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Lesson7_Data_Types.md

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Signal data can be formatted in several ways. The intended representation for samples in files can vary in length, endianness, and signedness. Some formats are more common than others and they can vary across applications. GNU Radio is heavily integrated into FISSURE and the descriptions of the data types are provided in this lesson. Some of the more common Python format characters are also described to demonstrate how to manipulate data from sources other than GNU Radio.

Table of Contents

  1. References
  2. GNU Radio Data Types
  3. Python

References

GNU Radio Data Types

Data Type Size (Bytes) Python Character Numpy
Complex Float 64 2*8 'd' np.float64
Complex/Complex Float 32 2*4 'f' np.float32
Complex Integer 64 2*8 'q' np.int64
Complex Integer 32 2*4 'i' np.int32
Complex Integer 16 2*2 'h' np.int16
Complex Integer 8 2*1 'b' np.int8
Float 64 8 'd' np.float64
Float/Float 32 4 'f' np.float32
Integer 64 8 'q' np.int64
Integer 32 4 'i' np.int32
Int/Integer 16 2 'h' np.int16
Byte/Integer 8 1 'b' np.int8

Python

From https://docs.python.org/3/library/struct.html:

Format C Type Python Type Standard Size
x pad byte no value
c char bytes of length 1 1
b signed char integer 1
B unsigned char integer 1
? _Bool bool 1
h short integer 2
H unsigned short integer 2
i int integer 4
I unsigned int integer 4
l long integer 4
L unsigned long integer 4
q long long integer 8
Q unsigned long long integer 8
n ssize_t integer
N size_t integer
e float 2
f float float 4
d double float 8
s char[] bytes
p char[] bytes
P void* integer

Examples of Converting and Writing

if (get_original_type == "Complex Float 64") and (get_new_type == "Complex Int 64"): 
    number_of_bytes = os.path.getsize(get_original_file)
    plot_data_formatted = struct.unpack((number_of_bytes/8)*'d', plot_data)
    np_data = np.asarray(plot_data_formatted, dtype=np.int64)
    np_data.tofile(get_new_file)
    
elif (get_original_type == "Complex Float 32") and ((get_new_type == "Complex Int 16") or (get_new_type == "Short/Int 16")):                
    number_of_bytes = os.path.getsize(get_original_file)
    plot_data_formatted = struct.unpack((number_of_bytes/4)*'f', plot_data)
    np_data = np.asarray(plot_data_formatted, dtype=np.int16)
    np_data.tofile(get_new_file)
    
elif (get_original_type == "Int/Int 32") and ((get_new_type == "Complex Float 32") or (get_new_type == "Float/Float 32")):                
    number_of_bytes = os.path.getsize(get_original_file)
    plot_data_formatted = struct.unpack((number_of_bytes/4)*'i', plot_data)
    np_data = np.asarray(plot_data_formatted, dtype=np.float32)
    np_data.tofile(get_new_file)    
    
elif ((get_original_type == "Complex Int 16") or (get_original_type == "Short/Int 16")) and ((get_new_type == "Complex Int 8") or (get_new_type == "Byte/Int 8")):                
    number_of_bytes = os.path.getsize(get_original_file)
    plot_data_formatted = struct.unpack((number_of_bytes/2)*'h', plot_data)
    np_data = np.asarray(plot_data_formatted, dtype=np.int8)
    np_data.tofile(get_new_file) 
    
elif ((get_original_type == "Complex Int 8") or (get_original_type == "Byte/Int 8")) and (get_new_type == "Complex Float 64"):                
    number_of_bytes = os.path.getsize(get_original_file)
    plot_data_formatted = struct.unpack((number_of_bytes)*'b', plot_data)
    np_data = np.asarray(plot_data_formatted, dtype=np.float64)
    np_data.tofile(get_new_file)