Edit data

Calculator

The Analyst calculator is a powerful tool for data editing and calculation. The calculator includes tools for column creation and deletion, calculation tools with common functions, and data selection:

• Create new columns.

• Calculate and write values into columns. Choose a column for calculation, insert the expression, and click on the "Calculate" button. Values in the column will be rewritten by new values.  

• Apply calculations on selected records only. Select the record in the table view, open the context menu (right-click), and choose "Add to selection". Write the expression into the box for selection and press the "Apply selection" button.

• Explore columns. Click on the column in the list and press the "Describe column" button.
  
  

• Check your results before saving. Choose table view and explore table records

• Save changes. Click on the "Save" button

Useful calculation expressions

Below there are some samples of useful calculation expressions for datasets.

*operations == and != support only date value, not datetime or time value

Copy a column

Example: Copy the column DNI from our dataset.

1. Open Calculator

2. Push "New" button under column list

3. Fill "Name" (eg. new_DNI), parameter type (DNI) and press "OK". For filling in other optional information you have to use Metadata editor.

4. Choose the new column in combobox (eg. col["new_DNI"])

5. Fill expression box (eg. col["old_DNI]).

6. Press "Validate" button

7. If validate step was without errors press "Calculate" button

8. For permanent saving of the results press "Save" button

Select records marked with a specific flag

Example: Select only those GHI values whose flag value is 2.

1. Open Calculator

2.  Double-click on column (eg. flg_GHI)

3.  Type "==" or click on "==" button

4.  Type value 2. Final expression will be: col["flg_GHI"] == 2

5. Press "Validate" button

6. If validate step was without errors press "Apply" button

Calculate parameters

Example: Diffuse horizontal irradiance (DIF) data was missing in the original imported file. 

1. Open Calculator

2. Push "New" button under column list

3. Fill "Name" (DIF), parameter type (DIF) and press "OK". For filling other optional information you have to use Metadata editor.

4. In expression combo-box choose column for calculation (col["DIF"])

5. Type in calculation formula into expression box (col["GHI"] - col["DNI"] * sin(radians(sun_elevation)))

6. Press "Calculate" for launching calculation.

7. Press "Save" for saving your calculation into your dataset.

Calculate parameters (only if values are empty)

Example: Calculate values for DIF, only for records with empty values:

1. Open Calculator

2. Push "Select data" button

3. Select DIF in column list and press "Describe column"

4. Double-click on DIF column (will bring column into expression box), press "==" button and double-click on "NaN" value in unique value list. Final expression looks like this: col["DIF"] == NaN

5. Press "Apply" and close window after the message informs you about the selection. Now you have selected all records with missing DIF value

6. In expression combo-box choose column for calculation (col["DIF"])

7. Type in calculation formula into expression box (col["GHI"] - col["DNI"] * sin(radians(sun_elevation)))

8. Press "Calculate" for launching calculation. Calculation is applied only for selected records

9. Press "Save" for saving your calculation into your dataset.

Units convertor

Converts between preselected types of data parameter units. You can do unit conversions in the Calculator tool as well. This tool is just more handy as it takes care of metadata. Conversion affects data values directly in the selected dataset without creating a copy.

Aggregate

Groups together multiple data records into one single record. Grouping is based on timestamps. Different aggregation rules and functions are used for different data parameters. Rules can be customized by the user. The result of the aggregation procedure is written in the new output dataset. Typical usage of the tool is when you want to reduce e.g., 5-minute data into an hourly dataset. Another example is the time step harmonization of different datasets before their comparison. 

Edit aggregation rules

Dialog window for editing aggregation rules specifically for every single column. Aggregation rule can be a simple summarization function (SUM, MAX, MIN, MEAN, MEDIAN, MODE, FIRST, LAST) or a more specific method (MEAN_WEIGHTED, ANGULAR) applied by the aggregation procedure.  Different rules can be set for aggregating into hourly and N-minute intervals and for daily, monthly or yearly. 

Opting for the 'None' rule in combo boxes (hourly and lower, daily and higher) will result in having empty aggregated intervals filled by Nan values.

Average aggregation for Albedo

Method of the albedo (ALB) aggregation depends on the presence of Global horizontal irradiation (GHI) in the dataset. If GHI is available:

• Analyst calculates RHI (Reflected horizontal irradiation) from GHI and albedo (by the formula RHI = GHI / ALB)

• mean is then calculated from calculated RHI

• ALB is recalculated from aggregated RHI (by the formula ALB = RHI / GHI)

• If GHI is not available:

• simple mean aggregation of ALB

Angular aggregation

For data parameters with circular quantities measured in degrees e.g., wind direction or sun azimuth, it isn't appropriate to calculate arithmetic means. The arithmetic mean of two values of the wind direction measured in geographical azimuth 1° (northern wind) and 359° (northern wind) is 180° (southern wind), which is misleading. The angular method converts all angles from polar coordinates to Cartesian coordinates and then computes arithmetic mean on these values. Results are again transposed back to angles.

When aggregating wind direction values (Analyst parameter code is WD), Analyst will always use wind speed data (if the column exists) for giving weight when averaging wind directions. This approach prefers wind directions with higher intensity of winds.

Average weighted (Mean weighted) aggregation

Standard MEAN aggregation function works only with records within the edges of desired output interval. Record exactly positioned on the right edge of given interval falls into the next interval (see the picture 'Standard MEAN aggregation rule'). Typical measured value of irradiation rather represents the time interval, not the instant moment in time.

For example record at 07:00 reads 227.7 W/m2. This value represents the average of all readings between 06:52:30 and 07:07:30. If you use standard mean rule with data like this, your aggregation result will not represent mean irradiation between 06:00 and 07:00. Instead, your result will represent mean irradiation between 05:52:30 and 06:52:30.  

MEAN_WEIGHTED aggregation takes into consideration the situation around interval borders. At the beginning of the process, weighted MEAN aggregation function densifies original records (see the picture 'MEAN_WEIGHTED aggregation rule'). The rule makes ten new records of the same value from one original record - giving the weight of each value by its occurrence. Then, the arithmetic mean is calculated.

Combine datasets 

Create a new dataset by combining two different datasets. The tool is useful for adding new records and columns to an existing dataset, e.g., every month, when new sensor readings are available. 

Select the primary dataset and the secondary dataset you want to combine. The order of datasets is important! The output dataset will contain the full copy of the primary dataset data (no loss) plus column values or additional rows from the secondary dataset. As a result of this tool, a new output dataset is created in the selected database.

Measured values

There are multiple options how to combine datasets:

• skip columns or rows in the secondary dataset that do not exist in the primary dataset, or 

• include all columns or all rows from the secondary dataset

There are two options how to handle case when a column with the same name exists in both datasets:

• columns from the secondary dataset will be renamed and added as a new column, or

• columns can be merged into one column

In case the value for one particular time stamp exists in both the primary and secondary dataset, value from the primary dataset is taken and the value from the secondary dataset is ignored. 

Optionally, the secondary dataset can be restricted by a period, so you have more control over which records will be added. 

Visualizations of different combined dataset options are below:

Flags

Flag columns are combined with the same logic as measured columns (parameters).  Flag column names are taken from the primary dataset. In case there are flag columns with identical names, some columns will be automatically renamed.

In case there are flag columns that are not assigned to any parameter, these columns will be part of the combined dataset. Such flag columns will not be merged, when there are columns with identical names, they are always renamed.

Maintenance log

Maintenance logs are combined as well. Based on parametrization maintenance log from the secondary dataset can be clipped to a shorter date range.

QC status

QC statuses are combined too. QC status is removed in case both primary and secondary datasets do not have particular QC status. It means that one of the datasets didn't have quality control done, and therefore also combined dataset is considered that it didn't have quality control done.

When is combining datasets not allowed

In the following cases, it is not allowed to combine datasets: 

• Database of the primary, secondary, or combined dataset is not available

• One of the datasets is empty

In case the option to merge columns is selected, the following scenarios are not allowed: 

• Merging columns of the same name but different parameter type

• Merging GTI columns of the same name but different GTI configuration

• Merging columns of the same name but values in different units

Warning while combining datasets

Users are informed about the following cases before datasets are combined. It is up to the user to reconsider whether to continue and combine datasets or to cancel the operation and check the dataset or metadata first.

To see more details about which columns are affected, use the "Show Details..." option.