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|---|---|---|---|---|
d3e35961fe54da16570344adb1d2aecbc7662d96590d6a889c1dca27bf5d6869 | Jupyter | 1,273 | 61 | # %% [markdown]
# First import libraries and set dictionary
# %%
%matplotlib inline
import os
import pandas as pd
import cPickle
import matplotlib.pyplot as plt
from pandas.tools import plotting
workingdir='../test/sampledata/ocn/'
folders=os.listdir(workingdir)
# %% [markdown]
# load all quantified files from all ... |
d395b8656a43091dd44c8706ef8a91b16b586d747c9f870df41627c8b357a7ad | Jupyter | 1,290 | 47 | # %%
import packerlabimaging as pkg
import packerlabimaging.plotting as plotting
# %%
# import data trialobj (TwoPhotonImaging Trial object)
expobj = pkg.import_obj(pkl_path='/home/pshah/Documents/code/packerlabimaging/tests/RL109_analysis.pkl')
print(expobj)
trialobj = expobj.load_trial(trialID='t-005')
# %% [markd... |
5c5a0a027bbb44e65d23e9a1a12f0b356f003e29e99f6467674da611d35dcfa3 | Jupyter | 1,296 | 47 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/TwitterDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Twitter Reader
# %%
%pip install llama... |
a3e4f2aba702c4f7b2021ac2953cead28c3f95d02e608f950637b3c5c3aa784a | Jupyter | 1,305 | 60 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/llm_predictor.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # LLM Predictor
# %% [markdown]
# If you're openin... |
17197b4216ee73523e1ea28752e95336057fb0db28aaa7102657056d38ca4b0b | Jupyter | 1,313 | 38 | # %% [markdown]
# ## Preprocessing data of Domcke et al., Science (2020)
# %% [markdown]
# #### Data from https://descartes.brotmanbaty.org/bbi/human-chromatin-during-development/
# ##### Cicero_coaccessibility_scores_by_cell_type.csv.gz
# %%
import os
import pandas as pd
from pybedtools import BedTool
domcke_cre = ... |
55c9904464339a0ef0a95756c36b0d6fe81fe04e15f90381aecee0a7cff499d6 | Jupyter | 1,329 | 54 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/everlyai.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # EverlyAI
# %% [markdown]
# If you're opening this Not... |
7eef164d702fe986f0e26fa3da914ed880b84ac647d253c380b064345aa61fc9 | Jupyter | 1,342 | 47 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/modelscope.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # ModelScope Embeddings
# %% [markdown]
# In t... |
46bdaa889331c84bf7e3660b99840cbb557f0eeb1608a690048f28fc689ccb1b | Jupyter | 1,353 | 55 | # %%
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
from oset.generic.lp_filter.lp_filter_zero_phase import lp_filter_zero_phase
# Load the MATLAB .mat file
# Example 1:
data = sio.loadmat("../../../datasets/sample-data/SampleECG1.mat")
x = data["data"]
# Example 2:
# data = sio.loadmat('.... |
d2d17a4c1ceb3c9d137996edd71cd550b9b5cc488a9c83efeb364c63e3b32056 | Jupyter | 1,353 | 46 | # %%
import numpy as np
%matplotlib inline
import matplotlib.pylab as plt
import pandas as pd
from arsenic import plotting, stats, wrangle, absolute, plotlying
# %%
raw_results = wrangle.read_csv('../data/example.csv')
# %%
plotting.plot_DDGs(raw_results,method_name='softwarename',target_name='made up protein', plo... |
0bfcb6e10666d19f6ce95cda974ec4466debd19670fa3886cc5282536b22a73a | Jupyter | 1,360 | 45 | # %% [markdown]
# # Tutorial 3 - Making Useful Plots
# %%
import packerlabimaging as pkg
import packerlabimaging.plotting as plotting
# %%
# import data trialobj (TwoPhotonImaging Trial object)
expobj = pkg.import_obj(pkl_path='/home/pshah/Documents/code/packerlabimaging/tests/RL109_analysis.pkl')
print(expobj)
tria... |
760ea4dbd42f36eded2a355abdef4dd083c249a98fc29ceb1225dd1334b528c1 | Jupyter | 1,364 | 35 | # %%
!git clone https://github.com/Merlinaphist/ReproduceANNA16.git
# %%
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from math import sqrt
from statistics import mean, stdev
from sklearn.neighbors import KNeighborsRegressor
# %%
rmse = []
for i in range(5):
X_train = pd.r... |
c39e81962c0a1c757707c271193f2ec81a599924ff07c64c091ef13288d78f23 | Jupyter | 1,415 | 59 | # %%
import numpy as np
import matplotlib.pyplot as plt
import functions_emt as f
from params_emt import *
import time
import pandas as pd
import anndata as ad
# %%
import importlib
importlib.reload(f)
tm = 1000. # simulation time (dt=0.1 is set in the time_course_emt() function )
b = 1.
np.random.seed(100)
# loa... |
df9463d31620c3ca456474f19bd087d3a22e3d1501e4c687ae6ce4d68a26047f | Jupyter | 1,423 | 61 | # %%
import numpy as np
import matplotlib.pyplot as plt
import functions_emt as f
from params_emt import *
import time
import pandas as pd
import anndata as ad
# %%
import importlib
importlib.reload(f)
tm = 1000. # simulation time (dt=0.1 is set in the time_course_emt() function )
b = 1.
np.random.seed(0)
# load i... |
ce10f9bebe590630afde15b91ffd40c7103ee59a89b9ef07a4e344d46ea8439a | Jupyter | 1,426 | 71 | # %% [markdown]
# Copyright (c) 2021, David Williams, Reza Sameni
#
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
# %% [markdown]
# ## Import dependencies
# %%
import numpy as np
import scipy.io as spio
imp... |
da9ce6e5bf4721ac50d4fa63771fecdf7574a6a75f5d0eb25d4ba15380b1a616 | Jupyter | 1,434 | 52 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/MongoDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # MongoDB Reader
# Demonstrates our MongoDB... |
8b50060c94d90038d84c9f65dd58b4a47d28c507de438a7bdb3dabf7eac6fbe4 | Jupyter | 1,438 | 50 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/customization/streaming/SimpleIndexDemo-streaming.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Streaming
# %% [... |
02ae2fbf18517b403aa9953672241b6143cc582087125af8ad4b64eef3271988 | Jupyter | 1,445 | 45 | # %% [markdown]
# ## Preprocessing data of Ziffra et al., Nature (2021)
# %%
import pandas as pd
import os, glob
from natsort import natsort_keygen
# %%
peaks = pd.read_excel("~/Dropbox/CWAS_paper_WD/Data/raw_annotations/Ziffra2021_TableS2-13.xlsx", sheet_name="ST2 AllPrimaryPeaks")
enhancers = pd.read_excel("~/Dropb... |
434b3cb734c4e6cdacbdd69e92d09b47a56d27be16fcfad790c7b779c25aefcd | Jupyter | 1,447 | 38 | # %% [markdown]
# # Reproducing topic heatmaps in the paper for SUPPORT cohorts
#
# Authors: Lexie Li, George H. Chen
# %%
import os, pickle, json, re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import colors
import seaborn as sb
from visualization_utils import *
import war... |
f9a377dc4e3adde105fe3bdd58fd11d5b068647280411fa259fb9e38fc69c03c | Jupyter | 1,450 | 63 | # %%
import pandas as pd
import pymaid
rm = pymaid.connect_catmaid()
# %%
n = pymaid.get_neuron(16)
# %%
# Get all connectors
cn = n.connectors
# Add a column with the URL to coordinates
cn['URL'] = pymaid.url_to_coordinates(cn,
stack_id=5,
... |
d25356424edf5116f29a17f2ce76bc7670a1d2a6d323c70e3c27168618e3261f | Jupyter | 1,451 | 50 | # %%
import os
# %%
os.getcwd()
# %%
import sys
print(sys.path)
# %%
print(sys.executable)
# %%
from packerlabimaging.imaging_utils import AllOptical
# %%
# 1) ### prepare trial and photostim experiment information below before running run_photostim_processing()
data_path_base = '/home/pshah/mnt/qnap/Data/2020-12-... |
65f4e578c3b2b8c6202fc2a960d9f1961c80b5f5ff0e12dcc04fe580cfa2c581 | Jupyter | 1,463 | 56 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/QdrantDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Qdrant Reader
# %%
%pip install llama-i... |
7c87aa8419f71bdca707d06eeb5f51dd7ae86cd563810d12c9c7ee0eff8c0285 | Jupyter | 1,464 | 48 | # %% [markdown]
# # Tutorial 3 - Making Useful Plots
# %%
import imagingplus as ip
import imagingplus.plotting as plotting
# %% [markdown]
# ### import data
# %%
# import data as a `trialobj` (here we are importing a TwoPhotonImaging Trial object)
expobj = ip.import_obj(pkl_path='/mnt/qnap_share/Data/imagingplus-ex... |
dacadb8b57b55c99668629db5269734c35c3f96ff3ad9401b7a1ea9a282c31eb | Jupyter | 1,495 | 63 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/chat_engine/chat_engine_repl.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Chat Engine - Simple Mode REPL
# %% [... |
66dc07a79386922348c37b28b29737cbd7af3ad711ec52cdf528092661cbee5f | Jupyter | 1,524 | 58 | # %%
import os
import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
df = pd.read_csv('allWordAnalysis_wNodeCount_shifted.zip')
# Define the threshold nodeCount
threshold_value = 20
# Set values above threshold to the fixed number
df = df[df['arcLe... |
a2a43e2bd12b070cf0fee3e1de83f4bf3b5ab09b557dfa14dc1a669ff69465d0 | Jupyter | 1,533 | 64 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/anyscale.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Anyscale
# %% [markdown]
# If you're opening this Not... |
3a4ee9f7b725a39aa9332d64b7b6b8665c6dc8c5e45ab53d81c2686f8a48c6ce | Jupyter | 1,537 | 73 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/upstage.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Upstage Embeddings
# %% [markdown]
# If you're ... |
fbd751957621b556adf64357902a436d02264bc8607ab7096136f8bc0eb1b5c5 | Jupyter | 1,538 | 45 | # %%
import shutil
import os
import pandas as pd
import numpy as np
from importlib.machinery import SourceFileLoader
# import the module from the given path
wfdb_csv = SourceFileLoader("csv", "/labs/samenilab/team/deepanshi/wfdb-python/wfdb/io/convert/csv.py").load_module()
print(wfdb_csv)
fname_in = 'sample_raw.csv... |
2c36c211b7b4c1a81fcd6d4db49a8e2425d96f35899ac1df807ca3ee26ac7675 | Jupyter | 1,539 | 65 | # %%
import cooler
import pandas as pd
import numpy as np
from statannot import add_stat_annotation
import seaborn as sns
import matplotlib.pyplot as plt
# %% [markdown]
# # Input data to run this notebook
# %%
# Merged Hi-C data
# Run 1_merge_Hi-C.sh script to obtain these maps
files=['../data/NeuNplus.sampled.1000.... |
2f6451f6a80b4f4169b9071a8ed87a76ab5ae2c1af3fd8c0e55f8106d760a32c | Jupyter | 1,539 | 50 | # %% [markdown]
# # Ollama Llama Pack Example
# %% [markdown]
# ### Setup Data
# %%
!wget "https://www.dropbox.com/s/f6bmb19xdg0xedm/paul_graham_essay.txt?dl=1" -O paul_graham_essay.txt
# %%
from llama_index.core import SimpleDirectoryReader
# load in some sample data
reader = SimpleDirectoryReader(input_files=["pa... |
b2ad87644e270188686459ff5d2bf72f8a9aff93050e8e617832757b99cd2e78 | Jupyter | 1,544 | 46 | # %% [markdown]
# # Venn diagram comparing female and male SZ differential expression results
# %%
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib_venn import venn2
# %%
plt.rcParams.update({'font.size': 22, 'font.weight': 'bold'})
# %%
def get_deg_df(tissue, feature):
fn1 = "../../../%s/fema... |
18db8105a7d9aa6f553abda429e51b80dbac7a5ec9b8fa0122943b808215726b | Jupyter | 1,546 | 54 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/ollama_embedding.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Ollama Embeddings
# %% [markdown]
# If... |
fb476f682302cbfa342112fb1723e447ad762492767103bfa46b105b1eadc854 | Jupyter | 1,552 | 56 | # %%
# 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... |
9ba344f404d27addb3f2a866910d4547d6b4f1722c6a3c8a08c226ef8509ea17 | Jupyter | 1,553 | 55 | # %%
import nbimporter
from functions import *
import glob, os
# %%
output_direction='/home/mahdi/python codes/start/s21_RND_another_ref'
input_direction='/home/mahdi/Desktop/data_selection_D7/S28_MSL.nii.gz'
reference=136
maximum_intensity=1705
model='200epoch_with_val.h5'
main(input_direction,reference,output_dir... |
41bb6b095d996f94702a48b06a317f4848e488d3309499f7a5495f807ef25b9d | Jupyter | 1,559 | 44 | # %% [markdown]
# MultiRAT environement preparation
# ================
# Joanes Grandjean
#
# 
#
# # Foreword
#
# This and the follow are jupyter files which contains all the code for
# reproducing my analysis and detail the process. The code is meant to be
# followed step-wise. ... |
6222fb963a7a17612958f61db049faadc9d84d261ee58a2c55614ac87dbcbdc5 | Jupyter | 1,562 | 56 | # %% [markdown]
# In this notebook, we are going to show how to use [BGE-M3](https://huggingface.co/BAAI/bge-m3) with LlamaIndex.
#
# BGE-M3 is a hybrid multilingual retrieval model that supports over 100 languages and can handle input lengths of up to 8,192 tokens. The model can perform (i) dense retrieval, (ii) spar... |
7d6879a67a03263e8526e2891eabf798236a989f60e3a1400e4401f52ab908ca | Jupyter | 1,562 | 55 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/DiscordDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Discord Reader
# Demonstrates our Disco... |
009f573e400d261ee0003118e1138b727a40ce306ff156e47ada48874d863895 | Jupyter | 1,563 | 42 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/text_embedding_inference.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Text Embedding Inference
#
# T... |
0c241dc89691b64a1ae97f986e61913114dc17e6548e792149af0eeba33bd292 | Jupyter | 1,583 | 61 | # %%
import pickle, numpy as np, pandas as pd, networkx as nx, warnings
warnings.filterwarnings('ignore')
import sys
import matplotlib, matplotlib.pyplot as plt
matplotlib.rcParams['font.sans-serif'] = "Arial"
matplotlib.rcParams['font.family'] = "Arial"
import seaborn as sns, OmicsIntegrator as oi
# %%
infile= "/mn... |
2141421a6e5a730e27c2856f18d7ecf773b0c71f8bd80c8a7dc81ed98bc680d4 | Jupyter | 1,587 | 59 | # %% [markdown]
# # Deplot Reader Demo
#
# In this notebook we showcase the capabilities of our ImageTabularChartReader, which is powered by the DePlot model https://arxiv.org/abs/2212.10505.
# %%
%pip install llama-index-readers-file
# %%
from llama_index.readers.file import ImageTabularChartReader
from llama_index... |
258a66fe6fd82b48162d0d9a85dd6f7aa6fc86380f6e970f168bfc694deb6cac | Jupyter | 1,601 | 50 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/MakeDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Make Reader
#
# We show how LlamaIndex ca... |
c795a8af00843e9459f7a006370aea88b1d308ee6c8e100e197ed9bdc8c40a28 | Jupyter | 1,615 | 80 | # %% [markdown]
# Copyright (c) 2021, David Williams, Reza Sameni
#
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
# %% [markdown]
# ## Import dependencies
# %%
import numpy as np
import scipy.io as spio
imp... |
b2b9e9843f1f1db4483c08f7f670e6fa974ed8c580da6dcf5b2ac39303406546 | Jupyter | 1,616 | 71 | # %%
import os
import pandas as pd
# %%
df_tcga = pd.read_excel("GBM_Methylom_TCGA_clinical-data.xlsx",
index_col=0)
df_tcga.columns = df_tcga.iloc[0].tolist()
df_tcga = df_tcga.iloc[1:]
df_tcga = df_tcga.set_index("Sample ID")
# %%
df_groups1 = pd.read_csv("/data/projects/robin/DNAm_revision... |
68199abf581a7ee0567613d88bf35c0b032d18deaa2f7ee2e8d84712e8cce0b4 | Jupyter | 1,636 | 60 | # %% [markdown]
# # MistralRS LLM
#
# **NOTE:** MistralRS requires a rust package manager called `cargo` to be installed. Visit https://rustup.rs/ for installation details.
# %%
%pip install llama-index-core
%pip install llama-index-readers-file
%pip install llama-index-llms-mistral-rs
%pip install llama-index-llms-h... |
d055160ba2410e53cff8744473326f4dd6a78a3318d57e81b5baa2ea142fe80f | Jupyter | 1,644 | 43 | # %%
import os
import numpy as np
import pandas as pd
from os import listdir
from os.path import isfile, join
# %%
directory = r'C:\Users\shirahad\Documents\research\electrophysiology\analyze_electrophysiology_data\psth\files'
# %% [markdown]
# # PAY ATTENTION TO 7 OR 10 INTENSITIES
# %%
intensities_file_path = r'C:... |
1a860f052ac4eab7178e0425080f13acdb8ab6e0cef18f1d3bb693490eb3aa27 | Jupyter | 1,650 | 47 | # %%
# -- coding: utf-8 --
import warnings
warnings.filterwarnings("ignore")
import sys
sys.path.append('../')
from analysis_utils import ttest_plot_utils
from analysis_utils import homo_random_config as config
import os
if __name__ == '__main__':
cfg = config._C
#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'... |
f775664a84846d48e69fe8cc878d158685f0e74af073d3ee7fe1402f574361ae | Jupyter | 1,651 | 84 | # %% [markdown]
# # Netmind AI LLM
#
# This notebook shows how to use `Netmind AI` as an LLM. Check out the full list of models [netmind.ai](https://www.netmind.ai/).
#
# Visit https://www.netmind.ai/ and sign up to get an API key.
# %% [markdown]
# ## Setup
# %% [markdown]
# If you're opening this Notebook on cola... |
fec9f7f17693923d43642d9c1bfc92c1fdc3a6620f12c64bed33126416a572a7 | Jupyter | 1,655 | 59 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/SlackDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Slack Reader
# Demonstrates our Slack dat... |
eaa8cbeb35ae4d43d166c9fc8adbeb3f547b81ed0050bef0d2d35a307b4d7a3d | Jupyter | 1,661 | 70 | # %% [markdown]
# # Agents with Structured Outputs
#
# When you run your agent or multi-agent framework, you might want it to output the result in a specific format. In this notebook, we will see a simple example of how to apply this to a FunctionAgent!🦙🚀
# %% [markdown]
# Let's first install the needed dependencie... |
bac47c7bd86dbec6fe4a463de74e91cb914e910630f226aec9a00ca5a1c1f711 | Jupyter | 1,667 | 57 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/chat_engine/chat_engine_best.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Chat Engine - Best Mode
# %% [markdow... |
fdfc276e47ef364a364d70079419fc05c2395fbf8406e98790a0d6138d6463f6 | Jupyter | 1,670 | 82 | # %% [markdown]
# # Featherless AI LLM
#
# This notebook shows how to use `Featherless AI` as an LLM. Check out the full list of models [featherless.ai](https://www.featherless.ai/).
#
# Visit https://www.featherless.ai/ and sign up to get an API key.
# %% [markdown]
# ## Setup
# %% [markdown]
# If you're opening t... |
c6c5345be19424c15c5c88f2982da3976efc6a072f7d8060df309569cb63f3b1 | Jupyter | 1,672 | 75 | # %% [markdown]
# # Gene term enrichment analysis
# %%
suppressMessages({
library(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
library(pathview)})
# %%
extract_geneList <- function(dft, keytype='EntrezID'){
dft = na.exclude(dft)
gg = as.vector(dft[, 'logFC'])
names(gg) = as.v... |
63524fbc8dc8eec1888031a8bbe9829ec1ec84dc57ad2fea83405b24b4440d84 | Jupyter | 1,678 | 57 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/alephalpha.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Aleph Alpha Embeddings
# %% [markdown]
# If ... |
1b89152e5bbbb0b8229b4279f3863f5aa680a09d3b4a613cbfde8a66318eab9e | Jupyter | 1,682 | 63 | # %%
import nbimporter
from functions import *
import voxelmorph as vxm
import neurite as ne
%matplotlib inline
# %%
data_dir='/home/mahdi/Desktop/data_selection_D1'
split=1
batch_size=32
#maximum=maxx(data_dir)
maximum=1584
# %%
# let's get some data
val_generator = affine_generator(data_dir,batch_size,maximum,split... |
1de1788fdf236d812a97159e4f826caed93803de461d967f1fe0aeb812dd63f5 | Jupyter | 1,701 | 68 | # %% [markdown]
# # CpG_Aggregate API usage
# %% [markdown]
# ## Import module
# %%
# Import main module
from pycoMeth.CpG_Aggregate import CpG_Aggregate
# Optionally inport jupyter helper functions
from pycoMeth.common import head, jhelp, stdout_print
# %% [markdown]
# ## Getting help
# %%
jhelp(CpG_Aggregate)
... |
878e6997dd150adb4b8dc7cda8c6f36ccc2f4b209457afe59ebd7d77e4ecde98 | Jupyter | 1,704 | 49 | # %%
# -- coding: utf-8 --
import warnings
warnings.filterwarnings("ignore")
import sys
sys.path.append('../')
from analysis_utils import ttest_plot_utils
from analysis_utils import homo_random_config as config
import os
if __name__ == '__main__':
cfg = config._C
#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'... |
594211167e156dec0ac1bf50e8f6059b3841c498c87982f03f4d4396ce041d56 | Jupyter | 1,708 | 54 | # %%
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Dictionary to manage paths and method names
method_files = {
'native': '../vectorizeFalse_assignFalse_deepcopyFalse.npy',
'module-based': '../vectorizeTrue_assignTrue_deepcopyTrue.npy',
'functional-based': '../vectorizeTrue_assig... |
854add82cdebd3e2e9b214e80da4e455de7820e4a94418f79ab1ccc9d01d1106 | Jupyter | 1,719 | 64 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/ChromaDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Chroma Reader
# %% [markdown]
# If you'... |
d77fa4b4a1a22df67df88cff0346a273906996b9e7ed1f2e1be06775d1a005e5 | Jupyter | 1,719 | 68 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/lmstudio.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # LM Studio
# %% [markdown]
# ## Setup
# %% [markdown]... |
c93867179fe97ee421284ead349a7b62a5b65c8eb578cde537f8e2c6966d16fe | Jupyter | 1,720 | 65 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/DashvectorReaderDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # DashVector Reader
# %% [markd... |
2664c0a28426555af2cd2ae2c950172a31898695bc6e0dca696eb2af2158902d | Jupyter | 1,731 | 74 | # %% [markdown]
# # Check sex chromosomes for caudate nucleus
# %%
suppressMessages({library(dplyr)
library(ggpubr)})
# %% [markdown]
# ## Get and annotated sex from genotypes
# %%
sexcheck_file = paste0('../../../input/genotypes/check_sex/_m/',
'LIBD_Brain_TopMed.sexcheck')... |
d47b1c1fc5c537044e1db3bd466be6cebfdb609906e53879cf48f9f68ebe766f | Jupyter | 1,737 | 75 | # %% [markdown]
# # Chat 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 `ChatMemoryBuffer` is a memory buffer that simply stores the las... |
68f5abf4e18215e0c531623b4ef4e82b5712d44e630453be38fd3632d6539331 | Jupyter | 1,753 | 73 | # %% [markdown]
# # Visualize GO analysis
# %%
import numpy as np
import pandas as pd
# %%
def get_top_GO(tissue, fn, label):
df = pd.read_csv(fn, sep='\t').sort_values('p_value').head(10)
df['Log10'] = -np.log10(df['p_value'])
df['Tissue'] = tissue
df['Direction'] = label
return df
# %%
tissue =... |
ecd5d407b1d26eab44e67e2531baf02063dbf53fe069a8439a6760c5ca712557 | Jupyter | 1,754 | 80 | # %% [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]
# # Fireworks
# %% [markdown]
# If you're opening this Note... |
1174e6ea215499d9e5acb437b05b8ae918269e46dae303af036fe4425da42f14 | Jupyter | 1,755 | 62 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
import scipy.io
# %%
df1 = pd.read_csv('htRmatrix.csv');
df2 = pd.read_csv('htPmatrix.csv');
# %%
df1.head()
# %%
df2.head()
# %%
np.zeros_like(df1)
# %%
mask = np.zeros_like(df1, dtype=np.bool)
# %%
mask[n... |
47d58917ebc8adc0f08db669cadde85fab938f2ff569fc032d546528585bc367 | Jupyter | 1,782 | 49 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/PsychicDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Psychic Reader
# Demonstrates the Psych... |
4245c02549b12ad4e921421e61ec1ece2f599ce85f9b0ef6e1a2f903c83ce8ca | Jupyter | 1,783 | 68 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import os
# %%
def get_start_index(df, cnv_start):
if cnv_start <= df.iloc[0].start:
return 0
else:
start_index = df.index[df["start"] <= cnv_start].tolist()[-1]
if df.iloc[start_index].end <= cnv_start:
start_index +=... |
ba280183f326d06623fca23c781faf484426af2e8ad22c8b159a0a3e573a985f | Jupyter | 1,785 | 70 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/customization/streaming/chat_engine_condense_question_stream_response.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
#... |
eb8afd29fe4babfd4a233becb9ff3a3a3c0b0578b5d13b65998cb113bab4d282 | Jupyter | 1,804 | 67 | # %%
import re
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.size'] = 20
import json
import random
# %%
def running_mean(x, N):
cumsum = np.cumsum(np.insert(x, 0, 0))
return (cumsum[N:] - cumsum[:-N]) / float(N)
# %%
def get_results(txt_file, number=15):
# Using readlines()
f... |
b866f76b7a53a4d9bf5a87649bdfea87d75abd5406258b7a6110c932fbc651ec | Jupyter | 1,810 | 63 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/elasticsearch.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Elasticsearch Embeddings
# %% [markdown]
... |
04da46526690334130d9aa10fef5c4d8b92ab1521dc55b22573ad35a8959cd1d | Jupyter | 1,812 | 66 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/chat_engine/chat_engine_personality.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Chat Engine with a Personality ... |
28683ba118b7ae1287e4b2ae6d3552c75911aafc157fcb865efbd0ba1154d529 | Jupyter | 1,814 | 72 | # %% [markdown]
# # Visualize KEGG analysis
# %%
import numpy as np
import pandas as pd
# %%
def get_top_GO(tissue, fn, label):
df = pd.read_csv(fn, sep='\t').sort_values('p_value')
df = df[(df['source'].isin(['KEGG', 'REAC']))].head(10)
df['Log10'] = -np.log10(df['p_value'])
df['Tissue'] = tissue
... |
191e96eaa18a5dc22a432ae9133b68cb1686d8d80857ec5adf69ee22f42e07ab | Jupyter | 1,819 | 61 | # %% [markdown]
# # Plot RXE comparisons
# %%
library(ggpubr)
library(tidyverse)
# %%
save_ggplots <- function(p, fn, w=7, h=7){
for(ext in c('.svg', '.png', '.pdf')){
ggsave(p, filename=paste0(fn, ext), width=w, height=h)
}
}
# %%
df = data.table::fread("../../_m/RXE_public.csv") %>%
mutate(Reg... |
815d39c13ee9278aaf4138097a69e64eb0768ca78705337b0b509f8af1f3094c | Jupyter | 1,820 | 63 | # %%
import pandas as pd
import numpy as np
import nibabel as nib
import nbimporter
from functions import *
from numpy import *
import matplotlib.pyplot as plt
%matplotlib inline
# %%
ndim = 2
unet_input_features = 2
loadable_model='200epoch_with_val.h5'
# data shape 64*64
s=(64,64)
inshape = (*s, unet_input_features... |
092ebea18b4f4580502a5f359b70923cb72c9eac65fd2c07170278c6157db9e2 | Jupyter | 1,846 | 109 | # %% [markdown]
# # DrugComb
#
# ### summary data (v1.5)
#
# https://drugcomb.org/download/
#
# * summary_v_1_5.csv
# %% [markdown]
# # CCLE
#
# ### omics data of cancer cell lines (Pubclic 22Q4)
#
# https://depmap.org/portal/download/all/
#
# * OmicsExpressionProteinCodingGenesTPMLogp1.csv
# * OmicsSomaticMutat... |
d1db8f0d2b41f42a5323c0f4dacf8b75d50c6cc0dcc9e055719b91046881fb40 | Jupyter | 1,858 | 71 | # %% [markdown]
# # Functional enrichment analysis with g:Profiler
# %%
library(tidyverse)
library(gprofiler2)
# %% [markdown]
# ## Load DEG results
# %%
deg <- data.table::fread('../../metrics_summary/_m/male_specific_DE_4features.txt') %>%
select(gencodeID, ensemblID, Symbol, logFC, "adj.P.Val")
deg %>% head(2... |
b676ba492578f966c7546adbc841b179a0b2368fbea2cd4ac2825b7c3dd91742 | Jupyter | 1,860 | 47 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/evaluation/BeirEvaluation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # BEIR Out of Domain Benchmark
# %% [markd... |
962b4685145363d562e1d1214c01b9a8162c73abbfe765341505f917919a176c | Jupyter | 1,864 | 80 | # %%
from pathlib import Path
import time
from huggingface_hub import create_repo, HfApi
import com_const as cc
# %%
repo_id = "treizh/oiv_ld_phenotyping"
# %%
create_repo(repo_id, exist_ok=True)
# %%
api = HfApi()
# %%
def upload_file(file_path: Path, dst_path=None):
api.upload_file(
path_or_fileobj=... |
a1dd2c7fb83bee4c82fda6d4bd9268b05e1a7962bb9175f8792564ddaa601068 | Jupyter | 1,866 | 63 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/cloudflare_workersai.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Cloudflare Workers AI Embeddings
#... |
9762469481d8ab183d2a2871162f8314395d9600aa8f55b2d0fc9e30b1b93990 | Jupyter | 1,871 | 76 | # %%
%load_ext autoreload
%autoreload 2
import os
from pathlib import Path
import Bio.PDB
# TODO: make a proper package for interactive usage, will remove need for hack
try:
if CORRECT_DIR:
pass
except:
os.chdir("..")
CORRECT_DIR = True
from matplotlib import pyplot as plt
from src import featu... |
ed88c8a046d9b3eed2ffcc03d059baab848e880eba1f4c41a5d086a531926e1a | Jupyter | 1,872 | 77 | # %% [markdown]
# # Functional enrichment analysis with g:Profiler
# %%
library(tidyverse)
library(gprofiler2)
# %% [markdown]
# ## Load DEG results
# %%
deg <- data.table::fread('../../metrics_summary/_m/female_specific_DE_4features.txt') %>%
select(gencodeID, ensemblID, Symbol, logFC, "adj.P.Val")
deg %>% head... |
9db154e15df980e899a404b9aebea8503bd5de77d52a97c2e07e4fbb07537943 | Jupyter | 1,880 | 73 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/openrouter.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # OpenRouter
# %% [markdown]
# OpenRouter provides a ... |
a23df4f490d53df82679f1a7afdbb100a41a16d2995c8d1b2fb2eea302afa81c | Jupyter | 1,888 | 49 | # %% [markdown]
# ## Mixing Sage and Amber force fields: Toluene in T4 Lysozyme
# %% [markdown]
# ### Parametrize a molecule with the Sage force field
#
# First, we parametrize the ligand (toluene) with the SMIRNOFF-format Sage force field through the usual route to create an OpenMM `System`.
# %%
from openff.toolki... |
ac4b7dc74b3eab1e7cacc62b2412da10b06ac89dd6ac3a19db61975c4d3a1283 | Jupyter | 1,890 | 97 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
%matplotlib inline
# %% [markdown]
# # direction
# %%
direction='/media/mahdi/HD710 PRO/R&d/result/tsnr_centerfix_result.csv'
df = pd.read_csv(direction)
df
# %% [markdown]
# # seperate spine ans csf part
# %%
csf_... |
403b60cf33844971fb82fe8a72fcb04b7f93ec47c0c8638fdcc99fb9c00227e2 | Jupyter | 1,894 | 49 | # %% [markdown]
# # Model-guided ligand optimization
#
#
# ## This notebook exemplifies our framework for model-guided ligand optimization.
#
# ### Optimization Objectives for Sequences:
# #### 1) Selective potency at GCGR
# #### 2) Selective potency at GLP-1R
# #### 3) High-potency at both receptors.
# %% [mark... |
63e8c444be25a76f3e96cef06e1bc9fa18e9fcaec248f1a7a2c2a553dfe61099 | Jupyter | 1,896 | 62 | # %% [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]
# # Embeddings with Clarifai
#
# LlamaIndex has su... |
9a5b2dfb8ac1315b87574d49e3b06b8bb95195e5925f4f66ca88a7f50c3db037 | Jupyter | 1,899 | 47 | # %% [markdown]
# ## notebook 04: repeatability plotting
#
# In this notebook we use the output from the linear mixed models and repeatability analyses from the previous notebook to generate Figure WWW from the publication.
# %%
# importing python packages
from matplotlib import pyplot as plt
import pandas as pd
impo... |
1057abe4a279cc939382bea986f317599cd5b7b76a79e15cb4d67e235bd63aab | Jupyter | 1,905 | 47 | # %% [markdown]
# ## notebook 04: repeatability plotting
#
# In this notebook we use the output from the linear mixed models and repeatability analyses from the previous notebook to generate Figure WWW from the publication.
# %%
# importing python packages
from matplotlib import pyplot as plt
import pandas as pd
impo... |
89a35533fb59402cba89e4c0721babc1f7b58502bc09ecf58b61584a6454787d | Jupyter | 1,916 | 72 | # %% [markdown]
# # Interval_Aggregate API usage
# %% [markdown]
# ## Import module
# %%
# Import main module
from pycoMeth.Interval_Aggregate import Interval_Aggregate
# optionally inport jupyter helper functions
from pycoMeth.common import head, jhelp, stdout_print
# %% [markdown]
# ## Getting help
# %%
jhelp (... |
299a2400820821531778b3990f45b71e63856dfa692ff127b974a14fa049d5ef | Jupyter | 1,920 | 59 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/lagecy_office_reader.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Legacy Office Reader
# %% [ma... |
ff21806b3e3bf3e52032f813856f59ef4edbd69ed03a139bf29aadb34112dab8 | Jupyter | 1,928 | 47 | # %% [markdown]
# ## notebook 03a3: repeatability plotting
#
# In this notebook we use the output from the linear mixed models and repeatability analyses from the previous notebook to generate Figure WWW from the publication.
# %%
# importing python packages
from matplotlib import pyplot as plt
import pandas as pd
im... |
76a07544897e34b119834b644e6371b3d4f4707a0c63edb08da15552497c2c71 | Jupyter | 1,937 | 49 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/bedrock.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Oracle Cloud Infrastructure Generative AI
#
# Oracle C... |
48bedc12314c1cac5ff53dfd56c1da1532f5046ba986dd8eb6c2c5af8a6ea5e9 | Jupyter | 1,955 | 81 | # %% [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]
# # OpenAI Embeddings
# %% [markdown]
# If you're op... |
b318bbb9c2b0967302bd7adc3d0cc2415262f56def67a9841ac45cf9401461f1 | Jupyter | 1,959 | 72 | # %% [markdown]
# # LlamaHub Demostration
#
# Here we give a simple overview of how to use data loaders and tools (for agents) within [LlamaHub](llamahub.ai).
#
# **NOTES**:
#
# - You can learn how to use everything in LlamaHub by clicking into each module and looking at the code snippet.
# - Also, you can find a [... |
c3b95a9699646924c371fa32072939554f549c51c038ab271e54bff533f60d16 | Jupyter | 1,961 | 47 | # %% [markdown]
# ## notebook 03p3: precision repeatability plotting
#
# In this notebook we use the output from the linear mixed models and repeatability analyses from the previous notebook to generate Figure WWW from the publication.
# %%
# importing python packages
from matplotlib import pyplot as plt
import panda... |
4cd1c24657bc8f3c4a5a9947c5847b36938e7a99d4b6c302fd7efc6281fba938 | Jupyter | 1,966 | 86 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/yi.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Yi LLMs
#
# This notebook shows how to use Yi series LLMs.
... |
0a010fa5651a7f03c49a5370765f1f4f0ebccf40d7e05780b2623825f2fefccb | Jupyter | 1,967 | 49 | # %% [markdown]
# # Llamafile Embeddings
# %% [markdown]
#
# One of the simplest ways to run an LLM locally is using a [llamafile](https://github.com/Mozilla-Ocho/llamafile). llamafiles bundle model weights and a [specially-compiled](https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file#technical-details) vers... |
5e1ef300484d4325b391b71cccb3350faea58e689f566de9d8cd545dc76634c9 | Jupyter | 1,974 | 69 | # %% [markdown]
# # Extract male bias genes on the X chromosome
# %%
import session_info
import pandas as pd
from pyhere import here
# %%
def get_deg():
fn = here("differential_expression/tissue_comparison/summary_table",
"_m/differential_expression_analysis_4features_sex.txt.gz")
df = pd.read_c... |
0d8d3591d2165d005785e655c3f99d4c3d98a62ae0aa1f1567c8ea202d8ce4fa | Jupyter | 1,982 | 87 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/together.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Together AI LLM
#
# This notebook shows how to use `T... |
6ef9631eb8abfd9fea7721d703d2f05eb8aaa5c4ebea287266cfd31539418d37 | Jupyter | 2,025 | 110 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/friendli.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Friendli
# %% [markdown]
# ## Basic Usage
# %% [mark... |
ed06744ed6d11106d9f6b91f3d29f45e22032b629a4b596ca9e6a979783c94cf | Jupyter | 2,040 | 72 | # %% [markdown]
# # Get References from PDFs
#
# This guide shows you how to use LlamaIndex to get in-line page number citations in the response (and the response is streamed).
#
# This is a simple combination of using the page number metadata in our PDF loader along with our indexing/query abstractions to use this ... |
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