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|---|---|---|---|---|
c2fa5e3a8adffb13f1208fb6e108db839c23583fbbbb9e41ecfbdf6d270981d7 | Jupyter | 6,787 | 196 | # %% [markdown]
# # Basic concepts of Mol2vec
#
# This notebook covers basic concepts of [Mol2vec](https://chemrxiv.org/articles/Mol2vec_Unsupervised_Machine_Learning_Approach_with_Chemical_Intuition/5513581) from generation of "molecular sentences" to plotting embeddings obtained with Mol2vec.
# * [Encoding substruct... |
f4f4b263f0ea14dea014a3ee924df95b7f22d270d76b4ce3125d0254f1296e09 | Jupyter | 6,802 | 256 | # %%
##
# Original author: @PavithraaSeenivasan
from pathlib import Path
import sys
from dotenv import load_dotenv, find_dotenv
load_dotenv()
PROJECT_ROOT = Path(find_dotenv()).parent
sys.path.append(str(PROJECT_ROOT.joinpath('src')))
print(f"Project root directory: {PROJECT_ROOT}")
import numpy as np
import plotly.g... |
be830cf4a9ef0ab8fc657701998d262051a74ea471accc45cd46df2c1b3b151e | Jupyter | 6,811 | 201 | # %% [markdown]
# (monitor)=
#
# # Saving & monitoring progress
#
# It is often useful to incrementally save the state of the chain to a file.
# This makes it easier to monitor the chain’s progress and it makes things a little less disastrous if your code/computer crashes somewhere in the middle of an expensive MCMC ... |
d42485da9522a4f20ef3c0d90967a82ba5f582fde085a8a9a4789f7c65f0bd25 | Jupyter | 6,839 | 213 | # %% [markdown]
# # OcampoATAC2
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weight... |
a76e4ed389e8ce00d70710a79cc033abc492945cbd8afbe3c892140f5813a1a4 | Jupyter | 6,883 | 203 | # %%
# Import of the libraries
import warnings
warnings.simplefilter('ignore')
import ipywidgets as widgets
from IPython.display import display, clear_output, Image
import numpy as np
import pandas as pd
import nibabel as nib
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from os.path import join, isd... |
edcf17706ec843dfa27934abb51bba2eaa5bee5e7cb1ebc073e758ffb11ea7cb | Jupyter | 6,884 | 225 | # %% [markdown]
# # PCHannum
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-i... |
b85ca7efd8d25d55f40617ccf3f47b4390290795d4df399a42011051843d9cd3 | Jupyter | 6,888 | 190 | # %% [markdown]
# # Example spike-sorting analysis with sample data
#
# This tutorial is also available as a [collab notebook](https://colab.research.google.com/drive/1gFZa8TEBDXmg_CB5RwuT_52Apl3hP0Ie?usp=sharing)
# if you would like to try Kilosort4 without installing the code locally.
# %% [markdown]
# ### 1. Downl... |
4a1c3d613b8f8e4c8d72a262799000e876fe2fe94176f6f916d001a08ecca745 | Jupyter | 6,907 | 178 | # %% [markdown]
# This tutorial assumes that you have installed NeatMS and familiarised yourself with the tool through the documentation at https://readthedocs.org/NeatMS. Example data and the default model used here are available on [NeatMS github repository](https://github.com/bihealth/NeatMS). The example data is co... |
367e5543359f6fd9e74f9574df92cc024c57598700d279df072f4a1846335d7b | Jupyter | 6,937 | 245 | # %% [markdown]
# # Figure 4D
# ## *in vivo* vs. *ex vivo* propagation
#
# For "Network-level encoding of local neurotransmitters in cortical astrocytes" (Cahill et al. 2024)
# %% [markdown]
# ---
# %% [markdown]
# ## Setup
# %% [markdown]
# #### Imports
# %%
from pathlib import Path
import numpy as np
import pan... |
bf192851f5877d8d5b96ff1522dec92c5634c28f221599a8286900610c504c11 | Jupyter | 6,938 | 203 | # %% [markdown]
# # TwelveCellDeconvoluteBloodEPIC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base ... |
5e462b2ad6257da7f1107c0f2790b4c59e4f133ab04dbdb5201b8cb0c67879a1 | Jupyter | 6,940 | 203 | # %% [markdown]
# # TwelveCellDeconvoluteBloodEPIC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base ... |
ab017d1d8d001ca1031b8c693e2b10d53b29abc4696957fb0390b19093572c5c | Jupyter | 6,942 | 203 | # %% [markdown]
# # TwelveCellDeconvoluteBloodEPIC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base ... |
0afa642987a4f249abf17e8588a21a67c574f2f7045a02d3a0f17813562386d6 | Jupyter | 6,943 | 203 | # %% [markdown]
# # TwelveCellDeconvoluteBloodEPIC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base ... |
2230de1b364150f064cfc8800ff060eb7637ec9ab3836984b35d7e270bd9262c | Jupyter | 6,946 | 203 | # %% [markdown]
# # TwelveCellDeconvoluteBloodEPIC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base ... |
ec0a666bb726e0c2c538dd3201795aafd9992f88aa818ad32cc694c463c39669 | Jupyter | 6,950 | 203 | # %% [markdown]
# # TwelveCellDeconvoluteBloodEPIC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base ... |
8f197f1b326e3af58bacc3810d9c85ba52256203424ce3cd72f31a4ce2cdf948 | Jupyter | 6,958 | 203 | # %% [markdown]
# # TwelveCellDeconvoluteBloodEPIC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base ... |
a605b7b60e785c585d70eda48c4846bef31e07ece7b91c204f1bd22597e4202f | Jupyter | 6,959 | 203 | # %% [markdown]
# # TwelveCellDeconvoluteBloodEPIC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base ... |
3b840423e2ae0a154e684cecbcf854a0850ad0fdbc5d623401e56e1dba075d8c | Jupyter | 6,960 | 203 | # %% [markdown]
# # TwelveCellDeconvoluteBloodEPIC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base ... |
a1a3174017d12e8a8f82c5202630eb9f163f66c0c2762e11da92a78f30e57be0 | Jupyter | 6,960 | 203 | # %% [markdown]
# # TwelveCellDeconvoluteBloodEPIC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base ... |
2e6a951e22e3eebb004d1147c7bc17c0cf0dc7cb4538ebad555fc3a2082ef16b | Jupyter | 6,964 | 203 | # %% [markdown]
# # TwelveCellDeconvoluteBloodEPIC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base ... |
d989edcc957fc9fc054641ad93c0d3f3867fbaa9e84649aa04d4be99d3027bb1 | Jupyter | 6,964 | 203 | # %% [markdown]
# # TwelveCellDeconvoluteBloodEPIC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base ... |
1ce71417202511918a199fc6854816726bea9ed78dc7de4f7db9baeb8e77645a | Jupyter | 6,970 | 162 | # %%
import pandas as pd
pd.set_option('display.max_rows', 1000) # or 1000
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 150
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
import seaborn as sns
import pingouin as pg
... |
536b19fdb6b1a89a791cec0ce7781a536a71f67d7789b8af7cba4b69510d3f44 | Jupyter | 6,979 | 149 | # %%
import pandas as pd
import numpy as np
import seaborn as sns
from scipy import stats
import matplotlib
import csv
import math
from matplotlib import pyplot as plt
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
# %%
count=pd.read_excel('seahorse count.xlsx', index_col=0)
well=list... |
c2992cc5e133f8a7f79d2c5a3a0689188ff6dcc6ee291857e50055cf559e1cb9 | Jupyter | 6,981 | 202 | # %% [markdown]
# # 2021-02 LC HPC Axon GcAMP TFC Analysis
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
# change pkg_path variable to the directory storing the package
pkg_path = '/Users/jake/Code/'
sys.path.insert(0, pkg_path)
from photometry_analysis impor... |
f88ffddfc599cb8152f9970d0e4d5811a342b4182f6265b2a82a6926c0572d74 | Jupyter | 6,987 | 225 | # %% [markdown]
# # PCPhenoAge
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights... |
441fc81c7c83dc088903f9852db84ed56daa29335dfcad04901d68f6097f2d1e | Jupyter | 7,010 | 193 | # %% [markdown]
# # MammalianLifespan
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-... |
5d371a5393ec1c278fddbb2cfacee4d65f6f0492b846370c70a17ee421d9c379 | Jupyter | 7,024 | 47 | # %% [markdown]
# # HPS
#
# Healthspan Proteomic Score (HPS) from the original Kuo lab R release: 86 Olink Explore 3072 plasma protein NPX values plus chronological age in years. Returns the modeled probability of remaining free of the study's major-disease-or-death endpoint over the next 10 years, on a 0-1 scale; hig... |
78d481b3fac8c1d2b87c4f2001fe590d61962205d6e26f848521554ec5592b55 | Jupyter | 7,048 | 236 | # %%
from samalg import SAM
import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
import scanpy as sc
# %%
samsporo=SAM()
# %%
samsporo.load_data('sporocysts3.counts.csv')
# %%
samsporo.preprocess_data()
# %%
samsporo.run(seed=20)
# %%
samsporo.scatter()
# %%
samsporo.dispersion_ranking_NN(s... |
3058126dea4d0ae6f8ee883b30445dc3b3f0593ce0d59fb52bdfa3681e344cab | Jupyter | 7,051 | 225 | # %% [markdown]
# # PCHorvath2013
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weig... |
0d5a52142b5a753649b2edcbaefea6a433e219b8c77192fe5bc0bf59fe7ef034 | Jupyter | 7,059 | 172 | # %%
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
import networkx as nx
import utils_description as utils
import networkx.algorithms.community as nx_comm
import numpy as np
# %%
sns.set(style="whitegrid", font='Arial', font_scale=1.)
meanprops = {'markeredgecolor': 'black', 'markerf... |
20eb6a4305a7d23373e5b35a5104e9ea4e8429db7b5015c9fd5a7bbefdafbffb | Jupyter | 7,076 | 173 | # %% [markdown]
# # Explainability: Grad-CAM and AAL2 region ranking
#
# Runs the native 3D Grad-CAM (`multimodal_ad.models.gradcam`) on a tiny synthetic volume/model, then the AAL2 region-ranking utility (`multimodal_ad.models.regions`) against the real AAL2 atlas bundled at the repository root (`atlas.nii.gz` + `AAL... |
bd3e1da319a07536f2bed15e910b167d3662ef7cdbfd31923e637a0df0982160 | Jupyter | 7,101 | 225 | # %% [markdown]
# # PCDNAmTL
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-i... |
5bd6b718a16b23fd031d99c433ba8b0878a62432932727d72952cdfc96742079 | Jupyter | 7,108 | 225 | # %% [markdown]
# # PCSkinAndBlood
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-wei... |
88c4ea71e7fd2948fbc831502c5fc09ef8df40a0589234b3e8d31ffcb95501f8 | Jupyter | 7,157 | 209 | # %% [markdown]
# # CamilloH3K9ac
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weig... |
712c1e4471bf48314f74cfbd163bf19c4f106dbb16faa600aa2e21e12bb50b94 | Jupyter | 7,165 | 209 | # %% [markdown]
# # CamilloH3K9me3
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-wei... |
c5ae1465cf42fcb2fa4d899991d8277ca7d12dfecd9b9e789d01ee841cf89d89 | Jupyter | 7,165 | 209 | # %% [markdown]
# # CamilloH3K4me1
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-wei... |
04137b9019fa71736474d421642fac6572f9c9c053c7ca6506d5be62433298d9 | Jupyter | 7,166 | 209 | # %% [markdown]
# # CamilloH3K27ac
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-wei... |
82221ba9c5bdec92b9c88d995cdb9f3ff52f9fc4ed49945f04fed3c20ea7393c | Jupyter | 7,166 | 209 | # %% [markdown]
# # CamilloH3K4me3
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-wei... |
e0b9abe8bf941519b90fbde35636849212e2182986224b0ad09cc14b4793a68f | Jupyter | 7,169 | 238 | # %% [markdown]
# # Example queries — main_survey_database_v2
#
# Database: `/scratch/duckdb-database/main_survey_database_v2.duckdb`
#
# Schema (5-level hierarchy):
# - `patients` → `brain_regions` → `cell_types` → `cells` → `oligomers`
#
# Key columns:
# - `cells.oligomer_concentration_per_um3` = `n_puncta_in_cell... |
4222e54029b9609532436d87e7a25d2b0f080b82d310028bb40bc997d46b504e | Jupyter | 7,172 | 209 | # %% [markdown]
# # CamilloH3K36me3
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-we... |
c328d998a44243dfd72c45991395e7b09c5402e279b07c865a4006ebec2a7313 | Jupyter | 7,172 | 209 | # %% [markdown]
# # CamilloH3K27me3
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-we... |
1669f3bb9dc059b60fcc7ebab2d2f205b4147560883014d1abedb629bf19a597 | Jupyter | 7,193 | 224 | # %% [markdown]
# # MammalianSkin2
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-wei... |
926d2eb4f871c57bab6d5b601803a948d671fcab94e9935539fccd2f47e4a5ac | Jupyter | 7,194 | 209 | # %% [markdown]
# # CamilloPanHistone
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-... |
745946f329a5168c487ca4a43b3ffe9bac1020765182a1fd303e61a83d8f7a5c | Jupyter | 7,201 | 224 | # %% [markdown]
# # MammalianBlood2
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-we... |
27db919a2f1190f2a95becf71b5232a5366882f0a42962ae9187d380ba62202e | Jupyter | 7,204 | 203 | # %% [markdown]
# # Graph network model training for SODAS prediction
#
# This notebook will guide you through graph network model training and inference for predicting the SODAS (structural orderness degree for atomic structures) metric. Before we proceed, make sure to install all the neccessary packages (please see ... |
8a4cf8e5ba8ea78dc9ef91944f68ba0c6a1d65ac249887830509e3825f147a58 | Jupyter | 7,213 | 224 | # %% [markdown]
# # Mammalian2
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights... |
9d4644163db9e9407640ddd8ad335a7721be536b88190195f464bd0bdb5b44ad | Jupyter | 7,215 | 224 | # %% [markdown]
# # MammalianSkin3
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-wei... |
f8b1a44d8f8f2b6158c08581158b89dc4e545bed4848089c68a5babfa4afdbec | Jupyter | 7,219 | 204 | # %%
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import networkx as nx
import networkx.algorithms.community as nx_comm
from sklearn.preprocessing import StandardScaler
from pingouin import partial_corr
import neurocognitive_analysis_utils as cog
import warnin... |
9d79a389509cde34462ef7717a8f77ee29247c8864c23d2037f7dbec1fb8f000 | Jupyter | 7,221 | 224 | # %% [markdown]
# # Mammalian3
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights... |
654f09901bfa0e21a6e8dc7d212388476aa3e8849a2cba08acf7b1783df16677 | Jupyter | 7,224 | 224 | # %% [markdown]
# # MammalianBlood3
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-we... |
8da160eeee14425c9a830865e292ea8eb50e7ea206a58e1bdbe9674bf7083cc7 | Jupyter | 7,243 | 204 | # %% [markdown]
# # Stubbs
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-int... |
c37cb6ec33d62c702a8b8805df11052be6abbf4368165ca6b9342d3a56e55077 | Jupyter | 7,266 | 185 | # %% [markdown]
# # NeuSin
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-int... |
aad9fc1ba26dc4e294e9d321818187862e16c33264f3a066446bb9870cb06e05 | Jupyter | 7,269 | 256 | # %% [markdown]
# # LC ArchT TFC 120s ITI Analysis
#
# ### Table of Contents
#
# 1. [Experiment Info](#ei)
# 2. [Imports and Set Up](#is)
# 3. [Training](#tr)
# 1. [Load and Clean Training Data](#lctr)
# 2. [Plot Training Data](#pltr)
# 4. [Tone Test](#to)
# 1. [Load and Clean Tone Test Data](#lcto)
# ... |
297f89ea15c152c83278469accdcdd6faaf80cf5e89d4ec888758816b5e9e0d8 | Jupyter | 7,364 | 207 | # %% [markdown]
# # CpGPTGrimAge3
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weig... |
46c3b96fc94b70e894c1eef202346e460a9885315d11b35b494123fb813c3dbb | Jupyter | 7,398 | 212 | # %% [markdown]
# # Merge normative model
#
#
# Suppose two remote locations independently train a normative model on their own patient population. These remote locations do not want to share their data with you. They can however share their trained models with you, and you can merge them into one bigger model that w... |
ea0b63663a0461a2eb529ccd34bae8f2a33773169c9e978e947e33363d7c88f8 | Jupyter | 7,459 | 250 | # %% [markdown]
# # StocZ
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into... |
df8d571581149d91c284a0d03e9aca5cf4eed00098d7dd55d7c70f2acc6bc03e | Jupyter | 7,467 | 250 | # %% [markdown]
# # StocH
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into... |
47f842b84f1001e8928ec9d6260fa67991138223d000c61a0830552946fbcc78 | Jupyter | 7,482 | 47 | # %% [markdown]
# # PAOPAC Conventional
#
# Conventional PAOPAC from the authors' MIT v1.0.0 release: 800 LightGBM trees with 189 Olink protein predictors and TDI, plus chronological age for cohort correction. Supply plasma Olink Explore 3072 NPX (assay-normalized log2 relative abundance), not linear abundance, concen... |
10b6a0564869e8833103e498f7cf02261c8485660b8ccbbcdcbc86f30d01d0e2 | Jupyter | 7,497 | 250 | # %% [markdown]
# # StocP
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into... |
2ae22b57ce754640088fe29dde8fd8f632349998bb738e6fbb640cedfb189b6b | Jupyter | 7,498 | 260 | # %%
import matplotlib.pyplot as plt
import torch
import numpy as np
import cv2
from ELD.utils import circle_size, colors, toImg, preprocess
from ELD.model import loadFan#, predict_landmarks
from torchgeometry.contrib import spatial_soft_argmax2d
from glob import glob
import tifffile as tiffv
from skimage.transform imp... |
37a1abf4ee84f3af9ef3559b642b8d2f37c96b827ccc48c0b63e1056fc2e9c8d | Jupyter | 7,514 | 235 | # %%
from __future__ import generators
import logging
import glob, os, functools
import sys
sys.path.append('../')
import SimpleITK as sitk
import numpy as np
import scipy
import nibabel as nib
import skimage
import matplotlib.pyplot as plt
import scipy.misc
from scipy import ndimage
from skimage.transform import re... |
5ba723eee04537dd565a232681733e4dbb9c44ffa70883f34d43cb3fac7a925c | Jupyter | 7,545 | 237 | # %% [markdown]
# # Pasta Mouse
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Map to mouse orthologs](#Map-to-mouse-orth... |
a350b5ae4de636fc36f9b569677d1e55449324c658ad1838804c7be0cd61338d | Jupyter | 7,554 | 234 | # %% [markdown]
# # DNAmFitAgeVO2Max
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-w... |
bb193a762f8831e61b2195822c6969858fb5208fda45336c7bd5454525dbdc12 | Jupyter | 7,560 | 187 | # %% [markdown]
# [](https://colab.research.google.com/github/PTB-MR/mrpro/blob/main/examples/notebooks/direct_reconstruction.ipynb)
# %%
import importlib
if not importlib.util.find_spec('mrpro'):
%pip install mrpro[notebooks]
# %% [markdo... |
2f607824175c579a9457a9332c20e923435a07d84fb716fdde1cec4f57a7cfc4 | Jupyter | 7,565 | 210 | # %% [markdown]
# # AltumAge
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-i... |
a5fa33fcd6b2b34a5acf1e081d1ef3714c4ac10a746b09a470c27670e58e7ee5 | Jupyter | 7,565 | 237 | # %% [markdown]
# # ⚡ Train and visualize a Lightning Pose model ⚡
#
# Using a toy dataset (a.k.a. "mirror-mouse") with 90 labeled images from Warren et al., 2022 (eLife).
# * [Environment setup](#Environment-setup)
# * [Train (via PyTorch Lightning)](#Training)
# * [Monitor optimization in real time (via TensorBoard ... |
bff0daedcc6a4d1eca2c2bb4525d107e7ac4249cfadde2b81750bae6f182d0ac | Jupyter | 7,628 | 222 | # %% [markdown]
# # Fit normative models on a compute cluster
#
# This notebook will go through the options of the runner class. We will show how to fit and evaluate a model in parallel, and how to do cross-validation.
#
# You can run this notebook on a login node, but it is recommended to run it on a compute node. ... |
311884f2ed14d4fdd5d0e47d22bd8ea353bcae9e5df2499302a171557aeebc02 | Jupyter | 7,670 | 291 | # %% [markdown]
# # Extended Data Fig. 7C
# ## Multi-round response overlap
#
# For "Network-level encoding of local neurotransmitters in cortical astrocytes" (Cahill et al. 2024)
# %% [markdown]
# ---
# %% [markdown]
# ## Setup
# %% [markdown]
# #### Imports
# %%
# Typing
from numpy.typing import(
ArrayLike,... |
ae4e1697758f441e0cdb7fdc00af334b4f0c1db94fbd5ed086c47937ce1b0366 | Jupyter | 7,711 | 331 | # %% [markdown]
# # Figure 4I
# ## NT decoding
#
# For "Network-level encoding of local neurotransmitters in cortical astrocytes" (Cahill et al. 2024)
# %% [markdown]
# ---
# %% [markdown]
# ## Setup
# %% [markdown]
# #### Imports
# %%
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
f... |
05a6a5cfe48af3233f6c699dc45270399216f884eb3ff58f95ffce8888d410ef | Jupyter | 7,713 | 236 | # %% [markdown]
# # Score dynamics
#
# This notebook will guide you through (conditional) diffusion model training and inference for iteratively generating future molecular configurations based on an initial condition.
# %%
import torch
import numpy as np
# %% [markdown]
# ## Data storage format
#
# Each molecular ... |
614e2a851a09a9c7c4fb6baf3cf7ab59f518ecde26f06fcdab16bd454cc28d11 | Jupyter | 7,723 | 189 | # %%
from __future__ import absolute_import, division, print_function
import argparse
from datetime import datetime
import numpy as np
import os
from keras import backend as K
from keras.callbacks import EarlyStopping, TensorBoard
import numpy as np
from sklearn.metrics import roc_auc_score
from data_handler import ... |
6381ec926f43e934e3f3d057ce2363cc0526770da2578a690e0450dba64fca69 | Jupyter | 7,732 | 209 | # %%
import figure_traub_eigensources as fte
import numpy as np
import h5py as h5
import scipy
import matplotlib.pyplot as plt
from traub_data_kcsd_column_figure import (prepare_electrodes, prepare_pots,
set_axis)
import os
# %%
def plot_eigensources(k, v, start=0, stop=6):
lett... |
ae4d4b69f8ca38029c7145665d57cd3c7155a47e1eb6818bbd21bcbe86317632 | Jupyter | 7,774 | 305 | # %% [markdown]
# # Gene_Disease_overlap
# %% [markdown]
# ## import libraries
# %%
#import libraries
suppressMessages({
library(biomaRt)
library(tidyr)
library(dplyr)
library(ggplot2)
library(GeneOverlap)
#library(overlapper)
})
# %%
setwd("/home/nhk/Desktop/")
# %%
#Create a list for al... |
db6ca53f631890f7df5161839e73e6ebbfd9c747ad318590d40616bad2605e26 | Jupyter | 7,787 | 169 | # %% [markdown]
# # DNAmFitAgeGrip
#
# Reproducible conversion of the published DNAmFitAge female and male grip strength regressions into one sex-gated pyaging artifact. The model keeps each coefficient table and reference-median vector independent; the public `female` input selects the corresponding branch.
# %% [ma... |
0062ac2465ea2d106ecf859893bc845b052c566a6e2b5902f4d0f6394c550a1e | Jupyter | 7,818 | 170 | # %% [markdown]
# # DNAmFitAgeGait
#
# Reproducible conversion of the published DNAmFitAge female and male gait speed regressions into one sex-gated pyaging artifact. The model keeps each coefficient table and reference-median vector independent; the public `female` input selects the corresponding branch.
# %% [markd... |
9f4269af2bd5e08d71cd8894616519f40ec824e18baa4ed8d3e3645fe0b804a3 | Jupyter | 7,819 | 135 | # %% [markdown]
# # fMRIPrep preprocessing details
# %% [markdown]
# Results included in this manuscript come from preprocessing
# performed using *fMRIPrep* 20.2.1
# (@fmriprep1; @fmriprep2; RRID:SCR_016216),
# which is based on *Nipype* 1.5.1
# (@nipype1; @nipype2; RRID:SCR_002502).
#
# ## Anatomical data preproces... |
6b5f84fa7bbc8a5e0f28cce25eeb1475ff6da2a9ee057f67809b1c706997b4c8 | Jupyter | 7,827 | 291 | # %% [markdown]
# # Import qbiome
# %%
import qbiome
from qbiome.data_formatter import DataFormatter
from qbiome.quantizer import Quantizer
from qbiome.qnet_orchestrator import QnetOrchestrator
from qbiome.mask_checker import MaskChecker
from qbiome.forecaster import Forecaster
from qbiome.hypothesis import Hypothesis... |
2ca2553086c0c962bedfc870613a46b195091146d2b6b6863ced9ca1e0a66878 | Jupyter | 7,875 | 229 | # %%
import numpy as np
import os
from sklearn.metrics import confusion_matrix
#import seaborn as sn; sn.set(font_scale=1.4)
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import cv2
import tensorflow as tf
from tqdm import tqdm... |
35ff7a1e5667cf743c6e50e4e6e2d1fd714361f3b411ec43835365276ccd108d | Jupyter | 7,875 | 300 | # %%
"""
This cell does the initial project setup.
If you start a new script or notebook, make sure to copy & paste this part.
A script with this code uses the location of the `.env` file as the anchor for
the whole project (= PROJECT_ROOT). Afterwards, code inside the `src` directory
are available for import.
"""
fro... |
091dbb10feeefeea7b8758abdff0d23d77cdb2d83113e32f9c7863663430e346 | Jupyter | 7,883 | 168 | # %%
# hide
%load_ext autoreload
%autoreload 2
# %% [markdown]
# # USPTO data Exploration
#
# > Looking into the USPTOyield data
# %%
# hide
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# %%
# hide
default_plot_kwargs = {
'color': '#541FA3',
}
title_fontsize = 18
l... |
b929262788b7458905012386c3e219d7a933bce5e55c4783ad4b7f2283f8d726 | Jupyter | 7,899 | 241 | # %%
# Step1: Load The Data
from Bio import SeqIO
# Function to load data from a FASTA file
def load_fasta_file():
sequences = []
virus_types = []
for record in SeqIO.parse(file_path, "fasta"):
sequences.append(str(record.seq))
virus_type = record.description.split()[
0
... |
058a0515e22d7ce2b18f9f0c9878aa221226dabda846d8424dd995ff47316834 | Jupyter | 7,912 | 276 | # %% [markdown]
# # **Locally Low Rank (LLR) Tutorial**
#
# ### Introduction:
#
# * LLR is an effective regularization technique
# * LLR enforces low rankness in the spatial-contrast dimension
#
# ### Authors:
#
# * Zhengguo Tan <zhengguo.tan@gmail.com>
# * Patrick A Liebig <patrick.liebig@siemens-healthine... |
5aca676659bb6c3f5ded1c377c0cb722a2e537ca18f3b18e513617c204fcdd52 | Jupyter | 7,918 | 183 | # %% [markdown]
# ## Feature Selection based on self organizing maps
# This notebook presents a way to achieve feature selection through self organizing maps. The code is based in an algorithm proposed in this [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-89935-0_6). **_The selection process strongly de... |
1d23445fb06e7fdeefdab18bf8754a2fa906238cc6b9abe52a9524a9b9cd7d3f | Jupyter | 7,971 | 207 | # %%
# figure S11-13: measuring brain alignment
# useful packages:
import torch
import torchvision.transforms as transforms
from torchvision import models
from scipy import stats
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy
import scipy.io as sio
from scipy.stats import pears... |
75035f8ad9fb07f45d96ce1aeb3d652f730589ce79e92d2997415540f5c918ae | Jupyter | 7,972 | 236 | # %% [markdown]
# # Save NIDM-Results packs to NiMARE dataset
# NOTE: This will ultimately be replaced with a simple `nidm2nimare` function in `nimare.io`.
# %%
import json
from glob import glob
import nibabel as nib
import pandas as pd
import numpy as np
from scipy import ndimage
from os.path import basename, join, i... |
461e448960038de2d9471fed1dc305772fbbbeb293671ba192c1b27ffd4f1da3 | Jupyter | 7,979 | 237 | # %%
#load libraries
import os
import glob
from eumap import parallel
from eumap.parallel import TilingProcessing
from eumap.raster import read_rasters, save_rasters
from eumap.misc import ttprint
from eumap.misc import find_files
import re
import pandas as pd
import numpy as np
import rasterio
from pathlib import Path... |
1509f99f331fa6901fe32b1bcdf14576da2d08cf96fe41b36e2ef194e86fa9cd | Jupyter | 7,980 | 268 | # %% [markdown]
# # Import qbiome
# %%
import qbiome
from qbiome.data_formatter import DataFormatter
from qbiome.quantizer import Quantizer
from qbiome.qnet_orchestrator import QnetOrchestrator
from qbiome.mask_checker import MaskChecker
from qbiome.forecaster import Forecaster
from qbiome.hypothesis import Hypothesis... |
d2d907821a3f6836e52bd70e3a617733a7d45b21394f1770a6d59506810049e8 | Jupyter | 7,987 | 208 | # %%
import scipy as sp
import scipy.io
import os
import numpy as np
import pandas as pd
import glob
import csv
import random as rand
from tqdm import tnrange, tqdm_notebook
from collections import Iterable
import matplotlib.pylab as plt
import random as rand
from ipywidgets import *
from scipy import stats
import impo... |
3088d5c664458c5a044ae1422b7f214f8ff03a9776e4270168750eae33746761 | Jupyter | 7,988 | 265 | # %%
%reset -f
%whos
# %%
%reset -f
%whos
#Misc
import time, os, sys, pdb
from glob import glob
from fnmatch import fnmatch
#Base
import numpy as np
import pandas as pd
import scipy.io as sio
import scipy.stats as st
from scipy.ndimage import gaussian_filter1d
from scipy.stats import zscore
#Save
import json
import... |
e0b771cd6c487eda3ee4fc5cef7084ea792f4f016310c7c6eeefeeb200a7a2d0 | Jupyter | 7,997 | 338 | # %%
library(ComplexHeatmap)
library(ComplexUpset)
library(dplyr)
library(ggplot2)
# %%
library(extrafont)
font_import(paths = "/Library/Fonts", prompt = FALSE)
loadfonts()
fonts()
# %%
plot_theme <- function() {
theme_bw() + theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
p... |
73cf34705391e690aeceb281dc8abff049712ab620b40abbf10cd7ead6fe888d | Jupyter | 8,001 | 193 | # %%
from stl import mesh
from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt, operator as op
import glob
from pathlib import Path
import seaborn as sns
from collections import defaultdict
import pandas as pd
# %%
base_path = Path("/Users/sbandya/Desktop/neurodegeneration_vacuole_quantification/inputs/")... |
30cfc37a3a59fd6b6289951dd8606ba8849b209a7dffdd9efccb41ba48338b22 | Jupyter | 8,028 | 252 | # %%
import os
import sys
sys.path.insert(0, os.path.abspath('../'))
import captum
import torch
import torchtext
import torchtext.data
import torch.nn as nn
import torch.nn.functional as F
from torchtext.vocab import Vocab
from captum.attr import LayerIntegratedGradients, TokenReferenceBase, visualization
from tor... |
81837694b2c3efff02ff36e1861524c538d3276db51c7a906d632a91a175a074 | Jupyter | 8,097 | 266 | # %%
import logging
import os
import warnings
import pandas as pd
import seaborn as sns
import pcntoolkit.util.output
from pcntoolkit import (
HBR,
BsplineBasisFunction,
CompositeBasisFunction,
NormalLikelihood,
NormativeModel,
NormData,
make_prior,
plot_centiles_advanced,
)
sns.set_s... |
972e396a94a8932520a6baa9a2b2b0a862210f0a4532bd92c771446aeb3902c6 | Jupyter | 8,098 | 194 | # %%
# default_exp augmentation
# %% [markdown]
# # Data Augmentation
#
# > Improve predictions doing data augmentation
# %%
# export
import random
import math
import numpy as np
import pandas as pd
from rdkit import Chem
# %%
# hide
from rxn_yields.data import generate_buchwald_hartwig_rxns
# %%
# hide
test_df = ... |
a1d6e72939d58c8b277d957afe6732d96fd97d79876695588d3a0bc00bef7000 | Jupyter | 8,134 | 245 | # %% [markdown]
# # GRAB-NE DREADDS
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
# change pkg_path variable to the directory storing the package
pkg_path = '/Users/jake/Code'
sys.path.insert(0, pkg_path)
from photometry_analysis import fp_dat, tfc_dat, fp_vi... |
d586c766998401f6d1b58039f7485c5589bc972aa827ad1554a76d7dbd079209 | Jupyter | 8,134 | 299 | # %% [markdown]
# # Tutorial
# %% [markdown]
# ### Setting up environment
# %%
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"]="1"
# %%
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
from skimage.io import imread, imshow
import sea... |
48a89760efd568abe9e9418348f4d2399d08798fa84b4a36606d58ef22687404 | Jupyter | 8,153 | 226 | # %% [markdown]
# ### What's SC-Track?
#
# ```
# SC-Track is an efficient algorithm for dynamic tracking of single cells on different time-lapse microscope images. It can use the segmentation results of various models to efficiently track single cells and reconstruct cell lines. It can track multi-generational cell di... |
d95078ebe48532295c14130bfcc1d4ac3b9da69f1c520534e1b898a1b67c0c5d | Jupyter | 8,168 | 202 | # %% [markdown]
# # Total-Variation based Bayesian Image Deblurring
#
# In this notebook, we will perform image deblurring via a Bayesian *Maximum a Posteriori* (MAP) approach. Our image prior will be composed of a total variation (TV) term and a positivity constraint.
#
# The forward model defining the blurring pro... |
3fae27547a49511ed72aa896fa0e1d53a4248f4e616a3187fe8f8b5160de7b44 | Jupyter | 8,169 | 256 | # %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
# change pkg_path variable to the directory storing the package
sys.path.insert(0, '../utility_code')
import fp_dat, fp_viz
import seaborn as sns
import statsmodels.api as sm
import scipy.stats as sci
# %%
fp_viz... |
f7d792219e1f8adf688958d5b3bd80a9026dcc2174818dfe3d80e4558c2ac96e | Jupyter | 8,197 | 290 | # %% [markdown]
# <h1 style="font-size: 24px;">Example Code: Least-squares Generative Adversarial Network to generate microbubble signals</h1>
# <h1 style="font-size: 14px;">Modified from 'tutorials/beginner_source/dcgan_faces_tutorials.py'</h1>
#
#
# %%
import os
import random
import torch
import torch.nn as nn
imp... |
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