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blobmodel-0.1.2
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مشاهده بیشترتوضیحات
Two dimensional model of propagating blobs
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | blobmodel-0.1.2 |
| نام | blobmodel |
| نسخه کتابخانه | 0.1.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | gregordecristoforo |
| ایمیل نویسنده | gregor.decristoforo@gmail.com |
| آدرس صفحه اصلی | https://github.com/uit-cosmo/2d_propagating_blobs/blob/main/ |
| آدرس اینترنتی | https://pypi.org/project/blobmodel/ |
| مجوز | MIT |
[](http://mypy-lang.org/)
[](https://codecov.io/gh/uit-cosmo/2d_propagating_blobs)
[](https://sourcery.ai)
# 2d_propagating_blobs
Two dimensional model of advecting and dissipating blobs.
The code has been developed originally to model profiles in the scrape-off layer of fusion experiments but it can be used to model any 1d or 2d system consisting of advecting pulses. Examples for one and two dimensions are shown below:
## Installation
The package is published to PyPI and can be installed with
```sh
pip install blobmodel
```
If you want the development version you must first clone the repo to your local machine,
then install the project in development mode:
```sh
git clone git@github.com:uit-cosmo/2d-propagating-blobs.git
cd 2d-propagating-blobs
poetry install
```
## Usage
Create the grid on which the blobs are discretized with using the `Model` class. The `make_realization()` method computes the output as an xarray dataset which can also be written out as a `netcdf` file if the argument `file_name` is specified. A simple example is shown below:
```Python
from blobmodel import Model, show_model
bm = Model(Nx=200, Ny=100, Lx=10, Ly=10, dt=0.1, T=20, blob_shape='gauss',num_blobs=100)
ds = bm.make_realization(file_name="example.nc")
```
The data can be shown as an animation using the `show_model` function:
```Python
show_model(ds)
```
You can specify the blob parameters with a BlobFactory class. The DefaultBlobFactory class has some of the most common distribution functions implemented. An example would look like this:
```Python
from blobmodel import Model, DefaultBlobFactory
# use DefaultBlobFactory to define distribution functions fo random variables
bf = DefaultBlobFactory(A_dist="exp", W_dist="uniform", vx_dist="deg", vy_dist="normal")
# pass on bf when creating the Model
tmp = Model(
Nx=100,
Ny=1,
Lx=10,
Ly=0,
dt=1,
T=1000,
blob_shape="exp",
t_drain=2,
periodic_y=False,
num_blobs=10000,
blob_factory=bf,
)
```
Alternatively, you can specify all blob parameters exactly as you want by writing your own class which inherits from BlobFactory. See `examples/custom_blobfactory.py` as an example.
## Input parameters
### `Model()`
- `Nx`: int, grid points in x
- `Ny`: int, grid points in y
- `Lx`: float, length of grid in x
- `Ly`: float, length of grid in y
- `dt`: float, time step
- `T`: float, time length
- `periodic_y`: bool, optional,
allow periodicity in y-direction
!!! this is only a good approximation if Ly is significantly bigger than blobs !!!
- `blob_shape`: str, optional,
switch between `gauss` and `exp` blob
- `num_blobs`: int, optional
number of blobs
- `t_drain`: float, optional,
drain time for blobs
- `blob_factory`: BlobFactory, optional,
object containing blob parameters
- `labels`: str, optional,
"off": no blob labels returned,
"same": regions where blobs are present are set to label 1,
"individual": different blobs return individual labels,
used for creating training data for supervised machine learning algorithms
- `label_border`: float, optional,
defines region of blob as region where density >= label_border * amplitude of Blob
only used if labels = True
### `DefaultBlobFactory()`
- `A_dist`: str, optional,
distribution of blob amplitudes
- `W_dist`: str, optional,
distribution of blob widths
- `vx_dist`: str, optinal,
distribution of blob velocities in x-dimension
- `vy_dist`: str, optinal,
distribution of blob velocities in y-dimension
- `A_parameter`: float, optional,
`free_parameter` for amplitudes
- `W_parameter`: float, optional,
`free_parameter` for widths
- `vx_parameter`: float, optional,
`free_parameter` for vx
- `vy_parameter`: float, optional,
`free_parameter` for vy
The following distributions are implemented:
- `exp`: exponential distribution with mean 1
- `gamma`: gamma distribution with `free_parameter` as shape parameter and mean 1
- `normal`: normal distribution with zero mean and `free_parameter` as scale parameter
- `uniform`: uniorm distribution with mean 1 and `free_parameter` as width
- `ray`: rayleight distribution with mean 1
- `deg`: array on ones
- `zeros`: array of zeros
### `make_realization()`
- `file_name`: str, optional,
file name for .nc file containing data as xarray dataset
- `speed_up`: bool, optional,
speeding up code by discretizing each single blob at smaller time window
when blob values fall under given error value the blob gets discarded
!!! this is only a good approximation for blob_shape='exp' !!!
- `error`: float, optional,
numerical error at x = Lx when blob gets truncated
### `show_model()`
- `ds`: xarray Dataset,
Model data
- `interval`: int, optional,
time interval between frames in ms
- `save`: bool, optional,
if True save animation as gif
- `gif_name`: str, optional,
set name for gif
- `fps`: int, optional,
set fps for gif
## Contact
If you have questions, suggestions or other comments you can contact me under gregor.decristoforo@uit.no
نیازمندی
| مقدار | نام |
|---|---|
| >=0.21.1,<0.22.0 | xarray |
| >=1.22.2,<2.0.0 | numpy |
| >=1.8.0,<2.0.0 | scipy |
| >=3.5.1,<4.0.0 | matplotlib |
| >=4.62.3,<5.0.0 | tqdm |
| >=7.0.1,<8.0.0 | pytest |
| >=1.4.4,<2.0.0 | nptyping |
| >=0.931,<0.932 | mypy |
| >=22.1.0,<23.0.0 | black |
| >=1.4,<2.0 | docformatter |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.10,<3.11 | Python |
نحوه نصب
نصب پکیج whl blobmodel-0.1.2:
pip install blobmodel-0.1.2.whl
نصب پکیج tar.gz blobmodel-0.1.2:
pip install blobmodel-0.1.2.tar.gz