328 if commas:329 return format(x,",")330 else:331 return x332dat = pd.read_csv("data.csv")333for country in dataDictionary.keys():334 row = dat.loc[(dat.country==country)].iloc[0]335 dataDictionary[country]["country"] = country336 337 # dataDictionary["Mozambique"]["table1"] = [["Gini index score*",Paragraph("Gini index rank<super>†</super>",style=whiteParaStyle),"Year"],[51,125,2011]]338 dataDictionary[country]["table1"][1] = [safeFormat(row["value_gini"]),safeFormat(row["rank_gini"]),safeFormat(row["year_gini"])]339 # dataDictionary["Mozambique"]["table2"] = [340 # ["Population (000)",format(12428,",d"),2015]341 # ,["Under-5 population (000)",format(1935,",d"),2015]342 # ,["Urban (%)",format(20,",d"),2015]343 # ,[">65 years (%)",format(5,",d"),2015]344 # ]345 dataDictionary[country]["table2"][0][1] = safeFormat(row["totalpop2017"],True)346 dataDictionary[country]["table2"][0][2] = safeFormat(2017)347 dataDictionary[country]["table2"][1][1] = safeFormat(row["under5pop"],True)348 dataDictionary[country]["table2"][1][2] = safeFormat(2017)349 dataDictionary[country]["table2"][2][1] = safeFormat(row["urbanpop"])350 dataDictionary[country]["table2"][2][2] = safeFormat(2017)351 dataDictionary[country]["table2"][3][1] = safeFormat(row["over65pop"],True)352 dataDictionary[country]["table2"][3][2] = safeFormat(2017)353 # dataDictionary["Mozambique"]["table3"] = [354 # ["Number of children under 5 affected (000)","",""]355 # ,[Paragraph("Stunting<super>a</super>",style=greyParaStyle),format(733,",d"),2015]356 # ,[Paragraph("Wasting<super>a</super>",style=greyParaStyle),format(43,",d"),2015]357 # ,[Paragraph("Overweight<super>a</super>",style=greyParaStyle),format(149,",d"),2015]358 # ,["Percentage of children under 5 affected","",""]359 # ,[Paragraph("Wasting<super>a</super>",style=greyParaStyle),format(2,"d"),2015]360 # ,[Paragraph("Severe wasting<super>a</super>",style=greyParaStyle),format(1,"d"),2015]361 # ,[Paragraph("Overweight<super>a</super>",style=greyParaStyle),format(8,"d"),2015]362 # ,[Paragraph("Low birth weight<super>b</super>",style=greyParaStyle),format(7,"d"),2015]363 # ]364 dataDictionary[country]["table3"][1][1] = safeFormat(row["number_stunting_current"],True)365 dataDictionary[country]["table3"][1][2] = safeFormat(row["year_stunting_current"])366 dataDictionary[country]["table3"][2][1] = safeFormat(row["number_wasting"],True)367 dataDictionary[country]["table3"][2][2] = safeFormat(row["year_wasting"])368 dataDictionary[country]["table3"][3][1] = safeFormat(row["number_u5overweight"],True)369 dataDictionary[country]["table3"][3][2] = safeFormat(row["year_u5overweight"])370 dataDictionary[country]["table3"][5][1] = safeFormat(row["prev_wasting"])371 dataDictionary[country]["table3"][5][2] = safeFormat(row["year_wasting"])372 dataDictionary[country]["table3"][6][1] = safeFormat(row["prev_sev_wasting"])373 dataDictionary[country]["table3"][6][2] = safeFormat(row["year_sev_wasting"])374 dataDictionary[country]["table3"][7][1] = safeFormat(row["prev_u5overweight"])375 dataDictionary[country]["table3"][7][2] = safeFormat(row["year_u5overweight"])376 dataDictionary[country]["table3"][8][1] = safeFormat(row["LBW"])377 dataDictionary[country]["table3"][8][2] = safeFormat(row["year_lbw"])378 # dataDictionary["Mozambique"]["table4"] = [379 # [Paragraph("Adolescent overweight<super>a</super>",style=greyParaStyle),"NA","NA"]380 # ,[Paragraph("Adolescent obesity<super>a</super>",style=greyParaStyle),"NA","NA"]381 # ,[Paragraph("Women of reproductive age, thinness<super>b</super>",style=greyParaStyle),format(5,"d"),2010]382 # ,[Paragraph("Women of reproductive age, short stature<super>b</super>",style=greyParaStyle),format(2,"d"),2010]383 # ]384 dataDictionary[country]["table4"][0][1] = safeFormat(row["AdolOW"])385 dataDictionary[country]["table4"][0][2] = safeFormat(row["year_adolOWOB"])386 dataDictionary[country]["table4"][1][1] = safeFormat(row["AdolOB"])387 dataDictionary[country]["table4"][1][2] = safeFormat(row["year_adolOWOB"])388 dataDictionary[country]["table4"][2][1] = safeFormat(row["prev_BMI185"])389 dataDictionary[country]["table4"][2][2] = safeFormat(row["year_BMI185"])390 dataDictionary[country]["table4"][3][1] = safeFormat(row["prev_height145"])391 dataDictionary[country]["table4"][3][2] = safeFormat(row["year_height145"])392 # dataDictionary["Mozambique"]["table5"] = [393 # [Paragraph("Women of reproductive age with anemia<super>a</super>",style=greyParaStyle),"",""]394 # ,["Total population affected (000)",format(467,",d"),2011]395 # ,["Total population affected (%)",format(17,"d"),2011]396 # ,[Paragraph("Vitamin A deficiency in children 6-59 months old (%)<super>b</super>",style=greyParaStyle),format(39,"d"),2013]397 # ,[Paragraph("Population classification of iodine nutrition (age group 5-19)<super>c</super>",style=greyParaStyle),Paragraph("Risk of iodine-induced hyperthyroidism (IIH) within 5-10 years following introduction of iodized salt in susceptible groups)",style=greyParaStyle),1996]398 # ]399 dataDictionary[country]["table5"][1][1] = safeFormat(row["WRAanaemia_NUMBER"],True)400 dataDictionary[country]["table5"][1][2] = safeFormat(row["year_WRAanaemia"])401 dataDictionary[country]["table5"][2][1] = safeFormat(row["WRAanaemia_RATE"])402 dataDictionary[country]["table5"][2][2] = safeFormat(row["year_WRAanaemia"])403 dataDictionary[country]["table5"][3][1] = safeFormat(row["prevalence_vita"])404 dataDictionary[country]["table5"][3][2] = safeFormat(row["year_vitA_def"])405 dataDictionary[country]["table5"][4][1] = Paragraph(safeFormat(row["Class_IodineNutrition"]),style=greyCenterParaStyle)406 dataDictionary[country]["table5"][4][2] = safeFormat(row["year_IodineNutrition"])407 # dataDictionary["Mozambique"]["table6"] = [408 # [409 # Paragraph("<b>Under-5 stunting, 2015<super>a</super></b>",style=whiteParaStyle)410 # ,Paragraph("<b>Under-5 wasting, 2015<super>b</super></b>",style=whiteParaStyle)411 # ,Paragraph("<b>Under-5 overweight, 2015<super>a</super></b>",style=whiteParaStyle)412 # ,Paragraph("<b>WRA Anemia, 2011<super>b</super></b>",style=whiteParaStyle)413 # ,Paragraph("<b>EBF, 2014-2015<super>a</super></b>",style=whiteParaStyle)414 # ]415 # ,["Off course, some progress","On course","Off course, no progress","Off course","On course"]416 # ]417 # dataDictionary[country]["table6"][0] = [418 # Paragraph("<b>Under-5 stunting, %s<super>1</super></b>" % safeFormat(row["year_stunting_current"]),style=whiteParaStyle)419 # ,Paragraph("<b>Under-5 wasting, %s<super>2</super></b>" % safeFormat(row["year_wasting"]),style=whiteParaStyle)420 # ,Paragraph("<b>Under-5 overweight, %s<super>1</super></b>" % safeFormat(row["year_u5overweight"]),style=whiteParaStyle)421 # ,Paragraph("<b>WRA anaemia, %s<super>2</super></b>" % safeFormat(row["year_WRAanaemia"]),style=whiteParaStyle)422 # ,Paragraph("<b>EBF, %s<super>1</super></b>" % safeFormat(row["year_ebf_current"]),style=whiteParaStyle)423 # ]424 dataDictionary[country]["table6"][1] = [425 Paragraph(safeFormat(row["stunting_progress"]),style=condStyle(row["stunting_progress"]))426 ,Paragraph(safeFormat(row["wasting_progress"]),style=condStyle(row["wasting_progress"]))427 ,Paragraph(safeFormat(row["u5overweight_progress"]),style=condStyle(row["u5overweight_progress"]))428 ,Paragraph(safeFormat(row["progress_WRAanaemia"]),style=condStyle(row["progress_WRAanaemia"]))429 ,Paragraph(safeFormat(row["EBF_progress"]),style=condStyle(row["EBF_progress"]))430 ]431 # dataDictionary["Mozambique"]["table6a"] = [432 # [433 # Paragraph("<b>Adult female obesity, 2015<super>a</super></b>",style=whiteParaStyle)434 # ,Paragraph("<b>Adult male obesity, 2015<super>a</super></b>",style=whiteParaStyle)435 # ,Paragraph("<b>Adult female diabetes, 2015<super>a</super></b>",style=whiteParaStyle)436 # ,Paragraph("<b>Adult male diabetes, 2015<super>a</super></b>",style=whiteParaStyle)437 # ]438 # ,["Off course, some progress","On course","Off course, no progress","Off course"]439 # ]440 # dataDictionary[country]["table6a"][0] = [441 # Paragraph("<b>Adult female obesity, %s<super>1</super></b>" % safeFormat(2017),style=whiteParaStyle)442 # ,Paragraph("<b>Adult male obesity, %s<super>1</super></b>" % safeFormat(2017),style=whiteParaStyle)443 # ,Paragraph("<b>Adult female diabetes, %s<super>1</super></b>" % safeFormat(2017),style=whiteParaStyle)444 # ,Paragraph("<b>Adult male diabetes, %s<super>1</super></b>" % safeFormat(2017),style=whiteParaStyle)445 # ]446 dataDictionary[country]["table6a"][1] = [447 Paragraph(safeFormat(row["ob_female_progress"]),style=condStyle(row["ob_female_progress"]))448 ,Paragraph(safeFormat(row["ob_male_progress"]),style=condStyle(row["ob_male_progress"]))449 ,Paragraph(safeFormat(row["dm_female_progress"]),style=condStyle(row["dm_female_progress"]))450 ,Paragraph(safeFormat(row["dm_male_progress"]),style=condStyle(row["dm_male_progress"]))451 ]452 # dataDictionary["Mozambique"]["table7"] = [453 # [Paragraph("Severe acute malnutrition, geographic coverage<super>a</super>",style=greyParaStyle),"9","2012"]454 # ,[Paragraph("Vitamin A supplementation, full coverage<super>b</super>",style=greyParaStyle),"65","2013"]455 # ,[Paragraph("Children under 5 with diarrhea receiving ORS<super>b</super>",style=greyParaStyle),"44","2011"]456 # ,[Paragraph("Immunization coverage, DTP3<super>b</super>",style=greyParaStyle),"78","2013"]457 # ,[Paragraph("Iodized salt consumption<super>b</super>",style=greyParaStyle),"87","2006"]458 # ]459 dataDictionary[country]["table7"][0][1] = safeFormat(row["SAMcoverage_rate"])460 dataDictionary[country]["table7"][0][2] = safeFormat(row["year_SAM"])461 dataDictionary[country]["table7"][1][1] = safeFormat(row["vitA_supp"])462 dataDictionary[country]["table7"][1][2] = safeFormat(row["year_vitA_supp"])463 dataDictionary[country]["table7"][2][1] = safeFormat(row["ors"])464 dataDictionary[country]["table7"][2][2] = safeFormat(row["yr_ors"])465 dataDictionary[country]["table7"][3][1] = safeFormat(row["DTP3"])466 dataDictionary[country]["table7"][3][2] = safeFormat(row["year_DTP"])467 dataDictionary[country]["table7"][4][1] = safeFormat(row["householdconsumingsalt"])468 dataDictionary[country]["table7"][4][2] = safeFormat(row["year_Iodised"])469 # dataDictionary["Mozambique"]["table8"] = [470 # ["Minimum acceptable diet","6","2011"]471 # ,["Minimum dietary diversity","13","2011"]472 # ]473 dataDictionary[country]["table8"][0][1] = safeFormat(row["mad"])474 dataDictionary[country]["table8"][0][2] = safeFormat(row["yr_mad"])475 dataDictionary[country]["table8"][1][1] = safeFormat(row["mdd"])476 dataDictionary[country]["table8"][1][2] = safeFormat(row["yr_mdd"])477 # dataDictionary["Mozambique"]["table9"] = [478 # [Paragraph("Early childbearing: births by age 18 (%)<super>a</super>",style=greyParaStyle),"33","2011"]479 # ,[Paragraph("Gender Inequality Index (score*)<super>b</super>",style=greyParaStyle),"0.529","2013"]480 # ,[Paragraph("Gender Inequality Index (country rank)<super>b</super>",style=greyParaStyle),"155","2013"]481 # ]482 dataDictionary[country]["table9"][0][1] = safeFormat(row["earlychild"])483 dataDictionary[country]["table9"][0][2] = safeFormat(row["year_early_child"])484 dataDictionary[country]["table9"][1][1] = safeFormat(row["index_genderinequality"],False,3)485 dataDictionary[country]["table9"][1][2] = safeFormat(row["year_genderinequality"])486 dataDictionary[country]["table9"][2][1] = safeFormat(row["rank_genderinequality"])487 dataDictionary[country]["table9"][2][2] = safeFormat(row["year_genderinequality"])488 # dataDictionary["Mozambique"]["table10"] = [489 # ["Physicians","0.117","2005"]490 # ,["Nurses and midwives","1.306","2005"]491 # ,["Community health workers","0.188","2005"]492 # ]493 dataDictionary[country]["table10"][0][1] = safeFormat(row["valuephysician"],False,3)494 dataDictionary[country]["table10"][0][2] = safeFormat(row["yearphysician"])495 dataDictionary[country]["table10"][1][1] = safeFormat(row["valuenurse"],False,3)496 dataDictionary[country]["table10"][1][2] = safeFormat(row["yearnurse"])497 dataDictionary[country]["table10"][2][1] = safeFormat(row["valuehealthworker"],False,3)498 dataDictionary[country]["table10"][2][2] = safeFormat(row["yearhealthworker"])499 # dataDictionary["Mozambique"]["table11"] = [500 # [Paragraph("National implementation of the International Code of Marketing of Breast-milk Substitutes<super>a</super>",style=greyParaStyle),"Law","2014"]501 # ,[Paragraph("Extent of constitutional right to food<super>b</super>",style=greyParaStyle),"High","2003"]502 # ,[Paragraph("Maternity protection (Convention 183)<super>c</super>",style=greyParaStyle),"No","2011"]503 # ,[Paragraph("Wheat fortification legislation<super>d</super>",style=greyParaStyle),"Mandatory","2015"]504 # ,[Paragraph("Undernutrition mentioned in national development plans and economic growth strategies<super>e</super>",style=greyParaStyle),"Rank: 39/126","2010-2015"]505 # ,[Paragraph("Overnutrition mentioned in national development plans and economic growth strategies<super>e</super>",style=greyParaStyle),"Rank: 57/116","2010-2015"]506 # ]507 dataDictionary[country]["table11"][0][1] = Paragraph(safeFormat(row["Code_breastfeeding"]),style=greyCenterParaStyle)508 dataDictionary[country]["table11"][0][2] = safeFormat(row["year_code_bf"])509 dataDictionary[country]["table11"][1][1] = Paragraph(safeFormat(row["RTF_level1"]),style=greyCenterParaStyle)510 dataDictionary[country]["table11"][1][2] = safeFormat(row["year_RTF"])511 dataDictionary[country]["table11"][2][1] = Paragraph(safeFormat(row["cat_maternityprotection"]),style=greyCenterParaStyle)512 dataDictionary[country]["table11"][2][2] = safeFormat(row["year_maternityprotection"])513 dataDictionary[country]["table11"][3][1] = Paragraph(safeFormat(row["Fortification"]),style=greyCenterParaStyle)514 dataDictionary[country]["table11"][3][2] = safeFormat(row["year_FFI"])515 dataDictionary[country]["table11"][4][1] = safeFormat(row["undernutritionrank_1to126"])516 dataDictionary[country]["table11"][4][2] = safeFormat(row["year_policyrank"])517 dataDictionary[country]["table11"][5][1] = safeFormat(row["overnutritionrank_1to116"])518 dataDictionary[country]["table11"][5][2] = safeFormat(row["year_policyrank"])519 # dataDictionary["Mozambique"]["table12"] = [520 # ["All major NCDs",Paragraph("Available, partially implemented",style=greyParaStyle),"2010"]521 # ]522 dataDictionary[country]["table12"][0][1] = Paragraph(safeFormat(row["ncd_policy"]),style=greyCenterParaStyle)523 dataDictionary[country]["table12"][0][2] = safeFormat(row["year_ncdpolicy"])524tableStyles = {}525tableStyles["table1"] = [526 ('TEXTCOLOR',(0,0),(-1,-1),"white")527 ,('BACKGROUND',(0,0),(2,0),"#7b1059")528 ,('FONTNAME',(0,1),(2,1),"Arial-Bold")529 # ,('FONTNAME',(0,1),(2,1),"Arial")530 ,('BACKGROUND',(0,1),(2,1),"#c79ec5")531 # ,('GRID',(0,0),(-1,-1),1,"white")532 ,('LINEAFTER',(0,0),(1,1),1,"white")533 ,('ALIGN',(0,0),(-1,-1),"CENTER")534 ,('VALIGN',(0,0),(-1,-1),"MIDDLE")535 ]536tableStyles["table2"] = [537 ('BACKGROUND',(0,1),(-1,1),"#fef5e7")538 ,('BACKGROUND',(0,3),(-1,3),"#fef5e7")539 ,('ALIGN',(0,0),(0,-1),"LEFT")540 ,('ALIGN',(1,0),(2,-1),"CENTER")541 ,('VALIGN',(0,0),(-1,-1),"MIDDLE")542 ,('BOX',(1,0),(1,-1),1,"#f79c2a")543 ,('LINEABOVE',(0,0),(-1,0),1,"#f79c2a")544 ,('LINEBELOW',(0,-1),(-1,-1),1,"#f79c2a")545 ,('TEXTCOLOR',(0,0),(-1,-1),"#443e42")546 ]547tableStyles["table3"] = [548 ('BACKGROUND',(0,1),(-1,1),"#fef5e7")549 ,('BACKGROUND',(0,3),(-1,3),"#fef5e7")550 ,('BACKGROUND',(0,5),(-1,5),"#fef5e7")551 ,('BACKGROUND',(0,7),(-1,7),"#fef5e7")552 ,('ALIGN',(0,0),(0,-1),"LEFT")553 ,('ALIGN',(1,0),(2,-1),"CENTER")554 ,('VALIGN',(0,0),(-1,-1),"MIDDLE")555 ,('BOX',(1,1),(1,3),1,"#f79c2a")556 ,('BOX',(1,5),(1,-1),1,"#f79c2a")557 ,('LINEABOVE',(0,0),(-1,0),1,"#f79c2a")558 ,('LINEABOVE',(0,1),(-1,1),1,"#f79c2a")559 ,('LINEABOVE',(0,4),(-1,4),1,"#f79c2a")560 ,('LINEABOVE',(0,5),(-1,5),1,"#f79c2a")561 ,('LINEBELOW',(0,-1),(-1,-1),1,"#f79c2a")562 ,('SPAN',(0,0),(-1,0))563 # ,('FONTNAME',(0,0),(-1,0),"Arial-Bold")564 ,('SPAN',(0,4),(-1,4))565 # ,('FONTNAME',(0,4),(-1,4),"Arial-Bold")566 ,('TEXTCOLOR',(0,0),(-1,-1),"#443e42")567 ]568tableStyles["table4"] = tableStyles["table2"]569tableStyles["table5"] = [570 ('LINEABOVE',(0,0),(-1,0),1,"#f79c2a")571 ,('LINEABOVE',(0,1),(-1,1),1,"#f79c2a")572 ,('LINEABOVE',(0,3),(-1,3),1,"#f79c2a")573 ,('LINEBELOW',(0,4),(-1,4),1,"#f79c2a")574 ,('SPAN',(0,0),(-1,0))575 ,('LINEAFTER',(0,1),(1,2),.5,"#fbcd99")576 ,('LINEBELOW',(0,1),(-1,1),.5,"#fbcd99")577 ,('LINEAFTER',(0,3),(1,-1),1,"#fbcd99")578 ,('LINEABOVE',(0,-1),(-1,-1),1,"#fbcd99")579 ,('VALIGN',(0,0),(-1,-1),"MIDDLE")580 ,('ALIGN',(0,0),(0,-1),"LEFT")581 ,('ALIGN',(1,0),(2,-1),"CENTER")582 ,('TEXTCOLOR',(0,0),(-1,-1),"#443e42")583]584tableStyles["table6"] = [585 ('TEXTCOLOR',(0,0),(-1,0),"white")586 # ,('BACKGROUND',(0,0),(-1,0),"#204d5e")587 # ,('BACKGROUND',(0,1),(-1,1),"white")588 # ,('GRID',(0,0),(-1,-1),1,"#386170")589 ,('ALIGN',(0,0),(-1,-1),"CENTER")590 ,('VALIGN',(0,0),(-1,-1),"MIDDLE")591 ,('TEXTCOLOR',(0,1),(-1,-1),"#443e42")592 ]593tableStyles["table6a"] = tableStyles["table6"]594tableStyles["table7"] = tableStyles["table2"]595tableStyles["table8"] = [596 ('BACKGROUND',(0,1),(-1,1),"#fef5e7")597 ,('ALIGN',(0,0),(0,-1),"LEFT")598 ,('ALIGN',(1,0),(2,-1),"CENTER")599 ,('VALIGN',(0,0),(-1,-1),"MIDDLE")600 ,('BOX',(1,0),(1,-1),1,"#f79c2a")601 ,('LINEABOVE',(0,0),(-1,0),1,"#f79c2a")602 ,('LINEBELOW',(0,-1),(-1,-1),1,"#f79c2a")603 ,('TEXTCOLOR',(0,0),(-1,-1),"#443e42")604 ]605tableStyles["table9"] = tableStyles["table8"]606tableStyles["table10"] = tableStyles["table8"]607tableStyles["table11"] = [608 ('BACKGROUND',(0,1),(-1,1),"#fef5e7")609 ,('BACKGROUND',(0,3),(-1,3),"#fef5e7")610 ,('BACKGROUND',(0,5),(-1,5),"#fef5e7")611 ,('ALIGN',(0,0),(0,-1),"LEFT")612 ,('ALIGN',(1,0),(2,-1),"CENTER")613 ,('VALIGN',(0,0),(-1,-1),"MIDDLE")614 ,('BOX',(1,0),(1,-1),1,"#f79c2a")615 ,('LINEABOVE',(0,0),(-1,0),1,"#f79c2a")616 ,('LINEBELOW',(0,-1),(-1,-1),1,"#f79c2a")617 ,('TEXTCOLOR',(0,0),(-1,-1),"#443e42")618 ]...

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tables_world.py

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1#!/usr/bin/python2# -*- coding: utf-8 -*-3from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle4from reportlab.platypus import Paragraph5from reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_RIGHT6import copy7import pandas as pd8import numbers9import re10from reportlab.pdfbase import pdfmetrics11from reportlab.pdfbase.ttfonts import TTFont12from reportlab.lib.fonts import addMapping13import pdb14dark_orange = "#DE5D09"15orange = "#F39000"16light_orange = "#FCC97A"17dark_aqua = "#007495"18aqua = "#93CAC9"19aqua_light = "#B2D8D7"20aqua_extra_light = "#D1E7E5"21dark_grey = "#475C6D"22grey = "#A0ADBB"23light_grey = "#CFD9E5"24pdfmetrics.registerFont(TTFont('Averta', 'fonts/Averta-Regular.ttf'))25pdfmetrics.registerFont(TTFont('Averta-Bold', 'fonts/Averta-Bold.ttf'))26pdfmetrics.registerFont(TTFont('Averta-Italic', 'fonts/Averta-RegularItalic.ttf'))27addMapping('Averta', 0, 0, 'Averta')28addMapping('Averta', 0, 1, 'Averta-Italic')29addMapping('Averta', 1, 0, 'Averta-Bold')30style = getSampleStyleSheet()31dark_greyParaBold = ParagraphStyle('dark_greyParaBold', parent=style['BodyText'], textColor=dark_grey, alignment=TA_LEFT, fontName="Averta-Bold")32dark_greyParaBoldCenter = ParagraphStyle('dark_greyParaBold', parent=style['BodyText'], textColor=dark_grey, alignment=TA_CENTER, fontName="Averta-Bold")33dark_greyParaStyle = ParagraphStyle('dark_greyParaStyle', parent=style['BodyText'], textColor=dark_grey, alignment=TA_LEFT)34dark_greyParaStyleRight = ParagraphStyle('dark_greyParaStyle', parent=style['BodyText'], textColor=dark_grey, alignment=TA_RIGHT)35NACourseStyle = ParagraphStyle('offCourseStyle', parent=style['BodyText'], textColor=grey, alignment=TA_LEFT)36offCourseStyle = ParagraphStyle('offCourseStyle', parent=style['BodyText'], textColor=orange, alignment=TA_LEFT)37progressStyle = ParagraphStyle('progressStyle', parent=style['BodyText'], textColor=aqua, alignment=TA_LEFT)38onCourseStyle = ParagraphStyle('onCourseStyle', parent=style['BodyText'], textColor=dark_aqua, alignment=TA_LEFT)39dataDictionary = {"World": {}}40dataDictionary["World"]["country"] = "World"41dataDictionary["World"]["table1"] = [42 [43 Paragraph("<b>Under-5 stunting</b>", style=dark_greyParaBold),44 Paragraph("<b>Under-5 wasting</b>", style=dark_greyParaBold),45 Paragraph("<b>Under-5 overweight</b>", style=dark_greyParaBold),46 Paragraph("<b>WRA anaemia</b>", style=dark_greyParaBold),47 Paragraph("<b>Exclusive breastfeeding</b>", style=dark_greyParaBold)48 ],49 ["Off course, some progress", "On course", "Off course, no progress", "Off course", "On course"]50]51dataDictionary["World"]["table1a"] = [52 [53 Paragraph("<b>Adult female obesity</b>", style=dark_greyParaBold),54 Paragraph("<b>Adult male obesity</b>", style=dark_greyParaBold),55 Paragraph("<b>Adult female diabetes</b>", style=dark_greyParaBold),56 Paragraph("<b>Adult male diabetes</b>", style=dark_greyParaBold)57 ],58 ["Off course, some progress", "On course", "Off course, no progress", "Off course"]59]60#61# dataDictionary["World"]["table2"] = [62# [Paragraph("Gini index score<super>1</super>", style=dark_greyParaBold), Paragraph("Gini index rank<super>2</super>", style=dark_greyParaBold), "Year"], [51, 125, 2011]63# ]64dataDictionary["World"]["table3"] = [65 ["Population (millions)", format(12428, ",d"), 2015],66 ["Under-5 population (millions)", format(1935, ",d"), 2015],67 ["Rural (%)", format(20, ",d"), 2015],68 ["≥65 years (millions)", format(5, ",d"), 2015],69]70dataDictionary["World"]["table4"] = [71 [Paragraph("Early childbearing: births by age 18 (%)", style=dark_greyParaBold), "33", "2011"]72]73dataDictionary["World"]["table5"] = [74 ["Physicians", "0.117", "2005"],75 ["Nurses and midwives", "1.306", "2005"],76 ["Community health workers", "0.188", "2005"]77]78dataDictionary["World"]["table6"] = [79 [Paragraph("Mandatory legislation for salt iodisation", style=dark_greyParaBold), "Yes"],80 [Paragraph("Sugar-sweetened beverage tax", style=dark_greyParaBold), "Yes"],81 [Paragraph("Food-based dietary guidelines", style=dark_greyParaBold), "Yes"],82 [Paragraph("Multisectoral comprehensive nutrition plan", style=dark_greyParaBold), "Yes"],83]84dataDictionary["World"]["table7"] = [85 [86 Paragraph("Stunting", style=dark_greyParaBoldCenter),87 Paragraph("Anaemia", style=dark_greyParaBoldCenter),88 Paragraph("Low birth weight", style=dark_greyParaBoldCenter),89 Paragraph("Child overweight", style=dark_greyParaBoldCenter),90 Paragraph("Exclusive breastfeeding", style=dark_greyParaBoldCenter),91 Paragraph("Wasting", style=dark_greyParaBoldCenter),92 Paragraph("Salt intake", style=dark_greyParaBoldCenter),93 Paragraph("Overweight adults and adolescents", style=dark_greyParaBoldCenter),94 ],95 ["Yes", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes"]96]97dataDictionary["World"]["table8"] = [98 [99 Paragraph("<b>Coverage/practice indicator</b>", style=dark_greyParaBold),100 Paragraph("<b>Number of countries with data</b>", style=dark_greyParaBold),101 Paragraph("<b>Minimum %</b>", style=dark_greyParaBold),102 Paragraph("<b>Maximum %</b>", style=dark_greyParaBold),103 Paragraph("<b>Mean %</b>", style=dark_greyParaBold),104 Paragraph("<b>Median % for countries with data</b>", style=dark_greyParaBold),105 ],106 [u"Children 0\u201359 months with diarrhoea who received zinc treatment", "46", "0.1", "50.2", "8.6", "2.8"],107 [u"Children 6\u201359 months who received vitamin A supplements in last 6 months", "58", "4.5", "86.4", "57.0", "60.9"],108 [u"Children 6\u201359 months given iron supplements in past 7 days", "56", "1.3", "45.4", "14.6", "11.6"],109 [Paragraph("Women with a birth in last five years who received iron and folic acid during their most recent pregnancy", style=dark_greyParaStyle), "62", "22.6", "96.6", "74.6", "81.0"],110 ["Household consumption of any iodised salt", "52", "18.0", "99.8", "82.7", "90.9"],111]112dat = pd.read_csv("data_world.csv")113country_names = dat.region.unique()114for country_name in country_names:115 dataDictionary[country_name] = copy.deepcopy(dataDictionary["World"])116def replaceDash(x):117 x = str(x)118 y = re.sub(r"((?:^|[^{])\d+)-(\d+[^}])", u"\\1\u2013\\2", x)119 return y120missingVals = [" ", ".", "", "No data","na","NA"]121def safeFormat(x, commas=False, precision=0, percent=False, divisor=False):122 if pd.isnull(x):123 return "NA"124 elif x in missingVals:125 return "NA"126 else:127 if percent:128 try:129 x = float(x) * 100130 except ValueError:131 return replaceDash(x)132 if divisor:133 try:134 x = float(x) / divisor135 if x < 1:136 precision += 1137 except ValueError:138 return replaceDash(x)139 if not isinstance(x, numbers.Number):140 try:141 x = float(x)142 except ValueError:143 return replaceDash(x)144 if precision == 0:145 x = int(round(x, precision))146 else:147 x = round(x, precision)148 if commas:149 return format(x, ",")150 else:151 return x152def indicator(ctryDat, indicator):153 try:154 return ctryDat.loc[(ctryDat["indicator"] == indicator)].iloc[0]["value"]155 except IndexError:156 return "NA"157def indicator_sum(ctryDat, indicator):158 try:159 return ctryDat.loc[(ctryDat["indicator"] == indicator)].iloc[0]["value.sum"]160 except IndexError:161 return "NA"162def indicator_n(ctryDat, indicator):163 try:164 return ctryDat.loc[(ctryDat["indicator"] == indicator)].iloc[0]["n"]165 except IndexError:166 return "NA"167def indicator_n_max(ctryDat, indicator):168 try:169 row = ctryDat.loc[(ctryDat["indicator"] == indicator)]170 max_year = row["year"].max()171 max_row = row[(row["year"] == max_year)].iloc[0]172 return max_row["n"]173 except IndexError:174 return "NA"175def indicator_n_disagg(ctryDat, indicator, disaggregation):176 try:177 return ctryDat.loc[(ctryDat["indicator"] == indicator) & (ctryDat["disaggregation"] == disaggregation)].iloc[0]["n"]178 except IndexError:179 return "NA"180def indicator_frac(ctryDat, indicator, value):181 try:182 row = ctryDat.loc[(ctryDat["indicator"] == indicator) & (ctryDat["value"] == value)].iloc[0]183 return "{}/{}".format(int(row["n"]), int(row["N"]))184 except IndexError:185 return "NA"186def indicator_disagg(ctryDat, indicator, disagg, disagg_value=None):187 if disagg_value:188 try:189 return ctryDat.loc[(ctryDat["indicator"] == indicator) & (ctryDat["disaggregation"] == disagg) & (ctryDat["disagg.value"] == disagg_value)].iloc[0]["value"]190 except IndexError:191 return "NA"192 else:193 try:194 return ctryDat.loc[(ctryDat["indicator"] == indicator) & (ctryDat["disaggregation"] == disagg)].iloc[0]["value"]195 except IndexError:196 return "NA"197def indicator_disagg_frac(ctryDat, indicator, disagg, value, disagg_value=None):198 if disagg_value:199 try:200 row = ctryDat.loc[(ctryDat["indicator"] == indicator) & (ctryDat["disaggregation"] == disagg) & (ctryDat["disagg.value"] == disagg_value) & (ctryDat["value"] == value)].iloc[0]201 return "{}/{}".format(int(row["n"]), int(row["N"]))202 except IndexError:203 return "NA"204 else:205 try:206 row = ctryDat.loc[(ctryDat["indicator"] == indicator) & (ctryDat["disaggregation"] == disagg) & (ctryDat["value"] == value)].iloc[0]207 return "{}/{}".format(int(row["n"]), int(row["N"]))208 except IndexError:209 return "NA"210def year(ctryDat, indicator):211 try:212 return ctryDat.loc[(ctryDat["indicator"] == indicator)].iloc[0]["year"]213 except IndexError:214 return "NA"215def year_range(ctryDat, indicator):216 try:217 return ctryDat.loc[(ctryDat["indicator"] == indicator)].iloc[0]["year_range"]218 except IndexError:219 return "NA"220for country in dataDictionary.keys():221 ctryDat = dat.loc[(dat.region == country)]222 dataDictionary[country]["country"] = country223 max_n = ctryDat["n"].max()224 if max_n >= 50:225 minimum_n = 10226 elif max_n >= 20:227 minimum_n = 5228 elif max_n > 2:229 minimum_n = 3230 else:231 minimum_n = 2232 summed_vars = [233 "population",234 "u5_pop",235 "65_years",236 "under_5_stunting_track",237 "under_5_wasting_track",238 "under_5_overweight_track",239 "wra_anaemia_track",240 "ebf_track",241 "adult_fem_obesity_track",242 "adult_mal_obesity_track",243 "adult_fem_diabetes_track",244 "adult_mal_diabetes_track",245 "salt_leg",246 "sugar_tax",247 "fbdg",248 "multi_sec",249 "stunting_plan",250 "anaemia_plan",251 "LBW_plan",252 "child_overweight_plan",253 "EBF_plan",254 "wasting_plan",255 "sodium_plan",256 "overweight_adults_adoles_plan",257 ]258 ctryDat_with_n = ctryDat.loc[(ctryDat["n"] >= minimum_n) & (~ctryDat["indicator"].isin(summed_vars))]259 ctryDat_without_n = ctryDat.loc[(pd.isnull(ctryDat["n"])) | (ctryDat["indicator"].isin(summed_vars))]260 ctryDat = pd.concat([ctryDat_with_n, ctryDat_without_n])261 dataDictionary[country]["table1"][1] = [262 Paragraph(safeFormat(indicator_frac(ctryDat, "under_5_stunting_track", "On course"))+" on course", style=offCourseStyle),263 Paragraph(safeFormat(indicator_frac(ctryDat, "under_5_wasting_track", "On course"))+" on course", style=offCourseStyle),264 Paragraph(safeFormat(indicator_frac(ctryDat, "under_5_overweight_track", "On course"))+" on course", style=offCourseStyle),265 Paragraph(safeFormat(indicator_frac(ctryDat, "wra_anaemia_track", "On course"))+" on course", style=offCourseStyle),266 Paragraph(safeFormat(indicator_frac(ctryDat, "ebf_track", "On course"))+" on course", style=offCourseStyle),267 ]268 dataDictionary[country]["table1a"][1] = [269 Paragraph(safeFormat(indicator_frac(ctryDat, "adult_fem_obesity_track", "On course"))+" on course", style=offCourseStyle),270 Paragraph(safeFormat(indicator_frac(ctryDat, "adult_mal_obesity_track", "On course"))+" on course", style=offCourseStyle),271 Paragraph(safeFormat(indicator_frac(ctryDat, "adult_fem_diabetes_track", "On course"))+" on course", style=offCourseStyle),272 Paragraph(safeFormat(indicator_frac(ctryDat, "adult_mal_diabetes_track", "On course"))+" on course", style=offCourseStyle),273 ]274 # dataDictionary[country]["table2"][1] = [275 # safeFormat(indicator(ctryDat, "gini")),276 # safeFormat(indicator(ctryDat, "gini_rank")),277 # safeFormat(year(ctryDat, "gini"))278 # ]279 dataDictionary[country]["table3"][0][1] = safeFormat(indicator_sum(ctryDat, "population"), True, divisor=1000)280 if safeFormat(indicator_sum(ctryDat, "population"), True, divisor=1000) == "0.0":281 dataDictionary[country]["table3"][0][0] = "Population (000)"282 dataDictionary[country]["table3"][0][1] = safeFormat(indicator_sum(ctryDat, "population"), True)283 dataDictionary[country]["table3"][0][2] = safeFormat(year(ctryDat, "population"))284 dataDictionary[country]["table3"][1][1] = safeFormat(indicator_sum(ctryDat, "u5_pop"), True, divisor=1000)285 if safeFormat(indicator_sum(ctryDat, "u5_pop"), True, divisor=1000) == "0.0":286 dataDictionary[country]["table3"][1][0] = "Under-5 population (000)"287 dataDictionary[country]["table3"][1][1] = safeFormat(indicator_sum(ctryDat, "u5_pop"), True)288 dataDictionary[country]["table3"][1][2] = safeFormat(year(ctryDat, "u5_pop"))289 dataDictionary[country]["table3"][2][1] = safeFormat(indicator(ctryDat, "rural_percent"))290 dataDictionary[country]["table3"][2][2] = safeFormat(year(ctryDat, "rural_percent"))291 dataDictionary[country]["table3"][3][1] = safeFormat(indicator_sum(ctryDat, "65_years"), True, divisor=1000)292 if safeFormat(indicator_sum(ctryDat, "65_years"), True, divisor=1000) == "0.0":293 dataDictionary[country]["table3"][3][0] = "≥65 years (000)"294 dataDictionary[country]["table3"][3][1] = safeFormat(indicator_sum(ctryDat, "65_years"), True)295 dataDictionary[country]["table3"][3][2] = safeFormat(year(ctryDat, "65_years"))296 dataDictionary[country]["table3_n1"] = safeFormat(indicator_n(ctryDat, "u5_pop"))297 dataDictionary[country]["table3_n2"] = safeFormat(indicator_n(ctryDat, "rural_percent"))298 dataDictionary[country]["table3_n3"] = safeFormat(indicator_n(ctryDat, "65_years"))299 dataDictionary[country]["table4"][0][1] = safeFormat(indicator(ctryDat, "early_childbearing_prev"))300 dataDictionary[country]["table4"][0][2] = Paragraph(str(safeFormat(year_range(ctryDat, "early_childbearing_prev"))), style=dark_greyParaStyleRight)301 dataDictionary[country]["table4_n"] = safeFormat(indicator_n(ctryDat, "early_childbearing_prev"))302 dataDictionary[country]["table5"][0][1] = safeFormat(indicator(ctryDat, "physicians"), False, 2)303 dataDictionary[country]["table5"][0][2] = Paragraph(str(safeFormat(year_range(ctryDat, "physicians"))), style=dark_greyParaStyleRight)304 dataDictionary[country]["table5"][1][1] = safeFormat(indicator(ctryDat, "nurses_and_midwives"), False, 2)305 dataDictionary[country]["table5"][1][2] = Paragraph(str(safeFormat(year_range(ctryDat, "nurses_and_midwives"))), style=dark_greyParaStyleRight)306 dataDictionary[country]["table5"][2][1] = safeFormat(indicator(ctryDat, "community_health_workers"), False, 2)307 dataDictionary[country]["table5"][2][2] = Paragraph(str(safeFormat(year_range(ctryDat, "community_health_workers"))), style=dark_greyParaStyleRight)308 dataDictionary[country]["table5_n"] = safeFormat(indicator_n(ctryDat, "physicians"))309 dataDictionary[country]["table6"][0][1] = safeFormat(indicator_frac(ctryDat, "salt_leg", "Yes"))310 dataDictionary[country]["table6"][1][1] = safeFormat(indicator_frac(ctryDat, "sugar_tax", "Yes"))311 dataDictionary[country]["table6"][2][1] = safeFormat(indicator_frac(ctryDat, "fbdg", "Yes"))312 dataDictionary[country]["table6"][3][1] = safeFormat(indicator_frac(ctryDat, "multi_sec", "Yes"))313 dataDictionary[country]["table7"][1] = [314 safeFormat(indicator_frac(ctryDat, "stunting_plan", "Yes")),315 safeFormat(indicator_frac(ctryDat, "anaemia_plan", "Yes")),316 safeFormat(indicator_frac(ctryDat, "LBW_plan", "Yes")),317 safeFormat(indicator_frac(ctryDat, "child_overweight_plan", "Yes")),318 safeFormat(indicator_frac(ctryDat, "EBF_plan", "Yes")),319 safeFormat(indicator_frac(ctryDat, "wasting_plan", "Yes")),320 safeFormat(indicator_frac(ctryDat, "sodium_plan", "Yes")),321 safeFormat(indicator_frac(ctryDat, "overweight_adults_adoles_plan", "Yes")),322 ]323 # dataDictionary[country]["table8"][1][1] = safeFormat(indicator_disagg(ctryDat, "diarrhea_zinc", "all"))324 #325 # dataDictionary[country]["table8"][2][1] = safeFormat(indicator_disagg(ctryDat, "vit_a", "gender", "Both"))326 # dataDictionary[country]["table8"][2][2] = safeFormat(indicator_disagg(ctryDat, "vit_a", "gender", "Boys"))327 # dataDictionary[country]["table8"][2][3] = safeFormat(indicator_disagg(ctryDat, "vit_a", "gender", "Girls"))328 #329 # dataDictionary[country]["table8"][3][1] = safeFormat(indicator_disagg(ctryDat, "iron_supp", "gender", "Both"))330 # dataDictionary[country]["table8"][3][2] = safeFormat(indicator_disagg(ctryDat, "iron_supp", "gender", "Boys"))331 # dataDictionary[country]["table8"][3][3] = safeFormat(indicator_disagg(ctryDat, "iron_supp", "gender", "Girls"))332 #333 # dataDictionary[country]["table8"][4][1] = safeFormat(indicator_disagg(ctryDat, "iron_and_folic", "all"))334 # dataDictionary[country]["table8"][4][3] = safeFormat(indicator_disagg(ctryDat, "iron_and_folic", "all"))335 #336 # dataDictionary[country]["table8"][5][1] = safeFormat(indicator_disagg(ctryDat, "iodised_salt", "all"))337 dataDictionary[country]["pov_percent_n"] = safeFormat(indicator_n(ctryDat, "190_percent"))338 n_indicators = [339 "190_percent",340 "GDP_capita_PPP",341 "fruit_veg_availability",342 "female_secondary_enroll_net",343 "basic_water",344 "basic_sanitation",345 "agriculture_expenditure",346 "coexistence",347 "adolescent_underweight",348 "adolescent_overweight",349 "adolescent_obesity",350 "adult_diabetes",351 "adult_overweight",352 "adult_obesity",353 "adult_blood_pressure",354 "adult_anemia",355 "adult_sodium",356 "Calcium",357 "diarrhea_zinc",358 "vit_a",359 "iron_supp",360 "iron_and_folic",361 "iodised_salt"362 ]363 for n_indicator in n_indicators:364 dataDictionary[country][n_indicator+"_n"] = safeFormat(indicator_n(ctryDat, n_indicator))365 # dataDictionary[country]["table8"][1][4] = safeFormat(indicator_n(ctryDat, "diarrhea_zinc"))366 # dataDictionary[country]["table8"][2][4] = safeFormat(indicator_n(ctryDat, "vit_a"))367 # dataDictionary[country]["table8"][3][4] = safeFormat(indicator_n(ctryDat, "iron_supp"))368 # dataDictionary[country]["table8"][4][4] = safeFormat(indicator_n(ctryDat, "iron_and_folic"))369 # dataDictionary[country]["table8"][5][4] = safeFormat(indicator_n(ctryDat, "iodised_salt"))370 n_indicators_disagg = [371 ("stunting_percent", "gender"),372 ("overweight_percent", "gender"),373 ("wasting_percent", "income"),374 ("stunting_percent", "income"),375 ("overweight_percent", "income"),376 ("wasting_percent", "location"),377 ("stunting_percent", "location"),378 ("overweight_percent", "location"),379 ]380 for n_indicator, disagg in n_indicators_disagg:381 dataDictionary[country][n_indicator+"_"+disagg+"_n"] = safeFormat(indicator_n_disagg(ctryDat, n_indicator, disagg))382 c_feeding_n_max = 0383 c_feeding_inds = [384 "continued_breastfeeding_2yr",385 "continued_breastfeeding_1yr",386 "minimum_accept_diet",387 "minimum_diet_diversity",388 "minimum_meal",389 "solid_foods",390 "exclusive_breastfeeding",391 "early_initiation"392 ]393 for ind in c_feeding_inds:394 this_n = indicator_n_max(ctryDat, ind)395 try:396 if this_n > c_feeding_n_max:397 c_feeding_n_max = this_n398 except TypeError:399 pass400 dataDictionary[country]["c_feeding_n"] = safeFormat(c_feeding_n_max)401generic_style = [402 ('TEXTCOLOR', (0, 0), (-1, -1), dark_grey),403 ('BACKGROUND', (0, 0), (-1, -1), "white"),404 ('LINEABOVE', (0, 0), (-1, 0), 1, dark_grey),405 ('ALIGN', (0, 0), (-1, -1), "LEFT"),406 ('VALIGN', (0, 0), (-1, -1), "MIDDLE"),407 ('LINEBELOW', (0, -1), (-1, -1), 1, dark_grey)408]409tableStyles = {}410tableStyles["table1"] = [411 ('TEXTCOLOR', (0, 0), (-1, -1), dark_grey),412 ('BACKGROUND', (0, 0), (-1, -1), "transparent"),413 ('ALIGN', (0, 0), (-1, -1), "LEFT"),414 ('VALIGN', (0, 0), (-1, -1), "MIDDLE"),415 ('FONTNAME', (0, 0), (-1, 0), "Averta-Bold")416]417tableStyles["table1a"] = tableStyles["table1"]418# tableStyles["table2"] = generic_style + [419# ('FONTNAME', (0, 0), (-1, 0), "Averta-Bold"),420# ('LINEABOVE', (0, 1), (-1, 1), 1, dark_grey)421# ]422tableStyles["table3"] = generic_style + [423 ('LINEABOVE', (0, 1), (-1, 1), 1, grey),424 ('LINEABOVE', (0, 2), (-1, 2), 1, grey),425 ('LINEABOVE', (0, 3), (-1, 3), 1, grey),426 ('FONTNAME', (0, 0), (0, -1), "Averta-Bold")427]428tableStyles["table4"] = generic_style + [429 ('LINEABOVE', (0, 1), (-1, 1), 1, grey),430 ('LINEABOVE', (0, 2), (-1, 2), 1, grey),431 ('LINEABOVE', (0, 3), (-1, 3), 1, grey)432]433tableStyles["table5"] = generic_style + [434 ('LINEABOVE', (0, 1), (-1, 1), 1, grey),435 ('LINEABOVE', (0, 2), (-1, 2), 1, grey),436 ('FONTNAME', (0, 0), (0, -1), "Averta-Bold")437]438tableStyles["table6"] = generic_style + [439 ('LINEABOVE', (0, 1), (-1, 1), 1, grey),440 ('LINEABOVE', (0, 2), (-1, 2), 1, grey),441 ('LINEABOVE', (0, 3), (-1, 3), 1, grey)442]443tableStyles["table7"] = generic_style + [444 ('TEXTCOLOR', (0, 0), (-1, -1), dark_grey),445 ('BACKGROUND', (0, 0), (-1, -1), "white"),446 ('ALIGN', (0, 0), (-1, -1), "CENTER"),447 ('VALIGN', (0, 0), (-1, -1), "MIDDLE"),448 ('LINEAFTER', (0, 0), (0, -1), 1, dark_grey),449 ('LINEAFTER', (1, 0), (1, -1), 1, dark_grey),450 ('LINEAFTER', (2, 0), (2, -1), 1, dark_grey),451 ('LINEAFTER', (3, 0), (3, -1), 1, dark_grey),452 ('LINEAFTER', (4, 0), (4, -1), 1, dark_grey),453 ('LINEAFTER', (5, 0), (5, -1), 1, dark_grey),454 ('LINEAFTER', (6, 0), (6, -1), 1, dark_grey),455]456tableStyles["table8"] = generic_style + [457 ('FONTNAME', (0, 0), (-1, 0), "Averta-Bold"),458 ('LINEABOVE', (0, 1), (-1, 1), 1, dark_grey),459 ('LINEABOVE', (0, 2), (-1, 2), 1, grey),460 ('LINEABOVE', (0, 3), (-1, 3), 1, grey),461 ('LINEABOVE', (0, 4), (-1, 4), 1, grey),462 ('LINEABOVE', (0, 5), (-1, 5), 1, grey),...

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tables_reg.py

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1#!/usr/bin/python2# -*- coding: utf-8 -*-3from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle4from reportlab.platypus import Paragraph5from reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_RIGHT6import copy7import pandas as pd8import numbers9import re10from reportlab.pdfbase import pdfmetrics11from reportlab.pdfbase.ttfonts import TTFont12from reportlab.lib.fonts import addMapping13import pdb14dark_orange = "#DE5D09"15orange = "#F39000"16light_orange = "#FCC97A"17dark_aqua = "#007495"18aqua = "#93CAC9"19aqua_light = "#B2D8D7"20aqua_extra_light = "#D1E7E5"21dark_grey = "#475C6D"22grey = "#A0ADBB"23light_grey = "#CFD9E5"24pdfmetrics.registerFont(TTFont('Averta', 'fonts/Averta-Regular.ttf'))25pdfmetrics.registerFont(TTFont('Averta-Bold', 'fonts/Averta-Bold.ttf'))26pdfmetrics.registerFont(TTFont('Averta-Italic', 'fonts/Averta-RegularItalic.ttf'))27addMapping('Averta', 0, 0, 'Averta')28addMapping('Averta', 0, 1, 'Averta-Italic')29addMapping('Averta', 1, 0, 'Averta-Bold')30style = getSampleStyleSheet()31dark_greyParaBold = ParagraphStyle('dark_greyParaBold', parent=style['BodyText'], textColor=dark_grey, alignment=TA_LEFT, fontName="Averta-Bold")32dark_greyParaBoldCenter = ParagraphStyle('dark_greyParaBold', parent=style['BodyText'], textColor=dark_grey, alignment=TA_CENTER, fontName="Averta-Bold")33dark_greyParaStyle = ParagraphStyle('dark_greyParaStyle', parent=style['BodyText'], textColor=dark_grey, alignment=TA_LEFT)34dark_greyParaStyleRight = ParagraphStyle('dark_greyParaStyle', parent=style['BodyText'], textColor=dark_grey, alignment=TA_RIGHT)35NACourseStyle = ParagraphStyle('offCourseStyle', parent=style['BodyText'], textColor=grey, alignment=TA_LEFT)36offCourseStyle = ParagraphStyle('offCourseStyle', parent=style['BodyText'], textColor=orange, alignment=TA_LEFT)37progressStyle = ParagraphStyle('progressStyle', parent=style['BodyText'], textColor=aqua, alignment=TA_LEFT)38onCourseStyle = ParagraphStyle('onCourseStyle', parent=style['BodyText'], textColor=dark_aqua, alignment=TA_LEFT)39dataDictionary = {"Asia": {}}40dataDictionary["Asia"]["country"] = "Asia"41dataDictionary["Asia"]["table1"] = [42 [43 Paragraph("<b>Under-5 stunting</b>", style=dark_greyParaBold),44 Paragraph("<b>Under-5 wasting</b>", style=dark_greyParaBold),45 Paragraph("<b>Under-5 overweight</b>", style=dark_greyParaBold),46 Paragraph("<b>WRA anaemia</b>", style=dark_greyParaBold),47 Paragraph("<b>Exclusive breastfeeding</b>", style=dark_greyParaBold)48 ],49 ["Off course, some progress", "On course", "Off course, no progress", "Off course", "On course"]50]51dataDictionary["Asia"]["table1a"] = [52 [53 Paragraph("<b>Adult female obesity</b>", style=dark_greyParaBold),54 Paragraph("<b>Adult male obesity</b>", style=dark_greyParaBold),55 Paragraph("<b>Adult female diabetes</b>", style=dark_greyParaBold),56 Paragraph("<b>Adult male diabetes</b>", style=dark_greyParaBold)57 ],58 ["Off course, some progress", "On course", "Off course, no progress", "Off course"]59]60#61# dataDictionary["Asia"]["table2"] = [62# [Paragraph("Gini index score<super>1</super>", style=dark_greyParaBold), Paragraph("Gini index rank<super>2</super>", style=dark_greyParaBold), "Year"], [51, 125, 2011]63# ]64dataDictionary["Asia"]["table3"] = [65 ["Population (millions)", format(12428, ",d"), 2015],66 ["Under-5 population (millions)", format(1935, ",d"), 2015],67 ["Rural (%)", format(20, ",d"), 2015],68 ["≥65 years (millions)", format(5, ",d"), 2015],69]70dataDictionary["Asia"]["table4"] = [71 [Paragraph("Early childbearing: births by age 18 (%)", style=dark_greyParaBold), "33", "2011"]72]73dataDictionary["Asia"]["table5"] = [74 ["Physicians", "0.117", "2005"],75 ["Nurses and midwives", "1.306", "2005"],76 ["Community health workers", "0.188", "2005"]77]78dataDictionary["Asia"]["table6"] = [79 [Paragraph("Mandatory legislation for salt iodisation", style=dark_greyParaBold), "Yes"],80 [Paragraph("Sugar-sweetened beverage tax", style=dark_greyParaBold), "Yes"],81 [Paragraph("Food-based dietary guidelines", style=dark_greyParaBold), "Yes"],82 [Paragraph("Multisectoral comprehensive nutrition plan", style=dark_greyParaBold), "Yes"],83]84dataDictionary["Asia"]["table7"] = [85 [86 Paragraph("Stunting", style=dark_greyParaBoldCenter),87 Paragraph("Anaemia", style=dark_greyParaBoldCenter),88 Paragraph("Low birth weight", style=dark_greyParaBoldCenter),89 Paragraph("Child overweight", style=dark_greyParaBoldCenter),90 Paragraph("Exclusive breastfeeding", style=dark_greyParaBoldCenter),91 Paragraph("Wasting", style=dark_greyParaBoldCenter),92 Paragraph("Salt intake", style=dark_greyParaBoldCenter),93 Paragraph("Overweight adults and adolescents", style=dark_greyParaBoldCenter),94 ],95 ["Yes", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes"]96]97# dataDictionary["Asia"]["table8"] = [98# [99# Paragraph("<b>Coverage/practice indicator</b>", style=dark_greyParaBold),100# Paragraph("<b>%</b>", style=dark_greyParaBold),101# Paragraph("<b>Male</b>", style=dark_greyParaBold),102# Paragraph("<b>Female</b>", style=dark_greyParaBold),103# Paragraph("<b>Year</b>", style=dark_greyParaBold)104# ],105# [u"Children 0\u201359 months with diarrhoea who received zinc treatment", "8.1", "NA", "NA", "2014"],106# [u"Children 6\u201359 months who received vitamin A supplements in last 6 months", "71.7", "71.6", "71.9", "2014"],107# [u"Children 6\u201359 months given iron supplements in past 7 days", "2.7", "2.6", "2.7", "2014"],108# [Paragraph("Women with a birth in last five years who received iron and folic acid during their most recent pregnancy", style=dark_greyParaStyle), "69.4", "", "NA", "2014"],109# ["Household consumption of any iodised salt", "99.5", "NA", "NA", "2014"],110# ]111dat = pd.read_csv("data_reg.csv")112country_names = dat.region.unique()113for country_name in country_names:114 dataDictionary[country_name] = copy.deepcopy(dataDictionary["Asia"])115def replaceDash(x):116 x = str(x)117 y = re.sub(r"((?:^|[^{])\d+)-(\d+[^}])", u"\\1\u2013\\2", x)118 return y119missingVals = [" ", ".", "", "No data","na","NA"]120def safeFormat(x, commas=False, precision=0, percent=False, divisor=False):121 if pd.isnull(x):122 return "NA"123 elif x in missingVals:124 return "NA"125 else:126 if percent:127 try:128 x = float(x) * 100129 except ValueError:130 return replaceDash(x)131 if divisor:132 try:133 x = float(x) / divisor134 if x < 1:135 precision += 1136 except ValueError:137 return replaceDash(x)138 if not isinstance(x, numbers.Number):139 try:140 x = float(x)141 except ValueError:142 return replaceDash(x)143 if precision == 0:144 x = int(round(x, precision))145 else:146 x = round(x, precision)147 if commas:148 return format(x, ",")149 else:150 return x151def indicator(ctryDat, indicator):152 try:153 return ctryDat.loc[(ctryDat["indicator"] == indicator)].iloc[0]["value"]154 except IndexError:155 return "NA"156def indicator_sum(ctryDat, indicator):157 try:158 return ctryDat.loc[(ctryDat["indicator"] == indicator)].iloc[0]["value.sum"]159 except IndexError:160 return "NA"161def indicator_n(ctryDat, indicator):162 try:163 return ctryDat.loc[(ctryDat["indicator"] == indicator)].iloc[0]["n"]164 except IndexError:165 return "NA"166def indicator_n_max(ctryDat, indicator):167 try:168 row = ctryDat.loc[(ctryDat["indicator"] == indicator)]169 max_year = row["year"].max()170 max_row = row[(row["year"] == max_year)].iloc[0]171 return max_row["n"]172 except IndexError:173 return "NA"174def indicator_n_disagg(ctryDat, indicator, disaggregation):175 try:176 return ctryDat.loc[(ctryDat["indicator"] == indicator) & (ctryDat["disaggregation"] == disaggregation)].iloc[0]["n"]177 except IndexError:178 return "NA"179def indicator_frac(ctryDat, indicator, value):180 try:181 row = ctryDat.loc[(ctryDat["indicator"] == indicator) & (ctryDat["value"] == value)].iloc[0]182 return "{}/{}".format(int(row["n"]), int(row["N"]))183 except IndexError:184 return "NA"185def indicator_disagg(ctryDat, indicator, disagg, disagg_value=None):186 if disagg_value:187 try:188 return ctryDat.loc[(ctryDat["indicator"] == indicator) & (ctryDat["disaggregation"] == disagg) & (ctryDat["disagg.value"] == disagg_value)].iloc[0]["value"]189 except IndexError:190 return "NA"191 else:192 try:193 return ctryDat.loc[(ctryDat["indicator"] == indicator) & (ctryDat["disaggregation"] == disagg)].iloc[0]["value"]194 except IndexError:195 return "NA"196def indicator_disagg_frac(ctryDat, indicator, disagg, value, disagg_value=None):197 if disagg_value:198 try:199 row = ctryDat.loc[(ctryDat["indicator"] == indicator) & (ctryDat["disaggregation"] == disagg) & (ctryDat["disagg.value"] == disagg_value) & (ctryDat["value"] == value)].iloc[0]200 return "{}/{}".format(int(row["n"]), int(row["N"]))201 except IndexError:202 return "NA"203 else:204 try:205 row = ctryDat.loc[(ctryDat["indicator"] == indicator) & (ctryDat["disaggregation"] == disagg) & (ctryDat["value"] == value)].iloc[0]206 return "{}/{}".format(int(row["n"]), int(row["N"]))207 except IndexError:208 return "NA"209def year(ctryDat, indicator):210 try:211 return ctryDat.loc[(ctryDat["indicator"] == indicator)].iloc[0]["year"]212 except IndexError:213 return "NA"214def year_range(ctryDat, indicator):215 try:216 return ctryDat.loc[(ctryDat["indicator"] == indicator)].iloc[0]["year_range"]217 except IndexError:218 return "NA"219for country in dataDictionary.keys():220 ctryDat = dat.loc[(dat.region == country)]221 dataDictionary[country]["country"] = country222 dataDictionary[country]["regional"] = ctryDat.iloc[0]["regional"]223 max_n = ctryDat["n"].max()224 if max_n >= 50:225 minimum_n = 10226 elif max_n >= 20:227 minimum_n = 5228 elif max_n > 2:229 minimum_n = 3230 else:231 minimum_n = 2232 summed_vars = [233 "population",234 "u5_pop",235 "65_years",236 "under_5_stunting_track",237 "under_5_wasting_track",238 "under_5_overweight_track",239 "wra_anaemia_track",240 "ebf_track",241 "adult_fem_obesity_track",242 "adult_mal_obesity_track",243 "adult_fem_diabetes_track",244 "adult_mal_diabetes_track",245 "salt_leg",246 "sugar_tax",247 "fbdg",248 "multi_sec",249 "stunting_plan",250 "anaemia_plan",251 "LBW_plan",252 "child_overweight_plan",253 "EBF_plan",254 "wasting_plan",255 "sodium_plan",256 "overweight_adults_adoles_plan",257 ]258 ctryDat_with_n = ctryDat.loc[(ctryDat["n"] >= minimum_n) & (~ctryDat["indicator"].isin(summed_vars))]259 ctryDat_without_n = ctryDat.loc[(pd.isnull(ctryDat["n"])) | (ctryDat["indicator"].isin(summed_vars))]260 ctryDat = pd.concat([ctryDat_with_n, ctryDat_without_n])261 dataDictionary[country]["table1"][1] = [262 Paragraph(safeFormat(indicator_frac(ctryDat, "under_5_stunting_track", "On course"))+" on course", style=offCourseStyle),263 Paragraph(safeFormat(indicator_frac(ctryDat, "under_5_wasting_track", "On course"))+" on course", style=offCourseStyle),264 Paragraph(safeFormat(indicator_frac(ctryDat, "under_5_overweight_track", "On course"))+" on course", style=offCourseStyle),265 Paragraph(safeFormat(indicator_frac(ctryDat, "wra_anaemia_track", "On course"))+" on course", style=offCourseStyle),266 Paragraph(safeFormat(indicator_frac(ctryDat, "ebf_track", "On course"))+" on course", style=offCourseStyle),267 ]268 dataDictionary[country]["table1a"][1] = [269 Paragraph(safeFormat(indicator_frac(ctryDat, "adult_fem_obesity_track", "On course"))+" on course", style=offCourseStyle),270 Paragraph(safeFormat(indicator_frac(ctryDat, "adult_mal_obesity_track", "On course"))+" on course", style=offCourseStyle),271 Paragraph(safeFormat(indicator_frac(ctryDat, "adult_fem_diabetes_track", "On course"))+" on course", style=offCourseStyle),272 Paragraph(safeFormat(indicator_frac(ctryDat, "adult_mal_diabetes_track", "On course"))+" on course", style=offCourseStyle),273 ]274 # dataDictionary[country]["table2"][1] = [275 # safeFormat(indicator(ctryDat, "gini")),276 # safeFormat(indicator(ctryDat, "gini_rank")),277 # safeFormat(year(ctryDat, "gini"))278 # ]279 dataDictionary[country]["table3"][0][1] = safeFormat(indicator_sum(ctryDat, "population"), True, divisor=1000)280 if safeFormat(indicator_sum(ctryDat, "population"), True, divisor=1000) == "0.0":281 dataDictionary[country]["table3"][0][0] = "Population (000)"282 dataDictionary[country]["table3"][0][1] = safeFormat(indicator_sum(ctryDat, "population"), True)283 dataDictionary[country]["table3"][0][2] = safeFormat(year(ctryDat, "population"))284 dataDictionary[country]["table3"][1][1] = safeFormat(indicator_sum(ctryDat, "u5_pop"), True, divisor=1000)285 if safeFormat(indicator_sum(ctryDat, "u5_pop"), True, divisor=1000) == "0.0":286 dataDictionary[country]["table3"][1][0] = "Under-5 population (000)"287 dataDictionary[country]["table3"][1][1] = safeFormat(indicator_sum(ctryDat, "u5_pop"), True)288 dataDictionary[country]["table3"][1][2] = safeFormat(year(ctryDat, "u5_pop"))289 dataDictionary[country]["table3"][2][1] = safeFormat(indicator(ctryDat, "rural_percent"))290 dataDictionary[country]["table3"][2][2] = safeFormat(year(ctryDat, "rural_percent"))291 dataDictionary[country]["table3"][3][1] = safeFormat(indicator_sum(ctryDat, "65_years"), True, divisor=1000)292 if safeFormat(indicator_sum(ctryDat, "65_years"), True, divisor=1000) == "0.0":293 dataDictionary[country]["table3"][3][0] = "≥65 years (000)"294 dataDictionary[country]["table3"][3][1] = safeFormat(indicator_sum(ctryDat, "65_years"), True)295 dataDictionary[country]["table3"][3][2] = safeFormat(year(ctryDat, "65_years"))296 dataDictionary[country]["table3_n0"] = safeFormat(indicator_n(ctryDat, "population"))297 dataDictionary[country]["table3_n1"] = safeFormat(indicator_n(ctryDat, "u5_pop"))298 dataDictionary[country]["table3_n2"] = safeFormat(indicator_n(ctryDat, "rural_percent"))299 dataDictionary[country]["table3_n3"] = safeFormat(indicator_n(ctryDat, "65_years"))300 dataDictionary[country]["table4"][0][1] = safeFormat(indicator(ctryDat, "early_childbearing_prev"))301 dataDictionary[country]["table4"][0][2] = Paragraph(str(safeFormat(year_range(ctryDat, "early_childbearing_prev"))), style=dark_greyParaStyleRight)302 dataDictionary[country]["table4_n"] = safeFormat(indicator_n(ctryDat, "early_childbearing_prev"))303 dataDictionary[country]["table5"][0][1] = safeFormat(indicator(ctryDat, "physicians"), False, 2)304 dataDictionary[country]["table5"][0][2] = Paragraph(str(safeFormat(year_range(ctryDat, "physicians"))), style=dark_greyParaStyleRight)305 dataDictionary[country]["table5"][1][1] = safeFormat(indicator(ctryDat, "nurses_and_midwives"), False, 2)306 dataDictionary[country]["table5"][1][2] = Paragraph(str(safeFormat(year_range(ctryDat, "nurses_and_midwives"))), style=dark_greyParaStyleRight)307 dataDictionary[country]["table5"][2][1] = safeFormat(indicator(ctryDat, "community_health_workers"), False, 2)308 dataDictionary[country]["table5"][2][2] = Paragraph(str(safeFormat(year_range(ctryDat, "community_health_workers"))), style=dark_greyParaStyleRight)309 dataDictionary[country]["table5_n"] = safeFormat(indicator_n(ctryDat, "physicians"))310 dataDictionary[country]["table6"][0][1] = safeFormat(indicator_frac(ctryDat, "salt_leg", "Yes"))311 dataDictionary[country]["table6"][1][1] = safeFormat(indicator_frac(ctryDat, "sugar_tax", "Yes"))312 dataDictionary[country]["table6"][2][1] = safeFormat(indicator_frac(ctryDat, "fbdg", "Yes"))313 dataDictionary[country]["table6"][3][1] = safeFormat(indicator_frac(ctryDat, "multi_sec", "Yes"))314 dataDictionary[country]["table7"][1] = [315 safeFormat(indicator_frac(ctryDat, "stunting_plan", "Yes")),316 safeFormat(indicator_frac(ctryDat, "anaemia_plan", "Yes")),317 safeFormat(indicator_frac(ctryDat, "LBW_plan", "Yes")),318 safeFormat(indicator_frac(ctryDat, "child_overweight_plan", "Yes")),319 safeFormat(indicator_frac(ctryDat, "EBF_plan", "Yes")),320 safeFormat(indicator_frac(ctryDat, "wasting_plan", "Yes")),321 safeFormat(indicator_frac(ctryDat, "sodium_plan", "Yes")),322 safeFormat(indicator_frac(ctryDat, "overweight_adults_adoles_plan", "Yes")),323 ]324 # dataDictionary[country]["table8"][1][1] = safeFormat(indicator_disagg(ctryDat, "diarrhea_zinc", "all"))325 # dataDictionary[country]["table8"][1][4] = safeFormat(year(ctryDat, "diarrhea_zinc"))326 #327 # dataDictionary[country]["table8"][2][1] = safeFormat(indicator_disagg(ctryDat, "vit_a", "gender", "Both"))328 # dataDictionary[country]["table8"][2][2] = safeFormat(indicator_disagg(ctryDat, "vit_a", "gender", "Boys"))329 # dataDictionary[country]["table8"][2][3] = safeFormat(indicator_disagg(ctryDat, "vit_a", "gender", "Girls"))330 # dataDictionary[country]["table8"][2][4] = safeFormat(year(ctryDat, "vit_a"))331 #332 # dataDictionary[country]["table8"][3][1] = safeFormat(indicator_disagg(ctryDat, "iron_supp", "gender", "Both"))333 # dataDictionary[country]["table8"][3][2] = safeFormat(indicator_disagg(ctryDat, "iron_supp", "gender", "Boys"))334 # dataDictionary[country]["table8"][3][3] = safeFormat(indicator_disagg(ctryDat, "iron_supp", "gender", "Girls"))335 # dataDictionary[country]["table8"][3][4] = safeFormat(year(ctryDat, "iron_supp"))336 #337 # dataDictionary[country]["table8"][4][1] = safeFormat(indicator_disagg(ctryDat, "iron_and_folic", "all"))338 # dataDictionary[country]["table8"][4][3] = safeFormat(indicator_disagg(ctryDat, "iron_and_folic", "all"))339 # dataDictionary[country]["table8"][4][4] = safeFormat(year(ctryDat, "iron_and_folic"))340 #341 # dataDictionary[country]["table8"][5][1] = safeFormat(indicator_disagg(ctryDat, "iodised_salt", "all"))342 # dataDictionary[country]["table8"][5][4] = safeFormat(year(ctryDat, "iodised_salt343 dataDictionary[country]["pov_percent_n"] = safeFormat(indicator_n(ctryDat, "190_percent"))344 n_indicators = [345 "190_percent",346 "GDP_capita_PPP",347 "fruit_veg_availability",348 "female_secondary_enroll_net",349 "basic_water",350 "basic_sanitation",351 "agriculture_expenditure",352 "coexistence",353 "adolescent_underweight",354 "adolescent_overweight",355 "adolescent_obesity",356 "adult_diabetes",357 "adult_overweight",358 "adult_obesity",359 "adult_blood_pressure",360 "adult_anemia",361 "adult_sodium",362 "Calcium"363 ]364 for n_indicator in n_indicators:365 dataDictionary[country][n_indicator+"_n"] = safeFormat(indicator_n(ctryDat, n_indicator))366 n_indicators_disagg = [367 ("stunting_percent", "gender"),368 ("overweight_percent", "gender"),369 ("wasting_percent", "income"),370 ("stunting_percent", "income"),371 ("overweight_percent", "income"),372 ("wasting_percent", "location"),373 ("stunting_percent", "location"),374 ("overweight_percent", "location"),375 ]376 for n_indicator, disagg in n_indicators_disagg:377 dataDictionary[country][n_indicator+"_"+disagg+"_n"] = safeFormat(indicator_n_disagg(ctryDat, n_indicator, disagg))378 c_feeding_n_max = 0379 c_feeding_inds = [380 "continued_breastfeeding_2yr",381 "continued_breastfeeding_1yr",382 "minimum_accept_diet",383 "minimum_diet_diversity",384 "minimum_meal",385 "solid_foods",386 "exclusive_breastfeeding",387 "early_initiation"388 ]389 for ind in c_feeding_inds:390 this_n = indicator_n_max(ctryDat, ind)391 try:392 if this_n > c_feeding_n_max:393 c_feeding_n_max = this_n394 except TypeError:395 pass396 dataDictionary[country]["c_feeding_n"] = safeFormat(c_feeding_n_max)397generic_style = [398 ('TEXTCOLOR', (0, 0), (-1, -1), dark_grey),399 ('BACKGROUND', (0, 0), (-1, -1), "white"),400 ('LINEABOVE', (0, 0), (-1, 0), 1, dark_grey),401 ('ALIGN', (0, 0), (-1, -1), "LEFT"),402 ('VALIGN', (0, 0), (-1, -1), "MIDDLE"),403 ('LINEBELOW', (0, -1), (-1, -1), 1, dark_grey)404]405tableStyles = {}406tableStyles["table1"] = [407 ('TEXTCOLOR', (0, 0), (-1, -1), dark_grey),408 ('BACKGROUND', (0, 0), (-1, -1), "transparent"),409 ('ALIGN', (0, 0), (-1, -1), "LEFT"),410 ('VALIGN', (0, 0), (-1, -1), "MIDDLE"),411 ('FONTNAME', (0, 0), (-1, 0), "Averta-Bold")412]413tableStyles["table1a"] = tableStyles["table1"]414# tableStyles["table2"] = generic_style + [415# ('FONTNAME', (0, 0), (-1, 0), "Averta-Bold"),416# ('LINEABOVE', (0, 1), (-1, 1), 1, dark_grey)417# ]418tableStyles["table3"] = generic_style + [419 ('LINEABOVE', (0, 1), (-1, 1), 1, grey),420 ('LINEABOVE', (0, 2), (-1, 2), 1, grey),421 ('LINEABOVE', (0, 3), (-1, 3), 1, grey),422 ('FONTNAME', (0, 0), (0, -1), "Averta-Bold")423]424tableStyles["table4"] = generic_style + [425 ('LINEABOVE', (0, 1), (-1, 1), 1, grey),426 ('LINEABOVE', (0, 2), (-1, 2), 1, grey),427 ('LINEABOVE', (0, 3), (-1, 3), 1, grey)428]429tableStyles["table5"] = generic_style + [430 ('LINEABOVE', (0, 1), (-1, 1), 1, grey),431 ('LINEABOVE', (0, 2), (-1, 2), 1, grey),432 ('FONTNAME', (0, 0), (0, -1), "Averta-Bold")433]434tableStyles["table6"] = generic_style + [435 ('LINEABOVE', (0, 1), (-1, 1), 1, grey),436 ('LINEABOVE', (0, 2), (-1, 2), 1, grey),437 ('LINEABOVE', (0, 3), (-1, 3), 1, grey)438]439tableStyles["table7"] = generic_style + [440 ('TEXTCOLOR', (0, 0), (-1, -1), dark_grey),441 ('BACKGROUND', (0, 0), (-1, -1), "white"),442 ('ALIGN', (0, 0), (-1, -1), "CENTER"),443 ('VALIGN', (0, 0), (-1, -1), "MIDDLE"),444 ('LINEAFTER', (0, 0), (0, -1), 1, dark_grey),445 ('LINEAFTER', (1, 0), (1, -1), 1, dark_grey),446 ('LINEAFTER', (2, 0), (2, -1), 1, dark_grey),447 ('LINEAFTER', (3, 0), (3, -1), 1, dark_grey),448 ('LINEAFTER', (4, 0), (4, -1), 1, dark_grey),449 ('LINEAFTER', (5, 0), (5, -1), 1, dark_grey),450 ('LINEAFTER', (6, 0), (6, -1), 1, dark_grey),451]452# tableStyles["table8"] = generic_style + [453# ('FONTNAME', (0, 0), (-1, 0), "Averta-Bold"),454# ('LINEABOVE', (0, 1), (-1, 1), 1, dark_grey),455# ('LINEABOVE', (0, 2), (-1, 2), 1, grey),456# ('LINEABOVE', (0, 3), (-1, 3), 1, grey),457# ('LINEABOVE', (0, 4), (-1, 4), 1, grey),458# ('LINEABOVE', (0, 5), (-1, 5), 1, grey),...

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ShoutOut.py

Source:ShoutOut.py Github

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1from base.module import ModuleBase, ModuleAdminCommand2from base.events import EVT_CHATCOMMAND3import re45class SafeFormat(dict):6 def __missing__(self, key):7 return key.join('{}')89class ShoutOut(ModuleBase):10 module_name = "shoutout"11 def setup(self):12 self._shoutout_data = self.get_module_data()1314 self.register_admin_command(ModuleAdminCommand(15 'command',16 self.set_command,17 usage = f'{self.module_name} command !<command>',18 description = 'Set chat command for shoutouts',19 ))2021 self.register_admin_command(ModuleAdminCommand(22 'message',23 self.set_message,24 usage = f'{self.module_name} message <message>',25 description = 'Set chat message for shoutouts. Avilable variables: {streamer}, {game}, {url}',26 ))2728 self.register_admin_command(ModuleAdminCommand(29 'account',30 self.set_response_account,31 usage = f'{self.module_name} account',32 description = 'Set the account to use for shoutouts',33 ))3435 self.event_listen(EVT_CHATCOMMAND, self.command)3637 def command(self, event):38 if event.command == self.so_command:39 match = re.search(r'^@?([^ ]+)$', event.message)40 if match:41 so_user = match.group(1)42 twitch_user = self.twitch_api.get_user(so_user)43 if not twitch_user:44 return False4546 twitch_channel = self.twitch_api.get_channel(twitch_user['id'])4748 chat_str = self.so_str.format_map(SafeFormat({49 'streamer' : twitch_user['display_name'],50 'game' : twitch_channel['game_name'],51 'url' : 'https://twitch.tv/{}'.format(twitch_user['login'])52 }))5354 twitch_id = self._shoutout_data.get('account', None)55 self.send_chat_message(chat_str, twitch_id)5657 return True5859 return False6061 def set_command(self, input, command):62 match = re.search(r'^!([^ ]+)$', input)6364 if match:65 command = match.group(1)66 self._shoutout_data['command'] = command67 self.save_module_data(self._shoutout_data)6869 self.print(f"Shoutout command changed to !{command}")7071 else:72 self.print(f'Current command: !{self.so_command}')73 self.print(f'Usage: {command.usage}')7475 def set_message(self, input, command):76 if not input:77 self.print(f'Usage: {command.usage}')78 self.print('Available variables: {streamer}, {game}, {url}')79 self.print(f'Current message: {self.so_str}')80 return8182 self._shoutout_data['message'] = input83 self.save_module_data(self._shoutout_data)8485 self.print('Shoutout message updated:')86 self.print(self.so_str)8788 def set_response_account(self, input, command):89 def account_selected(account):90 self._shoutout_data['account'] = account.twitch_user_id91 self.save_module_data(self._shoutout_data)9293 self.select_account(account_selected)9495 @property96 def so_str(self):97 return self._shoutout_data.get('message', 'Go check out {streamer}. They were last playing {game}. {url}')9899 @property100 def so_command(self):101 return self._shoutout_data.get('command', 'so')102103 def shutdown(self): ...

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