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[pre-commit.ci] auto fixes from pre-commit.com hooks
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notebooks/2021-08/2021-08-25/video.py

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channel_indexes = [1, 8, 9]
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satellite_data = []
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for channel_index in channel_indexes:
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# renormalize
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satellite_data.append(
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data["sat_data"][batch_index, :, :, :, channel_index] * SAT_STD.values[channel_index]

notebooks/2021-08/2021-08-26/video.py

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channel_indexes = [1, 9, 8]
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satellite_data = []
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for channel_index in channel_indexes:
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# renormalize
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satellite_data.append(
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data["sat_data"][batch_index, :, :, :, channel_index] * SAT_STD.values[channel_index]

notebooks/2021-09/2021-09-13/remove_hash.py

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for filenames in [train_filenames, validation_filenames]:
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for file in train_filenames:
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print(file)
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filename = file.split("/")[-1]

notebooks/2021-09/2021-09-14/gsp_centroid.py

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# for index in range(0, len(shape_data_raw)):
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for index in range(140, 150):
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# just select the first one
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shape_data = shape_data_raw.iloc[index : index + 1]
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shapes_dict = json.loads(shape_data["geometry"].to_json())

notebooks/2021-09/2021-09-14/gsp_duplicated.py

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duplicated_raw["Amount"] = range(0, len(duplicated_raw))
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for i in range(0, 8, 2):
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# just select the first one
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duplicated = duplicated_raw.iloc[i : i + 2]
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shapes_dict = json.loads(duplicated["geometry"].to_json())

notebooks/2021-09/2021-09-29/gsp_duplicated.py

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duplicated_raw["Amount"] = range(0, len(duplicated_raw))
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for i in range(0, 8, 2):
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# just select the first one
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duplicated = duplicated_raw.iloc[i : i + 2]
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shapes_dict = json.loads(duplicated["geometry"].to_json())

notebooks/2021-09/2021-09-29/video.py

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def get_trace(dt):
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# plot to check it looks right
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return go.Choroplethmapbox(
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geojson=shapes_dict,
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def get_frame(dt):
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# plot to check it looks right
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return go.Choroplethmapbox(
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z=gps_data[dt],

notebooks/2021-10/2021-10-01/pydantic.py

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class Satellite(BaseModel):
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# width: int = Field(..., g=0, description="The width of the satellite image")
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# height: int = Field(..., g=0, description="The width of the satellite image")
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# num_channels: int = Field(..., g=0, description="The width of the satellite image")
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class Batch(BaseModel):
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batch_size: int = Field(
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...,
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g=0,

notebooks/2021-10/2021-10-08/xr_compression.py

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def get_satellite_xrarray_data_array(
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batch_size, seq_length_5, satellite_image_size_pixels, number_sat_channels=10
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):
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r = np.random.randn(
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# self.batch_size,
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seq_length_5,

notebooks/2021-10/2021-10-08/xr_pydantic.py

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class Batch(BaseModel):
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batch_size: int = 0
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satellite: Satellite
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