Add csv reader

pyproject.toml

@@ -14,6 +14,8 @@ classifiers = [
 description = "package to calibrate bimorph mirrors to focus a beamline to a target position"
 dependencies = [
     "numpy",
+    "pandas",
+    "pandas-stubs",
 ] # Add project dependencies here, e.g. ["click", "numpy"]
 dynamic = ["version"]
 license.file = "LICENSE"
@@ -49,7 +51,7 @@ version_file = "src/bimorph_mirror_analysis/_version.py"
 
 [tool.pyright]
 typeCheckingMode = "strict"
-reportMissingImports = false  # Ignore missing stubs in imported modules
+reportMissingImports = false # Ignore missing stubs in imported modules
 
 [tool.pytest.ini_options]
 # Run pytest with all our checkers, and don't spam us with massive tracebacks on error

src/bimorph_mirror_analysis/__main__.py

@@ -1,8 +1,14 @@
 """Interface for ``python -m bimorph_mirror_analysis``."""
 
+import datetime
 from argparse import ArgumentParser
 from collections.abc import Sequence
 
+import numpy as np
+
+from bimorph_mirror_analysis.maths import find_voltages
+from bimorph_mirror_analysis.read_file import read_bluesky_plan_output
+
 from . import __version__
 
 __all__ = ["main"]
@@ -17,7 +23,49 @@
         action="version",
         version=__version__,
     )
-    parser.parse_args(args)
+    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]
+    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}', '')}\
+_optimal_voltages_{date}.csv"
+
+    np.savetxt(
+        output_path,
+        optimal_voltages,
+        fmt="%.2f",
+    )
+    print(f"The optimal voltages have been saved to {output_path}")
+    print(
+        f"The optimal voltages are: [{', '.join([str(i) for i in optimal_voltages])}]"
+    )
+
+
+# implement this into main
+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
+    data = pivoted[pivoted.columns[1:]].to_numpy()  # type: ignore
+
+    voltage_adjustments = find_voltages(data, increment)  # type: ignore
+    optimal_voltages = initial_voltages + voltage_adjustments
+    return optimal_voltages  # type: ignore
 
 
 if __name__ == "__main__":

src/bimorph_mirror_analysis/read_file.py

@@ -0,0 +1,37 @@
+import numpy as np
+import pandas as pd
+
+
+def read_bluesky_plan_output(
+    filepath: str,
+) -> tuple[pd.DataFrame, np.typing.NDArray[np.float64], float]:
+    """Read the csv file putput by the bluesky plan
+
+    Reads the file and returns the dataframe with individual pecil beam scans as
+    columns, the initial voltages and the voltage increment.
+
+    Args:
+        filepath (str): The path to the csv file to be read.
+
+    Returns:
+        A tuple containing the DataFrame, the initial voltages array and the voltage
+        incrememnt.
+    """
+    data = pd.read_csv(filepath)  # type: ignore
+    data = data.apply(pd.to_numeric, errors="coerce")  # type: ignore
+
+    voltage_cols = [col for col in data.columns if "voltage" in col]
+    initial_voltages = data.loc[0, voltage_cols].to_numpy()  # type: ignore
+    final_voltages = data.loc[len(data) - 1, voltage_cols].to_numpy()  # type: ignore
+
+    voltage_increment = final_voltages[0] - initial_voltages[0]  # type: ignore
+
+    pivoted = pd.pivot_table(  # type: ignore
+        data,
+        values="centroid_position_x",
+        index=["slit_position_x"],
+        columns=["pencil_beam_scan_number"],
+    )
+    pivoted.columns = ["pencil_beam_scan_" + str(col) for col in pivoted.columns]
+    pivoted.reset_index(inplace=True)
+    return pivoted, initial_voltages, voltage_increment  # type: ignore

tests/data/raw_data.csv

@@ -0,0 +1,85 @@
+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
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tests/data/raw_data_pivoted.csv

@@ -0,0 +1,22 @@
+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
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+4.5,0.0221760392544478,0.0896507221276781,0.521585333219087,0.474706589914094
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+5.5,0.7179675309214435,0.536044487634938,0.5528976167527797,0.5541329846866376
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+6.5,0.47779199219005,0.3847349424152309,0.2274410074020528,0.9755649555219787
+7.0,0.9336634732337328,0.1589756579076773,0.187455175861903,0.7228182241678169
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+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

tests/program_test.py

@@ -0,0 +1,16 @@
+import numpy as np
+import pytest
+
+from bimorph_mirror_analysis.__main__ import calculate_optimal_voltages
+
+
+@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)

tests/read_file_test.py

@@ -0,0 +1,32 @@
+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))
+
+
+@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))

tests/script_test.py

@@ -3,8 +3,6 @@
 
 from bimorph_mirror_analysis.maths import find_voltages
 
-# from bimorph_mirror_analysis.maths import find_voltages
-
 
 @pytest.mark.parametrize(
     ["input_path", "output_path"],

tests/test_cli.py

@@ -5,5 +5,11 @@
 
 
 def test_cli_version():
-    cmd = [sys.executable, "-m", "bimorph_mirror_analysis", "--version"]
+    cmd = [
+        sys.executable,
+        "-m",
+        "bimorph_mirror_analysis",
+        "tests/data/raw_data.csv",
+        "--version",
+    ]
     assert subprocess.check_output(cmd).decode().strip() == __version__