import numpy as np
import pandas as pd

Metadata file

The metadata file has information about each Patch-seq cell, including its different identifiers and assigned cell types.

metadata = pd.read_csv("downloads/20200711_patchseq_metadata_mouse.csv")
metadata.head()

	project	cell_specimen_id	cell_specimen_name	hemisphere	structure	donor_id	donor_name	biological_sex	age	ethnicity	...	apical_dendrite_status	neuron_reconstruction_type	cell_soma_normalized_depth	depth_from_pia_um	ephys_session_id	transcriptomics_sample_id	transcriptomics_batch	T-type Accession ID	T-type Label	MET-type Label
0	mIVSCC-MET	601506507	Vip-IRES-Cre;Ai14-331294.04.01.01	left	VISpm4	331294	Vip-IRES-Cre;Ai14-331294	M	P55	NaN	...	NaN	full	0.361015	NaN	601506492	PS0810_E1-50_S88	BT035	CS180626100018	Vip Gpc3 Slc18a3	Vip-MET-2
1	mIVSCC-MET	601790961	Sst-IRES-Cre;Ai14-331663.04.01.01	left	VISpm5	331663	Sst-IRES-Cre;Ai14-331663	F	P57	NaN	...	NaN	full	0.621829	NaN	601790945	PS0817_E1-50_S19	BT036	CS180626100041	Sst Tac2 Myh4	NaN
2	mIVSCC-MET	601803754	Sst-IRES-Cre;Ai14-331663.04.02.02	right	VISp2/3	331663	Sst-IRES-Cre;Ai14-331663	F	P57	NaN	...	NaN	full	0.164240	NaN	601803733	PS0817_E1-50_S25	BT036	CS180626100035	Sst Calb2 Pdlim5	Sst-MET-3
3	mIVSCC-MET	601808698	Sst-IRES-Cre;Ai14-331663.04.02.03	right	VISp5	331663	Sst-IRES-Cre;Ai14-331663	F	P57	NaN	...	NaN	full	0.483030	NaN	601808619	PS0817_E1-50_S26	BT036	CS180626100043	Sst Hpse Cbln4	Sst-MET-8
4	mIVSCC-MET	601810307	Sst-IRES-Cre;Ai14-331663.04.02.04	right	VISp2/3	331663	Sst-IRES-Cre;Ai14-331663	F	P57	NaN	...	NaN	full	0.295656	NaN	601810292	PS0817_E1-50_S27	BT036	CS180626100034	Sst Calb2 Necab1	Sst-MET-3

5 rows × 23 columns

File manifest

The file manifest contains URLs for the different data files associated with the Patch-seq cells that are located in various archives.

file_manifest = pd.read_csv("downloads/2020-07-08_mouse_file_manifest.csv")
file_manifest.head()

	file_id	file_name	file_size	sha256_checksum	md5_checksum	technique	file_type	archive	archive_uri	project	cell_specimen_id
0	601506507	601506507.marker	268	305938ce75d55c3f43fbbe2d1d57e203139fa0c357bd73...	ecf2285ccbf5274af7d6a31a855ce950	morphology	marker_swc	BIL	ftp://download.brainlib.org:8811/biccn/zeng/ps...	mIVSCC-MET	601506507.0
1	601506507	601506507_raw.swc	160503	6c82f5f974e4f6d2336a3606d4955823edd3c0173da60d...	7cc7ccdd627ef83fe54fcbf0feac79c2	morphology	raw_swc	BIL	ftp://download.brainlib.org:8811/biccn/zeng/ps...	mIVSCC-MET	601506507.0
2	601506507	601506507_transformed.swc	271764	bad13a99c8f7f8a375e43109d80fd23d7028555a7dc42b...	f6a365d86ff65370af26439f0f16caaf	morphology	transformed_swc	BIL	ftp://download.brainlib.org:8811/biccn/zeng/ps...	mIVSCC-MET	601506507.0
3	601506492	sub-599387254_ses-601506492_icephys.nwb	17881801	54003c5b599fa8081938713e8c7c3b69d1883b31b3ada1...	d854a21668f5f2229024d8cf0975de54	intracellular_electrophysiology	nwb	DANDI	https://girder.dandiarchive.org/api/v1/item/5e...	mIVSCC-MET	601506507.0
4	690250345	ar_687622505_STAR_Aligned.sortedByCoord.out.bam	106552715	015e535e886e4acbec4e37fd1db40576846ade5e1411bc...	bd83904f8ca2f61c7071becf0181ad9f	transcriptomics	bam_file	NEMO	http://data.nemoarchive.org/other/grant/AIBS_p...	mIVSCC-MET	601506507.0

Transcriptomic data

Here, we have already downloaded the (large) transcriptomic expression data file. In that file, expression is quantified as counts-per-million (cpm), and each row is a gene and each column is a cell.

gene_data = pd.read_csv(
    "downloads/20200513_Mouse_PatchSeq_Release_cpm.v2.csv",
    index_col=0,
)

gene_data

	PS0810_E1-50_S88	PS0817_E1-50_S19	PS0817_E1-50_S25	PS0817_E1-50_S26	PS0817_E1-50_S27	PS0817_E1-50_S28	PS0817_E1-50_S46	PS0817_E1-50_S52	PS0830_E1-50_S17	PS0830_E1-50_S19	...	SM-J39ZH_S561_E1-50	SM-J39ZH_S562_E1-50	SM-J39ZH_S565_E1-50	SM-J39ZH_S566_E1-50	SM-J39ZH_S571_E1-50	SM-J39ZH_S576_E1-50	SM-J3A1L_S592_E1-50	SM-J3A1L_S593_E1-50	SM-J3A1L_S603_E1-50	SM-J3A1L_S604_E1-50
0610005C13Rik	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	...	0.000000	0.0	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
0610006L08Rik	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	...	0.000000	0.0	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
0610007P14Rik	0.0	0.0	0.000000	152.239614	165.312294	58.189895	0.000000	0.000000	551.71563	109.361330	...	0.000000	0.0	32.223984	0.00000	24.467996	0.000000	51.366477	91.617703	82.706145	116.083588
0610009B22Rik	0.0	0.0	69.658825	0.000000	0.000000	0.000000	0.000000	57.335504	0.00000	78.641855	...	52.858114	0.0	27.620558	27.46475	40.197422	70.429453	0.000000	0.000000	0.000000	111.937746
0610009E02Rik	0.0	0.0	0.000000	0.000000	0.000000	1.662568	33.786897	0.000000	0.00000	0.000000	...	0.000000	0.0	20.715418	0.00000	1.747714	0.000000	0.000000	31.342899	0.000000	103.646061
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
n-R5s142	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	...	0.000000	0.0	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
n-R5s143	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	...	0.000000	0.0	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
n-R5s144	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	...	0.000000	0.0	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
n-R5s146	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	...	0.000000	0.0	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
n-R5s149	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	...	0.000000	0.0	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000

45768 rows × 4435 columns

Let's create a 2D projection of the data using the transcriptomic data to see the structure of the different subclasses and types. We will transform the data, then use the UMAP algorithm to create a nonlinear embedding of a subset of the differentially expressed genes.

import umap

marker_genes_for_umap = pd.read_csv("select_markers.csv", index_col=0)

embedding = umap.UMAP(n_neighbors=25).fit_transform(
    np.log2(gene_data.loc[marker_genes_for_umap["Gene"], :].values.T + 1)
)

import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline

plt.figure(figsize=(8, 8))
plt.scatter(*embedding.T, s=1, edgecolor="none")
sns.despine()

We can see where specific types fall in this embedding by using the metadata to identify specific cells.

# Identify the cells from a particular t-type (Lamp5 Plch2 Dock5)
my_ttype_metadata = metadata.loc[metadata["T-type Label"] == "Lamp5 Plch2 Dock5", :]

my_ttype_metadata

	project	cell_specimen_id	cell_specimen_name	hemisphere	structure	donor_id	donor_name	biological_sex	age	ethnicity	...	apical_dendrite_status	neuron_reconstruction_type	cell_soma_normalized_depth	depth_from_pia_um	ephys_session_id	transcriptomics_sample_id	transcriptomics_batch	T-type Accession ID	T-type Label	MET-type Label
128	mIVSCC-MET	640319052	Oxtr-T2A-Cre;Ai14-351472.04.01.02	left	VISp1	351472	Oxtr-T2A-Cre;Ai14-351472	F	P49	NaN	...	NaN	NaN	NaN	NaN	640318956	SM-GE65C_S142_E1-50	RSC-126	CS180626100005	Lamp5 Plch2 Dock5	NaN
140	mIVSCC-MET	642442513	Ndnf-IRES2-dgCre;Ai14-353735.03.02.01	right	VISp1	353735	Ndnf-IRES2-dgCre;Ai14-353735	M	P39	NaN	...	NaN	NaN	NaN	NaN	642442471	SM-GE65C_S188_E1-50	RSC-126	CS180626100005	Lamp5 Plch2 Dock5	NaN
143	mIVSCC-MET	642496703	Gad2-IRES-Cre;Ai14-352686.03.02.01	right	VISp1	352686	Gad2-IRES-Cre;Ai14-352686	M	P45	NaN	...	NaN	NaN	NaN	NaN	642496688	SM-GE65C_S189_E1-50	RSC-126	CS180626100005	Lamp5 Plch2 Dock5	NaN
153	mIVSCC-MET	642833509	Ndnf-IRES2-dgCre;Ai14-353737.04.02.01	right	VISp1	353737	Ndnf-IRES2-dgCre;Ai14-353737	M	P40	NaN	...	NaN	NaN	NaN	NaN	642833493	SM-GE652_S154_E1-50	RSC-127	CS180626100005	Lamp5 Plch2 Dock5	NaN
154	mIVSCC-MET	642842839	Ndnf-IRES2-dgCre;Ai14-353737.04.01.01	left	VISp2/3	353737	Ndnf-IRES2-dgCre;Ai14-353737	M	P40	NaN	...	NaN	NaN	NaN	NaN	642842794	SM-GE652_S164_E1-50	RSC-127	CS180626100005	Lamp5 Plch2 Dock5	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4104	mIVSCC-MET	891514933	Ndnf-IRES2-dgCre;Ai14-470196.04.01.02	left	VISp1	470196	Ndnf-IRES2-dgCre;Ai14-470196	F	P55	NaN	...	NaN	NaN	NaN	NaN	891514675	SM-GE4Y5_S168_E1-50	RSC-217	CS180626100005	Lamp5 Plch2 Dock5	NaN
4108	mIVSCC-MET	891652412	Ndnf-IRES2-dgCre;Ai14-470196.03.01.02	left	VISp1	470196	Ndnf-IRES2-dgCre;Ai14-470196	F	P55	NaN	...	NaN	NaN	NaN	NaN	891651975	SM-GE4Y5_S184_E1-50	RSC-217	CS180626100005	Lamp5 Plch2 Dock5	NaN
4109	mIVSCC-MET	891664096	Ndnf-IRES2-dgCre;Ai14-470196.05.01.01	left	VISp2/3	470196	Ndnf-IRES2-dgCre;Ai14-470196	F	P55	NaN	...	NaN	NaN	NaN	NaN	891663833	SM-GE4Y5_S179_E1-50	RSC-217	CS180626100005	Lamp5 Plch2 Dock5	NaN
4168	mIVSCC-MET	901179274	Gad2-IRES-Cre;Ai14-473098.03.01.02	left	VISp2/3	473098	Gad2-IRES-Cre;Ai14-473098	F	P56	NaN	...	NaN	NaN	NaN	NaN	901178941	SM-GE4WN_S087_E1-50	RSC-219	CS180626100005	Lamp5 Plch2 Dock5	NaN
4286	mIVSCC-MET	934799128	Oxtr-T2A-Cre;Ai14-484066.03.01.01	left	VISp2/3	484066	Oxtr-T2A-Cre;Ai14-484066	M	P55	NaN	...	NaN	NaN	NaN	NaN	934798929	SM-GE4Q2_S125_E1-50	RSC-226	CS180626100005	Lamp5 Plch2 Dock5	NaN

150 rows × 23 columns

my_ttype_mask = gene_data.columns.isin(my_ttype_metadata["transcriptomics_sample_id"].tolist())

plt.figure(figsize=(8, 8))
plt.scatter(*embedding.T, s=1, edgecolor="none")
plt.scatter(*embedding[my_ttype_mask, :].T, s=2, edgecolor="none")
sns.despine()

We can also look at how expression of a particular gene varies across types in this embedding.

plt.figure(figsize=(8, 8))
plt.scatter(
    *embedding.T,
    s=1,
    c=gene_data.loc["Npy", :].values,
    vmin=0,
    vmax=5e3,
    cmap="viridis",
    edgecolor="none"
)

<matplotlib.collections.PathCollection at 0x7f595f2d7850>

Finding cells with both electrophysiology and morphology data

All the cells in the public data set have both transcriptomic and electrophysiology data, but only some of them have morphological reconstructions.

my_ttype_with_recon_metadata = metadata.loc[
    (metadata["T-type Label"] == "Lamp5 Plch2 Dock5") &
    (metadata["neuron_reconstruction_type"].isin(["full"])),
    :]

my_ttype_with_recon_metadata.iloc[0, :]

project                                                  mIVSCC-MET
cell_specimen_id                                          645169930
cell_specimen_name            Ndnf-IRES2-dgCre;Ai14-355228.04.01.01
hemisphere                                                     left
structure                                                     VISp1
donor_id                                                     355228
donor_name                             Ndnf-IRES2-dgCre;Ai14-355228
biological_sex                                                    F
age                                                             P49
ethnicity                                                       NaN
medical_conditions                                              NaN
full_genotype                  Ndnf-IRES2-dgCre/wt;Ai14(RCL-tdT)/wt
dendrite_type                                                aspiny
apical_dendrite_status                                          NaN
neuron_reconstruction_type                                     full
cell_soma_normalized_depth                                0.0658506
depth_from_pia_um                                               NaN
ephys_session_id                                          645169915
transcriptomics_sample_id                       SM-GE643_S145_E1-50
transcriptomics_batch                                       RSC-131
T-type Accession ID                                  CS180626100005
T-type Label                                      Lamp5 Plch2 Dock5
MET-type Label                                          Lamp5-MET-1
Name: 226, dtype: object

Electrophysiology

Let's get the NWB file that has the electrophysiology data for this cell and use the IPFX library to process it.

my_specimen_id = my_ttype_with_recon_metadata.iloc[0, :]["cell_specimen_id"]

nwb_urls = file_manifest.loc[
    (file_manifest["cell_specimen_id"] == float(my_specimen_id)) &
    (file_manifest["file_type"] == "nwb"),
    :
]

nwb_urls["archive_uri"].values[0]

'https://girder.dandiarchive.org/api/v1/item/5edb30e22dace54b6f9b4051/download'

!dandi download https://girder.dandiarchive.org/api/v1/item/5edb30e22dace54b6f9b4051/download

2021-02-24 10:28:34,524 [ WARNING] A newer version (0.10.0) of dandi/dandi-cli is available. You are using 0.9.0
PATH                                    SIZE     DONE           DONE% CHECKSUM STATUS          MESSAGE
sub-643830424_ses-645169915_icephys.nwb 15.1 MB  15.1 MB         100%    ok    done                   
Summary:                                15.1 MB  15.1 MB                       1 done                 
                                                 100.00%                                              

nwb_path = nwb_urls["file_name"].values[0]
nwb_path

'sub-643830424_ses-645169915_icephys.nwb'

from ipfx.dataset.create import create_ephys_data_set
from ipfx.data_set_features import extract_data_set_features
from ipfx.utilities import drop_failed_sweeps

data_set = create_ephys_data_set(nwb_file=nwb_path)
drop_failed_sweeps(data_set)
cell_features, sweep_features, cell_record, sweep_records, _, _ = \
    extract_data_set_features(data_set, subthresh_min_amp=-100.0)

/local1/anaconda3/envs/ps-demo/lib/python3.7/site-packages/hdmf/spec/namespace.py:485: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.1.3 because version 1.3.0 is already loaded.
  % (ns['name'], ns['version'], self.__namespaces.get(ns['name'])['version']))
2021-02-24 10:29:26,251 root         INFO     sweep 23 skipping vrest criteria on ramp
2021-02-24 10:29:26,253 root         INFO     sweep 24 skipping vrest criteria on ramp
2021-02-24 10:29:26,254 root         INFO       
2021-02-24 10:29:26,255 root         INFO     ***** ***** ***** Analyzing cell features: ***** ***** *****
2021-02-24 10:29:26,257 root         INFO     ------------------------------------------------------------
2021-02-24 10:29:26,257 root         INFO       
2021-02-24 10:29:26,258 root         INFO       
2021-02-24 10:29:26,259 root         INFO     ***** ***** ***** Long Squares: ***** ***** *****
2021-02-24 10:29:26,259 root         INFO     -------------------------------------------------
2021-02-24 10:29:26,260 root         INFO       
2021-02-24 10:29:27,103 root         INFO       
2021-02-24 10:29:27,104 root         INFO     ***** ***** ***** Short Squares: ***** ***** *****
2021-02-24 10:29:27,106 root         INFO     --------------------------------------------------
2021-02-24 10:29:27,107 root         INFO       
2021-02-24 10:29:27,499 root         INFO       
2021-02-24 10:29:27,499 root         INFO     ***** ***** ***** Ramps: ***** ***** *****
2021-02-24 10:29:27,500 root         INFO     ------------------------------------------
2021-02-24 10:29:27,501 root         INFO       
2021-02-24 10:29:28,013 root         INFO       
2021-02-24 10:29:28,014 root         INFO     ***** ***** ***** Analyzing sweep features: ***** ***** *****
2021-02-24 10:29:28,014 root         INFO     -------------------------------------------------------------
2021-02-24 10:29:28,016 root         INFO       

cell_features.keys()

dict_keys(['long_squares', 'short_squares', 'ramps'])

cell_features["long_squares"].keys()

dict_keys(['sweeps', 'v_baseline', 'rheobase_i', 'rheobase_sweep', 'spiking_sweeps', 'fi_fit_slope', 'hero_sweep', 'sag', 'vm_for_sag', 'subthreshold_sweeps', 'subthreshold_membrane_property_sweeps', 'input_resistance', 'tau'])

cell_features["long_squares"]["rheobase_sweep"]

{'avg_rate': 5.000000000000001,
 'peak_deflect': (19.000002, 93400),
 'stim_amp': 260.0,
 'v_baseline': -69.02718353271484,
 'sag': -9.049047470092773,
 'adapt': -0.08286998815056433,
 'latency': 0.5081,
 'isi_cv': 0.3435623028567409,
 'mean_isi': 0.11080499999999999,
 'median_isi': 0.09931000000000001,
 'first_isi': 0.1734199999999999,
 'index': 4,
 'spikes': [{'threshold_index': 76405,
   'clipped': False,
   'threshold_t': 1.0081,
   'threshold_v': -30.875001907348633,
   'threshold_i': 260,
   'peak_index': 76432,
   'peak_t': 1.00864,
   'peak_v': 18.875001907348633,
   'peak_i': 260,
   'trough_index': 76720,
   'trough_t': 1.0144,
   'trough_v': -46.937503814697266,
   'trough_i': 260,
   'upstroke_index': 76420,
   'upstroke': 195.88039484902075,
   'upstroke_t': 1.0084,
   'upstroke_v': -5.031250476837158,
   'downstroke_index': 76452,
   'downstroke': -60.155100900715404,
   'downstroke_t': 1.00904,
   'downstroke_v': 2.718750238418579,
   'isi_type': 'detour',
   'fast_trough_index': 76582,
   'fast_trough_t': 1.0116399999999999,
   'fast_trough_v': -45.78125,
   'fast_trough_i': 260,
   'adp_index': nan,
   'adp_t': nan,
   'adp_v': nan,
   'adp_i': nan,
   'slow_trough_index': 76720.0,
   'slow_trough_t': 1.0144,
   'slow_trough_v': -46.937503814697266,
   'slow_trough_i': 260.0,
   'width': 0.0009799999999999809,
   'upstroke_downstroke_ratio': 3.2562557774163956},
  {'threshold_index': 85076,
   'clipped': False,
   'threshold_t': 1.18152,
   'threshold_v': -31.25,
   'threshold_i': 260,
   'peak_index': 85106,
   'peak_t': 1.18212,
   'peak_v': 17.562501907348633,
   'peak_i': 260,
   'trough_index': 85437,
   'trough_t': 1.18874,
   'trough_v': -47.093753814697266,
   'trough_i': 260,
   'upstroke_index': 85094,
   'upstroke': 165.72213467870418,
   'upstroke_t': 1.18188,
   'upstroke_v': -2.34375,
   'downstroke_index': 85141,
   'downstroke': -49.95692608657477,
   'downstroke_t': 1.18282,
   'downstroke_v': -10.46875,
   'isi_type': 'detour',
   'fast_trough_index': 85280,
   'fast_trough_t': 1.1856,
   'fast_trough_v': -45.500003814697266,
   'fast_trough_i': 260,
   'adp_index': nan,
   'adp_t': nan,
   'adp_v': nan,
   'adp_i': nan,
   'slow_trough_index': 85437.0,
   'slow_trough_t': 1.18874,
   'slow_trough_v': -47.093753814697266,
   'slow_trough_i': 260.0,
   'width': 0.001100000000000101,
   'upstroke_downstroke_ratio': 3.3173004758441236},
  {'threshold_index': 89813,
   'clipped': False,
   'threshold_t': 1.27626,
   'threshold_v': -30.250001907348633,
   'threshold_i': 260,
   'peak_index': 89840,
   'peak_t': 1.2768,
   'peak_v': 18.5,
   'peak_i': 260,
   'trough_index': 90198,
   'trough_t': 1.28396,
   'trough_v': -46.562503814697266,
   'trough_i': 260,
   'upstroke_index': 89829,
   'upstroke': 182.5993702185684,
   'upstroke_t': 1.27658,
   'upstroke_v': -2.031250238418579,
   'downstroke_index': 89873,
   'downstroke': -51.58705426100653,
   'downstroke_t': 1.27746,
   'downstroke_v': -8.687500953674316,
   'isi_type': 'detour',
   'fast_trough_index': 90006,
   'fast_trough_t': 1.28012,
   'fast_trough_v': -44.781253814697266,
   'fast_trough_i': 260,
   'adp_index': nan,
   'adp_t': nan,
   'adp_v': nan,
   'adp_i': nan,
   'slow_trough_index': 90198.0,
   'slow_trough_t': 1.28396,
   'slow_trough_v': -46.562503814697266,
   'slow_trough_i': 260.0,
   'width': 0.001060000000000061,
   'upstroke_downstroke_ratio': 3.539635531323429},
  {'threshold_index': 93372,
   'clipped': False,
   'threshold_t': 1.34744,
   'threshold_v': -31.65625,
   'threshold_i': 260,
   'peak_index': 93400,
   'peak_t': 1.348,
   'peak_v': 19.000001907348633,
   'peak_i': 260,
   'trough_index': 93726,
   'trough_t': 1.35452,
   'trough_v': -47.093753814697266,
   'trough_i': 260,
   'upstroke_index': 93388,
   'upstroke': 192.6202654248003,
   'upstroke_t': 1.34776,
   'upstroke_v': -3.781250238418579,
   'downstroke_index': 93420,
   'downstroke': -58.92077508269481,
   'downstroke_t': 1.3484,
   'downstroke_v': 2.875000238418579,
   'isi_type': 'detour',
   'fast_trough_index': 93587,
   'fast_trough_t': 1.35174,
   'fast_trough_v': -45.9375,
   'fast_trough_i': 260,
   'adp_index': nan,
   'adp_t': nan,
   'adp_v': nan,
   'adp_i': nan,
   'slow_trough_index': 93726.0,
   'slow_trough_t': 1.35452,
   'slow_trough_v': -47.093753814697266,
   'slow_trough_i': 260.0,
   'width': 0.001000000000000112,
   'upstroke_downstroke_ratio': 3.269140047028563},
  {'threshold_index': 98566,
   'clipped': False,
   'threshold_t': 1.45132,
   'threshold_v': -28.781251907348633,
   'threshold_i': 260,
   'peak_index': 98593,
   'peak_t': 1.45186,
   'peak_v': 17.65625,
   'peak_i': 260,
   'trough_index': 98976,
   'trough_t': 1.45952,
   'trough_v': -46.65625,
   'trough_i': 260,
   'upstroke_index': 98581,
   'upstroke': 171.18150678617957,
   'upstroke_t': 1.45162,
   'upstroke_v': -2.500000238418579,
   'downstroke_index': 98619,
   'downstroke': -53.039970180617686,
   'downstroke_t': 1.45238,
   'downstroke_v': -2.65625,
   'isi_type': 'detour',
   'fast_trough_index': 98775,
   'fast_trough_t': 1.4555,
   'fast_trough_v': -44.625003814697266,
   'fast_trough_i': 260,
   'adp_index': nan,
   'adp_t': nan,
   'adp_v': nan,
   'adp_i': nan,
   'slow_trough_index': nan,
   'slow_trough_t': nan,
   'slow_trough_v': nan,
   'slow_trough_i': nan,
   'width': 0.0010599999999998388,
   'upstroke_downstroke_ratio': 3.227405788563851}],
 'sweep_number': 10}

cell_features["long_squares"]["rheobase_sweep"]["latency"]

0.5081

We can also plot the particular sweep that we've been seeing the features of.

swp = data_set.sweep(cell_features["long_squares"]["rheobase_sweep"]["sweep_number"])

plt.figure(figsize=(10, 6))

plt.plot(swp.t, swp.v)
plt.xlabel("time (s)", fontsize=16)
plt.ylabel("membrane potential (mV)", fontsize=16)
sns.despine()

Morphology

Now we'll get the SWC file that has the morphological reconstruction of this cell and use the neuron_morphology library to process it.

swc_urls = file_manifest.loc[
    (file_manifest["cell_specimen_id"] == float(my_specimen_id)) &
    (file_manifest["file_type"] == "transformed_swc"),
    :
]

swc_urls["archive_uri"].values[0]

'ftp://download.brainlib.org:8811/biccn/zeng/pseq/morph/200526/645169930_transformed.swc'

!wget ftp://download.brainlib.org:8811/biccn/zeng/pseq/morph/200526/645169930_transformed.swc

--2021-02-24 10:31:25--  ftp://download.brainlib.org:8811/biccn/zeng/pseq/morph/200526/645169930_transformed.swc
           => ‘645169930_transformed.swc’
Resolving download.brainlib.org (download.brainlib.org)... 192.231.243.62, 192.231.243.61
Connecting to download.brainlib.org (download.brainlib.org)|192.231.243.62|:8811... connected.
Logging in as anonymous ... Logged in!
==> SYST ... done.    ==> PWD ... done.
==> TYPE I ... done.  ==> CWD (1) /bil/data/biccn/zeng/pseq/morph/200526 ... done.
==> SIZE 645169930_transformed.swc ... 949094
==> PASV ... done.    ==> RETR 645169930_transformed.swc ... done.
Length: 949094 (927K) (unauthoritative)

100%[======================================>] 949,094     1.89MB/s   in 0.5s   

2021-02-24 10:31:27 (1.89 MB/s) - ‘645169930_transformed.swc’ saved [949094]

swc_path = swc_urls["file_name"].values[0]
swc_path

'645169930_transformed.swc'

from neuron_morphology.swc_io import morphology_from_swc
from neuron_morphology.constants import AXON, BASAL_DENDRITE, SOMA

morph = morphology_from_swc(swc_path)

We can make a quick plot of the reconstruction.

axon_nodes = morph.get_node_by_types([AXON])
dend_nodes = morph.get_node_by_types([BASAL_DENDRITE])
soma_nodes = morph.get_node_by_types([SOMA])

axon_x = [node['x'] for node in axon_nodes]
axon_y = [node['y'] for node in axon_nodes]

dend_x = [node['x'] for node in dend_nodes]
dend_y = [node['y'] for node in dend_nodes]

soma_x = [node['x'] for node in soma_nodes]
soma_y = [node['y'] for node in soma_nodes]


plt.figure(figsize=(10, 10))

plt.scatter(axon_x, axon_y, s=1, edgecolor="none")
plt.scatter(dend_x, dend_y, s=1, edgecolor="none")
plt.scatter(soma_x, soma_y, s=20, c="black", edgecolor="none")


plt.gca().set(xticks=[], yticks=[])
plt.gca().set_aspect("equal")
sns.despine(left=True, bottom=True)

Then we can calculate a set of morphological features.

from neuron_morphology.feature_extractor.data import Data
from neuron_morphology.feature_extractor.feature_extractor import FeatureExtractor
from neuron_morphology.features.default_features import default_features

fe = FeatureExtractor()
fe.register_features(default_features)
feature_extraction_run = fe.extract(Data(morph))

2021-02-24 10:32:28,873 root         INFO     skipping mark (validation failed): type
2021-02-24 10:32:28,892 root         INFO     skipping mark (validation failed): type
2021-02-24 10:32:28,899 root         INFO     skipping mark (validation failed): type
2021-02-24 10:32:28,901 root         INFO     selected marks: <bound method FeatureExtractionRun.select_marks of <neuron_morphology.feature_extractor.feature_extraction_run.FeatureExtractionRun object at 0x7f597eb37450>>
2021-02-24 10:32:28,903 root         INFO     skipping feature: apical_dendrite.tip.dimension. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,904 root         INFO     skipping feature: apical_dendrite.bifurcation.dimension. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,905 root         INFO     skipping feature: apical_dendrite.compartment.dimension. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,906 root         INFO     skipping feature: apical_dendrite.node.dimension. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,907 root         INFO     skipping feature: apical_dendrite.num_nodes. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,908 root         INFO     skipping feature: apical_dendrite.num_branches. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,910 root         INFO     skipping feature: apical_dendrite.num_tips. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,911 root         INFO     skipping feature: apical_dendrite.mean_fragmentation. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,912 root         INFO     skipping feature: apical_dendrite.max_branch_order. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,913 root         INFO     skipping feature: apical_dendrite.num_outer_bifurcations. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,914 root         INFO     skipping feature: apical_dendrite.mean_bifurcation_angle_local. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,915 root         INFO     skipping feature: apical_dendrite.mean_bifurcation_angle_remote. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,916 root         INFO     skipping feature: apical_dendrite.total_length. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,916 root         INFO     skipping feature: apical_dendrite.total_surface_area. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,918 root         INFO     skipping feature: apical_dendrite.total_volume. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,922 root         INFO     skipping feature: apical_dendrite.mean_diameter. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,923 root         INFO     skipping feature: apical_dendrite.mean_parent_daughter_ratio. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,924 root         INFO     skipping feature: apical_dendrite.max_euclidean_distance. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,925 root         INFO     skipping feature: apical_dendrite.dendrite.overlap. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,927 root         INFO     skipping feature: apical_dendrite.apical_dendrite.overlap. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,928 root         INFO     skipping feature: basal_dendrite.apical_dendrite.overlap. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,929 root         INFO     skipping feature: dendrite.apical_dendrite.overlap. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,930 root         INFO     skipping feature: axon.apical_dendrite.overlap. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,931 root         INFO     skipping feature: apical_dendrite.basal_dendrite.overlap. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,932 root         INFO     skipping feature: apical_dendrite.axon.overlap. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,933 root         INFO     skipping feature: apical_dendrite.tip.moments. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,934 root         INFO     skipping feature: apical_dendrite.bifurcation.moments. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,935 root         INFO     skipping feature: apical_dendrite.compartment.moments. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,935 root         INFO     skipping feature: apical_dendrite.node.moments. Found extra marks: ['RequiresApical']
2021-02-24 10:32:28,936 root         INFO     skipping feature: all_neurites.normalized_depth_histogram. Found extra marks: ['RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,937 root         INFO     skipping feature: axon.normalized_depth_histogram. Found extra marks: ['RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,938 root         INFO     skipping feature: basal_dendrite.normalized_depth_histogram. Found extra marks: ['RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,939 root         INFO     skipping feature: apical_dendrite.normalized_depth_histogram. Found extra marks: ['RequiresApical', 'RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,940 root         INFO     skipping feature: dendrite.normalized_depth_histogram. Found extra marks: ['RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,941 root         INFO     skipping feature: apical_dendrite.dendrite.earth_movers_distance. Found extra marks: ['RequiresApical', 'RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,942 root         INFO     skipping feature: basal_dendrite.dendrite.earth_movers_distance. Found extra marks: ['RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,943 root         INFO     skipping feature: dendrite.dendrite.earth_movers_distance. Found extra marks: ['RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,943 root         INFO     skipping feature: axon.dendrite.earth_movers_distance. Found extra marks: ['RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,944 root         INFO     skipping feature: apical_dendrite.apical_dendrite.earth_movers_distance. Found extra marks: ['RequiresApical', 'RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,945 root         INFO     skipping feature: basal_dendrite.apical_dendrite.earth_movers_distance. Found extra marks: ['RequiresApical', 'RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,946 root         INFO     skipping feature: dendrite.apical_dendrite.earth_movers_distance. Found extra marks: ['RequiresApical', 'RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,947 root         INFO     skipping feature: axon.apical_dendrite.earth_movers_distance. Found extra marks: ['RequiresApical', 'RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,948 root         INFO     skipping feature: apical_dendrite.basal_dendrite.earth_movers_distance. Found extra marks: ['RequiresApical', 'RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,948 root         INFO     skipping feature: basal_dendrite.basal_dendrite.earth_movers_distance. Found extra marks: ['RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,949 root         INFO     skipping feature: dendrite.basal_dendrite.earth_movers_distance. Found extra marks: ['RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,950 root         INFO     skipping feature: axon.basal_dendrite.earth_movers_distance. Found extra marks: ['RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,954 root         INFO     skipping feature: apical_dendrite.axon.earth_movers_distance. Found extra marks: ['RequiresApical', 'RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,955 root         INFO     skipping feature: basal_dendrite.axon.earth_movers_distance. Found extra marks: ['RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,958 root         INFO     skipping feature: dendrite.axon.earth_movers_distance. Found extra marks: ['RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,959 root         INFO     skipping feature: axon.axon.earth_movers_distance. Found extra marks: ['RequiresReferenceLayerDepths', 'RequiresLayeredPointDepths']
2021-02-24 10:32:28,960 root         INFO     selected features: ['all_neurites.tip.dimension', 'axon.tip.dimension', 'basal_dendrite.tip.dimension', 'dendrite.tip.dimension', 'all_neurites.bifurcation.dimension', 'axon.bifurcation.dimension', 'basal_dendrite.bifurcation.dimension', 'dendrite.bifurcation.dimension', 'all_neurites.compartment.dimension', 'axon.compartment.dimension', 'basal_dendrite.compartment.dimension', 'dendrite.compartment.dimension', 'all_neurites.node.dimension', 'axon.node.dimension', 'basal_dendrite.node.dimension', 'dendrite.node.dimension', 'all_neurites.num_nodes', 'axon.num_nodes', 'basal_dendrite.num_nodes', 'dendrite.num_nodes', 'all_neurites.num_branches', 'axon.num_branches', 'basal_dendrite.num_branches', 'dendrite.num_branches', 'all_neurites.num_tips', 'axon.num_tips', 'basal_dendrite.num_tips', 'dendrite.num_tips', 'all_neurites.mean_fragmentation', 'axon.mean_fragmentation', 'basal_dendrite.mean_fragmentation', 'dendrite.mean_fragmentation', 'all_neurites.max_branch_order', 'axon.max_branch_order', 'basal_dendrite.max_branch_order', 'dendrite.max_branch_order', 'all_neurites.num_outer_bifurcations', 'axon.num_outer_bifurcations', 'basal_dendrite.num_outer_bifurcations', 'dendrite.num_outer_bifurcations', 'all_neurites.mean_bifurcation_angle_local', 'axon.mean_bifurcation_angle_local', 'basal_dendrite.mean_bifurcation_angle_local', 'dendrite.mean_bifurcation_angle_local', 'all_neurites.mean_bifurcation_angle_remote', 'axon.mean_bifurcation_angle_remote', 'basal_dendrite.mean_bifurcation_angle_remote', 'dendrite.mean_bifurcation_angle_remote', 'all_neurites.total_length', 'axon.total_length', 'basal_dendrite.total_length', 'dendrite.total_length', 'all_neurites.total_surface_area', 'axon.total_surface_area', 'basal_dendrite.total_surface_area', 'dendrite.total_surface_area', 'all_neurites.total_volume', 'axon.total_volume', 'basal_dendrite.total_volume', 'dendrite.total_volume', 'all_neurites.mean_diameter', 'axon.mean_diameter', 'basal_dendrite.mean_diameter', 'dendrite.mean_diameter', 'all_neurites.mean_parent_daughter_ratio', 'axon.mean_parent_daughter_ratio', 'basal_dendrite.mean_parent_daughter_ratio', 'dendrite.mean_parent_daughter_ratio', 'all_neurites.max_euclidean_distance', 'axon.max_euclidean_distance', 'basal_dendrite.max_euclidean_distance', 'dendrite.max_euclidean_distance', 'max_path_distance', 'early_branch_path', 'mean_contraction', 'basal_dendrite.dendrite.overlap', 'dendrite.dendrite.overlap', 'axon.dendrite.overlap', 'basal_dendrite.basal_dendrite.overlap', 'dendrite.basal_dendrite.overlap', 'axon.basal_dendrite.overlap', 'basal_dendrite.axon.overlap', 'dendrite.axon.overlap', 'axon.axon.overlap', 'all_neurites.tip.moments', 'axon.tip.moments', 'basal_dendrite.tip.moments', 'dendrite.tip.moments', 'all_neurites.bifurcation.moments', 'axon.bifurcation.moments', 'basal_dendrite.bifurcation.moments', 'dendrite.bifurcation.moments', 'all_neurites.compartment.moments', 'axon.compartment.moments', 'basal_dendrite.compartment.moments', 'dendrite.compartment.moments', 'all_neurites.node.moments', 'axon.node.moments', 'basal_dendrite.node.moments', 'dendrite.node.moments']

feature_extraction_run.results

{'all_neurites.tip.dimension': {'width': 749.7866746783784,
  'height': 266.8131293186376,
  'depth': 348.4693544674146,
  'min_xyz': array([-346.43802996, -185.410335  ,  -38.56081   ]),
  'max_xyz': array([403.34864472,  81.40279432, 309.90854446]),
  'bias_xyz': array([ 56.91061475, 104.00754068, 271.34773446])},
 'axon.tip.dimension': {'width': 749.7866746783784,
  'height': 266.8131293186376,
  'depth': 348.4693544674146,
  'min_xyz': array([-346.43802996, -185.410335  ,  -38.56081   ]),
  'max_xyz': array([403.34864472,  81.40279432, 309.90854446]),
  'bias_xyz': array([ 56.91061475, 104.00754068, 271.34773446])},
 'basal_dendrite.tip.dimension': {'width': 160.04351419243892,
  'height': 170.45021761579912,
  'depth': 124.17364049387336,
  'min_xyz': array([ -69.90063599, -115.67997622,  -36.53199587]),
  'max_xyz': array([90.1428782 , 54.77024139, 87.64164462]),
  'bias_xyz': array([20.24224221, 60.90973483, 51.10964874])},
 'dendrite.tip.dimension': {'width': 160.04351419243892,
  'height': 170.45021761579912,
  'depth': 124.17364049387336,
  'min_xyz': array([ -69.90063599, -115.67997622,  -36.53199587]),
  'max_xyz': array([90.1428782 , 54.77024139, 87.64164462]),
  'bias_xyz': array([20.24224221, 60.90973483, 51.10964874])},
 'all_neurites.bifurcation.dimension': {'width': 636.3707997033264,
  'height': 138.50738030869223,
  'depth': 288.43478853575857,
  'min_xyz': array([-286.22899059,  -65.98864488,  -30.87585723]),
  'max_xyz': array([350.14180911,  72.51873543, 257.55893131]),
  'bias_xyz': array([ 63.91281853,   6.53009055, 226.68307408])},
 'axon.bifurcation.dimension': {'width': 636.3707997033264,
  'height': 138.50738030869223,
  'depth': 288.43478853575857,
  'min_xyz': array([-286.22899059,  -65.98864488,  -30.87585723]),
  'max_xyz': array([350.14180911,  72.51873543, 257.55893131]),
  'bias_xyz': array([ 63.91281853,   6.53009055, 226.68307408])},
 'basal_dendrite.bifurcation.dimension': {'width': 80.27259121837159,
  'height': 72.48094989884794,
  'depth': 39.44104628804529,
  'min_xyz': array([-33.12364491, -35.49587155, -23.06368008]),
  'max_xyz': array([47.14894631, 36.98507835, 16.37736621]),
  'bias_xyz': array([14.02530139,  1.48920681,  6.68631386])},
 'dendrite.bifurcation.dimension': {'width': 80.27259121837159,
  'height': 72.48094989884794,
  'depth': 39.44104628804529,
  'min_xyz': array([-33.12364491, -35.49587155, -23.06368008]),
  'max_xyz': array([47.14894631, 36.98507835, 16.37736621]),
  'bias_xyz': array([14.02530139,  1.48920681,  6.68631386])},
 'all_neurites.compartment.dimension': {'width': 748.6712308294434,
  'height': 268.1468330566796,
  'depth': 347.8384853563372,
  'min_xyz': array([-345.88986685, -184.96425257,  -37.43907789]),
  'max_xyz': array([402.78136398,  83.18258048, 310.39940747]),
  'bias_xyz': array([ 56.89149713, 101.78167209, 272.96032958])},
 'axon.compartment.dimension': {'width': 748.6712308294434,
  'height': 268.1468330566796,
  'depth': 347.8384853563372,
  'min_xyz': array([-345.88986685, -184.96425257,  -37.43907789]),
  'max_xyz': array([402.78136398,  83.18258048, 310.39940747]),
  'bias_xyz': array([ 56.89149713, 101.78167209, 272.96032958])},
 'basal_dendrite.compartment.dimension': {'width': 748.6712308294434,
  'height': 268.1468330566796,
  'depth': 347.8384853563372,
  'min_xyz': array([-345.88986685, -184.96425257,  -37.43907789]),
  'max_xyz': array([402.78136398,  83.18258048, 310.39940747]),
  'bias_xyz': array([ 56.89149713, 101.78167209, 272.96032958])},
 'dendrite.compartment.dimension': {'width': 748.6712308294434,
  'height': 268.1468330566796,
  'depth': 347.8384853563372,
  'min_xyz': array([-345.88986685, -184.96425257,  -37.43907789]),
  'max_xyz': array([402.78136398,  83.18258048, 310.39940747]),
  'bias_xyz': array([ 56.89149713, 101.78167209, 272.96032958])},
 'all_neurites.node.dimension': {'width': 749.7866746783784,
  'height': 268.6190622789789,
  'depth': 348.986413225511,
  'min_xyz': array([-346.43802996, -185.410335  ,  -38.56081   ]),
  'max_xyz': array([403.34864472,  83.20872728, 310.42560322]),
  'bias_xyz': array([ 56.91061475, 102.20160772, 271.86479322])},
 'axon.node.dimension': {'width': 749.7866746783784,
  'height': 268.6190622789789,
  'depth': 348.986413225511,
  'min_xyz': array([-346.43802996, -185.410335  ,  -38.56081   ]),
  'max_xyz': array([403.34864472,  83.20872728, 310.42560322]),
  'bias_xyz': array([ 56.91061475, 102.20160772, 271.86479322])},
 'basal_dendrite.node.dimension': {'width': 160.04351419243892,
  'height': 170.92363127673545,
  'depth': 124.17364049387336,
  'min_xyz': array([ -69.90063599, -116.15338989,  -36.53199587]),
  'max_xyz': array([90.1428782 , 54.77024139, 87.64164462]),
  'bias_xyz': array([20.24224221, 61.38314849, 51.10964874])},
 'dendrite.node.dimension': {'width': 160.04351419243892,
  'height': 170.92363127673545,
  'depth': 124.17364049387336,
  'min_xyz': array([ -69.90063599, -116.15338989,  -36.53199587]),
  'max_xyz': array([90.1428782 , 54.77024139, 87.64164462]),
  'bias_xyz': array([20.24224221, 61.38314849, 51.10964874])},
 'all_neurites.num_nodes': 12755,
 'axon.num_nodes': 11157,
 'basal_dendrite.num_nodes': 1597,
 'dendrite.num_nodes': 1597,
 'all_neurites.num_branches': 396,
 'axon.num_branches': 327,
 'basal_dendrite.num_branches': 63,
 'dendrite.num_branches': 63,
 'all_neurites.num_tips': 199,
 'axon.num_tips': 164,
 'basal_dendrite.num_tips': 35,
 'dendrite.num_tips': 35,
 'all_neurites.mean_fragmentation': 32.22222222222222,
 'axon.mean_fragmentation': 34.11926605504587,
 'basal_dendrite.mean_fragmentation': 23.558823529411764,
 'dendrite.mean_fragmentation': 23.558823529411764,
 'all_neurites.max_branch_order': 17,
 'axon.max_branch_order': 17,
 'basal_dendrite.max_branch_order': 5,
 'dendrite.max_branch_order': 5,
 'all_neurites.num_outer_bifurcations': 9,
 'axon.num_outer_bifurcations': 9,
 'basal_dendrite.num_outer_bifurcations': 0,
 'dendrite.num_outer_bifurcations': 0,
 'all_neurites.mean_bifurcation_angle_local': 1.3569986904250544,
 'axon.mean_bifurcation_angle_local': 1.3655438592520177,
 'basal_dendrite.mean_bifurcation_angle_local': 1.3072536004680917,
 'dendrite.mean_bifurcation_angle_local': 1.3072536004680917,
 'all_neurites.mean_bifurcation_angle_remote': 1.4200143260193663,
 'axon.mean_bifurcation_angle_remote': 1.4353635898418622,
 'basal_dendrite.mean_bifurcation_angle_remote': 1.3306596830526922,
 'dendrite.mean_bifurcation_angle_remote': 1.3306596830526922,
 'all_neurites.total_length': 18760.542863889685,
 'axon.total_length': 16656.744824345344,
 'basal_dendrite.total_length': 2103.7980395443205,
 'dendrite.total_length': 2103.7980395443205,
 'all_neurites.total_surface_area': 16756.227456602548,
 'axon.total_surface_area': 11973.337379852555,
 'basal_dendrite.total_surface_area': 3372.6044170027603,
 'dendrite.total_surface_area': 3372.6044170027603,
 'all_neurites.total_volume': 3287.49336427227,
 'axon.total_volume': 684.9164275089337,
 'basal_dendrite.total_volume': 467.8096587531732,
 'dendrite.total_volume': 467.8096587531732,
 'all_neurites.mean_diameter': 0.26496000000000003,
 'axon.mean_diameter': 0.22881154432195036,
 'basal_dendrite.mean_diameter': 0.511210770194114,
 'dendrite.mean_diameter': 0.511210770194114,
 'all_neurites.mean_parent_daughter_ratio': 1.0097576457425315,
 'axon.mean_parent_daughter_ratio': 1.000050460475344,
 'basal_dendrite.mean_parent_daughter_ratio': 1.007962228536164,
 'dendrite.mean_parent_daughter_ratio': 1.007962228536164,
 'all_neurites.max_euclidean_distance': 420.5583251306083,
 'axon.max_euclidean_distance': 420.5583251306083,
 'basal_dendrite.max_euclidean_distance': 135.08360469803026,
 'dendrite.max_euclidean_distance': 135.08360469803026,
 'max_path_distance': 606.0596411814414,
 'early_branch_path': 0.8764521042382784,
 'mean_contraction': 0.8009361610523765,
 'basal_dendrite.dendrite.overlap': {'above': 0.0,
  'overlap': 1.0,
  'below': 0.0},
 'dendrite.dendrite.overlap': {'above': 0.0, 'overlap': 1.0, 'below': 0.0},
 'axon.dendrite.overlap': {'above': 0.0, 'overlap': 1.0, 'below': 0.0},
 'basal_dendrite.basal_dendrite.overlap': {'above': 0.0,
  'overlap': 1.0,
  'below': 0.0},
 'dendrite.basal_dendrite.overlap': {'above': 0.0,
  'overlap': 1.0,
  'below': 0.0},
 'axon.basal_dendrite.overlap': {'above': 0.0, 'overlap': 1.0, 'below': 0.0},
 'basal_dendrite.axon.overlap': {'above': 0.14197364883033073,
  'overlap': 0.8515730034955633,
  'below': 0.006453347674105943},
 'dendrite.axon.overlap': {'above': 0.14197364883033073,
  'overlap': 0.8515730034955633,
  'below': 0.006453347674105943},
 'axon.axon.overlap': {'above': 0.0, 'overlap': 1.0, 'below': 0.0},
 'all_neurites.tip.moments': {'mean': array([389.82372309, 348.88729309,  83.71767202]),
  'std': array([115.74138931,  38.27306887,  78.91192105]),
  'var': array([13396.06919944,  1464.82780107,  6227.09128317]),
  'skew': array([ 0.32331903, -1.18533534,  1.12598265]),
  'kurt': array([1.12248839, 3.46986233, 0.86665464])},
 'axon.tip.moments': {'mean': array([389.12180478, 352.81894898,  93.1198556 ]),
  'std': array([125.84091262,  37.03591891,  82.91145378]),
  'var': array([15835.93528956,  1371.65928981,  6874.30916826]),
  'skew': array([ 0.31965618, -1.34227129,  0.90285376]),
  'kurt': array([0.58674689, 4.8100245 , 0.31496669])},
 'basal_dendrite.tip.moments': {'mean': array([393.11271174, 330.46467693,  39.66172611]),
  'std': array([45.60212767, 39.12333225, 29.7150603 ]),
  'var': array([2079.55404769, 1530.63512639,  882.98480864]),
  'skew': array([ 0.07178201, -0.79535141,  0.99416707]),
  'kurt': array([-0.90266879,  0.62165608,  0.55440506])},
 'dendrite.tip.moments': {'mean': array([393.11271174, 330.46467693,  39.66172611]),
  'std': array([45.60212767, 39.12333225, 29.7150603 ]),
  'var': array([2079.55404769, 1530.63512639,  882.98480864]),
  'skew': array([ 0.07178201, -0.79535141,  0.99416707]),
  'kurt': array([-0.90266879,  0.62165608,  0.55440506])},
 'all_neurites.bifurcation.moments': {'mean': array([388.55855553, 345.24944793,  81.03807554]),
  'std': array([78.59936479, 28.73308828, 56.09126445]),
  'var': array([6177.86014573,  825.59036238, 3146.2299472 ]),
  'skew': array([ 0.64147157, -0.49741759,  1.38113356]),
  'kurt': array([ 2.94996389, -0.04360701,  2.27866112])},
 'axon.bifurcation.moments': {'mean': array([388.07036867, 348.57089745,  88.452092  ]),
  'std': array([84.65672605, 29.1479067 , 57.36063837]),
  'var': array([7166.76126622,  849.60046517, 3290.24283458]),
  'skew': array([ 0.61778158, -0.75828984,  1.2135647 ]),
  'kurt': array([2.2026247 , 0.35070527, 1.85593134])},
 'basal_dendrite.bifurcation.moments': {'mean': array([391.40050048, 325.9138668 ,  37.87790832]),
  'std': array([21.52785373, 16.05599181, 11.62090147]),
  'var': array([463.44848614, 257.79487307, 135.04535087]),
  'skew': array([0.32477966, 0.23180366, 0.0025201 ]),
  'kurt': array([-0.95870064, -0.0806142 , -1.15001275])},
 'dendrite.bifurcation.moments': {'mean': array([391.40050048, 325.9138668 ,  37.87790832]),
  'std': array([21.52785373, 16.05599181, 11.62090147]),
  'var': array([463.44848614, 257.79487307, 135.04535087]),
  'skew': array([0.32477966, 0.23180366, 0.0025201 ]),
  'kurt': array([-0.95870064, -0.0806142 , -1.15001275])},
 'all_neurites.compartment.moments': {'mean': array([386.7267636 , 343.40498466,  91.40757948]),
  'std': array([112.00564813,  35.91778852,  65.94494938]),
  'var': array([12545.26521305,  1290.08753184,  4348.7363489 ]),
  'skew': array([ 0.32969097, -1.06132336,  1.24973741]),
  'kurt': array([0.74238392, 2.46791361, 1.89636957])},
 'axon.compartment.moments': {'mean': array([386.7267636 , 343.40498466,  91.40757948]),
  'std': array([112.00564813,  35.91778852,  65.94494938]),
  'var': array([12545.26521305,  1290.08753184,  4348.7363489 ]),
  'skew': array([ 0.32969097, -1.06132336,  1.24973741]),
  'kurt': array([0.74238392, 2.46791361, 1.89636957])},
 'basal_dendrite.compartment.moments': {'mean': array([386.7267636 , 343.40498466,  91.40757948]),
  'std': array([112.00564813,  35.91778852,  65.94494938]),
  'var': array([12545.26521305,  1290.08753184,  4348.7363489 ]),
  'skew': array([ 0.32969097, -1.06132336,  1.24973741]),
  'kurt': array([0.74238392, 2.46791361, 1.89636957])},
 'dendrite.compartment.moments': {'mean': array([386.7267636 , 343.40498466,  91.40757948]),
  'std': array([112.00564813,  35.91778852,  65.94494938]),
  'var': array([12545.26521305,  1290.08753184,  4348.7363489 ]),
  'skew': array([ 0.32969097, -1.06132336,  1.24973741]),
  'kurt': array([0.74238392, 2.46791361, 1.89636957])},
 'all_neurites.node.moments': {'mean': array([386.73703172, 343.43753508,  91.43719142]),
  'std': array([112.26090462,  35.99259949,  66.12618666]),
  'var': array([12602.51070669,  1295.46721813,  4372.67256248]),
  'skew': array([ 0.32875932, -1.06321539,  1.2455017 ]),
  'kurt': array([0.7337745 , 2.4763935 , 1.87850195])},
 'axon.node.moments': {'mean': array([385.94110878, 345.83754318,  98.79662517]),
  'std': array([119.30032428,  36.23199138,  67.13421024]),
  'var': array([14232.56737217,  1312.75719945,  4507.00218471]),
  'skew': array([ 0.33065145, -1.16645259,  1.12135911]),
  'kurt': array([0.35276708, 2.78994021, 1.59621964])},
 'basal_dendrite.node.moments': {'mean': array([392.29706645, 326.68012818,  40.05439879]),
  'std': array([34.48022466, 29.23755138, 20.42311822]),
  'var': array([1188.88589292,  854.83441065,  417.10375784]),
  'skew': array([ 0.24569733, -1.07401827,  0.9440347 ]),
  'kurt': array([-0.65157761,  2.18555955,  1.57217495])},
 'dendrite.node.moments': {'mean': array([392.29706645, 326.68012818,  40.05439879]),
  'std': array([34.48022466, 29.23755138, 20.42311822]),
  'var': array([1188.88589292,  854.83441065,  417.10375784]),
  'skew': array([ 0.24569733, -1.07401827,  0.9440347 ]),
  'kurt': array([-0.65157761,  2.18555955,  1.57217495])}}

width = feature_extraction_run.results["axon.node.dimension"]["width"]
height = feature_extraction_run.results["axon.node.dimension"]["height"]
wh_ratio = width / height
print(width, height, wh_ratio)

749.7866746783784 268.6190622789789 2.791263837782572