updates to fitting routines

pxii_bec/macros/katscripts.py

@@ -1446,3 +1446,29 @@ def define_slits():
     ] 
     return slits
 
+def get_mirror_data(history_index: int):
+    """Read data from the BEC history and return the X and Y data as arrays."""
+    motor1 = "vfm_bu"
+    motor2 = "vfm_bd"
+
+    scan = bec.history[history_index]
+    md = scan.metadata["bec"]
+    x1_data = scan.devices[motor1][motor1].read()["value"]
+    x2_data = scan.devices[motor2][motor2].read()["value"]
+    y1_data = scan.devices['microscope_stats']['microscope_stats_data_sigma_y'].read()['value']
+    y2_data = scan.devices['microscope_stats']['microscope_stats_data_eccentricity'].read()['value']
+    np.savetxt("luts/x1_data", x1_data)
+    np.savetxt("luts/x2_data", x2_data)
+    np.savetxt("luts/y1_data", y1_data)
+    np.savetxt("luts/y2_data", y2_data)
+
+    
+    # plt.imshow(y1_data, cmap="hot")
+    # plt.clf
+    # return {
+    #     "signal_name": signal_name,
+    #     "x_data": x_data,
+    #     "y_data": y_data,
+    #     "motor_name": motor_name,
+    #     "scan_number": scan_number,
+    # }

pxii_bec/macros/mx_basics.py

@@ -15,6 +15,7 @@ import matplotlib.pyplot as plt
 
 def create_fit_parameters(
     deriv: bool = False,
+    negative: bool = False,
     model: str = "Voigt",
     baseline: str = "Linear",
     smoothing: None = None,
@@ -30,6 +31,7 @@ def create_fit_parameters(
     }
     return {
         "deriv": deriv,
+        "negative": negative,
         "model": model_mappings[model],
         "baseline": model_mappings[baseline],
         "smoothing": smoothing,
@@ -53,23 +55,39 @@ def get_data_from_h5(signal_name: str = "lu_bpmsum"):
         "scan_number": str(scan_number),
     }
 
+def get_history(scan):
+    if isinstance(scan, int):
+        if scan < 0:
+            return bec.history[scan]
+        return bec.history.get_by_scan_number(scan)
+        # return bec.history.get_by_dataset_number(scan)
+    return scan
+
 
 def get_data_from_history(
-    history_index: int,
+    scan: int,
     signal_name: str = "lu_bpmsum",
 ):
     """Read data from the BEC history and return the X and Y data as arrays."""
-    scan = bec.history[history_index]
-    md = scan.metadata["bec"]
+
+    data = get_history(scan)
+    md = data.metadata["bec"]
     motor_name = md["scan_report_devices"][0].decode()
+    mot_name = motor_name.replace(".","_")
+    if "." in motor_name:
+        root_name = motor_name.split(".")[0]
+        x_data = data.devices[root_name][mot_name].read()["value"]
+    else:
+        x_data = data.devices[mot_name][mot_name].read()["value"]
+
     scan_number = md["scan_number"]
-    x_data = scan.devices[motor_name][motor_name].read()["value"]
-    y_data = scan.devices[signal_name][signal_name].read()["value"]
+    
+    y_data = data.devices[signal_name][signal_name].read()["value"]
     return {
         "signal_name": signal_name,
         "x_data": x_data,
         "y_data": y_data,
-        "motor_name": motor_name,
+        "motor_name": mot_name,
         "scan_number": scan_number,
     }
 
@@ -78,31 +96,56 @@ def process_data(data, fit_params):
     """
     Process the signal data for fitting based on derivative or smoothing.
     """
-    smoothing, deriv = fit_params["smoothing"], fit_params["deriv"]
+    smoothing = fit_params["smoothing"]
+    deriv = fit_params["deriv"]
+    negative = fit_params["negative"]
+
     signal_name = data["signal_name"]
     y_data = data["y_data"]
 
-    if deriv:
-        if smoothing:
-            y_smooth = gaussian_filter1d(y_data, smoothing)
-            fitting_data = np.gradient(y_smooth)
-            signal_name = f"Derivative of smoothed {signal_name}"
-        else:
-            fitting_data = np.gradient(y_data)
-            signal_name = f"Derivative of {signal_name}"
-    elif smoothing and smoothing > 0.01:
+    if smoothing and smoothing > 0.01:
         fitting_data = gaussian_filter1d(y_data, smoothing)
         signal_name = f"Smoothed {signal_name}"
     else:
-        fitting_data = y_data
+        fitting_data = y_data.copy()
 
-    updated_data = {
-        "y_to_fit": fitting_data,
-        "signal_name": signal_name,
-    }
-    data.update(updated_data)
+    if deriv:
+        fitting_data = np.gradient(fitting_data)
+        signal_name = f"Derivative of {signal_name}"
+
+    if negative:
+        fitting_data = -fitting_data
+        signal_name = f"Negative {signal_name}"
+
+    data.update(
+        {
+            "y_to_fit": fitting_data,
+            "signal_name": signal_name,
+        }
+    )
     return data
 
+    # if deriv:
+    #     if smoothing:
+    #         y_smooth = gaussian_filter1d(y_data, smoothing)
+    #         fitting_data = np.gradient(y_smooth)
+    #         signal_name = f"Derivative of smoothed {signal_name}"
+    #     else:
+    #         fitting_data = np.gradient(y_data)
+    #         signal_name = f"Derivative of {signal_name}"
+    # elif smoothing and smoothing > 0.01:
+    #     fitting_data = gaussian_filter1d(y_data, smoothing)
+    #     signal_name = f"Smoothed {signal_name}"
+    # else:
+    #     fitting_data = y_data
+
+    # updated_data = {
+    #     "y_to_fit": fitting_data,
+    #     "signal_name": signal_name,
+    # }
+    # data.update(updated_data)
+    # return data
+
 
 def fit(data, fit_params):
     """Fit a signal to a model and return the fitting results."""
@@ -234,9 +277,9 @@ def plot_fitted_data_bec(
     """
     wf, text_box = select_bec_window()
     fit_text = (
-        f"Fit parameters: Centre = {fit_result['centre']:.4f}, "
-        f"FWHM = {fit_result['fwhm']:.3f}, "
-        f"Height = {fit_result['height']:.4f}\n"
+        f"Fit parameters: Centre = {fit_result['centre']:.5g}, "
+        f"FWHM = {fit_result['fwhm']:.5f}, "
+        f"Height = {fit_result['height']:.4g}\n"
         f"Model = {fit_result['model']}\n"
         f"Chi sq = {fit_result['chi_sq']:.3g}"
     )

pxii_bec/macros/mx_methods.py

@@ -198,6 +198,7 @@ def fit_history(
     history_index: int,
     signal_name: str,
     deriv: bool = False,
+    negative: bool = False,
     model: str = FitDefaults.MODEL,
     move_to_peak: bool = False,
 ):
@@ -226,7 +227,7 @@ def fit_history(
     data = get_data_from_history(history_index, signal_name)
 
     # Define fitting parameters
-    fit_params = create_fit_parameters(deriv, model, FitDefaults.BASELINE)
+    fit_params = create_fit_parameters(deriv, negative, model, FitDefaults.BASELINE)
 
     # Perform fit and plot the data
     fit_result = fit(data, fit_params)