Opening file: '../results-measurements/jent-conditioned.data' Error: could not open '../results-measurements/jent-conditioned.data' Error reading file. Usage is: ea_iid [-i|-c] [-a|-t] [-v] [-l , ] [bits_per_symbol] : 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' produces the 'H_bitstring' assessment using all read bits, '-t' truncates the bitstring used to produce the `H_bitstring` assessment to 1000000 bits. Test all data by default. Note: When testing binary data, no `H_bitstring` assessment is produced, so the `-a` and `-t` options produce the same results for the initial assessment of binary data. -v: Optional verbosity flag for more output. Can be used multiple times. -l , Read the substring of length . 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).