sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
5c5a043636a43170c1c0f6cfe09110c6c13fd076533d651a64104404928a046d | Jupyter | 7,595 | 157 | # %% [markdown]
# # Latin Hypercube Sampling
# This notebooks walks through [Latin Hypercube Sampling](https://en.wikipedia.org/wiki/Latin_hypercube_sampling) (LHS) and explains how it differs from the standard [Gaussian Random Sampling](https://en.wikipedia.org/wiki/Normal_distribution) implemented in LIME.
#
# Stan... |
6dbf08be74257d9b3f466edf542db3b2d1616ec77753d64b14f171409867b78b | Jupyter | 7,597 | 196 | # %%
import numpy as np
import emilys.optics.aperture as ap
import emilys.optics.mtf as mtf
import emilys.image.arrayplot as aplt
import matplotlib.pyplot as plt
import scipy.optimize as opt
import time
%matplotlib inline
# %%
# this calculates an aperture on a grid with mtf convolution
# aperture values are 1 for ful... |
dcb335a1a2abf9d6e04f819bfe958fb28a25515a068d478d1426f136193298d2 | Jupyter | 7,597 | 281 | # %%
import os, itertools
import cupy as cp
import numpy as np
from scipy.stats import norm
from numba import jit, njit, prange
from tqdm import trange, tqdm
# %% [markdown]
# ## Routines
# %% [markdown]
# #### Learning rules
# %%
class BilinearPlasticityRule:
def __init__(self):
self.f = lambda x:x
... |
9a55b27ed1dc13fdc911bf1a031e202482a1f92474faa19cfa234ddcc98d67d4 | Jupyter | 7,599 | 308 | # %% [markdown]
# # Figure 2 (Training and evaluating on the same database)
# %%
from collections import defaultdict
from mol2ccs.utils import performance_evaluation
import pandas as pd
import seaborn as sns
import matplotlib.gridspec as gridspec
from matplotlib import pyplot as plt
import numpy as np
from utils impo... |
67c43665b763ced6a22420fb67b7890557959b620fb32a252a8f0d17c6eefca3 | Jupyter | 7,607 | 215 | # %%
import json
from pathlib import Path
import nltk
import csv
from pytorch_pretrained_bert.tokenization import BasicTokenizer
#nltk.download('punkt')
# %% [markdown]
# **Convert BRAT-Format into JSON**
# %%
path_ann = Path('test').glob('*.ann')
path_txt = Path('test').glob('*.txt')
d = {}
for txt,ann in zip(path... |
9e10c5c734db7e5739979b565609dd1a45265eae6d16c17b8fa2fda4424c0076 | Jupyter | 7,609 | 295 | # %%
import pandas as pd
import numpy as np
import dask.dataframe as dd
import dask.array as da
import anndata
import scanpy as sc
from os.path import join
# %% [markdown]
# # Read data
# %%
PATH = '/mnt/dssmcmlfs01/merlin_cxg_2023_05_15_sf-log1p'
# %%
ddf = dd.read_parquet(join(PATH, 'train'), split_row_groups=Tru... |
7bfb7fd0e6649b5c2d0803f7f47d466853b3552e42923a5b075ab3c88d19ae69 | Jupyter | 7,667 | 181 | # %%
import os
import numpy as np
import cv2
import tensorflow as tf
import random
import numpy as np
import cv2
import face_recognition
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Multiply
from tensorflow.keras.models import Model
from tensorflo... |
454ac0a9686a511eae36a2896f25b02b351a7bcfe091a87487eeb3ffca1c986f | Jupyter | 7,686 | 233 | # %% [markdown]
# # Deep Neural Network acrchitecture
#
# Purpose: Create DNN architecture.
#
# Author: Gabriel Rodrigues Palma and Rafael de Andrade Moral
# %%
# visualisation modules
import matplotlib.pyplot as plt
# Data manipulation modules
import numpy as np
import pandas as pd
# Machine learning modules
from... |
bce3e0221787e92e978bae48879c5516e7849ab106ed59037d2abd5123b47171 | Jupyter | 7,698 | 167 | # %% [markdown]
# (dev_pytorch)=
# # PyTorch via NIRTorch
#
# PyTorch is a popular deep learning framework that many of the NIR-supported libraries are built on.
# We have built the [`nirtorch` package](https://github.com/neuromorphs/nirtorch) to make it easier to develop PyTorch extensions for the NIR-supported libra... |
82f381e3db3acdc887c75ae4692b37b27d839d1b82f39bd69bee68f4b3e2be5e | Jupyter | 7,706 | 199 | # %%
import numpy as np
import config.config as config
from validation import Validation
from Environment import Env
from Agent_EKF import *
from pathlib import Path
import sys; sys.path.append('../analysis/')
from my_utils import reset_seeds
# %%
def training(datapath, seed_number, task='gain',
TOTAL_EP... |
84f23912062d04da52ff6c938b65b1baaddb9c823b8879228625e0a38f3f0643 | Jupyter | 7,711 | 206 | # %% [markdown]
# # torch geometric + skorch @ CORA dataset
# %%
!date
# %% [markdown]
# This is an example for how to use skorch with [torch geometric](https://pytorch-geometric.readthedocs.io/). The code is based on the [introduction example](https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.htm... |
640e9f3b929dc47d7fd3d002fbf8dd6a2efd310161fb3c01fadb44f02de1bf45 | Jupyter | 7,849 | 298 | # %%
import numpy as np
import matplotlib.pyplot as plt
# %%
spks = [
[12, 11, 20, 33, 35, 36, 47, 48, 53, 53],
[8, 15, 19, 31, 32, 36, 45, 46, 50, 57],
[11, 11, 20, 30, 34, 38, 44, 47, 52, 56]
]
# %%
EV_LW = 7
ARR_S = 15
SP_LW = 4
Y_OFF = 0.2
X_OFF = 2
# %%
spk_t = [10, 13, 19, 32, 34, 37, 46, 48, 51... |
11aa031dcc430d12e64a4bc41ebf6f45c12f6f4966ae8fb99430980655f303c4 | Jupyter | 7,879 | 289 | # %% [markdown]
# # Calculate scDRS scores for different parameters
# %%
%load_ext lab_black
import os, sys
# %%
import scdrs.util as util
import scdrs.data_loader as dl
import scdrs.method as md
import submitit
import os
from os.path import join
import glob
import shutil
import yaml
import pandas as pd
import numpy ... |
e620baa5005aa92a0abd6e61b1c988c244072a1f6a9db2897a81c3a6e911edec | Jupyter | 7,879 | 239 | # %%
!pip install -e /dss/dsshome1/04/di93zer/git/cellnet --no-deps
# %% [markdown]
# # Visualize training dynamics
# %%
import os
import re
from os.path import join
import tensorboard as tb
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
# %%
# logs under: https://tenso... |
f5740a60e80e799d3b9893021a2537f4540e2b13e4feaa4d9d29e9ff95e664dd | Jupyter | 7,889 | 301 | # %% [markdown]
# # Figure 3 (Evaluating on a different chemical space)
# %%
from collections import defaultdict
from mol2ccs.utils import performance_evaluation
import pandas as pd
import seaborn as sns
import matplotlib.gridspec as gridspec
from matplotlib import pyplot as plt
import numpy as np
from utils import S... |
93b2fdac75d2a0d1478b56dc18e80c3899c646a41a906fb8a0b4f926c012ea0e | Jupyter | 7,898 | 236 | # %%
import json
import os
import numpy as np
import snntorch as snn
from snntorch import functional as SF
from snntorch import surrogate
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
seed = 42
np.random.seed(seed)
torch.manual_seed(seed)
torch.manual_seed(seed)
torch.use_de... |
5417e05c74f1181b92aa594432e1b5454d019ae9405b5c9b682287bae45ba609 | Jupyter | 7,914 | 254 | # %% [markdown]
# (tune-huggingface-example)=
#
# # Using Huggingface Transformers with Tune
#
# <a id="try-anyscale-quickstart-ray-tune-pbt_transformers" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=ray-tune-pbt_transformers">
# <img src="../../_... |
0c38019e97c5556521d650c9771478447a1d5103b4513894de1bfc098c2e4852 | Jupyter | 7,922 | 226 | # %% [markdown]
# This notebook uses an older version of snnTorch, install via:
# ```
# pip install git+https://github.com/jeshraghian/snntorch@40ad1360c644f948889cb9610121397c6ce943c8
# ```
# %%
import json
import os
import numpy as np
import snntorch as snn
from snntorch import functional as SF
from snntorch import ... |
6cab8011df8f2f30e2f793a5103a86b1d71bba86a4e7f26a4846e924d11ed95b | Jupyter | 7,931 | 196 | # %%
import scanpy as sc
import pandas as pd
import numpy as np
import scipy as sp
from statsmodels.stats.multitest import multipletests
import os
from os.path import join
import time
from anndata import read_h5ad
# scTRS tools
import scdrs.util as util
import scdrs.data_loader as dl
import scdrs.method as md
# auto... |
47c7c955425b3e6d6de69cd34efa71c817dea0e790eea6dbfe28e61f0841495d | Jupyter | 7,943 | 230 | # %%
#import
import pickle
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
from scipy.ndimage import gaussian_filter1d
import seaborn as sns
# %%
# Load data
folder = '/Volumes/Elements/data_for_uploaded/'
# Load pickle file
with open(folder + 'Fig.S12/' + 'dFovF.tx... |
31a968868b5f5ff8d8c906ddbc5fbc925919fa11ccc8c4e241ad0b3ed98b44ce | Jupyter | 7,946 | 163 | # %% [markdown]
# # A Simple MapReduce Example with Ray Core
#
# <a id="try-anyscale-quickstart-map_reduce" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=map_reduce">
# <img src="../../_static/img/run-on-anyscale.svg" alt="try-anyscale-quickstart">
... |
fbe3d396d1bc0bcdd9dacd31eb2247d4623f1eeb180287298c4da80d77ef24d3 | Jupyter | 7,953 | 214 | # %% [markdown]
# # Transfer Learning with skorch
# %% [markdown]
# In this tutorial, you will learn how to train a neural network using transfer learning with the `skorch` API. Transfer learning uses a pretrained model to initialize a network. This tutorial converts the pure PyTorch approach described in [PyTorch's T... |
59f87cc4e62162fcd1390a6d38f77646366ad7dd4276a334dd5e99ba51e2656a | Jupyter | 8,000 | 245 | # %% [markdown]
# ## This script plots the distribution of the closest distance between the cluster center to the UGUGUG motif, on FLARE confident clusters
# %% [markdown]
# ## MeOH samples compared with PFA samples
# %%
import os
cwd = os.getcwd()
main_dir = cwd
print(main_dir)
# %%
import pandas as pd
from glob im... |
d22766bc2500ab0b1a201d1122e4e348d47d528a90bfffeb17efe82aae0bae04 | Jupyter | 8,028 | 219 | # %% [markdown]
# ## Learning Rate Schedulers with Skorch
#
# This notebook demonstrates 3 learning rate schedulers in skorch:
#
# StepLR, ReduceROn Plateau and Cosine Annealing. This notebook was contributed by [Parag Ekbote](https://github.com/ParagEkbote).
#
# Firstly you will need to install the following libra... |
285f8df72974cf3bd6a14c3723bb8bbd44ae58217fe00678240a7615533dd879 | Jupyter | 8,038 | 247 | # %% [markdown]
# ## This script plots the distribution of the closest distance between the cluster center to the UGCAUG motif, on FLARE confident clusters
# %% [markdown]
# ## MeOH samples compared with PFA samples
# %%
import os
cwd = os.getcwd()
main_dir = cwd
print(main_dir)
# %%
import pandas as pd
from glob im... |
b18d0593e16609d39825e262a8888e18706b96dad752d6960244c019b106c861 | Jupyter | 8,066 | 224 | # %% [markdown]
# This notebook uses an older version of snnTorch, install via:
# ```
# pip install git+https://github.com/jeshraghian/snntorch@40ad1360c644f948889cb9610121397c6ce943c8
# ```
# %%
import json
import os
import numpy as np
import snntorch as snn
from snntorch import functional as SF
from snntorch import ... |
4220fbfd50df239b95c8d73e6f20b8729e17e43dc5b645946dde1929e1e7edc2 | Jupyter | 8,078 | 249 | # %% [markdown]
# ## Volumetric data processing
# This is a simple demo on toy 3d data for source extraction and deconvolution using CaImAn.
# For more information check demo_pipeline.ipynb which performs the complete pipeline for
# 2d two photon imaging data.
# %%
#%%
try:
get_ipython().magic(u'load_ext autorel... |
88ce8edcdcbaaba82deb9001fe974a8aaa9805baaf1988e6e7fa4e09b542480b | Jupyter | 8,078 | 218 | # %% [markdown]
# # 11 Synapse maps
# %% [markdown]
# ## Define paths and initialize packages
# %%
import numpy as np
paths = np.load('Paths.npy', allow_pickle=True).item();
import numpy as np
import pandas as pd
import matplotlib
import seaborn as sns
"""
Matplotlib backends:
matplotlib qt5 generates free floating... |
a2f6f2a664ba7a95733bcf476fec4ffe14f5e28d662fe9ccc374ac77117316bf | Jupyter | 8,099 | 227 | # %% [markdown]
# # PulseInputDDM neural model tutorial
#
# This is a not very detailed detailed crash course in using this code to fit the classic "Bing's model" to neural and choice data. Things you can learn:
#
# - How to load your data
# - How to plot some PSTHs, grouped by evidence strength
# - How to fit the mo... |
f0921ee50e11926eb561576a26dd0027d942f9e23f773d35dc9e3c5f8e1ed1bf | Jupyter | 8,117 | 218 | # %% [markdown]
# # 11 Synapse maps
# %% [markdown]
# ## Define paths and initialize packages
# %%
import numpy as np
paths = np.load('Paths.npy', allow_pickle=True).item();
import numpy as np
import pandas as pd
import matplotlib
import seaborn as sns
"""
Matplotlib backends:
matplotlib qt5 generates free floating... |
456b6d24f0338c68eb0ace78301ec2912bb26c832391881e54ca7a7deba8c7eb | Jupyter | 8,130 | 251 | # %% [markdown]
# # Evaluate DeepFace's Results In The Big Picture
# %%
import pandas as pd
from IPython.display import display, HTML
from sklearn import metrics
import matplotlib.pyplot as plt
# %%
alignment = [False, True]
models = ["Facenet512", "Facenet", "VGG-Face", "ArcFace", "Dlib", "GhostFaceNet", "SFace", "O... |
6b83ed33ba957ec7c7abf1affb49d808242b0f2458110b8ca5ea176b7d1722d9 | Jupyter | 8,163 | 276 | # %%
%load_ext lab_black
%load_ext autoreload
%autoreload 2
import matplotlib
matplotlib.rcParams["pdf.fonttype"] = 42
matplotlib.rcParams["ps.fonttype"] = 42
import numpy as np
import pandas as pd
import os, sys
import scTRS.data_loader as dl
import pandas as pd
import numpy as np
from os.path import join
from stat... |
950aa24267dd2742aebe0ca5a813437c4bed14d0303b6a340cf994c3a248f683 | Jupyter | 8,169 | 177 | # %% [markdown]
# # STARDUST (ROA-based version)
# %% [markdown]
# This Jupyter Notebook provides an interactive version of STARDUST for analysis using ROA-based fluorescence data. The required input for this analysis one mask/map and the time series generated from the map. Functions used in this script are defined an... |
28d3f46c055f4dfb57b65ed975c235c6ddb9b2c65c533b847e8f607b0cada43b | Jupyter | 8,180 | 312 | # %%
import pandas as pd
import numpy as np
# %%
def set_term(column1,column2):
if not pd.isnull(column1):
return column1
if not pd.isnull(column2):
return column2
else:
return np.nan
# %%
def separate_id(column,idx):
for i in column:
if idx in i:
column.re... |
dcc0d783d3a8d6a571f2deb542424fad359442f839a50f62f8c03d78c48cc867 | Jupyter | 8,189 | 285 | # %%
%load_ext lab_black
import os, sys
%load_ext autoreload
%autoreload 2
import matplotlib
matplotlib.rcParams["pdf.fonttype"] = 42
matplotlib.rcParams["ps.fonttype"] = 42
import scdrs.util as util
import scdrs.data_loader as dl
import scdrs.method as md
import submitit
import os
import pandas as pd
import numpy ... |
140e8961f9493b1190c2d36f39362bea70f8a62a557f1c32b3e1bd3c519bb464 | Jupyter | 8,240 | 223 | # %% [markdown]
# # Activity maps - Sup Figure 2
# %% [markdown]
# ## Define paths and initialize packages
# %%
import numpy as np
paths = np.load('Paths.npy', allow_pickle=True).item();
import numpy as np
import pandas as pd
import matplotlib
import seaborn as sns
"""
Matplotlib backends:
matplotlib qt5 generates ... |
b08be203a30cff06ca989955821dd25c49e48a59afe64169ace7e5d4dd96fffe | Jupyter | 8,247 | 226 | # %% [markdown]
# # Activity maps - Sup Figure 2
# %% [markdown]
# ## Define paths and initialize packages
# %%
import numpy as np
paths = np.load('Paths.npy', allow_pickle=True).item();
import numpy as np
import pandas as pd
import matplotlib
import seaborn as sns
"""
Matplotlib backends:
matplotlib qt5 generates ... |
bc87384ab77445f0d90d0bf3429d52f75c710d2f5e0c0f35fd08b89e86d3dc8a | Jupyter | 8,256 | 268 | # %% [markdown]
# # PerStruc-Denoiser
# %%
# Jupyter-notebook magic
# For the matplotlib
%matplotlib inline
# For reload functions explicitly
%load_ext autoreload
%autoreload 2
# %%
# Imports
## Add the modules to the system path
import os
import sys
sys.path.append(os.path.join(".."))
## Libs
import glob
import ... |
353ee72c0d6695a908b849e3fe9c343f5742737ec89b08c19c027877eafeb209 | Jupyter | 8,265 | 291 | # %% [markdown]
# # Evaluate Metlin-CCS train and test in CCSBase
# %%
from mol2ccs.utils import performance_evaluation
import pandas as pd
import seaborn as sns
import matplotlib.gridspec as gridspec
from matplotlib import pyplot as plt
from matplotlib_venn import venn3
from utils import (
compare_models_by_addu... |
3c7b5cfa933577eeec0430bf42a96995443289d54ba57d58e1e74eb1dbbd4c9e | Jupyter | 8,326 | 294 | # %% [markdown]
# # Evaluate CCSBase train and test in Metlin-CCS
# %%
from mol2ccs.utils import performance_evaluation
import pandas as pd
import seaborn as sns
import matplotlib.gridspec as gridspec
from matplotlib import pyplot as plt
from matplotlib_venn import venn3
from utils import (
compare_models_by_addu... |
74782beebae44a918c65254f38cec76da5025c84df7684d61cdd9b9d0c659421 | Jupyter | 8,328 | 96 | # %%
rsync -avzhe ssh \
/media/concha-eloko/Linux/PhageDEPOdetection/FP_2108/AY349011.3_prot_AAQ55000.1_65.pdb \
conchae@garnatxa.srv.cpd:/home/conchae/
rsync -avzhe ssh \
conchae@garnatxa.srv.cpd:/home/conchae/ \
/media/concha-eloko/Linux/PhageDEPOdetection/
/home/conchae/software/SWORD2/SWORD2.py -i /home/conchae/... |
fc00fe4943d88facd1409fe17130b043209ed406eb00acd0143f47fe86353b19 | Jupyter | 8,339 | 218 | # %% [markdown]
# # Neural space-time model example on structured illumination microscopy (SIM) data
# Jupyter lab demo for "Neural space-time model for dynamic multi-shot imaging" by Ruiming Cao, et al. (2024)
# %%
%load_ext autoreload
%autoreload 2
%matplotlib widget
import os
import numpy as np
import jax
import ja... |
510150b78ffe39f755f31d9bf81651be531a1a7c6f1134e59fbdba48e70238b6 | Jupyter | 8,356 | 246 | # %% [markdown]
# # Converting an ANN to an SNN
#
# This tutorial walks you through how to convert your pre-trained model to a spiking version.
# %% [markdown]
# ## Defining an ANN
# We define a simple convolutional architecture.
# %%
import torch.nn as nn
ann = nn.Sequential(
nn.Conv2d(1, 20, 5, 1, bias=False)... |
add02b311f5359249b12b60b06309128f8fd163897a501d7679bbfd6d8922f59 | Jupyter | 8,387 | 273 | # %%
import scanpy as sc
import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib import colors
import seaborn as sns
# %% [markdown]
# ## Load data
# %%
adata=sc.read_loom('../Interneurons_processed.loom')
# %%
adata.obs_names=adata.o... |
22298f51dbcb7b9c63288c40b24704e73e73cde81bb2cdcd04b18755eb101a18 | Jupyter | 8,416 | 212 | # %% [markdown]
# # Training an SNN with fewer SynOps and monitoring the firing rate
# In the previous tutorial we saw how we can train an ANN with a low number of synaptic operations (SynOps). The same can be done when training an SNN directly. For good measure we will also monitor the neuron firing rates to make sure... |
9343b6e434568fbf8c15117467c59456abe175cd0aadaf82701e6f563c508d49 | Jupyter | 8,435 | 199 | # %% [markdown]
# # `GCDM-SBDD` Tutorial, Part 2 - Filtering Generated Molecules using PoseBusters
# %% [markdown]
# ## Overview
#
# `GCDM-SBDD` provides a robust means of filtering new 3D molecules once generated within a target protein pocket of interest. As an illustrative real-world example, one can do so using t... |
eae78df50f058fbebaed38e714c27ce7cc4bad6eeb16cc2b91cf4e8cd2120595 | Jupyter | 8,474 | 211 | # %% [markdown]
# # Import backage
# %%
import sys
sys.path.append("../..")
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import random
%matplotlib inline
# %%
import torch
from torch import nn
from torch import distributions
from torch.nn.parameter import Parameter
from exp.sir.train i... |
6afd2e4deb1591c22df04fd032516b8472df262cde346fb7283f6ae8e1d9fbac | Jupyter | 8,486 | 269 | # %% [markdown]
# # Summarize results at cell-type level for the purpose of contrasting results across species
# 1. Proportion of FDR < 10% at various parameter for scDRS.
# 2. Cell type level p-value for scDRS
# 3. Geary's C statistics for 10kb, 1000 genes default settings.
# 4. LDSC-SEG p-value
# %%
%load_ext lab_b... |
52fdb679ef8d0a7f007dc880629b9eddbb8945d95d5e25717a3e03166fbad058 | Jupyter | 8,512 | 222 | # %%
import os
import numpy as np
import cv2
import tensorflow as tf
import random
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score
from scipy.stats import mode
import gc # Import the garbage collection module
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv... |
546eeec813204f769c5cb8085a1f9f71c84da4af63d72e533f9504631a2fbd52 | Jupyter | 8,512 | 305 | # %% [markdown]
# <a href="https://colab.research.google.com/github/shksherry/Dynamic-Offloading-of-ANN-Model-Algorithm-Analysis/blob/main/Dynamic_Offloading_of_ANN_Model_Algorithm_Analysis_in_(Smart_home_dataset)_Code_.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Ope... |
c1aa02d6f950e3b4e22d40ca30034609bd3a714c362900614a0770d5979b21af | Jupyter | 8,513 | 231 | # %% [markdown]
# # Tutorial on how to use dSpikesClassifier
#
# dSpikesClassifier can predict the type of dentate spike (i.e. either DS1 or DS2)
#
# To use it, you only need the local field potential (LFP) signal from the granule-cell layer of the dentate gyrus (DG) and the times of the dentate spikes.
#
# In this ... |
5c9665a56444b8bfbd7a546b69a566769ae235216895d857b08fb46998cb5d61 | Jupyter | 8,516 | 291 | # %% [markdown]
# ## This script produced 20 shuffled FLARE bed file (shuffle within the region)
# %%
import os
cwd = os.getcwd()
main_dir = cwd
print(main_dir)
# %%
import pandas as pd
import pybedtools
from glob import glob
# %% [markdown]
# ### Load peak files: cleaned confident peaks
# %%
peak_filepaths = sort... |
e2d2a3be5c647788804047e144bc2c6e5e39bdf83fb48fc44b5924813a7eed40 | Jupyter | 8,538 | 168 | # %% [markdown]
# ## Example of online analysis using OnACID
#
# Complete pipeline for online processing using CaImAn Online (OnACID).
# The demo demonstates the analysis of a sequence of files using the CaImAn online
# algorithm. The steps include i) motion correction, ii) tracking current
# components, iii) detecti... |
6ea09c1066ed4ec5344f472798b04eaa091184b9d0855e6f96e4dd0d3aa143b1 | Jupyter | 8,553 | 266 | # %%
using PulseInputDDM, MAT, LinearAlgebra
# %%
x_joint = Vector(undef, 3);
H_joint = Vector(undef, 3);
CIs_joint = Vector(undef, 3);
# %%
x_choice = Vector(undef, 3);
H_choice = Vector(undef, 3);
CIs_choice = Vector(undef, 3);
# %%
joint_model_path = joinpath(abspath(joinpath(pwd(), "..")), "models/joint");
choic... |
56759c9c82aa327e9621c689608ece52451c7b73857fc6fd43696ecdba4abb7c | Jupyter | 8,593 | 309 | # %% [markdown]
# # Goal : Save the Safada (or the ambiguous ones)
# ***
# ## I. Loading the dataframes
# ## II. Transform the features : PCA
# ## III. Clustering
# ## IV. Scan the results
# ***
# %%
import pandas as pd
import os
import json
path_df = "/media/concha-eloko/Linux/depolymerase_building/DBsuite_Dpo_fil... |
6750df8f2b6654131f5b73baa6073ba1fb8885103a54a95b8c2b5f39b3853a1b | Jupyter | 8,607 | 223 | # %% [markdown]
# # Basic characteristics of synaptic input development - Figure 3
# %% [markdown]
# ## Define paths and initialize packages
# %%
import numpy as np
paths = np.load('Paths.npy', allow_pickle=True).item();
import numpy as np
import pandas as pd
import matplotlib
import seaborn as sns
from scipy import... |
63afe831634ce1bed7f3b114b8a563645f7f245d31b6d6a0d1ed89200fa54752 | Jupyter | 8,652 | 262 | # %% [markdown]
# # Figure 3: STDP
# %%
# Imports
## General
import os
from copy import deepcopy
from itertools import product
from types import SimpleNamespace
import sys
sys.path.append("..")
from RNN_helpers import *
from IPython.display import display, Markdown
## Numerical
import numpy as np
import pandas as p... |
577e39587adf853ff9cc12ae381c47499d18c1458af90513d4786dc3622eba57 | Jupyter | 8,692 | 309 | # %% [markdown]
# # Distance to Confidence
#
# DeepFace performs a hard classification by clearly separating same person and different persons based on the pre-tuned threshold.
#
# This notebook builds a logistic regression model to convert calculated distances to probalistic estimate, indicating how likely the class... |
7afec9f4785c82af6a24fde88e72a10d74dbd3c3cc6d907d266f12f2221dc9c2 | Jupyter | 8,730 | 240 | # %%
# Matrix Manipulation/Management Libraries
import pandas as pd
import numpy as np
# Data Visualization Libaries
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.stats import fisher_exact
# Bioinformatics Libraries
import pybedtools
# Statistical Tests, Machine Learning, ... |
b8cb3a6a76d010e4bd8e6eb92a054c063f1044ef14253f267721397240651147 | Jupyter | 8,751 | 205 | # %% [markdown]
# # Introduction to ML using ML4H
# %% [markdown]
# ## Prerequisites
# - Basic comfort with python, some linear algebra, some data science
# - Follow the instructions in the main [README](https://github.com/broadinstitute/ml4h) for installing ML4H
# - Data used in this notebook is available here ([gs:/... |
2cb4ab6fd826291149f4fdb90769a4cdb6a4fdcff3a22c92a53434ad176ea5f9 | Jupyter | 8,758 | 167 | # %% [markdown]
# # Dpo Detection Tool script :
# ***
# %%
import pandas as pd
import os
from collections import Counter
import torch
path_work = "/media/concha-eloko/Linux/depolymerase_building"
#path_work = "/home/conchae/PhageDepo_pdb"
from transformers import AutoModelForTokenClassification, AutoTokenizer
model... |
d0be5995a01f4d2960dc2148d4606fee6d107c8692c010065afc4bddeac3bbff | Jupyter | 8,762 | 243 | # %%
# %load_ext autoreload
# %autoreload
# %matplotlib widget
# %%
import flammkuchen as fl
from matplotlib import pyplot as plt
import numpy as np
from scipy import io
import seaborn as sns
import matplotlib.gridspec as gridspec
from matplotlib.ticker import FormatStrFormatter
from pathlib import Path
import pandas ... |
2623ca9d7969d94f554e3d307ed4181fa651ea010f654a9f9d66dc2d405dc387 | Jupyter | 8,773 | 276 | # %% [markdown]
# # Flash responses
#
# This notebook introduces flash responses and the flash response index (FRI).
#
# The FRI measures whether a cell depolarizes to bright or to dark increments in a visual input.
# %% [markdown]
# **Select GPU runtime**
#
# To run the notebook on a GPU select Menu -> Runtime ->... |
78798d6f40009ab2d799a72ae1ad9a7610f86985c5c7dfc87a25c30b2400e025 | Jupyter | 8,782 | 254 | # %% [markdown]
# # Train a Pytorch Lightning Image Classifier
#
# <a id="try-anyscale-quickstart-lightning_mnist_example" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=lightning_mnist_example">
# <img src="../../../_static/img/run-on-anyscale.svg" ... |
545f84807bf006a804322fd87b95b28dba480a17b80703ff51d1b28f8e650499 | Jupyter | 8,789 | 304 | # %% [markdown]
# In this notebook, we propose a demonstartion of different expression analysis in omicverse. We apply our implementation as well as the original code on the bulk data of Alzheimer's disease.
#
# # Different expression analysis
#
# ## Setting up data
# %%
import scanpy as sc
import pandas as pd
impor... |
e44b533e3018c970d8ad62d7bc7607393e161cc63df52216fb093a3a9e02a027 | Jupyter | 8,805 | 309 | # %%
from gimpformats.gimpXcfDocument import GimpDocument
import cv2
import numpy as np
# Concave hull
import os
import sys
import pandas as pd
from descartes import PolygonPatch
import matplotlib.pyplot as plt
sys.path.insert(0, os.path.dirname(os.getcwd()))
import alphashape
# %%
def imageListFromXcf(fileName):
... |
eba42c6d101fbd4635dbfe99c97e51c03440d71d115fb793e38dbd34877104c4 | Jupyter | 8,808 | 273 | # %%
!pip install -e /dss/dsshome1/04/di93zer/git/cellnet --no-deps
# %%
from os.path import join
import numpy as np
import pandas as pd
import lightning.pytorch as pl
import dask.dataframe as dd
from cellnet.estimators import EstimatorCellTypeClassifier
from cellnet.models import TabnetClassifier
# %%
DATA_PATH = ... |
1fe2a5733e6285b7792c50730394cbe8856ff91fafbf652aa8022869ed7004a4 | Jupyter | 8,857 | 237 | # %%
from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch
from torch import nn
import torch.nn.functional as F
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score , matthews_corrcoef, confusion_matrix
from sklearn.model_selection import tra... |
86b4c943355def30619d5ec37b6d6e2da6409173642d8fcdd230c033aef675d1 | Jupyter | 8,859 | 224 | # %%
import numpy as np
import config.config as config
from validation import Validation
from Environment import Env
from Agent_RNN import *
from pathlib import Path
import sys; sys.path.append('../analysis/')
from my_utils import reset_seeds
# %%
def training(datapath, seed_number, Actor, Critic, task='gain',
... |
a9837ca7c68805474987e66c4145d09937499b19029b9852d916f50366cbc4b2 | Jupyter | 8,902 | 265 | # %% [markdown]
# # Fine-tuning a BERT model with skorch and Hugging Face
# %% [markdown]
# In this notebook, we follow the fine-tuning guideline from [Hugging Face documentation](https://huggingface.co/docs/transformers/training). Please check it out if we you want to know more about BERT and fine-tuning. Here, we as... |
4e75363f0fdd304f7814132ce18a4c46fd948784d09f7afe5647f72206af57f3 | Jupyter | 8,905 | 259 | # %%
from model import *
from attention import pHLA_attns_draw_save
from mutation import *
from utils import Logger, cut_peptide_to_specific_length
# %%
import math
from sklearn import metrics
from sklearn import preprocessing
import numpy as np
import pandas as pd
import re
import time
import datetime
import random
r... |
af4d6d6d121e5a303607fc69d0fc3cb257c022527ad86c02fb868c63cb3f1388 | Jupyter | 8,905 | 271 | # %% [markdown]
# # Creating checkpoints on the Hugging Face Hub
# %% [markdown]
# This short notebook explains how you can create a model checkpoint on [Hugging Face Hub](https://huggingface.co/docs/hub/repositories).
# %%
import subprocess
# Installation on Google Colab
try:
import google.colab
subprocess.... |
200215e8400683bd47ff9cb3b21e37e04777a9c8c487d6571d875b18bf8758a8 | Jupyter | 8,913 | 288 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os, sys, subprocess, time, pdb
import itertools, uuid
import numpy as np
import scipy
from scipy.stats import pearsonr
from tqdm.auto import tqdm, trange
import matplotlib
import matplotlib.pyplot as plt
sys.path.insert(0, '../network')
from measures import *
# %% ... |
17a40e20f8c01f88360198a3077cacfeff21ab409704b5e9f6bb54ca194ddea1 | Jupyter | 8,915 | 229 | # %% [markdown]
# # PFRL Quickstart Guide
#
# This is a quickstart guide for users who just want to try PFRL for the first time.
#
# If you have not yet installed PFRL, run the command below to install it:
# ```
# pip install pfrl
# ```
#
# If you have already installed PFRL, let's begin!
#
# First, you need to imp... |
c21184918d0c2e83afa473b7d8fe90ef62e9c5f05a2f203f0609e85ecf45c427 | Jupyter | 8,916 | 310 | # %% [markdown]
# # Process TDBRAIN
# > Preprocessing and feature computation for the TDBRAIN dataset
# %%
import pandas as pd
from pathlib import Path
from functools import partial
from dask.distributed import Client, LocalCluster, fire_and_forget
from dask_jobqueue import LSFCluster
from fastcore.transform import P... |
005368077750de0e72797b8a89527ea5c2dd24f6113ddc13d336f808749ed9dc | Jupyter | 8,935 | 195 | # %% [markdown]
# # Image Classification Batch Inference with Huggingface Vision Transformer
#
# <a id="try-anyscale-quickstart-huggingface_vit_batch_prediction" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=huggingface_vit_batch_prediction">
# <img... |
3c9612f12bc9eb64460895cc3333962538508419ef12901853902c839d614f24 | Jupyter | 9,000 | 199 | # %% [markdown]
# Install SimpleElastix and SimpleITK python module by the original tutorial
# https://simpleelastix.readthedocs.io/GettingStarted.html
# %%
#import necessary packages
import SimpleITK as sitk
import matplotlib.pyplot as plt
import os
import numpy as np
from skimage.io import imread, imsave
from skim... |
dfd6e9cf1a280946a92b657d8f0fd55e01f00049aa5c91887c4c881522eedae4 | Jupyter | 9,024 | 198 | # %%
import pandas as pd
from paintone_rsn.data_loading import load_subjectiid, exclusion, load_timeseries
from paintone_rsn.data_preproc import connectivity_matrix, connectivitylabelname
import paintone_rsn.mlfunctions as myml
from scipy.stats import pearsonr
import numpy as np
import pickle
# %%
# load behavior vari... |
419b9c696b3421f435155d4317a3d1f02e8ccb315d13fe1c5c1ad7ea6f6e628a | Jupyter | 9,126 | 326 | # %% [markdown]
# # init
# %% [markdown]
# ## import package
# %%
import os
import time
from subprocess import call
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import stats
import seaborn as sns
import matplotlib.pyplot as plt
import scipy as sc
from matplotlib.colors import ListedColorma... |
2ccbbd84d68208ecd472f0e77d7a127c31268a45fe5f6a602ad889de4721edda | Jupyter | 9,141 | 222 | # %% [markdown]
# # A quick (and over-simplified) introduction to spiking neurons
#
# Author: Sadique Sheik
#
# Spiking neural networks are considered to be the `third generation` of neural networks preceeded by McCulloch-Pitts threshold neurons (`first generation`) which produced digital outputs and Artificial Neura... |
82c155298b574a2fe05139a6f1dd5571925f735dba40ebc2d81b37ba361eb889 | Jupyter | 9,189 | 247 | # %%
import scanpy as sc
import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib import colors
import seaborn as sns
# %% [markdown]
# ## Load data
# %%
adata=sc.read_loom('../Interneurons_processed.loom')
# %%
adata.obs_names=adata.o... |
8c13fd5421bb7a7550d91d6a1572f75fdb87ccaa9e92c9a7bbc387c85bd9af5e | Jupyter | 9,191 | 227 | # %%
# Map manual subfield labels from 2D histology slice to volume
# %%
#import pyvips
print('get imports...')
import sys
import geopandas as gpd
import json
import matplotlib.pyplot as plt
import numpy as np
import rasterio.features
import os
import tirl
print(tirl.__version__)
from tirl.timage import TImage
#import... |
fb1adb31aad0c15a74fc7fd831e5c43df1dd7d9073ddc16b8dcbfc5f35f045df | Jupyter | 9,217 | 369 | # %%
# required packages: countdown-timer pyserial numpy av
# %%
%load_ext autoreload
%autoreload 2
# %%
import numpy as np
import subprocess
import datetime
import multiprocessing as mp
import sys, time
#sys.path.append('simple_pyspin/')
from multicamera_acquisition.simple_pyspin import Camera
import PySpin
from cou... |
4a094ca161c5b73d8c5c4161ce5951dbc497df3c9841e111fab35172e536317a | Jupyter | 9,256 | 355 | # %%
!pip install -e /dss/dsshome1/04/di93zer/git/cellnet --no-deps
# %%
from os.path import join
from typing import List
import anndata
import scanpy as sc
import dask.dataframe as dd
import dask.array as da
import pandas as pd
import numpy as np
# %%
def get_count_matrix_and_obs(ddf, columns: List[str] = None):
... |
f7f56e371b3414e72d195bcdb5f67469c7d56e6a6d6746b8ff6b9c74a928a661 | Jupyter | 9,256 | 224 | # %% [markdown]
# # London Air Quality Tutorial
#
# <a
# target="_blank" href="https://colab.research.google.com/github/google/bayesnf/blob/main/docs/tutorials/BayesNF_Tutorial_on_London_Air_Quality.ipynb">
# <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
# </a>
# %%
# Downl... |
cc156a8f5f566bed47f56326ee0071e2d0d1461333edc5be4b90b80ff82c5b49 | Jupyter | 9,308 | 292 | # %% [markdown]
# # Spiking CNN from NIR to SpiNNaker2
#
# In this notebook we will show how a pre-trained Spiking CNN NIR model can be deployed onto the SpiNNaker2 chip.
#
# py-spinnaker2, the high-level software interface for running spiking neural networks on SpiNNaker2, provides an API similar to PyNN and allows ... |
edd5b59f81428bc05f7fd22f29fa195f68722b32b7b8bd26ef0dc124bd136af4 | Jupyter | 9,331 | 239 | # %%
import os
import scipy.io
import numpy as np
#from tensorflow.keras.models import Model
from tensorflow.keras.layers import (Dense, Activation, Permute, Dropout, Conv2D,
MaxPooling2D, AveragePooling2D, SeparableConv2D,
DepthwiseConv2D, Batch... |
b17dae9a71e2d3bcb9b2511739a9dc76fb90970e56b367658fa6df72087a7eea | Jupyter | 9,337 | 351 | # %%
!pip install -e /dss/dsshome1/04/di93zer/git/cellnet --no-deps
# %%
!pip install -q SPARQLWrapper
# %%
# !pip install -q cellxgene-census
!pip install -q obonet
# %%
%load_ext autoreload
# %%
import pickle
import os
from os.path import join
import numpy as np
import pandas as pd
import seaborn as sns
import ... |
4c2c97ae269cf32d6a023bcd1807432dafa7c558048a2415dc9d6f8aca576650 | Jupyter | 9,346 | 272 | # %%
import os
import numpy as np
import seaborn as sns # for plots
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt
from scipy import stats as sps # to calcualte confidence intervals, etc
from adjustText import adjust_text # to adjust the text labels in the plots (pip install adjustText)
fr... |
a36b669caaaa42f3f6d18691693c4f555625d6bb38e26a350a39cbbe7e53207e | Jupyter | 9,357 | 241 | # %% [markdown]
# # Nuclei Image Segmentation Tutorial
# %% [markdown]
# In this tutorial, we will implement a [UNet](https://arxiv.org/abs/1505.04597) to solve Kaggle's [2018 Data Science Bowl Competition](https://www.kaggle.com/c/data-science-bowl-2018). The challenge asks participants to find the location of nuclei... |
9527537b7d60f79b7bbf027a87e9770f827e8a3041abe987bdb260eb18a0de85 | Jupyter | 9,360 | 212 | # %% [markdown]
# ## Introduction
# If you would like to add a reaction to the databases which does not currently appear in the database you should follow these steps here. If you already have initial and final frames, you can avoid this step and instead please see https://github.com/facebookresearch/fairchem/blob/4164... |
8121b0cb31a128d7e399a4ef15835d5fa3afdac8a0f863738e48b548a6701166 | Jupyter | 9,397 | 227 | # %%
import numpy as np
import pandas as pd
from collections import Counter
# %%
# Read msigdb immune signatures
DATA_PATH='/n/holystore01/LABS/price_lab/Users/mjzhang/scDRS_data'
def parse_term(term):
study_name = term.split('_')[0]
reg_dir = term.split('_')[-1]
gs_name = term.replace('%s_'%study_name,... |
da0cb31a1f7890c52875c1df44742d1350dafc6ec50a354da04c1926ee07e1ad | Jupyter | 9,402 | 317 | # %% [markdown]
# # Table of Contents
#
# 1. [Import & Set Up](#import_everything)
# 2. [Load data](#load_data)
# 3. [Get the parameter that's being adjusted and the values](#Get_the_parameter_thats_being_adjusted_and_the_values)
# 3. [Plot learning rate vs. within pair correlation (pre- and post-learning)](#Plot_lear... |
515aa502ad7ebf8289333b406961dd99aab0ba9e9e3fdfbd57157bb79ece32d2 | Jupyter | 9,425 | 306 | # %% [markdown]
# # Table of Contents
#
# 1. [Import & Set Up](#import_everything)
# 2. [Load data](#load_data)
# 3. [Get the parameter that's being adjusted and the values](#Get_the_parameter_thats_being_adjusted_and_the_values)
# 3. [Plot learning rate vs. within pair correlation (pre- and post-learning)](#Plot_lear... |
4f9ae8a682506a7763ac1971cdc0289a281fba683223714cf3502574fb19e1d8 | Jupyter | 9,430 | 231 | # %%
import numpy as np
import config.config as config
from validation import Validation
from Environment import Env
from Agent_RNN_EKF import *
from pathlib import Path
import sys; sys.path.append('../analysis/')
from my_utils import reset_seeds
# %%
def training(datapath, seed_number, Actor, Critic, task='gain',
... |
a7f1b8f276c61e14f5662ee46634e50024fb626a5378261df3679778e6df9efe | Jupyter | 9,434 | 179 | # %% [markdown]
# <div align="center">
#
# <a href="https://ultralytics.com/yolov8" target="_blank">
# <img width="1024", src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
#
#
# <br>
# <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src... |
872b0acdfd6a98d4bd824b3c56a40d494b313fc8a4fba344bcfd6a3f21acb6dd | Jupyter | 9,448 | 252 | # %% [markdown]
# This Notebook/demo uses spike interface, [here is how to install Spike Interface](https://spikeinterface.readthedocs.io/en/latest/installation.html)
# %%
import sys
from pathlib import Path
import spikeinterface as si
import spikeinterface.extractors as se
import spikeinterface.preprocessing as spre... |
d3dea55299dded621be3cd2e009e3737638677649154ea1167c4c368f5b28532 | Jupyter | 9,488 | 401 | # %%
import argparse
import sys
import os
import logging
import numpy as np
from scipy.stats import pearsonr
from scipy.stats import norm
from scipy.optimize import minimize
sys.path.insert(0, '../network')
from network import *
from transfer_functions import ErrorFunction, ReLU, LIF
from sequences import Sequence
fr... |
479508debcc17eefe5c807025b5c267b71a31050195cd91e78c2a4df052e41a3 | Jupyter | 9,498 | 220 | # %%
%matplotlib inline
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from skimage import data
from batchgenerators.dataloading.data_loader import DataLoaderBase
# %% [markdown]
# ## Let's create a DataLoader that gives us some image. Your implementation will vary depending on your dataset. He... |
dad0b7b69a10af59a549b9474198e297a67208c6afe658481a3ba97274976f17 | Jupyter | 9,532 | 407 | # %% [markdown]
# # Forward Simulation of 2D VEP model at Source level
# %%
import os
import sys
import time
import errno
import time
import timeit
import numpy as np
import numba
from numba import jit
import matplotlib.pyplot as plt
from matplotlib import colors, cm
# %%
import warnings
warnings.simplefilter("ign... |
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