sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
000fe4c806f3fcbc0f9ea897d2c380060055d944830bdf4f1787988aacc53c13 | Jupyter | 2,900 | 92 | # %% [markdown]
# # Rockpool
#
# Rockpool (https://rockpool.ai) is a deep learning toolchain for deep spiking neural networks and other dynamical NN architectures, supporting deployment to Neuromorphic computing hardware.
#
# Rockpool provides a convenient interface for designing, training and evaluating networks, wh... |
3c360904f23ce49e1422bc105525b0b6e46f70c98bc33b074c62908bab35f749 | Jupyter | 2,909 | 108 | # %%
!pip install -e /dss/dsshome1/04/di93zer/git/cellnet --no-deps
# %%
import os
import seaborn as sns
import torch
from lightning.pytorch.callbacks import ModelCheckpoint, LearningRateMonitor, TQDMProgressBar
from lightning.pytorch.loggers import TensorBoardLogger
from lightning.pytorch.utilities.model_summary imp... |
3171a2646d01456a804b90d182720fcd0b2d6537992442678e5566173b2587d6 | Jupyter | 2,912 | 60 | # %% [markdown]
# # PixAR: Replotter
# *Author: Nikhil Updated: 3 Oct 2021*
#
# *After analysing the data with Pixar, you can use this script to customize and replot some of the maps. Refer user manual or video tutorial for more details.
#
# #### To run the script:
# 1. Copy your PixAR 'Results_<your filename>' folde... |
306751aa2080b5faf011d81c5814933774082efebe99117eb312c4281c3e06df | Jupyter | 2,916 | 111 | # %%
from os.path import join
import anndata
import scanpy as sc
import numpy as np
import pandas as pd
import dask.dataframe as dd
# %%
DATA_PATH = '/mnt/dssmcmlfs01/merlin_cxg_2023_05_15_sf-log1p'
# %%
cell_type_mapping = pd.read_parquet(join(DATA_PATH, 'categorical_lookup/cell_type.parquet'))
cell_type_hierarchy ... |
fe7dda8f8e934283afc907bd8367b2267a58886181976e3f6f12bfd9f4e05944 | Jupyter | 2,926 | 93 | # %%
import scanpy as sc
from anndata import read_h5ad
import pandas as pd
import numpy as np
import scipy as sp
from statsmodels.stats.multitest import multipletests
import matplotlib.pyplot as plt
import seaborn as sns
import os
from os.path import join
import time
from gprofiler import GProfiler
# scTRS tools
impor... |
222d4ca5f73e713277ba9b107ac64ffd2890014bbb118a28dfb8bdce63376467 | Jupyter | 2,932 | 97 | # %%
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.pylab as pl
import matplotlib.gridspec as gridspec
import scipy.ndimage as ndimage
from matplotlib.ticker import AutoMinorLocator, FormatStrFormatter
import matplotlib.mlab as mlab
from matplotlib i... |
84b92068b7046b59efe001e597699b6fc839a999d8ae83a17c985e0e4f1dbf18 | Jupyter | 2,964 | 105 | # %% [markdown]
# # Notebook for demo how to get features with [neuron_morphology](https://github.com/AllenInstitute/neuron_morphology)
#
# To obtain the package, please see [https://neuron-morphology.readthedocs.io/en/readthedocs/](https://neuron-morphology.readthedocs.io/en/readthedocs/).
#
# This step-by-step guid... |
f2cd555818a49cff88b147a575fe2c020b53dab1510dded1d3a1965040c61fa9 | Jupyter | 2,966 | 81 | # %%
import os
import subprocess
from multiprocessing.pool import ThreadPool
path_work = "/home/conchae/depolymerase"
path_pdb = f"{path_work}/pdb_depolymerase_proteins_Bea"
path_out = f"{path_work}/Bea_sword2_out"
def sword2_pred(pdb_file) :
"""
This function makes a sword prediction from a pdb_file.
I... |
8b805da302f39d29c27d779c3e91a387f89f29f100deff9cba6293b88e3c3be3 | Jupyter | 2,986 | 93 | # %%
import sys
sys.path.append('..')
sys.path.append('../..')
sys.path.append('../../..')
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from statsmodels.stats.anova import AnovaRM # perform F test
import selective_recruitment.globals as gl
# import Functional_Fusion.Fu... |
9eae3ff395cff65349a7c6541fd604e089f9845458433266bd4c30e76707ccf7 | Jupyter | 2,987 | 95 | # %% [markdown]
# # Rockpool
#
# Rockpool (https://rockpool.ai) is a deep learning toolchain for deep spiking neural networks and other dynamical NN architectures, supporting deployment to Neuromorphic computing hardware.
#
# Rockpool provides a convenient interface for designing, training and evaluating networks, wh... |
b5f46465ad0fba296f3c17561d58eab04a163cfe4be34e38b9f5a56ce4eb7a06 | Jupyter | 2,987 | 82 | # %% [markdown]
# ### Given a recording, find any frames that have been skipped
# %% [markdown]
# - there are two types of dropped frames (1) frames that are correctly skipped in `frame_id` and (2) frames that are not correctly skipped in `frame_id`
# %%
import pandas as pd
import matplotlib.pyplot as plt
import num... |
00877c4c13cd954c8fbf03bcf0df352b5fba6c1918d8e6f16d14704ab35ed43f | Jupyter | 2,993 | 85 | # %%
#standard
import os
import warnings
warnings.filterwarnings('ignore')
import pickle
#third party
import numpy as np
from nilearn import plotting
import nibabel as nib
# %%
def calculate_noiseceiling(betas, n: int=1):
"""
Calculate the standard deviation across trials, square the result,
average acros... |
ecf11f2f3b1d4f2d8a34d9edcecae601104a8779cd8608c8f0de5336cf11ac69 | Jupyter | 3,013 | 75 | # %% [markdown]
# ## This script makes the FLARE input files and configuration files for sailor output results (after filtering), using default parameters
# %%
from glob import glob
import os
import json
import yaml
import subprocess
cwd = os.getcwd()
main_directory = cwd
print(main_directory)
# %%
input_dir = main... |
4e166f575425b1e806e0e942f7132f1aec80662ec6006bbfe8fda2df7fac6971 | Jupyter | 3,014 | 93 | # %%
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.pylab as pl
import matplotlib.gridspec as gridspec
import scipy.ndimage as ndimage
from matplotlib.ticker import AutoMinorLocator, FormatStrFormatter
import matplotlib.mlab as mlab
from matplotlib i... |
a3dab7c8d07004f8cfbd52ce77a4538f3ed723c4fd3600e29a676c7276860d04 | Jupyter | 3,018 | 110 | # %% [markdown]
# # Make the SWORD2 prediction
# ***
# ### I. The prediction
# ***
# %%
import os
import subprocess
from multiprocessing.pool import ThreadPool
path_work = "/home/conchae/PhageDepo_pdb"
path_pdb = f"{path_work}/phagedepo_out"
path_out = f"{path_work}/sword2_out"
def sword2_pred(pdb_file) :
""... |
f348fdd770173999b2ba7b9c4c1770289c32b44c46d4dd062d772e90e3ac5ab7 | Jupyter | 3,019 | 94 | # %%
import scanpy as sc
import pandas as pd
import numpy as np
import scipy as sp
from statsmodels.stats.multitest import multipletests
import matplotlib.pyplot as plt
import seaborn as sns
from anndata import AnnData
import os
from os.path import join
import time
from gprofiler import GProfiler
# scTRS tools
import... |
2fdfc39e4aa2318676a9fcd5443fb1261c36db96d010818d3e070537d4e66ecc | Jupyter | 3,022 | 147 | # %%
%load_ext autoreload
%autoreload 2
# %%
import pydicom
import glob
import numpy as np
import matplotlib.pyplot as plt
import cv2
import os
import os.path
import skimage
import skimage.color
import skimage.transform
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math... |
c1e3067cf45c488f1f7aff3a4a483ae6bab626bdafeb21d289d110af3220ad30 | Jupyter | 3,028 | 114 | # %%
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... |
14bdd5b17af6df97c314f2190265f9c76f71348b51b4d811b6a45df9d67cb2c0 | Jupyter | 3,030 | 79 | # %%
import h5py
import numpy
def extract_spikes(hd5_file, neuron_num=0):
"""Extracts the spiking data from the hdf5 file. Returns an array of
spike times.
Keyword arguments:
neuron_num -- the index of the neuron you would like to access.
"""
with h5py.File(hd5_file, "r+") as f:
neuro... |
8981df244d229d02d2506b045418bccbbe8e5b74cc00acf68eb05cdcce1c6d9a | Jupyter | 3,035 | 78 | # %%
import scvi
import tensorflow as tf
import numpy as np
import time
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import scanpy as sc
import matplotlib.pyplot as pl
from matplotlib import rcParams
from collections import Co... |
ce25af6de151f4f7a9abc3f5e9d83dfe014376c14db2e505b81a51bad570c9fc | Jupyter | 3,061 | 98 | # %% [markdown]
# ## YData Profiling: USA Air Pollution Data
# Source of data: https://data.world/data-society/us-air-pollution-data
# %% [markdown]
# The autoreload instruction reloads modules automatically before code execution, which is helpful for the update below.
# %%
%load_ext autoreload
%autoreload 2
# %% [m... |
31d4513cba4018d78bf63d8176a2232fc12c61a3cce0a02c32fef0ee63c1d59d | Jupyter | 3,088 | 66 | # %% [markdown]
# # PixAR: Pixel Based Analysis of Rhythms
# *[Author: Nikhil Updated: 26 Nov 2021]*
#
# *This script takes tiff image stacks and performs pixel level analysis of circadian rhythms. You can modify parameters for analysis in the **"Analysis Parameters"** cell if required. Refer user manual or video tut... |
3be214b76aa52e14b96fcca2c7c9d901db950e3b1394c1f3582732c58f5cee39 | Jupyter | 3,092 | 112 | # %% [markdown]
# # Goal : Scan the 3D predcitions of the FP with the Dpo fold database.
#
# ### A. Run Foldseek
# ### B. Get the hits with good proba
# ### C. Report the folds
# ***
# %% [markdown]
# # A
# %%
import subprocess
import os
path_project = "/media/concha-eloko/Linux/PhageDEPOdetection"
path_pdb = f"{p... |
109365bfa850c7eb72b5ce0ed0f308de7471aa5db17651a110686de0272d6e23 | Jupyter | 3,099 | 122 | # %%
import nir
# import nirtorch
import torch
# import numpy as np
import lava.lib.dl.slayer as slayer
import snntorch as snn
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [10, 2]
plt.rcParams['figure.dpi'] = 200
# %%
dt = 1e-4
scale = 1 << 6
# general params
lif_size = 2
vthr = ... |
2663a8b1ae4d688c25b266af4109f64a4c402f8a4d930905c66c2d037570c07b | Jupyter | 3,105 | 114 | # %%
!pip install -e /dss/dsshome1/04/di93zer/git/cellnet --no-deps
# %%
import os
import seaborn as sns
import torch
from lightning.pytorch.callbacks import ModelCheckpoint, LearningRateMonitor, TQDMProgressBar
from lightning.pytorch.loggers import TensorBoardLogger
from lightning.pytorch.utilities.model_summary imp... |
f75627c1cc4eb1ec1ebc97f68e07e4a3390fbb6b1e1964fbdc808fe58b37481c | Jupyter | 3,107 | 55 | # %% [markdown]
# # Step 3: Tomogram Refinement
# %%
from ddw import refine_tomogram
from ddw.utils.visualization import plot_tomo_slices
from ddw.utils.mrctools import load_mrc_data
from ddw.utils.print import print_help_for_function_arguments
# %% [markdown]
#
# ## Refining the Tutorial Data
#
# The `ddw refine-t... |
3b1c37e338ee4ccc606a59233151f8eca687ec1b9ef675fb6977eebaea6a3be1 | Jupyter | 3,118 | 101 | # %%
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import scanpy as sc
import numpy as np
import anndata
import matplotlib as mpl
import matplotlib.pyplot as plt
from copy import copy
import seaborn as sns
sc.settings.verbosity = 1
sc.logging.print_header()
sc.settings.set_figure_... |
5be43465e2b30d7b4f56aa9eb9763f79031f61329a04604c1aafd70d13e91f65 | Jupyter | 3,128 | 161 | # %%
%load_ext autoreload
%autoreload 2
# %%
import pandas as pd
# %% [markdown]
# # Get data
# %%
!aws s3 cp ccs-prediction/ccs_base_experimental.xlsx ../data/ccs_base_experimental.xlsx
# %%
ccsbase = pd.read_excel("../data/ccs_base_experimental.xlsx", index_col=0)
# %%
ccsbase["CCS Type"].unique()
# %%
ccsbase.... |
d30d45b9566e65c9eb4bc6c586176127d5bd39afd9be705d0e6975ff55d29ae7 | Jupyter | 3,128 | 98 | # %%
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 ListedColormap
import scipy.sparse as sparse
import importlib
import copy
im... |
d4bfb8e8c48df6e92092c44c130792a2149a8c9a5daa91735d2243df2f798505 | Jupyter | 3,136 | 114 | # %% [markdown]
# # imports
# %%
%matplotlib inline
import eelbrain as eel
import numpy as np
import scipy, pathlib, importlib, mne, time, os, sys, statsmodels, statsmodels.stats.multitest, matplotlib
from tqdm import tqdm
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
mne.set_log_level(verbose='e... |
734bf1a89bf5a239083d4c741867cc6c2e9f6b10f0b5130cb3041bab80bf05d3 | Jupyter | 3,165 | 98 | # %%
#load
import numpy as np
import scipy
from scipy import stats
import seaborn as sns
from matplotlib import pyplot as plt
import pandas as pd
# %%
#load data
upf = '/Volumes/Elements/data_for_uploaded/'
cells_con = np.load(upf+'FigS.15/cells_con.npy')
cells_ch= np.load(upf+'FigS.15/cells_ch.npy')
cl_con =np.load(u... |
f77912ebdbb03a90a4e1433a26537469a56353a92bb10f4794f37f4ca593b04c | Jupyter | 3,200 | 88 | # %% [markdown]
# # jaxsnn
#
# [jaxsnn](https://github.com/electronicvisions/jaxsnn) is an event-based training framework for spiking neural networks (SNNs) implemented in JAX. It provides support for numerical model simulation.
#
# Training of SNNs is done in the init/apply style, where an `init` function contains t... |
37ef3cf85990dca15b3117ec79e64fc1d6b51f25c7e7abe60f5d31d6fcfdff16 | Jupyter | 3,201 | 146 | # %%
import argparse
import sys
import os
import logging
import numpy as np
sys.path.insert(0, '../network')
from network import *
from transfer_functions import ErrorFunction, ReLU
from sequences import Sequence
from connectivity import *
from noise import WhiteNoise
sys.path.insert(0, '../scripts')
from simulation ... |
d1386a4ec815b51fbfd0c217741ae42bb8e87d47e6b6bb3b70958a2fc5545fd2 | Jupyter | 3,230 | 118 | # %% [markdown]
# # Goal : Scan the 3D predcitions of the FP with the Dpo fold database.
#
# ### A. Run Foldseek
# ### B. Get the hits with good proba
# ### C. Report the folds
# ***
# %% [markdown]
# # A
# %%
import subprocess
import os
path_project = "/media/concha-eloko/Linux/PhageDEPOdetection"
path_pdb = f"{p... |
08c9cbf5b48a56bf186dcb19f4ca8aa3495ae181b214bd365f42a8070b21e1ec | Jupyter | 3,251 | 125 | # %%
from os.path import join
import numpy as np
import pandas as pd
import dask.dataframe as dd
import xgboost as xgb
# %%
DATA_PATH = '/mnt/dssmcmlfs01/merlin_cxg_2023_05_15_sf-log1p'
CKPT_PATH = '/mnt/dssfs02/tb_logs/cxg_2023_05_15_xgboost/version_20_1.json'
# %%
cell_type_mapping = pd.read_parquet(join(DATA_PATH... |
7dc53597b6de09cf9bcab1d7085edf43550526edfb4f969d354e16008016975a | Jupyter | 3,257 | 96 | # %%
import scanpy as sc
import pandas as pd
import numpy as np
import scipy as sp
from statsmodels.stats.multitest import multipletests
import os
import time
from os.path import join
import glob
import scdrs.util as util
import scdrs.data_loader as dl
import scdrs.method as md
from anndata import read_h5ad
import wa... |
e7f85f57687111fdd85a782f77effc258c0e4d2a4b5e2f26d36abed797dfdae3 | Jupyter | 3,257 | 77 | # %%
using Distributed
addprocs(8);
# %%
@everywhere using PulseInputDDM, Flatten
# %% [markdown]
# ### Generate some data
#
# Create an instance of the `θchoice` [composite type](https://docs.julialang.org/en/v1/manual/types/#Composite-Types) which contains the 9 parameters of a choice DDM model.
# %%
θ_syn = θch... |
19467ed4ad83dac5c7b433d700bbdc4563912ead5741ca68103afe78f0e28a37 | Jupyter | 3,285 | 97 | # %%
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... |
7013aea5ca9d7e8405ce6121bea09d615d041d5eb40ef24e8e0d80cb2ad0f8c7 | Jupyter | 3,314 | 110 | # %%
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.formula.api import glm
import pandas as pd
import pingouin as pg
import numpy as np
import matplotlib.pyplot as plt
# %%
#Proximate/Distance Angle Analysis Between Focal and Target Mice
# %%
# read in files like this.
mouse_id =... |
632a139824c08038c4c2c9c53229cc4b4fdd575d18653300ae49c942c09dc9e7 | Jupyter | 3,318 | 113 | # %% [markdown]
# # LIF comparison
# %% [markdown]
# ## setup
# %%
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn
import matplotlib
filenames = [
["Exact", "lif_exact.csv"],
['Lava (CPU)', 'lif_lava_cpu_float.csv'],
['Lava (Loihi)', 'lif_lava_loihi_fixed.csv'],... |
169eb2aa92de676855f4d5a20daf405619740eaa3741bf561dc485cf083f4e11 | Jupyter | 3,331 | 124 | # %% [markdown]
# # SpiNNaker2
#
# SpiNNaker2 is a neuromorphic chip based around a grid of ARM Cortex-M4F processors which are tighly coupled with accelerators and a network-on-chip optimized for, but not limited to transmission of spikes.
#
# Running this requires the following library and installed SpiNNaker2 hard... |
7c6d925c016a06888d28a108995df81057bd8783c4a5000983b0de7bdb9bcbf1 | Jupyter | 3,334 | 88 | # %%
import glob
import os
import sys
import matplotlib.pyplot as plt
import nibabel as nib
import numpy as np
print(np.__version__)
from numpy import copy
import pandas as pd
from scipy.interpolate import griddata
import tirl
print(tirl.__version__)
from tirl.timage import TImage
from tirl import fsl
import shutil
pri... |
6908dc7ccc9d975d04292efb59523a42876295b64b7c56fee3e6dbde379e4284 | Jupyter | 3,342 | 95 | # %%
import scanpy as sc
from anndata import read_h5ad
import pandas as pd
import numpy as np
import scipy as sp
from statsmodels.stats.multitest import multipletests
import matplotlib.pyplot as plt
import seaborn as sns
import os
from os.path import join
import time
# scTRS tools
import scTRS.util as util
import scTR... |
fe69065d75bd333c1d2faa71a2eb6f4054f1abb000b7493058fbff60f893ca60 | Jupyter | 3,348 | 104 | # %%
import torch
import numpy as np
import nir
import json
# %%
graph = nir.read("braille_noDelay_bias_zero.nir")
# %%
graph.nodes.keys()
# %%
graph.nodes["lif1.w_rec"].weight.shape
# %%
def rework_braille_graph(graph):
graph = nir.read(graph)
hidden = np.array([graph.nodes["lif1.w_rec"].weight.shape[0]])
... |
cc6aa54226bad021c583efe393081a9f53c450b162099b78f4c6df5c719dea2d | Jupyter | 3,352 | 124 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import sak
import sak.signal.wavelet
import numpy as np
import scipy as sp
import scipy.signal
import pandas as pd
import glob
import wfdb
import json
import tqdm
import matplotlib.pyplot as plt
from scipy.stats import norm
from sak.signal import St... |
c5ea9e697f73f174eecb51413c38cf75a1bdd2614ce738eccb115e960c44a42f | Jupyter | 3,353 | 107 | # %%
import torch
import torch.nn as nn
from sinabs.backend.dynapcnn import DynapcnnNetwork
from sinabs.backend.dynapcnn import DVSLayer
from sinabs.layers import Merge, IAFSqueeze, SumPool2d
from sinabs.activation.surrogate_gradient_fn import PeriodicExponential
import sinabs.layers as sl
from torch.nn import CrossEn... |
f6a8d2c6b930a9e3412a8c0e500138b2fc5bd9c39e505de90e48adc9be9890d2 | Jupyter | 3,358 | 114 | # %% [markdown]
# This tutorial is about
# - Global constants in quantax
# - Define Symmetry
# - Define Ising Hamiltonian
# - Exact diagonalization
# - Define RBM neural quantum state
# - Exact reconfiguration
# - Stochastic reconfiguration (TDVP)
# %%
import numpy as np
import jax.numpy as jnp
import quantax as qtx
... |
9bb50c8f91d947fdfeca7d39b43b0fda3f65e5acfb8de19e030abac0366e1378 | Jupyter | 3,394 | 105 | # %% [markdown]
# Example for HRTEM image simulation of SrTiO$_3$ along the [001] zone axis
# %%
# Import necesary packages
import numpy as np
import matplotlib.pyplot as plt
import drprobe as drp
%matplotlib inline
# %%
# Read lattice parameters from cel file
a, b, c = np.genfromtxt('SrTiO3_001_ref.cel', skip_heade... |
4b206abd2cf03fcb3ddf71259aa187770e7e69e363d45aa82295d382da6cf193 | Jupyter | 3,398 | 145 | # %%
%load_ext autoreload
%autoreload 2
# %%
import sys
sys.path.insert(0, '../scripts')
from simulation import *
# %%
N = 80000
p = 0.005
K = N*p
P = 16
tau = 0.01
dt = 1e-3
A = 20
theta = 0.07
sigma = 0.05
x_f = 1.5
q_f = 0.8
# %%
patterns = np.random.RandomState(seed=1).normal(0,1,size=(P,N))
# %%
# Structural c... |
b013244a0ec32c9d705f6e4272759b1d394d8b2dbfa929aa531c0697632e8758 | Jupyter | 3,399 | 101 | # %%
from scdrs.util import test_gearysc
import submitit
from os.path import join
import glob
import pandas as pd
import scanpy as sc
from tqdm import tqdm
# %%
DATA_ROOT_DIR = "/n/holystore01/LABS/price_lab/Users/mjzhang/scDRS_data"
URL_SUPP_TABLE = "https://www.dropbox.com/s/qojbzu5zln33j7f/supp_tables.xlsx?dl=1"
... |
86be6a43a40e28f8c12c90fae0ff9469a0acadcabf849ec9faab43eb9d99bbbd | Jupyter | 3,416 | 111 | # %%
import torch
import torch.nn as nn
from sinabs.backend.dynapcnn import DynapcnnNetwork
from sinabs.backend.dynapcnn import DVSLayer
from sinabs.layers import Merge, IAFSqueeze, SumPool2d
from sinabs.activation.surrogate_gradient_fn import PeriodicExponential
import sinabs.layers as sl
from torch.nn import CrossEn... |
2f035915f328291fc103469deadfdec0cce46bc00e9aa950ca5ece1a839195bf | Jupyter | 3,418 | 126 | # %%
import numpy as np
import pandas as pd
import scanpy as sc
import matplotlib as mpl
import matplotlib.pyplot as plt
from copy import copy
vir = copy(mpl.cm.viridis)
vir.set_under("lightgray")
sc.settings.verbosity = 3
sc.logging.print_header()
sc.settings.set_figure_params(figsize = [12,10],dpi =100... |
3d606834590f0e06d25082e647c59db8d50aa0f03e0e429c25e3000c1568f1c1 | Jupyter | 3,420 | 127 | # %% [markdown]
# # SpiNNaker2
#
# SpiNNaker2 is a neuromorphic chip based around a grid of ARM Cortex-M4F processors which are tighly coupled with accelerators and a network-on-chip optimized for, but not limited to transmission of spikes.
#
# Running this requires the following library and installed SpiNNaker2 hard... |
5e8a511917adedc7b0335ffd92c105f36f6987bc9f768d058aab6d20b2d17725 | Jupyter | 3,422 | 103 | # %%
import torch
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pathlib
import pandas as pd
import seaborn
# %% [markdown]
# # Accuracies
# %%
accuracies = list(pathlib.Path(".").glob("*accuracy.npy"))
accuracies = [x for x in accuracies if "brian" not in str(x.name)]
acc_names = [' '.jo... |
14032183976051c39447e54e91f42ee082ff757d47df8390ae4042c0606a24ef | Jupyter | 3,435 | 151 | # %% [markdown]
# Main notebook containing relevant analysis steps, run for each ensemble.
#
# The script `notebook_per_ensemble.py' automatically copies this notebook to an ensemble directory and executes it for newly trained ensembles using papermill.
#
# **Warning:** You can loose your work! Don't edit automatica... |
41dc1e1781e093f296352d71f469a32bb2fe5f1574b0e5ed39729a45bfebbe31 | Jupyter | 3,435 | 197 | # %% [markdown]
# # Simulated base inference of SEEG signals using 2D VEP model
# %%
import os
import sys
import time
import errno
import time
import timeit
import numpy as np
import scipy as scp
import numba
from numba import jit
import matplotlib.pyplot as plt
from matplotlib import colors, cm
# %%
import warn... |
b0b2d302109e1ff0c8e87d9b6e07767b12954a6714fe332cd56c38813de2fc66 | Jupyter | 3,436 | 151 | # %% [markdown]
# Main notebook containing relevant analysis steps, run for each ensemble.
#
# The script `notebook_per_ensemble.py' automatically copies this notebook to an ensemble directory and executes it for newly trained ensembles using papermill.
#
# **Warning:** You can loose your work! Don't edit automatica... |
fba626878c6cec332bdb3502eca20d050c85496008cc360173fbb3e70172d948 | Jupyter | 3,438 | 95 | # %% [markdown]
# # Image annotations for a batch of patients
#
# In this notebook, for a batch of patients we view and annotate MRI images by drawing polygons on the images.
#
# <div class="alert alert-block alert-info">
#
# <p>Machine learning engineers work <b>iteratively</b> with domain experts, such as <b>cardi... |
62a972eaf9f3b43be9fd44434852996e4632400dc40a57a2ce56a105ce5b09a1 | Jupyter | 3,456 | 93 | # %%
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.pylab as pl
import matplotlib.gridspec as gridspec
import scipy.ndimage as ndimage
from matplotlib.ticker import AutoMinorLocator, FormatStrFormatter
import matplotlib.mlab as mlab
from matplotlib i... |
2bd1ee2fd6e0938bf9f7b399b38fa4153f5f651ffcea6e10a55c05314a958c6f | Jupyter | 3,484 | 115 | # %% [markdown]
# (tune-lightgbm-example)=
#
# # Using LightGBM with Tune
#
# <a id="try-anyscale-quickstart-ray-tune-lightgbm_example" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=ray-tune-lightgbm_example">
# <img src="../../_static/img/run-on-a... |
a6b1ea9120e598486839716a874a999ee9a6d2c02cb8beb036e3efefe038444a | Jupyter | 3,489 | 109 | # %%
%reload_ext autoreload
%autoreload 2
%matplotlib inline
# %% [markdown]
# ## Using transformation from external libraries inside `rising`
# > Note: Some external augmentation libraries are only supported at the beginning of
# the transformation pipeline. Generally speaking, if you need to resort to an
# external ... |
1d0f5dc8de037f8790e2d5d4d407ec039106172b08e15bb43a5127a1feb70e52 | Jupyter | 3,501 | 120 | # %% [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
# %%
def pro... |
23ceed890514445ae475d54a684059b9b79c2a95f5ba0517708948945aa10414 | Jupyter | 3,522 | 92 | # %%
from pathlib import Path
from dask.distributed import Client, LocalCluster, fire_and_forget
from core.config import cfg
from core.benchmark import create_benchmark_configs, run_benchmark_cv
# %%
def compute_benchmark(benchmark, output_root=None, sample=None):
"""
Executes a given benchmark and saves the ... |
869300b3d55591b9b004fdfe56d5e34dd120a803cb64db590319699404cbc8ff | Jupyter | 3,525 | 96 | # %% [markdown]
# # Batch Inference with LoRA Adapters
#
# In this example, we show how to perform batch inference using Ray Data LLM with LLM and a LoRA adapter.
#
# To run this example, we need to install the following dependencies:
#
# ```bash
# pip install -qU "ray[llm]"
# ```
# %%
import ray
from ray.data.llm... |
1aa2c8a75186c22823d8b3b2288a8a960434689cfe2f0f1c433d38b616beaff7 | Jupyter | 3,537 | 104 | # %%
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.pylab as pl
import matplotlib.gridspec as gridspec
import scipy.ndimage as ndimage
from matplotlib.ticker import AutoMinorLocator, FormatStrFormatter
import matplotlib.mlab as mlab
from matplotlib i... |
7c726816a5a4c0dad7d8e090a0ef3a2c903bc5efabb84a81a24b30d209f32656 | Jupyter | 3,538 | 110 | # %%
#import
import seaborn as sns
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
# %%
folder = '/Volumes/Elements/data_for_uploaded/'
hs_cont = np.load(folder +'Fig.S13/hist_all_control.npy')
hs_cont_clst =np.load(folder +'Fig.S13/hist_all_control_clst.npy')
hs_chrim =np.load(folder +'Fi... |
a0c364ef42661bd0227a784ed5a4130b868e45c696c44645524f0b32bb19430b | Jupyter | 3,539 | 114 | # %%
import torch
import torch.nn as nn
from sinabs.backend.dynapcnn import DynapcnnNetwork
from sinabs.backend.dynapcnn import DVSLayer
from sinabs.layers import Merge, IAFSqueeze, SumPool2d
from sinabs.activation.surrogate_gradient_fn import PeriodicExponential
import sinabs.layers as sl
from torch.nn import CrossEn... |
a22e3209d5828a1a02fdd1a4197afb6fcd7fb92834ada0196a7260693c3d417b | Jupyter | 3,556 | 152 | # %% [markdown]
# In this notebook, we propose a demonstartion of GLUE embedding. We apply our implementation as well as the original code on the bulk data of Alzheimer's disease.
#
# # GLUE
#
# ## Setting up data
# %%
import omicverse as ov
ov.utils.ov_plot_set()
# %%
rna=ov.utils.read('single_adata_ad.h5ad')
rna
... |
06b3b1f3fe7f6afbf9db2fc51767a3ec4bb1996d6c9e9832b656d43174287be3 | Jupyter | 3,567 | 172 | # %% [markdown]
# # Figure 1
# %% [markdown]
# ## a
# %%
%load_ext autoreload
%autoreload 2
from flyvis import EnsembleView
# %%
EnsembleView(
"flow/0000",
best_checkpoint_fn_kwargs={"validation_subdir": "validation", "loss_file_name": "loss"},
)
# %%
fig, ax, cbar, matrix = ensemble[0].connectivity_matrix... |
0663b3d2cfb872fdd6ee39dc9097c734c6c74229c9b2aed02a2354af4335461c | Jupyter | 3,568 | 120 | # %%
#import packages
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_validate
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %%
injection_site = pd.read_csv("./virus_inj_sites.csv", index_col = 0)
injection_site
# %%
#stati... |
0550ee9e9263bf3b5c295a3a31835698292145915290acb0bf3c92f49e9dbc02 | Jupyter | 3,589 | 150 | # %% [markdown]
# # Trainor Photometry DLC
# %%
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
from sklearn.linear_model import LinearRegression
from scipy.signal import butter, filtfilt
# %%
# Define project path and get training data
data = pd.read_csv('File Path/Raw_Photometry.csv', sk... |
034dbe7d758b68340658eb96b0a0870f4a4ffef17f4e7e4e892279f5d3e693de | Jupyter | 3,590 | 107 | # %% [markdown]
# # Optimize butterworth filter
# %%
import numpy as np
import matplotlib.pyplot as plt
from scipy import linalg, signal as sp_signal
# simulate sinusoidal timeseries
TR=1.0
time_len=300 # in sec
num_timepoints=int(time_len/TR)
t = np.linspace(0, time_len, num_timepoints, False)
orders=[1,3,5]
freq... |
f5dcc703f52ce9dc59ca0ffe9cd1c297d82d1231030e1ac365c4a0260952372c | Jupyter | 3,590 | 84 | # %%
# Jupyter notebook
# by J. Barthel, Ernst Ruska-Centre (ER-C 2)
# Forschungszentrum Juelich GmbH, Germany
# email: ju.barthel@fz-juelich.de
# ------------------------------------------
#
# Demonstration of measuring focus-spread from an image of thin amorphous
# recorded under tilted beam illumination and signific... |
b29aaac00819ad91496491183e4562a409a52ea197f7565b99360bcc928c41d6 | Jupyter | 3,594 | 85 | # %% [markdown]
# Here, we show how to apply FlowSig to a spatial Stereo-seq dataset of an E9.5 mouse embryo, as originally studied in [Chen et al. (2022)](https://doi.org/10.1016/j.cell.2022.04.003).
# The processed data and cell-cell communication inference, which was obtained using [COMMOT](https://commot.readthedoc... |
a8777cce400a6a09a7f3b04a51cf4b2a123882e6cdcc065bfeeee0d0499c41d4 | Jupyter | 3,600 | 165 | # %%
import numpy as np
import subprocess
from datetime import datetime, timedelta
import multiprocessing as mp
import sys, time
from countdown import countdown
import av
import glob
import serial
from tqdm import tqdm
# %%
from multicamera_acquisition.interfaces.arduino import (
packIntAsLong,
wait_for_serial... |
af06bc1bd7b2b6619a4d141325aa723c60bc7ba29c9da7b2e8b9809439f8a149 | Jupyter | 3,602 | 153 | # %%
%load_ext autoreload
%autoreload 2
# %%
import sys
sys.path.insert(0, '../scripts')
from simulation import *
# %%
N = 80000
p = 0.005
K = N*p
P = 16
tau = 0.01
dt = 1e-3
A = 2
theta = 0
sigma = 0.1
# %%
patterns = np.random.RandomState(seed=1).normal(0,1,size=(P,N))
# %%
# Structural connectivity
k = np.random... |
b6005839ca015b6d4134be28daf6fcdedf41c1ff0ddf771a214eec1f8654f738 | Jupyter | 3,633 | 117 | # %%
import torch
import torch.nn as nn
from sinabs.backend.dynapcnn import DynapcnnNetwork
from sinabs.backend.dynapcnn import DVSLayer
from sinabs.layers import Merge, IAFSqueeze, SumPool2d
from sinabs.activation.surrogate_gradient_fn import PeriodicExponential
import sinabs.layers as sl
from torch.nn import CrossEn... |
98446556bce702df8052e1cb2cad445de1a79169097af7bde120a1a5e575a505 | Jupyter | 3,637 | 117 | # %%
import torch
import torch.nn as nn
from sinabs.backend.dynapcnn import DynapcnnNetwork
from sinabs.backend.dynapcnn import DVSLayer
from sinabs.layers import Merge, IAFSqueeze, SumPool2d
from sinabs.activation.surrogate_gradient_fn import PeriodicExponential
import sinabs.layers as sl
from torch.nn import CrossEn... |
a46d2b72781679959e000843d2964a1c3e7b96324b3cbd06873451244e686288 | Jupyter | 3,641 | 129 | # %%
!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 LinearClassifier
# %%
DATA_PATH = ... |
e4753d9613be24b694859c1a440d4d39bca737f8174b7574d9b00fcb04a09bc0 | Jupyter | 3,641 | 109 | # %%
! pip install --upgrade transformers accelerate
! pip install torch
! pip install pandas==1.3.0 --user
# %%
from transformers import AutoTokenizer, EsmForProteinFolding
import torch
torch.backends.cuda.reserved_memory = 1024*1024*10 # 10 GB
torch.backends.cuda.max_split_size_mb = 1024 #4096
import gc
gc.collect(... |
9ad5ab35e9f2e840683f4b8aabd2e0e8a3d34ce2f393dac1665158db3be8edb9 | Jupyter | 3,684 | 157 | # %%
import scanpy as sc
import pandas as pd
import numpy as np
import scipy as sp
from statsmodels.stats.multitest import multipletests
import matplotlib.pyplot as plt
import seaborn as sns
from anndata import AnnData
import os
import time
from gprofiler import GProfiler
# scTRS tools
import scTRS.util as util
import... |
c0f3dc63b388f3e76d54962b90d222e6688bce483434ff13c882c3a6cf135c9d | Jupyter | 3,690 | 118 | # %%
import json
import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset
import snntorch
import nirtorch
from snntorch import import_nirtorch
import matplotlib.pyplot as plt
import nir
# %%
test_data_path = "data/ds_test.pt"
ds_test = torch.load(test_data_path)
device = "cpu"
# %% [ma... |
ada46e0c423ce4fd2ace15f4aaff922617c494aa8cbe7b1b949e3ddc2dcc88fa | Jupyter | 3,691 | 91 | # %%
#standard
import os
import warnings
warnings.filterwarnings('ignore')
import pickle
#third party
import numpy as np
from nilearn import plotting
import nilearn
import matplotlib.pyplot as plt
# %%
def calculate_noiseceiling(betas, n: int=1):
"""
Calculate the standard deviation across trials, square the ... |
6c8f276a41690898f29f7c6f540ef69aaf6d73dff31ee28041878ef8705854e9 | Jupyter | 3,695 | 141 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import sak
import numpy as np
import scipy as sp
import scipy.signal
import pandas as pd
import json
import tqdm
import matplotlib.pyplot as plt
from scipy.stats import norm
# Data loader to un-clutter code
def save_data(filepath, dic):
wit... |
44c22281d016414878e9138361165ad4bd5347f80107cadc1162db267ea3db40 | Jupyter | 3,719 | 107 | # %%
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.pylab as pl
import matplotlib.gridspec as gridspec
import scipy.ndimage as ndimage
from matplotlib.ticker import AutoMinorLocator, FormatStrFormatter
import matplotlib.mlab as mlab
from matplotlib i... |
e62cdacac03d689f0d63f8daece6bbb71ad34d2774912ec953bda5c3528ff8f3 | Jupyter | 3,722 | 140 | # %%
from samalg import SAM
import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
import scanpy as sc
from matplotlib.pyplot import rc_context
import seaborn as sns
# %%
path = '/Users/ta13/vscode_files/miravidia_v10/neuron1'
# %%
mira_neuron1=SAM()
# %%
mira_neuron1.load_data('miracidia.v10.Ne... |
4c0e4b3ab4923236f4b020912178129aecbcd5af8bb8172007e20cb50bdd6ff7 | Jupyter | 3,731 | 90 | # %% [markdown]
# Here, we show how to apply FlowSig to an scRNA-seq dataset of wildtype
# and stimulated human pancreatic islets, as originally studied in [Burkhardt et al. (2021)](https://www.nature.com/articles/s41587-020-00803-5).
# %%
import scanpy as sc
import pandas as pd
import flowsig as fs
# %% [markdown]
#... |
df573296268a70649656a873aa34900190a2964b9f2306bf7e98ebf3ec37bccd | Jupyter | 3,736 | 109 | # %% [markdown]
# # Reconstruct even/odd Tomograms
#
# __Note__: If you want to apply this notebook to your own data, you have to reconstruct the raw tomogram once manually with IMOD.
#
# The tomogram reconstruction is performed with [IMOD](https://bio3d.colorado.edu/imod/), which is already installed in the singula... |
db3722f0bbf1f255587a21330cb0ba3c44de2af642e5a365dad17ad88c72b059 | Jupyter | 3,741 | 154 | # %%
from collections import defaultdict
import pandas as pd
from tqdm import tqdm
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem.Scaffolds.MurckoScaffold import GetScaffoldForMol
from rdkit.DataStructs.cDataStructs import BulkTanimotoSimilarity
import seaborn as sns
import matplotlib.pyplot as... |
9ec129bb74d4c5156d780f91066764e788cfd8ea3ca08b0ee034fccfea39810d | Jupyter | 3,744 | 110 | # %% [markdown]
# # Batch Inference with Structural Outputs (Guided Decoding)
#
# Structural output (or named guided decoding, JSON mode) is a useful feature that ensures the LLM responses following the given output schema in either JSON or the context free grammar.
#
# In this example, we show how to perform batch i... |
594a10f52ef2560dd78f849761b8fa2bdac2bc39e238c2e4a7f366e30ef43106 | Jupyter | 3,749 | 116 | # %%
import pickle
import torch
import torch.nn.functional as F
from torch.nn import Linear
import torch_geometric as pyg
from torch_geometric.loader import DataLoader
from torch_geometric.nn import TopKPooling, GraphConv, global_mean_pool
# %%
with open('data/train_pyg.pkl','rb') as ftrain:
train_dataset=pickle.lo... |
1fd987fb1398953e537f6d028aef641554b213c4a86f4e02f479d20e8d787df5 | Jupyter | 3,755 | 153 | # %% [markdown]
# # Training NMNIST for deployment on Speck using EXODUS
# %%
import sinabs
import sinabs.layers as sl
import torch
import torch.nn as nn
import numpy as np
# %% [markdown]
# Let's visualize the neuron model that's supported on chip
# %%
iaf = sl.IAF(record_states=True, spike_threshold=5.0)
n_steps ... |
392f81045acd43dcecf64027b0ca4d534f31ab86228fb2420a2caf891d4de04b | Jupyter | 3,756 | 140 | # %% [markdown]
# # Analyses of brain diseases in TMS dataset
# %%
%load_ext lab_black
import matplotlib
from scdrs.util import test_gearysc
import submitit
from os.path import join
import glob
import pandas as pd
import scanpy as sc
import seaborn as sns
import matplotlib.pyplot as plt
# %%
DATA_ROOT_DIR = "/n/hol... |
248c0dc0175b8fba84486df2ce993b5e8190d5b70dcc46225acd4e1b2e77770a | Jupyter | 3,758 | 102 | # %%
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.pylab as pl
import matplotlib.gridspec as gridspec
import scipy.ndimage as ndimage
from matplotlib.ticker import AutoMinorLocator, FormatStrFormatter
import matplotlib.mlab as mlab
from matplotlib i... |
4eca051f4d9b5142752019d985ca74c15db827306c21c71774169ef18829802c | Jupyter | 3,772 | 124 | # %% [markdown]
# (tune-mnist-keras)=
#
# # Using Keras & TensorFlow with Tune
#
# <a id="try-anyscale-quickstart-tune_mnist_keras" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=tune_mnist_keras">
# <img src="../../_static/img/run-on-anyscale.svg" ... |
a1820e962132b8d937ca7409f55a17d8ed7bf3dcc8d8e80ebbfc9e8835ebf9a3 | Jupyter | 3,773 | 110 | # %% [markdown]
# # Split Movie Frames
#
# The first step in training a cryo-CARE network is to split the direct detector acquisition movies. To that end we will first align the frames with [MotionCor2](https://www.nature.com/articles/nmeth.4193) and then split the frames into even and odd acquistions.
#
# __Note__: ... |
8ebb80cda5862a4bb21f1538da47f6ff14ed8efa59d4410880e9f46865e93031 | Jupyter | 3,779 | 117 | # %%
import numpy as np
import pandas as pd
import scanpy as sc
import omicverse as ov
import scanpy as sc
import scvelo as scv
import matplotlib.pyplot as plt
ov.utils.ov_plot_set()
# %%
ov.utils.download_pathway_database()
ov.utils.download_geneid_annotation_pair()
# %%
sc.settings.verbosity = 3 # verb... |
d8de8e5e1b9b16f17ae981f5874fc5aebba1d575f493350e22cc81c18d000879 | Jupyter | 3,785 | 92 | # %%
from build_docker import *
import unittest
import pprint
DEBUG_MODE = True
this_script_path = os.path.realpath('__file__')
TEST_BUILD_TAG = 'mytest'
# %%
# test if build chain can be constructed in desired order
print( Project_Build.get_ordered_build_chain_single('manta') )
print( Project_Build.get_ordered_bui... |
e085c625e910c91aec3e617d86ac03bcf4e3ff33a57ed20fac3ae8dfe1f09960 | Jupyter | 3,786 | 121 | # %%
import torch
import torch.nn as nn
from sinabs.backend.dynapcnn import DynapcnnNetwork
from sinabs.backend.dynapcnn import DVSLayer
from sinabs.layers import Merge, IAFSqueeze, SumPool2d
from sinabs.activation.surrogate_gradient_fn import PeriodicExponential
import sinabs.layers as sl
from torch.nn import CrossEn... |
6360b15e387c1bbb0115b262bdb25f3dd8cfb47bdde21871f89528d3a0a55e1f | Jupyter | 3,790 | 109 | # %%
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.pylab as pl
import matplotlib.gridspec as gridspec
import scipy.ndimage as ndimage
from matplotlib.ticker import AutoMinorLocator, FormatStrFormatter
import matplotlib.mlab as mlab
from matplotlib i... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.