encointer / accounting-frontend Goto Github PK
View Code? Open in Web Editor NEWfrontend for historical events on Encointer Network. statstics and monthly statements
Home Page: https://accounting.encointer.org
frontend for historical events on Encointer Network. statstics and monthly statements
Home Page: https://accounting.encointer.org
It is undesireable that 2022 has to be analyzed with an Excel spreadsheet which doesn't even last until the end of 2022, but the accounting frontend only seems to show 2023. Moreover, we need to manually add Event's that didn't exist for previous runtime versions.
Our indexer should be enriched with the past (for consistent generic graphQL queries) and the frontend should use it
The explorer shows the current total number of reputables, which is the unique set of accounts with nonzero reputation (over the past 5 cycles)
This metric is dependent on the reputation lifetime, so it would probably make sense to recompute the entire history with the current lifetime config
y-axis: "number of reputables", linear
x-axis: "date", but indicate ticks with cycle index (or have a secondary axis with cindex)
As this metric is used for key results, we should also draw a regression curve (exponential curve) for the last 3 months and label the growth rate in percent per week
notes:
this plot should be public (and should be an embeddable component / iframe, i.e. for explorer.encointer.org)
Like Fig 6 in https://encointer.org/charting-the-development-of-encointers-longest-established-community-currency/
Provide a plot and CSV with all the relevant raw and calculated data in the "internal" space
this plot should be public
for each cycle
Verified*
entry in the reputation registryseparately:
x-axis should be date, but indicate cycle index too
See our manual statistics until now: https://docs.google.com/spreadsheets/d/1LQ4PCiLRS8P7z1rwphLgv6fOgTmzVRICZCJmhSDZ2B0/edit#gid=0
as a possible extension of #12 we could draw the entire landscape end2end (closing the circle to Retail wasn't possible with the tool)
CII [150] bizA
CII [1600] Retail
Retail [100] demurrage
Retail [860] bizA
bizA [30] demurrage
bizA [300] LEAbuyBack
bizA [200] unknownAccounts
bizA [90] bizB
bizB [30] demurrage
bizB [300] LEAbuyBack
bizB [200] unknownAccounts
Retail [860] bizB
bizB [200] bizC
bizC [20] demurrage
LEAbuyBack [20] demurrage
LEAbuyBack [200] vouchers
LEAbuyBack [200] salaries
Like Fig 7 in https://encointer.org/charting-the-development-of-encointers-longest-established-community-currency/
Provide a plot and CSV with all the relevant raw and calculated data in the "internal" space
data is there already...
For community A I'd like to know the total number of unique accounts which have received a reward at least once during the entire existence of the community
It would be nice to plot this as a monotonically increasing graph over time (cycle index)
I tried to do this query
query Query {
issueds(
filter: { arg0: { equalTo: "u0qj944rhWE" } }
orderBy: TIMESTAMP_DESC
) {
nodes {
id
blockHeight
timestamp
arg0
arg1
arg2
}
}
}
but I can't easily collect all entries because of pagination :-(
See Fig 5 of our blog: https://encointer.org/charting-the-development-of-encointers-longest-established-community-currency/
Provide a plot and CSV with all the relevant raw and calculated data in the "internal" space
this plot should be public (and should be an embeddable component / iframe, i.e. for explorer.encointer.org)
easiest first approach:
Like Fig 1 in https://encointer.org/charting-the-development-of-encointers-longest-established-community-currency/
The data is there already, just add a plot
https://accounting.encointer.org/rewards-report
this plot should be public (this issue includes setup of public page and embeddable iframe)
based on https://accounting.encointer.org/account-overview
a quick python hack
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
data = pd.read_csv('Account_Overview_Tue, 12 Mar 2024 13_14_00 GMT_Leu Zürich.csv', sep=',',header=1, index_col =0)
fig, ax = plt.subplots()
hist = data.hist(ax=ax, bins=np.linspace(0,1000,num=100), log=True, )
nr_meaningful_accounts = data[data > 5].count().values[0]
plt.title(f"{nr_meaningful_accounts} accounts with > 5 Leu")
plt.ylim([.5,100])
plt.xlabel("balance [LEU]")
plt.ylabel("number of accounts")
Like fig 3 in https://encointer.org/charting-the-development-of-encointers-longest-established-community-currency/
Provide a plot and CSV with all the relevant raw and calculated data in the "internal" space
this plot should be public
In order to quantify and categorize monetary flows, a Sankey diagram shall be used:
example:
Subject (bizA) (to choose via dropdown selector):
Time range (to choose interactively):
Categories:
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