| { | |
| "$schema": "https://vega.github.io/schema/vega/v5.json", | |
| "background": "white", | |
| "padding": 5, | |
| "width": 200, | |
| "height": 200, | |
| "style": "cell", | |
| "data": [ | |
| { | |
| "name": "brush_store", |
I want to output transactions so I can explore them with Huey pivot tables: https://rpbouman.github.io/huey/src/index.html
(update: below see Reports)
Testing using hledger_finance files
New columns:
amount_value = only the value of transactions (so I can use as a measure in Huey)debit_or_credit = 'debit' or 'credit' depending if a pos. or neg. transaction on account
| { | |
| "meta": { | |
| "title": "Histogram", | |
| "description": "Below I overlay 2x distribtuions (overall + selected), but to make comparisons it's helpful if I can normalise" | |
| }, | |
| "data": { | |
| "flights": { | |
| "file": "data/flights-200k.parquet" | |
| } | |
| }, |
| { | |
| "meta": { | |
| "title": "Density 1D", | |
| "description": "Density plots (`densityY` mark) for over 200,000 flights, created using kernel density estimation. Binning is performned in-database, subsequent smoothing in-browser. The distance density uses a log-scaled domain. To change the amount of smoothing, use the slider to set the kernel bandwidth.\n" | |
| }, | |
| "data": { | |
| "flights": { | |
| "file": "data/flights-200k.parquet" | |
| } | |
| }, |
| { | |
| "$schema": "https://vega.github.io/schema/vega/v5.json", | |
| "background": "white", | |
| "padding": 5, | |
| "data": [ | |
| {"name": "grid_store"}, | |
| {"name": "cashflow_store"}, | |
| {"name": "source_0", "values": [{"point_id_in":1,"t_in":-1,"zero_duration_in":false,"mort_rate_factor_in":3,"model_id":0,"input_cursor_id":1,"formula":"pv_premiums","value":94.84,"t_in_step_end":0,"A_keep_gte_0":true,"partition":"A","duration_n":0,"value_n":91.11563150937634,"t_in_mean":4.5},{"point_id_in":1,"t_in":-1,"zero_duration_in":false,"mort_rate_factor_in":3,"model_id":0,"input_cursor_id":1,"formula":"pv_premiums","value":94.84,"t_in_step_end":0,"A_keep_gte_0":true,"partition":"B","duration_n":0,"value_n":91.07068315793605,"t_in_mean":4.5},{"point_id_in":1,"t_in":-1,"zero_duration_in":false,"mort_rate_factor_in":3,"model_id":0,"input_cursor_id":1,"formula":"pv_premiums","value":94.84,"t_in_step_end":0,"A_keep_gte_0":true,"partition":"keep","duration_n":0,"value_n":91.07068315793605,"t_in_mean":4.5},{"point_id_in":1,"t_in":-1," |
| { | |
| "$schema": "https://vega.github.io/schema/vega-lite/v5.json", | |
| "description": "Google's stock price over time.", | |
| "data": {"url": "data/stocks.csv"}, | |
| "transform": [{"filter": "datum.symbol==='GOOG'"}], | |
| "layer": [ | |
| {"mark": {"type": "area", "fill": "orange"}}, | |
| { | |
| "mark": { | |
| "type": "area", |
| { | |
| "$schema": "https://vega.github.io/schema/vega-lite/v5.json", | |
| "description": "Google's stock price over time.", | |
| "data": {"url": "data/stocks.csv"}, | |
| "transform": [{"filter": "datum.symbol==='GOOG'"}], | |
| "layer": [ | |
| {"mark": {"type": "area", "fill": "red"}}, | |
| { | |
| "mark": { | |
| "type": "area", |
| { | |
| "$schema": "https://vega.github.io/schema/vega-lite/v5.json", | |
| "description": "Google's stock price over time.", | |
| "data": {"url": "data/stocks.csv"}, | |
| "transform": [{"filter": "datum.symbol==='GOOG'"}], | |
| "mark": { | |
| "type": "area", | |
| "line": {"color": "darkgreen"}, | |
| "fill": { | |
| "x1": 0.1, |
| { | |
| "$schema": "https://vega.github.io/schema/vega-lite/v5.json", | |
| "description": "Google's stock price over time.", | |
| "data": {"url": "data/stocks.csv"}, | |
| "transform": [{"filter": "datum.symbol==='GOOG'"}], | |
| "mark": { | |
| "type": "area", | |
| "line": {"color": "darkgreen"}, | |
| "fill": { | |
| "x1": 0.1, |