sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
22378afe5c86b36229709262b15f52eafacb914cc380740f5f1938de33ca9cf1 | Jupyter | 13,425 | 499 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
clean_ticks,
syllable_aliases,
savefig
)
from rl_analysis.util import rle, count_transitions
from rl_analysis.io.df import get_closed_loop_parquet_columns
im... |
42a9afe6e73278d63dd8f44f9331d3cd283aa20a9d78e891de22b8fd153d2f70 | Jupyter | 13,446 | 410 | # %% [markdown]
# This notebook compares CASM dataset to in-situ soil moistiure measurements.
#
# To run it, you'll need to have CASM data and in-situ measurements.
#
# Download the in-situ data from here [ISMN](https://ismn.geo.tuwien.ac.at/en/). Sample data is referenced in this notebook to check its functionality... |
61d924b34214e95061fefc93a8b7fcc2d121455193bca2d92c519121ffb0b45e | Jupyter | 13,486 | 439 | # %% [markdown]
# # Topological Bone Porosity Analysis Example Process
# %% [markdown]
# First load in a couple of images.
# You should see the first three (left) example images have 20 'pores' and the three on the right have 60 'pores'.
# %%
import os, sys
from tqdm import tqdm
import numpy as np
import pandas as ... |
ecd291a39421634b281f1fa7811ff3c88f5b8b061ac2bc8b12445cfa395ea19b | Jupyter | 13,513 | 448 | # %% [markdown]
# # Compute syllable sequences and entropy in an expanding window after syllable onset
#
# 1. Note that you can use a dask scheduler by setting `dask_address` to the address of the scheduler (see `../analysis_configuration.toml`)
# 1. Note that you must compute `syllable_stats_offline.toml` via `all_be... |
95018cf17191416b4dea2a845d33a4df408191c4102df3ff65007fc247c1e82c | Jupyter | 13,669 | 462 | # %% [markdown]
# # Table of Contents
# 1. [Parameters](#parameters)
# 2. [Imports](#imports)
# 1. [Packages](#packages)
# 2. [Data Files](#files)
# %% [markdown]
# ## Parameters <a name="parameters"></a>
# %%
n_neighbors = 20
vmax = 3
vmin = -3
cmap_z = "coolwarm" #'RdBu_r'
cmap_raw = "viridis"
ifdoublet = ... |
a578551b42b0ba86f200b910c37008eba212dd19122a33da15579633ff70d2f2 | Jupyter | 13,690 | 381 | # %%
import sys
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
sys.path.append("../../ndsvae/")
import ndsvae as ndsv
sys.path.append("..... |
1381d8374f4ca213df7bddc5caef499eb84ef4f71b997ec11c76eebd95c125cc | Jupyter | 13,693 | 341 | # %%
import json
from keplergl import KeplerGl
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
# %%
data = pd.read_csv('../data/lidar/bali_gedi/bali_gedi_data_all.csv')
with open('../data/tpa_polygons/tpa_polygons.json', 'r') as f:
tpa_sites = json.load(f)
# %%
filtered_df = data
... |
683e0e5238acff89c4c37c9236c6bd76a0021260497ccb1ef32320fc87f683c5 | Jupyter | 13,752 | 496 | # %%
# %% [markdown]
# %% [markdown]
# ```
# from dPCA import dPCA
# По обученной сети создается тензор
# actR = размером(num_trial, hidden_size, S, T)
# num_trial - число испытаний для каждого значения стимула
# hidden_size - число нейронов сети
# S - число стимулов (берем 10)
# T - число временных отсчетов в одно... |
1d87641fd630f466fa74850405a325239effa8a6d77328e1f49753650b096e50 | Jupyter | 13,813 | 391 | # %% [markdown]
# # spaCI manual and tutorial
#
# ### tutorial
# To run spaCI, the input data are shown in the required structure and the data path is assigned in the configure.yml files. Please refer to our preprocess manual [here](https://github.com/QSong-github/spaCI/blob/main/tutorials/tutorial_preprocess.md) fo... |
00d2b259cd22f2d238f1cfc3dcdfbd4dec94446d900db163be8e6dde5ca80851 | Jupyter | 13,859 | 476 | # %%
from common import colors, sbarlw
# %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
import pickle
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
import seaborn as sns
from scipy import stats
from grr.cell_class import Cell
from g... |
8ce19c82af5ae18a62a72e06dc339cdff53c0e1f1a468cee02bfa2b5a978fc0d | Jupyter | 13,875 | 401 | # %%
%load_ext autoreload
%autoreload 2
import os, sys
import numpy as np
import matplotlib.pyplot as plt
root_path = os.path.realpath('../')
sys.path.append(root_path)
from utils import eval, choicemodel, plotting
from hebbcl.parameters import parser
plt.rcParams['svg.fonttype'] = 'none'
plt.rcParams['pdf.use14coref... |
944299bb592b8a6f2487a367cdb007141a86c472ee47d633799dd7b13494c8b2 | Jupyter | 13,912 | 478 | # %% [markdown]
# # Compute syllable sequences and entropy in an expanding window after syllable onset (for fig. 4)
#
# 1. Note that you can use a dask scheduler by setting `dask_address` to the address of the scheduler (see `../analysis_configuration.toml`)
# 1. Note that you must compute `syllable_stats_offline.toml... |
056b8a62ec4a80c3bdc80e711025ac7d7c3ab7536df302000223e5a98933b41f | Jupyter | 13,952 | 528 | # %% [markdown]
# # Compute features for the encoding model (average variant)
# %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.batch import apply_parallel_joblib
from rl_analysis.io.df import dlight_exclude
from rl_analysis.photometry.encoding_average.feature... |
b8ad581b2f5bcc5d95c611741a0b7cfa5681ae6528b1e992421a0027f75dfbdf | Jupyter | 14,016 | 376 | # %% [markdown]
# # Train Pixel Classifier
# %%
%load_ext autoreload
%autoreload 2
# %%
from datetime import date
import json
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pickle
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics i... |
8ffeaff320183bf860cc5b0433e971e65762ccb9a3f7e05aac84f60736b88bc4 | Jupyter | 14,296 | 397 | # %% [markdown]
# # Signal flow
#
# The following is some derivation of the "signal flow" calculation for a directed network used in _Varshney et al. 2011_ [1], as well as my own implementation and some simple simulations to attempt to understand this function better.
#
# Feedback welcome!
# %%
import numpy as np
i... |
da47a6a4b8b5afc9cc64ac555a3363d1e58eb0914b627718839964ac221dee61 | Jupyter | 14,377 | 628 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
geno_palette,
clean_ticks,
savefig
)
from rl_analysis.photometry.signal import get_crossings, get_ncrossings
from rl_analysis.io.df import dlight_exclude_tom... |
cea4d07b24a941d4d9de3f76ea79d47101eafb5daad80542ac9595ae19e698b0 | Jupyter | 14,439 | 429 | # %%
import sys
import os
import itertools
import numpy as np
import scipy.stats as stats
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
sys.path.append("../... |
b1e9212792947ea06ed29077eff41dd36d7f5d21c950961b8027be4a8b3c495b | Jupyter | 14,467 | 501 | # %% [markdown]
# # Training FNOs on Darcy-Flow with TensorGRaD
#
# This notebook follows the same basic workflow as the NeuralOperator Darcy FNO example: load Darcy-Flow data, build an FNO, set up losses and optimizers, train, and visualize results.
#
# We compare three optimizers:
#
# - AdamW baseline;
# - TensorG... |
4af4decece6ada95763287ab2c1cacda33c6d3b0f43e4643fc064357b811bd01 | Jupyter | 14,485 | 418 | # %% [markdown]
# # EfficientDet Tutorial: inference, eval, and training
#
#
#
# <table align="left"><td>
# <a target="_blank" href="https://github.com/google/automl/blob/master/efficientdet/tf2/tutorial.ipynb">
# <img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on github
# <... |
0f10dd4cb21b23a91eebefffa8d54e8fd3d5274fd2c96304ae6b0537527efe64 | Jupyter | 14,521 | 362 | # %%
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
import circularFiltering as flt
from scipy.stats import vonmises
from scipy.stats import norm
import matplotlib
import glob
import os
import re
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helveti... |
0699ec4c09957c1b3b9085f428c102e3621f09ce2c790237d114f934bfd76c5b | Jupyter | 14,567 | 578 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from skimage.io import imread
from rl_analysis.plotting import setup_plotting_env, savefig
from skimage import morphology
from skimage import filters
from cellpose import models
from scipy.optimize import curve_fit
import matpl... |
a7bcad93c2e17b7d380e717da96279e7bbe46cfd02537a61ae80a82f86d726c2 | Jupyter | 14,600 | 488 | # %% [markdown]
# # Visualize and interpret netwas results
#
# 7/8/19: use less restrictive filtering critera for gwab and netwas (top 5%)
# %%
# 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
import myge... |
3394e645fc3c836db95a77e4a5d0682676a0d5d258d068d2647c4848d435783a | Jupyter | 14,691 | 430 | # %%
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
import pickle
import re
from pathlib import Path
import scipy.stats as ss
from scipy.optimize import curve_fit
import seaborn as sns
import sys
sys.path.append(os.path.join(Path(os.getcwd()).parents[0], "functions"))
import warnings
w... |
57eb35ef094c38a40fc999a689d0203dd8e7f230abca4800025e7a5c272b2760 | Jupyter | 14,815 | 368 | # %%
import brian2 as b
from brian2.units import amp, cm, ms, mV, ohm, pA, uF, um, uS, nS, mvolt, Hz
from dendrify import Dendrite, NeuronModel, Soma
# Brian params
INTEGRATION_METHOD = 'euler'
b.prefs.codegen.target = 'numpy'
# Simulation params
G_AMPA_DIST = 1*nS
G_AMPA_PROX = 2*nS
TAU_AMPA = 5*ms
# %%
%%timeit -r... |
ab282fbba056a99399176b477a4584db197588d8df44302f0a192852a0658b3a | Jupyter | 14,817 | 513 | # %%
from __future__ import print_function
feature_count = 42
predict_column = -1
target_cluster = 0
pop_size = 25
max_fitness = 3.0
max_gens = 80
pool_size = 15
seed_with = [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0]
# %%
import sy... |
ee5bcc7b12fc3cd943323e391bf1cca07f082b0c37a2c6636f459b0367bda3e5 | Jupyter | 14,830 | 494 | # %% [markdown]
# # Inspect single neuron model filters
#
# This notebook visualizes the membrane ($\kappa$), spike-triggered current ($\eta$), and spike-triggered threshold movement ($\gamma$) filters of fitted GIF-type models. Shown in fig. 4.
# %%
from common import colors, insetlw
# %%
import os
os.chdir(os.path... |
c1dbeec963be4d2044a15d308a3ca799d793947260b2b4a21cf775ca816bde5a | Jupyter | 14,933 | 473 | # %% [markdown]
# # TQIP ED Model
# %% [markdown]
# This notebook illustrates how we trained the ED Model for the paper: "Assesing the Utility of Deep Neural Networks in Dynamic Risk Prediction After Trauma"
#
# The TQIP database can be requested through the American College of Surgeons website at
# https://facs.org
... |
fadca406bd32aa927ec6945b46600637a7ed4d37e6d404f79ee64f4ace4a6db7 | Jupyter | 14,997 | 434 | # %%
import sys
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
sys.path.append("../../ndsvae/")
import ndsvae as ndsv
sys.path.append(".."... |
dccfbb12ace5f4b18899b288f72048fb33c91a59d0c909dbf540dde5124ce646 | Jupyter | 15,053 | 508 | # %% [markdown]
# # Effect of voltage history on spike timing in DRN SOM neurons
#
# Shown in fig. 1S2.
# %%
from common import colors, sbarlw, insetlw
# %%
import os
os.chdir(os.path.join('..', '..', '..'))
# %%
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
import seaborn as s... |
22598d911c28a88edf1b36a44e9cf66dd351139fa50b927a77c4d5a381fb60c7 | Jupyter | 15,089 | 362 | # %% [markdown]
# # Optimization with Reinforcement Learning
#
# In this tutorial, we show how different model types within DrugEx can be optimized under the reinforcement learning framework. The models can be taught to generate compounds according to multiple custom rewards and using several optimization strategies. ... |
0c6a3d09a754c53b5f569cae7bd08a8a9f1a7b2331cfc4ab9ee9142e3c8d150c | Jupyter | 15,093 | 456 | # %% [markdown]
# # TQIP Pre-Hospital Model
# %% [markdown]
# This notebook illustrates how we trained the Pre-Hospital Model for the paper: "Assesing the Utility of Deep Neural Networks in Dynamic Risk Prediction After Trauma"
#
# The TQIP database can be requested through the American College of Surgeons website at... |
b6c9de9445cf17e89645216eff518e4e960944a5dc6a057cda02b7d6f4e44178 | Jupyter | 15,118 | 619 | # %% [markdown]
# # Example on using RDKit, clustering techniques and xTB to find relevant conformers
#
# Based on conformer selection by **ReSCoSS, Anikó Udvarhelyi, Stephane Rodde, Rainer Wilcken, J. CAMD 2021, 10.1007/s10822-020-00337-7**
#
# An example flow for generating shape-diverse conformers.
#
# - Generate... |
9942b0fb219008b7ff15e8f020145baf697a17e6b408ba6896cc5b56666334ff | Jupyter | 15,123 | 328 | # %%
%load_ext autoreload
%autoreload 2
# %% [markdown]
# # Experiments
# We'll go through learning feature embeddings using different loss functions on MNIST dataset. This is just for visualization purposes, thus we'll be using 2-dimensional embeddings which isn't the best choice in practice.
#
# For every experimen... |
03619313bd9bd83e723b9ee5273637872f8573d9881b1c2459d8e7367f27a9db | Jupyter | 15,140 | 496 | # %% [markdown]
# ## Explore SynapseCLR representations and mark contaminations
# %%
import os
import numpy as np
import pandas as pd
import matplotlib.pylab as plt
import cuml
from cuml import PCA, TSNE, UMAP
from cuml.neighbors import NearestNeighbors, kneighbors_graph
import pickle
import igraph as ig
import leid... |
9e25e647dbda11db188997a0f98ce426525f7719e76377955e08b39635f98c73 | Jupyter | 15,226 | 385 | # %%
import brian2 as b
from brian2.units import amp, cm, ms, mV, ohm, pA, uF, um, uS, nS, mvolt, Hz
from dendrify import Dendrite, NeuronModel, Soma
# Brian params
INTEGRATION_METHOD = 'euler'
b.prefs.codegen.target = 'numpy'
# Simulation params
G_AMPA_DIST = 1*nS
G_AMPA_PROX = 2*nS
TAU_AMPA = 5*ms
# %%
%%timeit -r... |
207b840f70e956d9018e09b692ed4bb0cf8622a67a7b902277750ae0fda4f67f | Jupyter | 15,297 | 536 | # %%
import glob
import re
import numpy as np
import math
import pandas as pd
import pickle
import os
import subprocess
import seaborn as sns
%pylab inline
%matplotlib inline
# %%
FullAmino = ["ARG","HIS","LYS","ASP","GLU","SER","THR","ASN","GLN","CYS","GLY","PRO","ALA","VAL","ILE","LEU","MET","PHE","TYR","TRP"]
myAm... |
3ef15f98d318e48d85309e3d72f5e4d8276fe6b1086d7926a9f2ff498c4eb0a1 | Jupyter | 15,325 | 198 | # %% [markdown]
# # Cerebellar results priliminary results (Experimental codes)
#
# ## Cohorts
# - MNI ET+NC (38+30)
# - PPMI 116
# - ADNI2 freesurfer(308) cerebellar(110)
#
# ## Analysis
#
# - cerebellar white/gray matter volume
# - cerebellar lobules
#
# SUIT:
#
# MAGeTBrain:
#
# %%
## exploring data
#lib ... |
5d8088c6318f188a783415ae450b0aa8ee66763c999cf7926fff98ec41aed22f | Jupyter | 15,325 | 362 | # %% [markdown]
# # EfficientNetV2 with tf-hub
# %% [markdown]
# <table class="tfo-notebook-buttons" align="left">
# <td>
# <a target="_blank" href="https://github.com/google/automl/blob/master/efficientnetv2/tfhub.ipynb">
# <img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on gith... |
868b71c08dc7bde3aa2f770ea33a32fd5aab1c2d9f0e212ada1e33feccd6658e | Jupyter | 15,375 | 508 | # %%
import os
import sys
import itertools
import glob
import numpy as np
import pandas as pd
import scipy.stats as stats
import scipy.signal as sig
import scipy.io as spio
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import networkx as nx
from sklearn.decomposition import PCA
from sklearn... |
ee8f2065ea77b49a853c52542e4eef236b4911fbeddb93fb346ff8c194459750 | Jupyter | 15,377 | 514 | # %%
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import numpy as np
import os
import pickle
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import normalize as sklearn_normalize
from sklearn.utils import shuffle
from tensorflow import keras
np.random.seed(1)
# %... |
b2129cecd808782116e3a11ebbd9ca04d6032aaef1350db3ed6e914cfd38481a | Jupyter | 15,400 | 556 | # %% [markdown]
# # Setup
# %%
!pip install lxml
# %%
!pip install scikit-network
# %%
from lxml.html import fromstring
import requests, json
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm.notebook import tqdm
from functools import lru_cache
from sknetwork.clustering import Louva... |
13d63e25ae1763abcff89f0d0c0a65b4a437ab5497057967c4c1952544e79925 | Jupyter | 15,444 | 573 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import setup_plotting_env, geno_palette, savefig, clean_ticks
from rl_analysis.util import rle
import pandas as pd
import os
import seaborn as sns
import numpy as np
# %% [markdown]
# # Load in raw da... |
4592b298858e52e7afa1f68e6af2c7148e860b09b0cc5305d25aae508855e35c | Jupyter | 15,467 | 415 | # %%
import os, sys
import glob
import time
import subprocess
import multiprocessing
# %%
try:
from pfabnet import utils
except ModuleNotFoundError as e:
os.chdir(os.getcwd() + os.sep + '..')
CWD = os.getcwd()
from pfabnet import utils
from pfabnet import base
from pfabnet.base import SCHRODINGER_BA... |
2021d3556df2122d4cd19b9d8b96ff8016c00e1725c6def69e621a2f72937a93 | Jupyter | 15,493 | 488 | # %% [markdown]
# #### Imports
# %%
# Visualization
import matplotlib.pyplot as plt
# Builtin
from pathlib import Path
import sys
import os
import pdb
import subprocess
# 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 ... |
3a615eeff45d4f00778dd0ac4c30297b55d6bd0c47a3018cc4b63db5b0dc1d06 | Jupyter | 15,675 | 525 | # %% [markdown]
# # Inspect the effect of $dI/dt$ on population firing rate
#
# Shown in fig. 7.
# %%
from common import colors, savefig
# %%
from __future__ import division
import os; os.chdir(os.path.join('..', '..'))
print os.getcwd()
# %%
import warnings
import re
import copy
import pickle
import pandas as pd... |
4a2b75987221350b7dd2a0a0ae2014fa7e0cc035d080c4573d02c52903d5f83f | Jupyter | 15,730 | 261 | # %% [markdown]
# # Merging volumentric ROI measures and freesurfer surface measures.
# - Cortical thickness/volume/aseg/wmparc data generated by "../tab_data/freesurfer_stats2table.sh" from fMRIPrep_20.2.0 freesurfer_6.0.1
# - Cerebellar segmetations in both SUIT34 and MDTB10 spaces.
# - Uses participants data (../tab... |
ae12338e99d87f6bc4ef29513e1c62313e64bffc8428959b555e67f9f2239885 | Jupyter | 15,761 | 484 | # %% [markdown]
# # A549
# %% [markdown]
# library import
# %%
import os #importando biblioteca de organização de arquivos
import joblib #convertendo imagens em valores numéricos .gz
import numpy as np #estruturação de dados
import matplotlib.pyplot as plt #plotagem de gráficos
import matplotlib.image as mpimg #plot... |
e7f385629262fb87d4c5690f263a4c56316a46063565e8b9f0676b8caf3be51b | Jupyter | 15,781 | 608 | # %%
from common import colors, fliersize
# %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
import pickle
import re
import warnings
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
import seaborn as sns
from scipy import stats
from ezephys import pltools
f... |
e1f569eb8112c860b961872d5dc2b745efdef956522f92fc01e8ed28aaebf434 | Jupyter | 15,786 | 371 | # %% [markdown]
# # Lattice Proteins Notebook.
# LP are artificial protein sequences, used here to benchmark the algorithm against ground truth knowledge of the structure and of the fitness function
#
# ## Loading data and packages
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import sys
sys.path.append('.... |
618a86b71d202aeb0526a62b267d432278c1b8dd7fc44b02a218bc08f1fb6039 | Jupyter | 15,831 | 595 | # %%
import pickle
import pandas as pd
import seaborn as sns
import numpy as np
from matplotlib import pyplot as plt
import glob
import re
from mpl_toolkits import mplot3d
import os
from scipy.stats import rankdata
from sklearn import metrics
import math
%pylab inline
%matplotlib inline
# %%
myAmino = ['A', 'C', 'D',... |
a8bf4b5bf73ebb96b8db0fe86017d9e845b73b3225dfa6255ccb641b3a7e0c1b | Jupyter | 15,878 | 401 | # %%
import numpy as np
import os
import pandas as pd
import math
import pickle
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from matplotlib import gridspec
import seaborn as sns
sns.set_style("ticks")
hfont = {'fontname':'Myriad Pro'}
import sys
sys.path.append(os.path.join(os.getcwd(), "function... |
13fa63686bfd6940a060c5674e42ce80304ffe91267d23c725693f2d13f4f564 | Jupyter | 15,951 | 466 | # %% [markdown]
# This notebook is used to study model performance on the SARS-CoV-2 deep mutational scan dataset from Starr et al. (2020) https://www.cell.com/cell/fulltext/S0092-8674(20)31003-5
# %%
import numpy as np
import pandas as pd
import re
from matplotlib import pyplot as plt
from scipy import stats
from tq... |
6a2509f9516397300e45245c875985f2b9d6f2daa07c08945ba9428f7147b819 | Jupyter | 15,973 | 527 | # %% [markdown]
# # Plot limb speed vs dLight for all animals
#
# We must account for the speed of the animal's body if we want to independently consider limb speed.
# %%
import os
import toml
import joblib
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from glob import ... |
1f85aa35d3dc99ff78ac49bc25824127612c7ee91b918d3494c1c87ec25764c9 | Jupyter | 16,021 | 545 | # %%
from common import colors, sbarlw, savefig
# %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
import re
import pickle
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
import numpy as np
import pandas as pd
from ezephys import pltools
from grr.Spiketrain import PSTH
from g... |
d09c142ac0c9be3688be58520e539ed1186d337b31b1e001604778e74a7c774f | Jupyter | 16,050 | 477 | # %% [markdown]
# # Plot yeast RBD DMS escape calculator
# %% [markdown]
# ## Import modules and read data
# Import Python modules:
# %%
import itertools
import os
import re
import altair as alt
import numpy
import pandas as pd
# %% [markdown]
# Disable max rows specifier for Altair:
# %%
_ = alt.data_transforme... |
8628aba78b11f6886eb02f6e818d890dc935b803b9b897e02b52f33dc80e4cb0 | Jupyter | 16,074 | 409 | # %%
%cd ..
# %%
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
import torchvision
from dataloading.nvidia import NvidiaDataset, NvidiaValidationDataset, Normalize
from pilotnet import PilotnetControl, Pilo... |
9ab7923d9cd2034d249c1c91f25e5bb55c3d7d8d3cfcbe1f9b428f7d272c0380 | Jupyter | 16,164 | 468 | # %%
import numpy as np
import pandas as pd
from scipy.io import loadmat
import glob
import os
import sys
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="white")
from scipy import stats
from mne.viz import circular_layout, plot_connectivity_circle
import statsmodels.api as sm
from statsmodels.form... |
b6c6642f349edaacc71763426dfac596473005fe54f9610f5d2bea174d597afe | Jupyter | 16,291 | 445 | # %% [markdown]
# ### Getting started
# %%
import warnings
warnings.filterwarnings("ignore", message="Boolean inputs to the `inclusive` argument are deprecated infavour of `both` or `neither`.")
import numpy as np
import pandas as pd
import copy
import tqdm
import matplotlib.pyplot as plt
from collections import de... |
43bb8353c2ae2901c4394a1c6eb86f29deaa125873f8deed606e74f716ccf122 | Jupyter | 16,301 | 531 | # %% [markdown]
# # GIF filter cartoons
#
# This notebook produces cartoonized versions of the membrane and adaptation filters used in GIF models. Shown in figs. 5 and 6.
# %%
from common import colors
# %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
import pickle
import numpy as np
import... |
67c58c5ca21463e34ba54637d26565cbd1535c05a9f86e20e9e7dd49a8258c61 | Jupyter | 16,341 | 498 | # %% [markdown]
# # Import Packages
# %%
# Default packages
import os
from os.path import join
from datetime import date, datetime, timedelta
from joblib import load, dump
# Set current directory to "src"
os.chdir(join(os.getcwd(), os.pardir, "src"))
print(f"Current working directory: {os.getcwd()}")
# Installed pac... |
3a079ab7fd7dc42beedaf0584455ddc7424787c0a7823b1d1606cc57c6624840 | Jupyter | 16,413 | 507 | # %%
import os
import sys
import time
import random
import numpy as np
import imgaug
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.patches as patches
# from pycocotools.coco import COCO
# from pycocotools.cocoeval import COCOeval
# from pycocotools import mask as maskUtils... |
a6381de839e25bf5cd078db455f25a37963463eedbd7d9162aa24ad62234105c | Jupyter | 16,588 | 447 | # %% [markdown]
# ### Getting started
# %%
import warnings
warnings.filterwarnings("ignore", message="Boolean inputs to the `inclusive` argument are deprecated infavour of `both` or `neither`.")
import numpy as np
import pandas as pd
import copy
import tqdm
import matplotlib.pyplot as plt
from collections import de... |
6df304827b86ec3b8e230ea4a8875ed29f9900fd51038271a497c7ad90fa0c28 | Jupyter | 16,646 | 471 | # %% [markdown]
# <style>
# .extra-info p {
# color: firebrick;
# font-size: 13px;
# font-weight: bold;
# }
# </style>
#
# # Running simulations
#
# In this tutorial, we are going to cover the following topics:
#
# * How to merge compartments into compartmental neuron models
# * How to make Dendrify an... |
ab61fbc2404396b8566836eb7f8f1b9dd72d41284801744b7039384cafc9392f | Jupyter | 16,652 | 359 | # %%
from dask.distributed import Client, progress
client = Client(processes=False, threads_per_worker=4,
n_workers=10, memory_limit='16GB')
client
# %%
import dask
print(dask.__version__)
# %%
import os
import boto3
import numpy as np
import pandas as pd
from sklearn.cluster import DBSCAN
from tqdm i... |
175961e725e3e792d86e851863c0289dadae3bfa9711effbf742237f16da9ba6 | Jupyter | 16,694 | 598 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from functools import partial
from rl_analysis.plotting import (
setup_plotting_env,
geno_palette,
syllable_aliases,
savefig,
clean_ticks
)
from rl_analysis.util import (
rle,
)
from rl_analysis.batch imp... |
6ea7ce5b78806d7f8be15ff7249dc11637737eb0998f09ed706cffb5ed9aef82 | Jupyter | 16,701 | 556 | # %%
from pandas import DataFrame
import numpy as np
import pandas
from matplotlib import pyplot as plt
import string
from scipy.stats import pearsonr
from scipy import stats
import statsmodels.formula.api as smf
import hdbscan
from sklearn.cluster import KMeans
from scipy.spatial.distance import euclidean
pandas.se... |
f7aa0cce20d327f227b3a13044e6cdb48f288858e2a50a0fa5b72ba9390f49ee | Jupyter | 16,717 | 462 | # %%
import re
import os
from pathlib import Path
import pickle
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
import sys
sys.path.append(os.... |
82bddf420f0765c1231a8a82f856255557c1db64fea5ec95a83083683af28bdb | Jupyter | 16,844 | 479 | # %%
# set up enviroment
from __future__ import division
import sys
import numpy as np
import pandas as pd
import scipy
import importlib
import scanpy as sc
%matplotlib inline
from matplotlib import pyplot as plt
import matplotlib as mpl
import seaborn as sns
from matplotlib_venn import venn2
from sklearn.manifold i... |
110398b0127bf27b9245b526cb89ffa367adad4e9c4be06468c3452c263a3dcb | Jupyter | 16,877 | 448 | # %% [markdown]
# # WW domain notebook.
# The WW domain is a short binding domain targeting proline-rich motifs. WW domains are usually classified into 4 types, according to their binding specificity toward different motifs (e.g. PPXY, PPLP p[T/S]P...). We show here some features found by the algorithm.
#
# ## Loadin... |
81d163f60c828917e0c16c366e7d2f2effa5ceeaf85272a56f1465ac1877aad6 | Jupyter | 16,901 | 360 | # %%
%load_ext autoreload
%autoreload 2
# %% [markdown]
# # Experiments
# We'll go through learning feature embeddings using different loss functions on MNIST dataset. This is just for visualization purposes, thus we'll be using 2-dimensional embeddings which isn't the best choice in practice.
#
# For every experimen... |
f3a7645e3840d4b551c518c67bb775b61b75311ff210e8df34f04e410673efe5 | Jupyter | 16,936 | 580 | # %%
import os
import sys
import itertools
import numpy as np
import pandas as pd
import tensorflow as tf
import scipy.signal as sig
import scipy.stats as stats
import statsmodels.api as sm
import matplotlib
import matplotlib.pyplot as plt
sys.path.append("../../ndsvae/")
import ndsvae as ndsv
sys.path.append("../"... |
0ba9d5dd858fa85a3cd1edfab48e0ebcff35951273c0953cc8df2c22027d7448 | Jupyter | 16,943 | 553 | # %% [markdown]
# # Deception Detection supported by Machine Learning
# ## Literature review - Data collection and selection
# %% [markdown]
# ### Setup
# Instantiating a BiblioAlly Catalog in a SQLite file **DeceptionDetection.db**
# %%
import BiblioAlly as ally
base_path = "./"
base_file = base_path + "DeceptionDe... |
b4d5d15ab518461529bcf6edc1ed8ccea5208fa83654bef7a4e61d75e7b49ff0 | Jupyter | 16,996 | 482 | # %% [markdown]
# ### Notebook para la prueba del error de los ángulos de los brazos
# %%
# imports
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
import math
import os
import pandas as pd
import pickle as pk
import numpy as np
import cv2
from matplotlib import pyplot as plt
%matplo... |
bea534a5dc7bacdd42d512d05ac319f92f47f14445d341911418a0c01912cb43 | Jupyter | 17,012 | 416 | # %% [markdown]
# # CUR matrix decomposition for constructing PaiNN model
# This code introduces how to use CUR matrix decomposition for active learning. We use <a href=https://aip.scitation.org/doi/10.1063/1.3553717>symmetry function</a> as the fingerprints of atoms and select most representative atomic environments b... |
18eb9d2d816753056edcc4e90be13e005710119510d3f07c1ab846fb4fd27c9b | Jupyter | 17,057 | 589 | # %% [markdown]
# #imporation des librairies
# %%
import datetime
import matplotlib.pyplot as plt
import seaborn as sns
import statistics
from scipy import stats
from scipy.stats import pearsonr
from scipy.stats import ttest_ind
import pandas as pd
import numpy as np
from numpy.core.numeric import NaN
import tensorflo... |
41458a2b8bf7d9f991ef56ca23874571727dc6b5651d066562126981fbea4f0c | Jupyter | 17,232 | 380 | # %% [markdown]
# # Report result on participants' trust and willingness to use AI
# %%
from base_code import *
from scipy.stats import friedmanchisquare, wilcoxon
from statannot import add_stat_annotation
valid_data = pd.read_csv('../neurosurgeon35_data/neurosurgeon35.csv', index_col='Respondent ID')
dr_wise_acc = p... |
d18f1c1350bdd5f4236c3f788f1ad19403210f2fdb073c43edf3f03e1548b3f6 | Jupyter | 17,236 | 448 | # %% [markdown]
# # SETTINGS
# %%
import numpy as np
import numpy.matlib
import matplotlib
%matplotlib inline
import h5py
import matplotlib.pyplot as plt
import sys,os
import importlib
import sklearn
import sklearn.decomposition
import sklearn.preprocessing
import datetime
import seaborn as sns
import pandas as pd
imp... |
04314a4e19a6c23e3bfbc73e4253c6340f778c54f81849cdb885e6ef568a980c | Jupyter | 17,504 | 593 | # %%
import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
# %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import seaborn as sns
%matplotlib inline
import statsmodels.api as ... |
8f894b6edded0f7a54b1f461f12e24bae512aabf58ee6c3b35f4dddde2d728c0 | Jupyter | 17,578 | 518 | # %% [markdown]
# # Plot reduction over simulations (from pixel and Gaussian)
#
#
# This script loads the results of simulations produced by 'launch_RNN_simulations.ipynb'. This package contains the results of several runs and so it is not necessary to run launch_RNN_simulations.
# %%
import submitit
import os
impor... |
74bd100657a4f30dbfb42c4e7f657a5b6e6a5b800b836907f6811b2a8aea39b6 | Jupyter | 17,648 | 568 | # %%
from common import colors, pvalue_thresholds, insetlw
# %%
import os
os.chdir(os.path.join('..', '..', '..'))
print(os.getcwd())
# %%
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
imp... |
eb96c2553992bfed9f3dc2d889ade164743f81c07935b9fc43ba14a4e527af00 | Jupyter | 17,663 | 526 | # %%
import os
import numpy as np
import torch
import torch.nn as nn
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from scipy.stats import mannwhitneyu, binomtest
from statsmodels.stats.multitest import multipletests
# %%
def an_path(file):
an_et_al = '40_features_q_mpi_highQCsamples/An_e... |
132d5f40da81491859c07e44dffe95d56177d4507d6a49bbfea0a7c74cdfadea | Jupyter | 17,682 | 480 | # %%
# clear all variables
for i in list(globals().keys()):
if(i[0] != '_'):
exec('del {}'.format(i))
#suppress future warnings -- not really a good idea
from warnings import simplefilter
# ignore all future warnings
simplefilter(action='ignore', category=FutureWarning)
# suppress future warnings... |
388192acb566ff73701c95b038641c7e2a3ac1a56f9836185d8a9d8c9a041f23 | Jupyter | 17,729 | 453 | # %% [markdown]
# # Advanced API Usage
#
# DrugEx uses concepts of object-oriented programming and inheritance to allow for easy customization of the behaviour of the different components. The user can either implement their own versions of the classes or use the provided ones and override the methods that need to be ... |
3775994c39ba23274c17c5093924b4c6e770ecd4a8dfe91a162d36ba4632d979 | Jupyter | 17,747 | 449 | # %%
import numpy as np
import os
from pathlib import Path
import pandas as pd
import matplotlib.pyplot as plt
# import plotly.express as px
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 ... |
8534db35b40f06283a1011033f0f2986ad071310e1532af780e9c256d81dcbf1 | Jupyter | 17,760 | 444 | # %% [markdown]
# # Chargement des librairies
# %%
from google.colab import drive
drive.mount("/content/drive")
# %%
%cd drive/MyDrive/
# %%
import pandas as pad
import numpy as np
from numpy.core.numeric import NaN
import datetime
import matplotlib.pyplot as plt
import seaborn as sns
import statistics
from scip... |
df9b93b8eee430211ebf16902e9f743f9ab2ee7b0f116c76cc59db6179336bc1 | Jupyter | 17,764 | 573 | # %%
import os
import sys
import time
import random
import numpy as np
import imgaug
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.patches as patches
# from pycocotools.coco import COCO
# from pycocotools.cocoeval import COCOeval
# from pycocotools import mask as maskUtils... |
b6808e85d41ad201b5c31fd9a5d954e53146338bb3eeaa53e365f5d4641d3e9b | Jupyter | 17,771 | 573 | # %% [markdown]
# # Generate Predictions Images for a Site over Time
# This notebook is used to generate predictions overlays for a given location.
#
# Outputs are saved pngs where prediction score controls the opacity of a single-color image. The script also generates RGB patches for each prediction as well.
# %%
im... |
5a9b6e29789ef6a28d34e28016d9f5266952559197d46303309e22a980b00b44 | Jupyter | 17,818 | 676 | # %% [markdown]
# Whole-cell currents in 5-HT neurons recorded at 29-30 C.
# %%
from common import colors, sbarlw, insetlw, savefig
from gating_util import mark_time_interval
# %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
from __future__ import division, print_function
import pickle
impor... |
678733b8f3616a198564901d8ec9218e26a9dbfa0e1871ca956c85270e3ff08c | Jupyter | 18,180 | 573 | # %% [markdown]
# # TQIP In-Hospital Model
# %% [markdown]
# This notebook illustrates how we trained the In-Hospital Model for the paper: "Assesing the Utility of Deep Neural Networks in Dynamic Risk Prediction After Trauma"
#
# The TQIP database can be requested through the American College of Surgeons website at
#... |
1327e66eac281c0e7812ff5e949c3a2b234b64693d8aa33f37406f1fbd536b7a | Jupyter | 18,221 | 682 | # %% [markdown]
# # GO Annotation Analysis
#
# Analyzes the associations between genes and GO terms in the Gene Ontology (GO) database.
#
# In particular, categorizes associations based on:
#
# - whether they have been "retracted" (i.e., removed from the database)
# - whether they have been reviewed and accepted or ... |
a028d9e5f77563220efee9a4b4d0aa8a77af0077ef2c39fa6f6cbb9f21fa96e5 | Jupyter | 18,330 | 564 | # %% [markdown]
# # OAK Developers Tutorial
#
# This tutorial is primarily for **Python Developers** who wish to use OAK in their applications. These include
# applications such as:
#
# - building ontology-driven data portals
# - creating curation tools
# - data science and machine learning applications
# - web servi... |
448f2fecd9c6d2145ae200023d428f4d8f8ea6ad04ecd2a71be6beaf04ed646c | Jupyter | 18,424 | 727 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
import matplotlib.pyplot as plt
import seaborn as sns
import h5py
import os
import numpy as np
import pandas as pd
import joblib
import cv2
import itertools
import dask
from dask.distributed import Client
from tqdm.auto import tqdm
from ruamel.yaml impor... |
7bded7f7d404fb55a84ad2ec14b848ccbd3e31363ea90c6ffdd4a9e88954b7c5 | Jupyter | 18,662 | 546 | # %% [markdown]
# # Distance to soma
# Plotting quantifications of mitochondrial density and synapse density at different distances to the soma. All quantities we plot are computed over roughly 10 µm segments of basal dendrite.
# %%
from meshparty import trimesh_io
import math
from collections import Counter
import... |
32e6c89a2e3019edbb6570ee838ea2afa7e2f3bf6a27fe2e275b11598ff8004b | Jupyter | 19,003 | 441 | # %%
# set up enviroment
from __future__ import division
import sys
import numpy as np
import pandas as pd
import scipy
import importlib
import scanpy as sc
%matplotlib inline
from matplotlib import pyplot as plt
import matplotlib as mpl
import seaborn as sns
from sklearn.manifold import TSNE
import sklearn
# %%
sc... |
51e2ae0f6f5976d529562dbb0923a80132c45624c29295d9327693c92d92715e | Jupyter | 19,099 | 404 | # %% [markdown]
# # Comparing MAGeT ans SUIT results
# ## Data:
# - 1. Cerebellar SUIT segmentation;
# - SUIT: Left/right 1-X+Dentate+Interposed+Fastigial and Vermis
# - 2. Cerebellar MAGeTBrain [MAGeTBrain pipeline](https://github.com/CobraLab/documentation/wiki/MAGeTBrain#collecting-volumes)
# ... |
308f107960dd70e6d773bb6ddd65c8a528eeb3783bdb74eb0f68fb49a98da24c | Jupyter | 19,302 | 547 | # %%
import scipy.io as scio
import numpy as np
import os
from glob import *
import nibabel as nib
import pandas as pd
import random
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.pipeline import make_pipeline
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree im... |
2f5f87fecb71b344f35d30a5145954ba039cfbf150697ede52c2adc82f8ff304 | Jupyter | 19,550 | 497 | # %% [markdown]
# <a href="https://colab.research.google.com/github/Poirazi-Lab/dendrify/blob/main/paper_figures/Fig3_notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ## Overview
# > *In this interactive Python notebook, we... |
851bd4e53e27a0f2f74e9caf5ecd25c4b25f4c2249228fde4beae4ec8f579725 | Jupyter | 19,571 | 498 | # %% [markdown]
# <a href="https://colab.research.google.com/github/Poirazi-Lab/dendrify/blob/main/paper_figures/Fig3_notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ## Overview
# > *In this interactive Python notebook, we... |
24fdb986ea9707e8538bccc7ed57559adc74ffbd5d7c510fb2f98b50f7b6d028 | Jupyter | 19,629 | 419 | # %% [markdown]
# # Visualize the basic demographics before mathcing and recalculate power based on QC results
# This is the notebook for matching the control subjects with our ET subjects to creat new cohort to achieve 0.9 power for this study.
# ## Data:
# - 1. Freesurfer results: ICV. Lh/Rh_cerebellum_gm/wm (g... |
af199da3e1b322d8de15462bc330e28e81927b37620aa64bbaa9e28820894dd5 | Jupyter | 19,726 | 596 | # %%
import sys
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import scipy.stats as stats
from scipy.fft import fft, fftfreq
import scipy.signal as sig
from sklearn import linear_model
sys.path.append("../../ndsvae/")
import ndsvae as ndsv
sys.path.ap... |
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