Structure of the model input file and functionality
D-Eco Impact is steered through a YAML input file. This input file informs the model which data to use, what ecological knowledge rules to apply and where to write the output data. The easiest way to edit these YAML files is by using Notepad++. When starting with your first application with D-Eco Impact, make use of earlier models to setup your YAML input file and edit from there. When running the YAML file with D-Eco Impact, the model will inform you if there are inconsistencies in the file provided.
Importing and exporting data
Importing and exporting data is always arranged in the input-data and output-data header in the YAML file.
version: …………………….
input-data:
…………………….
rules:
…………………….
output-data:
…………………….
The variables present in the input data, provided through “filename”, are selected for use. The filename is able to accept a pattern including a * in the name. Instead of using one single input file, all files matching the pattern within the folder are being processed by the same input_file.yaml. So, for example, if in a folder there are two files test_1.nc and test_2.nc, the user can set the filename to "test_*.nc" and both files will be processed. It is possible to filter the input data by providing a start date or end date (format: "dd-mm-yyyy"); this is optional. The variables that are used can be selected under “variable_mapping”. Here, you are also able to rename variables as the name used for storage is often cryptic.
At output data the location where the output file needs to be written can be provided through “filename”. In this output file only variables that have been used from the input data and variables that have been created in the model are stored. If the user gives a pattern (filename with asterisk for partitions) in the input-data filename, the output-data filename needs to match the corresponding amount of files that are being processed. Again in the example of two files (test_1.nc and test_2.nc) and an input-data filename of "test_*.nc", the user can either give an output-data filename with or without an asterisk. Without an asterisk (eg "output.nc"), the partitioned part of the input filename is used and extended to the output-data filename ("output_1.nc" and "output_2.nc"). With an asterisk (eg "*_output.nc") the * will provide the place where the partitioned part of the input file will be placed ("1_output.nc" and "2_output.nc"). It is possible to reduce the file size with the optional parameter "save_only_variables", which can take the name of one or several variables. The model needs at least one rule under “rules” to execute.
#FORMAT
version: <D-Eco_Impact_version_nr>
input-data:
- dataset:
filename: <path_to_file_including_file_name_and_type>
start_date: "<start_date>"
end_date: "<end_date>"
variable_mapping:
<variable1_input_file>: "<variable1_name_in_model>"
<variable2_input_file>: "<variable2_name_in_model>"
………
rules:
………
output-data:
filename: <path_to_file_including_file_name_and_type>
save_only_variables: <variable, or list_of_variables>
#EXAMPLE : Reading and writing an example model of the Volkerak-Zoommeer
version: 0.1.5
# Mapping: mesh2d_sa1 : Salinity (PSU)
# mesh2d_s1 : Water level (m NAP)
# mesh2d_waterdepth : Water depth (m NAP)
input-data:
- dataset:
filename: examples/data/FM-VZM_0000_map.nc
start_date: "01-01-2011"
end_date: "31-12-2015"
variable_mapping:
mesh2d_sa1: "salinity"
mesh2d_s1: "water_level"
mesh2d_waterdepth: "water_depth"
rules:
- multiply_rule:
name: make variable test
description: Make a variable called test for testing purposes
multipliers: [1.0]
input_variable: water_depth
output_variable: test
output-data:
filename: examples/data_out/results_test8c.nc
save_only_variables: test
Functionality
The functionality is always arranged in the form of rules under the rules header in the yaml file.
version: …………………….
input-data:
…………………….
rules:
…………………….
output-data:
…………………….
The output of the following functionalities has been shown for a section of the Lake Volkerak 3D hydrodynamic model in the Netherlands. This hydrodynamic model output contains 6 years of data (2011 – 2016), with a timestep of 10 days. The 3D hydrodynamic model has been setup with 22 vertical layers and 3290 horizontal flexible mesh grid cells.
Rules
Multiply rule
- multiply_rule:
name: <name_of_rule_in_text>
description: <description_of_rule_in_text>
multipliers: [<value_to_multiply_with>]
input_variable: <one_input_variable_name>
output_variable: <one_output_variable_name>
- multiply_rule:
name: <name_of_rule_in_text>
description: <description_of_rule_in_text>
multipliers_table:
- ["start_date", "end_date", "multipliers"]
- [<DD-MM>, <DD-MM>, [<value_to_multiply_with>]]
- [<DD-MM>, <DD-MM>, [<value_to_multiply_with>]]
input_variable: <one_input_variable_name>
output_variable: <one_output_variable_name>
The multiply rule allows for multiplication of variables. This could be used for unit transformation (e.g., salinity to chloride) or scenario creation (e.g., water level 80% of existing value). The rule operates on all cells both 3D (in horizontal as vertical) as in the time axes. The same dimensions are returned at the output variable. The rule needs to be applied to an existing variable. A new variable is created when the rule is executed.
When using the multiply rule with a start and end date (or multiple start and end dates) all periods that are not covered will be set to NaN. In this way the multiply rule can also be used as a filter in time. NaNs are ignored by any further calculations (for example the time_aggregation_rule).
#EXAMPLE: Salinity (psu) to chloride (mg/l) in a freshwater environment.
- multiply_rule:
name: Salinity to chloride
description: Converts salinity (psu) to chloride (CL- mg/l) for fresh water environments
multipliers: [0.0018066, 1e5]
input_variable: salinity
output_variable: chloride
- multiply_rule:
name: Select only the summer half year for chloride
description: Select only the summer half year for chloride as this is important for plant growth
multipliers_table:
- ["start_date", "end_date", "multipliers"]
- ["15-04" , "15-09" , [1.0]]
input_variable: chloride
output_variable: chloride_grow_period
Layer filter rule
FORMAT
- layer_filter_rule:
name: <name_of_rule_in_text>
description: <description_of_rule_in_text>
layer_number: <integer_nr_of_layer>
input_variable: <one_3D_input_variable_name>
output_variable: <one_output_variable_name>
The layer filter rule allows for the extraction of a layer from 3D variables. This could be used for extracting the top layer or bottom layer (e.g., from a multi layered model result). The rule operates on all layers in a 3D variable (in the vertical) as in the time axes and returns a 2D result with the time axes intact. The rule needs to be applied to an existing 3D variable. A new 2D variable is created when the rule is executed.
#EXAMPLE : Extracts the chloride concentration at surface.
- layer_filter_rule:
name: Extract chloride at surface
description: Extracts the chloride concentration at surface
layer_number: 22
input_variable: chloride
output_variable: chloride_top_layer
Time aggregation rule
FORMAT
- time_aggregation_rule:
name: <name_of_rule_in_text>
description: <description_of_rule_in_text>
operation: <statistic_opperation_applied>
time_scale : <time_aggregation_applied>
input_variable: <one_input_variable_name>
output_variable: <one_output_variable_name>
The time aggregation rule allows for calculating a statistical summary over the time axes of 3D and 2D variables. This could be used for calculating the maximum value over a year (e.g., for water level) or the minimum value over a month (e.g., oxygen concentration). The rule operates both on 3D variables and 2D variables as long as they have a time axis and returns a 3D or 2D result depending on input with the statistic calculated for a new time axis (e.g., year or month). Operations available: Add, Average, Median, Min, Max, period statistics, Stdev and Percentile(n). When using percentile, add a number for the nth percentile with brackets like this: percentile(10). Stdev calculates the standard- deviation over the time period. Under period statistics are explained further in the text.
Time aggregation available: Year, Month
The rule needs to be applied to an existing 2D/3D variable with time axis. A new 2D/3D variable with new time axis is created when the rule is executed. With a year timestep the result is written to the last day of the year, with a month timestep the result is written to the last day of the month per year.
#EXAMPLE : Calculate the maximum water level in a year.
- time_aggregation_rule:
name: Maximum water level year
description: Get maximum water level in a year
operation: MAX
time_scale: year
input_variable: water_level
output_variable: MAX_water_level_year
Period statistics: Time aggregation rule with COUNT_PERIODS, AVG_DURATION_PERIODS, MIN_DURATION_PERIODS and MAX_DURATION_PERIODS
When the operation type period statistics is used, the user needs to make sure that the input data is always consisting of only 1 and 0. If there is no such layer, the user can make a combination of for example the classification rule together with the time aggregation rule. For example, water depth can be used to check whether the cells are dry or not (this can be done with a classification rule) and with the COUNT_PERIODS operation type in the time aggregation rule the number of consecutive periods within a year or month can be calculated (nr). AVG_DURATION_PERIODS, MIN_DURATION_PERIODS and MAX_DURATION_PERIODS take the respective statistic of the duration for those consecutive periods (duration).
#EXAMPLE:
Calculate the number of consecutive periods of dry time monthly
- classification_rule:
name: Classify dry time
description: Classify to 0 and 1 the dry time
criteria_table:
- ["output", "water_depth"]
- [0, ">0.10"]
- [1, "<0.10"]
input_variables: ["water_depth"]
output_variable: dry_time_classified
- time_aggregation_rule:
name: Count periods
description: Count periods
operation: COUNT_PERIODS
time_scale: month
input_variable: dry_time_classified
output_variable: COUNT_PERIODS_water_level_month
Step function rule
FORMAT
- step_function_rule::
name: <name_of_rule_in_text>
description: <description_of_rule_in_text>
limit_response_table:
- [ "limit", "response"]
- [<limit_value>, <response_value>]
- [<limit_value>, <response_value>]
input_variable: <one_input_variable_name>
output_variable: <one_output_variable_name>
The step function rule performs stepwise classification on the provided values of 3D and 2D variables time dependent arrays. This could be used for translating variables into classes (e.g., salinity classes based on salinity) or indicate suitable/unsuitable ranges (e.g., checking whether the water level falls between the maximum and minimum water level policy criteria). The rule operates both on 3D variables and 2D variables, independent of the time axes, and returns a binominal or classes in a 3D or 2D result, either with time axis, depending on input.
The rule needs to be applied to an existing 2D/3D variable with or without time axis. A new 2D/3D variable with or without time axis is created when the rule is executed.
#EXAMPLE : Salinity classes.
- step_function_rule:
name: Classify salinity
description: Make distinction between 0.0 – 0.5 , 0.5 – 1.2, 1.2 – 1.3 and >1.3 psu
limit_response_table:
- [ limit, response]
- [-999.0 , 0.0 ]
- [ 0.0 , 1.0 ]
- [ 0.5 , 2.0 ]
- [ 1.2 , 3.0 ]
- [ 1.3 , 4.0 ]
- [ 999.0 , 4.0 ]
input_variable: salinity
output_variable: salinity_class
#EXAMPLE : Check if the water level falls within the range of -0.10 and +0.15 m NAP.
- step_function_rule:
name: Check water level policy
description: Check if water level is within -0.10 (minimum) and +0.15 (maximum) m NAP
limit_response_table:
- [ limit, response]
- [-999.0 , 0.0 ]
- [ -0.10 , 1.0 ]
- [ 0.15 , 0.0 ]
- [ 999.0 , 0.0 ]
input_variable: water_level
output_variable : water_level_policy
Response curve rule
FORMAT
- response_curve_rule:
name: <name_of_rule_in_text>
description: <description_of_rule_in_text>
response_table:
- [ "input", "output"]
- [<limit_value>, <response_value>]
- [<limit_value>, <response_value>]
input_variable: <one_input_variable_name>
output_variable: <one_output_variable_name>
The response curve rule performs a linear interpolation over the provided values of the variables of 3D and 2D variables time dependent arrays. This could be used for a fuzzy logic translation of variables into ecological responses to these variables (e.g., suitability for aquatic plants based on light availability). The rule operates both on 3D variables and 2D variables, independent of the time axes, and returns decimal or fractional values in a 3D or 2D result, either with time axis, depending on input.
The rule needs to be applied to an existing 2D/3D variable with or without time axis. A new 2D/3D variable with or without time axis is created when the rule is executed.
#EXAMPLE : Response of the habitat suitability of Long-leaf pond weed
# (Potamogeton nodosus) to water depth.
# Suitable between 0.0 – 2.0 m and highly suitable between 0.5 – 1.0 m
- response_curve_rule:
name: HSI Pond weed water depth
description: Reponse of Pond weed (Potamogeton nodosus) to water depth
response_table:
- ["input", "output"]
- [-999.0 , 0.0 ]
- [ 0.0 , 0.0 ]
- [ 0.5 , 1.0 ]
- [ 1.0 , 1.0 ]
- [ 2.0 , 0.0 ]
- [ 999.0 , 0.0 ]
input_variable: water_depth
output_variable: HSI_Pnodosus_water_depth
Combine results rule
FORMAT
- combine_results_rule:
name: <name_of_rule_in_text>
description: <description_of_rule_in_text>
operation: <statistic_opperation_applied>
input_variables: [<list with_input_variable_names>]
output_variable: <one_output_variable_name>
The combine results rule combines the output of two or more variables to one output variable. The way this data is combined depends on the operation chosen. This could be used for adding mutual exclusive results (e.g., habitat suitability based on flow velocity and water depth) or asses difference between results (e.g., waterlevel and bathymetry to get the water depth).The rule operates one or multiple 3D variables or 2D variables, independent of the time axes, as long as these all have the same dimensions and returns a single 3D or 2D result, either with time axis, depending on input.
Operations available: Add, Subtract, Multiply, Average, Median, Min and Max
The rule needs to be applied to an existing 2D/3D variables with or without time axis. A new 2D/3D variable with or without time axis is created when the rule is executed.
#EXAMPLE : Calculate bathymetry over time
# This is just an example, there is a variable bed level without time (mesh2d_flowelem_bl)
- combine_results_rule:
name: Calculate bathymetry
description: Calculate bathymetry over time by adding water level and water depth
operation: subtract
input_variables: ["water_level","water_depth"]
output_variable: bathymetry_time
Formula rule
FORMAT
- formula_rule:
name: <name_of_rule_in_text>
description: <description_of_rule_in_text>
formula: <statistic_opperation_applied>
input_variables: [<list with_input_variable_names>]
output_variable: <one_output_variable_name>
With the formula based rule multiple variables can be combined in a flexible way. Operations that are supported are the standard operators.
The rule needs to be applied to an existing 2D/3D variables with or without time axis. A new 2D/3D variable with or without time axis is created when the rule is executed.
#EXAMPLE : Calculate bathymetry over time
# This is just an example, there is a variable bedlevel without time (mesh2d_flowelem_bl)
- formula_rule:
name: Calculate bathymetry
description: Calculate bathymetry over time by adding water level and water depth
formula: water_level + water_depth
input_variables: ["water_level","water_depth"]
output_variable: bathymetry_time
A lot of operators are supported with the formula based rule. Given two variables "x" and "y", formulas can be implemented for the following operators:
| Operator | Name | Example |
|---|---|---|
| + | Addition | x + y |
| - | Subtraction | x - y |
| * | Multiplication | x * y |
| / | Division | x / y |
| % | Modulus | x % y |
| ** | Exponentiation | x ** y |
| // | Floor division | x // y |
When a formula results in a boolean, it will be converted to a float result. Meaning that True = 1 and False = 0. Comparison, logical, identity, identity and bitwise operators are supported:
| Operator | Name | Example |
|---|---|---|
| == | Equal | x == y |
| != | Not equal | x != y |
| > | Greater than | x > y |
| < | Less than | x < y |
| >= | Greater than or equal to | x >= y |
| <= | Less than or equal to | x <= y |
| // | Floor division | x // y |
| and | Returns True if both statements are true | x < 5 and x < 10 |
| or | Returns True if one of the statements is true | x < 5 or x < 4 |
| not | Reverse the result, returns False if the result is true | not(x < 5 a |
| is | Returns True if both variables are the same object | x is y |
| is not | Returns True if both variables are not the same object | x is not y |
| in | Returns True if a sequence with the specified value is present in the object | x in y |
| not in | Returns True if a sequence with the specified value is not present in the object | x not in |
| Operator | Name | Description | Example |
|---|---|---|---|
| & | AND | Sets each bit to 1 if both bits are 1 | x & y |
| | | OR | Sets each bit to 1 if one of two bits is 1 | x | y |
| ^ | XOR | Sets each bit to 1 if only one of two bits is 1 | x ^ y |
| ~ | NOT | Inverts all the bits | ~x |
| << | Zero fill left shift | Shift left by pushing zeros in from the right and let the leftmost bits fall off | x << 2 |
| >> | Signed right shift | Shift right by pushing copies of the leftmost bit in from the left, and let the rightmost bits fall off | x >> 2 |
For more information on these operators click here.
(Multiple) Classification rule
FORMAT
- classification_rule:
name: <name_of_rule_in_text>
description: <description_of_rule_in_text>
criteria_table:
- [ "output" , <input_variable_name1>, <input_variable_name2>]
- [<response_value>, <criteria_range>, <criteria_range>]
- [<response_value>, <criteria_range>, <criteria_range>]
input_variables: [<list with_input_variable_names>]
output_variable: <one_output_variable_name>
The classification rule allows for the classification based on the range of one or multiple input vairables. The value range can be indicated in multiple ways. This rule can be used for indicating suitability (0 or 1) or specify categories (1,2,3 etc). The rule will start with the last given criteria range row and work upwards, hence overwriting is possible. Currently there is no check whether possible ranges have been missed or are overlapping.
The rule needs to be applied to an existing 2D/3D variables with or without time axis. A new 2D/3D variable with or without time axis is created when the rule is executed.
Criteria ranges available are:
| Criteria range | Example | Description |
|---|---|---|
| "-" | "-" | Value is not applicable to category, all is allowed |
| "criteria_value" | "5" | Value is exectly the criteria value (only applicable for integers) |
| ">criteria_value" | ">1" | Value needs to larger than criteria value |
| "<criteria_value" | "<0.5" | Value needs to be smaller than criteria value |
| ">criteria_value" | ">=1" | Value needs to larger than or equal to criteria value |
| "<criteria_value" | "<=0.5" | Value needs to be smaller than or equal to criteria value |
| "criteria_value1:criteria_value2" | "0.2:4" | Value needs to be equal or be in between criteria_value1 and criteria_value2 |
#EXAMPLE : Determine the suitability for aquatic vegetation based on classification
- classification_rule:
name: Classification for aquatic plants
description: Derive the classification for aquatic plants based on water depth, flow velocity and chloride levels
criteria_table:
- ["output", "MIN_water_depth_mNAP", "MAX_flow_velocity", "MAX_chloride"]
- [ 1 , "<0.10" , "-" , "-"] # too dry
- [ 2 , ">4.0" , "-" , "-"] # too deep
- [ 3 , "-" , "-" , ">400"] # too salty
- [ 4 , "-" , ">1.5" , "-"] # too fast flowing
- [ 5 , "0.10:4.0" , "0.0:1.5" , "0:400"] # perfect for aquatic plants
input_variables: ["MIN_water_depth_mNAP", "MAX_flow_velocity", "MAX_chloride"]
output_variable: aquatic_plant_classes
- classification_rule:
name: Suitability for aquatic plants
description: Derive the suitability for aquatic plants based on the classification
criteria_table:
- ["output", "aquatic_plant_classes"]
- [ 0 , "1:4"] # not suitable
- [ 1 , "5"] # suitable
input_variables: ["aquatic_plant_classes"]
output_variable: aquatic_plant_suitability
Rolling statistic rule
FORMAT
- rolling_statistics_rule:
name: <name_of_rule_in_text>
description: <description_of_rule_in_text>
operation: <statistic_opperation_applied>
time_scale : <time_step_unit_applied>
period: <time_step_value_applied>
input_variable: <one_input_variable_name>
output_variable: <one_output_variable_name>
The rolling statistic rule allows for a rolling statistic based on the chosen operation and the time period over which the statistic should be repeated. The calculated statistic will be written to each last timestep that falls within the period. Operations available: Add, Average, Median, Min, Max, count_periods, Stdev and Percentile(n). When using percentile, add a number for the nth percentile with brackets like this: percentile(10).
Time scales available: hour, day Period can be a float or integer value.
The rule needs to be applied to an existing 2D/3D variables with time axis. A new 2D/3D variable with the same time axis is created when the rule is executed.
An explanation of how the rolling statistic rule works is shown in the table below:
| timestep | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|
| period1 | - | - | - | i | ||||
| period2 | - | - | - | i | ||||
| period3 | - | - | - | i |
In the example shown above the stripe indicates the time period covered (4 timesteps in this case) and with i the location where the result of the statistic over that period is written. Hence, the first three timesteps in this example will not contain any values. This is repeated until the time series has been covered.
#EXAMPLE : Determine a rolling statistic over salinity levels
- rolling_statistics_rule:
name: test rolling statistic 12.5 hours
description: test rolling statistic 12.5 hours
operation: MAX
time_scale: hour
period: 12.5
input_variable: IN_salinity_PSU
output_variable: salinity_tl_hour_max
- rolling_statistics_rule:
name: test rolling statistic 7 days
description: test rolling statistic 7 days
operation: MAX
time_scale: day
period: 7
input_variable: IN_salinity_PSU
output_variable: salinity_tl_week_max
Axis filter rule
FORMAT
- axis_filter_rule:
name: <name_of_rule_in_text>
description: <description_of_rule_in_text>
axis_name: <name_of_axis_applied>
layer_number: <integer_nr_of_layer_in_axis_applied>
input_variable: <one_3D_input_variable_name>
output_variable: <one_output_variable_name>
The axis filter rule is close to the layer_filter_rule, however it allows for filtering on any axis present in the data. This allows for the selection of a specific time step, spatial cell or other data axis value.
The rule needs to be applied to an existing 2D/3D variables with or without time axis. A new 2D/3D variable with or without time axis is created when the rule is executed, with the exception of the axis that was filtered upon.
#EXAMPLE : Select only the salinity in the cell for the channel entrance from the faces
- axis_filter_rule:
name: Filter face of channel entrance (13th face cell)
description: Filter face of channel entrance (13th face cell)
axis_name: mesh2d_nFaces
layer_number: 13
input_variable: IN_salinity_PSU
output_variable: salinity_PSU_channel_entrance
Depth average rule
FORMAT
- depth_average_rule:
name: <name_of_rule_in_text>
description: <description_of_rule_in_text>
input_variable: <one_input_variable_name>
bed_level_variable: <variable_indicating_bed_level>
water_level_variable: <variable_indicating_water_level>
interfaces_variable: <variable_indicating_interfaces>
output_variable: <one_output_variable_name>
The depth average rule allows for an averaging over depth using the weighted values according to a mesh with z- or sigma-layers. The current implementation is only tested for input netCDF files generated by D-Hydro. The input file must include a variable containing the location of the horizontal interfaces between the layers over which the input variable will be averaged. Also two variables specifying the bedlevel and water level are needed. The input_variable will be a 2D/3D variable, with or without time axis. The output_variable has the same dimensions, excluding the dimension for the depth, as it will be represented as one averaged value per cell.
Note: combined z-sigma layers are currently not supported.
An explanation of how the depth rule works is shown in the example below.
The image shows a simplified model with the following dimensions: - mesh2d_nFaces = 6 (number of faces) - mesh2d_nLayers = 4 (number of layers in the z direction) - mesh2d_nInterfaces = 5 (number of interfaces that define the depth) - time = 2
Below are the variables belonging to this example:
This example results in the following output_variable.
Below is an example of an input_file for the depth average rule:
#EXAMPLE : Determine a depth average for over salinity
- depth_average_rule:
name: test depth average
description: Test depth average
input_variable: salinity
bed_level_variable: mesh2d_flowelem_bl
water_level_variable: mesh2d_s1
interfaces_variable: mesh2d_interfaces_sigma
output_variable: average_salinity
Filter extremes rule
FORMAT
- filter_extremes_rule:
name: <name_of_rule_in_text>
description: <description_of_rule_in_text>
input_variable: <one_input_variable_name>
output_variable: <one_output_variable_name>
extreme_type: troughs or peaks
distance: <int_of_time_scale>
time_scale: second, hour, day, month or year
mask: <boolean>
The filter extremes rule allows for temporal filtering of extremes in a dataset, i.e. peaks (local maxima) and troughs (local minima). The input variable can be any dimension, as long as it has a time dimension. If the variable mask = False, the output is a variable with the same shape as the input, but only values where the peaks occur and NaN values where no peak occur. If mask = True the output is a same sized variable with 1 (True) at the peak values and NaN elsewhere. Furthermore the user can add a distance (with timescale) as input to define the minimum distance between two peaks/troughs. This mask can be applied to another layer with the combine rule (operation: multiply).
Below an example of an input file to use the filter_extremes_rule.
#EXAMPLE : Determine the peak waterlevel values
- depth_average_rule:
name: test filter extremes
description: test filter extremes
input_variable: water_level
output_variable: water_level_mask
extreme_type: peaks
distance: 12
time_scale: hour
mask: True
The input above is part of a simple test to calculate the salinity at the peaks and troughs of the waterlevel. The extreme filter rule is first used to get the locations of the peaks and throughs of the water level (mask = True) and then with the combine rule the values of the salinity at these points are calculated. The figure below shows these results, the salinity (blue line) and water level are plotted (orange line). The calculated peaks and troughs are shown in purple and green respectively. This example can be reproduced with an iPython notebook (in D-EcoImpact/scripts/test_extreme_filter.ipynb), in this file is also the input_file.yaml included that is used for the calculation.
Including data from another YAML file
It is possible to include data in the YAML file that originates from another file. At the moment this is only applicable to another YAML file. This can be useful for storing large classification_rule tables in a separate file (for a better overview of the work file), but this functionality is not limited to that specific rule.
Here is the original rule:
#EXAMPLE : Original
# This is a simplified example, only top layer of flow velocity and chloride was used and year statistics
- classification_rule:
name: classification for aquatic plants
description: classification for aquatic plants based on water depth, flow velocity and chloride.
criteria_table:
- ["output", "MIN_water_depth_mNAP", "MAX_flow_velocity", "MAX_chloride"]
- [ 1 , "<0.10" , "-" , "-"] # too dry
- [ 2 , ">4.0" , "-" , "-"] # too deep
- [ 3 , "-" , "-" , ">400"] # too salty
- [ 4 , "-" , ">1.5" , "-"] # too fast flowing
- [ 5 , "0.10:4.0" , "0.0:1.5" , "0:400"] # perfect for aquatic plants
And this is the rule while making using of an inclusion from another file:
#EXAMPLE : Original
# This is a simplified example, only top layer of flow velocity and chloride was used and year statistics
- classification_rule:
name: classification for aquatic plants
description: classification for aquatic plants based on water depth, flow velocity and chloride.
criteria_table: !include tables/aquatic_plant_criteria.yaml
input_variables: ["MIN_water_depth_mNAP", "MAX_flow_velocity", "MAX_chloride"]
output_variable: aquatic_plant_classes
And this is the included file from tables/aquatic_plant_criteria.yaml:
- ["output", "MIN_water_depth_mNAP", "MAX_flow_velocity", "MAX_chloride"]
- [ 1 , "<0.10" , "-" , "-"] # too dry
- [ 2 , ">4.0" , "-" , "-"] # too deep
- [ 3 , "-" , "-" , ">400"] # too salty
- [ 4 , "-" , ">1.5" , "-"] # too fast flowing
- [ 5 , "0.10:4.0" , "0.0:1.5" , "0:400"] # perfect for aquatic plants