sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
5868d6bb47bf70fe9fc03a7499a44fbe6052e57b2bce44fd0795c8f22c3e3246 | Jupyter | 3,983 | 88 | # %% [markdown]
# **Log in to CATMAID instance**
# <br>
# After importing pymaid and contools, you must log in to your CATMAID instance. You will need to provide your username, password, token, and the url of the CATMAID instance.
# Here, these data are stored in pymaid_creds.py and imported into the script. Make sure ... |
fce61342987c6fdb9d8af9911c46fc20ad8ff8608f58dfcdaf3d162757455983 | Jupyter | 3,983 | 143 | # %%
import sys
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import seaborn as sns
import pandas as pd
sys.path.append("../../ndsvae/")
import ndsvae as ndsv
sys.path.append("../")
import util
import plotutils as pu
%matplotlib inline
# %%
config = "ns_3_mr... |
053de2db7be1b076f92ac8795973d430ea5ef381489fc7a0e376f9ef359fafa9 | Jupyter | 3,988 | 120 | # %%
from rdkit import Chem
from syba.syba import SybaClassifier
import pandas as pd
import matplotlib.pyplot as plt
# %%
syba = SybaClassifier()
syba.fitDefaultScore()
# %%
data = pd.read_excel('All_generated_SMILES_SYBA_filtration.xlsx')
# %%
data.head()
# %%
whole_data_after_QED_Lipinski = pd.read_excel('All_gen... |
601a1bcccee240e2b1073b0c3e7d28b0c5a3bd5ffc64a8d15eb82537f47e6c7d | Jupyter | 3,997 | 160 | # %%
import navis
# %%
# Load example neurons
nl = navis.example_neurons()
# %%
# Convert neurons into microns (they are 8nm)
nl_um = nl / (1000 / 8)
# Generate dotprops yourself
dps = navis.make_dotprops(nl_um, k=5, resample=False)
nbl = navis.nblast(dps[:3], dps[3:], progress=False)
nbl
# %%
# Alternatively:
nbl... |
e3ad2012e0ebb382f9db64447403cf59d9370507bd7250cf19213f5c87cd3f0a | Jupyter | 4,007 | 165 | # %% [markdown]
# # Compartment comparison
# Comparing the mitochondria volume and mitochondrial complexity index across axons, somas, basal dendrites, and apical dendrites.
# %%
from meshparty import trimesh_io # first import to remove static TLS error
from collections import Counter
import numpy as np
import pa... |
986c02869e92bee92b15dd6f9ff8c9531e09090c9bc5eff2bfbdf8d505a3c24b | Jupyter | 4,009 | 146 | # %% [markdown]
# # Using OAK for Environmental Ontologies
#
# * author: Chris Mungall
# * created: 2022-10-24
#
# This tutorial walks through the neuro-relevant subset of the [ENVO](https://obofoundry.org/ontology/envo) as well as SWEET.
#
# - to help understand the structure of ENVO
# - to show how to do advanced ... |
66c6309ef1f84b53e0c66cc86390c25b1c4014a20861eb757a07e8cbf8b5ef7b | Jupyter | 4,010 | 133 | # %% [markdown]
# # Use SMILES corrector for exploration
#
# This version demonstrates how to create analogues for desired molecule. It shows how to introduce errors into the molecule, initialize the SMILES corrector & fix the errors
# %% [markdown]
# # Setup
# %%
import torch
import os
import pandas as pd
import n... |
960a2bfd94b667ffc2c3f467aa24113ba77324c5ec7357e8a7905c0573863c39 | Jupyter | 4,092 | 95 | # %% [markdown]
# # Finetuning a Pretrained Model
#
# The pretrained model usually captures general chemistry rules and as such is best trained on a diverse set of molecules (see [pretraining](./pretraining.ipynb)). However, in drug discovery we usually want to bias such a model to a more focused area of chemical spac... |
6abd4bed9c5b2f08bf56ecfaae382c74628c6f5c4a67080c07271abd07221f24 | Jupyter | 4,100 | 121 | # %%
%load_ext autoreload
%autoreload 2
# %%
import descarteslabs as dl
import ee
import geopandas as gpd
import json
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import shapely
import shapely.geometry
from scripts import dl_utils
# %%
def get_country_boundary(country_name):
ee.Initiali... |
cf3f0b413b2ebda21edd07f69e59c1b432d3cebbcc31c2f0aeeda6e41bda6548 | Jupyter | 4,106 | 183 | # %%
import navis
import matplotlib.pyplot as plt
nl = navis.example_neurons(kind='skeleton')
# Plot using default settings
fig, ax = nl.plot2d(method='2d', view=('x', '-y'))
plt.show()
# %%
# Plot using matplotlib's 3D capabilities
fig, ax = nl.plot2d(method='3d_complex')
# For 3D plots you can use `azim` (azimuth)... |
dffb8905155d1baf3087726c3a55979e5a8c75723802208cc02d914f7caca1b3 | Jupyter | 4,106 | 102 | # %% [markdown]
# # 1. Get MRI wise accuracy
# %%
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score
valid_data = pd.read_csv('../neurosurgeon35_data/neurosurgeon35.csv', index_col='Respondent ID')
xai25 = pd.read_csv('../computational_data/xai25.csv')
# need XAI level
will_check_xai =... |
cb8635035960767c848d2a1b23565020b52869d6d69242073220b5a864e3b740 | Jupyter | 4,125 | 153 | # %% [markdown]
# # Population effects of $I_A$ in a toy model
#
# $I_A$ increases latency and decreases population firing rate. Shown in fig. 2.
# %%
from common import colors, sbarlw
# %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
from __future__ import division
import numpy as np
impor... |
39eafd1dbf35773d2444d47c0835215c70f7bf7b19c46d2967558bc749528e17 | Jupyter | 4,135 | 132 | # %% [markdown]
# #### Imports
# %%
# Visualization
import matplotlib.pyplot as plt
# Builtin
from pathlib import Path
import sys
import subprocess
import os
import pdb
# Add repo path to system path for additional imports.
# Note the relative depth from this notebook to system repo
# in case you want to move this ... |
3fb4d865dbb544d15947159b0f61b6587531b3879b1693495f0ebf02dcd6d594 | Jupyter | 4,149 | 202 | # %% [markdown]
# # Example of XTB on a ChEMBL compound
#
# From a ChEMBL SMILES
#
# - Generate 3D conformers with RDKit
# - Optimize the conformers with XTB
# - Update the molecule with new conformers
# - View the conformers
# - Calculate Fukui coefficients for the conformers
# %%
%load_ext autoreload
%autoreload 2... |
0512742cda41e9c83ecb973b98066185f1a5a9d7ad9f063ecf201f733355e698 | Jupyter | 4,159 | 166 | # %% [markdown]
# # Compute non-stationarities in behavior across a 30 minute sessions
# %% [markdown]
# 1. Get variables
# 1. Standardize variables
# 1. Cluster
# 1. Print to toml
# %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from rl_analysis.plotting import savefig, setup_plotting_env
from rl_ana... |
c844d72ff93d4276c29e0bee13e55ff9fc6e50f056768f3861db91f3e666254a | Jupyter | 4,169 | 156 | # %% [markdown]
# # GO Subset Analysis
#
# Analyze GO ribbons, exploring the degree of overlap between ribbon terms
# %%
# Basic OAK setup
from oaklib.datamodels.vocabulary import IS_A, PART_OF
from oaklib import get_adapter
go = get_adapter("sqlite:obo:go")
# %%
import pandas as pd
import numpy as np
# %%
# TODO:... |
7a83525ea510ec1c10f1f72fcc0242284293f8ff0376bd7ee4baf3904d65ead2 | Jupyter | 4,193 | 125 | # %%
from scipy import stats
from sklearn.cluster import AgglomerativeClustering, KMeans
from sklearn.decomposition import PCA
import json
import numpy as np
import pandas as pd
import seaborn as sns
# %% [markdown]
# ## Load DrCovid prediction matrix
# %%
! wget -O drcovid_results.json https://gist.github.com/EightS... |
5d655b10c864c45e2cb67abba7c30eb320ae1e68b1228be22fd0a6be50fbec8b | Jupyter | 4,209 | 97 | # %%
import os
os.chdir('/content')
CODE_DIR = 'TediGAN'
if not os.path.exists(CODE_DIR):
!git clone https://github.com/weihaox/TediGAN.git $CODE_DIR
os.chdir(f'./{CODE_DIR}')
MODEL_DIR = os.path.join('base', 'models', 'pretrain')
os.makedirs(MODEL_DIR, exist_ok=True)
!pip install ftfy regex tqdm
!pip install git+h... |
9f00fbb8dff777d862a46ca365db251c92b568fcfd08a609f966468f78bf550c | Jupyter | 4,230 | 120 | # %% [markdown]
# # Create Pixel Dataset
# This notebook is the source for downloading Sentinel data to produce inputs to the spectral classifier.
#
# ## Inputs
# The notebook operates by loading a set of sampling sites from a geojson. If the geojson contains `Point` features, a bounding rect is constructed. If the ge... |
4462d05fe0681bea87561fe61402997813c777f5dcd295d0e2fa7a7a4ba41f3e | Jupyter | 4,238 | 160 | # %% [markdown]
# # PhenIO tutorial
#
# * author: Chris Mungall
# * created: 2022-08-16
#
# This tutorial walks through phenio, the goals are:
#
# - to help understand the structure of phenio
# - to show how to do advanced OAK queries (CLI and programmatic) on PhenIO
# - to explore semsim techniques
# %% [markdown]... |
1eea13cf7061570705520a560cb4e62138a41c7b4c3a5df0f58d1e2f6f9e0da3 | Jupyter | 4,255 | 143 | # %%
import torch
from transformers import AutoTokenizer, AutoModel, AutoConfig
import pandas as pd
import pickle
# %%
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
# %%
data_dir='/home/mahbub/Desktop/sepsis_text_IoMT_project/data/embedding_generation_data/'
# %%
from transfo... |
bde9d7e78173ffb0bbfd527236e6fc9e2140a1f2e28fc0bf936236d5f78b5c5b | Jupyter | 4,305 | 144 | # %%
import cloudvolume
import pandas as pd
from imageryclient import ImageryClient
import matplotlib.pyplot as plt
import numpy as np
# %%
syn_df = pd.read_csv('data/soma_subgraph_synapses_spines_v185.csv')
# %%
img_source = "precomputed://https://storage.googleapis.com/microns_public_datasets/pinky100_v0/son_of_ali... |
04cba2befe7095b2b6082a72e3073ab4c6054035e4559d334d613126faa6db2c | Jupyter | 4,318 | 121 | # %%
import pickle
import numpy as np
import os
import warnings
import time
import cortex
# %%
def save_3d_views(data, root, base_name, list_views =['lateral'],list_surfaces = ['inflated'], with_labels = False,
size=(1024*5, 768*4), trim=True):
"""Saves 3D views of `data` in and around `root` und... |
ffc8fb4aca995ec5c4924e333e2d2cfb2682955db48ac3bd6029ea0361ce82af | Jupyter | 4,323 | 215 | # %%
from toolz import partial
from tqdm.auto import tqdm
from rl_analysis.plotting import setup_plotting_env
from rl_analysis.maya.process import (
add_area,
add_trial_count,
add_onset_and_duration,
rle,
)
from rl_analysis.util import zscore
from rl_analysis.io.df import exclude_uuids
import pandas as ... |
2fd213ff7bd9d27e51df87ab789169aaee5b89cbfe323efe6300e9a71b2b8e82 | Jupyter | 4,345 | 160 | # %%
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import time
from helper import *
from ALsim import ALsim
import sim
import sys
import os
from ALsimParameters import std_paras
import random
# %%
"""
This model run is designed to generate some example traces of neuron ac... |
acbf2a1633e9de3faac658ca1a450d80e6b1c9ded7163ac363c31264ff4f06b0 | Jupyter | 4,381 | 151 | # %% [markdown]
# # NIRSimple example
#
# → [Download this notebook](https://github.com/HanBnrd/NIRSimple/blob/master/docs/source/examples/simple-probe.ipynb)
# %%
import scipy.io
import numpy as np
import nirsimple as ns
# %% [markdown]
# The data used in this example is from the [Homer MATLAB toolbox tutorial... |
e01624b1ff78338048a03cc774a0a666810d7423afbc3106ef4b407414e5e41e | Jupyter | 4,402 | 153 | # %% [markdown]
# # Align dLight data (plus other features) to syllable onset within a window
#
# Each window is then concatenated together to create one (very large) dataframe.
# Useful for computing syllable-triggered averages and syllable averages.
#
# Note that you'll need to re-run to get online, offline, and mo... |
a254a0894c3938ac2bb6b4d26ee5d4268404ad61a35cc21f6543095310f7044b | Jupyter | 4,403 | 149 | # %% [markdown]
# # Test set predictions of best performing GIF models
#
# Representative samples are shown in fig 3.
# %%
from common import colors, sbarlw, savefig
# %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
import pickle
import warnings
import pandas as pd
import matplotlib.pyplot ... |
b6624e60d9dc3977724059f8282855c74427e3e749571a2e0cd07942dd41a9ad | Jupyter | 4,405 | 135 | # %%
import numpy as np
import os
from pathlib import Path
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
hfont = {'fontname':'Myriad Pro'}
import matplotlib.ticker as mtick
from matplotlib import gridspec
import matplotlib.colors as colors
import sys
sys.path.append(os.path.jo... |
77e6ef9a756f91e18c591c7961284760c7516bf83941c0b708901b69469c485a | Jupyter | 4,417 | 141 | # %% [markdown]
# # DELCODE WMH Segmentation Pipeline
# %%
from IPython.display import Image
# %%
Image('./imgs/tree.png')
# %% [markdown]
# # Integrating in another workflow
# %% [markdown]
# We want to segment the WMHs in our dataset while integrating our sysu_media implementation in a bigger preprocessing workf... |
44bdc5557f4269695bf7589144ff2974e6506749f0454cf231579f393755defd | Jupyter | 4,424 | 155 | # %%
import cxsystem2.neurodynlib as nd
from brian2.units import *
import brian2 as b2
import matplotlib.pyplot as plt
b2.codegen.target = 'numpy'
# %% [markdown]
# # Making networks
# %% [markdown]
# While neurodynlib is intended for exploring neuron models, you can use it to build network simulations as well. Some... |
71227a61e2ec4942212c598a9e20eb72778f5da9b43fb16426b7297febda4325 | Jupyter | 4,465 | 188 | # %% [markdown]
# # Compute the timecourse of cumulative syllable counts
# %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from collections import defaultdict
import pandas as pd
import os
import numpy as np
# %%
import toml
with open("../analysis_configuration.toml", "r")... |
0626488b2989e5afd1f98c91df93b6da1fc21278c5b7861ae6a62bb104b2dd4e | Jupyter | 4,469 | 127 | # %% [markdown]
# # Transformer Dose Calculation
# %% [markdown]
# ## Import libraries and define auxiliary functions
# %%
import h5py
import numpy as np
import random
import math
import json
import sys
sys.path.append('./src')
from models import dota_energies
from preprocessing import DataRescaler
from generators i... |
d6eb27a987a60a9e00be14ffc8068d8474b1a702c2a82a18bd178566800c67f5 | Jupyter | 4,492 | 125 | # %% [markdown]
# <div style="background: #DFF0D8; border-radius: 3px; padding: 10px;">
# </b> Importing the key modules and set parameters
#
# </div>
# %%
from meshparty import trimesh_io, trimesh_vtk
from caveclient import CAVEclient
import numpy as np
import pandas as pd
import cloudvolume
import itkwidgets
#set... |
4a637c001307a593b5bf780b29d100d974dc772cf62791cd7332b6978b5c27e0 | Jupyter | 4,514 | 112 | # %%
%pylab inline
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.ticker as tkr
TF_list = ["LHX2", "NeuN", "Olig2", "PU1"]
TF_actual_name = ["Astrocy... |
78039a161584d3eab175e2e73847a04fad4d832d76d581d6dddafd40466853ea | Jupyter | 4,515 | 131 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from os import listdir
# %%
def find_csv_filenames( path_to_dir, suffix=".csv" ):
filenames = listdir(path_to_dir)
return [ filename for filename in filenames if filename.endswith( suffix ) ]
# %%
sns.set_co... |
10eaab859cbeb2314d82c20eb0fcbc09ad19d52ec7e0241dbc56cda6d669654b | Jupyter | 4,521 | 111 | # %% [markdown]
# # Ubergraph Tutorial
#
#
# [Ubergraph](https://github.com/INCATools/ubergraph) is a SPARQL endpoint serving multiple OBO ontologies, pre-processed with:
#
# - relation-graph
# - information content scores
# - biolink categories
#
# This notebook demonstrates the use of OAK with the [ubergraph a... |
903f03ae1a266da99d76a5c99edf34fe77b34865692dc92a91f09b718dcebac2 | Jupyter | 4,541 | 147 | # %% [markdown]
# # OAK disjoints 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 `disjoints` command, which can be used to **lookup and summarize disjointness axioms**
#
# For more... |
c66b38910f20b81a5f0e0a47a8cf2223dad70cf165313930c5cde593314fea05 | Jupyter | 4,542 | 162 | # %% [markdown]
# # Coregistration
# %% [markdown]
# - Three different images need to be coregistered: EM, 2p structural stack, and 2p video.
# - 2p structural image is used as reference. EM and 2p video are coregistered to 2p structural image
# %%
import numpy as np
import pandas as pd
from pydrive.auth import Goog... |
d7d9b3c9d3ab5b8b8a54540dca34dd8077b67544f55b259ccc39f5f41a1afa1b | Jupyter | 4,581 | 168 | # %%
import os
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
sns.set(context='notebook', style='white', font_scale=1.2)
# %%
try:
import crossmapy as cmp
except:
import sys
sys.path.append('../')
... |
83a002f8b74d5fdd7a52b6318d419bc278569c934f2add2c529ed89184bac844 | Jupyter | 4,590 | 139 | # %% [markdown]
# Correlation
#
# For this analysis, I need to check if there is a relationship between the predicted values (model) and the values I know (y). Whether this relationship is positive or negative.
# %% [markdown]
# ### Organization of libraries
# %%
#salvar/carregar arquivos em diferentes formatos
impo... |
2ebd7b3fac12bfe07a2131424a9e0d83b7a08c44298aeade1aa0272a5f8b64f6 | Jupyter | 4,592 | 120 | # %% [markdown]
# # OAK relationships 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 `relationships` command, which can be used to lookup direct and inferred relationships
# between... |
d386a8c46fe7bed35d4b81ddc438d300b6c87a2ebd12c3a0f4240c8f11084eb6 | Jupyter | 4,606 | 186 | # %% [markdown]
# # Inspect single neuron model filters
#
# This notebook visualizes the membrane ($\kappa$), spike-triggered current ($\eta$), and spike-triggered threshold movement ($\gamma$) filters of GIF-type models fitted to 5-HT neurons. `5HT_heated` data was recorded at 29C to 30C by Michael Lynn in winter/spr... |
fcb4f6d9e498ccd74e15caea766b949251c2692bd4797e414b97b831b4b32801 | Jupyter | 4,628 | 177 | # %%
import csv
import numpy as np
import pandas as pd
from scipy import stats
from pycocotools.coco import COCO
import numpy as np
import skimage.io as io
import matplotlib.pyplot as plt
import pylab
import random
import numpy as np
import json
# %%
from scipy.cluster.vq import vq, kmeans, whiten
# %% [markdown]
# #... |
f85633aed85efc5b44829e4d295383af334b182ab4b16335290adcdb4600c1c0 | Jupyter | 4,654 | 150 | # %%
import os
import sys
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import pickle
import rbm, RBM_utils
num_threads = 10
os.environ["MKL_NUM_THREADS"] = "%s" % num_threads
os.environ["NUMEXPR_NUM_THREADS"] = "%s" % num_threads
os.environ["OMP_NUM_THREADS"] = "%s" % num_threads
os.environ["O... |
43e9b05e0cbb1057b864bb651cab64095315c4c4e5a4cd554a8cc185809ba9d3 | Jupyter | 4,660 | 168 | # %%
import os
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
beetle_img = []
img_dir = '/Users/ckruse/Downloads/assets'
for file in sorted(os.listdir(img_dir)):
print(file)
img = Image.open(os.path.join(img_dir, file)).convert('RGB')
beetle_img.append(np.asarray(img))
# %%
for ... |
f58da1e782306db2f0d431240e8d4be282b69b403535261a55e2ef1c53328d18 | Jupyter | 4,670 | 151 | # %% [markdown]
# # Code to find outliers from MAGeT segmentation
# ## Uses brain masks from fMRIPrep and MAGeT segmentation volumes from CSVs (../results/) generated by "collect_volumes.sh" script part of [MAGeTBrain pipeline](https://github.com/CobraLab/documentation/wiki/MAGeTBrain#collecting-volumes)
# %%
import n... |
281f60c830e9bcadbac82463f2d390e2ea90769dcc2d0853a2e345dd9bb90661 | Jupyter | 4,672 | 169 | # %%
import numpy as np
import matplotlib.pyplot as plt
import sys
from helper import *
# %%
label="2021-07-22-runs/run_hom"
gid="0.0"
pop= "PNs"
N= 800
spike_t= np.load(label+"_"+gid+"_"+pop+"_spike_t.npy")
spike_ID= np.load(label+"_"+gid+"_"+pop+"_spike_ID.npy")
t_total= spike_t[-1]
# %%
mlabel="2021-07-22-runs/ru... |
5f5ebf1b75850a551c616c294945365bac0d2424beec27cc206fda351b130973 | Jupyter | 4,681 | 163 | # %%
import pandas as pd
from rdkit import Chem
from rdkit.Chem import Draw
import warnings
warnings.filterwarnings('ignore')
# %%
all_set = pd.read_excel('All_generated_SMILES_SYBA_filtration.xlsx')
# %%
all_set.head()
# %%
all_set
# %%
all_mols = [Chem.MolFromSmiles(smi) for smi in all_set['SMILES']]
# %%
Draw.M... |
d223c61b16ca5e71cf732381374ef1ee12abb77baa3281076c70c0681802e083 | Jupyter | 4,681 | 163 | # %%
from rdkit import Chem
from syba.syba import SybaClassifier
import pandas as pd
import matplotlib.pyplot as plt
# %%
syba = SybaClassifier()
syba.fitDefaultScore()
# %% [markdown]
# ### ROR-gamma active used to predict new structures selection
# %%
initial_smi = pd.read_excel('ROR_gamma_active_QED_Lipinski.xlsx... |
b6630dfb7ac3d0f92dd9c2dfd6c9a87694bb854b3e15ade8a1be0cfc87df7cd2 | Jupyter | 4,683 | 189 | # %%
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", "ALL"]
# %% [markdown]
# %%
ic_map_by_taxon = {}
for tax in taxa:
... |
61acba84773b84e9e7304bf4f522a80cf315dcbe93ed3200f1e2fdfdb2831cbc | Jupyter | 4,693 | 123 | # %% [markdown]
# ## Seperately transformed
# %%
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
allDat = np.load('./tan_proj.npz', allow_pickle=True)
tr, rs = allDat['task_residuals'], allDat['resting_state']
trDist = np.linalg.norm(tr - np.mean(tr, axis=0), axis=-1)
rsDist = np.linalg.n... |
0ab9fdd21205d042349b98d2a0dd25cd5792892e95e4fd07b5eea8119b777078 | Jupyter | 4,704 | 132 | # %% [markdown]
# # Common Neighbor Rule in PyC Subgraph (Th=100)
# %%
import os
import random
from time import time
import pandas as pd
import numpy as np
import networkx as nx
from utils.utils import *
from utils.proximity import *
from utils.graph_creation import *
from utils.visualization import *
from utils.comm... |
9a96f0922fb11cd237d1c66ceb92d0ffca5ca439bfa69107075ccaf18643b1c6 | Jupyter | 4,704 | 175 | # %% [markdown]
# # Plot syllable waveform derivatives
# %%
import toml
import numba
import random
import joblib
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import signal
from os.path import join
from rl_analysis.behavior.moseq import syll_onset
from rl_analysis.plotting im... |
c918d824217ff0cd8e99191266052242f29c7b00edff2e1929e393f8c778d33b | Jupyter | 4,731 | 173 | # %%
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import time
from helper import *
from ALsim import ALsim
import sys
import os
from ALsimParameters import std_paras
import simimport random
# %%
"""
experiment to investigate the effect of decreasing response with higher ... |
8b8dc77f34da9703d42c4feedc9960e72787467e94bc4378f5146bb5e4ce7328 | Jupyter | 4,734 | 153 | # %%
import sys
sys.path.append('../source/')
sys.path.append('../utilities/')
from common_imports import set_num_threads
set_num_threads(2) # Set the number of cores. Must be executed before importing numpy&numba.
import numpy as np
import bm
import utilities
from scipy.linalg import eigh
import matplotlib.pyplot as p... |
b944573bbd8d9acb3719babee2e597132f1601123946a097a3a75e7fd78ef421 | Jupyter | 4,742 | 139 | # %%
import sys
sys.path.append('../Documents/GitHub/PGM/utilities/')
sys.path.append('../Documents/GitHub/PGM/source/')
import numpy as np
import rbm
import matplotlib.pyplot as plt
%matplotlib inline
# %% [markdown]
# # Generate synthetic dataset
# Generate dataset of continuous, non-negative, correlated signals.
# ... |
d74251c9510dc2a5584e170ba99166f3b7a963bf394ffbef6bb0bfe0f446606f | Jupyter | 4,755 | 167 | # %%
import cv2
import datetime
import descarteslabs as dl
import descarteslabs.workflows as wf
import glob
import json
import matplotlib.pyplot as plt
import moviepy.editor as mpy
import numpy as np
import os
from osgeo import gdal
# %%
# focus on Bengkala site for now
input_dir = '../data/s2/TPA_Bengkala'
input_file... |
fd6868916693a64f19f3018120271153e40543a8ecfc267a4d964ae71b70735c | Jupyter | 4,760 | 181 | # %% [markdown]
# #### Imports
# %%
# Visualization
import matplotlib.pyplot as plt
# Builtin
from pathlib import Path
import sys
import subprocess
import os
import pdb
# Add repo path to system path for additional imports.
# Note the relative depth from this notebook to system repo
# in case you want to move this... |
47dcfb77904a40ccb66d4136f40f22c14019ad8d19894fbc5ba59b970b8eb03a | Jupyter | 4,781 | 181 | # %% [markdown]
# # Plotting feature importances
# %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer, load_boston
from ... |
b35f1ab52152164494bf8bf868fcf1fed38c180a92da606fbb27acc09c60a65c | Jupyter | 4,787 | 137 | # %% [markdown]
# # Equivalence between RBM with Gaussian hidden units and BM
# RBM with Gaussian hidden units are equivalent to BM with a coupling matrix of rank n_h. Here we define the transformation
# %%
import sys
sys.path.append('../source/')
sys.path.append('../utilities/')
from common_imports import set_num_thr... |
37bf3ed266a5ee1b73cf5cbf894312e3aea8b2cf0a008fed3736fe5dcf41fb0f | Jupyter | 4,804 | 174 | # %% [markdown]
# # DataJoint usage example
# - Examples fetching data from the DataJoint database.
# %%
import numpy as np
import pandas as pd
import datajoint as dj
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns
# from scipy.stats import pearsonr, percentileofscore
f... |
0f6d8b72016e9175a7dff7554539a5fbdafe43eb4a111b2269b044f38b7862e5 | Jupyter | 4,817 | 169 | # %%
import pytorch_lightning as pl
# %%
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
# %% [markdown]
# # Example of subdiffusive 2D walks
# %%
models = ["fBM","sBM","LW"... |
82d5db08443986506b85c088d137fccd7233a4fc88ef35ed21eb64607a9bd254 | Jupyter | 4,843 | 158 | # %%
from oaklib.implementations.pronto.pronto_implementation import ProntoImplementation
from oaklib.resource import OntologyResource
import pandas as pd
#from pandasql import sqldf
import duckdb
from scipy import stats
# %%
yeast_annot = pd.read_csv("../4932.tab",sep="\t",header=0)
# %%
yeast_annot.head()
# %%
#ye... |
abbadb569ce6fe94281d7ec1c0562216503cf5d19fc8a44d5ad6856066a933c0 | Jupyter | 4,848 | 176 | # %% [markdown]
# # Example of selecting different configurations using xTB
#
# Problem: We want to find the lowest possible energy state of a molecule to describe how the compound is in solvents accurately. Quantum chemistry optimizers will only be able to find the closest local energy minima from a starting position... |
7bd438ab4a29c477076b8bbc5ae27e16371604717d52701f6d611d1073033ba0 | Jupyter | 4,858 | 164 | # %% [markdown]
# %%
import numpy as np
import matplotlib.pyplot as plt
import os, glob
from skimage import data
from skimage import io
from scipy.ndimage import fourier_shift
# for v0.16
# from skimage.feature import register_translation
# from skimage.feature.register_translation import upsampled_dft
# for >v0.1... |
49f65fb7c4d1814bfc1123bc2a0593bbe7955456f3275bba11aa27b1789cc333 | Jupyter | 4,860 | 170 | # %% [markdown]
# # Process the data from [Cao et al](https://www.biorxiv.org/content/10.1101/2021.12.07.470392v1)
#
# First read in the escape data:
# %%
import collections
import Bio.SeqIO
import pandas as pd
# add aliases for these, also anything originally named by alias
# is switched to have main name the non... |
89901ce2b624a19d08bdc25148a5c08ca3c8e6821b2f519c893dce81af8bd81d | Jupyter | 4,864 | 156 | # %%
import os
import sys
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import pickle
import rbm,RBM_utils
num_threads = 10
os.environ["MKL_NUM_THREADS"] = "%s" % num_threads
os.environ["NUMEXPR_NUM_THREADS"] = "%s" % num_threads
os.environ["OMP_NUM_THREADS"] = "%s" % num_threads
os.environ["OP... |
a60dd7578b0e3646ba260cf39f923d3af16bcf5c714a5d117151d1e72fb348b3 | Jupyter | 4,865 | 183 | # %% [markdown]
# # Here we're computing features for the decoding and reinforcement learning models
#
# 1. Re-run once with `file_suffix` set to `offline` and once with `file_suffix` set to `online`.
# %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.util imp... |
f8191ecc5d1b3d5778cd5aae8e995ab2bd5d891855636b6073ec3056543c36bb | Jupyter | 4,902 | 136 | # %%
%run ../setup_notebook.ipynb
import plotting_functions as pf
import analysis_functions as af
import swap_sign_RBM as ssrbm
# %%
## spike raster
save_fig = False
plot_labels = False
plot_ref = True
freq = 1 / np.mean(np.diff(rec.time))
n_neurons = 500
time_slice = slice(1450, 1900)
# random_neurons = np.random.c... |
8db13e532397452b8ea676dfe3cf27423977ff0615869916c46caa06d0f506d3 | Jupyter | 4,906 | 139 | # %%
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
import circularFiltering as flt
import matplotlib
matplotlib.rcParams.update({'font.size': 14,
'pdf.fonttype': 42,
'ps.fonttype': 42})
from matplotlib import rc
rc('font',*... |
5ec29e6f7d7d08f496b0cf1eeeb21b7ee1f678393e029c01510d62195fb78429 | Jupyter | 4,916 | 133 | # %% [markdown]
# # Generate Site Risk Scores
# %%
import geopandas as gpd
import json
import matplotlib.pyplot as plt
import numpy as np
import requests
from tqdm.notebook import tqdm
# %%
#endpoint = 'https://api.plastic.watch.earthrise.media/sites'
#response = requests.get(endpoint, params={'limit':5000})
#data = ... |
d48377e6293a63a92451de5252b002f2fdd3b1f3bf3a29d52071e5a719e5531f | Jupyter | 4,925 | 200 | # %%
import cv2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
from tensorflow.python import keras
from tensorflow.python.keras import Sequential
import matplotlib.pyplot as plt
from tensorflow.python.keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D
#from ... |
b6ca41629ddce3c4ce7edfeafc079553736846ed3bea1daa37ef36f5dc519ce6 | Jupyter | 4,927 | 176 | # %%
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
import phate
import seaborn as sns
from ggplot import *
import matplotlib.pyplot as plt
%matplotlib inline
# %% [markdown]
# # 0.load data
# %%
name = ["ASD","HP","MI","NORM","DCM"]
for index, i in enumerate(name):
data_dir = "...... |
c1a1bc1a626beac8bad78974342b8754da54e894de27196030e19fcc1da6d641 | Jupyter | 4,929 | 144 | # %% [markdown]
# # Check GPU
# %%
#Check for GPU
import tensorflow as tf
# if tf.test.gpu_device_name():
# print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
# else:
# print('Please install GPU version of TF')
tf.test.is_gpu_available()
# %%
#check keras and tf versions
import keras
print(kera... |
d7633a6afe3cf0f15559251d3703b1688080a7ea38eab719767ce839a8f0b313 | Jupyter | 4,970 | 136 | # %% [markdown]
# # Evaluating the VLAAI network proposed in [Accurate decoding of the speech envelope using the VLAAI deep neural network](./#) on the DTU dataset.
#
# In this example, a pre-trained VLAAI network will be evaluated on the publicly available dataset from [Fuglsang et al.](https://zenodo.org/record/1199... |
d590f5d3fa0a6233d651e2ef470934c6473c1c256f259b4d1fdb3aa9a840472c | Jupyter | 4,984 | 115 | # %% [markdown]
# <div style="background: #DFF0D8; border-radius: 3px; padding: 10px;">
# Import the key modules and set parameters
#
# </div>
# %%
from caveclient import CAVEclient
from meshparty import trimesh_io, trimesh_vtk
import cloudvolume
import numpy as np
# %%
client = CAVEclient('minnie65_public')
# ... |
93a24a8dabcdcc6eb0df5cc2176c6f8bc08afa684412a013c448972f69f1403d | Jupyter | 5,009 | 178 | # %%
import numpy as np
#for building dictionaries
print('Loading entities , relations......')
#load the entity and relation list
E = {}
R = {}
R1= {}
with open('entity2id.txt','r') as flr:
line=flr.readline()
line=flr.readline()
while(len(line)>0):
(name, idd)=line.strip().split('\t')
E[i... |
59177dcd3fad647f89826d6643d3d86f2832518f236cc8a737a67d23d9ac3025 | Jupyter | 5,014 | 124 | # %% [markdown]
# # This is the codes for free exploration
# %%
#reading/merging/selecting data
from pathlib import Path
import nipype.interfaces.io as nio
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import sys
codes_dir = Path("/codes")
data_dir = codes_dir / "tab_da... |
c84c45f36fba38c702d964fbe41c3fff17f38f5d65bc50d6222f83ac16823ce5 | Jupyter | 5,024 | 182 | # %% [markdown]
# # How to use the SMILES corrector.
# This version demonstrates how to create a training set and train & apply the model
# %% [markdown]
# # Setup
# %%
import torch
import os
import pandas as pd
import numpy as np
import random
from src.preprocess import standardize, train_valid_test_split, remov... |
4d9e220a5ad92c0eb7b5922d51799b4428c35bc105576bf2b37b07b7ee2b36f6 | Jupyter | 5,051 | 205 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from rl_analysis.plotting import savefig, setup_plotting_env, clean_ticks, format_pval
from rl_analysis.regression import model_fit
from tqdm.auto import tqdm
from joblib import delayed, Parallel
from scipy.stats import gaussian_kde
from sklearn import li... |
3d6296c7c3f41d3f26dd391613cef6a0fbbdcbcc36f8550d6a9f6e190504454d | Jupyter | 5,053 | 186 | # %% [markdown]
# # Plot syllable waveforms
# %%
import toml
import numba
import joblib
import random
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from os.path import join
from toolz import partial
from tqdm.auto import tqdm
from scipy import stats, sign... |
f6b80a2b2579f61943e03c17d977b3d1ac1b5fa292623d779dcc383a0973a9eb | Jupyter | 5,075 | 164 | # %% [markdown]
# # Subsetting Queries
#
# 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 of subsetting and graph operations.
#
# For more background, see:
#
# - [OAK Expression Language](https://inc... |
962593be3cb46487bb664114b663699ea065d826ea46a0687d72c530e5361e11 | Jupyter | 5,079 | 137 | # %% [markdown]
# This is a simple example of SimSwap on processing video with multiple faces. You can change the codes for inference based on our other scripts for image or single face swapping.
#
# Code path: https://github.com/neuralchen/SimSwap
#
# Paper path: https://arxiv.org/pdf/2106.06340v1.pdf or https://dl.... |
7f7fbbea11bf88ff2a6df61eff4e5e6100632838f6f3c45af43c7fe28519d595 | Jupyter | 5,084 | 166 | # %%
%run setup_notebook.ipynb
%load_ext autoreload
%autoreload 2
# %%
rbm_folder = '/media/thijs/hooghoudt/new_sweep_april20/RBM_sweep_combined/'
list_rbms = [x for x in os.listdir(rbm_folder) if x[-5:] == '.data']
arr_ts = np.array([x.split('_')[-1].rstrip('.data').lstrip('timestamp') for x in list_rbms])
# %%
ind... |
1f769ef83696b8ce6d05b3e01f371c97952134342f1689a17f746860263e6a34 | Jupyter | 5,089 | 121 | # %% [markdown]
# # Using Explainers and Metrics in GraphXAI
# Here, we'll walk through a quick example where we 1) generate a synthetic dataset that is ready for XAI evaluation, 2) train a GNN model, 3) explain the GNN model with two types of explainers, and 4) evaluate the output explanations from each explainer.
# ... |
dd00e75a8be6b13a9167bf92ca217ded218228bbd127bfa97bcefcfabfa404f1 | Jupyter | 5,093 | 174 | # %%
import matplotlib
import matplotlib.pyplot as plt
import IPython.display as ipd
import sys
sys.path.append('hifi-gan/')
import os
import numpy as np
import json
import librosa
import torch
import random
from omegaconf import OmegaConf
from env import AttrDict
from synthesizer import Synthesizer
from datasets ... |
1488465d8e9573a264e3be6da0699200afef4cb4797b3424a807bde943dc1e9f | Jupyter | 5,103 | 140 | # %% [markdown]
# # Common Neighbor Rule in PyC Subgraph (Th=0)
# %%
import os
import random
from time import time
import pandas as pd
import numpy as np
import networkx as nx
from utils.utils import *
from utils.proximity import *
from utils.graph_creation import *
from utils.visualization import *
from utils.common... |
6cc69759bb10a45796d163802df96a70b0af29f2f25414a1a6e9e43994f5f655 | Jupyter | 5,104 | 162 | # %% [markdown]
# #### Imports
# %%
# Visualization
import matplotlib.pyplot as plt
# Builtin
from pathlib import Path
import sys
import os
import pdb
# Add repo path to system path for additional imports.
# Note the relative depth from this notebook to system repo
# in case you want to move this file
nb_path = st... |
24b5a3707271659488846e00fec49e5aa94794936632dc456c452ba24fed89a9 | Jupyter | 5,110 | 189 | # %% [markdown]
# # Plot syllable classifier predicting syllable identity or syllable clusters from dLight waveforms
# %%
import toml
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from glob import glob
from os.path import join
from tqdm.auto import tqdm
f... |
99099743a55de361b0fad53a27fe3f238fec38bd3504d04c05ff1920230fb8c6 | Jupyter | 5,113 | 195 | # %% [markdown]
# # Population effects of $I_A$ in a toy model
#
# $I_A$ increases latency and decreases population firing rate. Shown in fig. 2.
# %%
from common import colors, sbarlw, savefig
# %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
from __future__ import division
import numpy as... |
bbabf3d97e18041f720062341bcc8421a8518761ab6a76fec7420b030d728846 | Jupyter | 5,143 | 176 | # %%
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# %%
data = np.random.rand(7, 8, 3)
data.shape
# data = np.arange(7*8*3)
# data = data.reshape(7, 8, 3)
# %%
# %%
data2 = np.random.rand(7, 3, 1)
data2.shape
# data2 = np.arange(7*8*1)
# data2 = data2.reshape(7, 8, 1)
# %%
lista_vectores =... |
28823bf7a9f9b5bef863efab0b2fbc143cc5970507142426c475509269d6f907 | Jupyter | 5,147 | 158 | # %%
from google.colab import drive
drive.mount('/content/drive')
# %%
%cd '/content/drive/MyDrive/Kimia'
# %%
import os
import sys
import torch
import numpy as np
import importlib
from torchvision import transforms
from data_utils.ModelNetDataLoader import ModelNetDataLoader, load_data, PointSampler, Normalize, Rand... |
6fea608ffbb2693f779bc702239e2d22239939274d2c251c12de585768662ac6 | Jupyter | 5,158 | 143 | # %% [markdown]
# # Common Neighbor Rule in PyC Subgraph (Th=300)
# %%
import os
import random
from time import time
import pandas as pd
import numpy as np
import networkx as nx
from utils.utils import *
from utils.proximity import *
from utils.graph_creation import *
from utils.visualization import *
from utils.comm... |
21f5eb5875162ea6de1307782a3ae43dbf133433a6344567d3487f263601b19f | Jupyter | 5,169 | 170 | # %% [markdown]
# # Remerge Model
# %%
from network import Remerge
from helpers import softmax, response_time
from plotting import plotting_init, matrix_plot, mds_plot
import numpy as np
from sklearn import manifold
from sklearn.metrics import euclidean_distances
import matplotlib.pyplot as plt
import seaborn as sns
... |
b74b3d635061ecd0caa9994349666de3410d47b9d6a1ba19c380e07ad28b9b69 | Jupyter | 5,188 | 164 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import os
from pathlib import Path
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split, KFold, cross_val_score, cross_val_predict, StratifiedKFold
from sklearn.metrics import fbeta_score, accuracy_score, co... |
bb7264d34cfa8a26f4e5d236586164ae83f562ac43e491f6f4f82e2e3ac86e0c | Jupyter | 5,221 | 206 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from rl_analysis.plotting import savefig, setup_plotting_env, clean_ticks
from rl_analysis.io.df import dlight_exclude_toml
from rl_analysis.photometry.signal import get_ncrossings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
im... |
0faf14400d2717849859955470aa511a715bee848105b9464ff7ec1cc10838b8 | Jupyter | 5,235 | 182 | # %% [markdown]
# ##### This notebook generates train / valid / test (0.8 / 0.1 / 0.1) splits for ClinTox, based on the random splitting method in MoleculeNet
#
# Splits were created for seeds of 122, 123, 124 (same seeds as MoleculeNet).
#
# All splits are saved in data/clintox/split_data folder
#
# Raw ClinTox d... |
9526b8e899ac9666f990e63db6c6410f59eaca27e35f6c29528ccdd6cb2f418e | Jupyter | 5,243 | 211 | # %%
import os
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
sns.set(context='notebook', style='white', font_scale=1.2)
# %%
try:
import crossmapy as cmp
except:
import sys
sys.path.append('../')
... |
967ee793841a34a56f71bc24a5b44a50c5f5dbb376d5e7374112412f47da6ad3 | Jupyter | 5,247 | 174 | # %%
import re
import os
from pathlib import Path
import pickle
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import gridspec
import seaborn as sns
from scipy.optimize import curve_fit
from lmfit import Model
from lmfit.models import LinearModel
sys.path.append(os.... |
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