sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
e32549d8f894fecf9e23e2e47c4bc4a85c4377418358b2034531992aadf12b69 | Jupyter | 5,454 | 195 | # %%
## Import Libraries
import pandas as pd
import numpy as np
import csv
import matplotlib.pyplot as plt
%matplotlib inline
import openpyxl
pd.set_option("max_colwidth", None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# %%
'''
function name: parse_df_columns
purpose: parsin... |
376aa52c3f8dd9c467219f7e899241f918f3fa99381baefd6b2658e6d3e74b06 | Jupyter | 5,462 | 177 | # %% [markdown]
# ## Annotated Data
#
# The AnnData library is the primary protocol that is used to store imaging data in an efficient, multi-functional format. It is created using the `anndata` sub-module and can be accessed using `trialobj.data`. By default, `trialobj.data` is a data array generated from Suite2p pro... |
0df5fc79ceab3f20ee5a474c22501f5753410974e48a6923bc36d1d445bae02e | Jupyter | 5,500 | 137 | # %%
import pandas as pd
import cooler
from cooltools import insulation
import glob, os
from pybedtools import BedTool as pbt
import seaborn as sns
import plotly.express as px
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
# %% [markdown]
# ### 1.... |
0559ad20d79ffa3789ce261153c9778ab78fcc455b24b53fab574e4d48231ce8 | Jupyter | 5,512 | 162 | # %%
from pathlib import Path
import numpy as np
import importlib
from matplotlib import pyplot as plt
import humanize
import sys
import os, psutil
import time
import shutil
import pandas as pd
import seaborn as sns
import warnings
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
import logging
impo... |
2476652a5efa9c5ecc4d06f70c29337511d23d7272ac54d18bb5b05ddac554b2 | Jupyter | 5,516 | 240 | # %% [markdown]
# # Ingestion Pipeline
#
# In this notebook we will demonstrate usage of Ingestion Pipeline in building RAG applications.
#
# [Ingestion Pipeline](https://docs.llamaindex.ai/en/stable/module_guides/loading/ingestion_pipeline/)
# %% [markdown]
# ## Installation
# %%
!pip install llama-index llama-ind... |
e0039848c565332272bfcd26ade58a47c5965888cc488de069cb5f604af75051 | Jupyter | 5,516 | 161 | # %% [markdown]
# # Llama Pack - Resume Screener 📄
#
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llama_hub/llama_pack_resume.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#
# This example sho... |
e009031f2e24b02d52ab8e13c1a0c3f8cc8d2dde9821fcc74eb85b122efa1b23 | Jupyter | 5,563 | 138 | # %%
# NBVAL_SKIP
from openff.toolkit.typing.engines.smirnoff import forcefield
from openff.toolkit.typing.engines.smirnoff import forcefield_utils as ff_utils
from simtk import openmm
from simtk.openmm import XmlSerializer
from simtk.openmm import app
from simtk import unit
from openmoltools import system_checker
impo... |
069b5575c1f558940f867695d0de4d41279588ccb69dcfdbdd8aa9c17d7d1ed6 | Jupyter | 5,567 | 116 | # %% [markdown]
# ## Using OpenFF force fields in Amber and GROMACS
#
# The OpenFF Toolkit can create create parametrized `openmm.System` objects and also, with an extra API call to [Interchange](https://docs.openforcefield.org/projects/interchange/en/stable/), input files for simulations in Amber and GROMACS. This ex... |
3e500dfaadb4a0de8bdb78c561dc2d3b8195b0767739df7f23ce615bb1b6623c | Jupyter | 5,572 | 198 | # %%
import os
from pathlib import Path
from astrocast.analysis import IO, Video, Events, Plotting
astroPlot = Plotting(None)
# %% [markdown]
# Here, we will showcase how to run the astrocast pipeline on a small sample dataset.
# %% [markdown]
# # 0. Explore the sample data
# %%
sample_path = Path("../../testdata/... |
0bf91df26c48af12cb2e592235c04b30ebda51278c676a3b9d313c0967be1df6 | Jupyter | 5,576 | 198 | # %% [markdown]
# # Cluster LVs
# This notebook will analyze the latent variables learned by MousiPLIER to see if any studies in the training data have strong clusters in LV space
# %%
import math
import pandas as pd
import pyreadr
from sklearn import cluster, metrics
from tqdm import tqdm
from plotnine import *
f... |
e4ae405f3b948e607029238b6d4b816ba58f20770e5ac3b93c64cfd004df0884 | Jupyter | 5,594 | 201 | # %%
import numpy as np
import xarray as xr
import pandas as pd
from pathta import Study
from frites import set_mpl_style
from frites.conn import conn_links
from research.study.pblt import get_model, get_anat_table
from visbrain.utils import normalize
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
f... |
b309bc1a8332a8d4cf0422552fdc408a8720a236e0ddf5e3e6782583573c00f4 | Jupyter | 5,606 | 163 | # %% [markdown]
# # Experimenting with the `AtomMapper` factory
#
# This notebook illustrates use of the `AtomMapper` factor, which proposes potentially valid atom mappings.
# Note that `perses` has specific additional requirements for atom mappings (such as the inability to turn constrained X-H bonds into unconstrain... |
c643d1dccc146bc891326c66e3e6d6020f75b13b16e067fb8aa14697a25a06d6 | Jupyter | 5,617 | 157 | # %% [markdown]
# # Visualizations in the OpenFF Toolkit
# %% [markdown]
# OpenFF [`Molecule`] and [`Topology`] objects provide some facilities for visualization in Jupyter notebooks. This can be useful for inspecting molecules and topologies!
#
# There are two ways to invoke the visualization:
# - implicitly, by _ev... |
e73cda2454c6e1c6c72525f53871ff9c32d9deb96ae1b28837ad8b8955e7ecd0 | Jupyter | 5,621 | 244 | # %% [markdown]
# # Neo4j Graph Store
# %%
%pip install llama-index-llms-openai
%pip install llama-index-graph-stores-neo4j
%pip install llama-index-embeddings-openai
%pip install llama-index-llms-azure-openai
# %%
# For OpenAI
import os
os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"
import logging
import sys
from ... |
3f1cc2d202e7b4dff4c23e3d52f849a7222aa405613df16ae8c4b0f76ce61281 | Jupyter | 5,626 | 190 | # %%
import h5py
import os
import matplotlib.pyplot as plt
import numpy as np
data_path = os.path.join(os.path.expanduser('~'), 'data', '3d-mnist',
'full_dataset_vectors.h5')
assert os.path.isfile(data_path), \
'Download the 3d-mnist data from https://www.kaggle.com/daavoo/3d-mnist'
wit... |
05a79912e447226d89509f802e2a3f0998ec4cca6f1ba73cb92e47e8701e0d8b | Jupyter | 5,638 | 162 | # %%
import os
import numpy
import matplotlib
from matplotlib import pyplot
import pandas
import scipy
import scipy.stats
import seaborn
# %%
%matplotlib inline
matplotlib.rcParams['figure.dpi'] = 120
matplotlib.rcParams['font.sans-serif'] = 'Helvetica'
# %%
# Combined replicates
datasets = [
{
'short_la... |
0ba17031a361efe0caf4d9ae885987501f9da0ac1f7d1c82a23b778d873164d2 | Jupyter | 5,672 | 164 | # %% [markdown]
# # Transformer Representations
#
# This notebook serves as the primary script to produce Transformer representations (embeddings, transformations, and transformation magnitudes) for the regression analyses in the paper.
#
# It relies on the `narrative-transcript-processing` notebook to produce suitab... |
fae0ea7ead56587d0a0b7e8d75d2af9226136019f146b193cdd4da49f06f09e1 | Jupyter | 5,677 | 126 | # %% [markdown]
# # Mixture simulation with Sage: Ethanol and cyclohexane
#
# This example shows how to use the OpenFF Toolkit to create a parametrized `openmm.System` object that can be used to run a molecular dynamic simulation with OpenMM. If you want to run MD with a different engine, see the example in `examples/... |
70f2471270ca8264198f5a2d593e819195dc22e94dbc777ab3bc0a89ad48d28a | Jupyter | 5,720 | 150 | # %% [markdown]
# # Plot RXE comparisons
# %%
library(ggpubr)
library(tidyverse)
# %%
save_ggplots <- function(p, fn, w=7, h=7){
for(ext in c('.svg', '.png', '.pdf')){
ggsave(p, filename=paste0(fn, ext), width=w, height=h)
}
}
# %%
df = data.table::fread("../../_m/RXE_public.csv") %>%
mutate_if(... |
bc46a85adf745985fa90718ca165c6f923893776f87576829e52d95fcc2f8436 | Jupyter | 5,731 | 188 | # %% [markdown]
# # Quantitative Phase Imaging from Defocus Demo
#
# This notebook demonstrates forward simulation and reconstruction for Quantitative Phase Imaging (QPI) from defocus.
# The simulation and reconstruction are based on partially coherent optical diffraction tomography (ODT):
#
# J. M. Soto, J. A. Rodri... |
87d9886981c173bcdf989b75ddc29268e89467334c6ab19876e5dcf088d15ee8 | Jupyter | 5,739 | 170 | # %% [markdown]
# # <font color='firebrick'><center>Transcript Profile Report</center></font>
# ### This report details all of the profiles that have been collected and collapsed accross the transcript profile.
# <br>
#
# %%
# %%
from IPython.display import display, Markdown
from IPython.display import HTML
import ... |
182b5179ec82f05fe482470fe118f190e7d67534de240fc298dd23db8918a7ea | Jupyter | 5,760 | 176 | # %%
import os
NUM_THREADS = 6
os.environ['OMP_NUM_THREADS'] = str(NUM_THREADS)
os.environ['MKL_NUM_THREADS'] = str(NUM_THREADS)
os.environ['NUMEXPR_NUM_THREADS'] = str(NUM_THREADS)
os.environ["OPENBLAS_NUM_THREADS"] = str(NUM_THREADS)
os.environ["VECLIB_MAXIMUM_THREADS"] = str(NUM_THREADS)
import pyscf
from pyscf imp... |
80dec76d28b2e2198d949cb480b9f3329be0432e80f7e1d19a432f52ace54766 | Jupyter | 5,783 | 112 | # %% [markdown]
# # Cookbook: Using PDB files with the OpenFF Toolkit
# %% [markdown]
# PDB files are a common way to represent biopolymer structures, but they don't natively contain all of the chemical information required to construct `Molecule` objects. However, PDB files are used widely in the molecular simulation... |
0bfc2014d9edf40cb243ce9e4600692c878dcc070924412205d98c4c1b161ad1 | Jupyter | 5,805 | 201 | # %% [markdown]
# MultiRAT analysis code
# ================
# Joanes Grandjean
#
# 
#
# # Foreword
#
# This is a jupyter notebook which contains all the code for reproducing my
# analysis. The code is meant to be followed step-wise.
#
# If re-using some of the scripts, please f... |
fb7f88269d2e97cd130a92972a8d876835f4af9a01c37ade30590e097441d7e3 | Jupyter | 5,816 | 152 | # %% [markdown]
# Copyright (c) MONAI Consortium
# 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
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
9944b6c05ec86b76a559001eb026383a06add241b8a09ac3fcefb813f1f67db1 | Jupyter | 5,837 | 198 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/monsterapi.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Monster API <> LLamaIndex
#
# MonsterAPI Hosts wide... |
b3f19f00ac15ba4f529fa82874f883bb1e580303df9af639e204afef1ada0bf1 | Jupyter | 5,846 | 190 | # %% [markdown]
# # PLS regression analysis for MEND DTI and clinical data
# %% [markdown]
# - Project: HCP-EP
# - File Name: PLS_regression_MEND
# - Author: Haley Wang
# - Date Created: 2023-07-06
# - Last Modified: 2023-07-06
# - Code Status: Finished
# %%
import numpy as np
import pandas ... |
7b429ea3f7c6301c62e7d43523ed4d324ec656be922a1b811267917b4ab65bc7 | Jupyter | 5,874 | 150 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/huggingface.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Hugging Face LLMs
#
# There are many ways to inter... |
7580a01f11d1ec3d45fe3bad84791adeaae3acecb3e30c470becbfb4da3bd278 | Jupyter | 5,897 | 93 | # %% [markdown]
# # ZephIR Demo
#
# [https://github.com/venkatachalamlab/ZephIR](https://github.com/venkatachalamlab/ZephIR)
#
# This notebook is a demo to illustrate a typical ZephIR workflow and is accompanied by a general [guide](https://github.com/venkatachalamlab/ZephIR/blob/main/docs/Guide-ZephIR.md) and [tutor... |
b895d17fe1b8716c25887447d0b9209d9b4e75c005a2b55e0f21e387e656e852 | Jupyter | 5,900 | 164 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/llama_2_llama_cpp.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # LlamaCPP
#
# In this short notebook, we sho... |
8242a16383352818ce04672015967ac798b21a51890bbfb54c8b616bd992e7f2 | Jupyter | 5,913 | 211 | # %% [markdown]
# # Tutorial 2. Imputation
#
# With masked self-suervised learning mechanisms, SEDR can impute the spatial transcriptomics data that have amount of dropouts.
#
# Testing data is a human Lymph node 10X Visium data, which can be downloaded from here: https://www.10xgenomics.com/resources/datasets/human-... |
b33504821d2b1875675610284ece31b6f644ad167727e31cc2f612c9938e0df5 | Jupyter | 5,921 | 211 | # %% [markdown]
# # Transcript-to-Transformer Processing
#
# This notebook pre-processes transcript data from the `Narratives` dataset to:
# 1. Generate nuisance vectors for use in regression analysis (phonemes, etc).
# 2. Produce a list-of-TRs output that can be fed into Transformer models.
#
#
# It accepts eit... |
5c3db9c5e8f9ac28e100f3af15e06b1a9c3970220475cfb3d453239a5883d6c5 | Jupyter | 5,936 | 195 | # %%
from py2cytoscape.data.cyrest_client import CyRestClient
from IPython.display import Image
import networkx as nx
import pandas as pd
import pymaid
rm = pymaid.CatmaidInstance('server_url', 'api_token', 'http_user', 'http_password')
print('pymaid version:', pymaid.__version__)
# %%
# Fire up the cytoscape int... |
e9a7b84ab48abb1b9ebfa5ff1e1f5f7be935d075ae45d0b2c578d91cd3eefe03 | Jupyter | 5,945 | 211 | # %%
## Import Libraries
import pandas as pd
import numpy as np
import csv
import matplotlib.pyplot as plt
%matplotlib inline
import openpyxl
pd.set_option("max_colwidth", None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# %%
'''
function name: parse_df_columns
purpose: parsin... |
7bbc7b41d56b85c0e8d88bed61710f88301064a6f20d63e687f272b29a3149ec | Jupyter | 5,969 | 159 | # %%
!git clone https://github.com/Merlinaphist/ReproduceANNA16.git
# %%
import pandas as pd
import numpy as np
import pickle, os, shutil
from math import sqrt
from zipfile import ZipFile
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimize... |
8cf488f313bb761e97f06edbbc2bb9fb5aa9e76586f36257c64a04e30df6de13 | Jupyter | 5,984 | 150 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn.metrics as skmts
import scipy.stats as scistat
import glob
plt.style.use('fivethirtyeight')
# %%
def eval_mts(y_list, y_pred_list, task_str):
"""return evaluation metrices"""
mask = ~np.isnan(y_pre... |
cf845ec2a239c5e5e68976383cf3ea2779d5f4de10065c60db7af2577b8fc253 | Jupyter | 6,015 | 170 | # %% [markdown]
# # Perplexity
#
# Perplexity's Sonar API offers a solution that combines real-time, grounded web search with advanced reasoning and deep research capabilities.
#
# When to use:
#
# - When your application requires timely, relevant data directly from the web, such as dynamic content updates or curre... |
0a2dd32bc80c0faf068d0d5a2c9e315706542299923b2e6da41b6ec38ad04b3a | Jupyter | 6,017 | 174 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn.metrics as skmts
import sklearn.preprocessing as skpre
import scipy.stats as scistat
import glob
import pickle
import statsmodels.stats.multitest as multi
plt.style.use('fivethirtyeight')
%matplotlib inline
... |
ed6f72b20e8f741e82cd278a553cce5317ef700e648c1cd1607b1fd45247f7ee | Jupyter | 6,032 | 193 | # %% [markdown]
# # Predicting Potency for human GCGR/GLP-1 Receptors Using a Pre-Trained Ensemble
#
# This notebook offers a trutorial on how to use a pre-trained enesamble of muli-task neural network models to predict potencies.
# %% [markdown]
# ## Imports
# %%
import time
from pathlib import Path
import numpy a... |
23de262b98a908171d41ea840c1e32b8f59ab31020414d4c833a79b92d82f8e8 | Jupyter | 6,053 | 143 | # %% [markdown]
# # Optimizing the Vectorized Simulations #
#
# Besides speed, the benefit of using the vectorized simulation based on the torch framework
# is that we can track all operations that happen across a simulation. This allows us to find
# the gradients with respect to any intermediate variable, and more im... |
c0cff83d3da7ec13ea1f455d74e58db0933a60c9459354c0ac3c9ce7bc05b114 | Jupyter | 6,057 | 139 | # %% [markdown]
# # Optimizing the Vectorized Simulations #
#
# Besides speed, the benefit of using the vectorized simulation based on the PyTorch framework
# is that we can track all operations that happen across a simulation. This allows us to find
# the gradients with respect to any intermediate variable, and, more... |
e010ed08fe7e64477b77ca8182bc45638e3c0c19a94b059fbe4b481c40cfbd5f | Jupyter | 6,067 | 289 | # %% [markdown]
# # Components Of LlamaIndex
#
# In this notebook we will demonstrate building RAG application and customize it using different components of LlamaIndex.
#
# 1. Question Answering
# 2. Summarization.
# 3. ChatEngine.
# 4. Customizing QA System.
# 5. Index as Retriever.
#
# [ChatEngine Documentation](... |
e95edff166212707f234972f2351f0c0f8223d00bcf2df90e5fa289feee4e4a5 | Jupyter | 6,073 | 196 | # %% [markdown]
# # Local excitation/lateral inhibition linear rate model
# %% [markdown]
# ### The following code will provide an example of the effect of various input patterns have on the outputs of a recurrant network with local excitation/lateral inhibition connectivity.
# %% [markdown]
# ### Imports
# %%
impor... |
1a5864edaa57bf01a48ee4e5b43f56d1b4ac78f5ecbc80efe58a83916ced888c | Jupyter | 6,085 | 207 | # %%
## Import Libraries
import pandas as pd
import numpy as np
import csv
import matplotlib.pyplot as plt
%matplotlib inline
import openpyxl
pd.set_option("max_colwidth", None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# %%
'''
function name: parse_df_columns
purpose: parsin... |
1d217d9d4ebc1528b2d5ce975b19bddb4523589daab76175c305d2d77858dbd5 | Jupyter | 6,087 | 163 | # %%
setwd("..")
# %%
source("Source/functions.R")
library("data.table")
# %%
mito_barcodes <- read.csv("Data/mito_barcodes.csv", as.is = TRUE)
mito_barcodes
mitoIDs <- mito_barcodes[, "ID"]
# %%
snv_info <- read.csv("Data/snv_loci_v2.csv", as.is = TRUE)
snv_info
# %%
snvIDs <- snv_info[,"SNVID"]
# %%
chrmbases <-... |
1f88c0e7446b8e9f857b8c2f204e6c48f15bf8bf96f682800bcc9599c70db6c3 | Jupyter | 6,155 | 215 | # %% [markdown]
# # Demo: Azure Table Storage as a Docstore
#
# This guide shows you how to use our `AzureDocumentStore` and `AzureIndexStore` abstractions which are backed by Azure Table Storage. By putting nodes in the docstore, this allows you to define multiple indices over the same underlying docstore, instead of... |
507cf2851bcee1905bd3f49530cf2db2546d835857a234c5226e81bc58052eec | Jupyter | 6,188 | 203 | # %% [markdown]
# Copyright (c) MONAI Consortium
# 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
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
c6c819d0b565f5d519c8943662b1edf57559016d39135c8e7ab2d53fe72eee3d | Jupyter | 6,193 | 162 | # %% [markdown]
# # mVEP Behavioral Analysis
#
# This notebook contains the analysis codes for mVEP BCI behavioral data.
#
# Scripts are primarily written by Joshua Kosnoff. I've tried to give proper credit to 3rd party sites where due.
# %% [markdown]
# ## Load in the behavioral data outputs
#
# The BCI behaviora... |
ae43a7187275ce85044d6668029599c4a01ef6d94c2af7d4ac4b33f3d4b08388 | Jupyter | 6,198 | 183 | # %%
!pip install --upgrade setuptools pip --user
!pip install --ignore-installed PyYAML
!pip install Pillow
!pip install nvidia-pyindex
!pip install --upgrade nvidia-tensorrt
!pip install pycuda
!pip install protobuf<4.21.3
!pip install onnxruntime-gpu
!pip install onnx>=1.9.0
!pip install onnx-simplifier>=0.3.6 --u... |
726ab4bd6cf2a708580e056c38ecde3600b8f60cd1f74212a4c48577ee70fdc8 | Jupyter | 6,246 | 185 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/ibm_watsonx.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # IBM watsonx.ai
#
# >WatsonxLLM is a wrapper for IB... |
b931fedfd91075f571488f1461d5fa8ec240382208459151434d27cab1667f66 | Jupyter | 6,248 | 189 | # %% [markdown]
# # Manipulating Memory at Runtime
#
# In this notebook, we cover how to use the `Memory` class to build an agentic workflow with dynamic memory.
#
# Specifically, we will build a workflow where a user can upload a file, and pin that to the context of the LLM (i.e. like the file context in Cursor).
# ... |
30f50c1f802ad5beca4f6dedf4bc6b2f05aa570fdbd0b46735c500879007a381 | Jupyter | 6,280 | 147 | # %% [markdown]
# Copyright (c) MONAI Consortium
# 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
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
dff13dd60a996ea293f3f1e9c52d773f8dad95fc79b40f798736fa431b88a83f | Jupyter | 6,285 | 79 | # %%
setwd("..")
# %%
source("Source/functions.R")
library("data.table")
library("uwot")
library("RColorBrewer")
# %%
inherited_altasin_bymito_byposmut <- fread(file = "Report/SNVs/hierarchy/inherited_noctrl_altasin_bymito_byposmut.csv")
inherited_altasin_bymito_byposmut <- inherited_altasin_bymito_byposmut[IsCtrl ==... |
5238db331216b655dc969a5aabc26b033fb1cdae7cd1bee5a2354464216e2c61 | Jupyter | 6,286 | 239 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/index_structs/doc_summary/DocSummary.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Document Summary Index
#
# Th... |
745f663718b10e7c4d0d47a68dc0bc774dd9da4665a2ee922166436eeb1f0c23 | Jupyter | 6,286 | 185 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/evaluation/answer_and_context_relevancy.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Answer Relevancy and Contex... |
ad30b42b71146192a8480b94938512b3f3d1c4818a7bb3b0da049bc71dc499e0 | Jupyter | 6,301 | 169 | # %% [markdown]
# # Compute conformer energies for a small molecule
#
# This notebook illustrates reading conformers of a molecule from an SDF file and computation of vacuum conformer energies using a SMIRNOFF force field.
#
# Note that absolute vacuum potential energies can be sensitive to small changes in partial c... |
d80581ecf8ce7b5eeca15baf30394cca035fa62a56dee781ebecf443caa3a504 | Jupyter | 6,312 | 221 | # %%
## Import Libraries
import pandas as pd
import numpy as np
import csv
import matplotlib.pyplot as plt
%matplotlib inline
import openpyxl
pd.set_option("max_colwidth", None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# %%
'''
function name: parse_df_columns
purpose: parsin... |
75cfa15bab5aa63943f691d41926a8160dc2fb02fb998c8e45f5647cba26c889 | Jupyter | 6,319 | 181 | # %% [markdown]
# # PLS regression analysis for EP DTI and clinical data
# %% [markdown]
# - Project: HCP-EP
# - File Name: EP_PLS
# - Author: Haley Wang
# - Date Created: 2023-07-10
# - Last Modified: 2023-07-10
# - Code Status: Finished
# %%
import numpy as np
import pandas as pd
import py... |
626db17239d579397422930904034674c7d4b1b53a7ffba263fd8e7be6482279 | Jupyter | 6,331 | 211 | # %% [markdown]
# # OpenAI JSON Mode vs. Function Calling for Data Extraction
# %% [markdown]
# OpenAI just released [JSON Mode](https://platform.openai.com/docs/guides/text-generation/json-mode): This new config constrain the LLM to only generate strings that parse into valid JSON (but no guarantee on validation aga... |
3ba3094ca0d3989bb8f70964066d9b76c26a853cf46ebf7a9af5733080985588 | Jupyter | 6,333 | 172 | # %% [markdown]
# # TMAlign all GB1 variant structures
# %%
import pandas as pd
import numpy as np
import random
import umap
from itertools import product
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
# %%
# Ignore UserWarning from UMAP
warnings.simplefilter(action='ignore', category=UserWar... |
15ae4a723c762222ef395f5a6df03f52dd340f4628b785e11cfd0af5d0621294 | Jupyter | 6,336 | 193 | # %%
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
import nibabel as nib
from nilearn.plotting import plot_glass_brain
# %%
# Convenience function for converting to NIfTI
def nii(data, affine, header=None):
return nib.Nifti1Image(data, affine, header=header)
# %%
# Create double... |
0f6ee84a04d6e40a833a428dc9f12f756d255f435eb558a2390d609cf0509ee4 | Jupyter | 6,339 | 224 | # %%
## Import Libraries
import pandas as pd
import numpy as np
import csv
import matplotlib.pyplot as plt
%matplotlib inline
import openpyxl
pd.set_option("max_colwidth", None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# %%
'''
function name: parse_df_columns
purpose: parsin... |
74fd6d1edf28ead79829e524323f96e4b695b776eec5d8182d304920d582a390 | Jupyter | 6,340 | 183 | # %% [markdown]
# # <font color='firebrick'><center>Report for Bam Stats</center></font>
# ### This report details the bamstats output tables that have been generated as part of running bamstats tool.
# <br>
#
# %%
# %%
from IPython.display import display, Markdown
from IPython.display import HTML
import IPython.co... |
0e2a3e09710c5551f622ab7c752d2976387b90f0596843326c24b6436241445c | Jupyter | 6,347 | 198 | # %% [markdown]
# # Building a Live RAG Pipeline over Google Drive Files
#
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/ingestion/ingestion_gdrive.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#... |
1be243c3543c05d20f8303d2cf7a18c43b1b024cdf7819072b93ae00c087d158 | Jupyter | 6,348 | 157 | # %%
## Import libraries
import pandas as pd
import numpy as np
## Display all rows of pandas dataframes
pd.set_option('display.max_rows', None)
# %%
'''
function name: parse_df_columns
purpose: parsing the last aggregate column of the gtf/gff3 into useful columns and cleaning non-relevant columns
input: dataframe ... |
16bdb509f429ef76c95b622861107794ac34f586f5d9c29498d840162e8e068d | Jupyter | 6,359 | 219 | # %% [markdown]
# # Visualization
# %%
import pandas as pd
import numpy as np
import os, cv2, copy
from PIL import Image, ImageFont, ImageDraw, ImageEnhance
import matplotlib.cm as mpl_color_map
import matplotlib.pyplot as plt
# %%
from ast import literal_eval
# %%
from matplotlib.colors import LinearSegmentedColor... |
7a929f3fc695f0097c2a5fa2a75ec3e7b2f2aaba83f269ce6d499181cfa76c70 | Jupyter | 6,373 | 201 | # %% [markdown]
# # Instrumentation: Basic Usage
# %% [markdown]
# The `instrumentation` module can be used for observability and monitoring of your llama-index application. It is comprised of the following core abstractions:
#
# - `Event` — represents a single moment in time that a certain occurrence took place with... |
101f73c616ba4004360923add6580c3be14593b92d5a44ce5d9444fcb2a0da75 | Jupyter | 6,375 | 229 | # %%
import numpy as np
import xarray as xr
import pandas as pd
from pathta import Study
from frites import set_mpl_style
from frites.conn import conn_links
from research.study.pblt import get_model, get_anat_table
from visbrain.utils import normalize
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
f... |
65dc5b377f628e513a4778ab802bb35517faaeebce6c306b893515f2bf6ac6c5 | Jupyter | 6,377 | 177 | # %% [markdown]
# <a href="https://colab.research.google.com/gist/iamdaniele/84cca60019384c4159df28e14e2dc61c/toolhouse-llamaindex-workflow.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Sales Prospecting Workflow with Toolhouse... |
b9b051f3e32ca05f5c5ca1c8d52a9b4f93a69e3b45e0e4fc34a199cd0f868a1c | Jupyter | 6,436 | 221 | # %%
import json
import numpy as np
import xarray as xr
import pandas as pd
from pathta import Study
from frites import set_mpl_style
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.lines import Line2D
from ipywidgets import interact
set_mpl_style()
with open("config.json", 'r') as f:
co... |
7f73532148e0b7d9846734ddbb2042a19862189aeaf750827decfd84d1c25e54 | Jupyter | 6,477 | 227 | # %% [markdown]
# # GPT Builder Demo
#
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/agent/agent_builder.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#
# Inspired by GPTs interface, presented a... |
1cfa1a84ddbc0c998094e8b3438fd08fbac93914733dda0f1683cb8a9ea30ccf | Jupyter | 6,480 | 201 | # %% [markdown]
# ## Object selectivity (Fig 4)
#
# This code analyzes basic properties of object selectivity and generate plots in Fig 4
# %%
import os
from pathlib import Path
import numpy as np
import pandas as pd
from scipy import stats
import statsmodels.api as sm
import pingouin as pg
import matplotlib as mp... |
3773ebc30d4597fded848bdb0ded893ab284ab189304e7b6e47343f833eb6276 | Jupyter | 6,481 | 211 | # %% [markdown]
# # Optimization Framework using auto-diff to predict binding rates
# %% [markdown]
# ## Import the necessary modules
# %%
# make sure jupyter path is correct for loading local moudules
import sys
# path to steric_simulator module relative to notebook
sys.path.append("../../")
import copy
from Kinetic... |
2fb647765f686f8cf04ef8ee474acfea9e89004732e47b5c319be80ae0f32b31 | Jupyter | 6,482 | 177 | # %% [markdown]
# # NVIDIA NIMs
#
# The `llama-index-llms-nvidia` package contains LlamaIndex integrations building applications with models on
# NVIDIA NIM inference microservice. NIM supports models across domains like chat, embedding, and re-ranking models
# from the community as well as NVIDIA. These models are ... |
f82961b4a481e309e31920c3a4472a482ddcdf2b41b3b2dbeb302615d43d8c1e | Jupyter | 6,499 | 256 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/mistralai.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # MistralAI
# %% [markdown]
# If you're opening this N... |
825ccbc5bf31aa08aa25bc3225a272881f99f8c5c1e52a2172317933bdc6e475 | Jupyter | 6,514 | 209 | # %% [markdown]
# # Optimization Framework using auto-diff to predict binding rates
# %% [markdown]
# ## Import the necessary modules
# %%
# make sure jupyter path is correct for loading local moudules
import sys
# path to steric_simulator module relative to notebook
sys.path.append("../../")
import copy
from Kinetic... |
3b76aa5191dc99366e75bdb2a868d99644054180365c6159326e0ae06e55370f | Jupyter | 6,536 | 250 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/ollama.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Ollama LLM
# %% [markdown]
# ## Setup
# First, follow t... |
6472af4fa873afe88078ec61fea8af51256315c81d50f33d1c9f309675df778d | Jupyter | 6,548 | 188 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/evaluation/RAGChecker.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # RAGChecker: A Fine-grained Evaluation Framewo... |
da2c9e060b512f0000b8878fef8d3a1de7d22f3d01f01f45ba6109f4d1abc9f7 | Jupyter | 6,583 | 246 | # %% [markdown]
# # Knowledge Graph Construction w/ WikiData Filtering
#
# In this notebook, we compare using [REBEL](https://huggingface.co/Babelscape/rebel-large) for knowledge graph construction with and without filtering from wikidata.
#
# This is a simplified version, find out more about using wikipedia for filt... |
441bf95ee06916f9a20e6e15a3dbb0b7486a95adb4b1bbb8c525fc7626ca10f2 | Jupyter | 6,602 | 280 | # %%
import os
import scipy.io
import numpy as np
import pandas as pd
import pyvista as pv
import matplotlib.pyplot as plt
from scipy import interpolate
from brainrender import Scene
from brainrender import settings
from brainrender.actors import Volume
from mpl_toolkits.mplot3d import Axes3D
from vedo import embedWind... |
30c73beb3bbee627da6d40790c8dbed71ffc1662328c65595e652d24352ad1b2 | Jupyter | 6,617 | 302 | # %% [markdown]
# # SambaNova Systems
#
# In this notebook you will know how to install, setup and use the [SambaNova Cloud](https://cloud.sambanova.ai/) and [SambaStudio](https://docs.sambanova.ai/sambastudio/latest/sambastudio-intro.html) platforms. Take a look and try it yourself!
# %% [markdown]
# # SambaNova Clo... |
6978642f4c65596c9b52bc5232cfbadc132eb586baf3b8aa747d331ad4429013 | Jupyter | 6,627 | 212 | # %% [markdown]
# # Optimization Framework using auto-diff to predict binding rates
# %% [markdown]
# ## Import the necessary modules
# %%
# make sure jupyter path is correct for loading local moudules
import sys
# path to steric_simulator module relative to notebook
sys.path.append("../../")
import copy
from Kinetic... |
b4ae7b618ae503187f54fc5f8d6d5e8c81af7b01a4fe2a74b9b978cca3551f0c | Jupyter | 6,655 | 197 | # %% [markdown]
# # CrewAI + LlamaIndex Cookbook
#
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/cookbooks/crewai_llamaindex.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#
# This cookbook shows... |
42d499efaaedb4c9baba7cc8ee602b925b404ecfdc2e741e3ecc4b0fd5f66132 | Jupyter | 6,678 | 207 | # %% [markdown]
# # Tutorial 2 - Data organization inside packerlabimaging
# %%
from IPython import display
# %%
import packerlabimaging as pkg
# %% [markdown]
# *This notebook demonstrates the basics of how data is organized inside of a trial object.*
#
#
# %% [markdown]
# ## Importing data analysis objects
# Th... |
7fe87ea2f403ccadd2ec9a608d49ffa54c5ce040500c74eba0186705aaf14360 | Jupyter | 6,691 | 181 | # %%
import csv
import random
import numpy as np
import json
import warnings
import operator
import h5py
from keras.models import model_from_json
from keras import backend as K
warnings.filterwarnings("ignore")
# %%
def read_file(file_path):
with open(file_path, 'r') as data_file:
data = json.loads(data_... |
927333e68ccc09ac664217246cebe31b455d51738f6d03d417189eca8f632038 | Jupyter | 6,702 | 206 | # %% [markdown]
# Copyright (c) MONAI Consortium
# 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
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
a026c3ee3838239253db7e2179f184322c5cf0a13071b80e965547787849bea9 | Jupyter | 6,703 | 246 | # %%
## Import Libraries
import pandas as pd
import numpy as np
import csv
import matplotlib.pyplot as plt
%matplotlib inline
pd.set_option("max_colwidth", None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# %%
'''
function name: parse_df_columns
purpose: parsing the last aggre... |
e3236fb3088fcf5a58d0b40ad909fccb9cddf2bfa91f95ed0deaaabfb5dd532e | Jupyter | 6,706 | 188 | # %%
!git clone https://github.com/Merlinaphist/ReproduceANNA16.git
# %%
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
from sklearn.metrics import mean_squared_error
from math import sqrt
from sklearn.model_selection import train_test_split
from tensorflow.keras.mod... |
3dd1a63c31cc3b7e0d28fdd317cac3826a1b47f09e5b10aee20e5a58a3b5fd9a | Jupyter | 6,711 | 244 | # %% [markdown]
# # `CleanSamples` model
# %% [markdown]
# # DeepLift
# %%
import deeplift
import deeplift.conversion.kerasapi_conversion as kc
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sys
import pkg_resources
# %%
# We have dense layers, using Genomics Def... |
45fbc8b05e3f7202f133939b7fc11095f7d28c98e05c5547b921f70141a67eb3 | Jupyter | 6,720 | 226 | # %% [markdown]
# # Plots and figures Rockland
#
# This notebook contains all code to generate the figures in the associated publications.
# %%
%load_ext autoreload
%autoreload 2
# %%
import logging
import os.path as path
import os
import socket
import sys
# %%
import matplotlib
import matplotlib as mpl
import matp... |
7f1583eab1f914d21b0eeac09940a4cc87ab5879c30ac9b05d2ba5e921d52cfc | Jupyter | 6,726 | 139 | # %% [markdown]
# ## notebook 04p1: quantifying condition and individuality effect in precision
#
# We quantify the effect from 0 to 3 objetcs, and separately from 3 to 99 objects, by plotting the differences in median individual position errors.
# %%
# importing python packages
import numpy as np
import pandas as pd... |
2ea08c050443435a823f99ff9714e1d7308e6efd012a49c844913b0f1273d230 | Jupyter | 6,750 | 257 | # %% [markdown]
# # Imports
# %%
import pandas as pd
import numpy as np
import os
import json
import altair as alt
import statsmodels
import statsmodels.api
# %% [markdown]
# # Declares
# %%
JSON_FILE = os.path.join("..",
"results",
"mammalian_REM2",
... |
a7608d497ed5b2f229cfc4a15303854e301dd13db37f52aa74ea12a1de68c4c7 | Jupyter | 6,768 | 264 | # %% [markdown]
# # Imports
# %%
import pandas as pd
import numpy as np
import os
import json
import altair as alt
import statsmodels
# %% [markdown]
# # Declares
# %%
JSON_FILE = os.path.join("..",
"results",
"mammalian_REM2",
"mammalian_... |
225ed10087337512107b23a1dc004dfa176179d38b820dda78a5d9dec4c48541 | Jupyter | 6,787 | 173 | # %% [markdown]
# MultiRAT dataset description code
# ================
# Joanes Grandjean
#
# 
#
# In this section, I provide a brief summary of the datasets that were
# supplied for this study.
#
# Normalization steps: - Columns with .orig have been left unaltered from
# how it ... |
3c81ce911e0cf1b8166a3c23e6f31b3cc7f7f64d77ececbee4de3675b4a51bc0 | Jupyter | 6,799 | 226 | # %% [markdown]
# # eqtplot for eqtl-gwas colocalization
# %%
import pandas as pd
import numpy as np
import subprocess
import functools
# %%
%load_ext rpy2.ipython
# %% [markdown]
# ## Functions
# %%
def flip_slope_by_allele(row):
return [-1, 1][bool(row["pgc2_a1_same_as_our_counted"])] * row["NES"]
def get_l... |
54565aa0e38fcbab5001e9fd9103f5910ee8c280274c89d8bf348ca9233e2813 | Jupyter | 6,810 | 167 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/PathwayReaderDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Pathway Reader
# %% [markdown]
#... |
5a37c1986e60c2f7a11b3c9cdfc90406227d3da65a11c7c7e013896636cb0298 | Jupyter | 6,812 | 251 | # %% [markdown]
# # Differential Expression with limma-voom pipeline - Genes
# %%
suppressMessages({library(ggplot2)
library(limma)
library(edgeR)
library(sva)
library(dplyr)
library(data.table)})
# %% [markdown]
# ## Prepare Da... |
87f22af49261a67c2c597cbec4d03cee5768b290003ab9330b29419fc38183b0 | Jupyter | 6,818 | 162 | # %%
from tqdm import tqdm
import numpy as np
import os
import pickle
import matplotlib.pyplot as plt
import cooler
import pandas as pd
import glob
import seaborn as sns
# %% [markdown]
# ### 1. Get the maps
# %%
chroms = ['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9',
'chr10', 'chr11'... |
8791baecd9f49b6df241111e07f7f122476d649763ee46582cbabb3347eec551 | Jupyter | 6,847 | 246 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/index_structs/struct_indices/duckdb_sql_query.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # SQL Query Engine with... |
f043ef79a93309a479db8d686472ebb05294eb147090e88540a753debd100ba1 | Jupyter | 6,847 | 225 | # %% [markdown]
# # Optimization Framework using auto-diff to predict binding rates
# %% [markdown]
# ## Import the necessary modules
# %%
# make sure jupyter path is correct for loading local moudules
import sys
# path to steric_simulator module relative to notebook
sys.path.append("../../")
import copy
from Kinetic... |
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