diff --git a/.coveragerc b/.coveragerc
index 1cedb8b..1c315c8 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -4,4 +4,5 @@ omit =
     */setup.py
     */tests/*
     */notebooks/*
-    */tinnsleep/config.py
\ No newline at end of file
+    */tinnsleep/config.py
+    */tinnsleep/external/*
diff --git a/.gitignore b/.gitignore
index a5b6d36..f79dd29 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,7 @@
 #Notebook draft
 drafts/
 Notebooks/*_vbrouillon.*
+tests/dummy_fif.edf
 
 # Byte-compiled / optimized / DLL files
 results/
diff --git a/tests/dummy_fif.edf b/tests/dummy_fif.edf
new file mode 100644
index 0000000..c0c7461
Binary files /dev/null and b/tests/dummy_fif.edf differ
diff --git a/tests/dummy_fif.fif b/tests/dummy_fif.fif
new file mode 100644
index 0000000..341baac
Binary files /dev/null and b/tests/dummy_fif.fif differ
diff --git a/tests/test_data.py b/tests/test_data.py
index b05bcf4..f847d28 100644
--- a/tests/test_data.py
+++ b/tests/test_data.py
@@ -282,5 +282,3 @@ def test_read_etiology_file_real():
     data_info_file = os.path.join(os.path.dirname(__file__), "../notebooks/data/data_info.csv")
     data_info = pd.read_csv(data_info_file, sep=";")
     data_info.merge(df_etiology, on="subject")
-
-
diff --git a/tests/test_io.py b/tests/test_io.py
new file mode 100644
index 0000000..98c021e
--- /dev/null
+++ b/tests/test_io.py
@@ -0,0 +1,35 @@
+import mne
+import os
+import numpy as np
+import numpy.testing as npt
+from tinnsleep.external.save_edf import write_edf
+import matplotlib.pyplot as plt
+
+def test_fif2edf():
+    fname = "./dummy_fif"
+    fname_edf = fname +".edf"
+    raw = mne.io.read_raw_fif(fname+".fif", preload=True)
+
+    # plt.close("all")
+    # plt.figure()
+    # raw.plot()
+    # plt.show()
+
+    signals = raw.get_data()
+    N, T = signals.shape
+    scaling = np.ones((signals.shape[0]))
+    scaling[np.median(np.abs(signals), axis=1)< 1e-3] = 1e6
+    #raw.info["ch_names"]
+    if os.path.exists(fname_edf):
+        os.remove(fname_edf)
+    write_edf(raw, fname_edf)
+
+    raw2 = mne.io.read_raw_edf(fname_edf)
+    signals2 = raw2.get_data()
+    is_uV = np.median(np.abs(signals), axis=1)< 1e-3
+    npt.assert_allclose(signals2[is_uV,:T], signals[is_uV, :], atol=0.01)
+    npt.assert_allclose(signals2[~is_uV,:T], signals[~is_uV, :], atol=0.01)
+
+    # plt.figure()
+    # raw2.plot()
+    # plt.show()
diff --git a/tinnsleep/data.py b/tinnsleep/data.py
index 01a52be..cfec87b 100644
--- a/tinnsleep/data.py
+++ b/tinnsleep/data.py
@@ -451,3 +451,5 @@ def read_etiology_file(etiology_file,
     df_etiology[new_key] = df_raw[key].replace(mapping) == 1
 
     return df_etiology
+
+
diff --git a/tinnsleep/external/requirements.txt b/tinnsleep/external/requirements.txt
new file mode 100644
index 0000000..761cd44
--- /dev/null
+++ b/tinnsleep/external/requirements.txt
@@ -0,0 +1,2 @@
+- mne
+- pyedflib
\ No newline at end of file
diff --git a/tinnsleep/external/save_edf.py b/tinnsleep/external/save_edf.py
new file mode 100644
index 0000000..a27653c
--- /dev/null
+++ b/tinnsleep/external/save_edf.py
@@ -0,0 +1,122 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Dec  5 12:56:31 2018
+
+@author: skjerns
+
+Gist to save a mne.io.Raw object to an EDF file using pyEDFlib
+(https://github.com/holgern/pyedflib)
+
+Disclaimer:
+    - Saving your data this way will result in slight
+      loss of precision (magnitude +-1e-09).
+    - It is assumed that the data is presented in Volt (V), it will be internally converted to microvolt
+    - Saving to BDF can be done by changing the file_type variable.
+      Be aware that you also need to change the dmin and dmax to
+      the corresponding minimum and maximum integer values of the
+      file_type: e.g. BDF+ dmin, dmax =- [-8388608, 8388607]
+"""
+
+import pyedflib  # pip install pyedflib
+from datetime import datetime
+import mne
+import os
+import numpy as np
+
+
+def write_edf(mne_raw, fname, picks=None, tmin=0, tmax=None, overwrite=False):
+    """
+    Saves the raw content of an MNE.io.Raw and its subclasses to
+    a file using the EDF+ filetype
+    pyEDFlib is used to save the raw contents of the RawArray to disk
+
+    Parameters
+    ----------
+    mne_raw : mne.io.Raw
+        An object with super class mne.io.Raw that contains the data
+        to save
+    fname : string
+        File name of the new dataset. This has to be a new filename
+        unless data have been preloaded. Filenames should end with .edf
+    picks : array-like of int | None
+        Indices of channels to include. If None all channels are kept.
+    tmin : float | None
+        Time in seconds of first sample to save. If None first sample
+        is used.
+    tmax : float | None
+        Time in seconds of last sample to save. If None last sample
+        is used.
+    overwrite : bool
+        If True, the destination file (if it exists) will be overwritten.
+        If False (default), an error will be raised if the file exists.
+    """
+    if not issubclass(type(mne_raw), mne.io.BaseRaw):
+        raise TypeError('Must be mne.io.Raw type')
+    if not overwrite and os.path.exists(fname):
+        raise OSError('File already exists. No overwrite.')
+    # static settings
+    file_type = pyedflib.FILETYPE_EDFPLUS
+    sfreq = mne_raw.info['sfreq']
+    date = datetime.now()#.strftime('%d %b %Y %H:%M:%S')
+    first_sample = int(sfreq * tmin)
+    last_sample = int(sfreq * tmax) if tmax is not None else None
+
+    # convert data
+    channels = mne_raw.get_data(picks,
+                                start=first_sample,
+                                stop=last_sample)
+
+    # convert to microvolts to scale up precision
+    #is_micro = np.median(channels, axis=1) < 1e-3
+
+    #channels *= 1e6
+
+    # set conversion parameters
+    dmin, dmax = [-32768, 32767]
+    pmin, pmax = [channels.min(), channels.max()]
+    n_channels = len(channels)
+
+    # create channel from this
+    try:
+        f = pyedflib.EdfWriter(fname,
+                               n_channels=n_channels,
+                               file_type=file_type)
+
+        channel_info = []
+        data_list = []
+
+        for i in range(n_channels):
+            if np.median(np.abs(channels[i])) < 1e-5:
+                scale = 1e9
+                unit = 'nV'
+            else:
+                if np.median(np.abs(channels[i])) < 1e-3:
+                    scale = 1e6
+                    unit = 'uV'
+                else:
+                    scale = 1
+                    unit = 'V'
+
+            ch_dict = {'label': mne_raw.ch_names[i],
+                       'dimension': unit,
+                       'sample_rate': sfreq,
+                       'physical_min': pmin,
+                       'physical_max': pmax,
+                       'digital_min': dmin,
+                       'digital_max': dmax,
+                       'transducer': '',
+                       'prefilter': ''}
+
+            channel_info.append(ch_dict)
+            data_list.append(scale*channels[i])
+
+        f.setTechnician('mne-gist-save-edf-skjerns')
+        f.setSignalHeaders(channel_info)
+        f.setStartdatetime(date)
+        f.writeSamples(data_list)
+    except Exception as e:
+        print(e)
+        return False
+    finally:
+        f.close()
+    return True
\ No newline at end of file
diff --git a/tinnsleep/io.py b/tinnsleep/io.py
new file mode 100644
index 0000000..d3c1739
--- /dev/null
+++ b/tinnsleep/io.py
@@ -0,0 +1,4 @@
+from tinnsleep.external.save_edf import write_edf
+
+def fif2edf(file):
+    return False
\ No newline at end of file