A library for headache-free data clean-up and validation.
sanitise is a CSV processing and validation library that generates code at compile time based on a YAML configuration file. The generated code is robust and will not panic.
Add sanitise to your dependencies in your Cargo.toml:
toml
[dependencies]
sanitise = "0.1"
Import the macro:
rust
use sanitise::sanitise;
And call: ```rust,ignore // main.rs use std::{fs, iter::zip};
use sanitise::sanitise;
fn main() { let csv = fs::readtostring("data.csv").unwrap(); let ((timemillis, pulse, movement), (timesecs,)) = sanitise!(includestr!("sanitiseconfig.yaml"), csv).unwrap();
println!("time_millis,time_secs,pulse,movement");
for (((time_millis, pulse), movement), time_secs) in zip(zip(zip(time_millis, pulse), movement), time_secs) {
println!("{time_millis},{time_secs},{pulse},{movement}")
}
} ```
```yaml
processes: - name: validate columns: - title: time type: integer - title: pulse type: integer max: 100 min: 40 on-invalid: average valid-streak: 3 - title: movement type: integer valid-values: [0, 1] output-type: boolean output: "value == 1" - name: process columns: - title: time type: integer output: "value / 1000" - title: pulse type: integer ignore: true - title: movement type: integer ignore: true
```
```csv
time,pulse,movement 0,67,0 15,45,1 126,132,1 ```
The first argument to sanitise! must be either a string literal or a macro call that expands to a string literal. The second argument must be an expression that resolves to a string in CSV format. In the above example, sanitise_config.yaml must be next to main.rs, and data.csv must be in the working directory at runtime.
These specify general information about how the file should be processed.
on-titleOptional.
Specifies what to do when a title row is found.
The valid options are once, which returns an error if more than one header row is found, and split, which splits the file at each title row and processes the resulting sections as individual files.
If once is selected, the macro will return a tuple containing the result of processing. If split is selected, a Vec of tuples will be returned, with one tuple per section of the file.
If no value is specified, the default is once.
processesRequired.
Describes the processes to be executed.
A specification of the contents of each process can be found under Processes.
The processes described here will be executed sequentially, with each process acting on the result of the previous. Every process returns a tuple containing the result from each column. Each file processed returns a tuple of these tuples.
Must be a list of maps.
These entries each specify one process. A process is an operation on a file, and returns the processed data.
nameRequired.
The name used to identify each process. In the future, this will be included in errors.
Must be a unique string.
columnsRequired.
Describes the operations on the columns in a file.
A specification of the contents of each column can be found under Columns;
The entries correspond to the columns in the input file if they are in the first process, or columns from the previous process otherwise. The titles of the columns in the first process must match the titles of the columns in the input file.
These entries specify an operation to be applied to each entry in a column. This operation accepts a value and returns either a validated value or an error.
titleRequired.
The title of a column.
The titles of the columns in the first process must correspond to the titles of the columns in the input file.
Must be a unique string.
column-typeRequired.
The data type of the corresponding column in the input file if this column is in the first process, or in the previous process otherwise.
Must be one of boolean, integer, real, string.
output-typeOptional.
The data type returned from this column. Likely to be the same as column-type, unless the output key is specified.
Must be one of boolean, integer, real, string.
Defaults to the value of column-type.
null-surrogateOptional.
A value to be treated as an empty entry if found.
The data type of this value must be column-type.
valid-valuesOptional.
A set of values to accept. All other values will be considered invalid.
Must be a list of column-type
on-invalidOptional.
What to do when an invalid value is found.
The valid options are:
- abort, which halts execution and returns an error if an invalid value is found.
- average, which averages the last valid value before a series of invalid values, and the first valid value after that series. This option requires that the key valid-streak be specified, which determines the number of consecutive valid values that must be found to end a series of invalid values.
- delete, which deletes the row if an invalid value is found.
- previous, which uses the previous value, or the value of invalid-sentinel if this is the first value. This option requires that the key invalid-sentinel be specified.
- sentinel, which uses the value of invalid-sentinel. This option requires that the key invalid-sentinel be specified.
If no value is specified, the default is abort.
on-nullOptional.
What to do when a null entry is found.
This will also be used if the value of null-surrogate is found.
The valid options are:
- abort, which halts execution and returns an error if a null entry is found.
- average, which behaves the same as if an invalid value was found. This option requires that on-invalid is set to average.
- delete, which deletes the row if a null entry is found.
- previous, which uses the previous value, or the value of null-sentinel if this is the first value. This option requires that the key null-sentinel be specified.
- sentinel, which uses the value of null-sentinel. This option requires that the key null-sentinel be specified.
If no value is specified, the default is abort.
maxOptional.
The maximum value to accept.
Any values found over this value will be considered invalid.
The data type of this value must be column-type.
minOptional.
The minimum value to accept.
Any values under this value will be considered invalid.
The data type of this value must be column-type.
outputOptional.
An expression used to calculate the result.
The permitted operations are +, - (both binary and unary), *, /, and %.
The following functions are provided:
- boolean: Convert the argument to a Boolean. Numbers will be false if they are equal to 0, and true otherwise. Strings will be false if they are empty, and true otherwise.
- integer: Convert the argument to an integer. Booleans will be 1 if they are true and 0 if they are false. Floats will be rounded down to the highest representable integer lower than them. Strings will be parsed into an integer, and return an error if the parsing fails.
- real: Convert the argument to a float. Booleans will be 1.0 if they are true and 0.0 if they are false. Strings will be parsed into an float, and return an error if the parsing fails. Note that very large integers may lose precison when converted to floats.
- string: Convert the argument to a string. All values will simply be converted to a textual representation.
For now, the only provided identifier is value, which represents the current value in this column. This may change in the future.
ignoreOptional.
Whether to ignore this column, and exclude it from the output of this process.
If this is set to true, all other settings for the column will be disregarded.
If no value is specified, the default is false.
The macro creates linear finite automata to process each column. If on-invalid is set to average for a given column, that column's automaton will use a state machine to keep track of valid and invalid values. If a column is ignored, no automaton will be generated for it. All data is stored in native Rust types.
Licensed under either of
at your option.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.