feat: realtime jupyter example (#29)

README.rst

@@ -693,10 +693,91 @@ The first example shows how to use a client capabilities to predict stock price
 
 * sources - `stock-predictions.ipynb <https://github.com/influxdata/influxdb-client-python/blob/master/notebooks/stock-predictions.ipynb>`_
 
-The second example shows how to use a client capabilities to realtime visualization via `hvPlot <https://hvplot.pyviz.org>`_, `Streamz <https://streamz.readthedocs.io/en/latest/>`_:
+The second example shows how to use a client capabilities to realtime visualization via `hvPlot <https://hvplot.pyviz.org>`_, `Streamz <https://streamz.readthedocs.io/en/latest/>`_, `RxPY <https://rxpy.readthedocs.io/en/latest/>`_:
 
 * sources - `realtime-stream.ipynb <https://github.com/influxdata/influxdb-client-python/blob/master/notebooks/realtime-stream.ipynb>`_
 
+.. code:: python
+
+    from datetime import timedelta
+    from typing import List
+
+    import hvplot.streamz
+    import pandas as pd
+    import rx
+    from rx import operators as ops
+
+    from streamz.dataframe import Random, DataFrame
+    from streamz import Stream
+    from influxdb_client import InfluxDBClient
+
+.. code:: python
+
+    def source_data(auto_refresh: int, query: str, sink: Stream):
+        rx \
+            .interval(period=timedelta(seconds=auto_refresh)) \
+            .pipe(ops.map(lambda start: f'from(bucket: "my-bucket") '
+                                        f'|> range(start: -{auto_refresh}s, stop: now()) '
+                                        f'{query}')) \
+            .pipe(ops.map(lambda query: client.query_api().query_data_frame(query, data_frame_index=['_time']))) \
+            .pipe(ops.map(lambda data_frame: data_frame.drop(columns=['result', 'table']))) \
+            .subscribe(observer=lambda data_frame: sink.emit(data_frame), on_error=lambda error: print(error))
+        pass
+
+.. code:: python
+
+    client = InfluxDBClient(url='http://localhost:9999', token='my-token', org='my-org')
+
+.. code:: python
+
+    cpu_query = '|> filter(fn: (r) => r._measurement == "cpu") ' \
+                '|> filter(fn: (r) => r._field == "usage_user") ' \
+                '|> filter(fn: (r) => r.cpu == "cpu-total") ' \
+                '|> keep(columns: ["_time", "_value"])'
+
+
+    cpu_sink = Stream()
+    cpu_example = pd.DataFrame({'_value': []}, columns=['_value'])
+    cpu_df = DataFrame(cpu_sink, example=cpu_example)
+
+    source_data(auto_refresh=5, sink=cpu_sink, query=cpu_query)
+
+.. code:: python
+
+    mem_query = '|> filter(fn: (r) => r._measurement == "mem") ' \
+                '|> filter(fn: (r) => r._field == "available" or r._field == "free" or r._field == "total" or r._field == "used") ' \
+                '|> map(fn: (r) => ({ r with _value: r._value / 1024 / 1024 }))' \
+                '|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")' \
+                '|> keep(columns: ["_time", "used", "total", "free", "available"])'
+
+    mem_sink = Stream()
+    mem_example = pd.DataFrame({'used': [], 'total': [], 'free': [], 'available': []}, columns=['available', 'free', 'total', 'used'])
+    mem_df = DataFrame(mem_sink, example=mem_example)
+
+    source_data(auto_refresh=5, sink=mem_sink, query=mem_query)
+
+.. code:: python
+
+    from bokeh.models.formatters import DatetimeTickFormatter
+
+    # Time formatter
+    formatter = DatetimeTickFormatter(
+        microseconds = ["%H:%M:%S"],
+        milliseconds = ["%H:%M:%S"],
+        seconds = ["%H:%M:%S"],
+        minsec = ["%H:%M:%S"],
+        minutes = ["%H:%M:%S"],
+        hourmin = ["%H:%M:%S"],
+        hours=["%H:%M:%S"],
+        days=["%H:%M:%S"],
+        months=["%H:%M:%S"],
+        years=["%H:%M:%S"],
+    )
+
+    cpu_df.hvplot(width=450, backlog=50, title='CPU % usage', xlabel='Time', ylabel='%', xformatter=formatter) +\
+    mem_df.hvplot.line(width=450, backlog=50, title='Memory', xlabel='Time', ylabel='MiB', xformatter=formatter, legend='top_left')
+
+.. image:: docs/images/realtime-result.gif
 
 
 Advanced Usage