sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
e63b536cfd93f0fd3e81ee4c05a3ad0301dc1b102dd46280ba76c1e37ba36b7b | Jupyter | 8,172 | 223 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llama_dataset/downloading_llama_datasets.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Downloading a LlamaDataset... |
919adf23155922f4ef862f084d2777a84338b8dc549ba78b24ae53bd45468efa | Jupyter | 8,211 | 232 | # %% [markdown]
# Peakcalling Peak Stats
# ================================================================
#
# This notebook is for the analysis of outputs from the peakcalling pipeline relating to the quality of the peakcalling steps
#
# There are severals stats that you want collected and graphed (topics covered ... |
8c41200033f1d6951d2602a575e90027c609276db94a7813e3ee90d6842ffc55 | Jupyter | 8,214 | 289 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/low_level/ingestion.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Building Data Ingestion from Scratch
#
# In th... |
48cfd687979aba02fa6b84009c69aba2941b94835c3930ceb765a3055ee1cd2a | Jupyter | 8,245 | 233 | # %%
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# %%
# Load in ROI labels and visualize
roi_labels = np.load('parcel_roi_labels.npy')
roi_names = ['L_PostTemp', 'L_AntTemp', 'L_AngG', 'L_IFG', 'L_MFG',
'L_IFGorb', 'L_vmPFC', 'L_dmPFC', 'L_PMC', 'L_HG', 'L_... |
35bafffe30576e773807253ee0cf0f36740467d5eb6b7f963bbfe67a40fbe93b | Jupyter | 8,269 | 231 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/jinaai_embeddings.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Jina Embeddings
# %% [markdown]
# If ... |
2a66de3b66cd5aa288f0bcd0e801a3e9fd97db65c73b0487b893a82789294f2b | Jupyter | 8,282 | 285 | # %%
import scipy.io as spio
import numpy as np
import numpy.matlib
import matplotlib.pyplot as plt
from skimage.measure import block_reduce
import keras
import tensorflow as tf
from tensorflow.keras import layers
import random
random.seed = 2020
import time
from sklearn.linear_model import LinearRegression
import pick... |
a96125c6cb80d7c9a8d1afdf1021a6c032bf40f27cca4ddba362af42e215c117 | Jupyter | 8,300 | 206 | # %% [markdown]
# # Start using Chroma
#
# <div style="border:2px solid #f7e097; padding:10px; margin-top:20px; background-color:#fefcd5; border-radius: 5px;">
# 🔑 <b>Note:</b> To generate proteins with Chroma, you'll need an API key from <a href="https://chroma-weights.generatebiomedicines.com">chroma-weights.ge... |
ce25283b8489684a05ba5f2c28eade1538320429bbecc8f05f19a6bc517e4e60 | Jupyter | 8,322 | 276 | # %% [markdown]
# # System Identification Models
# %% [markdown]
# This notebook describes how to build system identification models for any target distribution
# %%
!pip install git+https://github.com/sinzlab/nnsysident.git@ICLR2023
# %%
import torch
import numpy as np
import pandas as pd
from torch import nn
impo... |
320f854cedbd995eebdb2540037867da343ba25a70e62ae1c25995d52685b367 | Jupyter | 8,348 | 231 | # %% [markdown]
# # Rayx visualization toolbox
# %% [markdown]
# This [Jupyter notebook](https://jupyter.org/) allows for visualization of RAYX' output files. For this, the [environment variables](https://en.wikipedia.org/wiki/Environment_variable) `$RAYX_FILE` or `$RAYX_DIR` can be set.
#
# * If `$RAYX_FILE` is set,... |
2451a27b804fee30d6cc76ab310424a7a573012c25727f3c826984b0f2d8a9d3 | Jupyter | 8,355 | 227 | # %% [markdown]
# # Reliability Diagram
# %%
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from os.path import join
from cal_methods import HistogramBinning, TemperatureScaling
from betacal import BetaCalibration
from sklearn.isotonic import IsotonicRegression
from sklearn.linear_model i... |
15d3984765dc2d40ea99d0d3f0a43c8fb15e605f1b70f9b834aaf5ae5475650d | Jupyter | 8,373 | 250 | # %% [markdown]
# # Pre-Equilibrium Maximazation of Yield #
# %%
# 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 KineticAssembly_AD import ReactionNetwork, VectorizedRxnNet, VecSim, Opti... |
96422e42037c8800971a3670771a84e21f0e519c78883cdfd40359139f5de181 | Jupyter | 8,393 | 206 | # %% [markdown]
# # Multi-Agent Report Generation with AgentWorkflow
#
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/agent/agent_workflow_multi.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#
# ... |
58c9d9fd6f7df563ef3440da91b38702c2ebb43e80750d6bb5df864d5dc5654b | Jupyter | 8,407 | 250 | # %%
%load_ext autoreload
%autoreload 2
# imports
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import trajectory_reconstruction_tradeoff as T
import random
import scanpy as sc
import warnings
warnings.filterwarnings('ignore')
random.seed(20)
# %%
# params
dirname = '../'
datadir... |
ce5fbc2b310fef22081eb3e67d1ebb45d53b6291189fc41cc7a7865734d25f0a | Jupyter | 8,413 | 180 | # %% [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... |
45194b178f97379ea1a98d09a7dc10254062969e1cdd6f5fe5283387f1646596 | Jupyter | 8,441 | 283 | # %% [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... |
fbf0c672fd87fa3c5eecf6efdf46209f5a4eaf519b736d228c849debe6ddb537 | Jupyter | 8,450 | 266 | # %%
import os
import sys
import pandas as pd
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
#import os
from tqdm import tqdm
import numpy as np
# %%
df_tmalign = pd.read_csv(os.path.join("..",
"tables",
'TM_Align_Results.csv'), index_col=0)
d... |
1e5b6892718099073ae919c112a10ec6d93827b7375fd75db7eae42be781f62b | Jupyter | 8,467 | 217 | # %%
from scipy.spatial import cKDTree
import numpy as np
from scipy.spatial import distance_matrix
from sklearn.metrics import pairwise_distances
import pandas as pd
import matplotlib.pyplot as plt
from scipy.spatial import cKDTree
# %%
class dCrawler:
def __init__(self,threshold=1.5): #just a placeholder value ... |
488c7894c46c155dc88446f7797da81df90855b6951b0baab01489c7b41b8b6d | Jupyter | 8,503 | 290 | # %% [markdown]
# <div class="alert alert-success">
# <h1>Information transfer delays and $TE_{Rew}$, $TE_{Pun}$ networks</h1>
# <hr>
# <p>
# 1. TE max according to delays
# 2. Significant TE networks during the Reward and punishment conditions
# </p>
# </div>
# %%
import numpy as np
import xarray as xr
import pandas ... |
1f07eafc82b21beb76e9636fc70b106331fc6be2225904e7a39edf07ca92f638 | Jupyter | 8,511 | 286 | # %%
import pandas as pd
import numpy as np
import IPython
def progress(value, max=100):
return IPython.display.HTML("""
<progress
value='{value}'
max='{max}',
style='width: 100%'
>
{value}
</progress>
""".format(value=value, max=max))
# %... |
6d5a4156c9cfbef9289b8eb1a2cc15e3f549d56aa3ef76dc938d548b899c69be | Jupyter | 8,512 | 284 | # %% [markdown]
# # Determine kon and dG from observed yield vs time data #
# %%
# make sure jupyter path is correct for loading local moudules
import sys
# path to steric_simulator module relative to notebook
sys.path.append("/home/adip/mjohn218_KineticAssembly")
import copy
# %%
from KineticAssembly_AD import React... |
67240cacaf8abfee7b9b84171c7613c055c714b23df21c7e0cb4ee48098519d3 | Jupyter | 8,513 | 214 | # %% [markdown]
# ## Import necessary modules
#
# Every Jupyter Notebook requires the path to the KineticAssembly_AD modules (.py files in the root directory) to be mentioned. This can be done by adding the path to the 'PATH' variable of the system environment.
#
# Additonal modules are also imported which are requi... |
a87fdf3ee99476c664ebc7cf0bd82c0d36fda983035fed2aba49d981f2b9de62 | Jupyter | 8,522 | 252 | # %% [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... |
21e11330fafd60dfb65ef4a2d57133b771075d4564eb8339482a87c8a51dedea | Jupyter | 8,536 | 188 | # %% [markdown]
# <a href="https://colab.research.google.com/drive/1F0V_eClPOpS_2HIW-F2mCivgqBfFo2TR?usp=sharing" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # PremAI LlamaIndex
#
# [PremAI](https://premai.io/) is an all-in-one platf... |
9f46ac27f522dc8633180ca2be28db5811eecb283f1ba63879a152f527268d76 | Jupyter | 8,536 | 204 | # %% [markdown]
# # Test whether impropers are properly implemented in SMIRNOFF
#
# (Material from this will be migrated to a standard test once I work through it).
# By D. Mobley, 9/19/16.
#
# Initial testing on benzene and o-xylene. Add `CC(C)(C)c1ccccc1C(C)(C)C`, 1,2-ditert-butylbenzene, for a case where steric s... |
c9264decfda7380a40b07ac5674ae97b1fd35f042b755eb8d83752f1292d80ed | Jupyter | 8,548 | 171 | # %% [markdown]
# # Controlling the Micro-manager core through Python
# %% [markdown]
# This tutorial demonstrates how to control the Micro-Manager core through Pycro-Manager (see red arrows in figure below).
#
# The core is the lowest level of abstraction in Micro-Manager. It allows direct control of hardware devic... |
2c8b3373ea10aef61ffd5f04c472db05a8532c94a35d9f818dac2e842f16f460 | Jupyter | 8,589 | 233 | # %% [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... |
fc09d803c044a451f1a869a02336279a30b3c5d5fe109ca5308554b24ea225d9 | Jupyter | 8,600 | 282 | # %%
import os
import sys
import pandas as pd
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
#import os
from tqdm import tqdm
import numpy as np
# %%
df_tmalign = pd.read_csv(os.path.join("..",
"tables",
'TM_Align_Results.csv'), index_col=0)
d... |
5ee9d88fd31d70a868a502d71376620b38d3b7cc7cd13e4cf9448e43406e3cc3 | Jupyter | 8,607 | 271 | # %% [markdown]
# ## Imports
# %%
import pandas as pd
import numpy as np
import random
import csv
import os
import json
from tqdm import tqdm
import math
import altair as alt
import glob
import sys
module_path = os.path.abspath(os.path.join('..', 'scripts'))
if module_path not in sys.path:
sys.path.append(module_... |
c9dffb3e9f24108d43ff0a75369a0a200f04bb34c483158ab4d788606c5c17ae | Jupyter | 8,621 | 170 | # %% [markdown]
# # Structure of the State Dictionary
#
# In this tutorial, we will go over the different types of states used in MotorNet, how they flow during simulation, how they are structured and what information they carry.
#
# Let's start by importing what we need.
# %%
import os
import sys
import numpy as n... |
6f57aab72b54201661ea6dddc1ed9122e7ceef204a5fbbc3f877f019ea06c185 | Jupyter | 8,645 | 232 | # %% [markdown]
# ## Notebook for Figure 4 and related content: Beta Cells Subpopulations
# ### Author: J. Camunas-Soler
# ### Last Updated: Feb. 2020
# %%
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
import seaborn as sns
from singlet.dataset import Dataset
from singlet import SampleSh... |
84fc392d364e3d37900b3c4c8eb283c35dff8eb2db0f96e4a9ea50610527fb7b | Jupyter | 8,670 | 173 | # %%
%load_ext autoreload
%autoreload 2
from dotenv import load_dotenv
load_dotenv('../.env', override=True)
import pathlib
import jax
print(jax.devices())
from CEL.utils.config_reader import config_summoner
from CEL.utils.args import args_parser
import torch
from CEL.data.data_manager import load_dataset, load_datalo... |
d5a463d33a79d1a9f4f30f1eb4b01d3bc06bc4a12f21073eda9730c3d835d342 | Jupyter | 8,701 | 238 | # %% [markdown]
# # Multi-Agent Report Generation using Agents as Tools
#
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/agent/agents_as_tools.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#
# In... |
8fd267de994c5baacbe57e6ad605d10f73d1b9f81dd14288baf7f5ac06b1a4a2 | Jupyter | 8,707 | 256 | # %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_venn import venn2, venn2_circles, venn2_unweighted
from matplotlib_venn import venn3, venn3_circles
from matplotlib import pyplot as plt
%matplotlib inline
import se... |
3985865b2878fca06cb858809a553842ddf0cb9755113b6ef7cc59c17c9bfe8f | Jupyter | 8,737 | 223 | # %% [markdown]
# # Examining covariants for potential confounders
# %%
suppressPackageStartupMessages({
library(here)
library(dplyr)
library(ggplot2)
})
# %% [markdown]
# ## Functions
# %% [markdown]
# ### Cached functions
# %%
pca_norm_data <- function(region){
new_region = tolower(region)
## ... |
5fde833a2d423b4fb3cd3ecf9f9fe96605c3fd8115fdfd50aab70e691919c7a2 | Jupyter | 8,737 | 216 | # %% [markdown]
# # Google AlloyDB for PostgreSQL - `AlloyDBReader`
#
# > [AlloyDB](https://cloud.google.com/alloydb) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. AlloyDB is 100% compatible with PostgreSQL. Extend your database applicati... |
083318a41377f834e85e12a7b262e6285af63f36a09d64d6339f06b96a3b164d | Jupyter | 8,739 | 222 | # %%
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import rankdata, zscore, pearsonr, spearmanr
from scipy.spatial.distance import pdist, squareform
import pandas as pd
from statsmodels.tsa.stattools import acf
# %%
# Load in embeddings and transformations for black
black_em... |
921444609899a5779d7b9fbd84fe803565605c48174d7eda5ad8eee669d49dc0 | Jupyter | 8,743 | 248 | # %% [markdown]
# This notebook gives an example of how to extract energies and observables from the data saved to `.npy` files during an excited states calculation, as described in ["Natural Quantum Monte Carlo Calculation of Excited States"](https://arxiv.org/abs/2308.16848), especially Section IIB and Appendix C. No... |
f32ec6c320e873024dd28c25e0ce1b3c7d755c11e17c4bf7a06a2a6beaa1bd27 | Jupyter | 8,761 | 163 | # %% [markdown]
# ### notebook 05: constant vs linear guidance model
#
# In this notebook we plot in how many individual sessions a model with constant guidance weights or model with linearly changing guidance weights is best fitting the experimental data. Models were fit for each combination of 4 sessions, 5 conditio... |
340d104d3dda771e7e8e875416408c9caf82db4ed7a205cf1e632602baafd431 | Jupyter | 8,770 | 246 | # %% [markdown]
# # <font color='firebrick'><center>Idx Stats Report</center></font>
# ### This report provides information from the output of samtools idxstats tool. It outputs the number of mapped reads per chromosome/contig.
# <br>
#
# %%
# %%
from IPython.display import display, Markdown
from IPython.display im... |
2e39ea9f595df39626d72a49bc41df0933b445fd6c1427025ee095e08b52d3b1 | Jupyter | 8,807 | 247 | # %% [markdown]
# # <font color='firebrick'><center>Report for Picard Stats</center></font>
# ### This report details the picardstats output tables that have been generated as part of running picard tool.
# <br>
#
# %%
# %%
from IPython.display import display, Markdown
from IPython.display import HTML
import IPytho... |
bf0f381f758e32747ddff6b1b0dc52b0c7f998f564763081db13d4429fc1b286 | Jupyter | 8,837 | 243 | # %%
!pip install pingouin
# %%
from google.colab import drive
drive.mount("/content/gdrive")
# %%
from pathlib import Path
import h5py
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patheffects as pe
import seaborn as sns
import pandas as pd
import scipy.stats as sstats
import... |
83f46516f48859729bc201a856d4f479c7b82201f1719dbd9be305f146d6c41f | Jupyter | 8,870 | 286 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/low_level/fusion_retriever.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Building an Advanced Fusion Retriever fr... |
7613aecf3d1b6874d554d7025c8d115fb6e65d5e32bd3334ed6c9fac75c78a73 | Jupyter | 8,925 | 261 | # %%
# Nature Scientific Data Paper
# Author: Kelsey Montgomery
# Date: 20190604
# %%
# Dependencies
suppressMessages({library(CovariateAnalysis)
library(variancePartition)
library(BiocParallel)
library(data.table)
library(tidyverse)
... |
9b6b0d4837f16c40435171c4c8fa5ad2f413d55a43766bf1eeb2188982a9505c | Jupyter | 8,925 | 261 | # %%
# Nature Scientific Data Paper
# Author: Kelsey Montgomery
# Date: 20190604
# %%
# Dependencies
suppressMessages({library(CovariateAnalysis)
library(variancePartition)
library(BiocParallel)
library(data.table)
library(tidyverse)
... |
a702eaf8430f45be6c8a766cfd2b3cbcfeb2c795b88311537b40b47f7f4f6420 | Jupyter | 8,930 | 326 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import math
from tabulate import tabulate
from difflib import SequenceMatcher
import warnings
warnings.filterwarnings('ignore')
from collections import defaultdict
from itertools import chain, combinations
from fpgrowth_py import fpgr... |
9eb678deb92b1a640113f89afa0604e463fb526ce92cdc7f84c23e48c6be0105 | Jupyter | 8,939 | 319 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/finetuning/embeddings/finetune_embedding.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Finetune Embeddings
#
# I... |
e21e0b98753183f970a0baed26101a66dd0d03a0fa0f7f52089cd37f3eeeab77 | Jupyter | 8,947 | 246 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/agent/Chatbot_SEC.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # How to Build a Chatbot
#
# LlamaIndex serves as ... |
706f3efaf2315fb697bd694225634cce04097cc33fb43c0f2224385ac98840fb | Jupyter | 8,952 | 295 | # %% [markdown]
# <div class="alert alert-success">
# <h1>Context-dependent information transfer</h1>
# $TE_{Reward}$ vs. $TE_{Punishment}$
# </div>
# %%
import numpy as np
import xarray as xr
import pandas as pd
from pathta import Study
from frites import set_mpl_style
from frites.utils import kernel_smoothing
imp... |
1bca8de66b4e282d9c34de74ffcf9788ac5c4685dd7a274705292f8a32d05542 | Jupyter | 8,955 | 264 | # %%
# Nature Scientific Data Paper
# Author: Kelsey Montgomery
# Date: 20190604
# %%
# Dependencies
suppressMessages({library(CovariateAnalysis)
library(variancePartition)
library(BiocParallel)
library(data.table)
library(tidyverse)
... |
63977aa36c519db5a9384236301dd05483f22dfd195af507080175da36f60855 | Jupyter | 8,970 | 229 | # %%
# Import libraries
import os
import re
import pandas as pd
import numpy as np
import seaborn as sms
from datetime import datetime, date, time
from natsort import natsorted
from scipy.io import loadmat # this is the SciPy module that loads mat-files
import matplotlib.pyplot as plt
plt.rcParams['svg.fonttype'] = 'n... |
533478322fb5f9624fe13f751f5c305315ec7b93b992e8be2cd9a5674e302a37 | Jupyter | 8,981 | 295 | # %% [markdown]
# <div class="alert alert-success">
# <h1>Context-dependent information transfer</h1>
# $TE_{Reward}$ vs. $TE_{Punishment}$
# </div>
# %%
import numpy as np
import xarray as xr
import pandas as pd
from pathta import Study
from frites import set_mpl_style
from frites.utils import kernel_smoothing
imp... |
a8083b632ad2f6908c0291f9a8c7920feddec7b80c28846fbde002664a37b0e8 | Jupyter | 8,983 | 244 | # %% [markdown]
# # Tutorial 1 - Getting started with data
# %%
import os
from IPython import display
# %%
import imagingplus as ip
# %% [markdown]
# This notebook demonstrates a basic workflow of through `imaging+`, taking raw input files to create a multi-level data object that contains processed and extracted cal... |
63aaaf7f19d74393242bd5a1e8fbe7c39d0cd94d5d7785f231b72dee6cefb647 | Jupyter | 9,000 | 215 | # %%
# imports
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sn
import pandas as pd
# %%
# Pandas display options
pd.set_option('display.max_rows', 1200)
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_colwidth', 100)
# %%
# read each dataset in a dataframe
df_abide2 = pd.r... |
e7b821137f1d9077cbaa84be839b843d95d914bba002c0960793dd5b38f5a2d1 | Jupyter | 9,018 | 294 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/cookbooks/cohere_retriever_eval.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Cohere init8 and binary Embeddings ... |
a8f4a18fde5842435d71fa8925eaf680b8a50b2672c09b3c4eaf0fb6eb87d4ae | Jupyter | 9,049 | 168 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/managed/vectaraDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Vectara Managed Index
# In this notebook we are... |
bd2c64d44301ce9d0566cb499cb00246555d3f68dc9bb6ddb928ccf0cc44c333 | Jupyter | 9,071 | 263 | # %%
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
from sklearn.metrics import mean_squared_error, r2_score
from math import sqrt
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import ... |
abf27b58b1b297f9e35050e735d71772cf304d7079d295f124c5fde27657a892 | Jupyter | 9,078 | 194 | # %% [markdown]
# ## Import necessary modules
#
# Every Jupyter Notebook requires the path to the KineticAssembly_AD modules (.py files in the root directory) to be mentioned. This can be done by adding the path to the 'PATH' variable of the system environment.
#
# Additonal modules are also imported which are requi... |
65f34fed27aa3f40c3efc7d094606c27997ba17576114db9543280a85f3da920 | Jupyter | 9,082 | 322 | # %% [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... |
9b220d74b4cc64b0ca231d2e20720ba541d5798a2d3f4b12ca5f3b2b9f182447 | Jupyter | 9,108 | 308 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/isaacus.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Isaacus Embeddings
#
# The `llama-index-embeddi... |
2c85e8345236fa1f063b66ed98026506c221288ed3de28371a6d9b5c18701ba8 | Jupyter | 9,130 | 274 | # %% [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... |
d41556315ab7e1d8eb94c9af47efe5ad8b54e6765be23c37217ae69eea6a259c | Jupyter | 9,137 | 218 | # %% [markdown]
# # Google Cloud SQL for PostgreSQL - `PostgresReader`
#
# > [Cloud SQL](https://cloud.google.com/sql) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers MySQL, PostgreSQL, and SQL Server database engines. Extend your... |
0678e1c8bd0ddd99ca423b269144e578f443eaac55f680f90ce03f2f8fa5e91c | Jupyter | 9,146 | 253 | # %% [markdown]
# ## License
# This file is part of the project megFingerprinting. All of megFingerprinting code 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 3 of the License, or (at your option)... |
ae54e2910ae108564c17592bb67db6d307a43bb196d809ae09eb58a90fbe20c0 | Jupyter | 9,147 | 323 | # %%
import importlib
import pandas as pd
from tqdm import tqdm
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import warnings
import astrocast.helper as helper
import astrocast.reduction as red
import astroc... |
56ad8dd839a27c1104d2ac131591c16a3bf0090ed6cb644980c6c650ebbf1371 | Jupyter | 9,187 | 272 | # %% [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... |
12440580879cad66eb32e7ac4ba98023e6c9ed3331716fb9c5107937ded8405a | Jupyter | 9,203 | 240 | # %%
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
from sklearn.model_selection import train_test_split
import csv
from data.create_dataset import create_dataset
from data.load import load_dataset, load_dataset_kfold
import seaborn as sns
from collections import deque
i... |
a687ce4a8a6257e3d24fdb625de67d86857b86a50c70c1ae0c154b29a83e4d14 | Jupyter | 9,222 | 297 | # %%
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
plt.style.use('fivethirtyeight')
%matplotlib inline
# %% [markdown]
# # ONEIL vs. ALMANAC
# %%
df = pd.read_... |
601eca5df978e2ba2e65e0f1de2a420dc11fa6e13d77e8a9c8b329efc091831a | Jupyter | 9,249 | 182 | # %% [markdown]
# ### 1. Import SpaceFlow and squidpy package
# %%
import warnings
warnings.filterwarnings('ignore')
import squidpy as sq
from SpaceFlow import SpaceFlow
# %% [markdown]
# ### 2. Load the ST data from squidpy package
# %%
adata = sq.datasets.seqfish()
# %% [markdown]
# ### 3. Create SpaceFlow Object... |
9c9d7578ab9f58c37b64fe8d24fddac5ebff0473e1ea41529aa407d2cab662f3 | Jupyter | 9,257 | 285 | # %%
## 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... |
8ff21c034ce4e84de25036a68dcef3e29a91094d05a78eca3c7665189c9f47fa | Jupyter | 9,266 | 314 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/multi_modal/gemini.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#
# # Multi-Modal LLM using Google's Gemini model for image understa... |
aed13e4826dbc6f6a8b22b27d94b3ae49a7f3ae6d06c72da07e1bbae10ae61a2 | Jupyter | 9,274 | 408 | # %% [markdown]
# # Feature Test for Phi-3-mini-4k-instruct
#
# [Model card on Hugging Face](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
#
# Here's the [technical report](https://arxiv.org/abs/2404.14219).
# %%
!pip install llama-index llama-index-llms-huggingface llama-index-embeddings-huggingface tra... |
0ef8dfc4714d3f49510e4c12371b797f881de16d3b5a66f0b12530dda5e98e5d | Jupyter | 9,276 | 270 | # %% [markdown]
# ## Unimodal and crossmodal processing (Fig 5)
# This code examines unimodal and crossmodal firing patterns of PER single neurons.
#
# Neurons were classified into visual, auditory, and crossmodal cell categories based on beta coefficients derived from multiple linear regression analysis (2. Multiple... |
50d25202eb71a9572f2f77c60638b8b00c15476573dca7cc5c847851eac4306d | Jupyter | 9,388 | 314 | # %%
import os
import random
import numpy
import pandas
import matplotlib
from matplotlib import pyplot
import seaborn
import editdistance
import logomaker
# import nupack
# %%
%matplotlib inline
matplotlib.rcParams['figure.dpi'] = 120
matplotlib.rcParams['font.sans-serif'] = 'Helvetica'
# %% [markdown]
# # Helper... |
267c0057921bd0ed7639fc1c2a3160004077d336204dfe351e76473f3013b545 | Jupyter | 9,396 | 272 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/metadata_extraction/MetadataExtractionSEC.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Extracting Metadata for B... |
382990553de42cb7123de195981b41cb1fff588bbf8f76b1e518483e039c248a | Jupyter | 9,422 | 239 | # %% [markdown]
# # Tutorial 1 - Initializing an Experiment
# %%
from IPython import display
# %% [markdown]
# This notebook demonstrates a basic workflow of using the packerlabimaging package.
#
# The fundamental types of imaging trials accepted for this workflow are:
# - 2photon imaging
# - All Optical Experim... |
3741481d868bf692cbdeb993dbab234a8e6efe6fffa7a6ed3975a64d213845ca | Jupyter | 9,478 | 256 | # %% [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... |
7747b77aaa973b2e625b971e4cd9f820f6384c11e8d5d467fd7cee23207c4477 | Jupyter | 9,488 | 309 | # %% [markdown]
# # Running a network
#
# The purpose of this notebook is to showcase how to train, save, and re-load a network, as well as how to produce predictions once training is done.
#
# For how to build a plant from scratch, feel free to look up the `1-build-plant.ipynb` notebook.
# For how to build a network... |
4298d91a64c8d0851da3c87ee8b4afde8cace436290180b29b47b940dcf41584 | Jupyter | 9,495 | 220 | # %% [markdown]
# # Google Cloud SQL for PostgreSQL - `PostgresChatStore`
#
# > [Cloud SQL](https://cloud.google.com/sql) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers MySQL, PostgreSQL, and SQL Server database engines. Extend y... |
7e63869b312b5dd5bde63c47a7da95d57abc8a6cf6cf923adecd171c32f139bb | Jupyter | 9,512 | 283 | # %%
import os, sys, glob, tqdm
import matplotlib as mpl
sys.path.append('utils')
import utils_postprocessing, utils_image, utils_train
from tifffile import imread, imsave
from skimage import transform, img_as_bool, img_as_float
import numpy as np
import matplotlib.pyplot as plt
from skimage import morphology, io
from ... |
8dc20ccbfb669428b8d2753e0b991f8b10910379b7f7db7c3e57fa9957e19662 | Jupyter | 9,549 | 260 | # %% [markdown]
# # <font color='firebrick'><center>Exon Validation Report</center></font>
# ### This report details the results of running the cgat bam_vs_gtf script. The metrics are to look at the number of mapped and spliced reads per bam file.
# <br>
#
# %%
# %%
from IPython.display import display, Markdown
fro... |
b01ea5e8fafe899af0cf17deb7d5d1830c019c7ed90d6d6eaa517bc441f0c150 | Jupyter | 9,568 | 324 | # %% [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... |
23936b90e2ca2a7e02a7e601f559313ea5363553da064b273d7cf8f9c7434ac8 | Jupyter | 9,574 | 175 | # %% [markdown]
# # Oracle AI Vector Search: Document Processing
# Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords.
# One of the biggest benefits of Oracle AI Vector Search is that semantic search on unstructured data ... |
18320dc55543e00e65035a01ba754192541530a0a7285d54331d7fa2bc9d0cb1 | Jupyter | 9,603 | 259 | # %% [markdown]
# # Google Cloud SQL for PostgreSQL - `PostgresDocumentStore` & `PostgresIndexStore`
#
# > [Cloud SQL](https://cloud.google.com/sql) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers MySQL, PostgreSQL, and SQL Server... |
2ab411fc8e629e368ee712b2c2dac8824978f85478f9ba881702b5fe720e24fc | Jupyter | 9,616 | 300 | # %% [markdown]
# ## Import 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 KineticAssembly_AD import ReactionNetwork, VectorizedRxnNet, VecSim, Optimizer, EquilibriumSo... |
4f143dd4daea59c3869361ac7588a2b60c7588faf0e728b25d3c5ac2160c8af1 | Jupyter | 9,620 | 232 | # %% [markdown]
# ### Download the following utilities
# - http://hgdownload.soe.ucsc.edu/admin/exe/macOSX.x86_64/bigWigToBedGraph
# - http://hgdownload.soe.ucsc.edu/admin/exe/macOSX.x86_64/hgWiggle
# - bedtools
# - bedops
# - pandas (Python package)
# %% [markdown]
# ### Download the following annotations
# - https:/... |
d64413a3530f6e77bb7012daf6298d991642bb1359c0521fb65e24b2bb7599ae | Jupyter | 9,642 | 298 | # %% [markdown]
# ### Load model
# %%
import torch
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
torch.set_default_tensor_type(torch.FloatTensor)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = torch.load('model_snnet_trained.pkl')
# %% [markdown]
# ### 700-serum data
# %%
import... |
b3351a5a22e3e76ca55bfa502bf3e7325ca2d620ffd45e4143259487360b2a31 | Jupyter | 9,658 | 238 | # %%
import matplotlib.pyplot as plt
import seaborn as sns
import wandb
# %%
api = wandb.Api()
# %%
sweep = api.sweep("dive-ci/Invariant_Phy/8cnq0za9")
# %%
finished_runs = [run for run in sweep.runs if run.state == "finished"]
# %%
finished_runs
# %%
finished_runs[0].config
# %%
finished_runs[0].summary
# %%
f... |
2e49607d7fdcb0a62f9aedc3e0182d6858b42c2a11e98f51aa2936fc35ccb9ec | Jupyter | 9,685 | 269 | # %% [markdown]
# ## Create a CWAS gene set: Step 1
# %% [markdown]
# ### 0. Preparation
# %%
# Import statements
import os
import yaml
import numpy as np
import pandas as pd
from collections import defaultdict
# %%
# Path settings
project_dir = os.path.dirname(os.path.abspath('.'))
conf_dir = os.path.join(project_... |
cb58c0820572349ad91922553d2330b52668da0ccc65e8505c8d650d3df18b5d | Jupyter | 9,689 | 250 | # %% [markdown]
# # Filtering Peptide Samples by Biophysical Properties
#
# ### This notebook describes additional ranking of the sampled sequences based on their biophysical properties.
#
# ### Enhancing Sequence Selection
#
# - This ranking in useful in streamlining the selection of sequences. For instance, it can... |
bd25a9ad62ead896668dccd1dded00f27b0fdba15e9136b070685e983d7647e7 | Jupyter | 9,690 | 305 | # %% [markdown]
# # Compressive Ratio 8:1 Neural Network Training
#
# This notebook demonstrates the construction and training of a neural network optimized for a compressive ratio of 8:1. The network takes 64 inputs and produces 8 outputs.
# %% [markdown]
# # libs
# %%
import numpy as np
import matplotlib.pyplot as... |
8090f928debcbecedc600542af1f5f1e11dcb8f8521fc967c0cb151a3ad590a9 | Jupyter | 9,700 | 327 | # %% [markdown]
# # Import libraries
# %%
import pandas as pd
import numpy as np
import os
import json
import glob
from os.path import exists
import sys
module_path = os.path.abspath(os.path.join('..', 'scripts'))
if module_path not in sys.path:
sys.path.append(module_path)
#end if
import utils
from utils import... |
c8e52346d903b4de25c66639e6ea1fcc706afa849a3c220336a0389aa6a63024 | Jupyter | 9,701 | 322 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/low_level/oss_ingestion_retrieval.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Building RAG from Scratch (Open-s... |
6e61765da73ae9c383195adfc61f9b42283c02a3f58e747a45d6b22cd743f10c | Jupyter | 9,714 | 232 | # %%
import numpy as np
import nibabel as nib
import matplotlib.pyplot as plt
import nilearn.plotting as niplt
%matplotlib inline
import os
import seaborn as sns
from scipy.special import softmax
from scipy.stats import ttest_rel
import pandas as pd
# %%
#excluded 296 (less <25% comprehension score)
#Temporary... |
65ceb10c022c66abb149a8b22c864e97c285482ed2f300edfa7c487f42c8c82d | Jupyter | 9,722 | 239 | # %% [markdown]
# # Starter for Cores
# %% [markdown]
# In this notebook we provide an explanation of the different choices for Cores. For each of them we include:
# - a written description of the Core
# - a code demo how to use the Core
# %% [markdown]
# ---
# %% [markdown]
# ### The model has two main parts
# %%... |
c2c37dc5bec67c7d8b5472543324f18cf1ab9d681365e6cc9fa092b13d4cd432 | Jupyter | 9,762 | 322 | # %% [markdown]
# # Plots and figures Simulations
# %%
%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 matplotlib.pyplot as plt
print("Your style sheets are located at: {}".format(path.join(mpl.__path__... |
ba6985c83b11bd6d3cda65ef98765f49bc573cec814332b5f7eb28b442f0b6e8 | Jupyter | 9,765 | 280 | # %% [markdown]
# <hr style="height:2px;">
#
# # Denoising acquired images using deep learning
#
# Copy this notebook into a directory in your Google drive.
#
# In this tutorial we will create a deep learning denoising model trained on data aquired by Pycro-Manager on your microscope. We will then used this denois... |
68121d96003104282f55e8390896b4304b304a879a0ce207eac3f669e6f824df | Jupyter | 9,778 | 280 | # %% [markdown]
# # FunctionAgent / AgentWorkflow Basic Introduction
#
# The `AgentWorkflow` is an orchestrator for running a system of one or more agents. In this example, we'll create a simple workflow with a single `FunctionAgent`, and use that to cover the basic functionality.
# %%
%pip install llama-index
# %% ... |
a66b139aa99099b8ef2443fdc9d6baaf012ad593703ba8c8ee7d72168f58463f | Jupyter | 9,810 | 294 | # %% [markdown]
# #### set up packages, load path
# %%
on_work_computer = false;
project_folder = "/Users/erussek/Dropbox/meg_behavior";
plot_folder = "$project_folder/model_fitting/paper_plots"
using CSV
using DataFrames
using DataFramesMeta
using CategoricalArrays # for making data categorical
using Statistics #... |
74bc34b0f74e6f1537f1ace3935957e85df7eefae55d82a6cb05cc47f9dd2f7a | Jupyter | 9,815 | 320 | # %% [markdown]
# # Training of DNNs with different weights, predictor sets, and cost functions
# %% [markdown]
# ## Load necessary packages
# %%
# Import necessary packages
import pandas as pd
import numpy as np
import math
import os
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Weighte... |
62eb8cf9965a36ecad2e24ee8729246d269ef7b342ebdf70a955b15e50e7a597 | Jupyter | 9,851 | 272 | # %% [markdown]
# # Google AlloyDB for PostgreSQL - `AlloyDBDocumentStore` & `AlloyDBIndexStore`
#
# > [AlloyDB](https://cloud.google.com/alloydb) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. AlloyDB is 100% compatible with PostgreSQL. E... |
692c41b0295c28bc639d20f9946173823810e7d7e0372b9b392467f770cf6ce0 | Jupyter | 9,955 | 154 | # %% [markdown]
# MultiRAT preprocessing
# ================
# Joanes Grandjean
#
# 
#
# # Purpose
# In this section I re-submit failed `confound_correction` steps. These seemed to happen for two reasons: improper low pass filter for long TR dataset in the `aromal` strategy, or fai... |
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