In Odysee there in an analytics tab that I use quite rarely. Since it has almost no valuable information about anything at all. Also there is the whole block-chain, with the wallet history and other things that could be used as a data for analytics.
As more people watch a given publication, more LBC is being supported to it. And we can use this data to extract a graph that could be very useful to a lot of LBRY users.
I know that it doens't sound like a terminal feature. But a simple graph in a terminal is implementable. Also using something like pycairo we can generate a more precise image, documenting a given graph. Which could be saved as a file and opened in a default image viewer application.
A discussion of how this could be done, could happen here. I would like to see your ideas.
In Odysee there in an analytics tab that I use quite rarely. Since it has almost no valuable information about anything at all. Also there is the whole block-chain, with the wallet history and other things that could be used as a data for analytics.
As more people watch a given publication, more LBC is being supported to it. And we can use this data to extract a graph that could be very useful to a lot of LBRY users.
I know that it doens't sound like a terminal feature. But a simple graph in a terminal is implementable. Also using something like pycairo we can generate a more precise image, documenting a given graph. Which could be saved as a file and opened in a default image viewer application.
A discussion of how this could be done, could happen here. I would like to see your ideas.
... the analytics is half implemented. You can already tell with some precision what stuff is more popular and when people were watching / reading. But there are problems to still fix:
Access to raw data for the analytics. So far you can see on a graph the start and the ends time codes. And you have to infer from the image when a certain spike happened. Perhaps a way of interacting with spikes could be done. Either as a boring list of all entries. Or something a bit more clever. Like having a code for each vertical column of the graph. Which let's you access the list of only the entries in that column.
Search in Analytics. For now I need to look through a very slow loading list of publications to access analytics of a certain publication. Which makes it very hard to see analytics of very old publications. Perhaps a way to solve it, is to load analytics from a resolved publication directly. So in url.py we can add sales and analytics options for any resolved publication. That will just draw the graph.
Analytics / Sales for the entire Channel / Account. I would like to be able to see a graph for the entire history of all publications. This will require caching all transactions of any given channel, or all transactions, excluding personal transfers and the reward program.
Exporting analytics. An export of analytics into some easy to read analytics data files could be done. Like CSV. So a better graph could be generated from a dedicated software.
Caching / Saving. Analytics takes time to load. So a way to save the full report into Json and then re-open it right within the graph should exist.
With Commits:
- [Analytics! Real analytics. Damn!](https://notabug.org/jyamihud/FastLBRY-terminal/commit/9b5cc8723229a8c251587e6b3c4fc9b54c62d812)
- [Added Analytics into Help](https://notabug.org/jyamihud/FastLBRY-terminal/commit/24e726c28b20bdf369cb01c36cad19f608d21a69)
- [Softer Graphs. Antialiasing, so to speak.](https://notabug.org/jyamihud/FastLBRY-terminal/commit/fbd00632f51c8ed9fb4ec6b836296a95f1a1cc1c)
- [Settings for ASCII graphs and for more characters in tables.](https://notabug.org/jyamihud/FastLBRY-terminal/commit/fcd1b8916f3d3d3cb22a1c42466f8142b1943969)
... the analytics is half implemented. You can already tell with some precision what stuff is more popular and when people were watching / reading. But there are problems to still fix:
- [x] **Access to raw data for the analytics.** So far you can see on a graph the start and the ends time codes. And you have to infer from the image when a certain spike happened. Perhaps a way of interacting with spikes could be done. Either as a boring list of all entries. Or something a bit more clever. Like having a code for each vertical column of the graph. Which let's you access the list of only the entries in that column.
- [x] **Search in Analytics.** For now I need to look through a very slow loading list of publications to access analytics of a certain publication. Which makes it very hard to see analytics of very old publications. Perhaps a way to solve it, is to load analytics from a resolved publication directly. So in `url.py` we can add `sales` and `analytics` options for any resolved publication. That will just draw the graph.
- [x] **Analytics / Sales for the entire Channel / Account**. I would like to be able to see a graph for the entire history of all publications. This will require caching all transactions of any given channel, or all transactions, excluding personal transfers and the reward program.
- [x] **Exporting analytics**. An export of analytics into some easy to read analytics data files could be done. Like CSV. So a better graph could be generated from a dedicated software.
- [x] **Caching / Saving**. Analytics takes time to load. So a way to save the full report into Json and then re-open it right within the graph should exist.
In Odysee there in an analytics tab that I use quite rarely. Since it has almost no valuable information about anything at all. Also there is the whole block-chain, with the wallet history and other things that could be used as a data for analytics.
As more people watch a given publication, more LBC is being supported to it. And we can use this data to extract a graph that could be very useful to a lot of LBRY users.
I know that it doens't sound like a terminal feature. But a simple graph in a terminal is implementable. Also using something like pycairo we can generate a more precise image, documenting a given graph. Which could be saved as a file and opened in a default image viewer application.
A discussion of how this could be done, could happen here. I would like to see your ideas.
With Commits:
... the analytics is half implemented. You can already tell with some precision what stuff is more popular and when people were watching / reading. But there are problems to still fix:
url.py
we can addsales
andanalytics
options for any resolved publication. That will just draw the graph.DONE!