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import os
import pandas as pd
import streamlit as st
from meta_utils import run_subprocess, load_metagraphs
# from opendashboards.assets import io, inspect, metric, plot
from meta_plotting import plot_trace, plot_cabals
import asyncio
## TODO: Read blocks from a big parquet file instead of loading all the pickles -- this is slow
def get_or_create_eventloop():
try:
return asyncio.get_event_loop()
except RuntimeError as ex:
if "There is no current event loop in thread" in str(ex):
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
return asyncio.get_event_loop()
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
import bittensor
datadir='data/metagraph/1/'
blockfiles = sorted(int(filename.split('.')[0]) for filename in os.listdir(datadir))
DEFAULT_SRC = 'miner'
DEFAULT_BLOCK_START = blockfiles[0]
DEFAULT_BLOCK_END = blockfiles[-1]
DEFAULT_BLOCK_STEP = 1000
DEFAULT_NTOP = 10
DEFAULT_UID_NTOP = 10
# Set app config
st.set_page_config(
page_title='Validator Dashboard',
menu_items={
'Report a bug': "https://github.com/opentensor/dashboards/issues",
'About': """
This dashboard is part of the OpenTensor project. \n
"""
},
layout = "centered"
)
st.title('Metagraph :red[Analysis] Dashboard :eyes:')
# add vertical space
st.markdown('#')
st.markdown('#')
subtensor = bittensor.subtensor(network='finney')
current_block = subtensor.get_current_block()
current_difficulty = subtensor.difficulty(1, block=current_block)
bcol1, bcol2, bcol3 = st.columns([0.2, 0.6, 0.2])
with bcol1:
st.metric('Current **block**', current_block, delta='+7200 [24hr]')
# st.metric('Current **difficulty**', f'{current_difficulty/10e12:.0}T', delta='?')
block_start, block_end = bcol2.select_slider(
'Select a **block range**',
options=blockfiles,
value=(DEFAULT_BLOCK_START, DEFAULT_BLOCK_END),
format_func=lambda x: f'{x:,}'
)
bcol3.button('Refresh', on_click=run_subprocess)
with st.spinner(text=f'Loading data...'):
# df = load_metagraphs(block_start=block_start, block_end=block_end, block_step=DEFAULT_BLOCK_STEP)
df = pd.read_parquet('blocks_600100_807300_100')
blocks = df.block.unique()
df_sel = df.loc[df.block.between(block_start, block_end)]
# add vertical space
st.markdown('#')
st.markdown('#')
tab1, tab2, tab3, tab4 = st.tabs(["Overview", "Miners", "Validators", "Block"])
miner_choices = ['total_stake','ranks','incentive','emission','consensus','trust','validator_trust','dividends']
cabal_choices = ['hotkey','ip','coldkey']
### Overview ###
with tab1:
x_col = st.radio('X-axis', ['block','timestamp'], index=0, horizontal=True)
acol1, acol2 = st.columns([0.3, 0.7])
sel_ntop = acol1.slider('Number:', min_value=1, max_value=50, value=10, key='sel_ntop')
#horizontal list
miner_choice = acol2.radio('Select:', miner_choices, horizontal=True, index=0)
st.plotly_chart(
plot_trace(df_sel, time_col=x_col,col=miner_choice, ntop=sel_ntop),
use_container_width=True
)
col1, col2 = st.columns(2)
count_col = col1.radio('Count', cabal_choices, index=0, horizontal=True)
y_col = col2.radio('Agg on', cabal_choices, index=2, horizontal=True)
st.plotly_chart(
plot_cabals(df_sel, time_col=x_col, count_col=count_col, sel_col=y_col, ntop=sel_ntop),
use_container_width=True
)
with tab2:
# plot of miner weights versus time/block
pass |