sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1740dc7388dbabe7ec090cbe8e8424172b3a9b005b9b73fd96abaae65b244195 | Jupyter | 415 | 23 | # %%
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# %%
n = np.reshape(np.arange(1,100),(-1,1))
N = np.reshape(np.array([10,50,150,300]),(1,-1))
v = (2*n + 1/n)/(3*(N-n+1))
rel_err = np.sqrt(v)
# %%
for i, N_ in enumerate(N[0]):
plt.plot(n[n[:,0] < N_],rel_err[:,i][n[:,0] < N_],label="$N =... |
f0ec67798a768456e3fd870069c065a2214aa9352f7c363a01c4d7ae5e877b7f | Jupyter | 417 | 13 | # %% [markdown]
# #### This notebook navigates you through the pipeline of VCD-LFM.
#
# There are three notebooks to go through in total:
# 1. [datapre](./datapre/datapre.ipynb) for dataset generation
# 2. [train_demo](./vcdnet/train_demo.ipynb) for model training
# 3. [predict_demo](./vcdnet/predict_demo.ipynb) for... |
ec45529e637bc0d8a03f965536cde0e9e1b4ca77e538799742b86498501c1384 | Jupyter | 462 | 17 | # %% [markdown]
# # Running Models on Graph Datasets
# %%
import torch
from graphxai.datasets import Benzene
# %%
# Location of benzene.npz file (downloaded from Harvard Dataverse)
data_path = '../graphxai/datasets/real_world/benzene/benzene.npz'
dataset = Benzene(split_sizes = (0.75, 0.05, 0.2), data_path = data_pat... |
55a3ae21f63693e58b2ec17f2a578f077e7b0f0c31419a4923150e8f1bb70cf4 | Jupyter | 553 | 21 | # %% [markdown]
# # GCPDS - visualizations
# %% [markdown]
# ## Installation
# %%
pip install -U git+https://github.com/UN-GCPDS/python-gcpds.visualizations.git
# %% [markdown]
# <div class='gcpds-images0'>
#
# [](notebooks/01-connectivity_circosplot.html)
#
# [![Accuracy a... |
ef6d7d7de6f3ab6fc638f85d79c4980ef81e142c61f3a173c841e0c37285bcc0 | Jupyter | 565 | 31 | # %%
import navis
# Load example neurons
nl = navis.example_neurons()
# %%
# Without parallel processing
%time res = navis.resample_neuron(nl, resample_to=125)
# %%
# With parallel processing (by default uses half the available cores)
%time res = navis.resample_neuron(nl, resample_to=125, parallel=True)
# %%
%time ... |
922f6f230ba2b2f50a8e755b5eca5398ebffb0f24c87771f7d504b90f7a29f4e | Jupyter | 567 | 35 | # %%
import numpy as np
import pandas as pd
# %%
mouse = 'ERAB23_1b'
file = f'/camp/lab/rancze/working/tranvaa/brainsaw/classification/{mouse}/channel_1_inputs_v5_thr8/analysis/all_points.csv'
df = pd.read_csv(file)
# %%
df.head()
# %%
df[(df.iloc[:,0:5] < 0).all(1)]
# %%
df[df['hemisphere'] == 'right'].max()
# %... |
6f5d868db4f064d46fe78b1d758581f24950287516c5b79bcb55471116bf377a | Jupyter | 579 | 27 | # %%
%load_ext autoreload
%autoreload 2
from palmari.processing import *
from palmari import Experiment, ImagePipelineWidget
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
# %%
class MyExp(Experiment):
@property
def custom_fields(self):
return {"cell_zone":-2,"exp":-3,"condition":-4... |
0284f5269e1d76177c3a4a1df8f7b0e637abeb7e418396244c8bd0851e0f9a8f | Jupyter | 585 | 24 | # %% [markdown]
# ## Imports
# %%
from plot_metrics import plot_metrics
# %% [markdown]
# ## Input your files
# %%
files = [
"/home/arlapin/DeepCT_outputs/new_sampler_0008/selene_sdk.train_model.validation.txt",
"/home/arlapin/DeepCT_outputs/new_sampler_00008/selene_sdk.train_model.validation.txt",
"/hom... |
6222f6341088a5850576968ed7b348020dae52bb54e3964becceec3d01f69c4b | Jupyter | 599 | 47 | # %%
import navis
n = navis.example_neurons(n=1, kind='skeleton')
n
# %%
n.nodes.head()
# %%
help(navis.TreeNeuron.root)
# %%
help(navis.TreeNeuron.downsample)
# %%
# Downsample and return a copy of the original (default)
n_ds = n.downsample(10)
# Downsample the original neuron
n_ds2 = n.copy()
n_ds2.downsample(1... |
24b0c27bcc7ba742e082bb4bd2daab1e0a88447ca0c153675194a01f4f1eab81 | Jupyter | 603 | 21 | # %%
from mmdet.apis import init_detector, inference_detector, show_result_pyplot
import mmcv
# %%
config_file = '../configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
# download the checkpoint from model zoo and put it in `checkpoints/`
checkpoint_file = '../checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth'
... |
7cfdcd04f8588048249094f3d5a8205059b5a36625429efc356bcb574779c18e | Jupyter | 618 | 34 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import shutil
import sys
from tqdm.notebook import tqdm
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
from scripts import dl_utils
# %%
# User inputs
roi = 'algeria'
roi_file = f'../data/b... |
fa2d307fde07eb3f69872051855664e7cc73bf756f4e5476d6784f34bca745e0 | Jupyter | 634 | 24 | # %%
import numpy as np
import os
# %%
jer_sweep_name = '/media/thijs/hooghoudt/new_sweep_april20/RBM_sweep_21_04_2020/'
thijs_sweep_name = '/media/thijs/hooghoudt/new_sweep_april20/RBM_sweep_Thijs/'
# %%
jer_files = os.listdir(jer_sweep_name)
thijs_files = os.listdir(thijs_sweep_name)
jer_ts = [x.split('_')[-1].lst... |
ee7a74e913562e3868e7dde914d3b20879aba8e7a1fcf4858980111ef158136a | Jupyter | 664 | 27 | # %%
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
# %%
to_be_clean = pd.read_excel('dockingResults_ROR_gamma_SYBA_selected.xlsx')
# %%
to_be_clean.head()
# %%
to_be_clean['7npc'][0][1:5]
# %%
new_dataframe = pd.DataFrame(to_be_clean['smiles'])
# %%
new_dataframe['7npc'] = [to_be_clean['7np... |
291439fbd049ed488bd7bbbb281d99797ab905abbf7e3379d75de85a1c8374b9 | Jupyter | 668 | 26 | # %%
import geopandas as gpd
import pandas as pd
# %%
fn = 'negative_sites'
csv = pd.read_csv(f'../../data/sampling_locations/{fn}.csv', converters={'coords': eval})
gdf = gpd.GeoDataFrame(csv['name'], geometry=gpd.points_from_xy([coord[0] for coord in csv['coords']], [coord[1] for coord in csv['coords']]))
for name i... |
aa442cbe1ad1a0e5c65a6b1122f18a015b29929c1e9b5261a129df517c655d73 | Jupyter | 673 | 18 | # %%
from scripts import dl_utils
from descarteslabs.catalog import Image, properties
# %%
region = 'australia'
product_id = f'earthrise:{region}_v0.0.11_2019-01-01_2021-06-01'
search = Image.search().filter(properties.product_id == product_id)
image_list = [image.id for image in search]
image_keys = [k.split(':')[-1... |
dccf47903dcee85cef5239d034d90c707c60f92aa4ff9c9da58f62b9e959d627 | Jupyter | 674 | 32 | # %% [markdown]
# # OAK obsoletes command
#
# This notebook is intended as a supplement to the [main OAK CLI docs](https://incatools.github.io/ontology-access-kit/cli.html).
#
# This notebook provides examples for the `obsoletes` command, which shows all obsolete entities in an ontology,
# plus associated metadata
# ... |
6a63b23ff3261b8c4234058c14991df073d75d91e5dfcec9bf2275047819a960 | Jupyter | 679 | 33 | # %%
# Import neuprint wrapper by navis
import navis.interfaces.neuprint as neu
# %%
client = neu.Client('https://neuprint.janelia.org/', dataset='hemibrain:v1.1')
# %%
mbons, roi_info = neu.fetch_neurons(neu.SegmentCriteria(instance='.*MBON.*', regex=True))
mbons.head(3)
# %%
mb = neu.fetch_roi('MB(R)')
mb
# %%
mb... |
2167ad5454593fa7b4bd80c19ef612ec3a758c3cfad7a8cb52b8989729e78bd2 | Jupyter | 721 | 32 | # %% [markdown]
# ## Loading from obo files using Pronto
# %%
from oaklib.implementations.ubergraph.ubergraph_implementation import UbergraphImplementation
from oaklib.resource import OntologyResource
# %% [markdown]
# ### Remote (downloading from OBO)
#
# Next we use pronto's load from obo library feature
# %%
oi ... |
3d90dbd9a6ad875a1093bfdc8481f7c99d583af92a658df40c9754e7b8798e6a | Jupyter | 791 | 28 | # %%
import sys
sys.path.insert(0, '../src')
import numpy as np
import roboscientist.equation.equation as rs_equation
from experiments import run_experiment
# %% [markdown]
# This notebook shows how to recover equation from Nguyen benchmark using RoboScientist algorithm.
# $ Nguyen_1 = x ^ 3 + x ^ 2 + x$
# %%
X = ... |
e451709f5504d0286c92ff21dc13de72131a748cfb0db9b48a1ac80ac32f8000 | Jupyter | 799 | 27 | # %%
import visualkeras
from tensorflow import keras
from PIL import ImageFont
# %%
model_name = 'weak_labels_3.0_batchnorm'
model = keras.models.load_model(f'../../models/{model_name}.h5')
#model = keras.Model(input_img, encoded)
font = ImageFont.truetype("/Users/ckruse/Library/Fonts/Montserrat-SemiBold.ttf", 16)
... |
eec5c7eb00f7ea6a80a8df2d342e3519c008fc9ea8caabc4b9d3763aff22675c | Jupyter | 811 | 34 | # %%
import numpy as np
import matplotlib.pyplot as plt
# %%
lengths_1 = np.random.randint(7,24,size=1000)
lengths_2 = np.random.randint(8,18,size=100)
# %%
def get_target_lengths(L1, L2):
sorted_L1 = np.sort(L1)
sorted_L2 = np.sort(L2)
M = max(L1.shape[0],L2.shape[0])
for i in range(M):
... |
226c03391b335fa660264b1160f3acaa59f18929c424c04115f3de98e83e7367 | Jupyter | 849 | 42 | # %%
from polychrom.hdf5_format import list_URIs, load_URI
from polychrom.contactmaps import monomerResolutionContactMap, monomerResolutionContactMapSubchains, binnedContactMap
from multiprocessing import Pool
import glob
from polychrom import polymerutils
import numpy as np
# %%
# %%
URIs = list_URIs("trajectory... |
363bb37a61879c677fef56a4b3ddf2c8fb3e6ba0992dacfec925ef5a3cd222b8 | Jupyter | 853 | 27 | # %%
import pandas as pd
home_pth = '/home/thurs/DeepCT/DeepCT_outputs/biox/ct_v2/dnase/multi_ct/masked_train_LR0001_folds_shuffle/fold_00/'
validation_metrics = pd.read_csv(home_pth+'src.train.train_kfold.validation_metrics.txt',
sep='\t',
header=0,
# names=['loss', 'average_precision', 'baseline']
).... |
7ffc2e8f2baed7aa2c043909d4b85fdbe97be24ffdd8524c91961943367f908e | Jupyter | 886 | 28 | # %%
import geopandas as gpd
import requests
import pandas as pd
import country_converter
# %%
endpoint = 'http://api.plastic.watch.earthrise.media/sites?limit=10000'
response = requests.get(endpoint, params={'apikey': 'jahsdbvjahsdvaulhsdvoauysdg82973bq'})
data = response.json()
data = gpd.GeoDataFrame.from_features(... |
4c0a8f21f166ebbd17811533c13dc1853098d22915ee32ac58192b38de5e34e7 | Jupyter | 907 | 40 | # %%
import os
import sys
import numpy as np
from skimage.io import imread
import matplotlib.pyplot as plt
sys.path.append('..')
# %%
from keras.applications.imagenet_utils import decode_predictions
from efficientnet.keras import EfficientNetB0
from efficientnet.keras import center_crop_and_resize, preprocess_input
... |
99367f3ad2ad328add560cac545f5531599e5769e1ca1d2a333b4bc74324c689 | Jupyter | 907 | 38 | # %%
import sys
import os
if sys.platform == 'darwin':
import multiprocessing
else:
import psutil
import numpy as np
import pandas as pd
import itertools #install as more-itertools
from pathlib import Path
import networkx as nx
from networkx.algorithms import bipartite
import pickle #already part of standa... |
b88ad3a1cf4f231f7c8fecbe096bac7ba0cf46ad54f37c8b06ec144d3fdce2e1 | Jupyter | 917 | 39 | # %%
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
# %%
# import sys
# import os
# os.environ['WINGHOME'] = '/usr/local/lib/wingpro9'
# sys.path.append(os.getenv('WINGHOME]'))
# import wingdbstub
# wingdbstub.Ensure()
# %% [markdown]
# # EEG topoplots
# %%
from gcpds.visualizations.topo... |
cdd0708bd179943c27a55935d24d482fad1ca848198163dbed95562a50e28c74 | Jupyter | 928 | 41 | # %%
import pandas as pd
# %%
generated_smi1 = pd.read_excel('Selected_molecules_from_0_1_tensor_scaling.xlsx')
# %%
generated_smi2 = pd.read_excel('Selected_molecules_from_0_2_tensor_scaling.xlsx')
# %%
concate = (generated_smi1, generated_smi2)
# %%
whole_generated_and_selected_data = pd.concat(concate)
# %%
who... |
e929f01ec57d8b376c56b58d49476c94ce0b73259982ae79db93389d5f265b5e | Jupyter | 943 | 40 | # %%
import geopandas as gpd
import shapely
# %%
java = gpd.read_file('/Users/ckruse/Downloads/west_java.geojson')
java = java['geometry'][0]
# %%
contours = gpd.read_file('../data/model_outputs/site_contours/v2.1/indonesia_v0_mask_window_8_upsampled_4_contours_model_v0.0.11_ensemble-8-25-21.geojson')
# %%
contours.... |
0539af5c1d9cbc385158afcacdf2a1657a1096c55ef00591bcf5e1281cb4eee0 | Jupyter | 969 | 48 | # %%
#Read the relation2id file
R = {}
with open('relation2id.txt','r') as flr:
line=flr.readline()
line=flr.readline()
while(len(line)>0):
(name,idd)=line.strip().split('\t')
R[int(idd)]=name
line=flr.readline()
# %%
MRR = {}
MR = {}
Hit10 = {}
Hit3 = {}
Hit1 = {}
T = {}
#read the ... |
d651a541577226d958048e5c3cc5e5ba2d11a8fdb3d92928acfa528a30b20ee8 | Jupyter | 977 | 61 | # %%
%matplotlib inline
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
# %%
data = (
[1, 10, 10],
[1, 10, 10],
[1, 9, 9],
[1, 7, 7],
[2, 3, 3],
[1, 3, 2],
[5, 1, 1],
[3, 1, 1],
)
color = cm.get_cmap('Pastel1', len(data))
# %%
import mayavi... |
2d29ce81d90347f0025390380d24c4c38febb65d7bd96a7780609e1511b33410 | Jupyter | 1,006 | 40 | # %%
############Training
import tensorflow as tf
from keras.backend import tensorflow_backend
import os
from train import train
os.environ["CUDA_VISIBLE_DEVICES"]="0"
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
session = tf.Session(config=config)
tensorflow_backend.set_session(session)
#e... |
334456c8aa2b53fdd58511d5e52ed096addd4df29497f279ef596e8b864b4690 | Jupyter | 1,006 | 40 | # %%
############Training
import tensorflow as tf
from keras.backend import tensorflow_backend
import os
from train import train
os.environ["CUDA_VISIBLE_DEVICES"]="0"
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
session = tf.Session(config=config)
tensorflow_backend.set_session(session)
#e... |
061b39c089b8790e4a621f2e60e60f21d090520260ecee04b454333cfa2cd065 | Jupyter | 1,008 | 40 | # %%
import descarteslabs as dl
from descarteslabs.catalog import Image, Search, properties, Product
# %%
product_id = 'earthrise'
search = Search(Product).filter(properties.owners == 'org:earthrise')
img_search = Image.search().filter(properties.owners == 'org:earthrise')
img_search.summary()
# %%
product_ids = [p.i... |
1590e56ed855a9ca5a337e7cb9212e2e545c41b554894e508e382410146c082b | Jupyter | 1,011 | 54 | # %%
import numpy as np
import pickle
# %%
example_data = np.load('/media/thijs/hooghoudt/backup_zfrbm_paper_2021-04-15/all_fish_data/spike_only/20190109_Run04_spontaneous7_Tset=22__wb_spikes_only.npy')
example_data.shape
# %%
# %%
# %%
tmp = np.load('/home/thijs/Documents/matteo_data/Elisa_Latifa__wb_spikes_onl... |
f3041a0fc80502b95a09dbebce5df3262c2a2bcc26f0e02531f86394ec4222ea | Jupyter | 1,012 | 40 | # %%
############Training
import tensorflow as tf
from keras.backend import tensorflow_backend
import os
from train import train
os.environ["CUDA_VISIBLE_DEVICES"]="0"
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
session = tf.Session(config=config)
tensorflow_backend.set_session(session)
#e... |
ecdc9d44123ef718081537062bff250cb59354e34fd2e6b00aff9c7bb2ed258e | Jupyter | 1,015 | 53 | # %%
import navis
navis.set_pbars(hide=True, jupyter=False)
from navis.interfaces import r
import matplotlib.pyplot as plt
from rpy2.robjects.packages import importr
import rpy2.robjects as robjects
# %%
nat = importr('nat')
# %%
# Get neurons (Drosophila Kenyon cells) shipped with nat
kcs20 = robjects.r('kcs20')... |
6b06ff41d04861c29641dbaac3591161be9b0d1eea9069dbf07c700793ad6e93 | Jupyter | 1,018 | 44 | # %% [markdown]
# # command: crawl
#
# This notebook is intended as a supplement to the [main OAK CLI docs](https://incatools.github.io/ontology-access-kit/cli.html).
#
# This notebook provides examples for the `crawl` command, which is used to walk over multiple ontologies and endpoints.
#
# ## Help Option
#
# You... |
5239284c62ac631b9fc26c40c08827e6752350937d2af3a1c5ffdc77c65155f0 | Jupyter | 1,021 | 53 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from dask_jobqueue import SLURMCluster
from distributed import Client
# %%
use_gpu = True
# %%
# if we want to fit on cluster
# NOTE: if you're using the GPU make sure you spool the scheduler in the same env as your notebook
if not use_gpu:
extra = ... |
83ae0aed49a856289ad6df5557f2dda1feac4e08ae2b474037a4e92e6e9c8269 | Jupyter | 1,026 | 33 | # %% [markdown]
# # Mitochondria visualization
# A brief notebook to demonstrate visualizing a cell id and its mitochondria
# %%
import pandas as pd
from nglui import statebuilder as sb
from lib import ngl
# %% [markdown]
# Reading the dataframe that links mitochondria to cell IDs
# %%
mitodf = pd.read_csv("data/pn... |
5fbd8e30eaf56a857061ae54cc21ed84955409ca636a7c3c7f33950775ad72ef | Jupyter | 1,069 | 37 | # %%
%load_ext autoreload
%autoreload 2
import sys
from gratin.simulation.diffusion_models import generators, params_sampler
from gratin.simulation.traj_tools import get_traj_scale
import matplotlib.pyplot as plt
import numpy as np
# %%
# CTRW suit une pente bizarre au début
for dim in [1,2,3]:
for model in ["sBM"... |
76e5a16784ecaf9dc37ee02e7234aed7d11a0dc5d691cf9e0f230a0e020f0a1b | Jupyter | 1,076 | 52 | # %%
#Read the relation2id file
R = {}
R[24]='Merged:Treatment'
# %%
MRR = 0.0
MR = 0.0
Hit10 = 0.0
Hit3 = 0.0
Hit1 = 0.0
T = 0
#read the ranking file
with open('TransE,dist_Score.txt','r') as flr:
line=flr.readline()
while(len(line)>0):
(h,r,t,sh,st)=line.strip().split(' ')
(h,r,t,sh,st)=(int... |
5c576c75d54b33723309959c9fe639ab858be246aedf98caa833a9f914655e23 | Jupyter | 1,108 | 53 | # %% [markdown]
# # OBA tutorial
#
# * author: Harshad Hegde
# * created: 2023-01-25
#
# This tutorial walks through OBA.
# %% [markdown]
# ## Create an alias
#
# For convenience we will set a bash alias.
#
# This assumes you have built the sqlite version of oba in the `../../db` folder
#
# __NOTE__ an alternativ... |
c76231b0150b93b93f7434d91b4138512fd61f36b3e7b3e016c723909da4fcd1 | Jupyter | 1,130 | 53 | # %%
from dask_image.imread import imread
from dask.distributed import Client
from skimage.feature import peak_local_max
from dask.array import map_blocks
import dask.dataframe as dd
from dask import delayed
import numpy as np
from typing import Any
import pandas as pd
from dask.diagnostics import ProgressBar
# %%
c =... |
b282b4c90138cf82820e25aedf1618b4ec914fdee8338c3f822932ef7b91dd2f | Jupyter | 1,148 | 47 | # %% [markdown]
# # OAK rollup command
#
# This notebook is intended as a supplement to the [main OAK CLI docs](https://incatools.github.io/ontology-access-kit/cli.html).
#
# This notebook provides examples for the `rollup` command which produces a summarization of ontology associations
# between entities and "rolled... |
a1a7adcedefdc5082c0fa025aaa1fd87594376e060bd33e0136db8c8ae60c398 | Jupyter | 1,157 | 68 | # %%
import numpy as np
import pandas as pd
# %% [markdown]
# # Load original dataset.
# %%
data = pd.read_csv('SV_GQ_80.csv')
# %%
data.head()
# %% [markdown]
# # Filtering out the missing values.
# %%
num_of_missings = (data == -1).sum()
L = num_of_missings[0].tolist()
average = sum(L) / len(L)
average
# %%
x... |
eb9b9bce2fdc104b2805d02c4689dac4aeffd3093de92406dda40da506af54dc | Jupyter | 1,169 | 42 | # %%
!runoak prefixes GO ENVO CL OBI
# %%
!runoak prefixes dcterms owl EFO hgnc schema
# %%
!runoak --named-prefix-map obo prefixes dcterms owl EFO HGNC
# %%
!runoak --named-prefix-map linked_data prefixes dcterms owl EFO HGNC schema
# %%
!runoak --named-prefix-map bioregistry prefixes dcterms owl EFO hgnc schema
... |
63c356cb3ded6fc3e667670ce0210fb03778771c948ff2f249eedf876d54c08c | Jupyter | 1,176 | 67 | # %%
%pylab inline
from numpy import *
from numpy.random import rand, randn, randint
from dPCA import dPCA
# %%
# number of neurons, time-points and stimuli
N, T, S = 100, 250, 6
# noise-level and number of trials in each condition
noise, n_samples = 0.2, 10
# build two latent factors
zt = arange(T) / float(T)
zs = ... |
1afe0cb00479c3258bac33db9f9f72334c7f2c595777a37de817b104c768cade | Jupyter | 1,178 | 83 | # %% [markdown]
# # Example usage of XTB with XYZ files
# %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
import logging
import sys
# %%
from rdkit import Chem
# %%
try:
import ppqm
except ModuleNotFoundError:
import pathlib
cwd = pathlib.Path().resolve().parent
sys.path.append(str(cw... |
e1211a0914dc2fb3e3cbd062f2679890d81d5c330eaa5fb807a5a744d528ce3a | Jupyter | 1,193 | 27 | # %%
import requests
import geopandas as gpd
endpoint = 'http://api.globalplasticwatch.org/sites?limit=10000'
auth = requests.auth.HTTPBasicAuth('admin', 'plastics')
params = {'apikey': 'jahsdbvjahsdvaulhsdvoauysdg82973bq', 'limit': '10000'}
response = requests.get(endpoint, auth=auth, params=params)
data = response.j... |
ee801a46cc3cbf80f36bc8edb9f0a092c72a4adf49893880f6a0bbd09570c56e | Jupyter | 1,193 | 30 | # %%
import geopandas as gpd
file_name = 'vietnam-1.0'
data = gpd.read_file(f'../../data/site_metadata/{file_name}.geojson')
data.head()
# %%
data = data[data['waterway distance'] < 200]
data = data.sort_values(by='waterway distance')
print(len(data))
data.head()
# %%
coords = [[point.x, point.y] for point in data['g... |
5aee568fa8b6eba73afe02f1db847bfb0a02ddd130ed8807398aabd3569213c1 | Jupyter | 1,218 | 56 | # %% [markdown]
# # Analyzing Text Content of all Ontologies
#
# %%
# %%
!mkdir -p output
# %%
!python -m oaklib.utilities.metrics.text_content_analysis ~/repos/semantic-sql/db/*.db > output/ontologies-tc.tsv
# %%
import pandas as pd
df = pd.read_csv('output/ontologies-tc.tsv', sep='\t')
df
# %%
# replace ontolo... |
631233ed21877fd5b89f640b28a7c11888f6a64554bd98ac98bce1a2d9135216 | Jupyter | 1,221 | 74 | # %%
%load_ext autoreload
%autoreload 2
from palmari import Acquisition, Experiment, ImagePipeline, ImagePipelineWidget
from palmari.processing.steps import *
import tifffile
# %%
exp = Experiment(
data_folder="/Users/hverdier/Desktop/exemples_mifobio/short_only/",
export_folder="/Users/hverdier/Desktop/exampl... |
5110fdb242308df67ddd740300cc455499034b43cd9ef15415fb4ed757dcf0c9 | Jupyter | 1,257 | 50 | # %% [markdown]
# # Extract antibody information
# Extract the antibody information provided by Cao et al in an Excel file into a pandas DataFrame and save to a CSV.
#
# Import Python modules:
# %%
import pandas as pd
# %% [markdown]
# Read the data in the Excel file:
# %%
raw_info = pd.read_excel(
"covergent_R... |
8a7baefbee1af052f0129ec64578bff96727f598744d82e9406c40f87bf2b8cd | Jupyter | 1,260 | 42 | # %%
import pandas as pd
import json
from geojson import Point, Feature, FeatureCollection, dump
from shapely.geometry import box, shape, Point, Polygon
# %%
with open('../../data/sampling_locations/v12_java_bali_validated_positives.geojson', 'r') as f:
data = json.load(f)
# %%
rect_width = 0.006
rect_features = ... |
0bebb070de86d2a36a2f11048f45cb47c14a04e3772b390fef79eb475d41030c | Jupyter | 1,285 | 67 | # %%
%reset -f
# %%
from sssom.io import parse_sssom_table
import pandas as pd
fn = "demo.tsv"
# %% [markdown]
# ### Annotate
# %%
!runoak -i sqlite:obo:mondo annotate "Marfan syndrome" --matches-whole-text
# %% [markdown]
# ### Lexmatch
# %%
%%time
!runoak -i sqlite:obo:mondo -a sqlite:obo:doid lexmatch i^MONDO:... |
34dde78f365783aa35e8e0514b0355b7b4dd9f609a430beb81ce71d2be73599c | Jupyter | 1,294 | 56 | # %%
%load_ext autoreload
%autoreload 2
from gratin.standard import train_model, load_model, plot_demo, get_predictions
import matplotlib.pyplot as plt
import socket
# %%
from scipy.stats import truncexpon, expon
import numpy as np
# %%
if "jbmasson" in socket.gethostname():
export_path = "/home/hverdier/Gaia/hec... |
f795a0408e5a7af9560bb1bd168da111e5502bec1bf1074bf8c6f37577c1e392 | Jupyter | 1,300 | 56 | # %%
# load the packages
import os
import yaml
import pandas as pd
# load the runner
from finetune_transformer import FineTune
# %%
# load config file
config = yaml.load(open("config_ft_transformer.yaml", "r"), Loader=yaml.FullLoader)
config['dataloader']['randomSeed'] = 0
print(config)
if 'hMOF' in config['dataset... |
9f8b8284ec4181f7b631f619e15469a0962d6784eaa194bc9a249aee178190dc | Jupyter | 1,307 | 55 | # %% [markdown]
# # Mondo Phenotypes Example (IN PROGRESS)
#
# ## Help Option
#
# You can get help on any OAK command using `--help`
# %%
!runoak enrichment --help
# %% [markdown]
# ## Download example file and setup
#
# We will use the HPO Association file
# %%
!mkdir -p input
!curl -L -s http://purl.obolibrary.... |
b37159033d55578009fa2e1c55253bae1de3f180f8a858a42b0e88ac29d73d8a | Jupyter | 1,318 | 63 | # %%
from oaklib import get_adapter
# %%
adapter = get_adapter("semsimian:sqlite:obo:hp")
adapter2 = get_adapter("sqlite:obo:hp")
# %%
def labels2curies(labels):
return [adapter2.curies_by_label(label)[0] for label in labels]
terms1 = labels2curies(["Abnormal liver lobulation", "Focal white matter lesions"])
te... |
0f23c82a6caa4e6eaea48303ba39a7754d4cf5781992b64bfa7c67104b5859df | Jupyter | 1,326 | 52 | # %% [markdown]
# # Accessing data
#
# The dataset for each topology can be loaded using NumPy in python. After loading (np.load(file)), the following keys can be used:
#
# - positions; array of shape (N_configs $\times$ N_atoms $\times$ 3)
#
# - energies; 1-d array of N_configs datapoints
#
# - forces; array of sh... |
d92fc854d6c9e4a6b3c5f0ac8db798c22c7b85ebee543ed377ffc15bb51f0553 | Jupyter | 1,352 | 50 | # %% [markdown]
# ## SOI Localization Estimate
# %%
from ipywidgets import interactive, fixed
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
from PIL import Image
from utils.evaluation import evaluate_object_detection, evaluate_soi_localization, get_annotated_sl... |
9f23ccf5244f034da4871a3d49989f661a46646df32272326deab8d43ed495a8 | Jupyter | 1,397 | 87 | # %%
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
# %%
import sys
import os
os.environ['WINGHOME'] = '/usr/local/lib/wingpro8'
sys.path.append(os.getenv('WINGHOME'))
import wingdbstub
wingdbstub.Ensure()
# %% [markdown]
# # EEG plot
# %%
from gcpds.visualizations.series import plot_eeg... |
80173527a33813e98330534534758e251e8474494e079a81e32e300ca48033a8 | Jupyter | 1,452 | 51 | # %% [markdown]
# # Code for trial and error analysis and sanity checks
# %%
import pandas as pd
import os
import sys
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
import nibabel as nib
sys.path.append('../')
from lib.stats_... |
336487be13d391e39959a234e3d1093d36ba80b16284677ae33f0fa5e5dafa0d | Jupyter | 1,491 | 65 | # %%
%reset -f
# %%
# !pip install oaklib==0.5.16-rc1
# %%
%%time
from oaklib.selector import get_adapter
semsim = get_adapter(f"semsimian:sqlite:obo:phenio")
# %%
%%time
semsim.termset_pairwise_similarity(
subjects=["MP:0010771","MP:0002169","MP:0005391","MP:0005389","MP:0005367"],
objects=[... |
98adf2b899b8bcb1e9b3a03a7a2b9f10975a7f2964adfa4923a51f78ad9fe98a | Jupyter | 1,491 | 68 | # %% [markdown]
# # NCIT tutorial
#
# * author: Chris Mungall
# * created: 2022-11-11
#
# This tutorial walks through NCIT-OBO, the goals are:
#
# - to help understand the structure of ncit
# - to show how to do advanced OAK queries (CLI and programmatic) on NCIT
# %% [markdown]
# ## Create an alias
#
# For conven... |
4f99f0280f4b412fd31caf868daecfd1ad49f7dd9a81707bff9a126e1243f588 | Jupyter | 1,532 | 63 | # %% [markdown]
# # ClassEnrichmentCalculationInterface Examples
#
# See also [Python docs](https://incatools.github.io/ontology-access-kit/packages/interfaces/class-enrichment.html)
#
# %%
from oaklib.datamodels.input_specification import InputSpecification
from oaklib import get_adapter
spec = InputSpecification(
... |
4a1d9cf1f0653b42097714d4e243c93b00f52544cc852f777b3c6fe6c52e9220 | Jupyter | 1,536 | 64 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import descarteslabs as dl
import geopandas as gpd
from tqdm.notebook import tqdm
from scripts import deploy_candidate_detect, candidate_detect
# %%
roi = 'brazil'
model_name = '0.0.11'
product_name = f'{roi}_v{model_name}_2019-01-01_2021-06-01'
pred_threshold ... |
9f68a7a0a9c05b7f15b130bc16b5291d7fc14dc25d44368bfdc73b362fdc5c4b | Jupyter | 1,551 | 71 | # %% [markdown]
# ## Phenotypic Series queries
#
# We will walk through
#
# 1. Finding all MONDOs that correspond to an OMIM Phenotypic Series
# 2. Extend these to the leaf MODNO nodes
# 3. Query associations between these leaf nodes and genes
# %%
from oaklib.selector import get_adapter
# %%
handle = get_adapter("... |
02779e99aa3f77804c6e00d2c2debc19418b3671736b6f392fbaafb792b32c17 | Jupyter | 1,557 | 65 | # %% [markdown]
# # Parse the DE genes for pathview
# %%
# import matplotlib
# matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import networkx as nx
import seaborn as sns
# latex rendering of text in graphs
import matplotlib as mpl
from matplotlib import rcParams
rcPara... |
5abce1ba72d7fd2f1aac10193578cf5b09fa43f06033029fc02f8a7341046c4d | Jupyter | 1,567 | 60 | # %%
%alias go runoak -i sqlite:obo:go
# %%
go relationships --direction down -p RO:0002211 .desc//p=i 'signal transduction' > output/reg-pathway.tsv
# %%
import pandas as pd
# %%
df = pd.read_csv("output/reg-pathway.tsv", sep="\t")
df
# %%
from oaklib import get_adapter
from oaklib.datamodels.vocabulary import IS_... |
00ac9963823e78372a099ccf0b27f610feede699d50db43f7502e62274a87d90 | Jupyter | 1,570 | 61 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
from tqdm.auto import tqdm
import torch
import torch.nn as nn
from torch.utils.data import ConcatDataset
import torchvision
from torchvision import transforms
from dataloading.ouster import OusterCrop, OusterNormalize... |
01ce349b1ee712a606f70b1d383bc944e048b6fd318806c07165c9bf93632e27 | Jupyter | 1,576 | 45 | # %%
#import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # Uncomment if you don't want to use GPU.
import sys
sys.path.append('utilities')
sys.path.append('IPA_model')
#from GraphBuilder import GraphBuilder
from IPA import load_model
import numpy as np
import tensorflow as tf
# %%
model_path_d = 'weights/IPA_d'
mo... |
356fead27e294dc72871ac99ca1f7b314e4e6126417149f50894115a09dc3e7c | Jupyter | 1,576 | 64 | # %%
import numpy as np
import os
from pathlib import Path
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style("ticks")
hfont = {'fontname':'Myriad Pro'}
import plotly.express as px
# %%
path = Path(os.getcwd()).parents[0]
datapath = os.path.join(path, 'model_selection')
respath = ... |
3ba43676681d5edcc7a02a70946ad36e0cf76ce815f37f04db82227581ea2a60 | Jupyter | 1,577 | 46 | # %% [markdown]
# # LLM Tutorial
#
# This walks through using OAK through an LLM wrapper.
#
# See also [How-to guide](https://incatools.github.io/ontology-access-kit/howtos/use-llms.html).
#
# Note for this to work, you must either install OAK with llm extras, or do a separate install
# of `pipx install llm`.
#
# Y... |
dd8ce9097e7a8b20608d3544435fc01f1f9775b88fd1162b1c3b3ba1a175fe03 | Jupyter | 1,593 | 53 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import sys
sys.path.append('../../')
from scripts.viz_tools import animate_patch
from scripts.dl_utils import download_patch, rect_from_point
# %%
# This function takes a while to run as it is extracting data from GEE
rect_width = 0.02
coords = [95.347998212902... |
469a625447745799d90ac1a519eca445350e7674ac349b3ecf4907d50e1a5da3 | Jupyter | 1,609 | 52 | # %%
import numpy as np
from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D, Dropout
from keras import backend as K
from keras.optimize... |
33b3b1ccdedc6cebf1eb63fac1e4a19d19080c7231d3ec7328b5deb3415caa44 | Jupyter | 1,618 | 63 | # %% [markdown]
# # OAK validate-mappings command
#
# This notebook is intended as a supplement to the [main OAK CLI docs](https://incatools.github.io/ontology-access-kit/cli.html).
#
# This notebook provides examples for the `validate-mappings` command.
# This forms part of a suite of *validate* commands.
#
# #... |
2df45ecc95aa80fd01d1753340ade6d38e1f693789f566f3fd163cdc0c7368f7 | Jupyter | 1,619 | 79 | # %%
%load_ext autoreload
%autoreload 2
import tifffile
import numpy as np
import matplotlib.pyplot as plt
from skimage.filters import gaussian
from dask_image.ndfilters import gaussian_filter
from dask_image.imread import imread
from palmari.processing.steps import BaseDetector
from palmari import ImagePipeline, Acqui... |
f066ccbfa98b259af13e3916453a16eb20b12e2369649dd461235fa3c0f44b8b | Jupyter | 1,630 | 58 | # %% [markdown]
# # Plot cross-correlation between dlight and syllable frequency
# %%
import toml
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from os.path import join
from rl_analysis.plotting import save_factory, bootstrap_lineplot, add_legend, fg, setup_plotting_env, clean_ticks
# %%
... |
2e03cc5f8df05e9d25f6223a468b8b0ace803bb478d42b3e557b2c8ee122d92d | Jupyter | 1,640 | 48 | # %%
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy.sparse
import sys, os
from tqdm import tqdm
# %%
file_path = '/media/thijs/hooghoudt/Zebrafish_data/spontaneous_data_guillaume_T22/'
list_files = [x for x in os.listdir(file_path) if x[-3:] == '.h5']
n_ds = len(list_files)
list_files
#... |
433eb13592bdd2d3fd4c2bdc3f0b957691f2fec72a3370cc12aaa347ad726b3a | Jupyter | 1,657 | 72 | # %%
%load_ext autoreload
%autoreload 2
from palm_tools.data_structure.trackset import TrackSet, TrackSets
from glob import glob
import pandas as pd
import logging
logging.basicConfig(level=logging.DEBUG)
from palm_tools.utils.read_file import check_file_and_read
import os
# %%
def df_is_valid(df: pd.DataFrame, is3D: ... |
bb302f6664d83808a2be9ac04bda22ee5a55d090467ed60356378653e96c62a2 | Jupyter | 1,667 | 63 | # %% [markdown]
# * blog post: [Region of interest pooling explained - deepsense.io](https://deepsense.io/region-of-interest-pooling-explained/)
# * repository: [deepsense-io/roi-pooling](https://github.com/deepsense-io/roi-pooling)
# %%
from __future__ import print_function
import tensorflow as tf
import numpy as np... |
644f457e40cd77e7c11312a88998ff2043d3c7177808c52334729318c0f24e4a | Jupyter | 1,698 | 60 | # %% [markdown]
# # Run Pixel Classifier on a Test Sites
# Note: The script `nn_predict.py` also works well for this purpose if evaluating a single site.
#
# This functionality is also contained within the `train_pixel_classifier` notebook for the purposes of evaluating a trained model.
# %%
%load_ext autoreload
%aut... |
79c87e49eb242f47153a533072b2007e150cfd61a28099bbf122c1dd6f6ee18f | Jupyter | 1,702 | 56 | # %% [markdown]
# ## PDBBind preprocess
# %%
from data_pipeline.pdbbind_preprocess import PDBBindPreprocess, pdbbind_dataset_generate
preprocess = PDBBindPreprocess(core_path='.\data\\raw_files\PDBbind2016\core-set\\',
refined_path='.\data\\raw_files\PDBbind2016\\refined-set\\')
prepr... |
161f2464f464e0431719c687ea6020c4f48127cc27b1803558e5657d8ddc77d8 | Jupyter | 1,707 | 75 | # %% [markdown]
# # OAK paths command
#
# This notebook is intended as a supplement to the [main OAK CLI docs](https://incatools.github.io/ontology-access-kit/cli.html).
#
# This notebook provides examples for the `paths` command, which can be used to query for paths between ontology terms
#
# ## Help Option
#
# Yo... |
dde66dc7a8a4eb04096b6c3d0c0751b1072141465a75d55aae8b0b3422f07edc | Jupyter | 1,707 | 74 | # %% [markdown]
# ### Plot dF/F response to trains of APs
#
# * Simultaneous ephys-ophys data were recorded by Marton Rozsa <marton.rozsa@alleninstitute.org>
# * Parameter fits were done by Ziqiang Wei <weiz@janelia.hhmi.org>
# * Model parameters were reported in Table S8
# * Single-cell statistics were reported in Su... |
759214292450e81d5f61c1b5fc9bd746c9c076a9674844d8320876331af3e6da | Jupyter | 1,713 | 48 | # %% [markdown]
# # OAK termset-similarity command
#
# This notebook is intended as a supplement to the [main OAK CLI docs](https://incatools.github.io/ontology-access-kit/cli.html).
#
# This notebook provides examples for the `termset-similarity` command, which can be used to do an aggregate comparisons between
# tw... |
2e6b6144e09d5549cfe489d80961949637ca2d0991c80b68c247d5e595b08deb | Jupyter | 1,723 | 74 | # %%
import pandas as pd
from sklearn import preprocessing
from gnnad.graphanomaly import GNNAD
from gnnad.plot import plot_test_anomalies, plot_predictions, plot_sensor_error_scores
def normalise(X, scaler_fn):
scaler = scaler_fn.fit(X)
return pd.DataFrame(scaler.transform(X), index=X.index, columns = X.colu... |
3d2540da2049e8f756514c76633b1b1344e229af062936166e5184fbd130e179 | Jupyter | 1,724 | 61 | # %%
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy.sparse
import sys, os
from tqdm import tqdm
# %%
file_path = '/media/thijs/hooghoudt/Zebrafish_data/spontaneous_data_guillaume_T22/'
list_files = [x for x in os.listdir(file_path) if x[-3:] == '.h5']
n_ds = len(list_files)
list_files
#... |
fde8641897a64eb0aef3065492911821f3f09651e73d83086f0c313a44c628d6 | Jupyter | 1,727 | 69 | # %%
import os
import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import NMF
# %%
with open('../data/pixel_vectors_all.pkl', 'rb') as f:
pixel_vectors = pickle.load(f)
pixel_vectors = np.array(pixel_vectors) / 3000.0
# %%
with open('../data/pixel_vectors_labels_all.p... |
278236385d3b7aa2f04504edf29a06990cf6b3fb33dfcc157782014e0f1c1f9b | Jupyter | 1,733 | 61 | # %% [markdown]
# # Library
# %%
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.metrics import auc
from sklearn.metrics import RocCurveDisplay
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LogisticRegression
# %%
# mynewlist contains th... |
b26413ee293d20e61e687948390f6c4e25a187255b8b1dca814b78401482fe9d | Jupyter | 1,733 | 62 | # %% [markdown]
# # Library
# %%
# install missing libraries if failed to import libraries
import torch
import torchvision
import timm
import torch.nn as nn
# %% [markdown]
# # Model
# %%
class ImageAndClinicalModel(torch.nn.Module):
def __init__(self):
super(ImageAndClinicalModel, self).__init__()
... |
d085963cfd027937def6af7c926fc3be94723acd2bfd7976519eccb19d3e5d12 | Jupyter | 1,750 | 73 | # %% [markdown]
# # Plot the differentially expressed genes on some significant pathways
# %%
library(pathview)
# %%
# load the sample data
#data(gse16873.d)
# options for comp_name:
# LiLRvsNR
# CtrlLRvsNR
# comp_name = 'LiLRvsNR'
# # load the differentially expressed genes
# LFC_temp = read.table(paste('DE_',com... |
34a9ac07b436189f9f79b966ea4291eadea3c9e962dfd4110c14a524d24013af | Jupyter | 1,751 | 63 | # %%
import matplotlib.pyplot as plt
import numpy as np
# %%
ls
# %%
masif_fpr100 = np.load('masif_fpr100.npy')
masif_tpr100 = np.load('masif_tpr100.npy')
probis_fpr100 = np.load('probis_fpr100.npy')
probis_tpr100 = np.load('probis_tpr100.npy')
kripo_fpr100 = np.load('kripo_fpr100.npy')
kripo_tpr100 = np.load('krip... |
621f9d2f1584a165c40991841c2ecc2ee03a304504b734c01112e9b88b12911f | Jupyter | 1,757 | 94 | # %%
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
import math
from scipy import stats
# %%
# specify the datasets for your plot
df = pd.read_csv('/Users/lbreima/Documents/PhD_local/Manuscripts/2021_Sam_zipcodes/database/total_database.csv')
# %%
df.head()
# %%
map2 =... |
e91f19fc16e43587c54a80357990d45b42a78631ed0f1c3d31f68254a22b7f9e | Jupyter | 1,761 | 85 | # %%
import cxsystem2.neurodynlib as nd
from brian2.units import *
import brian2 as b2
import matplotlib.pyplot as plt
b2.codegen.target = 'numpy'
# %% [markdown]
# ## Creating a neuron
# %%
x = nd.EifNeuron() # try also with: EifNeuron, AdexNeuron, HodgkinHuxleyNeuron
# %%
x.get_neuron_equations()
# %% [markdown... |
d627473766115f2349b8baf88c5d34e74c4d463df2f8a96e5301a485f4e73ab2 | Jupyter | 1,807 | 102 | # %% [markdown]
# # Example of handling bad molecules
#
# If you give a bad conformer, of course xTB will not be able to calculate any properties.
# This is an example of how ppqm handles that
# %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
import logging
import sys
# %%
from rdkit import Chem
# %%... |
e14a07b216e5e5bcffc1e149d5586f2c2132c0433cb7d58f911ad594e844ad5e | Jupyter | 1,833 | 94 | # %%
from oaklib import get_adapter
from oaklib.utilities.associations.edge_information_util import associations_to_matrix
ont_adapter = get_adapter("sqlite:obo:go")
# %%
taxa = ["9606", "6239", "7955", "3702", "559292", "4896", "7227", "10090", ]
# %%
matrix_by_taxon = {}
for tax in taxa:
assoc_adapter = get_... |
6dffbd56f238b1cb933ea5c6f9a8a37b8975a9513914b17e8cc82a7c059775ea | Jupyter | 1,842 | 40 | # %% [markdown]
# CAVE is the Connectome Annotation Versioning Engine.
# It is a collection of services which manage proofreading and data annotation for large scale connectomics dataset.
# These services were developed during the IARPA MICrONS program, but now support data from several projects including FlyWire. You... |
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