Is it possible to use highcharts heat map chart using rally sdk? - rally
I'm building a rally custom HTML app. I would like to create a heat map chart. How can I do that?
I've tried to create Rally Chart of the type 'heatmap' (code below). As a result, I see the 404 error message in the console:
GET https://rally1.rallydev.com/slm/panel/highcharts/heatmap.js?_dc=1558434971290 404
When I try to use Highcharts library directly I'm getting a conflict with https://rally1.rallydev.com/apps/2.1/lib/analytics/analytics-all.js - highcharts being loaded twice. As a result analytics lib do not load I guess and I get an error like Lumenize.Time undefined.
this.chart = this.add(
Ext.create('Rally.ui.chart.Chart', {
loadMask: false,
chartData: {
series: [{
type:'heatmap',
name: 'Sales per employee',
borderWidth: 1,
data: [[0, 0, 10], [0, 1, 19], [0, 2, 8], [0, 3, 24], [0, 4, 67], [1, 0, 92], [1, 1, 58], [1, 2, 78], [1, 3, 117], [1, 4, 48], [2, 0, 35], [2, 1, 15], [2, 2, 123], [2, 3, 64], [2, 4, 52], [3, 0, 72], [3, 1, 132], [3, 2, 114], [3, 3, 19], [3, 4, 16], [4, 0, 38], [4, 1, 5], [4, 2, 8], [4, 3, 117], [4, 4, 115], [5, 0, 88], [5, 1, 32], [5, 2, 12], [5, 3, 6], [5, 4, 120], [6, 0, 13], [6, 1, 44], [6, 2, 88], [6, 3, 98], [6, 4, 96], [7, 0, 31], [7, 1, 1], [7, 2, 82], [7, 3, 32], [7, 4, 30], [8, 0, 85], [8, 1, 97], [8, 2, 123], [8, 3, 64], [8, 4, 84], [9, 0, 47], [9, 1, 114], [9, 2, 31], [9, 3, 48], [9, 4, 91]],
dataLabels: {
enabled: true,
color: '#000000'
}
}]
},
chartConfig: {
chart: {
marginTop: 40,
marginBottom: 80,
plotBorderWidth: 1
},
title: {
text: 'Sales per employee per weekday'
},
xAxis: {
categories: ['Alexander', 'Marie', 'Maximilian', 'Sophia', 'Lukas', 'Maria', 'Leon', 'Anna', 'Tim', 'Laura']
},
yAxis: {
categories: ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
title: null
},
colorAxis: {
min: 0,
minColor: '#FFFFFF',
//maxColor: Highcharts.getOptions().colors[0]
},
legend: {
align: 'right',
layout: 'vertical',
margin: 0,
verticalAlign: 'top',
y: 25,
symbolHeight: 280
},
tooltip: {
formatter: function () {
return '<b>' + this.series.xAxis.categories[this.point.x] + '</b> sold <br><b>' +
this.point.value + '</b> items on <br><b>' + this.series.yAxis.categories[this.point.y] + '</b>';
}
},
}
})
);
The problem you are going to hit is that the Highcharts v3.0.10 is bundled into the analytics code. This has been done so that there is a known Highcharts library used with the added Lumenize code - i.e. they match. The analytics library is dynamically loaded if the SDK can't find a Highcharts library as it starts up (i.e. window.Highcharts is undefined).
The result of this is that it looks like it is quite kludgy to change the Highcharts arrangement to add heatmaps. Someone who is better at javascript library loading/overloading might have a different view. You would have to load the heatmap.js file into your app after the app has started (in 'launch'?) to get around the dynamic loading.
I know that this is not directly related to your Highcharts question, but if it is just a 'heatmap' you are after, not necessarily a Highcharts heatmap, I have started to use d3 to visualise stuff in Rally. There is a d3 heatmap example here if you are interested: http://bl.ocks.org/tjdecke/5558084
I have done a bit of work to get d3 working inside a Rally custom app. There are a few examples on my github, but have a look here: https://github.com/nikantonelli/Radial-Density
Related
ax=b solving with numpy llinalg gives "Last 2 dimensions of the array must be square"
I have 3 matrices of shape (157, 4) , (157,) and (4,) respectively. The first matrix (an_array) contains the quantity of the items. the second, (Items matrix) contains the items names and the 3rd (Price matrix) one contains the price. I am trying to workout the price of individual items but I get an error saying "Last 2 dimensions of the array must be square" everytime I am trying to do X = np.linalg.solve(an_array,price) I am trying to solve ax=b but unfortunately its not working out. Any help would be very much appreciated. **Price matrix** array([499.25, 381. , 59.5 , 290. , 128.5 , 305.25, 336.25, 268.5 , 395.25, 136.5 , 194.5 , 498.75, 62.25, 312.75, 332. , 171. , 402.5 , 144.5 , 261.5 , 242.75, 381. , 371. , 355.5 , 373. , 65.5 , 228.75, 208.75, 204.5 , 86.5 , 143. , 70.5 , 36.5 , 82. , 302.5 , 365.5 , 158.5 , 316.5 , 508. , 86.5 , 359.75, 25.5 , 345.5 , 304.5 , 491.25, 181.5 , 343.75, 383.5 , 283.5 , 140.25, 426. , 386. , 337.25, 415.5 , 268.25, 406. , 149.5 , 200. , 122. , 510.25, 280. , 406.75, 191.25, 198. , 114.5 , 211.5 , 241.75, 195.75, 276.25, 276. , 165.25, 102. , 425. , 195. , 132.25, 86.75, 446.5 , 318. , 290.75, 286. , 232. , 520.5 , 382.75, 94. , 482.75, 233.25, 262. , 368.25, 438.75, 433.5 , 334.5 , 360. , 422. , 191. , 292.25, 151.75, 440.25, 370. , 105.25, 122. , 455.5 , 363. , 436. , 147.5 , 548.5 , 365.75, 185.5 , 348. , 342.5 , 509.25, 465.5 , 380.25, 361. , 271.25, 414.25, 366.75, 145.5 , 348. , 471.25, 254.5 , 329. , 441. , 253.25, 448.5 , 142. , 312.5 , 350. , 94. , 333. , 418. , 194.5 , 543. , 212.5 , 66.5 , 370. , 423. , 164. , 393.25, 299.75, 529.5 , 166.25, 228.5 , 476. , 373. , 383.25, 409. , 241. , 107.75, 194.5 , 350. , 221.75, 633.25, 444.25, 155.25, 76. , 542. , 346. , 159.75]) **Item matrix** array(['Cereal', 'Coffee', 'Eggs', 'Pancake'], dtype='<U7') #an_array matrix array([[ 7, 6, 6, 9], [ 6, 7, 0, 10], [ 0, 7, 1, 0], [ 4, 0, 10, 0], [ 0, 0, 4, 2], [10, 7, 0, 3], [ 9, 1, 4, 3], [ 9, 8, 0, 2], [10, 0, 4, 5], [ 6, 3, 0, 0], [ 0, 1, 9, 0], [ 3, 9, 9, 9], [ 2, 0, 0, 1], [ 6, 0, 6, 3], [ 9, 0, 3, 4], [ 8, 2, 0, 0], [ 9, 0, 0, 10], [ 5, 0, 0, 2], [ 8, 7, 3, 0], [ 0, 1, 6, 5], [ 7, 0, 3, 8], [ 0, 5, 10, 6], [ 7, 1, 10, 0], [ 4, 7, 10, 2], [ 0, 0, 1, 2], [ 2, 6, 0, 7], [ 6, 4, 0, 3], [ 8, 0, 0, 2], [ 0, 0, 2, 2], [ 3, 7, 0, 2], [ 0, 9, 1, 0], [ 1, 3, 0, 0], [ 3, 4, 0, 0], [ 4, 0, 0, 10], [ 4, 9, 7, 4], [ 6, 7, 0, 0], [ 6, 9, 7, 0], [ 6, 0, 10, 8], [ 0, 0, 2, 2], [ 6, 0, 4, 7], [ 1, 1, 0, 0], [ 3, 9, 7, 4], [ 0, 1, 10, 4], [ 6, 1, 10, 7], [ 0, 2, 6, 2], [ 4, 1, 7, 5], [ 0, 3, 9, 8], [ 3, 2, 8, 2], [ 0, 10, 3, 1], [ 4, 0, 8, 8], [ 2, 0, 8, 8], [ 7, 8, 2, 5], [ 7, 2, 2, 10], [ 0, 9, 3, 7], [ 4, 4, 6, 8], [ 5, 9, 0, 0], [ 0, 2, 9, 0], [ 4, 0, 2, 0], [10, 9, 5, 7], [ 4, 4, 0, 8], [ 9, 10, 5, 3], [ 4, 0, 0, 5], [ 1, 0, 0, 8], [ 1, 1, 0, 4], [ 4, 1, 6, 0], [ 0, 8, 2, 7], [ 1, 5, 6, 1], [ 4, 4, 3, 5], [ 9, 6, 3, 0], [ 2, 3, 2, 3], [ 4, 4, 0, 0], [ 8, 10, 10, 0], [ 6, 6, 2, 0], [ 0, 0, 1, 5], [ 1, 0, 0, 3], [ 7, 0, 4, 10], [ 4, 8, 5, 4], [ 9, 8, 0, 3], [ 4, 6, 4, 4], [10, 2, 1, 0], [10, 3, 6, 8], [ 6, 8, 3, 7], [ 0, 9, 0, 2], [ 8, 7, 4, 9], [ 0, 6, 0, 9], [ 0, 0, 4, 8], [ 0, 0, 8, 9], [ 8, 8, 8, 3], [ 3, 5, 8, 8], [ 7, 3, 0, 8], [ 9, 6, 7, 0], [ 8, 0, 4, 8], [ 9, 2, 0, 0], [ 6, 3, 0, 7], [ 0, 4, 3, 3], [10, 1, 8, 3], [ 5, 10, 6, 4], [ 0, 7, 0, 3], [ 4, 0, 2, 0], [10, 6, 0, 10], [ 4, 0, 5, 8], [10, 0, 7, 4], [ 1, 7, 0, 4], [10, 0, 6, 10], [ 8, 10, 4, 3], [ 9, 1, 0, 0], [ 9, 0, 8, 0], [ 6, 0, 0, 10], [ 9, 1, 8, 7], [ 1, 10, 8, 10], [ 0, 6, 7, 9], [10, 5, 0, 6], [ 0, 10, 5, 5], [ 8, 6, 1, 9], [ 0, 4, 9, 7], [ 4, 0, 1, 2], [ 3, 9, 5, 6], [10, 10, 5, 5], [ 5, 0, 1, 6], [ 9, 8, 5, 0], [ 8, 3, 2, 10], [ 0, 2, 2, 9], [ 5, 9, 10, 4], [ 6, 4, 0, 0], [ 8, 1, 7, 0], [ 6, 7, 7, 2], [ 0, 9, 0, 2], [10, 8, 0, 4], [10, 1, 8, 2], [ 0, 3, 0, 8], [10, 8, 10, 4], [ 0, 0, 8, 2], [ 0, 4, 0, 2], [ 8, 0, 10, 0], [ 7, 0, 5, 8], [ 6, 8, 0, 0], [ 8, 6, 0, 9], [10, 6, 0, 3], [ 9, 4, 5, 10], [ 0, 10, 0, 5], [ 6, 4, 2, 2], [ 9, 10, 3, 8], [ 8, 3, 3, 6], [ 6, 0, 3, 9], [ 3, 1, 10, 6], [ 0, 0, 3, 8], [ 4, 1, 0, 1], [ 5, 1, 0, 4], [ 7, 0, 10, 0], [ 5, 10, 0, 3], [10, 8, 9, 9], [10, 6, 9, 1], [ 0, 8, 0, 5], [ 0, 10, 1, 0], [ 8, 7, 10, 6], [ 0, 0, 8, 8], [ 0, 5, 1, 5]])
you have 4 variables that are present in 157 equations, and you need to solve for the 4 variables, this system has no solution and is not solved by a linear system solution, rather as a least squares solution using np.linalg.lstsq X = np.linalg.lstsq(an_array, price)[0] print(X)
Using the lstsqr as recommended by #Ahmed: In [88]: X = np.linalg.lstsq(quant, price)[0] C:\Users\paul\AppData\Local\Temp\ipykernel_6428\995094510.py:1: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions. To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`. X = np.linalg.lstsq(quant, price)[0] In [90]: X Out[90]: array([20. , 5.5 , 21. , 22.25]) Multiplying the quantities by these prices and summing gives the same numbers as your price array: In [91]: quant#X Out[91]: array([499.25, 381. , 59.5 , 290. , 128.5 , 305.25, 336.25, 268.5 , 395.25, 136.5 , 194.5 , 498.75, 62.25, 312.75, 332. , 171. , ... , 159.75]) So there's no noise in these prices - the values are exact. That means you could use solve with any 4 of the price and quant values. That addresses the "must be square" error. quant[:4,:] has (4,4) shape. In [92]: np.linalg.solve(quant[:4,:], price[:4]) Out[92]: array([20. , 5.5 , 21. , 22.25])
Keen-dataviz: Uncaught Requested parser does not exist
I keep getting the following error: "Uncaught Requested parser does not exist" from the devtool console and no chart is displayed on my dashboard. JS Code: var chart = new Keen.Dataviz() .el('#chart-01') .height(280) .title('Registered CSRs') .type('bar') .prepare(); // Fetch data from my server's API var json = $.ajax({ url: "/api/v1.0/registered_csrs/nwg", dataType: "json", success: function (jsonData) { chart .data(jsonData) .render(); } }); Here is the format of jsonData $ curl -GET http://localhost:4000/api/v1.0/registered_csrs/nwg [["Date", "Total", "Emergency", "High", "Medium", "Low"], ["2016-02-01", 1, 0, 0, 1, 0], ["2016-03-01", 6, 0, 0, 6, 0], ["2016-04-01", 11, 0, 1, 7, 3], ["2016-05-01", 19, 0, 1, 16, 2], ["2016-06-01", 27, 0, 12, 13, 2], ["2016-07-01", 27, 3, 12, 12, 0], ["2016-08-01", 25, 3, 8, 11, 3], ["2016-09-01", 21, 4, 10, 5, 2], ["2016-10-01", 19, 3, 4, 11, 1], ["2016-11-01", 29, 4, 12, 12, 1], ["2016-12-01", 26, 2, 9, 14, 1], ["2017-01-01", 16, 1, 3, 11, 1], ["2017-02-01", 22, 2, 8, 11, 1], ["2017-03-01", 28, 2, 10, 14, 2], ["2017-04-01", 15, 2, 6, 5, 2], ["2017-05-01", 28, 2, 7, 18, 1], ["2017-06-01", 22, 1, 11, 8, 2], ["2017-07-01", 10, 1, 4, 5, 0]]
Take a look Keen.io Dataviz to draw graph but keep getting error "Uncaught Requested parser does not exist" https://keen.io/docs/visualize/visualize-your-own-data/ Code below should work fine in your app chart .data({result: jsonData}) .render();
Splitting a number and assigning to elements in a row in a numpy array
How to place a list of numbers in to a 2D numpy array, where the second dimension of the array is equal to the number of digits of the largest number of that list? I also want the elements that don't belong to the original number to be zero in each row of the returning array. Example: From the list a = range(0,1001), how to get the numpy array of the below form: [[0,0,0,0], [0,0,0,1], [0,0,0,2], ... [0,9,9,8] [0,9,9,9], [1,0,0,0]] Please note how the each number is placed in-place in a np.zeros((1000,4)) array at the end of the each row. NB: A pythonic, vectorized implementation is expected
Broadcasting again! def split_digits(a): N = int(np.log10(np.max(a))+1) # No. of digits r = 10**np.arange(N,-1,-1) # 10-powered range array return (np.asarray(a)[:,None]%r[:-1])//r[1:] Sample runs - In [224]: a = range(0,1001) In [225]: split_digits(a) Out[225]: array([[0, 0, 0, 0], [0, 0, 0, 1], [0, 0, 0, 2], ..., [0, 9, 9, 8], [0, 9, 9, 9], [1, 0, 0, 0]]) In [229]: a = np.random.randint(0,1000000,(7)) In [230]: a Out[230]: array([431921, 871855, 636144, 541186, 410562, 89356, 476258]) In [231]: split_digits(a) Out[231]: array([[4, 3, 1, 9, 2, 1], [8, 7, 1, 8, 5, 5], [6, 3, 6, 1, 4, 4], [5, 4, 1, 1, 8, 6], [4, 1, 0, 5, 6, 2], [0, 8, 9, 3, 5, 6], [4, 7, 6, 2, 5, 8]])
Another concept using pandas str def pir(a): z = int(np.log10(np.max(a))) s = pd.Series(a.astype(str)) zfilled = s.str.zfill(z + 1).sum() a_ = np.array(list(zfilled)).reshape(-1, z + 1) return a_.astype(int) Using #Divakar's random array a = np.random.randint(0,1000000,(7)) array([ 57190, 29950, 392317, 592062, 460333, 639794, 983647]) pir(a) array([[0, 5, 7, 1, 9, 0], [0, 2, 9, 9, 5, 0], [3, 9, 2, 3, 1, 7], [5, 9, 2, 0, 6, 2], [4, 6, 0, 3, 3, 3], [6, 3, 9, 7, 9, 4], [9, 8, 3, 6, 4, 7]])
Select elements of a numpy array based on the elements of a second array
Consider a numpy array A of shape (7,6) A = array([[0, 1, 2, 3, 5, 8], [4, 100, 6, 7, 8, 7], [8, 9, 10, 11, 5, 4], [12, 13, 14, 15, 1, 2], [1, 3, 5, 6, 4, 8], [12, 23, 12, 24, 4, 3], [1, 3, 5, 7, 89, 0]]) together with a second numpy array r of the same shape which contains the radius of A starting from a central point A(3,2)=0: r = array([[3, 3, 3, 3, 3, 4], [2, 2, 2, 2, 2, 3], [2, 1, 1, 1, 2, 3], [2, 1, 0, 1, 2, 3], [2, 1, 1, 1, 2, 3], [2, 2, 2, 2, 2, 3], [3, 3, 3, 3, 3, 4]]) I would like to pick up all the elements of A which are located at the position 1 of r, i.e. [9,10,11,15,4,6,5,13], all the elements of A located at position 2 of r and so on. I there some numpy function to do that? Thank you
You can select a section of A by doing something like A[r == 1], to get all the sections as a list you could do [A[r == i] for i in range(r.max() + 1)]. This will work, but may be inefficient depending on how big the values in r go because you need to compute r == i for every i. You could also use this trick, first sort A based on r, then simply split the sorted A array at the right places. That looks something like this: r_flat = r.ravel() order = r_flat.argsort() A_sorted = A.ravel()[order] r_sorted = r_flat[order] edges = r_sorted.searchsorted(np.arange(r_sorted[-1] + 1), 'right') sections = [] start = 0 for end in edges: sections.append(A_sorted[start:end]) start = end
I get a different answer to the one you were expecting (3 not 4 from the 4th row) and the order is slightly different (strictly row then column), but: >>> A array([[ 0, 1, 2, 3, 5, 8], [ 4, 100, 6, 7, 8, 7], [ 8, 9, 10, 11, 5, 4], [ 12, 13, 14, 15, 1, 2], [ 1, 3, 5, 6, 4, 8], [ 12, 23, 12, 24, 4, 3], [ 1, 3, 5, 7, 89, 0]]) >>> r array([[3, 3, 3, 3, 3, 4], [2, 2, 2, 2, 2, 3], [2, 1, 1, 1, 2, 3], [2, 1, 0, 1, 2, 3], [2, 1, 1, 1, 2, 3], [2, 2, 2, 2, 2, 3], [3, 3, 3, 3, 3, 4]]) >>> A[r==1] array([ 9, 10, 11, 13, 15, 3, 5, 6]) Alternatively, you can get column then row ordering by transposing both arrays: >>> A.T[r.T==1] array([ 9, 13, 3, 10, 5, 11, 15, 6])
numpy custom array element retrieval
I have a question regarding how to extract certain values from a 2D numpy array Foo = array([[ 1, 2, 3], [ 4, 5, 6], [ 7, 8, 9], [10, 11, 12]]) Bar = array([[0, 0, 1], [1, 2, 3]]) I want to extract elements from Foo using the values of Bar as indices, such that I end up with an 2D matrix/array Baz of the same shape as Bar. The ith column in Baz correspond is Foo[(np.array(each j in Bar[:,i]),np.array(i,i,i,i ...))] Baz = array([[ 1, 2, 6], [ 4, 8, 12]]) I could do a couple nested for-loops but I was wondering if there is a more elegant, numpy-ish way to do this. Sorry if this is a bit convoluted. Let me know if I need to explain further. Thanks!
You can use Bar as the row index and an array [0, 1, 2] as the column index: # for easy copy-pasting import numpy as np Foo = np.array([[ 1, 2, 3], [ 4, 5, 6], [ 7, 8, 9], [10, 11, 12]]) Bar = np.array([[0, 0, 1], [1, 2, 3]]) # now use Bar as the `i` coordinate and 0, 1, 2 as the `j` coordinate: Foo[Bar, [0, 1, 2]] # array([[ 1, 2, 6], # [ 4, 8, 12]]) # OR, to automatically generate the [0, 1, 2] Foo[Bar, xrange(Bar.shape[1])]