diff --git a/decodertest.py b/decodertest.py
index 6c5b63e..70188cc 100644
--- a/decodertest.py
+++ b/decodertest.py
@@ -27,39 +27,7 @@
 from model.model import CompoundModel, DecoderModel
 from pipe.etl import etl, read
 import train.decoder_train as dt
-
-def trim(dataframe, column):
-
-    ndarray = np.array(dataframe.select(column).collect()) \
-            .reshape(-1, 32, 130)
-
-    return ndarray
-
-def transform(spark, dataframe, keys):
-    dataframe = dataframe.withColumn( 
-            "index", functions.monotonically_increasing_id()
-    )
-    bundle = {key: [
-        arr.tolist()
-        for arr in OneHotEncoder(sparse_output=False) \
-            .fit_transform(dataframe.select(key).collect())
-        ] for key in keys
-    }
-
-    bundle = [dict(zip(bundle.keys(), values))
-              for values in zip(*bundle.values())]
-    schema = types.StructType([
-        types.StructField(key, types.ArrayType(types.FloatType()), True)
-        for key in keys
-    ])
-    newframe = spark.createDataFrame(bundle, schema=schema).withColumn(
-            "index", functions.monotonically_increasing_id()
-    )
-    for key in keys:
-        dataframe = dataframe.withColumnRenamed(key, f"{key}_str")
-    dataframe = dataframe.join(newframe, on="index", how="inner")
-
-    return dataframe
+from visualize.plot import spectra_plot
 
 def build_dict(df, key):
     df = df.select(key, f"{key}_str").distinct()
@@ -68,37 +36,6 @@ def build_dict(df, key):
         lambda row: (str(np.argmax(row[key])), row[f"{key}_str"])
     ).collectAsMap()
 
-
-def get_data(spark, split=[0.99, 0.005, 0.005]):
-    path = Path("/app/workdir")
-
-    labels = []
-    with open(path / "train.csv", "r") as file:
-        for line in file:
-            labels.append(line.strip().split(",")[0])
-
-    pipe = SpectrogramPipe(spark, filetype="matfiles")
-    data = pipe.spectrogram_pipe(path, labels)
-    data.select("treatment").replace("virus", "cpv") \
-                            .replace("cont", "pbs") \
-                            .replace("control", "pbs") \
-                            .replace("dld", "pbs").distinct()
-
-    data = transform(spark, data, ["treatment", "target"])
-    category_dict = {
-        key: build_dict(data, key) for key in ["treatment", "target"]
-    }
-    splits = data.randomSplit(split, seed=42)
-    trainx, valx, testx = (trim(dset, "spectra") for dset in splits)
-    trainy, valy, testy = (
-            [np.array(dset.select("treatment").collect()).squeeze(),
-             np.array(dset.select("target").collect()).squeeze()]
-            for dset in splits
-    )
-
-
-    return ((trainx, trainy), (valx, valy), (testx, testy), category_dict)
-
 def multi_gpu():
     devices = jax.devices("gpu")
     mesh = keras.distribution.DeviceMesh(
@@ -147,14 +84,7 @@ def main():
     metrics = {key: None for key in keys}
     metrics, test_predict = dt.test_decoder(decoder, test_set, metrics)
 
-    plt.pcolor(test_predict[0], cmap='bwr', clim=(-1, 1))
-    plt.savefig("sample_spectra.png")
-    plt.close()
-    plt.pcolor(test_predict[0] - np.mean(np.array(test_predict[0])[:4], axis=0),
-               cmap='bwr', clim=(-1, 1))
-    plt.savefig("sample_spectrogram.png")
-    plt.close()
-    exit()
+    spectra_plot(test_predict[0], name=None)
 
 
 if __name__ == "__main__":