# Transforms (transform)¶

## Available Transforms (transform)¶

The following sub-types and transforms are implemented in pyCIF so far:

## Description¶

The transform class executes elementary operations in pyCIF. Basically, they form the execution chain of the observation operator.

The observation operator can be decomposed as follows in sub-operations:

$\mathcal{H}(\mathbf{x}) = ( \mathcal{H}_1 \circ \mathcal{H}_2 \circ \cdots \circ \mathcal{H}_N ) (\mathbf{x})$

Each operation is given by a transform.

They have a standardized input and output format to be fully inter-changeable. The input and output format is a datastore dictionary with the following structure:

## Input and output formats¶

The transform have a standardized format for their inputs and outputs. The format is a regular python dictionary dict, whose keys are the component/parameter IDs of each input/output. For each key, the input/output is split according to the dates of each sub-simulation corresponding to the transform.

inout_datastore = {
"inputs": {
(component0, param0): {
ddi0: data0,
ddi1: data1,
[...]
},
[...]
(componentN, paramN): {
ddi0: data0,
ddi1: data1,
[...]
}
},
"outputs": {
(component0, param0): {
ddi0: data0,
ddi1: data1,
[...]
},
[...]
(componentN, paramN): {
ddi0: data0,
ddi1: data1,
[...]
}
},
}


Note

Please note that the input components/parameters are not necessarily the same as for the outputs.

From here, there is two possibilities for the format of the data:

1. for gridded (from 1- to 4-D) data, the data is an xarray.Dataset. The structure of the Dataset is:

<xarray.Dataset>
Dimensions:  (time: ntime, lev: nlev, lat: nlat, lon: nlon)
Coordinates:
* time     (time) datetime64[ns] list_of_dates
* lev      (lev) int64 list_of_levels
Dimensions without coordinates: lat, lon
Data variables:
incr     (time, lev, lat, lon) float64 incr_values
spec     (time, lev, lat, lon) float64 spec_values


The xarray.DataArray spec contains the values of the corresponding parameter. incr includes the corresponding increments in the case of a tangent-linear simulation

2. for sparsed data (e.g., observations), the data is a pandas.DataFrame. The structure is the same as the one described here for observation inputs. The extra columns spec, and optionally incr for tangent-linear computations are included to store the local input/output parameter

## Required parameters, dependencies and functions¶

The following attributes, dependencies and functions should be defined for any transform, as they are called by other plugins. They can be parameters to define at the set-up step, functions to implement in the corresponding module, or dependencies to be attached to the transform class.

### Parameters and attributes¶

#### mapper¶

Each transform is defined by a so-called mapper. The mapper is a dictionary including all the metadata about the inputs, outputs, and, if applicable, sub-simulations.

It is defined in the function ini_mapper (see bellow) called at the initialization of the observation operator

Metadata about inputs/outputs are given for every component/parameter involved in the transform as input/output. All pieces of information are optional and depends on what is needed to compute the transform itself.

mapper = {
"inputs": {
(component1, tracer1): [...],
(component1, tracer2): [...],
(component2, tracer3): [...],
[...]
},
"outputs": {
(component1, tracer1): [...],
(component1, tracer2): [...],
(component2, tracer3): [...],
[...]

}
}


Note

The inputs and outputs do not necessarily have the same number of components/tracers

The pieces of information to specify in each component/tracer of the inputs/outputs are:

input_files

dictionary of input files as defined in the fetch functions of the class datastreams (see here)

input_dates

same as above for input dates

forces inputs prior to the transform to be loaded; this means that data needs to be handled by the transform itself; should be put to True in general;

force_dump

forces to dump inputs prior to the transform

forces to load outputs posterior to the transform

Note

the two arguments force_loadout and force_dump are used when the transform needs to read and returns data as external files.

This is typically the case for chemistry-transport models that cannot directly use the data in the memory, but rather use files

domain

the domain on which the data is given

continuous_hdomain

the domain is continuous in the horizontal direction; this means that the horizontal interpolation to fit to observations is done internally to the transform

continuous_vdomain

same as above in the vertical direction

Note

The two options continuous_hdomain and continuous_vdomain are used when interpolations to fit observations are done internally to the model/transform.

For instance, Lagrangian particle dispersion models naturally use these options as footprints are computed beforehand on given locations

On the opposite, for Eulerian models, it is recommended to switch off any interpolation function and let pyCIF do it itself, thus putting continuous_hdomain and continuous_vdomain to False

is_lbc

the data is used at the sides of the domain; used when regridding prior to the transform

is_top

same as above when the data is used at the top of the domain; used when vertically interpolating prior to the transform

sparse_data

the sparse data format (see above) is used/returned

sampled

the output data is a sample of the full 4D output on the full domain; typically this is used when a domain returns a list of concentrations at given grid cells / time stamps; a correspondance is thus needed to fit back simulated concentrations to the observation realm

tracer

the tracer plugin associated to the input/output

component

the component plugin associated to the input/output

Note

component and tracer are to be used if attributes from the component/tracer are needed to compute the transform

unit

the unit of the corresponding component/tracer; this is used to determine whether a unit conversion should be performed; if no unit is defined, unitless values are assumed, which may be incompatible with expected values elsewhere and possibly return an error. Please see how the unit_conversion transform behaves for further details here

There is extra information that can be specified in the mapper on top of the above-mentioned information about inputs and outputs:

subsimus

all information about sub-simulations and corresponding dates for all inputs and outputs.

mapper["subsimus"] = {
"inputs": {
(component1, tracer1): {
ddi1: [list of dates intervals],
ddi2: [list of dates intervals],
[...]
}
},
"outputs": {
(component1, tracer1): {
ddi1: [list of dates intervals],
ddi2: [list of dates intervals],
[...]
}
},
}

fixed_subsimus

when True sub-simulations need to be explicitly defined in subsimus and will not be influenced by the rest of the computation pipeling

### Functions¶

#### ini_mapper¶

The function ini_mapper is called at the initialization of the transform. It returns the mapper as defined above.

Click below for a full example of the ini_mapper function for the transform families (details here.

pycif.plugins.transforms.basic.families.ini_mapper()[source]

#### forward¶

The function forward computes in forward mode the transformation

The function forward computes in backward mode the transformation