✨ Jinja whitespaces and newlines #3657
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Login: chart-diff: ✅No charts for review.data-diff: ❌ Found differences~ Dataset garden/antibiotics/2024-11-12/antimicrobial_usage
- - update_period_days: 365
? ^^
+ + update_period_days: 308
? ^^
- - Table class_aggregated
- - Column ddd_anti_malarials
- - Column ddd_antibacterials_and_antituberculosis
- - Column ddd_antifungals
- - Column ddd_antituberculosis
- - Column ddd_antivirals
- - Column did_anti_malarials
- - Column did_antibacterials_and_antituberculosis
- - Column did_antifungals
- - Column did_antituberculosis
- - Column did_antivirals
= Table aware
~ Column ddd (changed metadata)
- - Total (#dod:defined-daily-doses) of AWaRe category: << awarelabel >> antibiotics used in a given year. <% if aware == "A" %> Access antibiotics have activity against a wide range of common pathogens and show lower resistance potential than antibiotics in the other groups. <% elif aware == "W" %> Watch antibiotic have higher resistance potential and include most of the highest priority agents among the Critically Important Antimicrobials for Human Medicine and/or antibiotics that are at relatively high risk of bacterial resistance. <% elif aware == "R" %> Reserve antibiotics should be reserved for treatment of confirmed or suspected infections due to multi-drug-resistant organisms. Reserve group antibiotics should be treated as “last resort” options. <% elif aware == "O" %> The use of the Not classified/Not recommended antibiotics is not evidence-based, nor recommended in high-quality international guidelines. WHO does not recommend the use of these antibiotics in clinical practice. <% endif %>
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ + Volume of AWaRe category: << awarelabel >> antibiotics used in a given year. <% if aware == "A" %> Access antibiotics have activity against a wide range of common pathogens and show lower resistance potential than antibiotics in the other groups. <% elif aware == "W" %> Watch antibiotic have higher resistance potential and include most of the highest priority agents among the Critically Important Antimicrobials for Human Medicine and/or antibiotics that are at relatively high risk of bacterial resistance. <% elif aware == "R" %> Reserve antibiotics should be reserved for treatment of confirmed or suspected infections due to multi-drug-resistant organisms. Reserve group antibiotics should be treated as “last resort” options. <% elif aware == "O" %> The use of the Not classified/Not recommended antibiotics is not evidence-based, nor recommended in high-quality international guidelines. WHO does not recommend the use of these antibiotics in clinical practice. <% endif %>
? ^^^^^^^^^
- - display:
- - numDecimalPlaces: 0
~ Column did (changed metadata)
- - Total (#dod:defined-daily-doses) of AWaRe category: <<awarelabel>> used per 1000 inhabitants per day. <% if aware == "A" %> Access antibiotics have activity against a wide range of common pathogens and show lower resistance potential than antibiotics in the other groups. <% elif aware == "W" %> Watch antibiotic have higher resistance potential and include most of the highest priority agents among the Critically Important Antimicrobials for Human Medicine and/or antibiotics that are at relatively high risk of bacterial resistance. <% elif aware == "R" %> Reserve antibiotics should be reserved for treatment of confirmed or suspected infections due to multi-drug-resistant organisms. Reserve group antibiotics should be treated as “last resort” options. <% elif aware == "O" %> The use of the Not classified/Not recommended antibiotics is not evidence-based, nor recommended in high-quality international guidelines. WHO does not recommend the use of these antibiotics in clinical practice. <% endif %>
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ + Volume of AWaRe category: <<awarelabel>> used per 1000 inhabitants per day. <% if aware == "A" %> Access antibiotics have activity against a wide range of common pathogens and show lower resistance potential than antibiotics in the other groups. <% elif aware == "W" %> Watch antibiotic have higher resistance potential and include most of the highest priority agents among the Critically Important Antimicrobials for Human Medicine and/or antibiotics that are at relatively high risk of bacterial resistance. <% elif aware == "R" %> Reserve antibiotics should be reserved for treatment of confirmed or suspected infections due to multi-drug-resistant organisms. Reserve group antibiotics should be treated as “last resort” options. <% elif aware == "O" %> The use of the Not classified/Not recommended antibiotics is not evidence-based, nor recommended in high-quality international guidelines. WHO does not recommend the use of these antibiotics in clinical practice. <% endif %>
? ^^^^^^^^^
- - display:
- - numDecimalPlaces: 1
= Table class
~ Column ddd (changed metadata)
- - Defined daily doses of <% if routeofadministration == "O" %> orally administered <% elif routeofadministration == "P" %> parentearally administered <% elif routeofadministration == "R" %> rectally administered4 <% elif routeofadministration == "I" %> inhaled <% endif %> << antimicrobialclass.lower()>> - << atc4name.lower() >> used
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ + Defined daily doses of <% if routeofadministration == "O" %> orally administered <% elif routeofadministration == "P" %> parentearally administered <% elif routeofadministration == "R" %> rectally administered4 <% elif routeofadministration == "I" %> inhaled <% endif %> << antimicrobialclass>> - << atc4name.lower() >> used
? ++++++++++++++++ ^^^
- - description_short: Total (#dod:defined-daily-doses) of antimicrobials used in a given year.
+ + description_short: Volume of antimicrobials used in a given year.
- - display:
- - numDecimalPlaces: 0
~ Column did (changed metadata)
- - description_short: Total (#dod:defined-daily-doses) of antimicrobials used per 1000 inhabitants per day.
+ + description_short: Volume of antimicrobials used per 1000 inhabitants per day.
- - display:
- - numDecimalPlaces: 1
= Dataset garden/artificial_intelligence/2024-11-03/epoch_regressions
= Table epoch_regressions
~ Dim days_since_1949
+ + New values: 12 / 874 (1.37%)
system days_since_1949
1.5x/year 547
1.5x/year 22080
2.0x/year 22280
2.4x/year 27670
4.1x/year 27670
- - Removed values: 12 / 874 (1.37%)
system days_since_1949
1.5x/year between 1950–2010 547
1.5x/year between 1950–2010 22080
2.0x/year between 2010–2025 22280
2.4x/year between 2010–2025 27670
4.1x/year between 2010–2025 27670
~ Dim system
+ + New values: 12 / 874 (1.37%)
days_since_1949 system
547 1.5x/year
22080 1.5x/year
22280 2.0x/year
27670 2.4x/year
27670 4.1x/year
- - Removed values: 12 / 874 (1.37%)
days_since_1949 system
547 1.5x/year between 1950–2010
22080 1.5x/year between 1950–2010
22280 2.0x/year between 2010–2025
27670 2.4x/year between 2010–2025
27670 4.1x/year between 2010–2025
~ Column domain (new data)
+ + New values: 12 / 874 (1.37%)
days_since_1949 system domain
547 1.5x/year NaN
22080 1.5x/year NaN
22280 2.0x/year NaN
27670 2.4x/year NaN
27670 4.1x/year NaN
- - Removed values: 12 / 874 (1.37%)
days_since_1949 system domain
547 1.5x/year between 1950–2010 NaN
22080 1.5x/year between 1950–2010 NaN
22280 2.0x/year between 2010–2025 NaN
27670 2.4x/year between 2010–2025 NaN
27670 4.1x/year between 2010–2025 NaN
~ Column organization_categorization (new data)
+ + New values: 12 / 874 (1.37%)
days_since_1949 system organization_categorization
547 1.5x/year NaN
22080 1.5x/year NaN
22280 2.0x/year NaN
27670 2.4x/year NaN
27670 4.1x/year NaN
- - Removed values: 12 / 874 (1.37%)
days_since_1949 system organization_categorization
547 1.5x/year between 1950–2010 NaN
22080 1.5x/year between 1950–2010 NaN
22280 2.0x/year between 2010–2025 NaN
27670 2.4x/year between 2010–2025 NaN
27670 4.1x/year between 2010–2025 NaN
~ Column parameters (new data, changed data)
+ + New values: 12 / 874 (1.37%)
days_since_1949 system parameters
547 1.5x/year <NA>
22080 1.5x/year <NA>
22280 2.0x/year 577927.0625
27670 2.4x/year <NA>
27670 4.1x/year <NA>
- - Removed values: 12 / 874 (1.37%)
days_since_1949 system parameters
547 1.5x/year between 1950–2010 <NA>
22080 1.5x/year between 1950–2010 <NA>
22280 2.0x/year between 2010–2025 577927.0625
27670 2.4x/year between 2010–2025 <NA>
27670 4.1x/year between 2010–2025 <NA>
~ Column publication_date (new data)
+ + New values: 12 / 874 (1.37%)
days_since_1949 system publication_date
547 1.5x/year NaT
22080 1.5x/year NaT
22280 2.0x/year NaT
27670 2.4x/year NaT
27670 4.1x/year NaT
- - Removed values: 12 / 874 (1.37%)
days_since_1949 system publication_date
547 1.5x/year between 1950–2010 NaT
22080 1.5x/year between 1950–2010 NaT
22280 2.0x/year between 2010–2025 NaT
27670 2.4x/year between 2010–2025 NaT
27670 4.1x/year between 2010–2025 NaT
~ Column training_computation_petaflop (new data, changed data)
+ + New values: 12 / 874 (1.37%)
days_since_1949 system training_computation_petaflop
547 1.5x/year 0.0
22080 1.5x/year 0.246361
22280 2.0x/year <NA>
27670 2.4x/year <NA>
27670 4.1x/year 466953248.0
- - Removed values: 12 / 874 (1.37%)
days_since_1949 system training_computation_petaflop
547 1.5x/year between 1950–2010 0.0
22080 1.5x/year between 1950–2010 0.246361
22280 2.0x/year between 2010–2025 <NA>
27670 2.4x/year between 2010–2025 <NA>
27670 4.1x/year between 2010–2025 466952832.0
~ Column training_dataset_size__datapoints (new data, changed data)
+ + New values: 12 / 874 (1.37%)
days_since_1949 system training_dataset_size__datapoints
547 1.5x/year <NA>
22080 1.5x/year <NA>
22280 2.0x/year <NA>
27670 2.4x/year 60797116416.0
27670 4.1x/year <NA>
- - Removed values: 12 / 874 (1.37%)
days_since_1949 system training_dataset_size__datapoints
547 1.5x/year between 1950–2010 <NA>
22080 1.5x/year between 1950–2010 <NA>
22280 2.0x/year between 2010–2025 <NA>
27670 2.4x/year between 2010–2025 60797116416.0
27670 4.1x/year between 2010–2025 <NA>
= Dataset garden/demography/2023-06-27/world_population_comparison
= Table world_population_comparison
= Dataset garden/missing_data/2024-03-26/children_out_of_school
= Table children_out_of_school
= Dataset garden/wb/2024-06-10/gender_statistics
= Table gender_statistics
~ Column sg_law_eqrm_wk (changed data)
~ Changed values: 24 / 15124 (0.16%)
country year sg_law_eqrm_wk - sg_law_eqrm_wk +
Slovakia 1980 1.0 0.0
Slovakia 1981 1.0 0.0
Slovakia 1984 1.0 0.0
Slovakia 1990 1.0 0.0
Slovakia 1992 1.0 0.0
= Dataset garden/wb/2024-06-10/gender_statistics_country_counts
= Table gender_statistics
~ Column sg_law_eqrm_wk_no_count (changed data)
~ Changed values: 48 / 378 (12.70%)
country year sg_law_eqrm_wk_no_count - sg_law_eqrm_wk_no_count +
Europe 1974 38 39
World 1972 186 187
World 1975 181 182
World 1976 180 181
World 1978 178 179
~ Column sg_law_eqrm_wk_no_pop (changed data)
~ Changed values: 48 / 378 (12.70%)
country year sg_law_eqrm_wk_no_pop - sg_law_eqrm_wk_no_pop +
Europe 1974 550689102 555379750
World 1972 3728735983 3733335785
World 1975 3877380047 3882119222
World 1976 3923263849 3928052362
World 1978 4011155699 4016041432
~ Column sg_law_eqrm_wk_yes_count (changed data)
~ Changed values: 48 / 378 (12.70%)
country year sg_law_eqrm_wk_yes_count - sg_law_eqrm_wk_yes_count +
Europe 1974 4 3
World 1972 3 2
World 1975 8 7
World 1976 9 8
World 1978 11 10
~ Column sg_law_eqrm_wk_yes_pop (changed data)
~ Changed values: 48 / 378 (12.70%)
country year sg_law_eqrm_wk_yes_pop - sg_law_eqrm_wk_yes_pop +
Europe 1974 123385892 118695244
World 1972 70537112 65937310
World 1975 145207798 140468623
World 1976 172060729 167272216
World 1978 230445466 225559733
= Dataset garden/who/2024-09-09/flu_test
= Table flu_test
~ Dim country
+ + New values: 7 / 72382 (0.01%)
date country
2024-08-26 Brazil
2024-09-02 Brazil
2024-09-09 Brazil
2024-09-16 Brazil
2024-11-11 Mexico
- - Removed values: 36 / 72382 (0.05%)
date country
2024-11-18 Ethiopia
2024-11-11 Iraq
2024-11-18 Jamaica
2024-10-21 Uganda
2024-10-28 Uganda
~ Dim date
+ + New values: 7 / 72382 (0.01%)
country date
Brazil 2024-08-26
Brazil 2024-09-02
Brazil 2024-09-09
Brazil 2024-09-16
Mexico 2024-11-11
- - Removed values: 36 / 72382 (0.05%)
country date
Ethiopia 2024-11-18
Iraq 2024-11-11
Jamaica 2024-11-18
Uganda 2024-10-21
Uganda 2024-10-28
~ Column denomcombined (new data, changed data)
+ + New values: 7 / 72382 (0.01%)
country date denomcombined
Brazil 2024-08-26 6810
Brazil 2024-09-02 7056
Brazil 2024-09-09 7321
Brazil 2024-09-16 6452
Mexico 2024-11-11 495
- - Removed values: 36 / 72382 (0.05%)
country date denomcombined
Ethiopia 2024-11-18 152
Iraq 2024-11-11 35
Jamaica 2024-11-18 19
Uganda 2024-10-21 49
Uganda 2024-10-28 68
~ Changed values: 87 / 72382 (0.12%)
country date denomcombined - denomcombined +
Argentina 2024-09-30 166 164
Brazil 2024-11-04 6224 5906
Indonesia 2024-08-12 48 47
Paraguay 2024-05-13 200 201
Paraguay 2024-08-19 328 327
~ Column pcnt_poscombined (new data, changed data)
+ + New values: 7 / 72382 (0.01%)
country date pcnt_poscombined
Brazil 2024-08-26 5.800294
Brazil 2024-09-02 5.782313
Brazil 2024-09-09 6.979921
Brazil 2024-09-16 7.377557
Mexico 2024-11-11 8.686869
- - Removed values: 36 / 72382 (0.05%)
country date pcnt_poscombined
Ethiopia 2024-11-18 18.421053
Iraq 2024-11-11 17.142857
Jamaica 2024-11-18 10.526316
Uganda 2024-10-21 14.285714
Uganda 2024-10-28 8.823529
~ Changed values: 94 / 72382 (0.13%)
country date pcnt_poscombined - pcnt_poscombined +
Argentina 2024-09-30 22.289156 21.95122
Brazil 2024-11-04 7.583548 7.162208
Chile 2024-04-29 45.319149 45.689655
Egypt 2024-09-30 4.118993 4.147465
Jamaica 2024-09-30 1.041667 1.428571
Legend: +New ~Modified -Removed =Identical Details
Hint: Run this locally with etl diff REMOTE data/ --include yourdataset --verbose --snippetAutomatically updated datasets matching weekly_wildfires|excess_mortality|covid|fluid|flunet|country_profile|garden/ihme_gbd/2019/gbd_risk are not included Edited: 2024-12-02 10:59:05 UTC |
lucasrodes
approved these changes
Nov 29, 2024
spoonerf
approved these changes
Nov 29, 2024
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A couple of improvements to alleviate some pain from #3654.
Note that this is still not perfect and doesn't solve
\n.problem from the issue (we still need to use<%- elifto get rid of it). Ideally, we should never have to use-and have the Jinja templates as intuitive as possible. To do this properly, we should:dynamic-yamlby something simpler and unify saving & loading of metadata files (while keeping it fast, there were tons of performance optimizations)jinjafunctionality toowid-catalogand add methodVariableMeta.render_jinja(dim_dict={"..."})\n., etc.