Merge branch 'fix-epics-push' into 'master'

EPICS records push fix

Closes #124

See merge request !142

conda/environment.yaml

@@ -16,4 +16,4 @@ dependencies:
   - python-nds2-client
   - declarative
   - dttxml
-  - ezca
+  - pyepics
\ No newline at end of file

pydarm/darm.py

@@ -17,7 +17,7 @@
 import numpy as np
 from scipy import signal
 from gwpy.time import tconvert, from_gps
-import time
+from epics import caget, caput
 
 from .model import Model
 from .sensing import SensingModel
@@ -395,7 +395,8 @@ class DARMModel(Model):
         Parameters
         ----------
         IFO : str
-            'LHO' or 'LLO'
+            'H1', 'L1', or similar. This will be prepended to all channel names
+            as per CDS convention.
         epics_output : `float`, dict
             the output from compute_epics_records
         filename : str
@@ -405,40 +406,47 @@ class DARMModel(Model):
         """
 
         with open(filename, 'w') as f:
-            for chan in sorted(epics_output.keys()):
-                chan_val = epics_output[chan]
+            for chname in sorted(epics_output.keys()):
+                chan_val = epics_output[chname]
+                # if value is complex then split operation into its
+                # corresponding channels ending in "_REAL" and
+                # "_IMAG"
                 if np.iscomplex(chan_val):
-                    chan = [f"{IFO}:{chan}_REAL", f"{IFO}:{chan}_IMAG"]
+                    chan = [f"{IFO}:{chname}_REAL", f"{IFO}:{chname}_IMAG"]
                     chan_val = [np.real(chan_val), np.imag(chan_val)]
                 else:
-                    chan = [chan]
+                    chan = [f"{IFO}:{chname}"]
                     chan_val = [chan_val]
                 for idx, ch in enumerate(chan):
                     f.write(f"{ch} {chan_val[idx]:0.16e}\n")
                     if push_to_epics:
-                        from ezca import ezca
-                        ezca = ezca.Ezca()
-                        ezca[ch] = '{:0.16e}'.format(chan_val[idx])
-        f.close()
+                        # using caput_many() would probably be better here
+                        caput(ch, f'{chan_val[idx]:0.16e}', wait=True, timeout=1)
 
-        # Ensure there is a moment for the EPICS records to get written
-        time.sleep(1)
+        # check if values written to EPICS channels were transferred properly
+        # Note: This assumes there is no race condition to write to any of the
+        # relevant channels.
 
         if push_to_epics:
             with open(filename, 'r') as f:
-                for chan in sorted(epics_output.keys()):
-                    chan_val = epics_output[chan]
+                for chname in sorted(epics_output.keys()):
+                    chan_val = epics_output[chname]
+                    # split channels if dealing with complex values, same as
+                    # above.
                     if np.iscomplex(chan_val):
-                        chan = [f"{IFO}:{chan}_REAL", f"{IFO}:{chan}_IMAG"]
+                        chan = [f"{IFO}:{chname}_REAL", f"{IFO}:{chname}_IMAG"]
                         chan_val = [np.real(chan_val), np.imag(chan_val)]
                     else:
-                        chan = [chan]
+                        chan = [f"{IFO}:{chname}"]
                         chan_val = [chan_val]
                     for idx, ch in enumerate(chan):
-                        readVal = ezca[ch]
-                        ratio = readVal / chan_val[idx]
-                    if abs(ratio[0] - 1) > 0.000001:
-                        print('Eeeeek! We had an EPICS connection error on '+chan+'. Check it!!')
+                        try:
+                            readVal = caget(ch)
+                            ratio = readVal / chan_val[idx]
+                            if abs(ratio - 1) > 0.000001:
+                                print(f"Eeeeek! We had an EPICS error on {ch}. Check it!!")
+                        except TypeError:
+                            print(f"Error: unable to validate record in channel {ch}.")
 
     def compute_etas(self, frequencies, sensing_syserr=None,
                      actuation_syserr_dict=None):

setup.cfg

@@ -36,7 +36,7 @@ install_requires =
     requests
     scikit-learn >= 1.1.0
     scipy
-    ezca
+    pyepics
 include_package_data = true
 
 [options.packages.find]