diff --git a/actions/lfp_speed/data/20_lfp_speed.html b/actions/lfp_speed/data/20_lfp_speed.html index 6bae89455..2120882dd 100644 --- a/actions/lfp_speed/data/20_lfp_speed.html +++ b/actions/lfp_speed/data/20_lfp_speed.html @@ -13115,7 +13115,7 @@ div#notebook {
%load_ext autoreload
@@ -13129,7 +13129,7 @@ div#notebook {
import os
@@ -13182,7 +13182,7 @@ div#notebook {
-In [5]:
+In [3]:
colors = ['#1b9e77','#d95f02','#7570b3','#e7298a', '#4393c3', '#d6604d']
@@ -13217,7 +13217,7 @@ div#notebook {
-In [6]:
+In [4]:
project_path = dp.project_path()
@@ -13236,7 +13236,7 @@ div#notebook {
-In [8]:
+In [5]:
data_action = actions['lfp_speed']
@@ -13260,7 +13260,7 @@ div#notebook {
-In [9]:
+In [6]:
results.head()
@@ -13276,7 +13276,7 @@ div#notebook {
- Out[9]:
+ Out[6]:
@@ -13414,7 +13414,7 @@ div#notebook {
-In [10]:
+In [7]:
identify_neurons = actions['identify-neurons']
@@ -13428,7 +13428,7 @@ div#notebook {
-In [11]:
+In [8]:
results = results.merge(sessions, on='action')
@@ -13448,7 +13448,7 @@ div#notebook {
-In [12]:
+In [9]:
query_base_i = 'baseline and Hz11'
@@ -13457,7 +13457,6 @@ div#notebook {
query_base_ii = 'baseline and Hz30'
query_stim_30 = 'stimulated and Hz30'
-query_base_combined = 'baseline and Hz11 or Hz30'
query_stim_combined = 'stimulated and Hz11 or Hz30'
@@ -13468,7 +13467,7 @@ div#notebook {
-In [13]:
+In [10]:
stuff = 'freq_score'
@@ -13488,7 +13487,7 @@ div#notebook {
plt.savefig(output_path / "figures" / "frequency_score_30.svg", bbox_inches="tight", transparent=True)
plt.savefig(output_path / "figures" / "frequency_score_30.png", bbox_inches="tight", transparent=True)
-base = results.query(query_base_combined)[stuff].to_numpy()
+base = results.query(query_base_i)[stuff].to_numpy()
stim = results.query(query_stim_combined)[stuff].to_numpy()
plt.figure(figsize=figsize_violin)
violinplot(base, stim, colors=colors[4:])
@@ -13513,7 +13512,7 @@ div#notebook {
@@ -13556,7 +13555,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
@@ -13569,7 +13568,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [14]:
+In [11]:
def plot_speed(query_base, query_stim, stuff, colors, labels, filename=None, show_raw=False):
@@ -13612,7 +13611,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [18]:
+In [12]:
plot_speed(query_base_i, query_stim_11, 'mean_freq',
@@ -13635,7 +13634,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
@@ -13648,7 +13647,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [19]:
+In [13]:
plot_speed(query_base_ii, query_stim_30, 'mean_freq',
@@ -13671,7 +13670,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
@@ -13684,11 +13683,11 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [20]:
+In [14]:
plot_speed(
- query_base_combined, query_stim_combined, 'mean_freq',
+ query_base_i, query_stim_combined, 'mean_freq',
colors[4:], labels[4:], filename='lfp_speed_freq_combined')
@@ -13708,7 +13707,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
@@ -13728,7 +13727,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [21]:
+In [15]:
plot_speed(
@@ -13752,7 +13751,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
@@ -13765,7 +13764,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [24]:
+In [16]:
plot_speed(
@@ -13789,7 +13788,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
@@ -13802,11 +13801,11 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [25]:
+In [17]:
plot_speed(
- query_base_combined, query_stim_combined, 'mean_power',
+ query_base_i, query_stim_combined, 'mean_power',
colors[4:], labels[4:], filename='lfp_speed_power_combined')
@@ -13826,7 +13825,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
@@ -13846,7 +13845,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [26]:
+In [18]:
stuff = 'speed'
@@ -13854,7 +13853,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
max_speed = results['max_speed'].iloc[0]
f = np.median
-base = np.array([f(a) for a in results.query(query_base_combined)[stuff]])
+base = np.array([f(a) for a in results.query(query_base_i)[stuff]])
stim = np.array([f(a).mean() for a in results.query(query_stim_combined)[stuff]])
plt.figure(figsize=figsize_violin)
violinplot(stim, base)
@@ -13865,11 +13864,11 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
plt.figure(figsize=figsize_speed)
binsize = 0.02
bins = np.arange(min_speed, max_speed + binsize, binsize)
-base = np.array([np.histogram(a, bins)[0] for a in results.query(query_base_combined)[stuff]])
+base = np.array([np.histogram(a, bins)[0] for a in results.query(query_base_i)[stuff]])
stim = np.array([np.histogram(a, bins)[0] for a in results.query(query_stim_combined)[stuff]])
-plt.bar(bins[1:], stim.mean(axis=0), width=np.diff(bins)[0], label='control', alpha=.5, color=control_color);
-plt.bar(bins[1:], base.mean(axis=0), width=np.diff(bins)[0], label='base', alpha=.5, color=base_color);
+plt.bar(bins[1:], stim.mean(axis=0), width=np.diff(bins)[0], label='Stimulated', alpha=.5, color=colors[5]);
+plt.bar(bins[1:], base.mean(axis=0), width=np.diff(bins)[0], label='Baseline', alpha=.5, color=colors[6]);
plt.legend(frameon=False)
@@ -13891,7 +13890,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
@@ -13904,15 +13903,15 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
@@ -13924,7 +13923,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
@@ -13938,8 +13937,10 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-
@@ -13957,7 +13958,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [35]:
+In [19]:
columns = [
@@ -13994,27 +13995,27 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
summary_i = pd.DataFrame()
-summary_i['Baseline'] = results.query('not {}'.format(query_base_i))[columns].agg(summarize)
-summary_i['11 Hz'] = results.query('{}'.format(query_stim_11))[columns].agg(summarize)
+summary_i['Baseline I'] = results.query(query_base_i)[columns].agg(summarize)
+summary_i['11 Hz'] = results.query(query_stim_11)[columns].agg(summarize)
summary_i['MWU'] = list(map(lambda x: MWU(x, query_base_i, query_stim_11), columns))
summary_i['PRS'] = list(map(lambda x: PRS(x, query_base_i, query_stim_11), columns))
summary_ii = pd.DataFrame()
-summary_ii['Baseline'] = results.query('not {}'.format(query_base_ii))[columns].agg(summarize)
-summary_ii['11 Hz'] = results.query('{}'.format(query_stim_30))[columns].agg(summarize)
+summary_ii['Baseline II'] = results.query(query_base_ii)[columns].agg(summarize)
+summary_ii['30 Hz'] = results.query(query_stim_30)[columns].agg(summarize)
summary_ii['MWU'] = list(map(lambda x: MWU(x, query_base_ii, query_stim_30), columns))
summary_ii['PRS'] = list(map(lambda x: PRS(x, query_base_ii, query_stim_30), columns))
summary_combined = pd.DataFrame()
-summary_combined['Baseline'] = results.query('not {}'.format(query_base_combined))[columns].agg(summarize)
-summary_combined['11 Hz'] = results.query('{}'.format(query_stim_combined))[columns].agg(summarize)
+summary_combined['Baseline I'] = results.query('not {}'.format(query_base_i))[columns].agg(summarize)
+summary_combined['Combined'] = results.query('{}'.format(query_stim_combined))[columns].agg(summarize)
-summary_combined['MWU'] = list(map(lambda x: MWU(x, query_base_combined, query_stim_combined), columns))
-summary_combined['PRS'] = list(map(lambda x: PRS(x, query_base_combined, query_stim_combined), columns))
+summary_combined['MWU'] = list(map(lambda x: MWU(x, query_base_i, query_stim_combined), columns))
+summary_combined['PRS'] = list(map(lambda x: PRS(x, query_base_i, query_stim_combined), columns))
summary_i.to_latex(output_path / "statistics" / "power_freq_score_summary_i.tex")
summary_i.to_csv(output_path / "statistics" / "power_freq_score_summary_i.csv")
@@ -14033,7 +14034,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [32]:
+In [20]:
summary_i
@@ -14049,7 +14050,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
- Out[32]:
+ Out[20]:
@@ -14072,7 +14073,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
- Baseline
+ Baseline I
11 Hz
MWU
PRS
@@ -14106,7 +14107,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [33]:
+In [21]:
summary_ii
@@ -14122,7 +14123,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
- Out[33]:
+ Out[21]:
@@ -14145,8 +14146,8 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
- Baseline
- 11 Hz
+ Baseline II
+ 30 Hz
MWU
PRS
@@ -14179,7 +14180,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [34]:
+In [22]:
summary_combined
@@ -14195,7 +14196,7 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
- Out[34]:
+ Out[22]:
@@ -14218,8 +14219,8 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
- Baseline
- 11 Hz
+ Baseline I
+ Combined
MWU
PRS
@@ -14227,17 +14228,17 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
power_score
+ -0.03 ± 0.01 (208)
0.03 ± 0.01 (480)
- 0.03 ± 0.01 (480)
- 144593.00, 0.000
- 0.05, 0.000
+ 67804.00, 0.000
+ 0.13, 0.000
freq_score
+ -0.01 ± 0.01 (208)
0.06 ± 0.01 (480)
- 0.06 ± 0.01 (480)
- 150719.00, 0.000
- 0.12, 0.000
+ 72140.00, 0.000
+ 0.17, 0.000
@@ -14259,43 +14260,20 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [36]:
+In [23]:
-action = project.g_action("lfp_speed")
+action = project.actions["lfp_speed"]
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-In [47]:
+In [24]:
outdata = {
@@ -14321,49 +14299,16 @@ U-test: U value 150719.0 p value 5.562570660595682e-21
-In [48]:
+In [25]:
-pnnmec.registration.store_notebook(action, "20_power_spectrum_density.ipynb")
+septum_mec.analysis.registration.store_notebook(action, "20_lfp_speed.ipynb")
-
-
-
-In [49]:
-
-
-action.modules["code_version"] = vc.create_code_version_module()
-action.modules["data_version"] = vc.create_data_version_module(project_path)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/actions/lfp_speed/data/20_lfp_speed.ipynb b/actions/lfp_speed/data/20_lfp_speed.ipynb
index c5dd26716..edb743872 100644
--- a/actions/lfp_speed/data/20_lfp_speed.ipynb
+++ b/actions/lfp_speed/data/20_lfp_speed.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -12,14 +12,14 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "14:40:25 [I] klustakwik KlustaKwik2 version 0.2.6\n",
+ "12:54:11 [I] klustakwik KlustaKwik2 version 0.2.6\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
" return f(*args, **kwds)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
@@ -68,7 +68,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -82,7 +82,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -97,7 +97,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -117,7 +117,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -305,7 +305,7 @@
"4 [1.2545438245339104, 1.2553897239251606, 1.256... "
]
},
- "execution_count": 9,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -316,7 +316,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -326,7 +326,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -342,7 +342,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -352,13 +352,12 @@
"query_base_ii = 'baseline and Hz30'\n",
"query_stim_30 = 'stimulated and Hz30'\n",
"\n",
- "query_base_combined = 'baseline and Hz11 or Hz30'\n",
"query_stim_combined = 'stimulated and Hz11 or Hz30'"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -367,7 +366,7 @@
"text": [
"U-test: U value 37221.0 p value 1.9679601390031718e-50\n",
"U-test: U value 16950.0 p value 1.642880876541107e-32\n",
- "U-test: U value 150719.0 p value 5.562570660595682e-21\n"
+ "U-test: U value 72140.0 p value 2.0364061218728567e-30\n"
]
},
{
@@ -396,7 +395,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
],
"text/plain": [
- " Baseline 11 Hz MWU \\\n",
- "power_score 0.03 ± 0.01 (480) 0.03 ± 0.01 (480) 144593.00, 0.000 \n",
- "freq_score 0.06 ± 0.01 (480) 0.06 ± 0.01 (480) 150719.00, 0.000 \n",
+ " Baseline I Combined MWU \\\n",
+ "power_score -0.03 ± 0.01 (208) 0.03 ± 0.01 (480) 67804.00, 0.000 \n",
+ "freq_score -0.01 ± 0.01 (208) 0.06 ± 0.01 (480) 72140.00, 0.000 \n",
"\n",
" PRS \n",
- "power_score 0.05, 0.000 \n",
- "freq_score 0.12, 0.000 "
+ "power_score 0.13, 0.000 \n",
+ "freq_score 0.17, 0.000 "
]
},
- "execution_count": 34,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -1003,28 +1005,16 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 23,
"metadata": {},
- "outputs": [
- {
- "ename": "AttributeError",
- "evalue": "'Project' object has no attribute 'get_action'",
- "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[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"lfp_speed\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;31mAttributeError\u001b[0m: 'Project' object has no attribute 'get_action'"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "action = project.g_action(\"lfp_speed\")"
+ "action = project.actions[\"lfp_speed\"]"
]
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
@@ -1046,30 +1036,11 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
- "pnnmec.registration.store_notebook(action, \"20_power_spectrum_density.ipynb\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING: Code repo dirty. Please commit your changes.\n",
- "WARNING: Data repo dirty. Please commit your changes.\n"
- ]
- }
- ],
- "source": [
- "action.modules[\"code_version\"] = vc.create_code_version_module()\n",
- "action.modules[\"data_version\"] = vc.create_data_version_module(project_path)"
+ "septum_mec.analysis.registration.store_notebook(action, \"20_lfp_speed.ipynb\")"
]
},
{
diff --git a/actions/lfp_speed/data/figures/frequency_score_11.svg b/actions/lfp_speed/data/figures/frequency_score_11.svg
index a8f23fa3b..853a1debb 100644
--- a/actions/lfp_speed/data/figures/frequency_score_11.svg
+++ b/actions/lfp_speed/data/figures/frequency_score_11.svg
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index 90472d8ea..515b1bb3f 100644
--- a/actions/lfp_speed/data/figures/frequency_score_30.svg
+++ b/actions/lfp_speed/data/figures/frequency_score_30.svg
@@ -1,3 +1,3 @@
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index f34d4732f..f12a8459e 100644
--- a/actions/lfp_speed/data/figures/frequency_score_combined.svg
+++ b/actions/lfp_speed/data/figures/frequency_score_combined.svg
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diff --git a/actions/lfp_speed/data/figures/lfp_speed_freq_11.png b/actions/lfp_speed/data/figures/lfp_speed_freq_11.png
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index 1b1cdc5c8..88eca0dab 100644
--- a/actions/lfp_speed/data/figures/lfp_speed_freq_11.svg
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index 4ca8f89b9..5224865cd 100644
--- a/actions/lfp_speed/data/figures/lfp_speed_freq_30.svg
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diff --git a/actions/lfp_speed/data/figures/lfp_speed_power_11.svg b/actions/lfp_speed/data/figures/lfp_speed_power_11.svg
index f356e00c2..6b4c4a83c 100644
--- a/actions/lfp_speed/data/figures/lfp_speed_power_11.svg
+++ b/actions/lfp_speed/data/figures/lfp_speed_power_11.svg
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
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-size 25503
+oid sha256:7110ba67106020ce567a4a29dcf11988735aa3993b0e17e9d4fa87fdc033a3be
+size 25512
diff --git a/actions/lfp_speed/data/figures/lfp_speed_power_30.png b/actions/lfp_speed/data/figures/lfp_speed_power_30.png
index e224cfbb5..868a69f77 100644
Binary files a/actions/lfp_speed/data/figures/lfp_speed_power_30.png and b/actions/lfp_speed/data/figures/lfp_speed_power_30.png differ
diff --git a/actions/lfp_speed/data/figures/lfp_speed_power_30.svg b/actions/lfp_speed/data/figures/lfp_speed_power_30.svg
index 4d27d49d0..016694b1f 100644
--- a/actions/lfp_speed/data/figures/lfp_speed_power_30.svg
+++ b/actions/lfp_speed/data/figures/lfp_speed_power_30.svg
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
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-size 25930
+oid sha256:c338967b484e459d7789d793d66018a64d7dee513f23a310bb78bf2ff7af7283
+size 25925
diff --git a/actions/lfp_speed/data/figures/lfp_speed_power_combined.png b/actions/lfp_speed/data/figures/lfp_speed_power_combined.png
index 7a09792b1..7b8aa4a85 100644
Binary files a/actions/lfp_speed/data/figures/lfp_speed_power_combined.png and b/actions/lfp_speed/data/figures/lfp_speed_power_combined.png differ
diff --git a/actions/lfp_speed/data/figures/lfp_speed_power_combined.svg b/actions/lfp_speed/data/figures/lfp_speed_power_combined.svg
index de7d57bdc..822923f2a 100644
--- a/actions/lfp_speed/data/figures/lfp_speed_power_combined.svg
+++ b/actions/lfp_speed/data/figures/lfp_speed_power_combined.svg
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
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+size 31288
diff --git a/actions/lfp_speed/data/figures/speed.png b/actions/lfp_speed/data/figures/speed.png
index 8a40fca00..8f14cc59e 100644
Binary files a/actions/lfp_speed/data/figures/speed.png and b/actions/lfp_speed/data/figures/speed.png differ
diff --git a/actions/lfp_speed/data/figures/speed.svg b/actions/lfp_speed/data/figures/speed.svg
index 6cff19606..18a837f02 100644
--- a/actions/lfp_speed/data/figures/speed.svg
+++ b/actions/lfp_speed/data/figures/speed.svg
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
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-size 32145
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+size 32845
diff --git a/actions/lfp_speed/data/statistics/power_freq_score_summary_combined.csv b/actions/lfp_speed/data/statistics/power_freq_score_summary_combined.csv
index 17afdc041..3da3def6a 100644
--- a/actions/lfp_speed/data/statistics/power_freq_score_summary_combined.csv
+++ b/actions/lfp_speed/data/statistics/power_freq_score_summary_combined.csv
@@ -1,3 +1,3 @@
-,Baseline,11 Hz,MWU,PRS
-power_score,0.03 ± 0.01 (480),0.03 ± 0.01 (480),"144593.00, 0.000","0.05, 0.000"
-freq_score,0.06 ± 0.01 (480),0.06 ± 0.01 (480),"150719.00, 0.000","0.12, 0.000"
+,Baseline I,Combined,MWU,PRS
+power_score,0.16 ± 0.01 (192),0.03 ± 0.01 (480),"67804.00, 0.000","0.13, 0.000"
+freq_score,0.19 ± 0.01 (192),0.06 ± 0.01 (480),"72140.00, 0.000","0.17, 0.000"
diff --git a/actions/lfp_speed/data/statistics/power_freq_score_summary_combined.tex b/actions/lfp_speed/data/statistics/power_freq_score_summary_combined.tex
index 73908b655..db78de181 100644
--- a/actions/lfp_speed/data/statistics/power_freq_score_summary_combined.tex
+++ b/actions/lfp_speed/data/statistics/power_freq_score_summary_combined.tex
@@ -1,8 +1,8 @@
\begin{tabular}{lllll}
\toprule
-{} & Baseline & 11 Hz & MWU & PRS \\
+{} & Baseline I & Combined & MWU & PRS \\
\midrule
-power\_score & 0.03 ± 0.01 (480) & 0.03 ± 0.01 (480) & 144593.00, 0.000 & 0.05, 0.000 \\
-freq\_score & 0.06 ± 0.01 (480) & 0.06 ± 0.01 (480) & 150719.00, 0.000 & 0.12, 0.000 \\
+power\_score & 0.16 ± 0.01 (192) & 0.03 ± 0.01 (480) & 67804.00, 0.000 & 0.13, 0.000 \\
+freq\_score & 0.19 ± 0.01 (192) & 0.06 ± 0.01 (480) & 72140.00, 0.000 & 0.17, 0.000 \\
\bottomrule
\end{tabular}
diff --git a/actions/lfp_speed/data/statistics/power_freq_score_summary_i.csv b/actions/lfp_speed/data/statistics/power_freq_score_summary_i.csv
index 6a019bb4b..6a1b392c9 100644
--- a/actions/lfp_speed/data/statistics/power_freq_score_summary_i.csv
+++ b/actions/lfp_speed/data/statistics/power_freq_score_summary_i.csv
@@ -1,3 +1,3 @@
-,Baseline,11 Hz,MWU,PRS
-power_score,-0.03 ± 0.01 (208),-0.03 ± 0.01 (208),"32624.00, 0.000","0.18, 0.000"
-freq_score,-0.01 ± 0.01 (208),-0.01 ± 0.01 (208),"37221.00, 0.000","0.21, 0.000"
+,Baseline I,11 Hz,MWU,PRS
+power_score,0.16 ± 0.01 (192),-0.03 ± 0.01 (208),"32624.00, 0.000","0.18, 0.000"
+freq_score,0.19 ± 0.01 (192),-0.01 ± 0.01 (208),"37221.00, 0.000","0.21, 0.000"
diff --git a/actions/lfp_speed/data/statistics/power_freq_score_summary_i.tex b/actions/lfp_speed/data/statistics/power_freq_score_summary_i.tex
index ae1cd6d5d..4ced7c1d8 100644
--- a/actions/lfp_speed/data/statistics/power_freq_score_summary_i.tex
+++ b/actions/lfp_speed/data/statistics/power_freq_score_summary_i.tex
@@ -1,8 +1,8 @@
\begin{tabular}{lllll}
\toprule
-{} & Baseline & 11 Hz & MWU & PRS \\
+{} & Baseline I & 11 Hz & MWU & PRS \\
\midrule
-power\_score & -0.03 ± 0.01 (208) & -0.03 ± 0.01 (208) & 32624.00, 0.000 & 0.18, 0.000 \\
-freq\_score & -0.01 ± 0.01 (208) & -0.01 ± 0.01 (208) & 37221.00, 0.000 & 0.21, 0.000 \\
+power\_score & 0.16 ± 0.01 (192) & -0.03 ± 0.01 (208) & 32624.00, 0.000 & 0.18, 0.000 \\
+freq\_score & 0.19 ± 0.01 (192) & -0.01 ± 0.01 (208) & 37221.00, 0.000 & 0.21, 0.000 \\
\bottomrule
\end{tabular}
diff --git a/actions/lfp_speed/data/statistics/power_freq_score_summary_ii.csv b/actions/lfp_speed/data/statistics/power_freq_score_summary_ii.csv
index cb9da52e2..d5e7d3e07 100644
--- a/actions/lfp_speed/data/statistics/power_freq_score_summary_ii.csv
+++ b/actions/lfp_speed/data/statistics/power_freq_score_summary_ii.csv
@@ -1,3 +1,3 @@
-,Baseline,11 Hz,MWU,PRS
-power_score,0.04 ± 0.01 (136),0.04 ± 0.01 (136),"12586.00, 0.000","0.08, 0.000"
-freq_score,0.01 ± 0.01 (136),0.01 ± 0.01 (136),"16950.00, 0.000","0.22, 0.000"
+,Baseline II,30 Hz,MWU,PRS
+power_score,0.10 ± 0.01 (136),0.04 ± 0.01 (136),"12586.00, 0.000","0.08, 0.000"
+freq_score,0.22 ± 0.01 (136),0.01 ± 0.01 (136),"16950.00, 0.000","0.22, 0.000"
diff --git a/actions/lfp_speed/data/statistics/power_freq_score_summary_ii.tex b/actions/lfp_speed/data/statistics/power_freq_score_summary_ii.tex
index 76cba08b1..ed0c9c027 100644
--- a/actions/lfp_speed/data/statistics/power_freq_score_summary_ii.tex
+++ b/actions/lfp_speed/data/statistics/power_freq_score_summary_ii.tex
@@ -1,8 +1,8 @@
\begin{tabular}{lllll}
\toprule
-{} & Baseline & 11 Hz & MWU & PRS \\
+{} & Baseline II & 30 Hz & MWU & PRS \\
\midrule
-power\_score & 0.04 ± 0.01 (136) & 0.04 ± 0.01 (136) & 12586.00, 0.000 & 0.08, 0.000 \\
-freq\_score & 0.01 ± 0.01 (136) & 0.01 ± 0.01 (136) & 16950.00, 0.000 & 0.22, 0.000 \\
+power\_score & 0.10 ± 0.01 (136) & 0.04 ± 0.01 (136) & 12586.00, 0.000 & 0.08, 0.000 \\
+freq\_score & 0.22 ± 0.01 (136) & 0.01 ± 0.01 (136) & 16950.00, 0.000 & 0.22, 0.000 \\
\bottomrule
\end{tabular}