Fix import typo and correct project name in README

LllC-mmd · Dec 13, 2023 · 0c5a85c · 0c5a85c
1 parent 37247f4
commit 0c5a85c
Show file tree

Hide file tree

Showing 4 changed files with 32 additions and 32 deletions.
diff --git a/README.md b/README.md
@@ -8,37 +8,37 @@ More details can be found in [the model descrition paper](https://gmd.copernicus
 
 ## Package Description
 
-This project focuses on patch-level building height and footprint mapping from Sentinel imagery. **SHAFT** is an abbreviation for **S**imultaneous building **H**eight **A**nd **F**ootprin**T** extraction from **S**entinel Imagery.
+This project focuses on patch-level building height and footprint mapping from Sentinel imagery. **SHAFTS** is an abbreviation for **S**imultaneous building **H**eight **A**nd **F**ootprin**T** extraction from **S**entinel Imagery.
 
 ### Installation
 
-SHAFT requires 64-bit `python` 3.7+ and can be installed with `pip` in command line prompt:
+SHAFTS requires 64-bit `python` 3.7+ and can be installed with `pip` in command line prompt:
 
 ```
-python3 -m pip install shaft --upgrade
+python3 -m pip install shafts --upgrade
 ```
 
 We recommend users to install `gdal>=3.2.0` using `conda` first.
 Otherwise, installation may raise error about the environment variables of `gdal`.
 
 ### Data Download
 
-The input data of SHAFT may include:
+The input data of SHAFTS may include:
 
 - [Sentinel-1](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1): VH band, VV band.
 
 - [Sentinel-2](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2): RGB band, NIR band.
 
 - [SRTM](https://www2.jpl.nasa.gov/srtm/): DEM (optional).
 
-SHAFT contains some functions which can download above data directly from [Google Earth Engine](https://earthengine.google.com/).
+SHAFTS contains some functions which can download above data directly from [Google Earth Engine](https://earthengine.google.com/).
 
 Note that according to the [guidance](https://developers.google.com/earth-engine/guides/exporting) for exporting data from Google Earth Engine, we can not export data to any local devices directly. Thus, Google Drive is recommended as a destination where data are export and then we can download exported data to our local devices.
 
 An example for downloading Sentinel-2's image via `sentinel2_download_by_extent` is given as follows:
 
 ```python {cmd}
-from shaft.utils.GEE_ops import sentinel2_download_by_extent
+from shafts.utils.GEE_ops import sentinel2_download_by_extent
 
 # ---specify the spatial extent and year for Sentinel-2's images
 lon_min = -87.740
@@ -57,7 +57,7 @@ sentinel2_download_by_extent(lon_min=lon_min, lat_min=lat_min, lon_max=lon_max,
                                 year=year, dst_dir=dst_dir, file_name=file_name, dst=dst)
 ```
 
-Also, SHAFT gives functions such as `sentinel1_download`, `sentinel2_download` and `srtm_download` to download images in a batch way by a `.csv` file.
+Also, SHAFTS gives functions such as `sentinel1_download`, `sentinel2_download` and `srtm_download` to download images in a batch way by a `.csv` file.
 
 ### Building Height and Footprint prediction
 
@@ -88,7 +88,7 @@ If users want to generate building height and footprint maps without downloading
 An example usage can be given as follows:
 
 ```python {cmd}
-from shaft.inference_gcloud import GBuildingMap
+from shafts.inference_gcloud import GBuildingMap
 
 # ---specify the mapping extent by the minimum/maximum of longitude and latitude
 lon_min = -0.50

diff --git a/example/case_run.py b/example/case_run.py
@@ -1,6 +1,6 @@
 import os
 import torch
-from shaft.inference import pred_height_from_tiff_DL_patch, pred_height_from_tiff_DL_patch_MTL
+from shafts.inference import pred_height_from_tiff_DL_patch, pred_height_from_tiff_DL_patch_MTL
 
 
 if __name__ == "__main__":
@@ -42,7 +42,7 @@
                 "DEM": {
                     "path": os.path.join(case_prefix, "infer_test_Glasgow", "raw_data", "Glasgow_srtm.tif"),
                     "patch_size_ratio": 1.0,
-                } 
+                }
             },
             "output_prefix": "Glasgow",
         },
@@ -61,7 +61,7 @@
                 "DEM": {
                     "path": os.path.join(case_prefix, "infer_test_Chicago", "raw_data", "Chicago_srtm.tif"),
                     "patch_size_ratio": 1.0,
-                } 
+                }
             },
             "output_prefix": "Chicago",
         },
@@ -74,15 +74,15 @@
 
     trained_record = {
         "STL": {
-            100: os.path.join("check_pt_{0}_100m".format(backbone), "experiment_1", "checkpoint.pth.tar"), 
-            250: os.path.join("check_pt_{0}_250m".format(backbone), "experiment_1", "checkpoint.pth.tar"), 
-            500: os.path.join("check_pt_{0}_500m".format(backbone), "experiment_1", "checkpoint.pth.tar"), 
+            100: os.path.join("check_pt_{0}_100m".format(backbone), "experiment_1", "checkpoint.pth.tar"),
+            250: os.path.join("check_pt_{0}_250m".format(backbone), "experiment_1", "checkpoint.pth.tar"),
+            500: os.path.join("check_pt_{0}_500m".format(backbone), "experiment_1", "checkpoint.pth.tar"),
             1000: os.path.join("check_pt_{0}_1000m".format(backbone), "experiment_1", "checkpoint.pth.tar")
         },
         "MTL": {
-            100: os.path.join("check_pt_{0}_100m_MTL".format(backbone), "experiment_1", "checkpoint.pth.tar"), 
-            250: os.path.join("check_pt_{0}_250m_MTL".format(backbone), "experiment_1", "checkpoint.pth.tar"), 
-            500: os.path.join("check_pt_{0}_500m_MTL".format(backbone), "experiment_1", "checkpoint.pth.tar"), 
+            100: os.path.join("check_pt_{0}_100m_MTL".format(backbone), "experiment_1", "checkpoint.pth.tar"),
+            250: os.path.join("check_pt_{0}_250m_MTL".format(backbone), "experiment_1", "checkpoint.pth.tar"),
+            500: os.path.join("check_pt_{0}_500m_MTL".format(backbone), "experiment_1", "checkpoint.pth.tar"),
             1000: os.path.join("check_pt_{0}_1000m_MTL".format(backbone), "experiment_1", "checkpoint.pth.tar")
         }
     }
@@ -113,9 +113,9 @@
                 pred_height_from_tiff_DL_patch(extent=extent, out_file=output_path, tif_ref=input_ref, patch_size=patch_size,
                                                 predictor=model, trained_record=pt_path, resolution=output_res,
                                                 s1_key=s1_key, s2_key=s2_key,
-                                                aux_feat_info=aux_feat_info, base_dir=tmp_dir, padding=padding, 
-                                                batch_size=batch_size, tmp_suffix=None, activation=var_ref[target_var]["activation"], 
-                                                log_scale=False, cuda_used=cuda_used, 
+                                                aux_feat_info=aux_feat_info, base_dir=tmp_dir, padding=padding,
+                                                batch_size=batch_size, tmp_suffix=None, activation=var_ref[target_var]["activation"],
+                                                log_scale=False, cuda_used=cuda_used,
                                                 v_min=var_ref[target_var]["min"], v_max=var_ref[target_var]["max"])
 
             # ------do inference by MTL models
@@ -126,12 +126,12 @@
             output_height_file = "_".join([case_loc[loc]["output_prefix"], "height", backbone + "_MTL"]) + ".tif"
             output_height_path = os.path.join(output_dir, output_height_file)
 
-            pred_height_from_tiff_DL_patch_MTL(extent=extent, out_footprint_file=output_footprint_path, out_height_file=output_height_path, 
+            pred_height_from_tiff_DL_patch_MTL(extent=extent, out_footprint_file=output_footprint_path, out_height_file=output_height_path,
                                                 tif_ref=input_ref, patch_size=patch_size,
                                                 predictor=model, trained_record=pt_path, resolution=output_res,
                                                 s1_key=s1_key, s2_key=s2_key,
-                                                aux_feat_info=aux_feat_info, crossed=False, base_dir=tmp_dir, padding=padding, 
+                                                aux_feat_info=aux_feat_info, crossed=False, base_dir=tmp_dir, padding=padding,
                                                 batch_size=batch_size, tmp_suffix=None, log_scale=False,
-                                                cuda_used=cuda_used, 
+                                                cuda_used=cuda_used,
                                                 h_min=var_ref["height"]["min"], h_max=var_ref["height"]["max"],
                                                 f_min=var_ref["footprint"]["min"], f_max=var_ref["footprint"]["max"])
diff --git a/example/data_download.py b/example/data_download.py
@@ -1,4 +1,4 @@
-from shaft.utils.GEE_ops import sentinel2_download_by_extent
+from shafts.utils.GEE_ops import sentinel2_download_by_extent
 
 
 # ---specify the spatial extent and year for Sentinel-2's images

diff --git a/example/minimum_case_run.py b/example/minimum_case_run.py
@@ -1,6 +1,6 @@
 import os
 import torch
-from shaft.inference import pred_height_from_tiff_DL_patch, pred_height_from_tiff_DL_patch_MTL
+from shafts.inference import pred_height_from_tiff_DL_patch, pred_height_from_tiff_DL_patch_MTL
 
 
 if __name__ == "__main__":
@@ -20,16 +20,16 @@
     f_max = 1.0
 
     # ---specify the settings of cases
-    target_resolution = 100                                               # target resolution for building height and footprint mapping 
-    target_extent = [-4.4786000, 55.6759000, -3.8828000, 56.0197000]      # target extent for building height and footprint mapping 
+    target_resolution = 100                                               # target resolution for building height and footprint mapping
+    target_extent = [-4.4786000, 55.6759000, -3.8828000, 56.0197000]      # target extent for building height and footprint mapping
 
     # ------specify the path of input Sentinel data (note: please use the following format for input data specification)
     s1_key = "sentinel_1"       # key which indicates the path of Sentinel-1's files
     s2_key = "sentinel_2"       # key which indicates the path of Sentinel-2's files
 
     input_img = {
         "50pt": {  # use annual medians as aggregation operation for one year data
-            s1_key: os.path.join(case_prefix, "infer_test_Glasgow", "raw_data", "Glasgow_2020_sentinel_1.tif"),      # path of input Sentinel-1 image 
+            s1_key: os.path.join(case_prefix, "infer_test_Glasgow", "raw_data", "Glasgow_2020_sentinel_1.tif"),      # path of input Sentinel-1 image
             s2_key: os.path.join(case_prefix, "infer_test_Glasgow", "raw_data", "Glasgow_2020_sentinel_2.tif"),      # path of input Sentinel-2 image
         }
     }
@@ -45,7 +45,7 @@
     model = "SEResNet18"            # name of pretrained models
     pretrained_model_path = os.path.join("check_pt_{0}_100m_MTL".format(backbone), "experiment_1", "checkpoint.pth.tar")  # path of files of pretrained models
     input_patch_size = [20]         # size of input sizes required by pretrained models
-    
+
     # ---specify the common settings of prediction
     padding = 0.03                                  # padding size outside the target region (it is recommended that padding should not be smaller than 0.03)
     cuda_used = torch.cuda.is_available()           # check whether CUDA can be used for prediction
@@ -60,12 +60,12 @@
     output_height_path = os.path.join(output_dir, output_height_file)                           # path of output building height files
 
     # ---start our prediction
-    pred_height_from_tiff_DL_patch_MTL(extent=target_extent, out_footprint_file=output_footprint_path, out_height_file=output_height_path, 
+    pred_height_from_tiff_DL_patch_MTL(extent=target_extent, out_footprint_file=output_footprint_path, out_height_file=output_height_path,
                                             tif_ref=input_img, patch_size=input_patch_size,
                                             predictor=model, trained_record=pretrained_model_path, resolution=target_res_mapping[target_resolution],
                                             s1_key=s1_key, s2_key=s2_key,
-                                            aux_feat_info=aux_data, crossed=False, base_dir=tmp_dir, padding=padding, 
+                                            aux_feat_info=aux_data, crossed=False, base_dir=tmp_dir, padding=padding,
                                             batch_size=batch_size, tmp_suffix=None, log_scale=False,
-                                            cuda_used=cuda_used, 
+                                            cuda_used=cuda_used,
                                             h_min=h_min, h_max=h_max,
                                             f_min=f_min, f_max=f_max)