Protect against failed fits

wirecell/test/__main__.py

@@ -22,6 +22,7 @@ def cli(ctx):
     '''
     pass
 
+
 @cli.command("plot")
 @click.option("-n", "--name", default="noise",
               help="The test name")
@@ -37,7 +38,6 @@ def plot(ctx, name, datafile, output):
     fp = ario.load(datafile)
     with plottools.pages(output) as out:
         mod.plot(fp, out)
-
 
 
 def ssss_args(func):
@@ -53,8 +53,11 @@ def ssss_args(func):
     @functools.wraps(func)
     def wrapper(*args, **kwds):
 
-        kwds["splat"] = ssss.load_frame(kwds.pop("splat"))
-        kwds["signal"] = ssss.load_frame(kwds.pop("signal"))
+        kwds["splat_filename"] = kwds.pop("splat")
+        kwds["signal_filename"] = kwds.pop("signal")
+
+        kwds["splat"] = ssss.load_frame(kwds["splat_filename"])
+        kwds["signal"] = ssss.load_frame(kwds["signal_filename"])
 
         channel_ranges = kwds.pop("channel_ranges")
         if channel_ranges:
@@ -74,6 +77,8 @@ def plot_ssss(channel_ranges, nsigma, nbins, splat, signal, output,
     Perform the simple splat / sim+signal process comparison test and make plots.
     '''
 
+    nminsig = 3                # sanity check
+
     with pages(output) as out:
 
         ssss.plot_frames(splat, signal, channel_ranges, title)
@@ -100,6 +105,13 @@ def plot_ssss(channel_ranges, nsigma, nbins, splat, signal, output,
 
             spl_qch = numpy.sum(spl.activity[bbox], axis=1)
             sig_qch = numpy.sum(sig.activity[bbox], axis=1)
+
+            nspl = len(spl_qch)
+            nsig = len(sig_qch)
+            if nspl != nsig or nsig < nminsig:
+                log.error(f'error: bad signals: {nspl=} {nsig=} {pln=} {ch=}')
+                raise ValueError(f'bad signals: {nspl=} {nsig=}')
+
             byplane.append((spl_qch, sig_qch))
 
 
@@ -132,7 +144,14 @@ def ssss_metrics(channel_ranges, nsigma, nbins, splat, signal, output, params, *
         spl_qch = numpy.sum(spl.activity[bbox], axis=1)
         sig_qch = numpy.sum(sig.activity[bbox], axis=1)
 
-        m = ssss.calc_metrics(spl_qch, sig_qch, nbins)
+        try:
+            m = ssss.calc_metrics(spl_qch, sig_qch, nbins)
+        except Exception as err:
+            splat_filename = kwds['splat_filename']
+            signal_filename = kwds['signal_filename']
+            log.error(f'error: ({err}) failed to calculate metrics for {pln=} {ch=} {splat_filename=} {signal_filename=}')
+            m = ssss.Metrics()
+
         metrics.append(dataclasses.asdict(m))
 
     if params:
@@ -149,8 +168,10 @@ def ssss_metrics(channel_ranges, nsigma, nbins, splat, signal, output, params, *
               help="PDF file in which to plot metrics")
 @click.option("--coordinate-plane", default=None, type=int,
               help="Use given plane number as global coordinates plane, default uses per-plane coordinates")
+@click.option("-t","--title", default="",
+              help="The title string")
 @click.argument("files",nargs=-1)
-def plot_metrics(output, coordinate_plane, files):
+def plot_metrics(output, coordinate_plane, title, files):
     '''Plot per-plane metrics from files.
 
     Files are as produced by ssss-metrics and must include a "params" key.
@@ -176,9 +197,15 @@ def add(k,v):
 
             pmet = met[plane]
             add('ineff', pmet['ineff'])
-            add('bias',  pmet['fit']['avg'])
-            hi = pmet['fit']['hi']
-            lo = pmet['fit']['lo']
+            fit = pmet['fit']
+            if fit is None:
+                add('bias', 1)  # fixme: best way to show failure?
+                add('reso', 1)
+                continue
+
+            add('bias',  fit['avg'])
+            hi = fit['hi']
+            lo = fit['lo']
             add('reso', 0.5*(hi+lo) )
             continue;
 
@@ -193,9 +220,15 @@ def add(k,v):
             add('ty',  par['theta_y_wps'][plane])
             add('txz', par['theta_xz_wps'][plane])
             add('ineff', pmet['ineff'])
-            add('bias',  pmet['fit']['avg'])
-            hi = pmet['fit']['hi']
-            lo = pmet['fit']['lo']
+            fit = pmet['fit']
+            if fit is None:
+                add('bias', 1)  # fixme: best way to show failure?
+                add('reso', 1)
+                continue
+
+            add('bias',  fit['avg'])
+            hi = fit['hi']
+            lo = fit['lo']
             add('reso', 0.5*(hi+lo) )
 
 
@@ -207,11 +240,13 @@ def add(k,v):
     pcolors = ('#58D453', '#7D99D1', '#D45853')
 
     fig, axes = plt.subplots(nrows=3, ncols=1, sharex=True)
+    if title:
+        title = ' - ' + title
     if coordinate_plane is None:
-        fig.suptitle("Per-plane angles")
+        fig.suptitle("Per-plane angles" + title)
     else:
         letter = "UVW"[coordinate_plane]
-        fig.suptitle(f'Global angles ({letter}-plane)')
+        fig.suptitle(f'Global angles ({letter}-plane)' + title)
 
     todeg = 180/numpy.pi
     # xlabs = [f'{txz}/{ty}' for txz,ty in zip(

wirecell/test/ssss.py

@@ -15,6 +15,9 @@
     baseline_noise, 
     gauss as gauss_func
 )
+import logging
+log = logging.getLogger("wirecell.test")
+
 
 def relbias(a,b):
     '''
@@ -197,17 +200,17 @@ def plot_plane(spl_act, sig_act, nsigma=3.0, title=""):
 class Metrics:
     '''Metrics about a signal vs splat'''
 
-    neor: int
+    neor: int = 0
     ''' Number of channels with activity in either the signal or splat (or both)
     and over which the rest are calculated.  This can be less than the number of
     channels in the original "activity" arrays if any given channel has zero
     activity in both "signal" and "splat".  '''
 
-    ineff: float
+    ineff: float = -1
     ''' The relative inefficiency.  This is the fraction of channels with splat
     but with zero signal.  '''
 
-    fit: BaselineNoise
+    fit: BaselineNoise | None = None
     '''
     Gaussian fit to relative difference.  .mu is bias and .sigma is resolution.
     '''
@@ -220,6 +223,12 @@ def calc_metrics(spl_qch, sig_qch, nbins=50):
     - nbins :: the number of bins over which to fit the relative difference.
     '''
 
+    nspl = len(spl_qch)
+    nsig = len(sig_qch)
+
+    if nspl != nsig:
+        raise ValueError(f'length mismatch {nspl=} != {nsig=}')
+
     # either-or, exclude channels where both are zero
     eor   = numpy.logical_or (spl_qch  > 0, sig_qch  > 0)
     # both are nonzero
@@ -247,7 +256,13 @@ def plot_metrics(splat_signal_activity_pairs, nbins=50, title="", letters="UVW")
     fig, axes = plt.subplots(nrows=2, ncols=3, sharey="row")
     for pln, (spl_qch, sig_qch) in enumerate(splat_signal_activity_pairs):
 
-        m = calc_metrics(spl_qch, sig_qch, nbins)
+        try:
+            m = calc_metrics(spl_qch, sig_qch, nbins)
+        except:
+            log.error(f'error: failed to get metric for {pln=} {spl_qch.size=} {sig_qch.size=} {nbins=} {title=}')
+            log.debug(f'skipped splat:  {spl_qch=}')
+            log.debug(f'skipped signal: {sig_qch=}')
+            continue
         counts, edges = m.fit.hist
         model = gauss_func(edges[:-1], m.fit.A, m.fit.mu, m.fit.sigma)
 

wirecell/util/peaks.py

@@ -16,6 +16,9 @@
 from wirecell.util.codec import dataclass_dictify
 from wirecell.util.bbox import union as union_bbox
 
+import logging
+log = logging.getLogger("wirecell.util")
+
 sqrt2pi = sqrt(2*pi)
 
 def gauss(x, A, mu, sigma, *p):
@@ -45,6 +48,11 @@ class BaselineNoise:
     Width (fit standard deviation)
     '''
 
+    N : int
+    '''
+    Number of samples
+    '''
+
     C : float
     '''
     Normalization (sum)
@@ -93,11 +101,18 @@ def baseline_noise(array, bins=200, vrange=100):
     defines an extent about the MEDIAN VALUE.  If it is a tuple it gives this
     extent explicitly or if scalar the extent is symmetric, ie median+/-vrange.
 
+    This will raise exceptions:
+
+    - ZeroDivisionError when the signal in the vrange is zero.
+
+    - RuntimeError when the fit fails.
+
     '''
+    nsig = len(array)
     lo, med, hi = numpy.quantile(array, [0.5-0.34,0.5,0.5+0.34])
 
     if not isinstance(vrange, tuple):
-        vrange=(-vrange, vrange)
+        vrange=(med-vrange, med+vrange)
     vrange=(med+vrange[0], med+vrange[1])
 
     hist = numpy.histogram(array, bins=bins, range=vrange)
@@ -113,11 +128,12 @@ def baseline_noise(array, bins=200, vrange=100):
         (A,mu,sig),cov = curve_fit(gauss, edges[:-1], counts, p0=p0)
     except RuntimeError:
         cov = None
+
     return BaselineNoise(A=A, mu=mu, sigma=sig,
+                         N=nsig,
                          C=C, avg=avg, rms=rms,
                          med=med, lo=lo, hi=hi,
                          cov=cov, hist=hist)
-
 
 @dataclasses.dataclass
 @dataclass_dictify