Quickstart
===================

Let’s simulate a use case where we take a june edition report Gummy Bear
Price Report June 2023 and we want to convert it to July report
automatically.

Step 1: We authenticate using our PW and Password
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: ipython3

    # Import necessary libraries
    import pandas as pd
    import numpy as np
    from richviewsdk import RichViewSession, ParagraphBlock

    with open('pws.txt') as f:
        email, password = f.read().split(',')
    session = RichViewSession.authenticate_with_password(email, password)
    session




.. parsed-literal::

    <richviewsdk.main.RichViewSession at 0x1c335450fd0>



Step 2: Let’s retrieve all the RichView reports in our account
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The reports are represented with their ID and title in our console.

.. code:: ipython3

    reports = session.get_reports()
    reports




.. parsed-literal::

    [RichViewReport(id=de270fac-2e8d-4107-b369-dc068290b2d7, title=RichView Tutorial),
     RichViewReport(id=dbed2de0-9cf1-4c16-a6bf-a25eb0021f57, title=Sample Report - Demise of SVB),
     RichViewReport(id=5de821c5-4ff0-4374-9725-2f78728031fc, title=GummiesJune23)]



Step 3: Let’s choose the report named GummiesJune23 of the reports and retrieve it
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: ipython3

    old_report = next(report for report in reports if report.title == 'GummiesJune23')
    old_report




.. parsed-literal::

    RichViewReport(id=5de821c5-4ff0-4374-9725-2f78728031fc, title=GummiesJune23)



Step 4: Let’s duplicate the report and renames it GummiesJuly23
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

We see it’s directly with the new title

.. code:: ipython3

    report = old_report.duplicate()
    report = report.set_title('GummiesJuly23')
    report




.. parsed-literal::

    RichViewReport(id=ada1e699-519a-4bfa-aae6-b6e51020fce5, title=GummiesJuly23)



Step 5: Let’s see how the report is built
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Each block type has its own unique index allowing for fast updates and
preventing report corruption. We can quickly query them based on their
text or content.

.. code:: ipython3

    report.get_blocks()




.. parsed-literal::

    [ImageBlock(id=BLI6RSGmTp, caption=),
     HeaderBlock(id=BacaW7lbzt, text=Gummy Bear...),
     ParagraphBlock(id=ayIQwbGZFq, text=Our analys...),
     EmbedBlock(id=6_fkJcT4h5, service=youtube),
     ParagraphBlock(id=rO30-mrKUT, text=Gummy Bear...),
     ParagraphBlock(id=cBAJQMsa0M, text=Here's a c...),
     TableBlock(id=1pMboyLLGd),
     ParagraphBlock(id=tK_WIGzNKl, text=We appreci...),
     ChartBlock(id=TT0Fw_xjqq, title=Gummy Bear Price Index)]



Step 6: Let’s query the report for all the charts
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Charts are what makes RichView special - we provide few unique functions
for ChartBlocks.

.. code:: ipython3

    charts = report.get_charts()
    charts




.. parsed-literal::

    [ChartBlock(id=TT0Fw_xjqq, title=Gummy Bear Price Index)]



Step 7: Let’s now get the chart we want to update by it’s ID
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: ipython3

    chart_id = [chart.block_id for chart in charts if chart.title == 'Gummy Bear Price Index'][0]
    chart = report.get_chart(chart_id)
    chart




.. parsed-literal::

    ChartBlock(id=TT0Fw_xjqq, title=Gummy Bear Price Index)



Step 8: Let’s retrieve the data of the chart to see how it’s structured
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: ipython3

    old_data = chart.get_data().round(2)
    old_data




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Gummy Bear Price Index | Line</th>
        </tr>
        <tr>
          <th>Category</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>2021-06-01</th>
          <td>6.11</td>
        </tr>
        <tr>
          <th>2021-06-02</th>
          <td>6.08</td>
        </tr>
        <tr>
          <th>2021-06-03</th>
          <td>6.15</td>
        </tr>
        <tr>
          <th>2021-06-04</th>
          <td>6.33</td>
        </tr>
        <tr>
          <th>2021-06-05</th>
          <td>6.29</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
        </tr>
        <tr>
          <th>2023-06-25</th>
          <td>4.50</td>
        </tr>
        <tr>
          <th>2023-06-26</th>
          <td>4.74</td>
        </tr>
        <tr>
          <th>2023-06-27</th>
          <td>4.78</td>
        </tr>
        <tr>
          <th>2023-06-28</th>
          <td>4.79</td>
        </tr>
        <tr>
          <th>2023-06-29</th>
          <td>4.70</td>
        </tr>
      </tbody>
    </table>
    <p>759 rows × 1 columns</p>
    </div>



Step 9: Let’s generate a fresh of data for the chart
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Some Simulated prices of delicious Gummy Bears!

.. code:: ipython3

    # Setting a seed for reproducibility
    np.random.seed(42)

    # Define parameters
    start_date = pd.to_datetime('2023-06-29')
    end_date = pd.to_datetime('2023-07-31')
    n_days = (end_date - start_date).days
    start_price = old_data.iloc[-1, 0]
    drift = 0.0001  # Slight upward trend
    volatility = 0.02  # Some randomness
    seasonality_amplitude = 0.10  # Amplitude of seasonal component

    # Generate dates
    dates = pd.date_range(start=start_date, periods=n_days)

    # Generate random walk component
    random_walk = np.random.normal(drift, volatility, n_days).cumsum()

    # Generate seasonal component
    seasonality = seasonality_amplitude * np.sin((dates.dayofyear / 365) * 2 * np.pi)

    # Combine components to generate prices
    prices = start_price * np.exp(random_walk + seasonality)

    # Create DataFrame
    new_data = pd.DataFrame(data={'Category': dates, 'Gummy Bear Price Index | Line': prices})
    new_data.set_index('Category', inplace=True)
    new_data.index = new_data.index.strftime('%Y-%m-%d')
    new_data




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Gummy Bear Price Index | Line</th>
        </tr>
        <tr>
          <th>Category</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>2023-06-29</th>
          <td>4.767867</td>
        </tr>
        <tr>
          <th>2023-06-30</th>
          <td>4.747002</td>
        </tr>
        <tr>
          <th>2023-07-01</th>
          <td>4.801104</td>
        </tr>
        <tr>
          <th>2023-07-02</th>
          <td>4.941580</td>
        </tr>
        <tr>
          <th>2023-07-03</th>
          <td>4.910525</td>
        </tr>
        <tr>
          <th>2023-07-04</th>
          <td>4.879671</td>
        </tr>
        <tr>
          <th>2023-07-05</th>
          <td>5.028104</td>
        </tr>
        <tr>
          <th>2023-07-06</th>
          <td>5.097623</td>
        </tr>
        <tr>
          <th>2023-07-07</th>
          <td>5.041834</td>
        </tr>
        <tr>
          <th>2023-07-08</th>
          <td>5.088631</td>
        </tr>
        <tr>
          <th>2023-07-09</th>
          <td>5.033580</td>
        </tr>
        <tr>
          <th>2023-07-10</th>
          <td>4.978914</td>
        </tr>
        <tr>
          <th>2023-07-11</th>
          <td>4.995065</td>
        </tr>
        <tr>
          <th>2023-07-12</th>
          <td>4.799870</td>
        </tr>
        <tr>
          <th>2023-07-13</th>
          <td>4.629736</td>
        </tr>
        <tr>
          <th>2023-07-14</th>
          <td>4.570713</td>
        </tr>
        <tr>
          <th>2023-07-15</th>
          <td>4.471993</td>
        </tr>
        <tr>
          <th>2023-07-16</th>
          <td>4.493120</td>
        </tr>
        <tr>
          <th>2023-07-17</th>
          <td>4.405363</td>
        </tr>
        <tr>
          <th>2023-07-18</th>
          <td>4.276009</td>
        </tr>
        <tr>
          <th>2023-07-19</th>
          <td>4.396395</td>
        </tr>
        <tr>
          <th>2023-07-20</th>
          <td>4.369855</td>
        </tr>
        <tr>
          <th>2023-07-21</th>
          <td>4.369070</td>
        </tr>
        <tr>
          <th>2023-07-22</th>
          <td>4.239880</td>
        </tr>
        <tr>
          <th>2023-07-23</th>
          <td>4.187639</td>
        </tr>
        <tr>
          <th>2023-07-24</th>
          <td>4.190651</td>
        </tr>
        <tr>
          <th>2023-07-25</th>
          <td>4.089195</td>
        </tr>
        <tr>
          <th>2023-07-26</th>
          <td>4.113957</td>
        </tr>
        <tr>
          <th>2023-07-27</th>
          <td>4.058885</td>
        </tr>
        <tr>
          <th>2023-07-28</th>
          <td>4.029420</td>
        </tr>
        <tr>
          <th>2023-07-29</th>
          <td>3.975496</td>
        </tr>
        <tr>
          <th>2023-07-30</th>
          <td>4.119657</td>
        </tr>
      </tbody>
    </table>
    </div>



Step 10: Let’s update the data of the chart and changes the chart type
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Any block can be directly updated only with set\_\* functoins. I do not
need to keep track what was the ID of the report where the chart was -
everything is neatly linked together. If I want to keep the old object
for safety purposes, I can, by default, all set\_\* functions return a
fresh object synced with the server, I can store the old one to be able
to revert to it

.. code:: ipython3

    all_data = pd.concat([old_data, new_data])
    all_data = all_data.rename(
        columns={'Gummy Bear Price Index | Line': 'Gummy Bear Price Index | Area'}) #Lets change chart type
    chart = chart.set_data(all_data.round(2))


Step 11: Let’s update the title of the chart
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: ipython3

    chart = chart.set_title('Gummy Bear Price Index July Update')

Step 12: Let’s add a paragraph of the text
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Then we use either the UI or the Python SDK to tweak and update the
remaining blocks.

.. code:: ipython3

    paragraph = ParagraphBlock(report.report_id, 'I can add some autogenerated maybe?', session)
    report.add_block(paragraph)




.. parsed-literal::

    ParagraphBlock(id=79xb69PMpu, text=I can add ...)