pxii_bec/device_configs/pxii-beamline-states.yaml

@@ -16,6 +16,7 @@ states:
     aerotech_u: mount
     smargon_x: mount
     smargon_y: mount
+    smargon_z: mount
     smargon_chi: mount
     smargon_phi: mount
     xrf_pos: out
@@ -32,8 +33,8 @@ states:
     diag_y: out
     fl_bright: 'on'
     aerotech_x: in
-    aerotech_y: work
-    aerotech_z: work
+    # aerotech_y: work
+    # aerotech_z: work
     # aerotech_u: mount
     # smargon_x: mount
     # smargon_y: mount
@@ -53,8 +54,6 @@ states:
     diag_y: out
     fl_bright: 'on'
     aerotech_x: in
-    aerotech_y: work
-    aerotech_z: work
     # aerotech_u: mount
     # smargon_x: mount
     # smargon_y: mount
@@ -92,6 +91,7 @@ states:
     coll_y: park
     cryo_pos: in
     det_cov: 'close'
+    det_z: 'mse'
     diag_y: park
     fl_bright: 'off'
     aerotech_x: in
@@ -115,9 +115,9 @@ states:
     diag_y: scint
     fl_bright: 'off'
     aerotech_x: out
-    aerotech_y: mount
-    aerotech_z: mount
-    aerotech_u: mount
+    # aerotech_y: mount
+    # aerotech_z: mount
+    # aerotech_u: mount
     smargon_x: mount
     smargon_y: mount
     smargon_chi: mount
@@ -135,9 +135,9 @@ states:
     diag_y: i1
     fl_bright: 'off'
     aerotech_x: out
-    aerotech_y: mount
-    aerotech_z: mount
-    aerotech_u: mount
+    # aerotech_y: mount
+    # aerotech_z: mount
+    # aerotech_u: mount
     smargon_x: mount
     smargon_y: mount
     smargon_chi: mount
@@ -155,8 +155,8 @@ states:
     diag_y: out
     fl_bright: 'on'
     aerotech_x: out
-    aerotech_y: mount
-    aerotech_z: mount
+    # aerotech_y: mount
+    # aerotech_z: mount
     aerotech_u: mount
     smargon_x: mount
     smargon_y: mount

pxii_bec/device_configs/pxii-devices.yaml

@@ -64,17 +64,16 @@ aerotech:
     u:
       mount: 0
       work: 0
-    work: null
     x:
       in: 0
       out: -10
       safe: -50
     y:
-      mount: 0
-      work: 0
+      mount: -0.0
+      work: -0.031
     z:
       mount: 0
-      work: 0
+      work: -0.01
 bcu_bpm1:
   description: BCU BPM Channel 1
   deviceClass: ophyd.EpicsSignalRO
@@ -155,6 +154,17 @@ bcu_bpmsum:
   onFailure: buffer
   readOnly: true
   readoutPriority: monitored
+bcu_shutter:
+  description: Fast Shutter
+  deviceClass: ophyd_devices.EpicsSignal
+  deviceConfig:
+    auto_monitor: true
+    read_pv: X10SA-ES-SHUTTER:SET
+  deviceTags:
+  - state
+  enabled: true
+  onFailure: buffer
+  readoutPriority: baseline
 bcu_sl_xcen:
   description: BCU Slit X Centre
   deviceClass: ophyd_devices.EpicsMotor
@@ -292,7 +302,7 @@ bs_x:
   onFailure: buffer
   readoutPriority: baseline
   userParameter:
-    in: 1.64
+    in: 0.3
 bs_y:
   description: Beamstop Y
   deviceClass: ophyd_devices.EpicsMotor
@@ -304,7 +314,7 @@ bs_y:
   onFailure: buffer
   readoutPriority: baseline
   userParameter:
-    in: -0.6
+    in: -3.35
 bs_z:
   description: Beamstop Z
   deviceClass: ophyd_devices.EpicsMotor
@@ -320,7 +330,7 @@ bs_z:
     max_blout: 70
     min: 13
     safe: 45.1
-    samp: 15.3
+    samp: 16.408
     type: continuous
     work_min: 20
 bsf_f1_y:
@@ -433,6 +443,8 @@ coll_x:
   enabled: true
   onFailure: buffer
   readoutPriority: baseline
+  userParameter:
+    in: -1.275
 coll_y:
   description: Collimator Y
   deviceClass: ophyd_devices.EpicsMotor
@@ -444,9 +456,9 @@ coll_y:
   onFailure: buffer
   readoutPriority: baseline
   userParameter:
-    in: 40.9
+    in: 39.06
     intermediate: 32
-    out: 20.0
+    out: 20.002
     park: 1
     tol: 0.05
     type: continuous
@@ -602,6 +614,9 @@ det_y:
   enabled: true
   onFailure: buffer
   readoutPriority: baseline
+  userParameter:
+    work: 70
+    xi: 213.601
 det_z:
   description: Detector Z
   deviceClass: ophyd_devices.EpicsMotor
@@ -612,6 +627,9 @@ det_z:
   enabled: true
   onFailure: buffer
   readoutPriority: baseline
+  userParameter:
+    mse: 800
+    vis: 800
 diag_y:
   description: Scintillator/diode Y
   deviceClass: ophyd_devices.EpicsMotor
@@ -623,10 +641,10 @@ diag_y:
   onFailure: buffer
   readoutPriority: baseline
   userParameter:
-    i1: 43.4
+    i1: 40.696
     out: 20.0
     park: 1
-    scint: 38.0
+    scint: 35.74
     tol: 0.3
     type: continuous
 diag_z:
@@ -1203,6 +1221,26 @@ mag_ref:
   onFailure: buffer
   readOnly: true
   readoutPriority: baseline
+microscope_stats:
+  description: Stats Signal of MS camera
+  deviceClass: pxii_bec.devices.stats_signal.StatsSignal
+  deviceConfig:
+    prefix: 'X10SA-ES-MS:'
+  enabled: true
+  readoutPriority: async
+  softwareTrigger: true
+samcam_ecc:
+  description: Sample Camera Eccentricity
+  deviceClass: ophyd.EpicsSignalRO
+  deviceConfig:
+    auto_monitor: true
+    read_pv: X10SA-ES-MS:Stats5:Eccentricity_RBV
+  deviceTags:
+  - scam
+  enabled: true
+  onFailure: buffer
+  readOnly: true
+  readoutPriority: monitored
 samcam_exp:
   description: Sample Camera Exposure
   deviceClass: ophyd.EpicsSignal
@@ -1237,6 +1275,30 @@ samcam_max:
   onFailure: buffer
   readOnly: true
   readoutPriority: monitored
+samcam_mean:
+  description: Sample Camera Mean
+  deviceClass: ophyd.EpicsSignalRO
+  deviceConfig:
+    auto_monitor: true
+    read_pv: X10SA-ES-MS:Stats1:MeanValue_RBV
+  deviceTags:
+  - scam
+  enabled: true
+  onFailure: buffer
+  readOnly: true
+  readoutPriority: monitored
+samcam_sigma:
+  description: Sample Camera Sigma
+  deviceClass: ophyd.EpicsSignalRO
+  deviceConfig:
+    auto_monitor: true
+    read_pv: X10SA-ES-MS:Stats1:Sigma_RBV
+  deviceTags:
+  - scam
+  enabled: true
+  onFailure: buffer
+  readOnly: true
+  readoutPriority: monitored
 samcam_x:
   description: Sample Camera X
   deviceClass: ophyd.EpicsSignalRO
@@ -1248,7 +1310,7 @@ samcam_x:
   enabled: true
   onFailure: buffer
   readOnly: true
-  readoutPriority: baseline
+  readoutPriority: monitored
 samcam_xsig:
   description: Sample Camera X Sigma
   deviceClass: ophyd.EpicsSignalRO
@@ -1272,7 +1334,7 @@ samcam_y:
   enabled: true
   onFailure: buffer
   readOnly: true
-  readoutPriority: baseline
+  readoutPriority: monitored
 samcam_ysig:
   description: Sample Camera Y Sigma
   deviceClass: ophyd.EpicsSignalRO
@@ -1285,6 +1347,16 @@ samcam_ysig:
   onFailure: buffer
   readOnly: true
   readoutPriority: monitored
+scam:
+  description: Sample camera image contrast
+  deviceClass: pxii_bec.devices.contrast.CameraContrast
+  deviceConfig:
+    prefix: 'X10SA-ES-MS:Stats1:'
+  deviceTags:
+  - detector
+  enabled: true
+  readOnly: true
+  readoutPriority: monitored
 scam_zoom:
   description: Sample Camera Zoom
   deviceClass: ophyd_devices.EpicsMotor
@@ -1635,6 +1707,16 @@ ss_xicam_ysig:
   onFailure: buffer
   readOnly: true
   readoutPriority: baseline
+transm:
+  description: Beam Transmission
+  deviceClass: ophyd_devices.EpicsSignal
+  deviceConfig:
+    read_pv: X10SA-ES-SSFI:TRANSM-SET
+    write_pv: X10SA-ES-SSFI:TRANSM-SET
+  deviceTags:
+  - transm
+  enabled: true
+  readoutPriority: baseline
 vfm_bd:
   description: VFM Downstream Bender
   deviceClass: ophyd_devices.EpicsMotorEC
@@ -1755,6 +1837,48 @@ vfm_yw:
   enabled: true
   onFailure: buffer
   readoutPriority: baseline
+xi_focus:
+  description: Xeye Zoom
+  deviceClass: ophyd_devices.EpicsMotor
+  deviceConfig:
+    prefix: X10SA-ES-XEYE:FOCUS
+  deviceTags:
+  - xeye
+  enabled: true
+  onFailure: buffer
+  readoutPriority: baseline
+xi_x:
+  description: Xeye X
+  deviceClass: ophyd_devices.EpicsMotor
+  deviceConfig:
+    prefix: X10SA-ES-XEYE:TRX
+  deviceTags:
+  - xeye
+  enabled: true
+  onFailure: buffer
+  readoutPriority: baseline
+xi_zoom:
+  description: Xeye Zoom
+  deviceClass: ophyd_devices.EpicsMotor
+  deviceConfig:
+    prefix: X10SA-ES-XEYE:ZOOM
+  deviceTags:
+  - xeye
+  enabled: true
+  onFailure: buffer
+  readoutPriority: baseline
+xidiode:
+  description: Xeye Diode
+  deviceClass: ophyd.EpicsSignalRO
+  deviceConfig:
+    auto_monitor: true
+    read_pv: X10SA-ES-XEYEDI:READOUT
+  deviceTags:
+  - xeye
+  enabled: true
+  onFailure: buffer
+  readOnly: true
+  readoutPriority: monitored
 xrf_pos:
   description: XRF Positioner
   deviceClass: ophyd.EpicsSignal

pxii_bec/device_configs/x10sa_device_config.yaml

@@ -1,5 +1,17 @@
 base_config:
   - !include ./pxii-devices.yaml
+
+microscope_stats:
+  readoutPriority: async
+  description: Stats Signal of MS camera
+  deviceClass: pxii_bec.devices.stats_signal.StatsSignal
+  deviceConfig:
+     prefix: 'X10SA-ES-MS:'
+  onFailure: retry
+  enabled: true
+  readOnly: false
+  softwareTrigger: true
+
 # sample_env:
 #   - !include ./pxii-state-devices.yaml
 # id_gap:

pxii_bec/devices/contrast.py

@@ -0,0 +1,75 @@
+import math
+import time
+
+from ophyd import Component as Cpt
+from ophyd import EpicsSignalRO, Kind, SignalRO
+from ophyd_devices.interfaces.base_classes.psi_device_base import PSIDeviceBase
+
+
+class ContrastSignal(SignalRO):
+    """Read-only signal calculated as sigma / mean."""
+
+    def __init__(
+        self,
+        *args,
+        minimum_mean: float = 1e-12,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.minimum_mean = minimum_mean
+
+    def get(self, **kwargs) -> float:
+        mean = float(self.parent.mean.get())
+        sigma = float(self.parent.sigma.get())
+
+        if (
+            not math.isfinite(mean)
+            or not math.isfinite(sigma)
+            or abs(mean) <= self.minimum_mean
+        ):
+            return math.nan
+
+        return sigma / mean
+
+    def read(self):
+        return {
+            self.name: {
+                "value": self.get(),
+                "timestamp": time.time(),
+            }
+        }
+
+    def describe(self):
+        return {
+            self.name: {
+                "source": (
+                    f"calculated:{self.parent.sigma.pvname}/"
+                    f"{self.parent.mean.pvname}"
+                ),
+                "dtype": "number",
+                "shape": [],
+                "units": "",
+            }
+        }
+
+
+class CameraContrast(PSIDeviceBase):
+    """Camera statistics and calculated contrast."""
+
+    mean = Cpt(
+        EpicsSignalRO,
+        "MeanValue_RBV",
+        kind=Kind.normal,
+    )
+
+    sigma = Cpt(
+        EpicsSignalRO,
+        "Sigma_RBV",
+        kind=Kind.normal,
+    )
+
+    contrast = Cpt(
+        ContrastSignal,
+        kind=Kind.hinted,
+    )
+

pxii_bec/devices/optics_shutter.py

@@ -0,0 +1,107 @@
+"""
+Module for the optics shutter device at PSI beamlines.
+
+Example config:
+shutter:
+    description: Optics Shutter A
+    deviceClass: ophyd_devices.optics_shutter.OpticsShutter
+    deviceConfig: {prefix: 'X10SA-EH1-PSYS:SH-A-'}
+    enabled: true
+    onFailure: retry
+    readOnly: false
+    readoutPriority: baseline
+    softwareTrigger: false
+    userParameter: {}
+"""
+
+from enum import IntEnum
+
+from ophyd import Component as Cpt
+from ophyd import Device, EpicsSignal, EpicsSignalRO, Kind, Signal
+
+from ophyd_devices import CompareStatus
+
+
+class ShutterOpenState(IntEnum):
+    """Enum for shutter open state."""
+
+    OPEN = 1
+    CLOSED = 0
+
+
+class ShutterEnabled(IntEnum):
+    """Enum for shutter enabled state."""
+
+    ENABLED = 1
+    DISABLED = 0
+
+
+class ShutterControl(Device):
+    """Control interface for the PVs related to the shutter."""
+
+    is_open = Cpt(EpicsSignalRO, "OPEN", kind=Kind.config, auto_monitor=True)
+    is_closed = Cpt(EpicsSignalRO, "CLOSE", kind=Kind.omitted)
+    is_enabled = Cpt(EpicsSignalRO, "ENABLE", kind=Kind.config, auto_monitor=True)
+    is_ok = Cpt(EpicsSignalRO, "OK", kind=Kind.omitted, auto_monitor=True)
+    alarm = Cpt(EpicsSignalRO, "ALARM", kind=Kind.omitted, auto_monitor=True)
+    set_open = Cpt(EpicsSignal, "OPEN-SET", kind=Kind.omitted)
+    set_closed = Cpt(EpicsSignal, "CLOSE-SET", kind=Kind.omitted)
+
+
+class ShutterOpenSignal(Signal):
+    """
+    ShutterOpenSignal. When called with 1, it will try to open the shutter.
+    If called with 0, it will try to close the shutter.
+    """
+
+    def enabled(self) -> ShutterEnabled:
+        """Check if the shutter is enabled."""
+        return ShutterEnabled(self.parent.epics_control.is_enabled.get())
+
+    def _check_enabled(self):
+        if self.enabled() != ShutterEnabled.ENABLED:
+            dev_name = self.parent.name if self.parent else "unknown"
+            raise RuntimeError(f"The shutter {dev_name} is disabled!")
+
+    def get(self) -> int:
+        return self.parent.epics_control.is_open.get()
+
+    def put(self, value: int, **kwargs) -> None:
+        self._check_enabled()
+        if value == ShutterOpenState.OPEN:
+            self.parent.epics_control.set_open.put(1, **kwargs)
+        elif value == ShutterOpenState.CLOSED:
+            self.parent.epics_control.set_closed.put(1, **kwargs)
+        else:
+            raise ValueError("Invalid value for ShutterOpenSignal. Use 0 (CLOSED) or 1 (OPEN).")
+
+    def set(self, value: int, **kwargs) -> CompareStatus:
+        self.put(value, **kwargs)
+        return CompareStatus(self.parent.epics_control.is_open, value)
+
+
+class OpticsShutter(Device):
+    """A shutter device with shutter open signal, and sub-device with full control PVs of the Epics implementation."""
+
+    set_open = Cpt(
+        ShutterOpenSignal,
+        name="open",
+        kind=Kind.omitted,
+        doc="Signal to open/close the shutter. Use 0 (CLOSED) or 1 (OPEN).",
+    )
+    is_open = Cpt(
+        EpicsSignalRO,
+        "OPEN",
+        kind=Kind.normal,
+        auto_monitor=True,
+        doc="Readback of the shutter open state. 0 (CLOSED) or 1 (OPEN).",
+    )
+    epics_control = Cpt(ShutterControl, suffix="", name="epics_control", kind=Kind.omitted)
+
+
+if __name__ == "__main__":
+    prefix = "X10SA-EH1-PSYS:SH-A-"
+    print(f"Testing shutter device with prefix {prefix}")
+    shutter = OpticsShutter(name="shutter", prefix=prefix)
+    shutter.wait_for_connection()
+    print(shutter.read())

pxii_bec/devices/stats_signal.py

@@ -0,0 +1,84 @@
+import time
+from collections import defaultdict
+from threading import RLock
+
+import numpy as np
+from bec_lib.logger import bec_logger
+from ophyd import Component as Cpt
+from ophyd import EpicsSignalRO, Kind, Signal
+from ophyd_devices import AsyncMultiSignal, StatusBase, TransitionStatus
+from ophyd_devices.interfaces.base_classes.psi_device_base import PSIDeviceBase
+
+logger = bec_logger.logger
+
+
+class StatsSignal(PSIDeviceBase):
+
+    data = Cpt(
+        AsyncMultiSignal,
+        name="data",
+        signals=["sigma_x", "sigma_y", "eccentricity"],
+        ndim=1,
+        async_update={"type": "add", "max_shape": [None]},
+        max_size=1000,
+        kind=Kind.normal,
+        doc="Stats data signal containing sigma_x, sigma_y, and eccentricity values",
+    )
+
+    sigma_x_raw = Cpt(EpicsSignalRO, "Stats1:SigmaX_RBV", kind=Kind.omitted)
+    sigma_y_raw = Cpt(EpicsSignalRO, "Stats1:SigmaY_RBV", kind=Kind.omitted)
+    eccentricity_raw = Cpt(EpicsSignalRO, "Stats1:Eccentricity_RBV", kind=Kind.omitted)
+
+    def on_init(self):
+        self._rlock = RLock()
+        self._cached_average_data = defaultdict(list)
+
+    def on_connected(self):
+        self._cached_average_data.clear()
+        for sig in (self.sigma_x_raw, self.sigma_y_raw, self.eccentricity_raw):
+            sig.subscribe(self._update_average_data, run=False)
+
+    def _update_average_data(self, value, obj, **kwargs):
+        if value is None:
+            return
+        if self.scan_info.msg is None:
+            return
+        if self.scan_info.msg.status != "open":
+            return
+
+        sig_name = obj.name
+        logger.info(f"Received update from signal {sig_name} with value{ value}")
+
+        with self._rlock:
+            self._cached_average_data[sig_name].append(value)
+
+    def on_trigger(self):
+        with self._rlock:
+            self._cached_average_data.clear()
+        status = StatusBase(obj=self)  # TransitionStatus(self.acquiring, transitions=[1, 0])
+        status.add_callback(self._publish_data)
+        time.sleep(self.scan_info.msg.scan_parameters.get("exp_time", 0))
+        status.set_finished()
+        return status
+
+    def _publish_data(self, status, **kwargs):
+        if status.done:
+            with self._rlock:
+
+                sigma_x = float(
+                    np.average(self._cached_average_data.get(self.sigma_x_raw.name, [0]))
+                )
+                sigma_y = float(
+                    np.average(self._cached_average_data.get(self.sigma_y_raw.name, [0]))
+                )
+                eccentricity = float(
+                    np.average(self._cached_average_data.get(self.eccentricity_raw.name, [0]))
+                )
+
+            ret = {
+                "sigma_x": {"value": sigma_x},
+                "sigma_y": {"value": sigma_y},
+                "eccentricity": {"value": eccentricity},
+            }
+            logger.info(f"Setting data for {self.name} data signal to {ret}.")
+            self.data.put(ret)

pxii_bec/macros/APscripts.py

@@ -9,7 +9,8 @@
 import numpy as np
 import math
 import matplotlib.pyplot as plt
-
+import sys 
+import time, datetime 
 
 ################################
 ### BASIC BL PROGRAMS
@@ -240,11 +241,8 @@ def rock(**kwargs):
 
 
 
-
-
-
 ################################
-### Help PROGRAMS
+### Helper PROGRAMS
 
 #############################
 ### just reconfigure beaml
@@ -254,8 +252,16 @@ def loadmac():
     bec.macros.load_all_user_macros()
     # d, planner = init_beamline_environment()
 
-    
 
+#############################
+### plotenv
+#############################
+def plotset(y):
+    import matplotlib.pyplot as plt
+    plt.ion()
+    plt.figure()
+    plt.plot(y)
+    
 ################################
 ### FITTING PROGRAMS
 
@@ -276,53 +282,49 @@ def justfit(data_x, data_y, model="gauss", ibg=0):
     from lmfit.models import LinearModel, GaussianModel, VoigtModel, QuadraticModel, LorentzianModel
     import matplotlib.pyplot as plt
 
-    peak = GaussianModel()
+    peak = GaussianModel(prefix = 'g_')
+    pre='g'
     if model == "voigt":
-        peak = VoigtModel()
+        peak = VoigtModel(prefix = 'v_')
+        pre='v'
     if model == "lorentz":
-        peak = LorentzianModel()
-    model = peak
+        peak = LorentzianModel(prefix = 'l_')
+        pre='l'
 
+    model = peak
+    ypeak = data_y 
+    pars = model.guess(data_y, x = data_x)
+    indmax = np.argmax(ypeak)
+    xm = data_x[indmax] 
+    
     if ibg:
-        # add a BG
-        background = LinearModel()
-        if ibg > 1:
-            background = QuadraticModel()
+        background = LinearModel(prefix = 'bg_')
         model = peak + background
 
-    ### define data_x and data_y:
-    maxy = max(data_y)
-    indmax = np.argmax(data_y)
-    xm = data_x[indmax]
-    sigma = 1.0
-    gamma = 0.2  # blurring/widening of the sigma ; the larger, the more of a Lorentzian profile
+        ### estimate initial params separately for both parts of the model
+        # 1) BG
+        n = len(data_x) 
+        edge= n//10
+        idx=np.r_[0:edge, n-edge:n] # first and last part of the range 
+        #m0, b0 = np.polyfit(data_x[idx], data_y[idx],1)
+        #ypeak= data_y - (m0*data_x + b0) # subtract linear part 
 
-    ## possible, but not necessarily better ... :
-    # sl = 0.00001 * (data_y[-2]-data_y[2])/(data_x[-2]-data_x[2]) # small slope
-    # ic =  min(data_y)
-    # print(f'slope and intercept initial conds= {sl, ic}')
-    ##
-    print("maxy, indmax, xm = ", maxy, indmax, xm)
+        pars = background.guess(data_y[idx], x = data_x[idx])
+        
+        # 2) Function - BG 
+        ypeak= data_y - (pars['bg_slope']*data_x + pars['bg_intercept'])# subtract linear part 
+        pars.update( peak.guess(ypeak, x= data_x) )
+    
 
-    p = model.make_params(amplitude=maxy, center=xm)
-    # other possibilities ... :
-    # p = model.make_params(amplitude = maxy, center=xm , slope=0, intercept = ic)
-    # p = model.make_params(amplitude = max(data_y), center=xm, slope=0, intercept = min(data_y))
-
-    p["center"].set(min=min(data_x), max=max(data_x))
-    p["sigma"].set(min=0, max=(max(data_x) - min(data_x)) / 2.0)
-    # other possibilities ... :
-    # p['amplitude'].set(min=min(data_y), max=max(data_y))
-    # p['intercept'].set(max=(max(data_y)-min(data_y))/2.)
-
-    g = model.fit(data_y, p, x=data_x)
+    g = model.fit(data_y, pars, x=data_x)
 
     # diagnostics
     # print(f'Gfit: {g.params}')
 
-    print(f'Center of {model} fit: {g.params["center"].value:.5f}')
-    print(f'Sigma: {g.params["sigma"].value:.5f}')
-    print(f'FWHM: {g.params["fwhm"].value:.5f}')
+    #print(f'Center of {model} fit: {g.pars["center"].value:.5f}')
+    #print(f'Sigma: {g.pars["sigma"].value:.5f}')
+    #print(f'FWHM: {g.pars["fwhm"].value:.5f}')
+    print(g.fit_report())
     print(f"Position of maximum: {xm}")
 
     return g, xm
@@ -345,13 +347,21 @@ def fit_plothist(hindex: int, signal_name: str, model="gauss", ibg=0):
     from lmfit.models import LinearModel, GaussianModel, VoigtModel, QuadraticModel, LorentzianModel
     import matplotlib.pyplot as plt
 
-    h = bec.history[hindex]
+    if hindex < 0:
+        h = bec.history[hindex]
+        print('by scan number')
+    else:
+        h = bec.history.get_by_scan_number(hindex)
+        print('by scan number')
     md = h.metadata["bec"]
     scanvar = list(md["args"].keys())[0]  # string, returns the variable of the last performed scan
     # data = h.devices[device_name][signal].read()["value"]
     # data_x = h.devices.dcm_pitch.dcm_pitch.read()["value"]
     # data_y = h.devices.lu_bpmsum.lu_bpmsum.read()["value"]
-    # data_x = h.devices[device_name][device_name].read()["value"]
+
+    ## that should work in any case:
+    device_name = scanvar
+    #data_x = h.devices[device_name][device_name].read()["value"]
 
     data_xd = md["positions"]  # last scan knows which device ... however, double array
     data_x = data_xd.flatten()
@@ -359,6 +369,10 @@ def fit_plothist(hindex: int, signal_name: str, model="gauss", ibg=0):
     data_y = h.devices[signal_name][signal_name].read()["value"]
     print("dy = ", data_y)
 
+    prefix='v_'
+    if model == 'gauss':
+         prefix='g_'
+    
     g, xm = justfit(data_x, data_y, model=model, ibg=ibg)
 
     plt.ion()
@@ -369,7 +383,9 @@ def fit_plothist(hindex: int, signal_name: str, model="gauss", ibg=0):
     plt.ylabel(signal_name)
     plt.show()
 
-    gcen = g.params["center"].value
+    # g.params
+    returncenvar = prefix+"center"
+    gcen = g.params[returncenvar].value
 
     return gcen, xm, data_x, data_y
 
@@ -421,39 +437,41 @@ def fit_plot(data_x, data_y, model="gauss", ibg=1, fitrange=0, fitclick=0):
         print("dy=", data_y)
 
     #####
-    peak = GaussianModel()
+    peak = GaussianModel(prefix = 'g_')
+    pre='g_'
     if model == "voigt":
-        peak = VoigtModel()
+        peak = VoigtModel(prefix = 'v_')
+        pre='v_'
     if model == "lorentz":
-        peak = LorentzianModel()
+        peak = LorentzianModel(prefix = 'l_')
+        pre='l_'
 
+    model = peak
+    ypeak = data_y 
+    pars = model.guess(data_y, x = data_x)
+    indmax = np.argmax(ypeak)
+    xm = data_x[indmax] 
+    
     if ibg:
-        background = LinearModel()
+        background = LinearModel(prefix = 'bg_')
         model = peak + background
 
-    ### define data_x and data_y:
-    maxy = max(data_y)
-    indmax = np.argmax(data_y)
-    xm = data_x[indmax]
-    sigma = 1.0
-    gamma = 0.2  # blurring/widening of the sigma ; the larger, the more of a Lorentzian profile
-    print("maxy, indmax, xm = ", maxy, indmax, xm)
-    # p = model.make_params(
-    #    amplitude=max(data_y), center=xm, slope=0, intercept=min(data_y)
-    # )
-    p = model.make_params(amplitude=maxy, center=xm)
-    p["center"].set(min=min(data_x), max=max(data_x))
-    p["sigma"].set(min=0, max=(max(data_x) - min(data_x)) / 2.0)
+        ### estimate initial params separately for both parts of the model
+        # 1) BG
+        n = len(data_x) 
+        edge= n//10
+        idx=np.r_[0:edge, n-edge:n] # first and last part of the range 
+        #m0, b0 = np.polyfit(data_x[idx], data_y[idx],1)
+        #ypeak= data_y - (m0*data_x + b0) # subtract linear part 
 
-    g = model.fit(data_y, p, x=data_x)
+        pars = background.guess(data_y[idx], x = data_x[idx])
+        
+        # 2) Function - BG 
+        ypeak= data_y - (pars['bg_slope']*data_x + pars['bg_intercept'])# subtract linear part 
+        pars.update( peak.guess(ypeak, x= data_x) )
+    
 
-    # diagnostics
-    # print(f'Gfit: {g.params}')
-
-    print(f'Center of {model} fit: {g.params["center"].value:.5f}')
-    print(f'Sigma: {g.params["sigma"].value:.5f}')
-    print(f'FWHM: {g.params["fwhm"].value:.5f}')
-    print(f"Position of maximum: {xm}")
+    g = model.fit(data_y, pars, x=data_x)
 
     #####
 
@@ -499,15 +517,24 @@ def fit_plot(data_x, data_y, model="gauss", ibg=1, fitrange=0, fitclick=0):
         plt.ylabel("signal")
         plt.show()
 
-    gcen = g.params["center"].value
 
-    return gcen, xm
+    # g.params
+    returncenvar = pre+"center"
+    gcen = g.params[returncenvar].value
+
+    print('gcen =', gcen) 
+    print(g.fit_report())
+
+    # fit_id = pre+'_center'
+    
+    #center = g.pars[fit_id].value
+    return gcen, xm 
 
 
 ####################################
 ### just SAVE DATA
 ####################################
-def save_data(hindex: int, device_name: str, signal_name: str):
+def save_data(hindex: int, device_name: str, signal_name: str, isave = 1):
     """
     Get data for a completed scan from the BEC history,
     and plot all the results, store in a CSV file
@@ -534,15 +561,16 @@ def save_data(hindex: int, device_name: str, signal_name: str):
     plt.ylabel(f"{signal_name} / AU")
 
     plt.show()
-    ans = "n"
-    ans = input("Store data in csv file? y/n ")
-    if ans == "y":
-        dirname = "/sls/x10sa/config/commissioning/Data/"
-        # writing output to simple data file for later analysis:
-        combined = np.column_stack((data_x, data_y))
-        filename = dirname + "Scan" + str(hindex) + device_name + ".txt"
-        with open(filename, "w") as f:
-            np.savetxt(f, combined, delimiter=",", fmt="%5f")
+    if isave: 
+        ans = "n"
+        ans = input("Store data in csv file? y/n ")
+        if ans == "y":
+            dirname = "/sls/x10sa/config/commissioning/Data/"
+            # writing output to simple data file for later analysis:
+            combined = np.column_stack((data_x, data_y))
+            filename = dirname + "Scan" + str(hindex) + device_name + ".txt"
+            with open(filename, "w") as f:
+                np.savetxt(f, combined, delimiter=",", fmt="%5f")
 
     return data_x, data_y
 
@@ -550,7 +578,7 @@ def save_data(hindex: int, device_name: str, signal_name: str):
 #########################################
 ### just retrieve the saved data from csv
 ########################################
-def read_data(filename: str):
+def read_data_csv(filename: str):
     """
     Get data stored in a CSV file
 
@@ -677,7 +705,7 @@ def getdiodepos(diode="i1"):
         diode_in = 0  # sys.exit(0)
 
     if diode == "i2":
-        detpos_diode = 214.5
+        detpos_diode = 213.6
         detpos_rb = dev.det_z.user_readback.get()
         print("using Det diode requires det up and close and beamstop out")
         if abs(detpos_diode - detpos_rb) > 0.5:
@@ -688,10 +716,10 @@ def getdiodepos(diode="i1"):
 
 
 def read_mon():
-    from datetime import datetime
+    #from datetime import datetime
 
     e = getenergy()
-    fesum = dev.fe_bpmsum.read()["fe_bpmsum"]["value"]
+    #fesum = dev.fe_bpmsum.read()["fe_bpmsum"]["value"]
     lusum = dev.lu_bpmsum.read()["lu_bpmsum"]["value"]
     sssum = dev.ss_bpmsum.read()["ss_bpmsum"]["value"]
 
@@ -710,28 +738,30 @@ def read_mon():
     vbpitch = dev.vfm_pitch.read()["vfm_pitch"]["value"]
     vbyaw = dev.vfm_yaw.read()["vfm_yaw"]["value"]
     vbroll = dev.vfm_roll.read()["vfm_roll"]["value"]
-    vblat = dev.vfm_lat.read()["vfm_lat"]["value"]
-    vbvert = dev.vfm_vert.read()["vfm_vert"]["value"]
+    vfm_x= dev.vfm_x.read()["vfm_x"]["value"]
+    vfm_y= dev.vfm_y.read()["vfm_y"]["value"]
+    
     # KB H
     hbu = dev.hfm_bu.read()["hfm_bu"]["value"]
     hbd = dev.hfm_bd.read()["hfm_bd"]["value"]
     hbpitch = dev.hfm_pitch.read()["hfm_pitch"]["value"]
     hbyaw = dev.hfm_yaw.read()["hfm_yaw"]["value"]
     hbroll = dev.hfm_roll.read()["hfm_roll"]["value"]
-    hblat = dev.hfm_lat.read()["hfm_lat"]["value"]
-    hbvert = dev.hfm_vert.read()["hfm_vert"]["value"]
+    hfm_x= dev.hfm_x.read()["hfm_x"]["value"]
+    hfm_y= dev.hfm_y.read()["hfm_y"]["value"]
+
 
     ## FE slits centre and size
-    fe_sx_cen = dev.fe_sxcen.read()["fe_sxcen"]["value"]
-    fe_sx_size = dev.fe_sxsize.read()["fe_sxsize"]["value"]
-    fe_sy_cen = dev.fe_sycen.read()["fe_sycen"]["value"]
-    fe_sy_size = dev.fe_sysize.read()["fe_sysize"]["value"]
+    fe_sl_xcen = dev.fe_sl_xcen.read()["fe_sl_xcen"]["value"]
+    fe_sl_xsize = dev.fe_sl_xsize.read()["fe_sl_xsize"]["value"]
+    fe_sl_ycen = dev.fe_sl_ycen.read()["fe_sl_ycen"]["value"]
+    fe_sl_ysize = dev.fe_sl_ysize.read()["fe_sl_ysize"]["value"]
 
     ## BSF slits centre and size
-    bsf_xcen = dev.bsf_sl_xcen.read()["bsf_sl_xcen"]["value"]
-    bsf_xsize = dev.bsf_sl_xsize.read()["bsf_sl_xsize"]["value"]
-    bsf_ycen = dev.bsf_sl_ycen.read()["bsf_sl_ycen"]["value"]
-    bsf_ysize = dev.bsf_sl_ysize.read()["bsf_sl_ysize"]["value"]
+    bsf_sl_xcen = dev.bsf_sl_xcen.read()["bsf_sl_xcen"]["value"]
+    bsf_sl_xsize = dev.bsf_sl_xsize.read()["bsf_sl_xsize"]["value"]
+    bsf_sl_ycen = dev.bsf_sl_ycen.read()["bsf_sl_ycen"]["value"]
+    bsf_sl_ysize = dev.bsf_sl_ysize.read()["bsf_sl_ysize"]["value"]
 
     ## SS slits centre and size
     ss_sl_xcen = dev.ss_sl_xcen.read()["ss_sl_xcen"]["value"]
@@ -761,30 +791,31 @@ def read_mon():
 
     ## move out again
 
-    xeye_x_pos = dev.xeye_x.read()["xeye_x"]["value"]
+    xeye_x_pos = dev.xi_x.read()["xi_x"]["value"]
     bcusum = 0
     i1signal = 0
-    if xeye_x_pos < 0:
-        bcusum = dev.bcu_bpmsum.read()["bcu_bpmsum"]["value"]
-        i1signal = dev.i1.read()["i1"]["value"]
+    #if xeye_x_pos < 0:
+    bcusum = dev.bcu_bpmsum.read()["bcu_bpmsum"]["value"]
+    i1signal = dev.i1.read()["i1"]["value"]
     print("Energy = ", e, " keV")
-    print(f"fesum,lusum,sssum,bcusum,i1signal =  {fesum,lusum,sssum,bcusum,i1signal}")
+    #print(f"fesum,lusum,sssum,bcusum,i1signal =  {fesum,lusum,sssum,bcusum,i1signal}")
+    print(f"lusum,sssum,bcusum,i1signal =  {lusum,sssum,bcusum,i1signal}")
 
     print(f"bragg, pitch, perp, fpitch, froll, gap =  {bragg, pitch,perp, fpitch, froll, gap}")
     # return e, fesum,lusum,sssum,bcusum,i1signal
     print("KB VERT")
     print(
-        f"vbu, vbd, vbpitch,vbyaw,vbroll,vblat,vbvert =  {vbu, vbd, vbpitch,vbyaw,vbroll,vblat,vbvert}"
+        f"vbu, vbd, vbpitch,vbyaw,vbroll,vbvert =  {vbu, vbd, vbpitch,vbyaw,vbroll,vfm_x, vfm_y}"
     )
     # return vbu, vbd, vbpitch,vbyaw,vbroll,vblat,vbvert
     print("KB HORI")
     print(
-        f"hbu, hbd, hbpitch,hbyaw,hbroll,hblat,hbvert = {hbu, hbd, hbpitch,hbyaw,hbroll,hblat,hbvert}"
+        f"hbu, hbd, hbpitch,hbyaw,hbroll,hblat,hbvert = {hbu, hbd, hbpitch,hbyaw,hbroll,hfm_x, hfm_y}"
     )
     # return hbu, hbd, hbpitch,hbyaw,hbroll,hblat,hbvert
     ## dump status in CSV
 
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
     dirname = "/sls/x10sa/config/commissioning/Data/"
     filename = dirname + f"BLstatus_{timestamp}.txt"
     with open(filename, "w") as f:
@@ -796,16 +827,16 @@ def read_mon():
         combined = np.column_stack((bragg, pitch, perp, fpitch, froll))
         np.savetxt(f, combined, delimiter=",", fmt="%5f")
 
-        f.write("vbu, vbd, vbpitch,vbyaw,vbroll,vblat,vbvert\n")
-        combined = np.column_stack((vbu, vbd, vbpitch, vbyaw, vbroll, vblat, vbvert))
+        f.write("vbu, vbd, vbpitch,vbyaw,vbroll,vbvert\n")
+        combined = np.column_stack((vbu, vbd, vbpitch, vbyaw, vbroll,  vfm_x, vfm_y))
         np.savetxt(f, combined, delimiter=",", fmt="%5f")
 
         f.write("hbu, hbd, hbpitch,hbyaw,hbroll,hblat,hbvert\n")
-        combined = np.column_stack((hbu, hbd, hbpitch, hbyaw, hbroll, hblat, hbvert))
+        combined = np.column_stack((hbu, hbd, hbpitch, hbyaw, hbroll,  hfm_x, hfm_y))
         np.savetxt(f, combined, delimiter=",", fmt="%5f")
 
         f.write("FE slits\n")
-        combined = np.column_stack((fe_sx_cen, fe_sx_size, fe_sy_cen, fe_sy_size))
+        combined = np.column_stack((fe_sl_xcen, fe_sl_xsize, fe_sl_ycen, fe_sl_ysize))
         np.savetxt(f, combined, delimiter=",", fmt="%5f")
 
         f.write("BSF slits\n")
@@ -820,8 +851,10 @@ def read_mon():
         combined = np.column_stack((bcu_sl_xcen, bcu_sl_xsize, bcu_sl_ycen, bcu_sl_ysize))
         np.savetxt(f, combined, delimiter=",", fmt="%5f")
 
-        f.write("fesum,lusum,sssum,bcusum,i1signal\n")
-        combined = np.column_stack((fesum, lusum, sssum, bcusum, i1signal))
+        #f.write("fesum,lusum,sssum,bcusum,i1signal\n")
+        #combined = np.column_stack((fesum, lusum, sssum, bcusum, i1signal))
+        f.write("lusum,sssum,bcusum,i1signal\n")
+        combined = np.column_stack((lusum, sssum, bcusum, i1signal))
         np.savetxt(f, combined, delimiter=",", fmt="%5f")
 
         return
@@ -873,7 +906,7 @@ def colliscan(direction: str, range=0.3, nsteps=30, stime=0.5, centre=1):
         settling time: default = 0.5
         move to centre or not [0/1]
 
-    Example: colliscan("h", centre=1)
+    Example: colliscan("h", range=0.2, nsteps=30, stime=0.5,centre=1)
 
     """
     import sys
@@ -972,14 +1005,15 @@ def slitscan(device_location: str, direction: str, range: 1, nsteps=50, centre=0
         default_h = 1.0  ## 1.4 or 0.4 or ???
         default_v = 1.0  ## 1.2
         det = dev.lu_bpmsum
-        if direction == "h":
-            mot = dev.fe_sxcen
-            size = dev.fe_sxsize
+        if direction in ["x","h"]:
+            mot = dev.fe_sl_xcen
+            size = dev.fe_sl_xsize
             s_closed = 0.1
             s_open = default_h
         else:
-            mot = dev.fe_sycen
-            size = dev.fe_sysize
+            mot = dev.fe_sl_ycen
+            size = dev.fe_sl_ysize
+            
             s_closed = 0.1
             s_open = default_v
 
@@ -989,7 +1023,7 @@ def slitscan(device_location: str, direction: str, range: 1, nsteps=50, centre=0
         default_v = 3.0  # ??? close more ???? # 8149.8
         det = dev.lu_bpmsum
         # det = dev.ss_bpmsum or #det = dev.bcu_bpmsum would also work
-        if direction == "h":
+        if direction in ["x", "h"]:
             mot = dev.bsf_sl_xcen
             size = dev.bsf_sl_xsize
             s_closed = 0.1
@@ -1006,7 +1040,7 @@ def slitscan(device_location: str, direction: str, range: 1, nsteps=50, centre=0
         default_h = 6.0  # ???
         default_v = 5.0  # ??? close more ???? # 8149.8
         det = dev.bcu_bpmsum
-        if direction == "h":
+        if direction in ["x", "h"]:
             mot = dev.ss_sl_xcen
             size = dev.ss_sl_xsize
             s_closed = 0.1
@@ -1030,7 +1064,7 @@ def slitscan(device_location: str, direction: str, range: 1, nsteps=50, centre=0
         #    print("moving diode i1 in")
         #    umv(dev.scin_y, dposm)
 
-        if direction == "h":
+        if direction in ["x", "h"]:
             mot = dev.bcu_sl_xcen
             size = dev.bcu_sl_xsize
             s_closed = 3.0  ## very large, else does not work !
@@ -1073,9 +1107,9 @@ def slitscan(device_location: str, direction: str, range: 1, nsteps=50, centre=0
     # FE slits ================================
     data_y = s.scan.live_data.lu_bpmsum.lu_bpmsum.val
 
-    if mot.name == "fe_sxcen":
+    if mot.name == "fe_sl_xcen":
         data_x = s.scan.live_data.fe_sxcen.fe_sxcen.val
-    if mot.name == "fe_sycen":
+    if mot.name == "fe_sl_ycen":
         data_x = s.scan.live_data.fe_sycen.fe_sycen.val
     # BSF slits ================================
     if mot.name == "bsf_sl_xcen":
@@ -1204,42 +1238,6 @@ def kbfocus(sizex, sizey):
     return
 
 
-##### ======================
-##### goto several energies with about the right gap opening
-
-
-def goto7():
-    perp_dist = 0.0389
-    umv(dev.dcm_perp, perp_dist)
-    gap = 6.9  # 3rd harm
-    umv(dev.id_gap, gap)
-    sete(7)
-
-
-def goto20():
-    perp_dist = 0.0389
-    umv(dev.dcm_perp, perp_dist)
-    gap = 4.736  # 13th harm
-    umv(dev.id_gap, gap)
-    sete(20)
-
-
-def goto27():
-    perp_dist = 0.0389
-    umv(dev.dcm_perp, perp_dist)
-    gap = 4.86  # 17th harm
-    umv(dev.id_gap, gap)
-    sete(27)
-
-
-def goto30():
-    perp_dist = 0.0389
-    umv(dev.dcm_perp, perp_dist)
-    gap = 4.842  # 19th harm
-    umv(dev.id_gap, gap)
-    sete(30)
-
-
 #########################
 ### Status of the BEC
 #########################
@@ -1263,22 +1261,23 @@ def bstatus():
 ###########################
 
 
-def detxeye_in():
+def detxeye_in(setdetzpos = 200, setdetypos = 213.6):
     """
     moves in the Xray eye and the Diode below detector
     """
     #
     setfoc = 1.0
     setzoom = 0.1
-    setxrpos = 2.0
+    setxrpos = 0.0
 
-    setdetzpos = 260  # 200 better?
-    setdetypos = 214.5
+    #setdetzpos = 200  # 200 better?
+    #setdetypos = 213.6
 
     detzpos = dev.det_z.user_readback.get()
     detypos = dev.det_y.user_readback.get()
 
     print(f"detector position now: z = {detzpos}, y = {detypos}")
+    print(f"... moving to detector position: z = {setdetzpos}, y = {setdetypos}")    
 
     ans = "n"
     ans = input("Is it ok to move detector in and up? y/n ")
@@ -1293,15 +1292,15 @@ def detxeye_in():
 
     print("moving X-ray eye below Eiger in")
 
-    xrpos = dev.det_xi_x.user_readback.get()
-    if abs(setxrpos - xrpos) > 1:
-        umv(dev.det_xi_x, setxrpos)
-    zoompos = dev.det_xi_zoom.user_readback.get()
-    if abs(setzoom - zoompos) > 1:
-        umv(dev.det_xi_zoom, setzoom)
-    focpos = dev.det_xi_focus.user_readback.get()
-    if abs(setfoc - focpos) > 1:
-        umv(dev.det_xi_focus, setfoc)
+    #xrpos = dev.det_xi_x.user_readback.get()
+    #if abs(setxrpos - xrpos) > 1:
+    #    umv(dev.det_xi_x, setxrpos)
+    #zoompos = dev.det_xi_zoom.user_readback.get()
+    #if abs(setzoom - zoompos) > 1:
+    #    umv(dev.det_xi_zoom, setzoom)
+    #focpos = dev.det_xi_focus.user_readback.get()
+    #if abs(setfoc - focpos) > 1:
+    #    umv(dev.det_xi_focus, setfoc)
 
 
 def detxeye_out():
@@ -1309,7 +1308,7 @@ def detxeye_out():
     moves det back and down
     """
     setdetzpos = 500
-    setdetypos = 0
+    setdetypos = 70
     umv(dev.det_z, setdetzpos)
     detzpos = dev.det_z.user_readback.get()
     if detzpos > 300:
@@ -1322,7 +1321,8 @@ def detxeye_out():
 
 
 def measure_samcam(zoom=1000):
-    scinti_inpos = 38.6  # mm
+    import sys 
+    scinti_inpos = 36.4  # mm
     sc_rb = dev.diag_y.user_readback.get()
     if abs(scinti_inpos - sc_rb) > 0.3:
         print("Scinti not in, please move")
@@ -1486,7 +1486,7 @@ def scan_eg(erange, nsteps=50, fit=True):
         data_e = energy_data
         data_y = bpm_data
 
-        dirname = "/home/gac-x10sa/Data/"
+        dirname = "/sls/x10sa/config/commissioning/Data"
         # writing output to simple data file for later analysis:
         combined = np.column_stack((data_x, data_e, data_y))
         tname = f"Scan_{timestamp}.txt"
@@ -1533,3 +1533,169 @@ def scan_window(wname="Scan", fit=True):
     
     ### if done, remove 
     #w2.remove()
+################################################
+### beam centre on detector with distance 
+################################################
+
+#/sls/x10sa/config/commissioning/Data
+
+def readdist(filename):
+    with open(filename, "r") as f:
+        combined_data = np.loadtxt(f, delimiter=",")
+        
+    ind1 = 0
+    ind2 = 1
+    ind3 = 2
+    detdist = combined_data[:, ind1]
+    bposx = combined_data[:, ind2]
+    bposy = combined_data[:, ind3]
+    
+    plt.ion()
+    plt.figure(1)
+    plt.plot(detdist,bposx, ".")
+    plt.title("Bpos X vs Detectordist")
+    plt.xlabel("dist / mm")
+    plt.ylabel("bpos X in det pix ")
+    
+    plt.figure(2)
+    plt.plot(detdist,bposy, ".")
+    plt.title("Bpos Y vs Detectordist")
+    plt.xlabel("dist / mm")
+    plt.ylabel("bpos Y in det pix ")
+    plt.show()
+
+    return 
+
+################################################
+###  flux cal 
+################################################
+
+def read_av(signal, avlen):
+    #signal = dev.lu_bpmsum
+    
+    buf = []
+    count =0 
+    while count < avlen:
+        new_value = signal.read()[signal.name]['value']
+        #new_value = dev.lu_bpmsum.read()['lu_bpmsum']["value"]
+        buf.append(new_value)
+        count+=1
+    return sum(buf) / len(buf)
+
+
+
+def fitpoly(x,y, order):
+    from scipy.optimize import curve_fit
+    
+    coeff = np.polyfit(x, y, order)
+    polynomial = np.poly1d(coeff)
+
+    print("Polynomial coefficients of the fit fct: ", order, coeff)
+    x_fit = np.linspace(min(x), max(x), 100)
+    y_fit = polynomial(x_fit)
+    
+    plt.ion()
+    plt.figure()
+    plt.title("X10SA Fl cal")
+    plt.ylabel("fl/bcubpmsum")
+    plt.title("E / keV")
+    
+    plt.plot(x, y, "*")
+    plt.plot(x_fit, y_fit, color="blue")
+    
+
+
+def prepare_fluxmeas():
+# check all slits
+    read_mon()
+#   
+    print("preparing devices ...")
+    ans = "n"
+    ans = input("Is it safe to move det in and up and BS down? y/n ")
+    if ans == "y":
+        detxeye_in()
+        bs_in = d["bs_y"].positions["in"]
+        #umv(dev.bs_y, bs_in +1) 
+    return
+
+def fluxcal(start_en=13, stop_en=14, step =1):
+    import time
+    #
+    tstart = time.perf_counter() # time logging
+    avlen= 10
+    
+    dat_en=[]
+    dat_lubpm=[]
+    dat_ssbpm=[]
+    dat_bcubpm=[]
+    dat_xi=[]
+    dat_fluxi=[]
+    dat_quot_flbsum=[]
+    dat_i1=[]
+
+    diode_inpos = dev.diag_y.user_parameter["i1"]
+    diode_outpos = dev.diag_y.user_parameter["out"]
+    for ei in range(start_en,stop_en+1, step):
+        bl_energy(ei*1000, plot = False) # in ev 
+        time.sleep(0.5)
+        #lusum = dev.lu_bpmsum.read()["lu_bpmsum"]["value"]
+        signal =  dev.lu_bpmsum    #  "lu_bpmsum"
+        lusum = read_av(signal, avlen)
+
+        #sssum = dev.ss_bpmsum.read()["ss_bpmsum"]["value"]
+        signal = dev.ss_bpmsum     # "ss_bpmsum"
+        sssum = read_av(signal, avlen)
+        
+        #bcusum = dev.bcu_bpmsum.read()["bcu_bpmsum"]["value"]
+        signal =   dev.bcu_bpmsum          #"bcu_bpmsum"
+        bcusum = read_av(signal, avlen)
+
+        
+        #xi =  dev.xidiode.read()["xidiode"]["value"]
+        signal =  dev.xidiode
+        xi = read_av(signal, avlen)
+        
+        fluxi = flux_x10sa(xi, ei, silent =1)
+        quot = fluxi/bcusum
+
+        
+        #i1diode =  dev.i1.read()["i1"]["value"]
+        umv(dev.diag_y,diode_inpos) 
+        signal =  dev.i1
+        i1diode = read_av(signal, avlen)       
+        umv(dev.diag_y,diode_outpos)
+        
+        print(f'En {ei}, BCUSUM {bcusum}, xi {xi}, flux {fluxi}')
+
+        dat_en.append(ei)
+        dat_lubpm.append(lusum)
+        dat_ssbpm.append(sssum)
+        dat_bcubpm.append(bcusum)
+        dat_xi.append(xi)
+        dat_fluxi.append(fluxi)
+        dat_quot_flbsum.append(quot)
+        dat_i1.append(i1diode)
+                
+    combined = np.column_stack((dat_en, dat_lubpm,dat_ssbpm,dat_bcubpm,dat_xi,dat_fluxi,dat_quot_flbsum, dat_i1))
+    
+    # open file for output
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    dirname = "/sls/x10sa/config/commissioning/Data/"
+    filename = dirname + f"FLUX_x10sa_{timestamp}.txt"
+
+    with open(filename, "w") as f:
+        np.savetxt(f, combined, delimiter=",", fmt="%.9f") # fmt for savetxt
+
+
+    elapsed_time = time.perf_counter()-tstart
+    print(f"Runtime: {elapsed_time: .6f} seconds, i.e., {elapsed_time/60: .6f} min")    
+    # CLOSE AND MOVE BACK ############     
+    # close shutter !
+    
+    print("close the shutter !")
+    dev.bcu_shutter.put(0) 
+    
+    # move beamstop back ! 
+    #umv(dev.bs_y, bs_in)
+
+    fitpoly(dat_en, dat_quot_flbsum, 3)

pxii_bec/macros/EvalFlux.py

@@ -0,0 +1,69 @@
+def evalflux():
+
+    from APscripts import read_data_csv, fitpoly
+
+    data=read_data_csv('FLUX_x10sa_20260709_174259.txt')
+
+    einv=data[:, 0]
+    fcalfinv = data[:, 6] # if energy top down
+
+
+    e = einv[::-1]
+    flcf = fcalfinv[::-1]
+
+    # omit the last values (>= 27 keV)
+
+    len(e) # = 25
+    e = e[0:21]
+    flcf =flcf[0:21]/1.e9 ## divide by 1e9 for better readability
+
+    fitpoly(e, flcf, 3)
+
+    # Polynomial coefficients of the fit fct:  3 [-0.00026213  0.01171065 -0.05379571  0.04478851]
+
+
+def csplinex(x,y,en):
+    from scipy.interpolate import CubicSpline
+
+    # Create spline
+    cs = CubicSpline(x, y)
+    y_cs = cs(en) 
+    return y_cs
+
+    
+def flux_bcu():
+    e = get_current_energy()/1000.
+    bcusum = read_av(dev.bcu_bpmsum,10)
+    flux = fl_bcubpm(e,bcusum)
+    return f"{flux:.2e}"  
+    
+def flux_i1():
+    e = get_current_energy()/1000
+    i1 = read_av(dev.i1,10)
+    flux = fl_i1(e, i1)
+    return f"{flux:.2e}"  
+    
+def fl_bcubpm(e,bcusum):
+    import numpy as np    
+    fact = 1.e9 
+    coeff = [-0.00026213,  0.01171065, -0.05379571,  0.04478851]
+    if e > 24 :
+        coeff = [4.83704287e-05, -3.17174523e-03,  7.33131161e-02, -5.91926617e-01, 1.66572494e+00]
+    polynomial = np.poly1d(coeff)
+    fl = polynomial(e) * bcusum * fact
+    #print(f'flux from {bcusum} with en {e} is {fl:.2e} ph/s')
+    
+    return fl 
+    
+def fl_i1(e,i1):
+    import pandas as pd
+
+    file = "luts/I1flcalf.csv"
+    df = pd.read_csv(file)  
+    a = df["En"]
+    b = df["Fl/I1"]
+    
+    fl_i1 =  csplinex(a,b,e) *i1 
+    print(f'flux from {i1} with en {e} is {fl_i1:.2} ph/s ')
+
+    return fl_i1

pxii_bec/macros/KB_Class.py

@@ -13,10 +13,10 @@ class KBMirror:
             self.P2 = 0.583490
             self.P3 = -0.372950
             self.L1 = -0.955798
-            self.curv_approx_fact = 1.0121
-            self.s1 = 24.15   #24.35
+            self.curv_approx_fact =  0.9550 # 0.9550 # origin: 1.0121 
+            self.s1 = 24.15   
             self.s2 = 1.45
-            self.asym0 = 0.08
+            self.asym0 = 0.114 ## origin: 0.0.08  + 0.034  # user asymm, eg 0.4750, will be added ( - sign) 
 
         elif self.mirror_type == "PXII_HFM":
             self.C0 = 0.060453
@@ -24,10 +24,10 @@ class KBMirror:
             self.P2 = 0.586057
             self.P3 = -0.088093
             self.L1 = -0.999445
-            self.curv_approx_fact = 0.995
-            self.s1 = 24.90   #25.10
+            self.curv_approx_fact = 0.8810   # origin: # 0.995 
+            self.s1 = 24.90   
             self.s2 = 0.7
-            self.asym0 = -0.0107
+            self.asym0 = 0.06455    ## origin: -0.0107 + 0.07525          # added user asymm 2.75 (-sign)  
 
         else:
             raise ValueError(f"Unknown mirror type: {self.mirror_type}")
@@ -56,23 +56,24 @@ class KBMirror:
         curv0 = curv / self.curv_approx_fact / 1000
         R = 1 / curv0
         s2 = self.s1 / (2 * self.s1 / (R * np.sin(th_mrad / 1000)) - 1)
-        return s2
+        return s2, R
 
-    def get_benders(self, th=2.0, defocus_mm=0.0, user_asymmetry=0.0):
-        asym = self.asym0 + user_asymmetry
+    def get_benders(self, th=3.0, defocus_mm=0.0, user_asymmetry=0.0):
+        asym = self.asym0 - user_asymmetry #### sign is important! 
         s2_target = self.s2 + defocus_mm / 1000
         curv = self.ellipse_to_curvature(s2_target, th)
         BU, BD = self.curvature_to_bend(curv, asym)
         return BU, BD
 
-    def readback(self, BU, BD, th=2.0):
+    def readback(self, BU, BD, th=3.0):
         curv, asym = self.bend_to_curvature(BU, BD)
-        s2_rb = self.curvature_to_ellipse(curv, th)
+        s2_rb, R0 = self.curvature_to_ellipse(curv, th)
         defocus_mm = (s2_rb - self.s2) * 1000
         bend_rad_m = 1/curv *1000
         return {
             "curvature": curv,
-            "bend_rad_m":  bend_rad_m, 
+            "bend_rad_m":  bend_rad_m,
+            "bend_rad0_m":  R0,
             "asymmetry": asym,
             "s2": s2_rb,
             "defocus_mm": defocus_mm

pxii_bec/macros/Undu_Helpers.py

@@ -214,19 +214,22 @@ def gap_harm(e=12.4):
 
 # test with estart = 6, end_en = 7.5 # should be 3 files , estimated time: 90 min 
 
-def long_gscan(estart=7, end_en=20.5, g_low=4.5, g_high=9.0, nsteps=1500):
+def long_gscan(estart=6, end_en=30.5, g_low=4.5, g_high=9.0, nsteps=1500):
     import time
     import numpy as np
 
     dirname = "/sls/x10sa/config/commissioning/Data/"
 
-    enstep = 0.5 
+    enstep = 1.0  
 
     print(
         f"scanning the U19 gap from {estart} keV to {end_en} keV, for a gapsize from {g_low} to {g_high} in {enstep} keV energy intervals"
     )
-    resol = (g_high - g_low) / nsteps
-    print(f"nsteps = {nsteps}; resolution is {resol} mm")
+    fe_h_size0 = dev.fe_sl_xsize.user_readback.get()
+    fe_v_size0 = dev.fe_sl_ysize.user_readback.get()
+    umv(dev.fe_sl_xsize, 0.5)
+    umv(dev.fe_sl_ysize, 0.3)
+    
     dock_area = bec.gui.new("LongGapScan", geometry = [4000, 900, 1200, 700])
     wr = dock_area.new(bec.gui.available_widgets.Waveform)
     mot = dev.id_gap
@@ -248,10 +251,16 @@ def long_gscan(estart=7, end_en=20.5, g_low=4.5, g_high=9.0, nsteps=1500):
     en = estart
 
     while en < end_en:
+        if en >= 17:
+            nsteps = 1000
+            g_high = 7.5 
         #sete(en)
         #time.sleep(0.2)
         #rock()
         # maybe use
+        resol = (g_high - g_low) / nsteps
+        print(f"nsteps = {nsteps}; resolution is {resol} mm")
+
         bl_energy(en, move_gap = False, plot = False)
         
         print(f"setting energy to {en}")
@@ -288,6 +297,12 @@ def long_gscan(estart=7, end_en=20.5, g_low=4.5, g_high=9.0, nsteps=1500):
     elapsed_time = time.perf_counter()-tstart
     print(f"Runtime: {elapsed_time: .6f} seconds, i.e., {elapsed_time: .6f}/60 min")
 
+    # moving slits back
+    umv(dev.fe_sl_xsize, fe_h_size0)
+    umv(dev.fe_sl_ysize, fe_v_size0)
+    # move back to standard En 
+    bl_energy(12400)
+    
     return
 
 

pxii_bec/macros/beamline_planner.py

@@ -95,20 +95,81 @@ class StateChangePlanner:
     def available_states(self):
         """Return a list of available states"""
         return list(self.states.keys())
+    
+    def state_devices(self):
+        """Return the set of devices that participate in
+        beamline state definitions"""
+
+        devices = set()
+
+        for state in self.states.values():
+            devices.update(state.keys())
+        
+        return sorted(devices)
 
     def get_positions(self):
-        """Return current positions of all SE devices"""
-        return {name: dev.pos for name, dev in self.devices.items()}
+        """Return current positions of devices that are used
+        in state transitions"""
+        return {name: self.devices[name].pos
+                for name in self.state_devices()}
 
     def print_positions(self):
         """Return current state of all devices"""
-        for name, device in self.devices.items():
-            print(f"{name:10s} : {device.pos:10s} value: {device.actual}")
+        for name in self.state_devices():
+            device = self.devices[name]
+
+            print(f"{name:10s} : {device.pos:10s} value: {device.actual:.5g}")
 
     def diff_states(self, before):
         """Return a dict of {device: (before, after)} pairs for devices that changed state"""
         after = self.get_positions()
         return {k: (before[k], after[k]) for k in before if before[k] != after[k]}
+    
+    def __get_closest_states(self):
+        """
+        Return states ordered by number of mismatches.
+        """
+
+        results = []
+
+        for state in self.states:
+
+            config = self._merged_state(state, None)
+
+            mismatches = []
+
+            for dev, expected in config.items():
+
+                if not self.devices[dev].is_at(expected):
+                    mismatches.append(
+                        (
+                            dev,
+                            self.devices[dev].pos,
+                            expected,
+                        )
+                    )
+
+            results.append(
+                (
+                    state.name,
+                    len(mismatches),
+                    mismatches,
+                )
+            )
+
+        return sorted(results, key=lambda x: x[1])
+
+    def closest_states(self, n=5):
+
+        for state, count, mismatches in self.__get_closest_states()[:n]:
+
+            print(f"\n{state}: {count} mismatch(es)")
+
+            for dev, actual, expected in mismatches:
+                print(
+                    f"  {dev:15s} "
+                    f"{actual:10s} -> {expected}"
+                )  
 
     def current_state(self):
         """Return all current matching BeamlineState and TemperatureMode combinations,

pxii_bec/macros/beampos_with_en.py

@@ -0,0 +1,19 @@
+def beampos_with_en(planner):
+    deltat = 2 
+    record_beampos_until("23:00",planner,interval_minutes=deltat)
+    bl_energy(6000)
+    record_beampos_until("00:00",planner,interval_minutes=deltat)
+    bl_energy(8000)    
+    record_beampos_until("01:00",planner,interval_minutes=deltat)
+    bl_energy(10000)    
+    record_beampos_until("02:00",planner,interval_minutes=deltat)
+    bl_energy(12000)
+    record_beampos_until("03:00",planner,interval_minutes=deltat)
+    bl_energy(12400)
+    record_beampos_until("04:00",planner,interval_minutes=deltat)
+    bl_energy(14000)
+    record_beampos_until("05:00",planner,interval_minutes=deltat)
+    bl_energy(16000)
+    record_beampos_until("06:00",planner,interval_minutes=deltat)
+    bl_energy(18000)
+    record_beampos_until("07:00",planner,interval_minutes=deltat)

pxii_bec/macros/beampos_with_en.py~

@@ -0,0 +1,9 @@
+def beampos_with_en(planner):
+    deltat = 5 
+    record_beampos_until("23:00",planner,interval_minutes=deltat)
+    bl_energy(16000)
+    record_beampos_until("05:00",planner,interval_minutes=deltat)
+    bl_energy(12400)    
+    record_beampos_until("11:00",planner,interval_minutes=deltat)
+
+    

pxii_bec/macros/build_devices.py

@@ -87,58 +87,276 @@ def build_devices(yaml_file, mock_devices):
     return state_devices
 
 
-# def update_position(device, position, value, axis=None):
-#     """Update the user parameter saved positions for a device"""
-#     params = device.user_parameter
 
-#     if axis is None:
-#         # Is this a nested structure?
-#         nested_keys = [k for k, v in params.items() if isinstance(v, dict)]
+def update_position(
+    device,
+    position,
+    value,
+    axis=None,
+    force=False,
+    max_delta=0.5,
+):
+    """
+    Update a single saved position in memory.
 
-#     if nested_keys:
-#         raise ValueError(
-#             f"{device.name} has nested parameters "
-#             f"({nested_keys}); specify axis=..."
-#         )
+    Does NOT save or reload. Use save_and_reload() or
+    update_positions() afterwards.
+    """
 
-#     device.update_user_parameter({position: value})
-#     return
-
-# axis_params = params[axis].copy()
-# axis_params[position] = value
-# device.update_user_parameter({axis: axis_params})
-
-
-# # Save the updated values to the device yaml file
-# # and re-initialise
-# beamline = os.environ["BEAMLINE"]
-# env = Environment(beamline = beamline)
-# bec.config.save_current_session(env.devices_file)
-# return init_beamline_environment()
-
-
-def update_position(device, position, value, axis=None):
-    """Update the user parameter saved positions for a device"""
     params = device.user_parameter
 
-    if axis is None:
-        nested_keys = [k for k, v in params.items() if isinstance(v, dict)]
+    # ------------------------------------------------------------------
+    # Nested device (e.g. aerotech.x.in)
+    # ------------------------------------------------------------------
+    if axis is not None:
 
-        if nested_keys:
-            raise ValueError(
-                f"{device.name} has nested parameters " f"({nested_keys}); specify axis=..."
+        if axis not in params:
+            raise KeyError(
+                f"Unknown axis '{axis}'. "
+                f"Valid axes: {list(params.keys())}"
             )
 
-        device.update_user_parameter({position: value})
-
-    else:
         axis_params = params[axis].copy()
-        axis_params[position] = value
-        device.update_user_parameter({axis: axis_params})
 
-    # Save the updated values to the device yaml file
+        if position not in axis_params:
+            raise KeyError(
+                f"Unknown position '{position}'. "
+                f"Valid positions: {list(axis_params.keys())}"
+            )
+
+        old_value = axis_params[position]
+
+        delta = abs(value - old_value)
+
+        if delta > max_delta and not force:
+            raise ValueError(
+                f"Refusing to change "
+                f"{device.name}.{axis}.{position} "
+                f"from {old_value} to {value} "
+                f"(Δ={delta:.3f} > {max_delta}). "
+                f"Use force=True if intentional."
+            )
+
+        axis_params[position] = value
+
+        device.update_user_parameter(
+            {axis: axis_params}
+        )
+
+        print(
+            f"Updated {device.name}.{axis}.{position}: "
+            f"{old_value} -> {value}"
+        )
+
+        return
+
+    # ------------------------------------------------------------------
+    # Flat device (e.g. bs_z)
+    # ------------------------------------------------------------------
+    nested_keys = [
+        k for k, v in params.items()
+        if isinstance(v, dict)
+    ]
+
+    if nested_keys:
+        raise ValueError(
+            f"{device.name} has nested parameters "
+            f"({nested_keys}); specify axis=..."
+        )
+
+    if position not in params:
+        raise KeyError(
+            f"Unknown position '{position}'. "
+            f"Valid positions: {list(params.keys())}"
+        )
+
+    old_value = params[position]
+
+    delta = abs(value - old_value)
+
+    if delta > max_delta and not force:
+        raise ValueError(
+            f"Refusing to change "
+            f"{device.name}.{position} "
+            f"from {old_value} to {value} "
+            f"(Δ={delta:.3f} > {max_delta}). "
+            f"Use force=True if intentional."
+        )
+
+    device.update_user_parameter(
+        {position: value}
+    )
+
+    print(
+        f"Updated {device.name}.{position}: "
+        f"{old_value} -> {value}"
+    )
+
+
+def save_and_reload():
+    """
+    Save current configuration to YAML and rebuild
+    the beamline environment.
+    """
+
     beamline = os.environ["BEAMLINE"]
+
     env = Environment(beamline=beamline)
+
+    print(f"(saving changes to {env.devices_file})")
+
     bec.config.save_current_session(env.devices_file)
 
     return init_beamline_environment()
+
+
+def update_positions(updates):
+    """
+    Apply many updates, then save and reload once.
+
+    updates = [
+        {
+            "device": dev.aerotech,
+            "axis": "x",
+            "position": "in",
+            "value": 0,
+        },
+        {
+            "device": dev.bs_z,
+            "position": "max_blout",
+            "value": 68,
+        },
+    ]
+    """
+
+    for update in updates:
+
+        update_position(
+            device=update["device"],
+            position=update["position"],
+            value=update["value"],
+            axis=update.get("axis"),
+            force=update.get("force", False),
+            max_delta=update.get("max_delta", 0.5),
+        )
+
+    return save_and_reload()
+
+
+def save_current_position(
+    device,
+    position,
+    axis=None,
+    force=False,
+    max_delta=0.5,
+):
+    """
+    Save the current motor position as a named position.
+    """
+    print(f"Saving current postion for {device}")
+
+    params = device.user_parameter
+
+    if axis is not None:
+
+        if axis not in params:
+            raise KeyError(
+                f"Unknown axis '{axis}'. "
+                f"Valid axes: {list(params.keys())}"
+            )
+
+        axis_params = params[axis].copy()
+
+        if position not in axis_params:
+            raise KeyError(
+                f"Unknown position '{position}'. "
+                f"Valid positions: {list(axis_params.keys())}"
+            )
+
+        old_value = axis_params[position]
+        print(f"Old value is {old_value}")
+
+        current_value = get_current_value(device, axis=None)
+
+        delta = abs(current_value - old_value)
+
+        if delta > max_delta and not force:
+            raise ValueError(
+                f"Current position differs from saved value:\n"
+                f"  saved   = {old_value}\n"
+                f"  current = {current_value}\n"
+                f"  delta   = {delta:.3f}\n"
+                f"Use force=True if intentional."
+            )
+
+        axis_params[position] = current_value
+
+        device.update_user_parameter(
+            {axis: axis_params}
+        )
+
+        print(
+            f"Updated {device.name}.{axis}.{position}: "
+            f"{old_value} -> {current_value}"
+        )
+
+        return
+    # ------------------------------------------------------------------
+    # Flat device (e.g. bs_z)
+    # ------------------------------------------------------------------
+    nested_keys = [
+        k for k, v in params.items()
+        if isinstance(v, dict)
+    ]
+
+    if nested_keys:
+        raise ValueError(
+            f"{device.name} has nested parameters "
+            f"({nested_keys}); specify axis=..."
+        )
+
+    if position not in params:
+        raise KeyError(
+            f"Unknown position '{position}'. "
+            f"Valid positions: {list(params.keys())}"
+        )
+
+    old_value = params[position]
+    current_value = get_current_value(device, axis=None)
+
+    delta = abs(current_value - old_value)
+
+    if delta > max_delta and not force:
+        raise ValueError(
+            f"Refusing to change "
+            f"{device.name}.{position} "
+            f"from {old_value} to {current_value} "
+            f"(Δ={delta:.3f} > {max_delta}). "
+            f"Use force=True if intentional."
+        )
+
+    device.update_user_parameter(
+        {position: current_value}
+    )
+
+    print(
+        f"Updated {device.name}.{position}: "
+        f"{old_value} -> {current_value}"
+    )
+
+
+def get_current_value(device, axis=None):
+    """
+    Return current motor position.
+    """
+
+    if axis is not None:
+        motor = getattr(device, axis)
+    else:
+        motor = device
+
+    data = motor.read()
+
+    signal_name = next(iter(data))
+
+    return round(data[signal_name]["value"],3)

pxii_bec/macros/calculator.py

@@ -17,6 +17,10 @@ class Constants:
     # d-spacings
     d_spacing = {120: 3.13481, 298: 3.13562}
 
+    # offset
+    # bragg_offset = -0.1457
+    bragg_offset = 0
+
 
 def speed_of_light_ang():
     """
@@ -116,7 +120,9 @@ def calculate_wavelength_from_angle(bragg_angle_mrad: float, temp=120) -> float:
         The calculated wavelength as a float value.
     """
     d = Constants.d_spacing[temp]
-    return 2 * d * np.sin(bragg_angle_mrad / 1000)
+    fudge = bragg_angle_mrad + Constants.bragg_offset
+    # return 2 * d * np.sin((bragg_angle_mrad / 1000)
+    return 2 * d * np.sin(fudge / 1000)
 
 
 def calculate_energy_from_wavelength(wavelength: float) -> float:
@@ -349,7 +355,7 @@ def calc_scam_microns(pixels, zoom=1000):
     """Convert pixels to microns for the sample camera"""
     return float(pixels / (CamConversion.a * np.exp(CamConversion.b * zoom)))
 
-def calc_bsccam_microns(pixels):
-    """Convert pixels to microns for the BSC camera"""
+def calc_sscam_microns(pixels):
+    """Convert pixels to microns for the ss camera"""
     return pixels*20
 

pxii_bec/macros/calibrate_bpm.py

@@ -0,0 +1,142 @@
+"""
+BPM calibration using inverse PCHIP spline.
+
+Calibration concept:
+    BPM signal -> beam position relative to BPM
+
+Final beam position:
+    beam_absolute = bpm_motor_position + local_beam_position
+    beam_relative_to_camera_crosshair = beam_absolute - camera_crosshair_position
+"""
+
+import time
+import os
+from datetime import datetime, timedelta
+from pathlib import Path
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from pyparsing import results
+from scipy.optimize import curve_fit
+import scipy.special as special
+from scipy.interpolate import PchipInterpolator, CubicSpline
+
+
+def read_currents():
+    """Read the individual bpm channels"""
+    vals_bpm1 = []
+    vals_bpm2 = []
+    vals_bpm3 = []
+    vals_bpm4 = []
+
+    for _ in range(5):
+        vals_bpm1.append(dev.bcu_bpm1.read()["bcu_bpm1"]["value"])
+        time.sleep(0.1)
+    for _ in range(5):
+        vals_bpm2.append(dev.bcu_bpm2.read()["bcu_bpm2"]["value"])
+        time.sleep(0.1)
+    for _ in range(5):
+        vals_bpm3.append(dev.bcu_bpm3.read()["bcu_bpm3"]["value"])
+        time.sleep(0.1)
+    for _ in range(5):
+        vals_bpm4.append(dev.bcu_bpm4.read()["bcu_bpm4"]["value"])
+        time.sleep(0.1)
+    bpm1 = np.mean(vals_bpm1)
+    bpm2 = np.mean(vals_bpm2)
+    bpm3 = np.mean(vals_bpm3)
+    bpm4 = np.mean(vals_bpm4)
+   
+    return {"bpm1": bpm1, "bpm2": bpm2, "bpm3": bpm3, "bpm4": bpm4}
+
+
+def compute_norm():
+    """Compute normalised x and y positions"""
+    readings = read_currents()
+    total = readings["bpm1"] + readings["bpm2"] + readings["bpm3"] + readings["bpm4"]
+    # print(f"Sum is {total:.2f}")
+    xn = (
+        (readings["bpm1"] + readings["bpm2"]) - (readings["bpm3"] + readings["bpm4"])
+    ) / total
+    yn = (
+        (readings["bpm1"] + readings["bpm3"]) - (readings["bpm2"] + readings["bpm4"])
+    ) / total
+    # return xn, yn, readings["bpm1"], readings["bpm2"], readings["bpm3"], readings["bpm4"]
+    return float(xn), float(yn)
+
+
+# def compute_pos():
+#     """Compute the beam x and y positions"""
+#     cal = {
+#         "bcu_bpm": {
+#             "x_slope": 7.778,
+#             "x_off": -1.019,
+#             "y_slope": -8.625,
+#             "y_off": -1.935,
+#         }
+#     }
+#     xn, yn, bpm1, bpm2, bpm3, bpm4 = compute_norm()
+#     # print(f"Normalised positions are {xn}, {yn}")
+#     x_pos = (xn - cal["bcu_bpm"]["x_off"]) / cal["bcu_bpm"]["x_slope"]
+#     y_pos = (yn - cal["bcu_bpm"]["y_off"]) / cal["bcu_bpm"]["y_slope"]
+#     return x_pos, y_pos
+
+
+def run_calibration():
+    """Scan bpm in x and y and record normalised x and y positions"""
+    print(f"moving bpm to {centred_x}, {centred_y}")
+    umv(dev.bcu_bpm_y, centred_y)
+    umv(dev.bcu_bpm_x, centred_x)
+    
+    # # Calibrate in X
+    umv(dev.ss_sl_xsize,0.02)
+    umv(dev.ss_sl_xsize, 3.0)
+    filename = f"luts/bpm_calib/{fnow}_bcu_bpm_x.csv"
+    xn_data = []
+    x_positions = np.linspace(centred_x - 0.6, centred_x + 0.6, 101)
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write(f"Xpos,Xnorm,BPM1,BPM2,BPM3,BPM4\n")
+    for x in x_positions:
+        umv(dev.bcu_bpm_x, x)
+        print(f"Moving BPM to {x} mm")
+        time.sleep(0.2)
+        xn, yn, bpm1, bpm2, bpm3, bpm4 = compute_norm()
+        xn_data.append(xn)
+        with open(filename, "a", encoding="utf-8") as f:
+            f.write(f"{x},{xn},{bpm1},{bpm2},{bpm3},{bpm4}\n")
+
+    # Calibrate in Y
+    umv(dev.bcu_bpm_x, centred_x)
+    umv(dev.ss_sl_xsize, 3.0)
+    umv(dev.ss_sl_ysize, 0.03)
+    filename = f"luts/bpm_calib/{fnow}_bcu_bpm_y.csv"
+    y_positions = np.linspace(centred_y - 0.1, centred_y + 0.1, 251)
+    yn_data = []
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write(f"Ypos,Ynorm,BPM1,BPM2,BPM3,BPM4\n")
+    for y in y_positions:
+        umv(dev.bcu_bpm_y, y)
+        print(f"Moving BPM to {y} mm")
+        time.sleep(0.2)
+        xn, yn, bpm1, bpm2, bpm3, bpm4 = compute_norm()
+        yn_data.append(yn)
+        with open(filename, "a", encoding="utf-8") as f:
+            f.write(f"{y},{yn},{bpm1},{bpm2},{bpm3},{bpm4}\n")
+
+    umv(dev.bcu_bpm_x, centred_x)
+    umv(dev.bcu_bpm_y, centred_y)
+    umv(dev.ss_sl_ysize, 3.0)
+
+
+
+def fit_bpm_data(x_y, rundate="02072130_", bpm_name="bcu_bpm"):
+    CALIB_DIR = "luts/bpm_calib/"
+    filename = os.path.join(CALIB_DIR, f"{rundate}{bpm_name}_{x_y}.csv")
+    data = np.loadtxt(filename, delimiter=",", skiprows=1)
+
+    xdata = data[:, 0]   # scan / motor position
+    ydata = data[:, 1]   # normalised BPM signal
+
+    return xdata, ydata
+
+
+

pxii_bec/macros/calibrate_bpm.py.bak

@@ -0,0 +1,670 @@
+"""
+BPM calibration using inverse PCHIP spline.
+
+Calibration concept:
+    BPM signal -> beam position relative to BPM
+
+Final beam position:
+    beam_absolute = bpm_motor_position + local_beam_position
+    beam_relative_to_camera_crosshair = beam_absolute - camera_crosshair_position
+"""
+
+import time
+import os
+from datetime import datetime, timedelta
+from pathlib import Path
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from pyparsing import results
+from scipy.optimize import curve_fit
+import scipy.special as special
+from scipy.interpolate import PchipInterpolator, CubicSpline
+
+
+def read_currents():
+    """Read the individual bpm channels"""
+    vals_bpm1 = []
+    vals_bpm2 = []
+    vals_bpm3 = []
+    vals_bpm4 = []
+
+    for _ in range(5):
+        vals_bpm1.append(dev.bcu_bpm1.read()["bcu_bpm1"]["value"])
+        time.sleep(0.1)
+    for _ in range(5):
+        vals_bpm2.append(dev.bcu_bpm2.read()["bcu_bpm2"]["value"])
+        time.sleep(0.1)
+    for _ in range(5):
+        vals_bpm3.append(dev.bcu_bpm3.read()["bcu_bpm3"]["value"])
+        time.sleep(0.1)
+    for _ in range(5):
+        vals_bpm4.append(dev.bcu_bpm4.read()["bcu_bpm4"]["value"])
+        time.sleep(0.1)
+    bpm1 = np.mean(vals_bpm1)
+    bpm2 = np.mean(vals_bpm2)
+    bpm3 = np.mean(vals_bpm3)
+    bpm4 = np.mean(vals_bpm4)
+   
+    return {"bpm1": bpm1, "bpm2": bpm2, "bpm3": bpm3, "bpm4": bpm4}
+
+
+def compute_norm():
+    """Compute normalised x and y positions"""
+    readings = read_currents()
+    total = readings["bpm1"] + readings["bpm2"] + readings["bpm3"] + readings["bpm4"]
+    # print(f"Sum is {total:.2f}")
+    xn = (
+        (readings["bpm1"] + readings["bpm2"]) - (readings["bpm3"] + readings["bpm4"])
+    ) / total
+    yn = (
+        (readings["bpm1"] + readings["bpm3"]) - (readings["bpm2"] + readings["bpm4"])
+    ) / total
+    # return xn, yn, readings["bpm1"], readings["bpm2"], readings["bpm3"], readings["bpm4"]
+    return float(xn), float(yn)
+
+
+# def compute_pos():
+#     """Compute the beam x and y positions"""
+#     cal = {
+#         "bcu_bpm": {
+#             "x_slope": 7.778,
+#             "x_off": -1.019,
+#             "y_slope": -8.625,
+#             "y_off": -1.935,
+#         }
+#     }
+#     xn, yn, bpm1, bpm2, bpm3, bpm4 = compute_norm()
+#     # print(f"Normalised positions are {xn}, {yn}")
+#     x_pos = (xn - cal["bcu_bpm"]["x_off"]) / cal["bcu_bpm"]["x_slope"]
+#     y_pos = (yn - cal["bcu_bpm"]["y_off"]) / cal["bcu_bpm"]["y_slope"]
+#     return x_pos, y_pos
+
+
+def run_calibration():
+    """Scan bpm in x and y and record normalised x and y positions"""
+    # centred_x = 0.749
+    # centred_y = -0.660
+    now = datetime.now()
+    fnow = now.strftime("%d%m%H%M")
+    centred_x = 0.46
+    centred_y = -1.51
+    print(f"moving bpm to {centred_x}, {centred_y}")
+    umv(dev.bcu_bpm_y, centred_y)
+    umv(dev.bcu_bpm_x, centred_x)
+    
+    # # Calibrate in X
+    umv(dev.ss_sl_xsize,0.02)
+    umv(dev.ss_sl_xsize, 3.0)
+    filename = f"luts/bpm_calib/{fnow}_bcu_bpm_x.csv"
+    xn_data = []
+    x_positions = np.linspace(centred_x - 0.6, centred_x + 0.6, 101)
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write(f"Xpos,Xnorm,BPM1,BPM2,BPM3,BPM4\n")
+    for x in x_positions:
+        umv(dev.bcu_bpm_x, x)
+        print(f"Moving BPM to {x} mm")
+        time.sleep(0.2)
+        xn, yn, bpm1, bpm2, bpm3, bpm4 = compute_norm()
+        xn_data.append(xn)
+        with open(filename, "a", encoding="utf-8") as f:
+            f.write(f"{x},{xn},{bpm1},{bpm2},{bpm3},{bpm4}\n")
+
+    # Calibrate in Y
+    umv(dev.bcu_bpm_x, centred_x)
+    umv(dev.ss_sl_xsize, 3.0)
+    umv(dev.ss_sl_ysize, 0.03)
+    filename = f"luts/bpm_calib/{fnow}_bcu_bpm_y.csv"
+    y_positions = np.linspace(centred_y - 0.1, centred_y + 0.1, 251)
+    yn_data = []
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write(f"Ypos,Ynorm,BPM1,BPM2,BPM3,BPM4\n")
+    for y in y_positions:
+        umv(dev.bcu_bpm_y, y)
+        print(f"Moving BPM to {y} mm")
+        time.sleep(0.2)
+        xn, yn, bpm1, bpm2, bpm3, bpm4 = compute_norm()
+        yn_data.append(yn)
+        with open(filename, "a", encoding="utf-8") as f:
+            f.write(f"{y},{yn},{bpm1},{bpm2},{bpm3},{bpm4}\n")
+
+    umv(dev.bcu_bpm_x, centred_x)
+    umv(dev.bcu_bpm_y, centred_y)
+    umv(dev.ss_sl_ysize, 3.0)
+
+
+
+def fit_bpm_data(x_y, rundate="02072130_", bpm_name="bcu_bpm"):
+    CALIB_DIR = "luts/bpm_calib/"
+    filename = os.path.join(CALIB_DIR, f"{rundate}{bpm_name}_{x_y}.csv")
+    data = np.loadtxt(filename, delimiter=",", skiprows=1)
+
+    xdata = data[:, 0]   # scan / motor position
+    ydata = data[:, 1]   # normalised BPM signal
+
+    return xdata, ydata
+
+def linear_calibration(
+        x_y,
+        bpm_motor_position,
+        crosshair_bpm_signal,
+        rundate = "05071923_",
+        bpm_name = "bcu_bpm",
+        sanity_limit = 0.2,
+        plot = True
+):
+    calib_dir = "luts/bpm_calib"
+    xdata, ydata = fit_bpm_data(x_y, rundate = rundate, bpm_name = bpm_name)
+    # Remove non-linear region
+    if x_y == 'x':
+        mask_limit = 0.59
+    else:
+        mask_limit = 0.9
+    mask = np.abs(ydata) < mask_limit
+    xdata = xdata[mask]
+    ydata = ydata[mask]
+
+    m, c = np.polyfit(xdata, ydata, 1)
+
+    # --- Plot ---
+    plt.figure(figsize=(7, 5))
+    plt.scatter(xdata, ydata, color="red", label=f"BPM {x_y} data")
+
+    x_fit = np.linspace(min(xdata), max(xdata), 100)
+    plt.plot(x_fit, m * x_fit + c, "r--", label=f"Fit: Linear")
+
+    plt.xlabel("label")
+    plt.ylabel("label")
+    plt.title("title")
+    plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+    plt.show()
+
+def make_inverse_spline_calibration(
+    x_y,
+    bpm_motor_position,
+    crosshair_bpm_signal,
+    rundate="05071923_",
+    bpm_name="bcu_bpm",
+    sanity_limit=0.2,
+    plot=True,
+):
+    """
+    Create and save inverse BPM calibration.
+
+    Parameters
+    ----------
+    x_y : str
+        "x" or "y"
+    bpm_motor_position : float
+        Current physical BPM motor position during calibration.
+    crosshair_bpm_signal : float
+        Normalised BPM reading when the beam is on the sample-camera crosshair.
+    mask_limit : float
+        Ignore saturated BPM readings outside +/- mask_limit.
+    sanity_limit : float
+        Warn later if BPM motor has moved by more than this.
+    """
+    CALIB_DIR = "luts/bpm_calib/"
+    xdata, ydata = fit_bpm_data(x_y, rundate=rundate, bpm_name=bpm_name)
+
+    # Remove saturated BPM response
+    if x_y == 'x':
+        mask_limit = 0.98
+    else:
+        mask_limit = 0.91
+    mask = np.abs(ydata) < mask_limit
+    xdata = xdata[mask]
+    ydata = ydata[mask]
+
+    # For inverse calibration: BPM signal -> beam position
+    order = np.argsort(ydata)
+    bpm_signal = ydata[order]
+    local_position = xdata[order]
+
+    # Remove duplicate BPM signal values
+    bpm_signal, unique_idx = np.unique(bpm_signal, return_index=True)
+    local_position = local_position[unique_idx]
+
+    spline = PchipInterpolator(bpm_signal, local_position)
+
+    # Local beam position when beam is visually on camera crosshair
+    crosshair_local_position = float(spline(crosshair_bpm_signal))
+
+    # Absolute beamline coordinate of camera crosshair
+    crosshair_absolute_position = bpm_motor_position + crosshair_local_position
+
+    save_file = os.path.join(CALIB_DIR, f"{bpm_name}_{x_y}_inverse_spline.npz")
+
+    np.savez(
+        save_file,
+        bpm_signal=bpm_signal,
+        local_position=local_position,
+        bpm_motor_position=bpm_motor_position,
+        crosshair_bpm_signal=crosshair_bpm_signal,
+        crosshair_local_position=crosshair_local_position,
+        crosshair_absolute_position=crosshair_absolute_position,
+        mask_limit=mask_limit,
+        sanity_limit=sanity_limit,
+    )
+
+    print(f"Saved {x_y} BPM calibration to:")
+    print(save_file)
+    print()
+    print(f"Calibration BPM motor position: {bpm_motor_position:.6f}")
+    print(f"Crosshair BPM signal:          {crosshair_bpm_signal:.6f}")
+    print(f"Crosshair local position:      {crosshair_local_position:.6f}")
+    print(f"Crosshair absolute position:   {crosshair_absolute_position:.6f}")
+
+    if plot:
+        signal_fit = np.linspace(np.min(bpm_signal), np.max(bpm_signal), 1000)
+        pos_fit = spline(signal_fit)
+
+        plt.figure()
+        plt.scatter(ydata, xdata, label="calibration data")
+        plt.plot(signal_fit, pos_fit, label="inverse PCHIP spline", color='red')
+        plt.axvline(crosshair_bpm_signal, linestyle="--", label="camera crosshair")
+        plt.xlabel(f"{bpm_name}_{x_y} normalised signal")
+        plt.ylabel(f"{x_y} local beam position")
+        plt.title(f"{bpm_name}_{x_y} inverse calibration")
+        plt.grid(True)
+        plt.legend()
+        plt.show()
+
+    return save_file
+
+class BPMCalibration:
+    def __init__(self, filename):
+        data = np.load(filename)
+
+        self.bpm_signal = data["bpm_signal"]
+        self.local_position = data["local_position"]
+
+        self.bpm_motor_position_calib = float(data["bpm_motor_position"])
+        self.crosshair_absolute_position = float(data["crosshair_absolute_position"])
+        self.sanity_limit = float(data["sanity_limit"])
+
+        self.spline = PchipInterpolator(
+            self.bpm_signal,
+            self.local_position,
+        )
+
+    def beam_position(self, bpm_signal, bpm_motor_position, warn=True):
+        """
+        Return beam position relative to the sample-camera crosshair.
+        """
+
+        motor_shift = bpm_motor_position - self.bpm_motor_position_calib
+
+        if warn and abs(motor_shift) > self.sanity_limit:
+            print(
+                "Warning: BPM motor has moved since calibration: "
+                f"delta = {motor_shift:.3f} mm"
+            )
+
+        local_beam_position = float(self.spline(bpm_signal))
+        absolute_beam_position = bpm_motor_position + local_beam_position
+
+        relative_to_crosshair = (
+            absolute_beam_position - self.crosshair_absolute_position
+        )
+
+        return relative_to_crosshair
+
+def get_beampos():
+    x_cal = BPMCalibration("luts/bpm_calib/bcu_bpm_x_inverse_spline.npz")
+    y_cal = BPMCalibration("luts/bpm_calib/bcu_bpm_y_inverse_spline.npz")
+
+    bpm_x_signal, bpm_y_signal = compute_norm()
+
+    beam_x = x_cal.beam_position(
+        bpm_signal=bpm_x_signal,
+        bpm_motor_position=dev.bcu_bpm_x.position,
+    )
+
+    beam_y = y_cal.beam_position(
+        bpm_signal=bpm_y_signal,
+        bpm_motor_position=dev.bcu_bpm_y.position,
+    )
+
+    beam_x_mic = 1000 * beam_x
+    beam_y_mic = 1000 * beam_y
+
+    print(f"Beam relative to camera crosshair:")
+    print(f"X = {beam_x_mic:.4f} um")
+    print(f"Y = {beam_y_mic:.4f} um")
+
+    return beam_x_mic, beam_y_mic
+
+#!/usr/bin/env python
+"""Calculate photon flux from a Si diode current."""
+
+import math
+from dataclasses import dataclass
+
+import numpy as np
+
+
+@dataclass
+class PXIIDiodeConfig:
+    """Diode/beamline-specific constants."""
+
+    si_thickness_um: float = 12.0
+    al_thickness_um: float = 20.0
+    diode_offset_mm: float = 15.0
+
+    si_density: float = 2.33        # g/cm^3
+    al_density: float = 2.699       # g/cm^3
+    air_density: float = 1.205e-3   # g/cm^3
+
+    eps_si: float = 3.62            # eV per electron-hole pair
+
+
+def flux(
+    current_a: float,
+    energy_kev: float | None = None,
+    det_z: float | None = None,
+    config = PXIIDiodeConfig,
+) -> float:
+    """
+    Calculate photon flux from Si diode current.
+
+    Parameters
+    ----------
+    current_a:
+        Diode current in A.
+    energy_kev:
+        Beam energy in keV. Will use the current energy if 
+        not specified.
+    det_z:
+        Detector distance (dev.det_z) (mm). Will use the current
+        detector distance if not specified.
+    config:
+        DiodeConfig containing Si/Al thicknesses and material constants.
+
+    Returns
+    -------
+    float
+        Flux in photons/s.
+    """
+    if energy_kev is None:
+        energy_kev = get_current_energy() / 1000
+
+    if det_z is None:
+        det_z = dev.det_z.position
+
+    air_path_mm = det_z + config.diode_offset_mm
+
+    si_thickness_cm = config.si_thickness_um / 10_000.0
+    al_thickness_cm = config.al_thickness_um / 10_000.0
+
+    log_energy = math.log10(energy_kev)
+
+    # Photoelectric mass attenuation coefficient fits
+    si_poly = np.poly1d([0.0789, -0.477, -2.238, 4.158])
+    al_poly = np.poly1d([0.0638, -0.413, -2.349, 4.106])
+    air_poly = np.poly1d([0.928, -2.348, -1.026, 3.153])
+
+    a_si = 10 ** si_poly(log_energy)
+    a_al = 10 ** al_poly(log_energy)
+    a_air = 10 ** air_poly(log_energy)
+
+    # Fraction of beam absorbed in active Si layer
+    si_absorbed_fraction = -np.expm1(
+        -a_si * config.si_density * si_thickness_cm
+    )
+
+    if si_absorbed_fraction <= 0:
+        raise ValueError("Calculated Si absorption fraction is zero or negative.")
+
+    # Flux before Al/air correction
+    flux = (
+        1000.0
+        * current_a
+        * config.eps_si
+        / 1.602
+        / energy_kev
+        / si_absorbed_fraction
+        * 1.0e13
+    )
+
+    # Transmission through Al and air
+    al_transmission = np.exp(
+        -a_al * config.al_density * al_thickness_cm
+    )
+
+    air_transmission = np.exp(
+        -a_air * config.air_density * air_path_mm / 10.0
+    )
+
+    flux /= al_transmission
+    flux /= air_transmission
+
+
+    print(f"Flux = {flux: .3g} ph/s")
+
+    return float(flux)
+
+def calibrate_bpm_flux(ave = 5):
+    # d['det_z'].move('mse')
+    # d['det_y'].move('xi')
+    # d['det_z'].move(200)
+
+    dir = "luts/bpm_calib/"
+    filename = dir + "bpm_flux_20260708b.csv"
+    with open(filename, 'w', encoding="utf-8") as f:
+        f.write("Energy,Transmission,BPMSum,Flux\n")
+    print("Energy,Transmission,BPMSum,Flux\n")
+    energies = [12400,7000,8000,9000,10000,11000,13000,14000,15000,16000,18000,20000,25000]
+    transmissions = [1, 0.9, 0.8, 0.7, 0.6, 0.5,0.4,0.3,0.2,0.1]
+
+    for energy in energies:
+        print(f"Moving to energy {energy}")
+        bl_energy(energy)
+
+        for set_trans in transmissions:
+            print(f"Setting transmission to {set_trans}")
+            dev.transm.put(set_trans)
+            time.sleep(5)
+
+            flux_values = []
+            bpm_values = []
+
+            print(f"Measuring flux and bpmsum")
+            for _ in range(ave):
+                diode_current = dev.xidiode.read()['xidiode']['value']
+                fl = flux(diode_current)
+                bpmsum = dev.bcu_bpmsum.read()['bcu_bpmsum']['value']
+                flux_values.append(fl)
+                bpm_values. append(bpmsum)
+
+            actual_trans = dev.transm.read()['transm']['value']
+            flux_ave = np.mean(flux_values)
+            bpmsum_ave = np.mean(bpm_values)
+            print(f"{energy:.5g},{actual_trans:.3g},{bpmsum_ave:.4g},{flux_ave:.4f}\n")
+
+            with open(filename, 'a') as f:
+                f.write(f"{energy:.5g},{actual_trans:.3g},{bpmsum_ave:.4g},{flux_ave:.4f}\n")
+    bl_energy(12400)
+    umv(dev.det_y, 70)
+    umv(dev.id_gap, 20)
+
+def analyse_bpm_flux_calibration(
+    filename="luts/bpm_calib/bpm_flux_20260708b.csv",
+    force_zero_intercept=False,
+):
+    data = pd.read_csv(filename)
+
+    results = []
+    with open("luts/bpm_calib/bpm_flux_fits.csv", "w") as f:
+        f.write("Energy,Slope,Intercept\n")
+    for energy, group in data.groupby("Energy"):
+        x = group["BPMSum"].to_numpy(dtype=float)
+        print(f"x is {x}")
+        y = group["Flux"].to_numpy(dtype=float)
+
+        if force_zero_intercept:
+            # y = m*x
+            slope = np.sum(x * y) / np.sum(x * x)
+            intercept = 0.0
+        else:
+            # y = m*x + c
+            slope, intercept = np.polyfit(x, y, deg=1)
+
+        y_fit = slope * x + intercept
+
+        # --- Plot ---
+        plt.figure(figsize=(7, 5))
+        plt.scatter(x, y, color="red")
+
+        x_fit = np.linspace(min(x), max(x), 100)
+        plt.plot(x_fit, slope * x_fit + intercept, 
+        "r--", label=f"Fit: Slope = {slope:.5g}, Intercept = {intercept:.5g}")
+
+        plt.xlabel("BPMSUm")
+        plt.ylabel("Flux")
+        plt.title(f"{energy} eV")
+        plt.legend()
+        plt.grid(True)
+        plt.tight_layout()
+        plt.savefig(f"luts/bpm_calib/{energy}_bpm_flux")
+        # plt.show()
+
+        residuals = y - y_fit
+        ss_res = np.sum(residuals**2)
+        ss_tot = np.sum((y - np.mean(y))**2)
+
+        r2 = 1 - ss_res / ss_tot if ss_tot > 0 else np.nan
+
+        results.append(
+            {
+                "Energy": energy,
+                "Slope": f"{slope:.5g}",
+                "Intercept": f"{intercept:.5g}",
+                "R2": f"{r2:.3g}",
+                # "N": len(group),
+            }
+        )
+
+    results = pd.DataFrame(results)
+
+    results.to_csv("luts/bpm_calib/bpm_flux_fits.csv", index=False)
+    dict = results.to_dict()
+    print(dict)
+    return results
+
+def linear_bpm_flux_calib():
+    filename = 'luts/bpm_calib/fits-25.csv'
+    df = pd.read_csv(filename)
+    energies = df["Energy"].to_numpy()
+    intercepts = df["Intercept"].to_numpy()
+    slopes = df["Slope"].to_numpy()
+    # --- Plot ---
+    plt.figure(figsize=(7, 5))
+    plt.scatter(energies, slopes, color="red")
+
+    x_fit = np.linspace(min(energies), max(energies), 100)
+    coeffs = np.polyfit(energies, slopes,1)
+    y_fit = np.poly1d(coeffs)
+    plt.plot(x_fit, y_fit(x_fit)),    
+    plt.xlabel("Energy eV")
+    plt.ylabel("Slope")
+    plt.title(f"Energies vs slopes, coeffs = {coeffs}")
+    # plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+    plt.savefig(f"luts/bpm_calib/slopes_vs_energy")
+    plt.show()
+    return coeffs
+
+
+# def get_flux_calibration(energy):
+#     # filename = "luts/bpm_calib/bpm_flux_fits.csv"
+#     filename = 'luts/bpm_calib/fits-25.csv'
+#     default_slope = 4.42e8
+#     default_intercept = -1.66e11
+
+#     try:
+#         df = pd.read_csv(filename)
+#         energies = df["Energy"].to_numpy()
+#         intercepts = df["Intercept"].to_numpy()
+#         slopes = df["Slope"].to_numpy()
+#         slope_interp = CubicSpline(energies, slopes)
+#         intercept_interp = CubicSpline(energies, intercepts)
+#         slope = slope_interp(energy)
+#         intercept = intercept_interp(energy)
+#         print(f"Using data from calibration file")
+#          # --- Plot ---
+#         plt.figure(figsize=(7, 5))
+#         plt.scatter(energies, slopes, color="red")
+
+#         x_fit = np.linspace(min(energies), max(energies), 100)
+#         coeffs = np.polyfit(energies, slopes,1)
+#         print("linear co-efficients: ", coeffs)
+#         y_fit = np.poly1d(coeffs)
+#         plt.plot(x_fit, y_fit(x_fit)), 
+#         # "r--", label=f"Fit: Slope = {slope:.5g}, Intercept = {intercept:.5g}")
+      
+#         plt.xlabel("Energy")
+#         plt.ylabel("Slope")
+#         plt.title(f"{energy} eV")
+#         # plt.legend()
+#         plt.grid(True)
+#         plt.tight_layout()
+#         plt.show()
+#     except FileNotFoundError:
+#         print("Cannot access calibration file, using default parameters")
+#         slope = default_slope
+#         intercept = default_intercept
+    
+#     return coeffs
+
+
+def bpm2flux(energy=None, bpmsum=None, transm=None):
+    """
+    Calculate the flux from BCU BPM reading.
+    Option to provide energy, bpmsum reading and transmission. 
+    If these are not given, the current values are used.
+
+    Returns the flux at the sample (after attenuators) and a calculated
+    value of the full flux if attenuation was 0.
+    
+    """
+    calibration_min_energy = 6500
+    calibration_max_energy = 21000
+
+    if energy is None:
+        energy = get_current_energy()
+
+    if bpmsum is None:
+        bpmsum = dev.bcu_bpmsum.read()["bcu_bpmsum"]["value"]
+
+    if transm is None:
+        transm = dev.transm.read()['transm']['value']
+
+    if not calibration_min_energy <= energy <= calibration_max_energy:
+        raise ValueError(
+            f"Energy {energy:.0f} eV is outside the calibrated range "
+            f"{calibration_min_energy:.0f}–"
+            f"{calibration_max_energy:.0f} eV"
+        )
+
+    coeffs = [
+        1.06436277e5,
+        -6.38943746e8,
+    ]
+
+    calibration_factor = np.polyval(coeffs, energy)
+    flux = calibration_factor * bpmsum
+    full_flux = flux/transm
+    if transm == 1:
+        print(f"Transmission = {transm*100:.5g} %, Flux = {flux: .3g} ph/s,\n")
+    else:
+        print(f"Transmission = {transm*100: .5g} %, Flux = {flux: .3g} ph/s,\n"
+            f"Flux with 100 % transmission would be {full_flux: .3g} ph/s")
+
+    return float(flux), float(full_flux)
+
+
+    

pxii_bec/macros/chmirrh.py

@@ -0,0 +1,136 @@
+import numpy as np 
+import math, time 
+import matplotlib.pyplot as plt
+import scipy
+########################################################
+### scan FE slits and check BPMs before and after mirror 
+########################################################
+
+def gaussfit(x,y,p):
+# define a gaussian fitting function where
+# p[0] = amplitude
+# p[1] = mean
+# p[2] = sigma
+# p[3] = const offset 
+    
+    fitfct = lambda p, x0: p[0]*np.exp(-(x0-p[1])**2/(2.0*p[2]**2))+p[3]
+    #fitfct = lambda p, x0: p[0]*np.exp(-(x0-p[1])**2/(2.0*p[2]**2))
+    errfct = lambda p, x0,y0: fitfct(p,x0)-y0  
+
+    # guess fit parameters
+    ind0 = np.where(y==max(y))[0] 
+    p0=np.c_[max(y), x[ind0], np.std(y), np.mean(y[0:5])]
+    #p0=scipy.c_[max(y), np.where(y==max(y))[0], np.std(y)]
+                  # array generation [[]], and taking only one dimens
+    # fit a gaussian to the correlation function
+
+    #print('p0 = ' , p0)
+    p1, success = scipy.optimize.leastsq(errfct, p0.copy()[0],
+                                     args=(x,y))
+
+    #plt.figure()
+    bestfit=fitfct(p1,x)
+    #print('************************', x,y)
+    plt.plot(x,y,'r*')    
+    plt.plot(x,bestfit,'r-')
+
+    # different fit fct
+    #popt, pcov = curve_fit(fitfct, x, y)
+    #newfit=fitfct(popt,x)
+    #plt.plot(x,newfit,'g+')
+
+    
+    plt.show()
+
+    return bestfit, p1
+
+def scan_fe_sl(dir = "y"):
+    if dir in ["y","v"]:
+        sdir ="vertical"
+        fe_size0 = dev.fe_sl_ysize.user_readback.get()
+        print('Closing vertical FE slits to 0.1')  
+        umv(dev.fe_sl_ysize, 0.1)
+        s = scans.line_scan(dev.fe_sl_ycen,-1,1, exp_time=02., steps=21, relative=True)
+        time.sleep(0.1)
+        umv(dev.fe_sl_ysize, fe_size0)
+        print(f'Opening {sdir} FE slits to {fe_size0}')
+    if dir in ["x","h"]:
+        sdir ="horizontal"
+        fe_size0 = dev.fe_sl_xsize.user_readback.get()
+        print('Closing horizontal FE slits to 0.1')  
+        umv(dev.fe_sl_xsize, 0.1)
+        s = scans.line_scan(dev.fe_sl_xcen,-1,1, exp_time=02., steps=21, relative=True)
+        time.sleep(0.1)
+        umv(dev.fe_sl_xsize, fe_size0)
+        print(f'Opening {sdir} FE slits to {fe_size0}')
+    else:
+        print('no valid direction, doing nothing') 
+    
+def mirrh(dir='y'):
+    
+    #dat_ss = read_data_csv("/home/anuschka/SS_pitch2p94.csv")
+    #dat_bcu = read_data_csv("/home/anuschka/BCU_pitch2p94.csv")
+
+    scan_fe_sl(dir=dir)
+    ind = -1
+    if dir in ["x","h"]:
+        slitmot = 'fe_sl_xcen'
+    if dir in ["y","v"]:
+        slitmot = 'fe_sl_ycen'        
+    
+    dat_ss = save_data(ind, slitmot, 'ss_bpmsum', isave = 0)
+    dat_bcu = save_data(ind, slitmot, 'bcu_bpmsum', isave = 0)
+    
+
+    data_xs = dat_ss[0]      # csv would be: dat_ss[:, 0]
+    data_ys = dat_ss[1]
+    
+    data_xb = dat_bcu[0]
+    data_yb = dat_bcu[1]
+
+    #plt.figure()
+
+    #plt.plot(data_xs, data_ys, "*")
+    #plt.plot(data_xb, data_yb, "+")
+
+    data_ys_norm = data_ys/max(data_ys) # looks better than data_ys/sum(data_ys)
+    data_yb_norm = data_yb/max(data_yb)
+
+    plt.figure()
+
+    plt.plot(data_xs, data_ys_norm, "*")
+    plt.plot(data_xb, data_yb_norm, "+")
+
+
+    y= data_ys_norm
+    xm = np.where(y==max(y))[0]
+    xmi = data_xs[xm]
+    #print('xm, xmi', xm, xmi) 
+
+    pf1 = [1, xmi , np.std(data_ys_norm), np.mean(y[0:5])]     # bg = 0 
+    fit_s, ps = gaussfit(data_xs,data_ys_norm, pf1) 
+
+    y= data_yb_norm
+    xm = np.where(y==max(y))[0]
+    xmi = data_xb[xm]
+    #print('xm, xmi', xm, xmi) 
+
+    pf2 = [1, xmi, np.std(data_yb_norm), np.mean(y[0:5])]  # bg = -11
+    fit_b, pb = gaussfit(data_xb,data_yb_norm, pf2) 
+
+
+    plt.figure()
+
+    plt.plot(data_xs, fit_s-ps[3], "*")
+    plt.plot(data_xs, fit_s-ps[3])
+    plt.plot(data_xb, fit_b-pb[3], "+")
+    plt.plot(data_xb, fit_b-pb[3])
+
+    print('Fit Params of First BPM, SS, ps', ps)
+    print('Fit Params of Second BPM, BCU, pb', pb)
+    print()
+    if abs(ps[1]-pb[1]) > 0.05:
+        print('***** WARNING ******, mirror height not ok')
+    if abs(ps[2]-pb[2]) > 0.05:
+        print('***** WARNING ******, mirror not catching full beam')
+    

pxii_bec/macros/devices_manager.py

@@ -1,9 +1,9 @@
 """
 This module manages the initialization of devices."""
 
-# from guards import attach_guards
-# from policies import attach_policies
-# from build_devices import build_devices
+#from guards import attach_guards
+#from policies import attach_policies
+#from build_devices import build_devices
 
 
 class DeviceManager:

pxii_bec/macros/enScan.py

@@ -0,0 +1,83 @@
+def en_plothist(hindex: int, signal_name: str, isave=1):
+    """
+    Get data for a completed scan from the BEC history,
+    and plot all the results.
+
+    Args:
+        history_index (int): scan to fetch, e.g. -1 for the most recent scan
+        signal_name (str): the monitor
+
+    Example:
+    s=scans.line_scan(dev.dcm_bragg, 178.5, 179.5, steps=240, relative=False
+    data_x, data_y = en_plothist(-1,"bcu_bpmsum")
+
+    scanned with 
+    scans.line_scan(dev.aerotech.y,-0.05,0.05, steps=101, settling_time=0.5, relative=True)
+    """
+
+    from lmfit.models import LinearModel, GaussianModel, VoigtModel, QuadraticModel, LorentzianModel
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    if hindex < 0:
+        h = bec.history[hindex]
+        print('by scan number')
+    else:
+        h = bec.history.get_by_scan_number(hindex)
+        print('by scan number')
+
+    md = h.metadata["bec"]
+    sno = md['dataset_number']
+    print(f"number of scan = {sno}")
+    scanvar = list(md["args"].keys())[0]  # string, returns the variable of the last performed scan
+    # data = h.devices[device_name][signal].read()["value"]
+    # data_x = h.devices.dcm_pitch.dcm_pitch.read()["value"]
+    # data_y = h.devices.lu_bpmsum.lu_bpmsum.read()["value"]
+    # data_x = h.devices[device_name][device_name].read()["value"]
+
+    # that works ... 
+    #print(list(md['args'].keys()))
+    val = list(md['args'].keys())
+    #val[0] = 'aerotech.x'
+    devicenam =  val[0]
+    print('device = ', devicenam)
+    data_xd = md["positions"]  # last scan knows which device ... however, double array
+    data_x = data_xd.flatten()
+    print("dx = ", data_x)
+    #data_y = h.devices[signal_name][signal_name].read()["value"]
+    data_y1 = h.devices["bcu_bpmsum"]["bcu_bpmsum"].read()["value"]
+    data_y2 = h.devices["ss_bpmsum"]["ss_bpmsum"].read()["value"]
+    
+    #print("dy = ", data_y)
+
+    data_y = data_y1/data_y2
+    rev = 0 
+    #if val[0][9] == 'x':
+    #     rev = 1
+    smoothplotgrad(data_x, data_y, reverse = rev)
+   # g, xm = justfit(data_x, data_y, model=model, ibg=ibg)
+
+#    plt.ion()
+#    plt.figure()
+#    plt.plot(data_x, data_y, "*")
+#    plt.plot(data_x, g.best_fit)
+#    plt.xlabel(scanvar)
+#    plt.ylabel(signal_name)
+#    plt.show()
+
+#    gcen = g.pars["center"].value
+
+
+    if isave: 
+        ans = "n"
+        ans = input("Store data in csv file? y/n ")
+        if ans == "y":
+            dirname = "/sls/x10sa/config/commissioning/Data/"
+            # writing output to simple data file for later analysis:
+            combined = np.column_stack((data_x, data_y))
+            filename = dirname + "Scan" + str(sno) + devicenam + ".txt"
+            with open(filename, "w") as f:
+                np.savetxt(f, combined, delimiter=",", fmt="%.9e")
+
+
+    return data_x, data_y

pxii_bec/macros/flux_di.py

@@ -89,38 +89,43 @@ def fluxcalc(curr, en, t_air, t_si, t_al):
 # Main
 #
 
-def flux_x10sa():
-    curr = input('Please enter the current [in A] ')
+def flux_x10sa(curr, en, silent = 0):
+    # curr = input('Please enter the current [in A] ')
     curr = float(curr)
-    print('Current =  ', curr)
-    en = input('Please enter the energy [in keV] ')
-    en = float(en) 
-    print('Energy =  ', en)
+    if not silent:
+        print('Current =  ', curr)
+    # en = input('Please enter the energy [in keV] ')
+    en = float(en)
+    if not silent:
+        print('Energy =  ', en)
     ## d_det = input('Please enter the detector distance [in mm] ')
     d_det = dev.det_z.user_readback.get()
     d_off = 15 ## at X10SA   ### input('Please enter the OFFSET of the diode to det surface [in mm] ') 
     
     t_air = float(d_det) +  float(d_off)
-    print('Air path length (CARE ! add det distance and offset of diode) =  ', t_air) # include additional diode distance
 
-    #  t_si = input('Please enter the Si thickness of diode [in um] ') 
-    #  t_si = float(t_si) / 10000. # to cm 
-
-    t_si = 12 # um ## at X10SA
-    #  t_al = input('Please enter the Al thickness of diode [in um] ') 
-    #  t_al = float(t_al) / 10000. # to cm
+    t_si = 12 # um ## at X10SA    
+    #  t_si = input('Please enter the Si thickness of diode [in um] ') #  if not silent:
+    t_si = float(t_si) / 10000. # to cm 
 
     t_al = 20 # um ## at X10SA
+    #  t_al = input('Please enter the Al thickness of diode [in um] ') 
+    t_al = float(t_al) / 10000. # to cm
+
+    if not silent:
+        print('Air path length (CARE ! add det distance and offset of diode) =  ', t_air) # include additional diode distance
+
+
     fl = fluxcalc(curr, en, t_air,t_si, t_al)
+    if not silent:
+        print( " Diode Current = %.6f  A " % curr)
+        print( " Energy = %.4f  keV "  % en )
 
-    print( " Diode Current = %.6f  A " % curr)
-    print( " Energy = %.4f  keV "  % en )
+        #print( " Thickness of active Si layer = %.4f\t  cm " % t_si) #*10000. 
+        #print( " Thickness of Al layer in front of diode = %.4f\t  cm "  % t_al) #*10000. 
+        print( " Length of path of air in front of diode = %.4f\t  mm " % t_air) #*10000.
 
-    print( " Thickness of active Si layer = %.4f\t  cm " % t_si) #*10000. 
-    print( " Thickness of Al layer in front of diode = %.4f\t  cm "  % t_al) #*10000. 
-    print( " Length of path of air in front of diode = %.4f\t  mm " % t_air) #*10000.
-
-    print (" Flux = %6.2e\t ph/s "  % fl)
+        print (" Flux = %6.2e\t ph/s "  % fl)
 
     return fl
 

pxii_bec/macros/init_beamline.py

@@ -30,8 +30,7 @@ class Environment:
         },
         "x06da": {
             "devdir": "/sls/x06da/config/bec/production/pxiii_bec/pxiii_bec/device_configs/",
-            "state": "pxiii-state-devices.yaml",
-            "rest": "pxiii-rest-devices.yaml",
+            "state": "pxiii-devices.yaml",
             "states": "pxiii-beamline-states.yaml",
         },
     }
@@ -85,7 +84,7 @@ class Environment:
         "aerotech_u": "mount",
         "bl_bright": "off",
         "bl_pos": "out",
-        "bs_pos": "out",
+        "bs_pos": "in",
         "bs_z": "safe",
         "coll_y": "park",
         "cryo_pos": "in",

pxii_bec/macros/knifehist.py

@@ -0,0 +1,79 @@
+def knife_plothist(hindex: int, signal_name: str, isave=1):
+    """
+    Get data for a completed scan from the BEC history,
+    and plot all the results.
+
+    Args:
+        history_index (int): scan to fetch, e.g. -1 for the most recent scan
+        signal_name (str): the monitor
+
+    Example:
+    data_x, data_y = knife_plothist(-1,"xidode")
+
+    scanned with 
+    scans.line_scan(dev.aerotech.y,-0.05,0.05, steps=101, settling_time=0.5, relative=True)
+    """
+
+    from lmfit.models import LinearModel, GaussianModel, VoigtModel, QuadraticModel, LorentzianModel
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    if hindex < 0:
+        h = bec.history[hindex]
+        print('by scan number')
+    else:
+        h = bec.history.get_by_scan_number(hindex)
+        print('by scan number')
+
+    md = h.metadata["bec"]
+    sno = md['dataset_number']
+    print(f"number of scan = {sno}")
+    scanvar = list(md["args"].keys())[0]  # string, returns the variable of the last performed scan
+    # data = h.devices[device_name][signal].read()["value"]
+    # data_x = h.devices.dcm_pitch.dcm_pitch.read()["value"]
+    # data_y = h.devices.lu_bpmsum.lu_bpmsum.read()["value"]
+    # data_x = h.devices[device_name][device_name].read()["value"]
+
+    # that works ... 
+    #print(list(md['args'].keys()))
+    val = list(md['args'].keys())
+    #val[0] = 'aerotech.x'
+    devicenam =  val[0]
+    print('device = ', devicenam)
+    data_xd = md["positions"]  # last scan knows which device ... however, double array
+    data_x = data_xd.flatten()
+    print("dx = ", data_x)
+    #data_y = h.devices[signal_name][signal_name].read()["value"]
+    data_y = h.devices["xidiode"]["xidiode"].read()["value"]    
+    print("dy = ", data_y)
+
+    rev = 0 
+    if val[0][9] == 'x':
+        rev = 1
+    smoothplotgrad(data_x, data_y, reverse = rev)
+   # g, xm = justfit(data_x, data_y, model=model, ibg=ibg)
+
+#    plt.ion()
+#    plt.figure()
+#    plt.plot(data_x, data_y, "*")
+#    plt.plot(data_x, g.best_fit)
+#    plt.xlabel(scanvar)
+#    plt.ylabel(signal_name)
+#    plt.show()
+
+#    gcen = g.pars["center"].value
+
+
+    if isave: 
+        ans = "n"
+        ans = input("Store data in csv file? y/n ")
+        if ans == "y":
+            dirname = "/sls/x10sa/config/commissioning/Data/"
+            # writing output to simple data file for later analysis:
+            combined = np.column_stack((data_x, data_y))
+            filename = dirname + "Scan" + str(sno) + devicenam + ".txt"
+            with open(filename, "w") as f:
+                np.savetxt(f, combined, delimiter=",", fmt="%.9e")
+
+
+    return data_x, data_y

pxii_bec/macros/luts/20260704-1508_ge_edge.csv

@@ -0,0 +1,6 @@
+Energy,bcu_bpmsum
+11125.712083383152,25032.909677419353
+11125.50607729644,25067.271582181264
+11125.300078962937,25072.67158218126
+11125.094088382204,25096.84485407066
+11124.888105553806,25110.599078341016

pxii_bec/macros/luts/20260704-1509_ge_edge.csv

@@ -0,0 +1,301 @@
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pxii_bec/macros/luts/27061952_vfm_pitch_energy.csv

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pxii_bec/macros/luts/27062057_vfm_pitch_energy.csv

@@ -0,0 +1,51 @@
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pxii_bec/macros/luts/27062107_vfm_pitch_energy.csv

@@ -0,0 +1,46 @@
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pxii_bec/macros/luts/27062148_lut.csv

@@ -0,0 +1,5 @@
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pxii_bec/macros/luts/27062203_lut.csv

@@ -0,0 +1,78 @@
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pxii_bec/macros/luts/2706_dcm_lut.csv

@@ -0,0 +1,83 @@
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pxii_bec/macros/luts/I1flcalf.csv

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pxii_bec/macros/luts/beampos/beam_pos_record_0107.csv

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pxii_bec/macros/luts/beampos/beam_pos_record_0207.csv

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pxii_bec/macros/luts/beampos/beam_pos_record_02072247.csv

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pxii_bec/macros/luts/beampos/beam_pos_record_02072249.csv

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pxii_bec/macros/luts/beampos/beam_pos_record_0307.csv

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pxii_bec/macros/luts/beampos/beam_pos_record_05072309.csv

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pxii_bec/macros/luts/beampos/beam_pos_record_05072312.csv

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pxii_bec/macros/luts/beampos/beam_pos_record_10072258.csv

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pxii_bec/macros/luts/beampos/beam_pos_record_10072259.csv

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pxii_bec/macros/luts/beampos/beam_pos_record_11072251.csv

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pxii_bec/macros/luts/beampos/beampos17_18Jul12p4_16_12p4.csv

@@ -0,0 +1,214 @@
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pxii_bec/macros/luts/beampos/beampos_0207.csv

@@ -0,0 +1,57 @@
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pxii_bec/macros/luts/beampos/beampos_17071707.csv

@@ -0,0 +1,71 @@
+Beam size: 25 x 25, Energy: 12400 eV, Zoom: 1000.0
+Time,Scin_x,Scin_y,BPM_x,BPM_y
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pxii_bec/macros/luts/beampos/beampos_17072256.csv

@@ -0,0 +1,74 @@
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pxii_bec/macros/luts/beampos/beampos_18070501.csv

@@ -0,0 +1,73 @@
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pxii_bec/macros/luts/beampos/beampos_18071135.csv

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pxii_bec/macros/luts/beampos/beampos_18071800.csv

@@ -0,0 +1,73 @@
+Beam size: 25 x 25, Energy: 7000 eV, Zoom: 1000.0
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pxii_bec/macros/luts/beampos/beampos_19070000.csv

@@ -0,0 +1,73 @@
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pxii_bec/macros/luts/beampos/beampos_19070559.csv

@@ -0,0 +1,73 @@
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pxii_bec/macros/luts/beampos/beampos_19071158.csv

@@ -0,0 +1,73 @@
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pxii_bec/macros/luts/beampos/beampos_19071756.csv

@@ -0,0 +1,41 @@
+Beam size: 25 x 25, Energy: 12400 eV, Zoom: 1000.0
+Time,Scin_x,Scin_y,BPM_x,BPM_y
+17:57:03, 1081.3, 875.8,-0.11122, 0.045013
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pxii_bec/macros/luts/beampos/beampos_19072236.csv

@@ -0,0 +1,14 @@
+Beam size: 25 x 25, Energy: 12400 eV, Zoom: 1000.0
+Time,Scin_x,Scin_y,BPM_x,BPM_y
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pxii_bec/macros/luts/beampos/beampos_19072303.csv

@@ -0,0 +1,29 @@
+Beam size: 25 x 25, Energy: 6000 eV, Zoom: 1000.0
+Time,Scin_x,Scin_y,BPM_x,BPM_y
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pxii_bec/macros/luts/beampos/beampos_20070000.csv

@@ -0,0 +1,31 @@
+Beam size: 25 x 25, Energy: 8000 eV, Zoom: 1000.0
+Time,Scin_x,Scin_y,BPM_x,BPM_y
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pxii_bec/macros/luts/beampos/beampos_20070100.csv

@@ -0,0 +1,31 @@
+Beam size: 25 x 25, Energy: 10000 eV, Zoom: 1000.0
+Time,Scin_x,Scin_y,BPM_x,BPM_y
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pxii_bec/macros/luts/beampos/beampos_20070200.csv

@@ -0,0 +1,30 @@
+Beam size: 25 x 25, Energy: 12000 eV, Zoom: 1000.0
+Time,Scin_x,Scin_y,BPM_x,BPM_y
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pxii_bec/macros/luts/beampos/beampos_20070258.csv

@@ -0,0 +1,31 @@
+Beam size: 25 x 25, Energy: 12400 eV, Zoom: 1000.0
+Time,Scin_x,Scin_y,BPM_x,BPM_y
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pxii_bec/macros/luts/beampos/beampos_20070359.csv

@@ -0,0 +1,31 @@
+Beam size: 25 x 25, Energy: 14000 eV, Zoom: 1000.0
+Time,Scin_x,Scin_y,BPM_x,BPM_y
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pxii_bec/macros/luts/beampos/beampos_20070459.csv

@@ -0,0 +1,31 @@
+Beam size: 25 x 25, Energy: 16000 eV, Zoom: 1000.0
+Time,Scin_x,Scin_y,BPM_x,BPM_y
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pxii_bec/macros/luts/beampos/beampos_20070559.csv

@@ -0,0 +1,31 @@
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pxii_bec/macros/luts/beampos/beampos_20260718-19_10-7-9-12p67-15-12p4.csv

@@ -0,0 +1,365 @@
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pxii_bec/macros/luts/beampos/beampos_20260719_20_12p4_6to18.csv

@@ -0,0 +1,243 @@
+Beam size: 25 x 25, Energy: 12400 eV, Zoom: 1000.0
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pxii_bec/macros/luts/beampos/beampos_3rdJul.csv

@@ -0,0 +1,50 @@
+Beam size: focus, Energy: 12400 eV, Zoom: 116.0
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pxii_bec/macros/luts/bpm_calib/bpm_flux_20260708b.csv

@@ -0,0 +1,131 @@
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pxii_bec/macros/luts/bpm_calib/bpm_flux_fits.csv

@@ -0,0 +1,13 @@
+Energy,Slope,Intercept,R2
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+20000,1.522e+09,0,0.997

pxii_bec/macros/luts/bpm_calib/fits-25.csv

@@ -0,0 +1,13 @@
+Energy,Slope,Intercept,R2
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pxii_bec/macros/luts/roll_20000.csv

@@ -0,0 +1,10 @@
+Roll, Y pos
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+2,351.5 
+3,388.1 
+4,425 
+5,460 
+6,494.7 
+7,526.9 
+8,558.8 
+9,590.4 

pxii_bec/macros/luts/roll_6000.csv

@@ -0,0 +1,12 @@
+Roll, Y pos
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+2.5,194.1 
+3,260.1 
+3.5,325.8 
+4,387.4 
+4.5,446.9 
+5,504.4 
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+7,705.2 

pxii_bec/macros/luts/vfm_pitch_energy.csv

@@ -0,0 +1,96 @@
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pxii_bec/macros/mx_basics.bak

@@ -0,0 +1,313 @@
+"""Get data from an h5 file or BEC history and perform fitting."""
+
+import numpy as np
+from lmfit.models import (
+    GaussianModel,
+    LorentzianModel,
+    VoigtModel,
+    ConstantModel,
+    LinearModel,
+)
+from scipy.ndimage import gaussian_filter1d
+import h5py
+import matplotlib.pyplot as plt
+
+
+def create_fit_parameters(
+    deriv: bool = False,
+    negative: bool = False,
+    model: str = "Voigt",
+    baseline: str = "Linear",
+    smoothing: None = None,
+):
+    """Store the fit parameters in a dictionary."""
+    # map input model to lmfit model name
+    model_mappings = {
+        "Gaussian": GaussianModel,
+        "Lorentzian": LorentzianModel,
+        "Voigt": VoigtModel,
+        "Constant": ConstantModel,
+        "Linear": LinearModel,
+    }
+    return {
+        "deriv": deriv,
+        "negative": negative,
+        "model": model_mappings[model],
+        "baseline": model_mappings[baseline],
+        "smoothing": smoothing,
+    }
+
+
+def get_data_from_h5(signal_name: str = "lu_bpmsum"):
+    """Get data from an h5 file."""
+    root, name = get_signal_values(signal_name)
+    with h5py.File("scan_676.h5", "r") as f:
+        entry = f["entry"]["collection"]
+        y_data = entry["devices"][root][name]["value"][:]
+        motor_data = entry["metadata"]["bec"]
+        motor_name = motor_data["scan_motors"][0].decode()
+        scan_number = motor_data["scan_number"][()]
+        x_data = entry["devices"][motor_name][motor_name]["value"][:]
+    return {
+        "x_data": x_data,
+        "y_data": y_data,
+        "signal_name": signal_name,
+        "motor_name": motor_name,
+        "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_signal_values(signal_name):
+
+    try:
+        mot = getattr(dev,signal_name)
+        root_name = signal_name
+    except:
+        root_name = signal_name.split("_", maxsplit=1)[0]
+    
+    return root_name, signal_name
+
+
+def get_data_from_history(
+    scan: int,
+    signal_name: str = "lu_bpmsum",
+):
+    """Read data from the BEC history and return the X and Y data as arrays."""
+
+    data = get_history(scan)
+    md = data.metadata["bec"]
+    motor_name = md["scan_report_devices"][0].decode()
+    mot_name = motor_name.replace(".","_")
+    print(data.devices)
+    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"]
+    
+    # y_data = data.devices[signal_name][signal_name].read()["value"]
+    root, name = get_signal_values(signal_name)
+    y_data = data.devices[root][name].read()["value"]
+    return {
+        "signal_name": signal_name,
+        "x_data": x_data,
+        "y_data": y_data,
+        "motor_name": mot_name,
+        "scan_number": float(scan_number),
+    }
+
+
+def process_data(data, fit_params):
+    """
+    Process the signal data for fitting based on derivative or smoothing.
+    """
+    smoothing = fit_params["smoothing"]
+    deriv = fit_params["deriv"]
+    negative = fit_params["negative"]
+
+    signal_name = data["signal_name"]
+    y_data = data["y_data"]
+
+    if smoothing and smoothing > 0.01:
+        fitting_data = gaussian_filter1d(y_data, smoothing)
+        signal_name = f"Smoothed {signal_name}"
+    else:
+        fitting_data = y_data.copy()
+
+    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."""
+    # Create the model
+    peak_model = fit_params["model"](prefix="peak_")
+    baseline_model = fit_params["baseline"](prefix="base_")
+    full_model = peak_model + baseline_model
+
+    # Prepare data
+    processed_data = process_data(data, fit_params)
+    params = full_model.make_params()
+    y_min = np.min(processed_data["y_to_fit"])
+
+    # Configure baseline parameters
+    if fit_params["baseline"] == ConstantModel:
+        params["base_c"].set(value=y_min)
+    elif fit_params["baseline"] == LinearModel:
+        params["base_intercept"].set(value=y_min)
+        params["base_slope"].set(value=0)
+
+    # Add peak-specific parameters
+    params.update(
+        peak_model.guess(processed_data["y_to_fit"], x=processed_data["x_data"])
+    )
+
+    # Perform the fitting
+    lmfit_result = full_model.fit(
+        processed_data["y_to_fit"], params, x=processed_data["x_data"]
+    )
+
+    # Find the X that gives the max Y
+    max_index = np.argmax(processed_data["y_to_fit"])
+    x_max = processed_data["x_data"][max_index]
+
+    # Generate data for a smoothed fit curve
+    fit_xdata = np.linspace(np.min(data["x_data"]), np.max(data["x_data"]), 500)
+    fit_ydata = lmfit_result.eval(x=fit_xdata, params=lmfit_result.params)
+
+    # Collect results
+    return {
+        "model": fit_params["model"].__name__,
+        "fwhm": lmfit_result.params["peak_fwhm"].value,
+        "centre": lmfit_result.best_values["peak_center"],
+        "height": lmfit_result.params["peak_height"].value,
+        "chi_sq": lmfit_result.chisqr,
+        "lmfit_result": lmfit_result,
+        "x_max": x_max,
+        "fit_xdata": fit_xdata,
+        "fit_ydata": fit_ydata,
+    }
+
+
+def plot_fitted_data(data, fit_result):
+    """Plot the original data and the fitted model."""
+    plt.plot(data["x_data"], data["y_to_fit"], label="Data")
+    plt.plot(fit_result['fit_xdata'], fit_result['fit_ydata'], label="Fit")
+    plt.xlabel(data["motor_name"])
+    plt.ylabel(data["signal_name"])
+    plt.title(f"Scan {data['scan_number']}, fitted with {fit_result['model']}")
+    plt.grid(True)
+    plt.legend()
+    plt.show()
+
+
+def select_bec_window(dock_area_name="Fitting"):
+    """Check to see if the fitting results dock is already open and re-create it if not"""
+    open_docks = bec.gui.windows
+    if open_docks.get(dock_area_name) is None:
+        dock_area = bec.gui.new(dock_area_name)
+        # wf = dock_area.new("Plot").new(bec.gui.available_widgets.Waveform)
+        wf = dock_area.new(widget='Waveform', object_name='Plot')
+        text_box = dock_area.new(widget='TextBox', object_name="Results", where="bottom")
+    else:
+        wf = bec.gui.Fitting.Plot
+        text_box = bec.gui.Fitting.Results
+    return wf, text_box
+
+
+def plot_live_data_bec(
+    motor_name,
+    signal_name,
+    window_name="Fitting"
+):
+    """
+    Plotting live data for motor and signal using BEC.
+
+    This function plots live data from a specified motor and signal.
+    It clears the current plot window, sets its title, labels the axes
+    with the provided motor and signal names, and initializes live plotting
+    on the given signal against the motor.
+
+    Args:
+        motor_name (str): The name of the motor to be used as the x-axis.
+        signal_name (str): The name of the signal to be used as the y-axis.
+
+    Returns:
+        None
+    """
+    root, name = get_signal_values(signal_name)
+    wf, text_box = select_bec_window(window_name)
+    text_box.set_plain_text("Plotting live data")
+    wf.clear_all()
+    wf.title = "Scan: Live scan"
+    wf.x_label = motor_name
+    wf.y_label = signal_name
+    wf.plot(device_x=motor_name, device_y=root)
+
+
+
+
+def plot_fitted_data_bec(
+    data,
+    fit_result,
+):
+    """
+    Plot fitted data and display fitting parameters in the specified window.
+
+    This function selects a BEC window and plots the original data along with the
+    fitted function. Additionally, it displays the fitting results in a text
+    box within the same window for better visualization of the fit results.
+
+    Parameters:
+    data : dict
+        Dictionary containing the original dataset, where 'x_data' and 'y_to_fit'
+        hold the independent variable and the dependent variable, respectively,
+        'scan_number' represents the scan number, 'motor_name' and 'signal_name'
+        provide axis labels.
+    fit_result : dict
+        Dictionary containing the results of the fit, including parameters such
+        as 'centre', 'fwhm', 'height', and the fitted model stored under
+        'lmfit_result', with its 'best_fit' attribute representing the fitted data.
+    """
+    wf, text_box = select_bec_window()
+    fit_text = (
+        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}"
+    )
+    text_box.set_plain_text(fit_text)
+    wf.clear_all()
+    wf.title = f"Scan: {data['scan_number']}"
+    wf.x_label = data["motor_name"]
+    wf.y_label = data["signal_name"]
+    wf.plot(x=data["x_data"], y=data["y_to_fit"], label="Data")
+    wf.plot(x=fit_result["fit_xdata"], y=fit_result["fit_ydata"], label="Fit")
+    # wf.Fit.set(symbol_size = 0)
+    wf.get_curve('Fit').set(symbol_size=0)
+

pxii_bec/macros/mx_basics.py

@@ -1,5 +1,5 @@
 """Get data from an h5 file or BEC history and perform fitting."""
-
+import re
 import numpy as np
 from lmfit.models import (
     GaussianModel,
@@ -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,
@@ -30,47 +31,94 @@ def create_fit_parameters(
     }
     return {
         "deriv": deriv,
+        "negative": negative,
         "model": model_mappings[model],
         "baseline": model_mappings[baseline],
         "smoothing": smoothing,
     }
 
 
-def get_data_from_h5(signal_name: str = "lu_bpmsum"):
-    """Get data from an h5 file."""
-    with h5py.File("scan_676.h5", "r") as f:
-        entry = f["entry"]["collection"]
-        y_data = entry["devices"][signal_name][signal_name]["value"][:]
-        motor_data = entry["metadata"]["bec"]
-        motor_name = motor_data["scan_motors"][0].decode()
-        scan_number = motor_data["scan_number"][()]
-        x_data = entry["devices"][motor_name][motor_name]["value"][:]
-    return {
-        "x_data": x_data,
-        "y_data": y_data,
-        "signal_name": signal_name,
-        "motor_name": motor_name,
-        "scan_number": str(scan_number),
-    }
+# def get_data_from_h5(signal_name: str = "lu_bpmsum"):
+#     """Get data from an h5 file."""
+#     root, name = get_signal_values(signal_name)
+#     with h5py.File("scan_676.h5", "r") as f:
+#         entry = f["entry"]["collection"]
+#         y_data = entry["devices"][root][name]["value"][:]
+#         motor_data = entry["metadata"]["bec"]
+#         motor_name = motor_data["scan_motors"][0].decode()
+#         scan_number = motor_data["scan_number"][()]
+#         x_data = entry["devices"][motor_name][motor_name]["value"][:]
+#     return {
+#         "x_data": x_data,
+#         "y_data": y_data,
+#         "signal_name": signal_name,
+#         "motor_name": motor_name,
+#         "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_value(dev_name, all=False):
+    if dev_name == dev.scam:
+        root = 'scam'
+        live_root = 'scam'
+        name = 'scam_contrast'
+        value = dev_name.read()[name]['value']
+    else:
+        try:
+            name = live_root = root = dev_name.name
+            value = dev_name.read()[name]['value']
+        except:
+            print("non-standard device")
+            root = dev_name.dotted_name
+            name = dev_name.full_name
+            live_root = dev_name.parent.name
+            value = dev_name.read()[name]['value']
+ 
+
+    if all:
+        return {"value": value, "root": root, "name": name, "live_root": live_root}
+
+    return value
 
 
 def get_data_from_history(
-    history_index: int,
-    signal_name: str = "lu_bpmsum",
+    scan: int,
+    signal_dev,
 ):
     """Read data from the BEC history and return the X and Y data as arrays."""
-    scan = bec.history[history_index]
-    md = scan.metadata["bec"]
-    motor_name = md["scan_motors"][0].decode()
+
+    data = get_history(scan) 
+    md = data.metadata["bec"]
+    motor_name = md["scan_report_devices"][0].decode()
+    if '.' in motor_name:
+        root = motor_name.split(".")[0]
+        name = motor_name.replace(".","_")
+    else:
+        root = motor_name
+        name = motor_name
+
+
+    x_data = data.devices[root][name].read()["value"]
+    y_res = get_value(signal_dev, True)
+    y_data = data.devices[y_res['root']][y_res['name']].read()["value"]
+    signal_name = y_res['name']
+
     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"]
+
     return {
         "signal_name": signal_name,
         "x_data": x_data,
         "y_data": y_data,
         "motor_name": motor_name,
-        "scan_number": scan_number,
+        "scan_number": float(scan_number),
     }
 
 
@@ -78,29 +126,33 @@ 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
 
 
@@ -172,7 +224,6 @@ def select_bec_window(dock_area_name="Fitting"):
     open_docks = bec.gui.windows
     if open_docks.get(dock_area_name) is None:
         dock_area = bec.gui.new(dock_area_name)
-        # wf = dock_area.new("Plot").new(bec.gui.available_widgets.Waveform)
         wf = dock_area.new(widget='Waveform', object_name='Plot')
         text_box = dock_area.new(widget='TextBox', object_name="Results", where="bottom")
     else:
@@ -210,6 +261,8 @@ def plot_live_data_bec(
     wf.plot(device_x=motor_name, device_y=signal_name)
 
 
+
+
 def plot_fitted_data_bec(
     data,
     fit_result,
@@ -234,9 +287,9 @@ 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

@@ -36,7 +36,7 @@ def calculate_step_size(start: float, stop: float, steps: int) -> float:
     """
     if steps < 1:
         raise ValueError("Number of steps must be at least 1.")
-    return round((stop - start) / steps, 3)
+    return round((stop - start) / (steps - 1), 5)
 
 
 def move_to_position(motor_device, motor_name: str, position: float, data: dict):
@@ -105,6 +105,7 @@ def go_to_peak(
     settle: float = FitDefaults.SETTLE_TIME,
     confirm: bool = True,
     gomax: bool = False,
+    negative: bool = False
 ):
     """
     Go to the peak of a signal by scanning a motor within a specified range and
@@ -132,8 +133,12 @@ def go_to_peak(
     Returns:
         None
     """
-    motor_name = motor_device.name
-    signal_name = signal_device.name
+    # Get motor and signal names
+    mres = get_value(motor_device, True)
+    motor_name = mres['root']
+    sres = get_value(signal_device, True)
+    signal_name = sres['root']
+    print(f"Signal name is {signal_name}")
     # wf.plot(x_name=motor_name, y_name=signal_name)
     if plot:
         plot_live_data_bec(motor_name, signal_name)
@@ -142,8 +147,9 @@ def go_to_peak(
     step_size = calculate_step_size(start, stop, steps)
 
     # Confirm the scan range
-    # current_motor_position = motor_device.user_readback.get()
-    current_motor_position = motor_device.read()[motor_name]["value"]
+    current_motor_position = mres['value']
+    print(f"Starting motor position = {current_motor_position}")
+
     if confirm:
         if relative:
             scan_start = current_motor_position + start
@@ -159,12 +165,15 @@ def go_to_peak(
             )
             print(f"Relative mode = {relative}")
         input("Press Enter to continue...")
+    
     # Perform the scan
     scan_result = scans.line_scan(
         motor_device, start, stop, steps=steps, relative=relative, settling_time=settle
     )
-    motor_data = scan_result.scan.live_data[motor_name][motor_name].val
-    signal_data = scan_result.scan.live_data[signal_name][signal_name].val
+    motor_data = scan_result.scan.live_data[mres['live_root']][mres['name']].val
+    # # motor_data = scan_result.scan.live_data[motor_name][motor_name].val
+    # sres = get_value(signal_name, True)
+    signal_data = scan_result.scan.live_data[sres['root']][sres['name']].val
     scan_number = "Current"
 
     data = {
@@ -177,7 +186,10 @@ def go_to_peak(
     }
 
     # Define and fit model to scan data
-    fit_params = create_fit_parameters(False, FitDefaults.MODEL, FitDefaults.BASELINE)
+    fit_params = create_fit_parameters(deriv = False,
+                                       negative = False, 
+                                       model = FitDefaults.MODEL, 
+                                       baseline = FitDefaults.BASELINE)
     fit_result = fit(data, fit_params)
 
     # Plot the fitted data if plot = True
@@ -198,6 +210,8 @@ def fit_history(
     history_index: int,
     signal_name: str,
     deriv: bool = False,
+    negative: bool = False,
+    smoothing: bool = False,
     model: str = FitDefaults.MODEL,
     move_to_peak: bool = False,
 ):
@@ -226,15 +240,17 @@ 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)
     plot_fitted_data_bec(data, fit_result)
+    
 
     # Optionally move the motor to the peak position
     if move_to_peak:
         move_to_position(data["motor_device"], data["motor_name"], fit_result["centre"], data)
+    return fit_result
 
 
 def scan_bpm(bpmname):

pxii_bec/macros/pxii_energy.py

@@ -85,7 +85,12 @@ def mono_pitch_scan(plot=True):
     energy = get_current_energy()
     pos = get_dcm_motors_positions(energy)
     print(f"Setting DCM Pitch to default value of {pos['dcm_pitch']}")
-    # scans.umv(EnergyDefaults.mono_pitch, pos["dcm_pitch"], relative=False)
+    # Set fine pitch to middle of its' travel range
+    scans.umv(EnergyDefaults.mono_fpitch, pos["dcm_fine_pitch"], relative=False)
+    # Move DCM pitch to starting value
+    scans.umv(EnergyDefaults.mono_pitch, pos["dcm_pitch"], relative=False)
+
+
     # Go to peak using default parameters from EnergyDefaults
     if plot:
         print("Scanning monochromator pitch and moving to peak, with plotting.")
@@ -162,13 +167,14 @@ def get_dcm_motors_positions(energy_ev):
     """
     # dcm_motor_values = get_value_from_lut(energy_ev)
     dcm_motor_values = {}
-    # pitch = float(np.poly1d(Calibration.pitch)(energy_ev))
-    pitch = -5.5
+    pitch = float(np.poly1d(Calibration.pitch_calib)(energy_ev))
+    # pitch = -5.5
+    fpitch = 5.0
     roll = EnergyDefaults.mono_roll_value
     perp = calc_perp_position(energy_ev, print_result=False)
     bragg_angle = convert_from_energy(energy_ev, print_result=False)["bragg_angle_mrad"]
     dcm_motor_values.update(
-        {"bragg_angle": bragg_angle, "perp": perp, "dcm_pitch": pitch, "dcm_roll": roll}
+        {"bragg_angle": bragg_angle, "perp": perp, "dcm_pitch": pitch, "dcm_roll": roll, "dcm_fine_pitch": fpitch}
     )
     return dcm_motor_values
 
@@ -192,6 +198,7 @@ def move_dcm_motors(energy_ev):
     # umv(EnergyDefaults.mono_pitch, dcm_pos["dcm_pitch"])
     # umv(EnergyDefaults.mono_roll, dcm_pos["dcm_roll"])
     # print("\n***DCM Roll movement is currently disabled ***\n")
+    scans.umv(EnergyDefaults.mono_fpitch,dcm_pos["dcm_fine_pitch"], relative=False )
     scans.umv(
         EnergyDefaults.energy,
         dcm_pos["bragg_angle"],
@@ -247,3 +254,34 @@ def bl_energy(energy_ev, move_gap=True, mono_scan=True, plot=True):
             mono_pitch_scan(plot=True)
         else:
             mono_pitch_scan(plot=False)
+
+def new_calc_gap(energy_ev):
+    """
+    Calculates the gap based on the given energy
+    """
+    gapval = Gap()
+    harmonic_values = gapval.get_harmonic_values(energy_ev)
+    if harmonic_values is not None:
+        fit = np.poly1d(harmonic_values)(energy_ev)
+        gap_value = fit * 0.9994
+        return gap_value
+    return None
+
+def set_gap(energy_ev, print_result=True):
+    """
+    set the gap
+    """
+    gap = new_calc_gap(energy_ev)
+    # else:
+    #     gap = calc_gap(energy_kev)
+    if gap is not None and Harmonics.min_gap_value <= gap <= 20:
+        umv(dev.id_gap, gap)
+        if print_result:
+            print(f"Setting gap to {gap:.3f} mm")
+    else:
+        msg = (
+            f"Position {gap: .3f} mm is outside the allowed scan range of "
+            f"4.5 to 20 mm. Not changing the gap."
+        )
+        raise ValueError(msg)
+    return gap