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chainlink_functions.py
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# coding: utf-8
def get_names_list(sheet_id, col_name_string, sheet_name=None):
import pandas as pd
url = f"https://docs.google.com/spreadsheets/d/{sheet_id}/gviz/tq?tqx=out:csv&sheet={sheet_name}"
df = pd.read_csv(url, usecols=[col_name_string])
df = df.dropna(how='all')
list_of_names = df[col_name_string].to_list()
list_of_names = [user.replace(' ', '-').lower() for user in list_of_names]
return list_of_names
def check_duplicates(listOfElems):
seen = set()
dupes = []
for x in listOfElems:
if x in seen:
dupes.append(x)
else:
seen.add(x)
if len(dupes)>0:
print("There are dupes: ", dupes)
else:
print('No dupes')
def get_responses(list_of_names):
'''gets the total value locked, includes staked tokens and outstanding collateral'''
import requests
import json
responses=[]
for user in list_of_names:
rr = requests.get('https://api.llama.fi/protocol/{}'.format(user), verify=False)
if rr.status_code == 200:
data=rr.json()
try:
data['name']
responses.append(data)
except KeyError:
print("400: {}".format(user))
return responses
def plot_single_tvl_over_time(responses_json, user_index):
import requests
import json
from datetime import datetime
import plotly.graph_objects as go
dates = []
values = []
for x in responses_json[user_index]['tvl']:
dates.append(datetime.fromtimestamp(int(x['date'])))
values.append(x['totalLiquidityUSD'])
fig = go.Figure()
fig.add_trace(go.Scatter(x=dates, y=values,
mode='lines',
name='lines',
line_color='blue'))
fig.update_layout(
title={'text':'{}'.format(responses_json[user_index]['name']),
'y':0.9, 'x':0.5,
'xanchor': 'center','yanchor': 'top'},
xaxis=dict(
showline=True,
showgrid=False,
showticklabels=True,
linewidth=2,
zeroline=True,
linecolor='#F4F4F4',
ticks='outside',
tickfont=dict(
family='Arial',
size=14,
color='rgb(82, 82, 82)',
),
),
yaxis=dict(
showgrid=True,
zeroline=True,
showline=True,
showticklabels=True,
gridcolor='#F4F4F4',
tickfont=dict(
family='Arial',
size=14,
color='blue',
),
),
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
autosize=True,
plot_bgcolor='white'
)
return fig
def plot_total_tvl_over_time(df_users, df_market):
users_tvl_grouped_bydate = df_users.groupby('date')['totalLiquidityUSD'].sum()
market_tvl_grouped_bydate = df_market.groupby('date')['totalLiquidityUSD'].sum()
import plotly.graph_objects as go
from plotly.subplots import make_subplots
trace1 = go.Scatter(x=users_tvl_grouped_bydate.index,
y=users_tvl_grouped_bydate.values,
mode='lines', name='Chainlink TVL',
line_color='orange')
trace2 = go.Scatter(x=market_tvl_grouped_bydate.index,
y=market_tvl_grouped_bydate.values,
mode='lines', name='Market TVL',
line_color='green')
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(trace1)
fig.add_trace(trace2) #, secondary_y=True
fig['layout'].update(
title={'text':'Chainlink vs market TVL',
'y':0.9, 'x':0.5,
'xanchor': 'center','yanchor': 'top'},
xaxis=dict(
showline=True,
showgrid=False,
showticklabels=True,
linewidth=2,
zeroline=True,
linecolor='#F4F4F4',
ticks='outside',
tickfont=dict(
family='Arial',
size=14,
color='rgb(82, 82, 82)',
),
),
yaxis=dict(
showgrid=True,
zeroline=True,
showline=True,
showticklabels=True,
gridcolor='#F4F4F4',
tickfont=dict(
family='Arial',
size=14,
# color='blue',
),
),
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
autosize=True,
plot_bgcolor='white'
)
return fig
def plot_catorchain_tvl(df, cat_or_chain_string, agg_np):
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
pivot_df = pd.pivot_table(df, values='totalLiquidityUSD',
index=['date'], columns=cat_or_chain_string, aggfunc=agg_np)
fig = go.Figure()
for col in pivot_df.columns:
fig.add_trace(go.Scatter(x=pivot_df.index, y=pivot_df[col].values,
name = col,
mode = 'lines',
line=dict(shape='linear'),
connectgaps=True))
fig.update_layout(
xaxis=dict(
showline=True,
showgrid=False,
showticklabels=True,
linewidth=2,
zeroline=True,
linecolor='#F4F4F4',
ticks='outside',
tickfont=dict(
family='Arial',
size=14,
color='rgb(82, 82, 82)',
),
),
yaxis=dict(
showgrid=True,
zeroline=True,
showline=True,
showticklabels=True,
gridcolor='#F4F4F4',
tickfont=dict(
family='Arial',
size=14,
# color='blue',
),
),
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
autosize=True,
plot_bgcolor='white'
)
return fig
# def get_percentage_per_catorchain(df, catorchain_string):
# rows_peruser_per_catorchain = df.groupby([catorchain_string,'name']).size()
# user_per_catorchain = rows_peruser_per_catorchain.reset_index().groupby(catorchain_string).size()
# total_users = sum(rows_peruser_per_catorchain.reset_index().groupby(catorchain_string).size().values)
# catorchain_percentage_indf = 100 * user_per_catorchain / total_users
#
# # # sanity check
# # sum((100*ser.reset_index().groupby('category').size() / total_users).values)
#
# return catorchain_percentage_indf
def plot_catorchain_pergroup(df_users, df_nonusers, catorchain_string):
# from chainlink_functions import get_percentage_per_catorchain
# cat_percentage_inusers = get_percentage_per_catorchain(df_users, catorchain_string)
# cat_percentage_innonusers = get_percentage_per_catorchain(df_nonusers, catorchain_string)
total_percat_inusers = df_users.groupby(catorchain_string)['name'].nunique()
total_percat_innonusers = df_nonusers.groupby(catorchain_string)['name'].nunique()
import plotly.graph_objects as go
from plotly.subplots import make_subplots
trace1 = go.Bar(x=total_percat_inusers.index,
y=total_percat_inusers.values,
name='Chainlink users')
trace2 = go.Bar(x=total_percat_innonusers.index,
y=total_percat_innonusers.values,
name='Non-Chainlink')
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(trace1)
fig.add_trace(trace2)
fig['layout'].update(title={'text': 'Breakdown of {}'.format(catorchain_string),
'y':0.9, 'x':0.4,
'xanchor': 'center','yanchor': 'top'})
fig.update_layout(
xaxis=dict(
showline=True,
showgrid=False,
showticklabels=True,
linewidth=2,
zeroline=True,
linecolor='#F4F4F4',
ticks='outside',
tickfont=dict(
family='Arial',
size=14,
color='rgb(82, 82, 82)',
),
),
yaxis=dict(
showgrid=True,
zeroline=True,
showline=True,
showticklabels=True,
gridcolor='#F4F4F4',
# ticksuffix='%',
tickfont=dict(
family='Arial',
size=14,
# color='blue',
),
),
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
autosize=True,
plot_bgcolor='white'
)
return fig