diff --git a/actions/stimulus-lfp-response/attributes.yaml b/actions/stimulus-lfp-response/attributes.yaml index a90dda4c5..3a47276e0 100644 --- a/actions/stimulus-lfp-response/attributes.yaml +++ b/actions/stimulus-lfp-response/attributes.yaml @@ -1,5 +1,5 @@ registered: '2019-10-14T17:03:05' data: results: results.csv - notebook: 20_stimulus-lfp-response.ipynb - html: 20_stimulus-lfp-response.html + notebook: 10-calculate-stimulus-lfp-response.ipynb + html: 10-calculate-stimulus-lfp-response.html diff --git a/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.html b/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.html index b452100a3..488fc5aed 100644 --- a/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.html +++ b/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.html @@ -13147,6 +13147,8 @@ div#notebook { import quantities as pq import shutil from distutils.dir_util import copy_tree +import scipy +import scipy.signal as ss from scipy.signal import find_peaks from scipy.interpolate import interp1d @@ -13171,7 +13173,9 @@ div#notebook {
-
10:26:07 [I] klustakwik KlustaKwik2 version 0.2.6
+
19:43:24 [I] klustakwik KlustaKwik2 version 0.2.6
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:24: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
+Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`
@@ -13200,8 +13204,16 @@ div#notebook {
In [4]:
-
output = pathlib.Path('output/stimulus-lfp-response')
-(output / 'data').mkdir(parents=True, exist_ok=True)
+
#############################
+
+perform_zscore = True
+
+if not perform_zscore:
+    zscore_str = "-no-zscore"
+else:
+    zscore_str = ""
+
+#################################
@@ -13212,6 +13224,20 @@ div#notebook {
In [5]:
+
+
+
output = pathlib.Path('output/stimulus-lfp-response' + zscore_str)
+(output / 'data').mkdir(parents=True, exist_ok=True)
+
+ +
+
+
+ +
+
+
+
In [6]:
identify_neurons = actions['identify-neurons']
@@ -13225,7 +13251,7 @@ div#notebook {
-
In [6]:
+
In [7]:
channel_groups = []
@@ -13242,7 +13268,7 @@ div#notebook {
-
In [7]:
+
In [8]:
channel_groups = pd.DataFrame(channel_groups)
@@ -13255,7 +13281,7 @@ div#notebook {
-
In [8]:
+
In [9]:
def get_lim(action_id):
@@ -13286,7 +13312,7 @@ div#notebook {
-
In [9]:
+
In [10]:
def signaltonoise(a, axis=0, ddof=0):
@@ -13313,7 +13339,7 @@ div#notebook {
-
In [10]:
+
In [11]:
def compute_energy(p, f, f1, f2):
@@ -13322,6 +13348,19 @@ div#notebook {
     mask = (f > f1) & (f < f2)
     df = f[1] - f[0]
     return np.sum(p[mask]) * df
+
+
+def compute_band_power(p, f, f1, f2):
+    if np.isnan(f1) or np.all(np.isnan(p)):
+        return [np.nan] * 2
+    from scipy.integrate import simps
+    dx = f[1] - f[0]
+    mask = (f > f1) & (f < f2)
+    # Compute the absolute power by approximating the area under the curve
+    band_power = simps(p[mask], dx=dx)
+    total_power = simps(p, dx=dx)
+    rel_power = band_power / total_power
+    return band_power, rel_power
@@ -13331,11 +13370,13 @@ div#notebook {
-
In [11]:
+
In [12]:
-
def find_theta_peak(p, f):
-    mask = (f > 6) & (f < 10)
+
def find_theta_peak(p, f, f1, f2):
+    if np.all(np.isnan(p)):
+        return np.nan, np.nan
+    mask = (f > f1) & (f < f2)
     p_m = p[mask]
     f_m = f[mask]
     peaks, _ = find_peaks(p_m)
@@ -13350,52 +13391,40 @@ div#notebook {
 

 

 

-
In [12]:

+
In [13]:

 

     

-
def compute_half_width(p, f, m_p, m_f):
-    if np.isnan(m_p):
-        return np.nan, np.nan
+
def compute_half_width(power, freq, max_power, max_frequency, band, band_width=1):
+    if np.isnan(max_power):
+        return [np.nan] * 3
+    
+    # estimate baseline power
+    low_baseline_mask = (freq > band[0] - band_width) & (freq < band[0])
+    high_baseline_mask = (freq > band[1]) & (freq < band[1] + band_width)
+    baseline = np.mean(np.concatenate([power[low_baseline_mask], power[high_baseline_mask]]))
+    p = power - baseline
+    m_p = max_power - baseline
+    m_f = max_frequency
+    f = freq
+    
+    # estimate half width
     m_p_half = m_p / 2
     half_p = p - m_p_half
     idx_f = np.where(f <= m_f)[0].max()
     idxs_p1, = np.where(np.diff(half_p[:idx_f + 1] > 0) == 1)
     if len(idxs_p1) == 0:
-        return np.nan, np.nan
+        return [np.nan] * 3
     m1 = idxs_p1.max()
     idxs_p2, = np.where(np.diff(half_p[idx_f:] > 0) == 1)
-    m2 = idxs_p2.min() + idx_f    
-#     plt.plot(f, p)
-#     plt.plot(m_f, m_p, marker='o', ls='none', markersize=10)
-#     plt.plot(f[m1], p[m1], marker='x', ls='none', markersize=10, c='r')
-#     plt.plot(f[m1+1], p[m1+1], marker='+', ls='none', markersize=10, c='r')
-    
-#     plt.plot(f[m2], p[m2], marker='x', ls='none', markersize=10, c='k')
-#     plt.plot(f[m2+1], p[m2+1], marker='+', ls='none', markersize=10, c='k')
-    
-#     plt.xlim(4,12)
-    assert p[m1] < m_p_half < p[m1+1], (p[m1], m_p_half, p[m1+1])
-    assert p[m2] > m_p_half > p[m2+1], (p[m2], m_p_half, p[m2+1])
+    if len(idxs_p2) == 0:
+        return [np.nan] * 3
+    m2 = idxs_p2.min() + idx_f
+#     assert p[m1] < m_p_half < p[m1+1], (p[m1], m_p_half, p[m1+1])
+#     assert p[m2] > m_p_half > p[m2+1], (p[m2], m_p_half, p[m2+1])
     
     f1 = interp1d([half_p[m1], half_p[m1 + 1]], [f[m1], f[m1 + 1]])(0)
     f2 = interp1d([half_p[m2], half_p[m2 + 1]], [f[m2], f[m2 + 1]])(0)
-    return f1, f2
-

-
-    

-

-

-
-

-

-

-
In [13]:

-

-    

-
def compute_stim_peak(p, f, s_f):
-    if np.isnan(s_f):
-        return np.nan
-    return interp1d(f, p)(s_f)
+    return f1, f2, m_p_half + baseline
 

 
     

@@ -13408,8 +13437,18 @@ div#notebook {
 
In [14]:

 

     

-
NFFT = 10000
-theta_band_f1, theta_band_f2 = 6, 10 
+
def compute_stim_peak(p, f, s_f):
+    if np.isnan(s_f):
+        return np.nan
+    return interp1d(f, p)(s_f)
+
+
+def compute_relative_peak(power, freq, max_power, band, band_width=1):
+    # estimate baseline power
+    low_baseline_mask = (freq > band[0] - band_width) & (freq < band[0])
+    high_baseline_mask = (freq > band[1]) & (freq < band[1] + band_width)
+    baseline = np.mean(np.concatenate([power[low_baseline_mask], power[high_baseline_mask]]))
+    return (max_power - baseline) / abs(baseline)
 

 
     

@@ -13419,12 +13458,28 @@ div#notebook {
 

 

 

-
In [15]:

+
In [ ]:

 

     

-
def process(row):
+
theta_band_f1, theta_band_f2 = 6, 10 
+

+
+    

+

+

+
+

+

+

+
In [ ]:

+

+    

+
psd_data, freq_data = {}, {}
+
+def process(row, perform_zscore):
     action_id = row['action']
     channel_group = row['channel_group']
+    name =  f'{action_id}_{channel_group}'
     lfp = data_loader.lfp(action_id, channel_group)
     clean_lfp = select_and_clean(lfp)
     snls = signaltonoise(clean_lfp)
@@ -13434,19 +13489,30 @@ div#notebook {
     lim = get_lim(action_id)
     
     mask = get_mask(lfp, lim)
-    signal = zscore(clean_lfp[mask, best_channel].ravel())
+    if perform_zscore:
+        signal = zscore(clean_lfp[mask, best_channel].ravel())
+    else:
+        signal = clean_lfp[mask, best_channel].ravel()
     
-    p_xx, freq = mlab.psd(signal, Fs=lfp.sampling_rate.magnitude, NFFT=NFFT)
+    window = 6 * lfp.sampling_rate.magnitude
     
-    theta_f, theta_p_max = find_theta_peak(p_xx, freq)
+#     p_xx, freq = mlab.psd(signal, Fs=lfp.sampling_rate.magnitude, NFFT=NFFT)
+    freq, p_xx = ss.welch(signal, fs=lfp.sampling_rate.magnitude, nperseg=window, nfft=scipy.fftpack.next_fast_len(window))
+    p_xx = 10 * np.log10(p_xx)
     
-    theta_energy = compute_energy(p_xx, freq, theta_band_f1, theta_band_f2) # theta band 6 - 10 Hz
+    theta_f, theta_p_max = find_theta_peak(p_xx, freq, theta_band_f1, theta_band_f2)
     
-    theta_half_f1, theta_half_f2 = compute_half_width(p_xx, freq, theta_p_max, theta_f)
+    theta_bandpower, theta_relpower = compute_band_power(p_xx, freq, theta_band_f1, theta_band_f2)
+    
+    theta_relpeak = compute_relative_peak(p_xx, freq, theta_p_max, [theta_band_f1, theta_band_f2])
+        
+    theta_half_f1, theta_half_f2, theta_half_power = compute_half_width(p_xx, freq, theta_p_max, theta_f, [theta_band_f1, theta_band_f2])
     
     theta_half_width = theta_half_f2 - theta_half_f1
     
-    theta_half_energy = compute_energy(p_xx, freq, theta_half_f1, theta_half_f2) # theta band 6 - 10 Hz
+    psd_data.update({name: p_xx})
+    freq_data.update({name: freq})
+
     
     # stim
     
@@ -13454,27 +13520,33 @@ div#notebook {
     
     stim_p_max = compute_stim_peak(p_xx, freq, stim_freq)
     
-    stim_half_f1, stim_half_f2 = compute_half_width(p_xx, freq, stim_p_max, stim_freq)
+    stim_half_f1, stim_half_f2, stim_half_power = compute_half_width(p_xx, freq, stim_p_max, stim_freq, [stim_freq - 1, stim_freq + 1])
+    
     stim_half_width = stim_half_f2 - stim_half_f1
     
-    stim_energy = compute_energy(p_xx, freq, stim_half_f1, stim_half_f2)
+    stim_bandpower, stim_relpower = compute_band_power(p_xx, freq, stim_freq - 1, stim_freq + 1)
+    
+    stim_relpeak = compute_relative_peak(p_xx, freq, stim_p_max, [stim_freq - 1, stim_freq + 1])
     
     result = pd.Series({
         'signal_to_noise': snl,
         'best_channel': best_channel,
         'theta_freq': theta_f,
         'theta_peak': theta_p_max,
-        'theta_energy': theta_energy,
+        'theta_bandpower': theta_bandpower,
+        'theta_relpower': theta_relpower,
+        'theta_relpeak': theta_relpeak,
         'theta_half_f1': theta_half_f1, 
         'theta_half_f2': theta_half_f2,
         'theta_half_width': theta_half_width,
-        'theta_half_energy': theta_half_energy,
         'stim_freq': stim_freq,
         'stim_p_max': stim_p_max,
         'stim_half_f1': stim_half_f1, 
         'stim_half_f2': stim_half_f2,
         'stim_half_width': stim_half_width,
-        'stim_energy': stim_energy
+        'stim_bandpower': stim_bandpower,
+        'stim_relpower': stim_relpower,
+        'stim_relpeak': stim_relpeak,
     })
     return result
 

@@ -13486,11 +13558,11 @@ div#notebook {
 

 

 

-
In [16]:

+
In [ ]:

 

     

 
results = channel_groups.merge(
-    channel_groups.progress_apply(process, axis=1), 
+    channel_groups.progress_apply(process, perform_zscore=perform_zscore, axis=1), 
     left_index=True, right_index=True)
 

 
@@ -13512,13 +13584,13 @@ div#notebook {
 
  
  
-

+

 

 
 
 

 
@@ -13529,8 +13601,19 @@ var element = $('#ccdb069e-4b63-4db1-9535-31624e57bc0e');
     

 
 
-

-
+

+
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:9: RuntimeWarning: invalid value encountered in greater
+  if __name__ == '__main__':
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:9: RuntimeWarning: invalid value encountered in less
+  if __name__ == '__main__':
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in greater
+  # Remove the CWD from sys.path while we load stuff.
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in less
+  # Remove the CWD from sys.path while we load stuff.
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.
+  out=out, **kwargs)
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in true_divide
+  ret = ret.dtype.type(ret / rcount)
 

 

 

@@ -13541,112 +13624,17 @@ var element = $('#ccdb069e-4b63-4db1-9535-31624e57bc0e');
 

 

 

-
In [17]:

+
In [ ]:

 

     

-
psd, freqs = {}, {}
-for i, row in tqdm(channel_groups.iterrows(), total=len(channel_groups)):
-    action_id = row['action']
-    channel_group = row['channel_group']
-    action_group = f'{action_id}_{channel_group}'
-    lfp = data_loader.lfp(action_id, channel_group)
-    clean_lfp = select_and_clean(lfp)
-    snls = signaltonoise(clean_lfp)
-    best_channel = np.argmax(snls)
-    snl = snls[best_channel]
-    
-    lim = get_lim(action_id)
-    
-    mask = get_mask(lfp, lim)
-    signal = zscore(clean_lfp[mask, best_channel].ravel())
-    
-    p_xx, freq = mlab.psd(signal, Fs=lfp.sampling_rate.magnitude, NFFT=NFFT)
-    psd.update({action_group: p_xx})
-    freqs.update({action_group: freq})
+
pd.DataFrame(psd_data).to_feather(output / 'data' / 'psd.feather')
+pd.DataFrame(freq_data).to_feather(output / 'data' / 'freqs.feather')
 

 
     

 

 

 
-

-

-
-
-

-
-    

-
-
-
-
-
- 
- 
-

-

-
-
-

-
-

-
-

-
-    

-
-
-

-
-

-

-

-
-

-

-
-

-

-

-
In [18]:

-

-    

-
(psd).to_feather(output / 'data' / 'psd.feather')
-freqs.to_feather(output / 'data' / 'freqs.feather')
-

-
-    

-

-

-
-

-

-
-
-

-
-    

-
-
-

-
----------------------------------------------------------------------------
-AttributeError                            Traceback (most recent call last)
-<ipython-input-18-7660e1d3307d> in <module>
-----> 1 psd.to_feather(output / 'data' / 'psd.feather')
-      2 freqs.to_feather(output / 'data' / 'freqs.feather')
-
-AttributeError: 'dict' object has no attribute 'to_feather'

-

-

-
-

-

-
 

 

 

@@ -13660,7 +13648,7 @@ var element = $('#6a05ea1e-3782-40af-8458-e7713b9c2fb7');
 
In [ ]:

 

     

-
action = project.require_action("stimulus-lfp-response")
+
action = project.require_action("stimulus-lfp-response" + zscore_str)
 

 
     

@@ -13674,7 +13662,7 @@ var element = $('#6a05ea1e-3782-40af-8458-e7713b9c2fb7');
 

     

 
action.modules['parameters'] = {
-    'NFFT': NFFT,
+    'window': 6,
     'theta_band_f1': theta_band_f1,
     'theta_band_f2': theta_band_f2
 }
diff --git a/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.ipynb b/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.ipynb
index 1b3d16471..03445578d 100644
--- a/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.ipynb
+++ b/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.ipynb
@@ -19,7 +19,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "10:26:07 [I] klustakwik KlustaKwik2 version 0.2.6\n"
+      "19:43:24 [I] klustakwik KlustaKwik2 version 0.2.6\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:24: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
+      "Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`\n"
      ]
     }
    ],
@@ -39,6 +41,8 @@
     "import quantities as pq\n",
     "import shutil\n",
     "from distutils.dir_util import copy_tree\n",
+    "import scipy\n",
+    "import scipy.signal as ss\n",
     "\n",
     "from scipy.signal import find_peaks\n",
     "from scipy.interpolate import interp1d\n",
@@ -66,8 +70,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output = pathlib.Path('output/stimulus-lfp-response')\n",
-    "(output / 'data').mkdir(parents=True, exist_ok=True)"
+    "#############################\n",
+    "\n",
+    "perform_zscore = True\n",
+    "\n",
+    "if not perform_zscore:\n",
+    "    zscore_str = \"-no-zscore\"\n",
+    "else:\n",
+    "    zscore_str = \"\"\n",
+    "\n",
+    "#################################"
    ]
   },
   {
@@ -75,6 +87,16 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "output = pathlib.Path('output/stimulus-lfp-response' + zscore_str)\n",
+    "(output / 'data').mkdir(parents=True, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "identify_neurons = actions['identify-neurons']\n",
     "sessions = pd.read_csv(identify_neurons.data_path('sessions'))"
@@ -82,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,17 +185,32 @@
     "        return np.nan\n",
     "    mask = (f > f1) & (f < f2)\n",
     "    df = f[1] - f[0]\n",
-    "    return np.sum(p[mask]) * df"
+    "    return np.sum(p[mask]) * df\n",
+    "\n",
+    "\n",
+    "def compute_band_power(p, f, f1, f2):\n",
+    "    if np.isnan(f1) or np.all(np.isnan(p)):\n",
+    "        return [np.nan] * 2\n",
+    "    from scipy.integrate import simps\n",
+    "    dx = f[1] - f[0]\n",
+    "    mask = (f > f1) & (f < f2)\n",
+    "    # Compute the absolute power by approximating the area under the curve\n",
+    "    band_power = simps(p[mask], dx=dx)\n",
+    "    total_power = simps(p, dx=dx)\n",
+    "    rel_power = band_power / total_power\n",
+    "    return band_power, rel_power"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def find_theta_peak(p, f):\n",
-    "    mask = (f > 6) & (f < 10)\n",
+    "def find_theta_peak(p, f, f1, f2):\n",
+    "    if np.all(np.isnan(p)):\n",
+    "        return np.nan, np.nan\n",
+    "    mask = (f > f1) & (f < f2)\n",
     "    p_m = p[mask]\n",
     "    f_m = f[mask]\n",
     "    peaks, _ = find_peaks(p_m)\n",
@@ -183,49 +220,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def compute_half_width(p, f, m_p, m_f):\n",
-    "    if np.isnan(m_p):\n",
-    "        return np.nan, np.nan\n",
+    "def compute_half_width(power, freq, max_power, max_frequency, band, band_width=1):\n",
+    "    if np.isnan(max_power):\n",
+    "        return [np.nan] * 3\n",
+    "    \n",
+    "    # estimate baseline power\n",
+    "    low_baseline_mask = (freq > band[0] - band_width) & (freq < band[0])\n",
+    "    high_baseline_mask = (freq > band[1]) & (freq < band[1] + band_width)\n",
+    "    baseline = np.mean(np.concatenate([power[low_baseline_mask], power[high_baseline_mask]]))\n",
+    "    p = power - baseline\n",
+    "    m_p = max_power - baseline\n",
+    "    m_f = max_frequency\n",
+    "    f = freq\n",
+    "    \n",
+    "    # estimate half width\n",
     "    m_p_half = m_p / 2\n",
     "    half_p = p - m_p_half\n",
     "    idx_f = np.where(f <= m_f)[0].max()\n",
     "    idxs_p1, = np.where(np.diff(half_p[:idx_f + 1] > 0) == 1)\n",
     "    if len(idxs_p1) == 0:\n",
-    "        return np.nan, np.nan\n",
+    "        return [np.nan] * 3\n",
     "    m1 = idxs_p1.max()\n",
     "    idxs_p2, = np.where(np.diff(half_p[idx_f:] > 0) == 1)\n",
-    "    m2 = idxs_p2.min() + idx_f    \n",
-    "#     plt.plot(f, p)\n",
-    "#     plt.plot(m_f, m_p, marker='o', ls='none', markersize=10)\n",
-    "#     plt.plot(f[m1], p[m1], marker='x', ls='none', markersize=10, c='r')\n",
-    "#     plt.plot(f[m1+1], p[m1+1], marker='+', ls='none', markersize=10, c='r')\n",
-    "    \n",
-    "#     plt.plot(f[m2], p[m2], marker='x', ls='none', markersize=10, c='k')\n",
-    "#     plt.plot(f[m2+1], p[m2+1], marker='+', ls='none', markersize=10, c='k')\n",
-    "    \n",
-    "#     plt.xlim(4,12)\n",
-    "    assert p[m1] < m_p_half < p[m1+1], (p[m1], m_p_half, p[m1+1])\n",
-    "    assert p[m2] > m_p_half > p[m2+1], (p[m2], m_p_half, p[m2+1])\n",
+    "    if len(idxs_p2) == 0:\n",
+    "        return [np.nan] * 3\n",
+    "    m2 = idxs_p2.min() + idx_f\n",
+    "#     assert p[m1] < m_p_half < p[m1+1], (p[m1], m_p_half, p[m1+1])\n",
+    "#     assert p[m2] > m_p_half > p[m2+1], (p[m2], m_p_half, p[m2+1])\n",
     "    \n",
     "    f1 = interp1d([half_p[m1], half_p[m1 + 1]], [f[m1], f[m1 + 1]])(0)\n",
     "    f2 = interp1d([half_p[m2], half_p[m2 + 1]], [f[m2], f[m2 + 1]])(0)\n",
-    "    return f1, f2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_stim_peak(p, f, s_f):\n",
-    "    if np.isnan(s_f):\n",
-    "        return np.nan\n",
-    "    return interp1d(f, p)(s_f)"
+    "    return f1, f2, m_p_half + baseline"
    ]
   },
   {
@@ -234,19 +263,41 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "NFFT = 10000\n",
+    "def compute_stim_peak(p, f, s_f):\n",
+    "    if np.isnan(s_f):\n",
+    "        return np.nan\n",
+    "    return interp1d(f, p)(s_f)\n",
+    "\n",
+    "\n",
+    "def compute_relative_peak(power, freq, max_power, band, band_width=1):\n",
+    "    # estimate baseline power\n",
+    "    low_baseline_mask = (freq > band[0] - band_width) & (freq < band[0])\n",
+    "    high_baseline_mask = (freq > band[1]) & (freq < band[1] + band_width)\n",
+    "    baseline = np.mean(np.concatenate([power[low_baseline_mask], power[high_baseline_mask]]))\n",
+    "    return (max_power - baseline) / abs(baseline)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "theta_band_f1, theta_band_f2 = 6, 10 "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def process(row):\n",
+    "psd_data, freq_data = {}, {}\n",
+    "\n",
+    "def process(row, perform_zscore):\n",
     "    action_id = row['action']\n",
     "    channel_group = row['channel_group']\n",
+    "    name =  f'{action_id}_{channel_group}'\n",
     "    lfp = data_loader.lfp(action_id, channel_group)\n",
     "    clean_lfp = select_and_clean(lfp)\n",
     "    snls = signaltonoise(clean_lfp)\n",
@@ -256,19 +307,30 @@
     "    lim = get_lim(action_id)\n",
     "    \n",
     "    mask = get_mask(lfp, lim)\n",
-    "    signal = zscore(clean_lfp[mask, best_channel].ravel())\n",
+    "    if perform_zscore:\n",
+    "        signal = zscore(clean_lfp[mask, best_channel].ravel())\n",
+    "    else:\n",
+    "        signal = clean_lfp[mask, best_channel].ravel()\n",
     "    \n",
-    "    p_xx, freq = mlab.psd(signal, Fs=lfp.sampling_rate.magnitude, NFFT=NFFT)\n",
+    "    window = 6 * lfp.sampling_rate.magnitude\n",
     "    \n",
-    "    theta_f, theta_p_max = find_theta_peak(p_xx, freq)\n",
+    "#     p_xx, freq = mlab.psd(signal, Fs=lfp.sampling_rate.magnitude, NFFT=NFFT)\n",
+    "    freq, p_xx = ss.welch(signal, fs=lfp.sampling_rate.magnitude, nperseg=window, nfft=scipy.fftpack.next_fast_len(window))\n",
+    "    p_xx = 10 * np.log10(p_xx)\n",
     "    \n",
-    "    theta_energy = compute_energy(p_xx, freq, theta_band_f1, theta_band_f2) # theta band 6 - 10 Hz\n",
+    "    theta_f, theta_p_max = find_theta_peak(p_xx, freq, theta_band_f1, theta_band_f2)\n",
     "    \n",
-    "    theta_half_f1, theta_half_f2 = compute_half_width(p_xx, freq, theta_p_max, theta_f)\n",
+    "    theta_bandpower, theta_relpower = compute_band_power(p_xx, freq, theta_band_f1, theta_band_f2)\n",
+    "    \n",
+    "    theta_relpeak = compute_relative_peak(p_xx, freq, theta_p_max, [theta_band_f1, theta_band_f2])\n",
+    "        \n",
+    "    theta_half_f1, theta_half_f2, theta_half_power = compute_half_width(p_xx, freq, theta_p_max, theta_f, [theta_band_f1, theta_band_f2])\n",
     "    \n",
     "    theta_half_width = theta_half_f2 - theta_half_f1\n",
     "    \n",
-    "    theta_half_energy = compute_energy(p_xx, freq, theta_half_f1, theta_half_f2) # theta band 6 - 10 Hz\n",
+    "    psd_data.update({name: p_xx})\n",
+    "    freq_data.update({name: freq})\n",
+    "\n",
     "    \n",
     "    # stim\n",
     "    \n",
@@ -276,133 +338,89 @@
     "    \n",
     "    stim_p_max = compute_stim_peak(p_xx, freq, stim_freq)\n",
     "    \n",
-    "    stim_half_f1, stim_half_f2 = compute_half_width(p_xx, freq, stim_p_max, stim_freq)\n",
+    "    stim_half_f1, stim_half_f2, stim_half_power = compute_half_width(p_xx, freq, stim_p_max, stim_freq, [stim_freq - 1, stim_freq + 1])\n",
+    "    \n",
     "    stim_half_width = stim_half_f2 - stim_half_f1\n",
     "    \n",
-    "    stim_energy = compute_energy(p_xx, freq, stim_half_f1, stim_half_f2)\n",
+    "    stim_bandpower, stim_relpower = compute_band_power(p_xx, freq, stim_freq - 1, stim_freq + 1)\n",
+    "    \n",
+    "    stim_relpeak = compute_relative_peak(p_xx, freq, stim_p_max, [stim_freq - 1, stim_freq + 1])\n",
     "    \n",
     "    result = pd.Series({\n",
     "        'signal_to_noise': snl,\n",
     "        'best_channel': best_channel,\n",
     "        'theta_freq': theta_f,\n",
     "        'theta_peak': theta_p_max,\n",
-    "        'theta_energy': theta_energy,\n",
+    "        'theta_bandpower': theta_bandpower,\n",
+    "        'theta_relpower': theta_relpower,\n",
+    "        'theta_relpeak': theta_relpeak,\n",
     "        'theta_half_f1': theta_half_f1, \n",
     "        'theta_half_f2': theta_half_f2,\n",
     "        'theta_half_width': theta_half_width,\n",
-    "        'theta_half_energy': theta_half_energy,\n",
     "        'stim_freq': stim_freq,\n",
     "        'stim_p_max': stim_p_max,\n",
     "        'stim_half_f1': stim_half_f1, \n",
     "        'stim_half_f2': stim_half_f2,\n",
     "        'stim_half_width': stim_half_width,\n",
-    "        'stim_energy': stim_energy\n",
+    "        'stim_bandpower': stim_bandpower,\n",
+    "        'stim_relpower': stim_relpower,\n",
+    "        'stim_relpeak': stim_relpeak,\n",
     "    })\n",
     "    return result"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e733903bb8d7406ea6c05bf5af093bfc",
+       "model_id": "0a39fef20b724ef186ce6ce5299b3719",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "HBox(children=(IntProgress(value=0, max=704), HTML(value='')))"
+       "HBox(children=(IntProgress(value=0, max=696), HTML(value='')))"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:9: RuntimeWarning: invalid value encountered in greater\n",
+      "  if __name__ == '__main__':\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:9: RuntimeWarning: invalid value encountered in less\n",
+      "  if __name__ == '__main__':\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in greater\n",
+      "  # Remove the CWD from sys.path while we load stuff.\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in less\n",
+      "  # Remove the CWD from sys.path while we load stuff.\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.\n",
+      "  out=out, **kwargs)\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
      ]
     }
    ],
    "source": [
     "results = channel_groups.merge(\n",
-    "    channel_groups.progress_apply(process, axis=1), \n",
+    "    channel_groups.progress_apply(process, perform_zscore=perform_zscore, axis=1), \n",
     "    left_index=True, right_index=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0184251a265a40a2a1e9910405f05db8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, max=704), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "psd, freqs = {}, {}\n",
-    "for i, row in tqdm(channel_groups.iterrows(), total=len(channel_groups)):\n",
-    "    action_id = row['action']\n",
-    "    channel_group = row['channel_group']\n",
-    "    action_group = f'{action_id}_{channel_group}'\n",
-    "    lfp = data_loader.lfp(action_id, channel_group)\n",
-    "    clean_lfp = select_and_clean(lfp)\n",
-    "    snls = signaltonoise(clean_lfp)\n",
-    "    best_channel = np.argmax(snls)\n",
-    "    snl = snls[best_channel]\n",
-    "    \n",
-    "    lim = get_lim(action_id)\n",
-    "    \n",
-    "    mask = get_mask(lfp, lim)\n",
-    "    signal = zscore(clean_lfp[mask, best_channel].ravel())\n",
-    "    \n",
-    "    p_xx, freq = mlab.psd(signal, Fs=lfp.sampling_rate.magnitude, NFFT=NFFT)\n",
-    "    psd.update({action_group: p_xx})\n",
-    "    freqs.update({action_group: freq})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'dict' object has no attribute 'to_feather'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpsd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'data'\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'psd.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mfreqs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'data'\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'freqs.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'dict' object has no attribute 'to_feather'"
-     ]
-    }
-   ],
-   "source": [
-    "(psd).to_feather(output / 'data' / 'psd.feather')\n",
-    "freqs.to_feather(output / 'data' / 'freqs.feather')"
+    "pd.DataFrame(psd_data).to_feather(output / 'data' / 'psd.feather')\n",
+    "pd.DataFrame(freq_data).to_feather(output / 'data' / 'freqs.feather')"
    ]
   },
   {
@@ -418,7 +436,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "action = project.require_action(\"stimulus-lfp-response\")"
+    "action = project.require_action(\"stimulus-lfp-response\" + zscore_str)"
    ]
   },
   {
@@ -428,7 +446,7 @@
    "outputs": [],
    "source": [
     "action.modules['parameters'] = {\n",
-    "    'NFFT': NFFT,\n",
+    "    'window': 6,\n",
     "    'theta_band_f1': theta_band_f1,\n",
     "    'theta_band_f2': theta_band_f2\n",
     "}"
@@ -490,5 +508,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/actions/stimulus-lfp-response/data/data/freqs.feather b/actions/stimulus-lfp-response/data/data/freqs.feather
index c8d0ab3a8..7d053599e 100644
Binary files a/actions/stimulus-lfp-response/data/data/freqs.feather and b/actions/stimulus-lfp-response/data/data/freqs.feather differ
diff --git a/actions/stimulus-lfp-response/data/data/psd.feather b/actions/stimulus-lfp-response/data/data/psd.feather
index 74119e0ab..726cf4394 100644
Binary files a/actions/stimulus-lfp-response/data/data/psd.feather and b/actions/stimulus-lfp-response/data/data/psd.feather differ
diff --git a/actions/stimulus-lfp-response/data/results.csv b/actions/stimulus-lfp-response/data/results.csv
index 684ea7ca2..9403466b7 100644
--- a/actions/stimulus-lfp-response/data/results.csv
+++ b/actions/stimulus-lfp-response/data/results.csv
@@ -1,705 +1,697 @@
-action,baseline,channel_group,entity,frequency,i,ii,session,stim_location,stimulated,tag,signal_to_noise,best_channel,theta_freq,theta_peak,theta_energy,theta_half_f1,theta_half_f2,theta_half_width,theta_half_energy,stim_freq,stim_p_max,stim_half_f1,stim_half_f2,stim_half_width,stim_energy
-1849-060319-3,True,0,1849,,False,True,3,,False,baseline ii,0.002325174631550908,3.0,8.1,0.33779249107561676,0.3880129398108474,7.744092633192099,8.297511539875476,0.553418906683377,0.129689268302729,,,,,,
-1849-060319-3,True,1,1849,,False,True,3,,False,baseline ii,0.005248440895229578,3.0,8.0,0.11020669960559534,0.2589190147417904,6.944846007095425,8.754280566164176,1.8094345590687517,0.1479721379167004,,,,,,
-1849-060319-3,True,2,1849,,False,True,3,,False,baseline ii,3.347351594129577e-05,0.0,8.1,0.4315653988524797,0.45982505898539094,7.747477446501496,8.28197517551151,0.5344977290100141,0.16560334973902324,,,,,,
-1849-060319-3,True,3,1849,,False,True,3,,False,baseline ii,0.00023674558906350285,3.0,8.1,0.3444389459211248,0.3688866289378556,7.7390971489950084,8.280323275670202,0.5412261266751939,0.13434570350536432,,,,,,
-1849-060319-3,True,4,1849,,False,True,3,,False,baseline ii,-0.0007146852440200746,0.0,8.1,0.34714910075052086,0.39234916310922135,7.742522583813493,8.510789496109712,0.7682669122962196,0.18896120853072274,,,,,,
-1849-060319-3,True,5,1849,,False,True,3,,False,baseline ii,-0.0004269914061296731,3.0,8.1,0.2382197105205953,0.2998343535828532,7.717653145328417,8.546157535182985,0.8285043898545679,0.13729238072330982,,,,,,
-1849-060319-3,True,6,1849,,False,True,3,,False,baseline ii,-0.0036540748551487923,1.0,8.1,0.1930226209661773,0.25655599117363787,7.598818532964633,8.33899767522237,0.7401791422577366,0.10838919145172765,,,,,,
-1849-060319-3,True,7,1849,,False,True,3,,False,baseline ii,-0.003039984731003642,3.0,8.1,0.13104010304696892,0.20190669436250663,7.597767485237029,8.598033827796227,1.0002663425591978,0.08623940433482297,,,,,,
-1839-120619-4,False,0,1839,30.0,False,True,4,ms,True,stim ii,0.04278777539730072,2.0,8.6,0.018140487382044684,0.04117396701745499,7.711365684177503,9.818004715417008,2.1066390312395047,0.026317238735641485,30.301877096368724,0.7136505937512442,30.231142056098097,30.37439388424781,0.1432518281497117,0.07228870131876733
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diff --git a/actions/stimulus-lfp-response/modules/parameters.yaml b/actions/stimulus-lfp-response/modules/parameters.yaml
index 17593dd5d..632f05bd5 100644
--- a/actions/stimulus-lfp-response/modules/parameters.yaml
+++ b/actions/stimulus-lfp-response/modules/parameters.yaml
@@ -1,3 +1,3 @@
-NFFT: 10000
+window: 6
 theta_band_f1: 6
 theta_band_f2: 10