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6eb4443d6fd0b7bb5cc76d2a6380bc1de5a0a4dfe386193d0e5e8be026c08b6c | Jupyter | 15,104 | 421 | # %%
gene_familly = "AP2"
# %%
# Parameters
gene_familly = "Dof"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rel... |
7288a18091eaf2b865ed2c5aaec1d092b4c743990c10bdcee8eacf2ccd991944 | Jupyter | 15,104 | 421 | # %%
gene_familly = "AP2"
# %%
# Parameters
gene_familly = "EIL"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rel... |
83591366d14f10afbbd8698a81b76993e49bf8d168d2b2bb917a8ca835b7e063 | Jupyter | 15,104 | 421 | # %%
gene_familly = "AP2"
# %%
# Parameters
gene_familly = "CPP"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rel... |
a89e7f8d92fdb209c5953ec7adafbe114073861818bce8ff9b23b2865970560c | Jupyter | 15,104 | 421 | # %%
gene_familly = "AP2"
# %%
# Parameters
gene_familly = "DBB"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rel... |
e8e5bdee7199c5b4ec36dc1be4dfbdec10633651d21a084d9127e389df659c88 | Jupyter | 15,104 | 421 | # %%
gene_familly = "AP2"
# %%
# Parameters
gene_familly = "ARF"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rel... |
7c2c639ac76d8459f4fafdea4c60ada4c21b60eaf16e51ddbeb6cb233c2426a3 | Jupyter | 15,105 | 421 | # %%
gene_familly = "AP2"
# %%
# Parameters
gene_familly = "GATA"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: re... |
36bca5b6792d80f0d3f32414efdfa5860561d63f538c0447ca8c0becbc3ac802 | Jupyter | 15,106 | 421 | # %%
gene_familly = "AP2"
# %%
# Parameters
gene_familly = "ARR-B"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: r... |
9193da76d18b4febf5ed3fd639d8e23463004c770c9a0ef2bc774adb68f1ec5a | Jupyter | 15,106 | 421 | # %%
gene_familly = "AP2"
# %%
# Parameters
gene_familly = "CAMTA"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: r... |
8b14b602455f1ca92835808eab47ef869c4c9755718eec0dab9e0bce81c01a32 | Jupyter | 15,107 | 421 | # %%
gene_familly = "AP2"
# %%
# Parameters
gene_familly = "E2F/DP"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: ... |
6ed1cd2094747f67f72114b8a3e518d330fe0b90f71166132c8e6c7d80bfc6f5 | Jupyter | 15,108 | 421 | # %%
gene_familly = "AP2"
# %%
# Parameters
gene_familly = "BBR-BPC"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position:... |
411335aa5ddbe5663cf5831e12eb77260fb30d9d5928112ad593ce2af4c3f0f0 | Jupyter | 15,115 | 280 | # %% [markdown]
# # An overview of `causallib` evaluation plots
#
# To make it easier to assess the quality of the causal models, `causallib` supplies a number of evaluation plots.
# The plots are accessed by first running `causallib.evaluation.evaluate` on a model and data. More useful results are obtained when the c... |
64052ae218f12c492602f97b54eb1d96b7091941fee469c3785447de1f8b39db | Jupyter | 15,130 | 473 | # %%
import brainpy as bp
import brainpy.math as bm
import numpy as np
import matplotlib.pyplot as plt
import scipy
from scipy.linalg import eig
#gridspec
import matplotlib.gridspec as gridspec
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import sys
sys.path.append('..')
from cann import CANN1D
# %% [... |
30d69f03018921472614ff99f21ea398fed26d577cdb73a79c4ea6c08fcc90ae | Jupyter | 15,171 | 361 | # %% [markdown]
# # DICOM-extraction
# This notebook outlines the procedure for extracting the desired MR-sequences from Philips' enhanced DICOM format to Niftii. The DICOM-files are not included, but we still provide this notebook for transparency. The MRI-data were located within it's own folder in the `mri_dataset`-... |
da6c3ea4e24791b8d3c1b2d402197215c298fd2f18a63061153830316704f761 | Jupyter | 15,205 | 316 | # %% [markdown]
# [](https://colab.research.google.com/github/sagar87/scPCA/blob/main/docs/notebooks/kang.ipynb)
# %% [markdown]
# <div class="admonition note">
# <p class="admonition-title">Note</p>
# <p>In order to run this notebook i... |
5e0bbeae00282f5ce19c5f3e43130978be42ac8187cc2ecc33fd83e6c77c5aa9 | Jupyter | 15,282 | 431 | # %% [markdown]
# # Figures 2 and 3: Human WM is associated with bursting in LMFG and RMTG
# %%
# Adjust the path to the root data folder containing dataset_description.json, sourcedata/, etc.
DATA_PATH = "/Volumes/External/dataset/"
SUBJECTS = [f"sub-P{k}" for k in range(1, 8)]
# %%
import utils
import numpy as np
... |
3181b90e398fcb43d9c95e81c73038c270e0c921857f03fb61788f95aa89dacf | Jupyter | 15,290 | 405 | # %%
PHONE_DEF_SIL = [
'BLANK','SIL','AA', 'AE', 'AH', 'AO', 'AW',
'AY', 'B', 'CH', 'D', 'DH',
'EH', 'ER', 'EY', 'F', 'G',
'HH', 'IH', 'IY', 'JH', 'K',
'L', 'M', 'N', 'NG', 'OW',
'OY', 'P', 'R', 'S', 'SH',
'T', 'TH', 'UH', 'UW', 'V',
'W', 'Y', 'Z', 'ZH', ]
import scipy.io
import numpy a... |
24af742c5c03818809ad7a5bfceffa6c9abd7de5a79b5e43bf9a9fe1f688528b | Jupyter | 15,320 | 423 | # %% [markdown]
# # GLM Between Analyis
# %% [markdown]
# This script performs within analysis including:
# - SM win 3,2,1 0 minus SC win 3,2,1, 0
# - OM win 3,2,1 0 minus OC win 3,2,1, 0
# %%
#!jupyter nbconvert --to script GLM-within-3-7-24.ipynb
# %% [markdown]
# ## Imports
# %%
import nibabel as nib
from nile... |
b499769aae94ec7f7c9ff38ba550a05c1de4d8c6d32db8dfed1371051d0b3ce5 | Jupyter | 15,350 | 413 | # %% [markdown]
# Before running the code, please download the required package first like torch, numpy ...
# %%
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
from torch.nn.parameter import Parameter, UninitializedParameter
from torch.nn ... |
15b5ab9a5caf232186ce1a023fef02931d93edb42edfbc7db7b8b6e1f497c51d | Jupyter | 15,352 | 330 | # %%
from infopath.model_loader import load_model_and_optimizer
from infopath.config import load_training_opt
import matplotlib.pyplot as plt
import torch
import numpy as np
from infopath.utils.functions import load_data, opto, run_with_perturbation, pear_corr, feature_pop_avg, mse_2d
from infopath.losses import noise... |
87504088ed2cfbed84de2303e01766c84e828e3239a0fe4db8afa198bf657253 | Jupyter | 15,368 | 504 | # %%
import warnings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import r2_score
warnings.filterwarnings("ignore")
# %%
# Loading NYT time series data
ts_df = pd.read_csv("./data/SWOW_prediction/eval/tim... |
b5cf21ed5a96e6280045791cf78f72ac4d485b62401227bbff2517bbbd965aa0 | Jupyter | 15,377 | 439 | # %% [markdown]
# # User Selection - Based Querying
# %% [markdown]
# ## Set up
# %% [markdown]
# ### Load Required Libraries
# %%
from QC_functions import *
# %%
#denote wich brain we're using, like S1 (this is the atlas image for the overlay)
# brain_img_orig=imread('/project/roysam/rwmills/repos/cluster-cont... |
3bc0dc7b3ee55932761be513db80bc3dfddbff629eb2b2f1d2727b7d71ef496c | Jupyter | 15,413 | 293 | # %% [markdown]
# <a href="https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/04a_das_models.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# **Hint:** To help with the high compute demand of DAS models,... |
8988a65e25b376021efc283a470e74dd1487365522b7afa6210927f38f2b73f1 | Jupyter | 15,441 | 418 | # %% [markdown]
# # Surface-Based NSD Data Exploration
#
# Explore fsaverage surface betas (MGH format), ROI labels, and noise ceiling
# for the surface-based Allen2022 fMRI benchmark.
#
# **Data location:** `/Volumes/Hagibis/nsd`
#
# **Key differences from volumetric pipeline:**
# - Surface betas are MGH files per ... |
e869906c01adbb528608fbe5eb622aca93bea99f4f2874a0c2ed45ae653b1155 | Jupyter | 15,443 | 417 | # %% [markdown]
# # fcHNN-based analyses of the ABIDE dataset
#
# ### Imports
# %%
import sys
sys.path.append('..')
import os
from os.path import join
import numpy as np
from nilearn.datasets.utils import _uncompress_file, _fetch_file
from nilearn.connectome import ConnectivityMeasure
import pandas as pd
import con... |
cfad85c9ac9a32501e757f46ca83896470439405dcc075fac171cf47199594ad | Jupyter | 15,507 | 410 | # %% [markdown]
# ## Distribution similarity metrics (122-D)
# This section adds metrics to compare empirical (all_ts) and simulated (state_sample) distributions in 122-D.
# - kNN KL/Jeffreys via Kozachenko–Leonenko estimator
# - Gaussian symmetric KL (Ledoit–Wolf covariance)
# - RBF kernel MMD (median heuristic)
# - S... |
faf4e82d98c51e9fbc83ec955f2af8925bcae0d1cde3db2430402a5b38d69281 | Jupyter | 15,524 | 365 | # %% [markdown]
# ## Figure S10A
# %%
import pandas as pd
import numpy as np
from difflib import SequenceMatcher
"""
Calculate GSI scores from csv of google form responses by participants. Code adapted from Giorgia Cantisani
"""
# %%
# HELPER FUNCTIONS
def similar(a, b):
# Function that recognized similar senten... |
f6e724e65e471d6b3147bebbe72ceaa4268db803b9a0624e1a8337d608c23446 | Jupyter | 15,614 | 343 | # %% [markdown]
# # Chapter 13: DSC/DCE MRI ANALYSIS
# %% [markdown]
# ### This is "worked example" walking through an example pipeline (as illustrated in Fig. 2) for DSC/DCE MRI analysis of a dual echo EPI acquisition.
# %% [markdown]
# ## 1. Import all the necessary packages
# %%
sys.path.append('..')
import os
i... |
bc11150e2ea789fc8c37d18fe5cf856f10448b889b138b2c6a265486586e61fa | Jupyter | 15,654 | 500 | # %%
import os
import re
from scipy import stats
import tempfile
from pathlib import Path
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib
from matplotlib.colors import rgb2hex
import pickle as pkl
from sklearn.model_selection import train_test_split
from sklearn.discriminan... |
c75cf0cbe748dca2df47ec88cc6792120eeb1f8a7a807ce61c0ae65bce28fb93 | Jupyter | 15,671 | 488 | # %%
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import liana as li
import scanpy as sc
from liana.method import singlecellsignalr, connectome, cellphonedb, natmi, logfc, cellchat, geometric_mean
matplotlib.rcParams['pdf.fonttype'] = 42
# %%
# prepare the scanpy object corr... |
510ce9a17fbe29a9343a1ad8d9885ee1731552ca74d79fc3e7da8553951a8a40 | Jupyter | 15,697 | 504 | # %%
! scp -r yroussel@bbpv1.epfl.ch:/gpfs/bbp.cscs.ch/data/project/proj84/csaba/aibs_10x_mouse_wholebrain/results/density_calculations_3/scaled_densities_t_types_as_keys.pickle ./
# %%
import pickle
# Open the pickle file in binary read mode
with open('./scaled_densities_t_types_as_keys.pickle', 'rb') as file:
#... |
370540ebdb3edbb8f1f3dd104fa5ed5654e2eb2ae37679b009c455d6418d780a | Jupyter | 15,723 | 317 | # %%
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from pylab import rcParams
import matplotlib
rcParams['figure.figsize'] = 3,5
sns.set(font_scale=1.3, style = "white")
# %%
df = pd.read_csv('/home/MRMelissaGrantPeters/MinaRyten/Melissa/teamWood/Abundance_final_data/proc... |
5beafda17a8440987bfd5f9df41d3bb3d4438e1bee3d8950e0f5a24606276ebf | Jupyter | 15,750 | 305 | # %% [markdown]
# <a href="https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/04b_training_deepsubdas.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# **Hint:** To help with the high compute demand of DA... |
089ee322ea339b6e3c0088f496ca5af994650e57d3d183b28c7ecfc56483d6bd | Jupyter | 15,755 | 469 | # %%
import sys
sys.path.append('..')
import os
from os.path import join
import numpy as np
import pandas as pd
import connattractor.network as network
import connattractor.analysis as analysis
from joblib import Parallel, delayed
import pickle
import matplotlib.pyplot as plt
from myst_nb import glue
import seaborn as... |
5206fbffc79d99b08e8af06a34137486223164bf3769bee80e7614d84d458fa4 | Jupyter | 15,762 | 431 | # %% [markdown]
# # Surface Data Preparation
#
# Extract session-z-scored, rep-averaged surface betas for ALL 1000 shared images,
# all 4 ROIs (V1, V2, V4, IT), all 8 subjects.
#
# **Output:** Per-region xarray DataArrays saved as netCDF.
# - `Allen2022_surface.V1.nc`, `Allen2022_surface.V2.nc`, etc.
# - Contains 100... |
9302f810573219aceb651fe443363cabb23d676629b0670816da22270b965130 | Jupyter | 15,822 | 279 | # %% [markdown]
# # LaLonde Dataset
#
# Economists have long-hypothesized that training programs could improve the labor market prospects of participants.
# In an attempt to test (or demonstrate) this, the National Supported Work (NSW) Demonstration was initiated using combined private and federal funding. This progra... |
4c39ada13a41123c054b2768260a5664483ebbbad209c97fc426348421968588 | Jupyter | 15,924 | 267 | # %% [markdown]
# # Instructions
# The following code was designed in order to track the location of a single animal across the course of a single video session. After initally loading in the video, the user is able to crop the video frame using a rectangle selection tool. A background reference frame is then specifi... |
97deeb4b8c95ec5b502d3493e1cdbc1098abaa9a229c0060da9e7f236e22c8d9 | Jupyter | 15,943 | 371 | # %% [markdown]
# We create a pickle file fom csv files
# - dict of dataframes
# %%
import pandas as pd
import os
import numpy as np
import copy
from voxcell import RegionMap
import pickle
# %%
import sys
sys.path.append('/gpfs/bbp.cscs.ch/data/project/proj84/csaba/aibs_10x_mouse_wholebrain/notebooks/scripts/')
fro... |
8b8b53a3c7f0eaf969594fbf6575d3cd85b3b3345503a46f25914919683f9a47 | Jupyter | 15,989 | 463 | # %% [markdown]
# # Introduction to TF2.4_IVIM-MRI_CodeCollection github and IVIM Analysis using Python
# %% [markdown]
# # Installation
#
# First, download the TF2.4_IVIM-MRI_CodeCollection github as zip-folder by clicking on Code > Download ZIP:
#
# 
#
# If you haven't done s... |
ce5f591e8447d2773aa7064011ffef0c9f0be0d20ffc73a6645e9f9177dd6d98 | Jupyter | 16,003 | 293 | # %% [markdown]
# ## X-learner
# X-learner is a Meta-algorithm by [künzel et al. (2018)](https://www.pnas.org/content/116/10/4156)
# that estimates heterogeneous treatment effect using five base-learners. Two are used for estimating the outcome (one for the treated and one for the non treated) Additional two are used ... |
ebb4742b1fb15aaf61430afe3c7e3b1f0d007d62bfa0c8d931ecf11b172e7db8 | Jupyter | 16,012 | 617 | # %% [markdown]
# Parse lentiMPRA data into format for modeling.
#
# Add the replicate-level standard deviation (aleatoric uncertainty) to this file, thereby removing samples with no standard deviation measurement.
#
# [Data source](https://www.biorxiv.org/content/10.1101/2023.03.05.531189v1.supplementary-material)
#... |
70f67224fa10a58098464a81741909ea097f4e0cf1328a933b87e242820ce75a | Jupyter | 16,037 | 376 | # %% [markdown]
# ### Imports
# %%
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from Utilities_and_Loading import *
from Global_parameters import *
from Plotting import *
from Preprocessing import *
from General import *
from Manifolds_analysis import *
fr... |
f43bf468e37197a77242b79f4b612190709f4f7e10880ef7b21a65c918779ff3 | Jupyter | 16,040 | 480 | # %%
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch import optim
import torchvision
from matplotlib import pyplot as plt
import angular_spectrum_tensor_v1 as AST
import cv2
import os
import pandas as pd
from torchvision.io import read_image
from torch.utils.data imp... |
27cf1b22f4e4092886d1ac5a6f811126eedf854755629b317c967d4da9008948 | Jupyter | 16,062 | 480 | # %%
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch import optim
import torchvision
from matplotlib import pyplot as plt
import angular_spectrum_tensor_v1 as AST
import cv2
import os
import pandas as pd
from torchvision.io import read_image
from torch.utils.data imp... |
3a041cbcc1fdb0f55f764207140fea6e9ae9fbd4044ef7e44084bb55b0bc60f6 | Jupyter | 16,063 | 532 | # %% [markdown]
# ## Importing packages and data
# %%
#!/usr/bin/env python3
# -*- coding: utf-8
import copy
import os
import sys
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scanpy as sc
import scvi
import torch
from scvi.model import SCVI, CondSCVI
from sklearn.cluster import KMean... |
4436f4b84d2b61be5422f7e98ce4688db7332873b2c545dea972abd45f403525 | Jupyter | 16,079 | 372 | # %%
library(tidyverse)
library(ggplot2)
library(ggthemes)
library(patchwork)
library(ggsci)
library(ggvenn)
library(ComplexHeatmap)
library(RColorBrewer)
library(ggbeeswarm)
library(tidyr)
library(circlize)
library(fastcluster)
library(ggrepel)
# %%
setwd("/data/wuqinhua/scPhase/CRC")
# %% [markdown]
# ## 1. Attribu... |
e9d2f7f7081b25105ef2a9d33ee197e6c5536e7b629721acbef65f61106dcd45 | Jupyter | 16,093 | 485 | # %%
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch import optim
import torchvision
from matplotlib import pyplot as plt
import angular_spectrum_tensor_v1 as AST
import cv2
import os
import pandas as pd
from torchvision.io import read_image
from torch.utils.data imp... |
8e8231a9ba934ef7d40eaf0007c294a8a499025174043e70c9ed915d2791d449 | Jupyter | 16,181 | 489 | # %% [markdown]
# # Colab Notebook for SAM2 segmentation
#
# Author: Dr.Yusuke Kimata (ORCID: https://orcid.org/0000-0002-1366-0636)<br>
# ref: Notebook by Meta Research (https://github.com/facebookresearch/sam2/blob/main/notebooks/)<br>
#
# ### **Revision for flexible reference frame selection (2025/09/22)**
# For i... |
a7d025676576ecd13605532e395a9c1000bbd1bf3f49ec803534626a28e8023c | Jupyter | 16,241 | 414 | # %% [markdown]
# # Positivity filtering
#
# This Notebooks presents several models that perform overlap exclusion
# %% [markdown]
# # Trimming Positivity Checker
# %%
from causallib.positivity import Trimming
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
import... |
5cb0a477c64fd6ecce2fedce42b484221aaa372c89ae7bec577aac2bee2b7191 | Jupyter | 16,269 | 408 | # %% [markdown]
# # Slow-waves detection
#
# This notebook demonstrates how to use YASA to automatically detect slow waves on single-channel EEG.
#
# Please make sure to install the latest version of YASA first by typing the following line in your terminal or command prompt:
#
# `pip install --upgrade yasa`
# %%
im... |
47ced40e936def41702457fe31836488868f4c8ddd03a7f71e9b024be00c3262 | Jupyter | 16,288 | 370 | # %%
library(tidyverse)
library(ggplot2)
library(ggthemes)
library(patchwork)
library(ggsci)
library(ggvenn)
library(ComplexHeatmap)
library(RColorBrewer)
library(ggbeeswarm)
library(tidyr)
library(circlize)
library(fastcluster)
library(ggrepel)
# %%
setwd("/data/wuqinhua/scPhase/Lung")
# %% [markdown]
# ## 1. Attrib... |
2524e6eff1d64b64f85801d30d58566576b2a696eeac64899a4a6c53c0e1636b | Jupyter | 16,335 | 377 | # %% [markdown]
# # Notebook 2: Position Interneurons
#
# ### General Overview
#
# Two position integrators (INs) are added to each joint, with the function of integrating all spikes originating from a given hair field into a position-dependent spike train. These spike trains are able to encode the varying joint angl... |
3393c2f76d11e1bad393a034fe9d393f25e990ea9abc3fdea67605d906726b40 | Jupyter | 16,378 | 389 | # %% [markdown]
# # Speckle Analysis Script
# %% [markdown]
# ## Script Parameters
# %%
output_path = 'C:/Users/u0119446/Desktop/Temp/Ongoing_projects/Laura/Analysis'
qupath_measurements = 'C:/Users/u0119446/Desktop/Temp/Ongoing_projects/Laura/measurements_detections.csv'
description_file = 'C:/Users/u0119446/Desktop... |
2de46944d31bceb1a568d02c9eb4678e648d3efb98c38f626a56f034a8e9af3b | Jupyter | 16,404 | 524 | # %%
import math
from collections import OrderedDict
import torch
from torch import nn
from torchvision import transforms
from torchmetrics.functional import pearson_corrcoef, spearman_corrcoef
import torch.utils.model_zoo
import os
from PIL import Image
from torch.utils.data import Dataset, DataLoader
import pandas as... |
c4f74c7453f231bf83a199878c87ae0438f00fcb4b1afd8ba58993f64767c323 | Jupyter | 16,520 | 463 | # %%
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch import optim
import torchvision
from matplotlib import pyplot as plt
import angular_spectrum_tensor_v1 as AST
import cv2
import os
import pandas as pd
from torchvision.io import read_image
from torch.utils.data imp... |
b02db05d543d43a62adacf24673daa94c57ccb0e68d74e929d44c0b1e3923bc5 | Jupyter | 16,612 | 464 | # %%
import numpy as np
import matplotlib.pyplot as plt
from pyDOE import lhs
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# %%
# -----------------------------
# Geometry Parameters (scaled)
# -----------------------------
L = 2.8 / 0.24
d = 0.2 / 0.24
l = 0.3 / 0.24
h = 0.2 / 0.24
L1 = 0.8... |
ee795ccda9a3efc09319246957cfc74de78a287d20060889241040559e61a23b | Jupyter | 16,624 | 467 | # %%
import os
import numpy as np
import pandas as pd
import json
# import seaborn as sns
from matplotlib import pyplot as plt
import pickle
# import tqdm.notebook as tq
from pcntoolkit.normative import predict, evaluate
from utils.training.nm_utils import load_2d
from pcntoolkit.util.utils import create_design_matrix... |
936a2b148c50225a327636fd58c5fcbf9a769549e4700af548d2c52c2d991a7d | Jupyter | 16,655 | 368 | # %% [markdown]
# # Chapter 13: DSC/DCE MRI ANALYSIS
# %% [markdown]
# ### This is "worked example" walking through an example pipeline (as illustrated in Fig. 2) for DSC/DCE MRI analysis of a dual echo EPI acquisition.
# %% [markdown]
# ## 1. Import all the necessary packages
# %%
import os
import numpy as np
impo... |
e6e07958b04ec256ba23cd61494d2b5c8182ec88cd841b4cfe8d776f73726341 | Jupyter | 16,705 | 517 | # %% [markdown]
# # Score SNPs
#
# Use trained models to score SNPs.
# %%
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pyfaidx
from collections import Counter, defaultdict, OrderedDict
from modisco.visualization import viz_sequence
import logomaker
from copy im... |
e1d3c1daf36e19ca37e577a58d80e2010dc26507b0bdb45e6d8697d7bd24054f | Jupyter | 16,762 | 534 | # %%
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import liana as li
import scanpy as sc
from liana.method import singlecellsignalr, connectome, cellphonedb, natmi, logfc, cellchat, geometric_mean
matplotlib.rcParams['pdf.fonttype'] = 42
# %%
# prepare the scanpy object corr... |
07c4399e7c0901ff6111219a8c243899bd3fe9f32781b6f32e6305f572abf423 | Jupyter | 16,774 | 768 | # %% [markdown]
# ## Import Packages
# %%
import os
import yaml
import math
import pandas as pd
import numpy as np
import scanpy as sc
import scipy.io
import humanfriendly
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker
import seaborn as sns
%matplotlib inline
%config InlineBackend.figure_f... |
e05b67c2c2920e1d8d0b2e9d518f08467dadafc65ac23fce71c82a9eca6a9bdf | Jupyter | 16,890 | 432 | # %%
library(ArchR)
library(BSgenome.Hsapiens.UCSC.hg38)
library(dplyr)
library(igraph)
library(glue)
source('../utils/utils.r')
addArchRThreads(threads = 16)
addArchRGenome("hg38")
set.seed(1)
# %%
projAUD <- loadArchRProject(path = "/gpfs/gibbs/pi/girgenti/JZhang/CL/multiome/data_AUD/fig1-UMAP/CNA-ATAC/", # /CNA-AT... |
c6745cacff24796bdc25796d53172a5bf957db79c6ad0204e75286ad05d4239d | Jupyter | 16,897 | 596 | # %%
import warnings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.formula.api as smf
warnings.filterwarnings("ignore")
# %%
# loading and preprocess time series data for COHA
def load_ts_df(path: str) -> pd.DataFrame:
df = pd.read_csv(path)
d... |
8dc48accc99dad3ca66e21959e983a3d708a4bfe42977eb85a7ddc7da9143867 | Jupyter | 16,899 | 774 | # %% [markdown]
# ## Import Packages
# %%
import os
import yaml
import math
import pandas as pd
import numpy as np
import scanpy as sc
import scipy.io
import humanfriendly
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker
import seaborn as sns
%matplotlib inline
%config InlineBackend.figure_f... |
fb8b570fb039599bffb4b8dff7ae5ec41bc2d77e74cc303591a99e92904d6189 | Jupyter | 16,913 | 459 | # %%
from pegasusio import UnimodalData, MultimodalData
import matplotlib.pyplot as plt
import pegasus as pg
import pandas as pd
import glob
import os
import itertools
import re
import sys
sys.path.append('../utils/')
from utils import *
from scipy.stats import mannwhitneyu
# %%
data = pg.read_input('/gpfs/gibbs/pi/g... |
b0d624f98146049c5a9994e0f42d0f14667e2345d3d9276ecd9e0b72b28ec877 | Jupyter | 16,934 | 500 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import precision_score, recall_score
from sklearn.metrics import mean_absolute_error, mean_squared_erro... |
3fe901f47916f1cd022531ac8083a7c6ff8f9d8df5d967999de8ebe28530e114 | Jupyter | 16,966 | 576 | # %%
%matplotlib notebook
import sys
import os
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import cartopy.crs as ccrs
import numpy as np
import obspy as obs
from obspy.signal.invsim import corn_freq_2_paz
import h5py
import pandas as pd
from math import sqrt
from scipy.spatial import Del... |
a57424a29178e9c231b1c5a11f4c90f11e0143b7a887bc363c554e86bf423381 | Jupyter | 17,027 | 478 | # %% [markdown]
# ## Figure 6F
# %%
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib as mpl
plt.style.use("article.mplstyle")
import mne
from statannotations.Annotator import Annotator
import itertools
import glob
import os
import sys
from scipy.io impo... |
5e0a51c8acb09a7d82c2b231e297c145d415ff706c7aa6edccd2cd1fe20566e0 | Jupyter | 17,039 | 431 | # %%
import torch
import torchvision
import torchvision.transforms as transforms
# %%
transform = transforms.Compose(
[torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
(0.1307,), (0.3081,))])
batch_size = 6000
trainset ... |
fd22f0f682b8417790bfddd8c942aa603305143285e3640f2e71a8ba9e9a317e | Jupyter | 17,055 | 529 | # %% [markdown]
# # Plotting the predictions of the single- and multi-center RPN signatures
# - This jupyter notebook is available on-line at:
# - https://github.com/spisakt/RPN-signature/blob/master/notebooks/3_compare_predictions.ipynb
# - Input data for the notebook and non-standard code (PAINTeR library) is avail... |
3acc3bc232d85fa9280aa2c5ecc05b8d2bd594384c26296e103ef306bfb78cf7 | Jupyter | 17,071 | 696 | # %%
#python resample.py $IMAGE_TO_RESAMPLE $REFERENCE_IMAGE
# %%
import nibabel as nib
from nilearn.input_data import NiftiMasker , MultiNiftiMasker
# %%
import numpy as np
import os
import scipy.io
import nibabel as nib
from nilearn.input_data import NiftiMasker
from nilearn.masking import compute_epi_mask
from s... |
db767b723991f2b7ac977ee3cfaec5fee81d12d706ab359fd431eb95cd5e3615 | Jupyter | 17,161 | 422 | # %% [markdown]
# ### Imports
# %%
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from Utilities_and_Loading import *
from Global_parameters import *
from Plotting import *
from Preprocessing import *
from General import *
from Manifolds_analysis import *
fr... |
012ebb1ee9ff0fc510000a9195e4487c1f1e46e0b040cb2f86b040b13f0a7901 | Jupyter | 17,194 | 467 | # %% [markdown]
# <h1><center>SpaGCN Tutorial</center></h1>
#
#
# <center>Author: Jian Hu,*, Xiangjie Li, Kyle Coleman, Amelia Schroeder, Nan Ma, David J. Irwin, Edward B. Lee, Russell T. Shinohara, Mingyao Li*
# %% [markdown]
# ### Outline
# 1. Installation
# 2. Import modules
# 3. Read in data
# 4. Integrate gene ... |
ab7d60efa96503afb5d8060f7a7f56c4004e077a04525f501807439650b365e6 | Jupyter | 17,210 | 450 | # %% [markdown]
# ### Imports
# %%
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from Utilities_and_Loading import *
from Global_parameters import *
from Plotting import *
from Preprocessing import *
from General import *
from Manifolds_analysis import *
fr... |
ef065c643cb699a439d0ee6cd095451f441823908b89357ef35ddcc8f788954b | Jupyter | 17,212 | 593 | # %% [markdown]
# # Compare Timings of MCML and Surrogate Model
# %%
import os
import pickle
import time
import warnings
import lightning as pl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly
import plotly.graph_objects as go
import torch
from omegaconf import DictConfig
from plot... |
ccaa20171738cb05169064be088e75e6b135ee5acb0c58e7fa1e3fdc3447cc4e | Jupyter | 17,217 | 392 | # %%
library(tidyverse)
library(ggplot2)
library(ggthemes)
library(patchwork)
library(ggsci)
library(ggvenn)
library(ComplexHeatmap)
library(RColorBrewer)
library(ggbeeswarm)
library(tidyr)
library(circlize)
library(fastcluster)
library(ggrepel)
# %%
setwd("/data/wuqinhua/scPhase/COVID")
# %% [markdown]
# ## 1. Attri... |
1decd7f2e0cfe65275ae2b98bc7a372da37d5ca7cbe4727f79f8c07b2d084a21 | Jupyter | 17,245 | 480 | # %%
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
# from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
# from mpl_toolkits.axes_grid... |
76c6b722a0c3ead844857f872257ce77d608bd7283ed833ea5e826e445ac7728 | Jupyter | 17,276 | 408 | # %%
import datajoint as dj
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from tqdm import tqdm
import scipy
from neuro_data.static_images import data_schemas
from staticnet_analyses import base
from staticnet_invariance import deis_reconfigured as deis_schema, dei_stimulu... |
4344f5a97c2e7494bc7b46aa0b5c3bbc76f9118b4f4a299b383fc6d5480b51fe | Jupyter | 17,283 | 504 | # %% [markdown]
# # HarveST Complete Analysis: Clustering + Spatial Gene Discovery
#
# This notebook demonstrates a complete spatial transcriptomics analysis workflow using HarveST:
# 1. **Clustering Analysis**: Identify spatial domains using graph neural networks
# 2. **Spatial Gene Discovery**: Find domain-specific ... |
9cf75ab39655bb4ee3499d64e006026817acfd4e84af42e94da800b00618fbcf | Jupyter | 17,321 | 421 | # %% [markdown]
# ## Code for parameters grid:
# Here is reported the code for obtaining the grid of digital biomarkers (in this case we use resting-state PSD and FC quantitites) for various combinations of model biomarkers:
#
# References:
#
# [1] L. G. Amato, A. A. Vergani, M. Lassi, C. Fabbiani, S. Mazzeo, R. Bur... |
8f747d2de3a5b089639ff355fb927aef2e97547055e8b4fc160c5143547dadca | Jupyter | 17,341 | 527 | # %% [markdown]
# # Tabula Data Loader
#
# This file contains instructions for creating scanpy anndata versions of:
#
#
# |Dataset|Paper|
# |-------|-----|
# |Tabula Sapiens|https://www.science.org/stoken/author-tokens/ST-495/full|
# |Tabula Microcebus|https://www.biorxiv.org/content/10.1101/2021.12.12.46946... |
4a04d25190096d56897a288f35b7e300909c252bf74cd9964e344a4544946fb9 | Jupyter | 17,367 | 443 | # %% [markdown]
# ### Imports
# %%
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from Utilities_and_Loading import *
from Global_parameters import *
from Plotting import *
from Preprocessing import *
from General import *
from Manifolds_analysis import *
fr... |
e35667979660368cf16d1a805669acd68caff76a79e484c8c17bb4259ab5484b | Jupyter | 17,367 | 505 | # %%
import os
import json
import pandas as pd
import numpy as np
from statsmodels.formula.api import mixedlm
from sklearn.preprocessing import StandardScaler
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.api as sm
import statsmodels.formula.api as smf
import scipy.stats as stats
from statsm... |
b2381f7b7d39d0723cdc6bc2c45dcbe5a810e70d61d1828dee04282a920049db | Jupyter | 17,407 | 613 | # %%
import pickle
import warnings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
warnings.filterwarnings("ignore")
from scipy.interpolate import UnivariateSpline
# %%
from nltk import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
from ast import literal_eval
# %%... |
2a05009a77408150b9ee394f956b560c022b6f94a1fb6aaf3abe184c17825dad | Jupyter | 17,490 | 571 | # %% [markdown]
# # 02-demographics-and-clinical-characteristics.ipynb
#
# Using `ibs-brain` conda environment
#
# Arvid Lundervold
#
# Updated: 2025-02-01; 2025-02-10
#
#
# Reporting demographics and clinical characteristics of the IBS-Brain cohort `demographics_fs7_rbans_78x48.csv`
#
# Saving to `latex/tables/d... |
f525df4c66e8766125c7dd89b611563ca1cdc51a25325cbf8f10542115ab8591 | Jupyter | 17,558 | 505 | # %% [markdown]
# # Initial Screening: Objectives, Inputs, Outputs, Method, and Rationale
#
# **Objective**
# - Perform comprehensive model screening across multiple machine learning algorithms to identify top-performing candidates for each brain region (ROI).
# - Generate reproducible, publication-ready accuracy comp... |
cadfec6ca16dd9ffc47ff6ad1c09c7cba5cfc1f95dbc7dcdc0bf23b8b1bfbe5a | Jupyter | 17,560 | 564 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from itertools import product
# %%
df = pd.read_csv("/Users/chris/projects/Nature2023MooneyScripts/results/learning_comparison.csv")
# %%
df.head()
# %% [markdown]
# ## Rank human performance
# %%
human_df = df[df["So... |
ce85adafe17a75b64434bf9f6cf8fb2dcdd984d9c70850a482e2e7976620b6e2 | Jupyter | 17,632 | 464 | # %% [markdown]
# ## READme:
#
# Jupyter Notebook & train data for recreating the normative charts from [Dimitrova et al., (2020)](https://www.biorxiv.org/content/10.1101/2020.08.05.228700v1.abstract). This notebook will allow the user to:
#
# - run Gaussian Process Regression (GPR) on the normative volumetric data p... |
3617a4f3685fae0dc5b9c091e8d274080afe236a49fcd83ccf3a7398b976fb02 | Jupyter | 17,689 | 367 | # %%
from infopath.model_loader import load_model_and_optimizer
from infopath.config import load_training_opt
import matplotlib.pyplot as plt
import torch
import numpy as np
from infopath.utils.functions import load_data, opto, run_with_perturbation, pear_corr, feature_pop_avg, mse_2d
from infopath.losses import noise... |
bd5c01908f192d53eb6d094d5f3c3ba4f5d98f43d62b8f48fe8507593bf14ab7 | Jupyter | 17,738 | 263 | # %% [markdown]
# <div align="center">
# <a href="https://www.ultralytics.com/yolo/yolo27?utm_source=github&utm_medium=social&utm_campaign=yolo27-launch-2026&utm_content=banner" target="_blank">
# <img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png">
# </a>
... |
74742d8642d484958009500cc25897d0175d509dccac06d8efe0f7556414b651 | Jupyter | 17,769 | 626 | # %%
import warnings
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
warnings.filterwarnings("ignore")
# %%
ts_df = pd.read_csv("./data/SWOW_prediction/eval/time_series/nyt_ts_df.csv")
ts_df = (
ts_df.groupby(["words", "year", "property"])[["outputs_z", "count"]]
.mean()
.reset... |
3bfacc8f5601e40fba1447348c7134b0624bd86044323625c260712af4945998 | Jupyter | 17,913 | 423 | # %%
import os
import numpy as np
import pandas as pd
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from skimage.morphology import remove_small_objects
from scipy.spatial.distance import directed_hausdorff
from sklearn.metrics import roc_curve, auc
import matplotlib
import matplotlib.pyplo... |
c65111307e6d1abd33176d2750dc6e20fee19e9de641609e7fccbc556d886568 | Jupyter | 18,042 | 538 | # %% [markdown]
# Parse CAGI5 data to get WT and mutant sequences for zero-shot prediction with ResidualBind models.
#
# Includes code to identify and add in missing alternate alleles.
# %%
import numpy as np
import os, h5py
import pandas as pd
from collections import defaultdict
import glob
import subprocess
# %% [... |
9681a18039a6dc7f5117fcea3c1e558babe3159f7e7473780e72ba5d3e05a92a | Jupyter | 18,160 | 524 | # %%
import brainpy as bp
import brainpy.math as bm
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import scipy
from scipy.signal import hilbert
import sys
sys.path.append('..')
from cann import CANN1D
import warnings
warnings.filterwarnings("ignore")
#set backend to cpu
b... |
14664e342d20254687c42cd96924fa56735221270420ba04638997c53ca601bb | Jupyter | 18,261 | 537 | # %%
# Depricated from Google Colab as of 2024-12-01
# !sudo apt install python3.9-full python3-pip
# !sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
# !pip uninstall -y scikit-learn scipy pyparsing -y
# !pip install Cython==0.29.36 scipy==1.9 pyparsing==2.4
# !pip install https://fi... |
7f4cdfeffbf3c13ec311ad4ba2335d5cc2f8d9638a24ebce274799ab7efecb52 | Jupyter | 18,369 | 533 | # %% [markdown]
# # Likelihood Stats
# Kai Sandbrink
#
# 2024-09-30
#
# This script aggregates the likelihood tests for both task 1 and task 2. Note that it requires re-running the networks on the human data to determine the likelihood of the trajectories.
# %% [markdown]
# ## Task 1
# ### Likelihood estimation: NOT... |
cad53077c4865e0f871785acb1e1fab890c02ba9cf8a33561b1c04c4630ab8db | Jupyter | 18,382 | 496 | # %% [markdown]
# <a href="https://colab.research.google.com/github/MRS-NIH/DeepJPRESS/blob/main/deepJPRESS_demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# Click **Run all** to reproduce in vivo test results.
# This notebook w... |
1b22274c61f4ad952fca837fe0e8a5c91770317ac6667190dc0671e580205578 | Jupyter | 18,408 | 479 | # %% [markdown]
# # Model comparison
#
# So how do we know if a model is good? The PCNtoolkit provides us with some useful statistics for fitted models, but those aren't always as unequivocal as we would like. Luckily, for HBR models, there is a sound way to rank models, based on the ELPD. This technique allows us to ... |
06ec8dca19a6e1d9c96ad2a2604cce1f765ceb2d876c68df0c41863547d41724 | Jupyter | 18,415 | 497 | # %%
import scanpy as sc, anndata as ad, numpy as np, pandas as pd
from scipy import sparse
import matplotlib.pyplot as plt
import plotly.express as px
from anndata import AnnData
from sklearn.neighbors import KernelDensity
import random
from matplotlib import pylab
from matplotlib.colors import TwoSlopeNorm
import wa... |
ce760f23a923c2a0dc2ef0aa0160c8f97f139b0518992cb9b4bfd67c61c8a55c | Jupyter | 18,437 | 669 | # %% [markdown]
# # Figure 4
# %%
#Import relevant packages
import numpy as np
import pandas as pd
from matplotlib import rcParams
import os
import scanpy as sc
import matplotlib as mpl
import matplotlib.pyplot as plt
import cmocean
import seaborn as sns
from scipy.stats import median_abs_deviation
import anndata... |
5913a721d0bf78c573a04008fb35b7e46a537289b538b1b44c71a840d3c822e8 | Jupyter | 18,483 | 358 | # %% [markdown]
# # Spindles detection
#
# This notebook demonstrates how to use YASA to perform **single-channel sleep spindles detection**. It also shows a step-by-step description of the detection algorithm.
#
# Please make sure to install the latest version of YASA first by typing the following line in your termi... |
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