#!/usr/bin/env python
# coding: utf-8

# In[1]:


get_ipython().run_cell_magic('time', '', '%matplotlib inline\nfrom new_import import *\n')


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get_ipython().run_cell_magic('time', '', '# Dask gateway\ncluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1,4))\ndc = datacube.Datacube()\n\n# Configure s3 access\nconfigure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n\nclient\n')


# In[3]:


## cấu hình thời gian lấy ảnh và tọa độ
date_range = ('2022-09-01', '2023-10-01')
longtitude_range = (105.86575, 105.94120)
latitude_range = (9.65070, 9.69850)


# In[4]:


## truy vấn ảnh vệ tinh sen2
data = load_data(dc, date_range, longtitude_range, latitude_range)
notebook_utils.heading(notebook_utils.xarray_object_size(data))
display(data)


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get_ipython().run_cell_magic('time', '', '# Tiến hành loại bỏ các vị trí bị mây ảnh hưởng\nresult = mask_clean(data)\nprogress(result)\n')


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# Tiến hành tính toán NDVI
ds1 = calculate_indices(result, index='NDVI', satellite_mission='s2')
ndvi = ds1["NDVI"]
display(ndvi)


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get_ipython().run_cell_magic('time', '', "## tính ndvi theo tháng\naverage_ndvi = ndvi.resample(time='1M').mean().persist()\nprogress(average_ndvi)\n")


# In[18]:


# compute average_ndvi
average_ndvi = average_ndvi.compute()


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# cấu hình vh vv file
name_vh = "ThuanHoa/ThuanHoa_VH.tif"
name_vv = "ThuanHoa/ThuanHoa_VV.tif"

# load dữ liệu sen1
bbox = [105.5, 9.2, 106.4, 10.0]
time_range = '2022-09-01/2023-10-01'
dsvh, dsvv = load_sen1(bbox, time_range)


# In[10]:


from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor


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mask = ~np.isnan(average_ndvi)
X_train = np.stack([dsvh.values[mask], dsvv.values[mask]], axis=1)
y_train = average_ndvi.values[mask]


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model = LinearRegression()
model.fit(X_train, y_train)


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X_pred = np.stack([dsvh.values[~mask], dsvv.values[~mask]], axis=1)
average_ndvi.values[~mask] = model.predict(X_pred)


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average_ndvi_filled = xr.DataArray(average_ndvi, dims=average_ndvi.dims)


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plt.imshow(average_ndvi_filled.isel(time=1))


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plt.imshow(average_ndvi.isel(time=1))


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