cập nhật chức năng predict các loại đất
This commit is contained in:
Victor Phan
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+152
-35
@@ -330,7 +330,7 @@ def update_prediction_progress(message: str):
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async def run_prediction(config: PredictionConfig):
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"""Chạy prediction process"""
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"""Chạy prediction process - Áp dụng phương pháp từ 02.predict_ODC.ipynb"""
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global prediction_status
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try:
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@@ -341,6 +341,7 @@ async def run_prediction(config: PredictionConfig):
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import numpy as np
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from datetime import datetime as dt
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import rioxarray
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import dask.array as da
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prediction_status["progress"] = "Đang load model..."
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@@ -364,6 +365,7 @@ async def run_prediction(config: PredictionConfig):
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# Import and use Microsoft Planetary Computer STAC API
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import pystac_client
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import planetary_computer
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from odc.stac import load
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catalog = pystac_client.Client.open(
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"https://planetarycomputer.microsoft.com/api/stac/v1",
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@@ -373,65 +375,168 @@ async def run_prediction(config: PredictionConfig):
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bbox = [config.min_lon, config.min_lat, config.max_lon, config.max_lat]
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time_range = f"{config.start_date}/{config.end_date}"
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# ============ BƯỚC 1: TẢI DỮ LIỆU SENTINEL-2 ============
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prediction_status["progress"] = "Đang tải dữ liệu Sentinel-2..."
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# Search Sentinel-2 data
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search = catalog.search(
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s2_search = catalog.search(
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collections=["sentinel-2-l2a"],
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bbox=bbox,
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datetime=time_range,
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query={"eo:cloud_cover": {"lt": config.cloud_cover}}
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)
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items = list(search.items()) # Changed from items_as_dicts() to items()
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if not items:
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s2_items = list(s2_search.items())
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if not s2_items:
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raise ValueError("Không tìm thấy dữ liệu Sentinel-2 cho khu vực và thời gian này")
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items = items[:config.max_scenes]
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prediction_status["progress"] = f"Đang xử lý {len(items)} scenes Sentinel-2..."
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# Load and process Sentinel-2 data (simplified)
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# Note: This is a simplified version. Full implementation would need more processing
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from odc.stac import load
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s2_items = s2_items[:config.max_scenes]
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prediction_status["progress"] = f"Đang xử lý {len(s2_items)} scenes Sentinel-2..."
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# Load Sentinel-2 data
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s2_data = load(
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items,
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s2_items,
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bbox=bbox,
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chunks={"time": 1, "x": 2048, "y": 2048},
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groupby="solar_day",
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resolution=config.resolution
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)
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prediction_status["progress"] = "Đang tính toán các chỉ số..."
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# ============ BƯỚC 2: TÍNH NDVI VÀ XỬ LÝ MÂY ============
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prediction_status["progress"] = "Đang tính toán NDVI và xử lý mây..."
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# Calculate NDVI using Sentinel-2 band names
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# B08 = NIR, B04 = Red
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nir = s2_data["B08"] # NIR band
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red = s2_data["B04"] # Red band
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ndvi = (nir - red) / (nir + red + 1e-8) # Add small value to avoid division by zero
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# Calculate NDVI using Sentinel-2 band names (B08 = NIR, B04 = Red)
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nir = s2_data["B08"].astype('float32')
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red = s2_data["B04"].astype('float32')
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ndvi = (nir - red) / (nir + red + 1e-8)
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# Resample to monthly
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ndvi_monthly = ndvi.resample(time="1M").mean()
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# Mask clouds using SCL band if available
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if "SCL" in s2_data:
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scl = s2_data["SCL"]
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# SCL values: 4=vegetation, 5=bare soil, 6=water - these are clear
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# 3=cloud shadow, 8=cloud medium, 9=cloud high, 10=cirrus - mask these
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cloud_mask = (scl == 3) | (scl == 8) | (scl == 9) | (scl == 10)
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ndvi = ndvi.where(~cloud_mask)
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prediction_status["progress"] = "Đang dự đoán..."
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# ============ BƯỚC 3: ĐIỀN GIÁ TRỊ NAN (FILL NAN) ============
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prediction_status["progress"] = "Đang điền giá trị bị che mây..."
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# Prepare features for prediction
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features_list = []
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for t in range(len(ndvi_monthly.time)):
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ndvi_t = ndvi_monthly.isel(time=t).values
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features_list.append(ndvi_t.flatten())
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# Fill NaN using forward fill and backward fill
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ndvi_filled = ndvi.ffill(dim='time').bfill(dim='time')
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# Stack features
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features = np.column_stack(features_list)
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# Resample to monthly average
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prediction_status["progress"] = "Đang tính trung bình NDVI theo tháng..."
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ndvi_monthly = ndvi_filled.resample(time="1ME").mean()
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# Compute NDVI (convert from dask to numpy)
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ndvi_monthly = ndvi_monthly.compute()
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# ============ BƯỚC 4: TẢI DỮ LIỆU SENTINEL-1 (VH, VV) ============
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prediction_status["progress"] = "Đang tải dữ liệu Sentinel-1 (Radar)..."
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# Search Sentinel-1 data
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s1_search = catalog.search(
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collections=["sentinel-1-rtc"],
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bbox=bbox,
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datetime=time_range,
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)
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s1_items = list(s1_search.items())
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if s1_items:
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s1_items = s1_items[:config.max_scenes]
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prediction_status["progress"] = f"Đang xử lý {len(s1_items)} scenes Sentinel-1..."
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# Load Sentinel-1 data
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s1_data = load(
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s1_items,
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bbox=bbox,
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chunks={"time": 1, "x": 2048, "y": 2048},
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groupby="sat:absolute_orbit",
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resolution=config.resolution,
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like=ndvi_monthly # Align with NDVI grid
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)
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# Extract VH and VV bands
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if "vh" in s1_data and "vv" in s1_data:
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vh = s1_data["vh"].astype('float32')
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vv = s1_data["vv"].astype('float32')
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# Resample to monthly average
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prediction_status["progress"] = "Đang tính trung bình VH/VV theo tháng..."
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vh_monthly = vh.resample(time="1ME").mean().compute()
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vv_monthly = vv.resample(time="1ME").mean().compute()
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use_radar = True
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else:
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prediction_status["progress"] = "Không tìm thấy bands VH/VV, tiếp tục với NDVI..."
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use_radar = False
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else:
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prediction_status["progress"] = "Không có dữ liệu Sentinel-1, tiếp tục với NDVI..."
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use_radar = False
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# ============ BƯỚC 5: CHUẨN BỊ FEATURES CHO DỰ ĐOÁN ============
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prediction_status["progress"] = "Đang chuẩn bị features cho dự đoán..."
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# Get shape information
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n_times_ndvi = len(ndvi_monthly.time)
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y_size = len(ndvi_monthly.y)
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x_size = len(ndvi_monthly.x)
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n_pixels = y_size * x_size
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# Prepare NDVI features (flatten each time step)
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ndvi_features = []
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for t in range(n_times_ndvi):
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ndvi_t = ndvi_monthly.isel(time=t).values.flatten()
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ndvi_features.append(ndvi_t)
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# Stack NDVI features
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features = np.column_stack(ndvi_features)
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# Add radar features if available
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if use_radar:
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n_times_vh = len(vh_monthly.time)
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n_times_vv = len(vv_monthly.time)
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# Add VH features
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for t in range(min(n_times_vh, n_times_ndvi)):
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vh_t = vh_monthly.isel(time=t).values.flatten()
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# Resize if needed
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if len(vh_t) != n_pixels:
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vh_t = np.resize(vh_t, n_pixels)
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features = np.column_stack([features, vh_t])
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# Add VV features
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for t in range(min(n_times_vv, n_times_ndvi)):
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vv_t = vv_monthly.isel(time=t).values.flatten()
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# Resize if needed
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if len(vv_t) != n_pixels:
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vv_t = np.resize(vv_t, n_pixels)
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features = np.column_stack([features, vv_t])
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# Handle NaN values in features
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features = np.nan_to_num(features, nan=0.0)
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# ============ BƯỚC 6: DỰ ĐOÁN ============
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prediction_status["progress"] = f"Đang dự đoán với {features.shape[1]} features..."
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# Make prediction
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predictions = model.predict(features)
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# Decode labels if label_encoder exists
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if label_encoder is not None:
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try:
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predictions = label_encoder.inverse_transform(predictions)
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except:
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pass # Keep numeric predictions if inverse_transform fails
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# Reshape to original shape
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pred_shape = ndvi_monthly.isel(time=0).shape
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pred_shape = (y_size, x_size)
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predictions_2d = predictions.reshape(pred_shape)
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# ============ BƯỚC 7: TẠO OUTPUT VÀ LƯU KẾT QUẢ ============
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prediction_status["progress"] = "Đang tạo bản đồ phân loại..."
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# Create output xarray
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prediction_da = xr.DataArray(
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predictions_2d,
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@@ -450,21 +555,33 @@ async def run_prediction(config: PredictionConfig):
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timestamp = dt.now().strftime("%Y%m%d_%H%M%S")
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output_file = output_dir / f"prediction_{timestamp}.tif"
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prediction_status["progress"] = "Đang lưu kết quả..."
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prediction_status["progress"] = "Đang lưu kết quả GeoTIFF..."
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# Save as GeoTIFF
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# Set CRS and save as GeoTIFF
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if hasattr(s2_data, 'rio') and s2_data.rio.crs is not None:
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prediction_da.rio.write_crs(s2_data.rio.crs, inplace=True)
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prediction_da.rio.to_raster(output_file, driver="GTiff")
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else:
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prediction_da.rio.write_crs("EPSG:4326", inplace=True)
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prediction_da.rio.to_raster(str(output_file), driver="GTiff")
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# Get unique classes for result
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unique_classes = np.unique(predictions_2d)
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unique_classes = unique_classes[~np.isnan(unique_classes)].tolist()
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prediction_status["is_predicting"] = False
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prediction_status["progress"] = "Hoàn thành!"
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prediction_status["output_file"] = str(output_file)
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prediction_status["result"] = {
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"output_file": str(output_file),
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"shape": pred_shape,
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"unique_classes": np.unique(predictions).tolist(),
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"shape": list(pred_shape),
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"unique_classes": unique_classes,
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"bbox": bbox,
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"time_range": time_range
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"time_range": time_range,
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"n_features": features.shape[1],
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"n_times_ndvi": n_times_ndvi,
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"used_radar": use_radar,
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"model_used": config.model_filename
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}
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prediction_status["end_time"] = dt.now().isoformat()
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@@ -0,0 +1,164 @@
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# 🌾 Giải Thích Quy Trình Phân Loại Đất Trồng Cây
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File notebook `02.predict_ODC.ipynb` sử dụng **Machine Learning** kết hợp với **dữ liệu vệ tinh** để phân loại các loại đất/cây trồng. Dưới đây là quy trình chi tiết:
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---
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## **Bước 1: Thu thập dữ liệu vệ tinh** (Cell 3-4)
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```python
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date_range = ('2022-09-01', '2023-10-01')
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longtitude_range = (105.86575, 105.94120)
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latitude_range = (9.65070, 9.69850)
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data = load_data(dc, date_range, longtitude_range, latitude_range)
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```
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- Lấy ảnh **Sentinel-2** (ảnh quang học) từ kho dữ liệu trong khoảng thời gian và vị trí cụ thể
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---
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## **Bước 2: Xử lý mây** (Cell 5)
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```python
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result = mask_clean(data)
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```
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- Loại bỏ các pixel bị mây che phủ để đảm bảo dữ liệu chính xác
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---
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## **Bước 3: Tính chỉ số NDVI** (Cell 6-10)
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```python
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ndvi = calculate_indices(result, index='NDVI', satellite_mission='s2')
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fill_nan_ndvi = fill_nan(ndvi, time_split)
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average_ndvi = fill_nan_ndvi.resample(time='1M').mean()
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```
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- **NDVI** (Normalized Difference Vegetation Index) = (NIR - Red) / (NIR + Red)
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- Giá trị từ **-1 đến 1**: cao = thực vật xanh tốt, thấp = đất trống/nước
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- Điền giá trị nan (mây) và tính trung bình theo tháng
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---
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## **Bước 4: Lấy dữ liệu Radar Sentinel-1** (Cell 11)
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```python
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dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)
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average_vv = calculate_average(dsvv, time_pattern='1M')
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average_vh = calculate_average(dsvh, time_pattern='1M')
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```
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- **VH, VV**: Dữ liệu radar (xuyên mây), cho biết cấu trúc bề mặt
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- Giúp phân biệt lúa ngập nước, cây trồng cạn, mặt nước...
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---
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## **Bước 5: Dự đoán bằng Model ML** (Cell 12) ⭐ **QUAN TRỌNG NHẤT**
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```python
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loaded_model = joblib.load("model_train/model_odc.joblib")
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data_array = predict(loaded_model, data.rio.crs, average_ndvi, average_vh, average_vv)
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```
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**Model đã được train trước** với dữ liệu mẫu (training data) gồm:
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- **Đầu vào (Features)**: NDVI theo tháng + VH + VV (chuỗi thời gian)
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- **Đầu ra (Labels)**: Loại đất đã được gắn nhãn thủ công
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### Cách model phân loại:
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| Đặc điểm | Loại đất |
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|----------|----------|
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| NDVI cao đều, VV thấp | Rừng |
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| NDVI biến đổi theo mùa vụ, VH cao (nước) | Lúa |
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| NDVI thấp, VV rất thấp | Sông/nước |
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| NDVI trung bình ổn định | Cây lâu năm (CLN) |
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---
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## **Bước 6: Hiển thị kết quả** (Cell 13-15)
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```python
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colors = ["#abcee9", "#ffef44", "#c4ff9e", "#ffd6a8", "#93ddda", "#1aeef7", "#ffa7f2", "#33ee33"]
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labels = ["Lúa tôm", "Lúa", "CHN", "CLN", "TS", "Sông", "Đất xây dựng", "Rừng"]
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```
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### 8 lớp phân loại:
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| Mã | Tên | Màu | Ý nghĩa |
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|----|-----|-----|---------|
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| 0 | Lúa tôm | 🔵 Xanh nhạt | Luân canh lúa-tôm |
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| 1 | Lúa | 🟡 Vàng | Đất trồng lúa |
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| 2 | CHN | 🟢 Xanh lá nhạt | Cây hàng năm |
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| 3 | CLN | 🟠 Cam nhạt | Cây lâu năm (cây ăn trái) |
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| 4 | TS | 🩵 Xanh ngọc | Thủy sản |
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| 5 | Sông | 🔷 Cyan | Mặt nước sông |
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| 6 | Đất XD | 💗 Hồng | Đất xây dựng |
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| 7 | Rừng | 💚 Xanh đậm | Rừng |
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---
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## **Bước 7: Lưu kết quả** (Cell 16)
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```python
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region_result.rio.to_raster("KetQuaPhanLoaiDatODC.tif")
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```
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- Xuất file GeoTIFF chứa mã phân loại (0-7) cho từng pixel
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---
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## 📊 **Tóm tắt quy trình:**
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```
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Ảnh vệ tinh (Sentinel-1 + Sentinel-2)
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↓
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Xử lý (loại mây, tính NDVI, VH, VV)
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↓
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Kết hợp features theo thời gian (13 tháng)
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↓
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Model ML (Random Forest/XGBoost) dự đoán
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↓
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Bản đồ phân loại 8 lớp đất
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↓
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File .tif (mỗi pixel = 1 mã loại đất)
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```
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---
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## 📁 Cấu trúc dữ liệu đầu vào cho Model
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|
||||
### Features (Đặc trưng):
|
||||
- **NDVI theo 13 tháng**: 13 bands
|
||||
- **VH (radar) theo 13 tháng**: 13 bands
|
||||
- **VV (radar) theo 13 tháng**: 13 bands
|
||||
- **Tổng cộng**: ~39 features cho mỗi pixel
|
||||
|
||||
### Labels (Nhãn):
|
||||
- Được lấy từ shapefile training: `train/ST_training data_updated_1130points_new.shp`
|
||||
- 1130 điểm mẫu đã được gắn nhãn thủ công bởi chuyên gia
|
||||
|
||||
---
|
||||
|
||||
## 🔧 Các thư viện sử dụng
|
||||
|
||||
| Thư viện | Mục đích |
|
||||
|----------|----------|
|
||||
| `datacube` | Truy vấn dữ liệu vệ tinh |
|
||||
| `xarray` | Xử lý dữ liệu đa chiều |
|
||||
| `rioxarray` | Đọc/ghi GeoTIFF |
|
||||
| `joblib` | Load/save model ML |
|
||||
| `sklearn` / `xgboost` | Training model |
|
||||
| `matplotlib` / `hvplot` | Trực quan hóa |
|
||||
|
||||
---
|
||||
|
||||
## 📝 Ghi chú
|
||||
|
||||
- **Độ phân giải**: 10-20m (tùy cấu hình)
|
||||
- **Thời gian xử lý**: Phụ thuộc vào kích thước vùng và số scenes
|
||||
- **Yêu cầu**: Cần kết nối internet để tải dữ liệu vệ tinh từ Planetary Computer hoặc ODC
|
||||
|
||||
---
|
||||
|
||||
*Tài liệu được tạo ngày 14/12/2025*
|
||||
+309
@@ -0,0 +1,309 @@
|
||||
affine @ file:///home/conda/feedstock_root/build_artifacts/affine_1733762038348/work
|
||||
aiobotocore==2.25.0
|
||||
aiohappyeyeballs==2.6.1
|
||||
aiohttp==3.12.15
|
||||
aioitertools==0.12.0
|
||||
aiosignal==1.4.0
|
||||
alembic==1.16.5
|
||||
annotated-doc==0.0.4
|
||||
annotated-types==0.7.0
|
||||
antimeridian @ file:///home/conda/feedstock_root/build_artifacts/antimeridian_1753706324394/work
|
||||
anyio @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_anyio_1758634638/work
|
||||
argon2-cffi @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi_1749017159514/work
|
||||
argon2-cffi-bindings @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi-bindings_1649500328244/work
|
||||
arrow @ file:///home/conda/feedstock_root/build_artifacts/arrow_1733584251875/work
|
||||
asciitree==0.3.3
|
||||
asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1733250440834/work
|
||||
async-lru @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_async-lru_1742153708/work
|
||||
async-timeout==3.0.1
|
||||
attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1741918516150/work
|
||||
babel @ file:///home/conda/feedstock_root/build_artifacts/babel_1738490167835/work
|
||||
beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1759146011391/work
|
||||
bleach @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_bleach_1737382993/work
|
||||
blinker==1.9.0
|
||||
bokeh==3.7.3
|
||||
boto3==1.40.18
|
||||
botocore==1.40.49
|
||||
Bottleneck @ file:///croot/bottleneck_1731058641041/work
|
||||
branca @ file:///croot/branca_1675157607453/work
|
||||
Brotli @ file:///croot/brotli-split_1736182456865/work
|
||||
brotlicffi @ file:///croot/brotlicffi_1736182461069/work
|
||||
cached-property @ file:///home/conda/feedstock_root/build_artifacts/cached_property_1615209429212/work
|
||||
cachetools==6.2.0
|
||||
Cartopy==0.25.0
|
||||
certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1759648874697/work/certifi
|
||||
cffi @ file:///croot/cffi_1736182485317/work
|
||||
cftime @ file:///home/conda/feedstock_root/build_artifacts/cftime_1649636873066/work
|
||||
chardet @ file:///home/conda/feedstock_root/build_artifacts/chardet_1649184137891/work
|
||||
charset-normalizer @ file:///croot/charset-normalizer_1721748349566/work
|
||||
ciso8601==2.3.3
|
||||
click @ file:///home/conda/feedstock_root/build_artifacts/click_1747811314515/work
|
||||
click-plugins @ file:///home/conda/feedstock_root/build_artifacts/click-plugins_1750848229740/work
|
||||
cligj @ file:///home/conda/feedstock_root/build_artifacts/cligj_1733749956636/work
|
||||
cloudpickle @ file:///home/conda/feedstock_root/build_artifacts/cloudpickle_1736947526808/work
|
||||
colorama==0.4.6
|
||||
colorcet==3.1.0
|
||||
comm @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_comm_1753453984/work
|
||||
contourpy @ file:///croot/contourpy_1732540045555/work
|
||||
cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work
|
||||
cytoolz==0.11.2
|
||||
dask @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_dask-core_1760473436/work
|
||||
dask-gateway @ file:///Users/runner/miniforge3/conda-bld/bld/rattler-build_dask-gateway_1744370153/work/dask-gateway
|
||||
dask-glm @ file:///home/conda/feedstock_root/build_artifacts/dask-glm_1701346265909/work
|
||||
dask-image==2024.5.3
|
||||
dask-ml @ file:///home/conda/feedstock_root/build_artifacts/dask-ml_1679705292494/work
|
||||
datacube==1.8.15
|
||||
datacube_ows==1.9.4
|
||||
datashader==0.18.2
|
||||
dea-tools==0.3.0
|
||||
debugpy @ file:///home/task_175706711740264/conda-bld/debugpy_1757067131873/work
|
||||
decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1740384970518/work
|
||||
deepdiff==8.6.1
|
||||
defusedxml @ file:///home/conda/feedstock_root/build_artifacts/defusedxml_1615232257335/work
|
||||
deprecat @ file:///home/conda/feedstock_root/build_artifacts/deprecat_1734684036993/work
|
||||
distributed @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_distributed_1760476147/work
|
||||
eo-tides==0.8.2
|
||||
exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1746947292760/work
|
||||
executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1756729339227/work
|
||||
fastapi==0.124.3
|
||||
fasteners @ file:///home/conda/feedstock_root/build_artifacts/fasteners_1734943108928/work
|
||||
fastjsonschema @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_python-fastjsonschema_1755304154/work/dist
|
||||
filelock==3.19.1
|
||||
fiona==1.10.1
|
||||
Flask==3.1.2
|
||||
flask-babel==4.0.0
|
||||
flatbuffers==25.2.10
|
||||
folium==0.20.0
|
||||
fonttools @ file:///croot/fonttools_1737039080035/work
|
||||
fqdn @ file:///home/conda/feedstock_root/build_artifacts/fqdn_1733327382592/work/dist
|
||||
frozenlist==1.7.0
|
||||
fsspec @ file:///home/conda/feedstock_root/build_artifacts/fsspec_1756908513222/work
|
||||
GDAL @ file:///croot/gdal-split_1734448174900/work/build/swig/python
|
||||
GeoAlchemy2 @ file:///home/conda/feedstock_root/build_artifacts/geoalchemy2_1753372953474/work
|
||||
geographiclib==2.1
|
||||
geojson==3.2.0
|
||||
geomad==1.0.0
|
||||
geopandas @ file:///croot/geopandas-split_1755761494241/work
|
||||
geopy==2.4.1
|
||||
greenlet @ file:///home/conda/feedstock_root/build_artifacts/greenlet_1648882383677/work
|
||||
h11 @ file:///home/conda/feedstock_root/build_artifacts/h11_1745526374115/work
|
||||
h2 @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_h2_1756364871/work
|
||||
h3==4.3.1
|
||||
hdstats==0.2.1
|
||||
holoviews==1.21.0
|
||||
hpack @ file:///home/conda/feedstock_root/build_artifacts/hpack_1737618293087/work
|
||||
httpcore @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_httpcore_1745602916/work
|
||||
httpx @ file:///home/conda/feedstock_root/build_artifacts/httpx_1733663348460/work
|
||||
hvplot==0.12.1
|
||||
hyperframe @ file:///home/conda/feedstock_root/build_artifacts/hyperframe_1737618333194/work
|
||||
idna==3.10
|
||||
imagecodecs==2025.3.30
|
||||
imageio==2.37.0
|
||||
importlib_metadata @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_importlib-metadata_1747934053/work
|
||||
ipykernel @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_ipykernel_1760459840/work
|
||||
ipyleaflet==0.20.0
|
||||
ipython @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_ipython_1748711175/work
|
||||
ipywidgets==8.1.7
|
||||
iso8601==2.1.0
|
||||
isoduration @ file:///home/conda/feedstock_root/build_artifacts/isoduration_1733493628631/work/dist
|
||||
itsdangerous==2.2.0
|
||||
jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1733300866624/work
|
||||
Jinja2 @ file:///croot/jinja2_1741710844255/work
|
||||
jmespath @ file:///home/conda/feedstock_root/build_artifacts/jmespath_1733229141657/work
|
||||
joblib @ file:///home/conda/feedstock_root/build_artifacts/joblib_1756321760188/work
|
||||
json5 @ file:///home/conda/feedstock_root/build_artifacts/json5_1755034879854/work
|
||||
jsonpointer @ file:///home/conda/feedstock_root/build_artifacts/jsonpointer_1756754132747/work
|
||||
jsonschema @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_jsonschema_1755595646/work
|
||||
jsonschema-specifications==2025.4.1
|
||||
jupyter-events @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_jupyter_events_1738765986/work
|
||||
jupyter-leaflet==0.20.0
|
||||
jupyter-lsp @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_jupyter-lsp_1756388269/work/jupyter-lsp
|
||||
jupyter-ui-poll==1.0.0
|
||||
jupyter_client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1733440914442/work
|
||||
jupyter_core @ file:///home/conda/feedstock_root/build_artifacts/jupyter_core_1748333051527/work
|
||||
jupyter_server @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_jupyter_server_1755870522/work
|
||||
jupyter_server_terminals @ file:///home/conda/feedstock_root/build_artifacts/jupyter_server_terminals_1733427956852/work
|
||||
jupyterlab @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_1758913905644/work
|
||||
jupyterlab_pygments @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_pygments_1733328101776/work
|
||||
jupyterlab_server @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_server_1733599573484/work
|
||||
jupyterlab_widgets==3.0.15
|
||||
kiwisolver @ file:///croot/kiwisolver_1737039087198/work
|
||||
lark==1.2.2
|
||||
lark-parser==0.12.0
|
||||
lazy_loader==0.4
|
||||
linkify-it-py==2.0.3
|
||||
llvmlite @ file:///croot/llvmlite_1741209858218/work
|
||||
locket @ file:///home/conda/feedstock_root/build_artifacts/locket_1650660393415/work
|
||||
lxml==5.4.0
|
||||
lz4 @ file:///croot/lz4_1736366683208/work
|
||||
Mako @ file:///home/conda/feedstock_root/build_artifacts/mako_1744317760971/work
|
||||
mapclassify @ file:///croot/mapclassify_1675157730177/work
|
||||
Markdown==3.9
|
||||
markdown-it-py==4.0.0
|
||||
MarkupSafe @ file:///croot/markupsafe_1738584038848/work
|
||||
matplotlib==3.10.5
|
||||
matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1733416936468/work
|
||||
mdit-py-plugins==0.5.0
|
||||
mdurl==0.1.2
|
||||
mistune @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_mistune_1756495311/work
|
||||
mpmath==1.3.0
|
||||
msgpack @ file:///home/conda/feedstock_root/build_artifacts/msgpack-python_1648745999384/work
|
||||
multidict @ file:///home/conda/feedstock_root/build_artifacts/multidict_1648882415384/work
|
||||
multipledispatch @ file:///home/conda/feedstock_root/build_artifacts/multipledispatch_1721907546485/work
|
||||
narwhals==2.3.0
|
||||
nbclient @ file:///home/conda/feedstock_root/build_artifacts/nbclient_1734628800805/work
|
||||
nbconvert @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_nbconvert-core_1738067871/work
|
||||
nbformat @ file:///home/conda/feedstock_root/build_artifacts/nbformat_1733402752141/work
|
||||
nest_asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1733325553580/work
|
||||
netCDF4 @ file:///croot/netcdf4_1743512888672/work
|
||||
networkx @ file:///croot/networkx_1737039604450/work
|
||||
notebook @ file:///home/conda/feedstock_root/build_artifacts/notebook_1759152069573/work
|
||||
notebook_shim @ file:///home/conda/feedstock_root/build_artifacts/notebook-shim_1733408315203/work
|
||||
numba @ file:///croot/numba_1750798165355/work
|
||||
numcodecs @ file:///croot/numcodecs_1707513121886/work
|
||||
numexpr @ file:///croot/numexpr_1755766469354/work
|
||||
numpy @ file:///croot/numpy_and_numpy_base_1755590845055/work/dist/numpy-1.26.4-cp310-cp310-linux_x86_64.whl#sha256=1096d33ad9a9757a1b4b46634d809e894263fc8b78780bff36801684b6e8cc88
|
||||
nvidia-cublas-cu12==12.8.4.1
|
||||
nvidia-cuda-cupti-cu12==12.8.90
|
||||
nvidia-cuda-nvrtc-cu12==12.8.93
|
||||
nvidia-cuda-runtime-cu12==12.8.90
|
||||
nvidia-cudnn-cu12==9.10.2.21
|
||||
nvidia-cufft-cu12==11.3.3.83
|
||||
nvidia-cufile-cu12==1.13.1.3
|
||||
nvidia-curand-cu12==10.3.9.90
|
||||
nvidia-cusolver-cu12==11.7.3.90
|
||||
nvidia-cusparse-cu12==12.5.8.93
|
||||
nvidia-cusparselt-cu12==0.7.1
|
||||
nvidia-nccl-cu12==2.27.3
|
||||
nvidia-nvjitlink-cu12==12.8.93
|
||||
nvidia-nvtx-cu12==12.8.90
|
||||
odc-algo==0.2.3
|
||||
odc-geo==0.4.10
|
||||
odc-io==0.2.2
|
||||
odc-loader @ file:///home/conda/feedstock_root/build_artifacts/odc-loader_1743656085024/work
|
||||
odc-stac @ file:///home/conda/feedstock_root/build_artifacts/odc-stac_1746136311934/work
|
||||
odc-ui==0.2.1
|
||||
orderly-set==5.5.0
|
||||
overrides @ file:///home/conda/feedstock_root/build_artifacts/overrides_1734587627321/work
|
||||
OWSLib==0.34.1
|
||||
packaging @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_packaging_1745345660/work
|
||||
pandas @ file:///home/task_175982153789305/conda-bld/pandas_1759822248912/work/dist/pandas-2.3.3-cp310-cp310-linux_x86_64.whl#sha256=0de7c83109c411cc2a74419a396c92f65e3d1e457fb4d835e5f100cfb04393a7
|
||||
pandocfilters @ file:///home/conda/feedstock_root/build_artifacts/pandocfilters_1631603243851/work
|
||||
panel==1.7.5
|
||||
param==2.2.1
|
||||
parso @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_parso_1755974222/work
|
||||
partd @ file:///home/conda/feedstock_root/build_artifacts/partd_1715026491486/work
|
||||
pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1733301927746/work
|
||||
pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1733327343728/work
|
||||
pillow @ file:///croot/pillow_1738010226202/work
|
||||
PIMS==0.7
|
||||
planetary-computer==1.0.0
|
||||
platformdirs @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_platformdirs_1756227402/work
|
||||
prometheus_client==0.22.1
|
||||
prometheus_flask_exporter==0.23.2
|
||||
prompt_toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1756321756983/work
|
||||
propcache==0.3.2
|
||||
psutil @ file:///home/conda/feedstock_root/build_artifacts/psutil_1653089181607/work
|
||||
psycopg2 @ file:///croot/psycopg2_1744919787325/work
|
||||
ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1733302279685/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl#sha256=92c32ff62b5fd8cf325bec5ab90d7be3d2a8ca8c8a3813ff487a8d2002630d1f
|
||||
pure_eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1733569405015/work
|
||||
pyarrow @ file:///home/task_175983338836370/conda-bld/pyarrow_1759833584228/work/python
|
||||
pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work
|
||||
pyct==0.5.0
|
||||
pydantic==2.11.7
|
||||
pydantic_core==2.33.2
|
||||
Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1750615794071/work
|
||||
pyogrio @ file:///croot/pyogrio_1741107161422/work
|
||||
pyows==0.3.1
|
||||
pyparsing @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_pyparsing_1753873557/work
|
||||
pyproj @ file:///croot/pyproj_1739284761968/work
|
||||
PyQt6==6.7.1
|
||||
PyQt6_sip @ file:///croot/pyqt-split_1753427276959/work/pyqt_sip
|
||||
pyshp==2.3.1
|
||||
PySocks @ file:///home/builder/ci_310/pysocks_1640793678128/work
|
||||
pystac @ file:///home/conda/feedstock_root/build_artifacts/pystac_1758218055393/work
|
||||
pystac-client==0.9.0
|
||||
python-dateutil==2.9.0.post0
|
||||
python-dotenv==1.1.1
|
||||
python-json-logger @ file:///home/conda/feedstock_root/build_artifacts/python-json-logger_1677079630776/work
|
||||
python-slugify==8.0.4
|
||||
pyTMD==2.2.8
|
||||
pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1742920838005/work
|
||||
pyviz_comms==3.0.6
|
||||
PyYAML==6.0.2
|
||||
pyzmq @ file:///croot/pyzmq_1734687138743/work
|
||||
rasterio @ file:///croot/rasterio_1740069178893/work
|
||||
rasterstats==0.20.0
|
||||
referencing==0.36.2
|
||||
regex==2025.9.1
|
||||
requests @ file:///croot/requests_1756709366904/work
|
||||
rfc3339_validator @ file:///home/conda/feedstock_root/build_artifacts/rfc3339-validator_1733599910982/work
|
||||
rfc3986-validator @ file:///home/conda/feedstock_root/build_artifacts/rfc3986-validator_1598024191506/work
|
||||
rfc3987==1.3.8
|
||||
rfc3987-syntax @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_rfc3987-syntax_1752876729/work
|
||||
rioxarray @ file:///home/conda/feedstock_root/build_artifacts/rioxarray_1737140588464/work
|
||||
rpds-py @ file:///croot/rpds-py_1736541261634/work
|
||||
ruamel.yaml @ file:///home/conda/feedstock_root/build_artifacts/ruamel.yaml_1649033201098/work
|
||||
ruamel.yaml.clib==0.2.12
|
||||
s3fs==2025.9.0
|
||||
s3transfer==0.13.1
|
||||
scikit-image==0.25.2
|
||||
scikit-learn==1.7.1
|
||||
scipy @ file:///croot/scipy_1747238027288/work/dist/scipy-1.15.3-cp310-cp310-linux_x86_64.whl#sha256=2a791554880ad4f358fcc4cd2a982ffe1e9d472e9241011216b2be797457f1f9
|
||||
seaborn==0.13.2
|
||||
Send2Trash @ file:///home/conda/feedstock_root/build_artifacts/send2trash_1733322040660/work
|
||||
setuptools-scm==9.2.0
|
||||
shapely @ file:///croot/shapely_1754380812723/work
|
||||
simplejson==3.20.1
|
||||
sip @ file:///croot/sip_1738856193618/work
|
||||
six==1.17.0
|
||||
slicerator==1.1.0
|
||||
sniffio @ file:///home/conda/feedstock_root/build_artifacts/sniffio_1733244044561/work
|
||||
snuggs @ file:///home/conda/feedstock_root/build_artifacts/snuggs_1733818638588/work
|
||||
sortedcontainers @ file:///home/conda/feedstock_root/build_artifacts/sortedcontainers_1738440353519/work
|
||||
soupsieve @ file:///home/conda/feedstock_root/build_artifacts/soupsieve_1756330469801/work
|
||||
sparse @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_sparse_1747799051/work
|
||||
SQLAlchemy==1.4.54
|
||||
stack_data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1733569443808/work
|
||||
starlette==0.50.0
|
||||
sympy==1.14.0
|
||||
tblib @ file:///home/conda/feedstock_root/build_artifacts/tblib_1743515515538/work
|
||||
terminado @ file:///home/conda/feedstock_root/build_artifacts/terminado_1710262609923/work
|
||||
text-unidecode==1.3
|
||||
threadpoolctl @ file:///home/conda/feedstock_root/build_artifacts/threadpoolctl_1741878222898/work
|
||||
tifffile==2025.5.10
|
||||
timescale==0.0.9
|
||||
timezonefinder==8.0.0
|
||||
tinycss2 @ file:///home/conda/feedstock_root/build_artifacts/tinycss2_1729802851396/work
|
||||
tomli @ file:///croot/tomli_1753774587605/work
|
||||
toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1733736030883/work
|
||||
torch==2.8.0
|
||||
tornado @ file:///croot/tornado_1748956929273/work
|
||||
tqdm==4.67.1
|
||||
traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1733367359838/work
|
||||
traittypes==0.2.1
|
||||
triton==3.4.0
|
||||
types-python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/types-python-dateutil_1759899809376/work
|
||||
typing-inspection==0.4.1
|
||||
typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_typing_extensions_1756220668/work
|
||||
typing_utils @ file:///home/conda/feedstock_root/build_artifacts/typing_utils_1733331286120/work
|
||||
tzdata @ file:///croot/python-tzdata_1746123641790/work
|
||||
uc-micro-py==1.0.3
|
||||
unicodedata2 @ file:///croot/unicodedata2_1736541023050/work
|
||||
uri-template @ file:///home/conda/feedstock_root/build_artifacts/uri-template_1733323593477/work/dist
|
||||
urllib3 @ file:///croot/urllib3_1750775463400/work
|
||||
uvicorn==0.38.0
|
||||
wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1733231326287/work
|
||||
webcolors @ file:///home/conda/feedstock_root/build_artifacts/webcolors_1733359735138/work
|
||||
webencodings @ file:///home/conda/feedstock_root/build_artifacts/webencodings_1733236011802/work
|
||||
websocket-client @ file:///home/conda/feedstock_root/build_artifacts/websocket-client_1759928050786/work
|
||||
Werkzeug==3.1.3
|
||||
widgetsnbextension==4.0.14
|
||||
wrapt @ file:///home/conda/feedstock_root/build_artifacts/wrapt_1651495243689/work
|
||||
xarray @ file:///home/conda/feedstock_root/build_artifacts/xarray_1749743207754/work
|
||||
xgboost==3.1.2
|
||||
xyzservices @ file:///croot/xyzservices_1675159059961/work
|
||||
yarl==1.20.1
|
||||
zarr @ file:///home/conda/feedstock_root/build_artifacts/zarr_1733237197728/work
|
||||
zict @ file:///home/conda/feedstock_root/build_artifacts/zict_1733261551178/work
|
||||
zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1749421620841/work
|
||||
Reference in New Issue
Block a user