sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
ff56b8f2a5450e03eb8dc908042e8f3f6a335d3ccc0036f208e53540a207b82a | JavaScript | 72,824 | 1,502 |
import * as json from './json.js';
import * as python from './python.js';
const keras = {};
const tfjs = {};
keras.ModelFactory = class {
async match(context) {
const identifier = context.identifier;
const extension = identifier.lastIndexOf('.') > 0 ? identifier.split('.').pop().toLowerCase() : ... |
f3f46b6dc8a003e536bde5c214b591f6e6bd2b2e585dc3cce9cd387c09cc3064 | JavaScript | 73,807 | 1 | var hpo_umls = {"HP:0003449": ["UMLS:C1861675"], "HP:0003565": ["UMLS:C0151632"], "HP:0003292": ["UMLS:C1837802"], "HP:0002584": ["UMLS:C0267373"], "HP:0002583": ["UMLS:C0009319"], "HP:0000729": ["UMLS:C0856975", "UMLS:C1510586"], "HP:0010976": ["UMLS:C1855067"], "HP:0010878": ["UMLS:C0948242"], "HP:0000722": ["UMLS:C0... |
e02b82992182f9561d9a8f6a4b2fdb51b6c8d4f9b87f46b6e22521fdce2867a6 | JavaScript | 75,448 | 1,274 |
import * as child_process from 'child_process';
import * as fs from 'fs/promises';
import * as os from 'os';
import * as path from 'path';
import * as process from 'process';
import * as readline from 'readline';
import * as tablegen from './tablegen.js';
import * as url from 'url';
const write = (message) => {
i... |
4eb1bae9c54e56fdea4dc686255011e7cd2680212e2b46e9a3f9cad20f6260c6 | JavaScript | 77,209 | 2,282 |
export const tosa = {};
tosa.v0 = {};
tosa.v0.DType = {
UNKNOWN: 0, '0': 'UNKNOWN',
BOOL: 1, '1': 'BOOL',
UINT8: 2, '2': 'UINT8',
INT4: 3, '3': 'INT4',
INT8: 4, '4': 'INT8',
INT16: 5, '5': 'INT16',
INT32: 6, '6': 'INT32',
INT48: 7, '7': 'INT48',
FP32: 8, '8': 'FP32',
UINT16: 9... |
5509b7fe6d175ad1143bdef9550f3e8bc4bf83be02736ec31ffccd81c4789224 | JavaScript | 77,363 | 31 | /* @preserve
* The MIT License (MIT)
*
* Copyright (c) 2013-2015 Petka Antonov
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the r... |
2aebc2552d7dadf4e3a0b80cc830c274e91146584dad8e29b04338b9ecedb363 | JavaScript | 78,468 | 7 | /*!
* Bootstrap v5.1.0 (https://getbootstrap.com/)
* Copyright 2011-2021 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
* Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
*/
!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=... |
cf6418ad1f0d70c24a6bc3a0257988d25933e8878ccf456ae6e5f74c981b0614 | JavaScript | 78,857 | 2,337 |
// Experimental
import * as base from './base.js';
const mlnet = {};
mlnet.ModelFactory = class {
async match(context) {
const entries = await context.peek('zip');
if (entries instanceof Map && entries.size > 0) {
const root = new Set(['TransformerChain', 'Predictor']);
... |
60eec6d07a0a0b638aca5b35985b08ac46c8be77d639fd9e3d0500e96c5b1778 | JavaScript | 83,962 | 1 | Search.setIndex({"docnames": ["example 0 hyperparameter groundtruth test", "example 1 load data", "example 2 heuristic parameter lgcp infer", "example 3 optimize hyperparameters", "example 4 confidence intervals", "example 5 heading dependence", "index", "lgcpspatial", "lgcpspatial.estimators", "lgcpspatial.gridsearch"... |
633ba16fa4ae95cc60b2a7dd1dceff0a235d31edc5340e6249ee9c0fcd96d0c8 | JavaScript | 86,350 | 4 | /*! jQuery v3.1.0 | (c) jQuery Foundation | jquery.org/license */
!function(a,b){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?wi... |
b10cbd29d770f3036a820ef73bb797e27499ff67c8ed770cb604f445c3d7750b | JavaScript | 87,357 | 2,429 |
export const mgb = {};
mgb.serialization = mgb.serialization || {};
mgb.serialization.fbs = mgb.serialization.fbs || {};
mgb.serialization.fbs.DTypeEnum = {
Float32: 0, '0': 'Float32',
Uint8: 1, '1': 'Uint8',
Int8: 2, '2': 'Int8',
Int16: 3, '3': 'Int16',
Int32: 4, '4': 'Int32',
IntB1: 5, '5'... |
7e8993540ebc6d27129b3e0cd6a124573a473baac465ffeff82991bf2db7b3cb | JavaScript | 92,728 | 2,715 |
export const onnx = {};
onnx.Version = {
"_START_VERSION": 0,
"IR_VERSION_2017_10_10": 1,
"IR_VERSION_2017_10_30": 2,
"IR_VERSION_2017_11_3": 3,
"IR_VERSION_2019_1_22": 4,
"IR_VERSION_2019_3_18": 5,
"IR_VERSION_2019_9_19": 6,
"IR_VERSION_2020_5_8": 7,
"IR_VERSION_2021_7_30": 8,
... |
71739e31aac58c067bdb8899a35fec39ad298ae96f1fa6034046ccb9703bf83b | JavaScript | 100,942 | 2,249 |
import * as base from './base.js';
const kmodel = {};
kmodel.ModelFactory = class {
async match(context) {
const reader = kmodel.Reader.open(context.stream);
if (reader) {
return context.set('kmodel', reader);
}
return null;
}
async open(context) {
co... |
f937ea654a669e942dc57687bf6967866bc140e891b408e0cbaf7df3fcaa5e60 | JavaScript | 102,220 | 2,221 |
// Experimental
import * as base from './base.js';
import * as protobuf from './protobuf.js';
import * as zip from './zip.js';
const tf = {};
tf.ModelFactory = class {
async match(context) {
const identifier = context.identifier;
const extension = identifier.lastIndexOf('.') > 0 ? identifier.sp... |
cbd5cd24d786593f01dc138958bbfce44c254bcde7c3724bf85e6bcd7fe2a6e1 | JavaScript | 104,796 | 2,455 |
const dagre = {};
// Dagre graph layout
// https://github.com/dagrejs/dagre
// https://github.com/dagrejs/graphlib
dagre.layout = (nodes, edges, layout, state) => {
let uniqueIdCounter = 0;
const uniqueId = (prefix) => {
const id = ++uniqueIdCounter;
return prefix + id;
};
const fla... |
6b239ec52155c4819d7ac89f8c4c530e9dde3490566cecb3c278fb368b5aaaac | JavaScript | 111,788 | 2,917 |
import * as protobuf from './protobuf.js';
const onnx = {};
onnx.ModelFactory = class {
async match(context) {
const identifier = context.identifier;
const extensions = [
'saved_model.pb', 'predict_net.pb', 'init_net.pb',
'predict_net.pbtxt', 'init_net.pbtxt', 'predict_ne... |
03b2b970a6add9aca3295f080511b58c0247f9279a462ea36841dc7053a8cb2e | JavaScript | 116,695 | 1 | var dischargeSummaryObj = [{"end": 152, "text": "Admission Date: [**2199-5-10**] Discharge Date: [**2199-5-11**]\n\nDate of Birth: [**2126-3-7**] Sex: M\n\nService: SURGERY\n\n", "section": "", "anns": [{"endNode": {"id": 4085, "offset": 60}, "startNode": {"id": 4084, "offset": 46}, "type... |
d58de4e899ed5730734820f733f22181975c96557a963141e49427611ff89a54 | JavaScript | 124,733 | 3,418 |
export const MNN = {};
MNN.NetSource = {
CAFFE: 0, '0': 'CAFFE',
TENSORFLOW: 1, '1': 'TENSORFLOW',
TFLITE: 2, '2': 'TFLITE',
ONNX: 3, '3': 'ONNX',
TORCH: 4, '4': 'TORCH'
};
MNN.DataType = {
DT_INVALID: 0, '0': 'DT_INVALID',
DT_FLOAT: 1, '1': 'DT_FLOAT',
DT_DOUBLE: 2, '2': 'DT_DOUBLE',... |
ef6d82272718401948b63c684f383eeff6dc74b013c134592f5585ad2d80d5b6 | JavaScript | 125,662 | 3,085 |
export const armnnSerializer = {};
armnnSerializer.ActivationFunction = {
Sigmoid: 0, '0': 'Sigmoid',
TanH: 1, '1': 'TanH',
Linear: 2, '2': 'Linear',
ReLu: 3, '3': 'ReLu',
BoundedReLu: 4, '4': 'BoundedReLu',
SoftReLu: 5, '5': 'SoftReLu',
LeakyReLu: 6, '6': 'LeakyReLu',
Abs: 7, '7': 'Ab... |
ada9fa2ba3f548947074d9b2580feb25738ba0cbc73263cb75af3387a13619fa | JavaScript | 130,917 | 2,812 |
// Experimental
import * as base from './base.js';
import * as flatbuffers from './flatbuffers.js';
import * as python from './python.js';
const pytorch = {};
const nnapi = {};
const numpy = {};
pytorch.ModelFactory = class {
async match(context) {
const reader = await pytorch.Reader.open(context);
... |
fc8f53ccfba3585554755c686f86309078cf067f9f1b7a72218dd1040c77ce8e | JavaScript | 134,619 | 3,675 |
export const circle = {};
circle.TensorType = {
FLOAT32: 0, '0': 'FLOAT32',
FLOAT16: 1, '1': 'FLOAT16',
INT32: 2, '2': 'INT32',
UINT8: 3, '3': 'UINT8',
INT64: 4, '4': 'INT64',
STRING: 5, '5': 'STRING',
BOOL: 6, '6': 'BOOL',
INT16: 7, '7': 'INT16',
COMPLEX64: 8, '8': 'COMPLEX64',
... |
de3cfdfca11fbe7a093a07d5d012b2b98754698d6d948f42773263b829bd3e13 | JavaScript | 142,627 | 3,731 | /* eslint-disable */
/* pako 1.0.11 nodeca/pako */
// added for lightweight web worker support
const _pakoMod = { exports: {} };
const _pakoExp = _pakoMod.exports;
(function (f) {
if (typeof _pakoExp === "object" && typeof _pakoMod !== "undefined") {
_pakoMod.exports = f();
} else if (typeof define === "functio... |
78c4f3ef9a486f7dd4b122fcf2891174adbbcb4c5fcba991ea8e8681ef109587 | JavaScript | 148,053 | 4,041 |
export const tflite = {};
tflite.TensorType = {
FLOAT32: 0, '0': 'FLOAT32',
FLOAT16: 1, '1': 'FLOAT16',
INT32: 2, '2': 'INT32',
UINT8: 3, '3': 'UINT8',
INT64: 4, '4': 'INT64',
STRING: 5, '5': 'STRING',
BOOL: 6, '6': 'BOOL',
INT16: 7, '7': 'INT16',
COMPLEX64: 8, '8': 'COMPLEX64',
... |
da4013b083a8c088200995e519c576424fec4bd8862719f5256c626cde21a252 | JavaScript | 148,331 | 3,495 |
import * as fs from 'fs/promises';
import * as path from 'path';
const tablegen = {};
tablegen.Location = class {
constructor(file, line, column) {
this.file = file;
this.line = line;
this.column = column;
}
toString() {
return `${this.file}:${this.line}:${this.column}`;... |
fedf48eca5b48690cdbe0a7a4470d4099b6215209c4ec6cb4813a5e462040de3 | JavaScript | 166,286 | 4,600 |
export const mindspore = {};
mindspore.schema = mindspore.schema || {};
mindspore.schema.ResizeMethod = {
UNKNOWN: -1, '-1': 'UNKNOWN',
LINEAR: 0, '0': 'LINEAR',
NEAREST: 1, '1': 'NEAREST',
CUBIC: 2, '2': 'CUBIC'
};
mindspore.schema.CoordinateTransformMode = {
ASYMMETRIC: 0, '0': 'ASYMMETRIC',
... |
0a816647e4df48aa9271fc16b5b39a3e878f9bf1be843f490274bcd9be533076 | JavaScript | 182,571 | 5,221 |
export const caffe = {};
caffe.BlobShape = class BlobShape {
constructor() {
this.dim = [];
}
static decode(reader, length) {
const message = new caffe.BlobShape();
const end = length === undefined ? reader.length : reader.position + length;
while (reader.position < end) ... |
028d1cfbe88ac0b08801b314598d5bfa8c5b270475a0aa15562243fb6e311ff6 | JavaScript | 200,000 | 5,214 |
export const nnabla = {};
nnabla.Shape = class Shape {
constructor() {
this.dim = [];
}
static decode(reader, length) {
const message = new nnabla.Shape();
const end = length === undefined ? reader.length : reader.position + length;
while (reader.position < end) {
... |
2e0115fa2dd5c2f5c56b3457d659f914602cba3ff2770722c34b4e2a79b3b3ea | JavaScript | 200,000 | 1 | var disease_model_100k = {"Severe multi-system atopic disease with high IgE": [{"hpo_label": "Eczema", "hpo_id": "HP:0000964"}, {"hpo_label": "Increased IgE level", "hpo_id": "HP:0003212"}, {"hpo_label": "Allergy", "hpo_id": "HP:0012393"}, {"hpo_label": "Cow milk allergy", "hpo_id": "HP:0100327"}, {"hpo_label": "Season... |
31efb20d75260d8e91ab9b7e147fed2c255f84ebd9d2c7ccdc821ad3e9305158 | JavaScript | 200,000 | 5,650 |
export const tensorflow = {};
export const google = {};
tensorflow.SavedModel = class SavedModel {
constructor() {
this.meta_graphs = [];
}
static decode(reader, length) {
const message = new tensorflow.SavedModel();
const end = length === undefined ? reader.length : reader.posit... |
346c98caa0c26fddac337315a5b96be16fd936e60604753ff2df9f91ae649e11 | JavaScript | 200,000 | 6,687 | /*! elasticsearch - v12.1.3 - 2016-12-14
* http://www.elastic.co/guide/en/elasticsearch/client/javascript-api/current/index.html
* Copyright (c) 2016 Elasticsearch BV; Licensed Apache-2.0 */
;(function () {
/* prevent lodash from detecting external amd loaders */var define;
/******/ (function(modules) { // webpackB... |
3a1581d077442ce14b6428be1f63157d36c783a81d13ed1ca61555269e33dfcb | JavaScript | 200,000 | 5,012 |
import * as base from './base.js';
import * as grapher from './grapher.js';
const view = {};
const markdown = {};
const png = {};
const metadata = {};
const metrics = {};
view.View = class {
constructor(host) {
this._host = host;
this._defaultOptions = {
weights: true,
at... |
8d984a80128c35a5fa0da48861c543f89c97d053ecb8fe3167dd22c65562733d | JavaScript | 200,000 | 1 | var hpo_umls = {"HP:0003566": ["UMLS:C3150358"], "HP:0003295": [], "HP:0003296": ["UMLS:C2673931"], "HP:0003297": ["UMLS:C4021733"], "HP:0003562": ["UMLS:C3277127"], "HP:0003563": ["UMLS:C0020597", "UMLS:C0853085"], "HP:0003292": ["UMLS:C1837802"], "HP:0003561": ["UMLS:C1855650"], "HP:0001151": ["UMLS:C1866753"], "HP:0... |
b7311faa13414737c0601e9e0e6334083c4be72436b86a751c0fb1ffeedb135d | JavaScript | 200,000 | 2,324 | var json = {
meta: [
{ chromosome: "1" startPoint: 1 endPoint: 249250621 color: "#FF0000FF" },
{ chromosome: "2" startPoint: 1 endPoint: 243199373 color: "#FF3D00FF" },
{ chromosome: "3" startPoint: 1 endPoint: 198022430 color: "#FF7A00FF" },
{ chromosome: "4" startPoint: 1 endPoint: 191154276 color: "#FFB800FF" }... |
b9de0f9c2990b9b2f9f9eaae7767e9e77826ad4a83237629c1a1e34d762c5ad8 | JavaScript | 200,000 | 1 | var _subconcepts = {"C2219717": [["C0002453", "RO"]], "C0043515": [["C0017152", "RN"], ["C0039082", "RN"], ["C0597168", "RN"], ["C0013395", "RO"], ["C0017150", "RO"], ["C0025267", "RO"], ["C0027662", "RO"], ["C0030920", "RO"], ["C0232567", "RO"], ["C0242003", "RO"], ["C0376180", "RO"]], "C1837084": [["C1836192", "RO"],... |
d396944f96472a3c4fb328ef99c35406367096ff40476f11bba7b113dfddb6dd | JavaScript | 200,000 | 5,414 |
// Experimental
import * as base from './base.js';
import * as text from './text.js';
const mlir = {};
const _ = {};
mlir.ModelFactory = class {
async match(context) {
const stream = context.stream;
const identifier = context.identifier;
const extension = identifier.split('.').pop().toL... |
fa33a2087cc4cd06899c57ccb42ede13b086034f712a2fd4cebfdd07852e36bc | JavaScript | 200,000 | 5,325 |
export const CoreML = {};
CoreML.Specification = {};
CoreML.Specification.Pipeline = class Pipeline {
constructor() {
this.models = [];
this.names = [];
}
static decode(reader, length) {
const message = new CoreML.Specification.Pipeline();
const end = length === undefine... |
7e13cb548c24278d6e3dfd7d3d7fe325ab8e1eac569f8504b5fa9d4e27e43136 | JavaScript | 200,001 | 2 | // https://d3js.org Version 4.13.0. Copyright 2018 Mike Bostock.
(function(t,n){"object"==typeof exports&&"undefined"!=typeof module?n(exports):"function"==typeof define&&define.amd?define(["exports"],n):n(t.d3=t.d3||{})})(this,function(t){"use strict";function n(t,n){return t<n?-1:t>n?1:t>=n?0:NaN}function e(t){return... |
e84861aeb022c84f3b574e47e13ed7cf67dec543749debbb79a803a98b4a717d | JavaScript | 200,002 | 3,877 |
// Experimental Python Execution
const python = {};
python.Execution = class {
constructor(sources) {
/* eslint-disable consistent-this */
const self = this;
/* eslint-enable consistent-this */
const execution = self;
this._sources = sources || new Map();
this._ev... |
20833df2bcc52f4d9688b9fad877e7ff8aefca8338b2f64740f03f043932a559 | Jupyter | 364 | 11 | # %%
#Only need to do once for each hemisphere
from brainsmash.workbench.geo import cortex
surface = "./SurfL10kConte.gii"
cortex(surface=surface, outfile="./SurfL10kConte.txt", use_wb=True,euclid=False)
from brainsmash.workbench.geo import cortex
surface = "./SurfR10kConte.gii"
cortex(surface=surface, outfile="./Surf... |
3491fc876e89963c7ba765c13a14c327233812ed707f0991e15611ef5ef0d896 | Jupyter | 371 | 21 | # %%
# test without view
import torch
import pandas as pd
import numpy as np
from simple_mlps.datas import ParcellationDataCorrelated
df = ParcellationDataCorrelated(1, 'all', correlation_coeff=3.0)
df = pd.DataFrame({"hit": df.hit, "time": df.time_obs})
df.groupby("hit").apply(np.mean)
# %%
import seaborn as sns
... |
d9439c658d59eaaaee3725a0138013d3caa489b3e57ed89f54bf6fa0c2bf1349 | Jupyter | 375 | 17 | # %%
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import yasa
# %%
data = np.load(r'C:\Users\Lenovo\1代码 REM中beta升高\upload_data\figure1d_data.npy')
hypno = np.load(r'C:\Users\Lenovo\1代码 REM中beta升高\upload_data\figure1d_hypno.npy')
sf = 100
# %%
fig = yasa.plot_spectrogram(data, sf, h... |
9fcf21d7fd931c0e09d419cd03fa7f59db15e23e8dd2cc0221f24037e0e248db | Jupyter | 515 | 27 | # %%
%load_ext autoreload
%autoreload 2
from config import *
fn = 'r2405_051216b_cell1816.mat'
data = Dataset.from_file(datadir+fn).prepare()
arena = data.arena
# Used to locate peaks in the autocorrelogram
kf = kernelft(
data.shape,
data.P,
data.V,
angle=data.angle,
style='grid')
result,model... |
ed52a6e9b02ac2c747ad9e9026e1c62eb6423d1bdcfab396cc989c32c688c3ec | Jupyter | 552 | 25 | # %%
import os
from rdkit import Chem
from serenityff.charge.dataset_preperation.MolMorganDataset import MolMorganDataset
# %%
dummy_set1 = MolMorganDataset('./dummyset1.sdf')
dummy_set2 = MolMorganDataset('./dummyset2.sdf')
# %%
dummy_set1.plot_compare(dummy_set2)
# %%
dummy_set1.missings(dummy_set2, DrawMolecules... |
b38c519dda3d1d66c430956026d12d7337a77fd2f93a0d08dd4cc618ad50fc6d | Jupyter | 578 | 21 | # %%
import numpy as np
# %%
def arclength(azi1, alt1, azi2, alt2, degrees=True, method="gs"):
# https://en.wikipedia.org/wiki/Great-circle_distance
from numpy import sin, cos
if degrees:
azi1, alt1, azi2, alt2 = np.radians((azi1, alt1, azi2, alt2))
delta_alt = np.abs(alt2 - alt1)
if meth... |
f6d2098968d528b007f596c72b343bb13bc6df8c3b90e3bc038497fb458ef741 | Jupyter | 640 | 29 | # %%
import pandas as pd
import grequests
import urllib.parse
from tqdm import tqdm
# %%
SERVER_URL = "https://npclassifier.ucsd.edu"
df = pd.read_csv("smiles.tsv", sep="\t")
all_urls = []
for entry in tqdm(df.to_dict(orient="records")):
smiles = str(entry["smiles"])
if len(smiles) > 5:
request_url... |
52931b2b9e5b3a7b1a545f54f994579aaabaae8247efaf70804a100c55556b66 | Jupyter | 667 | 32 | # %%
from statistics.survival_analysis import get_raw_kmf_by_group_full, load_metadata_mlp_pivot
mlp = load_metadata_mlp_pivot()
get_raw_kmf_by_group_full(mlp)
# %%
from statistics.shap_viz.shap_stats_cnn import bootstrap_pvals
bootstrap_pvals('cnn')
# %%
from statistics.survival_analysis import *
mlp_pivot = load... |
a6c374c56fc710f0e5cd4406f4ec0c225bdfad7188955e7fc802f24b3213a269 | Jupyter | 816 | 29 | # %%
from matplotlib.pyplot import imshow
from models.models import SegmentationModule, build_encoder, build_decoder
from src.eval import segment_image
from utils.constants import DEVICE
# %%
# path to image on which to run wall segmentation
path_image = 'PATH_TO_IMAGE'
# Model weights (encoder and decoder)
weights_... |
76ae075e9fdffec7e2173f58edc0964a31e483acde18deb24eac4860698f6a09 | Jupyter | 849 | 38 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#import seaborn as sns
%matplotlib inline
# %%
df = pd.read_csv('C:\\Users\\arif\\OneDrive\\Journal Paper\\Code\\Irshad\\Dataset\\4_fill_consectives_missing.csv', parse_dates=['date_and_hour'], index_col='date_and_hour')
# %%
df
# %%
df['Sys... |
a7f20d1b2f71e085993d423d79a620d004f18d30d46751b85e428a4af55cfa5f | Jupyter | 904 | 26 | # %% [markdown]
# # How to resume training
# %% [markdown]
# In many cases, you might want to continue training after an intitial training run (but with the same data).
#
# `sbi` supports this with the `.train(..., resume_training=True)` argument.
# %% [markdown]
# ## Main syntax
# %% [markdown]
# ```python
# infer... |
5d02eb57554159125f5cedde0b44841b3020f28a145028e62cd1400ede5e8795 | Jupyter | 957 | 63 | # %%
# mri-pet environment
from statistics import demographic_statistics
demographic_statistics.main()
# %%
# mri-pet
from statistics import survival_analysis
survival_analysis.main()
# %%
# mri-pet
from statistics import brain_visualization
brain_visualization.main()
# %%
# mri-surv
from statistics.radiology_an... |
717a08b5e7fc93aa73e786dfa07b7e692d7ffd9c21d55a330cbfc5614eee01a2 | Jupyter | 1,021 | 27 | # %%
from protein_mpnn_utils import StructureDataset
# By default pandas isn't in the enviroment that the dockerfile setup
# so run 'pip install pandas' if running this cell doesn't work
import pandas as pd
# %%
# This is a good preliminary test before running the save train cell below
# This is much smaller so if i... |
ec59ed93a4ae7fd1caa3802786a95897a3846dbaaff6711d324066371e59aa75 | Jupyter | 1,034 | 48 | # %%
import sys
import os
from os import path
import glob
import json
import re
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import pandas as pd
from tqdm import tqdm
from allensdk.core.brain_observatory_cache import BrainObservatoryCache
# Local imports (hacky approach)
if ".." not in sys.pa... |
2ba9142ff73126ef8f465ab908409b0206e38ca534519868531575ae849b2b6d | Jupyter | 1,048 | 11 | # %% [markdown]
# # How to choose neural nets
# %% [markdown]
# `sbi` implements a wide range of neural networks for the different inference methods. By and large, we expect that the default choices of `sbi` perform well. This tutorial describes how you can optimize the performance of `sbi` by choosing the best neural... |
377719a95714654c276161e0eca4b103796f9458006e2683c85c0e4fea535541 | Jupyter | 1,074 | 43 | # %%
import os
PODCAST_ROOT = os.environ.get("PODCAST_ROOT", "/engram/naplab/users/rs4613/Podcast") # set to your copy of the dataset
# %% [markdown]
# # Split the 1800s podcast audio into the 5 continguous chunks
# %%
import os
import librosa
import soundfile as sf
# Parameters
root_dir = f"{PODCAST_ROOT}/stimuli... |
f7668998c1134886055285c9ac99235f99dbe292aa7d8610cce5976db2408dad | Jupyter | 1,243 | 35 | # %% [markdown]
# # How to save and load objects
# %% [markdown]
# Save a trained inference object or posterior with `save()` and load it with the
# `load()` class method of the same class.
#
# ```python
# from sbi.inference import NPE, DirectPosterior
#
# # ... run inference
# posterior = inference.build_posterior(... |
74879c573d624cc6007a3e63c705c1217dbe868630466c06722d04546c46ee5f | Jupyter | 1,265 | 36 | # %%
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
# %%
path = '/home/brainstimmap/Documents/DATA/20xx_Projects/2025_DBSinDepression/03_Data/Patient3/VTA_tracts/plot/structural/data4heatmap.csv'
assessment_list = ['BDI', 'HAMD', 'MADRS', 'SHAPS', 'Sheehan', 'SOFAS', 'BARS']
complete_df = pd... |
db1eff62b2912e05dfd5e36f24afbf1b39844cb83c6f3b74222943f018828543 | Jupyter | 1,308 | 13 | # %% [markdown]
# # How to choose summary statistics
# %% [markdown]
# Many simulators produce outputs that are high-dimesional. For example, a simulator might
# generate a time series or an image. In the tutorial [04_embedding_networks](https://sbi.readthedocs.io/en/latest/tutorials/04_embedding_networks.html), we di... |
bf16c1e2dde6461a37a8612eba91140de499d9413c33f3189e569186c9aeae9c | Jupyter | 1,372 | 53 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#import seaborn as sns
%matplotlib inline
# %%
df = pd.read_csv('C:\\Users\\arif\\OneDrive\\Journal Paper\\Code\\Irshad\\Dataset\\2_Missing_Values_Identified.csv', parse_dates=['date_and_hour'], index_col='date_and_hour')
# %%
df.boxplot('Sys... |
267992f2bf993afd86e05cafe20a23c39f67f45535e75ff2d1843aa55240984c | Jupyter | 1,376 | 52 | # %%
import os
PODCAST_ROOT = os.environ.get("PODCAST_ROOT", "/engram/naplab/users/rs4613/Podcast") # set to your copy of the dataset
# %% [markdown]
# # Save Downsampled High-gamma signals first so that other codes can use them
# %%
import mne
import numpy as np
from mne_bids import BIDSPath
import os
import scipy... |
9fa9db4b5b3f12b892760e44a4a1f5df6eb6228d493ac2ed7e2807d15564ee4e | Jupyter | 1,393 | 41 | # %%
import glob
import pandas as pd
# FIXME: replace with dir to ISRUC data/annotations
f_path = "<path-to-ISRUC>"
xlsx_paths = glob.glob(f"{f_path}/**/*.xlsx", recursive=True)
s_id_to_l_events_map = {}
for xp in xlsx_paths:
ds_idx = xp.find("isruc-sg")
ds = xp[ds_idx:ds_idx + len("isruc-sg") + 1]
s_id ... |
1cde68f154ac6a1bd0b2aeae798cb3f4f60ab368c800218906c7a37bfb1ba00a | Jupyter | 1,423 | 37 | # %% [markdown]
# # How to refine posterior estimates with importance sampling
# %% [markdown]
# The `sbi` toolbox only requires samples from a simulator. When the likelihood can also be evaluated, importance sampling can refine a fixed-observation posterior estimate by using it as the proposal. Check importance-weigh... |
a688960c8ab69f2e2ec096c1f3625a62bff81281cf653718d604034c0f8c321e | Jupyter | 1,462 | 28 | # %% [markdown]
# # Example 2: Heuristic hyperparameters
# %% [markdown]
# We will use heuristic parameters to infer the posterior log-rate from an example dataset. The period of the grid can be inferred reasonably well from the radial autocorrelogram. The height of the kernel, reflecting the overall prior variance (u... |
c0f22e1648f3a1e238e454adf906b8702a4a561b0d027af9ac32ebe1ea299074 | Jupyter | 1,489 | 59 | # %% [markdown]
# # Scikit-learn API
#
# For those not familiar with PyTorch, we've created a wrapper for scikit-learn. This contains the familiar fit/predict-methods.
# %%
from binn import BINNClassifier, Network, SuperLogger
import pandas as pd
# %% [markdown]
# Similar to the PyTorch API, we load data and create... |
a2b04c10342b5c6553d9c6fcde72765e4d73d4685d40558e5abf580b066da9fb | Jupyter | 1,516 | 32 | # %% [markdown]
# # Example 2: Heuristic hyperparameters
# %% [markdown]
# We will use heuristic parameters to infer the posterior log-rate from an example dataset. The period of the grid can be inferred reasonably well from the radial autocorrelogram. The height of the kernel, reflecting the overall prior variance (u... |
72b21269532f38862de69eab3f3e9793538fb3f0336c4cd52a60666dad337f8e | Jupyter | 1,560 | 63 | # %%
import pandas as pd
import numpy as np
from tqdm import tqdm
from rdkit import Chem
from serenityff.charge.tree.dash_tree import DASHTree
import time
import os
from matplotlib import pyplot as plt
# %%
# set matplotlib font size
plt.rcParams.update({'font.size': 14})
# %%
folder_aa = "/localhome/mlehner/dash_dat... |
63ddcd4f74ea99426b3a9fa0888b5857651d834b70a2cba4fb89f18de64f7f8e | Jupyter | 1,569 | 83 | # %% [markdown]
# # Generate Heatmap of Cell Type Markers for Different Genetic Backgrounds
# %%
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
INPUT_CSV = "raw/genetic_backgrounds_marker_genes.csv"
OUTPUT_PATH = "out/genetic_backgrounds_heatmap.pdf"
V_MAX = 2000 # Max R... |
49a361b015583342d60e9f26fcbbae96a4f150daef166f2d346d7b012defb77b | Jupyter | 1,589 | 72 | # %%
%load_ext autoreload
%autoreload 2
#%matplotlib notebook
%matplotlib inline
# %%
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("../") if "../" not in sys.path else None # avoid adding multiple relave paths to sys.path
# custom packages
from ratsimulator import Agent, trajectory_g... |
acbd442d231f3a956d49ebcb3fb4a887b8533e425ad92a60760e3d7c6a659b5c | Jupyter | 1,672 | 28 | # %% [markdown]
# # How to sample the posterior given iid observations (for NLE and NRE)
# %% [markdown]
# > Important: This how-to guide is only relevant if you have iid observations **and** you are using NLE or NRE. You can use NPE with iid observations directly by passing `sample_with="mcmc"` or `sample_with="vi"` ... |
604eb30173eb8c3f2ba6dad7ab8a07cef3349ccde81ddb0d4ef3e8bc359e1716 | Jupyter | 1,674 | 73 | # %% [markdown]
# # Single Cell RGC Subtype Marker Expression Analysis in Liang et. al. 2023
# %%
import scanpy as sc
import anndata as ad
sc.settings.verbosity = 3
# %% [markdown]
# ### Data Collection
# %%
### Read .h5ad data into an AnnData object
# RGC subset obtained from
# https://cellxgene.cziscience.com/co... |
ef379aaf25ed647ddc12fc5e855e0bd275ca5c5c269ad6061dbb8a997d23926b | Jupyter | 1,683 | 50 | # %%
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from scipy.stats import spearmanr
# %%
pd_sub = pd.read_excel(r'C:\Users\Lenovo\1代码 REM中beta升高\upload_data\figure3ab_pdsub.xlsx',
index_col = [0])
pd_rem = pd_sub[pd_sub['... |
a8cf9a65b4bcf379a42772932c1b510fcdadb1b99dd7cd778b35d0aed0e8d69b | Jupyter | 1,715 | 100 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# %%
path='selected_data_ISONE.csv'
df=pd.read_csv(path, parse_dates=True, index_col='Date')
df.head(5)
# %%
df.reset_index('Date',inplace=True)
df['temp_date']=df['Date']
# %%
df.set_index('Date',in... |
3d3709cccf7a452c8b0afa8773b4cb38140d0c101ba1325a6be21f01a999d198 | Jupyter | 1,765 | 14 | # %% [markdown]
# # How to choose a diagnostic tool
# %% [markdown]
# `sbi` implements a range of diagnostic tools. Here, we provide an overview of what they do:
#
# - **expected coverage**: allows you to evaluate whether the joint posterior is over- or under-confident (on average across prior predictives). Unlik... |
be873df2b539ebf9c12e2f4b954d8a94610227c2f471a4b68147509cad3be62d | Jupyter | 1,768 | 70 | # %%
from binn import Network, BINN
import pandas as pd
input_data = pd.read_csv("../data/test_qm.csv")
translation = pd.read_csv("../data/translation.tsv", sep="\t")
pathways = pd.read_csv("../data/pathways.tsv", sep="\t")
network = Network(
input_data=input_data,
pathways=pathways,
mapping=translation,
... |
89a5d4455f59ac57af53931d8309b0df711904a1360b12624b9672e1a2b8660e | Jupyter | 1,772 | 61 | # %% [markdown]
# # BINN - Biologically Informed Neural Network
#
# This notebooks demonstrates some examples on how a BINN can be created.
# %% [markdown]
# Read some test data. This requires and input and a pathway file. These correspond to the first layer (input) and intermediary (hidden) layers in the model. We a... |
e346f6745a86a9567b2fd4d5c5b0dfc69d51c0a2406e209a8d52a01e1d3aa621 | Jupyter | 1,803 | 75 | # %%
import os
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import numpy as np
import pathlib
import cv2
import splitfolders
# %%
train_dir="data/intel_dataset/images/seg_train/seg_train"
test_dir="data/intel_dataset/images/se... |
f9595f3fd6bab66c8d05508752863bb5403c477fffddfc2b7782cef30222f3d3 | Jupyter | 1,878 | 85 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# %%
df=pd.read_csv('E:\\0_PYTHON\\9_Datasets\\PJM\\AEP_hourly.csv' ,parse_dates=True)
df.head(1)
# %%
# Drop Duplicates Except the First Occurrence
df.drop_duplicates(subset=['Datetime'], keep ='first... |
3f7fbbac16c5a711a62ce58a7fb93fa84f968e82c5d839e7b162f671381e98d6 | Jupyter | 1,939 | 73 | # %%
#!sudo pip3 install "foolbox==3.1.1"
# %%
import time
import torch
import torchvision
import foolbox
import eagerpy as ep
print(torch.__version__)
print(torchvision.__version__)
print(foolbox.__version__)
print(ep.__version__)
# %%
model = torchvision.models.resnet18(pretrained=True).eval()
# %%
preprocessing =... |
ab97ab726b24b9c6018fe6aae06597e640f341a80cc49564ac49e15b35e49748 | Jupyter | 2,007 | 71 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
# %% [markdown]
# # Prepare Data
# %%
import numpy as np
import matplotlib.pyplot as plt
import trefide.preprocess as preprocess
import scipy.io as io
# %%
# Load Data
stim = io.loadmat("../../data/sampleMovie.mat")['stimulationSignal'][1:-50].squeeze()
data... |
7b8c1c968de8da04872aed1b024b666d5b95f193bfbf8257d2b15f5f8f66b17a | Jupyter | 2,017 | 86 | # %%
#%matplotlib widget
%load_ext autoreload
%autoreload 2
#%matplotlib notebook
%matplotlib inline
# %%
import sys
from pathlib import Path
import torch
import numpy as np
import tqdm
from ripser import ripser
from persim import plot_diagrams
# Plotting
import matplotlib
from matplotlib import pyplot as plt
plt.st... |
f5990ee0bc0f00fba1cee0854861901bea37089d5d3660c7c9db420d8c951a99 | Jupyter | 2,052 | 54 | # %% [markdown]
# # How to visualize the conditional posterior distribution
#
# In all tutorials, we used the `pairplot` method to visualize the posterior. This method visualizes the posterior _marginals_. Another option to visualize the posterior _conditionals_. You can do this with the `conditional_pairplot` functio... |
0d1c4a8c4d00b02f7fc18a64bc8199e15708c4d9261ae1e87c26495b858b969a | Jupyter | 2,069 | 76 | # %%
#!sudo pip3 install "foolbox==2.4.0"
# %%
import time
import warnings
import torch
import torchvision
import foolbox
import numpy as np
print(torch.__version__)
print(torchvision.__version__)
print(foolbox.__version__)
print(np.__version__)
# %%
model = torchvision.models.resnet18(pretrained=True).eval()
# %%
p... |
2d174246c14eac43de4dd3b42a7d70fb77f9cdc73308127267e04e6ec3dd5d7d | Jupyter | 2,134 | 85 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem.Draw import rdMolDraw2D
import IPython.display
from collections import defaultdict
from tqdm import tqdm
from serenityff.charge.tree.dash_tree import DASHTree
# %%
dftd4_sdf_p... |
65cc9a3f2e5c9be09966ac4858651cdb8ec73e1daf7b3ec36baeca2d70150076 | Jupyter | 2,235 | 75 | # %% [markdown]
# # Usage example for the DASH tree
# %% [markdown]
# ## Import and initialize the DASH tree
# %%
from rdkit import Chem
from tqdm import tqdm
from serenityff.charge.tree.dash_tree import DASHTree
from serenityff.charge.tree.atom_features import AtomFeatures
# %% [markdown]
# The default dash tree is... |
7ce2fadba5d8921e480f7478b593c7cd8c150a2ed8ad28303f15c75079610758 | Jupyter | 2,246 | 59 | # %%
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
# %%
mean_nrem = np.load(r'C:\Users\Lenovo\1代码 REM中beta升高\upload_data\figure4f_nremmean.npy')
error_nrem = np.load(r'C:\Users\Lenovo\1代码 REM中beta升高\upload_data\figure4f_nremerror.npy')
data_nrem = pd.read_excel(... |
0df97472622e95c447bd05dffc3574bbdf6f82647c750639881330cae898afa7 | Jupyter | 2,262 | 59 | # %%
import numpy as np
import scanpy as sc
import scvelo as scv
import latentvelo as ltv
# %%
adata = scv.read_loom('simdata_bursts.loom')
adata.obs['celltype'] = ['cluster'+str(x) for x in adata.obs['celltype']]
# %%
adata = ltv.utils.anvi_clean_recipe(adata, spliced_key='spliced', unspliced_key='unspliced',
... |
3ce3468c2b2a796c6a7d8014825c1b9bab4184f0c504acdc590f15c243cdc319 | Jupyter | 2,282 | 88 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import pdb
import matplotlib
import seaborn as sns
# %%
df = pd.read_csv('C:\\Users\\arif\\OneDrive\\Journal Paper\\Code\\Irshad\\Dataset\\7_Dataset.csv', parse_dates=['date_and_hour'], index_col='date_an... |
02ea4e7cbd04ccd1e14a700e01593e8741dc34519fa9f876236c112afcbe5b58 | Jupyter | 2,306 | 85 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
# %% [markdown]
# # Prepare Data
# %%
import numpy as np
import matplotlib.pyplot as plt
import trefide.preprocess as preprocess
from trefide.pmd import batch_decompose
import scipy.io as io
# %%
X = np.load("../../data/demoMovie.npy")
X = np.tile(X, (10, 1... |
822c626671608ff0023a9a3e22d33bd5a631a6648262c1357efe96b4bb7a38ea | Jupyter | 2,325 | 71 | # %%
%load_ext autoreload
%autoreload 2
import sys
sys.path.append('../')
from config import *
import multiprocess
import pickle
configure_pylab()
L = 128
# %%
rp = 1.5 # Range (ratio) to search for optimal period
rv = 10 # Range (ratio) to search for optimal kernel height
Np = 101 # Period grid resolution
Nv = 1... |
218f4f9f21a830c754d74535fc399a34b0ba1e7fe672b326f2b7639b77794552 | Jupyter | 2,352 | 62 | # %%
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
# %%
nrem_mean = np.load(r'C:\Users\Lenovo\1代码 REM中beta升高\upload_data\figure4g_nremmean.npy')
nrem_error = np.load(r'C:\Users\Lenovo\1代码 REM中beta升高\upload_data\figure4g_nremerror.npy')
nrem_data = pd.read_exce... |
44b19368ea136e5336c35fd110c1699682785099f0bc3357d782017812b69da9 | Jupyter | 2,355 | 84 | # %%
#!sudo pip3 install "foolbox==1.8.0"
# %%
import time
import warnings
import torch
import torchvision
import foolbox
import numpy as np
print(torch.__version__)
print(torchvision.__version__)
print(foolbox.__version__)
print(np.__version__)
# %%
model = torchvision.models.resnet18(pretrained=True).eval()
# %%
p... |
1b823f53116026f7cbcdbf80a2ab82d65b6f362759565c0467a0e8729b164777 | Jupyter | 2,363 | 89 | # %%
import os
PODCAST_ROOT = os.environ.get("PODCAST_ROOT", "/engram/naplab/users/rs4613/Podcast") # set to your copy of the dataset
# %% [markdown]
# #### ChatGPT was used to assist writing code. The authors reviewed, edited, and take full responsibility for the content.
# %% [markdown]
# # Create a vector sample... |
8604639cb04d0f2ef65b67a465fa1a490c684c94b21f1f5acd0aa494b47f6328 | Jupyter | 2,406 | 91 | # %% [markdown]
# ### Imports
# %%
%load_ext autoreload
%autoreload 2
#%matplotlib notebook
%matplotlib inline
# %%
# Computational packages
import numpy as np
import matplotlib.pyplot as plt
import torch
import tqdm
# General packages
import datetime
import sys
import os
# custom packages
import ratsimulator
from ... |
739ee6297a3f9c969d7f602dbc4fdd4bf5af9610ac1cd2547a42124aa85c9e73 | Jupyter | 2,419 | 70 | # %%
import numpy as np
from matplotlib import pyplot as plt
from hf_scores import isruc_age, isruc_sex
# %%
_, axes = plt.subplots(1, 2, figsize=(8, 4))
axes = axes.flatten()
# panel ISRUC age prediction
scores_to_plot = {}
for i, (model_run, rmses_model) in enumerate(isruc_age.items()):
for sr in rmses_model.k... |
5aea174ddf2129b3708b24fd7b0948b944f3aebf546c062ca9c782bcaaee931a | Jupyter | 2,433 | 78 | # %%
import pandas as pd
import sys
import numpy as np
import matplotlib.pyplot as plt
sys.path.insert(1, '../utils/')
from import_data import load_suite2p_outputs
# %%
fishlabel = '201007_F02_chx10'
plane = '70um'
df_summary = pd.read_csv('/network/lustre/iss01/wyart/analyses/2pehaviour/MLR_analyses/data_summary_II... |
b2be0cb412e11b70d825e31ced71929ecbe947a08f061c912d55da06a1f58ca4 | Jupyter | 2,434 | 90 | # %%
# import module
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %%
acc = pd.read_csv('/ritter/share/projects/Lab_Rotation_Milena/posthoc_analysis/accuracy.csv')
# %%
acc_kSVM_FU3 = acc.loc[(acc['Model'] == 'Kernel SVM') & (acc['TP'] == 'FU3')]
# %%
# sort datafram... |
0164b155d89c881eea40fbb64cbbf49c70b5daf2290ff014ba80d683e805a225 | Jupyter | 2,484 | 77 | # %% [markdown]
# # Using SBI to infer parameter given observed data
#
# Once simulated data has been generated (see `1_simulation` notebook), applying SBI is straight forward using the `sbi` package:
#
# - select an SBI method, we use SNPE (see paper for details)
# - add the simulated data and corresponding paramet... |
de6f62592468aa3bd16b7976825c6f14238d37a9e2f1df3ad26f4df976ac2cee | Jupyter | 2,484 | 74 | # %% [markdown]
# # How to visualize MCMC diagnostics with ArviZ
#
# [ArviZ](https://python.arviz.org/en/latest/) provides standard diagnostics and visual summaries (rank plots,
# autocorrelation, ESS, and more) from an `InferenceData` container.
#
# ArviZ is available whenever `pymc` is installed (it is a PyMC depen... |
fcbc7b608137c1fd087f5e79c19412ed9658364701e7ff0e98eb671161fdf5e1 | Jupyter | 2,507 | 70 | # %% [markdown]
# # **DeepSlice notebook**
# this is an example notebook designed to teach you how to use DeepSlice
#
# %%
import os
# %%
#first lets ensure we are in the DeepSlice parent directory
# if we are in examples/example_notebooks, we need to go up two directories
os.chdir('../../')
# %% [markdown]
# ## *... |
24f80ef3f0d92357652cf79f5ae56d1cf77613496617308709c1d0b30da90d7a | Jupyter | 2,529 | 67 | # %%
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import sys
sys.path.append('../')
from jax.config import config
import tensorflow as tf
import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false'
tf.config.set_visible_devices([], device_type='GPU')
# %%
an... |
6369b73aa01428d3624566a39971f9ada2bbcdf14c5ed1c20062e4a06173feeb | Jupyter | 2,549 | 83 | # %%
%load_ext autoreload
%autoreload 2
from config import *
# %%
L = 128
fn = 'r2405_051216b_cell1816.mat'
data = Dataset.from_file(datadir+fn).prepare(L)
# Prepare hyperparameter grid
Po = data.P # Initial guess for kernel period
Vo = data.V # Initial guess for kernel height
rp = 2 # Range (ratio) to search f... |
1b3c4d53acfb4e48822233c8833e867af08ff2ff1f1a63b17910743a1d6a9014 | Jupyter | 2,554 | 75 | # %% [markdown]
# # How to use `PosteriorParameters` in sbi
#
# The `PosteriorParameters` classes in SBI are dataclasses used to initialize parameters for different types of posterior algorithms, when using the `build_posterior` method. This guide shows how to configure and use PosteriorParameters in your workflow.
# ... |
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