sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
04c59b824f04632ef2de928bae5f98a804590c34b63ffeeaf9a101f7a8d65872 | Jupyter | 10,163 | 381 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
clean_ticks,
savefig,
)
from rl_analysis.io.df import get_closed_loop_parquet_columns
from rl_analysis.util import rle, whiten
import pandas as pd
import matplo... |
69e015c8e64df67b6f2f27554f6994bd2e5dd4f131e08c3c44b28bd135e5cbb5 | Jupyter | 10,177 | 317 | # %%
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("../... |
e2a57f05d44a3d6a354db777c013368dc9ae3a653b34b2687797e8d92bed427f | Jupyter | 10,202 | 193 | # %% [markdown]
# ## Figures for The Paper
# %%
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %%
#This how we load txt files from each forsal model of 10
#Train
ventral1_t_avg_cos = np.loadtxt('Ventral_Train_AvgCosSim_1.txt', dtype="float",delimiter=",")
ventral2_t_avg_cos = np.loadtxt('... |
f36a431dc7a48899cb58b5d90d5994f1e5fc438c9ca958ea5396dcf29250d451 | Jupyter | 10,244 | 330 | # %% [markdown]
# # Ventral Decoding on Part of Speech
# %%
#Load the required libraries
import numpy as np
import pandas as pd
from tensorflow import keras
from keras.models import Model,load_model
from keras import backend as K
from keras import models, layers
from keras.models import Sequential, model_from_json
fro... |
70a0d9ff1dc3f7fc5cc58d4a690d19ebcc495b444f9beace7d0cb7415c0f517b | Jupyter | 10,262 | 333 | # %% [markdown]
# # Ventral Decoding on Onset Phoneme Monitoring
# %%
#Load the required libraries
import numpy as np
import pandas as pd
from tensorflow import keras
from keras.models import Model,load_model
from keras import backend as K
from keras import models, layers
from keras.models import Sequential, model_fro... |
4d30aaf827b1b0597453647b2c1c0dd99142fef9aafba2f5fa88d2badb1a395c | Jupyter | 10,268 | 333 | # %% [markdown]
# # Dorsal Decoding on Part of Speech
# %%
#Load the required libraries
import numpy as np
import pandas as pd
from tensorflow import keras
from keras.models import Model,load_model
from keras import backend as K
from keras import models, layers
from keras.models import Sequential, model_from_json
from... |
8082cf54c67ffd4684e8c51c0e4dd9cc3c6f7938e54c4f8bb7765c74873138b8 | Jupyter | 10,271 | 335 | # %% [markdown]
# # Dorsal Decoding on Onset Phoneme Monitoring
# %%
#Load the required libraries
import numpy as np
import pandas as pd
from tensorflow import keras
from keras.models import Model,load_model
from keras import backend as K
from keras import models, layers
from keras.models import Sequential, model_from... |
fff4b225f41014a136a582bf2909589b738e5d3b00bd5e9c957d1d8f5fc093aa | Jupyter | 10,296 | 327 | # %%
#from pandas import DataFrame
#import numpy as np
#df = DataFrame.from_csv('pca_normed.csv')
#np.unique(df['Multi_Spike_Cluster'])
# %%
from __future__ import print_function
feature_count = 39
predict_column = -3
#column_vector = [1] * feature_count
#target_cluster = 1
def train_model(column_vector = [1] * feat... |
88e2556fb27b016a19bc539302e73180c7a68abe9c208272b809169583f4d1ea | Jupyter | 10,303 | 244 | # %% [markdown]
# # Ontology Term Embeddings in OAK
#
# This notebook introduces the `EmbeddingProviderInterface`, which gives direct access to
# **vector embeddings of ontology terms** as numpy matrices and pandas DataFrames, along
# with operations built on them (similarity, nearest neighbours, text search, and
# be... |
3de5da0c4a2776ff2b7fa4cd19236f2d1bf9e472db1a6aba5c10ab18b200c607 | Jupyter | 10,356 | 381 | # %% [markdown]
# # Here we're computing features for the "syllable-time" or "kernel regression" model
# %% [markdown]
# The most important features here are:
#
# 1. counts
# 2. entropy
# 3. scalars
#
# Each syllable statistic is computed for the current syllable over a series of bin sizes, e.g. count_5 represents h... |
69f31ab2e937f0914d067c73699eec52d47a0c97cc981a3b0a9b706b55f93f0b | Jupyter | 10,427 | 264 | # %% [markdown]
# # Kunitz domain notebook.
# The Kunitz domain is a family of protease inhibitors. It is one of the simplest and shortest ($N = 53$) domain, with a well-known secondary and tertiary structure (2 beta strands, 2 alpha-helices, 3 disulfide bridges). We show here some features found by the algorithm, as ... |
638dede1d8cc68b004ddf3c9500887cd6fa966fbed1fd389aea734d8de29b5e7 | Jupyter | 10,463 | 265 | # %%
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
# %% [markdown]
# # Effect of a ramp stimulus on a leaky capacitor
#
# To develop a toy model of the peak population firing rate evoked by a ramp stimulus with a given derivative, we'll begin by considering the voltage created wh... |
c246bd786b5ce93c3c36b0c53e4a3118ce3766c9024abc59ae8bf32667a290da | Jupyter | 10,492 | 379 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
%matplotlib inline
from itertools import cycle
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import lab... |
b7fcafdda261d77fac85ee73ac4880e23fc06411c6ba3be705731fc0768bc74b | Jupyter | 10,493 | 370 | # %% [markdown]
# # Compute syllable counts and scalars 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`)
# %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# ... |
c7e48c2a62a171ef999c4160b11b9edc67cbcaff881d16e97a3bd0e68414cd13 | Jupyter | 10,508 | 293 | # %% [markdown]
# # Neuro-CL tutorial
#
# * author: Chris Mungall
# * created: 2022-09-9
#
# This tutorial walks through the neuro-relevant subset of the [Cell Ontology](https://obofoundry.org/ontology/cl) (CL), the goals are:
#
# - to help understand the structure of CL
# - to show how CL uses relationships lik... |
acf7df88cb8d475426cc53910d40c77992f76a86a76f936bab5f348e52682a00 | Jupyter | 10,541 | 408 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from rl_analysis.plotting import syllable_aliases, setup_plotting_env, savefig
from rl_analysis.util import rle, count_transitions
from rl_analysis.io.df import dlight_exclude
import pandas as pd
import os
import seaborn as sns
import numpy as np
import m... |
77b77a160e4af4b59a76c5ec78dbb9b46589eb77a32d3eeb7cfc1d213e9c001c | Jupyter | 10,565 | 405 | # %%
# Change directory to VSCode workspace root so that relative path loads work correctly. Turn this addition off with the DataScience.changeDirOnImportExport setting
# ms-python.python added
import os
try:
os.chdir(os.path.join(os.getcwd(), '../..'))
print(os.getcwd())
except:
pass
# %% [markdown]
# # EDA on f... |
ee17e881384644d908224cd2a5d19d67a1f5c229c03a73be8084936202a8af1c | Jupyter | 10,639 | 347 | # %% [markdown]
# # Whole-cell currents recorded in DRN SOM neurons
#
# Shown in fig. 1.
# %%
from common import colors, sbarlw, insetlw
# %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
from __future__ import division
import pickle
import warnings
import numpy as np
import matplotlib.pypl... |
029a16ff9e52ae0b85d3c5ab46996ea979799e63a242a40e3c29ce7d2777cf6c | Jupyter | 10,668 | 356 | # %% [markdown]
# # Temperature-dependence of derivative encoding by 5-HT neuron populations
#
# Shown in fig. 7S1.
# %%
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
... |
0e3e5f5bcaa93ec29c9f64c2627d04f67c562487bc8644613d4886b1771f319d | Jupyter | 10,722 | 295 | # %% [markdown]
# # Create Pixel Dataset (Deprecated in favor of Descartes pipeline)
# 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 coordinates either from a geojson or csv. For each location in the lis... |
97604eb1f96373ef7c61399ba2a120c0ac77da2b820a076d4ae22b235b889c46 | Jupyter | 10,750 | 346 | # %%
from common import colors, sbarlw
# %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
from __future__ import division
import pickle
import warnings
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
from mpl_toolkits.axes_grid1.inset_locator import inset_a... |
49423230f0d53e83e43f68f2a8fa3e55856ed6abe6cc8fad0ef847fae2965b2e | Jupyter | 10,820 | 357 | # %% [markdown]
# # Command Line Examples
#
# All lines starting `!` are commands that you can execute yourself on the command line
# %%
!pwd
# %%
!runoak --help
# %% [markdown]
# ## Ubergraph Examples
#
# Ubergraph is suitable for both graphy type operations as well as some inference/OWLy operations
#
# The defa... |
5dd010014d2b9a489851c59c2061313efdd56e066d1a3391245c4d093769590a | Jupyter | 10,835 | 329 | # %% [markdown]
# <a href="https://colab.research.google.com/github/saadkhi/genn/blob/saadkhi_MBT/docs/tutorials/mushroom_body/5_testing.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Testing
# In this final tutorial we load the... |
33a34c26ee6644b11d399de67c48ecfd24ee7e74af55e618770173bd672c604e | Jupyter | 10,843 | 415 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
clean_plot_labels,
clean_ticks,
savefig,
plot_pval
)
from sklearn.utils import resample
from sklearn.metrics import r2_score
from scipy.optimize import c... |
7fb62d62bb1db8bef2fefcbb3733f1e9195071e2201b3705e6945980ec33069a | Jupyter | 10,858 | 333 | # %%
import pandas as pd
import re
import string
import pickle
# %%
data_dir = '/home/mahbub/Desktop/sepsis_text_IoMT_project/data/embedding_generation_data/'
# %%
def calculate_start_end_time(file_name):
df = pd.read_csv(file_name)
column_list = ['start_time', 'end_time', 'icustay_id', 'subject_id'... |
c64384cfa0d84fb9ff347f18821b436ffca588869ee4e4cbe74c11648ccbf184 | Jupyter | 10,899 | 339 | # %%
import pandas as pd
import re
import string
import pickle
# %%
data_dir = '/home/mahbub/Desktop/sepsis_text_IoMT_project/data/embedding_generation_data/'
# %%
def calculate_start_end_time(file_name):
df = pd.read_csv(file_name)
column_list = ['start_time', 'end_time', 'icustay_id', 'subject_id'... |
3189a18990482ab24a963cad0f3bdd0d99182d108f275b5b994d145e9805fcf5 | Jupyter | 10,925 | 374 | # %% [markdown]
# # Compute syllable counts and scalars 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`)
# %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.... |
f44eea2f651e1fb3e4aa5b6dcb3141542481f836b189ec1a77e097ebccadd3f8 | Jupyter | 10,951 | 397 | # %% [markdown]
# ## Fires Exploration With Sentinel-2
# %% [markdown]
# Notebook that implements simple fire hotspot detection from infrared bands on Sentinel-2. This notebook utilizes the Descartes Labs platform to access all imagery in a more Python-native way.
# %% [markdown]
# ### Load in TPA points
# %%
import... |
22986b11b713e9614c03499bb0adbc4fa7802a972995d2746df739edba4fef45 | Jupyter | 10,995 | 371 | # %% [markdown]
# # Compute syllable counts with in expanding windows with a shift
#
# 1. Note that you can use a dask scheduler by setting `dask_address` to the address of the scheduler (see `../analysis_configuration.toml`)
# %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
... |
12c0fb120e3966f049bcaec30b8a8e9db2688f9ac33a96a4a2dd70d529f623a8 | Jupyter | 11,035 | 323 | # %%
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... |
f358a2cd4fb5a932dc740a55cb75447294bdcb206e7a728778a8daad71e00898 | Jupyter | 11,045 | 337 | # %% [markdown]
# # Passive membrane parameters
#
# Reported in table 1.
# %%
from common import colors, savefig
# %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
f... |
f87c6b3a9e8c4b372aa5f591463b7a2990a4f6371a2a29754294b94df1763cb3 | Jupyter | 11,054 | 240 | # %% [markdown]
# # Using a model to analyze sequences
#
# In this tutorial, we demonstrate how to use the individual classes and functions provided in Selene to make model predictions.
#
# If you want to run this notebook, please start it with `jupyter notebook --NotebookApp.iopub_data_rate_limit=10000000000`.
# The... |
70f106aef109a9062b87ff3672b943952bae2ac3ebc40ac41633145dbc5106b1 | Jupyter | 11,103 | 310 | # %%
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
sns.set_style("ticks")
hfont = {'fontname':'Myriad Pro'}
import matplotlib.ticker as mtick
from matplotlib import gridspec
from matplotlib import colors
import sys
sys.path... |
7aa4561f4f2276533a30aced766c62c76f3a3f2176d7ab1418099ce42e9144c9 | Jupyter | 11,126 | 282 | # %% [markdown]
# <a href="https://colab.research.google.com/github/Poirazi-Lab/dendrify/blob/main/paper_figures/Fig2_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... |
1f9bfeef8d9a00cfda147e50a5396d31e32ece1e15ede6bcda56bd27f28d4944 | Jupyter | 11,127 | 321 | # %% [markdown]
# # Train Spectrogram Classifier
# %%
%load_ext autoreload
%autoreload 2
# %%
import albumentations as A
from datetime import date
import json
import matplotlib.pyplot as plt
import numpy as np
import os
import pickle
from sklearn.model_selection import train_test_split
from sklearn.metrics import cla... |
18b3848ededa285b027d0063f294e739d316fa91d9fef3bf04849a3f488dc263 | Jupyter | 11,147 | 283 | # %% [markdown]
# <a href="https://colab.research.google.com/github/Poirazi-Lab/dendrify/blob/main/paper_figures/Fig2_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... |
8bcd3f05e7b573650f97d6557c61abcb5115fda170d6a9ee72091e9757d1266a | Jupyter | 11,154 | 262 | # %% [markdown]
# # Subsumption Recapitulation: Do LLM Embeddings Encode the Ontology Hierarchy?
#
# LLM embeddings of ontology terms are built only from text (labels, and possibly
# definitions). Ontologies encode **subsumption**: *finger syndactyly* is a kind of
# *syndactyly*. This notebook asks how much of that hi... |
7c428ffe8a0d06399236f0cbba6d62bf1dcfc84e93d9def48d2701f9f5e44767 | Jupyter | 11,168 | 292 | # %%
!pip install biopython
# %%
%pylab inline
import os
import pandas as pd
import seaborn as sns; sns.set(color_codes=True)
import matplotlib
from Bio.Seq import Seq
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
# %%
from google.colab import drive
drive.mount("/content/gdrive/")
... |
072b412936b1f38a1209b4a551d5196fe2e3d107d3f281da903cf484010f1557 | Jupyter | 11,188 | 402 | # %%
import toml
import numba
import joblib
import random
import warnings
import numpy as np
import pandas as pd
import colorcet as cc
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import signal
from toolz import get, partial
from os.path import join
from tqdm.auto import tqdm
from rl_analysis.boots... |
795172ee2df5098b65891c6ede5b3b073d134155a813f0ad95cc7989c423ec20 | Jupyter | 11,217 | 304 | # %% [markdown]
# - random seed to generate network and design locations (R)
# - random seed to generate gaussian field covariates (Python)
# - random seed to generate response, sim_river and sim_euc (R)
# - random seed to generate anomalies (Python)
#
# Then, the model is run
# %%
import pandas as pd
import numpy as... |
9bc5307f67f12a5b08344b87bd8c6a9b8ef1984a7d82fe562a0c9a1db8f7d75d | Jupyter | 11,218 | 334 | # %% [markdown]
# # LZW demonstrator
# %% [markdown]
# The aim of this note is to provide some reference facts for LZW---mostly from Thomas and Cover \cite{Cover:2006aa}---adapted to the needs of the Luminous project. LZW is an algorithm to compute a Kolmogorov Complexity estimate derived from a limited set of progr... |
449331482345800d750460aee9effe26159f5c6b8ed9e08712eb7ccbc08c9f16 | Jupyter | 11,325 | 348 | # %% [markdown]
# # Custom models
#
# <style>
# .extra-info p {
# color: firebrick;
# font-size: 13px;
# font-weight: bold;
# }
# </style>
#
# **With great power comes great responsibility!** - Uncle Ben
#
# In the following tutorial, we’ll explore how to modify neuron models created by
# Dendrify to gen... |
12c42c88f83004f3bd1aa294eb2b7f91999d20026ee3887974c2f221e5ad622f | Jupyter | 11,356 | 386 | # %%
# Change directory to VSCode workspace root so that relative path loads work correctly. Turn this addition off with the DataScience.changeDirOnImportExport setting
# ms-python.python added
import os
try:
os.chdir(os.path.join(os.getcwd(), '../..'))
print(os.getcwd())
os.chdir("/Users/bpedigo/JHU_code/m... |
103c8d65f7cbae2261120f5e1043d9d46a3b2d412fe6a7f491dc8cdd9f0065e5 | Jupyter | 11,378 | 361 | # %% [markdown]
# # General
# %%
from os import listdir
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
# %%
sns.set_context('talk')
# %%
def find_csv_filenames( path_to_dir, suffix=".csv" ):
filenames = li... |
bf8de18dc85063cb181ef8d746516797a7b256a36f91ed4b4530cd2b58df15cd | Jupyter | 11,438 | 299 | # %%
# stages:
# 1. Load a large set of geotiffs as xarray datasets
# 2. Select a few 32x32 pixel chips from each xarray
# 3. Generate embeddings for each chip
# 4. Create two lists. One containing the geometries of the chips and the other containing the embeddings
# %%
from datetime import datetime
import geopandas a... |
76ddbd74aa058ec86b3815e91ea9c599e44aa1c4e441bef9af9dc344d716a398 | Jupyter | 11,443 | 344 | # %%
import pickle
import pandas as pd
import seaborn as sns
import numpy as np
from matplotlib import pyplot as plt
%pylab inline
%matplotlib inline
# %%
ModCorDF = pickle.load(open("../computed_data/AffCor.pickle","rb"))
ModCorDF.sort_index(inplace=True)
ModAUCDF = pickle.load(open("../computed_data/AUCval.pickle"... |
81dad45d90e721667f0daf8fa7895a8381df75fc1aaf502954564df1371f8492 | Jupyter | 11,448 | 346 | # %%
import numpy as np
import pandas as pd
import scanpy as sc
from harmony import harmonize
import hdbscan
import seaborn as sns
from matplotlib import pyplot as plt
import sys
sys.path.append("../resources/")
import sct
# %%
adata = sc.read_h5ad('../data/adata_all_panPN_annotated_final.h5ad')
# %%
df_libs = pd.re... |
3922f20a26bcc94ba48e81feb7dfe023f50835e8097f72b874349e8e34916203 | Jupyter | 11,478 | 292 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from matplotlib import gridspec
import seaborn as sns
sns.set_style("ticks")
import os
from pathlib import Path
import itertools
import sys
import pickle
import sys
sys.path.append(os.path.join(Path(os.getcwd()... |
69ccc042a9881f481bfffe4d30382dfbec4316bf14a63a8d1e0a95ffdaac8dc1 | Jupyter | 11,495 | 459 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import setup_plotting_env, clean_ticks, savefig
from rl_analysis.util import count_transitions
from rl_analysis.io.df import dlight_exclude
from sklearn import model_selection
import joblib
import pand... |
e6fb1410f688b0904d3ce87cca81307d79a9ac5d66ff0c00ecbefa62bb2dd94b | Jupyter | 11,526 | 444 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
clean_plot_labels,
clean_ticks,
savefig,
plot_pval,
)
from functools import partial
from joblib import Parallel, delayed
import pandas as pd
import matp... |
955df9d1dca8efe59973505fa8cf11eb207b500410fb3397a0624b04980ba7b0 | Jupyter | 11,590 | 431 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
clean_ticks,
clean_plot_labels,
format_pval,
savefig
)
from rl_analysis.io.df import dlight_exclude
from rl_analysis.util import count_transitions, pd_zs... |
8a482cdd0df64d249b3cb2b74cd85f0a6551654c3492741e1d928b900545f07b | Jupyter | 11,665 | 364 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import shutil
import descarteslabs as dl
from descarteslabs.catalog import Image, properties
import geopandas as gpd
import rasterio as rs
from rasterio.merge import merge
from tensorflow.keras.models import load_model
from tensorflow import keras
from tqdm.noteb... |
abc4ddc9d0e5bca4d298832149dd21e503d74796e99558edd3b22a813b845291 | Jupyter | 11,708 | 363 | # %%
import sys
import os
import itertools
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as spopt
import scipy.integrate as spi
import scipy.stats as sps
import tensorflow as tf
import pandas as pd
sys.path.append("../../ndsvae/")
import ndsvae as ndsv
sys.path.append("../")
import util
... |
7230d37d977a388b4a40f580c983b2f65043b9a1e31c1f533c96a813d2aa792b | Jupyter | 11,738 | 335 | # %%
import ee
import geopandas as gpd
from tqdm.notebook import tqdm
import json
import numpy as np
import overpy
import requests
import shapely.geometry
import h3
# %%
class Metadata(object):
def __init__(self, coords):
self.coords = coords
self.metadata = {}
def get_population(self, rad... |
c63d113c49235b3c313bdd91f5d5aca119a4206b455eb8f16bab4c5cdbab63d4 | Jupyter | 11,753 | 416 | # %% [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 одном испыт... |
55ec31fb4f27a10eea0320d76369987966130285f055e6e4bcef40902089d7fa | Jupyter | 11,774 | 328 | # %% [markdown]
# # Fine tuning test
# Test the possibility to manually annotated a few frames of the experiment, then fine tune the network on them to predict the rest of the frames.
# This is kind of overfitting part of the test set to perform well on the rest, or sort of domain adaptation.
# %%
%matplotlib inline... |
568c97f35a24a6d9d22d140fa012e8f34c415138b3bbc86391ea2ced89ec836b | Jupyter | 11,861 | 427 | # %% [markdown]
# # Import modules
# %%
from common import colors, savefig, sbarlw, insetlw
# %%
import os
os.chdir(os.path.join('..', '..', '..'))
print(os.getcwd())
# %%
from __future__ import division
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
from mpl_tool... |
5a6de1ff3bf3af588369d0fd90d27f5145caa5a537a3faad809c8a96d4253101 | Jupyter | 11,867 | 377 | # %% [markdown]
# # Import Packages
# %%
import os
from os.path import join
from datetime import date, datetime, timedelta
from joblib import load, dump
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.backend import clear_sessi... |
272733e3a8ffaeb7c6b2e949550f4079759f2ea5e720e12d813606700eed0d69 | Jupyter | 11,880 | 387 | # %% [markdown]
# # Illustrate GIF models
#
# Model output (fig. 3A) and sample training/test set traces (fig. 3C and 3S1).
# %%
from common import colors, sbarlw
# %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplo... |
6b89cd4efbacdd7a61ee49a1d1e03f78296b172f81fcb8117b45ba0fd84c7783 | Jupyter | 11,898 | 362 | # %% [markdown]
# Shown in fig 5S2.
# %%
from common import colors, sbarlw
# %%
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.S... |
c10eda0b49f3cde8b1481d13a8e531c25c340dcfa12181bc8d6b291852a04c04 | Jupyter | 11,927 | 328 | # %%
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# plt.rcParams['svg.fonttype'] = 'none'
# plt.rcParams['pdf.use14corefonts'] = True
# %% [markdown]
# # Unsupervised exploration of feature representations
# %%
df = pd.read_csv('../datafiles/chexpert/chexpert... |
6c6ff781320def7607728ee3ad346cd8ab7fbecaf110fdd1f05f477627121767 | Jupyter | 11,930 | 424 | # %%
from common import colors, sbarlw, insetlw, savefig
from gating_util import mark_time_interval, load_gating_params, get_plot_coordinates_with_error
# %%
import os; os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
from functools import partial
import pandas as pd
import matplotlib.pyplot as plt
import ... |
0621dc4d8515a54b32ce403f8b7436abd12bfdae4ad6390972923c7336cabdb3 | Jupyter | 11,958 | 389 | # %%
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.utils import shuffle
from tqdm import tqdm
# %%
np.random.seed(42)
# %% [markdown]
# # Study population
# %%
data_dir = '../datafiles/mimic/'
# %%
metadata_df = pd.read_csv(data_dir + 'mimic-cxr-... |
9a9eb43de184ff3ac6b08f233d6ad69a2c51edd47d9a30ba42e38a1e32420457 | Jupyter | 11,980 | 337 | # %% [markdown]
# <a href="https://colab.research.google.com/github/ZwwWayne/mmdetection/blob/update-colab/demo/MMDet_Tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # MMDetection Tutorial
#
# Welcome to MMDetection! This... |
0c172cfaf7318d82441d2b534d3584c4d8442d514c367fc5e2abbc6b6faf4cbf | Jupyter | 12,037 | 253 | # %% [markdown]
# # Phenotype Profile Matching: Classic Semantic Similarity vs LLM Embeddings
#
# A core use of semantic similarity is matching a **patient's phenotype profile** to
# **disease profiles**, as tools like Exomiser and Phenomizer do. This notebook compares
# ontology-based methods with LLM-embedding metho... |
74c129859db9ea0879e660443c4885025ae3ef746c061f6198aaeb82c7a5e32f | Jupyter | 12,132 | 381 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import shutil
import sys
import descarteslabs as dl
from descarteslabs.catalog import Image, properties
import geopandas as gpd
import rasterio as rs
from rasterio.merge import merge
from tensorflow.keras.models import load_model
from tensorflow import keras
from... |
55f1613b2f9c56c5e3142028c6a08f8fb62778c7c58b4a1d77011d0d2ea4c60a | Jupyter | 12,220 | 402 | # %%
import numpy as np
import scipy as sp
import os
import pandas as pd
from tqdm.notebook import tqdm
from sklearn import metrics
import matplotlib.pyplot as plt
import seaborn as sns
import networkx as nx
from sklearn.decomposition import PCA
from sklearn.metrics import roc_curve
sns.set(context='notebook', style='... |
8b0131f516cf76554b8e1e9852160dca4a8b579313f62e11f6f121cb30c8f127 | Jupyter | 12,242 | 365 | # %%
# General imports
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.api as sm
from IPython.display import display
%load_ext autoreload
%autoreload 2
# Saving text not as a path
import matplotlib as mpl
new_rc_params = {'text.usetex': Fals... |
8b5b041d29a45be7dc14dc2a7909a80f18c5d131d19fdea062e46a74270844c9 | Jupyter | 12,310 | 300 | # %% [markdown]
# #### If you haven't looked at MostSynapsesInAndOut first, consider starting there, as this notebook builds on concepts described there.
# %%
from jupyter_dash import JupyterDash
import dash
import pandas as pd
from nglui.statebuilder import *
from dashdataframe import configure_app
import numpy as np... |
52e5d71174ddf0185dd52b62b659bff546b64437a15227b6f01debff84487967 | Jupyter | 12,324 | 330 | # %% [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... |
8baa8745bc8037586bdd6b97893cf370a3b7666cafb43a302d0aa6240c132440 | Jupyter | 12,348 | 353 | # %% [markdown]
# # EfficientDet Tutorial: inference, eval, and training
#
#
#
# <table align="left"><td>
# <a target="_blank" href="https://github.com/google/automl/blob/master/efficientdet/tutorial.ipynb">
# <img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on github
# </a>
... |
9910b71a96015e3c2c6553cdf15eb4e47e8baeb614ae507974634331b07686db | Jupyter | 12,369 | 316 | # %% [markdown]
# # CHEBI Predicates
#
# This notebook is intended as an explanatory guide to the importance of edge types (predicates) in ontologies.
#
# ## Citric acid and its ion forms
#
# For this guide, we are going to look at citric acid and it's conjugate forms such as citrate(3-). These
# chemical entities a... |
1416c6c2386ea1b0cab63d38c16d85a865518656263249592a83cddddc899b8c | Jupyter | 12,414 | 350 | # %% [markdown]
# # Train Spectrogram Classifier
# %%
%load_ext autoreload
%autoreload 2
# %%
from datetime import date
import json
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_repo... |
f1c17e8c17d1b09337725faf2e5b8820d562be7ef72cadb390ded77a2eebe993 | Jupyter | 12,430 | 226 | # %% [markdown]
# <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <img src="https://user-images.githubusercontent.com/26833433/98702494-b71c4... |
7d05a49d426896d5f18c46bb8862f10acd83514a0dc80ee9efb55a3c380197eb | Jupyter | 12,458 | 259 | # %% [markdown]
# # Report the main study result on physicians' task performance with and without the assistance of AI prediction and explanation
# %%
from base_code import *
from scipy.stats import friedmanchisquare, wilcoxon
from statannot import add_stat_annotation
valid_data = pd.read_csv('../neurosurgeon35_data/... |
0244823f3428b0768b1f84911d47015f558574b33e5a5753abae436a97925e31 | Jupyter | 12,459 | 388 | # %%
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)
# %... |
566c24d0fbc62153adc4110058dbc92742569a2f44bd47ccf1198a9b10223a5b | Jupyter | 12,487 | 276 | # %% [markdown]
# # Topology-preserving ring melt simulations
#
# This notebook shows how to run simulations in which chains **cannot pass through
# each other**, so the global topology (knots and links) is exactly conserved —
# and, just as importantly, how to *verify* that it actually is.
#
# The force set is the K... |
e3f08c1a2fa294d487f0aeef5646c4fb0c4b86d8bacdf85d251789f28efa22b7 | Jupyter | 12,498 | 510 | # %% [markdown]
# Shown in fig 1.
# %%
from common import colors, sbarlw, insetlw, savefig
# %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
from __future__ import division, print_function
import pickle
import warnings
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gri... |
bfa901cd279a11972acc07ce73fe4f3696751c6313b1563ba7dcad6203670a00 | Jupyter | 12,544 | 342 | # %%
! pip install --upgrade dgl-cu101
# %%
import numpy as np
import pandas as pd
from scipy import sparse, stats
import torch
from torch import nn
import torch.nn.functional as F
from functools import reduce
from pprint import pprint
from tqdm.notebook import tqdm
from sklearn.model_selection import train_test_split... |
b13fe85d2e9281730ba68a1fce3af26e7ef9f6128693543a7ed9539e7003db0e | Jupyter | 12,574 | 364 | # %% [markdown]
# ## NoteBook to Train and Visualize the U-Net Network
# %%
# Importing bunch of libraries
import os
import sys
import time
import random
import warnings
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
import tqdm
from skimage.io import imread, imshow
from skimage.transform import... |
390dc96948598c9519879b3aeb3754cf26678b031e4f93da3d270d646d2ebee0 | Jupyter | 12,609 | 439 | # %% [markdown]
# # Plot syllable waveforms
# %%
import toml
import numba
import joblib
import random
import warnings
import numpy as np
import rl_analysis
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
from os.path import join
from scipy ... |
fcc8cc88e615a48183ee18c361e55d3cb18b15a676ef351e3c303f154b49427e | Jupyter | 12,695 | 429 | # %%
# 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
import hdbscan
import scanpy as sc
from harmony import harmonize
from sklearn.manifold import TSNE
import sklearn
sys.path.append("../resources/")
import sct
... |
2dee256e8be6447bdd6173012dee0954cafaf6dc70caecf23443509a124271ee | Jupyter | 12,707 | 456 | # %% [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 #plotagem de imagens
# from uti... |
2125971bb3475a9926577494c45b60674b7e74c79afea09d6a76fded1a271a6b | Jupyter | 12,729 | 407 | # %% [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... |
6199966e30ce955b42d9a50d899962784dedd9787cfa0f36621afcd7c0fdf439 | Jupyter | 12,760 | 375 | # %%
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... |
1fca32b1c165f2d462a10ec995cc32f96842ba1b5fe1af974b7c38817c5f7ac6 | Jupyter | 12,765 | 378 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
from scipy.optimize import curve_fit
# %%
sns.set_context('talk')
# %%
cd O:\TUD\__eDICE-paper\__for-paper\data_pub... |
4e6e39a0ddf3e7d37be34f7c87b5e504bd206be8e47930c3105ee0bf839f912e | Jupyter | 12,773 | 483 | # %%
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
from rl_analysis.plotting import (
setup_plotting_env,
syllable_aliases,
clean_ticks,
savefig
)
from rl_analysis.util import rle, count_transitions
from rl_analysis.io.df import get_closed_loop_... |
4bf6465ce48f93aee77b109e47c5542889da0a66e94aed58d21541be733751e3 | Jupyter | 12,816 | 457 | # %% [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 #plotagem de imagens
# from uti... |
54830450e32dfab0e984eaa61ce1a40f5079e30f9bb1293b5eadfb5e73d16a4b | Jupyter | 12,829 | 639 | # %%
import os
import pandas as pd
import tarfile
import tmkit as tmk
from typing import List, Tuple
# from tmkit.topo import from_pdbtm, from_tmhmm, from_phobius
# from tests import dir_data, tmp_data, exp_data
from tests import tmp_data
# %% [markdown]
# # Example dataset
#
# We begin by introducing and downloading... |
eb679fd8b2a50bd2ee0a8d36883ca34396d90d3960142e86fac089f5e838a4ff | Jupyter | 12,906 | 442 | # %% [markdown]
# # This notebook generates plots of dynamics of model parameters as shown in section 2.4 and 2.5 of the paper
# %%
import sys
sys.path.append("../../")
from spectrome.utils import functions, path
import numpy as np
import xarray as xr
from pathlib import Path
import os
import pickle as pkl
from spec... |
331f72f306e71b6a2288d19655b72e02e5704449c207915a2d329be2a9dce614 | Jupyter | 12,915 | 408 | # %%
import sys
import os
import itertools
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as spopt
import scipy.integrate as spi
import scipy.stats as sps
import tensorflow as tf
import pandas as pd
sys.path.append("../../ndsvae/")
import ndsvae as ndsv
sys.path.append("../")
import util
... |
039ae50308f20e311f4be00b2ae248df471982b20b6f470a0322e97dbd5c2837 | Jupyter | 12,998 | 277 | # %% [markdown]
# # Volumetry Analysis
# ## Data:
# - 1. Freesurfer results: ICV. Lh/Rh_cerebellum_gm/wm (generated by "collect_volumes.sh" script);
# - Left/ Right cerebellum gray/white matter volumes
# - 2. Cerebellar SUIT segmentation with SUIT and MDTB atlas;
# - SUIT: Vermis VI, Vermis_... |
80acd7978ff8bd37ee586e2cb8f42f3e3b93803c6f8bb255d63a33344badef20 | Jupyter | 13,013 | 381 | # %%
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
# %%
AllName = ["x1", "m1", "... |
2f4def94616b4a3174b2f3d6354a41b714956e808d247022cc3ecd4a4e1adbc1 | Jupyter | 13,044 | 324 | # %%
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... |
da155548e1da8d6943a62ba344c54864d719d2e3c55303d85a0bc666c4daf455 | Jupyter | 13,295 | 497 | # %% [markdown]
# ## Prereq: python sim_cli.py --duration 120 --cores 24
# %%
import sim_analysis as plume_analysis
import sim_analysis
import tqdm
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from IPython import display as ipythondisplay
import config
print(config.dataset.keys())
# %%
# ... |
0b3d79692722521d48fff906046977fdb9d0ac4eb3a903443ad447459c2b198d | Jupyter | 13,316 | 388 | # %% [markdown]
# ## Preprocess raw 3D EM image chunks
#
# - Convert 256 x 256 x 256 EM and segmentation masks at ~ 8 nm x 8 nm x 40 nm resolution to 2 x 256 x 256 x 52 uint8 tensors. We only keep the central z-sections and remove the unwanted extra context. The two channels corresponds to EM intensity and integer-enc... |
1cfd4689674427f91e2a08e7397a48a9ee8b0594fb55e71cbef436b9f0b04ba9 | Jupyter | 13,331 | 406 | # %% [markdown]
# <div style="text-align: center;">
# <h1>T-N mismatch clustering tutorial</h1>
# </div>
#
# %%
#Load libraries
import pandas as pd
import numpy as np
from nilearn import plotting
import nibabel as nib
import os
import mismatch
from mismatch.TNMismatchClustering import TNMismatchClustering
from ... |
109ca89b00db163ed246e1c5f69e570ce6461bc521c88d2585c6dc5e5e00e70d | Jupyter | 13,343 | 426 | # %% [markdown]
# # Network propagation analysis for lithium response genes
#
#
# 8/23/18: add clustering and annotation
#
# 7/8/19: update with less stringent GWAB/NETWAS criteria (use top 5%)
#
# 9/27/19: find GWAB in top 2000 network
#
# 9/30/20: print out top 2000 genes at vipavee's request
# %%
# need to upd... |
a4aaf712c38f6c4df1e74287f3b9312a524cc02284e311e5a9e06b08211bf896 | Jupyter | 13,376 | 241 | # %%
import pandas as pd
import numpy as np
import json
# %%
#Interventions here are automatically counted, manual count is inserted below
results = """
BGR v1 (Nov)
{'mae': 0.27153275613021877, 'rmse': 0.38624637618598856, 'max': 1.7149375548548547, 'failure_rate': 2.816812454605741, 'distance': 7315.052901123929, 'd... |
c8aac29bd3c5e6e07b5ffb144a2773bace6931bcecd6c367c6b25c4e348f7295 | Jupyter | 13,407 | 437 | # %% [markdown]
# # Topological 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 pd
fr... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.