| node_remap_array = [8, 16, 24, 32, 41, 33, 57, 49, | |
| 7, 15, 23, 31, 42, 34, 58, 50, | |
| 6, 14, 22, 30, 43, 35, 59, 51, | |
| 5, 13, 21, 29, 44, 36, 60, 52, | |
| 4, 12, 20, 28, 45, 37, 61, 53, | |
| 3, 11, 19, 27, 46, 38, 62, 54, | |
| 2, 10, 18, 26, 47, 39, 63, 55, | |
| 1, 9, 17, 25, 48, 40, 64, 56] | |
| node_remap_dict = {} |
| import requests | |
| # Update or create storage bin | |
| def put_json_storage(to_publish, storage_name): | |
| headers = {'Content-Type': "application/json; charset=utf-8",'dataType': "json"} | |
| data = json.dumps({'data': to_publish}) | |
| url = f'https://jsonstorage.net/api/items/{storage_name}' | |
| resp = requests.put(url, headers=headers, data=data) | |
| if resp.status_code == 200 or 201: | |
| print(f'Data successfull uploaded: {resp.json()})') |
| import requests | |
| from blackfynn import Blackfynn | |
| from config import API_SECRET, API_TOKEN | |
| import os | |
| bf = Blackfynn(api_token=API_SECRET,api_secret=API_TOKEN) | |
| collection = 'N:collection:0eed07e7-d147-4a2c-9411-1ea8f9ceffa5' | |
| folder = 'downloads/' | |
| file_type = '.abf' |
| import plotly.graph_objects as go | |
| import plotly.figure_factory as ff | |
| from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot | |
| import numpy as np | |
| from scipy.spatial.distance import pdist, squareform | |
| import pandas as pd | |
| // How to use this example: | |
| // 1. Navigate to a race's odds page. This can be found by navigating to: | |
| // https://www.tabtouch.com.au/racing/<today's date>/<meeting abbreviation>/1 | |
| // 2. Paste the code below into the chrome console and run | |
| // Results: First horses name and odds should be printed to console | |
| parent = document.querySelector("tr[data-starter='1']") |
| # This document is to define the inputs and formatting outputs of the soon to be developed tabtouch scraper | |
| # Inputs: | |
| # date: string in form yyyy-mm-dd e.g. '2019-12-31' | |
| # event: sting of even name e.g. 'Albury' | |
| # race: int of race number e.g. 1 | |
| # Outputs: | |
| # odds: Either a python dictionary, json file, or json formatted string as defined below |
| !function(t,n){"object"==typeof exports&&"object"==typeof module?module.exports=n(require("plotly.js")):"function"==typeof define&&define.amd?define("vue-plotly",["plotly.js"],n):"object"==typeof exports?exports["vue-plotly"]=n(require("plotly.js")):t["vue-plotly"]=n(t["plotly.js"])}("undefined"!=typeof self?self:this,function(t){return function(t){function n(r){if(e[r])return e[r].exports;var o=e[r]={i:r,l:!1,exports:{}};return t[r].call(o.exports,o,o.exports,n),o.l=!0,o.exports}var e={};return n.m=t,n.c=e,n.d=function(t,e,r){n.o(t,e)||Object.defineProperty(t,e,{configurable:!1,enumerable:!0,get:r})},n.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return n.d(e,"a",e),e},n.o=function(t,n){return Object.prototype.hasOwnProperty.call(t,n)},n.p="",n(n.s=23)}([function(t,n){function e(t){var n=typeof t;return null!=t&&("object"==n||"function"==n)}t.exports=e},function(t,n,e){var r=e(14),o="object"==typeof self&&self&&self.Object===Object&&self,i=r||o||Function("return this" |
This document is a list of scicrunch queries currently and planned for use in ABI web apps for sparc.science All queries are made from a flask proxy server which is called from the vue front end
The below snippets are pulled fom @tehsurfer/sparc-api used for the sidebar
# Type map is used to map scicrunch paths to given facet
| # discover-simulation-search.py: Loop through all discover datasets finding keywords. | |
| # we then search across these keywords for terms that are stimulation: 'stimulation 'and | |
| import requests | |
| keywords = [] | |
| for i in range(1,106): | |
| r = requests.get(f'https://api.blackfynn.io/discover/datasets/{i}/versions/1/metadata') | |
| if r.status_code is 200: | |
| keywords.append(r.json()['keywords']) |