sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
00c7f22b6c524e0abd67e7cea16d4ee90995771d805d87b6f8af34eccd7ad82c | Jupyter | 3,376 | 98 | # %% [markdown]
# ### ECG Transformation Across Varying Heart Rates
# %%
import numpy as np
import neurokit2 as nk
import matplotlib.pyplot as plt
from sklearn.preprocessing import FunctionTransformer
from sklearn.impute import SimpleImputer
import rlign
# %%
normalizer = rlign.Rlign(scale_method="linear", template_... |
bb9fd29aed41f12f48f919437680caccb2034638bfff24abd478e97f629940d1 | Jupyter | 3,377 | 133 | # %% [markdown]
# # Create Graph Dataset
# %%
import sys
import os
import pickle as pkl
import pandas as pd
import torch
path = os.path.join('..', '.')
if path not in sys.path:
sys.path.append(os.path.abspath(path))
from src.protein_graph import pncaGraph
from tqdm import tqdm
# %%
train_seqs = pd.read_csv('..... |
0e626e858312da78bcc2f3495947ad9b6547e9bf145f4c5dc3a16bf5d47f4388 | Jupyter | 3,381 | 133 | # %%
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, KFold
from sklearn.metrics import roc_auc_score, precision_recall_curve
from sklearn.metrics import auc as calculate_auc
from sklearn.metrics import mean_squared_error
from sk... |
bdce07550e35b4ca6032aa3f5864e27534c113cdc8ed6e3d504bb38b62d2bb5e | Jupyter | 3,391 | 142 | # %%
import pandas as pd
import numpy as np
# %%
# %%
BASE_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/data_collection/"
OUT_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/processed_data/stitch"
# %%
## Pubchem_2_name
Pubchem = pd.read_pickle(f'{BASE_PATH}databases_for_mapping/pubchem/combined_df.pkl')... |
e5a8aa198e37b3ef53275a94d0c741426d46024300e7f8cd49b64c68e580041f | Jupyter | 3,400 | 83 | # %%
# %% Load source data and reproduce Fig 3 RT violin+slope
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.patches import Patch
in_xlsx = os.path.join(data_dir, "output", "SourceData_Fig3_SH_RT.xlsx")
roi_order = ["lc", "sn... |
6c4f8f883f417893b0f785591f2465ac977e807729d1ea67a4b8694fb2e776fa | Jupyter | 3,414 | 79 | # %% [markdown]
# # Visualization of Data Distribution for [THINGS-Mooney](https://github.com/wobc/things-mooney)
# %%
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
metadata = pd.read_csv('things_mooney_metadata.csv')
# %%
# Set the style for the plot
sns.set(style="whitegrid")
# Plot th... |
6657fe6032eb4422ac0efb4af91c49bbbfaf0310f60cc4ba0402a3b74ec27291 | Jupyter | 3,422 | 130 | # %%
# ===============================
# Encryption Pipeline
# ===============================
def encrypt_image(img_tensor, key_params):
"""
Applies chaotic encryption:
1) Arnold Cat Map (confusion)
2) Logistic Map + Zigzag diffusion
"""
img_np = img_tensor.squeeze(0).cpu().numpy()
N = im... |
d47982f280e0d51ec84944abde9fb372a5c0721baea6c6903254a1574a96d0da | Jupyter | 3,442 | 95 | # %% [markdown]
# # MolecularFunction → BiologicalProcess Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **MolecularFunction–BiologicalProcess** relation.
#
# **Output schema:** `head | relation | tail | head_type | relation_type | tail_type | kg_source | kg_type | head_id_is | tail_id_is | h... |
2ccc44b0e491f736fdf4b977e14c8e85ac82d9b1413a8fbbf49628e8ee156259 | Jupyter | 3,455 | 65 | # %% [markdown]
# # First steps
#
# After installing the plugin you can open it from the napari menu under the name ```Convpaint```.
#
# You can use the plugin with various types of images: simple gray-scale, multi-channel, time-lapse, RGB. Note that while you can annotate stacks of slices as a single 3D image, the l... |
77d4ca7c2c9b6d143e3ad3fc5fa1d25a8cbed60e405731361cf9981b44c8ab65 | Jupyter | 3,489 | 112 | # %% [markdown]
# # 不同类型的测量 API
# %% [markdown]
# ## 概述
#
# TensorCircuit 允许执行与测量结果相关的两种操作。
# 这些是 (i) 条件测量，其结果可用于控制下游条件量子门，以及 (ii) 后选择，它允许用户选择与特定测量结果相对应的测量后状态。
# %% [markdown]
# ## 设置
# %%
import tensorcircuit as tc
import numpy as np
K = tc.set_backend("tensorflow")
# %% [markdown]
# ## 条件测量
#
#
# `cond_measur... |
63dc1bfd29773a8ccd4eeb87c92705e8737b3495a27935480ca171cfd9649568 | Jupyter | 3,526 | 92 | # %% [markdown]
# Reproduce Fig2
#
# Here I have number of units that are close to the paper (I think because I did a mistake first in calculating constraints - using whole baseline instead of only 1500 ms?)
# %%
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import se... |
e0ac3b3b7ddb5873bdcadb264ab3971bf179da543ae94401b5a5ce970b1d960d | Jupyter | 3,533 | 118 | # %%
%load_ext autoreload
%autoreload 2
import numpy as np
import pandas as pd
import napari
from scribbles_testing.FoodSeg103_data_handler import *
# %% [markdown]
# ## Create scribbles
# %% [markdown]
# Load the ground truths as batch
# %%
img_nums = [n for n in range(0, 4983, 8)] #[1382] #2750 #1234 #2314
gts = ... |
91ce8ca9258348755c4a203ade93bb228050ae609e969d7599eadbb9d75a69c5 | Jupyter | 3,534 | 104 | # %% [markdown]
# # Stats for Figure 2
# %%
# imports
import pandas as pd
import numpy as np
import sys
sys.path.append('..') # needed for relative file path import with jupyter
from src.cc import ccptpt
from src.expparams import sim_dt
from src.expparams import sim_dur
from src.expparams import sim_step_num
from nump... |
3943995603479892615b65f6ed22482badecdad5390113153f0462b80be726ea | Jupyter | 3,538 | 85 | # %%
# %% Load saved source data and reproduce Fig 4 violin+slope (Gain → Loss)
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.patches import Patch
in_xlsx = os.path.join(data_dir, "output", "SourceData_Fig4_SH_GainLoss.xlsx")
... |
19d6eee7b4916da95a3c5f27f23f54556cbec965abb7c4dcc1ec26d8d3b0f05d | Jupyter | 3,540 | 111 | # %%
from deepscore import DeepScore
from preprocessing import *
import scanpy as sc
import anndata as ad
%load_ext rpy2.ipython
%load_ext tensorboard
sc.settings.set_figure_params(dpi=80, color_map='gist_earth')
sc.settings.set_figure_params(figsize=('10', '10'), color_map='gist_earth')
# %%
#!Rscript /home/pab/pro... |
2ac2af418bfff43d2ebaa968b8b7ccf4731266589e32a1f044fa720407569477 | Jupyter | 3,559 | 150 | # %%
#### !/usr/bin/env python
# coding: utf-8
from molmap.model import RegressionEstimator, MultiClassEstimator, MultiLabelEstimator
from molmap import loadmap
from molmap.show import imshow_wrap
import molmap
from molmap import MolMap
from sklearn.utils import shuffle
from joblib import load, dump
import numpy as n... |
a175a577d872b3ef834549d6fb8abfbb374f62f8238cd9133051834896f202d7 | Jupyter | 3,577 | 125 | # %%
import pickle as pkl
#import viz_sequence
from scipy.stats import spearmanr, pearsonr
import matplotlib.pyplot as plt
from scipy.spatial.distance import jensenshannon
import h5py
from scipy.special import softmax
import scipy
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from statsmodels.dis... |
6ba8a70536b60433ae9dcba718dd93f9ed8330a37a572575fe439ccc761d827c | Jupyter | 3,583 | 147 | # %%
# ===============================
# Environment & Reproducibility Setup
# ===============================
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import pennylane as qml
import numpy as np
import random
from pytorch_msssim i... |
d8fa63d1fc4c64a8a290978a8a3a17c9c661f2b9b95c9f9e1a81de39dcc5d01d | Jupyter | 3,598 | 87 | # %%
# %% Load source data and reproduce Fig 5 SH Accept vs SH Reject (violin + paired slope)
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.patches import Patch
in_xlsx = os.path.join(data_dir, "output", "SourceData_Fig5_SH_Ac... |
bb5e1fe7f611a88477d2a0f238fd765c18e82b16cc5d9f7548560fad203cd098 | Jupyter | 3,613 | 87 | # %%
import numpy as np
import pandas as pd
from PIL import Image
from seghub import segbox
from scribbles_testing import cellpose_data_handler
from scribbles_testing.image_analysis_helpers import single_img_stats
# %%
img_folder = "/mnt/imaging.data/rschwob/cellpose_run07/"
output_folder = "/mnt/imaging.data/rschwob... |
dbe233cb3831cdfd60dfb55b27671fab4ae2eb4c38555d5d805c4887950ed0ad | Jupyter | 3,616 | 94 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/apd.ipynb>`, or as a {download}`python script <converted/apd.py>` with code cells. We highly recommend us... |
7983bfe6149bbb3549d2e4864bc60d5c6ee0814d3d1e41f677ab79fd64533b5f | Jupyter | 3,625 | 108 | # %% [markdown]
# # Tabular Examples
# RNA3DB contains `Tabular`, a high-level API for interacting with Infernal's output tables for `cmscan`.
#
# Using the `.tbl` files we provide, we can look up Rfam family information for any RNA chain in the PDB.
# %%
# read an entire directory of *.tbl files, such as those provi... |
f00b25e07438620c9339a255842a1aa35bf14df5373933ea1e33253841beadd4 | Jupyter | 3,626 | 139 | # %% [markdown]
# # CellularComponent → ChemicalEntity Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **CellularComponent–ChemicalEntity** relation.
#
# **Output schema:** `head | relation | tail | head_type | relation_type | tail_type | kg_source | head_id_is | tail_id_is | head_detail_name ... |
84b69b7ea65ae463e8a02160cc90566088802dc843f835022064b595b987c6e6 | Jupyter | 3,628 | 136 | # %%
import numpy as np
import pandas as pd
from scipy import stats
import pingouin as pg
# ----------------------------
# 1) Raw LH frequecny data
# ----------------------------
data5D = {
"ConFoff": {
"Before": [18.3, 12.0, 15.0, 15.0, 17.5, 15.0],
"After": [22.5, 22.5, 27.5, 20.0, 25.0, 25.0],... |
982f646d990a25dd080cecdbc9d136285137e27e008201aa4fa84ddaaac5a4d0 | Jupyter | 3,631 | 112 | # %% [markdown]
# # 量子近似优化算法 (QAOA)
# %% [markdown]
# ## 概述
# %% [markdown]
# QAOA 是一种混合经典量子算法，它结合了量子电路与经典优化。
# 在本教程中，我们利用 QAOA 解决最大割 (MAX CUT) 组合优化问题：给定一个图 $G=(V, E)$，其中节点 $V$ 和边 $E$，找到一个子集 $S \in V$ 使得 $S$ 和 $S \backslash V$ 之间的边数最大化。
# 这个问题可以简化为寻找反铁磁伊辛模型的基态，其哈密顿量为：
#
# $$H_C = \frac{1}{2} \sum_{i,j\in E} C_{ij} \... |
6e340a51c9b9ca77a8716a41a95066c9ca3c501d1ca045427e3b190b16964a28 | Jupyter | 3,635 | 139 | # %% [markdown]
# This notebook shows how to use JOINT to cluster data with 2 cell types and 2 features (genes)
# %%
import numpy as np
from joint import joint
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from sklearn.metrics.cluster import adjusted_rand_score
%matplotlib inline
# %%
# generate ... |
a5815593162f40a7c1d164f7c95f2672cd419b58854c7210a1e2b3f5c27672dc | Jupyter | 3,719 | 148 | # %%
# --- Instantiate and Train ---
# Reset seed before each model for fair comparison
set_seed(42)
cnn_model = CNNAutoencoder()
print("Training CNN baseline...")
cnn_model, cnn_losses = train_model(cnn_model, "CNN")
torch.save(cnn_model.state_dict(), "cnn_model.pt")
set_seed(42)
qnn_model = QNNBranch()
print("\nTr... |
81a5a6fc0655b62fb65fa37dbc2591698615de05be505d626767c43e7693d8ce | Jupyter | 3,724 | 103 | # %%
%load_ext autoreload
%autoreload 2
import numpy as np
import pandas as pd
from scribbles_testing.FoodSeg103_data_handler import *
# %% [markdown]
# Load the images as batches
# %%
img_nums = [0]#[n for n in range(0, 4900, 500)] #2750 #1234 #2314
imgs, gts = load_food_batch(img_nums)
num_imgs = len(imgs)
print(f... |
5a34dc21e055acf6cb711e1700e49b89cd7bd31defe394b69fcce6aed067ea0c | Jupyter | 3,740 | 253 | # %%
import molmap
import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from molmap import feature, dataset
# %%
data = dataset.load_HIV()
# %%
aspirin = 'CC(=O)OC1=CC=CC=C1C(O)=O' #aspirin
NAC = 'CC(=O)NC1=CC=CC=C1C(O)=O' #N_acetylanthranilic_acid
smiles_list = [aspirin, NAC]
# %%
E ... |
0c505b26d1f778e1c3deaffed56750e4947dc765a68bd3913f6df14e9ec8cf00 | Jupyter | 3,753 | 118 | # %%
import numpy as np
import pandas as pd
import pickle
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import re
import scipy.stats as stats
# %%
matplotlib.rcParams.update({'font.size': 10})
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib... |
997dbefee657480bfd91b0d6753b2e0c20b9b1bef567c8f9001096370b64838c | Jupyter | 3,758 | 118 | # %%
import numpy as np
import pandas as pd
import pickle
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import re
import scipy.stats as stats
# %%
matplotlib.rcParams.update({'font.size': 10})
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib... |
a56d2a56505d92111285395c5078199c03747c16735d36c6c9f0d21f38f8b5e1 | Jupyter | 3,768 | 146 | # %% [markdown]
# # Gene ↔ Mutation Relation-Wise Merge
#
# Merges Gene–Mutation triples from EvoAGE;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BAS... |
b182b690c1fe56b4c03ce449600dfdbf7e33045586fa1118f0131f37f3bec2b8 | Jupyter | 3,779 | 143 | # %%
import pandas as pd
import numpy as np
# %%
# %%
# %%
BASE_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/data_collection/"
OUT_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/processed_data/stitch"
# %%
## Pubchem_2_name
Pubchem = pd.read_pickle(f'{BASE_PATH}databases_for_mapping/pubchem/combined_... |
deb20a8dd687d908253e5b439a9929436d72c9a4754e148edfd190c027e11d3a | Jupyter | 3,788 | 133 | # %% [markdown]
# # SDE integrator
# %%
#export
# Initialization
from DiffOperator import DifferentialOperator
# import derivativesTransferFunctions
import numpy as np
# import derivativesTransferFunctions
# %% [markdown]
# ## Reward-driven regulatory mechanism
# %%
#export
def RegulatoryPsi(psi0, stimulusA, stimu... |
c7662ae1647ce16a3383594c20a98bfa7d0710b53ef6bf78e95c07a50cd350b9 | Jupyter | 3,814 | 106 | # %%
%load_ext autoreload
%autoreload 2
import numpy as np
import napari
from scribbles_testing.FoodSeg103_data_handler import *
# %% [markdown]
# ## Prediction
# %% [markdown]
# Load the images as batches
# %%
img_nums = [1328]#[n for n in range(0, 4500, 1000)] #2750 #1234 #2314
imgs = load_food_batch(img_nums, lo... |
c5323f22e3e1581e20c1a5a14e8c9a36ff83d74a2a3db23e4b821d01bfae6438 | Jupyter | 3,818 | 113 | # %%
# This scripts creates events.tsv for the main sample of subjects
# Let's consider the boop and split the imagery block into 2 blocks
# %%
import os
from src.utils import seq2tsv
from src.my_settings import settings
sett = settings()
# %% [markdown]
# # Settings
# %%
feedback_task_list = ['nf','sham']
## Loca... |
ae004ce457b0f2e42dfd886ebe6fb072b9279621da040ab194ac74f35a6a418b | Jupyter | 3,830 | 175 | # %% [markdown]
# ## Pipeline for supervised modeling
# %%
import os
# Check if it's in the correct directory
print("Current working directory:", os.getcwd())
path = os.path.abspath(os.path.join(os.getcwd(), '..', 'path.py'))
%run $path
# %% [markdown]
# ##### Configure notebook
# %%
# Import data
train_file = '../... |
b8af7902062364b57c2e57695b2b3f6e5f35b62cf8d79cac234c9f53b6e61694 | Jupyter | 3,838 | 112 | # %%
import csv
# Load the csv file
def load_csv(file_path):
# input: csv with two columns: (id, sequence)
# output: list of tuples (id, sequence)
sequences = []
with open(file_path, "r") as f:
reader = csv.reader(f)
header = next(reader) # skip header
for row in reader:
... |
feed15d0738a6f2773a43b5225cd15c3c02981faab0d54cc0e1d79de394836d1 | Jupyter | 3,861 | 98 | # %%
# %% Load saved source data and reproduce Fig 2A violin (from SourceData_Wide)
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.patches import Patch
in_xlsx = os.path.join(data_dir, "output", "SourceData_Fig2_stakes.xlsx")
... |
c8ae2067a9eacdc2f077e29c18aa6e93ac19badba9c1e67557d44f03f1478eab | Jupyter | 3,871 | 157 | # %%
from molmap import model as molmodel
import molmap
from molmap import dataset
import matplotlib.pyplot as plt
import pandas as pd
from tqdm import tqdm
from joblib import load, dump
tqdm.pandas(ascii=True)
import numpy as np
import tensorflow as tf
import os
os.environ["CUDA_VISIBLE_DEVICES"]="3"
seed = 123
np.... |
4db9d6ccb28470e83bda8be432bf6920fe3b59e3abe395c8b03f91ad3bf77854 | Jupyter | 3,902 | 142 | # %% [markdown]
# # Protein ↔ Phenotype Relation-Wise Merge
#
# Merges Protein–Phenotype triples from CrossBAR;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DI... |
cafa98b36a5990dc45d6d5eeec5d100891fca8de7e12150b7abb1fd9109259d8 | Jupyter | 3,905 | 150 | # %% [markdown]
# # Gene ↔ Mutation Relation-Wise Merge
#
# Merges Gene–Mutation triples from EvoAGE;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BAS... |
ed6b39f897ba05192642ab0604758b2a738b445e04b29f7de7e7ace06dfffc94 | Jupyter | 3,908 | 78 | # %% [markdown]
# # `Exact` explainer
#
# This notebooks demonstrates how to use the Exact explainer on some simple datasets. The Exact explainer is model-agnostic, so it can compute Shapley values and Owen values exactly (without approximation) for any model. However, since it completely enumerates the space of maski... |
091be9b17e586cd246113fc9fe5c17a73f6e0ac160315a992a6ac14a86af96d6 | Jupyter | 3,914 | 109 | # %% [markdown]
# # Different Types of Measurement API
# %% [markdown]
# ## Overview
#
# TensorCircuit allows for two kinds of operations to be performed that are related to the outcomes of measurements. These are (i) conditional measurements, the outcomes of which can be used to control downstream conditional quant... |
f126906e6a5ee2cb76873089a033e99b46187575b0971a83e4cc92dfac5d0377 | Jupyter | 3,931 | 159 | # %%
# ===============================
# Quantum Device
# ===============================
dev = qml.device("default.qubit", wires=n_qubits, shots=None)
@qml.qnode(dev, interface="torch", diff_method="backprop")
def improved_qblock(inputs, weights):
# Angle embedding
qml.AngleEmbedding(inputs * np.pi, wires=... |
e64ca519b596cac0c6432463e73f43c3755aee118102dabdf0494ca5ac05d327 | Jupyter | 3,943 | 112 | # %%
# %% Load source data (Fig 7 Accept vs Reject) and reproduce laminar profile + superficial/deep plot
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
roi_tag = "dlPFC" # must match what you saved
in_xlsx = os.path.join(data_dir, "output", f"SourceData_Fig7_{roi_tag}_AR_laminar.xl... |
e7c1e7edfc82c6cd74ae42f737450b693fa2a72954545c78b631da049dff0155 | Jupyter | 3,948 | 223 | # %% [markdown]
# # Table 1. Fifteen feature vectors of DNA data calculated by repDNA
# %% [markdown]
# 
# %%
from Bio.Seq import Seq
from Bio import SeqIO
import pandas as pd
import numpy as np
# %%
df = pd.Series(SeqIO.to_dict(SeqIO.parse('./test.fasta', 'fasta')))
# %%
s = df.iloc[0]
# %%
my... |
14244f539aff41e5eb43d450bc623e3807f35fb3f30212fee4acdd6e7f44a551 | Jupyter | 3,950 | 158 | # %%
#### !/usr/bin/env python
# coding: utf-8
from molmap.model import RegressionEstimator, MultiClassEstimator, MultiLabelEstimator
from molmap import loadmap
from molmap.show import imshow_wrap
import molmap
from molmap import MolMap
from sklearn.utils import shuffle
from joblib import load, dump
import numpy as n... |
fc080b6fd425e3663054c01a4eb6ce57dd840f682c41377f3f5ed033968d5eb2 | Jupyter | 3,960 | 134 | # %% [markdown]
# # Create Sequences for Dataset
# %%
import pandas as pd
import numpy as np
import copy
import gumpy
# %% [markdown]
# ### Get sequences from mutations
# %%
reference = gumpy.Genome('../data/NC_000962.3.gbk')
pnca = reference.build_gene('pncA')
# %%
# from fowler-lab/predict-pyrazinamide-resistance... |
f796508ab9f4518c3e5f49ecefdef33ef6dd3aa7aafb4eb62e40cbd671f301bb | Jupyter | 3,961 | 127 | # %%
%load_ext autoreload
%autoreload 2
from PIL import Image
import napari
import numpy as np
import os
from scribbles_creator import *
from scribbles_testing.cellpose_data_handler import *
# %% [markdown]
# ## Define where the images are located
# %%
folder_path = "../cellpose_train_imgs/"
# %% [markdown]
# ## C... |
04d8db2c92a7fe45ceb22b8200e73a4491db9c28d87c399c690072c72e4dfebd | Jupyter | 3,968 | 158 | # %% [markdown]
# # Node Feature Importance
# %%
import sys
import os
import numpy as np
import pickle as pkl
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import torch
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
path = os.path.join('..', '.')
if path not in sys.... |
bc7346e33b06c684a9119a0753e046141cfc1289860d45d662a7d392d73e43c7 | Jupyter | 3,975 | 134 | # %%
from molmap import loadmap
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import seaborn as sns
from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem.Draw import IPythonConsole
#IPythonConsole.ipython_useSVG = True
import numpy as np
mp1 = loadmap('./descriptor.mp')
mp2 = lo... |
ba817226b7d8038bcae5770f8863eef94347b6f478a6a4e9301990ef4a950253 | Jupyter | 4,002 | 148 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ─────────────────────────... |
5fca1d8c1931adfc6d698ca7d7499a77cd8e4cefd6a77b0179fb956dba887a51 | Jupyter | 4,017 | 147 | # %%
import pandas as pd
import numpy as np
# %%
# %%
# %%
BASE_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/data_collection/"
OUT_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/processed_data/stitch"
# %%
# BTO
BTO = pd.read_csv(f'{BASE_PATH}databases_for_mapping/bto/Tissue.tsv', sep = '\t')
BTO
BTO_D... |
f1846fcc43da15f39d5cb685ddffde93dc4ee9dcf7e1d0c401f714e6e23ef597 | Jupyter | 4,033 | 121 | # %% [markdown]
# # Xarray Data Structures - an fNIRS example
#
# This example illustrates the usage of xarray-based data structures for calculating the Beer-Lambert transformation.
# %%
# This cells setups the environment when executed in Google Colab.
try:
import google.colab
!curl -s https://raw.githubuser... |
27e80eeddd1e18afd216d62b055cd1119efd5b060837439cf98a321dcee1665b | Jupyter | 4,036 | 151 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ─────────────────────────... |
5bb0a6c4c65d15527723c54163d95c855e04d9845a74cb6d7cd9f4ecd74c4a2f | Jupyter | 4,057 | 164 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ───────... |
6adfc53fbfdd48c23e28a33cccd383a8d2c5a8e2f04a031c29521003e4ad23f7 | Jupyter | 4,064 | 172 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ... |
68e860cd55791f56f44b7d988fcf4d949ceda746a90140a71e54d9aa8507a779 | Jupyter | 4,080 | 171 | # %%
#!/usr/bin/env python
# coding: utf-8
from molmap.model import RegressionEstimator, MultiClassEstimator, MultiLabelEstimator
from molmap import loadmap, dataset
from molmap.show import imshow_wrap
from sklearn.utils import shuffle
from joblib import load, dump
import numpy as np
import pandas as pd
import os
... |
45cfa96bfbbdb03cedbe1db6c8d5218e93eac3403a938a4e969e8b35cd9cdb84 | Jupyter | 4,102 | 120 | # %% [markdown]
# Makes summary df from abcTau run results.
# %%
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
import re
from pathlib import Path
import numpy as np
import pandas as pd
from scipy import stats
from scipy.stats import gaussian_kde
# add the path to the abcTau package
import sys
... |
ad32742529abf487691836d7d905135322ac14b474c746a052704cc761c4a530 | Jupyter | 4,124 | 129 | # %%
import shap
import tensorflow as tf
from tensorflow.keras.models import load_model
import chrombpnet.training.utils.losses as losses
import chrombpnet.training.utils.one_hot as one_hot
from tensorflow.keras.utils import get_custom_objects
from tensorflow.keras.models import load_model
import matplotlib.pyplot as p... |
66f9890b3c3e923d99b1040d6e221ae21b19c392666c6ab776f6184ca5098b99 | Jupyter | 4,133 | 117 | # %%
library(Seurat)
library(Signac)
library(glue)
library(ggplot2)
library(GenomicRanges)
set.seed(1234)
setwd("~/projects/deepscore")
source("utils/multiple_scataq_analysis.R")
# %%
kidney.rna <- readRDS("~/projects/kidney/Nuc/kidney.rna.rds")
kidney.atac <- readRDS("~/projects/kidney/Atac/kidney.atac.rds")
kidney.... |
3ba4ee6be587a5ef9f3644ae8ade38d554dfc7ad823a22b1495fd0543fc8a742 | Jupyter | 4,146 | 168 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ───────... |
bb87f0450f1252ff2d244fdab8df2ecdcf1caa5fcabae7415677073d0ce03917 | Jupyter | 4,154 | 130 | # %%
import sys
import os
# Add the parent directory of `notebook/` to sys.path
sys.path.append(os.path.abspath(".."))
import numpy as np
import pandas as pd
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.io.ase import AseAtomsAdaptor
from multiprocessing import Pool, cpu_count
from ase import... |
31d1993be4d710799ea5a7c70686f2a560845cda9f2f24391194786140429340 | Jupyter | 4,160 | 28 | # %% [markdown]
# # Description
# %% [markdown]
# **Classifiers**, such as Random Forest or Catboost are very powerful machine learning tools for image segmentation. The most famous example is the very popular open source software [Ilastik](https://www.ilastik.org/). There are also popular plugins to perform these tas... |
534d70d048703a48ff91f2254769843e2fe8ab510887a6d8dd56b277aaca2330 | Jupyter | 4,162 | 159 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# # Databases Having Gene Phenotype relation of Drosophila
# %%
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC... |
2490526b90846c63584c292134142a4b2070b02cec1cf25d62a68ffaac757831 | Jupyter | 4,164 | 116 | # %%
# %% Load source data (Fig 6) and reproduce laminar profile + superficial/deep plot
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
roi_tag = "vPFC" # <-- match what you saved ("vPFC" or "dlPFC")
in_xlsx = os.path.join(data_dir, "output",... |
942b5357c9d1c2d10cf7c809c3778bb963c295b6d62e92f0d92cd663e1d76f43 | Jupyter | 4,174 | 148 | # %%
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, KFold
from sklearn.metrics import roc_auc_score, precision_recall_curve
from sklearn.metrics import auc as calculate_auc
from sklearn.metrics import mean_squared_error
from sk... |
34fc4d83486f92734d5645166ef834c03df2651f9aef780cd8dd225a59377fbf | Jupyter | 4,176 | 154 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ─────────────────────────... |
3f416b2aa94984b6f3671ac295b59ec77b3e86bc47331251cef83247eae8ab68 | Jupyter | 4,179 | 126 | # %%
import os
import pandas as pd
import numpy as np
# %%
# ── Base directories ──────────────────────────────────────────────────────────
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR + 'processed_data/'
DB_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
# ── Output ... |
64897209f343b5da7696958807a4d32359b59b3aa7d202ba6f91342ee54ad848 | Jupyter | 4,179 | 145 | # %% [markdown]
# # NMF analysis
# %%
import os
import itertools
import joblib
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
import matplotlib.pyplot as plt
import seaborn as sns
# %%
%load_ext autoreload
%autoreload 2
from fruitfly_parkinson import settings as s
from fruitfly_p... |
5292c01e4eb3f11f61dfd3de5a695a682e59cd567d60d44beaffdb7d8587ac48 | Jupyter | 4,181 | 88 | # %% [markdown]
# # Demonstration of DCBC evaluation usage in volume space
# This notebook shows an example of a Distance controlled boundary coefficient (DCBC) evaluation of a striatum parcellation using the Multi-domain task battery (MDTB) functional dataset (glm7).
#
# ## Installation and Dependencies
#
# Ensure ... |
a45be363e1d50c4c325c9bdf16a1dfec66627b93897e9138b2d0ac0f611227c6 | Jupyter | 4,181 | 142 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
# sgd/ALL_YEAST_GENE_PHENOTYPE.csv
# %% [markdown]
# # Databases Having Gene PHENOTYPE relation of yeast
# %%
#
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_c... |
33b4cee2a4f82aa755af0407142ed4112e1bfe9a9e4a0e72a3adc7b1e8f4d0ca | Jupyter | 4,186 | 168 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ───────... |
a39083484e7e8cad13945704990d6fa02922996a873cbed39a3f761122f15528 | Jupyter | 4,186 | 168 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ───────... |
735ea22249223a32634573f7b3df6d4a106a2670b740d80223f0c878e7559a74 | Jupyter | 4,190 | 156 | # %% [markdown]
# # This tutorial shows how to run Cytocraft on subcell-resolution ST data (MERFISH ileum dataset)
# %% [markdown]
# ## Preprocessing
# %% [markdown]
# ### Load packages
# %%
import os
import pandas as pd
import cytocraft.craft as cc
gem_path = './demo/merfish_ileum/data/transcripts.gem.csv'
obs_path... |
d1f58daac0695e6b32e970af9b489e78e600a8e51bcdb6339ba56f4c849a3d95 | Jupyter | 4,195 | 117 | # %%
# %% Load source data (Fig 7) and reproduce laminar profile + superficial/deep plot
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
roi_tag = "vPFC" # match what you saved
in_xlsx = os.path.join(data_dir, "output", f"SourceData_Fig7_{roi_... |
056506877742dee7b1df2f90bf5dbf6883ca5425250cd0a076157bffb410fd70 | Jupyter | 4,197 | 96 | # %%
import pandas as pd
import numpy as np
# %%
# read volumes files
volumUNSAM = pd.read_csv('volumes_asegUNSAM.csv')
volumRRIB= pd.read_csv('volumes_asegRRIB.csv')
volumJUK = pd.read_csv('volumes_asegJUK.csv')
volumADNI = pd.read_csv('volumes_asegADNI.csv')
#read dataset's participants info
repo_dir='/Users/parri/... |
6d47748ef92395a31a8cc94d28b38d2e025c1e4ead00521c2b432deada9b89dd | Jupyter | 4,201 | 155 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ─────────────────────────... |
931643ceff6cb34e5dd64338cd4b043b6f2a526d7fca8ced87bfd0391b3be5a5 | Jupyter | 4,225 | 163 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ... |
db0789312223913bc22817e8999371a10f17505891d7b6be9714fe418570591b | Jupyter | 4,243 | 157 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# # Databases Having Phenotype biologicalprocess relation of celegans
# %%
# Monarch/Monarch_final/Celegans/Cele_PhenotypicFeature_BiologicalProcess.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAg... |
e8f67d8b30093f19952744a9c8439d2a44fc70a11038fe4c4703cc7b2f594c6a | Jupyter | 4,243 | 157 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# # Databases Having Phenotype biologicalprocess relation of celegans
# %%
# Monarch/Monarch_final/Celegans/Cele_PhenotypicFeature_BiologicalProcess.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAg... |
ae940b0610507eec13470c13ba891d9331c75da7fad1be04f0829108474de67a | Jupyter | 4,248 | 152 | # %% [markdown]
# Examples of ACF calculation with epoched methods:
# 1. isttc concat
# 2. PearsonR trial averaged
# 3. iSTTC trial averaged (works like PearsonR but with non binned data)
# %%
import numpy as np
import pandas as pd
import pickle
from isttc.scripts.cfg_global import project_folder_path
from isttc.spik... |
6cf51d326a6968876828b694cde1b2ff403da48a6fd579a3e2c2ae8ad74793e5 | Jupyter | 4,259 | 120 | # %% [markdown]
# # 梯度和变分优化
# %% [markdown]
# ## 概述
#
# TensorCircuit 旨在使参数化量子门的优化变得简单、快速和方便。 在本说明中，我们回顾了如何获得电路梯度和运行变分优化。
# %% [markdown]
# ## 设置
# %%
import numpy as np
import scipy.optimize as optimize
import tensorflow as tf
import tensorcircuit as tc
K = tc.set_backend("tensorflow")
# %% [markdown]
# ## PQC
#... |
fe44b644de402d7072a776818d7fcb8b23ab4984b009add1198b6b2c7cdc6c85 | Jupyter | 4,259 | 154 | # %%
from chembench import dataset
import pandas as pd
import os
random_seeds = [2, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]
data_save_dir = '/raid/shenwanxiang/08_Robustness/dataset_induces'
if not os.path.exists(data_save_dir):
os.makedirs(data_save_dir)
# %%
def random_split(df, random_state = 123, split... |
6665eccbd8098c968b6a8676982629e9b5959331aa12806b2f691f7dae608d3d | Jupyter | 4,263 | 200 | # %%
import warnings
warnings.filterwarnings('ignore')
import stereo as st
import random
import numpy as np
import scanpy as sc
import matplotlib.pyplot as plt
import os
import torch
import pandas as pd
import SpatialGlue
from SpatialGlue.preprocess import clr_normalize_each_cell, pca
# Fix random seed
from SpatialGl... |
2ba43a4ec34fba8022d24e7cc12856fe0a3f1c6a7c3d14ddf2adf2bd86e07837 | Jupyter | 4,279 | 167 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ───────... |
5e6fabc8ac492083a9f1be2abfef310348987d1528445924cb42e93a992c8281 | Jupyter | 4,283 | 151 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
!mkdir Celegans_chemical_noeffect_biologic... |
cf68cbc12d5d736c1de02848787806a1575855ec22a4facddce6a37d3db4bb5a | Jupyter | 4,283 | 160 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
# Gene → Anatomy
# Monarch/Monarch_final/Celegans/Gene_Cele_Anatomy.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_m... |
269209ab71dfb5858f99950e4d3c008f49d3f0455de4541d63384dbf99c98174 | Jupyter | 4,291 | 170 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ───────... |
5402e0cc7f5a497bb7ce79dddaa9dd8d4d2c210ae058cc48525ef1df355ebf7b | Jupyter | 4,300 | 138 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import math
# %%
#Load data using pandas. Make sure that the file is within the directory or use the full path to the file. ex. ('\C:\Users\mikep\etc')
data = pd.read_csv('MaleFemalePheno.csv')
# %%
#Get Categorical Arra... |
eb7ff07763e36aac72ef2f21c4cf3c475ecc4dd73e21c94ad0edf68025cda131 | Jupyter | 4,314 | 116 | # %% [markdown]
# # 00 settings
# %%
import scipy.io as scio
import h5py
import numpy as np
import tifffile as tf
from PIL import Image
import hdf5storage
import pandas as pd
import numpy as np
import math
import random
import copy
from itertools import chain
import matplotlib.pyplot as plt
import matplotlib.cm as c... |
dfb58be5fa371ca2846ce750d00aa85fb7cf9c05437bc5272a9712504a582ac8 | Jupyter | 4,315 | 138 | # %%
import os
import pandas as pd
import numpy as np
# %%
# ── Base directories ──────────────────────────────────────────────────────────
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR + 'processed_data/'
DB_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
# ── Output ... |
aca3e5d5d35f0c36d741a1f7aa7de6ba600abd3ba1003a5a98a5bf22653af2a4 | Jupyter | 4,323 | 171 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ───────... |
f611ae4657b75cf83533d46ffeee4b2c3487c0af1429ba1756d17ac772cbc0fc | Jupyter | 4,330 | 150 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ─────────────────────────... |
b673a2c7f8f387650da5b2ec8223281efe466e9901a2bdc3dd7b3535aca2c3c8 | Jupyter | 4,337 | 143 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# # Databases Having Gene gene relation of yeast
# %%
# Monarch/Monarch_final/Yeast/Gene_Yeast_MolecularFunction.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BAS... |
1278d8927a9d95bb62c373dc4c94fdecd7daf00196ec289cebc7674f232e1837 | Jupyter | 4,339 | 176 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
# flybase/Flybase_Droso_Gene_Phenotype.csv
# Monarch/Drosophila/Gene_Droso_Phenotype_MONARCH.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data... |
0669121485f9b13e6ae2e8ca2ba52a0f019896a0c1804fcfd1d509148c28cc52 | Jupyter | 4,353 | 148 | # %% [markdown]
# # MERA
# %% [markdown]
# ## Overview
#
# In this tutorial, we'll show you how to implement MERA (multi-scale entangled renormalization ansatz) with TensorCircuit, but not in physics perspective.
# %% [markdown]
# ## Background
#
# MERA is a kind of VQE starts from only one qubit in the $\ket{0}$ s... |
9d85737c68109be8668c81aa31805c38b36ee51af42321cf61119129293eb2ab | Jupyter | 4,354 | 167 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ───────... |
fc4a20254465fbc46e2ba6a031fd7617d9f07113788023ef1cab79d3fc6ddb44 | Jupyter | 4,378 | 150 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ─────────────────────────... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.