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estim8-0.0.1
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مشاهده بیشترتوضیحات
Conduct parameter estimations for Dymola and FMU models
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | estim8-0.0.1 |
| نام | estim8 |
| نسخه کتابخانه | 0.0.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Daniel Strohmeier |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/estim8/ |
| مجوز | MIT |
# Project Description: Estim8
*Estim8* is a tool for parameter estimation of differential-algebraic models (DAEs) using either model formulations in Dymola (installation required) or FMU models. It uses global meta-heuristic optimization to solve parameter estimation problems, and provides methods for uncertainty quantification.
## Installation
The installation via `pip install estim8Beta` is problematic, since the required PyGMO package is not available here. If you still installed this package via PyPI make sure to add PyGMO to your environment, using:
`$ conda config --add channels conda-forge`
`$ conda config --set channel_priority strict`
`$ conda install pygmo`
Alternatively, estim8 can be installed via conda-forge directly:
`conda install -c conda-forge estim8`
## Usage
The workflow of `estim8` can be divided into 5 steps:
1. Initializing the model
2. Defining Estimation Prerequisites
3. Starting Estimation
4. Conducting Uncertainty Qunatification
5. Visualize Results
### 1. Initializing the Model
To initialize a model, use the class `DymolaModel()`, or `FmuModel()` depending on your model formulation, and initialize it:
```python
MyModel = FmuModel('MyModel')
MyModel.initialize()
```
After the initialization all information on parameters and variables of the model are retrieved, and it can be simulated by the `simulate()` method:
```python
Results, Parameters = MyModel.simulate(0,20,0.1, #(t0, t_end, stepsize)
parameter={"par1":10},
observe=["Trajectory1","Trajectory2"],
tolerance=1e-4,
)
```
Where the 3 positional arguments in the beginning form the timevector to simulate (similar to numpy.linspace()).
### 2. Defining Estimation Prerequisites
To conduct a parameter estimation, it is necessary to provide at least:
* A model as FmuModel or DymolaModel
* Experimental data to fit the parameters to (as pandas.DataFrame with time as index and columns as variable names. For Replicates: {'Rid1':pd.DataFrame, ..., 'RidN':pd.DataFrame} )
* Boundaries for the parameters to estimate
* A dictionary, mapping the names of the experimental data columns to the variables of the model (`observation_mapping`)
Optional the following settings can be made:
* Metric for the discrepancy ('SS': Sum of squares, 'WSS' Weighted sum of squares, 'negLL': negative Log-Likelihood)
* ParameterMapping and Replicate IDs if multiple replicates or experiments are included
* Custom time vector in case a finer discretization as 0.1 is required
*Example Code:*
```python
# Data import
import pandas
MyData = pandas.read_excel('MyExpermentalData.xlsx',
index_col=0,
header=[0,1],
sheet_name=None
)
# Bound definition
bounds = {
"par1" : [8,12],
"par2" : [0.1,0.4],
}
# Observation mapping
obs_map = {
"ExpDat1" : "Obs1",
}
```
After specifying the prerequisites, the `Estimator()` can be instantiated:
```python
MyEstimator = Estimator(MyModel,
data = MyData,
bounds = bounds,
observation_mapping = obs_map,
)
```
### 3. Starting Estimation
For the estimation process there are 2 possibilities, which are `estimate()` and `estimate_parallel()`. Both return an optimal parameter vector according to the specified `bounds`, and an `info` struct:
```python
# Single Core
optimum, info = MyEstimator.estimate(
method = 'local', # optimization method
p0 = {'par1':10, 'par2':0.25}, # initial pnt
max_iter = 20, # maximum iterations
)
# Parallel
optimum, info = MyEstimator.estimate_parallel(
method = 'de', # optimization method
n_workers= 4, # number of parallel proc.
max_iter = 20,
)
# Parallel (PyGMOs generalized island approach)
optimum, info = MyEstimator.estimate_parallel(
method = ['pg_de1220','pg_sga']*2, # Islands
n_workers= 4, # overwritten by no. islands
max_iter = 20,
)
```
### 4. Uncertainty Qunatification
For UQ estim8 provides `profile_likelihood()`method, and classical Monte Carlo sampling method `mc_sampling()`.
```python
# MC sampling
MC_Samples = MyEstimator.mc_sampling(
method = ['pg_sea']*4, # Optimization method
n_samples = 200, # No. samples
evos2reset = 50, # No. Iter until RAM reset
# Terminate after:
no_progress = 10, #10 iters w.o. progress
tot_iter = 200, #More than 200 iters
final_loss = 500, #If loss of 500 is reached
}
```
### 5. Visualize Results
For visualization of the results, the following automated plotting functions are available in the `Visualization` module:
|Function |Arguments |Description |
|:---------------------|:------------------------|:--------------|
|`Visualization.plot_sim()`|Results, observe=[] | Plots simulation trajectories generated by a `Estim8Model`|
|`Visualization.plot_estimates()`|optimum, MyEstimator, data=None, only_measured=False|Plots simulation trajectories of optimum in comparison to experimental data|
|`Visualization.plot_correlations()`|MC_Samples, thresholds=5, show_vals=False|Plotting the correlations for parameter results of a Monte Carlo Sampling as a heatmap|
|`Visualization.plot_distribution()`|MC_Samples, bins=5, est=MyEstimator|Creates a corner plot showing historgrams for parameter values on the diagonal, and scatter plots between parameter pairs on the LTM|
|`Visualization.plot_many()`|MC_Samples, MyEstimator, observe=[]|Plots all trajectories resulting from the MC samples together with the experimental data|
|`Visualization.plot_bound_violation()`|Optimum, bounds|Shows relative location of optimum within the bounds|
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | scipy |
| - | pandas |
| - | openpyxl |
| - | joblib |
| - | pathlib |
| - | matplotlib |
| - | seaborn |
| - | fmpy |
| - | sdf |
| - | scikit-optimize |
| - | notebook |
| - | pygmo |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl estim8-0.0.1:
pip install estim8-0.0.1.whl
نصب پکیج tar.gz estim8-0.0.1:
pip install estim8-0.0.1.tar.gz