sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f878495067bcc4481b99bbb84f4b500396a821cc93ab64bef9ad62ab2b99a295 | Jupyter | 293 | 11 | # %%
from enigmatoolbox.datasets import load_summary_stats
# Load summary statistics for ENIGMA-Autism
sum_stats = load_summary_stats('asd')
# Get case-control cortical thickness table
CT = sum_stats['CortThick_case_vs_controls_meta_analysis']
# Extract Cohen's d values
CT_d = CT['d_icv']
|
e349c5c6ad7c3bc7aa353e8dbd7e5fe57fe2c7ef5880de3ac932d19f5512ccf6 | Jupyter | 380 | 23 | # %%
import yaml
# %%
with open('test_config.yaml') as file:
try:
databaseConfig = yaml.safe_load(file)
print(databaseConfig)
except yaml.YAMLError as exc:
print(exc)
# %%
print(databaseConfig["database"])
print(databaseConfig["author"])
# %%
print(databaseConfig["database"]["port"... |
68d4fd9b9861b3f42ddc59245117f277298bcfe132bd79531ce5b594a258ad9d | Jupyter | 503 | 28 | # %%
import pathlib
import scanpy as sc
import pandas as pd
# %%
metadata = pd.read_csv("study_overview.csv", index_col=0)
# %%
metadata
# %%
metadata["index"] = metadata.index.str.split("_").str[0] + "_" + metadata.index.str.split("_").str[2]
metadata = metadata[(metadata["Source"]=="3ca") & (metadata["Include"]=="... |
8809c5952c8c138408fb136987cf8894543948e052a0693a0ceff74747f37559 | Jupyter | 542 | 25 | # %%
import os
import pandas as pd
import numpy as np
import requests
import json
import logging
import time
from typing import List, Dict, Any, Optional
from enum import Enum
from dataclasses import dataclass
from sklearn.metrics import cohen_kappa_score, confusion_matrix
# Define the output directory
output_dir =... |
4d17d860ee8aab548dcf2a34831e8380b9bb34acf443a87672e860199a356f04 | Jupyter | 736 | 27 | # %%
from sklearn.decomposition import NMF
import matplotlib
from matplotlib import pyplot as plt
from matplotlib.lines import Line2D
import cv2
import numpy as np
from typing import List, Dict
import math
from visualizations import show_factorization_on_image
import glob
from PIL import Image
from matplotlib import py... |
72a8a793ff02a2ad27919f65b92d75f05e08720c67789dcd1cce5df97363e05d | Jupyter | 802 | 25 | # %% [markdown]
# # PyLossless Quality Control within a Jupyter Notebook
#
# This notebook demonstrate how the PyLossless Quality Control interface can be integrated in a Jupyter Notebook. This can even be done on a virtualized platform, like Google Colab! To try it, you can run this notebook from [here!](https://cola... |
f2ec788aabfcd7d17d454e18988c55cb8b7da6ade929a526f149b2c797976651 | Jupyter | 812 | 33 | # %%
import sys
sys.path.insert(0, "../")
import scanpy as sc
from model.embedding import embed
# %%
model_dir = "../model/assets"
adata_path = "../data/neftel_ss2.h5ad" # INSERT the path to your anndata object here
adata = sc.read_h5ad(adata_path)
batch_key = "sample" # The batch identity is used for highly variable... |
0b57c489a4e560323d7eb78815e88616a1b7eedb534e05c4e776be302fe3042a | Jupyter | 824 | 26 | # %%
import os
import torch # for determining device
from md.Simulator import Simulator
# %%
# define some constants and identify device
kSimulationName = "example-simulation"
kPdbInput = os.path.join("structures", "1em7.pdb")
kNumMaxIterations = 200
kNumSteps = 20_000
kRestartSimulation = False
kFixProtein = False
#... |
0f23cd7d276d2a70b427c8a9fc529261271f685ac84fa705f394a9517d267e3e | Jupyter | 847 | 35 | # %%
from enigmatoolbox.datasets import load_summary_stats
# Load summary statistics for ENIGMA-Epilepsy
sum_stats = load_summary_stats('epilepsy')
# Get case-control subcortical volume and cortical thickness tables
SV = sum_stats['SubVol_case_vs_controls_ltle']
CT = sum_stats['CortThick_case_vs_controls_ltle']
# Ex... |
ace290fa33fdaf361f78062ceb3bb7e93e5f09a26f41824f96dc5754434d6d6e | Jupyter | 907 | 32 | # %%
import matplotlib.pyplot as plt
import numpy as np
import scipy.io
import os
plt.style.use('ggplot')
path_root = os.environ.get('DECIDENET_PATH')
path_code = os.path.join(path_root, 'code')
if path_code not in sys.path:
sys.path.append(path_code)
from dn_utils.plotting import plot_z_convergence
from dn_utils... |
13534d23e82cb0c9a0fc026e06e94e639961268922da832c4a645bcabb2689bb | Jupyter | 944 | 53 | # %%
import sys
import os
from os.path import dirname, realpath, join
base_dir = dirname(dirname(os.getcwd()))
base_dir
import pandas as pd
from os.path import join
sys.path.insert(0, base_dir)
from config_path import PROSTATE_DATA_PATH, PLOTS_PATH
# %%
filename=join(PROSTATE_DATA_PATH, 'processed/P1000_final_analysis... |
630bb6bb0836c8b8500e5fc1a4de65d2a5e0b2ef897bbae7391f5738465e7ac5 | Jupyter | 958 | 32 | # %%
from os.path import join
from pathlib import Path
import pandas as pd
import numpy as np
from dn_utils.path import path_bsc, path_figures
from dn_utils.style import ORANGE, BLUE
from nilearn.plotting import plot_connectome
# %%
atlas = "combined_roi_4and5"
# %%
# Load atlas and filter PE ROIs
df_roi = pd.read_c... |
e0c146fdfa06047ea13b812e8d89e18baf48aa301c572659d5d46b208523db74 | Jupyter | 958 | 32 | # %%
import networkx as nx
import matplotlib.pyplot as plt
from faker import Faker
import random
# Initialize Faker
fake = Faker()
# Generate fake gene names
def generate_gene_names(n):
return [fake.unique.word().upper() for _ in range(n)]
# Create a random graph
num_nodes = 10
num_edges = 30
G = nx.gnm_random_g... |
9c611e3c6af708d631fb4c2e0a4343970d45d2a7fe04b0ec335c0749a36ed44b | Jupyter | 995 | 58 | # %%
import sys
import os
from os.path import dirname, realpath, join
base_dir = dirname(dirname(os.getcwd()))
base_dir
# %%
sys.path.insert(0, base_dir)
from config_path import PROSTATE_DATA_PATH
# %%
import pandas as pd
from os.path import join
# %%
filename = join(PROSTATE_DATA_PATH, 'external_validation/Met500/s... |
e6ec2d87bacd360e8197539a8d035c2b5ba9f73a0442fc58e04fd165e1d2836f | Jupyter | 1,040 | 38 | # %% [markdown]
# # Front page example (XGBoost)
#
# The code from the front page example using XGBoost.
# %%
import xgboost
import shap
# load JS visualization code to notebook
shap.initjs()
# train XGBoost model
X,y = shap.datasets.boston()
model = xgboost.train({"learning_rate": 0.01}, xgboost.DMatrix(X, label=y... |
a98c6483e94e5739b09b5d48790462ea0045f85407244ce35cf1ad89d6188c32 | Jupyter | 1,047 | 35 | # %% [markdown]
# # Cycles per degree
#
# Compute the spatial frequency (cycles per degree) of the grating stimuli.
# %%
import numpy as np
# %%
def cycles_per_degree(distance_to_screen_cm, spatial_period_cm):
"""
Calculate cycles per degree given the distance to the screen and the spatial period.
Param... |
fb16557e03c16cae1079fe78346000353cbdf9ea884cd95e0dc475624a516259 | Jupyter | 1,092 | 48 | # %%
import matplotlib.pyplot as plt
import numpy as np
from scipy import interpolate
# %%
pi = -np.array([4.3,4.4,4.2,3.2,2.6,2.2,4.1,3.0,4.2,5.3,5.4,4.1,3.6,2.8,3.4])
s2 = -np.array([5.1,5.0,4.5,4.1,3.1,3.7,4.2,4.8,5.0,6.2,6.4,5.4,5.0,4.7,4.9])
xpos = 10*np.arange(len(pi))
# %%
save = True
xnew = np.linspace(xpos[0... |
da5e389b4aea189760f96cac32f8351003f67f09d96efae781cbadc97ea66fea | Jupyter | 1,109 | 33 | # %% [markdown]
# ## Convert `.swc` files to `.nr` for `NeuRosetta`
# %%
T45_df = pd.read_pickle('/home/ndrummond@neuro.mpg.de/Datasets/Optic_lobe_neurons/T45_neurons.pkl')
# %%
swc_path = '/home/ndrummond@neuro.mpg.de/Datasets/Optic_lobe_neurons/swc/'
# %%
forest = nr.import_swc(swc_path, parallel = True, max_worke... |
e9f526c86b325ee09658f94946111f9901c0f572875fb0da54571b7217341842 | Jupyter | 1,116 | 49 | # %% [markdown]
# ### Get Dendrite Comparrtment
# %%
import NeuRosetta as nr
from tqdm import tqdm
# %%
fpath = '/home/ndrummond@neuro.mpg.de/Datasets/Optic_lobe_neurons/nr/Female/T4/'
forest = nr.load(fpath, parallel = True, progress = True, max_workers=10)
# %%
trees = []
for f in forest:
# if f.metadata['... |
1a4d269ebad6727cae0d640ff0fbfa9cfa7cc6b8c9a98fd55767e79352179266 | Jupyter | 1,140 | 39 | # %% [markdown]
# ### Swection angular deviation data for dendrites
#
# Create a dataframe of section angular mean and variances for each dendrite.
# %%
import NeuRosetta as nr
import pandas as pd
# %%
path = "/home/ndrummond@neuro.mpg.de/Datasets/Paper_data/nr/Flywire/Dendrites/"
forest = nr.load(path, parallel = T... |
5240dc2bc8ebb30c3c236d9c8316b37f2c2bcac1791940311a7b0f2dcf4922c8 | Jupyter | 1,143 | 40 | # %%
import scipy
from msi_visual.normalization import spatial_total_ion_count, total_ion_count, median_ion
import glob
import numpy as np
import UMAP
import matplotlib.pyplot as plt
from PIL import Image
import cv2
import importlib
import msi_visual.nonparametric_umap
importlib.reload(msi_visual.nonparametric_umap )... |
9cb982cc4e7e107d28239c07e7aa0336f3bf808298cfe41273757c2dd3403706 | Jupyter | 1,191 | 43 | # %%
import numpy as np
import matplotlib.pyplot as plt
# %%
se_ne = np.array([0.627194725, 0.627706144, 0.583127721, 0.493964843, 0.686143704, 0.667042701])
# %%
lumen_area = np.array([14810, 14666, 18500, 17482, 15433, 17436])/1.3**2
# %%
organoid_area = np.array([40963, 40901, 43631, 43939, 42968, 39899])/1.3**2
... |
ec35d156eff916ff71e9422f9b10600110ee8c497ae2bb8192da85c4ae1281e2 | Jupyter | 1,227 | 35 | # %% [markdown]
# # Explaining the Loss of a Tree Model
#
# Explaining the loss of a model can be very useful for debugging and model monitoring. This notebook gives a very simple example of how this works. Note that explaining the loss of a model requires passing the labels, and is only supported for the `feature_dep... |
15dab3e9431896b84b5fe9386e82a87aaa58e3c7574041132e2679f1a4b787db | Jupyter | 1,244 | 37 | # %%
import os
import numpy as np
import tifffile
# Set the directory path containing the images
directory_path = ''
# Output file to store results
output_file = 'image_non_black_pixel_counts3.txt'
# Open the output file in write mode
with open(output_file, 'w') as file:
# Write the header
file.write("Image ... |
2727b3fb67aa4e28491e6737e9ab6866a2e30e2869fa9e9ab0e6a47f548b83f9 | Jupyter | 1,247 | 39 | # %%
library(Seurat)
library(SeuratObject)
library(Matrix)
# %%
data_path = 'pancreas_cosmx_rds'
seurat_obj <- readRDS(paste0(data_path, "/Seurat_Pancreas_withTranscripts.rds"))
# %%
spatial_coords <- GetTissueCoordinates(seurat_obj)
head(spatial_coords)
# %%
head(seurat_obj@meta.data)
# %%
names(seurat_obj@meta.d... |
77efb51d5734fc640ecf43177d6771a9e5f022eac1eb27c79e5a3e557677f838 | Jupyter | 1,253 | 45 | # %%
import pandas as pd
import numpy as np
import scipy as sp
from scipy import io
from sklearn.metrics import pairwise_distances
import sys
sys.path.append("../topography/utils_py/mapalign")
from mapalign import dist
# %%
atlases = {'rm': 0, 'lv': 1, 'fv': 3, 'b05': 4, 'bb47': 5, 'pht00': 7}
def create_macaque_con... |
bed70c3a2e7ab31df8f0bedd73faeed5e909a1982e0380f3e08e59ad37686469 | Jupyter | 1,279 | 44 | # %% [markdown]
#
#
# # Example 1: Multiple Stat Maps
#
# This example shows multiple statistical maps on a surface with some extra
# stylizing for a clean-looking figure.
# %%
# Code source: Dan Gale
# License: BSD 3 clause
from surfplot import Plot
from surfplot.datasets import load_example_data
from neuromaps.... |
04abad79e26e8a6824d3762756ad1b7cee89a3aaed5a57de09c733bd75219387 | Jupyter | 1,285 | 44 | # %%
from enigmatoolbox.datasets import load_summary_stats
# Load summary statistics for ENIGMA-Epilepsy
sum_stats = load_summary_stats('epilepsy')
# Get case-control subcortical volume and cortical thickness tables
SV = sum_stats['SubVol_case_vs_controls_ltle']
CT = sum_stats['CortThick_case_vs_controls_ltle']
# Ex... |
1698f639d9e49c3ce89b3c1bc638801e1500f4f2850bf1daa99eb049e4d7b024 | Jupyter | 1,289 | 43 | # %%
from src.utils import collapse_isoforms_to_proteoforms, read_gtf
import polars as pl
# %% [markdown]
# # Add base_isoform to protein_classification
# %%
tx_classification = pl.read_parquet("nextflow_results/V47/final_classification.parquet")
orfanage_gtf = read_gtf("nextflow_results/V47/orfanage/orfanage.gtf", ... |
784b9a382a93f572d7f48a8d16a42bd4061cf873b7b13db5e8982b10033ed598 | Jupyter | 1,307 | 39 | # %% [markdown]
# Code from:
# Dear R, Wagstyl K, Seidlitz J, et al. Cortical gene expression architecture links healthy neurodevelopment to the imaging, transcriptomics and genetics of autism and schizophrenia[J]. Nature Neuroscience, 2024, 27(6): 1075-1086.
#
# More details can be found in paper
# ## Setup
# %%
# ... |
5087a22b90f70449f52459bd98233e7dd2792bc6790f94b2f7b6d8d813fb8d64 | Jupyter | 1,353 | 51 | # %%
! bash demo.sh
# %% [markdown]
# ## Example 1
# %%
from scRNA_workflow import *
sc.settings.figdir='./figures_new/' # set figure folder
path = './GSM4653863_HC1/'
adata = sc.read_10x_mtx(path) # read from 10x file
# %%
X_df= pd.DataFrame(sparse.csr_matrix.toarray(adata.X),index=adata.obs.index.tolist(),columns... |
6df2b2df63f333861d556056ae33e49f0c73c05ef52881fcaf3a6cc83d4ffcd8 | Jupyter | 1,355 | 59 | # %%
import numpy as np
import pandas as pd
import sys
loc = "../../tools/SERGIO/"
sys.path.append(loc+"SERGIO")
from sergio import sergio
# %%
sim = sergio(number_genes=1200, number_bins = 9, number_sc = 1000, noise_params = 1, decays=0.8, sampling_state=15, noise_type='dpd')
sim.build_graph(input_file_taregts =loc+... |
c8e45976ad9090f3b3687382d562e21f49f476e03451ab6752c05f97cd42c212 | Jupyter | 1,371 | 44 | # %%
import numpy as np
import glob
from matplotlib import pyplot as plt
import joblib
from visualizations import get_colors
from collections import Counter
from scipy.signal import find_peaks
NUM_COMPONENTS = 5
colors = get_colors(NUM_COMPONENTS)
centers = np.load("h.npy")
Percentile Ratioint(centers.shape)
all_mzs... |
9fc0b9a9f28170ce18f6d7d467307f0f900a437c60af7c7ad768353d401dc1c0 | Jupyter | 1,379 | 41 | # %%
import numpy as np
from sklearn.metrics import confusion_matrix
import seaborn as sns
import os
from matplotlib import pyplot as plt
# %%
audiocategory='breathing-deep,breathing-shallow,cough-heavy,cough-shallow,vowel-a,vowel-e,vowel-o,counting-normal,counting-fast'
# %%
results_folder = 'results'
classifier_nam... |
41eff91f8d882927d81e4bfd039bd360983a946edd57847f3fd8105ab5548cdb | Jupyter | 1,386 | 43 | # %%
import pandas as pd
import os
# %%
annot_snps_dir = "/mnt/seagate10/projects/cmm/experiments/hapgen10k11m.utr5_utr3_exon_other"
annot_snps_f_dict = {"UTR5":os.path.join(annot_snps_dir,"hapgen11m.utr5.snps.txt"),
"UTR3":os.path.join(annot_snps_dir,"hapgen11m.utr3.snps.txt"),
... |
e52a7deaa90b1be062f8bf110115dd5e40d7c03e9b33edcdc92773b9a536010d | Jupyter | 1,387 | 29 | # %%
import re
from oneqmc.analysis.energy import HARTREE_TO_KCAL
from uncertainties import ufloat_fromstr
from uncertainties.unumpy import nominal_values, std_devs
import matplotlib.pyplot as plt
import pandas as pd
# %%
results_df = pd.read_csv("../../experiment_results/10_atct_tae/processed_results.csv")
results_df... |
540092099e6c00e89306bc37ea1ec4ca9bd2815209757e4cd143ae9ce62bbc85 | Jupyter | 1,391 | 44 | # %% [markdown]
# ### Bifurcation geometry data for dendrites
#
# Create a dataframe of bifurcation geometry metrics for each dendrite
# %%
import NeuRosetta as nr
import pandas as pd
# %%
red_path = '/home/ndrummond@neuro.mpg.de/Datasets/Paper_data/nr/Flywire/Reduced_dendrites/'
full_path = '/home/ndrummond@neuro.m... |
7f90fa32212fe07cef368c8a1a83bc562ce672a9fa2063b2e002eae021e34121 | Jupyter | 1,391 | 39 | # %%
from extract import *
from extract import _bisect_spectrum
input_path = r"D:\\NRL4485-slide-4_lipids\\slide-4_lipids.d"
tsf = tsfdata.TsfData(input_path)
conn = tsf.conn
regions = conn.execute(f"SELECT RegionNumber FROM MaldiFrameInfo").fetchall()
regions = [r[0] for r in regions]
regions = list(set(regions))
reg... |
1a32884c39a951b1ddc702a72628ae09bb89605616a60f54665c8538a6f77265 | Jupyter | 1,404 | 51 | # %%
import pandas as pd
import matplotlib.pyplot as plt
from oneqmc.analysis import HARTREE_TO_KCAL, colours
from oneqmc.analysis.plot import set_defaults
set_defaults()
# %%
df = pd.read_csv("../../experiment_results/06_si_n2_ethene/processed.csv")
acpf_df = pd.read_csv("../../experiment_results/06_si_n2_ethene/r1... |
866492d3945307a1df4dd66dc68b22f332334b1a03707a87e40458c9a6fe558e | Jupyter | 1,412 | 42 | # %%
# Download HCP 820 dense connectome:
# HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii
# %%
import numpy as np
import nibabel as nib # git clone --branch enh/cifti2 https://github.com/satra/nibabel.git
from sklearn.metrics import pairwise_distances
# Load data and Fisher's z-to-r transform
dcon = np.... |
93986a1c6f4920edb29a6219745b3cb44be1fbe9df77a957815f80d3d7bd42fc | Jupyter | 1,417 | 32 | # %% [markdown]
# # Cosine Similarity
# %% [markdown]
# Prerequisites
#
# 1. Prepare the relevance matrix file containing the pair of PMIDs and the relevance score for that pair.
# 2. Generate the Doc2Vec model, using the [Embeddings Tutorial](https://github.com/zbmed-semtec/doc2vec-doc-relevance/blob/main/Docs/embed... |
be3e552aa0bb1123d35e80545e2929c20b9fe6db54f964e269c43a850f5032fb | Jupyter | 1,450 | 57 | # %%
%load_ext autoreload
%autoreload 2
import os
from os.path import join as ospj
import pickle
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from tqdm.notebook import tqdm
from einops import rearrange
from config import CONFIG
from model_v5 im... |
c604f77923a4bda06596425cbe05c179fdc8925da1aab62e2b052ec0d782168d | Jupyter | 1,453 | 33 | # %% [markdown]
# # Example notebook
# %%
import numpy as np
from anndata import AnnData
import pandas as pd
import nicheformer
# %%
adata = AnnData(np.random.normal(size=(20, 10)))
# %% [markdown]
# With myst it is possible to link in the text cell of a notebook such as this one the documentation of a function or a... |
d44b6d30aa94cdb494d5614a2dc9e56d81ef1c89ae672b36448afcfde0be0b42 | Jupyter | 1,457 | 53 | # %%
# reading data and working with arrays
import zarr
import numpy as np
from scipy.ndimage import map_coordinates, zoom, gaussian_filter
# data paths
inv_transform_path = '/nrs/scicompsoft/goinac/multifish/tiny/results/registration/local/t1-t2/inv-deform.n5'
inv_transform_subpath = 't2/c1/s2'
coords_path = '/nrs/sc... |
c093cfbec8dbea63e69d65e9e6b921aff047b60b0f428eab553f533285da05dc | Jupyter | 1,479 | 51 | # %%
# Script for NB-NC contact length ROC analysis shown in Figure S2
import numpy as np
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import pandas as pd
# %%
df = pd.read_csv('CombinedMatrixPreWindowFirstSignFrameFINAL0925.csv')
df = df.dropna(subset=['area', 'contact_length', 'contact... |
5e33bb7b211b7452d21f801e64f9b518cf153d467b08adfd9c3925bf5f1eb6b8 | Jupyter | 1,484 | 66 | # %%
import sys
import os
from os.path import dirname, realpath, join
base_dir = dirname(dirname(os.getcwd()))
import pandas as pd
from os.path import join
sys.path.insert(0, base_dir)
# %%
from config_path import PROSTATE_DATA_PATH, PLOTS_PATH, DATA_PATH
# %%
ge_file = join(PROSTATE_DATA_PATH,'processed/outputs_su... |
847f149c98ad2e68fb962caa741da4c07a1096c802cb710731dbe0779931e56b | Jupyter | 1,487 | 67 | # %% [markdown]
# ## Align Dendrite populations globaly
# %%
# import os
# os.environ["JAX_PLATFORMS"] = "cpu"
import numpy as np
# import vedo as vd
import pandas as pd
import NeuRosetta as nr
# import GeoJax as gj
# %%
col_df = pd.read_csv('/home/ndrummond@neuro.mpg.de/Desktop/Column_cell_type_ids/column_assignme... |
210c367684c52be2b9bed9025221c8432b38dedb6de96369c64f543c34ba3987 | Jupyter | 1,509 | 51 | # %%
# Script for NB-NC contact duration ROC analysis shown in Figure S2
import numpy as np
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import pandas as pd
# %%
df = pd.read_csv('CombinedMatrixPreWindowFirstSignFrameFINAL0925.csv')
df = df.dropna(subset=['area', 'contact_length', 'conta... |
f3fbf33712315c7451215ccaf3740c45b8b3b23ca3e5816a07eaba262f4d4daa | Jupyter | 1,509 | 38 | # %% [markdown]
# # TREC-repurposed Relevance Matrix
# %% [markdown]
# Prerequisites
#
# 1. Prepare the TREC relevance pairs file using the instructions given [here](https://github.com/zbmed-semtec/doc2vec-doc-relevance/blob/main/Docs/relevance%20matrix/TREC-simplified.ipynb).
# %%
import pandas as pd
os.chdir('../P... |
36ec9d23fa0efe947272f56e2e179f7ee324ea3abc5f2d185771c24bf16a881d | Jupyter | 1,520 | 90 | # %% [markdown]
# # Load & Parse Data
# %%
from utils.parse_data import load_dataset_main
dataset = load_dataset_main()
# %%
# %%
# %% [markdown]
# # Train Model
# %%
from utils.training_utils import run_loso_cv
from models.lstm import alstm_model_generator
n_roi, H = 19, 3
model_gen = lambda : alstm_model_ge... |
f8b091f300314312993e01277d2abe1d03dc156536a047113a2b529797f86563 | Jupyter | 1,545 | 66 | # %%
import sys
import os
from os.path import dirname, realpath, join
base_dir = dirname(dirname(os.getcwd()))
import pandas as pd
from os.path import join
# %%
sys.path.insert(0, base_dir)
from config_path import PROSTATE_DATA_PATH, PLOTS_PATH
# %%
mapping_file = join(PROSTATE_DATA_PATH, 'raw_data/sample_mapping.ts... |
bc3985d7bbd57ea1c8bb5e27812fdbb1de524dac1ada4b35465c234b14eb4cb6 | Jupyter | 1,598 | 58 | # %%
import numpy as np
a = np.load(r"E:\MSImaging-data\_msi_visual\Extractions\NRL4485-s2-new\5_bins\2.npy")
d = np.load(r"E:\MSImaging-data\_msi_visual\Extractions\NRL4485-s2\5_bins\2.npy")
print(a.max(), d.max())
print(a[30:40, 30:40].mean(), d[30:40, 30:40].mean())
print(a[:, :, :10].max(), d[:, :, :10].max())
pr... |
35499b51ce07d26d98be686952850bbfa8a7f78cf2b1fc0268b642d25ba64fb7 | Jupyter | 1,602 | 52 | # %%
import numpy as np
a = np.load(r"E:\MSImaging-data\_msi_visual\Extractions\NRL4485-s2-new\5_bins\2.npy")
d = np.load(r"E:\MSImaging-data\_msi_visual\Extractions\NRL4485-s2\5_bins\2.npy")
print(a.max(), d.max())
print(a[30:40, 30:40].mean(), d[30:40, 30:40].mean())
print(a[:, :, :10].max(), d[:, :, :10].max())
pri... |
0b8e74781365600f251244fdbd458dacfb4a849e6ae7caa694fd47cc08ded959 | Jupyter | 1,608 | 59 | # %%
# Script for NC apical area ROC analysis shown in Figure S2
import numpy as np
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import pandas as pd
# %%
df = pd.read_csv('CombinedMatrixPreWindowFirstSignFrameFINAL0925.csv')
df = df.dropna(subset=['area', 'contact_length', 'contact_durat... |
4466fb678bd157899489b187ca2c2d1c9cdfebbc41ce9b70531c1b430e5a7537 | Jupyter | 1,608 | 78 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/data/zebrafish_pair0.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
# a... |
991749350e345415d5e0f41f326f0ec759392c64b76a6b222a81b1471dbe00ad | Jupyter | 1,610 | 78 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/data/sample_pair.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
# assum... |
6ce77cb443702590333f878402e1f55b9cdb1fe9f5a559c0cc239d3497e59a9a | Jupyter | 1,615 | 78 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/data/zebrafish_pair1.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
# a... |
f631b1f2a4948446658cf0490b2fec0e7d6000e35cb24230129cf3c81d826167 | Jupyter | 1,628 | 63 | # %%
from binn import BINN, BINNDataLoader, BINNTrainer, BINNExplainer
import pandas as pd
# Load your data
data_matrix = pd.read_csv("../binn/data/sample_datamatrix.csv")
design_matrix = pd.read_csv("../binn/data/sample_design_matrix.tsv", sep="\t")
# Initialize BINN
binn = BINN(data_matrix=data_matrix, network_sour... |
f8d3f2d049bfd3d91baa55dc10f02ac003938777c15fe31983a27910c74b2fdd | Jupyter | 1,655 | 49 | # %%
import numpy as np
import matplotlib.pyplot as plt
# %%
# Load the data
data = np.load("Fig5.npz")
ActorToPartner = data["ActorToPartner"]
PartnerToActor = data["PartnerToActor"]
Modulation = data["Modulation"]
# %%
# Plot the data for Actor to Partner
fig, axs = plt.subplots(1,1)
# Figure 5A (left)
fac = axs.im... |
4157be5634b942c5641d65c5938db1ad4ea0398bb8918f12b01cf32264dd84df | Jupyter | 1,668 | 46 | # %%
library(spacexr)
library(Seurat)
library(ggplot2)
library(cowplot)
library(dplyr)
library(RColorBrewer)
cols = c(brewer.pal(9, "Set1"),brewer.pal(8,"Set2")[1:8],brewer.pal(12,"Paired")[1:12],brewer.pal(8,"Dark2")[1:8],brewer.pal(8,"Accent"),brewer.pal(12, "Set3"),brewer.pal(9,"Pastel1"),brewer.pal(8,"Pastel2"))
co... |
1ed346faa42193da9c0b37bc3084905bc48ba191d7d07b092704184cf6d3ae99 | Jupyter | 1,710 | 57 | # %%
import scanpy as sc
import os
from scib_metrics.benchmark import Benchmarker, BioConservation, BatchCorrection
import pandas as pd
# %%
scGPT = sc.read_h5ad("./data/scGPT_neftel_ss2.h5ad")
scGPT_cancer = sc.read_h5ad("./data/scGPT_cancer_neftel_ss2.h5ad")
CancerGPT = sc.read_h5ad("./data/CancerGPT_neftel_ss2.h5ad... |
6bca788d1c3a5a31fef15ad4bb6e621dbca0a7fdcf5247f0828cccf62d674a3a | Jupyter | 1,734 | 71 | # %% [markdown]
# # Figure S3
# %%
import sys
import os
root_path = os.path.abspath("..")
sys.path.append(root_path)
os.environ["JAX_PLATFORMS"] = "cpu"
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
import vedo as vd
import pickle
# from src import columns
from src.columns import Col... |
d03c02729c70b95944e702f06a0cf31e83c39bf2e7cbc5e822e556267c257a78 | Jupyter | 1,822 | 70 | # %%
import os
from dn_utils.behavioral_models import load_behavioral_data
from os.path import join
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from dn_utils.misc import tidy_data
from statsmodels.stats.anova import AnovaRM
from dn_utils.style import ORANGE, BLUE
# %%
p... |
eaec4936d12f1955ad70dc6bd0eb807ace62964311ce50056d93ec82235f4a31 | Jupyter | 1,838 | 59 | # %% [markdown]
# # Import packages
# %%
from src.utils import read_gtf
from scripts.splice_sites_annotation import add_phyloP, gtf_to_SJ
import polars as pl
import seaborn as sns
import matplotlib.pyplot as plt
# %%
ORFanage_ss = pl.read_csv("export/ORFanage_splice_sites.csv")
# %%
intronic_seq = read_gtf("nextflow... |
13e96c2422367c8b913a8bed63d5bb449affb5640542a5bac34935757af2a2e4 | Jupyter | 1,846 | 49 | # %%
import numpy as np
import matplotlib.pyplot as plt
# %%
# Load the data
data = np.load("Fig4.npz")
PartnerToActor = data["PartnerToActor"]
PartnerNotToActor = data["PartnerNotToActor"]
Modulation = data["Modulation"]
# %%
# Plot the data for Partner to Actor
fig, axs = plt.subplots(1,1)
# Figure 4A
sub = axs.ims... |
2481a99949fc31ccf54b93ff9b7ccfb6246419e203a8426d323d5c7c93ef7dd3 | Jupyter | 1,858 | 57 | # %%
############## Applicable Environment tereopy-1.5.1 #########
##############################################
import stereo as st
import warnings
warnings.filterwarnings('ignore')
# %%
st.__version__
# %%
import os
import glob
# %%
############ Extract all SN chip numbers for looping.
# Specify directory path
di... |
77fdd5dd2a70138d4bee48d5510256823a36b7778d8592e7dca21485f7704e8c | Jupyter | 1,859 | 68 | # %% [markdown]
# # Inference with MOJO - PyTorch version from HuggingFace
# %% [markdown]
#
# [](https://colab.research.google.com/github/instadeepai/nucleotide-transformer/blob/main/notebooks/mojo/inference_mojo_pytorch_example.ipynb)
# %%... |
b9976583e23aa477ab297667af9d2bfc22ab08bc296dc8426d3653b0d6f67fa4 | Jupyter | 1,870 | 71 | # %%
# Script that plots pre-transcription apical areas of presumptive NBs and NCs shown in Figure 3
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import ttest_ind
# Load dataset
df = pd.read_csv("AreasInPreSignallingWindows.csv") # replace with your actual file name
# ... |
5359b10e65d3b506b021758890fdba5ae64cb1ae84fd1bc1ef3cc54cd3c58093 | Jupyter | 1,872 | 73 | # %%
import pandas as pd
import torch
import scipy
import numpy as np
import scanpy as sc
import h5py
import pickle
import pylab as plt
import seaborn as sns
sns.set_context('paper')
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
# %%
fn_bin_strategy = './data/fig_bin_strategy.csv'
df_bin_strateg... |
6ab4d40d7ae900f02aa6f56aff9f0cd5b6631e2e92b96ce44749db31fc97f12a | Jupyter | 1,872 | 58 | # %%
import cv2
import numpy as np
import os
import tifffile
def crop_masked_regions(image_path, mask_path, output_dir, padding=10):
# Read the image and mask
image = cv2.imread(image_path)
mask = tifffile.imread(mask_path) # Use tifffile to read the mask
# Convert mask to 32-bit float if it's no... |
3561e7afe6e9259408003ac3e7447ea52d90508f446b32307878ddf38c87507a | Jupyter | 1,874 | 59 | # %%
import cellxgene_census
# %%
# or, directly open the census (don't forget to close it!)
census = cellxgene_census.open_soma()
# %%
var_df = cellxgene_census.get_var(census, "homo_sapiens")
# %%
var_df
# %%
var_df.n_measured_obs.max()
# %%
var_df.feature_name.str.contains("ENSG").sum() # 40k cannonical genes
... |
7c582fe3418b7d99eb6e33cd8e43417b643d94834a6401cae575417bccc3727b | Jupyter | 1,875 | 68 | # %%
import os
import sys
sys.path.append("..")
import warnings
warnings.filterwarnings('ignore')
from string import ascii_uppercase, ascii_lowercase
alphabet_list = list(ascii_uppercase+ascii_lowercase)
from utils.pdb_processing import seq_to_pdb, get_pdb_chains
from utils.protein_mpnn import score_complex, protein... |
083354eb6d020ddcedf331ca5e3b876f8153b09071a9a33eb638bcf0d334d411 | Jupyter | 1,891 | 90 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/data/zebrafish_pair3.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
# a... |
a95a14c252a8447c13f1aea45d338bda8bdf3fe23d7b1d426aacf104d30e1792 | Jupyter | 1,897 | 90 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/data/zebrafish_pair2.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
# a... |
bc6747737ed61adf0e63bc9035b681038824666bc5518b2621bba541a150a4bb | Jupyter | 1,904 | 56 | # %% [markdown]
#
#
# # Example 2: Plotting volumetric data
#
# This example shows how to project data from a NIFTI volume onto a surface, and
# then display the result.
#
# Data is a Left vs Right hand localizer contrast (t-map) for a single subject
# of the Midnight Scan Club dataset [1]_. Data is obtained fro... |
aabdeb465c1012cec4f6490179733cf865d9d6cf6e59e770b627db2e0cd7a7d0 | Jupyter | 1,906 | 87 | # %% [markdown]
# # Import modules
# %%
%reset -f
%load_ext autoreload
%autoreload 2
import sys
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy
import seaborn as sns
#from figure_functions import plot_trajectory
from utils import wrapToPi
from PFN_model import PFN, Build... |
d628d011a384acb854b53a8c158dbbf1b021422342f66c3a8cfa690a0d5ec067 | Jupyter | 1,919 | 81 | # %%
import os
import sys
import random
import matplotlib.pyplot as plt
import numpy as np
from prl_task import create_blocks, create_schedule
from dn_utils.style import BLUE_TASK, YELLOW_TASK, MPL_STYLES
%matplotlib inline
# %%
path_root = os.environ.get('DECIDENET_PATH')
path_code = os.path.join(path_root, 'code'... |
601e7499d509e15c15be13faacfb77c73e3f26a3814c057e0f63f58f911e94e2 | Jupyter | 1,936 | 55 | # %%
import os,sys
sys.path.append(os.path.realpath(os.path.join(os.getcwd(), '..')))
import warnings
warnings.filterwarnings("ignore")
# %%
import scanpy as sc
import pandas as pd
import CPS
# %%
opt = CPS.config()
args = opt.parse_args(['--seed', '2026'])
args.prep_scale = False
args.sigma=0.8
args.dataset_path = '... |
9e8d51e6aaaa7552705bfd56b257f97bcb03cd092a6c5265a827174c0e85a251 | Jupyter | 1,953 | 59 | # %%
from scipy.interpolate import interp1d
from scipy.special import hyp2f1, beta, gamma, gammaln
from numpy.random import uniform
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from statsmodels.distributions.empirical_distribution import ECDF
# %%
def rpdf(n, rs):
""" n = number of samples... |
2d894cb89fb07556caa2c1216dde5a853fbe9a5a8112076521a93a4fda74d3db | Jupyter | 1,972 | 69 | # %% [markdown]
# # Node profile dissimilarity visualization
#
# > **Analysis type**: Multiple γ (visualization)
# %%
import json
from os.path import join
import numpy as np
import pandas as pd
from dn_utils.path import path
# %%
atlas = "combined_roi"
gamma_range = np.arange(0.5, 2.5, 0.5)
# %%
path_corrmats = jo... |
a7f2008b7c8dec0a8b1d10643d64f4eb298299c3054948a4e4d7afe4e6fd2bcf | Jupyter | 1,990 | 69 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from oneqmc.analysis import HARTREE_TO_KCAL
from oneqmc.analysis.plot import set_defaults
set_defaults()
# %%
SAVE_FIGURES = False
# %%
df = pd.re... |
cc773ff2d7420d891f7c34430d245f8d8d2cb7bede1ec013c26aaf1e648ba9ee | Jupyter | 2,000 | 71 | # %% [markdown]
# # Simple Kernel SHAP
#
# This notebook provides a simple brute force version of Kernel SHAP that enumerates the entire $2^M$ sample space. We also compare to the full KernelExplainer implementation. Note that KernelExplainer does a sampling approximation for large values of $M$, but for small values ... |
d41001dfbc30cd56f23d4af2ffc73c75290966c348fadf3a01f73dcc4255c1ce | Jupyter | 2,003 | 78 | # %% [markdown]
# # Inference with BulkRNABert - Jax version
# %% [markdown]
# [](https://colab.research.google.com/github/instadeepai/nucleotide-transformer/blob/main/notebooks/bulk_rna_bert/inference_bulkrnabert_jax_example.ipynb)
# %% [mar... |
7d39caf386a26923fcecbae3ff246d80632269e339cea70d0cd4c4debb92e955 | Jupyter | 2,006 | 26 | # %% [markdown]
# ## Brachyury expression levels predict lineage potential and axis-forming ability of in vitro derived neuromesodermal progenitors.
#
# Anahí Binagui-Casas$^{1,*,\#}$, Anna Granés$^{1,*}$, Alberto Ceccarelli$^{2,\$}$, Matthew French$^{3,\$}$, Filip J. Wymeersch$^{4}$, Rosa Portero$^{1}$, Jen Annoh$^{1... |
bd9e989f37a52c58db5f5797c57f5f8b71d2f5285bcbe2814813bd8decfb65a4 | Jupyter | 2,023 | 88 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/test_data/mouse_pair7.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
# ... |
e0f5d3d2d46fdca88ae30e33c804b0b23bffc9f4cacb72bf4d7aec7fe709b744 | Jupyter | 2,023 | 88 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/test_data/mouse_pair6.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
# ... |
4f457c9c71e7aeb6725650c735d306ee0762b3cad6562036cf65db3276504849 | Jupyter | 2,028 | 96 | # %%
import torch
import os
import numpy as np
import matplotlib.pyplot as plt
from libs.codes import *
from libs.data import load_mindboggle
torch.cuda.memory_allocated('cuda')
# %%
moving_paths = np.loadtxt('partitions/validation.csv', dtype=str)
fixed_paths = np.loadtxt('partitions/fixed.csv', dtype=str)
# %%
... |
4f5536ed72c577002790df55e3c3ef1e8d38e77c4f9a840f4fd94491ccba1338 | Jupyter | 2,029 | 88 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/test_data/mouse_pair5.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
# ... |
f63eef5007bee7bb0e61938dfe55ca3344eca06f9ab563e5eadbf71addbee60c | Jupyter | 2,033 | 79 | # %% [markdown]
# # Inference with MOJO - Jax version
# %% [markdown]
# [](https://colab.research.google.com/github/instadeepai/nucleotide-transformer/blob/main/notebooks/mojo/inference_mojo_jax_example.ipynb)
# %% [markdown]
# ## Installatio... |
5aab93e1c61f1134d3ffb7087f45a1f39f1c53293e5d02333f77f8f348e41b09 | Jupyter | 2,035 | 92 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/test_data/zebrafish_pair5.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]... |
e4f241abb110ae6ccdaaf1514c3ec9aefc3b1324fda2e425051f0fadf83d867f | Jupyter | 2,035 | 92 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/test_data/zebrafish_pair6.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]... |
ed545e25d8cc5300527922a8ed4c494019c348f6a29fa3f4158565ac12186ab1 | Jupyter | 2,035 | 92 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/test_data/zebrafish_pair7.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]... |
da6714343a96f0d3dfe36e2d68b3dfff1c85ae4040a2f7ec64b065e89c04f999 | Jupyter | 2,044 | 44 | # %% [markdown]
# ### Force Plot Colors
#
# The dependence and summary plots create Python matplotlib plots that can be customized at will. However, the force plots generate plots in Javascript, which are harder to modify inside a notebook. In the case that the colors of the force plot want to be modified, the plot_cm... |
910a3bdf2e079e7b985430ce7dfc79451f0d34c1599a95186b6f9c63c552c70d | Jupyter | 2,059 | 73 | # %% [markdown]
# # Inference with BulkRNABert - PyTorch version from HuggingFace
# %% [markdown]
# [](https://colab.research.google.com/github/instadeepai/nucleotide-transformer/blob/main/notebooks/bulk_rna_bert/inference_bulkrnabert_pytorch_... |
8911eef43f7f2fe9a61c233cdde94955499fa3f4e101dd1999505ff0a4634666 | Jupyter | 2,063 | 66 | # %%
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
# --- Parameters ---
nPaths = 100 # number of Brownian bridges
nPoints = 26 # 0–25 inclusive
x = np.arange(nPoints)
# --- Simulate Brownian bridges ---
dW = np.random.randn(nPaths, nPoints - 1)
W = np.cumsum(dW, axis=1)
W =... |
863a321ffe13341fef339d3d7dcc2275f641aca7c2bc2d71c54f07d0a8954091 | Jupyter | 2,073 | 67 | # %%
import joblib
import numpy as np
import glob
from PIL import Image
import msi_visual
from importlib import reload
from msi_visual import parametric_umap
reload(parametric_umap )
from keras.callbacks import Callback
class MyCallback(Callback):
def on_epoch_end(self, epoch, logs=None):
Percentile Ratioi... |
1d57677d214752c4815a2a596c177d481d7f430691d956ee537fe8734bb20e57 | Jupyter | 2,079 | 62 | # %%
import sys
import os
from os.path import dirname, realpath, join
base_dir = dirname(dirname(os.getcwd()))
base_dir
import pandas as pd
from os.path import join
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn import metrics
# %%
sys.path.insert(0, base_dir)
from config_path import PROSTATE_... |
b09c7edb0ab08614bd87c305e3b3081d1cfb8a26c316fcd824c5bf520dfdf899 | Jupyter | 2,082 | 105 | # %%
import numpy as np
from matplotlib import pyplot as plt
import matplotlib as mpl
import scipy.signal as signal
import pandas as pd
import glob
import seaborn as sns
import scipy.ndimage as filters#changed from scipy.ndimage.filters as the namespace was deprecated 05/24/2023
import pickle
import scipy.stats as stat... |
f1a691871029b421f9eb386c114eb0cb932f28de54f8f9fef34e78b43978ff41 | Jupyter | 2,102 | 72 | # %%
import os
import sys
sys.path.append("..")
import warnings
warnings.filterwarnings('ignore')
from string import ascii_uppercase, ascii_lowercase
alphabet_list = list(ascii_uppercase+ascii_lowercase)
from utils.pdb_processing import seq_to_pdb, get_pdb_chains, ld_convert
from utils.protein_mpnn import score_comp... |
ab554a3e7d53b52b802cde0bbb850790cb5193a21e5db5ba65c52dd95bdfe1a9 | Jupyter | 2,132 | 62 | # %% [markdown]
# ### Vertex (Node) data for dendrites
#
# Create a dataframe of node level value metrics for each dendrite and save as pickle
# %%
import NeuRosetta as nr
import pandas as pd
from NeuRosetta.utils.geometry_utils import euclidean_distance
# %%
path = "/home/ndrummond@neuro.mpg.de/Datasets/Paper_data... |
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