Dimension elements "lost" when switching to Pie chart about cubesviewer HOT 8 CLOSED

In order to tell on which side it is, can you please turn on the browser inspect tools and check the network tab? Please post, for both queries, the request performed by CubesViewer and a sample of the response JSON from Cubes.

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Ok, so, bar chart:

request:
http://localhost:5000/cube/spending_amounts/aggregate?drilldown=spending_date@weekly:month|item@default:category&cut=currency@default:EUR

response (first several items, 22kb total):
{ "summary": { "amount_sum": 2558.0700000000006, "record_count": 1063 }, "remainder": {}, "cells": [ { "spending_date.year": 2018, "spending_date.month": 4, "item.category": "\u041f\u0440\u043e\u0435\u0437\u0434", "amount_sum": 220.6, "record_count": 1 }, { "spending_date.year": 2018, "spending_date.month": 5, "item.category": "\u041d\u0435\u0438\u0437\u0432\u0435\u0441\u0442\u043d\u043e \u043d\u0430 \u0447\u0442\u043e", "amount_sum": -12.0, "record_count": 2 }, { "spending_date.year": 2018, "spending_date.month": 5, "item.category": "\u041e\u0431\u044f\u0437\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u043f\u043b\u0430\u0442\u0435\u0436\u0438", "amount_sum": -1600.0, "record_count": 6 }, { "spending_date.year": 2018, "spending_date.month": 5, "item.category": "\u041e\u0434\u0435\u0436\u0434\u0430, \u043e\u0431\u0443\u0432\u044c", "amount_sum": -22.7, "record_count": 3 }, { "spending_date.year": 2018, "spending_date.month": 5, "item.category": "\u041e\u0442\u0434\u044b\u0445", "amount_sum": -68.21, "record_count": 12 }, { "spending_date.year": 2018, "spending_date.month": 5, "item.category": "\u041f\u0438\u0442\u0430\u043d\u0438\u0435", "amount_sum": -219.7, "record_count": 36 }, { "spending_date.year": 2018, "spending_date.month": 5, "item.category": "\u041f\u0440\u043e\u0432\u043e\u0434\u043a\u0430", "amount_sum": 2385.0, "record_count": 1 }, { "spending_date.year": 2018, "spending_date.month": 5, "item.category": "\u041f\u0440\u043e\u0435\u0437\u0434", "amount_sum": -25.0, "record_count": 1 }, { "spending_date.year": 2018, "spending_date.month": 5, "item.category": "\u0425\u043e\u0437\u0440\u0430\u0441\u0445\u043e\u0434\u044b", "amount_sum": -192.95, "record_count": 11 },

same but for Pie chart after "remove horizontal dimension":
Request:
http://localhost:5000/cube/spending_amounts/aggregate?drilldown=item@default:category&cut=currency@default:EUR

Response (complete response, 3 kb total):
{ "summary": { "amount_sum": 2558.0700000000006, "record_count": 1063 }, "remainder": {}, "cells": [ { "item.category": "\u0417\u0434\u043e\u0440\u043e\u0432\u044c\u0435", "amount_sum": -379.38, "record_count": 18 }, { "item.category": "\u041a\u043e\u0441\u043c\u0435\u0442\u0438\u043a\u0430", "amount_sum": -27.34, "record_count": 3 }, { "item.category": "\u041d\u0435\u0438\u0437\u0432\u0435\u0441\u0442\u043d\u043e \u043d\u0430 \u0447\u0442\u043e", "amount_sum": -212.15, "record_count": 15 }, { "item.category": "\u041e\u0431\u0443\u0447\u0435\u043d\u0438\u0435", "amount_sum": -3786.19, "record_count": 16 }, { "item.category": "\u041e\u0431\u044f\u0437\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u043f\u043b\u0430\u0442\u0435\u0436\u0438", "amount_sum": -6378.950000000001, "record_count": 55 }, { "item.category": "\u041e\u0434\u0435\u0436\u0434\u0430, \u043e\u0431\u0443\u0432\u044c", "amount_sum": -561.4700000000001, "record_count": 51 }, { "item.category": "\u041e\u0442\u0434\u044b\u0445", "amount_sum": -2875.150000000001, "record_count": 278 }, { "item.category": "\u041f\u0438\u0442\u0430\u043d\u0438\u0435", "amount_sum": -3991.5499999999965, "record_count": 424 }, { "item.category": "\u041f\u0440\u0438\u0445\u043e\u0434", "amount_sum": 22890.0, "record_count": 10 }, { "item.category": "\u041f\u0440\u043e\u0432\u043e\u0434\u043a\u0430", "amount_sum": 1235.0, "record_count": 6 }, { "item.category": "\u041f\u0440\u043e\u0435\u0437\u0434", "amount_sum": -1854.93, "record_count": 76 }, { "item.category": "\u0425\u043e\u0437\u0440\u0430\u0441\u0445\u043e\u0434\u044b", "amount_sum": -1499.8200000000002, "record_count": 111 } ], "total_cell_count": 12, "aggregates": [ "amount_sum", "record_count" ], "cell": [ { "type": "point", "dimension": "currency", "hierarchy": "default", "level_depth": 1, "invert": false, "hidden": false, "path": [ "EUR" ] } ], "levels": { "item": [ "category" ] }, "attributes": [ "item.category" ], "has_split": false }

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And here's how it looks like afterwards:

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I've been looking at this but I'm not able to reproduce it.

But seems the response does contain 12 values, so looks like a bug in CubesViewer.

1. Can you check if this might be due to Unicode characters? can you try changing the labels?
2. How many rows does the "series view" present? (it should present 12 rows also).
3. How many columns are presented using "vertical bars chart"? (it should be 12 too).
4. Does this happen if you remove the currency filter?
5. Does this happen if drilling down by other dimensions?
6. Does the Javascript console in the browser present any error or warning?

If we cannot pinpoint the root cause, ultimately, I might need a reproduction data (if you can reproduce the issue with a minimal dataset and model).

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It may also be of help if you could run a quick test with the latest jjmontes/mapchart branch (just checkout that branch and load studio.html in the html folder).

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damn, I think I've got the reason :( so, this is a cube of a household budget. One category holds incomes; the others are spendings. So, only one (or in the case of that slightly non-normalized data I've had, two) contains positive values; the rest of them contain negative ones.

Sorry for distraction (. Also, to be honest, I would prefer to be able to have a pie chart for negative values as well. Can you suggest anything ? I guess I could put "-amount" as an expression in this model.json section somehow ?
"aggregates": [ { "name": "amount_sum", "function": "sum", "measure": "amount" }, { "name": "record_count", "function": "count" } ],

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Regarding using -amount, and assuming you are mapping your transactional database (as oppossed to using a ETL process to customize your OLAP database): you could create a database view with your calculated columns and use it instead (my preference), or, depending on your cubes version you may be able to use an attribute mapping such as:

mappings {
    ...,
    "amount": {"table": "yourtable", "column": "amount", "function": "-"}
}

I have just tested this on the latest cubes (master) and seems to work correctly, but I seem to recall there might be an issue with functions. My preference is to either use database views or to create an ETL process and create an analytic database, but your mileage may vary.

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Regarding the negative values in the pie chart, I can confirm negative values are not shown by the pie chart, which imho is not reasonable. Thanks a lot.

So, the very minimum would be to inform the user that N slices are missing due to negative values (possibly shown as 0 value to force their inclusion in the legend?), and show it correctly if all of them are negative.

Would this work for you, or do you have something else in mind for positive+negative values in a pie chart?

I'm closing this issue as resolved and opening #101 to tackle this particular issue. Please post your comments there.

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