diff --git a/.gitignore b/.gitignore
index f563422..9aba18a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -129,4 +129,7 @@ dmypy.json
 .pyre/
 raw
 processed
-out
\ No newline at end of file
+out
+
+*.pdf
+*.png
\ No newline at end of file
diff --git a/README.md b/README.md
index caa8d49..c7bb5c3 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,9 @@
-# Predictive Maintenance (PM)
+# Predictive Maintenance
 
-This repository is intended to enable quick access to datasets for predictive maintenance tasks.
-The follwoing table summrizes the available features for the PM tasks,
+This repository is intended to enable quick access to datasets for predictive maintenance (PM) tasks.
+The following table summarizes the available features,
 where the marks show:
-- `x`: satisfying availablity
+- `x`: satisfying availability
 - `u`: univariate features
 - `m`: multivariate features
 
@@ -27,6 +27,20 @@ the richness of attributes you may check them up with higher priority.
 
 </center>
 
+## Usage
+
+Please put `datasets` directory into your workspace and import it like:
+
+```python
+import datasets
+
+# Exmaple
+datasets.ufd.load_data()
+```
+
+## Notebooks
+
+There are Jupyter notebooks for all datasets, which may help interactive processing and visualization of data.
 
 ## References
 
@@ -40,28 +54,28 @@ the richness of attributes you may check them up with higher priority.
 [https://square.github.io/pysurvival/index.html](https://square.github.io/pysurvival/index.html)
 1. Types of proactive maintenance:  
 [https://solutions.borderstates.com/types-of-proactive-maintenance/](https://solutions.borderstates.com/types-of-proactive-maintenance/)
-1. Common license types for datasets  
+1. Common license types for datasets:  
 [https://www.kaggle.com/general/116302](https://www.kaggle.com/general/116302)
 
 ### Dataset Sources
 
 1. ALPI: Diego Tosato, Davide Dalle Pezze, Chiara Masiero, Gian Antonio Susto, Alessandro Beghi, 2020. Alarm Logs in Packaging Industry (ALPI).  
 [https://dx.doi.org/10.21227/nfv6-k750](https://dx.doi.org/10.21227/nfv6-k750)
-1. UFD: Ultrasonic flowmeter diagnostics Data Set  
+1. UFD: Ultrasonic flowmeter diagnostics Data Set:  
 [https://archive.ics.uci.edu/ml/datasets/Ultrasonic+flowmeter+diagnostics](https://archive.ics.uci.edu/ml/datasets/Ultrasonic+flowmeter+diagnostics)
-1. NASA Bearing Dataset  
+1. NASA Bearing Dataset:  
 [https://www.kaggle.com/vinayak123tyagi/bearing-dataset](https://www.kaggle.com/vinayak123tyagi/bearing-dataset)
-1. CWRU Bearing Dataset  
+1. CWRU Bearing Dataset:  
 [https://www.kaggle.com/brjapon/cwru-bearing-datasets](https://www.kaggle.com/brjapon/cwru-bearing-datasets)
-1. MAPM: Microsoft Azure Predictive Maintenance  
+1. MAPM: Microsoft Azure Predictive Maintenance:  
 [https://www.kaggle.com/arnabbiswas1/microsoft-azure-predictive-maintenance](https://www.kaggle.com/arnabbiswas1/microsoft-azure-predictive-maintenance)
-1. HydSys: Predictive Maintenance Of Hydraulics System  
+1. HydSys: Predictive Maintenance Of Hydraulics System:  
 [https://www.kaggle.com/mayank1897/condition-monitoring-of-hydraulic-systems](https://www.kaggle.com/mayank1897/condition-monitoring-of-hydraulic-systems)
-1. GFD: Gearbox Fault Diagnosis  
+1. GFD: Gearbox Fault Diagnosis:  
 [https://www.kaggle.com/brjapon/gearbox-fault-diagnosis](https://www.kaggle.com/brjapon/gearbox-fault-diagnosis)
-1. PPD: Production Plant Data for Condition Monitoring  
+1. PPD: Production Plant Data for Condition Monitoring:  
 [https://www.kaggle.com/inIT-OWL/production-plant-data-for-condition-monitoring](https://www.kaggle.com/inIT-OWL/production-plant-data-for-condition-monitoring)
-1. GDD: Genesis demonstrator data for machine learning  
+1. GDD: Genesis demonstrator data for machine learning:  
 [https://www.kaggle.com/inIT-OWL/genesis-demonstrator-data-for-machine-learning](https://www.kaggle.com/inIT-OWL/genesis-demonstrator-data-for-machine-learning)
 
 <!-- 1. Condition Based Maintenance (CBM) of Naval Propulsion Plants Data Set  
diff --git a/datasets/__init__.py b/datasets/__init__.py
new file mode 100644
index 0000000..857a697
--- /dev/null
+++ b/datasets/__init__.py
@@ -0,0 +1,8 @@
+from . import alpi
+from . import cbm
+from . import gdd
+from . import gfd
+from . import hydsys
+from . import mapm
+from . import ppd
+from . import ufd
diff --git a/data/alpi/__init__.py b/datasets/alpi/__init__.py
similarity index 100%
rename from data/alpi/__init__.py
rename to datasets/alpi/__init__.py
diff --git a/data/alpi/alarms_log_data.zip b/datasets/alpi/alarms_log_data.zip
similarity index 100%
rename from data/alpi/alarms_log_data.zip
rename to datasets/alpi/alarms_log_data.zip
diff --git a/data/alpi/dataset.py b/datasets/alpi/dataset.py
similarity index 100%
rename from data/alpi/dataset.py
rename to datasets/alpi/dataset.py
diff --git a/data/alpi/readme.md b/datasets/alpi/readme.md
similarity index 100%
rename from data/alpi/readme.md
rename to datasets/alpi/readme.md
diff --git a/data/cbm/Features.txt b/datasets/cbm/Features.txt
similarity index 100%
rename from data/cbm/Features.txt
rename to datasets/cbm/Features.txt
diff --git a/data/cbm/README.txt b/datasets/cbm/README.txt
similarity index 100%
rename from data/cbm/README.txt
rename to datasets/cbm/README.txt
diff --git a/data/cbm/data.txt b/datasets/cbm/data.txt
similarity index 100%
rename from data/cbm/data.txt
rename to datasets/cbm/data.txt
diff --git a/data/gdd/Genesis_AnomalyLabels.csv.gz b/datasets/gdd/Genesis_AnomalyLabels.csv.gz
similarity index 100%
rename from data/gdd/Genesis_AnomalyLabels.csv.gz
rename to datasets/gdd/Genesis_AnomalyLabels.csv.gz
diff --git a/data/gdd/Genesis_StateMachineLabel.csv.gz b/datasets/gdd/Genesis_StateMachineLabel.csv.gz
similarity index 100%
rename from data/gdd/Genesis_StateMachineLabel.csv.gz
rename to datasets/gdd/Genesis_StateMachineLabel.csv.gz
diff --git a/data/gdd/Genesis_lineardrive.csv.gz b/datasets/gdd/Genesis_lineardrive.csv.gz
similarity index 100%
rename from data/gdd/Genesis_lineardrive.csv.gz
rename to datasets/gdd/Genesis_lineardrive.csv.gz
diff --git a/data/gdd/Genesis_normal.csv.gz b/datasets/gdd/Genesis_normal.csv.gz
similarity index 100%
rename from data/gdd/Genesis_normal.csv.gz
rename to datasets/gdd/Genesis_normal.csv.gz
diff --git a/data/gdd/Genesis_pressure.csv.gz b/datasets/gdd/Genesis_pressure.csv.gz
similarity index 100%
rename from data/gdd/Genesis_pressure.csv.gz
rename to datasets/gdd/Genesis_pressure.csv.gz
diff --git a/datasets/gdd/__init__.py b/datasets/gdd/__init__.py
new file mode 100644
index 0000000..bd0a958
--- /dev/null
+++ b/datasets/gdd/__init__.py
@@ -0,0 +1,176 @@
+import datetime
+import os
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+import pandas as pd
+import seaborn as sns
+
+
+def load_data(index='state'):
+
+    assert index in ['state', 'anomaly', 'normal', 'linear', 'pressure']
+    fp = os.path.dirname(__file__)
+
+    if index == 'state':
+        df = pd.read_csv(fp + '/Genesis_StateMachineLabel.csv.gz')
+    elif index == 'anomaly':
+        df = pd.read_csv(fp + '/Genesis_AnomalyLabels.csv.gz')
+    elif index == 'normal':
+        df = pd.read_csv(fp + '/Genesis_normal.csv.gz')
+        df.Timestamp = df.Timestamp / 1000
+    elif index == 'linear':
+        df = pd.read_csv(fp + '/Genesis_lineardrive.csv.gz')
+        df.Timestamp = df.Timestamp / 1000
+    elif index == 'pressure':
+        df = pd.read_csv(fp + '/Genesis_pressure.csv.gz')
+        df.Timestamp = df.Timestamp / 1000
+
+    df.Timestamp = df.Timestamp.apply(datetime.datetime.fromtimestamp)
+    
+    return df
+
+
+def plot_genesis_labels(df, figsize=(15, 20), cmap='tab10'):
+    """ Call this for machine states and anomaly labels """
+
+    fig, ax = plt.subplots(10, figsize=figsize)
+
+    df['MotorData.ActCurrent'].plot(ax=ax[0], legend=True, cmap=cmap)
+    df['MotorData.ActPosition'].plot(ax=ax[1], legend=True, cmap=cmap)
+    df['MotorData.ActSpeed'].plot(ax=ax[2], legend=True, cmap=cmap)
+
+    df['MotorData.IsAcceleration'].plot(ax=ax[3], legend=True, cmap=cmap)
+    df['MotorData.IsForce'].plot(ax=ax[4], legend=True, cmap=cmap)
+
+    df[['MotorData.Motor_Pos1reached',    # binary
+        'MotorData.Motor_Pos2reached',    # binary
+        'MotorData.Motor_Pos3reached',    # binary
+        'MotorData.Motor_Pos4reached',    # binary
+    ]].plot(ax=ax[5], legend=True, cmap=cmap)
+
+    df[['NVL_Recv_Ind.GL_Metall',  # binary
+        'NVL_Recv_Ind.GL_NonMetall',  # binary
+    ]].plot(ax=ax[6], legend=True, cmap=cmap)
+
+    df[['NVL_Recv_Storage.GL_I_ProcessStarted',  # binary
+        'NVL_Recv_Storage.GL_I_Slider_IN',  # binary
+        'NVL_Recv_Storage.GL_I_Slider_OUT',  # binary
+        'NVL_Recv_Storage.GL_LightBarrier',  # binary
+        'NVL_Send_Storage.ActivateStorage',  # binary
+    ]].plot(ax=ax[7], legend=True, cmap=cmap)
+
+    df[['PLC_PRG.Gripper',  # binary
+        'PLC_PRG.MaterialIsMetal',  # binary
+    ]].plot(ax=ax[8], legend=True, cmap=cmap)
+
+    df['Label'].plot(ax=ax[9], legend=True, cmap=cmap)
+
+    for axi in ax:
+        axi.set_xlim(0, df.shape[0])
+        axi.set_ylabel('Value')
+
+    ax[0].set_title('Date: {} to {}'.format(
+        df.Timestamp.min(), df.Timestamp.max()))
+    ax[-1].set_xlabel('Time')
+    fig.tight_layout()
+
+    return fig, ax
+    
+    
+def plot_genesis_nonlabels(df, figsize=(15, 20), cmap='tab10'):
+    """ Call this for non-labeled data """
+
+    fig, ax = plt.subplots(8, figsize=figsize)
+
+    df[['MotorData.SetCurrent',
+        'MotorData.ActCurrent',
+    ]].plot(ax=ax[0], legend=True, cmap=cmap)
+
+    df[['MotorData.SetSpeed',
+        'MotorData.ActSpeed',
+    ]].plot(ax=ax[1], legend=True, cmap=cmap)
+
+    df[['MotorData.SetAcceleration',
+        'MotorData.IsAcceleration',
+    ]].plot(ax=ax[2], legend=True, cmap=cmap)
+
+    df[['MotorData.SetForce',
+        'MotorData.IsForce'
+    ]].plot(ax=ax[3], legend=True, cmap=cmap)
+
+    df[['MotorData.Motor_Pos1reached',    # binary
+        'MotorData.Motor_Pos2reached',    # binary
+        'MotorData.Motor_Pos3reached',    # binary
+        'MotorData.Motor_Pos4reached',    # binary
+    ]].plot(ax=ax[4], legend=True, cmap=cmap)
+
+    df[['NVL_Recv_Ind.GL_Metall',  # binary
+        'NVL_Recv_Ind.GL_NonMetall',  # binary
+    ]].plot(ax=ax[5], legend=True, cmap=cmap)
+
+    df[['NVL_Recv_Storage.GL_I_ProcessStarted',  # binary
+        'NVL_Recv_Storage.GL_I_Slider_IN',  # binary
+        'NVL_Recv_Storage.GL_I_Slider_OUT',  # binary
+        'NVL_Recv_Storage.GL_LightBarrier',  # binary
+        'NVL_Send_Storage.ActivateStorage',  # binary
+    ]].plot(ax=ax[6], legend=True, cmap=cmap)
+
+    df[['PLC_PRG.Gripper',  # binary
+        'PLC_PRG.MaterialIsMetal',  # binary
+    ]].plot(ax=ax[7], legend=True, cmap=cmap)
+
+    for axi in ax:
+        axi.set_xlim(0, df.shape[0])
+        axi.set_ylabel('Value')
+
+    ax[0].set_title('Date: {} to {}'.format(df.Timestamp.min(), df.Timestamp.max()))
+    ax[-1].set_xlabel('Time')
+
+    fig.tight_layout()
+    return fig, ax
+
+
+def gen_summary(outdir='../out'):
+
+    os.makedirs(outdir, exist_ok=True)
+    fp = os.path.dirname(__file__)
+    sns.set(font_scale=1.1, style='whitegrid')
+
+    with PdfPages(outdir + '/gdd_summary.pdf') as pp:
+        
+        print('Plotting Genesis_StateMachineLabel...')
+        df = load_data(index='state')
+        fig, _ = plot_genesis_labels(df)
+        fig.savefig(pp, bbox_inches='tight', format='pdf')
+        plt.clf()
+        plt.close()
+
+        print('Plotting Genesis_AnomalyLabels...')
+        df = load_data(index='anomaly')
+        fig, _ = plot_genesis_labels(df)
+        fig.savefig(pp, bbox_inches='tight', format='pdf')
+        plt.clf()
+        plt.close()
+
+        print('Plotting Genesis_normal...')
+        df = load_data(index='normal')
+        fig, _ = plot_genesis_nonlabels(df)
+        fig.savefig(pp, bbox_inches='tight', format='pdf')
+        plt.clf()
+        plt.close()
+
+        print('Plotting Genesis_lineardrive...')
+        df = load_data(index='linear')
+        fig, _ = plot_genesis_nonlabels(df)
+        fig.savefig(pp, bbox_inches='tight', format='pdf')
+        plt.clf()
+        plt.close()
+
+        print('Plotting Genesis_pressure...')
+        df = load_data(index='pressure')
+        fig, _ = plot_genesis_nonlabels(df)
+        fig.savefig(pp, bbox_inches='tight', format='pdf')
+        plt.clf()
+        plt.close()
+
+        print("done!")
\ No newline at end of file
diff --git a/data/gfd/BrokenTooth/b30hz0.csv.gz b/datasets/gfd/BrokenTooth/b30hz0.csv.gz
similarity index 100%
rename from data/gfd/BrokenTooth/b30hz0.csv.gz
rename to datasets/gfd/BrokenTooth/b30hz0.csv.gz
diff --git a/data/gfd/BrokenTooth/b30hz10.csv.gz b/datasets/gfd/BrokenTooth/b30hz10.csv.gz
similarity index 100%
rename from data/gfd/BrokenTooth/b30hz10.csv.gz
rename to datasets/gfd/BrokenTooth/b30hz10.csv.gz
diff --git a/data/gfd/BrokenTooth/b30hz20.csv.gz b/datasets/gfd/BrokenTooth/b30hz20.csv.gz
similarity index 100%
rename from data/gfd/BrokenTooth/b30hz20.csv.gz
rename to datasets/gfd/BrokenTooth/b30hz20.csv.gz
diff --git a/data/gfd/BrokenTooth/b30hz30.csv.gz b/datasets/gfd/BrokenTooth/b30hz30.csv.gz
similarity index 100%
rename from data/gfd/BrokenTooth/b30hz30.csv.gz
rename to datasets/gfd/BrokenTooth/b30hz30.csv.gz
diff --git a/data/gfd/BrokenTooth/b30hz40.csv.gz b/datasets/gfd/BrokenTooth/b30hz40.csv.gz
similarity index 100%
rename from data/gfd/BrokenTooth/b30hz40.csv.gz
rename to datasets/gfd/BrokenTooth/b30hz40.csv.gz
diff --git a/data/gfd/BrokenTooth/b30hz50.csv.gz b/datasets/gfd/BrokenTooth/b30hz50.csv.gz
similarity index 100%
rename from data/gfd/BrokenTooth/b30hz50.csv.gz
rename to datasets/gfd/BrokenTooth/b30hz50.csv.gz
diff --git a/data/gfd/BrokenTooth/b30hz60.csv.gz b/datasets/gfd/BrokenTooth/b30hz60.csv.gz
similarity index 100%
rename from data/gfd/BrokenTooth/b30hz60.csv.gz
rename to datasets/gfd/BrokenTooth/b30hz60.csv.gz
diff --git a/data/gfd/BrokenTooth/b30hz70.csv.gz b/datasets/gfd/BrokenTooth/b30hz70.csv.gz
similarity index 100%
rename from data/gfd/BrokenTooth/b30hz70.csv.gz
rename to datasets/gfd/BrokenTooth/b30hz70.csv.gz
diff --git a/data/gfd/BrokenTooth/b30hz80.csv.gz b/datasets/gfd/BrokenTooth/b30hz80.csv.gz
similarity index 100%
rename from data/gfd/BrokenTooth/b30hz80.csv.gz
rename to datasets/gfd/BrokenTooth/b30hz80.csv.gz
diff --git a/data/gfd/BrokenTooth/b30hz90.csv.gz b/datasets/gfd/BrokenTooth/b30hz90.csv.gz
similarity index 100%
rename from data/gfd/BrokenTooth/b30hz90.csv.gz
rename to datasets/gfd/BrokenTooth/b30hz90.csv.gz
diff --git a/data/gfd/Healthy/h30hz0.csv.gz b/datasets/gfd/Healthy/h30hz0.csv.gz
similarity index 100%
rename from data/gfd/Healthy/h30hz0.csv.gz
rename to datasets/gfd/Healthy/h30hz0.csv.gz
diff --git a/data/gfd/Healthy/h30hz10.csv.gz b/datasets/gfd/Healthy/h30hz10.csv.gz
similarity index 100%
rename from data/gfd/Healthy/h30hz10.csv.gz
rename to datasets/gfd/Healthy/h30hz10.csv.gz
diff --git a/data/gfd/Healthy/h30hz20.csv.gz b/datasets/gfd/Healthy/h30hz20.csv.gz
similarity index 100%
rename from data/gfd/Healthy/h30hz20.csv.gz
rename to datasets/gfd/Healthy/h30hz20.csv.gz
diff --git a/data/gfd/Healthy/h30hz30.csv.gz b/datasets/gfd/Healthy/h30hz30.csv.gz
similarity index 100%
rename from data/gfd/Healthy/h30hz30.csv.gz
rename to datasets/gfd/Healthy/h30hz30.csv.gz
diff --git a/data/gfd/Healthy/h30hz40.csv.gz b/datasets/gfd/Healthy/h30hz40.csv.gz
similarity index 100%
rename from data/gfd/Healthy/h30hz40.csv.gz
rename to datasets/gfd/Healthy/h30hz40.csv.gz
diff --git a/data/gfd/Healthy/h30hz50.csv.gz b/datasets/gfd/Healthy/h30hz50.csv.gz
similarity index 100%
rename from data/gfd/Healthy/h30hz50.csv.gz
rename to datasets/gfd/Healthy/h30hz50.csv.gz
diff --git a/data/gfd/Healthy/h30hz60.csv.gz b/datasets/gfd/Healthy/h30hz60.csv.gz
similarity index 100%
rename from data/gfd/Healthy/h30hz60.csv.gz
rename to datasets/gfd/Healthy/h30hz60.csv.gz
diff --git a/data/gfd/Healthy/h30hz70.csv.gz b/datasets/gfd/Healthy/h30hz70.csv.gz
similarity index 100%
rename from data/gfd/Healthy/h30hz70.csv.gz
rename to datasets/gfd/Healthy/h30hz70.csv.gz
diff --git a/data/gfd/Healthy/h30hz80.csv.gz b/datasets/gfd/Healthy/h30hz80.csv.gz
similarity index 100%
rename from data/gfd/Healthy/h30hz80.csv.gz
rename to datasets/gfd/Healthy/h30hz80.csv.gz
diff --git a/data/gfd/Healthy/h30hz90.csv.gz b/datasets/gfd/Healthy/h30hz90.csv.gz
similarity index 100%
rename from data/gfd/Healthy/h30hz90.csv.gz
rename to datasets/gfd/Healthy/h30hz90.csv.gz
diff --git a/data/hydsys/CE.txt.gz b/datasets/hydsys/CE.txt.gz
similarity index 100%
rename from data/hydsys/CE.txt.gz
rename to datasets/hydsys/CE.txt.gz
diff --git a/data/hydsys/CP.txt.gz b/datasets/hydsys/CP.txt.gz
similarity index 100%
rename from data/hydsys/CP.txt.gz
rename to datasets/hydsys/CP.txt.gz
diff --git a/data/hydsys/EPS1.txt.gz b/datasets/hydsys/EPS1.txt.gz
similarity index 100%
rename from data/hydsys/EPS1.txt.gz
rename to datasets/hydsys/EPS1.txt.gz
diff --git a/data/hydsys/FS1.txt.gz b/datasets/hydsys/FS1.txt.gz
similarity index 100%
rename from data/hydsys/FS1.txt.gz
rename to datasets/hydsys/FS1.txt.gz
diff --git a/data/hydsys/FS2.txt.gz b/datasets/hydsys/FS2.txt.gz
similarity index 100%
rename from data/hydsys/FS2.txt.gz
rename to datasets/hydsys/FS2.txt.gz
diff --git a/data/hydsys/PS1.txt.gz b/datasets/hydsys/PS1.txt.gz
similarity index 100%
rename from data/hydsys/PS1.txt.gz
rename to datasets/hydsys/PS1.txt.gz
diff --git a/data/hydsys/PS2.txt.gz b/datasets/hydsys/PS2.txt.gz
similarity index 100%
rename from data/hydsys/PS2.txt.gz
rename to datasets/hydsys/PS2.txt.gz
diff --git a/data/hydsys/PS3.txt.gz b/datasets/hydsys/PS3.txt.gz
similarity index 100%
rename from data/hydsys/PS3.txt.gz
rename to datasets/hydsys/PS3.txt.gz
diff --git a/data/hydsys/PS4.txt.gz b/datasets/hydsys/PS4.txt.gz
similarity index 100%
rename from data/hydsys/PS4.txt.gz
rename to datasets/hydsys/PS4.txt.gz
diff --git a/data/hydsys/PS5.txt.gz b/datasets/hydsys/PS5.txt.gz
similarity index 100%
rename from data/hydsys/PS5.txt.gz
rename to datasets/hydsys/PS5.txt.gz
diff --git a/data/hydsys/PS6.txt.gz b/datasets/hydsys/PS6.txt.gz
similarity index 100%
rename from data/hydsys/PS6.txt.gz
rename to datasets/hydsys/PS6.txt.gz
diff --git a/data/hydsys/SE.txt.gz b/datasets/hydsys/SE.txt.gz
similarity index 100%
rename from data/hydsys/SE.txt.gz
rename to datasets/hydsys/SE.txt.gz
diff --git a/data/hydsys/TS1.txt.gz b/datasets/hydsys/TS1.txt.gz
similarity index 100%
rename from data/hydsys/TS1.txt.gz
rename to datasets/hydsys/TS1.txt.gz
diff --git a/data/hydsys/TS2.txt.gz b/datasets/hydsys/TS2.txt.gz
similarity index 100%
rename from data/hydsys/TS2.txt.gz
rename to datasets/hydsys/TS2.txt.gz
diff --git a/data/hydsys/TS3.txt.gz b/datasets/hydsys/TS3.txt.gz
similarity index 100%
rename from data/hydsys/TS3.txt.gz
rename to datasets/hydsys/TS3.txt.gz
diff --git a/data/hydsys/TS4.txt.gz b/datasets/hydsys/TS4.txt.gz
similarity index 100%
rename from data/hydsys/TS4.txt.gz
rename to datasets/hydsys/TS4.txt.gz
diff --git a/data/hydsys/VS1.txt.gz b/datasets/hydsys/VS1.txt.gz
similarity index 100%
rename from data/hydsys/VS1.txt.gz
rename to datasets/hydsys/VS1.txt.gz
diff --git a/datasets/hydsys/__init__.py b/datasets/hydsys/__init__.py
new file mode 100644
index 0000000..693493f
--- /dev/null
+++ b/datasets/hydsys/__init__.py
@@ -0,0 +1,72 @@
+import os
+import numpy as np
+import pandas as pd
+
+
+def make_pressure_dataframe(fp, cycle_id=0):
+    """ 100 Hz. 6000 samples in each cycle
+    """
+    data = pd.DataFrame(columns=[f'PS{i+1}' for i in range(6)])
+    for i in range(6):
+        data[f'PS{i+1}'] = np.loadtxt(fp + f'/PS{i+1}.txt.gz')[cycle_id]
+
+    return data
+
+
+def make_motor_power_dataframe(fp, cycle_id=0):
+    """ 100 Hz. 6000 samples in each cycle
+    """
+    return pd.DataFrame(np.loadtxt(fp + '/EPS1.txt.gz')[cycle_id], columns=['EPS1'])
+
+
+def make_volume_flow_dataframe(fp, cycle_id=0):
+    """ 10 Hz. 600 samples in each cycle
+    """
+    data = pd.DataFrame(columns=['FS1', 'FS2'])
+    data['FS1'] = np.loadtxt(fp + '/FS1.txt.gz')[cycle_id]
+    data['FS2'] = np.loadtxt(fp + '/FS2.txt.gz')[cycle_id]
+    return data
+
+
+def make_temp_dataframe(cycle_id=0):
+    """ 1 Hz. 60 samples in each cycle
+    """
+    fp = os.path.dirname(__file__)
+    data = pd.DataFrame(columns=[f'TS{i+1}' for i in range(4)])
+    data['TS1'] = np.loadtxt(fp + '/TS1.txt.gz')[cycle_id]
+    data['TS2'] = np.loadtxt(fp + '/TS2.txt.gz')[cycle_id]
+    data['TS3'] = np.loadtxt(fp + '/TS3.txt.gz')[cycle_id]
+    data['TS4'] = np.loadtxt(fp + '/TS4.txt.gz')[cycle_id]
+    return data
+
+
+def make_vibration_dataframe(cycle_id=0):
+    fp = os.path.dirname(__file__)
+    return pd.DataFrame(np.loadtxt(fp + '/VS1.txt.gz')[cycle_id], columns=['VS1'])
+
+
+def make_efficiency_dataframe(cycle_id=0):
+    fp = os.path.dirname(__file__)
+    return pd.DataFrame(np.loadtxt(fp + '/SE.txt.gz')[cycle_id], columns=['SE'])
+
+
+def make_cooling_dataframe(cycle_id=0):
+    fp = os.path.dirname(__file__)
+    data = pd.DataFrame(columns=['CE', 'CP'])
+    data['CE'] = np.loadtxt(fp + '/CE.txt.gz')[cycle_id]
+    data['CP'] = np.loadtxt(fp + '/CP.txt.gz')[cycle_id]
+    return data
+
+
+def make_condition_dataframe():
+    fp = os.path.dirname(__file__)
+    return pd.DataFrame(np.loadtxt(fp + '/profile.txt'),
+        columns=[
+            'cooler_condition',
+            'valve_condition',
+            'internal_pump_leakage',
+            'hydraulic_accumulator',
+            'stable_flag']).reset_index().rename(
+                columns={'index': 'cycle'})
+
+
diff --git a/data/hydsys/description.txt b/datasets/hydsys/description.txt
similarity index 100%
rename from data/hydsys/description.txt
rename to datasets/hydsys/description.txt
diff --git a/data/hydsys/documentation.txt b/datasets/hydsys/documentation.txt
similarity index 100%
rename from data/hydsys/documentation.txt
rename to datasets/hydsys/documentation.txt
diff --git a/data/hydsys/profile.txt b/datasets/hydsys/profile.txt
similarity index 100%
rename from data/hydsys/profile.txt
rename to datasets/hydsys/profile.txt
diff --git a/data/mapm/PdM_errors.csv.gz b/datasets/mapm/PdM_errors.csv.gz
similarity index 100%
rename from data/mapm/PdM_errors.csv.gz
rename to datasets/mapm/PdM_errors.csv.gz
diff --git a/data/mapm/PdM_failures.csv.gz b/datasets/mapm/PdM_failures.csv.gz
similarity index 100%
rename from data/mapm/PdM_failures.csv.gz
rename to datasets/mapm/PdM_failures.csv.gz
diff --git a/data/mapm/PdM_machines.csv.gz b/datasets/mapm/PdM_machines.csv.gz
similarity index 100%
rename from data/mapm/PdM_machines.csv.gz
rename to datasets/mapm/PdM_machines.csv.gz
diff --git a/data/mapm/PdM_maint.csv.gz b/datasets/mapm/PdM_maint.csv.gz
similarity index 100%
rename from data/mapm/PdM_maint.csv.gz
rename to datasets/mapm/PdM_maint.csv.gz
diff --git a/data/mapm/PdM_telemetry.csv.gz b/datasets/mapm/PdM_telemetry.csv.gz
similarity index 100%
rename from data/mapm/PdM_telemetry.csv.gz
rename to datasets/mapm/PdM_telemetry.csv.gz
diff --git a/data/mapm/__init__.py b/datasets/mapm/__init__.py
similarity index 100%
rename from data/mapm/__init__.py
rename to datasets/mapm/__init__.py
diff --git a/data/ppd/C11.csv.gz b/datasets/ppd/C11.csv.gz
similarity index 100%
rename from data/ppd/C11.csv.gz
rename to datasets/ppd/C11.csv.gz
diff --git a/data/ppd/C13-1.csv.gz b/datasets/ppd/C13-1.csv.gz
similarity index 100%
rename from data/ppd/C13-1.csv.gz
rename to datasets/ppd/C13-1.csv.gz
diff --git a/data/ppd/C13-2.csv.gz b/datasets/ppd/C13-2.csv.gz
similarity index 100%
rename from data/ppd/C13-2.csv.gz
rename to datasets/ppd/C13-2.csv.gz
diff --git a/data/ppd/C14.csv.gz b/datasets/ppd/C14.csv.gz
similarity index 100%
rename from data/ppd/C14.csv.gz
rename to datasets/ppd/C14.csv.gz
diff --git a/data/ppd/C15.csv.gz b/datasets/ppd/C15.csv.gz
similarity index 100%
rename from data/ppd/C15.csv.gz
rename to datasets/ppd/C15.csv.gz
diff --git a/data/ppd/C16.csv.gz b/datasets/ppd/C16.csv.gz
similarity index 100%
rename from data/ppd/C16.csv.gz
rename to datasets/ppd/C16.csv.gz
diff --git a/data/ppd/C7-1.csv.gz b/datasets/ppd/C7-1.csv.gz
similarity index 100%
rename from data/ppd/C7-1.csv.gz
rename to datasets/ppd/C7-1.csv.gz
diff --git a/data/ppd/C7-2.csv.gz b/datasets/ppd/C7-2.csv.gz
similarity index 100%
rename from data/ppd/C7-2.csv.gz
rename to datasets/ppd/C7-2.csv.gz
diff --git a/data/ppd/C8.csv.gz b/datasets/ppd/C8.csv.gz
similarity index 100%
rename from data/ppd/C8.csv.gz
rename to datasets/ppd/C8.csv.gz
diff --git a/data/ppd/C9.csv.gz b/datasets/ppd/C9.csv.gz
similarity index 100%
rename from data/ppd/C9.csv.gz
rename to datasets/ppd/C9.csv.gz
diff --git a/data/ufd/MeterA.txt b/datasets/ufd/MeterA.txt
similarity index 100%
rename from data/ufd/MeterA.txt
rename to datasets/ufd/MeterA.txt
diff --git a/data/ufd/MeterB.txt b/datasets/ufd/MeterB.txt
similarity index 100%
rename from data/ufd/MeterB.txt
rename to datasets/ufd/MeterB.txt
diff --git a/data/ufd/MeterC.txt b/datasets/ufd/MeterC.txt
similarity index 100%
rename from data/ufd/MeterC.txt
rename to datasets/ufd/MeterC.txt
diff --git a/data/ufd/MeterD.txt b/datasets/ufd/MeterD.txt
similarity index 100%
rename from data/ufd/MeterD.txt
rename to datasets/ufd/MeterD.txt
diff --git a/datasets/ufd/__init__.py b/datasets/ufd/__init__.py
new file mode 100644
index 0000000..88922d1
--- /dev/null
+++ b/datasets/ufd/__init__.py
@@ -0,0 +1,42 @@
+import os
+import pandas as pd
+import numpy as np
+
+
+def load_data(meter_id='A'):
+    fp = os.path.dirname(__file__)
+    data = np.loadtxt(fp + '/Meter{}.txt'.format(meter_id))
+
+    if meter_id == 'A':
+        columns =  ['flatness_ratio', 'symmetry', 'crossflow']
+        columns += ['flow_velocity_{}'.format(i+1) for i in range(8)]
+        columns += ['sound_speed_{}'.format(i+1) for i in range(8)]
+        columns += ['average_speed']
+        columns += ['gain_{}'.format(i+1) for i in range(16)]
+        columns += ['health_state']
+
+    if meter_id == 'B':
+        columns =  ['profile_factor', 'symmetry', 'crossflow', 'swirl_angle']
+        columns += ['flow_velocity_{}'.format(i+1) for i in range(4)]
+        columns += ['average_flow']
+        columns += ['sound_speed_{}'.format(i+1) for i in range(4)]
+        columns += ['average_speed']
+        columns += ['signal_strength_{}'.format(i+1) for i in range(8)]
+        columns += ['turbulence_{}'.format(i+1) for i in range(4)]
+        columns += ['meter_performance']
+        columns += ['signal_quality_{}'.format(i+1) for i in range(8)]
+        columns += ['gain_{}'.format(i+1) for i in range(8)]
+        columns += ['transit_time_{}'.format(i+1) for i in range(8)]
+        columns += ['health_state']
+
+    if meter_id == 'C' or meter_id == 'D':
+        columns =  ['profile_factor', 'symmetry', 'crossflow']
+        columns += ['flow_velocity_{}'.format(i+1) for i in range(4)]
+        columns += ['sound_speed_{}'.format(i+1) for i in range(4)]
+        columns += ['signal_strength_{}'.format(i+1) for i in range(8)]
+        columns += ['signal_quality_{}'.format(i+1) for i in range(8)]
+        columns += ['gain_{}'.format(i+1) for i in range(8)]
+        columns += ['transit_time_{}'.format(i+1) for i in range(8)]
+        columns += ['health_state']
+
+    return pd.DataFrame(data, columns=columns)
\ No newline at end of file
diff --git a/notebooks/view_gdd.ipynb b/notebooks/view_gdd.ipynb
new file mode 100644
index 0000000..b99a918
--- /dev/null
+++ b/notebooks/view_gdd.ipynb
@@ -0,0 +1,1260 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Genesis demonstrator data for machine learning\n",
+    "\n",
+    "- Dataset source:  \n",
+    "[https://www.kaggle.com/inIT-OWL/genesis-demonstrator-data-for-machine-learning](https://www.kaggle.com/inIT-OWL/genesis-demonstrator-data-for-machine-learning)\n",
+    "- Epoch & Unix Timestamp Conversion Tools:  \n",
+    "[https://www.epochconverter.com/](https://www.epochconverter.com/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import datetime\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "sns.set(font_scale=1.1, style='whitegrid')\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.backends.backend_pdf import PdfPages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GenesisStateMachineLabels\n",
+    "The demonstrator sorts two different materials (wood and metal)\n",
+    "into their corresponding target locations.  \n",
+    "The different modules:\n",
+    "1. Storage magazine\n",
+    "1. Sensor\n",
+    "1. Metal storage\n",
+    "1. Wood storage\n",
+    "\n",
+    "can be placed at any of the four positions\n",
+    "and the PLC program automatically adjusts for the change in location.  \n",
+    "(PLC: Programmable logic controller)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0        1.461149e+09\n",
+      "1        1.461149e+09\n",
+      "2        1.461149e+09\n",
+      "3        1.461149e+09\n",
+      "4        1.461149e+09\n",
+      "             ...     \n",
+      "16215    1.461149e+09\n",
+      "16216    1.461149e+09\n",
+      "16217    1.461149e+09\n",
+      "16218    1.461149e+09\n",
+      "16219    1.461149e+09\n",
+      "Name: Timestamp, Length: 16220, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp                               datetime64[ns]\n",
+       "Label                                            int64\n",
+       "MotorData.ActCurrent                             int64\n",
+       "MotorData.ActPosition                            int64\n",
+       "MotorData.ActSpeed                               int64\n",
+       "MotorData.IsAcceleration                         int64\n",
+       "MotorData.IsForce                                int64\n",
+       "MotorData.Motor_Pos1reached                      int64\n",
+       "MotorData.Motor_Pos2reached                      int64\n",
+       "MotorData.Motor_Pos3reached                      int64\n",
+       "MotorData.Motor_Pos4reached                      int64\n",
+       "NVL_Recv_Ind.GL_Metall                           int64\n",
+       "NVL_Recv_Ind.GL_NonMetall                        int64\n",
+       "NVL_Recv_Storage.GL_I_ProcessStarted             int64\n",
+       "NVL_Recv_Storage.GL_I_Slider_IN                  int64\n",
+       "NVL_Recv_Storage.GL_I_Slider_OUT                 int64\n",
+       "NVL_Recv_Storage.GL_LightBarrier                 int64\n",
+       "NVL_Send_Storage.ActivateStorage                 int64\n",
+       "PLC_PRG.Gripper                                  int64\n",
+       "PLC_PRG.MaterialIsMetal                          int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_state = pd.read_csv('../datasets/gdd/Genesis_StateMachineLabel.csv.gz')\n",
+    "print(df_state.Timestamp)\n",
+    "df_state.Timestamp = df_state.Timestamp.apply(datetime.datetime.fromtimestamp)\n",
+    "df_state.dtypes\n",
+    "# df_state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Timestamp('2016-04-20 19:35:12.938000'),\n",
+       " Timestamp('2016-04-20 19:47:53.354000'))"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_state.Timestamp.min(), df_state.Timestamp.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A linear drive with a pneumatic gripper transports the materials between the different stations.  \n",
+    "The procedure follows these steps:\n",
+    "1. Idle, waiting for start button press.\n",
+    "1. Homing, do homing when drive is not homed yet (only after first power on).\n",
+    "1. Move gripper to a position next to the storage module. This avoids collisions of the storage slider and the gripper when\n",
+    "material is ejected.\n",
+    "1. Eject material.\n",
+    "1. Move gripper to storage position.\n",
+    "1. Close the gripper to pick up material.\n",
+    "1. Move to the sensor to detect material type.\n",
+    "1. Move to the corresponding storage box of the detected material.\n",
+    "1. Open the gripper to release material.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'The internal state machine of the PLC code.'}>"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_state.Label.plot(\n",
+    "    figsize=(12, 2),\n",
+    "    title='The internal state machine of the PLC code.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_state[[\n",
+    "    # 'MotorData.ActCurrent',           # continuous\n",
+    "    # 'MotorData.ActPosition',          # continuous\n",
+    "    # 'MotorData.ActSpeed',             # continuous\n",
+    "    # 'MotorData.IsAcceleration',       # continuous\n",
+    "    'MotorData.IsForce',              # continuous\n",
+    "    # 'MotorData.Motor_Pos1reached',    # binary\n",
+    "    # 'MotorData.Motor_Pos2reached',    # binary\n",
+    "    # 'MotorData.Motor_Pos3reached',    # binary\n",
+    "    # 'MotorData.Motor_Pos4reached',    # binary\n",
+    "    # 'NVL_Recv_Ind.GL_Metall',  # binary\n",
+    "    # 'NVL_Recv_Ind.GL_NonMetall',  # binary\n",
+    "    # 'NVL_Recv_Storage.GL_I_ProcessStarted',  # binary\n",
+    "    # 'NVL_Recv_Storage.GL_I_Slider_IN',  # binary\n",
+    "    # 'NVL_Recv_Storage.GL_I_Slider_OUT',  # binary\n",
+    "    # 'NVL_Recv_Storage.GL_LightBarrier',  # binary\n",
+    "    # 'NVL_Send_Storage.ActivateStorage',  # binary\n",
+    "    # 'PLC_PRG.Gripper',  # binary\n",
+    "    # 'PLC_PRG.MaterialIsMetal',  # binary\n",
+    "]].plot(figsize=(12, 3), legend=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Genesis_AnomalyLabels\n",
+    "\n",
+    "This data is identical to GenesisStateMachineLabels except for the Label column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp                               datetime64[ns]\n",
+       "Label                                            int64\n",
+       "MotorData.ActCurrent                             int64\n",
+       "MotorData.ActPosition                            int64\n",
+       "MotorData.ActSpeed                               int64\n",
+       "MotorData.IsAcceleration                         int64\n",
+       "MotorData.IsForce                                int64\n",
+       "MotorData.Motor_Pos1reached                      int64\n",
+       "MotorData.Motor_Pos2reached                      int64\n",
+       "MotorData.Motor_Pos3reached                      int64\n",
+       "MotorData.Motor_Pos4reached                      int64\n",
+       "NVL_Recv_Ind.GL_Metall                           int64\n",
+       "NVL_Recv_Ind.GL_NonMetall                        int64\n",
+       "NVL_Recv_Storage.GL_I_ProcessStarted             int64\n",
+       "NVL_Recv_Storage.GL_I_Slider_IN                  int64\n",
+       "NVL_Recv_Storage.GL_I_Slider_OUT                 int64\n",
+       "NVL_Recv_Storage.GL_LightBarrier                 int64\n",
+       "NVL_Send_Storage.ActivateStorage                 int64\n",
+       "PLC_PRG.Gripper                                  int64\n",
+       "PLC_PRG.MaterialIsMetal                          int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_anom = pd.read_csv('../datasets/gdd/Genesis_AnomalyLabels.csv.gz')\n",
+    "df_anom.Timestamp = df_anom.Timestamp.apply(datetime.datetime.fromtimestamp)\n",
+    "df_anom.dtypes\n",
+    "# df_anom"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Timestamp('2016-04-20 19:35:12.938000'),\n",
+       " Timestamp('2016-04-20 19:47:53.354000'))"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_anom.Timestamp.min(), df_anom.Timestamp.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The anomaly Labels are manually annotated,\n",
+    "checked very carefully and are accurate for each data point!  \n",
+    "Only one type of Anomaly was simulated:\n",
+    "- 0: No anomaly\n",
+    "- 1: Linear drive jammed / tilted\n",
+    "- 2: Linear drive breaks free and corrects accumulated lag error\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'Anomaly labels'}>"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 864x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_anom.Label.plot(\n",
+    "    figsize=(12, 3),\n",
+    "    title='Anomaly labels'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Genesis_normal\n",
+    "\n",
+    "The Demonstrator worked as intended, without any failures or restrictions.  \n",
+    "It can be used to compare it with other files for predictive maintenance or anomaly detection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp                               datetime64[ns]\n",
+       "MotorData.ActCurrent                             int64\n",
+       "MotorData.ActSpeed                               int64\n",
+       "MotorData.IsAcceleration                         int64\n",
+       "MotorData.IsForce                                int64\n",
+       "MotorData.Motor_Pos1reached                      int64\n",
+       "MotorData.Motor_Pos2reached                      int64\n",
+       "MotorData.Motor_Pos3reached                      int64\n",
+       "MotorData.Motor_Pos4reached                      int64\n",
+       "MotorData.SetAcceleration                        int64\n",
+       "MotorData.SetCurrent                             int64\n",
+       "MotorData.SetForce                               int64\n",
+       "MotorData.SetSpeed                               int64\n",
+       "NVL_Recv_Ind.GL_Metall                           int64\n",
+       "NVL_Recv_Ind.GL_NonMetall                        int64\n",
+       "NVL_Recv_Storage.GL_I_ProcessStarted             int64\n",
+       "NVL_Recv_Storage.GL_I_Slider_IN                  int64\n",
+       "NVL_Recv_Storage.GL_I_Slider_OUT                 int64\n",
+       "NVL_Recv_Storage.GL_LightBarrier                 int64\n",
+       "NVL_Send_Storage.ActivateStorage                 int64\n",
+       "PLC_PRG.Gripper                                  int64\n",
+       "PLC_PRG.MaterialIsMetal                          int64\n",
+       "NVL_Recv_Storage.GL_X_TimeSlideIn                int64\n",
+       "NVL_Recv_Storage.GL_X_TimeSlideOut               int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_norm = pd.read_csv('../datasets/gdd/Genesis_normal.csv.gz')\n",
+    "df_norm.Timestamp = df_norm.Timestamp / 1000\n",
+    "df_norm.Timestamp = df_norm.Timestamp.apply(datetime.datetime.fromtimestamp)\n",
+    "df_norm.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Timestamp('2017-07-25 01:39:08.721000'),\n",
+       " Timestamp('2017-07-25 01:44:39.079000'))"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_norm.Timestamp.min(),df_norm.Timestamp.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_norm[[\n",
+    "    # 'MotorData.ActCurrent',           # continuous\n",
+    "    'MotorData.ActSpeed',             # continuous\n",
+    "    'MotorData.IsAcceleration',       # continuous\n",
+    "    # 'MotorData.IsForce',              # continuous\n",
+    "    # 'MotorData.Motor_Pos1reached',    # binary\n",
+    "    # 'MotorData.Motor_Pos2reached',    # binary\n",
+    "    # 'MotorData.Motor_Pos3reached',    # binary\n",
+    "    # 'MotorData.Motor_Pos4reached',    # binary\n",
+    "    # 'MotorData.SetAcceleration',      # continuous\n",
+    "    # 'MotorData.SetCurrent',           # continuous\n",
+    "    # 'MotorData.SetForce',             # continuous\n",
+    "    # 'MotorData.SetSpeed',             # continuous\n",
+    "    # 'NVL_Recv_Ind.GL_Metall',         # binary\n",
+    "    # 'NVL_Recv_Ind.GL_NonMetall',          # binary\n",
+    "    # 'NVL_Recv_Storage.GL_I_ProcessStarted',  # binary\n",
+    "    # 'NVL_Recv_Storage.GL_I_Slider_IN',    # binary\n",
+    "    # 'NVL_Recv_Storage.GL_I_Slider_OUT',  # binary\n",
+    "    # 'NVL_Recv_Storage.GL_LightBarrier',  # binary\n",
+    "    # 'NVL_Send_Storage.ActivateStorage',  # binary\n",
+    "    # 'PLC_PRG.Gripper',                # binary\n",
+    "    # 'PLC_PRG.MaterialIsMetal',         # binary\n",
+    "    # 'NVL_Recv_Storage.GL_X_TimeSlideIn',\n",
+    "    # 'NVL_Recv_Storage.GL_X_TimeSlideOut',\n",
+    "]].plot(figsize=(12, 3), legend=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Genesis_lineardrive\n",
+    "\n",
+    "The lineardrive was slightly impaired over time,\n",
+    "so that the Genesis Demonstrator does not work as intended.  \n",
+    "This file can be used for predictive maintenance or anomaly detection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp                               datetime64[ns]\n",
+       "MotorData.ActCurrent                             int64\n",
+       "MotorData.ActSpeed                               int64\n",
+       "MotorData.IsAcceleration                         int64\n",
+       "MotorData.IsForce                                int64\n",
+       "MotorData.Motor_Pos1reached                      int64\n",
+       "MotorData.Motor_Pos2reached                      int64\n",
+       "MotorData.Motor_Pos3reached                      int64\n",
+       "MotorData.Motor_Pos4reached                      int64\n",
+       "MotorData.SetAcceleration                        int64\n",
+       "MotorData.SetCurrent                             int64\n",
+       "MotorData.SetForce                               int64\n",
+       "MotorData.SetSpeed                               int64\n",
+       "NVL_Recv_Ind.GL_Metall                           int64\n",
+       "NVL_Recv_Ind.GL_NonMetall                        int64\n",
+       "NVL_Recv_Storage.GL_I_ProcessStarted             int64\n",
+       "NVL_Recv_Storage.GL_I_Slider_IN                  int64\n",
+       "NVL_Recv_Storage.GL_I_Slider_OUT                 int64\n",
+       "NVL_Recv_Storage.GL_LightBarrier                 int64\n",
+       "NVL_Send_Storage.ActivateStorage                 int64\n",
+       "PLC_PRG.Gripper                                  int64\n",
+       "PLC_PRG.MaterialIsMetal                          int64\n",
+       "NVL_Recv_Storage.GL_X_TimeSlideIn                int64\n",
+       "NVL_Recv_Storage.GL_X_TimeSlideOut               int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_linear = pd.read_csv('../datasets/gdd/Genesis_lineardrive.csv.gz')\n",
+    "df_linear.Timestamp = df_linear.Timestamp / 1000\n",
+    "df_linear.Timestamp = df_linear.Timestamp.apply(datetime.datetime.fromtimestamp)\n",
+    "df_linear.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Timestamp('2017-07-25 01:47:44.061000'),\n",
+       " Timestamp('2017-07-25 01:53:32.213000'))"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_linear.Timestamp.min(), df_linear.Timestamp.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_norm[[\n",
+    "    'MotorData.ActCurrent',           # continuous\n",
+    "    # 'MotorData.ActSpeed',             # continuous\n",
+    "    # 'MotorData.IsAcceleration',       # continuous\n",
+    "    # 'MotorData.IsForce',              # continuous\n",
+    "    # 'MotorData.Motor_Pos1reached',    # binary\n",
+    "    # 'MotorData.Motor_Pos2reached',    # binary\n",
+    "    # 'MotorData.Motor_Pos3reached',    # binary\n",
+    "    # 'MotorData.Motor_Pos4reached',    # binary\n",
+    "    # 'MotorData.SetAcceleration',      # continuous\n",
+    "    'MotorData.SetCurrent',           # continuous\n",
+    "    # 'MotorData.SetForce',             # continuous\n",
+    "    # 'MotorData.SetSpeed',             # continuous\n",
+    "    # 'NVL_Recv_Ind.GL_Metall',         # binary\n",
+    "    # 'NVL_Recv_Ind.GL_NonMetall',          # binary\n",
+    "    # 'NVL_Recv_Storage.GL_I_ProcessStarted',  # binary\n",
+    "    # 'NVL_Recv_Storage.GL_I_Slider_IN',    # binary\n",
+    "    # 'NVL_Recv_Storage.GL_I_Slider_OUT',  # binary\n",
+    "    # 'NVL_Recv_Storage.GL_LightBarrier',  # binary\n",
+    "    # 'NVL_Send_Storage.ActivateStorage',  # binary\n",
+    "    # 'PLC_PRG.Gripper',                # binary\n",
+    "    # 'PLC_PRG.MaterialIsMetal',         # binary\n",
+    "]].plot(figsize=(12, 3), legend=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Genesis_pressure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp                               datetime64[ns]\n",
+       "MotorData.ActCurrent                             int64\n",
+       "MotorData.ActSpeed                               int64\n",
+       "MotorData.IsAcceleration                         int64\n",
+       "MotorData.IsForce                                int64\n",
+       "MotorData.Motor_Pos1reached                      int64\n",
+       "MotorData.Motor_Pos2reached                      int64\n",
+       "MotorData.Motor_Pos3reached                      int64\n",
+       "MotorData.Motor_Pos4reached                      int64\n",
+       "MotorData.SetAcceleration                        int64\n",
+       "MotorData.SetCurrent                             int64\n",
+       "MotorData.SetForce                               int64\n",
+       "MotorData.SetSpeed                               int64\n",
+       "NVL_Recv_Ind.GL_Metall                           int64\n",
+       "NVL_Recv_Ind.GL_NonMetall                        int64\n",
+       "NVL_Recv_Storage.GL_I_ProcessStarted             int64\n",
+       "NVL_Recv_Storage.GL_I_Slider_IN                  int64\n",
+       "NVL_Recv_Storage.GL_I_Slider_OUT                 int64\n",
+       "NVL_Recv_Storage.GL_LightBarrier                 int64\n",
+       "NVL_Send_Storage.ActivateStorage                 int64\n",
+       "PLC_PRG.Gripper                                  int64\n",
+       "PLC_PRG.MaterialIsMetal                          int64\n",
+       "NVL_Recv_Storage.GL_X_TimeSlideIn                int64\n",
+       "NVL_Recv_Storage.GL_X_TimeSlideOut               int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ps = pd.read_csv('../datasets/gdd/Genesis_pressure.csv.gz')\n",
+    "df_ps.Timestamp = df_ps.Timestamp / 1000\n",
+    "df_ps.Timestamp = df_ps.Timestamp.apply(datetime.datetime.fromtimestamp)\n",
+    "df_ps.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_ps[[\n",
+    "    # 'MotorData.ActCurrent',           # continuous\n",
+    "    # 'MotorData.ActSpeed',             # continuous\n",
+    "    # 'MotorData.IsAcceleration',       # continuous\n",
+    "    # 'MotorData.IsForce',              # continuous\n",
+    "    'MotorData.Motor_Pos1reached',    # binary\n",
+    "    'MotorData.Motor_Pos2reached',    # binary\n",
+    "    'MotorData.Motor_Pos3reached',    # binary\n",
+    "    'MotorData.Motor_Pos4reached',    # binary\n",
+    "    # 'MotorData.SetAcceleration',      # continuous\n",
+    "    # 'MotorData.SetCurrent',           # continuous\n",
+    "    # 'MotorData.SetForce',             # continuous\n",
+    "    # 'MotorData.SetSpeed',             # continuous\n",
+    "    # 'NVL_Recv_Ind.GL_Metall',         # binary\n",
+    "    # 'NVL_Recv_Ind.GL_NonMetall',          # binary\n",
+    "    # 'NVL_Recv_Storage.GL_I_ProcessStarted',  # binary\n",
+    "    # 'NVL_Recv_Storage.GL_I_Slider_IN',    # binary\n",
+    "    # 'NVL_Recv_Storage.GL_I_Slider_OUT',  # binary\n",
+    "    # 'NVL_Recv_Storage.GL_LightBarrier',  # binary\n",
+    "    # 'NVL_Send_Storage.ActivateStorage',  # binary\n",
+    "    # 'PLC_PRG.Gripper',                # binary\n",
+    "    # 'PLC_PRG.MaterialIsMetal',         # binary\n",
+    "]].plot(figsize=(12, 3), legend=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_data(fp, index='state'):\n",
+    "\n",
+    "    assert index in ['state', 'anomaly', 'normal', 'linear', 'pressure']\n",
+    "\n",
+    "    if index == 'state':\n",
+    "        df = pd.read_csv(fp + '/Genesis_StateMachineLabel.csv.gz')\n",
+    "    elif index == 'anomaly':\n",
+    "        df = pd.read_csv(fp + '/Genesis_AnomalyLabels.csv.gz')\n",
+    "    elif index == 'normal':\n",
+    "        df = pd.read_csv(fp + '/Genesis_normal.csv.gz')\n",
+    "        df.Timestamp = df.Timestamp / 1000\n",
+    "    elif index == 'linear':\n",
+    "        df = pd.read_csv(fp + '/Genesis_lineardrive.csv.gz')\n",
+    "        df.Timestamp = df.Timestamp / 1000\n",
+    "    elif index == 'pressure':\n",
+    "        df = pd.read_csv(fp + '/Genesis_pressure.csv.gz')\n",
+    "        df.Timestamp = df.Timestamp / 1000\n",
+    "\n",
+    "    df.Timestamp = df.Timestamp.apply(datetime.datetime.fromtimestamp)\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Timestamp</th>\n",
+       "      <th>MotorData.ActCurrent</th>\n",
+       "      <th>MotorData.ActSpeed</th>\n",
+       "      <th>MotorData.IsAcceleration</th>\n",
+       "      <th>MotorData.IsForce</th>\n",
+       "      <th>MotorData.Motor_Pos1reached</th>\n",
+       "      <th>MotorData.Motor_Pos2reached</th>\n",
+       "      <th>MotorData.Motor_Pos3reached</th>\n",
+       "      <th>MotorData.Motor_Pos4reached</th>\n",
+       "      <th>MotorData.SetAcceleration</th>\n",
+       "      <th>...</th>\n",
+       "      <th>NVL_Recv_Ind.GL_NonMetall</th>\n",
+       "      <th>NVL_Recv_Storage.GL_I_ProcessStarted</th>\n",
+       "      <th>NVL_Recv_Storage.GL_I_Slider_IN</th>\n",
+       "      <th>NVL_Recv_Storage.GL_I_Slider_OUT</th>\n",
+       "      <th>NVL_Recv_Storage.GL_LightBarrier</th>\n",
+       "      <th>NVL_Send_Storage.ActivateStorage</th>\n",
+       "      <th>PLC_PRG.Gripper</th>\n",
+       "      <th>PLC_PRG.MaterialIsMetal</th>\n",
+       "      <th>NVL_Recv_Storage.GL_X_TimeSlideIn</th>\n",
+       "      <th>NVL_Recv_Storage.GL_X_TimeSlideOut</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2017-07-25 01:47:44.061</td>\n",
+       "      <td>344</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>76</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>77</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017-07-25 01:47:44.102</td>\n",
+       "      <td>344</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>76</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>77</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2017-07-25 01:47:44.151</td>\n",
+       "      <td>344</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>76</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>77</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2017-07-25 01:47:44.198</td>\n",
+       "      <td>344</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>78</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>77</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2017-07-25 01:47:44.244</td>\n",
+       "      <td>340</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>78</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>77</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Timestamp  MotorData.ActCurrent  MotorData.ActSpeed  \\\n",
+       "0 2017-07-25 01:47:44.061                   344                   0   \n",
+       "1 2017-07-25 01:47:44.102                   344                   0   \n",
+       "2 2017-07-25 01:47:44.151                   344                   0   \n",
+       "3 2017-07-25 01:47:44.198                   344                   0   \n",
+       "4 2017-07-25 01:47:44.244                   340                   0   \n",
+       "\n",
+       "   MotorData.IsAcceleration  MotorData.IsForce  MotorData.Motor_Pos1reached  \\\n",
+       "0                         0                 76                            0   \n",
+       "1                         0                 76                            0   \n",
+       "2                         0                 76                            0   \n",
+       "3                         0                 78                            0   \n",
+       "4                         0                 78                            0   \n",
+       "\n",
+       "   MotorData.Motor_Pos2reached  MotorData.Motor_Pos3reached  \\\n",
+       "0                            0                            0   \n",
+       "1                            0                            0   \n",
+       "2                            0                            0   \n",
+       "3                            0                            0   \n",
+       "4                            0                            0   \n",
+       "\n",
+       "   MotorData.Motor_Pos4reached  MotorData.SetAcceleration  ...  \\\n",
+       "0                            1                         77  ...   \n",
+       "1                            1                         77  ...   \n",
+       "2                            1                         77  ...   \n",
+       "3                            1                         77  ...   \n",
+       "4                            1                         77  ...   \n",
+       "\n",
+       "   NVL_Recv_Ind.GL_NonMetall  NVL_Recv_Storage.GL_I_ProcessStarted  \\\n",
+       "0                          1                                     0   \n",
+       "1                          1                                     0   \n",
+       "2                          1                                     0   \n",
+       "3                          1                                     0   \n",
+       "4                          1                                     0   \n",
+       "\n",
+       "   NVL_Recv_Storage.GL_I_Slider_IN  NVL_Recv_Storage.GL_I_Slider_OUT  \\\n",
+       "0                                1                                 0   \n",
+       "1                                1                                 0   \n",
+       "2                                1                                 0   \n",
+       "3                                1                                 0   \n",
+       "4                                1                                 0   \n",
+       "\n",
+       "   NVL_Recv_Storage.GL_LightBarrier  NVL_Send_Storage.ActivateStorage  \\\n",
+       "0                                 1                                 0   \n",
+       "1                                 1                                 0   \n",
+       "2                                 1                                 0   \n",
+       "3                                 1                                 0   \n",
+       "4                                 1                                 0   \n",
+       "\n",
+       "   PLC_PRG.Gripper  PLC_PRG.MaterialIsMetal  \\\n",
+       "0                0                        0   \n",
+       "1                0                        0   \n",
+       "2                0                        0   \n",
+       "3                0                        0   \n",
+       "4                0                        0   \n",
+       "\n",
+       "   NVL_Recv_Storage.GL_X_TimeSlideIn  NVL_Recv_Storage.GL_X_TimeSlideOut  \n",
+       "0                                240                                 200  \n",
+       "1                                240                                 200  \n",
+       "2                                240                                 200  \n",
+       "3                                240                                 200  \n",
+       "4                                240                                 200  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# df = load_data('../datasets/gdd/', index='state')\n",
+    "# df = load_data('../datasets/gdd/', index='anomaly')\n",
+    "# df = load_data('../datasets/gdd/', index='normal')\n",
+    "df = load_data('../datasets/gdd/', index='linear')\n",
+    "# df = load_data('../datasets/gdd/', index='state')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp                               datetime64[ns]\n",
+       "MotorData.ActCurrent                             int64\n",
+       "MotorData.ActSpeed                               int64\n",
+       "MotorData.IsAcceleration                         int64\n",
+       "MotorData.IsForce                                int64\n",
+       "MotorData.Motor_Pos1reached                      int64\n",
+       "MotorData.Motor_Pos2reached                      int64\n",
+       "MotorData.Motor_Pos3reached                      int64\n",
+       "MotorData.Motor_Pos4reached                      int64\n",
+       "MotorData.SetAcceleration                        int64\n",
+       "MotorData.SetCurrent                             int64\n",
+       "MotorData.SetForce                               int64\n",
+       "MotorData.SetSpeed                               int64\n",
+       "NVL_Recv_Ind.GL_Metall                           int64\n",
+       "NVL_Recv_Ind.GL_NonMetall                        int64\n",
+       "NVL_Recv_Storage.GL_I_ProcessStarted             int64\n",
+       "NVL_Recv_Storage.GL_I_Slider_IN                  int64\n",
+       "NVL_Recv_Storage.GL_I_Slider_OUT                 int64\n",
+       "NVL_Recv_Storage.GL_LightBarrier                 int64\n",
+       "NVL_Send_Storage.ActivateStorage                 int64\n",
+       "PLC_PRG.Gripper                                  int64\n",
+       "PLC_PRG.MaterialIsMetal                          int64\n",
+       "NVL_Recv_Storage.GL_X_TimeSlideIn                int64\n",
+       "NVL_Recv_Storage.GL_X_TimeSlideOut               int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_genesis_labels(df, figsize=(15, 20), cmap='tab10'):\n",
+    "    \"\"\" Call this for machine states and anomaly labels \"\"\"\n",
+    "\n",
+    "    fig, ax = plt.subplots(10, figsize=figsize)\n",
+    "\n",
+    "    df['MotorData.ActCurrent'].plot(ax=ax[0], legend=True, cmap=cmap)\n",
+    "    df['MotorData.ActPosition'].plot(ax=ax[1], legend=True, cmap=cmap)\n",
+    "    df['MotorData.ActSpeed'].plot(ax=ax[2], legend=True, cmap=cmap)\n",
+    "\n",
+    "    df['MotorData.IsAcceleration'].plot(ax=ax[3], legend=True, cmap=cmap)\n",
+    "    df['MotorData.IsForce'].plot(ax=ax[4], legend=True, cmap=cmap)\n",
+    "\n",
+    "    df[['MotorData.Motor_Pos1reached',    # binary\n",
+    "        'MotorData.Motor_Pos2reached',    # binary\n",
+    "        'MotorData.Motor_Pos3reached',    # binary\n",
+    "        'MotorData.Motor_Pos4reached',    # binary\n",
+    "    ]].plot(ax=ax[5], legend=True, cmap=cmap)\n",
+    "\n",
+    "    df[['NVL_Recv_Ind.GL_Metall',  # binary\n",
+    "        'NVL_Recv_Ind.GL_NonMetall',  # binary\n",
+    "    ]].plot(ax=ax[6], legend=True, cmap=cmap)\n",
+    "\n",
+    "    df[['NVL_Recv_Storage.GL_I_ProcessStarted',  # binary\n",
+    "        'NVL_Recv_Storage.GL_I_Slider_IN',  # binary\n",
+    "        'NVL_Recv_Storage.GL_I_Slider_OUT',  # binary\n",
+    "        'NVL_Recv_Storage.GL_LightBarrier',  # binary\n",
+    "        'NVL_Send_Storage.ActivateStorage',  # binary\n",
+    "    ]].plot(ax=ax[7], legend=True, cmap=cmap)\n",
+    "\n",
+    "    df[['PLC_PRG.Gripper',  # binary\n",
+    "        'PLC_PRG.MaterialIsMetal',  # binary\n",
+    "    ]].plot(ax=ax[8], legend=True, cmap=cmap)\n",
+    "\n",
+    "    df['Label'].plot(ax=ax[9], legend=True, cmap=cmap)\n",
+    "\n",
+    "    for axi in ax:\n",
+    "        axi.set_xlim(0, df.shape[0])\n",
+    "        axi.set_ylabel('Value')\n",
+    "\n",
+    "    ax[0].set_title('Date: {} to {}'.format(\n",
+    "        df.Timestamp.min(), df.Timestamp.max()))\n",
+    "    ax[-1].set_xlabel('Time')\n",
+    "    fig.tight_layout()\n",
+    "\n",
+    "    return fig, ax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_genesis_nonlabels(df, figsize=(15, 20), cmap='tab10'):\n",
+    "    \"\"\" Call this for non-labeled data \"\"\"\n",
+    "\n",
+    "    fig, ax = plt.subplots(8, figsize=figsize)\n",
+    "\n",
+    "    df[['MotorData.SetCurrent',\n",
+    "        'MotorData.ActCurrent',\n",
+    "    ]].plot(ax=ax[0], legend=True, cmap=cmap)\n",
+    "\n",
+    "    df[['MotorData.SetSpeed',\n",
+    "        'MotorData.ActSpeed',\n",
+    "    ]].plot(ax=ax[1], legend=True, cmap=cmap)\n",
+    "\n",
+    "    df[['MotorData.SetAcceleration',\n",
+    "        'MotorData.IsAcceleration',\n",
+    "    ]].plot(ax=ax[2], legend=True, cmap=cmap)\n",
+    "\n",
+    "    df[['MotorData.SetForce',\n",
+    "        'MotorData.IsForce'\n",
+    "    ]].plot(ax=ax[3], legend=True, cmap=cmap)\n",
+    "\n",
+    "    df[['MotorData.Motor_Pos1reached',    # binary\n",
+    "        'MotorData.Motor_Pos2reached',    # binary\n",
+    "        'MotorData.Motor_Pos3reached',    # binary\n",
+    "        'MotorData.Motor_Pos4reached',    # binary\n",
+    "    ]].plot(ax=ax[4], legend=True, cmap=cmap)\n",
+    "\n",
+    "    df[['NVL_Recv_Ind.GL_Metall',  # binary\n",
+    "        'NVL_Recv_Ind.GL_NonMetall',  # binary\n",
+    "    ]].plot(ax=ax[5], legend=True, cmap=cmap)\n",
+    "\n",
+    "    df[['NVL_Recv_Storage.GL_I_ProcessStarted',  # binary\n",
+    "        'NVL_Recv_Storage.GL_I_Slider_IN',  # binary\n",
+    "        'NVL_Recv_Storage.GL_I_Slider_OUT',  # binary\n",
+    "        'NVL_Recv_Storage.GL_LightBarrier',  # binary\n",
+    "        'NVL_Send_Storage.ActivateStorage',  # binary\n",
+    "    ]].plot(ax=ax[6], legend=True, cmap=cmap)\n",
+    "\n",
+    "    df[['PLC_PRG.Gripper',  # binary\n",
+    "        'PLC_PRG.MaterialIsMetal',  # binary\n",
+    "    ]].plot(ax=ax[7], legend=True, cmap=cmap)\n",
+    "\n",
+    "    for axi in ax:\n",
+    "        axi.set_xlim(0, df.shape[0])\n",
+    "        axi.set_ylabel('Value')\n",
+    "\n",
+    "    ax[0].set_title('Date: {} to {}'.format(df.Timestamp.min(), df.Timestamp.max()))\n",
+    "    ax[-1].set_xlabel('Time')\n",
+    "\n",
+    "    fig.tight_layout()\n",
+    "    return fig, ax\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate a summary PDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gen_summary(fp, outdir='../out'):\n",
+    "\n",
+    "    os.makedirs(outdir, exist_ok=True)\n",
+    "    with PdfPages(outdir + '/gdd_summary.pdf') as pp:\n",
+    "\n",
+    "        df = load_data(fp, index='state')\n",
+    "        fig, _ = plot_genesis_labels(df)\n",
+    "        fig.savefig(pp, bbox_inches='tight', format='pdf')\n",
+    "        plt.clf()\n",
+    "        plt.close()\n",
+    "\n",
+    "        df = load_data(fp, index='anomaly')\n",
+    "        fig, _ = plot_genesis_labels(df)\n",
+    "        fig.savefig(pp, bbox_inches='tight', format='pdf')\n",
+    "        plt.clf()\n",
+    "        plt.close()\n",
+    "\n",
+    "        df = load_data(fp, index='normal')\n",
+    "        fig, _ = plot_genesis_nonlabels(df)\n",
+    "        fig.savefig(pp, bbox_inches='tight', format='pdf')\n",
+    "        plt.clf()\n",
+    "        plt.close()\n",
+    "\n",
+    "        df = load_data(fp, index='linear')\n",
+    "        fig, _ = plot_genesis_nonlabels(df)\n",
+    "        fig.savefig(pp, bbox_inches='tight', format='pdf')\n",
+    "        plt.clf()\n",
+    "        plt.close()\n",
+    "\n",
+    "        df = load_data(fp, index='pressure')\n",
+    "        fig, _ = plot_genesis_nonlabels(df)\n",
+    "        fig.savefig(pp, bbox_inches='tight', format='pdf')\n",
+    "        plt.clf()\n",
+    "        plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gen_summary('../datasets/gdd')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "a79856092f03529f78cf72db1144171d864473d6b010bd73997d0bc77ba1f76e"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.5 64-bit ('poissonprocess': conda)",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/view_hydsys.ipynb b/notebooks/view_hydsys.ipynb
index da70105..5a4bc9b 100644
--- a/notebooks/view_hydsys.ipynb
+++ b/notebooks/view_hydsys.ipynb
@@ -11,15 +11,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import os\n",
     "import multiprocessing\n",
     "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "import numpy as np\n",
-    "import tqdm\n"
+    "import tqdm\n",
+    "\n",
+    "sns.set(font_scale=1.2)"
    ]
   },
   {
@@ -33,18 +37,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
     "def make_pressure_dataframe(fp, cycle_id=0):\n",
-    "    seqs = np.array([np.loadtxt(fp + f'/PS{i+1}.txt.gz')[cycle_id] for i in range(6)]).T\n",
-    "    return pd.DataFrame(seqs, columns=[f'PS{i+1}' for i in range(6)])"
+    "    \"\"\" 100 Hz. 6000 samples in each cycle\n",
+    "    \"\"\"\n",
+    "    data = pd.DataFrame(columns=[f'PS{i+1}' for i in range(6)])\n",
+    "    for i in range(6):\n",
+    "        data[f'PS{i+1}'] = np.loadtxt(fp + f'/PS{i+1}.txt.gz')[cycle_id]\n",
+    "    return data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -198,7 +206,7 @@
        "[6000 rows x 6 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -242,11 +250,274 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_motor_power_dataframe(fp, cycle_id=0):\n",
+    "    \"\"\" 100 Hz. 6000 samples in each cycle\n",
+    "    \"\"\"\n",
+    "    return pd.DataFrame(np.loadtxt(fp + '/EPS1.txt.gz')[cycle_id],\n",
+    "        columns=['EPS1'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_eps = make_motor_power_dataframe('../data/hydsys')\n",
+    "df_eps.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_volume_flow_dataframe(fp, cycle_id=0):\n",
+    "    \"\"\" 10 Hz. 600 samples in each cycle\n",
+    "    \"\"\"\n",
+    "    data = pd.DataFrame(columns=['FS1', 'FS2'])\n",
+    "    data['FS1'] = np.loadtxt(fp + '/FS1.txt.gz')[cycle_id]\n",
+    "    data['FS2'] = np.loadtxt(fp + '/FS2.txt.gz')[cycle_id]\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_fs = make_volume_flow_dataframe('../data/hydsys')\n",
+    "df_fs.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_temp_dataframe(fp, cycle_id=0):\n",
+    "    \"\"\" 1 Hz. 60 samples in each cycle\n",
+    "    \"\"\"\n",
+    "    data = pd.DataFrame(columns=[f'TS{i+1}' for i in range(4)])\n",
+    "    data['TS1'] = np.loadtxt(fp + '/TS1.txt.gz')[cycle_id]\n",
+    "    data['TS2'] = np.loadtxt(fp + '/TS2.txt.gz')[cycle_id]\n",
+    "    data['TS3'] = np.loadtxt(fp + '/TS3.txt.gz')[cycle_id]\n",
+    "    data['TS4'] = np.loadtxt(fp + '/TS4.txt.gz')[cycle_id]\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "make_temp_dataframe('../data/hydsys').plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_vibration_dataframe(fp, cycle_id=0):\n",
+    "    return pd.DataFrame(np.loadtxt(fp + '/VS1.txt.gz')[cycle_id], columns=['VS1'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "make_vibration_dataframe('../data/hydsys').plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_efficiency_dataframe(fp, cycle_id=0):\n",
+    "    return pd.DataFrame(np.loadtxt(fp + '/SE.txt.gz')[cycle_id], columns=['SE'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "make_efficiency_dataframe('../data/hydsys').plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def make_"
+    "def make_cooling_dataframe(fp, cycle_id=0):\n",
+    "    data = pd.DataFrame(columns=['CE', 'CP'])\n",
+    "    data['CE'] = np.loadtxt(fp + '/CE.txt.gz')[cycle_id]\n",
+    "    data['CP'] = np.loadtxt(fp + '/CP.txt.gz')[cycle_id]\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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NYmp5vvrKU4S2wEVSWCgS5c1PGnl21Q5eXl9PezjK2OJs/vd1k7h8Qkmyiyc9pC4UkTPcgdYQf1y7i8eWbWVjw2G+seBs7p43Dp/uvTLknSjA1SkmcobIzwxyx6wKXrjnYm6cNpJ/+8snfO2plRxqCyW7aNJLCnCRM0xmmp//d9t0/uG6SSzd0MBNP1nG5sbDyS6W9IIOYoqcgcyMr1xcxaSyPO75zSpu+s9lfHrKCHwGPjPMYvNUFWVzw/SRlOZlJLvI0g31gYuc4Xbsb+WbSz5gS2MzzkHUORwQjTqamtvxGcwdN5xbzh/FpyePICtN232DTQcxReS0bWk8zPOrd/Ds6h3U7WslK83PNefGHtR8bnl+sot3xlCAi0ivRaOO6tp9PLe6jhfX7KS5PcL5FcNYOKeSq6eUkRbQ4bSBpAAXkX5xsC3Ekuo6nnynhpqmFopz07n9gtHMm1DM1PJhuuJzACjARaRfRaOONzY28viyGt7c2IhzkJ3m58KzipgztoiLxw/nnBF5yS5mStCl9CLSr3w+4/IJJVw+oYS9ze28u6WJ5Zv3sHxTE69uaABgWnk+C+dUcu3UMj0WbgBoC1xE+t2uA628/FE9TyyvYXNjM8Nz0vibWRV8bvYYnZLYC+pCEZFB55zj7U17eGJ5DUs3NOA344bpI/nby8bq+Z6nQV0oIjLozIxLxhdzyfhitjW18NjyrSx6fzvPrtrBgkml3D1vLOdVFCS7mJ6lLXARGVT7mtt5fHkNjy+v4UBriIvOKmLKqDxCEceRcJRQJDaMyM/gorOKuKCy8Ix/wpC6UERkSGk+Eubp97fx2LIa9rW0E/T7CPp9pPmNgN/HrgOthCKOgM+YNnoYc8YWceXEUqaNHpbsog86BbiIeEpLe5iVtftYvrmJdzY38de6/UQdXHp2MfdeMZ4ZY86crhcFuIh42sG2EIve38bP39hCU3P7GRXkCnARSQkt7WGeere2M8gvrCpk/jklzB03nElleSn5gAoFuIiklJb2ML96p5ZnVtaxqSF2P/NhWcHOA5/FuenkZwbJzwySlxmkICv22ovPA+11gJtZBvAmkE7stMMlzrnvm1kh8FugEqgBPuuc23eyZSnARWQg1B9sY/nmPSzb1MTyTXvYeaCt2/nyMgKMK8npMlQUZjFqWBaZaUP3StG+BLgB2c65w2YWBN4G7gVuAfY65x40sweAAufct062LAW4iAw05xyNh4+wvyXEwdYQB1pDHGwLsbc5xNY9h9lYf5jNjYfZc7i9y/cKs9MYNSyTkcMymDGmgBumjWJE/tC4arTXF/K4WMJ3PG8pGB8ccCMwLz79CeB14KQBLiIy0MyMktwMSnJPHr77W9rZ1HCYun2t7NjfSt2+Vnbub2Vj/WH+/GE9P3xpA3PGFnHzeeVcNWUEOUPwXPQe9YGbmR9YCYwDfuKc+5aZ7XfODUuYZ59z7rjDwWZ2F3AXQEVFxYza2tr+KruIyIDY0niY59fs5PnVO9i2t4WMoI9rppRx58VVTBk1+A+y6JeDmGY2DHgO+F/A2z0J8ETqQhERL3HOsWrbPp5dtYPnV++guT3CrKpC7pxbxYJJpfgH6YyXfrkXinNuv5m9DlwF1JtZmXNul5mVAQ39U1QRkaHBzJgxppAZYwr51tXnsHjFdh5bVsPXnlpJRWEW104tw2cQirjOWwCkB/xcMbGEC6uKBjzge3IQsxgIxcM7E/gL8C/AZUBTwkHMQufcN0+2LG2Bi4jXhSNRXv6onkfe3kp17T78PiPot85bATQfCXMkHKU0L53rp47khukjOXdUfp9OX+zLWShTiR2k9AM+YLFz7gdmVgQsBiqAbcCtzrm9J1uWAlxEUolz7rhgbm2P8Mr6el78YCevf9xAKOKoGp7ND285l9lnFfVqPX05C+WvwHndTG8CruhVaUREUkB3W9WZaX6unzaS66eN5EBLiJfW7eKFNTsZMQAPshh658WIiKSI/Kwgt8+q4PZZFQOyfD0+WkTEoxTgIiIepQAXEfEoBbiIiEcpwEVEPEoBLiLiUQpwERGPUoCLiHiUAlxExKMU4CIiHqUAFxHxKAW4iIhHKcBFRDxKAS4i4lEKcBERj1KAi4h4lAJcRMSjFOAiIh6lABcR8SgFuIiIRynARUQ8SgEuIuJRCnAREY9SgIuIeJQCXETEoxTgIiIepQAXEfEoBbiIiEcpwEVEPEoBLiLiUQpwERGPUoCLiHiUAlxExKNOGeBmNtrMXjOz9Wb2oZndG59eaGYvm9nG+Lhg4IsrIiIderIFHga+4ZybCMwG7jGzScADwFLn3Hhgafy9iIgMklMGuHNul3NuVfz1IWA9MAq4EXgiPtsTwE0DVEYREenGafWBm1klcB7wHlDqnNsFsZAHSk7wnbvMrNrMqhsbG/tYXBER6dDjADezHOB3wH3OuYM9/Z5z7mHn3Ezn3Mzi4uLelFFERLrRowA3syCx8P61c+7Z+OR6MyuLf14GNAxMEUVEpDs9OQvFgEeA9c65HyV89CKwMP56IfBC/xdPREROJNCDeeYCXwDWmtma+LTvAA8Ci83sK8A24NYBKaGIiHTrlAHunHsbsBN8fEX/FkdERHpKV2KKiHiUAlxExKMU4CIiHqUAFxHxKAW4iIhHKcBFRDxKAS4i4lEKcBERj1KAi4h4lAJcRMSjFOAiIh6lABcR8SgFuIiIRynARUQ8SgEuIuJRCnAREY9SgIuIeJQCXETEoxTgIiIepQAXEfEoBbiIiEcpwEVEPEoBLiLiUQpwERGPUoCLiHiUAlxExKMU4CIiHqUAFxHxKAW4iIhHKcBFRDxKAS4i4lEKcBERj1KAi4h4lAJcRMSjThngZvaomTWY2bqEaYVm9rKZbYyPCwa2mCIicqyebIE/Dlx1zLQHgKXOufHA0vh7EREZRKcMcOfcm8DeYybfCDwRf/0EcFP/FktERE6lt33gpc65XQDxccmJZjSzu8ys2syqGxsbe7k6ERE51oAfxHTOPeycm+mcm1lcXDzQqxMROWP0NsDrzawMID5u6L8iiYhIT/Q2wF8EFsZfLwRe6J/iiIhIT/XkNMKngXeACWZWZ2ZfAR4EFpjZRmBB/L2IiAyiwKlmcM7dcYKPrujnsoiIyGnQlZgiIh6lABcR8SgFuIiIRynARUQ8SgEuIuJRCnAREY9SgIuIeJQCXETEoxTgIiIepQAXEfEoBbiIiEcpwEVEPEoBLiLiUQpwERGPUoCLiHiUAlxExKMU4CIiHqUAFxHxKAW4iIhHKcBFRDxKAS4i4lEKcBERj1KAi4h4lAJcRMSjFOAiIh6lABcR8ahAsgvQI40fw4G6Hs7sTjDdwAzMF3/ti83rokeHaDQ+T/xz88fHBs4ds3wDfxr4g7FxIB18gfj8vqPfMx9EI/F1RBJeJ643Ps0XgEBGbFnBzPgyg7HldJTZLLb6aCS+vPDR73dWNT5/R3ldxxCNvTc/+AOx9fmC8XG8LXcn+v0Sf0o79TwiMuC8EeDvPwwrfpnsUkgHf3qscQmkx177gwmhHh+bHW0g/B3jYNcGzhdvIDs+C8SX5U+PN46B2Ljjc5+/a0Pk4qvzBY5ZV8L6fIGj447PO5cZn9f88c/98SGhYesse1q8vGq8ZOjwRoBf9D/h3M/2fP7u/si6/OHHt3y7bC3HB+i6Vdw5b8JWrVlsWiQUH47Ex+1H19Mx4Lpuzfv88cDoZt3RCITb4sOR2LhjmR2B1bGl7fMlhI//6J5FxxZ3rCJd9zg6fhcXjW25R0IQDUEkfOrfr0M0EqtvuD0+PhIrY8dv3Pl7x9dx7HqiEXChhN8oEpseae+63M7ftj02z5Bg8UYmYc/r2EajoxGwY8adDcMxQ5dGJqHR6dLwJH6e2LgEOb7BiZcrsYyd60h4HcyMvVeD5GneCPDCqtggZ6ZoQmPQ2RDZ0a4tF4k3Et01GKETvI/Pl9gNFY0cXUY0FG+sQke/E2mPDeH2o6+7rDOc0KWV0F0WjXSdN5pY3tDRBqyzjPHpid1iA8IgmAXBjNjY5+9+nu4apG4bl24an86G7tj5/F33fizhcJwlbCh1bnzEh449tUDG0cGfFt8gStxI8iVsvFj3y+qc55guRhff8OnSSCbUbwg1et4IcDmz+XzgSwPSTjxPMHPQijNoOhuuYxqdjvcde4GdDUM4oaEJHd2rOa7hao/t3YVauw7d7el07o0mNkjHNFih1q4NZDTcdZ2JZY6GBv937G9duvVOEKGde1kJe17XPwRj5vRrURTgIkNVTxour+nY0k1sdKKR408S6Ow2jHbtluzY8wm1Hu1mDB/p2iXX0ejA0e93t6yO+bt0MSbs2XXZC0tsPNu7Nkwcu0XuutmjC0NaTr//nApwERk8Zke7YUhPdmk8T+eBi4h4VJ8C3MyuMrOPzWyTmT3QX4USEZFT63WAm5kf+AlwNTAJuMPMJvVXwURE5OT6sgU+C9jknNvinGsHFgE39k+xRETkVPoS4KOA7Qnv6+LTujCzu8ys2syqGxsb+7A6ERFJ1JcA7+5s9uNupOGce9g5N9M5N7O4uLgPqxMRkUR9CfA6YHTC+3JgZ9+KIyIiPdWXAF8BjDezKjNLA24HXuyfYomIyKmY68ntQ0/0ZbNrgIcAP/Coc+6fTzF/I1Dby9UNB/b08rtDUSrVJ5XqAqrPUJZKdYGe12eMc+64Pug+BfhgMrNq59zMZJejv6RSfVKpLqD6DGWpVBfoe310JaaIiEcpwEVEPMpLAf5wsgvQz1KpPqlUF1B9hrJUqgv0sT6e6QMXEZGuvLQFLiIiCRTgIiIe5YkA9/pta83sUTNrMLN1CdMKzexlM9sYHxcks4w9ZWajzew1M1tvZh+a2b3x6Z6rj5llmNn7ZvZBvC7/FJ/uubokMjO/ma02s/+Ov/dsfcysxszWmtkaM6uOT/NkfcxsmJktMbMN8b+fi/palyEf4Cly29rHgauOmfYAsNQ5Nx5YGn/vBWHgG865icBs4J74v4cX63MEmO+cmwZMB64ys9l4sy6J7gXWJ7z3en0ud85NTzhf2qv1+XfgT865c4BpxP6N+lYX59yQHoCLgD8nvP828O1kl6sX9agE1iW8/xgoi78uAz5Odhl7Wa8XgAVerw+QBawCLvRyXYjdk2gpMB/47/g0L9enBhh+zDTP1QfIA7YSP3Gkv+oy5LfA6eFtaz2o1Dm3CyA+LklyeU6bmVUC5wHv4dH6xLsb1gANwMvOOc/WJe4h4JtANGGal+vjgL+Y2Uozuys+zYv1OQtoBB6Ld2/90syy6WNdvBDgPbptrQwuM8sBfgfc55w7mOzy9JZzLuKcm05sy3WWmU1JcpF6zcyuAxqccyuTXZZ+NNc5dz6xLtR7zOzSZBeolwLA+cDPnHPnAc30Q9ePFwI8VW9bW29mZQDxcUOSy9NjZhYkFt6/ds49G5/s2foAOOf2A68TO1bh1brMBW4wsxpiT8iab2ZP4d364JzbGR83AM8RexKYF+tTB9TF9/AAlhAL9D7VxQsBnqq3rX0RWBh/vZBYX/KQZ2YGPAKsd879KOEjz9XHzIrNbFj8dSZwJbABD9YFwDn3bedcuXOuktjfyavOuc/j0fqYWbaZ5Xa8Bj4FrMOD9XHO7Qa2m9mE+KQrgI/oa12S3bnfwwMA1wCfAJuB7ya7PL0o/9PALiBErCX+ClBE7GDTxvi4MNnl7GFdLibWhfVXYE18uMaL9QGmAqvjdVkHfC8+3XN16aZu8zh6ENOT9SHWb/xBfPiw42/fw/WZDlTH/789DxT0tS66lF5ExKO80IUiIiLdUICLiHiUAlxExKMU4CIiHqUAFxHxKAW4iIhHKcBFRDzq/wPWH3iWhmMdegAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "make_cooling_dataframe('../data/hydsys').plot()"
    ]
   },
   {
@@ -280,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,7 +568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -464,7 +735,7 @@
        "[2205 rows x 6 columns]"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -476,35 +747,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df_pf.iloc[200:500].stable_flag.plot()\n",
+    "# df_pf.iloc[0:].valve_condition.plot(legend=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<AxesSubplot:>"
+       "<Figure size 720x720 with 5 Axes>"
       ]
      },
-     "execution_count": 73,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "# df_pf.iloc[200:500].stable_flag.plot()\n",
-    "df_pf.iloc[200:500].valve_condition.plot()"
+    "os.makedirs('../out', exist_ok=True)\n",
+    "fig, ax = plt.subplots(5, figsize=(10, 10))\n",
+    "for i, axi in enumerate(ax):\n",
+    "    df_pf.set_index('cycle').iloc[:, i].plot(legend=True, ax=axi)\n",
+    "    if i < 4:\n",
+    "        axi.set_xlabel(None)\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "fig.savefig('../out/hydsys_profile.png', \n",
+    "            bbox_inches='tight')"
    ]
   },
   {
@@ -517,10 +794,10 @@
  ],
  "metadata": {
   "interpreter": {
-   "hash": "16dbba425ba0731f581592bff1ceb33a70db2cdf9052cbf447b98983e28f2ced"
+   "hash": "a79856092f03529f78cf72db1144171d864473d6b010bd73997d0bc77ba1f76e"
   },
   "kernelspec": {
-   "display_name": "Python 3.9.7 64-bit ('survival': conda)",
+   "display_name": "Python 3.9.5 64-bit ('poissonprocess': conda)",
    "name": "python3"
   },
   "language_info": {
@@ -533,7 +810,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.5"
   },
   "orig_nbformat": 4
  },
diff --git a/notebooks/view_ufd.ipynb b/notebooks/view_ufd.ipynb
new file mode 100644
index 0000000..d668242
--- /dev/null
+++ b/notebooks/view_ufd.ipynb
@@ -0,0 +1,531 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_data(fp, meter_id='A'):\n",
+    "\n",
+    "    data = np.loadtxt(fp + '/Meter{}.txt'.format(meter_id))\n",
+    "    print(data.shape)\n",
+    "\n",
+    "    if meter_id == 'A':\n",
+    "        columns =  ['flatness_ratio', 'symmetry', 'crossflow']\n",
+    "        columns += ['flow_velocity_{}'.format(i+1) for i in range(8)]\n",
+    "        columns += ['sound_speed_{}'.format(i+1) for i in range(8)]\n",
+    "        columns += ['average_speed']\n",
+    "        columns += ['gain_{}'.format(i+1) for i in range(16)]\n",
+    "        columns += ['health_state']\n",
+    "\n",
+    "    if meter_id == 'B':\n",
+    "        columns =  ['profile_factor', 'symmetry', 'crossflow', 'swirl_angle']\n",
+    "        columns += ['flow_velocity_{}'.format(i+1) for i in range(4)]\n",
+    "        columns += ['average_flow']\n",
+    "        columns += ['sound_speed_{}'.format(i+1) for i in range(4)]\n",
+    "        columns += ['average_speed']\n",
+    "        columns += ['signal_strength_{}'.format(i+1) for i in range(8)]\n",
+    "        columns += ['turbulence_{}'.format(i+1) for i in range(4)]\n",
+    "        columns += ['meter_performance']\n",
+    "        columns += ['signal_quality_{}'.format(i+1) for i in range(8)]\n",
+    "        columns += ['gain_{}'.format(i+1) for i in range(8)]\n",
+    "        columns += ['transit_time_{}'.format(i+1) for i in range(8)]\n",
+    "        columns += ['health_state']\n",
+    "\n",
+    "    if meter_id == 'C' or meter_id == 'D':\n",
+    "        columns =  ['profile_factor', 'symmetry', 'crossflow']\n",
+    "        columns += ['flow_velocity_{}'.format(i+1) for i in range(4)]\n",
+    "        columns += ['sound_speed_{}'.format(i+1) for i in range(4)]\n",
+    "        columns += ['signal_strength_{}'.format(i+1) for i in range(8)]\n",
+    "        columns += ['signal_quality_{}'.format(i+1) for i in range(8)]\n",
+    "        columns += ['gain_{}'.format(i+1) for i in range(8)]\n",
+    "        columns += ['transit_time_{}'.format(i+1) for i in range(8)]\n",
+    "        columns += ['health_state']\n",
+    "\n",
+    "    return pd.DataFrame(data, columns=columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(180, 44)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>profile_factor</th>\n",
+       "      <th>symmetry</th>\n",
+       "      <th>crossflow</th>\n",
+       "      <th>flow_velocity_1</th>\n",
+       "      <th>flow_velocity_2</th>\n",
+       "      <th>flow_velocity_3</th>\n",
+       "      <th>flow_velocity_4</th>\n",
+       "      <th>sound_speed_1</th>\n",
+       "      <th>sound_speed_2</th>\n",
+       "      <th>sound_speed_3</th>\n",
+       "      <th>...</th>\n",
+       "      <th>gain_8</th>\n",
+       "      <th>transit_time_1</th>\n",
+       "      <th>transit_time_2</th>\n",
+       "      <th>transit_time_3</th>\n",
+       "      <th>transit_time_4</th>\n",
+       "      <th>transit_time_5</th>\n",
+       "      <th>transit_time_6</th>\n",
+       "      <th>transit_time_7</th>\n",
+       "      <th>transit_time_8</th>\n",
+       "      <th>health_state</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.104706</td>\n",
+       "      <td>1.004679</td>\n",
+       "      <td>0.994783</td>\n",
+       "      <td>2.345833</td>\n",
+       "      <td>2.604444</td>\n",
+       "      <td>2.580000</td>\n",
+       "      <td>2.347222</td>\n",
+       "      <td>1485.805556</td>\n",
+       "      <td>1485.930556</td>\n",
+       "      <td>1485.941667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.700000</td>\n",
+       "      <td>124.150000</td>\n",
+       "      <td>123.911944</td>\n",
+       "      <td>151.615833</td>\n",
+       "      <td>151.275000</td>\n",
+       "      <td>151.559444</td>\n",
+       "      <td>151.221111</td>\n",
+       "      <td>124.253611</td>\n",
+       "      <td>124.017778</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.089401</td>\n",
+       "      <td>0.997778</td>\n",
+       "      <td>0.998012</td>\n",
+       "      <td>3.399444</td>\n",
+       "      <td>3.711111</td>\n",
+       "      <td>3.711944</td>\n",
+       "      <td>3.414444</td>\n",
+       "      <td>1486.163889</td>\n",
+       "      <td>1486.302778</td>\n",
+       "      <td>1486.302778</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.700000</td>\n",
+       "      <td>124.171944</td>\n",
+       "      <td>123.831667</td>\n",
+       "      <td>151.652222</td>\n",
+       "      <td>151.164444</td>\n",
+       "      <td>151.595278</td>\n",
+       "      <td>151.113333</td>\n",
+       "      <td>124.274167</td>\n",
+       "      <td>123.937778</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.079671</td>\n",
+       "      <td>1.006056</td>\n",
+       "      <td>0.999027</td>\n",
+       "      <td>3.438056</td>\n",
+       "      <td>3.714722</td>\n",
+       "      <td>3.689722</td>\n",
+       "      <td>3.420000</td>\n",
+       "      <td>1486.322222</td>\n",
+       "      <td>1486.452778</td>\n",
+       "      <td>1486.455556</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.700000</td>\n",
+       "      <td>124.160000</td>\n",
+       "      <td>123.818056</td>\n",
+       "      <td>151.636111</td>\n",
+       "      <td>151.150833</td>\n",
+       "      <td>151.580000</td>\n",
+       "      <td>151.096944</td>\n",
+       "      <td>124.260833</td>\n",
+       "      <td>123.923611</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.090834</td>\n",
+       "      <td>1.013194</td>\n",
+       "      <td>0.994911</td>\n",
+       "      <td>3.399444</td>\n",
+       "      <td>3.725000</td>\n",
+       "      <td>3.660556</td>\n",
+       "      <td>3.371111</td>\n",
+       "      <td>1486.455556</td>\n",
+       "      <td>1486.575000</td>\n",
+       "      <td>1486.583333</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.700000</td>\n",
+       "      <td>124.144722</td>\n",
+       "      <td>123.808611</td>\n",
+       "      <td>151.622500</td>\n",
+       "      <td>151.139167</td>\n",
+       "      <td>151.564167</td>\n",
+       "      <td>151.084167</td>\n",
+       "      <td>124.250833</td>\n",
+       "      <td>123.913056</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.093816</td>\n",
+       "      <td>1.009716</td>\n",
+       "      <td>1.003622</td>\n",
+       "      <td>3.398889</td>\n",
+       "      <td>3.702778</td>\n",
+       "      <td>3.681389</td>\n",
+       "      <td>3.351944</td>\n",
+       "      <td>1486.600000</td>\n",
+       "      <td>1486.725000</td>\n",
+       "      <td>1486.730556</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.700000</td>\n",
+       "      <td>124.134444</td>\n",
+       "      <td>123.797500</td>\n",
+       "      <td>151.607500</td>\n",
+       "      <td>151.122778</td>\n",
+       "      <td>151.550278</td>\n",
+       "      <td>151.068889</td>\n",
+       "      <td>124.236111</td>\n",
+       "      <td>123.903056</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>4.593220</td>\n",
+       "      <td>5.153846</td>\n",
+       "      <td>0.123245</td>\n",
+       "      <td>-0.138611</td>\n",
+       "      <td>-1.703889</td>\n",
+       "      <td>-0.102778</td>\n",
+       "      <td>-0.254722</td>\n",
+       "      <td>1513.505556</td>\n",
+       "      <td>1507.080556</td>\n",
+       "      <td>1494.355556</td>\n",
+       "      <td>...</td>\n",
+       "      <td>44.600000</td>\n",
+       "      <td>121.266111</td>\n",
+       "      <td>122.244167</td>\n",
+       "      <td>145.081111</td>\n",
+       "      <td>155.030000</td>\n",
+       "      <td>147.680000</td>\n",
+       "      <td>153.622500</td>\n",
+       "      <td>117.866389</td>\n",
+       "      <td>117.880278</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>0.173768</td>\n",
+       "      <td>0.214250</td>\n",
+       "      <td>0.098034</td>\n",
+       "      <td>-0.200833</td>\n",
+       "      <td>-0.401389</td>\n",
+       "      <td>-0.103889</td>\n",
+       "      <td>-2.706944</td>\n",
+       "      <td>1521.897222</td>\n",
+       "      <td>1474.400000</td>\n",
+       "      <td>1500.705556</td>\n",
+       "      <td>...</td>\n",
+       "      <td>44.600000</td>\n",
+       "      <td>121.082222</td>\n",
+       "      <td>121.059167</td>\n",
+       "      <td>151.401944</td>\n",
+       "      <td>154.034722</td>\n",
+       "      <td>147.871389</td>\n",
+       "      <td>151.595278</td>\n",
+       "      <td>118.459444</td>\n",
+       "      <td>118.631111</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>0.049027</td>\n",
+       "      <td>0.115593</td>\n",
+       "      <td>0.134579</td>\n",
+       "      <td>-0.281111</td>\n",
+       "      <td>-0.050833</td>\n",
+       "      <td>-0.098889</td>\n",
+       "      <td>-2.772778</td>\n",
+       "      <td>1518.072222</td>\n",
+       "      <td>1439.802778</td>\n",
+       "      <td>1512.719444</td>\n",
+       "      <td>...</td>\n",
+       "      <td>44.169444</td>\n",
+       "      <td>121.298889</td>\n",
+       "      <td>121.370278</td>\n",
+       "      <td>158.391111</td>\n",
+       "      <td>154.248611</td>\n",
+       "      <td>149.020000</td>\n",
+       "      <td>148.266389</td>\n",
+       "      <td>118.569722</td>\n",
+       "      <td>118.807500</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>-0.053056</td>\n",
+       "      <td>0.099441</td>\n",
+       "      <td>0.143832</td>\n",
+       "      <td>-0.386667</td>\n",
+       "      <td>0.129722</td>\n",
+       "      <td>0.029444</td>\n",
+       "      <td>-2.613333</td>\n",
+       "      <td>1487.083333</td>\n",
+       "      <td>1422.211111</td>\n",
+       "      <td>1484.836111</td>\n",
+       "      <td>...</td>\n",
+       "      <td>44.100000</td>\n",
+       "      <td>125.552500</td>\n",
+       "      <td>122.318333</td>\n",
+       "      <td>161.174167</td>\n",
+       "      <td>155.306389</td>\n",
+       "      <td>149.217222</td>\n",
+       "      <td>153.626111</td>\n",
+       "      <td>118.638333</td>\n",
+       "      <td>118.872778</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>0.008931</td>\n",
+       "      <td>0.914347</td>\n",
+       "      <td>1.184204</td>\n",
+       "      <td>-2.617222</td>\n",
+       "      <td>0.144167</td>\n",
+       "      <td>-0.190000</td>\n",
+       "      <td>-2.514722</td>\n",
+       "      <td>1476.855556</td>\n",
+       "      <td>1413.877778</td>\n",
+       "      <td>1506.325000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>44.100000</td>\n",
+       "      <td>127.293611</td>\n",
+       "      <td>122.416111</td>\n",
+       "      <td>161.170278</td>\n",
+       "      <td>157.195000</td>\n",
+       "      <td>148.598611</td>\n",
+       "      <td>150.280556</td>\n",
+       "      <td>118.752222</td>\n",
+       "      <td>118.986389</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>180 rows × 44 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     profile_factor  symmetry  crossflow  flow_velocity_1  flow_velocity_2  \\\n",
+       "0          1.104706  1.004679   0.994783         2.345833         2.604444   \n",
+       "1          1.089401  0.997778   0.998012         3.399444         3.711111   \n",
+       "2          1.079671  1.006056   0.999027         3.438056         3.714722   \n",
+       "3          1.090834  1.013194   0.994911         3.399444         3.725000   \n",
+       "4          1.093816  1.009716   1.003622         3.398889         3.702778   \n",
+       "..              ...       ...        ...              ...              ...   \n",
+       "175        4.593220  5.153846   0.123245        -0.138611        -1.703889   \n",
+       "176        0.173768  0.214250   0.098034        -0.200833        -0.401389   \n",
+       "177        0.049027  0.115593   0.134579        -0.281111        -0.050833   \n",
+       "178       -0.053056  0.099441   0.143832        -0.386667         0.129722   \n",
+       "179        0.008931  0.914347   1.184204        -2.617222         0.144167   \n",
+       "\n",
+       "     flow_velocity_3  flow_velocity_4  sound_speed_1  sound_speed_2  \\\n",
+       "0           2.580000         2.347222    1485.805556    1485.930556   \n",
+       "1           3.711944         3.414444    1486.163889    1486.302778   \n",
+       "2           3.689722         3.420000    1486.322222    1486.452778   \n",
+       "3           3.660556         3.371111    1486.455556    1486.575000   \n",
+       "4           3.681389         3.351944    1486.600000    1486.725000   \n",
+       "..               ...              ...            ...            ...   \n",
+       "175        -0.102778        -0.254722    1513.505556    1507.080556   \n",
+       "176        -0.103889        -2.706944    1521.897222    1474.400000   \n",
+       "177        -0.098889        -2.772778    1518.072222    1439.802778   \n",
+       "178         0.029444        -2.613333    1487.083333    1422.211111   \n",
+       "179        -0.190000        -2.514722    1476.855556    1413.877778   \n",
+       "\n",
+       "     sound_speed_3  ...     gain_8  transit_time_1  transit_time_2  \\\n",
+       "0      1485.941667  ...  -0.700000      124.150000      123.911944   \n",
+       "1      1486.302778  ...  -0.700000      124.171944      123.831667   \n",
+       "2      1486.455556  ...  -0.700000      124.160000      123.818056   \n",
+       "3      1486.583333  ...  -0.700000      124.144722      123.808611   \n",
+       "4      1486.730556  ...  -0.700000      124.134444      123.797500   \n",
+       "..             ...  ...        ...             ...             ...   \n",
+       "175    1494.355556  ...  44.600000      121.266111      122.244167   \n",
+       "176    1500.705556  ...  44.600000      121.082222      121.059167   \n",
+       "177    1512.719444  ...  44.169444      121.298889      121.370278   \n",
+       "178    1484.836111  ...  44.100000      125.552500      122.318333   \n",
+       "179    1506.325000  ...  44.100000      127.293611      122.416111   \n",
+       "\n",
+       "     transit_time_3  transit_time_4  transit_time_5  transit_time_6  \\\n",
+       "0        151.615833      151.275000      151.559444      151.221111   \n",
+       "1        151.652222      151.164444      151.595278      151.113333   \n",
+       "2        151.636111      151.150833      151.580000      151.096944   \n",
+       "3        151.622500      151.139167      151.564167      151.084167   \n",
+       "4        151.607500      151.122778      151.550278      151.068889   \n",
+       "..              ...             ...             ...             ...   \n",
+       "175      145.081111      155.030000      147.680000      153.622500   \n",
+       "176      151.401944      154.034722      147.871389      151.595278   \n",
+       "177      158.391111      154.248611      149.020000      148.266389   \n",
+       "178      161.174167      155.306389      149.217222      153.626111   \n",
+       "179      161.170278      157.195000      148.598611      150.280556   \n",
+       "\n",
+       "     transit_time_7  transit_time_8  health_state  \n",
+       "0        124.253611      124.017778           1.0  \n",
+       "1        124.274167      123.937778           1.0  \n",
+       "2        124.260833      123.923611           1.0  \n",
+       "3        124.250833      123.913056           1.0  \n",
+       "4        124.236111      123.903056           1.0  \n",
+       "..              ...             ...           ...  \n",
+       "175      117.866389      117.880278           4.0  \n",
+       "176      118.459444      118.631111           4.0  \n",
+       "177      118.569722      118.807500           4.0  \n",
+       "178      118.638333      118.872778           4.0  \n",
+       "179      118.752222      118.986389           4.0  \n",
+       "\n",
+       "[180 rows x 44 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# data = load_data('../data/ufd/', meter_id='A')\n",
+    "# data = load_data('../data/ufd/', meter_id='B')\n",
+    "# data = load_data('../data/ufd/', meter_id='C')\n",
+    "data = load_data('../data/ufd/', meter_id='D')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.health_state.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "a79856092f03529f78cf72db1144171d864473d6b010bd73997d0bc77ba1f76e"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.5 64-bit ('poissonprocess': conda)",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/requirement.txt b/requirement.txt
index 87bc4c6..8820bf7 100644
--- a/requirement.txt
+++ b/requirement.txt
@@ -1,5 +1,7 @@
 numpy
 scipy
 pandas
+matplotlib
+seaborn
 scikit-learn
 pysurvival
\ No newline at end of file