sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a69cbf84dc3e0635e4f182cb99a994568037dc0c7aefaf834f48afad869c923b | Jupyter | 2,054 | 75 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/NotionDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Notion Reader
# Demonstrates our Notion ... |
4d90b51c814ebc97de58e1be39e8df8b928afd984001e64e10bd470b6acb6f72 | Jupyter | 2,058 | 63 | # %% [markdown]
# MultiRAT Quality Control
# ================
# Joanes Grandjean
#
# 
#
# ### Quality control
#
# RABIES outputs several QA/QC images that are directly relevant to assess
# image registration (func to anat, anat to template, template to
# standard), and motion par... |
c1a993d515bc3f200494072b3952a568836422bac9461e16cb219c1f9ce77480 | Jupyter | 2,058 | 82 | # %% [markdown]
# # Chat Summary Memory Buffer
#
# **NOTE:** This example of memory is deprecated in favor of the newer and more flexible `Memory` class. See the [latest docs](https://docs.llamaindex.ai/en/stable/module_guides/deploying/agents/memory/).
#
# The `ChatSummaryMemoryBuffer` is a memory buffer that stores... |
396e3b93ef32702f2096fe6c697e615ec09b131390a9ad99d97682dcd520f3ec | Jupyter | 2,059 | 58 | # %%
import torch
from torch_geometric.loader import DataLoader
import os
import pickle
from sklearn.model_selection import KFold
import numpy as np
import random
from torch_geometric.nn import global_mean_pool, GATConv
import torch.nn.functional as F
import torch.nn as nn
from torch import optim
import datetime
import... |
b76499e809b71ae1c63fa6c5edbf95c6abd052ba83ce87926ebedfdc646eca8b | Jupyter | 2,060 | 76 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/GithubRepositoryReaderDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Github Repo Reader
# %%... |
7cef16f79b4e0bdefdf8ed688b3d23a9d4a7d355d8d7e1e5e1bb0b96a422ed8d | Jupyter | 2,064 | 105 | # %%
from neuronir.methods import *
from neuronir.methods.plot_loss_maps import plot_loss_maps
from neuronir.methods.get_optimization_trajectory import get_optimization_trajectory
from neuronir.models.container import Container
from neuronir.utils.utils import *
%matplotlib inline
from tqdm.notebook import tqdm
# %%
... |
e49cc32e800d85f95a163a9d12b1bd4f75eb0667fee1cf3f2a27fb66c2c391d1 | Jupyter | 2,068 | 68 | # %%
import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
from utils.io import preprocess_data
import matplotlib.gridspec as gridspec
import numpy as np
from paths import TRAINING_DATA_PATH,EXAMPLE_PATH
import glob
import simpa as sp
imp... |
f41cc23191735752a074f7005a671537b76acef5ee4b0194a7edf4c0e8da4a80 | Jupyter | 2,070 | 58 | # %%
import numpy as np
import os
import miga_emblaze.emblaze as m_emb
from miga_emblaze.emblaze.utils import Field, ProjectionTechnique
import cv2
embedding_dir = '/rhome/lianyu.zhou/cache/cluster_test_set_gformer_l3_b256_atom60'
need = ['img', 'graph']
modal_num = len(need)
samples_num = 3000
embeddings_list = [np.... |
1e7b7c63775c10566840380045cb62593aac2595f39a8491e5d574e406dff31b | Jupyter | 2,076 | 67 | # %% [markdown]
# ## PBMC integration
#
# uniPort effectively integrates the scATAC and scRNA data by common, RNA-specific and ATAC-specific (in the form of gene activity matrix) highly variable genes without using pairing information.
# %%
import uniport as up
import numpy as np
import pandas as pd
import scanpy as ... |
3db86d498d187750933dcab354aca2dc5281825bfbae9c8b188a6701f1a86859 | Jupyter | 2,082 | 91 | # %% [markdown]
# # CCGtools
# %% [markdown]
# ## Installation
#
# To install the full parser run the following command:
# %%
! pip install -qU "ccgtools[parser]@git+https://github.com/stanojevic/ccgtools"
# %% [markdown]
# To have visualizations of CCG trees and dependencies you need to have GraphViz and LaTeX ins... |
488ef33c44965f97b8b1e8dc5fa80a5543c83fb97fd19ebb178ab27e213ecfe5 | Jupyter | 2,085 | 91 | # %% [markdown]
# # [Pipeshift](https://pipeshift.com)
# %% [markdown]
# If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.
# %%
%pip install llama-index-llms-pipeshift
# %%
%pip install llama-index
# %% [markdown]
# ## Basic Usage
# %% [markdown]
# Head on to the [models](h... |
3d86bbe150c60aa1d9c0c18fdef52ecd1820e9dd3efa2941760119bf685d5c6a | Jupyter | 2,101 | 69 | # %%
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression, PoissonRegressor
from inference import COSMIC, EM, EM_with_clock
# %%
import matplotlib.pyplot as plt
#plt.rc('text', usetex=True)
#font = {'weight': 'normal',
# 'size': 12,
# 'family': 'normal'}
#plt.rc('font'... |
ecc1e4dbf78dc5cf3c01b8a0f53ca74b784adfc8fdd4810cce92d8f332915167 | Jupyter | 2,114 | 80 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/deepinfra.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # DeepInfra
#
# With this integration, you can ... |
9e1b0e15b95bdcfe1f38d7de254be0212e1b73c0b4b9db79bea7fc523e4d8f01 | Jupyter | 2,123 | 90 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/deepseek.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # DeepSeek
#
# # LlamaIndex Llms Integration: DeepSeek
... |
a91cbe8a72b84328a6be823fe900904f0d989c049cbf7c69987b9f781f7d5904 | Jupyter | 2,126 | 71 | # %% [markdown]
# # API Call Observability
#
# Using the new `instrumentation` package, we can get direct observability into API calls made using LLMs and emebdding models.
#
# In this notebook, we explore doing this in order to add observability to LLM and embedding calls.
# %%
import os
os.environ["OPENAI_API_KE... |
8e1f3e85978980d3567ac3f42d45cc22499aa70ec0689c10ee8821f16f29a73d | Jupyter | 2,128 | 92 | # %%
import pymaid
from pymaid import tiles
rm = pymaid.CatmaidInstance('https://www.your.catmaid-server.org' ,
api_token='YOURTOKEN',
http_user='user', # omit if not required
http_password='password' # omit if not required
... |
b8e3bf6045f2daaaf15b332c8c9339e618159e752911cc3b6707669fd7703e51 | Jupyter | 2,149 | 105 | # %%
from astroCAST import preparation, analysis
from astroCAST.denoising import SubFrameGenerator, Network
from astroCAST.detection import Detector
from astroCAST.clustering import HdbScan, Distance, Linkage, Modules
from astroCAST.reduction import UMAP, CNN
from astroCAST.helper import Normalization
from astroCAST.an... |
c102d9837aa2485f622c4eca62bb8e1fbc888f9bd05e5a240c08f4f9c6c54ad8 | Jupyter | 2,154 | 86 | # %%
!pip install --upgrade setuptools pip --user
!pip install onnx
!pip install coremltools>=4.1
# %%
import sys
import torch
print(f"Python version: {sys.version}, {sys.version_info} ")
print(f"Pytorch version: {torch.__version__} ")
# %%
!nvidia-smi
# %%
!# Download YOLOv7 code
!git clone https://github.com/Wong... |
0f43b2411756e535adf09c2e0e819e9e12db9d3f50c9067d19996105516f6ae9 | Jupyter | 2,156 | 66 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/neutrino.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Neutrino AI
#
# Neutrino lets you intelligently route... |
7e5c8ef16486467bae4477528075cd12d4796cccc646ba35ea31d70bdaf41806 | Jupyter | 2,164 | 77 | # %%
import numpy as np
import seaborn as sns
import matplotlib.pylab as plt
# %%
LAYERS = range(0, 12)
MODEL = "bert-base-uncased"
STIMULUS = "black" # slumlordreach, black
# %%
z_reps = {}
for layer in LAYERS:
z_reps[layer] = np.load("{}_{}_layer_{}_z_representations.npy".format(STIMULUS, MODEL, layer))
numbe... |
87c978780fbb897abb52f78caffb8913ab70056528c546f20a43190f4469fe07 | Jupyter | 2,172 | 79 | # %% [markdown]
# # LlamaCloud Page Figure Retrieval
#
# This notebook shows an example of retrieving images embedded within a PDF document.
# More docs on using this feature can be found on the [LlamaCloud docs page](https://docs.cloud.llamaindex.ai/llamacloud/retrieval/images).
# %%
%pip install llama-index llama-i... |
206b51f5f738a1aeb51c9f18372b05c165e09f1ab15c4e64fe503cc68b684a6c | Jupyter | 2,177 | 86 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/cometapi.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # CometAPI
#
# CometAPI provides access to various stat... |
e1b280b12ae66f8f1156a600c4934cef3ce1083bf6a161f3f79525ffdc94146f | Jupyter | 2,210 | 79 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/nebius.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Nebius LLMs
#
# This notebook demonstrates how to use L... |
b923c73cda5c6c220b1e8e230c91d56d7e54b33e90bc43f3c40e5133c0c5e953 | Jupyter | 2,214 | 86 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/FaissDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Faiss Reader
# %% [markdown]
# If you're... |
0dd0e5e1fa4ed1c2aa044afaba4dd72597cfe667109618430d13859140c93781 | Jupyter | 2,216 | 99 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/openai.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # AlibabaCloud-PaiEas
# %% [markdown]
# If you're opening... |
dd41f467e7a914f245f793bd0a5aa18a2dfff044a6876b4ab50236f767fa444f | Jupyter | 2,219 | 81 | # %% [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... |
9a0390cc58d5423ee0bb749781683dc43c202d17d05d7fd15a39fac2dbd6fdad | Jupyter | 2,231 | 76 | # %% [markdown]
# # Retrieve ach_id
# %%
import pandas as pd
df = pd.read_pickle('../data/processed_data/ccle_model.pkl')
df[(df['cid'].str.contains('cid'))&(df['tcga abbrev'] != 'NA')]
# %%
ach_list = sorted(df[(df['cid'].str.contains('cid'))&(df['tcga abbrev'] != 'NA')]['depmap_id'].unique())
len(ach_list)
# %% [... |
d43a4fc2240b88d97621bddaab60d95b08d5a901ed327da3c5ba93df0d18d22e | Jupyter | 2,236 | 69 | # %% [markdown]
# ## Mannens et al., bioRxiv (2023)
# %% [markdown]
# #### https://github.com/linnarsson-lab/fetal_brain_multiomics?tab=readme-ov-file
# #### https://github.com/linnarsson-lab/fetal_brain_multiomics/blob/main/files/supplementals/Extended_data_2_Cluster_annotation.xlsx
# %%
import anndata
from pybedtoo... |
00b1a2d92849f84af501aca71dceb81fa28fa70570e1770f466db3bc0501dcd8 | Jupyter | 2,239 | 98 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/litellm.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # LiteLLM
#
# ### LiteLLM supports 100+ LLM APIs (Anthro... |
0f8dd53eec124ca7ebfaff4d75e1105e0d790ec747b6ac5c50f48dc6905d47e4 | Jupyter | 2,240 | 62 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/GoogleDriveDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Google Drive Reader
# Demonstrates ... |
9384934e3c5b97639fcc12053b274259ef4245ca97af0ac1aa0e7db30afa46dd | Jupyter | 2,240 | 96 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/WeaviateDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Weaviate Reader
# %%
%pip install lla... |
e8ade2137cc1443c8388c1b80c4592dcd3bb5b5de6238e7456c7e41f93ab1d9a | Jupyter | 2,270 | 76 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/llama_2_rap_battle.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # 🦙 x 🦙 Rap Battle
# %% [markdown]
# What h... |
c01105e89511f08eb4abc5e4d003ffbfd63d2fd1e4bbeca0a93a8321fb3a8dee | Jupyter | 2,274 | 48 | # %% [markdown]
# ## Example Bellybutton Workflow
#
# We recommend running Bellybutton through the command line / terminal. For instructions and further details see https://pypi.org/project/bellybuttonseg/.
#
# For users who prefer using a notebook, we provide an example workflow in this format below.
# %% [markdow... |
cd68018039fb851331e7c2137774ff43aa865ff7d31c360d7afe6315c60acf01 | Jupyter | 2,276 | 56 | # %% [markdown]
# # Model Training and Evaluation
#
# This notebook provides a training guide for the ensemble of deep multitask neural networks
# described in our paper.
#
# By default, the training is performed using the provided dataset of human GCG/GLP-1 analogs, however, below we describe how to train using yo... |
fda970d5bd7eb57484750ea9e4d2edb922219837bb9ac791acd982adb46c9ce7 | Jupyter | 2,278 | 50 | # %% [markdown]
# # Pairwise Reaction Energy Estimation #
#
# For real protein complexes for which we have a crystal structure, we may want to estimate true reaction free energies in
# order to get more realistic simulation results. This can be done using the Rosseta package, or specifically the pyrosetta
# API.
#
# ... |
840c9c09b807d39a9f2acd523bc9a35f8ea0ffeeba08ec1aae639d22f36469ed | Jupyter | 2,283 | 43 | # %% [markdown]
# # Pairwise Reaction Energy Estimation #
#
# For real protein complexes for which we have a crystal structure, we may want to estimate true reaction free energies in order to get more realistic simulation results. This can be done using the Rosetta package or, specifically, the PyRosetta API.
#
# Thi... |
9ccb725acb6a31e5dcf2cf7c7ba51ed7cdf91266dd3806697a3281d7b461288b | Jupyter | 2,297 | 88 | # %%
import pymaid
# Initialize connection
rm = pymaid.connect_catmaid()
# Fetch some neurons
nl = pymaid.get_neurons('annotation:glomerulus DA1 right')
# %%
cont = pymaid.get_contributor_statistics(nl, separate=True)
cont.head()
# %%
by_user = pymaid.get_user_contributions(nl)
by_user.head()
# %%
time_inv = pymai... |
001e82867130b722d93f44a47d829ea7ce3e76b86d83d9088f64bdfb6c21c9c3 | Jupyter | 2,302 | 86 | # %% [markdown]
# # Meth_Comp API usage
# %% [markdown]
# ## Import module
# %%
# Import main module
from pycoMeth.Meth_Comp import Meth_Comp
# optionally inport jupyter helper functions
from pycoMeth.common import head, jhelp
# %% [markdown]
# ## Getting help
# %%
jhelp(Meth_Comp)
# %% [markdown]
# ## Example u... |
d881a458eb57c36203edd7f59d6227f5f9029866d2afc418f5a586025fcd5a27 | Jupyter | 2,305 | 74 | # %%
%matplotlib agg
import copy
from utils.model import model_names_eps30, model_names_logr
from utils.ml import MLPNetwork, MOVotingRegressor
from utils.log import auto_log
from utils.vis_shap import summary_legacy
from utils.al import UniformCompositionGenerator
import shap
from shap.plots import colors
import p... |
70fd6667e283dd8877771929e1225eaa8c7ff3fc41ec4d47aa62b700ad88a914 | Jupyter | 2,312 | 106 | # %% [markdown]
# # Correlate variables to remove redundant metrics
# %%
suppressPackageStartupMessages({
library(here)
library(dplyr)
library(SummarizedExperiment)
})
# %% [markdown]
# ## Functions
# %%
load_phenotypes <- function(region){
pheno_file <- here("input/phenotypes/_m/phenotypes.csv")
... |
c30c3054b0cfcd933c3c18de59165398255b39c2438f85cccc2596a5f8df0ec5 | Jupyter | 2,316 | 99 | # %% [markdown]
# # Databricks
#
# Integrate with Databricks LLMs APIs.
# %% [markdown]
# ## Pre-requisites
#
# - [Databricks personal access token](https://docs.databricks.com/en/dev-tools/auth/pat.html) to query and access Databricks model serving endpoints.
#
# - [Databricks workspace](https://docs.databricks.co... |
03403d54744b598e6cabb24bc7f47c0aea21cc4991347779b384754aca2a6870 | Jupyter | 2,342 | 105 | # %%
import pymaid
import matplotlib.pyplot as plt
# Connect to CATMAID
rm = pymaid.connect_catmaid()
# Get two example neurons by their skeleton ID
nl = pymaid.get_neurons(['57311', '27295'])
# Plot using default settings
fig, ax = nl.plot2d()
plt.show()
# %%
import navis
# Plot using default settings
fig, ax = na... |
75385c123306f1905cbbf653b74fc0e5a9b040affda857bc9d77ceb89ee90f14 | Jupyter | 2,346 | 79 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/nebius.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Nebius Embeddings
#
# This notebook demonstrates... |
28c84b7b9ff4f02dc469be4e4b4ece681188257989531fdcf690a4bc0cea2c23 | Jupyter | 2,356 | 98 | # %%
# Imports
import os
import sys
import pandas as pd
from pandas.plotting import scatter_matrix
import random
from matplotlib import pyplot as plt
import itertools
import numpy as np
from matplotlib.pyplot import figure
from Bio.Seq import Seq
from Bio import SeqIO
from Bio.SeqRecord import SeqRecord
# %%
HUMAN_ID... |
64be31395775220a38f66eb5df9498753851ae648ca5f2f43a027ae4af35405a | Jupyter | 2,357 | 65 | # %% [markdown]
# # TextEmbed - Embedding Inference Server
#
# Maintained by Keval Dekivadiya, TextEmbed is licensed under the [Apache-2.0 License](https://opensource.org/licenses/Apache-2.0).
#
# TextEmbed is a high-throughput, low-latency REST API designed for serving vector embeddings. It supports a wide range of ... |
eaa7fd125af88595f87746d8a814774a4cd6cc33529ff3dedad702aceb243d55 | Jupyter | 2,374 | 82 | # %% [markdown]
# Here we make geo plots to show the localization of our collaborators
#
# based on https://geopandas.org/en/stable/gallery/create_geopandas_from_pandas.html
# %%
! pip install geopandas pygeos
# %%
import pandas as pd
import geopandas
import matplotlib.pyplot as plt
# %%
df_path='../table/Affiliati... |
9293c904d61c31cbbc6dd28f053cb17fb95520d7d2423d9f3e15b90ac4398882 | Jupyter | 2,377 | 77 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/alephalpha.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Aleph Alpha
#
# Aleph Alpha is a powerful language ... |
1cc6917611ce7b25a68c6a69c67fc417edd66e53b308c6588f61f25e11553174 | Jupyter | 2,380 | 133 | # %%
from zephir.methods import *
from zephir.models.container import Container
from zephir.utils.io import *
from zephir.methods.recommend_frames import recommend_frames
from zephir.methods.extract_traces import extract_traces
%matplotlib inline
import matplotlib.pyplot as plt
from tqdm.notebook import tqdm
# %%
# ... |
98e9227c5e490f2390e8b7e54f6c874cdf7d5736e60f44a317b80b9e601cb16d | Jupyter | 2,388 | 96 | # %%
# Imports
import os
import sys
import pandas as pd
from pandas.plotting import scatter_matrix
import random
from matplotlib import pyplot as plt
import itertools
import numpy as np
from matplotlib.pyplot import figure
from Bio.Seq import Seq
from Bio import SeqIO
from Bio.SeqRecord import SeqRecord
# %%
HUMAN_ID ... |
e8ed0038640c823a78b80f2c56227d85a477a45eef95b44f6239591f5354d12b | Jupyter | 2,390 | 133 | # %%
from neuronir.methods import *
from neuronir.models.container import Container
from neuronir.utils.io import *
from neuronir.methods.recommend_frames import recommend_frames
from neuronir.methods.extract_traces import extract_traces
%matplotlib inline
import matplotlib.pyplot as plt
from tqdm.notebook import tqd... |
ee8d6de41a0f37fa535d438f183fff6626a2b58bfd4cb22d21bc4d4721e074ac | Jupyter | 2,392 | 93 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/octoai.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # OctoAI
# %% [markdown]
# If you're opening this Notebo... |
52b68300ece18bcf567beec99f62b26108f432dee11e1ed622c3c9f987801229 | Jupyter | 2,412 | 73 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/helicone.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Helicone AI Gateway
# %% [markdown]
# Helicone is an ... |
c58276b202d37b9ac6f51639c060943c3f21ea7b0c32a8cf3006c1310d0427db | Jupyter | 2,415 | 64 | # %%
data_path = '/Users/rsameni/Documents/caramba.csv' # Update this path to where you have the CSV file
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import sys
# sys.path.append('/Users/rsameni/Documents/GitHub/OSET/python/src/')
current_working_dir = os.getcwd() # Get the curre... |
28b7bc42768ef52791defa74f2cc3a4619f45e9b2677f42954e94a2b3520e5a9 | Jupyter | 2,416 | 79 | # %%
%reload_ext autoreload
%autoreload 2
# after switching between these two commands, matplotlib has to be imported again
%matplotlib inline
# %matplotlib notebook
# %%
import numpy as np
from independent_vector_analysis import iva_g, consistent_iva
from independent_vector_analysis.visualization import calculate_c... |
e37a5a913fe4b6951cd6422b2a26e27c53d6f20abe0c155e1eb182ce64aaacde | Jupyter | 2,418 | 115 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/vicuna.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Replicate - Vicuna 13B
# %% [markdown]
# ## Setup
# %%... |
99df4402d2de8c301c4403c77b3bc7f991a9cb8b45af1939c462cae4a980eb85 | Jupyter | 2,428 | 80 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/bedrock.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Bedrock Embeddings
# If you're opening this Note... |
16e6a93b66266eb25ff4ed4a21f47f4f427726b29fa2fbd49b6918a631daf889 | Jupyter | 2,445 | 94 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/apertis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Apertis
# %% [markdown]
# Apertis provides a unified A... |
4b6def14e42aaf258e795ca586d3f4e2a01a753998b5c138ad504531ed379541 | Jupyter | 2,450 | 115 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/llama_2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Replicate - Llama 2 13B
# %% [markdown]
# ## Setup
# ... |
f273bba00c2b331bede599749c27a1b2bf59fed976d32484882338c1e8b709ae | Jupyter | 2,457 | 62 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/evaluation/HotpotQADistractor.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # HotpotQADistractor Demo
#
# This not... |
04e99283478bfd50f7e221670d5ac782216adc3778ab95a8006d38e0fe601dc1 | Jupyter | 2,460 | 99 | # %%
## Load libraries
import pandas as pd
import numpy as np
# %%
'''
function name: fix_column_names
purpose: Fixing the column names, making them smaller, informative, and consistent
input: The raw counts dataframe for either genes or transcripts
output: Same dataframe with improved column names
'''
def fix_co... |
dcf61632fa2b2ed03a07309ebbc8923569cc933e804f39547c7a125632892185 | Jupyter | 2,469 | 91 | # %%
#memory usage
import sys
# path to steric_simulator module relative to notebook
sys.path.append("C:\\Users\\denys\\AMGEN\\")
import copy
from KineticAssembly_AD import ReactionNetwork, VectorizedRxnNet, VecSim, Optimizer, EquilibriumSolver
import networkx as nx
import torch
from torch import DoubleTensor as Tenso... |
93d05ff7ce3111cf5f57b5645cbf1dde2d698c6dfa07f2f041d32fb2d77e43b6 | Jupyter | 2,480 | 83 | # %% [markdown]
# # Metadata Extraction and Augmentation w/ Marvin
#
# This notebook walks through using [`Marvin`](https://github.com/PrefectHQ/marvin) to extract and augment metadata from text. Marvin uses the LLM to identify and extract metadata. Metadata can be anything from additional and enhanced questions and ... |
cd5b723ec6cc76e15d997465e41225ddc535e849619619c19e56fe92147ee908 | Jupyter | 2,494 | 85 | # %%
## Load libraries
import pandas as pd
import numpy as np
# %%
'''
function name: fix_column_names
purpose: Fixing the column names, making them smaller, informative, and consistent
input: The raw counts dataframe for either genes or transcripts
output: Same dataframe with improved column names
'''
def fix_co... |
4766a8185fe96e66db80d746f0005d7bfd5880df2b13f2e93ff4a74b9d754c61 | Jupyter | 2,499 | 76 | # %%
import scanpy as sc
import pandas as pd
import numpy as np
import spatialprompt as sp
# %% [markdown]
# # import the data
# %%
merfish_h5ad = sc.read_h5ad("/media/swainasish/main_disk_2/Datasets_swain_asish/spatial_prompt/reproduce_results/merfish_full.h5ad")
merfish_h5ad
# %% [markdown]
# ## simulate 100 k dat... |
a6afe4309667dc01ae087cf74140bf262d44c4471a93624cc7e83eeac62f1bf6 | Jupyter | 2,506 | 88 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/vercel-ai-gateway.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Vercel AI Gateway
# %% [markdown]
# The AI G... |
19b9a6b4068d828e8473a5db196751488489dbd8489ec63489d3c5986a3e70bf | Jupyter | 2,515 | 53 | # %%
# read in experiment results data
separator = .Platform$file.sep
data_path = paste("..", separator, "data", separator, "processed_data.csv", sep="")
save_path = paste("..", separator, "data", separator, sep="")
data_raw = read.csv(data_path, skip=0)
# %%
# average the position error by condition and ppid by taki... |
79e9fa992251e38e2eb38e3952ae5cde233802cba349019f6ff8d993c1d19f23 | Jupyter | 2,518 | 110 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/clarifai.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Clarifai LLM
#
# ## Example notebook to call differen... |
6a9176f9fa6dca08b5e11b57337cd022f7facb7e6e1f4eb39efd17a093ea6cf9 | Jupyter | 2,519 | 99 | # %% [markdown]
# # Using multi_d_acquistion_events
#
# `multi_d_acquisition_events` is a powerful tool that allows you to specify the acquisition events in a similar manner to using the μmanager gui.
#
# You should also read the [Features page for this function](https://pycro-manager.readthedocs.io/en/latest/acq_eve... |
2eb9c5122ab8dee5442dc202d833df02811bbe00365b453680dbc09204bacdef | Jupyter | 2,521 | 70 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/agent/memory/vector_memory.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Vector Memory
#
# **NOTE:** This exampl... |
dac558eab9978105843f8bda482799afc0ddb80c1707dbac14875d4bea63a68c | Jupyter | 2,534 | 62 | # %%
import torch
import scanpy as sc
import pandas as pd
import numpy as np
# read h5ad rna/seqfish data
device = torch.device('cuda:2')
adata_rna = sc.read_h5ad('/data/msyuan/Cross-omics-Annotator/SPANN_Package/sample_data/adata_rna.h5ad')
adata_seqfish = sc.read_h5ad('/data/msyuan/Cross-omics-Annotator/SPANN_Packag... |
3e7ea68e72e2ded3888489eec82e95b16a0fe5437e8bc9b07f047d5663e3f6fa | Jupyter | 2,545 | 75 | # %% [markdown]
# ## Integration with PACKIO outputs (.paq data)
# %% [markdown]
# PACKIO is a software for temporal synchronization of experimental data acquisition and simultaneous hardware triggering. More information can be found at: https://github.com/apacker83/PackIO
#
# The outputs of PACKIO is a .paq file tha... |
8b1f304a2d9d0794a7deb46b11cdd453421293b4fe23d86050a04ae6ea18defb | Jupyter | 2,545 | 103 | # %%
import numpy as np
from astrocast.helper import DummyGenerator as DG
from dtwParallel import dtw_functions as dtw
# %%
A = np.random.random((8, 16))
# display(A)
dg = DG(num_rows=8, trace_length=16, ragged=True)
Bl = dg.get_list()
Ba = dg.get_array()
Bla = np.array(dg.get_list())
local_dissimilarity = "norm1"
... |
bfdd93ff8f1f31b5ca454387f7b8ef280e0dbfa0d309a6c02e9e4d3bad1f40f4 | Jupyter | 2,549 | 83 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/evaluation/guideline_eval.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Guideline Evaluator
# %% [markdown]
# Th... |
3da2261bf31498f3378474c6234ea3183216aed0bd6ff03a503b08a7390c72d5 | Jupyter | 2,556 | 95 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/cookbooks/codestral.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Codestral from MistralAI Cookbook
#
# MistralA... |
3fef3c2489dd32e0d07f0b156fc0827932e85f68516667db76611aeb37741bd1 | Jupyter | 2,560 | 93 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/cookbooks/mixedbread_reranker.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # mixedbread Rerank Cookbook
#
# mixed... |
9bc0974b3009b0d1b074766dc3767d6b0a39205a4fdf28ced391748bd33afdd9 | Jupyter | 2,567 | 71 | # %% [markdown]
# # MyMagic AI LLM
# %% [markdown]
# ## Introduction
# This notebook demonstrates how to use MyMagicAI for batch inference on massive data stored in cloud buckets. The only enpoints implemented are `complete` and `acomplete` which can work on many use cases including Completion, Summariation and Extrac... |
e38cd7327ca894ef53b32aebf5dc2b40ab1de7727cb45caaff03517a1248a3ee | Jupyter | 2,583 | 66 | # %% [markdown]
# ### Allen download
# ##### The following function downloads the tracer-data for the WT-experiments and exports the data as a nifti file. In addition it also creates a cvs file with the experiment ids, which is needed for the allen function. It accepts the following arguments:
# - resolution: the resol... |
fdffd6278f39452ec8546b6a7a494b0129a1ed56002b146c221d98cd83b5e140 | Jupyter | 2,588 | 107 | # %% [markdown]
# # Feature summary analysis
# %%
import numpy as np
import pandas as pd
# %% [markdown]
# ## Summary plots
# %% [markdown]
# ### MSSM Penn Pitt
# %%
mpp = pd.read_csv('../../_m/mssm_penn_pitt_maleVfemale.tsv', sep='\t')
mpp = mpp[(mpp['adj.P.Val'] < 0.05)].sort_values('adj.P.Val')
mpp.head()
# %% ... |
ef4af48c44b706eb0dae75a2c59d6b98223c9a2128196be3188a4a4abee72466 | Jupyter | 2,616 | 95 | # %%
import pandas as pd
import shutil
import yaml
from zephir.utils.utils import *
%matplotlib inline
from tqdm.notebook import tqdm
# %%
# path to Zephir dataset
dataset = Path('.')
# path to DeepLabCut project directory
path_to_dlc_project = Path('.')
# %%
with open(str(path_to_dlc_project / 'config.yaml'), 'r'... |
cc742f1b410a1dbfab5495aef2faee3000959191b476c9047748fecd31905409 | Jupyter | 2,623 | 102 | # %%
# imports
# local imports
import voxelmorph as vxm
import neurite as ne
import os, sys
# third party imports
import numpy as np
import tensorflow as tf
assert tf.__version__.startswith('2.'), 'This tutorial assumes Tensorflow 2.0+'
import matplotlib.pyplot as plt
import cv2
%matplotlib inline
import nbimporter
... |
ef90b983646a183c20a4fa1477b5b12ea3ae49f494133f3e7b02d8d195cd4ca6 | Jupyter | 2,623 | 63 | # %%
def MeanECGExtraction(x, phase, bins, flag):
meanPhase = np.zeros(bins)
ECGmean = np.zeros(bins)
ECGmedian = np.zeros(bins)
ECGsd = np.zeros(bins)
SamplesPerBin = np.zeros(bins)
I = np.nonzero(np.logical_or(phase >= (np.pi - np.pi/bins),
phase < (-np.pi+n... |
d5692131aa049024766f94e12ab7dd19797460d92ea5935ee9b4b49153d7df57 | Jupyter | 2,624 | 75 | # %% [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... |
7794038297a12494d2925845367ddf753e722251240ad025d2db517d8fc65447 | Jupyter | 2,630 | 70 | # %%
import os
import json
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import iirnotch, lfilter
from notch_filter_cls import notch_filter_cls # Assuming notch_filter_cls function is in notch_filter_cls.py
Qfactor = 45.0 # standard IIR notch filter's Q-factor
lambda_ = 25000.0 # notch_filter_c... |
a4758b55144e5167c532fa92129ea0215358aa85e58e31eb2a2bf7237f0eabc0 | Jupyter | 2,631 | 67 | # %% [markdown]
# # Tutorial 1
#
# Annotate unlabeled seqfish data with novel cell detection
# %%
import torch
import scanpy as sc
import pandas as pd
import numpy as np
# read h5ad rna/seqfish data
device = torch.device('cuda:2')
adata_rna = sc.read_h5ad('/data/msyuan/Cross-omics-Annotator/SPANN_Package/sample_da... |
9a46f718fa4da7bc2a21785a300c53219df96cfb0bbfdeaa723889b2cf46f0f0 | Jupyter | 2,638 | 100 | # %%
from IPython import display
# %%
import packerlabimaging as pkg
# %% [markdown]
# ### In this notebook, we demonstrate the basics of how data is organized inside of a trial object.
#
#
# First, an already existing trial object is loaded:
# %%
# import trial object to use as example
trialobj = pkg.import_obj... |
3c8aba2dc79dec8c400b45848417dc29b792dabe44545d9280b93504a0c6f13e | Jupyter | 2,649 | 67 | # %% [markdown]
# # Tutorial 1
#
# Annotate unlabeled seqfish data with novel cell detection
# %%
import torch
import scanpy as sc
import pandas as pd
import numpy as np
# read h5ad rna/seqfish data
device = torch.device('cuda:2')
adata_rna = sc.read_h5ad('/data/msyuan/Cross-omics-Annotator/SPANN_Package/sample_da... |
139842c675fc6c35af21d0922eb8a77f9d7561101e9b96bbe2b7deaea8fe285b | Jupyter | 2,656 | 79 | # %% [markdown]
# ## Predict scRNA-seq through MERFISH
#
# uniPort trained an encoder network to project cells with common genes across datasets into a
# common cell-embedding latent space and a decoder network to reconstruct cells with common and
# specific genes. Therefore, once the coupled-VAE is trained well, it c... |
234c7e018a7fadc5e511bc9e57cdbc38cfa02a251ee15df98291576c3a15b626 | Jupyter | 2,659 | 81 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/fireworks_cookbook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Fireworks Function Calling Cookbook
#
# Fir... |
ccf9857190fb48c56ce7d2f76161ab336fda3317dfcf81f87a16a93aa54fd6ee | Jupyter | 2,662 | 68 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/GoogleMapsTextSearchReaderDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Google Maps Text Sea... |
baeb86cc82798575688727efc86744b9d9bddecdbad7052573eb623fdc561a1f | Jupyter | 2,681 | 78 | # %% [markdown]
# ## Vertical integration for paired datasets
#
# uniPort also involves a vertical integration method for paired-cell datasets. Here we use uniPort to integrates paired SNARE-seq CellLineMixture datasets from [GSE126074](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126074).
# %%
import unipor... |
71b8daf9e823e2510ce840ed92cef94757cb29d23b0b80da1dd84e428273647e | Jupyter | 2,698 | 98 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/dashscope.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # DashScope LLMS
# %% [markdown]
# In this notebook, w... |
6da9ae0aa0da26fa82e46a5bf2ea25950fc607d675cf190a42700fbcef13c097 | Jupyter | 2,703 | 79 | # %% [markdown]
# # Tutorial 7 - Integration with PACKIO outputs (.paq data)
# %% [markdown]
# PACKIO is a software for temporal synchronization of experimental data acquisition and simultaneous hardware triggering. More information can be found at: https://github.com/apacker83/PackIO
#
# The outputs of PACKIO is a .... |
a5db387cf07975a52e9548e5e28fdc6f30aa60fc45e19e7943c587999f5cffa9 | Jupyter | 2,703 | 65 | # %% [markdown]
# # Conformal Prediction Quickstart
#
# End-to-end **conformal prediction** for regression and classification using [crepes](https://github.com/henrikbostrom/crepes) and scikit-learn. The intervals and prediction sets are guaranteed to contain the true label with the user-specified coverage (e.g. 90%) ... |
0c1e47a81aa3ff37b958dac1308f1cf8aa3021bb7f565c4773470a6221ddbdff | Jupyter | 2,706 | 83 | # %%
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from src.drop_width.preprocessing import DropDataProcessor
from src.drop_width.model import build_lstm_model
from src.drop_width.side_to_width ... |
89cf3b9d3ab6426e6fee5c6985df5d58448605499355c321d5d2c83628c6d55c | Jupyter | 2,707 | 92 | # %%
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from anndata import AnnData
import scanpy as sc
import json
# %%
sc.set_figure_params(dpi=100)
# %%
with open('/data/projects/robin/cell_free/proteomics_WGCNA/module_genes.json') as handle: # '/data/projects/ro... |
4d690a4d1bdfb0a481c6c8dfc7e9c55431246ea3df37c417414fc4f39593dc29 | Jupyter | 2,721 | 77 | # %%
import packerlabimaging.imaging_utils as pkgb
# %%
# experiment initialization dictionary
initialization_dict = {
'dataPath': '/home/pshah/mnt/qnap/Data/2020-12-19',
'analysisSavePath': '/home/pshah/Documents/code/packerlabimaging/tests/',
'microscope': "Bruker",
"expID": 'RL109',
'date': '20... |
b09baadb9f3407f7e30001c7b8cbeb3ee37d00cd683846216737f312694e392e | Jupyter | 2,721 | 116 | # %%
from zephir.methods import *
from zephir.methods.plot_loss_maps import plot_loss_maps
from zephir.methods.get_optimization_trajectory import get_optimization_trajectory
from zephir.models.container import Container
from zephir.utils.utils import *
%matplotlib inline
# %%
# path to dataset
dataset = Path('.')
# ... |
c9a60db947ffa13989a5d988f14cf2bdadfed404df5cc596db9c4cd4a034ff03 | Jupyter | 2,725 | 94 | # %%
import emblaze
from emblaze.utils import Field, ProjectionTechnique
import numpy as np
import pandas as pd
# %%
def load_bnhp():
df = pd.read_csv("housing.csv", header=None, sep='\s+')
df.columns = ["crim", "zn", "indus", "chas", "nox", "rm", "age", "dis", "rad", "tax", "ptratio", "black", "lstat", "medv"... |
ad9012d14f4317e4e00e1b32a457f33c40f552e3729c7e510a9f52a0a4a2e889 | Jupyter | 2,731 | 53 | # %% [markdown]
# # Equilibrium Solver #
# The equilibrium solver takes a reaction network as input and solves for a correct equilibrium solution. It does this by writing the system as a system of equations that can be solved to give the expected concentrations of all species at equilibrium.
#
# It is useful to have t... |
953501345c019c7fbd6beb4f06d4b3da4928b37f4c3517ed928c7823b8757b92 | Jupyter | 2,745 | 102 | # %%
suppressMessages({
library(tidyverse)
library(data.table)
})
save_plot <- function(p, fn, w, h){
for(ext in c(".pdf", ".png")){
ggsave(filename=paste0(fn,ext), plot=p, width=w, height=h)
}
}
dir.create('../_m/')
setwd('../_m/')
# %%
#file_list
# %%
# create a list of all the filenames... |
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