The flow itself is not backed by any kind of computation and does not have any state by itself until it starts to be col
It appears that the Date column is being treated as a string rather than as dates. Let’s make things right. For this, we shall use the pandas’ to_datetime feature, which converts the arguments to dates. Lastly, we want to make sure that the Date column is the index column.
,But how do you handle things like user actions, external device events, state updates, etc? They are operating independently of whether there is any code that is interested in them. They should support multiple observers inside the application. These are so-called hot sources of events.
,A popular way to deal with buffer overflow is to drop the oldest events and retain only the most recent, newest events. In particular, it is a great way to model state variables in an application. It is such a wide-spread use-case that it has its own specialized StateFlow type which serves as a replacement for a ConflatedBroadcastChannel, which became obsolete, too.
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software can sometimes be overwhelming, especially when there are so many tools and buttons present on the interface. It’s one of the reasons why I was hesitant to learn at first. But this tutorial made me realize I don’t need to know everything — just the tools, that I’ll use the most.,class BroadcastEventBus {
private val _events = MutableSharedFlow
Time resampling is a way to aggregate data with respect to a defined time period. We have the stock price data for each day, but this doesn’t make much sense if we want to see the trend for a financial institution. What is useful is the aggregated information for every month or every quarter. This helps the management to get an overview instantly and then make decisions based on this overview.
,# Importing required modules import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates import datetime as dt from datetime import datetime # To access datetime from pandas import Series # To work on series
,Instead of working with the entire data, it is prudent to slice the time series data to highlight the portion of the data we are interested in. Since the volume-weighted average price (VWAP) is a trading benchmark, we shall limit our analysis to only that column.
,It has tunable parameters such as the number of old events to keep and replay for new subscribers and the extraBufferCapacity to provide cushion for fast emitters and slow subscribers.
,So there is a dip in stock prices around the last week of October and the first week of November. One could investigate it further by finding out if there was some special event on that day.
,That’s where the concept of aSharedFlow comes in. A shared flow exists regardless of whether it is being collected or not. A collector of the shared flow is called a subscriber. All subscribers of a shared flow receive the same sequence of values. It effectively works like a “broadcast channel”, without most of the channel overhead. It makes the concept of a broadcast channel obsolete.
,class StateModel { private val _state = MutableStateFlow(initial) val state = _state.asStateFlow() // read-only public view fun update(newValue: Value) { _state.value = newValue // NOT suspending } }
,It appears that Maruti had a more or less steady increase in its stock price from 2004 to the mid-2018 window. There seems to be some drop in 2019, though. Let’s further analyze the data for the year 2018.
,All subscribers of a shared flow are asynchronously collecting events in their own context. Emitter does not wait until subscribers finish processing the events. However, when the shared flow buffer is full, the emitter suspends until there is room in the buffer. This suspension of emitter on buffer overflow provides back-pressure to slow down emission when collectors cannot keep up. Alternative strategies to deal with buffer overflow are supported via BufferOverlow parameter.
,Python’s necessary objects for working with dates and times reside in the built-in datetime module. In pandas, a single point in time is represented as a Timestamp And we can use datetime() function to create Timestamps from strings in a wide variety of date/time formats.