Book - 3.5) Algorithms - Iteration in Blockly (Part 2)
Having seen how decisions are represented in Blockly we can now see how iteration and decision are combined to form the filter pattern. We can also then consider how a new list of values can be created from an existing list using the transform pattern.
Iteration: Filter¶
In other cases there are certain items in a data stream of particular interest. We can “filter” the data stream by iterating through the stream looking for the items of interest and ignoring those not of interest. Only those items of interest are processed. For example, for our forecast temperature data stream we might want to print only the mild temperatures in the forecast. According to our earlier definition, a mild temperature is one that is at least 60 degrees and below 75 degrees. The BlockPy workspace below illustrates this pattern of filtering a data stream.
In this example an iteration is used to select each "temp" in the "temps" list of temperatures forecast for "Blacksburg, VA". Each "temp" is tested in the decisions to determine if it satisfied the requirements of a mild temperature. The condition in the decision uses the Boolean "and" to combine into a single true/false outcome the true/false outcomes of the two individual tests. If a given "temp" satisfies both of these individual tests the "and" is also true and the value of "temp" is output. Any "temp" value that does not satisfy the condition is not output.
In general, the filter pattern can involve a condition of any degree of complexity to determine whether the element satisfies the "filter test". The "filter test" may also require some computation; for example, the "filter test" may be to output only those temperatures that are below 52 degrees Celsius when the temperature is expressed in Fahrenheit. This requires that the Fahrenheit temperature be converted into its Celsius equivalent before the condition is tested.
Iteration: Transform¶
The transform pattern produces a new data stream from a given data stream. Each item of the given data stream is changed in some way to generate each item of the new data stream stream. For example, each item in the forecast stream is a temperature on the Fahrenheit scale. What might be needed is a data stream where each item is the same temperature but measured in the Celsius scale. The transform pattern can be used to produce the Celsius data stream from the Fahrenheit data stream. This pattern is illustrated in the BlockPy workspace below.
The Blockly program creates a new list named “Cstream” to represent the data stream to be created. The “Cstream” list is initialized to be empty because it contains no items at the beginning. The forecast data stream is used to initialize the variable “Fstream”. The “Fstream” list is printed. Each of the printed values will be in Fahrenheit. The iteration produces the transformed data stream, “Cstream”. One each iteration:
- an item, denoted by “temp”, is selected from the Fahrenheit data stream
- the value of “celsius” is set to the result of converting “temp” to the equivalent temperature on the Celsius scale. For ease of readability later, the result of the conversion is rounded to the nearest integer value.
- the value of “celsius” is added to the “Cstream” list that represents the Celsius data stream.
When the iteration is completed the “Cstream” list is printed. You can run the program and see the correspondence between each item in the original data stream and its transformed value in the new data stream.
Combining the Patterns¶
The individual patterns of uniform, filter, accumulate, and transform can be combined. For example, might want to generate a data stream whose items are only the temperatures above 85 degrees, the hot temperatures, and where the temperatures in the generated data stream are in Celsius. The program to generate this data stream uses both a transform pattern and a filter pattern.