
%load_ext autotime

%load_ext autoreload

%autoreload 2

time: 7.6 ms (started: 2021-08-16 20:02:35 +00:00)


# External dependences
import pandas as pd
import numpy as np
import plotly.express as px
from prophet import Prophet
import matplotlib.pyplot as plt

# Move path to parent folder
import sys
sys.path.insert(1, '../')

import plotly
plotly.offline.init_notebook_mode()

time: 781 ms (started: 2021-08-16 20:02:35 +00:00)


# Create a connection object from a conn string
from filecoin_metrics.connection import get_connection, get_connection_string
conn_string = get_connection_string('../config/sentinel-conn-string.txt')
connection = get_connection(conn_string)

time: 323 ms (started: 2021-08-16 20:02:35 +00:00)


QUERY = """
/* Get the last state of the sectors */
with sector_states as ( 
	select 
	msi.*,
	max(msi.height) over (partition by msi.sector_id, msi.miner_id) as max_height
	from miner_sector_infos msi 
	where msi.activation_epoch > 0
	and msi.expiration_epoch > msi.height /* Get only active sectors */
	order by max_height 
)
select 
count(*) as sector_count,
sum(ss.initial_pledge::numeric) / 1e18 as initial_pledge_in_fil,
count(*) * 32 as network_power_in_gb,
date_trunc('DAY', to_timestamp(height_to_unix(ss.activation_epoch))) as activation_date,
date_trunc('DAY', to_timestamp(height_to_unix(ss.expiration_epoch))) as expiration_date
from sector_states as ss
where ss.max_height = ss.height /* get the last state of the info */
group by activation_date, expiration_date
order by activation_date, expiration_date
"""

query_df = (pd.read_sql(QUERY, connection)
              .assign(network_power_in_pib=lambda df: df.network_power_in_gb / (1024 ** 2))
              .assign(initial_pledge_in_thousand_fil=lambda df: df.initial_pledge_in_fil / 1000))

time: 4min 42s (started: 2021-08-16 20:02:36 +00:00)


# Maximum date for V1 sectors
UPGRADE_DATE = '2020-11-25 00:00:00'

metrics = {'is_v1': lambda x:  x['activation_date'] < UPGRADE_DATE}

query_df = query_df.assign(**metrics)

time: 14.3 ms (started: 2021-08-16 20:07:18 +00:00)


def resample_and_bar_plot(df, resample_rule, time_column, value_column, title, **kwargs):
    fig_df = df.resample(resample_rule, on=time_column, label='left').sum()
    fig = px.bar(fig_df.reset_index(),
                 x=time_column,
                 y=value_column,
                 title=title,
                 **kwargs)
    return fig


def resample_and_bar_plot_relative(df, resample_rule, time_column, value_column, title, **kwargs):
    fig_df = df.resample(resample_rule, on=time_column, label='left').sum()
    y = fig_df.groupby(time_column).sum()
    fig_df /= y
    fig = px.bar(fig_df.reset_index(),
                 x=time_column,
                 y=value_column,
                 title=title,
                 **kwargs)
    return fig

time: 13.4 ms (started: 2021-08-16 20:07:18 +00:00)


df = query_df.copy()

print("Basic stats")
print("---")
print(f"Total sectors (#): {df.sector_count.sum()}")
print(f"Raw bytes power (PiB): {df.network_power_in_gb.sum() / (1024 ** 2) :.3g}")
print(f"Initial pledge (FIL): {df.initial_pledge_in_fil.sum()}")
print("---")

Basic stats
---
Total sectors (#): 76983949
Raw bytes power (PiB): 2.35e+03
Initial pledge (FIL): 22906027.384519055
---
time: 14 ms (started: 2021-08-16 20:07:18 +00:00)


resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'sector_count'
title = 'Count of Expiring  Sectors (#)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
         )

fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             color='is_v1',
             title=title)
fig.show()

time: 301 ms (started: 2021-08-16 20:07:18 +00:00)


resample_rule = '1d'
time_column = 'expiration_date'
value_column = 'sector_count'
title = 'Count of Expiring  Sectors Before 15Jun2021 (log #)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']



fig_df = (df.query("expiration_date < '2021-06-15 00:00+00:00'")
            .groupby(groups)
            .sum()
            .reset_index()
         )

if fig_df.empty:
   print('WARNING: DataFrame is Empty')
else:
   fig = px.bar(fig_df,
               x=time_column,
               y=value_column,
               facet_row=fig_df.is_v1,
               title=title,
               log_y=True)
   fig.show()

WARNING: DataFrame is Empty
time: 24.6 ms (started: 2021-08-16 20:07:18 +00:00)


sector_count = df.sector_count.sum()
v1_count = df.query("is_v1 == True").sector_count.sum()

v1_6mo_count = (df.query("is_v1 == True & expiration_date < '2021-06-01 00:00+00:00'")
                           .sector_count
                           .sum())

v1_12mo_count = (df.query("is_v1 == True & expiration_date < '2021-12-01 00:00+00:00' & expiration_date >= '2021-06-01 00:00+00:00'")
                           .sector_count
                           .sum())


v1_18mo_count = (df.query("is_v1 == True & expiration_date < '2022-06-01 00:00+00:00' & expiration_date >= '2021-12-01 00:00+00:00'")
                           .sector_count
                           .sum())

print("---")
print(f"Total sectors (#): {sector_count}")
print(f"V1 sectors (#): {v1_count}")
print(f"V1 sectors share (%): {v1_count / sector_count :.1%}")
print("---")
print(f"6mo V1 sectors share (%) of total sectors: {v1_6mo_count / sector_count :.2%}")
print(f"12mo V1 sectors share (%) of total sectors: {v1_12mo_count / sector_count :.2%}")
print(f"18mo V1 sectors share (%) of total sectors: {v1_18mo_count / sector_count :.2%}")
print("---")
print(f"6mo V1 sectors share (%) of total sectors: {v1_6mo_count / v1_count :.2%}")
print(f"12mo V1 sectors share (%) of total sectors: {v1_12mo_count / v1_count :.2%}")
print(f"18mo V1 sectors share (%) of total sectors: {v1_18mo_count / v1_count :.2%}")
print("---")

---
Total sectors (#): 76983949
V1 sectors (#): 1189468
V1 sectors share (%): 1.5%
---
6mo V1 sectors share (%) of total sectors: 0.00%
12mo V1 sectors share (%) of total sectors: 0.00%
18mo V1 sectors share (%) of total sectors: 1.55%
---
6mo V1 sectors share (%) of total sectors: 0.00%
12mo V1 sectors share (%) of total sectors: 0.00%
18mo V1 sectors share (%) of total sectors: 100.00%
---
time: 28.7 ms (started: 2021-08-16 20:07:19 +00:00)


resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'sector_count'
title = 'Upcoming Sector Expiration Count, grouped by sector version (#)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
            .reset_index()
         )

fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             facet_row='is_v1',
             title=title)
fig.show()

time: 88.5 ms (started: 2021-08-16 20:07:19 +00:00)


resample_rule = '1w'
time_column = 'activation_date'
value_column = 'sector_count'
title = 'Activated Sector Count, grouped by sector version (#)'


groups = [pd.Grouper(key='activation_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
            .reset_index()
         )

fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             facet_row='is_v1',
             title=title)
fig.show()

time: 84.5 ms (started: 2021-08-16 20:07:19 +00:00)


rename_cols = {'activation_date': 'ds',
               'sector_count': 'y'}

proj_df = (df.resample("1d", on="activation_date")
             .sector_count
             .sum()
             .reset_index()
             .rename(columns=rename_cols)
             .assign(ds=lambda df: df.ds.dt.tz_localize(None))
             .assign(y=lambda df: df.y.cumsum() / (32 * 1024 * 1024)))

m = Prophet(changepoint_prior_scale=0.4)
m.fit(proj_df)
future = m.make_future_dataframe(periods=60)
forecast = m.predict(future)

fig = m.plot(forecast, figsize=(10, 4))
plt.title('Past and Forecasted RB Storage Power')
plt.xlabel("Time")
plt.ylabel("Total RB Storage Power (EiB)")
plt.show()

INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

Initial log joint probability = -34.86
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
      99       1143.27      0.022935       1613.26      0.3619           1      119   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     199       1238.34   0.000966434       452.201      0.3022           1      236   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     299       1275.24     0.0059398       139.084           1           1      355   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     399       1288.35    0.00662802        301.25           1           1      465   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     499       1293.15    0.00146768       91.2477      0.7303      0.7303      576   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     599       1296.95    0.00076636       171.424      0.2215      0.2215      698   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     699       1300.09   0.000830021       48.8602           1           1      807   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     799       1302.23    0.00257937       67.8508           1           1      920   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     899       1302.98    0.00209483       366.517      0.1783           1     1039   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     999       1303.21    0.00620624       54.0371           1           1     1160   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1014       1303.27   9.38817e-06       15.8519   2.155e-07       0.001     1233  LS failed, Hessian reset 
    1099       1303.36   3.53407e-05       22.4954           1           1     1337   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1120       1303.37   9.83812e-06       24.2391   3.985e-07       0.001     1405  LS failed, Hessian reset 
    1199       1303.42    0.00123869       60.0257           1           1     1503   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1299       1303.45   1.81636e-05       8.12707      0.1367           1     1632   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1399       1303.45   2.33894e-05       8.63394       2.276      0.5979     1763   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1409       1303.45   2.07816e-06        7.3013       1.343      0.1343     1780   
Optimization terminated normally: 
  Convergence detected: relative gradient magnitude is below tolerance

time: 2.69 s (started: 2021-08-16 20:07:19 +00:00)


rename_cols = {'activation_date': 'ds',
               'sector_count': 'y'}

proj_df = (df.query("activation_date > '2020-11-01 00:00+00:00'")
             .resample("1d", on="activation_date")
             .sector_count
             .sum()
             .reset_index()
             .rename(columns=rename_cols)
             .assign(ds=lambda df: df.ds.dt.tz_localize(None)))

m = Prophet(changepoint_prior_scale=0.2)
m.fit(proj_df)
future = m.make_future_dataframe(periods=60)
forecast = m.predict(future)

fig = m.plot(forecast, figsize=(10, 4))
plt.title('Past and Forecasted Daily New Sectors')
plt.xlabel("Time")
plt.ylabel("Daily New Sectors (#)")
plt.show()

INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

Initial log joint probability = -10.0724
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
      99       529.418     0.0187003       149.773      0.3614           1      122   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     148       533.193    0.00060227       37.8832   1.658e-05       0.001      232  LS failed, Hessian reset 
     199        535.49    0.00114505       19.5532      0.8739      0.8739      294   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     299       539.479    0.00103885       61.6846       0.234       0.234      419   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     373       539.836   1.03298e-07        16.441           1           1      518   
Optimization terminated normally: 
  Convergence detected: relative gradient magnitude is below tolerance

time: 1.54 s (started: 2021-08-16 20:07:21 +00:00)


rename_cols = {'activation_date': 'ds',
               'sector_count': 'y'}

proj_df = (df.query("activation_date > '2020-11-01 00:00+00:00' & activation_date < '2021-03-01 00:00+00:00'")
             .resample("1d", on="activation_date")
             .sector_count
             .sum()
             .reset_index()
             .rename(columns=rename_cols)
             .assign(ds=lambda df: df.ds.dt.tz_localize(None)))

m = Prophet(changepoint_prior_scale=0.2)
m.fit(proj_df)
future = m.make_future_dataframe(periods=90)
forecast = m.predict(future)

fig = m.plot(forecast, figsize=(10, 4))
plt.title('Past and Forecasted Daily New Sectors without March data')
plt.xlabel("Time")
plt.ylabel("Daily New Sectors (#)")
plt.show()

INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

Initial log joint probability = -4.476
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
      72       151.528    0.00255944       21.4481   7.658e-05       0.001      120  LS failed, Hessian reset 
      99       151.713   0.000149277       17.1013       2.201      0.2201      163   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     144       151.717   0.000179243       13.4875   8.639e-06       0.001      264  LS failed, Hessian reset 
     199       151.718   9.43985e-06       17.6273           1           1      342   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     226       151.733   5.13839e-05       17.9329   3.145e-06       0.001      416  LS failed, Hessian reset 
     252       151.733   9.53697e-09       15.6199     0.06896     0.06896      461   
Optimization terminated normally: 
  Convergence detected: absolute parameter change was below tolerance

time: 1.69 s (started: 2021-08-16 20:07:23 +00:00)


rename_cols = {'activation_date': 'ds',
               0: 'y'}

proj_df = (df.query("activation_date > '2020-11-01 00:00+00:00'")
             .resample("1d", on="activation_date")
             .apply(lambda x: (x.initial_pledge_in_fil / x.sector_count).mean())
             .reset_index()
             .rename(columns=rename_cols)
             .assign(ds=lambda df: df.ds.dt.tz_localize(None))
          )

m = Prophet(changepoint_prior_scale=0.2)
m.fit(proj_df)
future = m.make_future_dataframe(periods=60)
forecast = m.predict(future)

fig = m.plot(forecast, figsize=(10, 4))
plt.title('Past and Forecasted Initial Pledge per Sector')
plt.xlabel("Time")
plt.ylabel("Mean Initial Pledge per Sector (FIL)")
plt.show()

INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

Initial log joint probability = -5.13883
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
      67       332.998    0.00241765       47.1703   0.0001122       0.001      114  LS failed, Hessian reset 
      83       333.444    0.00137575       31.2096   8.656e-05       0.001      163  LS failed, Hessian reset 
      99       333.514     0.0068239       31.2128           1           1      189   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     199       335.002    0.00030881        14.688           1           1      323   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     296       336.538    0.00260695       25.3458   0.0001489       0.001      478  LS failed, Hessian reset 
     299        336.56   0.000602843       16.2874      0.3774           1      484   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     327       336.796   0.000639859       16.6146   4.489e-05       0.001      561  LS failed, Hessian reset 
     399       336.928   0.000704987       18.0159      0.3497           1      658   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     450       337.031    0.00127428       35.4843    6.93e-05       0.001      757  LS failed, Hessian reset 
     499       337.044   1.25545e-07       14.6231           1           1      828   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     501       337.044   4.96423e-08       18.3408      0.3679           1      832   
Optimization terminated normally: 
  Convergence detected: relative gradient magnitude is below tolerance

time: 1.64 s (started: 2021-08-16 20:07:25 +00:00)


m.fit

<bound method Prophet.fit of <prophet.forecaster.Prophet object at 0x7f19022cfc10>>

time: 22.7 ms (started: 2021-08-16 20:07:26 +00:00)


resample_rule = '7d'
time_column = 'activation_date'
value_column = 'sector_count'
title = 'Count of Sector Activation Date (#)'


groups = [pd.Grouper(key='activation_date', freq=resample_rule),
          pd.Grouper(key='expiration_date', freq=resample_rule)]
fig_df = df.groupby(groups).sum().reset_index()
px.density_heatmap(fig_df,
                   x='activation_date',
                   y='expiration_date',
                   z='sector_count')

time: 110 ms (started: 2021-08-16 20:07:26 +00:00)


resample_rule = '1d'
time_column = 'activation_date'
value_column = 'sector_count'
title = 'Count of Sector Activation Date (#)'


groups = [pd.Grouper(key='activation_date', freq=resample_rule),
          pd.Grouper(key='expiration_date', freq=resample_rule)]
fig_df = df.groupby(groups).sum().reset_index()
fig = px.density_contour(fig_df,
                   x='activation_date',
                   y='expiration_date',
                   z='sector_count',
                   histfunc='sum')
fig.show()

time: 372 ms (started: 2021-08-16 20:07:26 +00:00)


sector_count = df.initial_pledge_in_fil.sum()
v1_count = df.query("is_v1 == True").initial_pledge_in_fil.sum()

v1_6mo_count = (df.query("is_v1 == True & expiration_date < '2021-06-01 00:00+00:00'")
                           .initial_pledge_in_fil
                           .sum())

v1_12mo_count = (df.query("is_v1 == True & expiration_date < '2021-12-01 00:00+00:00' & expiration_date >= '2021-06-01 00:00+00:00'")
                           .initial_pledge_in_fil
                           .sum())


v1_18mo_count = (df.query("is_v1 == True & expiration_date < '2022-06-01 00:00+00:00' & expiration_date >= '2021-12-01 00:00+00:00'")
                           .initial_pledge_in_fil
                           .sum())

print("---")
print(f"Total collateral (Million FIL): {sector_count / 1e6 :.3g}")
print(f"V1 collateral (Million FIL): {v1_count / 1e6 :.3g}")
print(f"V1 sectors share (%): {v1_count / sector_count :.1%}")
print("---")
print(f"6mo V1 sectors share (%) of total collateral: {v1_6mo_count / sector_count :.2%}")
print(f"12mo V1 sectors share (%) of total collateral: {v1_12mo_count / sector_count :.2%}")
print(f"18mo V1 sectors share (%) of total collateral: {v1_18mo_count / sector_count :.2%}")
print("---")
print(f"6mo V1 sectors share (%) of total collateral: {v1_6mo_count / v1_count :.2%}")
print(f"12mo V1 sectors share (%) of total collateral: {v1_12mo_count / v1_count :.2%}")
print(f"18mo V1 sectors share (%) of total collateral: {v1_18mo_count / v1_count :.2%}")
print("---")

---
Total collateral (Million FIL): 22.9
V1 collateral (Million FIL): 0.376
V1 sectors share (%): 1.6%
---
6mo V1 sectors share (%) of total collateral: 0.00%
12mo V1 sectors share (%) of total collateral: 0.00%
18mo V1 sectors share (%) of total collateral: 1.64%
---
6mo V1 sectors share (%) of total collateral: 0.00%
12mo V1 sectors share (%) of total collateral: 0.00%
18mo V1 sectors share (%) of total collateral: 100.00%
---
time: 34.2 ms (started: 2021-08-16 20:07:27 +00:00)


resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'initial_pledge_in_fil'
title = 'Initial Pledge (FIL) of Expiring  Sectors (#)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
         )


fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             color='is_v1',
             title=title)
fig.show()

time: 171 ms (started: 2021-08-16 20:07:27 +00:00)


resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'initial_pledge_in_fil'
title = 'Initial Pledge (FIL) of Expiring Sectors, grouped by Sector Version'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
            .reset_index()
         )


fig = px.bar(fig_df,
             x=time_column,
             y=value_column,
             facet_col='is_v1',
             title=title)
fig.show()

time: 90.3 ms (started: 2021-08-16 20:07:27 +00:00)


resample_rule = '1m'
time_column = 'activation_date'
value_column = ['initial_pledge_in_thousand_fil']
title = 'Sum of Initial Pledge (FIL) across activation dates'
resample_and_bar_plot(df, resample_rule, time_column, value_column, title).show()

time: 71.8 ms (started: 2021-08-16 20:07:27 +00:00)


resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'network_power_in_pib'
title = 'RB Network Power (PiB) of Expiring  Sectors (#)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
         )


fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             color='is_v1',
             title=title)
fig.show()

time: 77.8 ms (started: 2021-08-16 20:07:27 +00:00)


resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'network_power_in_pib'
title = 'RB Network Power (PiB) of Expiring Sectors, grouped by Sector Version'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
            .reset_index()
         )


fig = px.bar(fig_df,
             x=time_column,
             y=value_column,
             facet_col='is_v1',
             title=title)
fig.show()

time: 88.5 ms (started: 2021-08-16 20:07:27 +00:00)


resample_rule = '1m'
time_column = 'activation_date'
value_column = ['network_power_in_pib']
title = 'Sum of RB Network Power (PiB) across activation dates'
resample_and_bar_plot(df, resample_rule, time_column, value_column, title).show()

time: 71.8 ms (started: 2021-08-16 20:07:27 +00:00)

FIP 0014 thematic notebook¶

Setup and dependences¶

Sector Count¶

Forecasts¶

Initial Pledge¶

RB Network Power¶