mitkrieg

// SPDX-License-Identifier: MIT
pragma solidity ^0.6.0;

interface AggregatorV3Interface {

  function decimals()
    external
    view
    returns (
      uint8

mitkrieg

# Edited run_test() in ExampleTest class in bitcoin/test/functional/example_test.py
# Test mines a new block on node1, sends block to node2 and checks to see if node2 recieved.
def run_test(self):
        """Main test logic"""

        # Create P2P connections will wait for a verack to make sure the connection is fully up
        peer_messaging = self.nodes[0].add_p2p_connection(BaseNode())

        # Generating a block on one of the nodes will get us out of IBD
        blocks = [int(self.nodes[0].generate(nblocks=1)[0], 16)]

mitkrieg

import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
from layouts import example_graph1, example_graph2
from app import app

@app.callback(
    Output("tab-content", "children"),
    Input("tabs", "active_tab"),
)

mitkrieg

import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from app import app
import plotly.express as px

#####################################
# Add your data
#####################################

mitkrieg

import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output

from app import app, server 
#import your navigation, styles and layouts from layouts.py here
from layouts import nav_bar, layout1, layout2, CONTENT_STYLE 
import callbacks

# Define basic structure of app:

mitkrieg

import dash
import dash_bootstrap_components as dbc

#Instantiates the Dash app and identify the server
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
server = app.server

mitkrieg

# instantiate the model and fit the timeseries
prophet = Prophet(weekly_seasonality=False, changepoint_range=1,changepoint_prior_scale=0.75)
prophet.fit(ts)

# create a future data frame 
future = prophet.make_future_dataframe(periods=25)
forecast = prophet.predict(future)

# display the most critical output columns from the forecast
forecast[['ds','yhat','yhat_lower','yhat_upper']].head()

mitkrieg

from fbprophet.plot import add_changepoints_to_plot
a = add_changepoints_to_plot(fig.gca(),m,forcast)
fig

mitkrieg

from fbprophet import Prophet

# instantiate the model and fit the timeseries
prophet = Prophet()
prophet.fit(ts)

# create a future data frame 
future = prophet.make_future_dataframe(periods=25)
forecast = prophet.predict(future)

mitkrieg

import pandas as pd

# read data from NY times
df = pd.read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us.csv")

# engineer new cases
df['new_cases'] = df.cases - df.cases.shift().fillna(0)

# create pandas time series
df.date = pd.to_datetime(df.date)