csaxs_bec/bec_ipython_client/plugins/LamNI/lamni_optics_mixin.py

@@ -6,6 +6,8 @@ from rich.console import Console
 from rich.table import Table
 
 from csaxs_bec.bec_ipython_client.plugins.cSAXS import epics_put, fshclose
+from csaxs_bec.bec_ipython_client.plugins.omny.omny_general_tools import OMNYTools
+
 
 # import builtins to avoid linter errors
 dev = builtins.__dict__.get("dev")
@@ -18,25 +20,32 @@ class LamNIInitError(Exception):
 
 
 class LaMNIInitStagesMixin:
+    def __init__(self, client):
+        super().__init__()
+        self.client = client
+        self.OMNYTools = OMNYTools(self.client)
+
     def lamni_init_stages(self):
-        user_input = input("Starting initialization of LamNI stages. OK? [y/n]")
-        if user_input == "y":
+
+        if self.OMNYTools.yesno("Start initialization of LamNI stages. OK?"):
             print("staring...")
             dev.lsamrot.enabled = True
         else:
             return
 
+
+
+
         if self.check_all_axes_of_lamni_referenced():
-            user_input = input("Continue anyways? [y/n]")
-            if user_input == "y":
+            if self.OMNYTools.yesno("All axes are referenced. Continue anyways?"):
                 print("ok then...")
             else:
                 return
 
         axis_id_lsamrot = dev.lsamrot._config["deviceConfig"].get("axis_Id")
         if dev.lsamrot.controller.get_motor_limit_switch(axis_id_lsamrot)[1] == False:
-            user_input = input("The rotation stage will be moved to one limit [y/n]")
-            if user_input == "y":
+
+            if self.OMNYTools.yesno("The rotation stage will be moved to one limit"):
                 print("starting...")
             else:
                 return
@@ -47,10 +56,7 @@ class LaMNIInitStagesMixin:
             print("The controller will be disabled in bec. To enable dev.lsamrot.enabled=True")
             return
 
-        user_input = input(
-            "Init of loptz. Can the stage move to the upstream limit without collision?? [y/n]"
-        )
-        if user_input == "y":
+        if self.OMNYTools.yesno("Init of loptz. Can the stage move to the upstream limit without collision?"):
             print("ok then...")
         else:
             return
@@ -81,8 +87,7 @@ class LaMNIInitStagesMixin:
         time.sleep(0.1)
         self.find_reference_mark(dev.lsamrot)
 
-        user_input = input("Init of leye. Can the stage move to -x limit without collision? [y/n]")
-        if user_input == "y":
+        if self.OMNYTools.yesno("Init of leye. Can the stage move to -x limit without collision?"):
             print("starting...")
         else:
             return
@@ -134,8 +139,7 @@ class LaMNIInitStagesMixin:
         return ord(axis_id.lower()) - 97
 
     def _align_setup(self):
-        user_input = input("Start moving stages to default initial positions? [y/n]")
-        if user_input == "y":
+        if self.OMNYTools.yesno("Start moving stages to default initial positions?"):
             print("Start moving stages...")
         else:
             print("Stopping.")

csaxs_bec/bec_ipython_client/plugins/LamNI/x_ray_eye_align.py

@@ -15,13 +15,19 @@ from bec_lib.pdf_writer import PDFWriter
 from typeguard import typechecked
 
 from csaxs_bec.bec_ipython_client.plugins.cSAXS import epics_get, epics_put, fshopen
+from csaxs_bec.bec_ipython_client.plugins.omny.omny_general_tools import OMNYTools
 
 from .lamni_optics_mixin import LaMNIInitStagesMixin, LamNIOpticsMixin
 
 logger = bec_logger.logger
-bec = builtins.__dict__.get("bec")
 
 
+if builtins.__dict__.get("bec") is not None:
+    bec = builtins.__dict__.get("bec")
+    dev = builtins.__dict__.get("dev")
+    umv = builtins.__dict__.get("umv")
+    umvr = builtins.__dict__.get("umvr")
+
 class XrayEyeAlign:
     # pixel calibration, multiply to get mm
     # PIXEL_CALIBRATION = 0.2/209 #.2 with binning
@@ -510,8 +516,9 @@ class LamNI(LamNIOpticsMixin):
     def __init__(self, client):
         super().__init__()
         self.client = client
+        self.device_manager = client.device_manager
         self.align = XrayEyeAlign(client, self)
-        self.init = LaMNIInitStagesMixin()
+        self.init = LaMNIInitStagesMixin(client)
         self.check_shutter = True
         self.check_light_available = True
         self.check_fofb = True
@@ -524,6 +531,16 @@ class LamNI(LamNIOpticsMixin):
         self._beam_is_okay = True
         self._stop_beam_check_event = None
         self.beam_check_thread = None
+        self.OMNYTools = OMNYTools(self.client)
+        # Progress tracking
+        self.progress = {}
+        self.progress["tomo_type"] = "Equally spaced sub-tomograms"
+        self.progress["subtomo"] = 0
+        self.progress["subtomo_projection"] = 0
+        self.progress["subtomo_total_projections"] = 1
+        self.progress["projection"] = 0
+        self.progress["total_projections"] = 1
+        self.progress["angle"] = 0
 
     def get_beamline_checks_enabled(self):
         print(
@@ -598,12 +615,12 @@ class LamNI(LamNIOpticsMixin):
         if val == 1:
             # equally spaced tomography with 8 sub tomograms
             self.client.set_global_var("tomo_type", val)
-        # elif val == 2:
-        #    # golden ratio tomography (sorted bunches)
-        #    self.client.set_global_var("tomo_type", val)
-        # elif val == 3:
-        #    # equally spaced tomography with starting angles shifted by golden ratio
-        #    self.client.set_global_var("tomo_type", val)
+        elif val == 2:
+            # golden ratio tomography (sorted bunches)
+            self.client.set_global_var("tomo_type", val)
+        elif val == 3:
+            # equally spaced tomography with starting angles shifted by golden ratio
+            self.client.set_global_var("tomo_type", val)
         else:
             raise ValueError("Unknown tomo_type.")
 
@@ -740,6 +757,41 @@ class LamNI(LamNIOpticsMixin):
     def tomo_stitch_overlap(self, val: float):
         self.client.set_global_var("tomo_stitch_overlap", val)
 
+    @property
+    def golden_max_number_of_projections(self):
+        val = self.client.get_global_var("golden_max_number_of_projections")
+        if val is None:
+            return 1000.0
+        return val
+
+    @golden_max_number_of_projections.setter
+    def golden_max_number_of_projections(self, val: float):
+        self.client.set_global_var("golden_max_number_of_projections", val)
+
+    @property
+    def golden_ratio_bunch_size(self):
+        val = self.client.get_global_var("golden_ratio_bunch_size")
+        if val is None:
+            return 20
+        return val
+
+    @golden_ratio_bunch_size.setter
+    def golden_ratio_bunch_size(self, val: float):
+        if val < 20:
+            raise ValueError("golden_ratio_bunch_size must be at least 20.")
+        self.client.set_global_var("golden_ratio_bunch_size", val)
+
+    @property
+    def golden_projections_at_0_deg_for_damage_estimation(self):
+        val = self.client.get_global_var("golden_projections_at_0_deg_for_damage_estimation")
+        if val is None:
+            return 0
+        return val
+
+    @golden_projections_at_0_deg_for_damage_estimation.setter
+    def golden_projections_at_0_deg_for_damage_estimation(self, val: float):
+        self.client.set_global_var("golden_projections_at_0_deg_for_damage_estimation", val)
+
     @property
     def sample_name(self):
         val = self.client.get_global_var("sample_name")
@@ -909,6 +961,49 @@ class LamNI(LamNIOpticsMixin):
         except Exception:
             logger.warning("Failed to send update to SciLog.")
 
+    def rt_off(self):
+        dev.rtx.enabled = False
+        dev.rty.enabled = False
+
+    def rt_on(self):
+        dev.rtx.enabled = True
+        dev.rty.enabled = True
+        if dev.rtx.enabled == True:
+            print("rt is enabled")
+        else:
+            print("failed to enable rt")
+
+    def feedback_enable_with_reset(self):
+        self.device_manager.devices.rtx.controller.feedback_enable_with_reset()
+        self.feedback_status()
+
+    def feedback_enable_without_reset(self):
+        self.device_manager.devices.rtx.controller.feedback_enable_without_reset()
+        self.feedback_status()
+
+    def feedback_disable(self):
+        self.device_manager.devices.rtx.controller.feedback_disable()
+        self.feedback_status()
+
+    def feedback_disable_and_reset_angle(self):
+        self.device_manager.devices.rtx.controller.feedback_disable_and_even_reset_lamni_angle_interferometer()
+        self.feedback_status()
+
+    def feedback_status(self):
+        if self.device_manager.devices.rtx.controller.feedback_is_running():
+            print("The rt feedback is \x1b[92mrunning\x1b[0m.")
+        else:
+            print("The rt feedback is \x1b[91mNOT\x1b[0m running.")
+
+    def show_interferometer_positions(self):
+        self.device_manager.devices.rtx.controller.show_interferometer_positions()
+
+    def show_signal_strength(self):
+        self.device_manager.devices.rtx.controller.show_signal_strength_interferometer()
+
+    def show_analog_signals(self):
+        return self.device_manager.devices.rtx.controller.show_analog_signals()
+
     def add_sample_database(
         self, samplename, date, eaccount, scan_number, setup, sample_additional_info, user
     ):
@@ -935,9 +1030,8 @@ class LamNI(LamNIOpticsMixin):
         # self.tomo_scan_projection(angle)
         # self.tomo_reconstruct()
 
-    def sub_tomo_scan(self, subtomo_number, start_angle=None):
-        """start a subtomo"""
-        dev = builtins.__dict__.get("dev")
+    def _write_subtomo_to_scilog(self, subtomo_number):
+        """Write subtomo start information to scilog."""
         bec = builtins.__dict__.get("bec")
         if self.tomo_id > 0:
             tags = ["BEC_subtomo", self.sample_name, f"tomo_id_{self.tomo_id}"]
@@ -948,6 +1042,10 @@ class LamNI(LamNIOpticsMixin):
             tags,
         )
 
+    def sub_tomo_scan(self, subtomo_number, start_angle=None):
+        """start a subtomo"""
+        self._write_subtomo_to_scilog(subtomo_number)
+
         if start_angle is None:
             if subtomo_number == 1:
                 start_angle = 0
@@ -980,13 +1078,14 @@ class LamNI(LamNIOpticsMixin):
         if not (subtomo_number % 2):
             angles = np.flip(angles)
         for angle in angles:
+            self.progress["tomo_type"] = "Equally spaced sub-tomograms"
             self.progress["subtomo"] = subtomo_number
-            self.progress["subtomo_projection"] = angles.index(angle)
-            self.progress["subtomo_total_projections"] = 180 / self.tomo_angle_stepsize
+            self.progress["subtomo_projection"] = np.where(angles == angle)[0][0]
+            self.progress["subtomo_total_projections"] = 360 / self.tomo_angle_stepsize
             self.progress["projection"] = (subtomo_number - 1) * self.progress[
                 "subtomo_total_projections"
             ] + self.progress["subtomo_projection"]
-            self.progress["total_projections"] = 180 / self.tomo_angle_stepsize * 8
+            self.progress["total_projections"] = 360 / self.tomo_angle_stepsize * 8
             self.progress["angle"] = angle
             self._tomo_scan_at_angle(angle, subtomo_number)
 
@@ -1049,6 +1148,11 @@ class LamNI(LamNIOpticsMixin):
                 for scan_nr in range(start_scan_number, end_scan_number):
                     self._write_tomo_scan_number(scan_nr, angle, subtomo_number)
 
+                if self._was_beam_okay() and not error_caught:
+                    successful = True
+                else:
+                    self._wait_for_beamline_checks()
+
     def _write_tomo_scan_number(self, scan_number: int, angle: float, subtomo_number: int) -> None:
         tomo_scan_numbers_file = os.path.expanduser(
             "~/Data10/specES1/dat-files/tomography_scannumbers.txt"
@@ -1059,13 +1163,74 @@ class LamNI(LamNIOpticsMixin):
                 f"{scan_number} {angle} {dev.lsamrot.read()['lsamrot']['value']:.3f} {self.tomo_id} {subtomo_number} {0} {'lamni'}\n"
             )
 
-    def tomo_scan(self, subtomo_start=1, start_angle=None):
-        """start a tomo scan"""
+    def _golden(self, ii, howmany_sorted, maxangle=360, reverse=False):
+        """Return the ii-th golden ratio angle within sorted bunches of size howmany_sorted,
+        and its subtomo number. Operates over maxangle degrees (360 for LamNI)."""
+        golden = []
+        for iji in range(
+            (ii - (ii % howmany_sorted)), (ii - (ii % howmany_sorted)) + howmany_sorted, 1
+        ):
+            golden.append(
+                ((iji * maxangle * (1 + pow(5, 0.5)) / 2) * 1000 % (maxangle * 1000)) / 1000
+            )
+        golden.sort()
+        subtomo_number = int(ii / howmany_sorted) + 1
+        if reverse and not subtomo_number % 2:
+            golden.reverse()
+        return (golden[ii % howmany_sorted], subtomo_number)
+
+    def _golden_equally_spaced(self, ii, number_of_projections_per_subtomo, maxangle=360, reverse=True, verbose=False):
+        """Return angles for equally spaced tomography with sub-tomogram starting angles
+        shifted according to the golden ratio. Operates over maxangle degrees (360 for LamNI).
+        ii is the projection number starting at 0."""
+        angular_step = maxangle / number_of_projections_per_subtomo
+        subtomo_number = int(((ii) * angular_step) / maxangle) + 1
+        start_angle = self._golden(subtomo_number - 1, 1, angular_step)[0]
+        projection_number_of_subtomo = (
+            ii - (subtomo_number - 1) * number_of_projections_per_subtomo
+        )
+
+        if reverse:
+            if subtomo_number % 2:
+                angle = start_angle + projection_number_of_subtomo * angular_step
+            else:
+                angle = (
+                    start_angle
+                    + (number_of_projections_per_subtomo - 1) * angular_step
+                    - projection_number_of_subtomo * angular_step
+                )
+        else:
+            angle = start_angle + projection_number_of_subtomo * angular_step
+
+        if verbose:
+            print(
+                f"Equally spaced golden ratio tomography.\n"
+                f"Angular step: {angular_step}\n"
+                f"Subtomo Number: {subtomo_number}\n"
+                f"Angle: {angle}"
+            )
+
+        return angle, subtomo_number
+
+    def tomo_scan(self, subtomo_start=1, start_angle=None, projection_number=None):
+        """Start a tomo scan.
+
+        Args:
+            subtomo_start (int): For tomo_type 1, the sub-tomogram number to start from. Defaults to 1.
+            start_angle (float, optional): Override the starting angle of the first sub-tomogram. Defaults to None.
+            projection_number (int, optional): For tomo_types 2 and 3, resume from this projection index. Defaults to None.
+        """
         bec = builtins.__dict__.get("bec")
         scans = builtins.__dict__.get("scans")
         self._current_special_angles = self.special_angles.copy()
 
-        if self.tomo_type == 1 and subtomo_start == 1 and start_angle is None:
+        # Register a new tomo scan in the database and write the PDF report
+        # only when starting fresh (not resuming mid-scan)
+        if (
+            (self.tomo_type == 1 and subtomo_start == 1 and start_angle is None)
+            or (self.tomo_type == 2 and projection_number is None)
+            or (self.tomo_type == 3 and projection_number is None)
+        ):
             # pylint: disable=undefined-variable
             self.tomo_id = self.add_sample_database(
                 self.sample_name,
@@ -1077,10 +1242,108 @@ class LamNI(LamNIOpticsMixin):
                 "BEC",
             )
             self.write_pdf_report()
+
         with scans.dataset_id_on_hold:
-            for ii in range(subtomo_start, 9):
-                self.sub_tomo_scan(ii, start_angle=start_angle)
-                start_angle = None
+            if self.tomo_type == 1:
+                # 8 equally spaced sub-tomograms over 360 degrees
+                self.progress["tomo_type"] = "Equally spaced sub-tomograms"
+                for ii in range(subtomo_start, 9):
+                    self.sub_tomo_scan(ii, start_angle=start_angle)
+                    start_angle = None
+
+            elif self.tomo_type == 2:
+                # Golden ratio tomography (sorted bunches) over 360 degrees
+                self.progress["tomo_type"] = "Golden ratio tomography"
+                previous_subtomo_number = -1
+                ii = 0 if projection_number is None else projection_number
+                while True:
+                    angle, subtomo_number = self._golden(
+                        ii, self.golden_ratio_bunch_size, maxangle=360, reverse=True
+                    )
+                    if previous_subtomo_number != subtomo_number:
+                        self._write_subtomo_to_scilog(subtomo_number)
+                        if (
+                            subtomo_number % 2 == 1
+                            and ii > 10
+                            and self.golden_projections_at_0_deg_for_damage_estimation == 1
+                        ):
+                            self._tomo_scan_at_angle(0, subtomo_number)
+                        previous_subtomo_number = subtomo_number
+
+                    self.progress["subtomo"] = subtomo_number
+                    self.progress["projection"] = ii
+                    self.progress["angle"] = angle
+                    if self.golden_ratio_bunch_size > 0:
+                        self.progress["subtomo_total_projections"] = self.golden_ratio_bunch_size
+                        self.progress["subtomo_projection"] = (
+                            ii - (subtomo_number - 1) * self.golden_ratio_bunch_size
+                        )
+                    else:
+                        self.progress["subtomo_total_projections"] = 0
+                        self.progress["subtomo_projection"] = 0
+                    self.progress["total_projections"] = (
+                        self.golden_max_number_of_projections
+                        if self.golden_max_number_of_projections > 0
+                        else 0
+                    )
+
+                    self._tomo_scan_at_angle(angle, subtomo_number)
+                    ii += 1
+                    if (
+                        self.golden_max_number_of_projections > 0
+                        and ii > self.golden_max_number_of_projections
+                    ):
+                        print(
+                            f"Golden ratio tomography stopped automatically after the requested"
+                            f" {self.golden_max_number_of_projections} projections."
+                        )
+                        break
+
+            elif self.tomo_type == 3:
+                # Equally spaced tomography with golden ratio starting angles over 360 degrees
+                self.progress["tomo_type"] = "Equally spaced, golden ratio starting angles"
+                previous_subtomo_number = -1
+                ii = 0 if projection_number is None else projection_number
+                while True:
+                    angle, subtomo_number = self._golden_equally_spaced(
+                        ii, int(360 / self.tomo_angle_stepsize), maxangle=360, reverse=True
+                    )
+                    if previous_subtomo_number != subtomo_number:
+                        self._write_subtomo_to_scilog(subtomo_number)
+                        if (
+                            subtomo_number % 2 == 1
+                            and ii > 10
+                            and self.golden_projections_at_0_deg_for_damage_estimation == 1
+                        ):
+                            self._tomo_scan_at_angle(0, subtomo_number)
+                        previous_subtomo_number = subtomo_number
+
+                    self.progress["subtomo"] = subtomo_number
+                    self.progress["projection"] = ii
+                    self.progress["angle"] = angle
+                    self.progress["subtomo_total_projections"] = 360 / self.tomo_angle_stepsize
+                    self.progress["subtomo_projection"] = (
+                        ii - (subtomo_number - 1) * self.progress["subtomo_total_projections"]
+                    )
+                    self.progress["total_projections"] = (
+                        self.golden_max_number_of_projections
+                        if self.golden_max_number_of_projections > 0
+                        else 0
+                    )
+
+                    self._tomo_scan_at_angle(angle, subtomo_number)
+                    ii += 1
+                    if (
+                        self.golden_max_number_of_projections > 0
+                        and ii > self.golden_max_number_of_projections
+                    ):
+                        print(
+                            f"Golden ratio tomography stopped automatically after the requested"
+                            f" {self.golden_max_number_of_projections} projections."
+                        )
+                        break
+            else:
+                raise ValueError(f"Unknown tomo_type: {self.tomo_type}.")
 
     def tomo_parameters(self):
         """print and update the tomo parameters"""
@@ -1103,9 +1366,37 @@ class LamNI(LamNIOpticsMixin):
         print(f"   _tomo_fovy_offset       <mm>  =  {self.align.tomo_fovy_offset}")
         print(f"   _manual_shift_x         <mm>  =  {self.manual_shift_x}")
         print(f"   _manual_shift_y         <mm>  =  {self.manual_shift_y}")
-        print(f"Angular step within sub-tomogram:   {self.tomo_angle_stepsize} degrees")
-        print(f"Resulting in number of projections: {360/self.tomo_angle_stepsize*8}")
-        print(f"Sample name: {self.sample_name}\n")
+        print("")
+        if self.tomo_type == 1:
+            print("\x1b[1mTomo type 1:\x1b[0m 8 equally spaced sub-tomograms")
+            print(f"Angular step within sub-tomogram:   {self.tomo_angle_stepsize} degrees")
+            print(f"Resulting in number of projections: {360/self.tomo_angle_stepsize*8}")
+        elif self.tomo_type == 2:
+            print("\x1b[1mTomo type 2:\x1b[0m Golden ratio tomography")
+            print(f"Sorted in bunches of: {self.golden_ratio_bunch_size}")
+            if self.golden_max_number_of_projections > 0:
+                print(f"Ending after {self.golden_max_number_of_projections} projections.")
+            else:
+                print("Ending by manual interruption.")
+            if self.golden_projections_at_0_deg_for_damage_estimation == 1:
+                print(
+                    "Repeating projections at 0 degrees at the beginning of every second subtomogram."
+                )
+        elif self.tomo_type == 3:
+            print(
+                "\x1b[1mTomo type 3:\x1b[0m Equally spaced tomography, golden ratio starting angle"
+            )
+            print(f"Angular step within sub-tomogram:   {self.tomo_angle_stepsize} degrees")
+            print(f"Number of projections per sub-tomogram: {360/self.tomo_angle_stepsize}")
+            if self.golden_max_number_of_projections > 0:
+                print(f"Ending after {self.golden_max_number_of_projections} projections.")
+            else:
+                print("Ending by manual interruption.")
+            if self.golden_projections_at_0_deg_for_damage_estimation == 1:
+                print(
+                    "Repeating projections at 0 degrees at the beginning of every second subtomogram."
+                )
+        print(f"\nSample name: {self.sample_name}\n")
 
         user_input = input("Are these parameters correctly set for your scan? ")
         if user_input == "y":
@@ -1125,13 +1416,61 @@ class LamNI(LamNIOpticsMixin):
             self.ptycho_reconstruct_foldername = self._get_val(
                 "Reconstruction queue ", self.ptycho_reconstruct_foldername, str
             )
-            tomo_numberofprojections = self._get_val(
-                "Number of projections", 360 / self.tomo_angle_stepsize * 8, int
-            )
 
-            print(f"The angular step will be {360/tomo_numberofprojections}")
-            self.tomo_angle_stepsize = 360 / tomo_numberofprojections * 8
-            print(f"The angular step in a subtomogram it will be {self.tomo_angle_stepsize}")
+            print("Tomography type:")
+            print("  1: 8 equally spaced sub-tomograms (360 deg)")
+            print("  2: Golden ratio tomography")
+            print("  3: Equally spaced tomography, golden ratio starting angle")
+            self.tomo_type = self._get_val("Tomography type", self.tomo_type, int)
+
+            if self.tomo_type == 1:
+                tomo_numberofprojections = self._get_val(
+                    "Number of projections", 360 / self.tomo_angle_stepsize * 8, int
+                )
+                print(f"The angular step will be {360/tomo_numberofprojections}")
+                self.tomo_angle_stepsize = 360 / tomo_numberofprojections * 8
+                print(f"The angular step in a subtomogram it will be {self.tomo_angle_stepsize}")
+
+            elif self.tomo_type == 2:
+                while True:
+                    bunch_size = self._get_val(
+                        "Number of projections sorted per bunch (minimum 20)",
+                        self.golden_ratio_bunch_size,
+                        int,
+                    )
+                    if bunch_size >= 20:
+                        self.golden_ratio_bunch_size = bunch_size
+                        break
+                    print("Bunch size must be at least 20. Please try again.")
+                self.golden_max_number_of_projections = self._get_val(
+                    "Stop after number of projections (0 for endless)",
+                    self.golden_max_number_of_projections,
+                    int,
+                )
+                self.golden_projections_at_0_deg_for_damage_estimation = self._get_val(
+                    "Repeat projections at 0 deg every second subtomo 1/0?",
+                    self.golden_projections_at_0_deg_for_damage_estimation,
+                    int,
+                )
+
+            elif self.tomo_type == 3:
+                numprj = self._get_val(
+                    "Number of projections per sub-tomogram",
+                    int(360 / self.tomo_angle_stepsize),
+                    int,
+                )
+                self.tomo_angle_stepsize = 360 / numprj
+                self.golden_max_number_of_projections = self._get_val(
+                    "Stop after number of projections (0 for endless)",
+                    self.golden_max_number_of_projections,
+                    int,
+                )
+                self.golden_projections_at_0_deg_for_damage_estimation = self._get_val(
+                    "Repeat projections at 0 deg every second subtomo 1/0?",
+                    self.golden_projections_at_0_deg_for_damage_estimation,
+                    int,
+                )
+
             self.sample_name = self._get_val("sample name", self.sample_name, str)
 
     @staticmethod
@@ -1197,6 +1536,7 @@ class LamNI(LamNIOpticsMixin):
             (
                 f"{'Angular step within sub-tomogram:':<{padding}}{self.tomo_angle_stepsize:>{padding}.2f}\n"
             ),
+            f"{'Tomo type:':<{padding}}{self.tomo_type:>{padding}}\n",
         ]
         content = "".join(content)
         user_target = os.path.expanduser(f"~/Data10/documentation/tomo_scan_ID_{self.tomo_id}.pdf")
@@ -1389,3 +1729,4 @@ class DataDrivenLamNI(LamNI):
             shapes.append(data.shape)
         if len(set(shapes)) > 1:
             raise ValueError(f"Tomo data file has entries of inconsistent lengths: {shapes}.")
+        

csaxs_bec/bec_ipython_client/plugins/flomni/flomni.py

@@ -35,32 +35,32 @@ class FlomniInitError(Exception):
 class FlomniError(Exception):
     pass
 
-class FlomniTools:
-    def yesno(self, message: str, default="none", autoconfirm=0) -> bool:
-        if autoconfirm and default == "y":
-            self.printgreen(message + " Automatically confirming default: yes")
-            return True
-        elif autoconfirm and default == "n":
-            self.printgreen(message + " Automatically confirming default: no")
-            return False
-        if default == "y":
-            message_ending = " [Y]/n? "
-        elif default == "n":
-            message_ending = " y/[N]? "
-        else:
-            message_ending = " y/n? "
-        while True:
-            user_input = input(self.OKBLUE + message + message_ending + self.ENDC)
-            if (
-                user_input == "Y" or user_input == "y" or user_input == "yes" or user_input == "Yes"
-            ) or (default == "y" and user_input == ""):
-                return True
-            if (
-                user_input == "N" or user_input == "n" or user_input == "no" or user_input == "No"
-            ) or (default == "n" and user_input == ""):
-                return False
-            else:
-                print("Please expicitely confirm y or n.")
+# class FlomniTools:
+#     def yesno(self, message: str, default="none", autoconfirm=0) -> bool:
+#         if autoconfirm and default == "y":
+#             self.printgreen(message + " Automatically confirming default: yes")
+#             return True
+#         elif autoconfirm and default == "n":
+#             self.printgreen(message + " Automatically confirming default: no")
+#             return False
+#         if default == "y":
+#             message_ending = " [Y]/n? "
+#         elif default == "n":
+#             message_ending = " y/[N]? "
+#         else:
+#             message_ending = " y/n? "
+#         while True:
+#             user_input = input(self.OKBLUE + message + message_ending + self.ENDC)
+#             if (
+#                 user_input == "Y" or user_input == "y" or user_input == "yes" or user_input == "Yes"
+#             ) or (default == "y" and user_input == ""):
+#                 return True
+#             if (
+#                 user_input == "N" or user_input == "n" or user_input == "no" or user_input == "No"
+#             ) or (default == "n" and user_input == ""):
+#                 return False
+#             else:
+#                 print("Please expicitely confirm y or n.")
 
 
 

csaxs_bec/devices/omny/galil/lgalil_ophyd.py

@@ -25,6 +25,34 @@ logger = bec_logger.logger
 
 
 class LamniGalilController(GalilController):
+
+
+    # ============================================================
+    # Error status
+    # ============================================================
+
+    caperr_bits = {
+        0x01: "Cap1 outside expected left-stop range (early check)",
+        0x02: "Cap2 outside expected left-stop range (early check)",
+        0x04: "Cap1 too low during pressure-off check (near right boundary)",
+        0x08: "Cap2 too low during pressure-off check (near right boundary)",
+        0x10: "Cap1 exceeded allowed left-stop boundary during movement",
+        0x20: "Cap2 exceeded allowed left-stop boundary during movement (disabled in code)",
+        0x40: "Cap1 did not respond to test movement",
+        0x80: "Cap2 did not respond to test movement"
+    }
+
+    allaxrer_table = {
+        1: "Not all axes referenced after reference search",
+        2: "Pressure-loss emergency stop (pressure 14/15 active while motor C off)",
+        3: "Unexpected pressure OFF while soft-limits not yet set",
+        4: "Pressure valve mismatch (OUT13=0 but IN13=1)",
+        5: "Capacitive sensor boundary violations (caperr > 0)",
+        6: "Emergency Stop triggered (IN[5]=0)",
+        7: "Following error detected on one or more axes"
+    }
+
+
     USER_ACCESS = [
         "describe",
         "show_running_threads",
@@ -37,6 +65,8 @@ class LamniGalilController(GalilController):
         "get_motor_limit_switch",
         "is_motor_on",
         "all_axes_referenced",
+        "lamni_lights_off",
+        "lamni_lights_on"
     ]
 
     def show_status_other(self):
@@ -60,6 +90,47 @@ class LamniGalilController(GalilController):
             print("There is air pressure at the outer rotation radial.")
         swver = float(self.socket_put_and_receive("MGswver"))
         print(f"Lgalil LAMNI firmware version {swver:2.0f}.")
+        allaxref = int(float(self.socket_put_and_receive("MGallaxref")))
+        print(f"Error statuts:")
+        if allaxref == 1:
+            print(f"Allaxref = 1, all OK.")
+        else:
+            print(f"Allaxref = {allaxref}. Not all axes are referenced or error introduced preventing motion.")
+            allaxrer = int(float(self.socket_put_and_receive("MGallaxrer")))
+            print("\nallaxrer =", allaxrer)
+            print(self.decode_allaxrer(allaxrer))
+            caperr = int(float(self.socket_put_and_receive("MGcaperr")))
+            print("\nDecoding caperr =", caperr)
+            self.visualize_caperr(caperr)
+
+    def decode_allaxrer(self, code: int) -> str:
+        """Return human-readable meaning of allaxrer code."""
+        return self.allaxrer_table.get(code, "Unknown allaxrer code")
+
+    def visualize_caperr(self, mask: int):
+        """Pretty-print a bitmask visualization for caperr."""
+        print("\n=== CAPERR BITMASK VISUALIZER ===")
+        print(f"Raw value: {mask}   (0x{mask:02X})")
+        print("----------------------------------\n")
+
+        print("Bit | Hex  | Active | Meaning")
+        print("----------------------------------")
+
+        for bit, meaning in self.caperr_bits.items():
+            active = "YES" if mask & bit else "no"
+            print(f"{bit:3d} | 0x{bit:02X} | {active:6} | {meaning}")
+
+        print("\nActive flags:")
+        active_flags = [meaning for bit, meaning in self.caperr_bits.items() if mask & bit]
+
+        if active_flags:
+            for f in active_flags:
+                print("  ✓", f)
+        else:
+            print("  (none)")
+
+        print("\n==================================\n")
+
 
     def lamni_lights_off(self):
         self.socket_put_confirmed("SB1")
@@ -93,7 +164,7 @@ class LamniGalilReadbackSignal(GalilSignalRO):
         val = super().read()
         if self.parent.axis_Id_numeric == 2:
             try:
-                rt = self.parent.device_manager.devices[self.parent.rtx]
+                rt = self.parent.device_manager.devices[self.parent.rt]
                 if rt.enabled:
                     rt.obj.controller.set_rotation_angle(val[self.parent.name]["value"])
             except KeyError:
@@ -147,7 +218,7 @@ class LamniGalilMotor(Device, PositionerBase):
             raise BECConfigError(
                 "device_mapping has been specified but the device_manager cannot be accessed."
             )
-        self.rt = self.device_mapping.get("rt")
+        self.rt = self.device_mapping.get("rt", "rtx")
 
         super().__init__(
             prefix,

csaxs_bec/devices/omny/rt/rt_lamni_ophyd.py

@@ -11,6 +11,7 @@ from ophyd.status import wait as status_wait
 from ophyd.utils import LimitError, ReadOnlyError
 from ophyd_devices.utils.controller import Controller, threadlocked
 from ophyd_devices.utils.socket import SocketIO, SocketSignal, raise_if_disconnected
+from prettytable import PrettyTable
 
 from csaxs_bec.devices.omny.rt.rt_ophyd import RtCommunicationError, RtError
 
@@ -51,6 +52,7 @@ class RtLamniController(Controller):
     _axes_per_controller = 3
     USER_ACCESS = [
         "socket_put_and_receive",
+        "socket_put",
         "set_rotation_angle",
         "feedback_disable",
         "feedback_enable_without_reset",
@@ -62,6 +64,11 @@ class RtLamniController(Controller):
         "_set_axis_velocity_maximum_speed",
         "_position_sampling_single_read",
         "_position_sampling_single_reset_and_start_sampling",
+        "show_signal_strength_interferometer",
+        "show_interferometer_positions",
+        "show_analog_signals",
+        "show_feedback_status",
+
     ]
 
     def __init__(
@@ -208,8 +215,9 @@ class RtLamniController(Controller):
 
     @threadlocked
     def start_scan(self):
-        interferometer_feedback_not_running = int((self.socket_put_and_receive("J2")).split(",")[0])
-        if interferometer_feedback_not_running == 1:
+        # interferometer_feedback_not_running = int((self.socket_put_and_receive("J2")).split(",")[0])
+        # if interferometer_feedback_not_running == 1:
+        if not self.feedback_is_running():    
             logger.error(
                 "Cannot start scan because feedback loop is not running or there is an interferometer error."
             )
@@ -270,6 +278,44 @@ class RtLamniController(Controller):
             "average_lamni_angle": {"value": self.average_lamni_angle / (int(return_table[0]) + 1)},
         }
         return signals
+    
+    def feedback_is_running(self) -> bool:
+        status = int(float((self.socket_put_and_receive("J2")).split(",")[0]))
+        return status == 0  # 0 means running, 1 means error/disabled
+
+    def show_feedback_status(self):
+        if self.feedback_is_running():
+            print("Loop is running, no error on interferometer.")
+        else:
+            print("Loop is not running, either it is turned off or an interferometer error occurred.")
+
+
+    def show_analog_signals(self) -> dict:
+        self.socket_put("As")  # start sampling
+        time.sleep(0.01)
+        return_table = (self.socket_put_and_receive("Ar")).split(",")
+        
+        number_of_samples = int(float(return_table[0]))
+        signals = {
+            "number_of_samples":    number_of_samples,
+            "piezo_0":              float(return_table[1]),
+            "piezo_1":              float(return_table[2]),
+            "cap_0":                float(return_table[3]),
+            "cap_1":                float(return_table[4]),
+            "cap_2":                float(return_table[5]),
+            "cap_3":                float(return_table[6]),
+            "cap_4":                float(return_table[7]),
+        }
+
+        t = PrettyTable()
+        t.title = f"LamNI Analog Signals ({number_of_samples} samples)"
+        t.field_names = ["Signal", "Value"]
+        for key, val in signals.items():
+            if key != "number_of_samples":
+                t.add_row([key, f"{val:.4f}"])
+        print(t)
+
+        return
 
     def read_positions_from_sampler(self):
         # this was for reading after the scan completed
@@ -347,6 +393,48 @@ class RtLamniController(Controller):
         )
         return bool(return_table[0])
 
+    def show_signal_strength_interferometer(self):
+        # trigger SSI averaging before reading
+        self.socket_put("J3")
+        time.sleep(0.05)
+        return_table = (self.socket_put_and_receive("J2")).split(",")
+        ssi_0 = float(return_table[1])
+        ssi_1 = float(return_table[2])
+
+        return_table_angle = (self.socket_put_and_receive("J7")).split(",")
+        angle_running = bool(int(float(return_table_angle[0])))
+        angle_position = float(return_table_angle[1])
+        angle_signal = float(return_table_angle[2])
+
+        t = PrettyTable()
+        t.title = "Interferometer signal strength"
+        t.field_names = ["Axis", "Description", "Value", "Running"]
+        t.add_row([0, "ST FZP horizontal",   ssi_0,        "-"])
+        t.add_row([1, "ST FZP vertical",     ssi_1,        "-"])
+        t.add_row([2, "Angle interferometer", angle_signal, angle_running])
+        print(t)
+
+        if angle_running:
+            print(f"Angle interferometer position: {angle_position:.4f} um")
+        else:
+            print("Warning: angle interferometer is not running.")
+
+    def show_interferometer_positions(self) -> dict:
+        return_table = (self.socket_put_and_receive("J4")).split(",")
+        loop_status = bool(int(float(return_table[0])))
+        pos_y = float(return_table[1])
+        pos_x = float(return_table[2])
+
+        t = PrettyTable()
+        t.title = "LamNI Interferometer Positions"
+        t.field_names = ["Axis", "Description", "Position (um)"]
+        t.add_row([0, "X", f"{pos_x:.4f}"])
+        t.add_row([1, "Y", f"{pos_y:.4f}"])
+        print(t)
+        print(f"Feedback loop running: {loop_status}")
+
+        return {"x": pos_x, "y": pos_y, "loop_running": loop_status}    
+
     def feedback_enable_with_reset(self):
         if not self.feedback_status_angle_lamni():
             self.feedback_disable_and_even_reset_lamni_angle_interferometer()