My personal research for NightCafe and Disco Diffusion painting generation. Just for my personal reference. This post will be continuously updated. Landscape snow scene by Vincent Salvas-Savard,Katarina Persson,Jan Degas,Michal Kváč,Andres Munar,zhang gan,Goedele Godfroid,Au Au,Jay Versluis,malo chen 3D Artist,Mike Reid MaxQ3D,llya Landshut 3D Artist cityscape by sathish kumar,Logan Feliciano,Matt Birks,Guillem H. Pongiluppi,Liya Bohnat,Michal Kváč,Christian Hecker,Daniel Lieske,Jason ..
人工智能绘画作品分享_池塘 Products of AI art generators nightcafe and disco diffusion. From above, we see a pond in a garden with a variety of trees covered with yellow, red, and orange blossoms.There is a pool in the center, the water is blue, and the shadows of the trees are..
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Ignore warnings # Ignore warnings import sys import warnings if not sys.warnoptions: warnings.simplefilter(‘ignore’) Ignore some errors # function FN does not work when there are nan in X try: mktest=FN(X) trend=mktest except: trend=np.nan From my original code:Indexes_ERA5_GreatLakesRegion_sens_s..
Method to write and read a single N-Dimension numpy array to a .npy file. Sample code import numpy as np #write a 3-dimension varaible trend38 np.save(‘indexes_trend38_sens_slope.npy’,trend38) #read the 3-dimention variable to trend38 (use different name is ok) trend38=np.load(‘indexes_trend38_sens_slope.npy’) My original full version code:Indexes_ERA5_GreatLakesRegion_sens_s..
Add multiple variables with the same dimension to an existing nc file. Read the target nc file and include the new variables into the file and save to the new file. Sample code import xarray as xr #read nc file fn1 = ‘Annual_average_temperature.nc’ TxTn90p10p=xr.open_dataset(fn1) #add new variables TxTn90p10p[‘TX90p’]=xr.DataArray(TX90p.astype(np.float32), coords=TxTn90p10p.coords, dims=TxTn90p10p.t2m.dims, attrs=TxTn90p10p.attrs) TxTn90p10p[‘TX10p’]=xr.DataArray(TX10p.astype(np.float32), coords=TxTn90p10p.coords, dims=TxTn90p10p.t2m.dims, attrs=TxTn90p10p.attrs) ..
pr_data[‘PrTot’]=xr.DataArray(PrTot.astype(np.float32), coords=pr_data.coords, dims=pr_data.tp.dims, attrs=pr_data.attrs) pr_data[‘R1mm’]=xr.DataArray(R1mm.astype(np.int16), coords=pr_data.coords, dims=pr_data.tp.dims, attrs=pr_data.attrs) pr_data[‘R10mm’]=xr.DataArray(R10mm.astype(np.int16), coords=pr_data.coords, dims=pr_data.tp.dims, attrs=pr_data.attrs) pr_data[‘R20mm’]=xr.DataArray(R20mm.astype(np.int16), coords=pr_data.coords, dims=pr_data.tp.dims, attrs=pr_data.attrs) pr_data[‘SDII’]=xr.DataArray(SDII.astype(np.float32), coords=pr_data.coords, dims=pr_data.tp.dims, attrs=pr_data.attrs) pr_data[‘Rx1day’]=xr.DataArray(Rx1day.astype(np.float32), coords=pr_data.coords, dims=pr_data.tp.dims, attrs=pr_data.attrs) pr_data[‘Rx5day’]=xr.DataArray(Rx5day.astype(np.float32), coords=pr_data.coords, dims=pr_data.tp.dims, attrs=pr_data.attrs) See example code: /media/Data1/OnClimate/Precipitation_indexes_EAR5_GreatLakesRegion.ipynb Pay attention to line: dims=pr_dat..
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Introduction This article includes some Python functions for calculating the core climate extreme index. I post them here for my future reference, and share them with people who are interested in python or climate change. No guarantee of accuracy. The definitions of the core climate extreme indices 1.Description of Climate Extreme Indexes by OCDP 2.Definitions ..