check if wafer and band exist for qa (#1362)

Co-authored-by: Michael McCrackan <mmccrackan@compute11.simonsobs.org>

Author: mmccrackan

sotodlib/preprocess/pcore.py

@@ -586,32 +586,34 @@ def gen_metric(
                 frac_flagged = np.array([
                     np.dot(r.ranges(), [-1, 1]).sum() for r in proc_aman[key1][key2][subset]
                 ], dtype=float)
-                num_valid = np.array([
-                    np.dot(r.ranges(), [-1, 1]).sum() for r in proc_aman[key1].valid[subset]
-                ])
-                with np.errstate(divide="ignore"):
-                    frac_flagged *= np.where(num_valid > 0, 1 / num_valid, 0)
-
-                # record percentiles over detectors and fraction of samples flagged
-                perc = np.percentile(frac_flagged, percentiles)
-                mean = frac_flagged.mean()
-
-                # get the tags for this wafer (all detectors in this subset share these)
-                tags_base = {
-                    k: _get_tag(meta.det_info, k, subset[0]) for k in tag_keys if _has_tag(meta.det_info, k)
-                }
-                tags_base["telescope"] = meta.obs_info.telescope
-
-                # add tags and values to respective lists in order
-                tags_perc = [tags_base.copy() for i in range(perc.size)]
-                for i, t in enumerate(tags_perc):
-                    t["det_stat"] = f"percentile_{percentiles[i]}"
-                vals += list(perc)
-                tags += tags_perc
-                tags_mean = tags_base.copy()
-                tags_mean["det_stat"] = "mean"
-                vals.append(mean)
-                tags.append(tags_mean)
+
+                if len(frac_flagged) > 0:
+                    num_valid = np.array([
+                        np.dot(r.ranges(), [-1, 1]).sum() for r in proc_aman[key1].valid[subset]
+                    ])
+                    with np.errstate(divide="ignore"):
+                        frac_flagged *= np.where(num_valid > 0, 1 / num_valid, 0)
+
+                    # record percentiles over detectors and fraction of samples flagged
+                    perc = np.percentile(frac_flagged, percentiles)
+                    mean = frac_flagged.mean()
+
+                    # get the tags for this wafer (all detectors in this subset share these)
+                    tags_base = {
+                        k: _get_tag(meta.det_info, k, subset[0]) for k in tag_keys if _has_tag(meta.det_info, k)
+                    }
+                    tags_base["telescope"] = meta.obs_info.telescope
+
+                    # add tags and values to respective lists in order
+                    tags_perc = [tags_base.copy() for i in range(perc.size)]
+                    for i, t in enumerate(tags_perc):
+                        t["det_stat"] = f"percentile_{percentiles[i]}"
+                    vals += list(perc)
+                    tags += tags_perc
+                    tags_mean = tags_base.copy()
+                    tags_mean["det_stat"] = "mean"
+                    vals.append(mean)
+                    tags.append(tags_mean)
 
         obs_time = [meta.obs_info.timestamp] * len(vals)
         return {

sotodlib/preprocess/processes.py

@@ -826,46 +826,47 @@ def gen_metric(cls, meta, proc_aman):
                     (meta.det_info.wafer_slot == ws) & (meta.det_info.wafer.bandpass == bp)
                 )[0]
 
-                # get the coefficients for every detector
-                coeff = proc_aman.hwpss_stats.coeffs[subset]
-                # mask those that were not set
-                nonzero = np.any(coeff != 0.0, axis=1)
-
-                # calculate amplitude of each mode
-                mode_labels = list(proc_aman.hwpss_stats.modes.vals)
-                num_re = re.compile("^[SC](\d+)$")
-                nums = sorted(list(set([num_re.match(l).group(1) for l in mode_labels])))
-                coeff_amp = np.zeros((coeff.shape[0], len(nums)), coeff.dtype)
-                amp_labels = []
-                for i, n in enumerate(nums):
-                    c_ind = mode_labels.index(f"C{n}")
-                    s_ind = mode_labels.index(f"S{n}")
-                    coeff_amp[:, i] = np.sqrt(coeff[:, c_ind]**2 + coeff[:, s_ind]**2)
-                    amp_labels.append(f"A{n}")
-
-                # record percentiles over detectors and fraction of samples flagged
-                perc = np.percentile(coeff_amp[nonzero], cls._influx_percentiles, axis=0)
-                mean = coeff_amp[nonzero].mean(axis=0)
-
-                tags_base = {
-                    k: _get_tag(meta.det_info, k, subset[0]) for k in tag_keys if _has_tag(meta.det_info, k)
-                }
-                tags_base["telescope"] = meta.obs_info.telescope
-
-                # loop over percentiles and coefficient labels
-                for pi, p in enumerate(cls._influx_percentiles):
+                if len(subset) > 0:
+                    # get the coefficients for every detector
+                    coeff = proc_aman.hwpss_stats.coeffs[subset]
+                    # mask those that were not set
+                    nonzero = np.any(coeff != 0.0, axis=1)
+
+                    # calculate amplitude of each mode
+                    mode_labels = list(proc_aman.hwpss_stats.modes.vals)
+                    num_re = re.compile("^[SC](\d+)$")
+                    nums = sorted(list(set([num_re.match(l).group(1) for l in mode_labels])))
+                    coeff_amp = np.zeros((coeff.shape[0], len(nums)), coeff.dtype)
+                    amp_labels = []
+                    for i, n in enumerate(nums):
+                        c_ind = mode_labels.index(f"C{n}")
+                        s_ind = mode_labels.index(f"S{n}")
+                        coeff_amp[:, i] = np.sqrt(coeff[:, c_ind]**2 + coeff[:, s_ind]**2)
+                        amp_labels.append(f"A{n}")
+
+                    # record percentiles over detectors and fraction of samples flagged
+                    perc = np.percentile(coeff_amp[nonzero], cls._influx_percentiles, axis=0)
+                    mean = coeff_amp[nonzero].mean(axis=0)
+
+                    tags_base = {
+                        k: _get_tag(meta.det_info, k, subset[0]) for k in tag_keys if _has_tag(meta.det_info, k)
+                    }
+                    tags_base["telescope"] = meta.obs_info.telescope
+
+                    # loop over percentiles and coefficient labels
+                    for pi, p in enumerate(cls._influx_percentiles):
+                        for l in amp_labels:
+                            t_new = tags_base.copy()
+                            t_new.update({"mode": l, "det_stat": f"percentile_{p}"})
+                            tags.append(t_new)
+                        vals += list(perc[pi])
+
+                    # finally also record the mean
                     for l in amp_labels:
                         t_new = tags_base.copy()
-                        t_new.update({"mode": l, "det_stat": f"percentile_{p}"})
+                        t_new.update({"mode": l, "det_stat": "mean"})
                         tags.append(t_new)
-                    vals += list(perc[pi])
-
-                # finally also record the mean
-                for l in amp_labels:
-                    t_new = tags_base.copy()
-                    t_new.update({"mode": l, "det_stat": "mean"})
-                    tags.append(t_new)
-                vals += list(mean)
+                    vals += list(mean)
 
         obs_time = [meta.obs_info.timestamp] * len(tags)
         return {

sotodlib/qa/metrics.py

@@ -212,24 +212,25 @@ def _process(self, meta):
                     (meta.det_info.wafer_slot == ws) & (meta.det_info.wafer.bandpass == bp)
                 )[0]
 
-                # Compute the number of samples that are valid
-                frac_valid = np.array([
-                    np.dot(r.ranges(), [-1, 1]).sum() / len(subset)
-                    for r in meta.preprocess[self._key].valid[subset]
-                ])
-
-                # Count detectors with fraction valid above threshold
-                n_good = (frac_valid > self._thresh).sum()
-
-                # get the tags for this wafer (all detectors in this subset share these)
-                tags_i = {
-                    k: _get_tag(meta.det_info, k, subset[0]) for k in tag_keys if _has_tag(meta.det_info, k)
-                }
-                tags_i["telescope"] = meta.obs_info.telescope
-
-                # add tags and values to respective lists in order
-                vals.append(n_good)
-                tags.append(tags_i)
+                if len(subset) > 0:
+                    # Compute the number of samples that are valid
+                    frac_valid = np.array([
+                        np.dot(r.ranges(), [-1, 1]).sum() / len(subset)
+                        for r in meta.preprocess[self._key].valid[subset]
+                    ])
+
+                    # Count detectors with fraction valid above threshold
+                    n_good = (frac_valid > self._thresh).sum()
+
+                    # get the tags for this wafer (all detectors in this subset share these)
+                    tags_i = {
+                        k: _get_tag(meta.det_info, k, subset[0]) for k in tag_keys if _has_tag(meta.det_info, k)
+                    }
+                    tags_i["telescope"] = meta.obs_info.telescope
+
+                    # add tags and values to respective lists in order
+                    vals.append(n_good)
+                    tags.append(tags_i)
 
         obs_time = [meta.obs_info.timestamp] * len(vals)
         return {
@@ -290,14 +291,12 @@ def _process(self, meta):
                 good_indices = np.nonzero(mask)[0]
                 if good_indices.size > 0:
                     vals.append(np.sqrt(1.0 / np.nansum(1.0 / (white_noise[good_indices])**2)))
-                else:
-                    vals.append(0.0)
 
-                tags_base = {
-                    k: _get_tag(meta.det_info, k, subset[0]) for k in tag_keys if _has_tag(meta.det_info, k)
-                }
-                tags_base["telescope"] = meta.obs_info.telescope
-                tags.append(tags_base)
+                    tags_base = {
+                        k: _get_tag(meta.det_info, k, subset[0]) for k in tag_keys if _has_tag(meta.det_info, k)
+                    }
+                    tags_base["telescope"] = meta.obs_info.telescope
+                    tags.append(tags_base)
 
         obs_time = [meta.obs_info.timestamp] * len(tags)
         return {
@@ -358,14 +357,12 @@ def _process(self, meta):
                 good_indices = np.nonzero(mask)[0]
                 if good_indices.size > 0:
                     vals.append(np.sqrt(1.0 / np.nansum(1.0 / (white_noise[good_indices])**2)) * np.sqrt(len(good_indices)))
-                else:
-                    vals.append(0.0)
-
-                tags_base = {
-                    k: _get_tag(meta.det_info, k, subset[0]) for k in tag_keys if _has_tag(meta.det_info, k)
-                }
-                tags_base["telescope"] = meta.obs_info.telescope
-                tags.append(tags_base)
+
+                    tags_base = {
+                        k: _get_tag(meta.det_info, k, subset[0]) for k in tag_keys if _has_tag(meta.det_info, k)
+                    }
+                    tags_base["telescope"] = meta.obs_info.telescope
+                    tags.append(tags_base)
 
         obs_time = [meta.obs_info.timestamp] * len(tags)
         return {