sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
fb24a44121178e6660dc41df6b0fb29846b8eb2663603fc463dd624e7557a82b | Jupyter | 1,282 | 59 | # %%
import os
import sys
sys.path.insert(0, os.path.abspath('../'))
import bz2
import pickle
import _pickle as cPickle
import numpy as np
# load Classifier
model_name = "BoW_20230220-1206"
model_path = os.path.join(r"C:\Users\jjnunez\PycharmProjects\scar_nlp_psych\results\final_results\dspln_PSYCHIATRY_12\BoW", model... |
04a32fc4affc7d3a233eec4a20739752c16d470f47311be5b0b65d406c80297c | Jupyter | 1,283 | 59 | # %%
import os
import sys
sys.path.insert(0, os.path.abspath('../'))
import bz2
import pickle
import _pickle as cPickle
import numpy as np
# load Classifier
model_name = "BoW_20230220-1316"
model_path = os.path.join(r"C:\Users\jjnunez\PycharmProjects\scar_nlp_psych\results\final_results\dspln_SOCIALWORK_12\BoW", model... |
5db446511fddef2b8c6deceaa2fccff5f9e238b461f7229195ec227cc6de8640 | Jupyter | 1,285 | 49 | # %%
%load_ext autoreload
%autoreload 2
import lzma
import dill
import json
import pandas as pd
import shap
from tqdm.auto import tqdm
import numpy as np
import tensorflow as tf
from pathlib import Path
import scienceplots
import matplotlib.pyplot as plt
import matplotlib as mpl
from matal import load_model, load_d... |
79efefae3bd26997b94b802b1ef6269fac0f6667f589a37f9b291637a292a6aa | Jupyter | 1,288 | 43 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
from moseq2_lda.data import load_representations
from moseq2_lda.model import create_lda_pipeline, run_cross_validation, train_lda_pipeline, train_lda_model
from moseq2_lda.viz import plot_lda_results, plot_validation_curve, plot_permutation_score
fro... |
2bf4122a3555ec9071b1c3cfe1002d0315baa2cae93952a08978410a217b1bee | Jupyter | 1,311 | 41 | # %% [markdown]
# For more details about the problems and an introduction to the idea behind the code see: https://glowingpython.blogspot.com/2020/06/solving-travelling-salesman-problem.html
# %%
from minisom import MiniSom
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# %%
np.random.RandomSt... |
c72f2f903f3aa0b63db6ad526d72a726a45df9af3359a382bd511c58cbdecfc4 | Jupyter | 1,317 | 60 | # %% [markdown]
# # SCH23390 TFC Analysis
# %% [markdown]
# <a id='is'></a>
# ## Imports and Script-Wide Set Up
# %%
import sys
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pingouin as pg
# insert path to fearData.py below
sys.path.insert(0, '../utility_code')
im... |
99200daeff756b43fcacd7185d11b51d8d4bb6c4712622141419937fb8799cea | Jupyter | 1,338 | 47 | # %%
import pandas as pd
import numpy as np
from sklearn.model_selection import StratifiedKFold
# %%
df = pd.read_csv('yeast_pairs.tsv', sep='\t', header=None)
df.columns = ['p1', 'p2', 'label']
df = df.sample(frac=1).reset_index(drop=True)
df
# %%
df_sub = df[:100]
list_df_sub = list(set(list(df_sub['p1'].values) + ... |
7ccf98feb1325f8916ddbe24c654eafdec7703b83fe8cb65b2bc277e3c822e1e | Jupyter | 1,347 | 60 | # %%
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as mpl
from matplotlib.gridspec import GridSpec
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams['text.usetex'] = True
plt.rc('text.latex', preamble=r'\usepackage{amsmath,dsfont}')
# %%
#loading data for activ... |
f15d9a4627dc79be4896b5ab5e32450e04994a38ba46887177b6c110b5a0792d | Jupyter | 1,351 | 60 | # %% [markdown]
# # Prazosin and Propranolol TFC
# %% [markdown]
# <a id='is'></a>
# ## Imports and Script-Wide Set Up
# %%
import sys
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pingouin as pg
# insert path to fearData.py below
sys.path.insert(0, '../utility_co... |
295ab2bbe3de0960ed22ace610cdacf83ca48ad121663487f079ce6c24cb5bcf | Jupyter | 1,370 | 61 | # %%
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as mpl
from matplotlib.gridspec import GridSpec
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams['text.usetex'] = True
plt.rc('text.latex', preamble=r'\usepackage{amsmath,dsfont}')
# %%
#loading data for activ... |
cabdd886ecb5d871e1536453234ac5d8b919382ab4e06e1fcc4571ac41f42e43 | Jupyter | 1,373 | 60 | # %%
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as mpl
from matplotlib.gridspec import GridSpec
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams['text.usetex'] = True
plt.rc('text.latex', preamble=r'\usepackage{amsmath,dsfont}')
# %%
#loading data for activ... |
1787986f3de1225e2b4c235be5f89f54b33b0dabcaf5880bc603b9177d3d6956 | Jupyter | 1,389 | 44 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
from moseq2_lda.data import load_representations
from moseq2_lda.model import create_lda_pipeline, run_cross_validation, train_lda_pipeline
from moseq2_lda.viz import plot_lda_results, plot_validation_curve, plot_permutation_score, Aesthetics
#%matplo... |
a5e6e80b3be4bad60e75de12e96aa498ccbd2847f3b19254c73f40030fca89bd | Jupyter | 1,395 | 62 | # %%
import os
import sys
sys.path.insert(0, os.path.abspath('../../qbiome'))
from data_formatter import DataFormatter
from quantizer import Quantizer
from qnet_orchestrator import QnetOrchestrator
# %%
dirname = 'example_data/'
data = dirname + 'SamplesByMetadata_otuDADA2_EMP_10249_ECAM_RSRC_TaxaRelativeAbundance.cs... |
f861dcded531b3e64e4a3342ebd6b629cc2ce70bddaa3a16c7f9b2f442dc4276 | Jupyter | 1,430 | 53 | # %%
import os
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
import torch
import numpy as np
import random
import argparse
from Bernoulli_bandits_generator import StationaryBbs, Flip_flop, Smooth_change
from RNN_bandit_algorthims import RecurrentRNN, RecurrentGRU, RecurrentLSTM
from NeuAstro_algorithm import NeuroA... |
357e55e7531fcc9313bec2b79dfa150fefc94ab1a31ef88397e70f597c8f00e9 | Jupyter | 1,431 | 64 | # %% [markdown]
# # LC ChR2 TFC
# %% [markdown]
# <a id='is'></a>
# ## Imports and Script-Wide Set Up
# %%
import sys
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# insert path to fearData.py below
sys.path.insert(0, '../utility_code')
import fearData as fd
%matplotlib ... |
ea2249ee41ab1ced6a58160c904e91b9afe10b8798e6101bd67cb652e41b51ef | Jupyter | 1,450 | 64 | # %% [markdown]
# # Propranolol TFC
# %% [markdown]
# <a id='is'></a>
# ## Imports and Script-Wide Set Up
# %%
import sys
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pingouin as pg
# insert path to fearData.py below
sys.path.insert(0, '../utility_code')
import f... |
e32745cb97d9b4f779c2bea8a4c7706a370e62f1b4c3ac95ca32fc4747cf1bfb | Jupyter | 1,456 | 67 | # %% [markdown]
# ## Load dataset
# %%
from urso import Urso
from config import Config
import utils
import cv2
# Dataset location
dataset_path = '../datasets/soyuz_easy'
# Load dataset
dataset = Urso()
config = Config()
dataset.load_dataset(dataset_path, config, "train")
# %% [markdown]
# ## Load and show image an... |
28f75dde77bba83553f25b88fe8ad4e8969d1800666f4d57840dea2878344e02 | Jupyter | 1,457 | 46 | # %%
import logging
import msmfcode_src
from msmfcode.core.logging import log
from msmfcode.core.config import *
from msmfcode.execution.parallel import ParallelExecutor, ParallelEvaluationExecutor
from msmfcode.models.cann import DMSMF, FMSMF, Grid, SSSF
from msmfcode.evaluation.plot import plot_weight_distribution, ... |
a338c5b7cffe21a68e22af750e9440d34e63bfdce976ece511412d996746842a | Jupyter | 1,471 | 76 | # %%
import os
import sys
sys.path.insert(0, os.path.abspath('../../qbiome'))
from data_formatter import DataFormatter
# %%
dirname = '../example_data/'
data = dirname + 'SamplesByMetadata_otuDADA2_EMP_10249_ECAM_RSRC_TaxaRelativeAbundance.csv'
meta = dirname + 'SamplesByMetadata_otuDADA2_EMP_10249_ECAM_RSRC_Characte... |
e023fbee71971360da5fe40bf756452eef996710a660d0b16ac70ad43b005bd1 | Jupyter | 1,496 | 57 | # %%
from bokeh.plotting import output_notebook
output_notebook()
%load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
# %%
import pandas as pd
embeddings = pd.read_csv('data/metadata_with_embeddings.csv', index_col=0, parse_dates=['Collection Date'])
embeddings.head()... |
153849269c9873a771c822915b3e991e54adc5f4f9047fa3bf4c4d731d9c13bc | Jupyter | 1,547 | 50 | # %% [markdown]
# # 3. Network
# %% [markdown]
# Right now this notebook simply makes a network. For more examples see train.py in protein_holography/network. For how the network works see network/hnn and dependencies.
# %%
import tensorflow as tf
import sys
sys.path.append("../network")
import hnn
import clebsch
imp... |
c5206cc5769a3de2d2bd698ee8b63053fa5a6f1eb700eec3f060b193a8ee8f53 | Jupyter | 1,557 | 46 | # %%
%matplotlib inline
import os, glob
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from scipy.optimize import curve_fit
import scipy.optimize as opt
import matplotlib.lines as mlines
import pandas as pd
import csv
from skimage import io
import matplotlib
from matplotlib.backends.backend... |
8b89a3832abd19f038d4a63594b1bf8b9e33b6ca88363409dd4efc5afe99ab44 | Jupyter | 1,583 | 68 | # %%
%load_ext autoreload
%autoreload 2
from sbrnet_core.sbrnet.model import SBRNet
from sbrnet_core.config_loader import load_config
import torch
# %%
from pandas import read_parquet
p = '/ad/eng/research/eng_research_cisl/jalido/sbrnet/data/training_data/UQ/1/metadata.pq'
df = read_parquet(p)
df[df['sbr']<1.15]
... |
b54445a6165c4e6686599cff31306208168a4477e8af6c968b443566d45885ca | Jupyter | 1,604 | 58 | # %%
# import requisite packages
import numpy as np
import matplotlib.pyplot as plt
import sys,os
import pandas as pd
import seaborn as sns
# %%
df = pd.read_csv('pairwise_symm.csv')
# %%
df=df.loc[df.Type_Real!='NR',:]
# %%
df
# %%
pal=["#d4af37","#0000d5","#cc1a1e"]
jama_palette=sns.set_palette(pal,);
sns.set(fon... |
0977801a954f9a009a66b2148b3992f3a7ce87f351133deb3e1550fd592d1147 | Jupyter | 1,607 | 69 | # %% [markdown]
# # Generating forecasts and plotting microbiome trajectories
# %%
import numpy as np
import pandas as pd
import qbiome.forecaster
import qbiome.qnet_orchestrator
import qbiome.quantizer
from quasinet import qnet
# %% [markdown]
# Load existing quantizer
# %%
quantizer = qbiome.quantizer.Quantizer(n... |
650ab7ed756bb87cb25a3f3db5bead1b8fbc8bb5077071b93c1c25c74c51bf0d | Jupyter | 1,622 | 76 | # %% [markdown]
# # `Artist` class
# - class for exporting graphical schematics
# - by default, figures are exported in `out` with designated file names
# %%
from tools import Artist
# %%
constructor = Artist()
# %% [markdown]
# ## `comparison_schematic`
# - file name: `comparison_schematic.png`
# %%
constructo... |
2b2b184f54d8bc7028eec630b2097b8514a218b9ff37808f2b6b64c21d9bed72 | Jupyter | 1,675 | 46 | # %%
%matplotlib inline
import os, glob
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import scipy.optimize as opt
import matplotlib.lines as mlines
import pandas as pd
import csv
from skimage import io
import matplotlib
from matplotlib.backends.backend_pdf import PdfPages
matp... |
6696c1c7a835c8025df9976c28503039656bec7ad120319bddcd9775f79090a7 | Jupyter | 1,694 | 74 | # %%
import os.path as op
from glob import glob
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# %%
result_dir = op.abspath("../results")
corr_dir = op.join(result_dir, "gradient_decoding", "lda_neurosynth_KMeans_corr")
corr_lst = sorted(glob(op.join(corr_dir, "*_corr.np... |
24c85f07d65a5b267dbe57be8a6e1af35b739d415c824b8cdd401ca64638365c | Jupyter | 1,739 | 58 | # %% [markdown]
# # PyCaret 4.0.0a3 — Colab smoke test
#
# Verifies that `pycaret==4.0.0a3` installs and runs a full classification
# loop on Google Colab. Upload this `.ipynb` to Colab and click **Runtime → Run all**.
#
# If Colab prompts you to restart the runtime after the install cell, do so,
# then re-run from t... |
951d33e00d01f53f118cb22e81a67e298c20390c2aaa7835d3163aca3afd18a2 | Jupyter | 1,742 | 81 | # %%
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
if os.environ.get('DISPLAY') is not None:
%matplotlib tk
import msmfcode_src
from msmfcode.core.config import *
from msmfcode.models.cann import DMSMF, FMSMF, Grid, SSSF
from msmfcode.evaluation.plot import gen_plot_error_box
f... |
ec9888b38a35a1ec68bbbe277e996310ba484080b9e17ea841b25729cd6b2c1a | Jupyter | 1,749 | 64 | # %% [markdown]
# # LC HPC Axon GcAMP TFC Analysis
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
# change pkg_path variable to the directory storing the package
pkg_path = '../utility_code'
sys.path.insert(0, pkg_path)
import fp_viz, fp_dat
import seaborn as ... |
c36376000cd9f9debf1cb7c8738ef67e7bc7ba7c29bff87292de61e6a47189a9 | Jupyter | 1,758 | 64 | # %% [markdown]
# In this example we will take a small set of colors in the RGB space and use the intensity values to train a SOM. After the som is trained we will plot the map coloring each neuron using its weights as RGB coordinates.
#
# Let's first define our colors:
# %%
from minisom import MiniSom
import numpy ... |
197d441d9d1935b1409a548b957d50cf555d080a908c6322c92caee7bd3d9271 | Jupyter | 1,767 | 50 | # %% [markdown]
# This example is derived from: https://cellpose.readthedocs.io/en/latest/notebook.html
# %%
import rand_cmap
new_cmap = rand_cmap.rand_cmap(1000, type='bright', first_color_black=True, last_color_black=False, verbose=False)
# %%
import numpy as np
import matplotlib.pyplot as plt
from cellpose import ... |
8cb691c5ad5ac8a57ffc41c380739f0ac96e80feaabd9e6017e25c91627d2216 | Jupyter | 1,793 | 79 | # %%
import pandas as pd
import seaborn as sns
import math
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# %%
bpall='GO:0002181~cytoplasmic translation GO:0006412~translation GO:0043043~peptide biosynthetic process GO:0006518~peptide metabolic process GO:0043604~amide biosynthetic process GO... |
47d213874d0b59cb36cd608177bc9fe5a412458b99ca243e15c9b8694bc35334 | Jupyter | 1,794 | 48 | # %%
library(ggplot2)
library(gridExtra)
library(ggpubr)
# %% [markdown]
# # Read in data
# %%
data_UKBB=read.csv('/dagher/dagher11/filip/MAPT_OB//data/dataset_excluded_nooutlierdexclusions.csv', header=T, quote='"')
data_ABCD=read.csv('/dagher/dagher11/filip/Obesity_maps/data/dataset_ABCD.csv',header=T, quote='"', s... |
4efad12e0619e95473a948627ca214473ce419247401d38a6c618a8d08e2baab | Jupyter | 1,826 | 83 | # %% [markdown]
# # Computation of risk from abundance data
# %%
import numpy as np
import pandas as pd
from quasinet import qnet
import qbiome.quantizer
# %% [markdown]
# Load models:
# %%
ahctg = qnet.load_qnet("appr.joblib")
shctg = qnet.load_qnet("sub.joblib")
# %%
def risk(
data,
ahctg_model=ahctg,
... |
d7573c379f353fe844df32ad983d909aa68cac1fdbb4de561e3ca74ae4bd6d11 | Jupyter | 1,838 | 71 | # %%
import numpy as np
import import_ipynb
import functions_feature_construction as ffc
# %% [markdown]
# Load Data
# %%
folder_name = 'sample' #list folder containing images and outputted data directories
date = '051923' #date of analysis
# %%
#import dataset
groups={
'1 control' : {'label': 0 , 'bat... |
20aba6ac985ba9fca91f814199ac1780449022508059020c8f09978773ad5449 | Jupyter | 1,854 | 85 | # %%
from xml.etree import ElementTree
import glob
from tensorflow import keras
import tensorflow as tf
import sklearn
from skimage import draw
from skimage.io import imshow, imread
from skimage import morphology
import numpy as np
from matplotlib import pyplot
# %%
et = ElementTree.parse("CVATdata/annotations.xml")
... |
0abeb60fb1b782c5075194ee1c6440a0a295fabe680777e4fb440080aac22036 | Jupyter | 1,855 | 57 | #Imports
import numpy as np
import os
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.gridspec import GridSpec
import matplotlib.backends.backend_pdf
matplotlib.rcParams['lines.linewidth'] = 0.5
matplotlib.rcParams['lines.markersize'] = 1
matplotlib.rcParams['pdf.fonttype'] = 42... |
60f206b92544a2d7eb02ea72327b4756e4a66526abe59ad20d2d50d7d7f2e64d | Jupyter | 1,859 | 58 | #%%
#Imports
import numpy as np
import os
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.gridspec import GridSpec
import matplotlib.backends.backend_pdf
matplotlib.rcParams['lines.linewidth'] = 0.5
matplotlib.rcParams['lines.markersize'] = 1
matplotlib.rcParams['pdf.fonttype'] ... |
3de7a6790bb70f483e3317fcfa55a02dcba5fdc03e14347fe0403bfe727d0a7e | Jupyter | 1,877 | 54 | # %%
%matplotlib inline
import matplotlib
from matplotlib.backends.backend_pdf import PdfPages
matplotlib.rcParams.update({'font.size': 20, 'font.family':'sans-serif', 'font.sans-serif':'Arial', 'pdf.fonttype':42})
import os, glob
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from scipy.op... |
8f1797e51529df0846020276dca790003d88b12d8cc2b47b589caf63c52c512e | Jupyter | 1,878 | 52 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
from moseq2_lda.data import load_representations
from moseq2_lda.model import create_lda_pipeline, run_cross_validation, train_lda_pipeline
from moseq2_lda.viz import plot_lda_results, plot_validation_curve, plot_permutation_score
#%matplotlib qt
# %... |
4dc876cb9664266e74f84d2842f6ecf47dde500b165f355aa6353866d5983223 | Jupyter | 1,888 | 52 | # %% [markdown]
# # Creating a spycon test
#
# In this notebook we demonstrate, how a test can be created, and immediately used for benchmarking. For simplicity, we reconstruct the same test, that is already in the `data` folder. Let's load all the information, we need for a connectivity test. That means the
#
# 1. S... |
026d987beb8bc237aaa694bf37ae511389ce886df2e5651768e6259bfb2b6692 | Jupyter | 1,890 | 72 | # %% [markdown]
# # GRAB-NE DREADDS
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
import pingouin as pg
# change pkg_path variable to the directory storing the package
sys.path.insert(0, '../utility_code')
import fp_dat, fp_viz
import seaborn as sns
import st... |
e166fda330b8ff74664a4924b782f46cc9667ef6e5b5df0fae9612b944ce1814 | Jupyter | 1,911 | 48 | # %%
%matplotlib inline
import matplotlib
from matplotlib.backends.backend_pdf import PdfPages
matplotlib.rcParams.update({'font.size': 20, 'font.family':'sans-serif', 'font.sans-serif':'Arial', 'pdf.fonttype':42})
import os, glob
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from scipy.op... |
bdb1bb917faa5aba1265f84681b5cd4a4a27bdf9d6c124b33da51748973d62b7 | Jupyter | 1,934 | 76 | # %%
%load_ext autoreload
%autoreload 2
import lzma
import dill
import json
import pandas as pd
import shap
from tqdm.auto import tqdm
import numpy as np
import tensorflow as tf
from pathlib import Path
import scienceplots
import matplotlib.pyplot as plt
import matplotlib as mpl
from matal import load_model, load_d... |
9def0ef19b48274e75adfa446df6ad3c3f1b331950f822e05cbedc9c4a0d5789 | Jupyter | 1,942 | 57 | # %% [markdown]
# # Simple forward evaluation
#
# This example shows how the emulator can be used to simply emulate an action potential from given maximum conductances.
#
# We start by loading the data (maximum conductances, time $t$ and APs $V_m(t)$) and from our dataset #1 (some is provided with this repository).
... |
7470ad9bcb5abc15d7f000dcefe1b8358ebb698958c8b42d5298614949561db9 | Jupyter | 1,949 | 61 | # %%
import os
import re
from med_associates_utils import parse_ma_directory, SessionCollection
# %%
base_path = r'../sample_data/CRF MedAssoc Files/CRF MedAssoc Files'
data_sources = {
os.path.join(base_path, "MT - Day 1"): {'Genotype': 'MT', 'Day': 'Day1'},
os.path.join(base_path, "MT - Day 2"): {'Genotype'... |
55274daa64e462479b503c3fc3b66bdcf9bdd8b5c910dfe7243bc7f94321bd36 | Jupyter | 1,950 | 65 | # %% [markdown]
# In this example we will load an image and then buil a matrix that we will call `pixels` and will look like this:
#
# \begin{bmatrix}
# r_1 & g_1 & b_1\\
# \vdots & \vdots & \vdots\\
# r_n & g_n & b_n
# \end{bmatrix}
#
# In this matrix each row represent the color of a given pixel in the image in the... |
e6b3fdfee00cf22095e4aa7564e91611a206d877e0c2a00c3005ea629aefde0a | Jupyter | 1,951 | 52 | # %%
import pandas as pd
from rdkit.Chem import PandasTools
from helper_functions import string_to_int_list, string_to_dict
from mol_drawing import draw_mol_with_highlight_varying_colour, show_png
# %%
#import val data and model explanations
df_val = PandasTools.LoadSDF('../data/val_set_Ames.sdf', idName='internalID',... |
994797fd74ed3929a930d21bfbcbd5ec9c3744a9538deeae49b39a970134834d | Jupyter | 1,963 | 52 | # %% [markdown]
# In this example we will see how to use MiniSom to cluster the seeds dataset.
#
# First, let's load the data and train our SOM:
# %%
from minisom import MiniSom
import numpy as np
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.tx... |
40eafb755e75b8e211e9a1d269bf566b1b421b86556c66f6545ff53a312405d6 | Jupyter | 1,987 | 38 | # %% [markdown]
# # Contributions
#
# The initial API was started by Hanad Sharmarke[^1] and Pierre Bellec[^1][^7].
# The implementation was completed by Hao-Ting Wang[^1], Steven Meisler[^2][^3], François Paugam[^1][^4], Pierre Bellec.
# Hao-Ting Wang migrated the code base to `nilearn`.
# Nicolas Gensollen[^5] and B... |
a3d6337f8e7683ec78349f34a29719a74f9fa0d26eb26123f08de3f7e9ce6ee8 | Jupyter | 1,991 | 61 | # %%
import os
import re
from med_associates_utils.session import parse_directory, SessionCollection
# %%
base_path = r'C:\Users\thackray\git\med-associates-utils\sample_data\CRF MedAssoc Files\CRF MedAssoc Files'
data_sources = {
os.path.join(base_path, "MT - Day 1"): {'Genotype': 'MT', 'Day': 'Day1'},
os.pa... |
01fd6a8b18366a601dc756f6fb8639880057e18932c4432d3164ae2e04d1c718 | Jupyter | 2,002 | 101 | # %% [markdown]
# # **Demo on single-shot multi-band EPI reconstruction**
#
# ### Authors:
#
# * Zhengguo Tan <zhengguo.tan@gmail.com>
# %% [markdown]
# # 1. Setup
# %% [markdown]
# ## Install SigPy
#
# * The original [SigPy](https://github.com/mikgroup/sigpy.git) was forked for the development of HD-MRI recon... |
18d92f818c688e9515e62085aaae948e5a3e7b483195dbb37c546ccc3d7c9fa1 | Jupyter | 2,016 | 62 | # %%
import pandas as pd
from torch.utils.data import TensorDataset, DataLoader,Dataset
from training.engine import *
from training.model import RNNModel
from training.config import *
from training.dataset import *
device = torch.device("cuda:1")
import warnings
warnings.filterwarnings("ignore")
# %%
grouped = pd.r... |
beae5198030bdb6caae38eded475a03227f8b8f222aca008d6888ff74e883f78 | Jupyter | 2,044 | 70 | # %%
from pymodules import sim_functions as sf
from pymodules.sim_functions import b2
import time
import json
from pymodules.ML_params import *
# %%
cell = "NMO_00892"
# %%
morph_soma = b2.spatialneuron.morphology.Morphology.from_swc_file(f"morphs/{cell}_somas.swc", spherical_soma=True)
# %%
morph_d8 = b2.spatialneu... |
d3f1299568491328daaa60ddd39f43956e11b5a85d9fd3180f925ecb3797437a | Jupyter | 2,062 | 86 | # %%
import pandas as pd
import seaborn as sns
import math
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
# %%
bpall='GO:0002181~cytoplasmic translation GO:0006412~translation GO:0043043~peptide... |
026702b1b13017cdc1e0f2672a6b421fefc40679e2fe566c42e6aa384016b5b9 | Jupyter | 2,075 | 92 | # %% [markdown]
# # GRAB-DA Signaled vs Unsignaled
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
# change pkg_path variable to the directory storing the package
sys.path.insert(0, '../utility_code')
import fp_dat, fp_viz
import seaborn as sns
import statsmode... |
3643a3e51d7904b2ff2c0e2f958ac150ac3a0fb88ded573e30576663a078f55c | Jupyter | 2,083 | 59 | # %%
import os
import re
from med_associates_utils import parse_ma_directory, SessionCollection
from med_associates_utils.viz import plot_cumulative_events
# %%
base_path = r'../sample_data/CRF MedAssoc Files/CRF MedAssoc Files'
data_sources = {
os.path.join(base_path, "MT - Day 1"): {'Genotype': 'MT', 'Day': 'Da... |
81a206e20a76f15f35a2b475b099fe2639bf133446f608b622cb97da0a0eeb4c | Jupyter | 2,124 | 64 | # if you don't need to change anything, just do like this:
from SCTrack import track
image = './examples/mask_annotation/01.tif'
annotation = './examples/mask_annotation/SEG.tif'
track.start_track(fannotation=annotation,
fout='./examples/mask_annotation/',
basename='test',
... |
c31a8fb47af910079a213ee9b79d5669bea35aebd7083eddde754da765118457 | Jupyter | 2,126 | 65 | # %%
import os
import re
import matplotlib as mpl
from med_associates_utils.session import parse_directory, SessionCollection
from med_associates_utils.viz import plot_event_raster
mpl.rcParams['pdf.fonttype'] = 42
# %%
base_path = r'C:\Users\thackray\git\med-associates-utils\sample_data\CRF MedAssoc Files\CRF MedAs... |
dd2564a84bdbdede43450cf6822c7591f48ceb8cf73e06cafc7a0bd2520a800e | Jupyter | 2,128 | 127 | # %%
import os.path as op
from nilearn.plotting.cm import _cmap_d as nilearn_cmaps
from topics_models import load_files, model_figures
# %%
project_dir = op.abspath("../")
data_dir = op.join(project_dir, "data")
DPI = 300
lda_cmap = nilearn_cmaps["cold_hot"]
gclda_cmap = "afmhot"
# %% [markdown]
# # Neurosynth
# %... |
1874cc1251df5d23bf2010f3c31398eb29fbbe2099252bab1ea6a9b8fe629ee9 | Jupyter | 2,133 | 68 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
from moseq2_lda.data import load_representations
from moseq2_lda.model import create_lda_pipeline, run_cross_validation, train_lda_pipeline
from moseq2_lda.viz import plot_lda_results, plot_validation_curve, plot_permutation_score, Aesthetics
#%matplo... |
de534657a03d38161b634f483b5b79f204a3461768040fa769e8899a541976f1 | Jupyter | 2,150 | 98 | # %% [markdown]
# ## 3D
# %%
import sys
sys.path.append('../')
# %%
import matplotlib.pyplot as plt
import torch
import numpy as np
import cv2
from utils import circle_size, colors, toImg, preprocess
from model import loadFan#, predict_landmarks
from torchgeometry.contrib import spatial_soft_argmax2d
from glob import... |
bb1d64f9e6a1e5e988772c0fc05fe8705f0f77b153b4e1f3d3813d1cc29f9b76 | Jupyter | 2,155 | 73 | # %%
# plot the similarity between model and neural rdms
# useful packages:
import torch
import torchvision.transforms as transforms
from torchvision import models
from scipy import stats
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy
import scipy.io as sio
from scipy.stats imp... |
d5c5d66b00ece009c1752489773cf61dbcf8834701fe8341c0c646b0fafed47f | Jupyter | 2,181 | 78 | # %%
from pathlib import Path
import sys
import numpy as np
import pandas as pd
from dotenv import load_dotenv, find_dotenv
load_dotenv()
PROJECT_ROOT = Path(find_dotenv()).parent
sys.path.append(str(PROJECT_ROOT.joinpath('src')))
from utils import olc_client
c = olc_client.connect(verbose=True)
# %%
from utils... |
b2879bf9793e5791a8b77ccb1cc24b23e3a231560a502ede6fd33fe45729d5ed | Jupyter | 2,192 | 40 | # %% [markdown]
# - prepare data in h5 formant
# - ECoG
# - Spec
# - Waveform
# - onset offset
# %% [markdown]
# 'ecog_alldataset','label_alldataset',\
# 'formant_re_alldataset','pitch_re_alldataset','intensity_re_alldataset',\
# 'start_ind_re_valid_alldataset','end_ind_re_valid_alldataset','wave_re_sp... |
d14d8d38c0f795b7e63f9fa302fc874dc8513ad151c13e554d07552d88ea3741 | Jupyter | 2,194 | 99 | # %% [markdown]
# # 3D
# %%
import sys
sys.path.append('../')
# %%
import matplotlib.pyplot as plt
import torch
import numpy as np
import cv2
from ELD.utils import circle_size, colors, toImg, preprocess
from ELD.model import loadFan#, predict_landmarks
from torchgeometry.contrib import spatial_soft_argmax2d
from glob... |
b02763bbccb4033afca8279e1290b5b7c4bcf3fcfa76fa0fa3a211ed27907ca1 | Jupyter | 2,196 | 63 | # %%
import os
import re
import matplotlib as mpl
from med_associates_utils.session import parse_directory, SessionCollection
from med_associates_utils.viz import plot_cumulative_events
mpl.rcParams['pdf.fonttype'] = 42
# %%
base_path = r'C:\Users\thackray\git\med-associates-utils\sample_data\CRF MedAssoc Files\CRF ... |
8c10df594b40ba64c4b503f4a0ecdbb7ad32b4df73a50efe5d1dafc892249546 | Jupyter | 2,208 | 77 | # %% [markdown]
# ## Read in data files from WebGestalt
# %%
library('ggplot2')
library('viridis')
library('ggpubr')
library('colorspace')
library('pals')
library('ggchicklet')
# %%
files=c('OEA_pos_biol_proc.txt','OEA_pos_cell_comp.txt', 'OEA_neg_biol_proc.txt', 'OEA_neg_cell_comp.txt',
'GSEA_biol_proc.txt', ... |
dee0ef664bc20871c5d18a6e2e81916f86612321278dcf8dc4663018fdefee46 | Jupyter | 2,219 | 88 | # %%
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import ipywidgets as widgets
from scipy.ndimage import gaussian_filter1d as gauss
from scipy import stats
from scipy import signal as sig
def Normalize(arr):
return (arr - arr.min())/(arr.max() - arr.min())
# %%
mice = ['', '']
IIS_resp =... |
df4e1f93483fb9aebfe27faee6b05021f73a1025a28ebefd433f9022d28db396 | Jupyter | 2,219 | 69 | # %%
import os, datetime
from pathlib import Path
import sys
import plotly.graph_objects as go
from dotenv import load_dotenv, find_dotenv
load_dotenv()
PROJECT_ROOT = Path(find_dotenv()).parent
sys.path.append(str(PROJECT_ROOT.joinpath('src')))
from utils.ROI_columns import create_center_column_pins, smooth_center_... |
9d06d1c1c2cc32002b7a262a220c30af419220fd0e518e503890697bf9a7fba6 | Jupyter | 2,242 | 83 | # %% [markdown]
# ### Imports and Configuration
# %%
import os
if os.environ.get('DISPLAY') is not None:
%matplotlib tk
import msmfcode_src
from msmfcode.core.logging import log
from msmfcode.core.config import *
from msmfcode.execution.optimization import ContGeneticAlgorithm
from msmfcode.models.cann import Gri... |
94de3706b15a62b6225fe82e722c2c6ebe1ec769e5706e0fd86e974333728161 | Jupyter | 2,254 | 85 | # %%
import copy
import os
import csv
import statistics
from functools import reduce
from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
if os.environ.get('DISPLAY') is not None:
%matplotlib tk
import msmfcode_src
from msmfcode.core.logging import log
from msmfcode.core.config import *
f... |
3caff8a59bb688b10848990e85f519b06e3ddbf6bf54e2c9532969669c9c4a5f | Jupyter | 2,264 | 84 | # %%
%load_ext autoreload
from pathlib import Path
from IPython.display import display
import sys
from dotenv import load_dotenv, find_dotenv
load_dotenv()
PROJECT_ROOT = Path(find_dotenv()).parent
sys.path.append(str(PROJECT_ROOT.joinpath('src')))
print(f"Project root directory: {PROJECT_ROOT}")
from utils import olc... |
80b538e4801733ad86b29613b8d6661d496e014696214ddcdaece900ac83f341 | Jupyter | 2,296 | 73 | # %%
import os
import pickle
import pandas as pd
import numpy as np
import collections
import matplotlib.pyplot as plt
from src.util import splitData, preprocess
from tensorflow.keras.models import load_model
from src.eval import rmse, get_prediction
# %%
# You may change the following paths to your own paths
test_gen... |
e29c1eee76840e26510f8482fcf8ce5b8f6d36f11834ae9b5ac90601e7ea962c | Jupyter | 2,298 | 79 | # %% [markdown]
# # GRAB-DA hM3Dq
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import pingouin as pg
import sys
# change pkg_path variable to the directory storing the package
sys.path.insert(0, '../utility_code')
import fp_dat, fp_viz
import seaborn as sns
import stat... |
65934e8516fc70ac8ec5e3de5b593d02e7a6c9c43753a3fba7e74e2954bbd993 | Jupyter | 2,300 | 86 | # %%
# import snf
import numpy as np
import numpy as np
import networkx as nx
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import spectral_clustering
import snf
# %% [markdown]
# ### Load data & preprocessing
# %%
sns.s... |
2e56f69c9d8ce8d3d1d70bba092bce73d13d84e020832f50f4ca0140366e39d4 | Jupyter | 2,302 | 92 | # %%
import torch
import torch_geometric
import logging
from pathlib import Path
from tqdm import tqdm
import os
from ocpmodels import models
from ocpmodels.common import logger
from ocpmodels.common.utils import setup_logging, load_config
from ocpmodels.datasets import LmdbDataset
from ocpmodels.common.registry import... |
537142b9b260f09fbc51f0a11b5f282b6e4720629ea066288472da1043c371b6 | Jupyter | 2,305 | 92 | # %% [markdown]
# [](https://colab.research.google.com/github/lucascamillomd/pyaging/blob/main/tutorials/tutorial_metadata.ipynb) [](https://nbviewer.jupyter.org/github/lu... |
6d5b446c545d71fb5a2e7f19e80a2dc026a96608bd25f97fea5f7d6864bce289 | Jupyter | 2,326 | 96 | # %% [markdown]
# # ClusterProfiler_Gene_set_enrichment_analysis_GSEA
# %% [markdown]
# ## import libraries
# %%
#import libraries
suppressMessages({
library("clusterProfiler")
library("enrichplot")
library("ggplot2")
library("org.Mm.eg.db", character.only = TRUE)
library(dplyr)
})
# %%
# SET THE... |
9e97737202a88e5a101a378d4b231e3ace5c1b04c7aea6a4a8a616315d9de372 | Jupyter | 2,347 | 96 | # %% [markdown]
# # LC Tone Response
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
# change pkg_path variable to the directory storing the package
sys.path.insert(0, '../utility_code')
import fp_dat, fp_viz
import seaborn as sns
import statsmodels.api as sm
i... |
e1d0546692c0a15900500bb805d83b30e0493ba1f4eece5f77c2635e0b2a59ab | Jupyter | 2,347 | 56 | # %% [markdown]
# In this example we will see how to perform topic extraction using MiniSom. The goal is to extract the main topics (represented as a set of words) that occur in a collection of documents.
# %%
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from minisom import MiniSom
from sklea... |
a58d033bfeb9ac141fec9446040d7220ed39fbc58cb423f6511a94f092835194 | Jupyter | 2,349 | 69 | # %%
import os
import re
from med_associates_utils import parse_ma_directory, SessionCollection
from med_associates_utils.viz import plot_event_raster
# %%
base_path = r'../sample_data/CRF MedAssoc Files/CRF MedAssoc Files'
data_sources = {
os.path.join(base_path, "MT - Day 1"): {'Genotype': 'MT', 'Day': 'Day1'},... |
8b0866c3f12d0042680f131fcb683dac1ecdb4e4498a8134069986e63effd43b | Jupyter | 2,363 | 72 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
from moseq2_lda.data import load_representations
from moseq2_lda.model import create_lda_pipeline, run_cross_validation, train_lda_pipeline
from moseq2_lda.viz import plot_lda_results, plot_validation_curve, plot_permutation_score
#%matplotlib qt
# %... |
b594bb0d307306bf72565392fa5036b0c0351c7029f39d3d9ea4b5262a6b3b0b | Jupyter | 2,374 | 69 | # %%
import numpy as np
import pandas as pd
import import_ipynb
#import Mean_and_Variance_Comparisons as mv
from Mean_and_Variance_Comparisons import SD_con_rec_variance_mean_tables
from Mean_and_Variance_Comparisons import pvals_ttest_SD_vs_con_region_means_vars
from Mean_and_Variance_Comparisons import boxplots_norm... |
1caa65fe2b8dd2172c8e43eecf89a688b805ed7e3d7007a151be399a19775183 | Jupyter | 2,412 | 75 | # %% [markdown]
# In this example we will see how to train a SOM to create a map of hand-written digits using the <a href="https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits">UCI ML hand-written digits datasets</a>.
#
# First, we'll 1) load the data using the sklearn wrapper, 2) scale th... |
1d1b3e70f225aaf9b44469369b2a732efa14004d56e0d6c99ee36c1500716546 | Jupyter | 2,412 | 101 | # %%
import os
import sys
sys.path.insert(0, os.path.abspath('../../qbiome'))
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme()
plt.style.use('ggplot')
from data_formatter import DataFormatter
from quantizer import Quantizer
from qnet_orchestrator import Qnet... |
dc7c69bcc530b08a9340ad9418a737327d7bc5d4d2e0912e7af38607983f9d03 | Jupyter | 2,415 | 67 | # %%
library(data.table)
library(Seurat)
library(ggplot2)
library(ComplexUpset)
library(readxl)
options(repr.plot.width=10, repr.plot.height=10)
# %%
bm_preprocess <- read_excel("bm.xlsx", sheet = "preprocessing")
bm_preprocess$Run <- factor(bm_preprocess$Run, levels= bm_preprocess$Run)
bm_preprocess$totaltime<- row... |
72b88b48cd7d6507bd2956dca2e40f54bd7be6b4b4ccc60ba7407f9a92166d12 | Jupyter | 2,419 | 85 | # %% [markdown]
# This example is derived from: https://github.com/stardist/stardist/blob/master/examples/2D/3_prediction.ipynb
# %%
import rand_cmap
new_cmap = rand_cmap.rand_cmap(1000, type='bright', first_color_black=True, last_color_black=False, verbose=False)
# %%
from __future__ import print_function, unicode_l... |
c071d9f66f299e21d67577f4bbe70bb8b9e77dc7da745fa18640a592bbef9781 | Jupyter | 2,424 | 71 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
from moseq2_lda.data import load_representations
from moseq2_lda.model import create_lda_pipeline, run_cross_validation, train_lda_pipeline
from moseq2_lda.viz import plot_lda_results, plot_validation_curve, plot_permutation_score, Aesthetics
#%matplo... |
22eab82ad30507ff77bb864d30511434d7d472eb320c65b58e974cc291adfd18 | Jupyter | 2,433 | 55 | # %% [markdown]
# This examples show how to use MiniSom to solve a classification problem. The classification mechanism will be implemented with MiniSom and the evaluation will make use of sklearn.
#
# First, let's load a dataset and scale its columns:
# %%
from minisom import MiniSom
import numpy as np
import pandas... |
b3f5110391ec13a7919b63bc7a227ad4a6a58052d54334770dd2a40a0aa86a7f | Jupyter | 2,455 | 135 | # %%
import sys
sys.path.append('../../')
from script.Model_run import Speech_analysis
import numpy as np
# %%
Prob_list_1 = []
BARS_total_list_1 = []
BARS_speech_list_1 = []
Prob_list_2 = []
BARS_total_list_2 = []
BARS_speech_list_2 = []
# %%
test_1 = Speech_analysis()
# %%
test_2 = Speech_analysis()
# %%
#Impor... |
9396429b8614a0d08f2961134f1a3c9ea609ded3dfced455da794dfffb52b692 | Jupyter | 2,456 | 58 | # %% [markdown]
# # Create your own Connectivity method
#
# Here we explain quickly how one can incorparate custom algorithms in the `spycon` pipeline. To this end we implement a pretty dumb algorithm that draws random confidence scores and weights. It is just to showcase, what is needed.
#
# Below we implement a cl... |
26b1ca97e1f54d854e5a8611e4728ec700265f0d850ac51abefee62e977a539d | Jupyter | 2,483 | 89 | # %%
from pymodules import sim_functions as sf
from pymodules.sim_functions import b2
import time
import json
from pymodules.ML_params import *
# %%
cell = "NMO_00892"
# %%
morph_d8 = b2.spatialneuron.morphology.Morphology.from_swc_file(f"morphs/{cell}_depth8.swc", spherical_soma=True)
morph_d13 = b2.spatialneuron.mo... |
144baf365fd450851735f7033524eca7c78cb11abb94c3ebf14116ebc5595a96 | Jupyter | 2,488 | 74 | # %% [markdown]
# # Creating a Kilosort4 probe dictionary
#
# Kilosort4 uses a dictionary to track probe interformation.
# The dictionary needs the following keys, all of which correspond to NumPy ndarrays.
#
# ```
# 'chanMap': the channel indices that are included in the data.
# 'xc': the x-coordinates (in micr... |
570f6ec293c076495b859a931b10556351bc8e861b7eaf7d62656f13488c5dae | Jupyter | 2,492 | 96 | # %% [markdown]
# # HPC GRAB-DA Shock Response
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
# change pkg_path variable to the directory storing the package
sys.path.insert(0, '../utility_code')
import fp_dat, fp_viz
import seaborn as sns
import statsmodels.a... |
bfe91d505928e5da865c5420782725c73d54385c9f300564f09dd96f91e43167 | Jupyter | 2,519 | 72 | # %% [markdown]
# # Version Configuration
# ## Configuration Files
# - package dependencies are listed in following files:
# - `requirements_py.txt`: python pkgs
# - `requirements_r.csv`: R pkgs
# %%
import glob
import pandas as pd
# %%
glob.glob("/home/jovyan/tools/requirements*")
# %%
# python pkgs
print("... |
42607bf414035d0e1f91da0cec09ca61ac7a27513cdc066f54d1c8ae51b04b80 | Jupyter | 2,521 | 94 | # %% [markdown]
# This program loads movie cover images, preprocesses them, trains a Self-Organizing Map on these images, and visualizes the images organized according to the SOM grid, grouping similar images together
# %%
import sys
sys.path.insert(0, '../')
%load_ext autoreload
# %% [markdown]
# Covers of top 100 m... |
fe2a643549265f089d3cc0a080c9e26f52926d766fe98fb7c31009fe8e64af48 | Jupyter | 2,522 | 94 | # %% [markdown]
# ## LC Shock Response
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
# change pkg_path variable to the directory storing the package
sys.path.insert(0, '../utility_code')
import fp_dat,fp_viz
import seaborn as sns
import statsmodels.api as sm
... |
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