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TotBPPImageTeselaMapVideo
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Mario Gil 2023-09-15 19:24:44 -05:00
parent 986ed56ed7
commit 614110d3af
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.gitignore vendored Normal file
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1Teselas/map/*
2nc2image/img/*
3image2video/ConCoralN/*
3image2video/env/*
3image2video/img/*
3image2video/SinCoralN/*
3image2video/Videos/*
Data/*
DataF/*
DataOriginal/*
DataVieja/*

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0PreProc/CorrectNameandFormat.py Normal file
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import xarray as xr
import glob
import numpy as np
def ModAttrs(DictOri,site,label,data):
keys=list(DictOri.keys())
NewDict={}
for key in range(len(keys)):
if site==key:
NewDict[label]=data
NewDict[keys[key]]=DictOri[keys[key]]
return NewDict
def date2float(df,var):
V1=df[var].astype("float64")
return V1
def cleanAttr(var,T):
A=T.attrs
if var in A.keys():
A.pop(var)
return A
def Corrector2(file,DataEntry,DataSal):
T=xr.open_dataset(DataEntry+file)
T.DoY_DHW4cal.attrs={'long_name': 'first day of the year when DHW exceeds 4 degree-weeks',
'units': 'day of the year',
'comment': 'considering January 1st the first day of the year'}
T.DoY_DHW8cal.attrs={'long_name': 'first day of the year when DHW exceeds 8 degree-weeks',
'units': 'day of the year',
'comment': 'considering January 1st the first day of the year'}
data =T.DHW_q99[0,:,:].values
T["nCellsTotal"]=np.count_nonzero(np.isnan(data))+np.count_nonzero(~np.isnan(data))
T["nCellsValid"]=np.count_nonzero(~np.isnan(data))
T.nCellsTotal.attrs={"long_name": "number of data cells in selected area"}
T.nCellsValid.attrs={ "long_name": "number of valid data cells in selected area"}
T.to_netcdf(path=DataSal+file)
def CorrectNc(Map1="/home/mario/Documentos/Ocean/NetcdfToPng/NC2023Patron/",SalidaDir="/home/mario/Documentos/Ocean/NetcdfToPng/MapsModificados/",file="DHW_ssp245_BCC-CSM2-MR_DHW.nc"):
nc1 = xr.open_dataset(Map1+file)#,decode_times=False,decode_timedelta=False
#nc1["DoY_DHW4cal"]=nc1["DoY_DHW4"]
#nc1["DoY_DHW8cal"]=nc1["DoY_DHW8"]
#nc1["nDays_DHW4cal"]=nc1["nDays_DHW4"]
#nc1["nDays_DHW8cal"]=nc1["nDays_DHW8"]
#nc1=nc1.drop("DoY_DHW4")
#nc1=nc1.drop("DoY_DHW8")
#nc1=nc1.drop("nDays_DHW4")
#nc1=nc1.drop("nDays_DHW8")
#nc1["DoY_DHW4"]=nc1["DoYrel_DHW4"]
#nc1["DoY_DHW8"]=nc1["DoYrel_DHW8"]
#nc1=nc1.drop("DoYrel_DHW4")
#nc1=nc1.drop("DoYrel_DHW8")
#nc1=nc1.drop_sel({"time":1986})
df=nc1
try:
df=df.drop("quantile")
except:
print("noq")
df.attrs.keys()
#df.attrs=ModAttrs(df.attrs,5,"label","data")
df.attrs["time_coverage_start"]=1986
data=df.DHW_q99[0,:,:].values
df.attrs=ModAttrs(df.attrs,5,"region_name","Global")
#df["nCellsTotal"]=np.count_nonzero(np.isnan(data))+np.count_nonzero(~np.isnan(data))
#df["nCellsValid"]=np.count_nonzero(~np.isnan(data))
file=file.replace("_start1986","").replace("_dec22","")
HH=SalidaDir+file
# df.DoY_DHW4.attrs={'long_name': "first day of the year when DHW exceeds 4 degree-weeks, relative to the climatological coldest DOY",
# 'units': 'day of the year',
# 'comment': "considering the coldest climatological DoY as the first day of the year"}
# df.DoY_DHW8.attrs={'long_name': "first day of the year when DHW exceeds 8 degree-weeks, relative to the climatological coldest DOY",
# 'units': 'day of the year',
# 'comment': "considering the coldest climatological DoY as the first day of the year"}
# df.nDays_DHW8.attrs={"long_name" : "number of days above 8 degrees-week",
# "units" : "days"}
# df.nDays_DHW4.attrs={"long_name" : "number of days above 4 degrees-week",
# "units" : "days"}
mask_land = 1 * np.ones((df.dims['lat'], df.dims['lon'])) * np.isnan(df.DHW_q99.isel(time=0))
df["mask_land"]=mask_land
df["nDays_DHW4"]=df["nDays_DHW4"].astype(np.float64)/1e9/60/60/24
#if np.nanmax(df["nDays_DHW4"].values)>1000:
#df["nDays_DHW4"]=df["nDays_DHW4"]/1e9/60/60/24
#print("NDais")
df["nDays_DHW8"]=df["nDays_DHW8"].astype(np.float64)/1e9/60/60/24
df["nDays_DHW8"]=df["nDays_DHW8"].where(df.mask_land != 1)
df["nDays_DHW4"]=df["nDays_DHW4"].where(df.mask_land != 1)
#if np.nanmax(df["nDays_DHW8"].values)>1000:
#df["nDays_DHW8"]=df["nDays_DHW8"]
#print("NDais")
# df["nDays_DHW4"]=df["nDays_DHW4"].astype("float64")
# if np.nanmax(df["nDaysrel_DHW4"].values)>1000:
# df["nDaysrel_DHW4"]=df["nDaysrel_DHW4"]/1e9/60/60/24
# print("NDais")
# df["nDaysrel_DHW8"]=df["nDaysrel_DHW8"].astype("float64")
# if np.nanmax(df["nDaysrel_DHW8cal"].values)>1000:
# df["nDaysrel_DHW8"]=df["nDaysrel_DHW8"]/1e9/60/60/24
# print("NDais")
#df["nDays_DHW8"].attrs={'long_name': 'number of days above 8 degrees-week, relative to the climatological coldest DOY'}
#df["nDays_DHW4"].attrs={'long_name': 'number of days above 4 degrees-week, relative to the climatological coldest DOY'}
#df["nDays_DHW8cal"].attrs={'long_name': 'number of days above 8 degrees-week, considering January 1st the first day of the year'}
#df["nDays_DHW4cal"].attrs={'long_name': 'number of days above 4 degrees-week, considering January 1st the first day of the year'}
#df["nDays_DHW8cal"]=df["nDays_DHW8cal"].where(df.mask_land != 1)
#df["nDays_DHW4cal"]=df["nDays_DHW4cal"].where(df.mask_land != 1)
df=df.drop("mask_land")
Tempp=list(df.var().keys())
for i in Tempp:
df[i].attrs=cleanAttr("coordinates",df[i])
# df.DoY_DHW8cal.attrs={'long_name': 'first day of the year when DHW exceeds 8 degree-weeks',
# 'units': 'day of the year',
# 'comment': 'considering January 1st the first day of the year'}
# df.DoY_DHW4cal.attrs={'long_name': 'first day of the year when DHW exceeds 4 degree-weeks',
# 'units': 'day of the year',
# 'comment': 'considering January 1st the first day of the year'}
comp = dict(zlib=True, complevel=5)
encoding = {var: comp for var in df.data_vars}
data =df.DHW_q99[0,:,:].values
df["nCellsTotal"]=np.count_nonzero(np.isnan(data))+np.count_nonzero(~np.isnan(data))
df["nCellsValid"]=np.count_nonzero(~np.isnan(data))
df.nCellsTotal.attrs={"long_name": "number of data cells in selected area"}
df.nCellsValid.attrs={ "long_name": "number of valid data cells in selected area"}
print(df)
df.to_netcdf(path=HH, encoding=encoding)
nn=0
for i in glob.glob("../DataOriginal/*"):
A=i.split("/")[-1]
SalidaDir="../Data/"
nn+=1
print(A)
CorrectNc(Map1="../DataOriginal/",SalidaDir=SalidaDir,file=A)
print(1)
#if nn>1:
#break
#SalidaDir2="../DataF/"
#Corrector2(A,SalidaDir,SalidaDir2)
#print(2)
#break

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import xarray as xr
import glob
import numpy as np
def ModAttrs(DictOri,site,label,data):
keys=list(DictOri.keys())
NewDict={}
for key in range(len(keys)):
if site==key:
NewDict[label]=data
NewDict[keys[key]]=DictOri[keys[key]]
return NewDict
def date2float(df,var):
V1=df[var].astype("float64")
return V1
def cleanAttr(var,T):
A=T.attrs
if var in A.keys():
A.pop(var)
return A
def Corrector2(file,DataEntry,DataSal):
T=xr.open_dataset(DataEntry+file)
T.DoY_DHW4cal.attrs={'long_name': 'first day of the year when DHW exceeds 4 degree-weeks',
'units': 'day of the year',
'comment': 'considering January 1st the first day of the year'}
T.DoY_DHW8cal.attrs={'long_name': 'first day of the year when DHW exceeds 8 degree-weeks',
'units': 'day of the year',
'comment': 'considering January 1st the first day of the year'}
data =T.DHW_q99[0,:,:].values
T["nCellsTotal"]=np.count_nonzero(np.isnan(data))+np.count_nonzero(~np.isnan(data))
T["nCellsValid"]=np.count_nonzero(~np.isnan(data))
T.nCellsTotal.attrs={"long_name": "number of data cells in selected area"}
T.nCellsValid.attrs={ "long_name": "number of valid data cells in selected area"}
T.to_netcdf(path=DataSal+file)
def FusionNc(Map1="/home/mario/Documentos/Ocean/NetcdfToPng/NC2023Patron/",Map2="/home/mario/Documentos/Ocean/NetcdfToPng/MapsAModificar/",SalidaDir="/home/mario/Documentos/Ocean/NetcdfToPng/MapsModificados/",file="DHW_ssp245_BCC-CSM2-MR_DHW.nc"):
nc1 = xr.open_dataset(Map1+file,decode_times=False,decode_timedelta=False)
nc2b = xr.open_dataset(Map2+file,decode_times=False,decode_timedelta=False)
#nc2b=nc2b.drop_sel({"time":1986})
#nc2b=nc2b.drop_sel({"time":1985})
nc1["DoY_DHW4cal"]=nc2b["DoY_DHW4"]
nc1["DoY_DHW8cal"]=nc2b["DoY_DHW8"]
nc1["nDays_DHW4cal"]=nc2b["nDays_DHW4"]
nc1["nDays_DHW8cal"]=nc2b["nDays_DHW8"]
df=nc1
try:
df=df.drop("quantile")
except:
print("noq")
df.attrs.keys()
#df.attrs=ModAttrs(df.attrs,5,"label","data")
df.attrs["time_coverage_start"]=1987
data=df.DHW_q99[0,:,:].values
df.attrs=ModAttrs(df.attrs,5,"region_name","Global")
#df["nCellsTotal"]=np.count_nonzero(np.isnan(data))+np.count_nonzero(~np.isnan(data))
#df["nCellsValid"]=np.count_nonzero(~np.isnan(data))
HH=SalidaDir+file
df.DoY_DHW4.attrs={'long_name': "first day of the year when DHW exceeds 4 degree-weeks, relative to the climatological coldest DOY",
'units': 'day of the year',
'comment': "considering the coldest climatological DoY as the first day of the year"}
df.DoY_DHW8.attrs={'long_name': "first day of the year when DHW exceeds 8 degree-weeks, relative to the climatological coldest DOY",
'units': 'day of the year',
'comment': "considering the coldest climatological DoY as the first day of the year"}
df.nDays_DHW8.attrs={"long_name" : "number of days above 8 degrees-week",
"units" : "days"}
df.nDays_DHW4.attrs={"long_name" : "number of days above 4 degrees-week",
"units" : "days"}
df["nDays_DHW4"]=df["nDays_DHW4"].astype("float64")
if np.nanmax(df["nDays_DHW4"].values)>1000:
df["nDays_DHW4"]=df["nDays_DHW4"]/1e9/60/60/24
df["nDays_DHW8"]=df["nDays_DHW8"].astype("float64")
if np.nanmax(df["nDays_DHW8"].values)>1000:
df["nDays_DHW8"]=df["nDays_DHW8"]/1e9/60/60/24
df["nDays_DHW4cal"]=df["nDays_DHW4cal"].astype("float64")
if np.nanmax(df["nDays_DHW4cal"].values)>1000:
df["nDays_DHW4cal"]=df["nDays_DHW4cal"]/1e9/60/60/24
df["nDays_DHW8cal"]=df["nDays_DHW8cal"].astype("float64")
if np.nanmax(df["nDays_DHW8cal"].values)>1000:
df["nDays_DHW8cal"]=df["nDays_DHW8cal"]/1e9/60/60/24
df["nDays_DHW8"].attrs={'long_name': 'number of days above 8 degrees-week, relative to the climatological coldest DOY'}
df["nDays_DHW4"].attrs={'long_name': 'number of days above 4 degrees-week, relative to the climatological coldest DOY'}
df["nDays_DHW8cal"].attrs={'long_name': 'number of days above 8 degrees-week, considering January 1st the first day of the year'}
df["nDays_DHW4cal"].attrs={'long_name': 'number of days above 4 degrees-week, considering January 1st the first day of the year'}
mask_land = 1 * np.ones((df.dims['lat'], df.dims['lon'])) * np.isnan(df.DHW_q99.isel(time=0))
df["mask_land"]=mask_land
df["nDays_DHW8"]=df["nDays_DHW8"].where(df.mask_land != 1)
df["nDays_DHW4"]=df["nDays_DHW4"].where(df.mask_land != 1)
df["nDays_DHW8cal"]=df["nDays_DHW8cal"].where(df.mask_land != 1)
df["nDays_DHW4cal"]=df["nDays_DHW4cal"].where(df.mask_land != 1)
df=df.drop("mask_land")
Tempp=list(df.var().keys())
for i in Tempp:
df[i].attrs=cleanAttr("coordinates",df[i])
df.DoY_DHW8cal.attrs={'long_name': 'first day of the year when DHW exceeds 8 degree-weeks',
'units': 'day of the year',
'comment': 'considering January 1st the first day of the year'}
df.DoY_DHW4cal.attrs={'long_name': 'first day of the year when DHW exceeds 4 degree-weeks',
'units': 'day of the year',
'comment': 'considering January 1st the first day of the year'}
comp = dict(zlib=True, complevel=5)
encoding = {var: comp for var in df.data_vars}
df.to_netcdf(path=HH, encoding=encoding)
for i in glob.glob("/home/mario/Documentos/Ocean/NetcdfToPng/NC2023Patron/*"):
A=i.split("/")[-1]
#try:
#SalidaDir="/home/mario/Documentos/Ocean/NetcdfToPng/MapsModificados/"
#file=A
print(A)
#FusionNc(Map1="/home/mario/Documentos/Ocean/NetcdfToPng/NC2023Patron/",Map2="/home/mario/Documentos/Ocean/NetcdfToPng/MapsAModificar/",SalidaDir=SalidaDir,file=file)
#print(1)
#Corrector2(file,SalidaDir,"/home/mario/Documentos/Ocean/NetcdfToPng/MapCorregidoV2/")
#print(2)
#break

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docker run -it --mount type=bind,src=/home/mario/Documentos/Ocean/ScuenciaOrganizadaPre/Teselas,dst=/temporal ghcr.io/osgeo/gdal:ubuntu-full-latest bash
gdal2tiles.py -p geodetic -z 0-7 -r average -s EPSG:4326 --xyz -w mapml /temporal/P1_96P6.jpeg /temporal/map

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import xarray as xr
import rioxarray as rio
from matplotlib import cm
import numpy as np
import time
import matplotlib
import matplotlib.image as imgg
import os
from PIL import Image, ImageDraw, ImageFont
def write_png(data,name, origin='upper', colormapD=None):
"""
Transform an array of data into a PNG string.
This can be written to disk using binary I/O, or encoded using base64
for an inline PNG like this:
>>> png_str = write_png(array)
>>> "data:image/png;base64,"+png_str.encode('base64')
Inspired from
https://stackoverflow.com/questions/902761/saving-a-numpy-array-as-an-image
Parameters
----------
data: numpy array or equivalent list-like object.
Must be NxM (mono), NxMx3 (RGB) or NxMx4 (RGBA)
origin : ['upper' | 'lower'], optional, default 'upper'
Place the [0,0] index of the array in the upper left or lower left
corner of the axes.
colormap : callable, used only for `mono` image.
Function of the form [x -> (r,g,b)] or [x -> (r,g,b,a)]
for transforming a mono image into RGB.
It must output iterables of length 3 or 4, with values between
0. and 1. Hint: you can use colormaps from `matplotlib.cm`.
Returns
-------
PNG formatted byte string
"""
if colormapD is None:
def colormapD(x):
return (x, x, x, 1)
arr = np.atleast_3d(data)
height, width, nblayers = arr.shape
if nblayers not in [1, 3, 4]:
raise ValueError('Data must be NxM (mono), '
'NxMx3 (RGB), or NxMx4 (RGBA)')
assert arr.shape == (height, width, nblayers)
if nblayers == 1:
arr = np.array(list(map(colormapD, arr.ravel())))
nblayers = arr.shape[1]
if nblayers not in [3, 4]:
raise ValueError('colormap must provide colors of r'
'length 3 (RGB) or 4 (RGBA)')
arr = arr.reshape((height, width, nblayers))
assert arr.shape == (height, width, nblayers)
if nblayers == 3:
arr = np.concatenate((arr, np.ones((height, width, 1))), axis=2)
nblayers = 4
assert arr.shape == (height, width, nblayers)
assert nblayers == 4
# Normalize to uint8 if it isn't already.
if arr.dtype != 'uint8':
with np.errstate(divide='ignore', invalid='ignore'):
arr = arr * 255./np.array([1., 1., 1., 1.]).reshape((1, 1, 4))
arr[~np.isfinite(arr)] = 0
arr = arr.astype('uint8')
# Eventually flip the image.
if origin == 'lower':
arr = arr[::-1, :, :]
r3 = arr.copy(order='C')
matplotlib.image.imsave(name, r3)
def image_to_url(image,name, colormapD=None, origin='upper'):
"""
Infers the type of an image argument and transforms it into a URL.
Parameters
----------
image: string, file or array-like object
* If string, it will be written directly in the output file.
* If file, it's content will be converted as embedded in the
output file.
* If array-like, it will be converted to PNG base64 string and
embedded in the output.
origin: ['upper' | 'lower'], optional, default 'upper'
Place the [0, 0] index of the array in the upper left or
lower left corner of the axes.
colormap: callable, used only for `mono` image.
Function of the form [x -> (r,g,b)] or [x -> (r,g,b,a)]
for transforming a mono image into RGB.
It must output iterables of length 3 or 4, with values between
0. and 1. You can use colormaps from `matplotlib.cm`.
"""
if 'ndarray' in image.__class__.__name__:
img = write_png(image, origin=origin, colormapD=colormapD,name=name)
def get_color(x,colormap,Min,Max):
decimals = 2
try:
#print(x,Min,Max)
x=(x*1.-Min)/(Max*1.-Min)
#print(x)
if x < 0:
x=0.0
if x > 1.0:
x=1.0
x = np.around(x, decimals=decimals)
if colormap=="Spectral" or colormap=='ocean' or colormap=="RdYlBu":
Tempcm=cm.get_cmap(colormap).reversed()
else:
Tempcm=cm.get_cmap(colormap)
#print(x)
ls = np.around(np.linspace(0,1,10**decimals+1),decimals=decimals)
#print(ls)
if 0 <= x <= 1:
#print(cm.get_cmap('viridis')(ls)[np.argwhere(ls==x)][0][0].shape,cm.get_cmap('viridis')(ls)[np.argwhere(ls==x)])
#print(x,np.argwhere(ls==x))
return Tempcm(ls)[np.argwhere(ls==x)][0][0]
else:
#print(np.array(np.zeros(4)).shape,np.array(np.zeros(4)))
#print(x)
return np.zeros(4)
except:
print(x,np.argwhere(ls==x))
return np.zeros(4)
def ExtractMapImage(file,colormap,countyear,name,nc,geometry=""):
nc = nc.rio.write_crs(4326)
if geometry=="":
try:
nc = nc.rio.clip(geometries)
except:
pass
else:
pass
bounds=[[float(nc.lat.min().values),float(nc.lon.min().values)],[float(nc.lat.max().values),float(nc.lon.max().values)]]
i=countyear
year=int(nc.time[i].values)
print(i,year)
ncVar=nc.DHW_q99
data = ncVar[i,:,:].values
Min=np.around(np.nanquantile(ncVar[int(year)-1986,:,:].values, 0.01),2)
Max=np.around(np.nanquantile(ncVar[int(year)-1986,:,:].values, 0.99),2)#np.around(np.nanmax(ncVar[year-1985,:,:].values),2)
image_to_url(data,name, colormapD=lambda x: get_color(x,colormap,Min,Max), origin="lower")
def ProcessAllImage(ssp,model,Colorpalete,ExportDirectory,DataDirectory):
cc=0
Var="DHW"
for i in ssp:
for j in model:
ff=DataDirectory+"%s_%s_%s_DHW.nc"%(Var,i,j)
for CM in Colorpalete:
for countyear in range(115):
nc = xr.open_dataset(ff, decode_coords="all")
year=int(nc.time[countyear].values)
Evaluado=ExportDirectory+"%s_%s"%(CM,ff.split("/")[-1].replace(".nc","_%s.png"%(year)))
if not os.path.isfile(Evaluado):
cc=cc+1
Total=cc
start=time.time()
cc=0
for i in ssp:
for j in model:
ff=DataDirectory+"%s_%s_%s_DHW.nc"%(Var,i,j)
for CM in Colorpalete:
for countyear in range(115):
nc = xr.open_dataset(ff, decode_coords="all")
year=int(nc.time[countyear].values)
Evaluado=ExportDirectory+"%s_%s"%(CM,ff.split("/")[-1].replace(".nc","_%s.png"%(year)))
if not os.path.isfile(Evaluado):
ExtractMapImage(ff,CM,countyear,Evaluado,nc)
cc=cc+1
now=time.time()
print(cc,(start-now)/cc,(start-now)/cc*(Total-cc))
ssp=["ssp245","ssp370","ssp585"]
model=("BCC-CSM2-MR","CESM2","CanESM5","EC-Earth3","IPSL-CM6A-LR","MIROC6","MRI-ESM2-0","NorESM2-MM")#,"ensemble5","ensemble8")
Colorpalete=["RdYlBu",'Spectral','ocean',"coolwarm",]
ExportDirectory="img/"
DataDirectory="../Data/"
ProcessAllImage(ssp,model,Colorpalete,ExportDirectory,DataDirectory)

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import xarray as xr
import rioxarray as rio
from matplotlib import cm
import numpy as np
import time
import matplotlib
import matplotlib.image as imgg
import os
def write_png(data,name, origin='upper', colormapD=None):
"""
Transform an array of data into a PNG string.
This can be written to disk using binary I/O, or encoded using base64
for an inline PNG like this:
>>> png_str = write_png(array)
>>> "data:image/png;base64,"+png_str.encode('base64')
Inspired from
https://stackoverflow.com/questions/902761/saving-a-numpy-array-as-an-image
Parameters
----------
data: numpy array or equivalent list-like object.
Must be NxM (mono), NxMx3 (RGB) or NxMx4 (RGBA)
origin : ['upper' | 'lower'], optional, default 'upper'
Place the [0,0] index of the array in the upper left or lower left
corner of the axes.
colormap : callable, used only for `mono` image.
Function of the form [x -> (r,g,b)] or [x -> (r,g,b,a)]
for transforming a mono image into RGB.
It must output iterables of length 3 or 4, with values between
0. and 1. Hint: you can use colormaps from `matplotlib.cm`.
Returns
-------
PNG formatted byte string
"""
if colormapD is None:
def colormapD(x):
return (x, x, x, 1)
arr = np.atleast_3d(data)
height, width, nblayers = arr.shape
if nblayers not in [1, 3, 4]:
raise ValueError('Data must be NxM (mono), '
'NxMx3 (RGB), or NxMx4 (RGBA)')
assert arr.shape == (height, width, nblayers)
if nblayers == 1:
arr = np.array(list(map(colormapD, arr.ravel())))
nblayers = arr.shape[1]
if nblayers not in [3, 4]:
raise ValueError('colormap must provide colors of r'
'length 3 (RGB) or 4 (RGBA)')
arr = arr.reshape((height, width, nblayers))
assert arr.shape == (height, width, nblayers)
if nblayers == 3:
arr = np.concatenate((arr, np.ones((height, width, 1))), axis=2)
nblayers = 4
assert arr.shape == (height, width, nblayers)
assert nblayers == 4
# Normalize to uint8 if it isn't already.
if arr.dtype != 'uint8':
with np.errstate(divide='ignore', invalid='ignore'):
arr = arr * 255./np.array([1., 1., 1., 1.]).reshape((1, 1, 4))
arr[~np.isfinite(arr)] = 0
arr = arr.astype('uint8')
# Eventually flip the image.
if origin == 'upper':
arr = arr[::-1, :, :]
r3 = arr.copy(order='C')
matplotlib.image.imsave(name, r3)
def image_to_url(image,name, colormapD=None, origin='lower'):
"""
Infers the type of an image argument and transforms it into a URL.
Parameters
----------
image: string, file or array-like object
* If string, it will be written directly in the output file.
* If file, it's content will be converted as embedded in the
output file.
* If array-like, it will be converted to PNG base64 string and
embedded in the output.
origin: ['upper' | 'lower'], optional, default 'upper'
Place the [0, 0] index of the array in the upper left or
lower left corner of the axes.
colormap: callable, used only for `mono` image.
Function of the form [x -> (r,g,b)] or [x -> (r,g,b,a)]
for transforming a mono image into RGB.
It must output iterables of length 3 or 4, with values between
0. and 1. You can use colormaps from `matplotlib.cm`.
"""
if 'ndarray' in image.__class__.__name__:
img = write_png(image, origin=origin, colormapD=colormapD,name=name)
def get_colorD(x,Tempcm,Min,Max,np):#*
"""Calculate color of pixel
Args:
x (float): Value of pixel
Tempcm (array): Colormap values
Min (float): min value
Max (float): max value
ls (_type_): linear interpolation function
decimals (_type_): number of decimals
np (_type_): library numpy
Returns:
array (4x1): array of color rbga
"""
import numpy as np
try:
if x <= 0:
x = 0
else:
x = x+1
if x > 21:
x = 21
x = int(np.fix(x))
except:
return Tempcm[-1]
return Tempcm[x]
def ExtractMapImage(file,colormap,countyear,name,nc,geometry=""):
import matplotlib as mpl
nc = nc.rio.write_crs(4326)
if geometry=="":
try:
nc = nc.rio.clip(geometries)
except:
pass
else:
pass
bounds=[[float(nc.lat.min().values),float(nc.lon.min().values)],[float(nc.lat.max().values),float(nc.lon.max().values)]]
i=countyear
year=int(nc.time[i].values)
print(i,year)
ncVar=nc.DHW_q99
data = ncVar[i,:,:].values
if colormap=="noaa":
Min=np.around(np.nanmin(data),0)
Max=np.around(np.nanmax(data),0)
cmap=np.array([[200, 250, 250, 255],
[ 69, 49, 120, 255],
[ 99, 79, 149, 255],
[129, 110, 179, 255],
[159, 140, 209, 255],
[255, 252, 0, 255],
[253, 220, 0, 255],
[251, 185, 0, 255],
[251, 149, 1, 255],
[248, 2, 1, 255],
[209, 1, 0, 255],
[159, 1, 0, 255],
[110, 0, 0, 255],
[229, 125, 69, 255],
[179, 90, 39, 255],
[125, 60, 31, 255],
[ 84, 45, 19, 255],
[239, 33, 239, 255],
[200, 25, 200, 255],
[159, 18, 159, 255],
[120, 10, 120, 255],
[ 49, 2, 49, 255],
[200, 250, 250, 255],
[ 49, 2, 49, 255],
[ 0, 0, 0, 0]], dtype=np.uint8)
image_to_url(data, name,colormapD=lambda x: get_colorD(x,cmap,Min,Max,np), origin='upper')
def ProcessAllImage(ssp,model,Colorpalete,ExportDirectory,DataDirectory):
cc=0
Var="DHW"
for i in ssp:
for j in model:
ff=DataDirectory+"%s_%s_%s_DHW.nc"%(Var,i,j)
for CM in Colorpalete:
for countyear in range(115):
nc = xr.open_dataset(ff, decode_coords="all")
year=int(nc.time[countyear].values)
Evaluado=ExportDirectory+"%s_%s"%(CM,ff.split("/")[-1].replace(".nc","_%s.png"%(year)))
if not os.path.isfile(Evaluado):
cc=cc+1
Total=cc
start=time.time()
cc=0
for i in ssp:
for j in model:
ff=DataDirectory+"%s_%s_%s_DHW.nc"%(Var,i,j)
for CM in Colorpalete:
for countyear in range(115):
nc = xr.open_dataset(ff, decode_coords="all")
year=int(nc.time[countyear].values)
Evaluado=ExportDirectory+"%s_%s"%("noaa",ff.split("/")[-1].replace(".nc","_%s.png"%(year)))
if not os.path.isfile(Evaluado):
ExtractMapImage(ff,"noaa",countyear,Evaluado,nc)
cc=cc+1
now=time.time()
print(cc,(start-now)/cc,(start-now)/cc*(Total-cc))
ssp=("ssp245","ssp370","ssp585")
model=("ensemble5","ensemble8","BCC-CSM2-MR","CESM2","CanESM5","EC-Earth3","IPSL-CM6A-LR","MIROC6","MRI-ESM2-0","NorESM2-MM")
Colorpalete=['CRW-NOAA']
ExportDirectory="img/"
DataDirectory="../Data/"
ProcessAllImage(ssp,model,Colorpalete,ExportDirectory,DataDirectory)

54
2nc2image/requirimments.txt Normal file
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@ -0,0 +1,54 @@
affine==2.3.0
attrs==21.4.0
beautifulsoup4==4.10.0
branca==0.4.2
certifi==2021.10.8
cftime==1.5.2
charset-normalizer==2.0.10
click==8.0.4
click-plugins==1.1.1
cligj==0.7.2
contourpy==1.0.5
cycler==0.11.0
docopt==0.6.2
folium==0.12.1.post1
fonttools==4.37.4
h5netcdf==0.14.0
h5py==3.6.0
idna==3.3
imageio==2.16.1
Jinja2==3.0.3
joblib==1.1.0
kiwisolver==1.3.2
MarkupSafe==2.0.1
matplotlib==3.6.0
netCDF4==1.5.8
networkx==2.7
numpy==1.22.0
packaging==21.3
pandas==1.4.1
Pillow==9.0.1
plotly==5.6.0
Pydap==3.2.2
pyparsing==3.0.7
pyproj==3.3.0
python-dateutil==2.8.2
pytz==2021.3
PyWavelets==1.2.0
rasterio==1.2.10
requests==2.27.1
rioxarray==0.12.2
scikit-image==0.19.2
scikit-learn==1.0.2
scipy==1.8.0
Shapely==1.8.4
six==1.16.0
sklearn==0.0
snuggs==1.4.7
soupsieve==2.3.1
tenacity==8.0.1
threadpoolctl==3.1.0
tifffile==2022.2.9
urllib3==1.26.8
WebOb==1.8.7
xarray==0.21.1

134
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import xarray as xr
from PIL import Image, ImageDraw, ImageFont
import os
import os
import moviepy.video.io.ImageSequenceClip
def calculategetValuesColormap(nc2,year):#*
""" Calculate values of colormap
Args:
nc (nc data): all layer of nc
year (int): year
Returns:
Min (float): Minimum value
Max (float): Maximun value
q1 (float): q1 value
q50 (float): q50 value
q25 (float): q25 value
q75 (float): q75 value
q99 (float): q99 value
"""
import numpy as np
try:
q1=np.around(np.nanquantile(nc2[int(year)-1986,:,:].values, 0.01),2)
Min=np.around(np.nanmin(nc2[int(year)-1986,:,:].values),2)
Max=np.around(np.nanmax(nc2[int(year)-1986,:,:].values),2)
q99=np.around(np.nanquantile(nc2[int(year)-1986,:,:].values, 0.99),1)
q50= np.around((q1+q99)/2,2)#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.50),1)
q25= np.around((q1+q50)/2,2)#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.25),1)
q75= np.around((q50+q99)/2,2)#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.75),1)
except Exception:
pass
return Min,Max,q1,q50,q25,q75,q99
def textdraw(back_im,text,x,y,color,size=18,colormap=False):
draw = ImageDraw.Draw(back_im)
title_font = ImageFont.truetype('Roboto/Roboto-Regular.ttf', size)
textwidth, textheight = draw.textsize(str(text))
#print(textwidth, textheight,text)
if colormap:
x=x-textheight
draw.text((x, y), str(text),color,title_font)
return back_im
def ProcessAllImage(ssp,model,Colormap):
cc=0
Var="DHW"
for i in ssp:
for j in model:
ff="../Data/%s_%s_%s_DHW.nc"%(Var,i,j)
print(ff)
nc = xr.open_dataset(ff, decode_coords="all")
for countyear in range(len(nc.time)):
#try:
year=int(nc.time[countyear].values)
path='./SinCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year)
isFile = os.path.isfile(path)
if isFile:
print(" Ya existe "+ path)
continue
#print(ff,year)
im1 = Image.open('./CapasJuntas_%s.png'%(Colormap))
im2 = Image.open('../2nc2image/img/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
back_im = im1.copy()
back_im.paste(im2, (0, 27))
#final2 = Image.new("RGBA", back_im.size)
#final2 = Image.alpha_composite(final2, back_im)
#im3 = Image.open("/home/mario/Documentos/Ocean/NetcdfToPng/CapasCoralJunta.png")
#final2 = Image.alpha_composite(final2, im3)
#back_im=final2
im3 = Image.open("./FondoRosa.png")
final2 = Image.alpha_composite(im3,back_im)
back_im=final2
x=10
y=0
back_im=textdraw(back_im,year,x,y+2,(0, 0, 0))
#back_im.save('rocket_pillow_paste_pos.png', quality=95)
x=500
y=0
back_im=textdraw(back_im,i,x,y+2,(0, 0, 0))
x=570
y=0
back_im=textdraw(back_im,j,x,y+2,(0, 0, 0))
#back_im.save('rocket_pillow_paste_pos.png', quality=95)
back_im.save('./SinCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year), quality=100)
ListY=[]
path="Videos/Animated_%s_%s_%s_%s_OFF"%(Colormap,Var,i,j)+".webm"
isFile = os.path.isfile(path)
if isFile:
print(" Ya existe "+ path)
continue
for year in range(1986,2101):
ListY.append('./SinCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
movie_clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(ListY, 2)
movie_clip.write_videofile("Videos/Animated_%s_%s_%s_%s_OFF"%(Colormap,Var,i,j)+".avi",codec="rawvideo")
#except:
# print(3432)
#break
#break
#break
ssp=("ssp245","ssp370","ssp585")
#model=("ensemble5","ensemble8")
model=("ensemble5","ensemble8","BCC-CSM2-MR","CESM2","CanESM5","EC-Earth3","IPSL-CM6A-LR","MIROC6","MRI-ESM2-0","NorESM2-MM")
Colormaps=["noaa"]
for Colormap in Colormaps:
ProcessAllImage(ssp,model,Colormap)
import moviepy.video.io.ImageSequenceClip
Var="DHW"
ssp=("ssp245","ssp370","ssp585")
model=("ensemble5","ensemble8","BCC-CSM2-MR","CESM2","CanESM5","EC-Earth3","IPSL-CM6A-LR","MIROC6","MRI-ESM2-0","NorESM2-MM")
Colormaps=["noaa"]
for Colormap in Colormaps:
for i in ssp:
for j in model:
ListY=[]
for year in range(1986,2101):
ListY.append('ConCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
movie_clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(ListY, 2)
movie_clip.write_videofile("Videos/Animated_%s_%s_%s_%s_ON"%(Colormap,Var,i,j)+".webm")

206
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import xarray as xr
from PIL import Image, ImageDraw, ImageFont
import os
import moviepy.video.io.ImageSequenceClip
def calculategetValuesColormap(nc2,year):#*
""" Calculate values of colormap
Args:
nc (nc data): all layer of nc
year (int): year
Returns:
Min (float): Minimum value
Max (float): Maximun value
q1 (float): q1 value
q50 (float): q50 value
q25 (float): q25 value
q75 (float): q75 value
q99 (float): q99 value
"""
import numpy as np
try:
q1=np.around(np.nanquantile(nc2[int(year)-1986,:,:].values, 0.01),2)
Min=np.around(np.nanmin(nc2[int(year)-1986,:,:].values),2)
Max=np.around(np.nanmax(nc2[int(year)-1986,:,:].values),2)
q99=np.around(np.nanquantile(nc2[int(year)-1986,:,:].values, 0.99),2)
q50= int(np.around((q1+q99)/2,2))#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.50),1)
q25= int(np.around((q1+q50)/2,2))#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.25),1)
q75= int(np.around((q50+q99)/2,2))#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.75),1)
if q99<10:
q99=8
q1=0
q25=2
q50=4
q75=6
except Exception:
pass
return Min,Max,q1,q50,q25,q75,q99
def textdraw(back_im,text,x,y,color,size=18,colormap=False):
draw = ImageDraw.Draw(back_im)
title_font = ImageFont.truetype('Roboto/Roboto-Regular.ttf', size)
textwidth, textheight = draw.textsize(str(text))
#print(textwidth, textheight,text)
if colormap:
x=x-textheight
draw.text((x, y), str(text),color,title_font)
return back_im
def ProcessAllImage(ssp,model,Colormap):
cc=0
Var="DHW"
for i in ssp:
for j in model:
ff="../Data/%s_%s_%s_DHW.nc"%(Var,i,j)
print(ff)
try:
print(ff)
nc = xr.open_dataset(ff, decode_coords="all")
except:
pass
for countyear in range(len(nc.time)):
#try:
nc2=nc.DHW_q99
year=int(nc.time[countyear].values)
path='SinCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year)
isFile = os.path.isfile(path)
if isFile:
print(" Ya existe "+ path)
continue
#print(ff,year)
Min,Max,q1,q50,q25,q75,q99=calculategetValuesColormap(nc2,year)
#print(Min,Max,q1,q50,q25,q75,q99)
print(year)
if q1<0.01:
q1=0.01
im1 = Image.open('./CapasJuntas_%s.png'%(Colormap))
im2 = Image.open('../2nc2image/img/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
back_im = im1.copy()
back_im.paste(im2, (0, 27))
final2 = Image.new("RGBA", back_im.size)
final2 = Image.alpha_composite(final2, back_im)
im3 = Image.open("./FondoRosa.png")
final2 = Image.alpha_composite(im3,final2)
back_im=final2
x=10
y=0
back_im=textdraw(back_im,year,x,y+2,(0, 0, 0))
#back_im.save('rocket_pillow_paste_pos.png', quality=95)
x=500
y=0
back_im=textdraw(back_im,i,x,y+2,(0, 0, 0))
x=570
y=0
back_im=textdraw(back_im,j,x,y+2,(0, 0, 0))
#back_im.save('rocket_pillow_paste_pos.png', quality=95)
x=230
y=197
back_im=textdraw(back_im,"<"+str(q1),x,y,(205, 205, 205),12,True)
#back_im.save('rocket_pillow_paste_pos.png', quality=95)
x1=470#91
back_im=textdraw(back_im,">"+str(q99),x1,y,(205, 205, 205),12,True)
x2=(x+x1)/2#53
back_im=textdraw(back_im,str(q50),x2,y,(205, 205, 205),12,True)
x3=(x2+x1)/2
back_im=textdraw(back_im,str(q75),x3,y,(205, 205, 205),12,True)
x4=(x+x2)/2
back_im=textdraw(back_im,str(q25),x4,y,(205, 205, 205),12,True)
back_im.save('SinCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year), quality=95)
ListY=[]
path="Videos/Animated_%s_%s_%s_%s_OFF"%(Colormap,Var,i,j)+".webm"
isFile = os.path.isfile(path)
if isFile:
print(" Ya existe "+ path)
continue
for year in range(1986,2101):
ListY.append('./SinCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
movie_clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(ListY, 2)
movie_clip.write_videofile("./Videos/Animated_%s_%s_%s_%s_OFF"%(Colormap,Var,i,j)+".webm")
#except:
# print(3432)
#break
#break
#break
ssp=("ssp245","ssp370","ssp585")
model=("BCC-CSM2-MR","CESM2","CanESM5","EC-Earth3","IPSL-CM6A-LR","MIROC6","MRI-ESM2-0","NorESM2-MM","ensemble5","ensemble8",)
Colormaps=['Spectral','ocean',"coolwarm","RdYlBu"]
for Colormap in Colormaps:
ProcessAllImage(ssp,model,Colormap)
import xarray as xr
from PIL import Image, ImageDraw, ImageFont
import os
# for e in os.walk('./SinCoralN'):
# pass
# for file in e[2]:
# #print(file)
# back_im = Image.open("./SinCoralN/"+file)
# final2 = Image.new("RGBA", back_im.size)
# #print(back_im.size,final2.size)
# im1 = Image.open("./3CoralesOrig.png")
# final2.paste(im1, (0, 27))
# #print(back_im.size,final2.size)
# final2 = Image.alpha_composite(back_im,final2,)
# final2.save("./ConcoralN/"+file, quality=95)
for e in os.walk('./SinCoralN'):
pass
for file in e[2]:
print(file)
isFile = os.path.isfile("./ConCoralN/"+file)
if isFile:
print(" Ya existe "+ "./ConCoralN/"+file)
continue
back_im = Image.open("./SinCoralN/"+file)
final2 = Image.new("RGBA", back_im.size)
#print(back_im.size,final2.size)
im1 = Image.open("./3CoralesOrig.png")
final2.paste(im1, (0, 27))
#print(back_im.size,final2.size)
final2 = Image.alpha_composite(back_im,final2,)
final2.save("./ConCoralN/"+file, quality=95)
import moviepy.video.io.ImageSequenceClip
Var="DHW"
ssp=("ssp245","ssp370","ssp585")
model=("BCC-CSM2-MR","CESM2","CanESM5","IPSL-CM6A-LR","MIROC6","NorESM2-MM","MRI-ESM2-0","EC-Earth3")
Colormaps=['Spectral','ocean',"coolwarm","RdYlBu"]
for Colormap in Colormaps:
for i in ssp:
for j in model:
ListY=[]
path="Videos/Animated_%s_%s_%s_%s_ON"%(Colormap,Var,i,j)+".webm"
isFile = os.path.isfile(path)
if isFile:
print(" Ya existe "+ path)
continue
for year in range(1986,2101):
ListY.append('./ConCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
print("%s_%s_%s_%s_ON"%(Colormap,Var,i,j))
movie_clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(ListY, 2)
movie_clip.write_videofile(path)

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import xarray as xr
from PIL import Image, ImageDraw, ImageFont
import os
import moviepy.video.io.ImageSequenceClip
def calculategetValuesColormap(nc2,year):#*
""" Calculate values of colormap
Args:
nc (nc data): all layer of nc
year (int): year
Returns:
Min (float): Minimum value
Max (float): Maximun value
q1 (float): q1 value
q50 (float): q50 value
q25 (float): q25 value
q75 (float): q75 value
q99 (float): q99 value
"""
import numpy as np
try:
q1=np.around(np.nanquantile(nc2[int(year)-1987,:,:].values, 0.01),2)
Min=np.around(np.nanmin(nc2[int(year)-1987,:,:].values),2)
Max=np.around(np.nanmax(nc2[int(year)-1987,:,:].values),2)
q99=np.around(np.nanquantile(nc2[int(year)-1987,:,:].values, 0.99),2)
q50= np.around((q1+q99)/2,2)#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.50),1)
q25= np.around((q1+q50)/2,2)#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.25),1)
q75= np.around((q50+q99)/2,2)#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.75),1)
if q99<10:
q99=8
q1=0
q25=2
q50=4
q75=6
except Exception:
pass
return Min,Max,q1,q50,q25,q75,q99
def textdraw(back_im,text,x,y,color,size=18,colormap=False):
draw = ImageDraw.Draw(back_im)
title_font = ImageFont.truetype('Roboto/Roboto-Regular.ttf', size)
textwidth, textheight = draw.textsize(str(text))
#print(textwidth, textheight,text)
if colormap:
x=x-textheight
draw.text((x, y), str(text),color,title_font)
return back_im
def ProcessAllImage(ssp,model,Colormap):
cc=0
Var="DHW"
for i in ssp:
for j in model:
ListY=[]
for year in range(1987,2101):
ListY.append('/home/mario/Documentos/Ocean/NetcdfToPng/SinCoral/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
movie_clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(ListY, 2)
movie_clip.write_videofile("Videos/Animated_%s_%s_%s_%s_OFF"%(Colormap,Var,i,j)+".webm")
#except:
# print(3432)
#break
#break
#break
ssp=("ssp245","ssp370","ssp585")
model=("BCC-CSM2-MR","CESM2","CanESM5","EC-Earth3","IPSL-CM6A-LR","MIROC6","MRI-ESM2-0","NorESM2-MM")
Colormaps=['Spectral','ocean',"coolwarm","RdYlBu"]
for Colormap in Colormaps:
ProcessAllImage(ssp,model,Colormap)