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api.py
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api.py
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from fastapi import FastAPI, Response
from datetime import datetime
from urllib.request import urlopen
from PIL import Image,ImageDraw, ImageFont
import requests
import numpy as np
import pandas as pd
from bs4 import BeautifulSoup
import io
from starlette.responses import StreamingResponse
import xarray as xr
import httplib2
def getValueFromColor(pixel_color,color_entry_dict):
try:
val = color_entry_dict[tuple(pixel_color)]
except KeyError:
val = None
return val
app = FastAPI()
# http://ec2-3-65-18-201.eu-central-1.compute.amazonaws.com:8080/get_stats?minx=-160&miny=-5&maxx=-150&maxy=0×tamp=2016-06-09T00:00:00Z
@app.get("/get_stats", tags=["Home"])
def get_stats(minx:float,miny:float,maxx:float,maxy:float,timestamp:str):
timestamp = timestamp.replace('-T','T') # Addreessing timestamp formatting issue
resolution = 2
map_height=180*resolution
map_width=360*resolution
wms_url = 'https://gibs.earthdata.nasa.gov/wms/epsg4326/best/wms.cgi?SERVICE=WMS&VERSION=1.3.0&REQUEST=GetMap&BBOX=-90,-180,90,180&CRS=EPSG:4326&WIDTH='+str(map_width)+'&HEIGHT='+str(map_height)+'&LAYERS=GHRSST_L4_MUR_Sea_Surface_Temperature&STYLES=&FORMAT=image/png&TIME='+timestamp
#img_bytes = urlopen(wms_url).read()
im = Image.open(requests.get(wms_url, stream=True).raw)
data = np.array(im)
colormap_url = 'https://gibs.earthdata.nasa.gov/colormaps/v1.3/GHRSST_Sea_Surface_Temperature.xml'
colormap_document = requests.get(colormap_url)
colormap_document_soup= BeautifulSoup(colormap_document.content,"lxml-xml")
colormap_entries = colormap_document_soup.find_all("ColorMapEntry",{"sourceValue": True})
color_entry_dict={}
for row in colormap_entries:
color_entry={tuple(map(int, (row.attrs['rgb']+',255').split(","))):float(row.attrs['sourceValue'][1:-1].split(',')[0].replace("INF", "0"))}
color_entry_dict.update(color_entry)
# pixels = im.load() # create the pixel map
pixels = np.asarray(im)
out = np.zeros((im.size[1], im.size[0]))
for i in range(im.size[0]): # for every pixel:
for j in range(im.size[1]):
out[j-1,i-1] = getValueFromColor(pixels[j,i],color_entry_dict)
out_pandas = pd.DataFrame(out)
minx_grid = int((180 + minx)*resolution)
maxx_grid = int((180 + maxx)*resolution)
miny_grid = int((90-miny)*resolution)
maxy_grid = int((90-maxy)*resolution)
mean_value = np.nanmean(out_pandas[maxy_grid:miny_grid].iloc[:, minx_grid:maxx_grid].values).round(2)
min_value = np.nanmin(out_pandas[maxy_grid:miny_grid].iloc[:, minx_grid:maxx_grid].values).round(2)
max_value = np.nanmax(out_pandas[maxy_grid:miny_grid].iloc[:, minx_grid:maxx_grid].values).round(2)
stdev_value = np.nanstd(out_pandas[maxy_grid:miny_grid].iloc[:, minx_grid:maxx_grid].values).round(2)
msg = {
"Mean": mean_value,
"Max": max_value,
"Min": min_value,
"StDev": stdev_value
}
datem = datetime.strptime(timestamp, "%Y-%m-%dT%H:%M:%SZ")
year_day = datem.timetuple().tm_yday
ghrsst_resolution=4 # 0.25 degree
ghrsst_minx_grid = int((180 + minx)*ghrsst_resolution)
ghrsst_maxx_grid = int((180 + maxx)*ghrsst_resolution)
ghrsst_miny_grid = int((90+miny)*ghrsst_resolution)
ghrsst_maxy_grid = int((90+maxy)*ghrsst_resolution)
ghrsst_url = 'https://podaac-opendap.jpl.nasa.gov/opendap/hyrax/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR25/v4.2/'+str(datem.year)+'/'+str(year_day)+'/'+datem.strftime("%Y%m%d")+'090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2.nc?time[0:1:0],lat['+str(ghrsst_miny_grid)+':1:'+str(ghrsst_maxy_grid)+'],lon['+str(ghrsst_minx_grid)+':1:'+str(ghrsst_maxx_grid)+'],analysed_sst[0:1:0]['+str(ghrsst_miny_grid)+':1:'+str(ghrsst_maxy_grid)+']['+str(ghrsst_minx_grid)+':1:'+str(ghrsst_maxx_grid)+']'
ds =xr.open_dataset(ghrsst_url, decode_times=False).to_dataframe()
ds['sst']=ds['analysed_sst']-273.15 # Convert to Celsius
msg_ghrsst = {
"Mean": ds['sst'].mean().round(2),
"Max": ds['sst'].max().round(2),
"Min": ds['sst'].min().round(2),
"StDev": ds['sst'].std().round(2)
}
img = Image.new('RGB', (280, 160), color = (0,0,0))
fnt = ImageFont.truetype('/home/admin/WorldView/Calibri.ttf', 15)
d = ImageDraw.Draw(img)
d.text((10,10), "Timestamp: "+timestamp+"\n\nDerived from GIBS:\n Mean: "+str(mean_value)+"°C\n Max: "+str(max_value)+"°C\n"+" Min: "+str(min_value)+"°C\n"+" StdDev: "+str(round(stdev_value,2))+"°C\n",font=fnt,fill=(218, 247, 166))
d.text((140,10), "\n\nUnderlying GHRSST:\n Mean: "+str(msg_ghrsst['Mean'])+"°C\n Max: "+str(msg_ghrsst['Max'])+"°C\n"+" Min: "+str(msg_ghrsst['Min'])+"°C\n"+" StdDev: "+str(msg_ghrsst['StDev'])+"°C\n",font=fnt,fill=(218, 247, 166))
image = io.BytesIO()
img.save(image, format='PNG')
#imsave(image, img, format='PNG', quality=100)
image.seek(0)
#return StreamingResponse(image, media_type="image/png")
return Response(content=image.getvalue(), media_type="image/png")
# return StreamingResponse(io.BytesIO(img.tobytes()), media_type="image/png")
#return StreamingResponse(image.read(), media_type="image/png")
# Get SST from OIIS aggregated dataset
# http://ec2-3-65-18-201.eu-central-1.compute.amazonaws.com:8080/get_stats?minx=-160&miny=-5&maxx=-150&maxy=0×tamp=2016-06-09T00:00:00Z
@app.get("/get_stats_oiip", tags=["OIIP"])
def get_stats_oiip(minx:float,miny:float,maxx:float,maxy:float,timestamp:str):
timestamp = timestamp.replace('-T','T') # Addreessing timestamp formatting issue
#full_url ='https://thredds.jpl.nasa.gov/thredds/ncss/ncml_aggregation/SalinityDensity/smap/aggregate__SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5.ncml?var=anc_sss&var=anc_sst&var=land_fraction&var=smap_high_spd&var=smap_spd&var=smap_sss&var=smap_sss_uncertainty&north='+str(maxy)+'&west='+str(minx)+'&east='+str(maxx)+'&south='+str(miny)+'&disableProjSubset=on&horizStride=1&time_start='+timestamp+'&time_end='+timestamp+'&timeStride=1&addLatLon=true'
full_url = 'https://coverage.ceos.org/thredds/ncss/grid/scan-aggregation/NOAA_MSL12-NRT-CHL-Daily-L4.nc?var=chlor_a&north='+str(maxy)+'&west='+str(minx)+'&east='+str(maxx)+'&south='+str(miny)+'&horizStride=1&time_start='+timestamp+'&time_end='+timestamp+'&timeStride=1&vertCoord=&accept=netcdf3' #Chlorophyll
http = httplib2.Http(".cache")
response, content = http.request(full_url, "GET")
with open('/tmp/file.nc', 'wb') as f:
f.write(content)
ds = xr.open_dataset('/tmp/file.nc',engine='netcdf4')
#mask_lon = (ds.longitude >= minx) & (ds.longitude <= maxx)
#mask_lat = (ds.latitude >= miny) & (ds.latitude <= maxy)
#cropped_ds = ds.where(mask_lon & mask_lat, drop=True)
ds_value= round(ds.mean().chlor_a.item(),2),
msg_anc_sst = {
"Mean": round(ds.mean().chlor_a.item(),2),
"Max": round(ds.max().chlor_a.item(),2),
"Min": round(ds.min().chlor_a.item(),2),
"StDev": round(ds.std().chlor_a.item(),2)
}
img = Image.new('RGB', (200, 160), color = (0,0,0))
fnt = ImageFont.truetype('/home/admin/WorldView/Calibri.ttf', 15)
d = ImageDraw.Draw(img)
d.text((10,10), "Timestamp: "+timestamp+"\n\nChlorophyll concentration:\n Mean: "+str(msg_anc_sst['Mean'])+"mg/m3\n Max: "+str(msg_anc_sst['Max'])+"mg/m3\n"+" Min: "+str(msg_anc_sst['Min'])+"mg/m3\n"+" StdDev: "+str(msg_anc_sst['StDev'])+"mg/m3\n",font=fnt,fill=(218, 247, 166))
image = io.BytesIO()
img.save(image, format='PNG')
return Response(content=image.getvalue(), media_type="image/png")