Refactor cli to typer

pyproject.toml

@@ -16,6 +16,7 @@ dependencies = [
     "numpy",
     "pandas",
     "pandas-stubs",
+    "typer",
 ] # Add project dependencies here, e.g. ["click", "numpy"]
 dynamic = ["version"]
 license.file = "LICENSE"
@@ -36,7 +37,7 @@ dev = [
 ]
 
 [project.scripts]
-bimorph-mirror-analysis = "bimorph_mirror_analysis.__main__:main"
+bimorph-mirror-analysis = "bimorph_mirror_analysis.__main__:app"
 
 [project.urls]
 GitHub = "https://github.com//bimorph-mirror-analysis"

src/bimorph_mirror_analysis/__main__.py

@@ -1,10 +1,9 @@
 """Interface for ``python -m bimorph_mirror_analysis``."""
 
 import datetime
-from argparse import ArgumentParser
-from collections.abc import Sequence
 
 import numpy as np
+import typer
 
 from bimorph_mirror_analysis.maths import find_voltages
 from bimorph_mirror_analysis.read_file import read_bluesky_plan_output
@@ -13,37 +12,25 @@
 
 __all__ = ["main"]
 
+app = typer.Typer()
 
-def main(args: Sequence[str] | None = None) -> None:
-    """Argument parser for the CLI."""
-    parser = ArgumentParser()
-    parser.add_argument(
-        "-v",
-        "--version",
-        action="version",
-        version=__version__,
-    )
-    parser.add_argument(
-        "file_path",
-        type=str,
-        help="Path to the file containing the output of the Bluesky plan.",
-    )
-    parser.add_argument(
-        "-o",
-        "--output",
-        type=str,
-        help="Path to save the optimal voltages.",
-    )
-    a = parser.parse_args(args)
-    file_path = a.file_path
-    file_type = a.file_path.split(".")[-1]
+
+@app.command(name=None)
+def calculate_voltages(
+    file_path: str = typer.Argument(help="The path to the csv file to be read."),
+    output_path: str | None = typer.Option(
+        None,
+        help="The path to save the output\
+optimal voltages to, optional.",
+    ),
+):
+    file_type = file_path.split(".")[-1]
     optimal_voltages = calculate_optimal_voltages(file_path)
     optimal_voltages = np.round(optimal_voltages, 2)
     date = datetime.datetime.now().date()
-    if a.output:
-        output_path = a.output
-    else:
-        output_path = f"{a.file_path.replace(f'.{file_type}', '')}\
+
+    if output_path is None:
+        output_path = f"{file_path.replace(f'.{file_type}', '')}\
 _optimal_voltages_{date}.csv"
 
     np.savetxt(
@@ -57,7 +44,26 @@ def main(args: Sequence[str] | None = None) -> None:
     )
 
 
-# implement this into main
+def version_callback(value: bool):
+    if value:
+        typer.echo(f"Version: {__version__}")
+        raise typer.Exit()
+
+
+@app.callback()
+def main(
+    version: bool = typer.Option(
+        None,
+        "--version",
+        "-v",
+        callback=version_callback,
+        is_eager=True,
+        help="Show the application's version and exit",
+    ),
+):
+    pass
+
+
 def calculate_optimal_voltages(file_path: str) -> np.typing.NDArray[np.float64]:
     pivoted, initial_voltages, increment = read_bluesky_plan_output(file_path)
     # numpy array of pencil beam scans
@@ -69,4 +75,4 @@ def calculate_optimal_voltages(file_path: str) -> np.typing.NDArray[np.float64]:
 
 
 if __name__ == "__main__":
-    main()
+    app()

tests/conftest.py

@@ -1,6 +1,7 @@
 import os
 from typing import Any
 
+import pandas as pd
 import pytest
 
 # Prevent pytest from catching exceptions when debugging in vscode so that break on
@@ -19,3 +20,130 @@ def pytest_exception_interact(call: pytest.CallInfo[Any]):
     @pytest.hookimpl(tryfirst=True)
     def pytest_internalerror(excinfo: pytest.ExceptionInfo[Any]):
         raise excinfo.value
+
+
+@pytest.fixture
+def raw_data() -> pd.DataFrame:
+    data = """voltage_channel_1,voltage_channel_2,voltage_channel_3,slit_position_x,\
+slit_width_x,slit_position_y,slit_width_y,centroid_position_x,centroid_position_y,pencil_beam_scan_number
+    0,0,0,0.0,1,0,1,0.902155003650946,0.9059778816847021,0
+    0,0,0,0.5,1,0,1,0.974760013427511,0.048632530864663126,0
+    0,0,0,1.0,1,0,1,0.628935425613454,0.11203132765051338,0
+    0,0,0,1.5,1,0,1,0.7762649318706349,0.2719190769515324,0
+    0,0,0,2.0,1,0,1,0.5071122837043811,0.611609088123056,0
+    0,0,0,2.5,1,0,1,0.3474659308224035,0.03510213867906076,0
+    0,0,0,3.0,1,0,1,0.3284992604576076,0.99208831627924,0
+    0,0,0,3.5,1,0,1,0.7295178626505394,0.5103013553152209,0
+    0,0,0,4.0,1,0,1,0.14850099035491915,0.07969729764666311,0
+    0,0,0,4.5,1,0,1,0.022176039254447888,0.049619315174349476,0
+    0,0,0,5.0,1,0,1,0.35295771305012513,0.9557024629963558,0
+    0,0,0,5.5,1,0,1,0.7179675309214435,0.2055059586891408,0
+    0,0,0,6.0,1,0,1,0.6499835221868125,0.606285488948628,0
+    0,0,0,6.5,1,0,1,0.47779199219005,0.6546623731441139,0
+    0,0,0,7.0,1,0,1,0.9336634732337328,0.3136740595705516,0
+    0,0,0,7.5,1,0,1,0.009520569061363338,0.8063235605316648,0
+    0,0,0,8.0,1,0,1,0.7158395576862129,0.5122984261799449,0
+    0,0,0,8.5,1,0,1,0.2716444289557093,0.5507234769303935,0
+    0,0,0,9.0,1,0,1,0.9930168604136324,0.8152365237183438,0
+    0,0,0,9.5,1,0,1,0.4129681812988907,0.8023526931635533,0
+    0,0,0,10.0,1,0,1,0.6674668944557347,0.4174468364062732,0
+    100,0,0,0.0,1,0,1,0.5546590484621201,0.8304502838039677,1
+    100,0,0,0.5,1,0,1,0.13906662116959356,0.17335719779661474,1
+    100,0,0,1.0,1,0,1,0.8798442985528538,0.6082544403694868,1
+    100,0,0,1.5,1,0,1,0.18419363414361012,0.8916318274563698,1
+    100,0,0,2.0,1,0,1,0.8711036413523057,0.7004663717770345,1
+    100,0,0,2.5,1,0,1,0.9099533074121638,0.5582690692187812,1
+    100,0,0,3.0,1,0,1,0.7802219284321528,0.5448810133045838,1
+    100,0,0,3.5,1,0,1,0.9206330646867245,0.9704364812040612,1
+    100,0,0,4.0,1,0,1,0.40940536712510833,0.5267501604276668,1
+    100,0,0,4.5,1,0,1,0.0896507221276781,0.9367451946136546,1
+    100,0,0,5.0,1,0,1,0.2712519699376731,0.8318670004689493,1
+    100,0,0,5.5,1,0,1,0.536044487634938,0.8376715817809228,1
+    100,0,0,6.0,1,0,1,0.43025265873767304,0.3787406941706395,1
+    100,0,0,6.5,1,0,1,0.3847349424152309,0.10274579669497463,1
+    100,0,0,7.0,1,0,1,0.15897565790767731,0.20601860566024477,1
+    100,0,0,7.5,1,0,1,0.33067777617929583,0.6736142304595877,1
+    100,0,0,8.0,1,0,1,0.11858344701673051,0.6782032580859674,1
+    100,0,0,8.5,1,0,1,0.4286248262035558,0.9895136712839148,1
+    100,0,0,9.0,1,0,1,0.7180884861749929,0.056036359595403096,1
+    100,0,0,9.5,1,0,1,0.26499046575096896,0.39912112255467724,1
+    100,0,0,10.0,1,0,1,0.14379259711280845,0.14106802929088103,1
+    100,100,0,0.0,1,0,1,0.7233918949216122,0.982168455516603,2
+    100,100,0,0.5,1,0,1,0.290742792709767,0.12776420966496438,2
+    100,100,0,1.0,1,0,1,0.13799656543141725,0.6898436988997922,2
+    100,100,0,1.5,1,0,1,0.7966501007242092,0.10121665939974611,2
+    100,100,0,2.0,1,0,1,0.6035548318312184,0.5266494061788435,2
+    100,100,0,2.5,1,0,1,0.8745250527670112,0.2783673425712273,2
+    100,100,0,3.0,1,0,1,0.2199662395829104,0.8628343609007396,2
+    100,100,0,3.5,1,0,1,0.21397922232247635,0.14932168483955655,2
+    100,100,0,4.0,1,0,1,0.6491136779321542,0.9677149734414517,2
+    100,100,0,4.5,1,0,1,0.521585333219087,0.3555822165846366,2
+    100,100,0,5.0,1,0,1,0.2878937119272532,0.9483878657266134,2
+    100,100,0,5.5,1,0,1,0.5528976167527797,0.2989377930464553,2
+    100,100,0,6.0,1,0,1,0.2348080763889996,0.27979916967784824,2
+    100,100,0,6.5,1,0,1,0.22744100740205286,0.8218958168136187,2
+    100,100,0,7.0,1,0,1,0.18745517586190308,0.6605989632183804,2
+    100,100,0,7.5,1,0,1,0.6481645591784894,0.6263681865745402,2
+    100,100,0,8.0,1,0,1,0.16260780954429876,0.27381083790754324,2
+    100,100,0,8.5,1,0,1,0.1579859256512237,0.9062488979864969,2
+    100,100,0,9.0,1,0,1,0.37772126256203997,0.7955668100253056,2
+    100,100,0,9.5,1,0,1,0.2295136220510665,0.8019070476612383,2
+    100,100,0,10.0,1,0,1,0.8681643230023325,0.5923396832700695,2
+    100,100,100,0.0,1,0,1,0.232850273843354,0.3203071762815356,3
+    100,100,100,0.5,1,0,1,0.39422707347590746,0.9169444346778486,3
+    100,100,100,1.0,1,0,1,0.5195777869079362,0.933578081913559,3
+    100,100,100,1.5,1,0,1,0.3055357289931444,0.13087372577687528,3
+    100,100,100,2.0,1,0,1,0.7483418094894336,0.07346239072990834,3
+    100,100,100,2.5,1,0,1,0.696346546725722,0.7225571582190516,3
+    100,100,100,3.0,1,0,1,0.9724618758170118,0.9471159228590665,3
+    100,100,100,3.5,1,0,1,0.21177070577236934,0.6092750600252596,3
+    100,100,100,4.0,1,0,1,0.9916829027154486,0.9626893747544218,3
+    100,100,100,4.5,1,0,1,0.474706589914094,0.8168984615795551,3
+    100,100,100,5.0,1,0,1,0.28338124640130735,0.9619912615534008,3
+    100,100,100,5.5,1,0,1,0.5541329846866376,0.6162100171902787,3
+    100,100,100,6.0,1,0,1,0.4041186292722393,0.7502255535642713,3
+    100,100,100,6.5,1,0,1,0.9755649555219787,0.6290253229311905,3
+    100,100,100,7.0,1,0,1,0.7228182241678169,0.2973982599315711,3
+    100,100,100,7.5,1,0,1,0.29495423356560835,0.005345110272705567,3
+    100,100,100,8.0,1,0,1,0.2785374723069234,0.2728282979215154,3
+    100,100,100,8.5,1,0,1,0.004064948618126452,0.658081995540037,3
+    100,100,100,9.0,1,0,1,0.6907922378507042,0.17092071374050632,3
+    100,100,100,9.5,1,0,1,0.3965148737759935,0.04638829999092675,3
+    100,100,100,10.0,1,0,1,0.8706751462033909,0.7206080303974453,3"""
+    df = pd.DataFrame(
+        [row.split(",") for row in data.split("\n")[1:]],
+        columns=data.split("\n")[0].split(","),
+    )  # type: ignore
+    return df.apply(pd.to_numeric, errors="coerce")  # type: ignore
+
+
+@pytest.fixture
+def raw_data_pivoted() -> pd.DataFrame:
+    data = """slit_position_x,pencil_beam_scan_0,pencil_beam_scan_1,pencil_beam_scan_2\
+,pencil_beam_scan_3
+    0.0,0.902155003650946,0.5546590484621201,0.7233918949216122,0.232850273843354
+    0.5,0.974760013427511,0.1390666211695935,0.290742792709767,0.3942270734759074
+    1.0,0.628935425613454,0.8798442985528538,0.1379965654314172,0.5195777869079362
+    1.5,0.7762649318706349,0.1841936341436101,0.7966501007242092,0.3055357289931444
+    2.0,0.5071122837043811,0.8711036413523057,0.6035548318312184,0.7483418094894336
+    2.5,0.3474659308224035,0.9099533074121638,0.8745250527670112,0.696346546725722
+    3.0,0.3284992604576076,0.7802219284321528,0.2199662395829104,0.9724618758170118
+    3.5,0.7295178626505394,0.9206330646867243,0.2139792223224763,0.2117707057723693
+    4.0,0.1485009903549191,0.4094053671251083,0.6491136779321542,0.9916829027154486
+    4.5,0.0221760392544478,0.0896507221276781,0.521585333219087,0.474706589914094
+    5.0,0.3529577130501251,0.2712519699376731,0.2878937119272532,0.2833812464013073
+    5.5,0.7179675309214435,0.536044487634938,0.5528976167527797,0.5541329846866376
+    6.0,0.6499835221868125,0.430252658737673,0.2348080763889996,0.4041186292722393
+    6.5,0.47779199219005,0.3847349424152309,0.2274410074020528,0.9755649555219787
+    7.0,0.9336634732337328,0.1589756579076773,0.187455175861903,0.7228182241678169
+    7.5,0.0095205690613633,0.3306777761792958,0.6481645591784894,0.2949542335656083
+    8.0,0.7158395576862129,0.1185834470167305,0.1626078095442987,0.2785374723069234
+    8.5,0.2716444289557093,0.4286248262035558,0.1579859256512237,0.0040649486181264
+    9.0,0.9930168604136324,0.7180884861749929,0.3777212625620399,0.6907922378507042
+    9.5,0.4129681812988907,0.2649904657509689,0.2295136220510665,0.3965148737759935
+    10.0,0.6674668944557347,0.1437925971128084,0.8681643230023325,0.8706751462033909"""
+    df = pd.DataFrame(
+        [row.split(",") for row in data.split("\n")[1:]],
+        columns=data.split("\n")[0].split(","),
+    )  # type: ignore
+    return df.apply(pd.to_numeric, errors="coerce")  # type: ignore

tests/data/raw_data_optimal_voltages_2024-12-19.csv

@@ -0,0 +1,3 @@
+72.14
+50.98
+18.59

tests/program_test.py

@@ -1,16 +1,35 @@
+from unittest.mock import patch
+
 import numpy as np
-import pytest
+import pandas as pd
 
 from bimorph_mirror_analysis.__main__ import calculate_optimal_voltages
+from bimorph_mirror_analysis.maths import find_voltages
+
 
+def test_calculate_optimal_voltages_mocked(raw_data_pivoted: pd.DataFrame):
+    with (
+        patch(
+            "bimorph_mirror_analysis.__main__.read_bluesky_plan_output"
+        ) as mock_read_bluesky_plan_output,
+        patch("bimorph_mirror_analysis.__main__.find_voltages") as mock_find_voltages,
+    ):
+        # set the mock return values
+        mock_read_bluesky_plan_output.return_value = (
+            raw_data_pivoted,
+            np.array([0.0, 0.0, 0.0]),
+            100,
+        )
+        mock_find_voltages.side_effect = find_voltages
+        voltages = calculate_optimal_voltages("input_file")
+        voltages = np.round(voltages, 2)
+        # assert correct voltages calculated
+        np.testing.assert_almost_equal(voltages, np.array([72.14, 50.98, 18.59]))
 
-@pytest.mark.parametrize(
-    ["input_file", "expected_voltages"],
-    [["tests/data/raw_data.csv", np.array([72.14, 50.98, 18.59])]],
-)
-def test_calculate_optimal_voltages(
-    input_file: str, expected_voltages: np.typing.NDArray[np.float64]
-):
-    voltages = calculate_optimal_voltages(input_file)
-    voltages = np.round(voltages, 2)
-    np.testing.assert_almost_equal(voltages, expected_voltages)
+        # assert mock was called
+        mock_read_bluesky_plan_output.assert_called()
+        mock_read_bluesky_plan_output.assert_called_with("input_file")
+        mock_find_voltages.assert_called()
+        expected_data = raw_data_pivoted[raw_data_pivoted.columns[1:]].to_numpy()  # type: ignore
+        np.testing.assert_array_equal(mock_find_voltages.call_args[0][0], expected_data)  # type: ignore
+        np.testing.assert_almost_equal(mock_find_voltages.call_args[0][1], 100)

tests/read_file_test.py

@@ -1,32 +1,37 @@
+from unittest.mock import patch
+
 import numpy as np
 import pandas as pd
 import pytest
 
 from bimorph_mirror_analysis.read_file import read_bluesky_plan_output
 
 
-@pytest.mark.parametrize(
-    ["input_path", "output_path"],
-    [
-        ["tests/data/raw_data.csv", "tests/data/raw_data_pivoted.csv"],
-    ],
-)
-def test_read_raw_data(input_path: str, output_path: str):
-    pivoted, initial_voltages, increment = read_bluesky_plan_output(input_path)
-    expected_output = pd.read_csv(output_path)  # type: ignore
-    pd.testing.assert_frame_equal(pivoted, expected_output)
-    np.testing.assert_array_equal(initial_voltages, np.array([0.0, 0.0, 0.0]))
-    np.testing.assert_equal(increment, np.float64(100.0))
+def test_read_raw_data(raw_data: pd.DataFrame, raw_data_pivoted: pd.DataFrame):
+    with patch("bimorph_mirror_analysis.read_file.pd.read_csv") as mock_read_csv:
+        mock_read_csv.return_value = raw_data
+        pivoted, initial_voltages, increment = read_bluesky_plan_output("input_path")
+        # expected_output = pd.read_csv(output_path)  # type: ignore
+        pd.testing.assert_frame_equal(pivoted, raw_data_pivoted)
+        np.testing.assert_array_equal(initial_voltages, np.array([0.0, 0.0, 0.0]))
+        np.testing.assert_equal(increment, np.float64(100.0))
+        mock_read_csv.assert_called()
 
 
 @pytest.mark.xfail(
     reason="This test is expected to fail, the incrememnt should be 100, not 101"
 )
-def test_read_raw_data_fail():
-    pivoted, initial_voltages, increment = read_bluesky_plan_output(
-        "tests/data/raw_data.csv"
-    )
-    expected_output = pd.read_csv("tests/data/raw_data_pivoted.csv")  # type: ignore
-    pd.testing.assert_frame_equal(pivoted, expected_output)
-    np.testing.assert_array_equal(initial_voltages, np.array([0.0, 0.0, 0.0]))
-    np.testing.assert_equal(increment, np.float64(101.0))
+def test_read_raw_data_fail(raw_data_pivoted: pd.DataFrame):
+    with patch(
+        "bimorph_mirror_analysis.read_file.read_bluesky_plan_output"
+    ) as mock_read_bluesky_plan_output:
+        mock_read_bluesky_plan_output.return_value = (
+            raw_data_pivoted,
+            np.array([0.0, 0.0, 0.0]),
+            np.float64(101.0),
+        )
+        pivoted, initial_voltages, increment = mock_read_bluesky_plan_output()
+        expected_output = pd.read_csv("tests/data/raw_data_pivoted.csv")  # type: ignore
+        pd.testing.assert_frame_equal(pivoted, expected_output)
+        np.testing.assert_array_equal(initial_voltages, np.array([0.0, 0.0, 0.0]))
+        np.testing.assert_equal(increment, np.float64(100.0))