sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
889081131c481d4ca209d759643877fa9dd1334cd445149bd1fcdde2c58b410e | Jupyter | 20,607 | 484 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
# %%
#monkey patching violion plot
import matplotlib as mpl
import warnings
from seaborn.categorical import _Categorica... |
126ad1b7fccbac7e98177662101c31ece12576f8fbf538171f50ba8661da719f | Jupyter | 20,625 | 487 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
import pymaid
import navis as nv
# %%
#monkey patching violion plot
import matplotlib as mpl
import warnings
from seabo... |
026149b377c1acbe46993179200819356fdcfa9614cf53864e9dd1d9032c9ad6 | Jupyter | 20,644 | 484 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
# %%
#monkey patching violion plot
import matplotlib as mpl
import warnings
from seaborn.categorical import _Categorica... |
0a6ee57704f6ba2f1930bc2d9f73015391b8a914ce0ffe9baa9adad3e7585bf2 | Jupyter | 20,651 | 487 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
import pymaid
import navis as nv
# %%
#monkey patching violion plot
import matplotlib as mpl
import warnings
from seabo... |
dc6e315167ce7061b665f56d3cc4bbade145eda0dfa2043595a521e71b1f7156 | Jupyter | 20,828 | 611 | # %% [markdown]
# # Event Scheduling & Time
# %% [markdown]
# **Important:**
# - If you are just exploring Mesa and want the fastest way to execute the code we recommend executing this tutorial online in a Colab notebook. [](https://colab.research.googl... |
122e38a28b0396fb4c1180b33272eef691ea0b5af71f646eb3b290f84cc584e7 | Jupyter | 21,251 | 614 | # %% [markdown]
# # Molly exercise
#
# This exercise will introduce you more to the [Molly.jl](https://github.com/JuliaMolSim/Molly.jl) package for molecular simulation. Julia 1.10 and Molly 0.22.3 or later are recommended. Basic familiarity with Julia and molecular dynamics concepts is assumed. You may find the [Moll... |
595a44589bbc43ba621dc54198dd6a7d9d726a5f3555b71ec7d783471b6bc18b | Jupyter | 21,309 | 641 | # %% [markdown]
# ## Figure 4
#
# The input files are available at [our repository on Zenodo](https://doi.org/10.5281/zenodo.19499423).
# %%
import os
import numpy as np
import pandas as pd
from PIL import Image
Image.MAX_IMAGE_PIXELS = 553190400
from statsmodels.stats.proportion import proportion_confint
from mat... |
80145fee7887c757d4071f3650630b4b83b84e14fce4baf3ce94e12475a972e6 | Jupyter | 21,535 | 506 | # %%
#allslow
# %% [markdown]
# # Example: Constructing RNAmaps
#
# > An example of how to perform RNAmap analyses
# %% [markdown]
#
# If we want to create an RNAmap based on the skipped exons in an rMATS analysis, we first need a table listing each of the splice sites in the rMATS output. We can use the [`splicefo... |
2fd23aabeddb3a9bf5a5764200049516cb28fa4bee55d17746e0bc09be6e839f | Jupyter | 21,756 | 509 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
import cld_lettering
# %%
def create_ordered_post_hoc_result(measurement_df):
measurement_df_long = pd.melt(measureme... |
a2dba648654610b611263931f0e1a40ae9e0214dbd49a88b7d909b64b15acba6 | Jupyter | 21,787 | 509 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
import cld_lettering
# %%
def create_ordered_post_hoc_result(measurement_df):
measurement_df_long = pd.melt(measureme... |
229d15ab7dbdc80460a0ad224d4344315bfd32e917c1f298a1a460c67ce034cb | Jupyter | 21,892 | 509 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
import cld_lettering
# %%
def create_ordered_post_hoc_result(measurement_df):
measurement_df_long = pd.melt(measureme... |
70386a6ceed21e5b7b45f14d50cc08afabf620584e8332ff19b46c7603a69cab | Jupyter | 21,917 | 509 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
import cld_lettering
# %%
def create_ordered_post_hoc_result(measurement_df):
measurement_df_long = pd.melt(measureme... |
42a49734223aa0010f03c0b632c69671e5c7a937478abbe1777bfb00b07624c4 | Jupyter | 21,924 | 509 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
import cld_lettering
# %%
def create_ordered_post_hoc_result(measurement_df):
measurement_df_long = pd.melt(measureme... |
222aae6edca106bdd602f6322a715af0d0b63266495d29e915d73211e58c2be4 | Jupyter | 21,950 | 509 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
import cld_lettering
# %%
def create_ordered_post_hoc_result(measurement_df):
measurement_df_long = pd.melt(measureme... |
63e4fd78c646825ca96e94b75e9d50eea0e6e13304804f77d3f0bcd1a46390a7 | Jupyter | 22,321 | 508 | # %%
import os
os.environ["OMP_NUM_THREADS"] = "1"
import sys
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import patches
import umap
from scipy.stats import mannwhitneyu
from spatial_separation.classification_utils import perform_cross_validation
from sklearn.mixture import GaussianMixture as GMM... |
91996daccf674d6b613eaa4eba174d86540c1b13e6356f76182f857b5147ac26 | Jupyter | 22,596 | 738 | # %% [markdown]
# # Occlusion robustness analysis
#
# Robustness of learned feature selectivity (left-convex boundary element) to progressive visual occlusion.
#
# **Overview**
#
# - Load inference recordings with occlusion applied at multiple levels
# - Compute stimulus-specific information measures for L4 neurons ... |
5175e9f70e7c3349034b90416830c63da5141ec6869d136e90cd42a5d0644437 | Jupyter | 22,993 | 532 | # %%
import numpy as np
import pandas as pd
import os
import pickle
from pathlib import Path
import psutil
import re
import matplotlib.pyplot as plt
# %%
root_path = "C://Users//franc//Documents//Mapping_Recurrent_Inhibition_minimal_data_to_run_scripts//one_example_experimental_MUedit_file//"
mu_properties_filename = ... |
4b28f1b2ab0e56c39d0a050b83d75fc89464068f5aa584b422c4b0fdbcff9f58 | Jupyter | 23,318 | 627 | # %%
from neurodsp.spectral import compute_spectrum
from neurodsp.utils import create_times
from neurodsp.plts.spectral import plot_power_spectra
import pingouin as pg
from fooof import FOOOFGroup
from natsort import natsorted
from os import listdir
from os.path import join
import numpy as np
import pandas as pd
imp... |
ca71686d987e8b504aeef9a30b882d529ac2fa6fa45989629ad5c8ebeef8a96b | Jupyter | 24,340 | 794 | # %%
import os, sys
from pathlib import Path
PROJECT_ROOT = Path("/home/mame_hil")
os.chdir(PROJECT_ROOT)
sys.path.insert(0, str(PROJECT_ROOT))
os.makedirs("output/ana", exist_ok=True)
from abx_app.AttackCNN.generate_image_from_condition import generate_image_from_condition
import torch
import torchvision.transform... |
926bffb059e5c3476ddf43552d98f0cf4a4c6abbc79e039c288ef68d20b7319a | Jupyter | 24,817 | 623 | # %%
#default_exp bamfiles.converters
# %% [markdown]
# # bamfiles.converters
#
# > A submodule for condensing bamfiles. I could probably have used pre-existing tools, but I wanted to code for the exact outputs that I desire.
# %%
#export
from tqdm import tqdm
import numpy
from pyranges import PyRanges
import pandas... |
e438e60fb27dbaf582a87ca8619d38e4ea4c5715942b67499b67f05aae5e0854 | Jupyter | 24,892 | 696 | # %%
import os
# https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html#
#https://allensdk.readthedocs.io/en/latest/_static/examples/nb/ecephys_quickstart.html
from ipywidgets import FloatProgress
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle
from allensdk.brain_... |
5990e88ea08671673011cbdff6ddcf48536ca3514f87d14d3b73b79a46a08a96 | Jupyter | 24,998 | 544 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
# %%
#monkey patching violion plot
import matplotlib as mpl
import warnings
from seaborn.categorical import _Categoric... |
2efc5b0a4c3709b9ac3f160e838365653b42b2035555462336c7cd42022f340c | Jupyter | 25,437 | 543 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
# %%
#monkey patching violion plot
import matplotlib as mpl
import warnings
from seaborn.categorical import _Categorica... |
4f2758db3225e7e00010508ea12cc9fdc3cacfca420f9c67496cd0de0bb30017 | Jupyter | 25,480 | 705 | # %% [markdown]
# # Shapley value analysis for Chemprop models
# %% [markdown]
# This notebook demonstrates how to perform Shapley (SHAP) value analysis for a chemprop model. In addition, it also serves as an example on how to customize chemprop featurizers.
#
# * Example 1: Shapley value analysis to explain importan... |
e54aeb74544f319bc145b433d0e82665cf74da8cc9e554c45164c0b7ba892f2c | Jupyter | 25,592 | 543 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
# %%
#monkey patching violion plot
import matplotlib as mpl
import warnings
from seaborn.categorical import _Categorica... |
a7a70be4fa87a141a63164388b53e57bbe915401084ec80423d8d1a6963b34f7 | Jupyter | 25,708 | 580 | # %%
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.gridspec as gridspec
# %%
plt.rcParams["font.family"] = "arial"
plt.rcParams["font.size"] = 7
plt.rcParams['axes.linewidth'] = 0.5
plt.rcParams['xtick.major.width'] = 0.25
plt.rcPar... |
9d757981bd0fdf25b8de4c35b628021f9c70f89b787430d7f9f0ddfb5044221d | Jupyter | 25,780 | 543 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
# %%
#monkey patching violion plot
import matplotlib as mpl
import warnings
from seaborn.categorical import _Categorica... |
16cddf77d895a314a9f6337fde8cafbf608b02f03f46e52b5b5c1c4bbc2970cd | Jupyter | 25,792 | 543 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
# %%
#monkey patching violion plot
import matplotlib as mpl
import warnings
from seaborn.categorical import _Categorica... |
1d0056da980cadec2ad22b01d50959279ed85defbeaef35f8fcd79e36f445dfc | Jupyter | 25,910 | 543 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
# %%
#monkey patching violion plot
import matplotlib as mpl
import warnings
from seaborn.categorical import _Categorica... |
030680c9678a1515f2ace7285da90c1739e2643cfc6e354889cc9b37bd1a9b3d | Jupyter | 26,045 | 543 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
# %%
#monkey patching violion plot
import matplotlib as mpl
import warnings
from seaborn.categorical import _Categorica... |
461e04285e20a813182f4baadbef096d8bc41828ea4635e5560edc8f5a971b8d | Jupyter | 26,952 | 613 | # %%
import os
os.environ["OMP_NUM_THREADS"] = "1"
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import patches
from scipy.stats import mannwhitneyu
from scipy.stats import pearsonr
from statsmodels.stats.multitest import multipletests
from analysis_utils import *
# %%
from sklearn.mixture import ... |
62036639bbc328bdbcd0a859f1ee5310a9774a5db0cc179003e25b7e9e8a2ead | Jupyter | 27,635 | 713 | # %%
import re
import gzip
import zipfile
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import subprocess as sp
import seaborn as sns
import tqdm
import time
import os
import io
import json
import shutil
import random
import yaml
import collections
# # to setup the environment for... |
f1e04dcd3f5c83d49745fa9d7c7dab7506f51a6d3ae38b717bbb0d2f41b40709 | Jupyter | 27,713 | 822 | # %%
import numpy as np
import pickle
import matplotlib.pyplot as plt
from scipy.spatial.distance import squareform, pdist, cdist
plt.rcParams['figure.figsize'] = (6.0, 4.0)
plt.rcParams['figure.dpi'] = 72.0
from utils import *
# %%
from scipy.optimize import lsq_linear
def getPermutedTensor(factors, lambdas, tensorX... |
46f45d956e8157c6ceef90be9f1428cce5b8e1a56f747c5257621206a15157c3 | Jupyter | 27,883 | 465 | # %% [markdown]
# # Organize data
# %%
import numpy as np
import pandas as pd
import scipy
import glob, os
from tqdm.notebook import tqdm
# %% [markdown]
# # Test
# %%
condition_arr = []
cell_num_arr = []
cutoff_arr = []
angle_arr = []
run_length_arr = []
frac = []
frac_percent = []
cutoff = 15
angle = 20
run_length... |
d7c0a6f72269936e2d6fec0b56f2665cf9467c31579290895a1d7a0f3d407748 | Jupyter | 27,998 | 696 | # %% [markdown]
# # Dipole Benchmark Against SPICE QM Dipoles
#
# This notebook compares force-field dipoles from OpenFF, Garnet, and Espaloma against the QM `scf_dipole` values stored in `SPICE-dipoles.hdf5`.
#
# The comparison is done on Garnet's training test split from `training/splits/molecules_test.txt`. The sp... |
8c2bd48b84245cfe8fa32cf77e89631388e9772f651f11bee087c82a5d6df308 | Jupyter | 28,878 | 697 | # %%
# Import general libraries
import numpy as np
import pandas as pd
import os
import glob
from pathlib import Path
import pickle
import json
import warnings
import h5py
from simulator import SimulationParameters, run_simulation
from analyzer import AnalyzesParams, analyze_data
# %%
parent_folder_to_load_from = 'C:... |
9232e17b4fb56d79be9ed58a5390676e7709229d62814da420e4b50fce11d59d | Jupyter | 29,753 | 834 | # %% [markdown]
# # Annotation
# %%
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
import numba
from numba.core.errors import NumbaDeprecationWarning, NumbaPendingDeprecationWarning
warnings.simplefilter("ignore", category=NumbaDeprecationWarning)
# %%
import scanpy as sc
import pan... |
11d945ab59fd715e90f10a29aec5d15a956db6d83041cebf3e3c32bec27a0626 | Jupyter | 30,393 | 557 | # %% [markdown]
# # Branch Analysis
#
# ##### This script identifies branch points on filaments. Credits to Marc Siggel (Kosinski/Mahamid lab) for the initial translation of matlab code to python.
# %% [markdown]
# ## Initialization
# %%
import numpy as np
import pandas as pd
from scipy.spatial.distance import cdis... |
c8c493cf08c463d1bb0633baeafedab0ff541169871fa26049acf64456074224 | Jupyter | 30,527 | 686 | # %%
import pandas as pd
import matplotlib.pyplot as plt
from fafbseg import flywire
import pymaid
import navis as nv
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
from sklearn import metrics
from sklearn.cluster import KMean... |
0e99fa202774c6bf89b8be2ad84a256047519069db165e81ead904da3b6cb3f2 | Jupyter | 32,820 | 761 | # %%
#default_exp inference.diagnostics
# %% [markdown]
# # experimental.diagnostics
#
# > A submodule containing diagonistics for interpreting inferred models.
# %%
#export
from katmap.utilities import progbar
from arviz import psislw
import pandas as pd
import numpy
import scipy, scipy.stats, scipy.special
import ... |
6ee0a6a458a9a62a0e9bf4275e798ed54e8ca85d790f90ed93f4d1c97899f2b2 | Jupyter | 33,442 | 725 | # %%
import pandas as pd
import matplotlib.pyplot as plt
from fafbseg import flywire
import pymaid
import navis as nv
import numpy as np
import seaborn as sns
import scipy.stats as stats
import statsmodels
import scikit_posthocs as sp
import sys
import scipy
import random
import string
from sklearn import metrics
from ... |
d03de895f1040ff038332f65634bd4656720eb4aedeffaad90154f7a9e9560b0 | Jupyter | 33,973 | 730 | # %%
# Import general libraries
import numpy as np
import pandas as pd
import re
import pickle
import torch
import itertools
from brian2 import *
from sbi import utils, inference
import matplotlib.pyplot as plt
import os
from pathlib import Path
import getpass
import psutil
import torch
from copy import deepcopy
from d... |
8b78e464815fa5248be3766d5a58e7c564e7e875162a529961416fbf110520f3 | Jupyter | 35,543 | 880 | # %% [markdown]
# # ECLIPSE — Part III: Clustering and Prioritization
#
# This notebook takes the two output CSVs from Part II directly as input.
# No Atlas files needed — all filtering and species proportion calculation
# was already done in Part II.
#
# ### Workflow
# ```
# eclipse_search_results_component_dark_gen... |
c13f91013e5c9f3a7ad4884bcdf2d926603c84c42f9c5657735aac143863bb5e | Jupyter | 39,394 | 873 | # %%
import os
os.environ["OMP_NUM_THREADS"] = "1"
import torch as tc
tc.set_num_threads(1)
import sys
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import patches
import re
import pickle
import umap
from sklearn.decomposition import PCA
from spatial_separation.classification_utils import *
from an... |
45565715404e0ff6f9584065f9e973887a3bd3da05b461e84dde072dfb5a8375 | Jupyter | 41,666 | 905 | # %% [markdown]
# # init
# %%
import os
import sys
import copy
import glob
import numpy as np
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
import pickle
from scipy import stats
import importlib
import time
import tifffile as tf
import shutil
from matplotlib.backends.backend_pdf import PdfPages
import jso... |
4b58738e516f308e6f23352d9123ea2efca9f8c8d894d9a03691cd672d39fc4c | Jupyter | 45,487 | 1,175 | # %%
#default_exp parsers
# %% [markdown]
# # parsers
#
# > A submodule containing classes and functions for organizing and parsing different file formats.
# %%
#hide
from nbdev.showdoc import show_doc
# %%
# %%
#export
import numpy
import scipy
from matplotlib import pyplot
import seaborn
import pandas as pd
imp... |
27b0da879304f3c66c89890c7e51879842505e54af117de1b0522e39d9aabf2c | Jupyter | 47,237 | 954 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
# %%
# setup the colours to use in the plots
openfe_color = '#8A2283'
fep_plus_color = '#50CAF5'
fep_plus_text_color = "#0078E4"
garnet_color = "tab:green"
# %% [markdown]
# # Introduction
# %% [markdown]
# This noteb... |
628c73f75c919dd48da7855609567277f9c27d6c500aab123ea2912a71162300 | Jupyter | 47,344 | 1,257 | # %%
#default_exp seq.summaries
# %% [markdown]
# # seq.summaries
#
# > A submodule containing classes and functions for summarizing sequences, such as in terms of motif similarity or GC-composition.
# %%
#hide
from nbdev.showdoc import *
# %%
#export
import numpy
import scipy
import scipy.signal
from matplotlib im... |
6b19ddf26f981119ea5a3140a8cfaacccb32fa881f9120d0e6357c95556bcaa6 | Jupyter | 48,325 | 1,316 | # %% [markdown]
# # init
# %%
import os
import sys
import copy
import glob
import numpy as np
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
import pickle
from scipy import stats
import importlib
import time
import tifffile as tf
import shutil
from matplotlib.backends.backend_pdf import PdfPages
import jso... |
17ae18721d70dd820b3012d7e984e435a4449342e81dc69ad478d71c31b5b106 | Jupyter | 48,768 | 1,096 | # %% [markdown]
# Quick inspection of the Excel file containing binary ML results to check sheet
# names, structure, and column layout.
#
# This cell:
# 1. Mounts Google Drive in Colab and loads the `SI_1_ML_Results_Binary_Multiclass.xlsx`
# workbook, printing all available sheet names to verify the file structure.... |
884967e6256d5a185bb14949e01be7ac058bb85042d154c4d3f5e0b49808cfc5 | Jupyter | 57,825 | 1,530 | # %%
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import cmasher as cmr
import os
import re
from matplotlib.colors import to_rgb
from scipy.stats import gaussian_kde
from scipy.ndimage import zoom
import matplotlib.cm as cm
from matplotlib.colors import Norm... |
5d2bc0f1ac4e160d1d06afb4141af37fd2424c10f81f20f2600771dd820ab3dd | Jupyter | 58,033 | 1,348 | # %%
#default_exp rnamap
# %% [markdown]
# # rnamap
#
# > A submodule containing the different classes need to construct an RNA map.
# %%
#hide
from nbdev.showdoc import show_doc
from matplotlib.ticker import ScalarFormatter, FormatStrFormatter
import matplotlib
# %%
#export
import numpy
import scipy
from matplotli... |
110682e6a3f61ee3d1385b2648f6911eb0259b98822f8ab75a4d68cc8ec05068 | Jupyter | 68,941 | 1,698 | # %% [markdown]
# # ECLIPSE — Complete Pipeline (All-in-One)
#
# Runs the full ECLIPSE pipeline in one notebook:
# **Part I** (darkness estimation) → **Part II** (two-track stratification) → **Part III** (DPPS scoring).
#
# > The MMseqs2 `easy-search` is run separately on the command line and produces the
# > `.m8` f... |
0bb74b01f2cd8dbfcb70db6d0b046bd4809e93026df60b8931bb1e71e791620c | Jupyter | 74,586 | 1,995 | # %% [markdown]
# # init
# %%
import os
import sys
import copy
import glob
import numpy as np
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
import pickle
from scipy import stats
import importlib
import time
import tifffile as tf
import shutil
from matplotlib.backends.backend_pdf import PdfPages
import jso... |
9a98083b4ded52126ddf97bf71a3047cbec507929276758ed94f2eb529f43654 | Jupyter | 81,304 | 1,546 | # %%
#hide
from katmap.commandline import MakeConfigCommandParser, UpdateConfigCommandParser
# %%
#hide
from nbdev.showdoc import *
import matplotlib.pyplot as plt
plt.rc("axes.spines", top=False, right=False)
# %% [markdown]
# # katmap
#
# > A library for interpreting splicing changes in terms of RBP binding.
# ... |
c175d9026ddcf91e5f951507410c2a20de4fab080c9f3b3d0487e1a959fc80a1 | Jupyter | 84,525 | 2,121 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.inspection import partial_dependence, PartialDependenceDisplay
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error, mean_absolute_percentage_error
from sklearn.model_selection import train_test_split, Gri... |
ff13ab2261a4db6521246e22910d7670c627b2484ae356f79e6325c6f278438e | Jupyter | 90,028 | 2,588 | # %% [markdown]
# <a href="https://colab.research.google.com/github/Armaan-Raina/Estrous-Phase-Classification/blob/main/Estrous_Classification.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # **Estrous Cycle Phase Classification**... |
af862ff3088545dcbac84a004e36b133b51c4213294bd019e5cd0fa56a5f36ab | Jupyter | 93,226 | 2,664 | # %% [markdown]
# # Project description: Survey
#
# This project develops a human-machine framework to classify frost-related features in archaeological thin sections, addressing the challenges of subjective and time-consuming manual analysis. We combine five different Convolutional Neural Network (CNN) models with a ... |
6883b905f593fc2ff4c34ee1f72d188c3581e60a3201899d34b5b6c637bc8023 | Jupyter | 96,676 | 2,293 | # %% [markdown]
# # init
# %%
import os
import sys
import copy
import glob
import numpy as np
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
import pickle
from scipy import stats
import importlib
import time
import tifffile as tf
import shutil
from matplotlib.backends.backend_pdf import PdfPages
from stats... |
624df307aefb447ccd36bbd4bad8d77e88ca8fccd61acb5f857b70cad3678c77 | Jupyter | 106,658 | 2,693 | # %% [markdown]
# # init
# %%
import os
import sys
import copy
import glob
import numpy as np
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
import pickle
from scipy import stats
import importlib
import time
import tifffile as tf
import shutil
from matplotlib.backends.backend_pdf import PdfPages
import jso... |
50cf49dc1876adcfa5ae71ec131dc95b8bba620e9ea104e92bc90126bee68dd1 | Jupyter | 113,832 | 2,290 | # %%
import utils
import numpy as np
import pandas as pd
import skimage
import scipy
from scipy.spatial import distance_matrix
from scipy.spatial.distance import cdist
import math
import os
import glob
import csv
import matplotlib.pyplot as plt
from tqdm.notebook import tqdm
import seaborn as sns
import mrcfile
import ... |
3054158799aa7a1df1addae92e59744500c664c275170e30637993f481c4743c | Jupyter | 155,638 | 3,384 | # %%
# Import general libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import glob
from pathlib import Path
import pickle
import json
import cmasher as cmr
import warnings
from matplotlib.gridspec import GridSpec
import gc
import re
import math
from scipy.stats import gaussian_... |
8c8a1433fc2b928063991125f22966f1cf294aa21cab3f4cb8e5cfa21a2b3f8e | Jupyter | 156,179 | 3,605 | # %% [markdown]
# Builds a rich human–AI alignment dataset by combining expert survey ratings,
# model predictions, and multiple consensus/difficulty metrics.
#
# This script:
# 1. Loads the expert voting survey (`voting_poll.xlsx`), separates the
# ground-truth row (ID=20) from expert responses, and decodes each
#... |
d28922be9fa2654237557f4a666b36cb75a0e98a109f856f34fcf6cea0d55a30 | Jupyter | 179,849 | 4,085 | # %%
#default_exp experimental.INLA
# %% [markdown]
# # experimental.INLA
#
# > A submodule containing implementing the KATMAP additive model and the PMC-INLA algorithm used for inference.
# %%
#hide
from nbdev.showdoc import *
# %%
#export
import numpy
import scipy
from functools import partial
from jax.tree_ut... |
7d529b43a152f6ae90646edf7c2c042aaea7fd9a19e2960a64d22ce1af59ccd9 | Jupyter | 187,773 | 3,824 | # %%
#default_exp mikesmaps
# %%
#hide
from nbdev import showdoc
# %% [markdown]
# # Mike's Inferential (Kinda Exact) Statistical Models Are Predicting Splicing
#
# > Contains functions and classes for performing analyses with the additive regression model.
# %% [markdown]
# Internal API:
#
# ```python
#
# from k... |
1de927b777da633fc4e3d698aa189a22269beb328827f083208fdda47e7aa3db | Jupyter | 200,024 | 5,297 | # %% [markdown]
# # Visualization of Dataset
# %%
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the participants.aparc.tsv data into a DataFrame
df2 = pd.read_csv('derivatives_fsaverage/freesurfer7.4.1/participants.aparc.tsv', sep='\t')
# Define the age intervals and correspondin... |
ede3950370f9347ec8fa956248b9c160011ef1ee22832b853473a1d026f08c04 | Jupyter | 200,296 | 3,839 | # %%
# Install dependencies that are not included in the environment
# Please make sur to have selected the right Python environment/kernel (mapping_RI_env) before running these commands
%pip install factor_analyzer
%pip install seaborn
%pip install PyWavelets
%pip install cmasher
%pip install fsspec
# %%
import os
im... |
19cdc7bf26b6653dae5ae8d157e0120380675e6aead7be9f58b27c102c691f4e | Jupyter | 200,603 | 4,590 | # %%
import numpy as np
import pandas as pd
import torch
from torch.distributions import constraints, MultivariateNormal
import random
import os
import inspect
import pickle
import re
from sklearn.neighbors import NearestNeighbors
import pathlib
import json
import ast
from tqdm.auto import tqdm
# For plotting
import m... |
65769bd8f1c4a89205d66a39f80fad5523d93c6c891a5938b3fb26dde9657646 | License | 279 | 7 | MIT License
Copyright (c) 2025 Haofei Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
… [and so on—paste full text from https://opensource.org/licenses/MIT]
|
28b26e3185c388f0bee7c0fffda58b5cfd1f4ad43839de482d486062d42de0a5 | License | 670 | 14 | Copyright 2011-2020 Biomedical Imaging Group Rotterdam, Departments of
Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
... |
f10f6c6957a1a5de0fe45ba9fa71cf50f40c3932e769343dd446f21463f56df2 | License | 696 | 16 | SMILES2Docking
Copyright (C) 2026 Adriano Marques Gonçalves
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is di... |
cb29146c4c1638ca446db0a43910d6ff850aeda01bbb0dc8cfa1f42f9d1a449e | License | 757 | 14 | Copyright 2026 Motional
Licensed under the Apache License, Version 2.0 (the "License").
You may not use the software in this repository except in compliance with the License.
You may obtain a copy of the License at:
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in wr... |
0a04c15243d33d76e4eaa054325f2b1af86b04b76adebb43380ebcdf00f61b2e | License | 924 | 18 | Creative Commons Attribution 4.0 International (CC BY 4.0)
Copyright (c) 2025 Morteza Esmaeili
This work is licensed under the Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the ma... |
5824b152b5f620e9c78f1e6824ac5b55a68dd83d38b1b54ce1f07a6fec344a36 | License | 1,050 | 7 | Copyright © 2026
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or ... |
81c17a78bd43f9df592d00dcac53218c936bbcd44a2b130a6108193e93eeb5fe | License | 1,057 | 7 | Copyright (c) 2024 Noam Teyssier
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, subl... |
99d992fdf80a283bea327f2adaf7304f0e94200105e122a793615097608f5a10 | License | 1,059 | 7 | Copyright 2019 Brain Products GmbH
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, su... |
61e2ad90a3b30a5d48d12438c79a2a1d585a4ef60f5317cdb6470d10b59405dd | License | 1,060 | 21 | MIT License
Copyright (c) 2026 Ray
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, s... |
32bdc2c38b75c0c1532878fa5fc4a5dae5f405ed8e44f70c82e3d63cb5fd96b0 | License | 1,061 | 21 | MIT License
Copyright (c) 2026 CBJYB
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute,... |
cb0c6c145cc9e8d45470b1855601229d14430bd5d6f552a0d6a91150a0889623 | License | 1,062 | 21 | MIT License
Copyright (c) 2026 Denys
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute,... |
d84f2785e586165502667c0622c579c09aead145f1d4edae6436066ef6da3924 | License | 1,062 | 21 | MIT License
Copyright (c) 2026 Emily
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute,... |
5db746ac3641e717ffb372a13b510fbea69d835a160e3a4159589c7bd022b213 | License | 1,063 | 21 | MIT License
Copyright (c) 2026 leohog
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute... |
ebdeaa05f41a5850a66021fdf80f3f60e78fb5d0dc25ecec184e6b902a1fad76 | License | 1,063 | 21 | MIT License
Copyright (c) 2019 ay-lab
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute... |
0887246832ee6b2f8254747f7911877ff53e0da1ca5c5da3bf79079ceb38946b | License | 1,064 | 21 | MIT License
Copyright (c) 2026, see DOI
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribu... |
118b331289eaf0382a67ab2be9e1162f0f36c80819f749ec1f1117c9953da382 | License | 1,064 | 21 | MIT License
Copyright (c) 2024 Narayan
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
55f330330a8ce45649dabff6b3a2c2d33e495e28bdb4a7d51be336d9861e0007 | License | 1,064 | 21 | MIT License
Copyright (c) 2020 zixuans
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
618b16e82fa2b1f7f78889be3d930fb66f65f0e32bb1fc3a676d1d62a8272980 | License | 1,064 | 21 | MIT License
Copyright (c) 2026 UVA-LIU
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
6e0eff88d9e13a32246429abbccec21748a360e329e87809f09f33628a842601 | License | 1,064 | 21 | MIT License
Copyright (c) 2026 Yu Pang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
884f90a39a09469add1226cab8cef9557021bdd38fd9f2c011fea11666275dbf | License | 1,064 | 21 | MIT License
Copyright (c) 2026 milecap
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
fb24115b517208bad70794cfb1094bd12bf05386eb991921a677e8342e6c4df5 | License | 1,064 | 7 | Copyright 2025 McGill University
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, subl... |
1fbdbf74e284b0f66e69981ac115c7b81eadda75e1720bb44916d8a05968383e | License | 1,065 | 21 | MIT License
Copyright (c) 2024 ehsansyh
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribu... |
82d642620291a58eaa448c9de432c43dd7157d3426fa6d0f9ef0ad384afa77c4 | License | 1,065 | 21 | MIT License
Copyright (c) 2020 Ali Khan
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribu... |
c12a8c399c016b9b01c7a1142f4ee5ab3ddd24e086de5f285a145f177a070e1d | License | 1,065 | 21 | MIT License
Copyright (c) 2026 BioX-NKU
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribu... |
d16ba326a1bd87609d337efad99983153653567f6267316de9c0ca076beab835 | License | 1,065 | 21 | MIT License
Copyright (c) 2026 hbyrne07
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribu... |
ddf7b6020ed2b1f208973382535393fa7e6ffbcafd3e72c0ea326234af8f025f | License | 1,065 | 21 | MIT License
Copyright (c) 2025 Zaineb18
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribu... |
3b7900ad72a749ee23f1125acd2f7340860b44c86ce356c83f96edc36f3dcb42 | License | 1,066 | 21 | MIT License
Copyright (c) 2026 YaromirKo
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distrib... |
40b66d913bf45e5f4475ea10c395eb5dc4523651168479531745fde0068e5410 | License | 1,066 | 21 | MIT License
Copyright (c) 2026 takaoarai
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distrib... |
4a74d39aebe6f5b99d54bb3a48f1716a4f91dae2de123e1868b005910b260c45 | License | 1,066 | 21 | MIT License
Copyright (c) 2025 Ziwei Liu
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distrib... |
7c1dd63425937f27f381576b0fec94ad57a6f560cdf46fee5eb0ab864573b47b | License | 1,066 | 21 | MIT License
Copyright (c) 2026 hassonlab
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distrib... |
01ff614993de66e4378cdf59547195fc880033264c89ca7f2302408fbceaf21e | License | 1,067 | 21 | MIT License
Copyright (c) 2026 kerlin-lab
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distri... |
0ad1d799e7d922f164a663fd03c21991dd10d86255822df6582c55e3051a5793 | License | 1,067 | 21 | MIT License
Copyright (c) 2025 Tilgner Lab
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distr... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.