weewx/docs/xtypes.md

Extensible types (XTypes)

Abstract

This proposal allows new derived observation types to be added to WeeWX.

Motivation

Right now, the set of observation types is fixed:

• The set of types that StdWXCalculate knows how to calculate is limited to a set of "magic types," such as dewpoint, or heatindex.
• Heating and cooling degree days are also special types, defined in subclass WXDaySummaryManager.
• There is no way to add new types to the tag system, unless they appear in the "current" record, or in the database.

In theory, new types can be introduced by subclassing, but this allows only new types to be accreted in a linear fashion: it would not be possible for two extensions to both introduce new types. One would have to inherit from the other.

The goal of XTypes is to allow the user to add new types to the system with a minimum of fuss.

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Summary

• A new module, weewx.xtypes, will be introduced. Similar to the existing weewx.units, it would be responsible for managing new types. New types can be added dynamically, using a Python API.

• The service StdWXCalculate will no longer have a fixed set of "special" types. Instead, it will be extensible, by using weewx.xtypes. The existing options of hardware, prefer_hardware, and software would continue. It would come out-of-the-box with the existing types it now handles (dewpoint, heatindex, etc.), but new types could be added by the user. This allows new types to appear in the current LOOP packet or archive record, allowing their use elsewhere in WeeWX.

• When resolving tags, the Cheetah generator would first look in the present record, then in the database, as it does now. But, then it would look to weewx.types to try and calculate any unresolved types. This would allow the products of StdWXCalculate to be used by wee_reports, resolving Issue #95

• In a similar manner, the Image generator would first try the database to resolve any series. If that doesn't work, it would then try weewx.xtypes.

• The class WXDaySummaryManager would be deprecated, and the two types heatdeg and cooldeg would no longer depend on it. Instead, the tag system would use weewx.xtypes to calculate them.

• The schema system would be expanded to allow explicit declaration of the schema for the daily summaries. This replaces some functionality presently done by WXDaySummaryManager.

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Overview of adding new types

Adding a new observation type is done by subclassing the abstract base class XTypes, then overriding one to three functions:

class XTypes:
    get_scalar(obs_type, record, db_manager=None)
    get_series(obs_type, timespan, db_manager, aggregate_type=None, aggregate_interval=None)
    get_aggregate(obs_type, timespan, aggregate_type, db_manager, **option_dict)

An instance of the subclass is then instantiated and registered with weewx.xtypes. Note that it is not always necessary to supply all three functions. Details follow

Calculating scalars

To calculate a custom scalar value, the user should subclass XTypes, then override the member function get_scalar:

class MyTypes(XTypes):

    def get_scalar(obs_type, record, db_manager):
        ...

Where

• obs_type is the name of type to be computed.
• record is a WeeWX record. It will include at least types dateTime and usUnits.
• db_manager is an instance of weewx.manager.Manager, or a subclass. The connection will be open and usable.

The function should return:

• A ValueTuple scalar. The value held by the ValueTuple can be None.

The function should raise:

• An exception of type weewx.UnknownType, if the type obs_type is not known to the function.
• An exception of type weewx.CannotCalculate if the type is known to the function, but all the information necessary to calculate the type is not there.

Calculating series

The user should subclass XTypes, then override the member function get_series:

class MyTypes(XTypes):

    def get_series(obs_type, timespan, db_manager, aggregate_type, aggregate_interval):
       ...

Where

• obs_type is the name of type to be computed.
• timespan is an instance of weeutil.weeutil.TimeSpan. It defines bounding start and ending times of the series, exclusive on the left, inclusive on the right.
• db_manager is an instance of weewx.manager.Manager, or a subclass. The connection will be open and usable.
• aggregate_type defines the type of aggregation, if any. Typically, it is one of sum, avg, min, or max, although there is nothing stopping the user-defined extension from defining new types of aggregation. If set to None, then no aggregation should occur, and the full series should be returned.
• aggregate_interval is an aggregation interval. If aggregation is to be done (i.e., aggregate_type is not None), then the series should be grouped by the aggregation interval.

The function should return:

• A three-way tuple: (start_list_vt, stop_list_vt, data_list_vt) where

  • start_list_vt is a ValueTuple, whose first element is a list of start times;
  • stop_list_vt is a ValueTuple, whose first element is a list of stop times;
  • data_list_vt is a ValueTuple, whose first element is a list of aggregated values.

The function should raise:

• An exception of type weewx.UnknownType, if the type obs_type is not known to the function.
• An exception of type weewx.UnknownAggregation if the aggregation aggregate_type is not known to the function.
• An exception of type weewx.CannotCalculate if the type and aggregation are known to the function, but all the information necessary to perform the calculate is not there.

Calculating aggregates

To calculate a custom aggregation, the user should override the member function get_aggregate:

class MyTypes(XTypes):

    def get_aggregate(obs_type, timespan, aggregate_type, db_manager, **option_dict):
        ...

Where

• obs_type is the type over which the aggregation is to be computed.
• timespan is an instance of weeutil.weeutil.TimeSpan. It defines bounding start and ending times of the aggregation, exclusive on the left, inclusive on the right.
• aggregate_type is the type of aggregation to be performed, such as avg, or last, or it can be some new, user-defined aggregation.
• db_manager is an instance of weewx.manager.Manager, or a subclass. The connection will be open and usable.
• option_dict is a dictionary with possible, additional, values to be used by the aggregation. (Need details)

The function should return:

• A ValueTuple holding the aggregated value.

The function should raise:

• An exception of type weewx.UnknownType, if the type obs_type is not known to the function.
• An exception of type weewx.UnknownAggregation if the aggregation aggregate_type is not known to the function.
• An exception of type weewx.CannotCalculate if the type and aggregation are known to the function, but all the information necessary to perform the aggregation is not there.

See the extension weewx-xaggs for an example of how to add a new aggregate type.

Registering your subclass

The last step is to tell the XTypes system about the existence of your extension. The module weewx.xtypes keeps a simple list of extensions. When it comes time to evaluate a derived type, the list is scanned, and the first entry that successfully resolves a type, is the one that is used.

Order matters! Your new class should be prepended or appended to the list, depending on whether you want it to override other extensions. If it is at the front of the list (prepended), it will be the first to be tried, and so will take precedence over anything further back in the list. See the section XTypes API.

There are two general ways of arranging to have your XTypes extension registered.

Statically

In this approach, you create an instance of your class in a file, then add it to the xtypes list. This works well when a file is likely to get loaded for some other reasons, perhaps because a WeeWX service extension is in it. It does not work so well if it needs special information, such as information that can only be obtained from the configuration file weewx.conf.

import weewx.xtypes

class MyXType(weewx.xtypes.XType):
    def get_scalar(self, obs_type, record, db_manager=None):
        # Perform some calculation...
        ...
        return value

# Instantiate an instance, and append it to the list:
weewx.xtypes.xtypes.append(MyXType())

As a service

In this approach, you explicitly create a WeeWX service, which will load and unload your extension. This is a good approach when you need information out of weewx.conf.

Here's the general pattern. Take a look in the files weewx/wxxtypes.py for some other comprehensive examples used internally by WeeWX.

from weewx.engine import StdService
import weewx.xtypes

# This is the actual XTypes extension:
class OtherXType(weewx.xtypes.XType):
    def __init__(self, info1=1, info2=2):
        self.info1 = info1
        self.info2 = info2
    def get_scalar(self, obs_type, record, db_manager=None):
        # Perform some calculation involving info1 and info2
        ...
        return value

# This is a WeeWX service, whose only job is to register and unregister the extension:
class MyService(StdService):
    def __init__(self, engine, config_dict):
        super(MyService, self).__init__(engine, config_dict)

        # Get some options out of the configuration dictionary
        info1 = config_dict['OtherXType']['info1']
        info2 = config_dict['OtherXtype']['info2']

        # Instantiate an instance of the class OtherXType, using the options:
        self.xt = OtherXType(info1, info2)

        # Register the class
        weewx.xtypes.xtypes.append(self.xt)

    def shutDown(self):
        # Engine is shutting down. Remove the registration
        weewx.xtypes.xtypes.remove(self.xt)

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A comprehensive example

In this example, we are going to write an extension to calculate the vapor pressure of water. The observation type will be called vapor_p, and we will offer two algorithms for calculating it. This example is included in WeeWX V4.3 and later in the examples subdirectory.

The extension

Here's what the XTypes extension looks like:

# File user/vaporpressure.py

import math

import weewx
import weewx.units
import weewx.xtypes
from weewx.units import ValueTuple

class VaporPressure(weewx.xtypes.XType):

    def __init__(self, algorithm='simple'):
        # Save the algorithm to be used.
        self.algorithm = algorithm.lower()

    def get_scalar(self, obs_type, record, db_manager):
        # We only know how to calculate 'vapor_p'. For everything else, raise an exception UnknownType
        if obs_type != 'vapor_p':
            raise weewx.UnknownType(obs_type)

        # We need outTemp in order to do the calculation.
        if 'outTemp' not in record or record['outTemp'] is None:
            raise weewx.CannotCalculate(obs_type)

        # We have everything we need. Start by forming a ValueTuple for the outside temperature.
        # To do this, figure out what unit and group the record is in ...
        unit_and_group = weewx.units.getStandardUnitType(record['usUnits'], 'outTemp')
        # ... then form the ValueTuple.
        outTemp_vt = ValueTuple(record['outTemp'], *unit_and_group)

        # Both algorithms need temperature in Celsius, so let's make sure our incoming temperature
        # is in that unit. Use function convert(). The results will be in the form of a ValueTuple
        outTemp_C_vt = weewx.units.convert(outTemp_vt, 'degree_C')
        # Get the first element of the ValueTuple. This will be in Celsius:
        outTemp_C = outTemp_C_vt[0]

        if self.algorithm == 'simple':
            # Use the "Simple" algorithm.
            # We need temperature in Kelvin.
            outTemp_K = weewx.units.CtoK(outTemp_C)
            # Now we can use the formula. Results will be in mmHg. Create a ValueTuple out of it:
            p_vt = ValueTuple(math.exp(20.386 - 5132.0 / outTemp_K), 'mmHg', 'group_pressure')
        elif self.algorithm == 'teters':
            # Use Teter's algorithm.
            # Use the formula. Results will be in kPa:
            p_kPa = 0.61078 * math.exp(17.27 * outTemp_C_vt[0] / (outTemp_C_vt[0] + 237.3))
            # Form a ValueTuple
            p_vt = ValueTuple(p_kPa, 'kPa', 'group_pressure')
        else:
            # Don't recognize the exception. Fail hard:
            raise ValueError(self.algorithm)

        # We have the vapor pressure as a ValueTuple. Convert it back to the units used by
        # the incoming record and return it
        return weewx.units.convertStd(p_vt, record['usUnits'])

We have subclassed XTypes as a class called VaporPressure. By default, it uses a "simple" algorithm to calculate the vapor pressure, but the constructor for the class gives us a chance to use another algorithm, Teter's algorithm.

Registering the extension

Now we need to register the extension with the XTypes system, and to provide some way of specifying which algorithm we want to use. We can accomplish both by writing a simple WeeWX service. Here's what it looks like:

from weewx.engine import StdService

class VaporPressureService(StdService):
    def __init__(self, engine, config_dict):
        super(VaporPressureService, self).__init__(engine, config_dict)
        
        # Get the desired algorithm. Default to "simple".
        try:
            algorithm = config_dict['VaporPressure']['algorithm']
        except KeyError:
            algorithm = 'simple'
            
        # Instantiate an instance of VaporPressure:
        self.vp = VaporPressure(algorithm)
        # Register it with the XTypes system:
        weewx.xtypes.xtypes.append(self.vp)
        
    def shutDown(self):
        # Remove the registered instance:
        weewx.xtypes.xtypes.remove(self.vp)

Like any other WeeWX service, VaporPressureService needs to be added to the list of services to be run, so that the engine will instantiate it. We do this by adding it to the end of the service group xtype_services.

[Engine]

    [[Services]]
        # This section specifies the services that should be run. They are
        # grouped by type, and the order of services within each group
        # determines the order in which the services will be run.
        xtype_services = weewx.wxxtypes.StdWXXTypes, weewx.wxxtypes.StdPressureCooker, weewx.wxxtypes.StdRainRater, user.vaporpressure.VaporPressureService
        ...

The final step is to tell the unit system what group our new observation type, vapor_p, is in:

weewx.units.obs_group_dict['vapor_p'] = "group_pressure"

The resultant file, vaporpressure.py, can be found in the examples subdirectory.

Using the extension

There are several different ways you can use your extension:

1. In an expression in a Cheetah template;
2. As a type to be plotted;
3. To populate data packets and records;
4. In other extensions.

Cheetah

Suppose we want to show the current vapor pressure in a Cheetah template:

<p>The current vapor pressure is $current.vapor_p </p>

The Cheetah engine will first look for the type vapor_p in the current record. It won't be there, because it's a derived type. Then it will look in the database. Unless we've made other arrangements, it won't be there either. Finally, the Cheetah engine invokes the XTypes system to see if it can calculate it. Because of our extension, this works.

Plot images

We can also use our XTypes extension in a plot. For example, suppose we wanted to plot today's vapor pressure. Here's what the entry in the skin configuration file, skin.conf would look like:

    ...
    [[day_images]]
        ...
        [[[dayvaporp]]]
            [[[[vapor_p]]]]

This would instruct the plotting engine to create an image file dayvaporp.png containing a plot with a single variable: vapor_p:

This works, because the XTypes system includes a version of get_series() which hits the database, then feeds the result into the XTypes system to calculate any derived variables, such as vapor_p. However, this version does not know how to calculate aggregations (although there's no reason why this couldn't be added in the future).

Populate packets and records

In the above examples, the type vapor_p was evaluated "just in time" to be used in a template or plot. If you want vapor_p to appear in packets and records, perhaps because you want to store it in a database, you can ask the service StdWXCalculate to calculate it for you and put it in the packet / record.

[StdWXCalculate]
    [[Calculations]]
        ...
        vapor_p = prefer_hardware
        ...

Like any other derived type, this will cause StdWXCalculate to first look in the packet or record to see if it already has a value for vapor_p from your hardware station. Most likely, it doesn't, so StdWXCalculate will arrange for the XTypes system to calculate a value, then puts it in record.

If your database schema includes a slot for vapor_p, then the value would be put in the database.

XTypes API

Getting a value from the XTypes system can also be useful inside an extension. Most XTypes users will not need to do this, but if you're writing an extension, it can be helpful.

Module weewx.xtypes supplies 3 functions for using user-supplied extensions:

get_scalar(obs_type, record, db_manager=None)
get_series(obs_type, timespan, db_manager, aggregate_type=None, aggregate_interval=None)
get_aggregate(obs_type, timespan, aggregate_type, db_manager, **option_dict)

Example: function weewx.xtypes.get_scalar() searches the list weewx.xtypes.xtypes, trying member function get_scalar() of each object in turn. If the member function raises weewx.UnknownType or weewx.CannotCalculate, weewx.xtypes.get_scalar() moves on to the next object in the list. If no function can be found to do the evaluation, it raises weewx.UnknownType.

The other functions work in a similar manner.

Subtlety

One must be careful when sharing data structures across threads. Most of the time, this is not a problem in WeeWX because the only thing that is shared is the configuration dictionary, and it is treated as readonly.

However, because XType extensions can be used within the main thread (by StdWXCalculate), and the reporting thread (by Cheetah, and the image generator), it is possible to end up sharing a data structure. In particular, this can happen when you use a WeeWX service to initialize the extension.

For an example of this, see the class wxxtypes.RainRater. Its job is to calculate the rainfall rate, by calculating the amount of rain received per unit time. It binds to loop events, and uses it to update an internal cache of rain events. This happens in the main thread.

However, it is possible that it could get used in the reporting thread if, say, the Cheetah template engine needs to evaluate rainRate, and it's not available in the database, nor in the present record. In this case, the extension will attempt to calculate rainRate, which involves scanning the internally cached list of rain events.

The danger is if a thread context switch happens in the middle of that scan. The cached data structure could be in an unstable state. To guard agains this, RainRater locks the structure before using or changing it.

This is a problem only because RainRater binds to something in the main thread. Most of the time, this is not an issue.

Other examples

It's worth taking a look in file weewx/wxxtypes.py for examples of XTypes used by WeeWX itself.

See the project weewx-xaggs for examples of adding new aggregation types, such as the historical highs and lows for a date.

The repository weepwr contains a more complex example. This is a device driver for the Brultech energy monitors. It registers many new types, and does this dynamically.

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Alternatives to the chosen design

Alternative: register functions with weewx.conf

The chosen design registered new types through a Python API. An alternative is to declare the types and function to be called in weewx.conf, in a manner similar to search list extensions. This approach has the advantage that it requires a bit less programming and, most importantly, it leaves a concise record of what extensions are being used in the configuration file weewx.conf.

However, it has a big disadvantage. The example above shows why. It is difficult to predict what data a user might need to write an extension. In our example, we needed the altitude of the station. Where would that come from? The answer is that it would have to be supplied by a standardized interface to the user function, which would make all manner of information available. This means the user might potentially have to know everything, so you end up with a system where everything is connected to everything.

This is avoided by supplying a Python API that the type must adhere to. The new type can get any information it wants, then register with the API. This is what our example does.

Alternative: register functions through the API

With this alternative, new types register with a Python API, but register functions, rather than instances of classes.

The disadvantage is that this results in a proliferation of small functions. The chosen method has the advantage that all the functions needed for a type can be held held under one roof.

Alternative: declare types

In the chosen design, if an observation type is not known to a type extension, it raises an exception of type weewx.UnknownType. An alternative that was considered is to require extensions to declare what types they can handle in advance, much like a dictionary. This allows types to be discoverable.

But, it has a disadvantage that all known types must be declared. That's not always practical. For example, a type extension would be useful to calculate power from a running aggregate of energy from an energy monitor. But, some monitors are capable of emitting 48+ channels, each of which would have to be declared. Add polarized and absolute values, and we are looking at nearly 100 types!

Instead, we allow extensions to recognize what types they know about, possibly from a regular expression of the observation type. If they don't recognize the type, they raise weewx.UnknownType. Types do not have to be declared in advance.

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Open issues