*[Data harmonization and custom functions](#data-harmonization-and-custom-functions)
*[Save outputs to file](#save-outputs-to-file)
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The following passage guides you through the essentials of the usage of SaQC via a toy dataset and a toy configuration.
### 3.1 Get toy data and configuration
### Get toy data and configuration
If you take a look into the folder *saqc/ressources/data* you will find a toy dataset *data.csv* which contains the following:
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In this case, we define a range-test that flags all values outside the range [10,60] and a test to detect spikes using the MAD-method. You can find an overview of all available quality control tests in the [documentation](FunctionDescriptions.md). Note that the tests are _executed in the order that you define in the configuration file_. The quality flags that are set during one test are always passed on to the subsequent one.
### 3.2 Run SaQC
### Run SaQC
Remember to have your virtual environment activated:
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* Following our definition in the config-file, first the *range*-test that flags all values outside the range [10,60] was executed and after that, the *spikes_simpleMad*-test to identify spikes in the data
* In the config, we set the plotting option to *True* for *spikes_simpleMad*, only. Thus, the plot aggregates all preceeding tests (here: *range*) to black points and highlights the flags of the selected test as red points.
### 3.3 Configure SaQC
### Configure SaQC
#### Change test parameters
Now you can start to change the settings in the config-file and investigate the effect that has on how many datapoints are flagged as "BAD". When using your own data, this is your way to configure the tests according to your needs. For example, you could modify your *myconfig.csv* and change the parameters of the range-test:
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You can see that the changes that we made to the parameters of the range test take effect so that only the values >60 are flagged by it (black points). This, in turn, leaves more erroneous data that is then identified by the proceeding spike-test (red points).
### 3.4 Explore the functionality
### Explore the functionality
#### Process multiple variables
You can also define multiple tests for multiple variables in your data. These are then executed sequentially and can be plotted seperately. E.g. you could do something like this:
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