I'm building an app using Bubble and I have the Toolbox plugin which enables me to use the "Run javascript" step in a Workflow.
From the following NASA API url, I'm planning to read and analyze 20 years of data with values for every month (240 values):
Nonetheless, I'd just have a list of 13 averages instead of 12 due to the data I'm getting
let api_url="https://power.larc.nasa.gov/api/temporal/monthly/point?parameters=ALLSKY_SFC_SW_DNI&community=RE&longitude=48.0000&latitude=27.0000&format=JSON&start=2001&end=2020"
var list={} //will store data points on each month "number"
let response = await fetch(api_url)
let parseData = await response.json()
let interest=parseData.properties.parameter.ALLSKY_SFC_SW_DNI
for(const entry of Object.entries(interest)){
let [key,value]=entry //value is value xD
key=key.substring(4,6) //key becomes month "number"
list[key]=list[key]||{average:0,count:0}
list[key].count ; list[key].average =value
}
Object.entries(list).forEach(([_,month])=>{
month.average/=month.count
})
console.log(list[12]) //sample of a month data
console.log(list) //full thing
Here's what it looks like
CodePudding user response：
We know the data exists @ data.properties.parameter.ALLSKY_SFC_SW_DNI
The year and month are combined so the month must be extracted. Here is one simple way. (note I chose to remove the leading zero for a reason)
/* Given '202001' returns '1', given 202012' returns '12' */
function extractMonth(yearmonth){
let month = yearmonth.substring(yearmonth.length-2);
return month.startsWith('0')? month[month.length-1] : month;
}
If you store the data in a simple Array, the indexes can serve as the Month number. So index 1 will represent January. In order to do that the first element (index 0) will be set to null.
The reasons I took this approach is because the data structure is simple and it makes finding the lowest and highest values quite easy.
Now it is as easy as populating each Array element with the average value inside the loop. Each iteration of the loop updates the average for the month.
for(let [yearmonth, measurement] of Object.entries(data.properties.parameter.ALLSKY_SFC_SW_DNI)){
const month = extractMonth(yearmonth);
if(!tempsByMonth[month]) tempsByMonth[month] = 0;
tempsByMonth[month] = (tempsByMonth[month] measurement)/2;
}
It looks like this:
const data = {"type":"Feature","geometry":{"type":"Point","coordinates":[48.0,27.0,191.14]},"properties":{"parameter":{"ALLSKY_SFC_SW_DNI":{"200101":5.65,"200102":4.88,"200103":5.28,"200104":5.05,"200105":4.84,"200106":5.21,"200107":5.44,"200108":6.07,"200109":5.72,"200110":5.3,"200111":4.52,"200112":3.03,"200113":5.08,"200201":4.09,"200202":4.89,"200203":4.49,"200204":4.19,"200205":5.17,"200206":6.12,"200207":6.15,"200208":5.64,"200209":5.92,"200210":5.5,"200211":4.57,"200212":3.26,"200213":5.0,"200301":4.55,"200302":3.9,"200303":4.09,"200304":4.29,"200305":4.39,"200306":6.1,"200307":5.29,"200308":6.14,"200309":6.3,"200310":5.28,"200311":3.93,"200312":3.39,"200313":4.81,"200401":3.51,"200402":4.45,"200403":5.37,"200404":4.01,"200405":5.39,"200406":6.4,"200407":6.42,"200408":6.11,"200409":6.21,"200410":5.86,"200411":3.66,"200412":3.89,"200413":5.11,"200501":4.05,"200502":4.03,"200503":4.52,"200504":4.49,"200505":5.14,"200506":5.33,"200507":5.06,"200508":5.85,"200509":6.08,"200510":5.45,"200511":3.89,"200512":4.06,"200513":4.83,"200601":3.65,"200602":3.43,"200603":4.85,"200604":4.0,"200605":4.57,"200606":6.08,"200607":5.01,"200608":5.5,"200609":6.32,"200610":4.81,"200611":4.08,"200612":3.99,"200613":4.7,"200701":4.27,"200702":4.56,"200703":4.39,"200704":3.51,"200705":4.75,"200706":5.4,"200707":5.18,"200708":5.77,"200709":5.88,"200710":5.43,"200711":4.7,"200712":3.76,"200713":4.8,"200801":3.26,"200802":4.12,"200803":4.64,"200804":3.77,"200805":4.03,"200806":4.52,"200807":4.84,"200808":5.3,"200809":4.71,"200810":4.76,"200811":3.97,"200812":5.08,"200813":4.42,"200901":4.08,"200902":3.23,"200903":3.68,"200904":3.66,"200905":4.57,"200906":4.9,"200907":4.23,"200908":4.73,"200909":5.03,"200910":4.57,"200911":4.15,"200912":3.89,"200913":4.23,"201001":4.73,"201002":3.99,"201003":4.51,"201004":3.52,"201005":4.36,"201006":5.12,"201007":4.8,"201008":5.17,"201009":5.28,"201010":5.14,"201011":5.39,"201012":4.65,"201013":4.73,"201101":3.56,"201102":3.47,"201103":4.1,"201104":2.83,"201105":4.52,"201106":4.37,"201107":4.63,"201108":5.31,"201109":5.53,"201110":4.8,"201111":3.71,"201112":4.81,"201113":4.31,"201201":4.19,"201202":3.51,"201203":3.36,"201204":3.34,"201205":3.68,"201206":4.9,"201207":5.33,"201208":5.18,"201209":5.7,"201210":4.84,"201211":4.43,"201212":3.52,"201213":4.33,"201301":3.44,"201302":4.4,"201303":4.03,"201304":3.79,"201305":4.96,"201306":4.94,"201307":4.97,"201308":5.45,"201309":5.39,"201310":5.11,"201311":4.14,"201312":4.38,"201313":4.58,"201401":3.86,"201402":4.49,"201403":4.28,"201404":4.19,"201405":4.78,"201406":5.71,"201407":5.4,"201408":5.37,"201409":5.82,"201410":4.61,"201411":4.21,"201412":4.66,"201413":4.78,"201501":4.24,"201502":3.73,"201503":4.01,"201504":3.79,"201505":4.18,"201506":5.02,"201507":4.7,"201508":5.67,"201509":4.94,"201510":4.17,"201511":3.77,"201512":3.45,"201513":4.31,"201601":4.19,"201602":5.15,"201603":3.77,"201604":4.8,"201605":5.0,"201606":5.73,"201607":4.92,"201608":5.69,"201609":5.49,"201610":5.5,"201611":4.16,"201612":3.46,"201613":4.82,"201701":3.8,"201702":4.54,"201703":3.76,"201704":4.32,"201705":4.89,"201706":7.03,"201707":6.06,"201708":6.37,"201709":6.41,"201710":5.69,"201711":4.48,"201712":4.72,"201713":5.17,"201801":4.6,"201802":4.12,"201803":5.46,"201804":3.69,"201805":5.18,"201806":5.6,"201807":5.71,"201808":5.88,"201809":6.16,"201810":4.48,"201811":3.29,"201812":3.9,"201813":4.85,"201901":2.72,"201902":4.73,"201903":4.92,"201904":4.62,"201905":5.73,"201906":7.4,"201907":5.76,"201908":6.02,"201909":6.66,"201910":5.02,"201911":4.33,"201912":4.65,"201913":5.21,"202001":4.6,"202002":4.85,"202003":5.12,"202004":4.48,"202005":6.14,"202006":6.77,"202007":6.52,"202008":6.37,"202009":6.58,"202010":6.86,"202011":4.21,"202012":3.89,"202013":5.54}}},"header":{"title":"NASA/POWER CERES/MERRA2 Native Resolution Monthly and Annual","api":{"version":"v2.2.12","name":"POWER Monthly and Annual API"},"fill_value":-999.0,"start":"20010101","end":"20201231"},"messages":[],"parameters":{"ALLSKY_SFC_SW_DNI":{"units":"kW-hr/m^2/day","longname":"All Sky Surface Shortwave Downward Direct Normal Irradiance"}},"times":{"data":0.745,"process":0.02}};
const tempsByMonth = [null];
for(let [yearmonth, measurement] of Object.entries(data.properties.parameter.ALLSKY_SFC_SW_DNI)){
const month = extractMonth(yearmonth);
if(!tempsByMonth[month]) tempsByMonth[month] = 0;
tempsByMonth[month] = (tempsByMonth[month] measurement)/2;
}
function extractMonth(yearmonth){
let month = yearmonth.substring(yearmonth.length-2);
return month.startsWith('0')? month[month.length-1] : month;
}
console.log(tempsByMonth);
Finally, to find the lowest and highest average, you'll use Math.max and Math.min. Note that for the lowest average the null value was filtered out of the list:
console.log('Month with greatest value: ', tempsByMonth.indexOf(Math.max(...tempsByMonth)));
console.log('Month with lowest value: ', tempsByMonth.indexOf(Math.min(...tempsByMonth.filter(v=>v!==null))));
And here is the final result in action:
const data = {"type":"Feature","geometry":{"type":"Point","coordinates":[48.0,27.0,191.14]},"properties":{"parameter":{"ALLSKY_SFC_SW_DNI":{"200101":5.65,"200102":4.88,"200103":5.28,"200104":5.05,"200105":4.84,"200106":5.21,"200107":5.44,"200108":6.07,"200109":5.72,"200110":5.3,"200111":4.52,"200112":3.03,"200113":5.08,"200201":4.09,"200202":4.89,"200203":4.49,"200204":4.19,"200205":5.17,"200206":6.12,"200207":6.15,"200208":5.64,"200209":5.92,"200210":5.5,"200211":4.57,"200212":3.26,"200213":5.0,"200301":4.55,"200302":3.9,"200303":4.09,"200304":4.29,"200305":4.39,"200306":6.1,"200307":5.29,"200308":6.14,"200309":6.3,"200310":5.28,"200311":3.93,"200312":3.39,"200313":4.81,"200401":3.51,"200402":4.45,"200403":5.37,"200404":4.01,"200405":5.39,"200406":6.4,"200407":6.42,"200408":6.11,"200409":6.21,"200410":5.86,"200411":3.66,"200412":3.89,"200413":5.11,"200501":4.05,"200502":4.03,"200503":4.52,"200504":4.49,"200505":5.14,"200506":5.33,"200507":5.06,"200508":5.85,"200509":6.08,"200510":5.45,"200511":3.89,"200512":4.06,"200513":4.83,"200601":3.65,"200602":3.43,"200603":4.85,"200604":4.0,"200605":4.57,"200606":6.08,"200607":5.01,"200608":5.5,"200609":6.32,"200610":4.81,"200611":4.08,"200612":3.99,"200613":4.7,"200701":4.27,"200702":4.56,"200703":4.39,"200704":3.51,"200705":4.75,"200706":5.4,"200707":5.18,"200708":5.77,"200709":5.88,"200710":5.43,"200711":4.7,"200712":3.76,"200713":4.8,"200801":3.26,"200802":4.12,"200803":4.64,"200804":3.77,"200805":4.03,"200806":4.52,"200807":4.84,"200808":5.3,"200809":4.71,"200810":4.76,"200811":3.97,"200812":5.08,"200813":4.42,"200901":4.08,"200902":3.23,"200903":3.68,"200904":3.66,"200905":4.57,"200906":4.9,"200907":4.23,"200908":4.73,"200909":5.03,"200910":4.57,"200911":4.15,"200912":3.89,"200913":4.23,"201001":4.73,"201002":3.99,"201003":4.51,"201004":3.52,"201005":4.36,"201006":5.12,"201007":4.8,"201008":5.17,"201009":5.28,"201010":5.14,"201011":5.39,"201012":4.65,"201013":4.73,"201101":3.56,"201102":3.47,"201103":4.1,"201104":2.83,"201105":4.52,"201106":4.37,"201107":4.63,"201108":5.31,"201109":5.53,"201110":4.8,"201111":3.71,"201112":4.81,"201113":4.31,"201201":4.19,"201202":3.51,"201203":3.36,"201204":3.34,"201205":3.68,"201206":4.9,"201207":5.33,"201208":5.18,"201209":5.7,"201210":4.84,"201211":4.43,"201212":3.52,"201213":4.33,"201301":3.44,"201302":4.4,"201303":4.03,"201304":3.79,"201305":4.96,"201306":4.94,"201307":4.97,"201308":5.45,"201309":5.39,"201310":5.11,"201311":4.14,"201312":4.38,"201313":4.58,"201401":3.86,"201402":4.49,"201403":4.28,"201404":4.19,"201405":4.78,"201406":5.71,"201407":5.4,"201408":5.37,"201409":5.82,"201410":4.61,"201411":4.21,"201412":4.66,"201413":4.78,"201501":4.24,"201502":3.73,"201503":4.01,"201504":3.79,"201505":4.18,"201506":5.02,"201507":4.7,"201508":5.67,"201509":4.94,"201510":4.17,"201511":3.77,"201512":3.45,"201513":4.31,"201601":4.19,"201602":5.15,"201603":3.77,"201604":4.8,"201605":5.0,"201606":5.73,"201607":4.92,"201608":5.69,"201609":5.49,"201610":5.5,"201611":4.16,"201612":3.46,"201613":4.82,"201701":3.8,"201702":4.54,"201703":3.76,"201704":4.32,"201705":4.89,"201706":7.03,"201707":6.06,"201708":6.37,"201709":6.41,"201710":5.69,"201711":4.48,"201712":4.72,"201713":5.17,"201801":4.6,"201802":4.12,"201803":5.46,"201804":3.69,"201805":5.18,"201806":5.6,"201807":5.71,"201808":5.88,"201809":6.16,"201810":4.48,"201811":3.29,"201812":3.9,"201813":4.85,"201901":2.72,"201902":4.73,"201903":4.92,"201904":4.62,"201905":5.73,"201906":7.4,"201907":5.76,"201908":6.02,"201909":6.66,"201910":5.02,"201911":4.33,"201912":4.65,"201913":5.21,"202001":4.6,"202002":4.85,"202003":5.12,"202004":4.48,"202005":6.14,"202006":6.77,"202007":6.52,"202008":6.37,"202009":6.58,"202010":6.86,"202011":4.21,"202012":3.89,"202013":5.54}}},"header":{"title":"NASA/POWER CERES/MERRA2 Native Resolution Monthly and Annual","api":{"version":"v2.2.12","name":"POWER Monthly and Annual API"},"fill_value":-999.0,"start":"20010101","end":"20201231"},"messages":[],"parameters":{"ALLSKY_SFC_SW_DNI":{"units":"kW-hr/m^2/day","longname":"All Sky Surface Shortwave Downward Direct Normal Irradiance"}},"times":{"data":0.745,"process":0.02}};
const tempsByMonth = [null];
for(let [yearmonth, measurement] of Object.entries(data.properties.parameter.ALLSKY_SFC_SW_DNI)){
const month = extractMonth(yearmonth);
if(!tempsByMonth[month]) tempsByMonth[month] = 0;
tempsByMonth[month] = (tempsByMonth[month] measurement)/2;
}
function extractMonth(yearmonth){
let month = yearmonth.substring(yearmonth.length-2);
return month.startsWith('0')? month[month.length-1] : month;
}
console.log(tempsByMonth);
console.log('Month with greatest value: ', tempsByMonth.indexOf(Math.max(...tempsByMonth)));
console.log('Month with lowest value: ', tempsByMonth.indexOf(Math.min(...tempsByMonth.filter(v=>v!==null))));