# The ' from ... import ' structure here allows us to only import the module ' python_version ' from the library ' platform ':
import platform
from platform import python_version # The ` python_version ` will allow us to display our evvironment details to help you replicate this code.
print("This code runs on Python version " + python_version() + " in " + platform.architecture()[0])

        
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import pandas as pd  # `pandas` is a widely-used Python library that allows us to manipulate data-frames.
import numpy as np  # `numpy` is a widely-used Python library that allows us to manipulate number arrays.
import datetime  # `datetime` allows us to manipulate time as it it was data points
import warnings  # Some data manipulations throw warnings that polute the display we'll build; it is thus best to remove them for now; this `warnings` library will allow us to do just that.

        
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import DatastreamDSWS as DSWS
 
# The username and password is placed in a text file so that it may be used in this code without showing it itself.
dsws_username = open("Datastream_username.txt", "r")
dsws_password = open("Datastream_password.txt", "r")
 
ds = DSWS.Datastream(username = str(dsws_username.read()), password = str(dsws_password.read()))
 
# It is best to close the files we opened in order to make sure that we don't stop any other services/programs from accessing them if they need to.
dsws_username.close()
dsws_password.close()

        
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startDate = '2020-06-01'
 
_reportActual = ds.get_data(
    tickers = 'USCONPRCE', # USCONPRCE for US CPI and USPENOACO for NonFarmPayrolls.
    fields = ['X', 'TREL1', 'DREL1'],
    start = startDate,
    freq = 'M')

        
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            _reportActual.columns
        
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_reportActualColumns = [[_reportActual.columns[i][j] for i in range(len(_reportActual.columns))] for j in range(len(_reportActual.columns[0]))]
reportActualColumns = [k + "_" + l for k, l in zip(_reportActualColumns[0], _reportActualColumns[1])]

        
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reportActual = pd.DataFrame(
    data=_reportActual.values,
    columns=reportActualColumns,
    index=_reportActual.index)
reportActual

        
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def GetDSWSDataAndFalattenColumns(tickers, fields, freq, start=startDate, adjustment=0):
    '''GetDSWSDataAndFalattenColumns V1.0:
    One needs a LSEG Refinitiv's Datastream DSWS API(https://developers.refinitiv.com/en/api-catalog/eikon/datastream-web-service) and a working account/license.
    It works by collecting DSWS data for the `tickers`, `fields`, `freq` and `start` and flattens the MultiIndex columns it usually comes out with.
 
    Dependencies
    ----------
 
    Python library 'pandas' version 1.2.4
    Python library 'DatastreamDSWS' as DSWS (!pip install DatastreamDSWS)
 
    Parameters
    ----------
 
    `tickers`, `fields`, `freq` and `start`: str
        For information on these function parameters, please run:
            import DatastreamDSWS as DSWS
            ds = DSWS.Datastream(username = "USERNAME", password = "PASSWORD"))
            help(ds)
 
    adjustment: int
        I found that someties the column number was not nessesarily correct, so you can adjust it here.
        Default: adjustment=0
 
    Returns
    -------
 
    pandas dataframe
 
    '''
 
    df = ds.get_data(tickers=tickers, fields=fields, start=start, freq=freq)
    columns = [[df.columns[i][j] for i in range(len(df.columns))]
               for j in range(len(df.columns[0]) + adjustment)]
    columns = [k + "_" + l for k, l in zip(columns[0], columns[1])]
    df = pd.DataFrame(data=df.values, columns=columns, index=df.index)
    return df

        
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reportPoll = GetDSWSDataAndFalattenColumns(
    tickers = 'US&CPNM.F',
    fields = "X",
    freq = 'M')
reportSurprise = GetDSWSDataAndFalattenColumns(
    tickers = 'US&CPNS.F',
    fields = 'X',
    freq = 'M')
reportNames = ds.get_data(
    tickers = 'USCONPRCE,US&CPNM.F,US&CPNS.F',
    fields = ['NAME'],
    kind = 0) # `kind = 0` is for static data, i.e.: data that is not meant to change through time, like the name of a company.
reportNames

        
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            reportActual
        
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# Let's put all this data together
reportDf = pd.merge(
    pd.merge(
        reportActual, reportPoll, left_index=True, right_index=True),
    reportSurprise, left_index=True, right_index=True)
reportDf.dropna()
 
# Let's create a plot data-frame to get a nice looking graph, 
plotDf = reportDf.dropna()[['USCONPRCE_X', 'US&CPNM.F_X', 'US&CPNS.F_X']]
plotDf.index = reportDf.dropna().USCONPRCE_DREL1
 
# Unfortunutally, some of the data provided are of different data types. We can homogenise it for easier proccessing:
for i in plotDf.columns:
    plotDf[i] = [float(j) for j in plotDf[i].to_list()]

        
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pd.options.plotting.backend = "plotly" # https://plotly.com/python/pandas-backend/
plotDf.dropna().plot(
    title='USCONPRCE (US CPI - ALL URBAN: ALL ITEMS SADJ), US&CPNM.F (US CPI INDEX, NSA-MEDIAN NADJ), US&CPNS.F (US CPI INDEX, NSA-SURPRISE NADJ)',
    template='plotly_dark') # need `pip install nodejs` & the `@jupyterlab/plotly-extension` if in Jupyter Lab outside CodeBook.

        
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            reportDf.dropna()
        
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import os # This module provides a portable way of using operating system dependent functionality.
os.environ["RD_LIB_CONFIG_PATH"] = "C:\\Example.DataLibrary.Python-main\\Example.DataLibrary.Python-main\\Configuration" # Here we point to the path to our configuration file.
 
# To install the RD library, you may need to run `!pip install refinitiv-data` in Jupyter Lab/Notebook1.
import refinitiv.data as rd  # pip install httpx==0.21.3
 
rd.open_session("desktop.workspace") # Send info to the Refinitiv system (i.e.: the API)
# rd.open_session("desktop.workspace") # If running this code outside CodeBook. You can also try "platform.rdp"
# rd.open_session("") # If running this code within CodeBook. # 'DEFAULT_CODE_BOOK_APP_KEY' is also often used in CodeBook. More on CodeBook here: https://www.refinitiv.com/content/dam/marketing/en_us/documents/fact-sheets/codebook.pdf

        
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# Let's get the last report's release date and time:
lastReportTime = datetime.datetime.strptime(reportDf.dropna().USCONPRCE_DREL1[-1] + "T" + reportDf.dropna().USCONPRCE_TREL1[-1], "%Y-%m-%dT%H:%M:%S")
# Let's collect tick-data for the next 15 minutes after that last report's release:
toTime = lastReportTime + datetime.timedelta(minutes=15) # This object will be the time we're collecting tick-fx-data until.
print(f"The last report was on {lastReportTime.strftime('%Y-%m-%dT%H:%M:%S')} and we'll get FX data till {toTime.strftime('%Y-%m-%dT%H:%M:%S')}")

        
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# Now that we have the date and time for which we would like to collect data, we can send our tick data request:
fxDf = rd.content.historical_pricing.events.Definition(
    universe="GBP=",
    fields=['BID', 'ASK', 'MID_PRICE'],  # ['TR.BIDPRICE', 'TR.BIDPRICE.date']
    start=lastReportTime.strftime("%Y-%m-%dT%H:%M:%S"),
    end=toTime.strftime("%Y-%m-%dT%H:%M:%S")).get_data().data.df
fxDf.drop('EVENT_TYPE', inplace=True, axis=1) # This is a column of str's, which can mess up with the maths and plots we want to do
for i in fxDf.columns:
    fxDf[i] = fxDf[i].astype(float) # `MID_PRICE` data is returned in str form, we would like it in float so that we can do some maths with it.
 
# Let's go through our calculations:
fxDf['RatioChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")] = (
    fxDf['MID_PRICE'] - fxDf['MID_PRICE'][0])/fxDf['MID_PRICE'][0]
fxDf['%ChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")] = fxDf[
    'RatioChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")]*100
fxDf['Average%ChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")] = [
    np.average(fxDf['%ChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[0:i]) for i in range(len(fxDf))]
fxDf['StandardDeviationOf%ChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")] = [
    fxDf['%ChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[0:i].std() for i in range(len(fxDf))]
fxDf['UpperBoundOfStandardDeviationOf%ChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")] = [
    fxDf['Average%ChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[i] +
    fxDf['StandardDeviationOf%ChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[i]
    for i in range(len(fxDf))]
fxDf['LowerBoundOfStandardDeviationOf%ChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")] = [
    fxDf['Average%ChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[i] -
    fxDf['StandardDeviationOf%ChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[i]
    for i in range(len(fxDf))]
fxDf

        
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# Now let's have a quick plot to look at our work:
fxPlotDf = fxDf.drop(
    ['BID', 'ASK', 'MID_PRICE',
     'StandardDeviationOf%ChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S"),
     'RatioChangeSince' + lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")], axis=1)
fxPlotDf.plot(
    title=f'GBP= since last report publication at {lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")}',
    template='plotly_dark') # need `pip install nodejs` & the `@jupyterlab/plotly-extension` if in Jupyter Lab outside CodeBook.

        
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            fxPlotDf
        
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df = pd.DataFrame(
    data=reportDf.dropna()[['USCONPRCE_X', 'US&CPNM.F_X', 'US&CPNS.F_X']].values,
    index=[reportDf.dropna()['USCONPRCE_DREL1'].iloc[i] + 'T' + reportDf.dropna()['USCONPRCE_TREL1'].iloc[i]
           for i in range(len(reportDf.dropna()))],
    columns=['Reported Value', 'Poll', 'Surprise'])
df

        
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dfs = {'fromTime': [], 'toTime': [], 'fxDf': [], 'prctChg': [], 'fxPlotDf' : []}  # In this dictionary, there will be a list full of all the datafames collected 7 the percent change that we're after
for i in range(len(reportDf.dropna())):
    _lastReportTime = datetime.datetime.strptime(  # The `_` in front of variables usually denotes that they are temporary and can be over written in the future, e.g.: over every loop.
        reportDf.dropna().USCONPRCE_DREL1[i] + "T" + reportDf.dropna().USCONPRCE_TREL1[i], "%Y-%m-%dT%H:%M:%S")  # We are writing time codes here in the Refinitiv default way for consistency's sake.
    _toTime = _lastReportTime + datetime.timedelta(minutes=15)  # This object will be the time we're collecting tick-fx-data until.
    try:  # There isn't nessesarily going to be all the data what we're after, going back as far as we want; that's why we're going through a try loop.
        _fxDf = rd.content.historical_pricing.events.Definition(
            universe="GBP=",
            fields=['MID_PRICE'],  # ['TR.BIDPRICE', 'TR.BIDPRICE.date']
            start=_lastReportTime.strftime("%Y-%m-%dT%H:%M:%S"),
            end=_toTime.strftime("%Y-%m-%dT%H:%M:%S")).get_data().data.df
        print(f"We'll get data from {_lastReportTime.strftime('%Y-%m-%dT%H:%M:%S')} till {_toTime.strftime('%Y-%m-%dT%H:%M:%S')}")  # This line is here so that we can tell exactly what data we're calling for.
        _fxDf.drop('EVENT_TYPE', inplace=True, axis=1) # This is a column of str's, which can mess up with the maths and plots we want to do
        for i in _fxDf.columns:
            _fxDf[i] = _fxDf[i].astype(float) # `MID_PRICE` data is returned in str form, we would like it in float so that we can do some maths with it.
        _fxDf['%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")] = ((
            _fxDf['MID_PRICE'] - _fxDf['MID_PRICE'][0])/_fxDf['MID_PRICE'][0])*100
        _fxDf['Average%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")] = [
            np.average(_fxDf['%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[0:i])
            for i in range(len(_fxDf))]
        _fxDf['StandardDeviationOf%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")] = [
            _fxDf['%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[0:i].std()
            for i in range(len(_fxDf))]
        _fxDf['UpperBoundOfStandardDeviationOf%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")] = [
            _fxDf['Average%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[i] +
            _fxDf['StandardDeviationOf%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[i]
            for i in range(len(_fxDf))]
        _fxDf['LowerBoundOfStandardDeviationOf%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")] = [
            _fxDf['Average%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[i] -
            _fxDf['StandardDeviationOf%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[i]
            for i in range(len(_fxDf))]
        _fxPlotDf = _fxDf.drop(
            ['MID_PRICE',
             'StandardDeviationOf%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")],
            axis=1)
 
        dfs['fromTime'].append(_lastReportTime.strftime('%Y-%m-%dT%H:%M:%S'))
        dfs['toTime'].append(_toTime.strftime("%Y-%m-%dT%H:%M:%S"))
        dfs['fxDf'].append(_fxDf)
        dfs['prctChg'].append(_fxDf['%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[-1])
        dfs['fxPlotDf'].append(_fxPlotDf)
 
    except:
        pass

        
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# In order to find out the % change of our tick data over the chosen window, we need to only add data as far back as we have this tick data. The `NaN` will be there to complete the dataframe.
prctChg = [np.nan for i in range(len(df) - len(dfs['prctChg']))]  # `NaN`s in question.
for i in dfs['prctChg']: prctChg.append(i)  # Now we can append with the data we actually have.
df['PrctChg'] = prctChg  # And now we can add the data to our dataframe.
df.insert(0, 'TimeStamp', df.index)  # This is only just to have a column with Timestamps that show up in the table in our app displayed at the end.
df

        
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import plotly.graph_objects as go  # Plotly is a great library to display interactive graphs and tables; `go` will allow us to add them on top of one-another.
from plotly.subplots import make_subplots  # To have plotly elements next to each other, we'll use `make_subplots`.
# import re  # If you would like to programatically change column names, you will need `re`.

        
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import plotly
plotly.__version__

        
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fig = make_subplots(  # `fig` will be our overall figure in which we will add objects.
    rows=2, cols=1,  # Let's put our objects on top of each other, so 2 rows and 1 column.
    shared_xaxes=True,
    vertical_spacing=0.1,
    specs=[[{"type": "scatter"}],
           [{"type": "table"}]])
for i in fxPlotDf.columns:
    fig.add_trace(  # Here we're adding line traces on the graph
        go.Scatter(
            x=fxDf.index,
            y=fxPlotDf[i],
            name=i),
        row=1, col=1) # Here we're indicating taht we are adding lines to a plot that is 1st (i.e.: on top) in the figure.
fig.add_trace(
    go.Table(  # Now we can add our table.
        header=dict(
            values=df.columns.to_list(),
            font=dict(size=10),
            align="left"),
        cells=dict(
            values=[df[k].tolist() for k in df.columns],
            align = "left")),
    row=2, col=1)
fig.update_layout(
    height=800,
    showlegend=True,
    title_text=f'GBP= since last report publication at {df.index[-1]}', # note that `lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")` and `df.index[-1]` are the same.
    template = 'plotly_dark')
fig.show()

        
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            dfs['fxPlotDf'][0]
        
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%matplotlib inline
import ipywidgets as widgets
 
out1 = widgets.Output()
out2 = widgets.Output()
out3 = widgets.Output()
 
# In our example here, were are only 3 rows in `df` for which we have tick data, but we could have more and use: `outL = [widgets.Output() for i in range(len(df.dropna()))]`
 
tab = widgets.Tab(children=[out1, out2, out3])
for i, j in enumerate([df.index[i] for i in range(-1, -4, -1)]): # Note that instead of `enumerate(`, we could have put `zip(range(3), `.
    tab.set_title(i, j)
display(tab)
 
# Let's use a defined function for our figure creation:
def FigureOfTableAndGraph(dfsPlotDfNo, RIC='GBP=', df=df, plotDf=dfs['fxPlotDf'], plotTemplate=None, graphHeight=800):
    '''FigureOfTableAndGraph(dfsPlotDfNo, RIC='GBP=', df=df, plotDf=dfs['fxPlotDf'], plotTemplate=None, graphHeight=800) V1.0:
    This python function outputs a plotly figure layout with two elements, two subplots (rows=2, cols=1); a plotly line graph above a table.
 
    Dependencies
    ----------
 
    Python library 'pandas' version 1.2.4
    plotly '5.8.2' molules 'graph_objects' and 'subplots':
        import plotly.graph_objects as go  # Plotly is a great library to display interactive graphs and tables; `go` will allow us to add them on top of one-another.
        from plotly.subplots import make_subplots  # To have plotly elements next to each other, we'll use `make_subplots`.
 
    Parameters
    ----------
 
    RIC: str
        Refinitiv IdentifiCation for which the line plot is made.
        Default: RIC='GBP='
 
    df: pandas dataframe
        pandas dataframe containing data wished to be in the table.
        Default: df=df
 
    plotDf: list of pandas dataframe
        list of pandas dataframes, each containing data wished to be graphed.
        Default: plotDf=dfs['fxPlotDf']
 
    plotTemplate: Plotly plot template
        Dictates the plotly template chosen for the line graph.
        Can be one of the following: "plotly", "plotly_white", "plotly_dark", "ggplot2", "seaborn", "simple_white", "none" or None.
        More on templates: https://plotly.com/python/templates/
        Default: plotTemplate=None
 
    graphHeight: int
        Dictates the height of the figure in which the line graph and table will be displayed.
        Default: graphHeight=800
 
    dfsPlotDfNo: int
        Dictates which of the `plotDf` will be plotted.
 
    Returns
    -------
 
    plotly fig
 
    '''
 
    fig = make_subplots(
        rows=2, cols=1,
        shared_xaxes=True,
        vertical_spacing=0.1,
        specs=[[{"type": "scatter"}],
               [{"type": "table"}]])
    for i in plotDf[dfsPlotDfNo].columns:
        fig.add_trace(
            go.Scatter(
                x=plotDf[dfsPlotDfNo].index,
                y=plotDf[dfsPlotDfNo][i],
                name=i),
            row=1, col=1)
    fig.add_trace(
        go.Table(header=dict(values=df.columns.to_list(), font=dict(size=10), align="left"),
                 cells=dict(values=[df[k].tolist() for k in df.columns], align = "left")),
        row=2, col=1)
    fig.update_layout(
        height=graphHeight,
        showlegend=True, template=plotTemplate,
        title_text=f'{RIC} since last report publication')
 
    return fig
 
with out1: # Each `out` will be a tab.
    fig = FigureOfTableAndGraph(dfsPlotDfNo = -1)
    fig.show()
 
with out2:
    fig = FigureOfTableAndGraph(dfsPlotDfNo = -2)
    fig.show()
 
with out3:
    fig = FigureOfTableAndGraph(dfsPlotDfNo = -3)
    fig.show()

        
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# # Some imports to start with:
import pandas as pd  # `pandas` is a widely-used Python library that allows us to manipulate data-frames.
import numpy as np  # `numpy` is a widely-used Python library that allows us to manipulate number arrays.
import datetime  # `datetime` allows us to manipulate time as it it was data points
import warnings  # Some data manipulations throw warnings that polute the display we'll build; it is thus best to remove them for now; this `warnings` library will allow us to do just that.
%matplotlib inline
import ipywidgets
import ipywidgets as widgets
import plotly
import plotly.graph_objects as go  # Plotly is a great library to display interactive graphs and tables; `go` will allow us to add them on top of one-another.
from plotly.subplots import make_subplots  # To have plotly elements next to each other, we'll use `make_subplots`.
# import re  # If you would like to programatically change column names, you will need `re`.
for i, j in zip(['pandas', 'numpy', 'ipywidgets', 'plotly'],
                [pd, np, ipywidgets, plotly]):
    print(i + " version " + j.__version__)

        
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def GetDSWSDataAndFalattenColumns(tickers, fields, freq, start, adjustment=0):
    '''GetDSWSDataAndFalattenColumns(tickers, fields, freq, start, adjustment=0) V1.0:
    One needs a LSEG Refinitiv's Datastream DSWS API(https://developers.refinitiv.com/en/api-catalog/eikon/datastream-web-service) and a working account/license.
    It works by collecting DSWS data for the `tickers`, `fields`, `freq` and `start` and flattens the MultiIndex columns it usually comes out with.
 
    Dependencies
    ----------
 
    Python library 'pandas' version 1.2.4
    Python library 'DatastreamDSWS' as DSWS (!pip install DatastreamDSWS), e.g.:
        >>> import DatastreamDSWS as DSWS
        >>> # The username and password is placed in a text file so that it may be used in this code without showing it itself.
        >>> dsws_username = open("Datastream_username.txt", "r")
        >>> dsws_password = open("Datastream_password.txt", "r")
        >>> ds = DSWS.Datastream(username = str(dsws_username.read()), password = str(dsws_password.read()))
        >>> # It is best to close the files we opened in order to make sure that we don't stop any other services/programs from accessing them if they need to.
        >>> dsws_username.close()
        >>> dsws_password.close()
 
    Parameters
    ----------
 
    `tickers`, `fields`, `freq` and `start`: str
        For information on these function parameters, please run:
            import DatastreamDSWS as DSWS
            ds = DSWS.Datastream(username = "USERNAME", password = "PASSWORD"))
            help(ds)
 
    adjustment: int
        I found that someties the column number was not nessesarily correct, so you can adjust it here.
        Default: adjustment=0
 
    Returns
    -------
 
    pandas dataframe
 
    '''
 
    df = ds.get_data(tickers=tickers, fields=fields, start=start, freq=freq)
    columns = [[df.columns[i][j] for i in range(len(df.columns))]
               for j in range(len(df.columns[0]) + adjustment)]
    columns = [k + "_" + l for k, l in zip(columns[0], columns[1])]
    df = pd.DataFrame(data=df.values, columns=columns, index=df.index)
    return df

        
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def FigureOfTableAndGraph(dfsPlotDfNo, df, plotDf, RIC='GBP=', plotTemplate=None, graphHeight=600):
    '''FigureOfTableAndGraph(dfsPlotDfNo, RIC='GBP=', df=df, plotDf=dfs['fxPlotDf'], plotTemplate=None, graphHeight=800) V1.0:
    This python function outputs a plotly figure layout with two elements, two subplots (rows=2, cols=1); a plotly line graph above a table.
 
    Dependencies
    ----------
 
    Python library 'pandas' version 1.2.4
    plotly '5.8.2' molules 'graph_objects' and 'subplots':
        import plotly.graph_objects as go  # Plotly is a great library to display interactive graphs and tables; `go` will allow us to add them on top of one-another.
        from plotly.subplots import make_subplots  # To have plotly elements next to each other, we'll use `make_subplots`.
 
    Parameters
    ----------
 
    RIC: str
        Refinitiv IdentifiCation for which the line plot is made.
        Default: RIC='GBP='
 
    df: pandas dataframe
        pandas dataframe containing data wished to be in the table.
        Default: df=df
 
    plotDf: list of pandas dataframe
        list of pandas dataframes, each containing data wished to be graphed.
        Default: plotDf=dfs['fxPlotDf']
 
    plotTemplate: Plotly plot template
        Dictates the plotly template chosen for the line graph.
        Can be one of the following: "plotly", "plotly_white", "plotly_dark", "ggplot2", "seaborn", "simple_white", "none" or None.
        More on templates: https://plotly.com/python/templates/
        Default: plotTemplate=None
 
    graphHeight: int
        Dictates the height of the figure in which the line graph and table will be displayed.
        Default: graphHeight=800
 
    dfsPlotDfNo: int
        Dictates which of the `plotDf` will be plotted.
 
    Returns
    -------
 
    plotly fig
 
    '''
    fig = make_subplots(
        rows=2, cols=1,
        shared_xaxes=True,
        vertical_spacing=0.1,
        specs=[[{"type": "scatter"}],
               [{"type": "table"}]])
    for i in plotDf[dfsPlotDfNo].columns:
        fig.add_trace(
            go.Scatter(
                x=plotDf[dfsPlotDfNo].index,
                y=plotDf[dfsPlotDfNo][i],
                name=i),
            row=1, col=1)
    fig.add_trace(
        go.Table(header=dict(values=df.dropna().loc[::-1].columns.to_list(), font=dict(size=10), align="left"),
                 cells=dict(values=[df.dropna().loc[::-1][k].tolist() for k in df.dropna().loc[::-1].columns], align = "left")),
        row=2, col=1)
    fig.update_layout(
        height=graphHeight,
        showlegend=True, template=plotTemplate,
        title_text=f'{RIC} since last report publication')
    return fig

        
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def DisplayApp(startDate='2020-06-01', reportTicker='USCONPRCE',
               reportPollTicker='US&CPNM.F', reportSurpriseTicker='US&CPNS.F',
               RIC="GBP=", field='MID_PRICE', timeWindow=5, SDAway=1, plotTemplate=None):
    '''DisplayApp(startDate='2020-06-01', reportTicker='USCONPRCE', reportPollTicker='US&CPNM.F', reportSurpriseTicker='US&CPNS.F', RIC="GBP=", field='MID_PRICE', timeWindow=5, SDAway=1, plotTemplate=None) V1.0:
    This python function outputs a plotly widget with several tabs, each showing a plotly fig with two elements: a plotly line graph above a table.
 
    Dependencies
    ----------
 
    os (native)
    numpy version 1.21.2 as np
    pandas version 1.2.4 as pd
    plotly version 5.8.2:
        import plotly.graph_objects as go  # Plotly is a great library to display interactive graphs and tables; `go` will allow us to add them on top of one-another.
        from plotly.subplots import make_subplots  # To have plotly elements next to each other, we'll use `make_subplots`.
    ipywidgets version 7.6.3:
        import ipywidgets as widgets
    DatastreamDSWS and instance `ds`, e.g.:
        >>> import DatastreamDSWS as DSWS
        >>> # The username and password is placed in a text file so that it may be used in this code without showing it itself.
        >>> dsws_username = open("Datastream_username.txt", "r")
        >>> dsws_password = open("Datastream_password.txt", "r")
        >>> ds = DSWS.Datastream(username = str(dsws_username.read()), password = str(dsws_password.read()))
        >>> # It is best to close the files we opened in order to make sure that we don't stop any other services/programs from accessing them if they need to.
        >>> dsws_username.close()
        >>> dsws_password.close()
    refinitiv.data 1.0.0b10 as rd, e.g.:
        >>> import os # This module provides a portable way of using operating system dependent functionality.
        >>> os.environ["RD_LIB_CONFIG_PATH"] = "C:\\Example.DataLibrary.Python-main\\Example.DataLibrary.Python-main\\Configuration" # Here we point to the path to our configuration file.
        >>> # To install the RD library, you may need to run `!pip install refinitiv-data` in Jupyter Lab/Notebook1.
        >>> import refinitiv.data as rd  # pip install httpx==0.21.3
        >>> rd.open_session("desktop.workspace") # Send info to the Refinitiv system (i.e.: the API)
        >>> # rd.open_session("desktop.workspace") # If running this code outside CodeBook. You can also try "platform.rdp"
        >>> # rd.open_session("") # If running this code within CodeBook. # 'DEFAULT_CODE_BOOK_APP_KEY' is also often used in CodeBook. More on CodeBook here: https://www.refinitiv.com/content/dam/marketing/en_us/documents/fact-sheets/codebook.pdf
 
    Parameters
    ----------
 
    startDate: str
        String in the form YYY-MM-DD as to when user would like data collected from.
        Default: startDate='2020-06-01'
 
    reportTicker: str
        String of the numonic for the report in question in DSWS.
        Default: reportTicker='USCONPRCE'
 
    reportPollTicker: str
        String of the numonic for the poll expected value for the report in question in DSWS.
        Default: reportPollTicker='US&CPNM.F'
 
    reportSurpriseTicker: str
        String of the numonic for the surprise between the poll expected value and the realised value for the report in question in DSWS.
        Default: reportSurpriseTicker='US&CPNS.F'
 
    RIC: str
        Refinitiv IdentifiCation for which the line plot is made.
        Default: RIC='GBP='
 
    field: str
        the field for which the RIC's data will be ploted.
        Default: field='MID_PRICE'
 
    timeWindow: int
        The time window, in minutes, for which tickdata is asked for, called for from the rd library and plotted. The expanding average over that window will also be plotted as well as lower and upper standard deviation bounds.
        Default: timeWindow=5
 
    SDAway: int
        The standard deviation of the tick data for which the lower and upper standard deviation bounds will be plotted.
        Default: SDAway=1
 
    plotTemplate: Plotly plot template
        Dictates the plotly template chosen for the line graph.
        Can be one of the following: "plotly", "plotly_white", "plotly_dark", "ggplot2", "seaborn", "simple_white", "none" or None.
        More on templates: https://plotly.com/python/templates/
        Default: plotTemplate=None
 
    Returns
    -------
 
    plotly fig
 
    '''
 
    # # Collect report data:
 
    warnings.filterwarnings('ignore')  # certain manipulations below will create warning messsages that will polute our widget, this line removes them.
    reportActual = GetDSWSDataAndFalattenColumns(
        tickers = reportTicker,
        fields = ['X', 'TREL1', 'DREL1'],
        start = startDate, freq = 'M')
    reportPoll = GetDSWSDataAndFalattenColumns(
        start = startDate,
        tickers = reportPollTicker,
        fields = "X", freq = 'M')
    reportSurprise = GetDSWSDataAndFalattenColumns(
        start = startDate,
        tickers = reportSurpriseTicker,
        fields = 'X', freq = 'M')
    reportDf = pd.merge(
        pd.merge(
            reportActual, reportPoll, left_index=True, right_index=True),
        reportSurprise, left_index=True, right_index=True).dropna()
    plotDf = reportDf[[reportTicker + '_X', reportPollTicker + '_X', reportSurpriseTicker + '_X']]
    plotDf.index = reportDf[reportTicker + '_DREL1']
    for i in plotDf.columns:
        plotDf[i] = [float(j) for j in plotDf[i].to_list()]
 
    df = plotDf.copy()
 
 
    # # Collect Tick Data:
 
    _dfs = {'fromTime': [], 'toTime': [], 'df': [], 'prctChg': [], 'plotDf' : []} # In this dictionary, there will be a list full of all the datafames collected 7 the percent change that we're after
    for i in range(len(reportDf.dropna())):
        _lastReportTime = datetime.datetime.strptime(
            reportDf.dropna()[reportTicker + '_DREL1'][i] + "T" + reportDf.dropna()[reportTicker + '_TREL1'][i], "%Y-%m-%dT%H:%M:%S")
        _toTime = _lastReportTime + datetime.timedelta(minutes=timeWindow) # This object will be the time we're collecting tick-data until.
        try:
            _df = rd.content.historical_pricing.events.Definition(
                universe=RIC,
                fields=[field], # ['TR.BIDPRICE', 'TR.BIDPRICE.date']
                start=_lastReportTime.strftime("%Y-%m-%dT%H:%M:%S"),
                end=_toTime.strftime("%Y-%m-%dT%H:%M:%S")).get_data().data.df
            _df.drop('EVENT_TYPE', inplace=True, axis=1) # This is a column of str's, which can mess up with the maths and plots we want to do
            for i in _df.columns:
                _df[i] = _df[i].astype(float) # `MID_PRICE` data is returned in str form, we would like it in float so that we can do some maths with it.
            _df['%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")] = ((
                _df[field] - _df[field][0])/_df[field][0])*100
            _df['Average%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")] = [
                np.average(_df['%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[0:i])
                for i in range(len(_df))]
 
            _dfs['fromTime'].append(_lastReportTime.strftime('%Y-%m-%dT%H:%M:%S'))
            _dfs['toTime'].append(_toTime.strftime("%Y-%m-%dT%H:%M:%S"))
            _dfs['df'].append(_df)
            _dfs['prctChg'].append(_df['%ChangeSince' + _lastReportTime.strftime("%Y-%m-%dT%H:%M:%S")].iloc[-1])
            _dfs['plotDf'].append(1)
 
        except:
            pass
 
    np.seterr(all="ignore")  # certain manipulations below will create warning messsages that will polute our widget, this line removes them.
    for i in range(len(_dfs['df'])):
        _dfs['df'][i][f'StandardDeviation{SDAway}Of%ChangeSince' + _dfs['fromTime'][i]] = [
            _dfs['df'][i]['%ChangeSince' + _dfs['fromTime'][i]].iloc[0:j].std() * SDAway
            for j in range(len(_dfs['df'][i]))]
        _dfs['df'][i][f'UpperBound{SDAway}StandardDeviationsAwayFromAverage%ChangeSince' + _dfs['fromTime'][i]] = [
            _dfs['df'][i]['Average%ChangeSince' + _dfs['fromTime'][i]].iloc[j] +
            _dfs['df'][i][f'StandardDeviation{SDAway}Of%ChangeSince' + _dfs['fromTime'][i]].iloc[j]
            for j in range(len(_dfs['df'][i]))]
        _dfs['df'][i][f'LowerBound{SDAway}StandardDeviationsAwayFromAverage%ChangeSince' + _dfs['fromTime'][i]] = [
            _dfs['df'][i]['Average%ChangeSince' + _dfs['fromTime'][i]].iloc[j] -
            _dfs['df'][i][f'StandardDeviation{SDAway}Of%ChangeSince' + _dfs['fromTime'][i]].iloc[j]
            for j in range(len(_dfs['df'][i]))]
        _dfs['plotDf'][i] = _dfs['df'][i][[
            '%ChangeSince' + _dfs['fromTime'][i],
            'Average%ChangeSince' + _dfs['fromTime'][i],
            f'LowerBound{SDAway}StandardDeviationsAwayFromAverage%ChangeSince' + _dfs['fromTime'][i],
            f'UpperBound{SDAway}StandardDeviationsAwayFromAverage%ChangeSince' + _dfs['fromTime'][i]]]
        # # If you would like to rename columns in a programatic way, you can import `re` and use the lines below:
        # _dfs['fxPlotDf'][i].columns = [
        #     re.sub(r"(\w)([A-Z])", r"\1 \2", j)
        #     for j in _dfs['fxPlotDf'][i].columns]
 
 
    # # Proccess data:
    df.index=[reportDf[reportTicker + '_DREL1'] + 'T' + reportDf[reportTicker + '_TREL1']]
    prctChg = [np.nan for i in range(len(df) - len(_dfs['prctChg']))]
    for i in _dfs['prctChg']: prctChg.append(i)
    df[RIC + ' PrctChg ' + str(timeWindow) + 'min after report'] = prctChg
    df.rename(columns={reportTicker + '_X':'Value from Report',
                       reportPollTicker + '_X' :'Poll',
                       reportSurpriseTicker + '_X' :'Surprise'},
              inplace = True)
    df.insert(0, 'TimeStamp', df.index.values)
 
 
    # # Create widget and plots:
    outL = [widgets.Output() for i in range(len(df.dropna()))]
 
    tab = widgets.Tab(children=outL)
    for i, j in enumerate([df.index[k] for k in range(-1, (len(outL)*-1)-1, -1)]):
        tab.set_title(i, j)
    display(tab)
 
    for i, out in enumerate(outL):
        with out:
            fig = FigureOfTableAndGraph(
                dfsPlotDfNo=-1*i, df=df, plotDf=_dfs['plotDf'], RIC=RIC, plotTemplate=plotTemplate)
            fig.show()

        
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# # Authentification to LSG Refinitiv's Datastream Service:
import DatastreamDSWS as DSWS # We can use our Refinitiv's Datastream Web Socket (DSWS) API keys that allows us to be identified by Refinitiv's back-end services and enables us to request (and fetch) data
import getpass as gp # This library will allow users to entre their passwords without showing it to anyone watching.
dsws_username = input("Enter DSWS username:")
dsws_password = gp.getpass("Enter DSWS password:")
if dsws_username == "" and dsws_password == "": # Users might have their credentials in .txt files along side their code, in which case we code bellow so that if the username and password are left empty, we automatically look for and use those txt files.
    dsws_username = open("Datastream_username.txt", "r")
    dsws_password = open("Datastream_password.txt", "r")
    ds = DSWS.Datastream(username = str(dsws_username.read()), password = str(dsws_password.read()))
    dsws_username.close()
    dsws_password.close()
else:
    ds = DSWS.Datastream(username = dsws_username, password = dsws_password)
 
# # Let's use the Refinitiv Data Library for Python (RD)
import refinitiv.data as rd  # pip install httpx==0.21.3
try:  # To use the RD library in Codebook, we don't need any user credentials
    rd.open_session("")
    connectionTest = rd.content.historical_pricing.events.Definition(
        'VOD.L').get_data().data.df # `connectionTest` is just a test value to see if we're connected to rd.
except:  # To use the RD library outside of codebook, we need to ask user where the config file is:
    import os  # This module provides a portable way of using operating system dependent functionality.
    creds = input("Enter the path to your 'refinitiv-data.config.json' file  and the session wished (e.g.1: C:\\Example.DataLibrary.Python-main\\Configuration,desktop.workspace) (e.g.2: C:\\Con,platform.rdp): ")
    os.environ["RD_LIB_CONFIG_PATH"] = creds.split(",")[0] # Here we point to the path to our configuration file.
    rd.open_session(creds.split(",")[1]) # Send info to the Refinitiv system (i.e.: the API)
    try:
        connectionTest = rd.content.historical_pricing.events.Definition(
            'VOD.L').get_data().data.df # `connectionTest` is just a test value to see if we're connected to rd.
    except:
        print("Connection to RD could not be established")
 
# # Now let's start up the Widget:
widgets.interact( # NonFarmPayRoll: reportTicker = 'USPENOACO', reportPollTicker = 'US&ADPM.O', reportSurpriseTicker = 'US&ADPS.O'
    DisplayApp,
    timeWindow=[i for i in range(1, 61)],
    SDAway=[i/10 for i in range(1, 31)],
    plotTemplate=['ggplot2', 'seaborn', 'simple_white', 'plotly',
                  'plotly_white', 'plotly_dark', 'presentation',
                  'xgridoff', 'ygridoff', 'gridon', 'none']); # The `;` is there to stop the widget message from showing up.

        
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