Opening file: '../results-analysis-runtime/jent-raw-noise-0001.0Fbitout.single.data'
Error: '../results-analysis-runtime/jent-raw-noise-0001.0Fbitout.single.data' is empty
Error reading file.
Usage is: ea_non_iid [-i|-c] [-a|-t] [-v] [-l <index>,<samples> ] <file_name> [bits_per_symbol]

	 <file_name>: Must be relative path to a binary file with at least 1 million entries (samples).
	 [bits_per_symbol]: Must be between 1-8, inclusive. By default this value is inferred from the data.
	 [-i|-c]: '-i' for initial entropy estimate, '-c' for conditioned sequential dataset entropy estimate. The initial entropy estimate is the default.
	 [-a|-t]: '-a' tests all bits in bitstring, '-t' truncates bitstring to 1000000 bits. Test all data by default.
	 -v: Optional verbosity flag for more output. Can be used multiple times.
	 -l <index>,<samples>	Read the <index> substring of length <samples>.

	 Samples are assumed to be packed into 8-bit values, where the least significant 'bits_per_symbol'
	 bits constitute the symbol.

	 -i: Initial Entropy Estimate (Section 3.1.3)

		 Computes the initial entropy estimate H_I as described in Section 3.1.3
		 (not accounting for H_submitter) using the entropy estimators specified in
		 Section 6.3.  If 'bits_per_symbol' is greater than 1, the samples are also
		 converted to bitstrings and assessed to create H_bitstring; for multi-bit symbols,
		 two entropy estimates are computed: H_original and H_bitstring.
		 Returns min(H_original, bits_per_symbol X H_bitstring). The initial entropy
		 estimate H_I = min(H_submitter, H_original, bits_per_symbol X H_bitstring).

	 -c: Conditioned Sequential Dataset Entropy Estimate (Section 3.1.5.2)

		 Computes the entropy estimate per bit h' for the conditioned sequential dataset if the
		 conditioning function is non-vetted. The samples are converted to a bitstring.
		 Returns h' = min(H_bitstring).