diff --git a/pipe/extract.py b/pipe/extract.py
index 40c650a..d905972 100644
--- a/pipe/extract.py
+++ b/pipe/extract.py
@@ -138,46 +138,52 @@ def read_matfiles(self, specpath: Path,
 
         for label in labels:
             matdata = sp.io.loadmat(specpath/"matfiles"/label)
-            header = matdata["header"]
-            constants_array = np.zeros((1,5), dtype="float16")
-            constants_array[0,0] = header["f1"][0][0][0]
-            constants_array[0,1] = header["f2"][0][0][0]
-            constants_array[0,2] = header["t0"][0][0][0]
-            constants_array[0,4] = header["dt"][0][0][0]
-            timeseries_array = np.zeros((nloops, nfreq+3), dtype="float16")
-            spec = matdata["SP"]
-            for _ in range(len(spec.shape)-2):
-                spec = spec[0]
-                spec[np.abs(spec) == np.inf] = pad_value
-                spec[np.isnan(spec)] = pad_value
-            try:
-                constants_array[0,3] = header["tf"][0][0][0]
-            except ValueError:
-                constants_array[0,3] = float(spec.shape[-2])
-            mean += np.mean(spec)
-            maxv += max(np.max(spec), maxv)
-            minv += min(np.min(spec), minv)
-            time_offset = int(nloops-constants_array[0,3])
-            timeseries_array[time_offset:, :130] = \
-                (spec - spec_meanshift) / spec_scale
-            timeseries_array[:time_offset, :130] = \
-                timeseries_array[time_offset, :130]
-            timeseries_array[time_offset:, 130] = \
-                (np.log10(matdata["BB"][0]) - bb_meanshift) / bb_scale
-            timeseries_array[time_offset:, 131] = \
-                (matdata["NSD"][0] - nsd_meanshift)
-            timeseries_array[time_offset:, 132] = \
-                np.log10(matdata["NCNT"][0]) / ncnt_scale
-            row["timeseries"] = timeseries_array.tolist()
-
-            if DataKind.TREATMENT in datakinds:
-                row["treatment"] = matdata["header"]["drug"][0][0][0].lower()
-            if DataKind.TARGET in datakinds:
+            ncnt = np.log10(matdata["NCNT"][0])
+            if np.min(ncnt) < 2:
+                print(f"Skipping file {label} due to low counts.")
+                print(np.min(ncnt))
+                continue
+            else:
+                header = matdata["header"]
+                constants_array = np.zeros((1,5), dtype="float16")
+                constants_array[0,0] = header["f1"][0][0][0]
+                constants_array[0,1] = header["f2"][0][0][0]
+                constants_array[0,2] = header["t0"][0][0][0]
+                constants_array[0,4] = header["dt"][0][0][0]
+                timeseries_array = np.zeros((nloops, nfreq+3), dtype="float16")
+                spec = matdata["SP"]
+                for _ in range(len(spec.shape)-2):
+                    spec = spec[0]
+                    spec[np.abs(spec) == np.inf] = pad_value
+                    spec[np.isnan(spec)] = pad_value
                 try:
-                    row["target"] = matdata["header"]["cell"][0][0][0].lower()
-                except:
-                    row["target"] = "unknown"
-            data.append(Row(**row))
+                    constants_array[0,3] = header["tf"][0][0][0]
+                except ValueError:
+                    constants_array[0,3] = float(spec.shape[-2])
+                mean += np.mean(spec)
+                maxv += max(np.max(spec), maxv)
+                minv += min(np.min(spec), minv)
+                time_offset = int(nloops-constants_array[0,3])
+                timeseries_array[time_offset:, :130] = \
+                    (spec - spec_meanshift) / spec_scale
+                timeseries_array[:time_offset, :130] = \
+                    timeseries_array[time_offset, :130]
+                timeseries_array[time_offset:, 130] = \
+                    (np.log10(matdata["BB"][0]) - bb_meanshift) / bb_scale
+                timeseries_array[time_offset:, 131] = \
+                    (matdata["NSD"][0] - nsd_meanshift)
+                timeseries_array[time_offset:, 132] = \
+                    np.log10(matdata["NCNT"][0]) / ncnt_scale
+                row["timeseries"] = timeseries_array.tolist()
+
+                if DataKind.TREATMENT in datakinds:
+                    row["treatment"] = matdata["header"]["drug"][0][0][0].lower()
+                if DataKind.TARGET in datakinds:
+                    try:
+                        row["target"] = matdata["header"]["cell"][0][0][0].lower()
+                    except:
+                        row["target"] = "unknown"
+                data.append(Row(**row))
         
         return self.spark.createDataFrame(data)