sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
35df5135dffa5eae979378ac64e9d8c4b94a4ed906fdc36b59e67f77576067d7 | Jupyter | 8,202 | 237 | # %% [markdown]
# (parallel)=
#
# # Parallelization
# %%
%config InlineBackend.figure_format = "retina"
from matplotlib import rcParams
rcParams["savefig.dpi"] = 100
rcParams["figure.dpi"] = 100
rcParams["font.size"] = 20
import multiprocessing
multiprocessing.set_start_method("fork")
# %% [markdown]
# :::{note}... |
836720379cdd82df527d92b597ee58bebb0f07f91ecbd1ff4dd3338e25729d40 | Jupyter | 8,202 | 238 | # %% [markdown]
# # `EDA` class
# - This class is a platform for the EDA analysis including:
# - PCA
# - data visualization with a scatter plot
# - visualization of PC components (an alias for eigenvectors used in `skelearn.decomposition.PCA`)
# - Definition of upregulated/downregulated genes by Cohen's d value... |
4906afb7346281f2f3d67882ac2cf78669ad395476dbf3b102104869639d969b | Jupyter | 8,218 | 321 | # %% [markdown]
# # Han
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into-b... |
8f038b0a1b9accf76c61d4383d0e5f59f31af6e6eb8be265591dde198668446f | Jupyter | 8,244 | 290 | # %% [markdown]
# # Generate network hypotheses (Local Marginal Regulation/LOMAR coefficients)
# %%
import glob
import os
from datetime import datetime
import numpy as np
import pandas as pd
import qbiome.data_formatter
import qbiome.forecaster
import qbiome.hypothesis
import qbiome.network
import qbiome.qnet_orchest... |
c9655c9a92759cf7b0a1129b06add10d5c7a4ad0124f6595b2340a15a62bea1b | Jupyter | 8,276 | 205 | # %% [markdown]
# # A sinusoidal weight matrix can be learned by Hebbian learning when forcing a sinusoidal input
#
# ## Fixing the activity and running the weight updates
# %%
import numpy as np
import numpy.lib.stride_tricks
import matplotlib.pyplot as plt
import scipy.linalg
import matplotlib.scale
plt.style.use('... |
66c7601d303cd8a4070fb31b789655f597d77e1eef3b9cd319a4ffb14b3fdc52 | Jupyter | 8,303 | 264 | # %% [markdown]
# ### 1. V-MSMF
# %%
import numpy as np
import matplotlib.pyplot as plt
from msmfcode.core.config import *
from msmfcode.evaluation.plot import plot_optimization_results_3d, plot_optimization_results_3d_sequential
from msmfcode.models.cann import DMSMF
import plotly.io as pio
pio.renderers.default = ... |
5a5d6bcd3c39775d76f964986a65cc0a65f50ae68691a636ccd5f9ce8676a31c | Jupyter | 8,313 | 244 | # %% [markdown]
# # Imports
# %%
import os
import rippl_AI
import importlib
importlib.reload(rippl_AI)
import aux_fcn
import matplotlib.pyplot as plt
import numpy as np
%matplotlib qt
# %% [markdown]
# # Basic detection example
# In this section, a use example of the predict and detection functions are provided
# %... |
7c02f3f04006234860446cec0c507645e6bc59c13223bd0537380c55621fed0d | Jupyter | 8,344 | 232 | # %% [markdown]
# [](https://colab.research.google.com/github/PTB-MR/mrpro/blob/main/examples/notebooks/qmri_sg_challenge_2024_t1.ipynb)
# %%
import importlib
if not importlib.util.find_spec('mrpro'):
%pip install mrpro[notebooks]
# %% [ma... |
9163dde48f512a7fa10585e0858fe1127374056565786df3b2a62c68817b9ae8 | Jupyter | 8,355 | 261 | # %% [markdown]
# # The NormData class
#
# A key component of the PCNtoolkit is the NormData object. It is a container for the data that will be used to fit the normative model. The NormData object keeps track of the all the dimensions of your data, the features and response variables, batch effects, preprocessing ste... |
497da7971fb577242a722b7e20836d1af6b5091fc236091bfba336d8c4c3fba5 | Jupyter | 8,388 | 254 | # %% [markdown]
# # Transfer and extend
#
# Training a normative model on a large reference cohort takes a lot of data and a lot of compute. Rather than training one from scratch, PCNtoolkit lets you take a model that was already trained on tens of thousands of subjects and adapt it to your own, much smaller, dataset.... |
0e4ef27d2b03bf48817a11b4a249756e92e396e90442560a0b9e09a3c8a0838c | Jupyter | 8,407 | 258 | # %%
"""Plot MED OFF vs. ON TRGC (Figure 3C)."""
import os
import sys
from pathlib import Path
import numpy as np
import copy
import pandas as pd
import pte_stats
import pte_decode
from matplotlib import pyplot as plt
from scipy.stats import sem
cd_path = Path(os.getcwd()).absolute().parent
sys.path.append(os.path.jo... |
360431635dc3bd82eaeda574ff57f64e5d0691a355eb8e0512d6b93a44659d12 | Jupyter | 8,414 | 230 | # %%
"""Example for plotting gradient data"""
import os.path as op
from glob import glob
import itertools
import gc
import numpy as np
from neuromaps.datasets import fetch_fslr
from surfplot import Plot
from gradec.fetcher import _fetch_metamaps
from surfplot.utils import add_fslr_medial_wall, threshold
import matplot... |
07e8aadf53e10a89b4c3d98c6d8c7b0ef01dab6b7eb463b9d581282c69ff5b3c | Jupyter | 8,424 | 236 | # %%
library(R.matlab)
library(psych)
library(officer)
library(dplyr)
# %% [markdown]
# # HCP
# %%
data_beh=read.table('/dagher/dagher11/filip/Obesity_maps/data_old/analysis_noethn.fsgd',sep='\t',header=T)
data_thick_lh=read.table('/dagher/dagher11/filip/Obesity_maps/data_old/CT_BMI_LH.txt',sep='\t',header=T)[1:32]
d... |
6668aa43c0a714a5bf4a3daf8f30fbf51fc3d42ea66b1e481369e110e339160e | Jupyter | 8,457 | 215 | # %% [markdown]
# # SVM parameter exploration
# This notebook is a template for finding the SVM model best suited for your needs
# %%
import os
import matplotlib.pyplot as plt
import numpy as np
import sklearn as sk
from sklearn import svm,calibration
from imblearn.under_sampling import RandomUnderSampler
import sys
i... |
26e3c27b0461582266f1a95023da6c02bfb6f288708375416788e1f2d4fc20d4 | Jupyter | 8,487 | 228 | # %% [markdown]
# # Netherlands Neurogenetics Database
# Author: Nienke Mekkes <br>
# Date: 9-Nov-2022. <br>
# Correspond: n.j.mekkes@umcg.nl <br>
#
# ## Script: clinical history predictions
# Steps: <br>
# - (when model not yet trained: load cleaned training data)
# - (when model not yet trained: train model on clean... |
919fab6099ba39a6e3267ca24ca72d2ce98c13b573ca35ef303a31d0e34805f5 | Jupyter | 8,515 | 240 | # %% [markdown]
# # Netherlands Neurogenetics Database
# Author: Nienke Mekkes <br>
# Date: 9-Nov-2022. <br>
# Correspond: n.j.mekkes@umcg.nl <br>
#
# ## Script: clinical history predictions
# Steps: <br>
# - (when model not yet trained: load cleaned training data)
# - (when model not yet trained: train model on clean... |
48f8d9c71f19ab0a0e4449eeda41b8bae33ec107b04503309351d7a6194204c3 | Jupyter | 8,521 | 221 | # %%
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use("seaborn-paper")
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 150
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
import pingouin as pg
import warnings
warnings.filterwarnings('ignore')
# %% [markdo... |
a541af5cbdf1777d754ef379d317c6f120206ef596424827d0345698c49df8b2 | Jupyter | 8,532 | 257 | # %%
import pickle
import numpy as np
import pandas as pd
import networkx as nx
import seaborn as sns
from matplotlib import pyplot as plt
import gseapy as gp
import matplotlib_venn as mvenn
import utils_description as utils
# %%
sns.set(style="white", font='Arial', font_scale=1.)
# %%
infile_edges = 'results/edg... |
5c888960231b65b817e15ad221fc6f3bb9e883ae96a252e2e2c49df83852ddb9 | Jupyter | 8,615 | 203 | # %% [markdown]
# # A sinusoidal weight matrix is most robust to noise
#
# * If we want a circulant network to have stable activity with no input
# * and the network is operating in the linear regime
# * and the network needs to encode 2 variables (vector direction and magnitude)
# * and the neurons can only have posi... |
47554f1483b478d7447be9ea2208bd7229eae45bf033f785fae497c67615e13b | Jupyter | 8,668 | 224 | # %% [markdown]
# # Compare normative models
# %%
import logging
import warnings
import pandas as pd
import seaborn as sns
import pcntoolkit.util.output
from pcntoolkit import (
HBR,
BsplineBasisFunction,
NormalLikelihood,
NormativeModel,
NormData,
load_fcon1000,
make_prior,
)
from pcntoo... |
e64114bb9fa908853667dc8b9929fe37defcc04564b74d759f49bafe58fb13d1 | Jupyter | 8,669 | 226 | # %% [markdown]
# # CpGPTPCGrimAge3
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-we... |
1550533983f7a90254ce9e6b18a7b665ad3a31099a861e542566272dd7942f46 | Jupyter | 8,687 | 175 | # %%
# default_exp data
# %% [markdown]
# # Data
#
# > Prepare reaction yield data from different sources
# %%
# export
from rdkit import Chem
from rdkit.Chem import rdChemReactions
# %%
import pandas as pd
# %% [markdown]
# ### Buchwald-Hartwig high-throughput experiment data set
#
# For the Buchwald-Hartwig rea... |
59585949da2c3a440293b543d4d033a1dcb6b5e7f4c89169deb766fedc9e9b1f | Jupyter | 8,695 | 150 | # %%
import warnings
warnings.filterwarnings("ignore")
from fmriprep_denoise.visualization import figures, utils
from myst_nb import glue
path_root = utils.repo2data_path() / "denoise-metrics"
# Load metric data
dataset = "ds000228"
fmriprep_version = "fmriprep-20.2.1lts"
atlas_name = "schaefer7networks"
dimension = ... |
91d9afcb82c28a2a771cc9fe6619c2c00094550ded3784b8bc809054b52a6a01 | Jupyter | 8,708 | 182 | # %%
import os
import sys
sys.path.append('..')
from src import RASPRoutines
RASP = RASPRoutines.RASP_Routines()
import pandas as pd
from src import AnalysisFunctions
A_F = AnalysisFunctions.Analysis_Functions()
from src import IOFunctions
from src import Image_Analysis_Functions
IA_F = Image_Analysis_Functions.Ima... |
45e1e11d86932b6746eba808e5eb3f22bad42d962239ac64eec17fec63daccee | Jupyter | 8,713 | 224 | # %% [markdown]
# # Lin
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into-b... |
43ed4df548cabaea9e79cf3d4c1dfb230dc03610192f741c9b7b7053fef38a5a | Jupyter | 8,747 | 239 | # %% [markdown]
# # Maths for the 2 population sinusoidal activity case
#
# Going from the abstract case to the case with 2 populations.
#
# First we look at the 4 population fly network and see that if we compress the 3 excitatory populations together, we get the following connectivity for an EI network
#
# * EE -... |
aff58dcad0663fee6f7eaf930c4caedb733cb495b4fbbb0e4feefaaebe9478d2 | Jupyter | 8,751 | 316 | # %% [markdown]
# In this example we will see how to use MiniSom to draw some insights from the <a href="https://en.wikipedia.org/wiki/Democracy_Index">Democracy Index data</a> from Wikipedia.
#
# First, let's load the dataset:
# %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotli... |
66e83ba1288b594ad24b0747787a8a6c34fdc29ca81413bae06f9f66c6750b4c | Jupyter | 8,827 | 268 | # %% [markdown]
# # 2. Zernike Projection
# %% [markdown]
# This notebook covers how to apply the 3D Zernike Fourier transformation to point clouds.
#
# %% [markdown]
# ### Import statements
# %%
import plotly.express as px
import pandas as pd
import numpy as np
import math
import matplotlib as mpl
import matplotli... |
b1d052cc4797735241f604cd3257cdf16f58564c3560e988f153210a038c57b7 | Jupyter | 8,839 | 273 | # %%
EXTRACTED_FASTQ = '/scratch/users/yigit.babal/ferret/corrected_output/ED221124_barcode_extracted.fastq' # path to extracted fasq files containing lineage barcodes
REFORMATTED_FASTA = "/scratch/users/yigit.babal/ferret/corrected_output/ED221124_barcode_reformat.fa" # path to reformatted fasta fileRE
LIB = "ED221124... |
e0240c0380d220c060f9d1ca618689a19b60e229ffa45e7eeafcf863a7bc2948 | Jupyter | 8,849 | 247 | # %%
import pandas as pd
pd.set_option('display.max_rows', 1000) # or 1000
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 150
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
import seaborn as sns
import pingouin as pg
from statsmodels.sandb... |
38b1c08a5dd8a89413cd8656e31b59bdbfafce4482e8f07e9298f71a392bb980 | Jupyter | 8,882 | 301 | # %% [markdown]
# # LC GcAMP TFC Analysis
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
# change pkg_path variable to the directory storing the package
sys.path.insert(0, '../utility_code')
import fp_dat, fp_viz
import seaborn as sns
import statsmodels.api as... |
fc84a81bcf68eccb2dc555a6c3447d12b6a89828f19e34b294151b8baf052700 | Jupyter | 8,921 | 357 | # %%
from pathlib import Path
import sys
from dotenv import load_dotenv, find_dotenv
load_dotenv()
PROJECT_ROOT = Path(find_dotenv()).parent
sys.path.append(str(PROJECT_ROOT.joinpath('src')))
print(f"Project root directory: {PROJECT_ROOT}")
import pandas as pd
from utils import olc_client
from utils.layer_tools impor... |
4f2395805bd9021d45a15ed7cb9f4f3a195488b9175737137b0f4f8e892023d5 | Jupyter | 8,935 | 282 | # %% [markdown]
# # Import qbiome
# %%
import qbiome
from qbiome.data_formatter import DataFormatter
from qbiome.quantizer import Quantizer
from qbiome.qnet_orchestrator import QnetOrchestrator
from qbiome.mask_checker import MaskChecker
from qbiome.forecaster import Forecaster
from qbiome.hypothesis import Hypothesis... |
023e6647dd64a29b229fb397780709d6a3437232b7c8ae8ba029a004a98f72d4 | Jupyter | 9,058 | 329 | # %% [markdown]
# # LC ArchT TFC 120s ITI Analysis
#
# ### Table of Contents
#
# 1. [Experiment Info](#ei)
# 2. [Imports and Set Up](#is)
# 3. [Training](#tr)
# 1. [Load and Clean Training Data](#lctr)
# 2. [Plot Training Data](#pltr)
# 4. [Tone Test](#to)
# 1. [Load and Clean Tone Test Data](#lcto)
# ... |
9a8fad0cb2f73a3bc6d1d7c628c1a7ddb7ed96f3006d50fcbd0828f349cc9a6d | Jupyter | 9,146 | 315 | # %% [markdown]
# # IsoformSwitchAnalyser_Differential_transcript_usage
# %% [markdown]
# ## import libraries
# %%
#import libraries
suppressMessages({
library("DESeq2")
library("IsoformSwitchAnalyzeR")
packageVersion('IsoformSwitchAnalyzeR')
})
# %%
setwd("/run/user/1002/gvfs/smb-share:server=wfs-medizi... |
ba5da09bac554259f50e115fdccd0026f21884a994b300b0594840bbf7923378 | Jupyter | 9,150 | 256 | # %% [markdown]
# # We can look at the trajectories of both the weights and activity in phase space
#
# Because we allow negative activity, we can characterise both the weight and activity profiles just in terms of their sinusoidal amplitude - first Fourier mode.
#
# Therefore, we can look at the trajectory of both s... |
7c261403ec3746e39c94ced2fdca32225b0f52412f62b4e1681cc285d732f18b | Jupyter | 9,185 | 223 | # %% [markdown]
# # Loading other data formats with SpikeInterface
# %% [markdown]
# Kilosort 4 natively supports data in binary format, `.bin`. The simplest
# way to save your data in this format is to load it into memory one chunk at a
# time and save it to a `.bin` file using `NumPy's memmap` function. However,
# i... |
712f22bfd013fced191896125c22fd22d7c41395f75df59a9731bfc29aa5ec62 | Jupyter | 9,247 | 192 | # %% [markdown]
# # sKCSD tutorial
# In this tutorial we will cover three topics: data format for sKCSD estimation, sKCSD method and source visualization.
# %%
from kcsd import sKCSDcell, sKCSD, sample_data_path
from kcsd import utility_functions as utils
from kcsd.sKCSD_utils import LoadData
import numpy as np
impor... |
b193697d283ab61f62a9f4467e62eb189ba050e12fb518b5a9c079fa6cfed9db | Jupyter | 9,311 | 210 | # %%
import os
import numpy as np
import pandas as pd
from glob import glob
import sns
import matplotlib
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import patches
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.backends.backend_pdf import PdfPages
from scipy.sparse... |
3898f59991f90d9e0b721c7ec0fc743450acad1b370763f9f98b9a406f5263d2 | Jupyter | 9,320 | 205 | # %% [markdown]
# # Imports
# %%
import pandas as pd
import numpy as np
import glob
import seaborn as sns
pd.set_option('display.max_rows', 500)
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 150
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
import pingouin a... |
8d310ec55e242fe0e416d834e8a54f437adc00a0afee68756992c74df3bbb19e | Jupyter | 9,329 | 241 | # %% [markdown]
# [](https://colab.research.google.com/github/PTB-MR/mrpro/blob/main/examples/notebooks/qmri_cardiac_fingerprinting.ipynb)
# %%
import importlib
if not importlib.util.find_spec('mrpro'):
%pip install mrpro[notebooks]
# %% [... |
4e5f5ecbc7d3a7ee4cdf411214eaa67d6e591a9f06ff6f98def7f7e1c267061b | Jupyter | 9,348 | 317 | # %% [markdown]
# In this notebook, we shall use a single-cell RNAseq data to deconvolve a bulk RNAseq data.
#
# ## Datasets needed for tutorial
#
# The datasets used in this tutorial are available as part the [DISSECT](https://github.com/robinredX/DISSECT/tutorials/data) repository.
#
# Sources:
#
# The bulk RNAs... |
481324c78542efae8d487241d566893f7770f16dc0f58b626ece1c1372ff018b | Jupyter | 9,410 | 275 | # %% [markdown]
# # GRAB-DA hM3Dq
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
# change pkg_path variable to the directory storing the package
pkg_path = '/Users/jake/Code/'
sys.path.insert(0, pkg_path)
from photometry_analysis import fp_dat, tfc_dat, fp_viz... |
555b37f2b4357e7f61fc6b692195194956d5a3f163ec4cb1786a2bf5c028c2d8 | Jupyter | 9,434 | 263 | # %%
import os
import glob
import sys
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from umap import UMAP
import seaborn as sns
sys.path.append('../')
import src.utils as my_u
from src.utils import df_cp
from src.utils import df_log
from src.uti... |
029dfad8012d2a7461847acb88a2478cba335df98d90f08114d6ea76418d68a6 | Jupyter | 9,445 | 310 | # %% [markdown]
# # HBR with ZINB likelihood
#
# This tutorial shows you how you can use HBR to model zero inflated negative binomial (ZINB) distributed data. ZINB data might come from questionnaires, for example in measures of substance use, where many participants report no use while others report varying levels of ... |
3bc56c461bbbf2f3a92b1477d9d7eccd56b99c48831e95d92f759f84879bff84 | Jupyter | 9,453 | 338 | # %% [markdown]
# Scripts for producing plots shown in Hoyer et al. "Combinatorial selective ER-phagy remodels the ER during neurogenesis"
#
# doi: https://doi.org/10.1101/2023.06.26.546565
#
# Plots related to cryo-ET and correlative cryo-FLM analysis
#
# author: capitanio cristina
# %%
import pandas as pd
impor... |
4d3111a5d8c00655bb14e5808f72a06411213d54dd464682b0f1bbb0c865f31a | Jupyter | 9,503 | 320 | # %% [markdown]
# # Replication for results in Davidson et al. 2017. "Automated Hate Speech Detection and the Problem of Offensive Language"
# %%
import pandas as pd
import numpy as np
import pickle
import sys
from sklearn.feature_extraction.text import TfidfVectorizer
import nltk
from nltk.stem.porter import *
import... |
4950165a11a41064a2e386fac1972061bc446801dc4ca9ecab48106213eedfe1 | Jupyter | 9,552 | 226 | # %% [markdown]
# # LSTM parameter exploration
# This notebook is a template for finding the LSTM model best suited for your needs
# %%
import os
import matplotlib.pyplot as plt
import numpy as np
from tensorflow import keras
import sys
parent_dir=os.path.dirname(os.getcwd())
sys.path.insert(0,parent_dir)
import rippl... |
7a9c01872133e820d80612620738725ac7c4d50513bace72111f539e7241fe5d | Jupyter | 9,553 | 342 | # %%
import glob
from tensorflow.keras import utils, applications as apps
from tensorflow.data import Dataset
from tensorflow.keras import layers, Model, losses
from tensorflow.keras.optimizers import Adam, SGD
from tensorflow.keras.utils import Sequence
import pathlib
import numpy as np
from tensorflow import Tensor
f... |
5002483fb93fc49f0743451d7f1c07c486453149b7592bd567d53def06383c62 | Jupyter | 9,588 | 244 | # %% [markdown]
# [](https://colab.research.google.com/github/PTB-MR/mrpro/blob/main/examples/notebooks/iterative_sense_reconstruction_radial2D.ipynb)
# %%
import importlib
if not importlib.util.find_spec('mrpro'):
%pip install mrpro[notebo... |
dc98014bac6ca78aef8071d390a2db4b8a4bea7b331e2a8608746378cc4a7195 | Jupyter | 9,590 | 293 | # %% [markdown]
# # Load Files and libraries
# %%
import aicspylibczi
import napari
import numpy as np
import os
import pathlib
import tifffile
import time
from datetime import timezone, datetime, timedelta
import glob
from skimage import data, io
from skimage.feature import register_translation
from skimage.feature... |
8bcec465c4f9d5f0a612ce9984b8712c3f9c626a9f256e27e14691ea2c229d1e | Jupyter | 9,621 | 233 | # %% [markdown]
# # Build the self-contained DNAmFitAge clock
#
# This notebook embeds the packaged gait, grip, VO2max, and original GrimAge models into one DNAmFitAge artifact. The public inputs are one ordered assay-feature union followed by `female` and `age`; no precomputed `grimage` input is exposed.
# %% [markd... |
8a007444e36d2f100c68f6c0a843841041475224d9526e7fa7167a3921004e33 | Jupyter | 9,627 | 232 | # %% [markdown]
# # CNN1D parameter exploration
# This notebook is a template for finding the CNN1D model best suited for your needs
#
# This architecture is based in the CNN1D designed by A. Navas-Olivé (https://doi.org/10.7554/eLife.77772).
# %%
import os
import importlib
import matplotlib.pyplot as plt
import nump... |
317265024673f7b0f0017286f4669902bc6971d0b1be2af3e88530635209e7fb | Jupyter | 9,645 | 260 | # %% [markdown]
# # Transfer a pretrained model to your own data
#
# The [PCN lab](https://predictiveclinicalneuroscience.com/) shares a collection of pretrained models on
# [SURFdrive](https://surfdrive.surf.nl/s/Mb6mZyFmJeCaPcZ?dir=/zip), covering
# structural MRI, functional MRI and diffusion MRI. In this tutorial ... |
10fb1d57516d60695deb9b1d35e9b141069354d4ac2fd3026a0f4e5f21ef80f7 | Jupyter | 9,655 | 311 | # %%
import itertools
import os.path as op
import gc
import matplotlib
matplotlib.use("Agg")
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from matplotlib.gridspec import GridSpec
import pandas as pd
import ptitprince as pt
from matplotlib.ticker import FormatStrFormatter
# %... |
147e50a4d1e8872aed66d70b97f5c5f8507b049898483a59197907ec214fcafd | Jupyter | 9,672 | 193 | # %%
import pandas as pd
import numpy as np
import matplotlib as mpl
from matplotlib import pyplot as plt
plt.style.use('default')
from matplotlib.patches import Rectangle
import seaborn as sns
import pingouin as pg
mpl.rcParams['figure.dpi']= 300
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
impo... |
d23cb9e617174176c3725fe14f35cae7f575420ec2cdc8a615d4616a52eccbc6 | Jupyter | 9,871 | 270 | # %%
import matplotlib
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from helper import *
from tqdm import tqdm_notebook
import brainconn as bc
import pandas as pd
import seaborn as sns
import nilearn.plotting as plotting
import palettable as pal
import matplotlib.colors as mcolors
# %%
conn_m... |
9a72d69a9f69bee086cea7cbc7b4e9f994cdf652ae62aa1117ecc7827e9a93b1 | Jupyter | 9,881 | 245 | # %% [markdown]
# ## Classification accuracy by population size
#
# This notebook implements the simulations described in Jwa et al. to simulate the effects of population size on classification accuracy. The simulations are based on the results of [Schwartz et al., 2021](https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/... |
42c617f687dfb010cc7e93635c166be9dcb3e100419698a5105ef4ee5e3ce600 | Jupyter | 9,887 | 245 | # %% [markdown]
# ## Classification accuracy by population size
#
# This notebook implements the simulations described in Jwa et al. to simulate the effects of population size on classification accuracy. The simulations are based on the results of [Schwartz et al., 2021](https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/... |
7eb2fb5a42f9279a4d43ef31ea2f055fa250e259a45b3cc2eba3d91fa20ada3e | Jupyter | 9,947 | 310 | # %% [markdown]
# [](https://colab.research.google.com/github/lucascamillomd/pyaging/blob/main/tutorials/tutorial_cpgptgrimage3.ipynb) [](https://nbviewer.jupyter.org/gith... |
8116d38f2b15e9512189156a24de908c3c76d51ef47b069d34865b9e86775185 | Jupyter | 9,997 | 257 | # %% [markdown]
# # Paper figures: the legacy glass-brain / cohort Grad-CAM plots
#
# Ports the useful parts of `notebooks/legacy/3d-brain-plot.ipynb` (the paper's
# figure-generation notebook, formerly `images/3D Brain Plot.ipynb`) onto the
# real, committed root assets: `mri1.nii` (a real reference MRI volume) and t... |
7b35c7133113c1bb0d6f93e126b3983ea936d9f17471a7d58b10bdddc809661e | Jupyter | 10,025 | 204 | # %% [markdown]
# # Tutorial: Analyzing single cell calling cards collected from the mouse cortex
# %% [markdown]
# In this tutorial, we will analyze single cell calling data that describes Brd4 binding in the mouse cortex. The dataset comes from [Moudgil et al., Cell. (2020)](https://doi.org/10.1016/j.cell.2020.06.03... |
b7c534e74c6edf4c0c0552fe1fcc9332345bd1f4fb46afa783427e8a69f0668e | Jupyter | 10,070 | 288 | # %%
import keypoint_moseq as kpms
project_dir = 'demo_project'
config = lambda: kpms.load_config(project_dir)
# %%
#only with GPU version
import os
cuda_path = os.environ['/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.3'] # or maybe cuda_path='/usr/local/cuda-11.0'
os.environ['XLA_FLAGS'] = '--xla_gpu_c... |
ddd99a2028d424d2e95c5fcd86e2264e5dd0950bfb37fb4a95a519f221057d6b | Jupyter | 10,116 | 336 | # %%
# initialization
import tensorflow as tf
from tensorflow import keras
# test for GPU use in tensorflow
physical_gpus = tf.config.list_physical_devices('GPU')
print(physical_gpus)
# define variables and constants
DIMX = 256
IMAGE_SIZE = (DIMX, DIMX)
BATCH_SIZE = 32
EPOCHS = 30
BASE_DIR = './'
IMAGE_DIR = BASE_DI... |
6ef864c56c2ca0da3c7b5b556f42ac0ba691a5403235761ee18d38ccb3328ff7 | Jupyter | 10,146 | 348 | # %% [markdown]
# # Replication for results in Davidson et al. 2017. "Automated Hate Speech Detection and the Problem of Offensive Language"
# %%
import pandas as pd
import numpy as np
import pickle
import sys
from sklearn.feature_extraction.text import TfidfVectorizer
import nltk
from nltk.stem.porter import *
import... |
ca214a980a10191b96b72afda27c6f8ecb60eaf1bc123b3a0214106ece622e6c | Jupyter | 10,239 | 274 | # %% [markdown]
# # T-DAPIA : Two dimensional Analysis Pipeline "Intelligence Artificial"
# %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyimzml.ImzMLParser import ImzMLParser
from scipy.ndimage.filters import gaussian_filter
from scipy.ndimage import zoom
from bisect import bisect_le... |
eba6c652ac35e70279b6fe68d74b6f3d2ca323261ab03e53687fe4cc16ae42f5 | Jupyter | 10,310 | 350 | # %% [markdown]
# # Import qbiome
# %%
import qbiome
from qbiome.data_formatter import DataFormatter
from qbiome.quantizer import Quantizer
from qbiome.qnet_orchestrator import QnetOrchestrator
from qbiome.mask_checker import MaskChecker
from qbiome.forecaster import Forecaster
from qbiome.hypothesis import Hypothesis... |
27013da8ce4b90f79f15f7aa21059b6ddd811718e211c87b12c7d10de6f168e8 | Jupyter | 10,326 | 291 | # %%
from med_associates_utils.session import parse_directory, SessionCollection
import os
import re
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
import time
import numpy as np
from scipy import stats
from sklearn.linear_model import LinearRegression
from scipy.optimize import curve_fi... |
1a265c3aa57608f76ad36f219fe9040790e46dfbcd0140e04e577b92298ceb42 | Jupyter | 10,462 | 237 | # %% [markdown]
# # CNN2D parameter exploration
# This notebook is a template for finding the CNN2D model best suited for your needs
#
# This arquitecture is inspired by the UNet (https://doi.org/10.48550/arXiv.1505.04597) and YOLOR (https://doi.org/10.48550/arXiv.2105.04206)
# The 1st half uses convolution and MaxPo... |
c301f32246066ab9e025312ce8d2ee50b9cf81d586ce965cd6353014f259c4ff | Jupyter | 10,559 | 243 | # %% [markdown]
# [](https://colab.research.google.com/github/PTB-MR/mrpro/blob/main/examples/notebooks/iterative_sense_reconstruction_with_regularization.ipynb)
# %%
import importlib
if not importlib.util.find_spec('mrpro'):
%pip install m... |
3f282865ba9bc4b8649ba87a519738aac87f666b04625fcb286b0ff7a83edb7b | Jupyter | 10,560 | 339 | # %%
"""Analyse relationship between MIC patterns and hyperdirect connectivity."""
from copy import deepcopy
import numpy as np
import pandas as pd
from statsmodels.regression.mixed_linear_model import MixedLM
import os
import sys
from pathlib import Path
import matplotlib
from matplotlib import pyplot as plt
import m... |
860977687f2718f00164d572b0aa5564514076a3aa381aea722419596c7a7a8e | Jupyter | 10,604 | 298 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import os
import re
import numpy as np
# %%
root = "/cluster/projects/p33/users/alexeas/elleke/pleioprs"
noda_export = os.path.join(root, "pheno", "noda_query_2021-12-03_13.02_8fd09a4.csv")
df = pd.read_csv(noda_export, sep=';')
print(df.shape)
#df.head()
# %%
... |
e180c7f1d082beff78741a0f582813db8ed9c9bfc849dc5efeb32c6a5d7b22ad | Jupyter | 10,811 | 375 | # %% [markdown]
# # Module preservation based on one one network
# %% [markdown]
# ## Load libraries
# %%
library('gplots')
library('ggplot2')
library('knitr')
library('limma')
library('reshape2')
library('RColorBrewer')
library('WGCNA')
library("broman")
# %% [markdown]
# ### Use the log values from WGNCA analsis i... |
316d88890d9c626944f338f7ddefd0e2a3ba48245e8bcb31dad822996f5cf2e3 | Jupyter | 10,828 | 207 | # %% [markdown]
# # Polar coordinates noise distribution
#
# * The neural activity represents a location on a 2D plane
# * the distance and angle from the nest are encoded as the amplitude and phase of a sinusoid respectively
# * this corresponds to each neuron having a preferred direction, distributed evenly arou... |
aa8751f34ae15f2b4a09c5dff33ad4fdddf489ebc3a3b340731af97e920831ed | Jupyter | 10,840 | 282 | # %% [markdown]
# [](https://colab.research.google.com/github/PTB-MR/mrpro/blob/main/examples/notebooks/tv_minimization_reconstruction_pdhg.ipynb)
# %%
import importlib
if not importlib.util.find_spec('mrpro'):
%pip install mrpro[notebooks]... |
03552f1ac198d737fb0e14ec12cedbab2077d6f4e70dabbd74c35bef15d36471 | Jupyter | 10,922 | 382 | # %%
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import skimage.io
import scipy.signal
import scipy.ndimage as ndimage
from jupyter_compare_view import compare
import warnings
from bm3d import rgb_to
from functools import partial
from pycsou.runtime import Precision, Width
import cupy as cp
fr... |
53639f70fee1f54e72a94a351a8f4df7dec5837f1ee2e435244d3305fcfa6f5d | Jupyter | 10,983 | 368 | # %%
"""Example for plotting gradient data"""
import os.path as op
from glob import glob
import itertools
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
import matplotlib.pyplot as plt
from utils import plot_gradient, plot_subcortical_gradient
# %%
import numpy as... |
a03f3311a8e3091833d3b8a855b59c25f26c42d291573783e94634c0d6c897dc | Jupyter | 11,031 | 257 | # %%
import pandas as pd
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt
# %%
project_dir = Path.cwd().parent
input_dir = Path.cwd().parent / 'input'
output_dir = Path.cwd().parent / 'output' / "mediation_analysis"
output_dir.mkdir(parents=True, exist_ok=True)
# %%
scores_df = pd.read_csv(... |
3c8039071037f43f55e0eb4d816e52781d38aa715bd7f537070e934dec0b8dec | Jupyter | 11,084 | 360 | # %%
# hide
%load_ext autoreload
%autoreload 2
# %% [markdown]
# # Test-time augmentation for uncertainty estimation
#
# > Compare the uncertainty estimate of the different test-time augmentation methods. The models to compute the results can be found: [Augmented yield prediction models](https://ibm.box.com/v/Augmen... |
754405bf2947f0454007c8947a2eebf416367356666a67430e42c80c8b9c7a04 | Jupyter | 11,104 | 309 | # %% [markdown]
# # Figure 5 Reconstruction
# %%
# import requisite packages
import mne
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
import sys,os
from mne.preprocessing import (ICA, create_eog_epochs, create_ecg_epochs,corrmap)
from mne.datasets import fetch_fsaverage
import os.path... |
188da4e8affc6f25a71c3d0b91682b5044419f437a2aba145b4930c654e8e220 | Jupyter | 11,117 | 251 | # %% [markdown]
# # A sinusoidal weight matrix can be learned by Hebbian Learning when forcing a sinusoidal input
#
# ## Fixing the activity and running the weight updates
# %%
import numpy as np
import numpy.lib.stride_tricks
import matplotlib.pyplot as plt
import scipy.linalg
import matplotlib.scale
plt.style.use('... |
9f7ffec998dac81079880b408022985a9738cf0f6a94ff99031f84f951dca65b | Jupyter | 11,143 | 218 | # %% [markdown]
# # Tutorial: SP1 bindings in Cre-driver mouse lines.
# %% [markdown]
# In this tutorial, we will analyze the binding of the transcription factor Sp1, collected using cre-dependant calling cards from a Syn1::Cre-driver mouse line. Bulk unfused (Brd4 directed) data was also collected as backgound. Thi... |
7d81c8f7a505b7edc9289a5610a1d146db8956e07792899175b596366e0cbb7a | Jupyter | 11,183 | 385 | # %% [markdown]
# # Import qbiome
# %%
import qbiome
from qbiome.data_formatter import DataFormatter
from qbiome.quantizer import Quantizer
from qbiome.qnet_orchestrator import QnetOrchestrator
from qbiome.mask_checker import MaskChecker
from qbiome.forecaster import Forecaster
from qbiome.hypothesis import Hypothesis... |
147788a61e1811f3658eeb4041ff3a3cb6723689742c4515e6e92e16d042b47b | Jupyter | 11,217 | 329 | # %%
%reset -f
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle
import mne
# %%
fileName = 'sub-05/ses-SITUATION1A/ieeg/sub-05_ses-SITUATION1A_task-acute_ieeg.edf'
data = mne.io.read_raw_edf(fileName)
raw_data = data.get_data()
channels = np.array(data.ch_names)
fs = data.info['sfre... |
36dd4ce24c22897569943bbe0cea7c25626799d6bb26c0ea0fad89d4b1b1e39b | Jupyter | 11,239 | 383 | # %%
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import skimage.io
import scipy.signal
import scipy.ndimage as ndimage
from jupyter_compare_view import compare
import warnings
from bm3d import rgb_to
from functools import partial
from pycsou.runtime import Precision, Width
import cupy as cp
fr... |
9fcba654893307002780965b66675b3d46c611fc168634f8669dcdabc7ee6675 | Jupyter | 11,240 | 404 | # %% [markdown]
# # Weighted_gene_co-expression_network_analysis_WGCNA
# %% [markdown]
# ## import libraries
# %%
#import libraries
suppressMessages({
library('gplots')
library('ggplot2')
library('knitr')
#library('limma')
library('reshape2')
library('RColorBrewer')
library('WGCNA')
li... |
1b0e3fc17c807fbc163430327d1cd88cb5708e5f9d87687067ba2c320e7a3c3a | Jupyter | 11,296 | 290 | # %% [markdown]
# # Running CELLEX on MOCA dataset
#
# # The data
# The full version of Mouse Organogenesis Cell Atlas (MOCA) contains ~2 million cells, derived from 61 embryos staged between 9.5 and 13.5 days of gestation. Please see https://oncoscape.v3.sttrcancer.org/atlas.gs.washington.edu.mouse.rna/landing for mo... |
324ec80a40530617c32757445c67c4401cfefe150d8db0ca6bcfbf9caefee2ed | Jupyter | 11,334 | 391 | # %%
!pip install nibabel
import os
import random
from pathlib import Path
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import nibabel as nib
DATA_DIR = Path("Your data directory")
RANDOM_SEED = 1234
random.seed(RANDOM_SEED)
# %%
!mkdir /datasets -p
from google.colab import drive
drive.mount(... |
6346983f66da31d6a24854df74b08ca176d146c81bf4f71cdbd19d626d05d759 | Jupyter | 11,345 | 323 | # %%
import scipy as sp
import scipy.io
import os
import numpy as np
import pandas as pd
import glob
import csv
import random as rand
from tqdm import tnrange, tqdm_notebook
from collections import Iterable
import matplotlib.pylab as plt
import random as rand
from ipywidgets import *
from scipy import stats
import impo... |
bce8402d425f16c62b264be72dda5f0c18520de00cc1dd5ed55148416fc7a5f9 | Jupyter | 11,394 | 328 | # %% [markdown]
# # Egg detection via bounding box regression
#
# A notebook outlining the training procedure using a variety of best performing image classification models as the model 'backbone' for this problem. Because this is an object detection problem involving only single instances of embryos in a given image,... |
fd53a33bb8a0a3233f03937cbbddb49dcab0ac850a3bc059841570e72d584096 | Jupyter | 11,439 | 253 | # %% [markdown]
# # How do the weights learned by Oja's rule change when the input has errors?
#
# But what effect do errors in the neural activity have?
# * fixed activity
# * constant offset in waveform
# * constant offset in one neuron
# * additional white noise in neural activity
#
#
#
# * dynamic activit... |
5fbc6f5ecea1de8d3c08060f0a09a2088f10ae93b83391e9ee475f07d6058d30 | Jupyter | 11,514 | 345 | # %%
"""Analyse relationship between MIC patterns and fMRI connectivity."""
from copy import deepcopy
import numpy as np
import pandas as pd
from statsmodels.regression.mixed_linear_model import MixedLM
import os
import matplotlib.ticker as ticker
from matplotlib import pyplot as plt
from matplotlib.offsetbox import A... |
b64f078bc3327a6b1064ef5806ffd8d800b51ebaa2d1259f3b478c24232b3133 | Jupyter | 11,535 | 293 | # %% [markdown]
# [](https://colab.research.google.com/github/PTB-MR/mrpro/blob/main/examples/notebooks/qmri_t1_mapping_with_grad_acq.ipynb)
# %%
import importlib
if not importlib.util.find_spec('mrpro'):
%pip install mrpro[notebooks]
# %%... |
033b82ab8c873f5559fd02d95a55d34f15aeab9fb687276f5b7d803b83f93e71 | Jupyter | 11,569 | 282 | # %%
import scipy as sp
import scipy.io
import scipy.stats
import os
import numpy as np
import pandas as pd
import glob
import random as rand
from tqdm import tnrange, tqdm_notebook
import sys
sys.path.append(os.getcwd()+'\\..')
import cmocean
from utils import utils
from utils import plotting_utils as pu
from utils im... |
5b757c34db324d77f78372c37b151b20358af5006976decefea91e46cb5c6468 | Jupyter | 11,571 | 361 | # %% [markdown]
# # Amorphous structure generation
#
# This notebook is broken down into the following components:
# - Train the prior score model, which is the basis for the unconditional diffusion model.
# - Load a trained prior score model, and perform unconditional generation.
# - Lastly, load a trained forward mo... |
543553c81ecd412916f4e3d470403791a94c6e0ca44d8d0da29aa9a24cf3f85c | Jupyter | 11,634 | 263 | # %% [markdown]
# # GrimAge
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-in... |
678a4fc56457ea7cb51cd8e21cf93d2640577c99d149d163e6f912b109e3cd2c | Jupyter | 11,637 | 313 | # %% [markdown]
# This tutorial assumes that you have installed NeatMS and familiarised yourself with the tool through the documentation at https://readthedocs.org/NeatMS.
# Example data and the default model used here are available on [NeatMS github repository](https://github.com/bihealth/NeatMS). The example data is... |
a9f308417f99ec9c55eeb2372a510f0e7f305ff84c803478124e713b2d48b12e | Jupyter | 11,665 | 388 | # %% [markdown]
# ## Part4. Discussion
#
#
#
# %%
import pandas as pd
import os
import numpy as np
# %%
import numpy as np
import torch
import dgl
from dgl import DGLGraph
from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem.Draw import rdMolDraw2D
from rdkit.Chem import rdDepictor
from IPython.displ... |
469a839b8d45b2fbc85c06e3e0773f6459cf8dd6d2ee64843d04e5e866a3b97d | Jupyter | 11,690 | 382 | # %%
import pandas as pd
# %% [markdown]
# This notebook is used to :
#
# - Prepare the downsampled barcodes
# - De-anonymize the donors after deconvolution
# %% [markdown]
# Create barcodes for downsampling.
# %%
donor_barcodes = pd.read_csv(
"../data/pipeline_output/gx12/donor_match/all_assignment_after_match... |
a0edd6eaeb8e44b0c98ed8894abb7fdf73d0ada36a7984f6ed5a099f133e390c | Jupyter | 11,794 | 356 | # %%
"""Plot STIM OFF vs. ON TRGC (Figures 3C & S4)."""
import os
import sys
from pathlib import Path
import numpy as np
import copy
import pandas as pd
import pte_stats
import pte_decode
from matplotlib import pyplot as plt
from scipy.stats import sem
cd_path = Path(os.getcwd()).absolute().parent
sys.path.append(os.... |
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