sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
faec1475a9b9e096b8c0ef593f787b8720b8fc480f2cbd29b9664ab0e1a171fa | Jupyter | 11,756 | 358 | # %% [markdown]
# # Table of contents
#
# ### - Import libraries and define functions + Initial setup
#
# ### - Import data
#
# ### - Make Plots & Tables
# %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_venn im... |
d6cf984b4f48bd53c978e655a9249c0cc09d816496054eecb588edbcf2cfdafd | Jupyter | 11,801 | 278 | # %%
import polyfingerprints as pfp
import pandas as pd
import numpy as np
from wrapconfig import YAMLWrapConfig
import os
# %%
RAW_XLS_PATH = "./excel_template.xlsx"
TARGET_VALUE="numerical_attr2"
OUTDIR="out"
if not os.path.exists(OUTDIR):
os.makedirs(OUTDIR)
# %%
df, info = pfp.datareader.excel_to_data(RAW_XL... |
6ed450edb16af3b356809f61a80eff60fc956f49d9eb43bda48b829354cbc236 | Jupyter | 11,802 | 358 | # %% [markdown]
# Copyright (c) MONAI Consortium<br>
# Licensed under the Apache License, Version 2.0 (the "License");<br>
# you may not use this file except in compliance with the License.<br>
# You may obtain a copy of the License at<br>
# http://www.apache.org/licenses/LICENSE-2.0<br>
# Unless required by applic... |
de02b49037574ac7427c4604bfab8f40cb97a942d3599dbfcbe1e25cac0a96a0 | Jupyter | 11,805 | 420 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/finetuning/router/router_finetune.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Router Fine-tuning
#
# In this n... |
14d8799826d6452229292a47b72bcfda8c33ade5334820e09de29ce40757e425 | Jupyter | 11,837 | 225 | # %% [markdown]
# MultiRAT preprocessing
# ================
# Joanes Grandjean
#
# 
#
# # Purpose
# In this section I submit jobs to my high performance computer to perform the preprocessing, confound_correction, and analysis steps as implemented in RABIES.
# This section relies ... |
e5d6e08ec1d7d151454c5978a4fb6b3272c81a127b836a3e0f11efc5bbd72123 | Jupyter | 11,841 | 361 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/cookbooks/rerank_llamaparsed
# _pdfs.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#
# # Reranking top pages from PDF using LlamaPars... |
2861d4104998a9212f21ac580f46afe8a0b5f0ccee661432befaa6a2146ea763 | Jupyter | 11,887 | 369 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/multi_modal/gpt4v_experiments_cot.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # GPT4-V Experiments with General, ... |
97c7823e44c6ab75661a1f78e0cf081d9a22269e3b70ce75dc7dfa5c563ec34b | Jupyter | 11,897 | 398 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/finetuning/embeddings/finetune_embedding_adapter.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Finetuning an Adap... |
79b2bd3fbd486963e78ec7b6c79e033bd1535155b1d86aaad3751d4648a4a40f | Jupyter | 11,912 | 316 | # %% [markdown]
# # Library Import and Functions
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_venn import venn2, venn2_circles, venn2_unweighted
from matplotlib_venn import venn3, venn3_circles
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
from wordclo... |
ea4bded004d7d4e711ca91aecf774a353125e378333f465ff69b774b5f89520e | Jupyter | 12,031 | 201 | # %% [markdown]
# StandardRAT preprocessing
# ================
# Joanes Grandjean
#
# 
#
# # Purpose
# In this section I submit jobs to my high performance computer to perform the preprocessing, confound_correction, and analysis steps as implemented in RABIES.
# This section reli... |
bab03ba6cfb2218a59990f12c4f9dce5900f1778485449f602de8381cde6a84b | Jupyter | 12,054 | 271 | # %% [markdown]
# # Stain Normalization
#
# Click to open in: \[[GitHub](https://github.com/TissueImageAnalytics/tiatoolbox/blob/develop/examples/02-stain-normalization.ipynb)\]\[[Colab](https://colab.research.google.com/github/TissueImageAnalytics/tiatoolbox/blob/develop/examples/02-stain-normalization.ipynb)\]
#
#... |
9e3580c525061ab2ec46be9019bf412b27c42940b97f7444316ce4d1dba03218 | Jupyter | 12,092 | 327 | # %% [markdown]
# # Library Import and Functions
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_venn import venn2, venn2_circles, venn2_unweighted
from matplotlib_venn import venn3, venn3_circles
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
from wordclo... |
036bc8804335bfa7dd227c3b94e69c8b8e85f6e298bd965da0e839f0ecce1618 | Jupyter | 12,114 | 423 | # %% [markdown]
# # Plots and figures HCP
#
# This notebook contains all code to generate the figures in the associated publications.
# %%
%load_ext autoreload
%autoreload 2
# %%
import logging
import os.path as path
import os
import socket
import sys
# %%
import matplotlib
import matplotlib as mpl
import matplotli... |
df969feaf8e342ab0f67b868bcc3dfb0d8e2a83c7ba92806769888eee388aa85 | Jupyter | 12,178 | 427 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/cookbooks/contextual_retrieval.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Contextual Retrieval
#
# In this no... |
3da8f30a372ca4feeaae869efd5296cdc8d763d0a24b8b14751fa5a66541ee75 | Jupyter | 12,259 | 310 | # %% [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... |
849b00fcc0f687abf1e90e72a1e95202d314b4175f40b8141bbd6be571aa9648 | Jupyter | 12,263 | 374 | # %%
import os
import pickle
import re
import matplotlib
from matplotlib import pyplot
import numpy
import pandas
import scipy
import seaborn
import tensorflow.keras
from sklearn import preprocessing
import logomaker
# %% [markdown]
# # Helper functions
# %%
%matplotlib inline
matplotlib.rcParams['figure.dpi'] = 12... |
e967824a5d4be59ae668b7aa073e8f0f9bca09c6d454aca3b3256a91d2cdf9af | Jupyter | 12,270 | 385 | # %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_venn import venn2, venn2_circles, venn2_unweighted
from matplotlib_venn import venn3, venn3_circles
from matplotlib import pyplot as plt
%matplotlib inline
import se... |
183ea68df5b6b8e538eebf2e2df8aa57682af7b84c1a98e65b38fd1b1d16f5f8 | Jupyter | 12,276 | 305 | # %% [markdown]
# # Testdata creation
# ## Table of Contents
# 1. Create artificial data functions
# 2. Create testdata
# > 2.1 Training data
# > 2.2 Validation data
# > 2.3 Prediction data
# > 2.4 Extra files
# 4. Look into one file
# %% [markdown]
# ## 1. Create artificial data fun... |
1cc1b8875c6c9c0125763267e03e61c79fe145386c1c7c99d23e890c65a23768 | Jupyter | 12,365 | 335 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/multi_modal/gpt4o_mm_structured_outputs.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Multimodal Structured Outpu... |
7515699f672a172c47561b76d0c5da0b696725e0c6dc5269212558134bababf4 | Jupyter | 12,394 | 362 | # %%
import numpy as np
import matplotlib.pyplot as plt
from skimage.io import*
from scipy import ndimage as ndi
import napari
from skimage.morphology import*
from skimage.segmentation import*
from skimage.measure import*
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.metrics impor... |
3a5296c01653274b6a52524c31b1e2f5b15ec21ab6f5ce221f7fb8969b44e410 | Jupyter | 12,412 | 210 | # %% [markdown]
# Jupyter Notebooks can sometime hard to work with. Some magic methods will be really handy when things don't seem to work out. The following cell reloads all changed modules.
# %%
import tempfile
%load_ext autoreload
%autoreload 2
# %% [markdown]
# # Table of Contents
# 1. [Installation](#installatio... |
2c732123aea36dd27bf7e0cf73d163a65d031f85e13ccb977b69cc2bf03632c5 | Jupyter | 12,485 | 459 | # %% [markdown]
# # Llama3 Cookbook with Groq
#
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/cookbooks/llama3_cookbook_groq.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#
# Meta developed and ... |
ac6e4813cdd340c9b3774a0669ab6a5d5a6cf2748feadfd12715d29e3f2c2a3d | Jupyter | 12,556 | 364 | # %% [markdown]
# # Table of contents
#
# ### - Import libraries and define functions + Initial setup
#
# ### - Pre-process data
#
# ### - Make figures
# %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_venn impo... |
31f09c0792d63c1ac38cc8892fb7abf29162ce22970ef394ec718d58e7d891a8 | Jupyter | 12,557 | 364 | # %%
import numpy as np
import nibabel as nib
import matplotlib
import matplotlib.pyplot as plt
import nilearn.plotting as niplt
import os
import seaborn as sns
from scipy.special import softmax
import pandas as pd
import sys
# %%
#excluded 296
black_subs=['sub-300', 'sub-304', 'sub-293', 'sub-273', 'sub-265... |
18cfe26466591ec0ddfbe511326a74aea9a77a65bdbc6b2e4daa8c2449813fb0 | Jupyter | 12,571 | 385 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/index_structs/struct_indices/SQLIndexDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Text-to-SQL Guide (Query ... |
f7ed007ace02e507de0e1b16cfdf2b2f46961ba4ae7a4cc9e56a3d78676be600 | Jupyter | 12,604 | 355 | # %%
import os
import numpy as np
import seaborn as sns
from praatio import tgio
import pandas as pd
import json
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from scipy.stats import chisquare
import time
# %matplotlib qt
def fillDB_syntAndGpt2(datainp, dataSpacy, dataGpt2, durati... |
1dc6b14b1f6b5755ccb8a8e8873ab84ad221faadd9767da64c49d141d6eba078 | Jupyter | 12,641 | 463 | # %% [markdown]
# # LLM Cookbook with Intel Gaudi
#
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/cookbooks/llama3_cookbook_gaudi.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#
# Meta developed... |
ed9899d837e1866d730d41c1c476d5334e8a602c30cfb4fd3e5996a39c1bb5fc | Jupyter | 12,736 | 360 | # %%
import numpy as np
import pandas as pd
import scanpy as sc
import anndata as ad
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm
# %%
x_meta = pd.read_csv("../DATA/TNBC/Wu_EMBO_metadata.csv", skiprows= lambda t: t in [1])
# %%
'''
cell_types = ['B_cell','CD4_helper','C... |
1abd4fddff4f8a2a1d1e6684126a74d85a288084ec8ab0f38db87924c4aa33ad | Jupyter | 12,743 | 353 | # %% [markdown]
# # Table of contents
#
# ### - Import libraries and define functions + Initial setup
#
# ### - Figure 2
#
# ### - Figure 3
#
# ### - Figure 4
#
# ### - Figure 5
#
# ### - Output novel high-confidence only annotation
# %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
... |
888200ac2c7bcb9a50a92f15af3eadec5ad7bddcfa36b6cd01cd4c5784a2e44b | Jupyter | 12,744 | 353 | # %% [markdown]
# # Table of contents
#
# ### - Import libraries and define functions + Initial setup
#
# ### - Figure 2
#
# ### - Figure 3
#
# ### - Figure 4
#
# ### - Figure 5
#
# ### - Output novel high-confidence only annotation
# %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
... |
904be29c7b9ce44b101b990c5db368652d3133019c1ff3e57d99a0b366f972ae | Jupyter | 12,752 | 330 | # %% [markdown]
# # Analysis code for mVEP BCI EEG sensor domain analysis
#
# ### Written by Joshua Kosnoff except where otherwise noted.
# %% [markdown]
# ## Load in Preprocessed Data
# %%
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1 import make_axes_locatable
import mne
import n... |
ca625f4e895f8f7e19b8b2683f5dad8818d5bb6abefbe0ab24d1053029b60134 | Jupyter | 12,790 | 364 | # %% [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... |
667c479377f5daa6b3f68722f462a8c269dfe8e08115fd117887c90685e4bce5 | Jupyter | 12,797 | 353 | # %% [markdown]
# # Table of contents
#
# ### - Import libraries and define functions + Initial setup
#
# ### - Figure 2
#
# ### - Figure 3
#
# ### - Figure 4
#
# ### - Figure 5
#
# ### - Output novel high-confidence only annotation
# %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
... |
7dbdc62748ba08f967198df70e0a6d69773e985e9a8426471db19654b53265cc | Jupyter | 12,832 | 367 | # %% [markdown]
# # Knowledge Distillation For Fine-Tuning A GPT-3.5 Judge (Correctness)
#
# This notebook has to do with fine-tuning an LLM Judge that evaluates the responses of another LLM to a user query. More specifically, we demonstrate how to use the `llama_index` library to distill knowledge from a GPT-4 Judge ... |
ad12dd2bbc24257932dcdd9c852fa13812c6b0994e54986d0e79e4639a57e0e4 | Jupyter | 12,965 | 421 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/low_level/router.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Building a Router from Scratch
#
# In this tutori... |
afd9ab768f58ec3e80e33b0620480b54cc53e7ca1c4ef55b24e4061e7f34d6d9 | Jupyter | 12,995 | 354 | # %% [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... |
f44a252d7efbb0e33aedc885c41a3375f0078ff514b8410e136031400e6edfed | Jupyter | 13,028 | 398 | # %%
import os
import sys
import pathlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.mixture import GaussianMixture
from sklearn.preprocessing import StandardScaler
from matplotlib.patches import Ellipse
from matplotlib.backends.backend_pdf i... |
ac4686012eeeb7835d4ebc326c8d00428d1c4a414753678cd572d3df297ef888 | Jupyter | 13,063 | 370 | # %% [markdown]
# # Finetuning corpus embeddings using NUDGE
# [NUDGE](https://www.arxiv.org/abs/2409.02343) is a novel simple and lightweight fine-tuning method that boosts accuracy when retrieving text using semantic similarity with pre-trained embedding models. NUDGE directly modifies the embeddings of data records ... |
671da5015eddd8cf81d51cf2fc408c04c453a4d3a3b0521aacc600a66a1c5023 | Jupyter | 13,079 | 408 | # %%
import humanize
from astrocast.preparation import Delta, IO
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
# %%
test_path = Path("./quickstart.h5")
assert test_path.exists()
pixels = [(167, 153), (199, 306), (214, 109), (337, 72)]
# %%
io = IO()
inf = io.load(test_path, lazy=False,... |
fc4cb8fbc7dd48278ad2c36fb3c888905ff4214d479b50590a6293100e3e68e0 | Jupyter | 13,106 | 333 | # %% [markdown]
# # <font color='firebrick'><center>CONTEXT STATS REPORT</center></font>
# ### This report details the context stats that have been generated as a result of running the bamstats tool.
# <br>
# %%
from IPython.display import display, Markdown
from IPython.display import HTML
import IPython.core.display ... |
d67af15343d6f8b4b76d9195348c21f5441ac74d2b3883e04e4b9e5a15294ae8 | Jupyter | 13,138 | 424 | # %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_venn import venn2, venn2_circles, venn2_unweighted
from matplotlib_venn import venn3, venn3_circles
from matplotlib import pyplot as plt
%matplotlib inline
import se... |
631fcc8acf8ddc5f7ac1c0778976374238d932c64043d04dca11ded4fdd027e9 | Jupyter | 13,187 | 370 | # %% [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... |
7bf4aea5fb32680ad5470873b87b773e36d904fa675ac39c392a2b50054f8f69 | Jupyter | 13,205 | 281 | # %% [markdown]
# # Model evaluation
# %%
import os
import numpy as np
import h5py
import copy
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.axes_grid1 import make_axes_locatable
import seaborn as sns
# %%
def prep_metrics(prefixes, reps, types, input_dir, epochs=None):
metrics_dict =... |
f28108f5738020fd152f99477ff8d72daf1e01815d0a87386ae2e51f3f1c8a11 | Jupyter | 13,214 | 226 | # %% [markdown]
# MultiRAT preprocessing
# ================
# Joanes Grandjean
#
# 
#
# # Purpose
# In this section I submit jobs to my high performance computer to perform the preprocessing, confound_correction, and analysis steps as implemented in RABIES.
# This section relies ... |
3ca12c15fc1e1b0b60d3c2d6849bb22e936dd22e1afb5c81d0a4c994f29e6aec | Jupyter | 13,223 | 283 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/cookbooks/cleanlab_tlm_rag.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Trustworthy RAG with the Trustworthy Lan... |
2a0385b23cb99dd46542c5c926d5a51aa6a33b1428ea07b8730005c32c6e5604 | Jupyter | 13,227 | 345 | # %% [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... |
63a9c202ea28733ed2b0b67f70319a73da5f3610a1986e394de6f63d50d57172 | Jupyter | 13,325 | 405 | # %% [markdown]
# # Comparison with other datasets
# %%
import functools, session_info
import numpy as np
import pandas as pd
from plotnine import *
from pyhere import here
from scipy.stats import binom_test, fisher_exact, linregress
from warnings import filterwarnings
from matplotlib.cbook import mplDeprecation
filt... |
352ab2f2137bb108a82faa42bbdeda21371d44a3454cbd6d17cea15ca7777cfe | Jupyter | 13,396 | 290 | # %% [markdown]
# # 1. Gradient extraction of template and individual connectomes + alignment
# %%
import numpy as np
import pandas as pd
import scipy
from scipy import ndimage
import seaborn as sns
import os
import matplotlib.pyplot as plt
import glob
from brainspace.gradient import GradientMaps
from brainspace.utils... |
9bbc40958aa9fbcfb3fd6c22031bc3efdeae6a6352a19b114f9317254e686328 | Jupyter | 13,411 | 265 | # %% [markdown]
# # Tool recommendation
# ## (Gated recurrent units neural network with weighted cross-entropy loss)
# %%
import numpy as np
import json
import warnings
import operator
import h5py
from keras.models import model_from_json
from keras import backend as K
warnings.filterwarnings("ignore")
def read_fi... |
c849e5f08164dd6eefa6a7c3d03fe57e60502eac60572de9021456020215794a | Jupyter | 13,481 | 315 | # %% [markdown]
# # Scanless two-photon voltage imaging (example)
# %%
import tifffile
import numpy as np
import scipy
from matplotlib import pyplot as plt
import skimage
import warnings
import copy
from sklearn import linear_model
# %%
# data directories
# change as necessary
data_dir = r"./example_data_scanless_two... |
8ecedfad8eb782d061743b1981d9d6786843f51c8a667ba65df83b6abfee7b30 | Jupyter | 13,492 | 229 | # %%
import itertools
import sys
import traceback
from pathlib import Path
import yaml
from astrocast.denoising import Network, PyTorchNetwork, SubFrameDataset, SubFrameGenerator
# %% [markdown]
# # General considerations
#
# This training is generally a one-time requirement; once completed, the model can be reappli... |
4ec6c49e819fcf9b72422386d3cc8811244eb77d9c68469af6f15086c852cba4 | Jupyter | 13,567 | 407 | # %% [markdown]
# # Custom Planning Multi-Agent System
#
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/agent/custom_multi_agent.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#
# In this notebook... |
30e951d99d8b6521372ff826f29aeae7ee4b1fb0c109aed295159da1bd0ef370 | Jupyter | 13,601 | 439 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/low_level/vector_store.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Building a (Very Simple) Vector Store from S... |
37a8a28fe83bd555cddc9eb8b6c109f19ab3ff059670cb07ae408108de09f6bf | Jupyter | 13,633 | 503 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/openai_responses.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # OpenAI Responses API
#
# This notebook shows ... |
fb94a3ad73b36080e1471e6b565cac175a027d552ec8d8433596f7f2b014a235 | Jupyter | 13,633 | 351 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn.metrics as skmts
import sklearn.preprocessing as skpre
import scipy.stats as scistat
import glob
import pickle
import statsmodels.stats.multitest as multi
plt.style.use('fivethirtyeight')
%matplotlib inline
... |
40db7bde421b67fd8989857fcde8199f123fff0e39c02dade581d8cf325372a0 | Jupyter | 13,647 | 416 | # %%
import numpy
import matplotlib
import scipy
print("NumPy version:", numpy.__version__)
print("Matplotlib version:", matplotlib.__version__)
print("SciPy version:", scipy.__version__)
# %%
import os
import datetime
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
def printbar():
nowtime = datetime.datetime.now()... |
74ee7f9fb333d7ff26b9e9298a056fb782b3b00c14d9e47080b6172e6949812e | Jupyter | 13,693 | 459 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/finetuning/mistralai_fine_tuning.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Fine Tuning MistralAI models using... |
542883c91716fef3cf72de3f1bbce352ad9f86f819856a27caf1d52924aa7a12 | Jupyter | 13,756 | 415 | # %%
import os
import time
import tensorflow as tf
import numpy as np
import pandas as pd
import scanpy as sc
import anndata as ad
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm
import tensorflow_probability as tfp
from tensorflow.keras import backend as K
import pdb
# %... |
6e8ae13929e7d44b2a2049c5d84cfa75771a15d98e0a1e28f2823014583252c1 | Jupyter | 13,762 | 385 | # %%
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, BatchNormalization, LocallyConnected2D, Permute
from keras.layers import Conca... |
869d6dde9c5f3988460fc02de6e55e5ea2f3126ace3f5fe7a61c1ed3290dc344 | Jupyter | 13,780 | 485 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import math
from tabulate import tabulate
from difflib import SequenceMatcher
import warnings
warnings.filterwarnings('ignore')
from collections import defaultdict
from itertools import chain, combinations
from fpgrowth_py import fpgr... |
7e71431cedb5aecffe7bb9a1af4aa9d7ae206a652deda21525dd6de0708233ec | Jupyter | 13,782 | 485 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import math
from tabulate import tabulate
from difflib import SequenceMatcher
import warnings
warnings.filterwarnings('ignore')
from collections import defaultdict
from itertools import chain, combinations
from fpgrowth_py import fpgr... |
acd91706dc8649bddc22cee61d0cc587693017107072f021980d6becd490ed6e | Jupyter | 13,800 | 452 | # %%
import os
import sys
sys.path.append('/home/esten/repos/FastSurfer/FastSurferCNN')
# %%
from pyment.data import NiftiDataset, AsyncNiftiGenerator
from pyment.data.preprocessors import NiftiPreprocessor
ixi_folder = os.path.join(os.path.expanduser('~'), 'data', 'IXI')
image_folder = os.path.join(ixi_folder, 'cr... |
dc62bd923b42f22766d68b91818c8574b1cc6a6c8238652823b4e61242006188 | Jupyter | 13,848 | 452 | # %%
import os
import sys
sys.path.append('/home/esten/repos/FastSurfer/FastSurferCNN')
# %%
from pyment.data import NiftiDataset, AsyncNiftiGenerator
from pyment.data.preprocessors import NiftiPreprocessor
ixi_folder = os.path.join(os.path.expanduser('~'), 'data', 'IXI')
image_folder = os.path.join(ixi_folder, 'cr... |
37956c31733c6545eb52d2f010f6a832f5ce8ca1d3794b65b5a268b8853625a8 | Jupyter | 13,881 | 351 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn.metrics as skmts
import sklearn.preprocessing as skpre
import scipy.stats as scistat
import glob
import pickle
import statsmodels.stats.multitest as multi
plt.style.use('fivethirtyeight')
%matplotlib inline
... |
7390bc6615373ea43915917133c6b40603385fe78ded1ce43e9594db43504a7a | Jupyter | 13,934 | 286 | # %% [markdown]
# ## notebook 02: position error plotting
#
# In this notebook we read in the experiment result dataframe and plot boxplots of individual performance separated by condition overlaid by scattered single participant data. We additonally calculate a quadratic regression and its local minimum to motivate s... |
dab21cdef676fd69d7fe49682e963b4db9b100df002ceb13230ca3986013f7bc | Jupyter | 13,962 | 451 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/agent/multi_document_agents-v1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Multi-Document Agents (V1)
#
# In t... |
8613877421f7b26961b3b37dc3cfc729019558fc42fd095090d9a60dba23072c | Jupyter | 14,004 | 286 | # %% [markdown]
# ## notebook 02: position error plotting
#
# In this notebook we read in the experiment result dataframe and plot boxplots of individual performance separated by condition overlaid by scattered single participant data. We additonally calculate a quadratic regression and its local minimum to motivate s... |
0b218387576eebc38a35845e3bedf533fd1930e7b5c8c2c50699edb948e56afb | Jupyter | 14,164 | 552 | # %% [markdown]
# [](https://colab.research.google.com/github/mahdimplus/DeepRetroMoco/blob/main/network/train_with_validtiondatafunctions.ipynb)
# %%
from google.colab import drive
drive.mount('/content/drive')
# %%
!apt-get install -y -qq s... |
b6fac51029c054abbd0826a8e3317aa6d46cb2f6f72e50936f3fb193ae155dae | Jupyter | 14,176 | 440 | # %%
# make sure jupyter path is correct for loading local moudules
import sys
# path to steric_simulator module relative to notebook
sys.path.append("../../../")
import copy
from KineticAssembly_AD import ReactionNetwork, VectorizedRxnNet, VecSim, Optimizer, EquilibriumSolver, TrapMetric
import networkx as nx
import t... |
6fad68382b88625b72fba36c38ae4577c880975aadfe16d143a1d51166611be3 | Jupyter | 14,196 | 436 | # %%
## Import dependencies
import numpy as np
from os import path
import matplotlib.pyplot as plt
import os
import nmrglue as ng
import seaborn as sns
import sklearn
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import torch
import torch.nn as nn
import torch.opti... |
22806fc25bc78f7c92b5ff8fe77dc1a309ef20fa896a252128d4481493908a95 | Jupyter | 14,214 | 521 | # %% [markdown]
# # Llama3 Cookbook with Ollama and Replicate
#
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/cookbooks/llama3_cookbook_ollama_replicate.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/><... |
977c39950a2ba6ae72e841fe526cb30bbec5acf5c8c782966eb341d983b2b32c | Jupyter | 14,220 | 428 | # %% [markdown]
# <div class="alert alert-success">
# <h1>Local encoding of prediction error signals</h1>
# <hr>
# <p>
# I(HGA; PE)
# </p>
# </div>
# %%
import numpy as np
import xarray as xr
import pandas as pd
from pathta import Study
from frites import set_mpl_style
from frites.utils import kernel_smoothing
imp... |
290b28eda27e8462eccd8836b1eaba4035127607e94c142941241fba25f56839 | Jupyter | 14,244 | 364 | # %% [markdown]
# # notebook 01: trajectory plotting
# In this notebook we read in trajectory data of example participants, plot trajectories and endpoints separated by condition to generate Figures 1, 2, 3, S1, and the exemplary participants a-f in Figure 7 of the paper.
# %%
# importing python packages
import numpy ... |
21c943ecfbfb18a20557226465614d34d782ddc9cd91c6ac72f69d432a87311d | Jupyter | 14,258 | 404 | # %% [markdown]
# Peakcalling Bam Stats and Filtering Report - Reads per Chromsome
# ================================================================
#
# This notebook is for the analysis of outputs from the peakcalling pipeline
#
# There are severals stats that you want collected and graphed (topics covered in thi... |
39d01d7ddb24b1f77e8dcc9c719b91b0d4236384802609ec212dd205c01912fe | Jupyter | 14,258 | 421 | # %%
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
import random
import math
import copy
import pickle
from torch.utils.data import DataLoader, Dataset, ConcatDataset
from torch.utils.data.dataset import Subset
from sklearn.model_selection i... |
e60673238327cb1313afb16f8a3c69956124b241c2300e695c979361f92d8991 | Jupyter | 14,279 | 312 | # %% [markdown]
# <a href="https://colab.research.google.com/github/dauparas/ProteinMPNN/blob/main/colab_notebooks/quickdemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# #ProteinMPNN
# This notebook is intended as a quick demo, ... |
88edac747bfb45deceb0f78e458a2320c7525f343b60ce4fe2e69bc5a0a052eb | Jupyter | 14,280 | 309 | # %% [markdown]
# <a href="https://colab.research.google.com/github/dauparas/ProteinMPNN/blob/main/colab_notebooks/ca_only_quickdemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# #ProteinMPNN
# This notebook is intended as a quic... |
0a410c82669b68c4fc8ff9801b5667e6a1fd56bbe2d420769bb498e4aeba2add | Jupyter | 14,327 | 436 | # %% [markdown]
# # AICONS Lab Functional Gradient Workflow
# ## Read before beginning
# Before running anything in this notebook, generate connectomes for each subject using a method of your choosing and save them as a `csv` with the first column being region labels. Before beginning, you will need:
# 1. A text file c... |
d4fbccde4bd59fdd68f28380c4a630efe0a798b4a1ac810d3fc31efc00eeefff | Jupyter | 14,387 | 649 | # %% [markdown]
# # Read & parse snpEFF data
# %%
_SAMPLE_TABLE = pd.read_excel("<sampletable")
# %%
import pandas as pd
import seaborn as sns
import pathlib
import tqdm.auto as tqdm
tqdm.tqdm.pandas()
# %%
files = pd.Series(
pathlib.Path("variantcalling/annotation/").rglob("*snpEff.ann.vcf.gz")
)
# %%
files.i... |
086ddab62a07b92c0b5ffe3a02488ad8b82e0ef0078d823b33fd0cf44ec30211 | Jupyter | 14,398 | 366 | # %%
!pip install pingouin pycirclize
# %%
from google.colab import drive
drive.mount("/content/gdrive")
# %%
from pathlib import Path
import h5py
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import scipy.stats as sstats
import pingouin
# %%
matplotli... |
03566491663d49c7d6eff0fc640a5fca17b5f1b6d90ab91845eec07879ae310a | Jupyter | 14,402 | 366 | # %% [markdown]
# # notebook 01: trajectory plotting
# In this notebook we read in trajectory data of example participants, plot trajectories and endpoints separated by condition to generate Figures 1, 2, 3, S1, and the exemplary participants a-f in Figure 7 of the paper.
# %%
# importing python packages
import numpy ... |
29932b590eff794f631082542f4c1d9bfa76deb27cdb1f3673fc27d96d72b534 | Jupyter | 14,406 | 508 | # %% [markdown]
# # `Individual` models
# %%
# Sconda conda activate tensorflow2; cd /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/results/deeplift_analysis/; python
# %%
%load_ext lab_black
# %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import json
from... |
d14d9d2d9e8f000008318a6a97035663999b12f026c7d90ba1ce4a970c173c37 | Jupyter | 14,414 | 525 | # %% [markdown]
# # `Combined` Model generalizable
#
# Generate another combined model but exclude 10 samples from training such that we get a set of totally unseen data
# %%
# Sconda conda activate tensorflow2; cd /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/results/deeplift_analys... |
3c1dd9c085fcabb206845e5d2d89594ddff2ca70ff95ca20d716c544fe34a0bf | Jupyter | 14,420 | 282 | # %% [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... |
b4f44302ef553bc47c9bd4d44854fffcab44b48984223eaf5068e99710c768a9 | Jupyter | 14,459 | 333 | # %% [markdown]
# # Readouts
# %% [markdown]
# 
# %% [markdown]
# All the models in this library are constructed around two main components:
# - **core**: the core aims to (nonlinearly) extract features that are common between neurons. That is, we assume there exist a shared set of f... |
fe8538dec236bce93b1e1aa33718407db9479e6d7c692c87a41c94962ba62076 | Jupyter | 14,475 | 357 | # %% [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... |
e770034b43cf8308ff746c1f96d3f7098a09ad6bb83ea91d87b4a4387265835a | Jupyter | 14,490 | 431 | # %% [markdown]
# # Building a Knowledge Graph with LlamaCloud & Neo4J
#
# RAG is as a powerful technique for enhancing LLMs with external knowledge, but traditional semantic similarity search often fails to capture nuanced relationships between entities, and can miss critical context that spans across multiple docume... |
8d2c6ed6e92d59fd4cf9f4977afdb3d0116b409f640be1b91818691cb53c46a4 | Jupyter | 14,512 | 260 | # %%
from IPython.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))
# %%
import pandas as pd
#-------
database_name = 'QTDataBase'
#-------
target_noise_db_simulation = -5
rep = 5
#-------
csv_name_clean_sig = 'CleanSignals' + str(rep)
csv_name_noisy_sig = 'Noisy... |
475e86fdebc19248e5eea45a64c6040d56d2ee11f7aa1ae2ad5b84116a3b0591 | Jupyter | 14,610 | 348 | # %% [markdown]
# ## Defining selective epoch
#
# This code defines object-selectivity based on firing rate difference between object BOY and EGG using ANOVA. (Is the main effect for object ideneity significant?)
# %%
import os
from pathlib import Path
import numpy as np
import pandas as pd
from scipy import stats
f... |
613b5723195a3aca387276cf358e956bb4620a0137eda456bd12b3d3314bae8e | Jupyter | 14,624 | 325 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/evaluation/Cleanlab.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Trustworthy RAG with LlamaIndex and Cleanlab
# ... |
eb3dbdbfc21c32f66890b43629ebaec238847c9d8caad0521f4fd00c5837ba93 | Jupyter | 14,701 | 543 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/asi1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # ASI LLM
# ASI1-Mini is an advanced, agentic LLM designed b... |
d16f41067f660681fd8db6c69d135f0d7cfd190fe68133ad291db0c486ab16ac | Jupyter | 14,702 | 405 | # %%
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, BatchNormalization, LocallyConnected2D, Permute
from keras.layers import Conca... |
c8b40cd11e2e956c5cf309f2de6dfbc7fcb4d0e6113a603dd25e47f245d0b35d | Jupyter | 14,762 | 466 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/finetuning/rerankers/cohere_custom_reranker.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Custom Cohere Reranker
... |
d5c87d6d2e3b0892db6e976cc591b8946db6e99c25c6aa833e9b92014fd7e91b | Jupyter | 14,814 | 526 | # %%
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
import shutil
import os
import subprocess
# %%
D7_dir='/home/mahdi/Desktop/data_selection_D7'
n,name=count(D7_dir)
out_dir='/home/mahdi/Des... |
3e7f055c68bb2b96b9b16fca92efe8142506bae18fc52862450d377e26c93667 | Jupyter | 14,934 | 451 | # %%
# import dependencies
import os
import numpy as np
import pandas as pd
import scanpy as sc
import loompy as lp
from MulticoreTSNE import MulticoreTSNE as TSNE
import json
import base64
import zlib
from pyscenic.plotting import plot_binarization
from pyscenic.export import add_scenic_metadata
from pyscenic.cli.util... |
8557d4270f6af132f25eb7d4adc20e56388d6e67d6ef7537655f2ddf31cc3522 | Jupyter | 14,971 | 348 | # %% [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... |
9feb98c1dceec497b633a85d93b0c2e26042c46a6957212c3087d98c80ce9665 | Jupyter | 14,980 | 450 | # %%
# import dependencies
import os
import numpy as np
import pandas as pd
import scanpy as sc
import loompy as lp
from MulticoreTSNE import MulticoreTSNE as TSNE
import json
import base64
import zlib
from pyscenic.plotting import plot_binarization
from pyscenic.export import add_scenic_metadata
from pyscenic.cli.util... |
ba8f635e658a0afb8eb77f6a56276e15acae5c3d090b7ad10506b56adb950817 | Jupyter | 14,983 | 421 | # %% [markdown]
# # Results summary
# %%
# utils
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sincfold.metrics import f1_strict
from sincfold.utils import dot2bp
import json
# %% [markdown]
# ## Ablation study
# %%
dotbracket_format = ["IPknot", "ProbKnot", "RNA... |
ee33d076852ea910e8b151816c075f5d008a559caf45df518aa42642ad4b750d | Jupyter | 14,983 | 603 | # %% [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]
# # OpenAI
#
# This notebook shows how to use the OpenAI LL... |
71f1756fc6d51eb95dc3d2125fd94d91db82ca0155427a436654cae23cf0780a | Jupyter | 15,083 | 412 | # %% [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... |
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