sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
4deb6b673893f2bea5f6db177205a511931e9f19cd6de20efae7cf569285d03e | Julia | 20,801 | 436 | # ============================================================================
# Optimal Control for Cramér-Rao Bound Minimization
#
# State vector convention (11 components per gradient parameter):
# [xf, yf, zf, xs, zs, ∂xf/∂θ, ∂yf/∂θ, ∂zf/∂θ, ∂xs/∂θ, ∂zs/∂θ, 1]
# 1 2 3 4 5 6 7 8 ... |
42240eb04ea5c2f019a3091cc5eb897827527f9f6a39c2eac0a7e85315fd1677 | Julia | 22,141 | 457 | using CSV
using DataFrames
using DataFramesMeta
using CategoricalArrays # for making data categorical
using Statistics # for median
using StatsBase # for mad
using Gadfly # for plotting
using Cairo # for plotting
using Fontconfig # for plotting
using MixedModels # for linear mixed effects models
using Loess # for Loess... |
1772a3e74b703f9f78713488a6370bc79b431a7c14c0b0e4e516142599a1762c | Julia | 24,982 | 401 | ##
"""
hamiltonian_linear(ω1, B1, ω0, T, m0s, R1f, R2f, Rx, R1s, R2s[, dR2sdT2s, dR2sdB1, grad_type])
Calculate the hamiltonian of the linear approximation of the generalized Bloch model.
If no gradient is supplied, it returns a 6x6 (static) matrix with the dimensions (in this order) `[xf, yf, zf, xs, zs, 1]`; th... |
a16fc45072858d03c578ca7586367e54b225cb2d1e2e3ece5a98282633e7fd11 | Julia | 27,501 | 651 | #md # [](@__BINDER_ROOT_URL__/build_literate/Analyze_NMR_IR_Data.ipynb)
# # Inversion Recovery Experiments
# The following code replicates the NMR data analysis in Figs. 4-6 and complements the paper with additional analyses that are not shown in the paper in the interest of bre... |
cf59829bc9a4316781c3a6152e2c34979c8fb3086318993a5346450dbfd1c123 | Julia | 27,637 | 651 | #md # [](@__BINDER_ROOT_URL__/build_literate/Analyze_NMR_IR_Data.ipynb)
# # Inversion Recovery Experiments
# The following code replicates the NMR data analysis in Figs. 4-6 and complements the paper with additional analyses that are not shown in the paper in the interest of bre... |
557b19212a755e562a820950e65226e1ddb7b9eca0d060b9b6384300f675d833 | Julia | 32,125 | 745 | ###################################################
# generalized Bloch Hamiltonians that can take any
# Green's function as an argument.
###################################################
"""
apply_hamiltonian_gbloch!(∂m∂t, m, mfun, p, t)
Apply the generalized Bloch Hamiltonian to `m` and write the resulting der... |
779506a0a482dcf09d72e6c2908e1daf9d4edbaf7beb8883dfac9987c5424bf1 | Julia | 35,954 | 800 | ###################################################
# generalized Bloch Hamiltonians that can take any
# Green's function as an argument.
#
# Dispatch variants for apply_hamiltonian_gbloch!:
# p::NTuple{6,Any} — isolated semi-solid pool: (ω1, B1, ω0, R1s, T2s, g)
# p::NTuple{10,Any} — coupled two-pool, scalar ω1... |
7ac09fd89a3955c4cd2ac0967ac59585a549dc8d02ecf02c787ce85d7edf463d | Julia | 51,799 | 1,251 | #region output
"""
Results struct
state: Table with arguments (n=num generations):
τ² = Array{Float64,3} dimension (n,1,1) variance of error term,
u = Array{Float64,3} dimension (n,R,V) R-dim latent variables for each node,
ξ = Array{Float64,3} dimension (n,V,1) binary vector to denote if a node is inf... |
4aeead4efe9827fcb18c6cc383b70e813e4af49f9bd1a630762ca748313b5f2c | Jupyter | 39 | 5 | # %%
pip install dynamo-release
# %%
|
7401aa5d5d295a9081035637cb35621a96e3812501162f7e154e3dc79c56c311 | Jupyter | 89 | 13 | # %%
import numpy as np
import pandas as pd
from utilities import *
# %%
# %%
# %%
|
273ce5ff5db8dbd30477914688d9cfdeda5caf0f2817587558b8d3758969517b | Jupyter | 96 | 8 | # %%
file_path = 'DO_FACTORY_RESTORE'
with open(file_path, 'w') as f:
pass
file_path
# %%
|
d621778da303d1421a4b8ecbe99c0e3708289fa978d08003756102173601c367 | Jupyter | 125 | 11 | # %%
import packerlabimaging as pkg
# %%
pkg.Utils
# %%
pkg.Utils.s2pRun
# %%
# updating ops fields as an optional action
|
52eda1f6cca97d94bc65875b2d16f57a00d5710741d30e1f8b350fb77cc7e303 | Jupyter | 133 | 5 | # %%
def PhaseShifting(phasein, theta):
phase = phasein + theta;
phase = (phase + np.pi) % 2*np.pi - np.pi;
return phase
|
6056b6b19cd8d8a7d1eb677ea116c73770cc5198ba89b886c53ba8d22873de79 | Jupyter | 133 | 3 | version https://git-lfs.github.com/spec/v1
oid sha256:e0baba4ac3a68926405c55c55f18e86a03e58db10eb26d91c6b2e496d6c11108
size 12983025
|
26d582a6bec349725528a05813ce8258b603228730f6dd8a8124e2598f42170b | Jupyter | 303 | 13 | # %%
import sys
import numpy as np
np.set_printoptions(precision=3, suppress=True)
import brkraw
import pprint as pp
# %%
path = '/project/4180000.19/multiRat/to_convert/BIL_antwerpen/data/20211103_115428_BMvdB_UA_17367_21_03112021_rsfMRI_standard_2_1_2'
pvdset = brkraw.load(path)
# %%
pvdset.info()
|
f51ee8ee74803b78d5dfa52aba074e7c216fb62240d6142ffd861602bfe52474 | Jupyter | 304 | 18 | # %%
import pandas as pd
file_path = './Macaque_Mouse.tsv'
df = pd.read_csv(file_path, sep='\t')
print(df)
df.to_csv('./Macaque_Mouse_multi2multi.csv', index=False)
df = df[df['Mouse homology type'].isin(['ortholog_one2one'])]
print(df)
df.to_csv('./Macaque_Mouse_one2one.csv', index=False)
# %%
|
c61f4cb098bcfc1d55cecf1909508211d8639d8fc955a5b5184a4dafe3017a88 | Jupyter | 366 | 15 | # %%
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... |
f6f2b99b51d9401d5804029802876a64205711768f1685c571343befd1a125c6 | Jupyter | 474 | 20 | # %%
%matplotlib agg
import copy
from utils.ml import MLPNetwork, MOVotingRegressor
from utils.log import auto_log
from utils.da import augmente_data
import pandas as pd
import numpy as np
# %%
train_df = pd.read_csv('data/eps30_data/eps_train_rd08.csv')
x_cols = ['CNF', 'MXN', 'GEL', 'Mass_loading', 'GA']
y_cols =... |
91bfc68161ec21d05e0eb96759683ee78430b2b655579489b37e97176f8eb51f | Jupyter | 505 | 34 | # %%
import pymaid
rm = pymaid.connect_catmaid()
# %%
%time n = pymaid.get_neuron(16)
# %%
%time n2 = pymaid.get_neuron(16)
# %%
rm.setup_cache(size_limit=128, time_limit=900)
# %%
rm.cache_size
# %%
rm.clear_cache()
rm.cache_size
# %%
rm = pymaid.CatmaidInstance('server_url', 'api_token', 'http_user', 'http_pas... |
5795f1e31a339740a746b1416e82f037a8560070b88eb3a58d12bc0d7927fa1e | Jupyter | 516 | 32 | # %%
library(minfi)
# %%
df_betas <- read.csv("clinical_cohort_betas_all.csv", row.names=1)
df_meta <- read.csv("clinical_cohort_metadata_all.csv", row.names=1)
# %%
dim(df_betas)
# %%
df_betas <- t(df_betas)
# %%
head(df_meta[1:2,1:2])
# %%
df_meta <- df_meta[colnames(df_betas),]
rownames(df_meta)==colnames(df_be... |
13f77a8403b1139fd53d5ce157fb9607d587e41079b8c3ac0c14db07325be817 | Jupyter | 522 | 23 | # %% [markdown]
# # YandexGPT
# %%
%pip install llama-index-embeddings-yandexgpt
# %%
!pip install llama-index
# %%
from llama_index.embeddings.yandexgpt import YandexGPTEmbedding
yandexgpt_embedding = YandexGPTEmbedding(
api_key="your-api-key", folder_id="your-folder-id"
)
text_embedding = yandexgpt_embedding... |
59919794e6acb7e93e1ac3f1d279e96ba9e045ed8046ef38361619ffe2f18e7f | Jupyter | 529 | 31 | # %% [markdown]
# # CGI_Finder CLI usage
# %% [markdown]
# ## Activate virtual environment
# %%
# Using virtualenvwrapper here but can also be done with Conda
workon pycoMeth
# %% [markdown]
# ## Getting help
# %%
pycoMeth CGI_Finder --help
# %% [markdown]
# ## Example usage
# %% [markdown]
# #### Basic usage wi... |
ea431d414646aef4d6538af0458c9cd4c6d3a47c9b5817ce0bbe6f5abc3f0271 | Jupyter | 534 | 25 | # %% [markdown]
# # Grok 4
#
# Grok from xAI uses an OpenAI-compatible API, so you can use it with the OpenAILike integration class.
# %%
!pip install llama-index-llms-openai-like
# %%
grok_api_key = "xai-xxxxxxxx"
# %%
from llama_index.llms.openai_like import OpenAILike
llm = OpenAILike(
model="grok-4-0709",
... |
2134428127089964cc3aa9bd8830459e1de2b0b00c69694cbb34ad0348b93f6d | Jupyter | 557 | 28 | # %%
import sqlite3
import pandas as pd
# %% [markdown]
# # Table of pass or failed pipelines
# %%
conn = sqlite3.connect("csvdb")
df = pd.read_sql_query("SELECT * FROM md5_compare;", conn)
df
# %% [markdown]
# # Files with different lines
# %%
pd.set_option('display.max_colwidth', -1)
df = pd.read_sql_query("SELE... |
3ef50b4cbf1b599dade8af7e3c1f932730f2b2d0b3ed9537ff6fab3e247e2a58 | Jupyter | 571 | 10 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/gemini.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Gemini
# %% [markdown]
# `llama-index-llms-gemini` supp... |
6eba11986c8405fc1311477dffeceadca09534467e73a96bbf319b9d3a94cd24 | Jupyter | 582 | 28 | # %% [markdown]
# ## Integration
# %%
import uniport as up
import numpy as np
import pandas as pd
import scanpy as sc
# %% [markdown]
# Read preprocessed data.
# %%
adata_atac = sc.read_h5ad('PBMC/atac.h5ad')
adata_rna = sc.read_h5ad('PBMC/rna.h5ad')
adata_cm = sc.read_h5ad('PBMC/atac_and_rna.h5ad')
# %%
adatas = [... |
a19331073cd5b66146a3993641eddd69b07cbed09915444a965efa8ac4e219ba | Jupyter | 583 | 24 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/langchain.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # LangChain LLM
# %%
%pip install llama-index-llms-lan... |
71aaef3d2ee355a863a5b33a7d817daa0e1d85118b3cbbb9cf1decaa85bc946e | Jupyter | 588 | 7 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/gemini.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Google Palm Embeddings
#
# Support for `llama-in... |
1bed6fa09b46d9be1d3eaafbd7f64ae4b5ba178b9bbb1473124661f2a8c2888f | Jupyter | 590 | 34 | # %%
import pandas as pd
file_path = './Mouse_Human.tsv'
df = pd.read_csv(file_path, sep='\t')
print(df)
df.to_csv('./Mouse_Human_multi2multi.csv', index=False)
df = df[df['Human homology type'].isin(['ortholog_one2one'])]
print(df)
df.to_csv('./Mouse_Human_one2one.csv', index=False)
# %%
import pandas as pd
fi... |
4bba4855edff68ffd0824b609d34abf00d7defd74b8d358ebba230cc30182bdf | Jupyter | 590 | 7 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/gemini.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Google Gemini Embeddings
#
# Support for `llama-... |
13ed0dc34b14c300523d65a06b05c77227006702ca888fc207831832baa6506c | Jupyter | 600 | 6 | # %% [markdown]
# ### information on analysis pipeline
#
# For notebook nb_03 we will focus on the position error. We run the same basic analysis for accuracy and precision, except for some adaptations and internal differences. The analysis notebooks concerning accuracy are labeled with "a", and the notebook concernin... |
e0a4eb7de2a1684437c56d44a5c447690c93154f21400e92d54810b28a32dbec | Jupyter | 611 | 24 | # %% [markdown]
# # GigaChat
# %%
%pip install llama-index-embeddings-gigachat
# %%
!pip install llama-index
# %%
from llama_index.embeddings.gigachat import GigaChatEmbedding
gigachat_embedding = GigaChatEmbedding(
auth_data="your-auth-data",
scope="your-scope", # Set scope 'GIGACHAT_API_PERS' for persona... |
a87d61d25d950d550479b0655f9a60ba368f4a68d992b658b428dabb63e128ae | Jupyter | 617 | 34 | # %% [markdown]
# # CGI_Finder API usage
# %% [markdown]
# ## Import module
# %%
# Import main module
from pycoMeth.CGI_Finder import CGI_Finder
# optionally inport jupyter helper functions
from pycoMeth.common import head, jhelp
# %% [markdown]
# ## Getting help
# %%
jhelp(CGI_Finder)
# %% [markdown]
# ## Examp... |
39859b3f7dccea0db6d60e2740ceca9e27d07a5a4ab683187c47c07eb9e80fda | Jupyter | 672 | 40 | # %%
skids = pymaid.get_neurons_in_volume('AL_L')
nl = pymaid.get_neurons(skids)
nl.head()
# %%
subset = nl[nl.n_nodes > 6000]
subset.head()
# %%
subset = nl[nl.soma != None]
subset.head()
# %%
subset = nl.skid['57499']
subset
# %%
subset = nl.skid [[57499, 57479]]
subset
# %%
subset = nl['PN glomerulus VL1 57500 ... |
cc7b4337bed753aad8bc36615f3914ce3ba221f48a51d23e8aa8ccb524a85f70 | Jupyter | 690 | 27 | # %% [markdown]
# # **Google Sheets Reader**
# Demonstrates Google Sheets Reader in LlamaIndex
#
#
# * Make Sure you have token.json or credentials.json file in the Environment, More on that [here](https://developers.google.com/workspace/guides/create-credentials)
#
# %%
from llama_index.readers.google import Goo... |
4976569e241997fed1fa4f1ec6786c487c1ebd977945fd296a21c200802b48af | Jupyter | 700 | 27 | # %%
import scipy.io
import numpy as np
import matplotlib.pyplot as plt
from oset.ecg.peak_detection.peak_det_pan_tompkins import (
peak_det_pan_tompkins,
)
from oset.generic.lp_filter import lp_filter_zero_phase
# %%
mat = scipy.io.loadmat("../../../datasets/sample-data/SampleECG1.mat")["data"][0]
f = 1
fc = 0.5
... |
39ec008e2a45fb29a75a6e8d3d55134a136240e18fd44f6157bf1440144e781b | Jupyter | 713 | 28 | # %%
import scipy.io
import numpy as np
import matplotlib.pyplot as plt
from oset.generic.lp_filter.lp_filter_zero_phase import lp_filter_zero_phase
from oset.ecg.peak_detection.peak_det_local_search import (
peak_det_local_search,
)
# %%
mat = scipy.io.loadmat("../../../datasets/sample-data/SampleECG1.mat")["data... |
30788644b738c9691cbcd196bc043c4e6441e8ac496274861cbde8fa6ba7f3c5 | Jupyter | 721 | 33 | # %%
import squidpy as sq
# %%
import scanpy as sc
data_dir = '../data/'
adata = sc.read_h5ad(data_dir+'E7-9h_cellbin_tdr_v2.h5ad')
adata1 = adata[adata.obs['slices']==adata.obs['slices'].unique()[15]].copy()
adata2 = adata[adata.obs['slices']==adata.obs['slices'].unique()[16]].copy()
# %%
adata.obs['slices'].unique(... |
3fbf10d74855871ed66880e17745c0e319d88f8ea69805c59296eb1ec629fdb0 | Jupyter | 768 | 25 | # %% [markdown]
# # Databricks Embeddings
# If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.
# %%
%pip install llama-index
%pip install llama-index-embeddings-databricks
# %%
import os
from llama_index.core import Settings
from llama_index.embeddings.databricks import Databri... |
a7e0b159789972f86bddc1403f6df2a507f41c831eb56442a8135698d039f777 | Jupyter | 796 | 42 | # %%
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
from embedding_viewer import CMEViewer
from utils.distribution import DeviceManager
import os
os.environ['RANK'] = '-1'
DeviceManager('1')
path = '/rhome/lianyu.zhou/dataset/Omics/train.csv'
smiles_list = []
add_samples = 10
with open(path) as ... |
fc63c12984d3f9736a6702cc45c6cc87e5afb197fa973af2d4e63d11330e13bf | Jupyter | 806 | 36 | # %%
pip install dynamo-release --user
# %%
import dynamo as dyn
dyn.configuration.set_figure_params('dynamo', background='white') # jupter notebooks
# %%
adata = dyn.read('../data/toggle_switch/data_toggle.h5ad')
# %%
dyn.pp.recipe_monocle(adata)
dyn.tl.dynamics(adata)
# %%
dyn.tl.reduceDimension(adata, basis='uma... |
5dbf981e08b504f0d9df22547201b99efa09f56682bb645f713d942e5e6042fa | Jupyter | 807 | 27 | # %% [markdown]
# # Metrics summary for splicing
# %%
import numpy as np
import pandas as pd
# %%
df = pd.read_csv('../../_m/cluster_ds_results_annotated.txt', sep='\t')
df['chr'] = df.coord.str.split(":", expand=True)[0]
df.head(2)
# %%
print("There are %d unique DS!" % len(df.clusterID.unique()))
print("There are ... |
97e86acbb498f232c49fe79318f7dcdf1bc4e03c7a334afa90aeddced6a03030 | Jupyter | 807 | 41 | # %% [markdown]
# # 1.Clone source code from GitHub
# %%
!git clone https://github.com/NathanUA/BASNet.git
# %%
%cd BASNet
# %% [markdown]
# # 2.Download pre trained weight
# %%
!pip install gdown
# %%
import os
import gdown
os.makedirs('./saved_models/basnet_bsi', exist_ok=True)
gdown.download('https://drive.g... |
a4487b2866a59b03fb76b995f3f72ca05fe86fcbfcbb60971f3cce9cf79c6945 | Jupyter | 807 | 28 | # %%
import scipy.io
import numpy as np
import matplotlib.pyplot as plt
from oset.generic.lp_filter.lp_filter_zero_phase import lp_filter_zero_phase
from oset.ecg.peak_detection.peak_det_modified_pan_tompkins import (
peak_det_modified_pan_tompkins,
)
# %%
mat = scipy.io.loadmat("../../../datasets/sample-data/Samp... |
028785eef0592a4ab9ae605644298755bf03fd9f34d19a580b013082289bb0ec | Jupyter | 820 | 41 | # %% [markdown]
# # Netmind AI Embeddings
#
# This notebook shows how to use `Netmind AI` for embeddings.
#
# Visit https://www.netmind.ai/ and sign up to get an API key.
# %% [markdown]
# ## Setup
# %% [markdown]
# If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.
# %%
%pi... |
0b79c89c10c73c8afffb76e0b00df51bfd8baa4ad2660eae651d2d0ee24ec461 | Jupyter | 835 | 44 | # %%
import celldancer as cd
import anndata
import pandas as pd
import numpy as np
import scipy as sp
import os
# %%
adata = anndata.read_h5ad('../data/toggle_switch/data_toggle.h5ad')
# %%
adata.layers['Mu']=adata.layers['unspliced']
adata.layers['Ms']=adata.layers['spliced']
# %%
df = cd.adata_to_df_with_embed(ada... |
9862fdcdb89f66cccaff9c737b990d9bfb5af70aed63195ac7214bfe0b7b4d04 | Jupyter | 847 | 44 | # %%
import celldancer as cd
import anndata
import pandas as pd
import numpy as np
import scipy as sp
import os
# %%
adata = anndata.read_h5ad('../data/emt_sim/emt_circuit_larger_noise.h5ad')
# %%
adata.layers['Mu']=adata.layers['unspliced']
adata.layers['Ms']=adata.layers['spliced']
# %%
df = cd.adata_to_df_with_em... |
2facfbc163bde8f2ca25e062aa2df0f8eca6b6622622b41bc1b7a45c2bcb75dc | Jupyter | 848 | 43 | # %% [markdown]
# # Meth_Comp CLI usage
# %% [markdown]
# ## Activate virtual environment
# %%
# Activate Conda env
conda activate pycoMeth
# %% [markdown]
# ## Getting help
# %%
pycoMeth Comp_Report --help
# %% [markdown]
# ## Example usage
# %% [markdown]
# #### Example with a single significant result
# %%
p... |
f8e3818d9d3c94151c2b2ee10948c4ea8a31e2e751c48c2e120fcbbb161334dc | Jupyter | 850 | 28 | # %%
import pickle, numpy as np, pandas as pd, networkx as nx, warnings
warnings.filterwarnings('ignore')
import matplotlib, matplotlib.pyplot as plt
import sys
matplotlib.rcParams['font.sans-serif'] = "Arial"
matplotlib.rcParams['font.family'] = "Arial"
import seaborn as sns, OmicsIntegrator as oi
# %%
infile= ... |
32c7f210e7cb55485071bcb1095372baa8a6b10bcff877a967b358749c903281 | Jupyter | 852 | 35 | # %%
import os
# %%
from packerlabimaging.imaging_utils import *
# %%
metainfo = {
'animal prep.': 'RL109',
'trial': 't-013',
't series id': 'RL109 t-013',
'date': '2020-12-19',
'exptype': 'pre 4ap 2p all optical',
'comments': '<comments about the experiment>'
}
tiff_path = '/home/pshah/mnt/... |
015d5f6a85546ca78940f6b8ebde4dd640f9831ebc8015f394e864bd2ad2595d | Jupyter | 858 | 52 | # %%
import scanpy as sc
import matplotlib.pyplot as plt
from run_stMDA import run_stMDA
# %%
adata = sc.read_h5ad('../PDAC/adata/GSM6505134_DonorB_FFPE-probes.h5ad')
# %%
sc.pp.highly_variable_genes(adata, n_top_genes=3000, flavor='seurat_v3', subset=True)
# %%
sc.pp.log1p(adata)
# %%
run_stMDA(adata, alpha1=0.5,... |
6b8caec06b8246b20276c6410438edd2f3fb74f5ebdfd61d7e5590545eb263b8 | Jupyter | 892 | 34 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/llm_rails.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # LLMRails Embeddings
# %% [markdown]
# If you'... |
1bd949e875f60b0d99fba9af33b6b89a36be56054c7c13101904821928d206bf | Jupyter | 894 | 49 | # %%
import celldancer as cd
import anndata
import pandas as pd
import numpy as np
import scipy as sp
import os
# %%
adata = anndata.read_h5ad('../data/A549_TGFB1.h5ad')
# %%
import scanpy as sc
sc.pp.neighbors(adata, n_neighbors = 20)
sc.tl.umap(adata)
# %%
df = cd.adata_to_df_with_embed(adata,
... |
a510dee2d099a759f8b2e15716cb438e1d08f7d8aea16f95e83e964bf736b7a6 | Jupyter | 896 | 32 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/mistralai.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # MistralAI Embeddings
# %% [markdown]
# If you... |
affae37d0037b75a583a1659356a18d66748aef5d45d437f40f0c69a137186d0 | Jupyter | 897 | 52 | # %%
import pandas as pd
# %%
wf_path = "../data/workflow-connections_Feb_22_1.csv"
# %%
wf_frame = pd.read_csv(wf_path, sep="|", header=None)
# %%
wf_frame
# %%
wf_frame_1 = wf_frame[1:len(wf_frame.index)-1]
# %%
wf_frame_1 = wf_frame_1[1:]
wf_frame_1
# %%
wf_frame_1 = wf_frame_1.sample(frac=1)
wf_frame_1
# %%
... |
75b81c48efd56ef8b4044aeeab8f7446db88680fece6c56946caa7bc0bba96c8 | Jupyter | 899 | 53 | # %%
import scanpy as sc
import matplotlib.pyplot as plt
from run_stMDA import run_stMDA
# %%
adata = sc.read_h5ad('../human_embryo/adata/s2.h5ad')
adata.obsm['spatial'] = adata.obsm['spatial'][:, [1,0]]
# %%
sc.pp.highly_variable_genes(adata, n_top_genes=3000, flavor='seurat_v3', subset=True)
# %%
sc.pp.log1p(adat... |
11bbe542bb12e500e28b878fdb17c96a80b35344f155b5bfec8d47e7b4f73aba | Jupyter | 900 | 40 | # %%
import scipy.stats as st
from matplotlib.pyplot import figure
import sys, os
import matplotlib.pyplot as plt
import pandas as pd
# %%
TN93_DST_FILE = os.path.join("..",
"results",
"mammalian_REM2",
"mammalian_REM2_codons.SA.FilterOutlie... |
95926bd1e0430773f90aa2bf69652bb7d32f830e5fe7b244d3c0c8c779a43ee7 | Jupyter | 901 | 58 | # %%
import pymaid
# %%
rm = pymaid.CatmaidInstance(server='https://www.your.catmaid-server.org',
api_token='YOURTOKEN',
http_user='user', # omit if not required
http_password='pw') # omit if not required
# %%
rm = pymaid.connect_catm... |
1dd36ae585be5b9988af1b1d328b4b1e177482d486b0c6d8f78fe90f670cad0d | Jupyter | 908 | 49 | # %%
import celldancer as cd
import anndata
import pandas as pd
import numpy as np
import scipy as sp
import os
# %%
adata = anndata.read_h5ad('../data/mouse_brain.h5ad')
# %%
adata
# %%
df = cd.adata_to_df_with_embed(adata,
cell_type_para='Region',
embed... |
2feaeb51da7dd07c71c883eb885d47f5ae2bd391600c66d02754c32153df3e2a | Jupyter | 908 | 33 | # %%
# set the working directory to be the SSCD folder (hit Ctrl+Enter to run cell)
import os
path_parent = os.path.dirname(os.getcwd())
os.chdir(path_parent)
%config Completer.use_jedi = False
# %% [markdown]
# ### Example run
#
# For example purposes, the following run uses a couple of scale images shipped with the... |
f07c7b22f19103d58789681c88b3124cf6d3512c17f243edb1082539192c6562 | Jupyter | 930 | 30 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/OpenAI.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Anyscale Embeddings
#
# This guide shows you how... |
3ad457fb46c384542a39bc272c44a2dac88837ae324a80eb28cc7501d1c10b6b | Jupyter | 932 | 29 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/OpenAI.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Fireworks Embeddings
#
# This guide shows you ho... |
47652f8a318a68cdf0e22914ded1b6d529a69c3afd0158d62d406a198c2b245b | Jupyter | 945 | 54 | # %%
import os
import glob
import numpy as np
import pandas as pd
from tqdm import tqdm
# %%
def read_file(lbpath):
fl = open(lbpath, 'r')
data = fl.readlines()
fl.close()
return data
# %%
def save_file(filename, content):
with open(filename, 'w') as f:
f.writelines(content)
# ... |
5dde8f4cb9cce4d84f09ffca6d17e23645d6f66c0b751115ad00e9357f429f37 | Jupyter | 967 | 45 | # %%
import pymaid
# We will deactivate caching so that we always have the live data
rm = pymaid.connect_catmaid(caching=False)
# %%
resp = pymaid.add_annotations(16, "favourite neuron of Philipp")
# %%
resp
# %%
pymaid.get_skids_by_annotation('favourite neuron of Philipp')
# %%
resp = pymaid.remove_annotations(16... |
35ed1e4dc4ea3fc48a6a1b234dcc69debbeb919baad0fb98fe410ef021734175 | Jupyter | 974 | 38 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/MboxReaderDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Mbox Reader
# %% [markdown]
# If yo... |
d155a3e71e05b8e47c475e947e593aec8248941354a7da341ee67367b97d66e6 | Jupyter | 986 | 45 | # %%
import numpy as np
import scipy.io as sio
import matplotlib.pyplot as plt
from lp_filter_zero_phase import lp_filter_zero_phase
# Load the MATLAB .mat file
# Example 1:
data = sio.loadmat('../../datasets/sample-data/SampleECG1kHz1.mat')
x = data['data']
# Example 2:
# data = sio.loadmat('../../datasets/sample-d... |
80392b6df1aa61b9737deb3eed493804145808fb901660b2727a1941b51663b6 | Jupyter | 987 | 39 | # %%
import scipy.io
import numpy as np
import matplotlib.pyplot as plt
from oset.generic.lp_filter.lp_filter_zero_phase import lp_filter_zero_phase
from oset.ecg.peak_detection.peak_det_simple import peak_det_simple
# %%
mat = scipy.io.loadmat("../../../datasets/sample-data/SampleECG1.mat")["data"][0]
f = 1
fs = 1000... |
866522066e43cc4ac9ec2efdbd3fcb90f72d9eee2443c7212bde4fca64c2688f | Jupyter | 991 | 41 | # %%
import nibabel as nib
def load_m (file_path):
img = nib.load(file_path)
img_data = img.get_fdata()
if img.shape[0:2]!=(64,64):
img_data = img_data[23:87,23:87,:,:]
if not (file_path.endswith(".nii") or file_path.endswith(".nii.gz")):
raise ValueError(
... |
f3ea68ce60617e38016d0442ddd687d3cd67d61bd19082de92ccac1cdbc8ecc0 | Jupyter | 991 | 38 | # %%
import pickle, numpy as np, pandas as pd, networkx as nx, warnings
warnings.filterwarnings('ignore')
import matplotlib, matplotlib.pyplot as plt
import sys
matplotlib.rcParams['font.sans-serif'] = "Arial"
matplotlib.rcParams['font.family'] = "Arial"
import seaborn as sns, OmicsIntegrator as oi
# %%
infile="/mnt... |
e85e4fc6fd2bbd2b15ae67b5cfe776789472e90a6f9c0a2ff07b83aba3e10ba8 | Jupyter | 1,000 | 34 | # %% [markdown]
# # Partial bond orders and benzene
#
# This notebook applies a SMIRNOFF FFXML involving partial bond orders (reducing the number of parameters) to assign bond parameters for benzene.
# %%
# Import stuff we need
from openff.toolkit.typing.engines.smirnoff import *
import openeye.oechem as oechem
impo... |
a90e7f9d93bfd229568393b475ea0ee395871ea91cb815d1e5993754025030f1 | Jupyter | 1,015 | 43 | # %%
import numpy as np
import matplotlib.pyplot as plt
%run MeanECGExtraction.ipynb
%run PhaseCalculation.ipynb
%run PhaseShifting.ipynb
# %%
# test PhaseShifting
peaks = [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0]
print('peaks:', peaks)
phase, phasepos = PhaseCalculation(peaks)
pr... |
036945ed004afc2a39385ee2c1410ba8a62bf59c38a4c5e9fdb3d15ea5beee61 | Jupyter | 1,038 | 38 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/MilvusReaderDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # MilvusReader
#
# %% [markdown]
#... |
a5534c03c1fdaabc5622a248b527e48da0805a73c43cb5ce63db26f2b4df6559 | Jupyter | 1,038 | 44 | # %%
import os
# %%
os.getcwd()
# %%
from IPython import display
display.Image("files/TwoPhoton Imaging Workflow #1.png")
# %%
from packerlabimaging.imaging_utils import *
# %%
from packerlabimaging import imaging_utils as iu
expobj = iu.TwoPhotonImaging(...)
# %%
metainfo = {
'animal prep.': 'RL109',
'tri... |
d856198102a7e87d190ccc7300f7e79898b150555fea63e78d32dc97fa757e70 | Jupyter | 1,039 | 36 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/html_tag_reader.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # HTML Tag Reader
# %% [markdown]
# ... |
230a152de532e1ef9438c7c59344dad0520c65b693e43bd818a28ab0c466478a | Jupyter | 1,040 | 43 | # %%
import scipy.io
import numpy as np
import matplotlib.pyplot as plt
from oset.generic.lp_filter.lp_filter_zero_phase import lp_filter_zero_phase
from oset.ecg.peak_detection.peak_det_amp_threshold import (
peak_det_amp_threshold,
)
# %%
mat = scipy.io.loadmat("../../../datasets/sample-data/SampleECG1.mat")["da... |
1ff6a14f27926e4670f21f8663b87147dc5a6005da547807df06f6ddc58bd9e8 | Jupyter | 1,043 | 120 | # %%
nl = pymaid.get_neurons('annotation:glomerulus DA1 right')
nl.head()
# %%
1 == 1
# %%
2 == 1
# %%
n1 = nl[0]
n2 = nl[0]
n1 == n2
# %%
n1 = nl[0]
n2 = nl[1]
n1 == n2
# %%
nl == nl
# %%
nl == nl[:-1]
# %%
nl[[0, 1, 2]] == nl[[2, 1, 0]]
# %%
n1 = nl[0]
n2 = n1.copy()
n1 == n2
# %%
n1 = nl[0]
n2 = n1.reroot(n... |
f68cfbda7647887c4b90a34191e0920f5a41995894bea618d0a4303d91b50dc8 | Jupyter | 1,045 | 31 | # %% [markdown]
# # LangChain Embeddings
#
# This guide shows you how to use embedding models from [LangChain](https://python.langchain.com/docs/integrations/text_embedding/).
#
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/Langchain.ipynb" target="_paren... |
2c1634572a3d16ef1754628a17908f79cd422243ba405ab2bc9c2796c957a6ac | Jupyter | 1,056 | 63 | # %%
import pymaid
rm = pymaid.connect_catmaid()
# %%
da1 = pymaid.get_neuron('annotation:glomerulus DA1 right')
da1
# %%
fig, ax = da1.plot2d(lw=1.5)
# %%
vol = pymaid.get_volume('v14.neuropil')
vol
# %%
# For visualization we rely on navis
import navis
# Make the volume more transparent
vol.color = (230, 230, 23... |
67ef760ce9010bf9438cc1642c261e79a2e441eb29f8001087f7a58f7bf68f08 | Jupyter | 1,068 | 49 | # %% [markdown]
# # CpG_Aggregate CLI usage
# %% [markdown]
# ## Activate virtual environment
# %%
# Using virtualenvwrapper here but can also be done with Conda
workon pycoMeth
# %% [markdown]
# ## Getting help
# %%
pycoMeth CpG_Aggregate --help
# %% [markdown]
# ## Example usage
# %% [markdown]
# ### Basic usa... |
8489813286e96b189a1526728558005bd14f2ba1426576286bbfd3fc616fba18 | Jupyter | 1,085 | 35 | # %%
%load_ext autoreload
%autoreload 2
import os
import numpy as np
import matplotlib.pyplot as plt
import trajectory_reconstruction_tradeoff as T
# %%
# config
outdir = '../output/'
to_save = True
dataset = 'hayashi'
method = 'dpt'
# %%
L_tradeoff = T.io.read_output(dataset, outdir=outdir, sample='exp')
# %%
# ... |
6db63069a55b40eabea8d6d5447632e1971516a7ae9700564dfc67e1cc73647e | Jupyter | 1,090 | 42 | # %%
import numpy as np
%matplotlib inline
import matplotlib.pylab as plt
from arsenic import plotting, stats
# %%
class Result(object):
def __init__(self,ligandA,ligandB,exp_DDG,exp_dDDG,calc_DDG,mbar_error,other_error):
self.ligandA = str(ligandA)
self.ligandB = str(ligandB)
self.exp_DD... |
51c147c6347bab599c2c9a750c8d7726ffac77644e84d493b0361c9c7383698c | Jupyter | 1,093 | 47 | # %%
import pymaid
# Initialize connection
rm = pymaid.connect_catmaid()
# %%
pns = pymaid.get_skids_by_annotation('glomerulus DA1 right excitatory')
cn_table = pymaid.get_partners(pns)
cn_table.head()
# %%
downstream_only = cn_table[cn_table.relation == 'downstream']
strong_only = cn_table[cn_table.total >= 10]
... |
78cf3893ac0173caf83516b9d4b230c7e753f1d3e34da259e57215ed339b301a | Jupyter | 1,105 | 35 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/clarifai.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Qdrant FastEmbed Embeddings
#
# LlamaIndex sup... |
ab9e51bf749706bd916e8fb9818667742759f5c4ddf0209220a400aff151707b | Jupyter | 1,107 | 41 | # %%
def PhaseCalculation(peaks):
phasepos = np.zeros(len(peaks))
# print(phasepos)
# print(len(phasepos))
I = np.nonzero(peaks)[0]
# print('I is',I,'\n')
for i in np.arange(len(I)-1):
m = I[i+1]-I[i]
# print(i, i+1, I[i], I[i+1],m)
phasepos[I[i] : I[i+1]] = np.arange(2*n... |
3cbcaadc1e3d43db6d4acd4c82577ffa2427a3b199aad247b10b7c7099ea06bb | Jupyter | 1,117 | 50 | # %% [markdown]
# # Meth_Comp CLI usage
# %% [markdown]
# ## Activate virtual environment
# %%
# Activate Conda env
conda activate pycoMeth
# %% [markdown]
# ## Getting help
# %%
pycoMeth Meth_Comp --help
# %% [markdown]
# ## Example usage
# %% [markdown]
# #### Usage with CpG Aggregate output
# %%
pycoMeth Met... |
2fe5382df451b6dc10e5744dafd479f948e5cffa53d74442cf7c9290c7be8f3c | Jupyter | 1,151 | 41 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/DeepLakeReader.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # DeepLake Reader
# %% [markdown]
# I... |
e49d29f8b43546b83dc022d0de1fbaddd5ace4724aafa4c24f4d4a71c2c7bc6b | Jupyter | 1,169 | 40 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/modelscope.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # ModelScope LLMS
# %% [markdown]
# In this notebook,... |
fcc9c755c3665155be29186b0bb8427d0e269f4b0140f85aff6e28985853a7d0 | Jupyter | 1,169 | 44 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/together.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Together AI Embeddings
#
# This notebook shows... |
29c5425a1a4e0ac87c3393d777d1a5f9875ad73f596928eee315e5db6d397f13 | Jupyter | 1,177 | 57 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/palm.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # PaLM
#
# In this short notebook, we show how to use the ... |
3d07cff67a73dacb5ada1e769c4e8b9edaffd5a098854a303d3b1fdedf596400 | Jupyter | 1,180 | 46 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/ObsidianReaderDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Obsidian Reader
# %% [markdown]... |
8d40f8acc436763d4b3a2e84ce0a7590b01a86e6e268dfb8f3f4bf46966a2402 | Jupyter | 1,183 | 57 | # %% [markdown]
# # Extracting DE analysis from CMC synapse
# %%
library(synapser)
library(tidyverse)
library(data.table)
# %%
# Login to Synapse with apikey - https://r-docs.synapse.org/articles/manageSynapseCredentials.html
synLogin()
# %% [markdown]
# ## Download results
# %%
dt = readRDS(synGet('syn24180858')$p... |
5c5a592d7dec06b9526e22da165d7ce2e0274a5ad40538c1c574169cf862acfb | Jupyter | 1,185 | 33 | # %% [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]
# # Baseten Embeddings
# %% [markdown]
# This guide how to ... |
7045d85841722ef7e37899fe5a04c57323c037872ec678decba7b682010948a0 | Jupyter | 1,200 | 56 | # %% [markdown]
# # Basic usage of sincFold package
#
# ## Install
# %%
%pip install -U sincfold > /dev/null
# %% [markdown]
# ## Fold a short sequence
# %%
!sincFold pred "AACCGGGUCAGGUCCGGAAGGAAGCAGCCCUAA"
# %% [markdown]
# Adding drawing options
# %%
!sincFold pred "AACCGGGUCAGGUCCGGAAGGAAGCAGCCCUAA" --name s... |
2e5e649f66f3a2012534d99b7c6d67a9e0921114f8f90f76a4f0178603cd88ff | Jupyter | 1,213 | 36 | # %% [markdown]
# # Select LVs
# Having 200 LVs is way more manageable than tens of thousands of genes, but that's still a lot of hypotheses to test. Inspired by the discussion section of the MultiPLIER paper, we'll filter out the LVs with low variance in the target dataset under the hypothesis that they are more likel... |
038abda958866ffb56edded9730594d2595dffa01924af21a700b5c5c50ad310 | Jupyter | 1,216 | 45 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/MyScaleReaderDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # MyScale Reader
# %% [markdown]
#... |
05e67ca06e6a575f35fe612b34ce80de51f6955905020e0ac3af6a934e37d1f6 | Jupyter | 1,234 | 51 | # %% [markdown]
# # Interval_Aggregate CLI usage
# %% [markdown]
# ## Activate virtual environment
# %%
# Using virtualenvwrapper here but can also be done with Conda
workon pycoMeth
# %% [markdown]
# ## Getting help
# %%
pycoMeth Interval_Aggregate --help
# %% [markdown]
# ## Example usage
# %% [markdown]
# ###... |
2a9d62742cc71d9fb7cfca9b56c5e6d3c1174722beed302f45f9d003b7c4c9ba | Jupyter | 1,238 | 47 | # %%
import numpy as np
import matplotlib.pyplot as plt
import h5py
# %%
epoch_earlystop = 60
folder_name_r_5_rep_120_diff_data = []
data_file_r_5_rep_120_diff_data = []
def load_h5py_data(file_path):
with h5py.File(file_path, 'r') as f:
data = {}
for key in f.keys():
data[key] = np.ar... |
0ddf84dae6ed25e662eb92eecbfb7879d3ef4d7c1ffee8940a9908a29ef9f0a6 | Jupyter | 1,239 | 34 | # %%
## Import Libraries
import pandas as pd
import numpy as np
import csv
pd.set_option("max_colwidth", None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# %%
df = pd.read_csv("C:/Users/bag22/Desktop/current_files/python_files/ebbert_lab/data/2022-07-14_cDNA_data_ebbert_lab_ne... |
85582be575b03967ab429b1178580373867cd93a9914d76fa8082127594d8e93 | Jupyter | 1,250 | 44 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/GoogleDocsDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Google Docs Reader
# Demonstrates ou... |
10c9d471b339801b76ff7e0401a742ffe3de1747bb3edca09ba2685d3fee0cbd | Jupyter | 1,271 | 48 | # %% [markdown]
# # Visualization
# %%
import cv2, copy, os
import pandas as pd
from PIL import Image
import matplotlib.cm as mpl_color_map
import matplotlib.pyplot as plt
# %%
df_apolo = pd.read_json(os.path.join('./data', 'apolo.json'))
df_apolo
# %%
# change this index to view more annotations
idx = 0
paint = df... |
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