refactor: Renamed methods and dataclasses

README.md

@@ -32,7 +32,7 @@ into the correct format for the Timelapse Feature Explorer.
 ```python
 import pandas as pd
 from io import StringIO
-from tfe_data import convert_colorizer_data
+from tfe_data import convert_tfe_data
 
 from pathlib import Path
 
@@ -49,7 +49,7 @@ source_dir = Path("some/source/directory")
 data: pd.DataFrame = pd.read_csv(StringIO(csv))
 output_dir = Path("some/directory/my-dataset")
 
-convert_colorizer_data(
+convert_tfe_data(
     data,
     output_dir,
     source_dir=source_dir,

documentation/DATA_FORMAT.md

@@ -832,7 +832,7 @@ A collection file can also include optional metadata fields, saved under the `me
 Once you get the first frame in your dataset, you'll need to save the `starting_timepoint` to your dataset's metadata and include the information when generating the frame paths.
 
 ```python
-writer = ColorizerDatasetWriter(...)
+writer = TfeDatasetWriter(...)
 starting_timepoint = 5
 num_frames = 100
 
@@ -841,6 +841,6 @@ frame_paths = generate_frame_paths(num_frames, start_frame=starting_timepoint)
 writer.set_frame_paths(frame_paths)
 
 # Including starting frame number in the metadata
-metadata = ColorizerMetadata(start_frame_num=starting_timepoint)
+metadata = DatasetMetadata(start_frame_num=starting_timepoint)
 writer.write_manifest(metadata)
 ```

documentation/MIGRATION.md

@@ -0,0 +1,22 @@
+# Migration
+
+## From v1 to v2
+
+1. Update imports to reflect the new package name and repository location:
+
+```
+# pip
+pip install git+https://github.com/AllenCell/tfe-data.git@v2.0.0
+
+# requirements.txt
+tfe_data @ git+https://github.com/AllenCell/tfe-data.git@v2.0.0
+```
+
+2. Rename the package in any imports from `colorizer_data` to `tfe_data`.
+
+3. Replace renamed methods and classes. The old methods/classes will continue to
+   work, but are marked as deprecated and will be removed in the next major
+   version.
+   - Replace `convert_colorizer_data()` with `convert_tfe_data()`.
+   - Replace `ColorizerMetadata` with `DatasetMetadata`.
+   - Replace `ColorizerDatasetWriter` with `TfeDatasetWriter`.

documentation/bin/example_scripts/check_conversion_api.py

@@ -1,6 +1,6 @@
 import pathlib
 import pandas as pd
-from tfe_data import convert_colorizer_data
+from tfe_data import convert_tfe_data
 
 """
 To test, run the following commands from the root directory.
@@ -14,7 +14,7 @@
     source_dir = pathlib.Path("./documentation/getting_started_guide/raw_dataset")
     data = pd.read_csv(source_dir / "data.csv")
     output_dir = pathlib.Path("./documentation/getting_started_guide/processed_data")
-    convert_colorizer_data(
+    convert_tfe_data(
         data,
         output_dir,
         source_dir=source_dir,

documentation/bin/example_scripts/getting_started_guide.py

@@ -28,10 +28,10 @@
 from pathlib import Path
 
 from tfe_data import (
-    convert_colorizer_data,
+    convert_tfe_data,
     FeatureInfo,
     FeatureType,
-    ColorizerMetadata,
+    DatasetMetadata,
     CollectionMetadata,
     update_collection,
 )
@@ -90,7 +90,7 @@ def main():
         dataset_out_dir.mkdir(parents=True, exist_ok=True)
 
         # Dataset metadata
-        metadata = ColorizerMetadata(
+        metadata = DatasetMetadata(
             name="Example dataset {}".format(i + 1),
             description="An example dataset for the Timelapse Feature Explorer.",
             author="Author name et al.",
@@ -107,7 +107,7 @@ def main():
 
         # Convert the dataset
         data: pd.DataFrame = pd.read_csv(dataset_src_dir / "data.csv")
-        convert_colorizer_data(
+        convert_tfe_data(
             data,
             dataset_out_dir,
             source_dir=dataset_src_dir,

documentation/getting_started_guide/GETTING_STARTED.ipynb

@@ -162,7 +162,7 @@
 			"source": [
 				"from pathlib import Path\n",
 				"\n",
-				"from tfe_data import convert_colorizer_data\n",
+				"from tfe_data import convert_tfe_data\n",
 				"import pandas as pd\n",
 				"\n",
 				"source_directory = Path(\"raw_datasets/dataset_1\")\n",
@@ -171,12 +171,12 @@
 				"# Load the dataset\n",
 				"data: pd.DataFrame = pd.read_csv(source_directory / \"data.csv\")\n",
 				"\n",
-				"# `convert_colorizer_data` is a helper function for dataset conversion! You can pass in a pandas DataFrame\n",
+				"# `convert_tfe_data` is a helper function for dataset conversion! You can pass in a pandas DataFrame\n",
 				"# and the output directory, as well as the names of the data columns for the id, time, track, and image data.\n",
 				"# Any other columns are treated as features. We'll show off more advanced behavior in later steps, but this will\n",
 				"# output a complete readable dataset.\n",
 				"\n",
-				"convert_colorizer_data(\n",
+				"convert_tfe_data(\n",
 				"    data,\n",
 				"    output_directory,\n",
 				"    source_dir=source_directory,\n",
@@ -206,7 +206,7 @@
 				"\n",
 				"By default, any columns that aren't mapped to data columns (e.g. object ID, times, tracks, etc.) are automatically handled as feature data. You can specify which columns are features by passing a list of column names to the `feature_columns` parameter.\n",
 				"\n",
-				"While `convert_colorizer_data` will infer feature types for you, you can also provide metadata about each feature, such as its label, key, type, units, and description using the `feature_info` argument.\n",
+				"While `convert_tfe_data` will infer feature types for you, you can also provide metadata about each feature, such as its label, key, type, units, and description using the `feature_info` argument.\n",
 				"\n",
 				"Features can be one of three types:\n",
 				"\n",
@@ -262,7 +262,7 @@
 				"# Providing it is optional, and `feature_info` will still update metadata without it.\n",
 				"feature_column_names = [\"area\", \"radius\", \"location\"]\n",
 				"\n",
-				"convert_colorizer_data(\n",
+				"convert_tfe_data(\n",
 				"    data,\n",
 				"    output_directory,\n",
 				"    source_dir=source_directory,\n",
@@ -293,11 +293,11 @@
 			"outputs": [],
 			"source": [
 				"from tfe_data import (\n",
-				"    ColorizerMetadata,\n",
+				"    DatasetMetadata,\n",
 				")\n",
 				"\n",
 				"# Define the metadata for this dataset.\n",
-				"metadata = ColorizerMetadata(\n",
+				"metadata = DatasetMetadata(\n",
 				"    name=\"Example dataset 1\",\n",
 				"    description=\"An example dataset for the Timelapse Feature Explorer!\",\n",
 				"    author=\"Jane Doe et al.\",\n",
@@ -311,7 +311,7 @@
 				"    frame_duration_sec=1,\n",
 				")\n",
 				"\n",
-				"convert_colorizer_data(\n",
+				"convert_tfe_data(\n",
 				"    data,\n",
 				"    output_directory,\n",
 				"    source_dir=source_directory,\n",
@@ -359,7 +359,7 @@
 				"output_directory = Path(\"processed_dataset/dataset_2\")\n",
 				"data: pd.DataFrame = pd.read_csv(source_directory / \"data.csv\")\n",
 				"\n",
-				"convert_colorizer_data(\n",
+				"convert_tfe_data(\n",
 				"    data,\n",
 				"    output_directory,\n",
 				"    source_dir=source_directory,\n",
@@ -501,7 +501,7 @@
 				"# If the download doesn't start (can be browser-dependent), you can manually\n",
 				"# navigate to and download the zip file from the file browser on the left side\n",
 				"# of the screen. It will be in\n",
-				"# `tfe-data/documentation/getting_started_guide/`."
+				"# `tfe_data/documentation/getting_started_guide/`."
 			]
 		},
 		{
@@ -587,7 +587,7 @@
 				"For example, for 3D data, you can specify a 3D frame source like this:\n",
 				"\n",
 				"```python\n",
-				"from tfe_data import convert_colorizer_data\n",
+				"from tfe_data import convert_tfe_data\n",
 				"from tfe_data.types import Frames3dMetadata, Backdrop3dMetadata\n",
 				"\n",
 				"frames_3d = Frames3dMetadata(\n",
@@ -620,7 +620,7 @@
 				"    ]\n",
 				")\n",
 				"\n",
-				"convert_colorizer_data(\n",
+				"convert_tfe_data(\n",
 				"    ...\n",
 				"    frames_3d=frames_3d,\n",
 				"    image_column=None,  # Disable 2D frame generation\n",

pyproject.toml

@@ -34,8 +34,9 @@ dependencies = [
     "pandas",
     "pillow",
     "pyarrow",
-    "scikit-image",
     "requests",
+    "scikit-image",
+    "typing-extensions",
 ]
 
 [project.optional-dependencies]