diff --git a/actions/stimulus-spike-lfp-response/data/10-calculate-stimulus-spike-lfp-response.ipynb b/actions/stimulus-spike-lfp-response/data/10-calculate-stimulus-spike-lfp-response.ipynb
index c1e4bbb2c..6bdbd8f08 100644
--- a/actions/stimulus-spike-lfp-response/data/10-calculate-stimulus-spike-lfp-response.ipynb
+++ b/actions/stimulus-spike-lfp-response/data/10-calculate-stimulus-spike-lfp-response.ipynb
@@ -19,7 +19,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "16:56:09 [I] klustakwik KlustaKwik2 version 0.2.6\n"
+ "10:25:26 [I] klustakwik KlustaKwik2 version 0.2.6\n"
]
}
],
@@ -68,7 +68,7 @@
"outputs": [],
"source": [
"output = pathlib.Path('output/stimulus-spike-lfp-response')\n",
- "(output / 'figures').mkdir(parents=True, exist_ok=True)"
+ "(output / 'data').mkdir(parents=True, exist_ok=True)"
]
},
{
@@ -122,7 +122,7 @@
" return np.where(sd == 0, 0, m / sd)\n",
"\n",
"\n",
- "def clean_lfp(anas, width=500, threshold=2):\n",
+ "def compute_clean_lfp(anas, width=500, threshold=2):\n",
" anas = np.array(anas)\n",
"\n",
" for ch in range(anas.shape[1]):\n",
@@ -148,7 +148,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -165,7 +165,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -191,7 +191,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -203,7 +203,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -217,7 +217,7 @@
" mask = (anas.times > t_start) & (anas.times < t_stop)\n",
" anas = np.array(anas)[mask,:]\n",
"\n",
- " cleaned_anas = zscore(clean_lfp(anas))\n",
+ " cleaned_anas = zscore(compute_clean_lfp(anas))\n",
" \n",
" cleaned_anas = neo.AnalogSignal(\n",
" signal=cleaned_anas * units, sampling_rate=sampling_rate, t_start=t_start\n",
@@ -234,7 +234,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -262,7 +262,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -272,7 +272,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@@ -281,7 +281,7 @@
" channel_group = row['channel_group']\n",
" unit_name = row['unit_name']\n",
" \n",
- " lfp = data_loader.lfp(action_id, channel_group)\n",
+ " lfp = data_loader.lfp(action_id, channel_group) # TODO consider choosing strongest stim response\n",
" \n",
" sptr = data_loader.spike_train(action_id, channel_group, unit_name)\n",
" \n",
@@ -292,7 +292,8 @@
" snls = signaltonoise(clean_lfp)\n",
" best_channel = np.argmax(snls)\n",
" snl = snls[best_channel]\n",
- " p_xy = p_xys[:,best_channel]\n",
+ " p_xy = p_xys[:,best_channel].magnitude\n",
+ " freq = freq.magnitude\n",
" \n",
" theta_f, theta_p_max = find_theta_peak(p_xy, freq, theta_band_f1, theta_band_f2)\n",
" \n",
@@ -337,13 +338,13 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e28818ca79be42abaf9c2b6106580c75",
+ "model_id": "564d1ef5339f46eebee42ec41cfcfe62",
"version_major": 2,
"version_minor": 0
},
@@ -361,6 +362,13 @@
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/signal/spectral.py:1578: RuntimeWarning: invalid value encountered in true_divide\n",
" Cxy = np.abs(Pxy)**2 / Pxx / Pyy\n"
]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
}
],
"source": [
@@ -370,12 +378,86 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "%debug"
+ "# plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "b20a9a26e0864f578e1a9aa0c021999b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(IntProgress(value=0, max=1298), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "coher, freqs = {}, {}\n",
+ "for i, row in tqdm(units.iterrows(), total=len(units)):\n",
+ " action_id = row['action']\n",
+ " channel_group = row['channel_group']\n",
+ " unit_name = row['unit_name']\n",
+ " \n",
+ " name = f'{action_id}_{channel_group}_{unit_name}'\n",
+ " \n",
+ " lfp = data_loader.lfp(action_id, channel_group) # TODO consider choosing strongest stim response\n",
+ " \n",
+ " sptr = data_loader.spike_train(action_id, channel_group, unit_name)\n",
+ " \n",
+ " lim = get_lim(action_id)\n",
+ "\n",
+ " p_xys, freq, clean_lfp = compute_spike_lfp(lfp, sptr, *lim, NFFT=NFFT)\n",
+ " \n",
+ " snls = signaltonoise(clean_lfp)\n",
+ " best_channel = np.argmax(snls)\n",
+ " snl = snls[best_channel]\n",
+ " p_xy = p_xys[:,best_channel].magnitude\n",
+ " freq = freq.magnitude\n",
+ " \n",
+ " coher.update({name: p_xy})\n",
+ " freqs.update({name: 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[0mcoher\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'coherence.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": [
+ "coher.to_feather(output / 'data' / 'coherence.feather')\n",
+ "freqs.to_feather(output / 'data' / 'freqs.feather')"
]
},
{
diff --git a/actions/stimulus-spike-lfp-response/data/20_stimulus-spike-lfp-response.html b/actions/stimulus-spike-lfp-response/data/20_stimulus-spike-lfp-response.html
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271 rows × 67 columns
+
+
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+
Out[19]:
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+
+
+
+
+
+
+
+
+ |
+ action |
+ baseline |
+ entity |
+ frequency |
+ i |
+ ii |
+ session |
+ stim_location |
+ stimulated |
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+ ... |
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+ stim_half_width |
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+ bs_ctrl |
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+
+
+
+ 33 |
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+
5 rows × 68 columns
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+
Store results in Expipe action¶
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Out[29]:
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+
['/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/data/freqs.feather',
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diff --git a/actions/stimulus-spike-lfp-response/data/20_stimulus-spike-lfp-response.ipynb b/actions/stimulus-spike-lfp-response/data/20_stimulus-spike-lfp-response.ipynb
new file mode 100644
index 000000000..e524ceaa1
--- /dev/null
+++ b/actions/stimulus-spike-lfp-response/data/20_stimulus-spike-lfp-response.ipynb
@@ -0,0 +1,2743 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "19:14:03 [I] klustakwik KlustaKwik2 version 0.2.6\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import expipe\n",
+ "import pathlib\n",
+ "import numpy as np\n",
+ "import spatial_maps.stats as stats\n",
+ "import septum_mec\n",
+ "import septum_mec.analysis.data_processing as dp\n",
+ "import septum_mec.analysis.registration\n",
+ "import head_direction.head as head\n",
+ "import spatial_maps as sp\n",
+ "import speed_cells.speed as spd\n",
+ "import re\n",
+ "import joblib\n",
+ "import multiprocessing\n",
+ "import shutil\n",
+ "import psutil\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib\n",
+ "import seaborn as sns\n",
+ "from distutils.dir_util import copy_tree\n",
+ "from neo import SpikeTrain\n",
+ "import scipy\n",
+ "\n",
+ "from tqdm import tqdm_notebook as tqdm\n",
+ "from tqdm._tqdm_notebook import tqdm_notebook\n",
+ "tqdm_notebook.pandas()\n",
+ "\n",
+ "from spike_statistics.core import permutation_resampling\n",
+ "\n",
+ "from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n",
+ "\n",
+ "from septum_mec.analysis.plotting import violinplot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "plt.rc('axes', titlesize=12)\n",
+ "plt.rcParams.update({\n",
+ " 'font.size': 12, \n",
+ " 'figure.figsize': (6, 4), \n",
+ " 'figure.dpi': 150\n",
+ "})\n",
+ "\n",
+ "output_path = pathlib.Path(\"output\") / \"stimulus-spike-lfp-response\"\n",
+ "(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
+ "(output_path / \"figures\").mkdir(exist_ok=True, parents=True)\n",
+ "output_path.mkdir(exist_ok=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_loader = dp.Data()\n",
+ "actions = data_loader.actions\n",
+ "project = data_loader.project"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "identification_action = actions['identify-neurons']\n",
+ "sessions = pd.read_csv(identification_action.data_path('sessions'))\n",
+ "units = pd.read_csv(identification_action.data_path('units'))\n",
+ "session_units = pd.merge(sessions, units, on='action')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lfp_action = actions['stimulus-spike-lfp-response']\n",
+ "lfp_results = pd.read_csv(lfp_action.data_path('results'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# lfp_results has old unit id's but correct on (action, unit_name, channel_group)\n",
+ "lfp_results = lfp_results.drop('unit_id', axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "statistics_action = actions['calculate-statistics']\n",
+ "shuffling = actions['shuffling']\n",
+ "\n",
+ "statistics_results = pd.read_csv(statistics_action.data_path('results'))\n",
+ "statistics_results = session_units.merge(statistics_results, how='left')\n",
+ "quantiles_95 = pd.read_csv(shuffling.data_path('quantiles_95'))\n",
+ "action_columns = ['action', 'channel_group', 'unit_name']\n",
+ "data = pd.merge(statistics_results, quantiles_95, on=action_columns, suffixes=(\"\", \"_threshold\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data['unit_day'] = data.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = data.merge(lfp_results, how='left')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "waveform_action = actions['waveform-analysis']\n",
+ "waveform_results = pd.read_csv(waveform_action.data_path('results')).drop('template', axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = data.merge(waveform_results, how='left')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']\n",
+ "labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n",
+ "queries = ['baseline and Hz11', 'frequency==11', 'baseline and Hz30', 'frequency==30']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data.bs = data.bs.astype(bool)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of gridcells 225\n"
+ ]
+ }
+ ],
+ "source": [
+ "grid_query = 'gridness > gridness_threshold and information_rate > information_rate_threshold'\n",
+ "sessions_above_threshold = data.query(grid_query)\n",
+ "print(\"Number of gridcells\", len(sessions_above_threshold))\n",
+ "# print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
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271 rows × 67 columns
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+ " action baseline entity frequency i ii session \\\n",
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+ "\n",
+ " stim_location stimulated tag ... stim_half_f1 stim_half_f2 \\\n",
+ "17 ms True stim ii ... 30.173035 30.443743 \n",
+ "19 ms True stim ii ... 30.128480 30.460705 \n",
+ "21 ms True stim ii ... 30.188571 30.437126 \n",
+ "29 ms True stim ii ... 30.155404 30.445467 \n",
+ "30 ms True stim ii ... 30.195374 30.437554 \n",
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+ "33 NaN False baseline i ... NaN NaN \n",
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+ "54 ms True stim i ... 10.995334 11.259054 \n",
+ "57 NaN False baseline ii ... NaN NaN \n",
+ "76 ms True stim ii ... 30.136513 30.450445 \n",
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+ "106 ms True stim i ... 10.998382 11.225055 \n",
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+ "1155 mecl True stim i ... 10.981120 11.242957 \n",
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+ "1255 ms True stim i ... 11.016969 11.215476 \n",
+ "1257 ms True stim i ... 11.023725 11.224735 \n",
+ "1263 ms True stim i ... 10.994052 11.225703 \n",
+ "1264 ms True stim i ... 11.000518 11.216176 \n",
+ "1268 ms True stim i ... 11.034662 11.197408 \n",
+ "1275 ms True stim i ... 11.016058 11.203307 \n",
+ "\n",
+ " stim_half_width stim_energy half_width peak_to_trough \\\n",
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+ "[271 rows x 67 columns]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gridcell_sessions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data.loc[:,'gridcell'] = False\n",
+ "data['gridcell'] = data.isin(gridcell_sessions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
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+ " 39 | \n",
+ " 1833-260619-1 | \n",
+ " True | \n",
+ " 1833 | \n",
+ " NaN | \n",
+ " True | \n",
+ " False | \n",
+ " 1 | \n",
+ " NaN | \n",
+ " False | \n",
+ " baseline i | \n",
+ " ... | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 0.284542 | \n",
+ " 0.644111 | \n",
+ " 17.471520 | \n",
+ " True | \n",
+ " NaN | \n",
+ " 1.0 | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 40 | \n",
+ " 1833-260619-1 | \n",
+ " True | \n",
+ " 1833 | \n",
+ " NaN | \n",
+ " True | \n",
+ " False | \n",
+ " 1 | \n",
+ " NaN | \n",
+ " False | \n",
+ " baseline i | \n",
+ " ... | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 0.259920 | \n",
+ " 0.581698 | \n",
+ " 5.891739 | \n",
+ " True | \n",
+ " NaN | \n",
+ " 1.0 | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 68 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " action baseline entity frequency i ii session \\\n",
+ "33 1833-260619-1 True 1833 NaN True False 1 \n",
+ "34 1833-260619-1 True 1833 NaN True False 1 \n",
+ "35 1833-260619-1 True 1833 NaN True False 1 \n",
+ "39 1833-260619-1 True 1833 NaN True False 1 \n",
+ "40 1833-260619-1 True 1833 NaN True False 1 \n",
+ "\n",
+ " stim_location stimulated tag ... stim_half_f2 stim_half_width \\\n",
+ "33 NaN False baseline i ... NaN NaN \n",
+ "34 NaN False baseline i ... NaN NaN \n",
+ "35 NaN False baseline i ... NaN NaN \n",
+ "39 NaN False baseline i ... NaN NaN \n",
+ "40 NaN False baseline i ... NaN NaN \n",
+ "\n",
+ " stim_energy half_width peak_to_trough average_firing_rate bs \\\n",
+ "33 NaN 0.272875 0.602667 5.945508 True \n",
+ "34 NaN 0.226452 0.274814 2.860048 False \n",
+ "35 NaN 0.247266 0.570104 3.365674 True \n",
+ "39 NaN 0.284542 0.644111 17.471520 True \n",
+ "40 NaN 0.259920 0.581698 5.891739 True \n",
+ "\n",
+ " bs_stim bs_ctrl gridcell \n",
+ "33 NaN 1.0 True \n",
+ "34 NaN 0.0 True \n",
+ "35 NaN 1.0 True \n",
+ "39 NaN 1.0 True \n",
+ "40 NaN 1.0 True \n",
+ "\n",
+ "[5 rows x 68 columns]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.query('baseline and Hz11 and gridcell').head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
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+ "