sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
109e05020de0c33b678e8045e0eaff23ff58a2ccabec72efa476f62c8ad5efa7 | Jupyter | 991 | 35 | # %% [markdown]
# ## Example of InfoGraph
# %% [markdown]
# ### Graph level representation learning
# %%
from dig.sslgraph.utils import Encoder
from dig.sslgraph.dataset import get_dataset
from dig.sslgraph.evaluation import GraphUnsupervised
from dig.sslgraph.method import InfoGraph
# %% [markdown]
# #### Loading d... |
fe0bd0e3e71de5087820cd5f58b0db7cb159681d80f2b377d1516a9c4c0434ac | Jupyter | 1,029 | 41 | # %% [markdown]
# ## Load necessary imports for downloading LDA50 database and converting to NiMARE dataset file
# %%
import os
from pprint import pprint
from nimare.extract import download_abstracts, fetch_neuroquery, fetch_neurosynth
from nimare.io import convert_neurosynth_to_dataset
# %% [markdown]
# ## Download... |
b69d25a0c486fed3b33c8c11f00e8de5fbe5fe0d698f949933ff9685a9db2acb | Jupyter | 1,042 | 36 | # %%
import os
os.chdir('../')
# %%
from DeepPurpose import DTI as models
from DeepPurpose import utils, dataset
# %% [markdown]
# There are two ways to load the pretrained model, the first way is to load from local model directory.
#
# A model directory should consist of two files:
#
# config.pkl that describes... |
bc3f73491c12a28a3bcbc4be5f79ca1e226bb489b6de7a8bbf6da60c77215084 | Jupyter | 1,066 | 48 | # %%
class Matter(object):
pass
lump = Matter()
# %%
from transitions import Machine, State
# %%
states = ['solid','liquid','gas','plasma']
transitions = [
{ 'trigger': 'melt', 'source': 'solid', 'dest': 'liquid' },
{ 'trigger': 'evaporate', 'source': 'liquid', 'dest': 'gas' },
{ 'trigger': 'sublima... |
24f0fd76df8ba89b1ed917deb472bd759b7319bbc07cf60c69cd1397bf67d94e | Jupyter | 1,068 | 45 | # %%
import scanpy as sc
import matplotlib.pyplot as plt
import pickle
# %%
with open('DLPFC.pkl', 'rb') as fp:
adata = pickle.load(fp)
# %%
fig, ax = plt.subplots(2, 9, figsize=(18,4))
for key, i in zip(mapping_names.keys(), ax[0].flatten()):
adata = data[key]
sc.pl.umap(adata, color="batch", ax=i,... |
5e3be8943c2569cb9e17aab7b498437b7fee1bcca307517ee60f746076542676 | Jupyter | 1,073 | 41 | # %%
import torch
from monai.transforms import Resize
from models import InversionTimeLSTM, FeatureExtractor
from utils import load_parameters
import json
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
# %%
def load_model(p):
with open(f'{p}/config.json', 'r') as f:
config = ... |
7f7956c98e7848195b17ef451f82d751d43bc7fa9ccc8e9946953edfae8361a7 | Jupyter | 1,091 | 42 | # %%
import os
os.chdir('../')
# %%
import DeepPurpose.oneliner as oneliner
from DeepPurpose import dataset
# %%
target, target_name = dataset.load_SARS_CoV_Protease_3CL()
train_drug, train_target, train_y = dataset.load_AID1706_SARS_CoV_3CL('./data', seed = 1234)
X_repurpose, drug_names, drug_CID = dataset.load_anti... |
5968643407fdf800265091a67a111e31f338a511f18be629c1d874528fad6fca | Jupyter | 1,130 | 41 | # %%
import pandas as pd
# %%
xtb_cols = ['GFN2_TOTAL_ENERGY', 'GFN2_ATOMIC_ENERGY', 'GFN2_FORMATION_ENERGY', 'GFN2_HOMO_ENERGY', 'GFN2_LUMO_ENERGY',
'GFN2_ROT_CONSTANT_A', 'GFN2_ROT_CONSTANT_B', 'GFN2_ROT_CONSTANT_C', 'GFN2_DIPOLE_X', 'GFN2_DIPOLE_Y',
'GFN2_DIPOLE_Z', 'GFN2_DIPOLE_TOT']
dft_co... |
ca91b7ebbdc9a5e700bf6c2b05a92378d7d1f7d5eefff079cdcf996679acd91e | Jupyter | 1,166 | 36 | # %%
import os
os.chdir('../')
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
X_drug, X_target, y = load_process_KIBA('./data/', binary=False)
drug_encoding = 'CNN'
target_encoding = 'CNN'
train, val, test = data_process(X_drug, X_target, y,
... |
914d74314c524258340e086ea5a1a494daf461f7fc8672766d16313024e6194e | Jupyter | 1,178 | 40 | # %%
import os
os.chdir('../')
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
X_drug, X_target, y = load_process_DAVIS('./data/', binary=True)
drug_encoding = 'CNN'
target_encoding = 'CNN'
train, val, test = data_process(X_drug, X_target, y,
... |
be21a7da3b8aeaeb3fb010bebb3a08e71cce7ca6a44aa94aab8ce13e2daaff61 | Jupyter | 1,184 | 35 | # %% [markdown]
# ## Example of MVGRL
# %% [markdown]
# ### Graph level representation learning
# %%
from dig.sslgraph.utils import Encoder
from dig.sslgraph.evaluation import GraphUnsupervised
from dig.sslgraph.dataset import get_dataset
from dig.sslgraph.method import MVGRL
# %% [markdown]
# #### Loading dataset
... |
28d66cc9ba779a4c9d330e20a6e14594ae492c0ec3ad35f735513bc24bf2ee85 | Jupyter | 1,185 | 43 | # %%
import os
os.chdir('../')
# %%
import DeepPurpose.oneliner as oneliner
from DeepPurpose import dataset
# %%
target, target_name = dataset.load_SARS_CoV_Protease_3CL()
train_drug, train_target, train_y = dataset.load_AID1706_SARS_CoV_3CL('./data', seed = 1234)
X_repurpose, drug_names, drug_CID = dataset.load_anti... |
89b1f51f5c5e7b3fa29a408bfff6f8151876fbc928a535e7166de58fce285ce4 | Jupyter | 1,187 | 63 | # %%
import sys
sys.path.append('../../../../')
# %%
%load_ext autoreload
%autoreload 2
import torch
from jtvae import JTVAE
import jtnn
import fast_jtnn
path_adj = "./"
# %%
from rdkit import Chem
from rdkit import RDLogger
RDLogger.DisableLog('rdApp.*')
# %%
from dig.ggraph.dataset import ZINC800
from dig.ggrap... |
11140a42f5a636c87bf7f7097417e77bab7893d7e198e495d39c171b4d1d2a75 | Jupyter | 1,195 | 34 | # %%
import os
os.chdir('../')
from DeepPurpose import utils
from DeepPurpose import DTI as models
import gradio
model_binding = models.model_pretrained(model = 'MPNN_CNN_BindingDB')
model_kiba = models.model_pretrained(model = 'MPNN_CNN_KIBA')
model_davis = models.model_pretrained(model = 'MPNN_CNN_DAVIS')
def DTI_... |
fb8f551c2167cb4952c8b3e30bfb910d6edaa99017b5ce088e79b3baa2e3e75c | Jupyter | 1,201 | 39 | # %%
import os
os.chdir('../')
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
X_drug, X_target, y = process_BindingDB('../data/BindingDB_All.tsv',
y = 'Kd',
binary = False,
... |
fb915aa0d547fe046a1fc45d16829c178da4fac68f17a305aa1425b2e7c93328 | Jupyter | 1,209 | 39 | # %%
import os
os.chdir('../')
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
X_drug, X_target, y = load_process_DAVIS('./data/', binary=False)
drug_encoding = 'CNN'
target_encoding = 'CNN'
train, val, test = data_process(X_drug, X_target, y,
... |
a6e76b9b08427510a345f36b63003782a8a683fb212d422efb276f94d9bb8521 | Jupyter | 1,214 | 41 | # %%
import numpy as np
import jPCA
import matplotlib.pyplot as plt
from jPCA.util import load_churchland_data, plot_projections
# %%
# Load publicly available data from Mark Churchland's group
path = "/Users/Bantin/Documents/Stanford/Linderman-Shenoy/jPCA_ForDistribution/exampleData.mat"
datas, times = load_churchlan... |
12cc9d9af29b5516a6ed00fa85ac901eca35665aa8268a9aa6f87dcbc7dcef3e | Jupyter | 1,246 | 46 | # %%
import numpy as np
import pandas as pd
# %%
# -*- coding: utf-8 -*-
import gibbslda
import pandas as pd
# load dataset
f = 'E:/word_embeding_TM/GLDAW_model/test_rus_docs.csv'
df = pd.read_csv(f, delimiter=';',decimal='.', encoding='ANSI')
texts = [[text for text in doc.split()] for doc in df['original data']]
... |
5b540468234d8a5694909f1999ae8dbab33ee0dc126e047902c59690b69a4128 | Jupyter | 1,247 | 36 | # %%
import sys
sys.path.append('../')
import pandas as pd
import seaborn as sns; sns.set(style='darkgrid', context='notebook')
from figures.plotting import plot_eigenspectrum, annotate_corr
from matplotlib import pyplot as plt
eigmetric = 'effective dimensionality'
eigspec = pd.read_csv(f'../results/counter-example|... |
6a0e33d511d34f1c6b623dad7f22780ee42f882ed04e553535980a67ba54b202 | Jupyter | 1,250 | 59 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
# %%
from configuration import *
from figure_estimation import *
# %%
plt.rcParams.update({
'font.size': 12,
'xtick.labelsize': 12,
'ytick.labelsize': 12,
})
# %%
figure_folder = base_folder / 'figures' / 'estimation'
figure_folder.mkdir(exist_o... |
424e8a2507aebbaf233faa320afd6995f30965c73a764c019e80ec1f41b32372 | Jupyter | 1,253 | 46 | # %% [markdown]
# ## Example of GraphCL with grid search
# %%
from dig.sslgraph.utils import Encoder
from dig.sslgraph.dataset import get_dataset
from dig.sslgraph.evaluation import GraphSemisupervised
from sslgraph.method import GraphCL
# %% [markdown]
# ### 1. Semi-supervised learning on DD
#
# #### Load dataset
#... |
ed68871f1bfcb37e853f06beb22e20503a0fffa5bc83081ddeb52da8c923466b | Jupyter | 1,266 | 64 | # %%
import os, glob
import numpy as np
import matplotlib. pyplot as plt
import pandas as pd
import seaborn as sns
from skimage.io import imread, imsave
from tqdm.notebook import tqdm
from scipy.fftpack import dct
import pickle
from skimage.morphology import remove_small_holes, binary_dilation, disk, binary_erosion
fro... |
6ad6595f27f56b6ce41ef3f1f88cbe1fc7ca0672c0da1930783c252dc6546acc | Jupyter | 1,301 | 43 | # %% [markdown]
# ## Sheet to process the Archer (2018) proteomics and RNA data
# Generally used for Figure 4 and SHHa/SHHb split
# %%
import os
import pandas as pd
# %% [markdown]
# ### Load and process proteomics data per Archer 2018
# %%
base_folder = '../data/'
# %%
## Load proteomics Data
bpdf = pd.read_excel(... |
5bb965909666eb4f38a13019dd2f018e8d5d3c799ff5f984fc8e4a2621411bb2 | Jupyter | 1,338 | 36 | # %% [markdown]
# ## Example of GRACE
# %% [markdown]
# ### Node-level representation learning on CORA
# %%
from dig.sslgraph.utils import Encoder
from dig.sslgraph.evaluation import NodeUnsupervised
from dig.sslgraph.dataset import get_node_dataset
from dig.sslgraph.method import GRACE
# %% [markdown]
# #### Loadin... |
0c121370ca67bde99ceb08c16550e0843f24767315babcc1c9269adf090ad107 | Jupyter | 1,400 | 47 | # %%
import os
os.chdir('../')
# %%
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
from time import time
t1 = time()
X_drug, X_target, y = load_process_KIBA('./data/', binary=False)
drug_encoding = 'MPNN'
target_encoding = 'AAC'
train, val, test = data_proces... |
5b9084e0187e73e789c5d211f49550059a0da3f22d8090cea808a247bd4b53fe | Jupyter | 1,400 | 47 | # %%
import os
os.chdir('../')
# %%
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
from time import time
t1 = time()
X_drug, X_target, y = load_process_KIBA('./data/', binary=False)
drug_encoding = 'MPNN'
target_encoding = 'CNN'
train, val, test = data_proces... |
a8e9dd480a201b0e838b0548cc927e25c3ca77064b9cbc94e02e0b826395bd1c | Jupyter | 1,400 | 65 | # %%
library(DESeq2)
library(tidyverse)
library(ggplot2)
# %%
counts = read_csv('../03_build_meta_and_counts/counts.csv') %>% column_to_rownames('gene')
counts %>% head
# %%
meta = read_csv('../04_cleanup/meta_qc.csv') %>% filter(!is.na(name)) %>% column_to_rownames("name") %>% mutate(name = rownames(.))
meta %>% hea... |
893c9b226eef124dac15020aeb304e2bcf66c0d5c694ac302cebbaa73ab1141e | Jupyter | 1,401 | 37 | # %%
import chromadb
from chromadb.utils import embedding_functions
sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
client = chromadb.Client()
# client.heartbeat()
# client.reset()
collection = client.get_or_create_collection("test-where-list", embed... |
8dd572e629a200872a3b3c0f1df7a3ce4f9909f14b93573bc717f06b3a0a923f | Jupyter | 1,402 | 47 | # %%
import os
os.chdir('../')
# %%
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
from time import time
t1 = time()
X_drug, X_target, y = load_process_DAVIS('./data/', binary=False)
drug_encoding = 'MPNN'
target_encoding = 'AAC'
train, val, test = data_proce... |
cec27695e3928ff73a1cf2dc08b7634bf297cd0a35b9ff056f98465367a33c9d | Jupyter | 1,402 | 47 | # %%
import os
os.chdir('../')
# %%
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
from time import time
t1 = time()
X_drug, X_target, y = load_process_DAVIS('./data/', binary=False)
drug_encoding = 'MPNN'
target_encoding = 'CNN'
train, val, test = data_proce... |
6378fd280ff700eceddc9e824ee559658b6814052487ccdb2c669eb5b3a0d411 | Jupyter | 1,409 | 44 | # %%
import os
os.chdir('../')
# %%
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
from time import time
t1 = time()
X_drug, X_target, y = load_process_DAVIS('./data/', binary=False)
drug_encoding = 'CNN'
target_encoding = 'Transformer'
train, val, test = dat... |
efb3bac025285ce7ed2b83ac44cd4a5132c9a76b933420704bd1519f38717816 | Jupyter | 1,414 | 47 | # %%
import os
os.chdir('../')
# %%
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
from time import time
t1 = time()
X_drug, X_target, y = load_process_KIBA('./data/', binary=False)
drug_encoding = 'CNN'
target_encoding = 'Transformer'
train, val, test = data... |
bff1be9ec04e3270c8a08c83c52b521b0bde1f54eab1a666e82550e74d5c7ab0 | Jupyter | 1,425 | 48 | # %% [markdown]
# # trying to check that the res model is working sitewise
# %% [markdown]
# # 1 checking that the data loaded is working site-wise
# %%
import gvp
import gvp.atom3d
from atom3d.datasets import LMDBDataset
from functools import partial
import torch_geometric
# %%
# inspecting the first element in da... |
76c2a8e5bec04a538520c7dc7b22ed30f8d8bf465d8baeb3782f04c58c56854f | Jupyter | 1,452 | 29 | # %%
def forced_choice_permutation_accuracy(data1, data2, model, n_permutations=10000, metric='correlation'):
'''Compute one-tailed p-values for forced choice accuracy using permutation test
Args:
data1: (Brain_Data) Brain_Data instance of Condition1(must be same subject order across conditions)
... |
67d72d995d814550546e4a1826f0591acc2ce1d6ebb9db139492513ffb73f267 | Jupyter | 1,461 | 68 | # %%
import sys
import os
import glob
import random
from itertools import combinations
import numpy as np
import scipy
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import ticker
import seaborn as sns
import prettypyplot as pplt
import pyemma
import mdtraj as md
import MDAnalysis as mda
#... |
8da23156b47a39cccf82fa28c411754d49477438b051820cf996d724c3bcd098 | Jupyter | 1,469 | 72 | # %%
import os
import time
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm.auto import tqdm
from foldingdiff import sampling, plotting
# %%
def time_to_sample_length(length: int, num_samples: int, **kwargs) -> float:
"""Returns the time to sample a given number of samples f... |
67070db24d2d8ea650374dbf7a29a136990b4ba9543716236d9943ea64c8e044 | Jupyter | 1,479 | 42 | # %%
import os
os.chdir('../')
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
X_drug, X_target, y = load_AID1706_SARS_CoV_3CL('./kh278/DeepPurpose/data', oversample_num = 30)
drug_encoding = 'CNN'
target_encoding = 'CNN'
train, val, test = data_process(X_drug,... |
251cfbd42297a59c3c2961a97fa666ac96922b4084a81ce5226a8968919835a6 | Jupyter | 1,481 | 56 | # %%
import sys
sys.path.append('./../..')
import dynaphos
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
from dynaphos import utils
import seaborn as sns
import matplotlib as mpl
results = pd.read_csv('results/performance_best_GPU.csv',)
# Load colorstyles
plt.style.use('../_figure... |
35c5b73acce6fe0dcc3187cf22e5f88dbfbfeae23cabb599407d1e023bd3f193 | Jupyter | 1,490 | 68 | # %% [markdown]
# # Compare Embedding Models
#
# This notebook walks through how to compare various embedding models with your custom benchmark results.
# %% [markdown]
# ## 1. Setup
# %% [markdown]
# ### 1.1 Install & Import
#
# Install the necessary packages.
# %%
%pip install -r requirements.txt
# %%
%load_ext... |
e286ac5f9ad354de8b17cf41af8de0f45463e63fdf0b040be46554e502d07a87 | Jupyter | 1,500 | 69 | # %%
import sys
sys.path.append('../../../../')
# %%
%load_ext autoreload
%autoreload 2
import torch
from jtvae import JTVAE
from fast_jtnn.datautils import MolTreeFolder, PairTreeFolder, MolTreeDataset
path_adj = "./"
# %%
from rdkit import Chem
from rdkit import RDLogger
RDLogger.DisableLog('rdApp.*')
# %%
from ... |
8362eb81ca20f3c826f6a972e992a33dcab8ff11613b83d80d5c4b4a623a675d | Jupyter | 1,501 | 59 | # %%
import sys
sys.path.append('./../..')
import dynaphos
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
from dynaphos import utils
import seaborn as sns
import matplotlib as mpl
results = pd.read_csv('results/performance_best_GPU.csv',)
# Load colorstyles
plt.style.use('../_figure... |
68d702e793774ac3649f048f76d64b378d35cdc9a8432c19f29a7fe8b60ee58d | Jupyter | 1,508 | 46 | # %%
## if you are using the pip version, please comment out the below two lines
import os
os.chdir('../')
from DeepPurpose import CompoundPred as models
from DeepPurpose.utils import *
from tdc import BenchmarkGroup
group = BenchmarkGroup(name = 'ADMET_Group', path = 'data/')
import warnings
warnings.filterwarnings(... |
c6944a7c9aed0cdc9d15004235f76622bc5d73ce110af570a815fb2a7d955e4f | Jupyter | 1,533 | 52 | # %%
import os
os.chdir('../')
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
X_drug, X_target, y = load_AID1706_SARS_CoV_3CL('./kh278/DeepPurpose/data', oversample_num = 30)
drug_encoding = 'MPNN'
target_encoding = 'CNN'
train, val, test = data_process(X_drug... |
2b2cea84d3f015ddf82266bfecd0b83bdc8061cf8aea4db7705d9d3ab855bc66 | Jupyter | 1,551 | 54 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
from visualize import PyramidVisualizer, get_dataset, simple_visualizer
from metroem.aligner import Aligner
from collections import defaultdict
import random
import cloudvolume as cv
import numpy as np
# %%
cv_path = 'gs://h01-release/data/20210601/c3'
# %%
... |
ea42e0b554c734a6e1c592c447956d7ee7575cf99ea0a5c1dc30c9bc110f7e19 | Jupyter | 1,559 | 66 | # %%
import numpy as np
# %%
import tensorflow as tf
# %%
saved_model_dir = "../../data/osfstorage/deep-learning-qc/saved-models/report-model"
model = tf.keras.models.load_model(saved_model_dir)
# %%
def rename(model, layer, new_name):
def _get_node_suffix(name):
for old_name in old_nodes:
if... |
07382522b324ed847e8225934636ceb0026df7163fde17dc6b16f05ce100c749 | Jupyter | 1,561 | 114 | # %%
import numpy as np
import dynfc as dyn
import scipy as sc
import scipy.io
import matplotlib.pyplot as plt
# %%
mat_dbg = sc.io.loadmat('../data/dbg.mat',
squeeze_me = True)
# %%
signal = mat_dbg['RSsig_t'].transpose()
# %%
flp = 0.03
fhi = 0.07
delt = 0.392
k = 2
base = 2
nBits = 8
# %%
timeserie = np.zero... |
c7b4c002d3978b959dfb0e651cdd4b2c6da505ee7bad70368c8bcda62c5d4822 | Jupyter | 1,571 | 39 | # %% [markdown]
# ## Example of GraphEBM: Compositional Generation
# %%
import os
import torch
from torch_geometric.loader import DenseDataLoader
from rdkit import RDLogger
from dig.ggraph.dataset import ZINC250k
from dig.ggraph.method import GraphEBM
# %%
device = torch.device('cuda:1')
# %% [markdown]
# #### Prep... |
c4c6c7db87aef67c76d235a36a21b5ca239ec08ef1d4eda66a6509480f5b461e | Jupyter | 1,658 | 76 | # %%
import os, glob
import numpy as np
import matplotlib. pyplot as plt
import pandas as pd
import seaborn as sns
from skimage.io import imread, imsave
from tqdm.notebook import tqdm
from scipy.fftpack import dct
import pickle
from skimage.morphology import remove_small_holes, binary_dilation, disk, binary_erosion
fro... |
96ecc29d7171282fe12d226ace6c39d386110175f44279bcd1ba5fe4a9bc45be | Jupyter | 1,660 | 75 | # %%
import pickle
# %%
with open("/media/Data/zhangchao/zhangchao/BatchEffectEvaluationPipeline/analysis/OB/OB_res.pkl", "rb") as f:
data = pickle.load(f)
# %%
names = ["Raw",
"Harmony",
"Combat",
"BBKNN",
"Scanorama",
"MNN",
"PRECAST",
"spatiA... |
f8e66595a4e6e6417b45358c6765326edce128111ed075cb41e8df66ebac5fe6 | Jupyter | 1,668 | 77 | # %%
CODE_REPO_PATH='/YOUR_PATH/Converting_allo_goal_to_ego_steering/analysis/'
import warnings
warnings.filterwarnings('ignore')
from importlib import reload
import sys
import os
import neuprint
from neuprint import Client
sys.path.insert(0, CODE_REPO_PATH)
import em
%config InlineBackend.figure_format = 'retina... |
1c55caa2c6ca480fb2fae5ac6eb197dc58aa069be1641b7f79e81fb17d704b9b | Jupyter | 1,683 | 64 | # %%
import os
import pickle
import pandas as pd
import numpy as np
# %%
# run
# указываем каталог, в котором лежат матрицы phi
results0 = 'E:/word_embeding_TM/Renyi_entropy/phi_matrix/'
results1 = 'E:/word_embeding_TM/Renyi_entropy/'
# %%
#for i in os.listdir(results0):
# print('file name: ', i)
# df = pd.re... |
9296cb7e44612409d69392bb99b698f5c7ce4ce34bc199fc8f26af1d67cbce37 | Jupyter | 1,705 | 65 | # %%
import numpy as np
import os, sys
sys.path.append(os.getcwd()+"/..")
from rnn_scripts.utils import *
from rnn_scripts.train import *
cls= green_blue_colours()
import matplotlib.pyplot as plt
%matplotlib inline
# %%
# Load training data from three models
fig_dir=os.getcwd()+"/../figures/"
model_dir = os.getcwd(... |
09b2bafa58d85855b3fb238c4606f522eeb185ce936054955690c468e8ec8dd1 | Jupyter | 1,706 | 69 | # %% [markdown]
# # Local Peristence Demo
# This notebook demonstrates how to configure Chroma to persist to disk, then load it back in.
# %%
import chromadb
# %%
# Create a new Chroma client with persistence enabled.
persist_directory = "db"
client = chromadb.PersistentClient(path=persist_directory)
# Create a n... |
2809780cb48c5adf2de78de9ad6b3045d4a4040e33cad4e2a241e0eeb286f22f | Jupyter | 1,725 | 62 | # %% [markdown]
# ## Start with the necessary imports
# %%
import os
import nipype
import nibabel as nib
import numpy as np
from nilearn.plotting import plot_roi
from nilearn import plotting
from nimare.dataset import Dataset
from nimare.decode import discrete
from nimare.utils import get_resource_path
# %% [markdo... |
bfbbf79ca977e5be6c9ac3812ce8cf8acab2a7ac42de8f1e63ce04c8b2a8bc80 | Jupyter | 1,729 | 52 | # %%
library(tidyverse)
library(DESeq2)
# %%
preprocessed_list = qs::qread('../04_cleanup/preprocessed_list.qs')
# %%
deg_results_tibble = tibble()
for (name in c("0010_HY", "0010_HPF", "0010_SC",
"0010_IC", "0010_PFC", "0037_HY", "0037_HPF", "0037_SC", "0037_IC",
"0037_ZI")){
seq_run = str_split(name, '_') %>%... |
64788144ccd43a7f59f163c1cf15d9681d1ce5f27ea889347d5f16b3761755cd | Jupyter | 1,796 | 98 | # %%
import numpy as np
import pandas as pd
from glob import glob
# %%
with open('../scripts/preprocessing-stimulus/splits.txt') as f:
splits = f.readlines()
# %%
def convert_to_seconds(hhmmss):
h, m, s = hhmmss.split(':')
s = float(h) * 3600 + float(m) * 60 + float(s)
return s
# %%
splits = [s.strip... |
78cd0316390288188bca91c7ed4f44b788ef1465fcc514775817ca180cee99e5 | Jupyter | 1,808 | 63 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
# %%
loc = sys.argv[1]
loc = "IZH.csv"
df = pd.read_csv(loc, index_col=0)
# %%
list(df.script_name.unique())
# %%
sizes = df["size"].unique()
sizes.sort()
groups = df.groupby(["size"])
all_df = []
all_index = []
for size in s... |
ddc2f44d0cc1361c66af70f0050483a3d361c30988fc013b05a7210524c0ed27 | Jupyter | 1,827 | 79 | # %%
import importlib
import os
import sys
import matplotlib as mpl
import matplotlib.pyplot as plt
import mdtraj as md
import numpy as np
import prettypyplot as pplt
import scipy
import seaborn as sns
from matplotlib import ticker
import extq
# %%
pplt.load_cmaps()
plt.style.use("custom") # custom style sheet
plt.... |
192b88288405bfe564be34e998fe7b6cab30e0f59b54f2f24a86b257e9a56cff | Jupyter | 1,829 | 72 | # %%
library(tidyverse)
library(gprofiler2)
# %%
dir.create('wgcna_output/renamed_tables', recursive = TRUE, showWarnings = FALSE)
# %%
kme = read_csv('wgcna_output/tables/dmb_ds4_mcs20_pF_kME_cellModEmbed.csv.gz')
kme %>% head
# %%
modules = colnames(kme) %>%
`[`(-(1:3)) %>%
enframe %>%
rename(index = name, old_nam... |
47885c9e27f3bee940268b54606a686bbc4dea430c34082c3643d168e7710686 | Jupyter | 1,852 | 60 | # %%
import sys
sys.path.append('../')
import pandas as pd
import seaborn as sns; sns.set(style='darkgrid', context='poster')
from figures.plotting import plot_eigenspectrum
from matplotlib import pyplot as plt
pooling = True
dataset = 'imagenet'
grayscale = False
data = pd.read_csv(f'../results/eigspectra|dataset:{da... |
c21e9146080e9c394f7288c9f61bbcb3d867d530891de11e3f3b613e76818e54 | Jupyter | 1,873 | 67 | # %%
library(tidyverse)
library(broom)
# %%
# load the mouse brain ESMU. Part of this repo and at https://github.com/perslab/CELLECT/wiki/Precomputed-CELLEX-datasets
esmu = read_csv('esmu/mousebrain.mu.csv.gz')
esmu %>% head
# %%
# load the kmes file
kmes = read_csv('wgcna_output/renamed_tables/wgcna_output_ds4_mcs20... |
4a2af403912b3abae032c1bc32876ee45324dd887ba3ddbdcf9cd98d5c4c43c1 | Jupyter | 1,880 | 72 | # %% [markdown]
# Step 1: Define our imports (assumes `Binary.py` lives in the same directory as this notebook)
# %%
import Binary
from glob import glob
import os
import numpy as np
import matplotlib.pyplot as plt
# %% [markdown]
# Step 2: Point to data directory and load. This directory contains data from one cont... |
20c214e55eca674e34d17e0e738a6310f1d42e5977c46377bdee2ab01225d760 | Jupyter | 1,924 | 64 | # %%
CODE_REPO_PATH='/YOUR_PATH/Converting_allo_goal_to_ego_steering/analysis/'
import warnings
warnings.filterwarnings('ignore')
from importlib import reload
import sys
sys.path.insert(0, CODE_REPO_PATH)
import model as model
%config InlineBackend.figure_format = 'retina'
# %%
FIGURE_PATH='/YOUR_PATH/Converting_all... |
670ebcc6cdc0450ccd2d1c92ff0c6580a51cec19572afdcbd41777787761c4d6 | Jupyter | 1,948 | 43 | # %% [markdown]
# ## Example of GCA
# %% [markdown]
# ### Node-level representation learning on Amazon Photos
# %%
from dig.sslgraph.utils import Encoder
from dig.sslgraph.evaluation import NodeUnsupervised
from dig.sslgraph.dataset import get_node_dataset
from dig.sslgraph.method import pGRACE
from dig.sslgraph.util... |
92279fdb3946b0b06037fa1b6bb4eeee3bc0e86ede40bf637e5ad8f9651380a2 | Jupyter | 1,963 | 68 | # %%
!pip install --no-use-pep517 "napari[all]"
!sudo apt-get install --reinstall libxcb-xinerama0
!python3 -m pip install pyvirtualdisplay
!sudo apt-get install xvfb
!sudo apt-get install python3-pyqt5
# %%
import random
import torch
import numpy as np
import cloudvolume as cv
import torch
from visualize import Pyr... |
2a18e812961d38abc89d6ada2ee45c78e8d34d580406d631858827b8ef7ce9a3 | Jupyter | 1,972 | 111 | # %%
import live2p
from live2p.analysis import process_data, clean_data, min_subtract, baseline_subtract
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import json
import scipy.stats as stats
import sklearn
# %%
import h5py
# %%
%load_ext autoreload
%autoreload 2
# %%
base = 'D:/Frankenr... |
7fd11307a288c2978d39abae077706375f56ca1656db8ee13d77b91868cffb3f | Jupyter | 1,983 | 81 | # %%
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import convolve
# %%
with open('recordedWaveforms.bin','rb') as file:
data = np.fromfile(file,dtype='int16').astype('float32')
LED = data[0::4]
x_feedback = data[1::4]
x_control = data[2::4]
photodiode = data[3::4]
# %%
# Define a functio... |
135f4ab3278ef770e5962bd8f7cd90ed3946a6d609c93f4984285a09dd4344b5 | Jupyter | 2,006 | 67 | # %%
# check if used pathways match
# MelBrainSys_PhDProject_TKraft/DataSets/Annotations/geneset_with_cancerGenes_merged.txt & Co
# compared to annotation/pathway-definitions.ods
# %%
library(xlsx)
# %%
basePath = "/data/bcu_projects/MelBrainSys_PostdocProject_Gruetzmann/publications/2022-my-MelBrainSys-paper/scripts... |
3faa7782dcc5b4c8f3c729d8864d20cc06f9d0cd41dcc4c5b614197f32ef5a2c | Jupyter | 2,037 | 62 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
# %%
loc = sys.argv[1]
loc = "LIF.csv"
df = pd.read_csv(loc, index_col=0)
# %%
list(df.script_name.unique())
# %%
sizes = df["size"].unique()
sizes.sort()
groups = df.groupby(["size"])
all_df = []
all_index = []
for size in s... |
5f83705d12d9337f6bc0318e93a7e56c71cde26850925befeb841a5fe7416c13 | Jupyter | 2,103 | 64 | # %%
import sys
sys.path.append('../')
import pandas as pd
import seaborn as sns; sns.set(style='darkgrid', context='notebook')
from matplotlib import pyplot as plt
from figures.plotting import annotate_corr_grid, annotate_corr
benchmark = 'dicarlo.MajajHong2015.IT.public-pls'
eigmetric = 'effective dimensionality'
po... |
cf63aac5748af6c828fdddc316817b9363e0b4e99823d16bfdfbb406b49e4506 | Jupyter | 2,106 | 54 | # %%
## loading package
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import pylab
import seaborn as sns
from sklearn.metrics.pairwise import cosine_similarity
from statannot import add_stat_annotation
import matplotlib as mpl
from scipy import stats, cluster
import glob
impo... |
39bb6ff1601060001747a445e5092bed21043f3b7c0d8422117f7d13aa577087 | Jupyter | 2,114 | 91 | # %%
import os
import sys
from pprint import pprint
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
project_root = ".."
sys.path.append(project_root)
from sleeprnn.common import viz
DATASETS_PATH = os.path.join(project_root, 'resources', 'datasets', 'nsrr')
viz.notebook_full_width()
# %% [ma... |
3844bddcbeccac745e7d647bb11e217d8cde7bb8e2566b757ef811f815f33b57 | Jupyter | 2,139 | 74 | # %%
import pandas as pd
from glob import glob
import numpy as np
# %%
subjects = [s.split('/')[-1] for s in sorted(glob('../data/sub-*'))]
# %%
def load_confounds(subject):
confounds = sorted(glob(f"../outputs/fmriprep/{subject}/func/*.tsv"))
dfs = [pd.read_csv(c, sep='\t') for c in confounds]
return dfs... |
3e05668f166137bb53ec2e0148fe0b0f5676697619e0911739836717fb1f86df | Jupyter | 2,153 | 75 | # %%
# import libraries
import serial
import pandas as pd
from pandas import DataFrame
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import threading
import os
# configure Matplotlib backend
import matplotlib
matplotlib.use('TkAgg')
# functions
# reads in the press... |
f40a5aa7bbfeab70e681daab6a8ad0559fe87809fef23d12ee4f2f746a06f73b | Jupyter | 2,162 | 57 | # %%
import eelbrain as eel
import numpy as np
import cochlea, tqdm, pathlib, time
from pathnames import *
# %% [markdown]
# # load stimulus recorded on EEG Erg1 sensor channel
# %%
datadict = eel.load.unpickle(preprocessed_path / f'part_001_in_speech_Cz_reref_ergs.pkl')
ergs = [e.sub(time=(1, 245)) for e in datadict... |
8356dbd5046e34668daa21f957ee5834e119679f0821bb7c1f9e59d8e3c9e1fc | Jupyter | 2,181 | 75 | # %%
import os
os.chdir('../')
from DeepPurpose import dataset
import numpy as np
import pandas as pd
# %%
SMILES, Target_seq, y = dataset.load_process_DAVIS()
# %%
X_drug, X_target, y = dataset.process_BindingDB('../data/BindingDB_All.tsv',
y = 'Kd',
... |
21bce97165ff673245a154665f1845e05840877fbce7e826664d41bd37dde6d6 | Jupyter | 2,185 | 84 | # %% [markdown]
# # Tutorial 4: Integration of heterogeneous-structure datasets
# %% [markdown]
# In this tutorial, we utilized three mouse hippocampal slices from Slide-seq to assess the performance of spatiAlign in integrating heterogeneous-structure datasets with distinct biological characteristics.
# %% [markdown... |
3ca38f0f88092cacc8806a788cf8e86bd5232dcbe071f1d29fad889fc21dd96e | Jupyter | 2,185 | 64 | # %% [markdown]
# ## Taurine Barplots for Figure 6
# Used for Figure 6
#
# Author: Max Gold
# %%
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
# %%
# Say, "the default sans-serif font is COMIC SANS"
matplotlib.rcParams['font.sans-serif'] = "Helvetica"
# Then, "ALWAYS use... |
12eb78938aa8f4d19ec13a2734849e9c25d03b589942cf3070a4f7222ec4c75a | Jupyter | 2,199 | 84 | # %%
import os, sys
from pathlib import Path
import json
import numpy as np
from scipy import stats
from matplotlib import pyplot as plt
import seaborn as sns
PLOT_DIR = Path(os.path.join(os.path.dirname(os.getcwd()), "plots"))
assert PLOT_DIR.is_dir()
PLOT_DIR
# %%
BASELINE_SCTM_SCORES_JSON = "/home/wukevin/project... |
77b57d870eabaa20a3133066ab1071199e296a13d8bb4df2e1c09f5bdf48b41e | Jupyter | 2,215 | 78 | # %% [markdown]
# # K-Means clustering on a Riemannian Manifold
#
# Lead authors: Thomas Gerald and Hadi Zaatiti.
#
# In this notebook, we demonstrate how run a K-mean clustering algorithm on a Riemannian Manifold. Generate data on a sphere from a Von Mises Fisher distribution and apply a rotation sampled randomly fr... |
bf4eb3d45d1db98782069f5e9f503ed307d44851047b4d63957bc40a457ebbfb | Jupyter | 2,215 | 75 | # %%
import nibabel as nib
import scipy.linalg as la
import pandas as pd
from glob import glob
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
# %%
import seaborn as sns
# %%
import nilearn.plotting
# %%
!ls ../outputs/datapaper/tsnr/
# %%
subjects = [path.split('/')[-1] for path in glob('../o... |
d267aaad9d89d838c9f657bd75b17481a77e488e4d359a1c8a1764ba68d454dc | Jupyter | 2,260 | 72 | # %%
library(xlsx)
# %%
basePath = "/data/bcu_projects/MelBrainSys_PostdocProject_Gruetzmann/publications/2022-my-MelBrainSys-paper/scripts-etc-for-publication/"
setwd(basePath)
# %%
immunePWs = read.xlsx(file = "annotation/pathway-definitions.xls", header = T, sheetName = "ImmunePathways")
colnames(immunePWs) = gsub... |
4ab49e3911111821798aed59517507a3beb383a28821c1a547f828c66f4c7ab7 | Jupyter | 2,273 | 105 | # %%
import os, glob
from tqdm import tqdm
import numpy as np
from skimage.io import imread, imsave
import matplotlib.pyplot as plt
from matplotlib import rc
plt.rcParams.update({'font.size': 20})
# plt.style.use('dark_background')
rc('pdf', fonttype=42)
import funcs
import imp
imp.reload(funcs)
# %%
folder = os.pat... |
2ce55dd0864dd3c35358ae446ea346d2c42eb0bbb04b83ffe5fa014dea38f4dd | Jupyter | 2,283 | 77 | # %% [markdown]
# # Tutorial 3: Integrating DLPFC dataset
# %% [markdown]
# In this tutorial, we illustrate the application of spatiAlign for integrating a series of 10x Genomics Visium datasets from the human dorsolateral prefrontal cortex (DLPFC).
# %% [markdown]
# - ### Import packages
# %%
import scanpy as sc
f... |
402314c1398c776efba82c9fc8180f95334524c7bbe6799ae002b29c844a4928 | Jupyter | 2,306 | 74 | # %% [markdown]
# # scoring antitoxin mutations with evMutation
#
# ### Inputs:
# - the experimentally measured antitoxin variant effects
# - evCouplings model
#
# ### Outputs:
# - score file
# %%
import evcouplings
from evcouplings.couplings import CouplingsModel
import pandas as pd
plt.style.use('../../src/paper_... |
7de311d6b7ec630d81d9603f71e5b64cf44c3cd207d01d1da11872b42e27a05b | Jupyter | 2,402 | 71 | # %%
import sys
sys.path.append('../')
import pandas as pd
import seaborn as sns; sns.set(style='darkgrid', context='poster')
from matplotlib import pyplot as plt
from matplotlib.gridspec import GridSpec
benchmark = 'dicarlo.MajajHong2015.IT.public-pls'
pooling = False
data = pd.read_csv(f'../results/encoding|benchma... |
07fbe47d036db6ef1310e853bc12e9237f1f0f9f2fd9c6b78839fe4fd78ee151 | Jupyter | 2,403 | 62 | # %% [markdown]
# ## Example of GraphEBM: Random Generation
# %%
import os
import torch
from torch_geometric.loader import DenseDataLoader
from rdkit import RDLogger
from dig.ggraph.dataset import QM9, ZINC250k
from dig.ggraph.method import GraphEBM
from dig.ggraph.evaluation import RandGenEvaluator
# %%
device = to... |
9d60d80411829ddba753d3a6b84b069de6fa520a8f2c0a3db59ef8f33a887966 | Jupyter | 2,405 | 65 | # %%
import sys
sys.path.append('../')
import pandas as pd
import seaborn as sns; sns.set(style='darkgrid', context='notebook')
from matplotlib import pyplot as plt
from figures.plotting import annotate_corr_grid
benchmark = 'bonner.Object2Vec.pls.roi=LOC'
intrinsic_metric = 'intra-ID'
pooling_enc = False
dataset_intr... |
d2fbd8633d6742637840b324dc4143ee3863a06b96acaf544d7e824e84e33c3c | Jupyter | 2,456 | 72 | # %%
import sys
sys.path.append('../')
import pandas as pd
import seaborn as sns; sns.set(style='darkgrid', context='poster')
from matplotlib import pyplot as plt
from matplotlib.gridspec import GridSpec
eigmetric = 'effective dimensionality'
pooling = True
dataset = 'imagenet'
grayscale = False
data = pd.read_csv(f'.... |
6f7864e9d5c270cf028afe3e1badfc82092276d91c3700cc52e478887bd8d4e6 | Jupyter | 2,471 | 67 | # %%
# Bunch of imports
# System path should be changed to where the git repo is.
import sys
sys.path.append('..')
import tensorflow as tf
import src.models as models
import lattice_filter_op_loader
custom_module = lattice_filter_op_loader.module
# %%
# Set GPU and batch size
# Can set a different batch size, or skip... |
cead519f455b72ce135ac4247dd552c025156e844dfbe0c87e762c3b81972d21 | Jupyter | 2,474 | 87 | # %%
import pandas as pd
import numpy as np
# %%
slideseq_df = pd.DataFrame(
data=np.array([0.6230, 0.3583, 0.5567, 0.7615, 0.6074, 0.5778, 0.4514, 0.4348, 0.5064]).reshape(1, -1),
columns=["spatiAlign", "PRECAST", "GraphST", "SCALEX", "Harmony", "Combat", "BBKNN", "Scanorama", "MNN"],
)
# %%
slideseq_df
# ... |
70a316b7bd17f53599ec0cb880036123394f83c8ad9a4702d911cab13a9f110e | Jupyter | 2,478 | 91 | # %% [markdown]
# ## Tutorial 2: Integrating datasets measured by different platforms.
# In this tutorial, we demonstrate how to apply Spatialign to integrate datasets that were measured by different platforms. As an example, we used three mouse olfactory bulb datasets. One slice was profiled by 10x Genomics Vi... |
70c28bcf3dbc0bfd83c6168ab90ec7fb2ff56cd65b2946983f6089c968191728 | Jupyter | 2,512 | 69 | # %%
# Bunch of imports
# System path should be changed to where the git repo is.
import sys
import tensorflow as tf
import models as models
import lattice_filter_op_loader
import os
custom_module = lattice_filter_op_loader.module
# %%
# Set GPU and batch size
# Can set a different batch size, or skip if CPU
strategy... |
2b35f6de486abd73c60fd0c5d8a69ef67bbd6f218c454abfb2bfa8c308d501f0 | Jupyter | 2,522 | 71 | # %% [markdown]
# It seems that redirecting error message works only in jupyter notebook (in another process)
#
# http://rdkit.blogspot.com/2016/03/capturing-error-information.html
# %%
from io import StringIO
import sys
from rdkit import Chem
import os.path as osp
from tqdm import tqdm
import pickle
# %%
with open(... |
71114dcc9412c2f164b92dc12912743334eef1be3bba40ee6066dbe7fd803b7f | Jupyter | 2,546 | 99 | # %% [markdown]
# # Tutorial 5: Integration of time-series mouse embryonic brain
# %% [markdown]
# In this tutorial, we utilized a series of mouse brain datasets extracted from multiple developing mouse embryos, measured by Stereo-seq, to demonstrate the benefits of spatiAlign for downstream gene-level analysis.
# %... |
8e97a800421b777a52128c4402b4334a1f74e90a24fe42f80ea8d67df38f0c88 | Jupyter | 2,552 | 84 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
# %%
from configuration import *
import pandas as pd
from spikeinterface.sortingcomponents.benchmark.benchmark_motion_interpolation import BenchmarkMotionInterpolationMearec
from figure_interpolation import (figure_sorting_accuracies,
... |
9286c4774f40b919243dfd6ff33add7ccd66c634b7cd73e72b7cec44628cbfca | Jupyter | 2,566 | 69 | # %%
# computes network flow/impact matrix of each gene on each other gene for each sample
# outputs in TrainNetwork-*/NetworkPropagation/NetworkFlow/
# %%
library(parallel)
# %%
basePath = "/data/bcu_projects/MelBrainSys_PostdocProject_Gruetzmann/publications/2022-my-MelBrainSys-paper/scripts-etc-for-publication/"
m... |
fb512932627eb81ff7cb82053cb43f3b8cc5e773a3e2ec901aceac77889e6795 | Jupyter | 2,567 | 114 | # %%
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %%
class UnnormedMVNormalPDF:
def __init__(self, eigvecs):
if not isinstance(eigvecs, np.ndarray):
eigvecs = np.array(eigvecs)
self.eigvecs = eigvecs
self.cov = eigvecs.T @ eigvecs
self.cov_... |
eac0d71aa83f8c2d1765953a48f93d6a833e21a521af9379d5366602358eef3b | Jupyter | 2,632 | 91 | # %%
import numpy as np
from torch.utils.data import DataLoader
import os, sys
sys.path.append(os.getcwd()+"/..")
from rnn_scripts.model import *
from rnn_scripts.train import *
from rnn_scripts.utils import *
from tasks.seqDS import *
import matplotlib.pyplot as plt
%matplotlib inline
# %%
# Load models
fig_dir=os.ge... |
970d0723cd4acf7376c30d5700859b2301604140c6822a1896c3b243123cf7e2 | Jupyter | 2,655 | 74 | # %%
import os
import pandas as pd
# supress warnings about having no GPU
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from Humatch.align import get_padded_seq
from Humatch.model import load_cnn
from Humatch.classify import predict_from_list_of_seq_strs, get_class_and_score_of_max_predictions_only
from Humatch.plot import... |
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