sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
d64bb7b83dce42b3cfa3a7ff3f2bd2a9d9ec9281559679fd260610aee263fd03 | Jupyter | 5,274 | 184 | # %%
import mido
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.markers import MarkerStyle
import pandas as pd
# %%
#plot params
axis_fontsize = 20
xtick_fontsize = 15
marker = MarkerStyle('s')
marker._transform.scale(0.1, 2) # Scale x by 0.5 (narrower), y by 2 (taller)
# %%
def onsets_pitch_fr... |
22a14ca54253d21285eae9064e45c5766ca9b321656aaf26f25bfc53b8822569 | Jupyter | 5,275 | 169 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/Prot-Transformers/blob/master/Embedding/Advanced/Electra.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3> Extracting protein sequences' features us... |
8893540e1098cfb5a03d630e57782d77d02e06e98091786d30b87a19c7837a7b | Jupyter | 5,276 | 129 | # %% [markdown]
# # EI network - Dend Target Prop (LDS)
# - **Linear synaptic weight update (LDS)**
# - **Top-down weight symmetry**
# %% [markdown]
# Here we will show the analysis for a single example seed of a network described in [Galloni et al. 2025.](https://www.biorxiv.org/content/10.1101/2025.05.22.655599... |
afa336335e3172536390d44f3c35565cd02cb0b29a62807cdc787c576ae912e2 | Jupyter | 5,276 | 214 | # %%
"""
Random Forest Model for Predicting Flexural Strength of Gypsum Composites
Author: Haseeb Ahmad
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics impo... |
84e6adec9ef18a1add3c52b40503efb5f1a76ea50aee0c2adda0fd21d6e6e116 | Jupyter | 5,282 | 169 | # %% [markdown]
# # Camera Models
#
# Everything starts with the cameras. Given a set of images, we need a way to project their pixels into 3D space. This is accomplished by computing the camera ray for each pixel given information about the type of camera and the location of the camera. This guide describes the camer... |
42b15f71dce4a6615a43b6021d6b4864ffe6409ed9f592c2924b1d1d117a1d61 | Jupyter | 5,290 | 199 | # %% [markdown]
# # Gallery
# %%
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import yasa
# Download the EEG data and hypnogram
eeg_filepath = yasa.fetch_sample("full_6hrs_100Hz_Cz+Fz+Pz.npz")
hypno_filepath = yasa.fetch_sample("full_6hrs_100Hz_hypno_30s.txt")
# Load E... |
723ef1aceb613bf8297ece39d2f4666c86f96c62b49c5d08a0e391aefa627d60 | Jupyter | 5,299 | 173 | # %%
import os
import json
import pandas as pd
import numpy as np
from joblib import Parallel, delayed
from matplotlib.gridspec import GridSpec
import matplotlib.lines as mlines
import seaborn as sns
import matplotlib.pyplot as plt
from utils.setup_paths_and_configs import setup_paths_and_config
# %%
dataset = 'OASIS... |
8eb3ab0496f21da0391ce6eb791e95d8e739744ee3541335e7bab2c95992dde3 | Jupyter | 5,302 | 186 | # %%
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import pearsonr
filename_pattern = 's669_DDGjanus{variant}_TRANSITIVITY_N{n}.csv'
variants = ['BASE', 'BASEANTY', '_FINALE']
steps = [1, 3, 5, 9]
name_mapping = {
"BASE": "JanusDDG Base",
... |
d81b099769a52135c2df2bb7752d0b96092ec356c8632933e6d580bce4430101 | Jupyter | 5,314 | 198 | # %%
import cebra
from cebra import CEBRA
import cebra.models
import os
from scipy import stats
import tempfile
from pathlib import Path
import torch
from torch import nn
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib
from matplotlib.colors import rgb2hex
import pickle as ... |
3314d418328e9c9f61fe120327abca294a688a797393b5d701c60d0c64ad04ae | Jupyter | 5,332 | 275 | # %%
import os
import yaml
import pandas as pd
import numpy as np
import scipy.io
import scanpy as sc
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
%config InlineBackend.figure_format = "retina"
# %% [markdown]
# ## Parameters
# %%
# this cell is tagged `parameters` and... |
8f20a2df3e015126674893609ab5a669bfdce9836a7322e3431e05378fe9e693 | Jupyter | 5,340 | 151 | # %% [markdown]
# # PRS for WGS data
# %%
#set up Hail requirements on instance
import os
os.system('wget https://github.com/adoptium/temurin21-binaries/releases/download/jdk-21.0.4%2B7/OpenJDK21U-jre_x64_linux_hotspot_21.0.4_7.tar.gz')
!tar -xvzf OpenJDK21U-jre_x64_linux_hotspot_21.0.4_7.tar.gz
!pip install pyspark
#... |
4fa453d2a9fe301a11c6ee7be56a441dfe3bd2f77683c8a68247ba12d749106f | Jupyter | 5,348 | 176 | # %%
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import warnings
warnings.filterwarnings('ignore')
# %% [markdown]
# ## Explore correlations among HCN channel genes, features etc in M. Nemestrina
# %%
##L23 data
data_df = pd.read_csv('../Data/Supplementary_Data2.csv')
... |
97477d606294a4abc5211bff641c74bedab38984b884a72823ebf5eb8a5abea0 | Jupyter | 5,367 | 169 | # %%
# import library
import numpy as np
import pandas as pd
pd.set_option("display.max_columns", 1000)
import matplotlib
from matplotlib import rcParams
rcParams["font.family"] = "Arial"
import matplotlib.pyplot as plt
from adjustText import adjust_text
import seaborn as sns
# %% [markdown]
# # Load data
# %%
DEG_df... |
bba369c28eb1e75e53dbeaf3c2fee11c662ac68ab514d300a0d87543d3c6fb9d | Jupyter | 5,368 | 174 | # %%
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
from scipy.stats import pearsonr
warnings.filterwarnings("ignore")
# %% [markdown]
# ## This notebook studies the relationship between frequency and moral relevance ... |
e533bb55bf63af983512fd4a5c68c46bd4fde02c8bb28490b4eab6f4017446dd | Jupyter | 5,374 | 157 | # %% [markdown]
# <a href="https://colab.research.google.com/github/CPernet/OpenNeuro_MetaPrivAssessment/blob/main/TheDallasLifespanBrainStudy.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Link to the repo and import local data... |
a87f082e273cbe90500ec2731350fcb150895583c1828ab7555a9fa129d3da8c | Jupyter | 5,402 | 171 | # %% [markdown]
# # Utilities
# %% [markdown]
# ### Metrics calculation
# %% [markdown]
# 1. Set dataset path and scale applied to spines during visualization.
# %%
from notebook_widgets import SpineMeshDataset
# set dataset path and scale applied to spines during visualization
dataset_path = "example dataset"
scal... |
f0ed69dc2ed7df4e5f20c6d89bdd3a29c1c6e41bbc80b79e8c6c3a81575b4af9 | Jupyter | 5,415 | 210 | # %%
import os, json
import pandas as pd
from Bio import SeqIO
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import f1_score
# %%
csv_folder = "../data/testset"
fasta_folder = "../data/raw_data"
output_path = "../test/results/analysis-by-species/species_mapping.csv"
r... |
ea55b6eb12286a0639e2e4064e9ac12de0c9a88fed3d1b8d36cb6a0f66f984f0 | Jupyter | 5,442 | 179 | # %%
from tomocupy_stream import GPURecRAM
import numpy as np
import tifffile
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
import matplotlib.pyplot as plt
%matplotlib inline
# %% [markdown]
# # Download data with the direct link
# %%
!wget -nc https://anl.box.com/s... |
d406353d97166b83cbf947d7ba9c11b04105c26a98486e621f552bf246e8ecc2 | Jupyter | 5,446 | 139 | # %% [markdown]
# # Multi-channel slow-wave detection
#
# This notebook demonstrates how to use YASA to perform **multi-channel slow-waves detection** from a NumPy array (example 1) or a MNE Raw object (example 2). If you haven't done so already, check out the notebook on [single-channel slow-waves detection](05_sw_de... |
4e8a37881ae6d1c0504a5dd78249d5d8ce60cbe355246567a93706aa7105c2c5 | Jupyter | 5,450 | 180 | # %% [markdown]
# # CellDiscoveryNet training notebook
#
# This notebook assumes you have already run the `make_CellDiscoveryNet_input` notebook. It trains a CellDiscoveryNet model on the data generated by that notebook.
# %%
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1" # adjust which GPU you want to use here
... |
85955a328347cb7248a969e6e0b818040167d3f2d010334e7e5ccd819bbe9eee | Jupyter | 5,462 | 144 | # %% [markdown]
# # SpikeTrain Smoothing
# %% [markdown]
# ## Goal:
# - exact convolution for spike density estimation (as opposed to convolution over a discretized, binned, space)
# %% [markdown]
# In particular, given a `SpikeTrainArray` or a collection of events, we want to compute the exact smoothed version by co... |
203bc22795ef1bb4391772e68a7b03e0e95df84bb49d2d2b3cc65a9c08d337df | Jupyter | 5,486 | 168 | # %%
import os
import sys
from pathlib import Path
import anndata as ad
import scanpy as sc
import numpy as np
import warnings
import math
import torch
from GEASO.utils.utils import intersect, feature_extract
from GEASO.alignment.coarse_alignment import coarse_alignment
from GEASO.alignment.elastic_registration import ... |
a0bbdcd179ddef9489ff012b290d2b5db9441700904ced4e521158b5c2e504af | Jupyter | 5,511 | 160 | # %% [markdown]
# <a href="https://colab.research.google.com/github/CPernet/OpenNeuro_MetaPrivAssessment/blob/main/EarlyStressfulExperiencesInChildren.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ## Link to the repo and import l... |
b085d29428c24d33b7c63f33e8692c30067cbc195d925db5882412fa33dc7aaa | Jupyter | 5,515 | 151 | # %% [markdown]
# ## Import distinctipy
# %%
import distinctipy
from distinctipy import colorblind
from distinctipy import examples
import matplotlib.pyplot as plt
# %% [markdown]
# ## Generating Colours
#
# Say you have already used red, green, blue, white and black in your application. What should you use next? D... |
d02c0e1b9ef06158f216cd17f546a2910e6809856019669e885a042d2bfcdb0b | Jupyter | 5,516 | 173 | # %% [markdown]
# # 01-freesurfer-freeview-t1-aseg-bga-046.ipynb
#
# Using `ibs-brain` conda environment
#
# Arvid Lundervold
#
# Last updated: 2024-12-09; 2025-02-10
#
# A total of 85 participants had their brain scaned 1, 2, 3 or 4 times. (/Volumes/LaCie/BrainGut/FS_732/nifti)
#
#
# ### Freeview BGA_046
#
# ... |
5e5bdcf19ad83389e1dba20297d938d9774dbbf7d38034c874c7ca7cf07ca6fc | Jupyter | 5,520 | 175 | # %% [markdown]
# # Tutorial 2: Test on simulated data
# %%
from dlim.model import DLIM
from dlim.dataset import Data_model
from dlim.api import DLIM_API
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import pearsonr, spearmanr
from numpy.random import choice
from src_simulate_data.sim_data im... |
b7fad9ed6db2592093f5f9c02d32e557656f4d9a5af3b02775abe74ad2b6a8db | Jupyter | 5,529 | 154 | # %% [markdown]
# ## Usage
# %% [markdown]
# ### Import distinctipy
# %%
import distinctipy
from distinctipy import colorblind
from distinctipy import examples
import matplotlib.pyplot as plt
# %% [markdown]
# ### Generating Colours
#
# Say you have already used red, green, blue, white and black in your applicatio... |
79d2ae968358264c9bb3dd2b7742128b2cde164b87167c295fb761ced22c7942 | Jupyter | 5,533 | 222 | # %%
"""
Artificial Neural Network (ANN) for Predicting Compressive Strength
Author: Haseeb Ahmad
Description:
This script trains a feedforward ANN model to predict compressive strength
of gypsum-based composites using mix design parameters.
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot... |
8479c0f61d3869ad42034e72f915708819686a005ec68b4d4c6209f28dee8df4 | Jupyter | 5,539 | 129 | # %% [markdown]
# # Behavior Analysis: Binned-Ratio Timeline
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_acc_MiniscopeCap'
# Select group and condition, see meta.py to see how you previously defined it for your project
selected_... |
1866ed8a2fb27b6d972f81463d2602f33b4f31cbc2f4fbd71a1c081774bae757 | Jupyter | 5,542 | 212 | # %%
import os
# %%
import re
# %%
import zipfile
# %%
import numpy as np
# %%
import pandas as pd
# %%
import xarray as xr
# %%
import mkgu
# %%
from mkgu.knownfile import KnownFile as kf
# %%
from mkgu.knownfile import FileRecord, Sighting
# %%
from mkgu.lookup import pwdb
# %%
from mkgu.assemblies import A... |
bf27be4ed6579cc21015e411371b72591e8b01798604498dcdae1fedb3ebaf97 | Jupyter | 5,553 | 164 | # %% [markdown]
# # Reproducibility 5: figure 7
# - Application on protein interaction data
# - Diss, Guillaume, and Ben Lehner. "The genetic landscape of a physical interaction." Elife 7 (2018): e32472.
# %% [markdown]
# ## Import packages
# %%
import sys
sys.path.append('../')
from dlim.model import DLIM
from dli... |
b9a137b6e0c9b81e22c4a75df2c7a0be39878f9ab8b3996f7ff7385eef07da22 | Jupyter | 5,554 | 164 | # %% [markdown]
# # Reproducibility 5: figure 7
# - Application on protein interaction data
# - Diss, Guillaume, and Ben Lehner. "The genetic landscape of a physical interaction." Elife 7 (2018): e32472.
# %% [markdown]
# ## Import packages
# %%
import sys
sys.path.append('../')
from dlim.model import DLIM
from dli... |
07b894d2947c5f4df5231d4f28aa30ce0d7fa27e183c7061c820a6de4a354e74 | Jupyter | 5,563 | 104 | # %% [markdown]
# # Concentration Maps
# This notebook details how to combine the $T_1$-maps from the Look-Locker and Mixed sequences, and to estimate tracer concentration maps from them.
# We will assume that you've already registered the $T_1$-maps from both the [Look-Locker](./looklocker-processing.ipynb) and [Mixed... |
09197fa4a73c9e8994e5482b1ef44ae82221f5f6c4969bc689beccc42c602506 | Jupyter | 5,564 | 150 | # %%
#!/usr/bin/env python
"""Shows effectiveness of post-spike plasticity. This time with non-disjoint input assemblies."""
import os
import time
import numpy as np
from core import core_global as core
from core.spike_monitor import SpikeMonitor
from core.voltage_monitor import VoltageMonitor
from core.weight_matri... |
01906608cfc635776d444d7289267d102b0f74beca262082ea4918eab8346171 | Jupyter | 5,570 | 166 | # %% [markdown]
# # Load data from the allen and plot reference structures
#
# The first cell takes a while. It needs to download data for the allen common reference framework and it takes a while to actually isolate the contours for each reference.
#
# The references are in milimeters and in reference to a reference... |
cb8f4238a689c88438d71f6074409cf64a1d003f03db6a0f9bf3b6b24b6945b2 | Jupyter | 5,579 | 161 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/Prot-Transformers/blob/master/Benchmark/ProtElectra.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <b>Benchmark ProtElectra Model using GPU or CPU</b>... |
fca4fb1587e01cc50b1dda4ec85c9d5d9e1e33d05cf846a7d35d3e9cd20a8429 | Jupyter | 5,588 | 171 | # %% [markdown]
# Imports
# %%
import numpy as np
import openpyxl
import pandas as pd
from openpyxl import load_workbook, Workbook
from openpyxl.styles import PatternFill
from openpyxl.styles import Font
from scipy.stats import spearmanr
import matplotlib.pyplot as plt
import scipy
from google.colab import files
from ... |
e2b699797d7b42dee35de1f8e4bedd4ede0195e2b6aecb5dfa2f52075901231e | Jupyter | 5,600 | 170 | # %%
import glob
import os
from typing import List
import numpy as np
import pandas as pd
from visualization_util import *
import matplotlib.pyplot as plt
COLORS = ["#DD7739", "#3371AD", "#000000"]
COLORS = [color_orange, color_blue, "#101010"]
OUTPUT_DIR_EVALUATION = "_figures"
os.makedirs(OUTPUT_DIR_EVALUATION, ... |
a7e29c729ac3942120c4c8ca35fa34ec33a383c4ab394c87ad00b6cb104749a1 | Jupyter | 5,601 | 132 | # %% [markdown]
# # Behavior Analysis: Binned-Ratio Timeline
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MiniscopeSNI_3WeekMorphine'
# Select group and condition, see meta.py to see how you previously defined it for your pro... |
c09dabfa5a8214650aefbd6ae58f233a791274627ff77d93dc314fedbe91933f | Jupyter | 5,619 | 201 | # %%
from pathlib import Path
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# %%
import re
# %%
p = Path("/Users/xujinliu/projects/cluster_results/behavior_predictions")
# Iterate over the csv files
df_lst = []
for file in p.glob("*.csv"):
df = pd.read_csv(file)
# Only take CV, p... |
4033b6bf4ac2a41657fd98e662ad7c44746c33ea1e3b8fe3ab4e904a82035eb1 | Jupyter | 5,626 | 181 | # %% [markdown]
# # 1. INIT - Import packages
# %%
import torch
import os
import sys
from pathlib import Path
# --- Start of the robust path modification ---
try:
# This variable is defined in VS Code notebooks
current_file_path = Path(__vsc_ipynb_file__)
except NameError:
# Fallback for other environment... |
10e3c9bcab1f56922a76d47841a4a5564d1b8cecbf8b25f7f342cdb2bf7e6820 | Jupyter | 5,627 | 215 | # %% [markdown]
# # Multiverse recipes
#
# There are many ways of specifying and customizing complex multiverse analyses. The following code snippets aim to provide some examples/recipes for this.
# %% [markdown]
# ## Specifying classes/functions
#
# You can define the method either as a direct function call or by p... |
74620b29178b3a14d07bead2abe0c4c4650842fa176073350f046e4de98258ac | Jupyter | 5,634 | 132 | # %% [markdown]
# # Behavior Analysis: Binned-Ratio Timeline
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_Study4_NociFormalinResponse'
# Select group and condition, see meta.py to see how you previously defined it for your pr... |
07af0b5952c7593a54553a8191c9a4d731157a8c73e1671fd4c34f5d1bf8fa6f | Jupyter | 5,647 | 181 | # %% [markdown]
# # Static Networks - Training and evaluating neural networks
# %% [markdown]
# Once the static image stimulus $s$ is paired with the corresponding population response $\mathbf{r}$, it is time for us to train neural networks to predict the population resopnse from the static image, thereby approximatin... |
172d31fb25a12f6f09e13a911fc331a850637eead0edd2e7569f2ed41e0400bd | Jupyter | 5,656 | 211 | # %%
import os, json
import pandas as pd
from Bio import SeqIO
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import f1_score
# %%
testset_path = "../test/BLAST/src/testset.csv"
fasta_folder = "../data/raw_data"
output_path = "../test/results/analysis-by-species/species... |
6b2e1023c085302cb8b9af3272116cb303c82153e57da5d18102d99e6f2bef88 | Jupyter | 5,660 | 132 | # %% [markdown]
# # Behavior Analysis: Binned-Ratio Timeline
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MiniscopeSNI_Female'
# Select group and condition, see meta.py to see how you previously defined it for your project
se... |
61d6f824692983e4b0be7494f1e16bd12b96b60478f0a8094b5bb2d292431b00 | Jupyter | 5,663 | 170 | # %%
import numpy as np
import pandas as pd
import pegasus as pg
import json
import os
import pegasusio
from pegasusio import UnimodalData, MultimodalData
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import itertools
import random
import matplotlib.cm
import glob
from collections import Counte... |
5375cbc38a19b744c7fdb87ff19e0ddde7e4de7b94df30a53da599f0fb0666fd | Jupyter | 5,668 | 132 | # %% [markdown]
# # Behavior Analysis: Binned-Ratio Timeline
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MORPDreadd_Combined'
# Select group and condition, see meta.py to see how you previously defined it for your project
se... |
d9f3c90164e3a866249c37b457f2b60a20c982a101b9a3b908b224f5e5c8eb99 | Jupyter | 5,697 | 210 | # %% [markdown]
# # Integration of Skynet models into Seisbench
# ### Small details will differ due to slight differences in the implementation, but qualitatively they are consistent.
# ### As shown below, the picks differ by a fraction of a second, I will look into it but for now it works well.
# %%
import torch
impo... |
a1564d39d40fb9717718aa873f054a714b282f15a1be7a3e2ca0911cb8aa91f3 | Jupyter | 5,713 | 204 | # %%
import re, time, warnings, os, sys, copy
import gzip, pickle
import numpy as np
import pandas as pd
import gzip, pickle
import scipy, sklearn, mne
import auxiliary as aux
import data, rsa
sys.path.append('../../spaces/')
import embeddings as emb
import pubplot as pub
import matplotlib.pyplot as plt
pub.styles.s... |
c849764b217cc8d5e9e5ace7577c3806156a16be5aa20540e2efe410168cad34 | Jupyter | 5,723 | 192 | # %%
import mne
import os
import os.path as op
import numpy as np
from mne.time_frequency import tfr_morlet
import matplotlib.pyplot as plt
import matplotlib.patches as patches
# %%
task = 'MI'
sit_rest_lst = []
stand_rest_lst = []
sit_me_lst = []
stand_me_lst = []
for s_id in range(1, 24, 1):
if s_id == 5: cont... |
107918782c8ce62ddc90a7b8b166e87b49f1556028d3c0136c8d1cedf27bc3c9 | Jupyter | 5,743 | 170 | # %%
import re, time, warnings, os, sys, copy
import gzip, pickle
import numpy as np
import pandas as pd
import gzip, pickle
import scipy, sklearn, mne
import auxiliary as aux
import data, rsa
sys.path.append('../../spaces/')
import embeddings as emb
import pubplot as pub
import matplotlib.pyplot as plt
pub.styles.s... |
553ea692536bce770bb33c1a7e5d71793e39874a61e01a3cb4d1fd9cccb4199e | Jupyter | 5,745 | 111 | # %% [markdown]
#
# # Organisation of code submissions and standardisation to a common interface
#
# ## General structure
# Code submissions are located in the src/original folder, where submissions are named as `<initials>_<institution>`. Due to code submissions having different authors, it is expected that they all... |
b0ae618183c9033586f34e51e0cefe725aab4155887634f5a43a58e212057705 | Jupyter | 5,746 | 167 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import glob
import os
from scipy import stats
import seaborn as sns
# %%
def load_volreg(path, mean_radius=9):
"""
Load motion parameters estimated from AFNI's 3dvolreg and convert radian
values to distance based on the given mean ... |
2995e4390bb0c443127995827fdd1e63f49048944a76724bad0d4352e91012ca | Jupyter | 5,767 | 168 | # %%
import pandas as pd
import matplotlib.pyplot as plt
# %%
df1 = pd.read_csv('F_LUPE-hab_likelihood.csv')
df2 = pd.read_csv('M_LUPE-hab_likelihood.csv')
# %%
df1
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Load CSV files into DataFrame objects
df1 = pd.read_csv('F_LUPE-hab_likel... |
d0d63e7af6d29d55f0ae6c3787dae330607a1101f8f0380bfb3aaa72f87effd0 | Jupyter | 5,768 | 179 | # %% [markdown]
# # Batch variant scoring
# %% [markdown]
# In this notebook, we demonstrate how to score many variants using AlphaGenome.
#
# To prepare numerous variants for scoring, provide the following information as
# columns in a tab-separated Variant Call Format (VCF) file: - `variant_id`: A
# unique identifi... |
07f62f11ac2fa9513b5b7f41844a9627186b8566537848ea981b22fae2d0f7a5 | Jupyter | 5,772 | 158 | # %%
#!/usr/bin/env python
"""Shows that catastrophic forgetting can be avoided."""
import os
import time
from core import core_global as core
from core.spike_monitor import SpikeMonitor
from core.voltage_monitor import VoltageMonitor
from core.weight_matrix_monitor import WeightMatrixMonitor
from layers.rewiring_con... |
fb406552ccb142d70e80e16e5d3dac457a86d957dc977f112c8a1f82cf18ab5e | Jupyter | 5,800 | 231 | # %% [markdown]
# ### This notebook was used to convert the original CRED weights to SeisBench and ensure binary compatibility. It is not intended for further use, but only serves as a reference
# %%
%load_ext autoreload
%autoreload 2
# %%
from seisbench.models import CRED
import torch
import h5py
import numpy as np
... |
d72ed70ef3dccda2cb9555c1a0c3beb51b2a9d929e270f1619e41b56cfeb421b | Jupyter | 5,802 | 138 | # %% [markdown]
# # Behavior Analysis: Distance Traveled Stats + Movement Heatmaps
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MiniscopeSNI_3WeekMorphine'
# Select group and condition, see meta.py to see how you previously de... |
6f3d5b936c3f1ab03f0097c7887fb7d1ae875427cab687f03df1f937db37f1d7 | Jupyter | 5,811 | 153 | # %% [markdown]
# # JuSpyce API test: group permutation test
# %%
import sys
import os
from glob import glob
import pathlib
import numpy as np
import pandas as pd
from IPython.display import display
import seaborn as sns
import matplotlib.pyplot as plt
# current path
wd = pathlib.Path().resolve().parent
print(wd)
# ... |
187f77db90df21b941d291d4c4a2ba72dcfd699ac1bc97b26bcbcaa87f47aa89 | Jupyter | 5,822 | 235 | # %%
"""
ANN Model for Predicting Flexural Strength of Gypsum-Based Composites
Author: Haseeb Ahmad
Description:
This script trains an Artificial Neural Network (ANN) to predict
flexural strength using mix design parameters.
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sk... |
62e99f6eabf7684ea21e548ef6c909fe39644fc7f9cd420250d81484c93fff5d | Jupyter | 5,825 | 233 | # %%
#Copyright (c) 2019 Primoz Ravbar UCSB Licensed under BSD 2-Clause [see LICENSE for details] Written by Primoz Ravbar
import tensorflow.keras as K
import numpy as np
import scipy
from scipy import ndimage
from scipy import misc
import pickle
import time
import matplotlib.pyplot as plt
import cv2
import os
import... |
dacdb91a73e92c61221be06a518775396f75fcd3945e73bf4eda0e4315cf3bcb | Jupyter | 5,835 | 137 | # %% [markdown]
# # Behavior Analysis: Distance Traveled Stats + Movement Heatmaps
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_Study4_NociFormalinResponse'
# Select group and condition, see meta.py to see how you previously d... |
3fea793696eb15e6c416a7a911a6a3370f409480fb3298c6184aee401bcba37f | Jupyter | 5,840 | 138 | # %% [markdown]
# # Behavior Analysis: Distance Traveled Stats + Movement Heatmaps
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_acc_MiniscopeCap'
# Select group and condition, see meta.py to see how you previously defined it for y... |
ea976dc960bcf575be16f84f44ef9dcb1592dee887f517d048744a6474330c66 | Jupyter | 5,858 | 127 | # %% [markdown]
# # Hurricane multiverse (non-neuroimaging)
#
# The Comet toolbox is developed primarily for fMRI brain network analyses. However, we here showcases how the versatile multiverse workflow can also be applied outside of neuroimaging.
#
# This script is a re-implementation og the famous *“Female hurrican... |
465d1d8d9596388a49e4c09ac52469e6037fe7d0b83a61992b937acb4cc38444 | Jupyter | 5,861 | 187 | # %% [markdown]
# # Tutorial 2: Test on simulated data
# %%
from dlim.model import DLIM
from dlim.dataset import Data_model
from dlim.api import DLIM_API
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import pearsonr, spearmanr
from numpy.random import choice
from src_simulate_data.sim_data im... |
4ef7d9929c73aca1a5147ddd6d3e34d4b2085e394faa5e26ae613cdffc60d245 | Jupyter | 5,861 | 137 | # %% [markdown]
# # Behavior Analysis: Distance Traveled Stats + Movement Heatmaps
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MiniscopeSNI_Female'
# Select group and condition, see meta.py to see how you previously defined i... |
61af15b59af2075632e49b864006926cfad7986364b19c66099f6db241bf2886 | Jupyter | 5,864 | 192 | # %%
import warnings
warnings.filterwarnings('ignore',
message='.*Attempting to register factory for plugin.*')
warnings.filterwarnings('ignore',
message='.*computation placer already registered.*')
import os
os.chdir('/app/NBT-Classifier/')
import glob
import numpy as np
import pandas as pd
import openslide... |
ffbb22cb493c799a3704a9ce89dc3db455553669126c9961249d95e5f6e9e8a6 | Jupyter | 5,867 | 210 | # %% [markdown]
# Copyright (c) 2019 Primoz Ravbar UCSB
# Licensed under BSD 2-Clause [see LICENSE for details]
# Written by Primoz Ravbar
# %%
# load ST-images and labels for the ConvNet training
import numpy as np
import scipy
from scipy import ndimage
from scipy import misc
import pickle
import pandas as pd
imp... |
01910eade2bfd14b9a7fbb97d52cbd6980dde64f55aa2cf0c10ce465a6144262 | Jupyter | 5,868 | 137 | # %% [markdown]
# # Behavior Analysis: Distance Traveled Stats + Movement Heatmaps
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MORPDreadd_Combined'
# Select group and condition, see meta.py to see how you previously defined i... |
5522f184cc3b228766be9c83fd9aed6705ce6a1d1d0b26df5d1e1e109bb61955 | Jupyter | 5,868 | 138 | # %% [markdown]
# # Behavior Analysis: Distance Traveled Stats + Movement Heatmaps
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_Study3_FormalinMorphine'
# Select group and condition, see meta.py to see how you previously defin... |
f1f7b5c86464ed73a98d20463578662b239f3c889ed872a1284581f3ce80abf7 | Jupyter | 5,871 | 146 | # %%
import sys
sys.path.append('../utils')
import scipy.io
import mat_to_tfrecord_imagined
from pathlib import Path
import numpy as np
import os
from sklearn.metrics import f1_score
session = 't17.2024.05.23'
#block 1: Phonemes
blocks = [1]
trialsToRemove = []
dataDir = str(Path('../Data', 't17', session, 'brainToT... |
da3094cf708299ae66daf45019496eb96584979618469ff3d749b9f3dcd5039b | Jupyter | 5,898 | 145 | # %%
import sys
sys.path.append('../utils')
import scipy.io
import mat_to_tfrecord_imagined
from pathlib import Path
import numpy as np
import os
from sklearn.metrics import f1_score
session = 't17.2024.05.23'
#block 2: Words
blocks = [2]
trialsToRemove = []
dataDir = str(Path('../Data', 't17', session, 'brainToText... |
ab3170e38d95d790be7407a43e4790b600374d50566c917df4404b7e90e46f1f | Jupyter | 5,899 | 133 | # %% [markdown]
# # 2. Self-organising mosaic (Stage 1a: retinal mosaic)
# This Jupyter notebook demonstrates how to generate a self-organising mosaic that uses reaction-diffusion modelling. ERICA uses the Numba JIT compiler (http://numba.pydata.org/) to speed up the reaction-diffusion computations, however, this is st... |
300f93326bafa5077558ffe3516f3911a411007f9f17c07bae7d6759510267a4 | Jupyter | 5,906 | 148 | # %% [markdown]
# ### Interpretation of the LR models
#
# Here, we compare the coefficients learned by the different linear regression models.
#
# These coefficients can be downloaded from Zenodo.
# %%
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
import matplotl... |
547b57e6ff4adf76a1db69f033e5dc69e5d0e2d625f130d127de8924adebf709 | Jupyter | 5,920 | 169 | # %%
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.stats import pearsonr
from sklearn.metrics import r2_score
# %%
test_results_path = "./data/SWOW_prediction/eval/"
# %%
properties = ["previous_link", "polarity"]
model_name = "bert-base-uncased"
re... |
551b4ea2ef8dc45585e9ad1efdb0b4140dde0dda8a57860b4d8d7fa4acaf8cea | Jupyter | 5,945 | 170 | # %% [markdown]
# Author: Chris J. Dallmann \
# Affiliation: University of Wuerzburg \
# Last revision: 19-February-2025\
# Dyrad Repository: DOI: 10.5061/dryad.fxpnvx153
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy
from sklearn.neighbors import KernelDensity
# %%
# Load d... |
db9a863b99ab86015d227516b361f18fa19f86be76695a663c912e5eb9761a0b | Jupyter | 5,952 | 161 | # %%
import os
# The current working directory needs to be in explainable_TGCNN
print(os.getcwd())
os.chdir('..\\')
print(os.getcwd())
import numpy as np
import pandas as pd
from src import whole_model_demographics_gradcam, graph_plot, gc, utils, create_fake_patients
import tensorflow as tf
import math
# %%
second_... |
c319eb79c99f1895c8b760c23c4c67e1dfe96943310069731b8c88722bcd2b11 | Jupyter | 5,953 | 203 | # %%
"""
Support Vector Machine (SVM) Regression Model
for Predicting Gypsum Compressive Strength
Author: Haseeb AHmad
Date: 2026
"""
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, learning_curve
from sklearn.svm ... |
517cb261cd5942e746f374ada54ac0c340254b780942e85f25af6fcdc50b7fd8 | Jupyter | 5,959 | 208 | # %% [markdown]
# note: this notebook should be run under the same directory as `scripts`
# %% [markdown]
# ### Euler registeration on RFP images
# %%
from euler_register import EulerRegistrationProcessor
# %%
target_image_shape = (284, 120, 64)
save_directory = "/home/alicia/notebook/alicia/BrainAlignNet/demo/euler... |
bd9ef133fb7246d2b8af15604cdbd3ef8d310e92620888067c7968a6500e3759 | Jupyter | 5,974 | 148 | # %% [markdown]
# # Aperiodic and oscillatory components of the power spectrum
#
# This notebook demonstrates how to use YASA to separate the aperiodic (= fractal = $1/f$) components of the EEG power spectra using the IRASA method.
#
# Please make sure to install the latest version of YASA first by typing the followi... |
1419e381b339090dc5692ac31b5d1dbd39f729f0fe52b3777ca7800b97239991 | Jupyter | 6,007 | 187 | # %% [markdown]
# ## Multi-class classification example
#
# In this example we show how the ecgxai package can be used to easily build a classification system for multiple classes (in this case for Sinus Rhythm (NSR), Sinus Arrhythmia (SA), Sinus Bradycardia (SB) and Sinus tachycardia (STach)). We train the model on t... |
ddf9574fb5aed36ad48e254fd3c8e6bca8fdd9f62f5527869374e05a7a6b2027 | Jupyter | 6,014 | 131 | # %% [markdown]
# # Confounder analysis for the multi-center RPN-signature
# - This jupyter notebook is available on-line at:
# - https://github.com/spisakt/RPN-signature/blob/master/notebooks/5_confounder_analysis.ipynb
# - Input data for the notebook and non-standard code (PAINTeR library) is available in the repo:... |
f4e573fcf984719460757ed7d99210fb46e6c0710535561474053a680d29507e | Jupyter | 6,015 | 202 | # %%
import pandas as pd
import seaborn as sns
import matplotlib.colors as mcolors
import matplotlib.cm as cm
import matplotlib.pyplot as plt
# %%
df = pd.read_csv('/home/MRMelissaGrantPeters/MinaRyten/Melissa/MRIxST/processed_data/08_transcriptomics/annot_l2_enrich_test_lists_fdr_all.csv')
df
# %%
df_mat = df[df['t... |
368e9bafb6247fcf8fce9a5ae59b5a72e292e69bddc5028f9400a06034b360bb | Jupyter | 6,019 | 151 | # %% [markdown]
# ## Recall dynamics for simulator comparison
# %% [markdown]
# ### Imports and definitions
# %%
import sys
import os
from subprocess import run, PIPE
import shutil
import re
from pathlib import Path
current_folder = globals()['_dh'][0]
sys.path.append(os.path.join(current_folder, "../../analysis"))
... |
9e61dace9243b843142fca4163ea4ce06deb0d3398ddc23ec8a2be96e8eac590 | Jupyter | 6,020 | 164 | # %% [markdown]
# <a href="https://colab.research.google.com/github/yetinam/pyocto/blob/examples/03_interfaces.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ![image](https://raw.githubusercontent.com/yetinam/pyocto/main/docs/_sta... |
9c4b9cacd0b1d5292bd847edc871686cbded096b4a7cf25a6055533f7fd65bb5 | Jupyter | 6,028 | 189 | # %%
import numpy as np
import os
import nibabel as nib
from tqdm import tqdm
import json
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from scipy.ndimage import label
from scipy import ndimage
from collections import defaultdict
from scipy.stats import bootstrap
def median_ci(data, confid... |
3411d67f8b290668826b47315aab177eaf4712f53c69889b98edbbf58849661e | Jupyter | 6,036 | 192 | # %%
import warnings
import pandas as pd
import pingouin as pg
warnings.filterwarnings("ignore")
# %%
# Loading COHA time series data
ts_df = pd.read_csv("./data/SWOW_prediction/eval/time_series/ts_df.csv")
ts_df = (
ts_df.groupby(["words", "year", "property"])[["count", "outputs_z"]]
.mean()
.reset_inde... |
a9a8140cd0e27a82c5574eb085ae1f50298c01a64f1af5c95036d739b4dafe31 | Jupyter | 6,039 | 145 | # %%
import sys
sys.path.append('../utils')
import scipy.io
import mat_to_tfrecord_imagined
from pathlib import Path
import numpy as np
import os
from sklearn.metrics import f1_score
session = 't17.2024.05.23'
#block 3: Phrases
blocks = [3]
trialsToRemove = []
dataDir = str(Path('../Data', 't17', session, 'brainToTe... |
b3cdaa623e23cf877f6b2a69b297c26f0b6034c167f6a779c659aaa9d2b8f07c | Jupyter | 6,039 | 132 | # %% [markdown]
# <a href="https://colab.research.google.com/github/beatLaboratory/TIMBRE/blob/main/LFP_demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %%
# %% [markdown]
# # Run TIMBRE on real data
#
# In this notebook we import the exper... |
ce5d17f838019826cffad250b89371c4670e072fac21130b71208ed8850f24d4 | Jupyter | 6,044 | 179 | # %%
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.stats import pearsonr
from sklearn.metrics import r2_score
# %%
test_results_path = "./data/SWOW_prediction/eval/"
# %%
properties = ["previous_link", "polarity"]
model_name = "bert-base-uncased" #... |
40eb68dccd7fae03ba46e597d7b2b354ff71d7932119dc20b8c060b0ec3be348 | Jupyter | 6,053 | 200 | # %%
import mlflow
from interpret.glassbox import ExplainableBoostingClassifier
import pandas as pd
import numpy as np
import os
# %%
# Create the MLflow client
mlflow.set_tracking_uri("http://localhost:5000")
# automatically parse string values from MLflow as int, float or str
def parse_value(value):
if isinstan... |
b5ce2edea379bbc5996a70316f315293102897f520be3ed62098138e8dbcea5e | Jupyter | 6,055 | 136 | # %%
import pandas as pd
import numpy as np
import gseapy as gp
import matplotlib.pyplot as plt
from gseapy import barplot, dotplot
import seaborn as sns
from matplotlib import rcParams
import re
# %%
import os
os.chdir("/data/wuqinhua/scPhase/CRC")
# %%
import pandas as pd
def _df_append(self, other, ignore_index=Fa... |
d707237afd8b3b08b0e734884b845c9dff6c418b338c8fb3c9e6cf486ff83540 | Jupyter | 6,065 | 240 | # %% [markdown]
# ### This notebook was used to convert the original PhaseNet weights to SeisBench and ensure binary compatibility. It is not intended for further use, but only serves as a reference
#
# **Last run on 05/01/2023** - note that this notebook will not be kept up to date with changes made to Phasenet or Se... |
e67c4adcc9af2b965785481e1608d91a947c27b08cbddc9e4b6a74113b7e6cd4 | Jupyter | 6,100 | 211 | # %%
"""
Support Vector Regression (SVR)
Flexural Strength Prediction of Gypsum-Based Composites
Author: Haseeb AHmad
Year: 2026
"""
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, learning_curve
from sklearn.svm i... |
3117eac3a971e389a3ec1f29df6f7c6baa5239b2c6de8e9cd85f2e37f1e78372 | Jupyter | 6,105 | 158 | # %% [markdown]
# # Dendritic Spine Clustering
# %% [markdown]
# Glossary:
# - Group — a division of spines defined by experiment design, e.g., division of spines into experimental and control groups;
# - Class — a group of spines that meet specific criteria, e.g., stubby, mushroom, thin and filopodia groupings. In c... |
37f772b22981b6a5c2e015cd24c789df659fe4e5111d1c0aa4b9f72f61b44436 | Jupyter | 6,110 | 136 | # %%
import pandas as pd
import numpy as np
import gseapy as gp
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import rcParams
import re
import os
os.chdir("/data/wuqinhua/scPhase/Lung")
# %%
import pandas as pd
def _df_append(self, other, ignore_index=False, verify_integrity=False, sort=False)... |
fa3ae93f3b4e6b54aaaa85c75144d3bf724884ff9da3c3c94337bfc9923e2a00 | Jupyter | 6,123 | 172 | # %% [markdown]
# ## Description:
#
# This notebook uses manual interpolation as well as a grid of points spaced "PIXELS_PER_POINT" apart to re-interpolate a Moving image onto a Reference. The code currently assumes that the reference and moving image share exact dimensions along all 3 axes, e.g. as would be the case ... |
20d1fd607a56ac9274120efa3719a9d3ed16ad9be660014b0cb97a804b2a07b5 | Jupyter | 6,125 | 217 | # %%
import subprocess, numpy as np, tifffile, matplotlib.pyplot as plt, os
from skimage.measure import label, regionprops
import joblib as jl
from tqdm.notebook import tqdm
import glob, os, re, shutil, subprocess, pgzip, time, traceback, tifffile, numpy as np, numba as nb, \
scipy.stats, joblib as jl
from itertoo... |
6182167da496c0bef9378f78966134c946b4c95b967aea13112ccc12576f21c1 | Jupyter | 6,129 | 212 | # %%
from omegaconf import OmegaConf
import sys
sys.path.append('../utils')
from brainToText_trainDecoder import brainToText_decoder
args = OmegaConf.load('T17_trainArgs.yaml')
decoder = brainToText_decoder(args)
out, out_by_day = decoder.inference()
# %%
import re
import numpy as np
from g2p_en import G2p
PHONE_DEF... |
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