diff --git a/panoramix/data/__init__.py b/panoramix/data/__init__.py index b472b42f52a..547854acb8f 100644 --- a/panoramix/data/__init__.py +++ b/panoramix/data/__init__.py @@ -1,14 +1,11 @@ -from datetime import datetime -import csv import gzip import json import os -from flask.ext.appbuilder import Base import pandas as pd -from sqlalchemy import Column, String, DateTime, Table, Integer +from sqlalchemy import String, DateTime -from panoramix import app, db, models +from panoramix import app, db, models, utils config = app.config @@ -29,31 +26,6 @@ def get_or_create_db(session): def load_world_bank_health_n_pop(): - """ - Details on how the data was loaded from - http://data.worldbank.org/data-catalog/health-nutrition-and-population-statistics - DIR = "" - df_country = pd.read_csv(DIR + '/HNP_Country.csv') - df_country.columns = ['country_code'] + list(df_country.columns[1:]) - df_country = df_country[['country_code', 'Region']] - df_country.columns = ['country_code', 'region'] - - df = pd.read_csv(DIR + '/HNP_Data.csv') - del df['Unnamed: 60'] - df.columns = ['country_name', 'country_code'] + list(df.columns[2:]) - ndf = df.merge(df_country, how='inner') - - dims = ('country_name', 'country_code', 'region') - vv = [str(i) for i in range(1960, 2015)] - mdf = pd.melt(ndf, id_vars=dims + ('Indicator Code',), value_vars=vv) - mdf['year'] = mdf.variable + '-01-01' - dims = dims + ('year',) - - pdf = mdf.pivot_table(values='value', columns='Indicator Code', index=dims) - pdf = pdf.reset_index() - pdf.to_csv(DIR + '/countries.csv') - pdf.to_json(DIR + '/countries.json', orient='records') - """ tbl = 'wb_health_population' with gzip.open(os.path.join(DATA_FOLDER, 'countries.json.gz')) as f: pdf = pd.read_json(f) @@ -75,10 +47,10 @@ def load_world_bank_health_n_pop(): obj = db.session.query(TBL).filter_by(table_name=tbl).first() if not obj: obj = TBL(table_name='wb_health_population') + obj.description = utils.readfile(os.path.join(DATA_FOLDER, 'countries.md')) obj.main_dttm_col = 'year' obj.database = get_or_create_db(db.session) - models.Table - db.session.add(obj) + db.session.merge(obj) db.session.commit() obj.fetch_metadata() @@ -112,13 +84,13 @@ def load_birth_names(): obj.main_dttm_col = 'ds' obj.database = get_or_create_db(db.session) models.Table - db.session.add(obj) + db.session.merge(obj) db.session.commit() obj.fetch_metadata() tbl = obj print("Creating some slices") - def get_slice_json(slice_name, **kwargs): + def get_slice_json(**kwargs): defaults = { "compare_lag": "10", "compare_suffix": "o10Y", @@ -135,7 +107,6 @@ def load_birth_names(): "metrics": ["sum__num"], "row_limit": config.get("ROW_LIMIT"), "since": "100 years", - "slice_name": slice_name, "until": "now", "viz_type": "table", "where": "", @@ -147,205 +118,191 @@ def load_birth_names(): Slice = models.Slice slices = [] - slice_name = "Girls" - slc = db.session.query(Slice).filter_by(slice_name=slice_name).first() - if not slc: - slc = Slice( - slice_name=slice_name, + def merge_slice(slc): + o = db.session.query( + Slice).filter_by(slice_name=slc.slice_name).first() + if o: + db.session.delete(slc) + db.session.add(slc) + session.commit() + slices.append(slc) + + merge_slice( + Slice( + slice_name="Girls", viz_type='table', datasource_type='table', table=tbl, params=get_slice_json( - slice_name, groupby=['name'], flt_eq_1="girl", row_limit=50)) - session.add(slc) - slices.append(slc) + groupby=['name'], flt_eq_1="girl", row_limit=50))) - slice_name = "Boys" - slc = session.query(Slice).filter_by(slice_name=slice_name).first() - if not slc: - slc = Slice( - slice_name=slice_name, + merge_slice( + Slice( + slice_name="Boys", viz_type='table', datasource_type='table', table=tbl, params=get_slice_json( - slice_name, groupby=['name'], flt_eq_1="boy", row_limit=50)) - session.add(slc) - slices.append(slc) + groupby=['name'], flt_eq_1="boy", row_limit=50))) - slice_name = "Participants" - slc = session.query(Slice).filter_by(slice_name=slice_name).first() - if not slc: - slc = Slice( - slice_name=slice_name, + merge_slice( + Slice( + slice_name="Participants", viz_type='big_number', datasource_type='table', table=tbl, params=get_slice_json( - slice_name, viz_type="big_number", granularity="ds", - compare_lag="5", compare_suffix="over 5Y")) - session.add(slc) - slices.append(slc) + viz_type="big_number", granularity="ds", + compare_lag="5", compare_suffix="over 5Y"))) - slice_name = "Genders" - slc = session.query(Slice).filter_by(slice_name=slice_name).first() - if not slc: - slc = Slice( - slice_name=slice_name, + merge_slice( + Slice( + slice_name="Genders", viz_type='pie', datasource_type='table', table=tbl, params=get_slice_json( - slice_name, viz_type="pie", groupby=['gender'])) - session.add(slc) - slices.append(slc) + viz_type="pie", groupby=['gender']))) - slice_name = "Gender by State" - slc = session.query(Slice).filter_by(slice_name=slice_name).first() - if not slc: - slc = Slice( - slice_name=slice_name, + merge_slice( + Slice( + slice_name="Genders by State", viz_type='dist_bar', datasource_type='table', table=tbl, params=get_slice_json( - slice_name, flt_eq_1="other", viz_type="dist_bar", + flt_eq_1="other", viz_type="dist_bar", metrics=['sum__sum_girls', 'sum__sum_boys'], - groupby=['state'], flt_op_1='not in', flt_col_1='state')) - session.add(slc) - slices.append(slc) + groupby=['state'], flt_op_1='not in', flt_col_1='state'))) - slice_name = "Trends" - slc = session.query(Slice).filter_by(slice_name=slice_name).first() - if not slc: - slc = Slice( - slice_name=slice_name, + merge_slice( + Slice( + slice_name="Trends", viz_type='line', datasource_type='table', table=tbl, params=get_slice_json( - slice_name, viz_type="line", groupby=['name'], - granularity='ds', rich_tooltip='y', show_legend='y')) - session.add(slc) - slices.append(slc) + viz_type="line", groupby=['name'], + granularity='ds', rich_tooltip='y', show_legend='y'))) - slice_name = "Title" - slc = session.query(Slice).filter_by(slice_name=slice_name).first() code = """ -### Birth Names Dashboard -The source dataset came from [here](https://github.com/hadley/babynames) - -![img](http://monblog.system-linux.net/image/tux/baby-tux_overlord59-tux.png) +
+

Birth Names Dashboard

+

The source dataset came from [here]

+ +
""" - if not slc: - slc = Slice( - slice_name=slice_name, + merge_slice( + Slice( + slice_name="Title", viz_type='markup', datasource_type='table', table=tbl, params=get_slice_json( - slice_name, viz_type="markup", markup_type="markdown", - code=code)) - session.add(slc) - slices.append(slc) + viz_type="markup", markup_type="html", + code=code))) - slice_name = "Name Cloud" - slc = session.query(Slice).filter_by(slice_name=slice_name).first() - if not slc: - slc = Slice( - slice_name=slice_name, + merge_slice( + Slice( + slice_name="Name Cloud", viz_type='word_cloud', datasource_type='table', table=tbl, params=get_slice_json( - slice_name, viz_type="word_cloud", size_from="10", + viz_type="word_cloud", size_from="10", series='name', size_to="70", rotation="square", - limit='100')) - session.add(slc) - slices.append(slc) + limit='100'))) - slice_name = "Pivot Table" - slc = session.query(Slice).filter_by(slice_name=slice_name).first() - if not slc: - slc = Slice( - slice_name=slice_name, + merge_slice( + Slice( + slice_name="Pivot Table", viz_type='pivot_table', datasource_type='table', table=tbl, params=get_slice_json( - slice_name, viz_type="pivot_table", metrics=['sum__num'], - groupby=['name'], columns=['state'])) - session.add(slc) - slices.append(slc) + viz_type="pivot_table", metrics=['sum__num'], + groupby=['name'], columns=['state']))) print("Creating a dashboard") Dash = models.Dashboard dash = session.query(Dash).filter_by(dashboard_title="Births").first() - if not dash: - dash = Dash( - dashboard_title="Births", - position_json=""" - [ - { - "size_y": 4, - "size_x": 2, - "col": 3, - "slice_id": "1", - "row": 3 - }, - { - "size_y": 4, - "size_x": 2, - "col": 1, - "slice_id": "2", - "row": 3 - }, - { - "size_y": 2, - "size_x": 2, - "col": 1, - "slice_id": "3", - "row": 1 - }, - { - "size_y": 2, - "size_x": 2, - "col": 3, - "slice_id": "4", - "row": 1 - }, - { - "size_y": 3, - "size_x": 7, - "col": 5, - "slice_id": "5", - "row": 4 - }, - { - "size_y": 5, - "size_x": 11, - "col": 1, - "slice_id": "6", - "row": 7 - }, - { - "size_y": 3, - "size_x": 3, - "col": 9, - "slice_id": "7", - "row": 1 - }, - { - "size_y": 3, - "size_x": 4, - "col": 5, - "slice_id": "8", - "row": 1 - } - ] - """ - ) - session.add(dash) + + if dash: + db.session.delete(dash) + js = """ +[ + { + "size_y": 4, + "size_x": 2, + "col": 8, + "slice_id": "85", + "row": 7 + }, + { + "size_y": 4, + "size_x": 2, + "col": 10, + "slice_id": "86", + "row": 7 + }, + { + "size_y": 2, + "size_x": 2, + "col": 1, + "slice_id": "87", + "row": 1 + }, + { + "size_y": 2, + "size_x": 2, + "col": 3, + "slice_id": "88", + "row": 1 + }, + { + "size_y": 3, + "size_x": 7, + "col": 5, + "slice_id": "89", + "row": 4 + }, + { + "size_y": 4, + "size_x": 7, + "col": 1, + "slice_id": "90", + "row": 7 + }, + { + "size_y": 3, + "size_x": 3, + "col": 9, + "slice_id": "91", + "row": 1 + }, + { + "size_y": 3, + "size_x": 4, + "col": 5, + "slice_id": "92", + "row": 1 + }, + { + "size_y": 4, + "size_x": 4, + "col": 1, + "slice_id": "93", + "row": 3 + } +] + """ + l = json.loads(js) + for i, pos in enumerate(l): + pos['slice_id'] = str(slices[i].id) + dash = Dash( + dashboard_title="Births", + position_json=json.dumps(l, indent=4), + ) for s in slices: dash.slices.append(s) session.commit() diff --git a/panoramix/data/countries.md b/panoramix/data/countries.md new file mode 100644 index 00000000000..253a68b9f50 --- /dev/null +++ b/panoramix/data/countries.md @@ -0,0 +1,355 @@ +This data was download from the +[World's Health Organization's website](http://data.worldbank.org/data-catalog/health-nutrition-and-population-statistics) + +Here's the script that was used to massage the data: + + DIR = "" + df_country = pd.read_csv(DIR + '/HNP_Country.csv') + df_country.columns = ['country_code'] + list(df_country.columns[1:]) + df_country = df_country[['country_code', 'Region']] + df_country.columns = ['country_code', 'region'] + + df = pd.read_csv(DIR + '/HNP_Data.csv') + del df['Unnamed: 60'] + df.columns = ['country_name', 'country_code'] + list(df.columns[2:]) + ndf = df.merge(df_country, how='inner') + + dims = ('country_name', 'country_code', 'region') + vv = [str(i) for i in range(1960, 2015)] + mdf = pd.melt(ndf, id_vars=dims + ('Indicator Code',), value_vars=vv) + mdf['year'] = mdf.variable + '-01-01' + dims = dims + ('year',) + + pdf = mdf.pivot_table(values='value', columns='Indicator Code', index=dims) + pdf = pdf.reset_index() + pdf.to_csv(DIR + '/countries.csv') + pdf.to_json(DIR + '/countries.json', orient='records') + +Here's the description of the metrics available: + +Series | Code Indicator Name +--- | --- +NY.GNP.PCAP.CD | GNI per capita, Atlas method (current US$) +SE.ADT.1524.LT.FM.ZS | Literacy rate, youth (ages 15-24), gender parity index (GPI) +SE.ADT.1524.LT.MA.ZS | Literacy rate, youth male (% of males ages 15-24) +SE.ADT.1524.LT.ZS | Literacy rate, youth total (% of people ages 15-24) +SE.ADT.LITR.FE.ZS | Literacy rate, adult female (% of females ages 15 and above) +SE.ADT.LITR.MA.ZS | Literacy rate, adult male (% of males ages 15 and above) +SE.ADT.LITR.ZS | Literacy rate, adult total (% of people ages 15 and above) +SE.ENR.ORPH | Ratio of school attendance of orphans to school attendance of non-orphans ages 10-14 +SE.PRM.CMPT.FE.ZS | Primary completion rate, female (% of relevant age group) +SE.PRM.CMPT.MA.ZS | Primary completion rate, male (% of relevant age group) +SE.PRM.CMPT.ZS | Primary completion rate, total (% of relevant age group) +SE.PRM.ENRR | School enrollment, primary (% gross) +SE.PRM.ENRR.FE | School enrollment, primary, female (% gross) +SE.PRM.ENRR.MA | School enrollment, primary, male (% gross) +SE.PRM.NENR | School enrollment, primary (% net) +SE.PRM.NENR.FE | School enrollment, primary, female (% net) +SE.PRM.NENR.MA | School enrollment, primary, male (% net) +SE.SEC.ENRR | School enrollment, secondary (% gross) +SE.SEC.ENRR.FE | School enrollment, secondary, female (% gross) +SE.SEC.ENRR.MA | School enrollment, secondary, male (% gross) +SE.SEC.NENR | School enrollment, secondary (% net) +SE.SEC.NENR.FE | School enrollment, secondary, female (% net) +SE.SEC.NENR.MA | School enrollment, secondary, male (% net) +SE.TER.ENRR | School enrollment, tertiary (% gross) +SE.TER.ENRR.FE | School enrollment, tertiary, female (% gross) +SE.XPD.TOTL.GD.ZS | Government expenditure on education, total (% of GDP) +SH.ANM.CHLD.ZS | Prevalence of anemia among children (% of children under 5) +SH.ANM.NPRG.ZS | Prevalence of anemia among non-pregnant women (% of women ages 15-49) +SH.CON.1524.FE.ZS | Condom use, population ages 15-24, female (% of females ages 15-24) +SH.CON.1524.MA.ZS | Condom use, population ages 15-24, male (% of males ages 15-24) +SH.CON.AIDS.FE.ZS | Condom use at last high-risk sex, adult female (% ages 15-49) +SH.CON.AIDS.MA.ZS | Condom use at last high-risk sex, adult male (% ages 15-49) +SH.DTH.COMM.ZS | Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total) +SH.DTH.IMRT | Number of infant deaths +SH.DTH.INJR.ZS | Cause of death, by injury (% of total) +SH.DTH.MORT | Number of under-five deaths +SH.DTH.NCOM.ZS | Cause of death, by non-communicable diseases (% of total) +SH.DTH.NMRT | Number of neonatal deaths +SH.DYN.AIDS | Adults (ages 15+) living with HIV +SH.DYN.AIDS.DH | AIDS estimated deaths (UNAIDS estimates) +SH.DYN.AIDS.FE.ZS | Women's share of population ages 15+ living with HIV (%) +SH.DYN.AIDS.ZS | Prevalence of HIV, total (% of population ages 15-49) +SH.DYN.MORT | Mortality rate, under-5 (per 1,000 live births) +SH.DYN.MORT.FE | Mortality rate, under-5, female (per 1,000 live births) +SH.DYN.MORT.MA | Mortality rate, under-5, male (per 1,000 live births) +SH.DYN.NMRT | Mortality rate, neonatal (per 1,000 live births) +SH.FPL.SATI.ZS | Met need for contraception (% of married women ages 15-49) +SH.H2O.SAFE.RU.ZS | Improved water source, rural (% of rural population with access) +SH.H2O.SAFE.UR.ZS | Improved water source, urban (% of urban population with access) +SH.H2O.SAFE.ZS | Improved water source (% of population with access) +SH.HIV.0014 | Children (0-14) living with HIV +SH.HIV.1524.FE.ZS | Prevalence of HIV, female (% ages 15-24) +SH.HIV.1524.KW.FE.ZS | Comprehensive correct knowledge of HIV/AIDS, ages 15-24, female (2 prevent ways and reject 3 misconceptions) +SH.HIV.1524.KW.MA.ZS | Comprehensive correct knowledge of HIV/AIDS, ages 15-24, male (2 prevent ways and reject 3 misconceptions) +SH.HIV.1524.MA.ZS | Prevalence of HIV, male (% ages 15-24) +SH.HIV.ARTC.ZS | Antiretroviral therapy coverage (% of people living with HIV) +SH.HIV.KNOW.FE.ZS | % of females ages 15-49 having comprehensive correct knowledge about HIV (2 prevent ways and reject 3 misconceptions) +SH.HIV.KNOW.MA.ZS | % of males ages 15-49 having comprehensive correct knowledge about HIV (2 prevent ways and reject 3 misconceptions) +SH.HIV.ORPH | Children orphaned by HIV/AIDS +SH.HIV.TOTL | Adults (ages 15+) and children (0-14 years) living with HIV +SH.IMM.HEPB | Immunization, HepB3 (% of one-year-old children) +SH.IMM.HIB3 | Immunization, Hib3 (% of children ages 12-23 months) +SH.IMM.IBCG | Immunization, BCG (% of one-year-old children) +SH.IMM.IDPT | Immunization, DPT (% of children ages 12-23 months) +SH.IMM.MEAS | Immunization, measles (% of children ages 12-23 months) +SH.IMM.POL3 | Immunization, Pol3 (% of one-year-old children) +SH.MED.BEDS.ZS | Hospital beds (per 1,000 people) +SH.MED.CMHW.P3 | Community health workers (per 1,000 people) +SH.MED.NUMW.P3 | Nurses and midwives (per 1,000 people) +SH.MED.PHYS.ZS | Physicians (per 1,000 people) +SH.MLR.NETS.ZS | Use of insecticide-treated bed nets (% of under-5 population) +SH.MLR.PREG.ZS | Use of any antimalarial drug (% of pregnant women) +SH.MLR.SPF2.ZS | Use of Intermittent Preventive Treatment of malaria, 2+ doses of SP/Fansidar (% of pregnant women) +SH.MLR.TRET.ZS | Children with fever receiving antimalarial drugs (% of children under age 5 with fever) +SH.MMR.DTHS | Number of maternal deaths +SH.MMR.LEVE | Number of weeks of maternity leave +SH.MMR.RISK | Lifetime risk of maternal death (1 in: rate varies by country) +SH.MMR.RISK.ZS | Lifetime risk of maternal death (%) +SH.MMR.WAGE.ZS | Maternal leave benefits (% of wages paid in covered period) +SH.PRG.ANEM | Prevalence of anemia among pregnant women (%) +SH.PRG.ARTC.ZS | Antiretroviral therapy coverage (% of pregnant women living with HIV) +SH.PRG.SYPH.ZS | Prevalence of syphilis (% of women attending antenatal care) +SH.PRV.SMOK.FE | Smoking prevalence, females (% of adults) +SH.PRV.SMOK.MA | Smoking prevalence, males (% of adults) +SH.STA.ACSN | Improved sanitation facilities (% of population with access) +SH.STA.ACSN.RU | Improved sanitation facilities, rural (% of rural population with access) +SH.STA.ACSN.UR | Improved sanitation facilities, urban (% of urban population with access) +SH.STA.ANV4.ZS | Pregnant women receiving prenatal care of at least four visits (% of pregnant women) +SH.STA.ANVC.ZS | Pregnant women receiving prenatal care (%) +SH.STA.ARIC.ZS | ARI treatment (% of children under 5 taken to a health provider) +SH.STA.BFED.ZS | Exclusive breastfeeding (% of children under 6 months) +SH.STA.BRTC.ZS | Births attended by skilled health staff (% of total) +SH.STA.BRTW.ZS | Low-birthweight babies (% of births) +SH.STA.DIAB.ZS | Diabetes prevalence (% of population ages 20 to 79) +SH.STA.IYCF.ZS | Infant and young child feeding practices, all 3 IYCF (% children ages 6-23 months) +SH.STA.MALN.FE.ZS | Prevalence of underweight, weight for age, female (% of children under 5) +SH.STA.MALN.MA.ZS | Prevalence of underweight, weight for age, male (% of children under 5) +SH.STA.MALN.ZS | Prevalence of underweight, weight for age (% of children under 5) +SH.STA.MALR | Malaria cases reported +SH.STA.MMRT | Maternal mortality ratio (modeled estimate, per 100,000 live births) +SH.STA.MMRT.NE | Maternal mortality ratio (national estimate, per 100,000 live births) +SH.STA.ORCF.ZS | Diarrhea treatment (% of children under 5 receiving oral rehydration and continued feeding) +SH.STA.ORTH | Diarrhea treatment (% of children under 5 who received ORS packet) +SH.STA.OW15.FE.ZS | Prevalence of overweight, female (% of female adults) +SH.STA.OW15.MA.ZS | Prevalence of overweight, male (% of male adults) +SH.STA.OW15.ZS | Prevalence of overweight (% of adults) +SH.STA.OWGH.FE.ZS | Prevalence of overweight, weight for height, female (% of children under 5) +SH.STA.OWGH.MA.ZS | Prevalence of overweight, weight for height, male (% of children under 5) +SH.STA.OWGH.ZS | Prevalence of overweight, weight for height (% of children under 5) +SH.STA.PNVC.ZS | Postnatal care coverage (% mothers) +SH.STA.STNT.FE.ZS | Prevalence of stunting, height for age, female (% of children under 5) +SH.STA.STNT.MA.ZS | Prevalence of stunting, height for age, male (% of children under 5) +SH.STA.STNT.ZS | Prevalence of stunting, height for age (% of children under 5) +SH.STA.WAST.FE.ZS | Prevalence of wasting, weight for height, female (% of children under 5) +SH.STA.WAST.MA.ZS | Prevalence of wasting, weight for height, male (% of children under 5) +SH.STA.WAST.ZS | Prevalence of wasting, weight for height (% of children under 5) +SH.SVR.WAST.FE.ZS | Prevalence of severe wasting, weight for height, female (% of children under 5) +SH.SVR.WAST.MA.ZS | Prevalence of severe wasting, weight for height, male (% of children under 5) +SH.SVR.WAST.ZS | Prevalence of severe wasting, weight for height (% of children under 5) +SH.TBS.CURE.ZS | Tuberculosis treatment success rate (% of new cases) +SH.TBS.DTEC.ZS | Tuberculosis case detection rate (%, all forms) +SH.TBS.INCD | Incidence of tuberculosis (per 100,000 people) +SH.TBS.MORT | Tuberculosis death rate (per 100,000 people) +SH.TBS.PREV | Prevalence of tuberculosis (per 100,000 population) +SH.VAC.TTNS.ZS | Newborns protected against tetanus (%) +SH.XPD.EXTR.ZS | External resources for health (% of total expenditure on health) +SH.XPD.OOPC.TO.ZS | Out-of-pocket health expenditure (% of total expenditure on health) +SH.XPD.OOPC.ZS | Out-of-pocket health expenditure (% of private expenditure on health) +SH.XPD.PCAP | Health expenditure per capita (current US$) +SH.XPD.PCAP.PP.KD | Health expenditure per capita, PPP (constant 2011 international $) +SH.XPD.PRIV | Health expenditure, private (% of total health expenditure) +SH.XPD.PRIV.ZS | Health expenditure, private (% of GDP) +SH.XPD.PUBL | Health expenditure, public (% of total health expenditure) +SH.XPD.PUBL.GX.ZS | Health expenditure, public (% of government expenditure) +SH.XPD.PUBL.ZS | Health expenditure, public (% of GDP) +SH.XPD.TOTL.CD | Health expenditure, total (current US$) +SH.XPD.TOTL.ZS | Health expenditure, total (% of GDP) +SI.POV.NAHC | Poverty headcount ratio at national poverty lines (% of population) +SI.POV.RUHC | Rural poverty headcount ratio at national poverty lines (% of rural population) +SI.POV.URHC | Urban poverty headcount ratio at national poverty lines (% of urban population) +SL.EMP.INSV.FE.ZS | Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment) +SL.TLF.TOTL.FE.ZS | Labor force, female (% of total labor force) +SL.TLF.TOTL.IN | Labor force, total +SL.UEM.TOTL.FE.ZS | Unemployment, female (% of female labor force) (modeled ILO estimate) +SL.UEM.TOTL.MA.ZS | Unemployment, male (% of male labor force) (modeled ILO estimate) +SL.UEM.TOTL.ZS | Unemployment, total (% of total labor force) (modeled ILO estimate) +SM.POP.NETM | Net migration +SN.ITK.DEFC | Number of people who are undernourished +SN.ITK.DEFC.ZS | Prevalence of undernourishment (% of population) +SN.ITK.SALT.ZS | Consumption of iodized salt (% of households) +SN.ITK.VITA.ZS | Vitamin A supplementation coverage rate (% of children ages 6-59 months) +SP.ADO.TFRT | Adolescent fertility rate (births per 1,000 women ages 15-19) +SP.DYN.AMRT.FE | Mortality rate, adult, female (per 1,000 female adults) +SP.DYN.AMRT.MA | Mortality rate, adult, male (per 1,000 male adults) +SP.DYN.CBRT.IN | Birth rate, crude (per 1,000 people) +SP.DYN.CDRT.IN | Death rate, crude (per 1,000 people) +SP.DYN.CONU.ZS | Contraceptive prevalence (% of women ages 15-49) +SP.DYN.IMRT.FE.IN | Mortality rate, infant, female (per 1,000 live births) +SP.DYN.IMRT.IN | Mortality rate, infant (per 1,000 live births) +SP.DYN.IMRT.MA.IN | Mortality rate, infant, male (per 1,000 live births) +SP.DYN.LE00.FE.IN | Life expectancy at birth, female (years) +SP.DYN.LE00.IN | Life expectancy at birth, total (years) +SP.DYN.LE00.MA.IN | Life expectancy at birth, male (years) +SP.DYN.SMAM.FE | Mean age at first marriage, female +SP.DYN.SMAM.MA | Mean age at first marriage, male +SP.DYN.TFRT.IN | Fertility rate, total (births per woman) +SP.DYN.TO65.FE.ZS | Survival to age 65, female (% of cohort) +SP.DYN.TO65.MA.ZS | Survival to age 65, male (% of cohort) +SP.DYN.WFRT | Wanted fertility rate (births per woman) +SP.HOU.FEMA.ZS | Female headed households (% of households with a female head) +SP.MTR.1519.ZS | Teenage mothers (% of women ages 15-19 who have had children or are currently pregnant) +SP.POP.0004.FE | Population ages 0-4, female +SP.POP.0004.FE.5Y | Population ages 0-4, female (% of female population) +SP.POP.0004.MA | Population ages 0-4, male +SP.POP.0004.MA.5Y | Population ages 0-4, male (% of male population) +SP.POP.0014.FE.ZS | Population ages 0-14, female (% of total) +SP.POP.0014.MA.ZS | Population ages 0-14, male (% of total) +SP.POP.0014.TO | Population ages 0-14, total +SP.POP.0014.TO.ZS | Population ages 0-14 (% of total) +SP.POP.0509.FE | Population ages 5-9, female +SP.POP.0509.FE.5Y | Population ages 5-9, female (% of female population) +SP.POP.0509.MA | Population ages 5-9, male +SP.POP.0509.MA.5Y | Population ages 5-9, male (% of male population) +SP.POP.1014.FE | Population ages 10-14, female +SP.POP.1014.FE.5Y | Population ages 10-14, female (% of female population) +SP.POP.1014.MA | Population ages 10-14, male +SP.POP.1014.MA.5Y | Population ages 10-14, male (% of male population) +SP.POP.1519.FE | Population ages 15-19, female +SP.POP.1519.FE.5Y | Population ages 15-19, female (% of female population) +SP.POP.1519.MA | Population ages 15-19, male +SP.POP.1519.MA.5Y | Population ages 15-19, male (% of male population) +SP.POP.1564.FE.ZS | Population ages 15-64, female (% of total) +SP.POP.1564.MA.ZS | Population ages 15-64, male (% of total) +SP.POP.1564.TO | Population ages 15-64, total +SP.POP.1564.TO.ZS | Population ages 15-64 (% of total) +SP.POP.2024.FE | Population ages 20-24, female +SP.POP.2024.FE.5Y | Population ages 20-24, female (% of female population) +SP.POP.2024.MA | Population ages 20-24, male +SP.POP.2024.MA.5Y | Population ages 20-24, male (% of male population) +SP.POP.2529.FE | Population ages 25-29, female +SP.POP.2529.FE.5Y | Population ages 25-29, female (% of female population) +SP.POP.2529.MA | Population ages 25-29, male +SP.POP.2529.MA.5Y | Population ages 25-29, male (% of male population) +SP.POP.3034.FE | Population ages 30-34, female +SP.POP.3034.FE.5Y | Population ages 30-34, female (% of female population) +SP.POP.3034.MA | Population ages 30-34, male +SP.POP.3034.MA.5Y | Population ages 30-34, male (% of male population) +SP.POP.3539.FE | Population ages 35-39, female +SP.POP.3539.FE.5Y | Population ages 35-39, female (% of female population) +SP.POP.3539.MA | Population ages 35-39, male +SP.POP.3539.MA.5Y | Population ages 35-39, male (% of male population) +SP.POP.4044.FE | Population ages 40-44, female +SP.POP.4044.FE.5Y | Population ages 40-44, female (% of female population) +SP.POP.4044.MA | Population ages 40-44, male +SP.POP.4044.MA.5Y | Population ages 40-44, male (% of male population) +SP.POP.4549.FE | Population ages 45-49, female +SP.POP.4549.FE.5Y | Population ages 45-49, female (% of female population) +SP.POP.4549.MA | Population ages 45-49, male +SP.POP.4549.MA.5Y | Population ages 45-49, male (% of male population) +SP.POP.5054.FE | Population ages 50-54, female +SP.POP.5054.FE.5Y | Population ages 50-54, female (% of female population) +SP.POP.5054.MA | Population ages 50-54, male +SP.POP.5054.MA.5Y | Population ages 50-54, male (% of male population) +SP.POP.5559.FE | Population ages 55-59, female +SP.POP.5559.FE.5Y | Population ages 55-59, female (% of female population) +SP.POP.5559.MA | Population ages 55-59, male +SP.POP.5559.MA.5Y | Population ages 55-59, male (% of male population) +SP.POP.6064.FE | Population ages 60-64, female +SP.POP.6064.FE.5Y | Population ages 60-64, female (% of female population) +SP.POP.6064.MA | Population ages 60-64, male +SP.POP.6064.MA.5Y | Population ages 60-64, male (% of male population) +SP.POP.6569.FE | Population ages 65-69, female +SP.POP.6569.FE.5Y | Population ages 65-69, female (% of female population) +SP.POP.6569.MA | Population ages 65-69, male +SP.POP.6569.MA.5Y | Population ages 65-69, male (% of male population) +SP.POP.65UP.FE.ZS | Population ages 65 and above, female (% of total) +SP.POP.65UP.MA.ZS | Population ages 65 and above, male (% of total) +SP.POP.65UP.TO | Population ages 65 and above, total +SP.POP.65UP.TO.ZS | Population ages 65 and above (% of total) +SP.POP.7074.FE | Population ages 70-74, female +SP.POP.7074.FE.5Y | Population ages 70-74, female (% of female population) +SP.POP.7074.MA | Population ages 70-74, male +SP.POP.7074.MA.5Y | Population ages 70-74, male (% of male population) +SP.POP.7579.FE | Population ages 75-79, female +SP.POP.7579.FE.5Y | Population ages 75-79, female (% of female population) +SP.POP.7579.MA | Population ages 75-79, male +SP.POP.7579.MA.5Y | Population ages 75-79, male (% of male population) +SP.POP.80UP.FE | Population ages 80 and above, female +SP.POP.80UP.FE.5Y | Population ages 80 and above, female (% of female population) +SP.POP.80UP.MA | Population ages 80 and above, male +SP.POP.80UP.MA.5Y | Population ages 80 and above, male (% of male population) +SP.POP.AG00.FE.IN | Age population, age 0, female, interpolated +SP.POP.AG00.MA.IN | Age population, age 0, male, interpolated +SP.POP.AG01.FE.IN | Age population, age 01, female, interpolated +SP.POP.AG01.MA.IN | Age population, age 01, male, interpolated +SP.POP.AG02.FE.IN | Age population, age 02, female, interpolated +SP.POP.AG02.MA.IN | Age population, age 02, male, interpolated +SP.POP.AG03.FE.IN | Age population, age 03, female, interpolated +SP.POP.AG03.MA.IN | Age population, age 03, male, interpolated +SP.POP.AG04.FE.IN | Age population, age 04, female, interpolated +SP.POP.AG04.MA.IN | Age population, age 04, male, interpolated +SP.POP.AG05.FE.IN | Age population, age 05, female, interpolated +SP.POP.AG05.MA.IN | Age population, age 05, male, interpolated +SP.POP.AG06.FE.IN | Age population, age 06, female, interpolated +SP.POP.AG06.MA.IN | Age population, age 06, male, interpolated +SP.POP.AG07.FE.IN | Age population, age 07, female, interpolated +SP.POP.AG07.MA.IN | Age population, age 07, male, interpolated +SP.POP.AG08.FE.IN | Age population, age 08, female, interpolated +SP.POP.AG08.MA.IN | Age population, age 08, male, interpolated +SP.POP.AG09.FE.IN | Age population, age 09, female, interpolated +SP.POP.AG09.MA.IN | Age population, age 09, male, interpolated +SP.POP.AG10.FE.IN | Age population, age 10, female, interpolated +SP.POP.AG10.MA.IN | Age population, age 10, male +SP.POP.AG11.FE.IN | Age population, age 11, female, interpolated +SP.POP.AG11.MA.IN | Age population, age 11, male +SP.POP.AG12.FE.IN | Age population, age 12, female, interpolated +SP.POP.AG12.MA.IN | Age population, age 12, male +SP.POP.AG13.FE.IN | Age population, age 13, female, interpolated +SP.POP.AG13.MA.IN | Age population, age 13, male +SP.POP.AG14.FE.IN | Age population, age 14, female, interpolated +SP.POP.AG14.MA.IN | Age population, age 14, male +SP.POP.AG15.FE.IN | Age population, age 15, female, interpolated +SP.POP.AG15.MA.IN | Age population, age 15, male, interpolated +SP.POP.AG16.FE.IN | Age population, age 16, female, interpolated +SP.POP.AG16.MA.IN | Age population, age 16, male, interpolated +SP.POP.AG17.FE.IN | Age population, age 17, female, interpolated +SP.POP.AG17.MA.IN | Age population, age 17, male, interpolated +SP.POP.AG18.FE.IN | Age population, age 18, female, interpolated +SP.POP.AG18.MA.IN | Age population, age 18, male, interpolated +SP.POP.AG19.FE.IN | Age population, age 19, female, interpolated +SP.POP.AG19.MA.IN | Age population, age 19, male, interpolated +SP.POP.AG20.FE.IN | Age population, age 20, female, interpolated +SP.POP.AG20.MA.IN | Age population, age 20, male, interpolated +SP.POP.AG21.FE.IN | Age population, age 21, female, interpolated +SP.POP.AG21.MA.IN | Age population, age 21, male, interpolated +SP.POP.AG22.FE.IN | Age population, age 22, female, interpolated +SP.POP.AG22.MA.IN | Age population, age 22, male, interpolated +SP.POP.AG23.FE.IN | Age population, age 23, female, interpolated +SP.POP.AG23.MA.IN | Age population, age 23, male, interpolated +SP.POP.AG24.FE.IN | Age population, age 24, female, interpolated +SP.POP.AG24.MA.IN | Age population, age 24, male, interpolated +SP.POP.AG25.FE.IN | Age population, age 25, female, interpolated +SP.POP.AG25.MA.IN | Age population, age 25, male, interpolated +SP.POP.BRTH.MF | Sex ratio at birth (male births per female births) +SP.POP.DPND | Age dependency ratio (% of working-age population) +SP.POP.DPND.OL | Age dependency ratio, old (% of working-age population) +SP.POP.DPND.YG | Age dependency ratio, young (% of working-age population) +SP.POP.GROW | Population growth (annual %) +SP.POP.TOTL | Population, total +SP.POP.TOTL.FE.IN | Population, female +SP.POP.TOTL.FE.ZS | Population, female (% of total) +SP.POP.TOTL.MA.IN | Population, male +SP.POP.TOTL.MA.ZS | Population, male (% of total) +SP.REG.BRTH.RU.ZS | Completeness of birth registration, rural (%) +SP.REG.BRTH.UR.ZS | Completeness of birth registration, urban (%) +SP.REG.BRTH.ZS | Completeness of birth registration (%) +SP.REG.DTHS.ZS | Completeness of death registration with cause-of-death information (%) +SP.RUR.TOTL | Rural population +SP.RUR.TOTL.ZG | Rural population growth (annual %) +SP.RUR.TOTL.ZS | Rural population (% of total population) +SP.URB.GROW | Urban population growth (annual %) +SP.URB.TOTL | Urban population +SP.URB.TOTL.IN.ZS | Urban population (% of total) +SP.UWT.TFRT | Unmet need for contraception (% of married women ages 15-49) diff --git a/panoramix/static/widgets/viz_table.js b/panoramix/static/widgets/viz_table.js index f307acc1ccd..c3f10399459 100644 --- a/panoramix/static/widgets/viz_table.js +++ b/panoramix/static/widgets/viz_table.js @@ -20,7 +20,7 @@ px.registerViz('table', function(slice) { maxes[metrics[i]] = d3.max(col(metrics[i])); } var table = d3.select(slice.selector).append('table') - .attr('class', 'dataframe table table-striped table-bordered table-condensed table-hover'); + .classed('dataframe dataframe table table-striped table-bordered table-condensed table-hover dataTable no-footer', true); table.append('thead').append('tr') .selectAll('th') .data(data.columns).enter() @@ -75,7 +75,7 @@ px.registerViz('table', function(slice) { else return d.val; }); - var datatable = slice.container.find('table').DataTable({ + var datatable = slice.container.find('.dataTable').DataTable({ paging: false, searching: form_data.include_search, }); diff --git a/panoramix/utils.py b/panoramix/utils.py index c9f6d38d561..fdb939d48ac 100644 --- a/panoramix/utils.py +++ b/panoramix/utils.py @@ -229,3 +229,9 @@ def json_iso_dttm_ser(obj): def markdown(s): return md(s, ['markdown.extensions.tables']) + + +def readfile(filepath): + with open(filepath) as f: + content = f.read() + return content diff --git a/panoramix/viz.py b/panoramix/viz.py index 8dc9df163c3..2f788dae9a2 100644 --- a/panoramix/viz.py +++ b/panoramix/viz.py @@ -283,7 +283,10 @@ class TableViz(BaseViz): 'all_columns', ) },) - css_files = ['lib/dataTables/dataTables.bootstrap.css'] + css_files = [ + 'lib/dataTables/dataTables.bootstrap.css', + 'widgets/viz_table.css', + ] is_timeseries = False js_files = [ 'lib/d3.min.js', @@ -291,7 +294,6 @@ class TableViz(BaseViz): 'lib/dataTables/dataTables.bootstrap.js', 'widgets/viz_table.js', ] - css_files = ['widgets/viz_table.css'] def query_obj(self): d = super(TableViz, self).query_obj()