Entropy Coverter app for iPhone and iPad

Entropy is a measure of the amount of information or uncertainty in a system. In information theory, entropy quantifies the average amount of information contained in each message or data element. Entropy can be calculated for various systems, such as information sources, data streams, or random variables.
In the context of entropy conversion, we often encounter different units of measurement for entropy. Here is a detailed description of common entropy units and their conversions:
Bits (bit):
• Bit is the fundamental unit of information entropy.
• It represents the amount of information required to resolve a choice between two equally likely options.
• 1 bit can have two possible states: 0 or 1.
Nats (nat):
• Nat is a unit of entropy based on the natural logarithm.
• It is often used in mathematical and statistical contexts.
• 1 nat corresponds to approximately 0.69315 bits.
• Conversion: 1 nat ≈ 0.69315 bits.
Shannons (Sh):
• Shannon is a unit of entropy named after Claude Shannon.
• It measures the amount of information in a system.
• 1 Shannon is equivalent to 1 bit.
• Conversion: 1 Shannon = 1 bit.
Hartleys (Hart):
• Hartley is a unit of entropy named after Ralph Hartley.
• It measures information in decimal form based on the logarithm to the base 10.
• 1 Hartley is approximately equal to log2(10) bits.
• Conversion: 1 Hartley ≈ log2(10) bits.
These units allow for conversion between different measures of entropy. Conversion factors between these units can be used to convert the entropy values from one unit to another. The conversion factors mentioned above provide approximate equivalences between different units of entropy.