# climpy
<center> <img src="climpy.png" alt="logo" style="width:100px;"/></center>
Climpy is working to help climate researchers to analyse climate data, write in formats ready to be used with machine learning models and analyse the accuracy of model predictions
https://github.com/climai/climpy/actions/workflows/python-app.yml/badge.svg
The package is divided into three parts
- Transform: The `transform` module transforms by applying different conditions on your dataset. The class diagram below will detail on the application of the module.
```mermaid
---
Transform
---
classDiagram
Hazard <|-- Criterion
class Condition{
args
returns_event
func()
}
class Criterion{
sequence
apply_conditions()
valid_conditions(sequence) bool
}
class Hazard{
event_locations
n_events
apply_conditions()
valid_conditions()
get_event(n) Event
all_events() EventList
}
class DataArray{
xr.DataArray variables
xr.DataArray functions()
}
class LinkDataHazard{
on_events()
get_values()
}
class Event{
data
location
start_time
end_time
r
tau
set_intensity()
}
class EventList{
event_list
}
DataArray -- LinkDataHazard
LinkDataHazard -- Hazard
Hazard *-- EventList
EventList *--Event
Condition .. Criterion
Condition .. Hazard
```
- ml_data: The `ml_data` module creates/writes data to be used conveniently for different kinds of machine learning models. The class diagram below will detail on the application of the module.
```mermaid
---
Data ML
---
classDiagram
List *-- DataArray
class X{
values
meta
}
class Y{
values
meta
}
class MLData{
X
Y
tvt_split()
}
class DataArray{
xr.DataArray variables
xr.DataArray functions()
}
class Split
```
- Metrics: The `metrics` module can be applied to observed and simulated variables. This would include exhaustive set of different metrics that can be used on climate related data
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"description": "# climpy\n\n<center> <img src=\"climpy.png\" alt=\"logo\" style=\"width:100px;\"/></center> \n\nClimpy is working to help climate researchers to analyse climate data, write in formats ready to be used with machine learning models and analyse the accuracy of model predictions\n\nhttps://github.com/climai/climpy/actions/workflows/python-app.yml/badge.svg\n\nThe package is divided into three parts\n- Transform: The `transform` module transforms by applying different conditions on your dataset. The class diagram below will detail on the application of the module.\n\n```mermaid\n---\nTransform\n---\n\nclassDiagram\n\nHazard <|-- Criterion\n\nclass Condition{\n args\n returns_event\n func()\n}\n\nclass Criterion{\n sequence\n apply_conditions()\n valid_conditions(sequence) bool\n}\n\nclass Hazard{\n\n event_locations\n n_events\n apply_conditions()\n valid_conditions()\n get_event(n) Event\n all_events() EventList\n}\n\nclass DataArray{\n xr.DataArray variables\n xr.DataArray functions()\n}\n\nclass LinkDataHazard{\n on_events()\n get_values()\n}\n\nclass Event{\n data\n location\n start_time\n end_time\n \n r\n tau\n\n set_intensity()\n}\n\nclass EventList{\n event_list\n}\n\nDataArray -- LinkDataHazard\nLinkDataHazard -- Hazard\nHazard *-- EventList\nEventList *--Event\nCondition .. Criterion\nCondition .. Hazard\n```\n\n- ml_data: The `ml_data` module creates/writes data to be used conveniently for different kinds of machine learning models. The class diagram below will detail on the application of the module.\n\n```mermaid\n---\nData ML\n---\nclassDiagram\n\n\nList *-- DataArray\n\nclass X{\n values\n meta\n}\n\nclass Y{\n values\n meta\n}\n\nclass MLData{\n X\n Y\n tvt_split()\n}\n\n\nclass DataArray{\n xr.DataArray variables\n xr.DataArray functions()\n}\n\nclass Split\n\n```\n\n- Metrics: The `metrics` module can be applied to observed and simulated variables. This would include exhaustive set of different metrics that can be used on climate related data",
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