Init
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import xarray as xr
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import rioxarray as rio
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from matplotlib import cm
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import numpy as np
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import time
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import matplotlib
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import matplotlib.image as imgg
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import os
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def write_png(data,name, origin='upper', colormapD=None):
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"""
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Transform an array of data into a PNG string.
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This can be written to disk using binary I/O, or encoded using base64
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for an inline PNG like this:
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>>> png_str = write_png(array)
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>>> "data:image/png;base64,"+png_str.encode('base64')
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Inspired from
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https://stackoverflow.com/questions/902761/saving-a-numpy-array-as-an-image
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Parameters
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----------
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data: numpy array or equivalent list-like object.
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Must be NxM (mono), NxMx3 (RGB) or NxMx4 (RGBA)
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origin : ['upper' | 'lower'], optional, default 'upper'
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Place the [0,0] index of the array in the upper left or lower left
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corner of the axes.
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colormap : callable, used only for `mono` image.
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Function of the form [x -> (r,g,b)] or [x -> (r,g,b,a)]
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for transforming a mono image into RGB.
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It must output iterables of length 3 or 4, with values between
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0. and 1. Hint: you can use colormaps from `matplotlib.cm`.
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Returns
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-------
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PNG formatted byte string
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"""
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if colormapD is None:
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def colormapD(x):
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return (x, x, x, 1)
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arr = np.atleast_3d(data)
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height, width, nblayers = arr.shape
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if nblayers not in [1, 3, 4]:
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raise ValueError('Data must be NxM (mono), '
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'NxMx3 (RGB), or NxMx4 (RGBA)')
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assert arr.shape == (height, width, nblayers)
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if nblayers == 1:
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arr = np.array(list(map(colormapD, arr.ravel())))
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nblayers = arr.shape[1]
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if nblayers not in [3, 4]:
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raise ValueError('colormap must provide colors of r'
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'length 3 (RGB) or 4 (RGBA)')
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arr = arr.reshape((height, width, nblayers))
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assert arr.shape == (height, width, nblayers)
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if nblayers == 3:
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arr = np.concatenate((arr, np.ones((height, width, 1))), axis=2)
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nblayers = 4
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assert arr.shape == (height, width, nblayers)
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assert nblayers == 4
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# Normalize to uint8 if it isn't already.
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if arr.dtype != 'uint8':
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with np.errstate(divide='ignore', invalid='ignore'):
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arr = arr * 255./arr.max(axis=(0, 1)).reshape((1, 1, 4))
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arr[~np.isfinite(arr)] = 0
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arr = arr.astype('uint8')
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# Eventually flip the image.
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if origin == 'lower':
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arr = arr[::-1, :, :]
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r3 = arr.copy(order='C')
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matplotlib.image.imsave(name, r3)
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def image_to_url(image,name, colormapD=None, origin='upper'):
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"""
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Infers the type of an image argument and transforms it into a URL.
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Parameters
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----------
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image: string, file or array-like object
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* If string, it will be written directly in the output file.
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* If file, it's content will be converted as embedded in the
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output file.
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* If array-like, it will be converted to PNG base64 string and
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embedded in the output.
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origin: ['upper' | 'lower'], optional, default 'upper'
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Place the [0, 0] index of the array in the upper left or
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lower left corner of the axes.
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colormap: callable, used only for `mono` image.
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Function of the form [x -> (r,g,b)] or [x -> (r,g,b,a)]
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for transforming a mono image into RGB.
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It must output iterables of length 3 or 4, with values between
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0. and 1. You can use colormaps from `matplotlib.cm`.
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"""
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if 'ndarray' in image.__class__.__name__:
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img = write_png(image, origin=origin, colormapD=colormapD,name=name)
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def get_color(x,colormap,Min,Max):
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decimals = 2
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try:
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#print(x,Min,Max)
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x=(x*1.-Min)/(Max*1.-Min)
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#print(x)
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if x < 0:
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x=0.0
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x = np.around(x, decimals=decimals)
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if colormap=="Spectral" or colormap=='ocean' or colormap=="RdYlBu":
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Tempcm=cm.get_cmap(colormap).reversed()
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else:
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Tempcm=cm.get_cmap(colormap)
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#print(x)
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ls = np.around(np.linspace(0,1,10**decimals+1),decimals=decimals)
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#print(ls)
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if 0 <= x <= 1:
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#print(cm.get_cmap('viridis')(ls)[np.argwhere(ls==x)][0][0].shape,cm.get_cmap('viridis')(ls)[np.argwhere(ls==x)])
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#print(x,np.argwhere(ls==x))
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return Tempcm(ls)[np.argwhere(ls==x)][0][0]
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else:
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#print(np.array(np.zeros(4)).shape,np.array(np.zeros(4)))
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#print(x)
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return np.zeros(4)
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except:
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print(x,np.argwhere(ls==x))
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return np.zeros(4)
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def ExtractMapImage(file,colormap,countyear,name,nc,geometry=""):
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nc = nc.rio.write_crs(4326)
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if geometry=="":
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try:
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nc = nc.rio.clip(geometries)
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except:
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pass
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else:
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pass
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bounds=[[float(nc.lat.min().values),float(nc.lon.min().values)],[float(nc.lat.max().values),float(nc.lon.max().values)]]
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i=countyear
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year=int(nc.time[i].values)
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print(i,year)
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ncVar=nc.DHW_q99
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data = ncVar[i,:,:].values
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Min=np.around(np.nanquantile(ncVar[int(year)-1985,:,:].values, 0.01),2)
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Max=np.around(np.nanmax(ncVar[year-1985,:,:].values),2)
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image_to_url(data,name, colormapD=lambda x: get_color(x,colormap,Min,Max), origin="lower")
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def ProcessAllImage(ssp,model,Colorpalete,ExportDirectory,DataDirectory):
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cc=0
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Var="DHW"
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for i in ssp:
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for j in model:
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ff=DataDirectory+"%s_%s_%s_DHW.nc"%(Var,i,j)
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for CM in Colorpalete:
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for countyear in range(116):
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nc = xr.open_dataset(ff, decode_coords="all")
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year=int(nc.time[countyear].values)
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Evaluado=ExportDirectory+"%s_%s"%(CM,ff.split("/")[-1].replace(".nc","_%s.png"%(year)))
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if not os.path.isfile(Evaluado):
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cc=cc+1
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Total=cc
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start=time.time()
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cc=0
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for i in ssp:
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for j in model:
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ff=DataDirectory+"%s_%s_%s_DHW.nc"%(Var,i,j)
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for CM in Colorpalete:
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for countyear in range(116):
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nc = xr.open_dataset(ff, decode_coords="all")
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year=int(nc.time[countyear].values)
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Evaluado=ExportDirectory+"%s_%s"%(CM,ff.split("/")[-1].replace(".nc","_%s.png"%(year)))
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if not os.path.isfile(Evaluado):
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ExtractMapImage(ff,CM,countyear,Evaluado,nc)
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cc=cc+1
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now=time.time()
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print(cc,(start-now)/cc,(start-now)/cc*(Total-cc))
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ssp=("ssp245","ssp370",)#"ssp585",
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model=("BCC-CSM2-MR","CanESM5",)#"CanESM5","CESM2","EC-Earth3","IPSL-CM6A-LR","MIROC6","MRI-ESM2-0",,"NorESM2-MM"
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Colorpalete=["RdYlBu",'Spectral','ocean',"coolwarm"]
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ExportDirectory="images/"
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DataDirectory="maps/"
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ProcessAllImage(ssp,model,Colorpalete,ExportDirectory,DataDirectory)
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@ -0,0 +1,34 @@
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import imageio
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import os
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import sys
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import glob
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def create_gif(source, name, duration):
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"""
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La función de generar gif, la imagen original solo supports png
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fuente: lista de imágenes png (ordenadas)
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nombre: el nombre del archivo generado
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duración: el intervalo de tiempo entre cada imagen
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"""
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frames = [] # Leer en el búfer
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for img in source:
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frames.append(imageio.imread(img))
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imageio.mimsave(name, frames, 'GIF', duration=duration)
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print("Procesamiento completado")
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path = "/home/mario/Documentos/Ocean/web2py/applications/BPP/views/default/maps2/"
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pic_list =[]
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cm="Spectral"
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scenario="ssp370"
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model="CanESM5"
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Rut=path+"%s*%s*%s*.png"%(cm,scenario,model)
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for name in glob.glob(Rut):
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pic_list.append(name)
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pic_list.sort()
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gif_name = path+"Animated_%s_%s_%s.gif"%(cm,scenario,model)
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print(gif_name)
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duration_time = 0.5
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create_gif(pic_list, gif_name, duration_time)
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