lisflood-code | Lisflood OS - LISFLOOD | Predictive Analytics library
kandi X-RAY | lisflood-code Summary
kandi X-RAY | lisflood-code Summary
Lisflood OS - LISFLOOD
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Top functions reviewed by kandi - BETA
- Determine and store dynamic data
- Decompress map into numpy array
- Compress a masked array
- Get landuse and index from a veg
- This method is used to instantiate the time series
- Writes a header file to disk
- Write a netCDF file to a netCDF file
- Unpack masked array
- Initiate dynamic loops
- Calculate the total catchment error
- Reservoir inflow
- Calculate population variables
- Determine dynamic watering for a given season
- Main function for lisflood
- Generate dynamic variables for each surface
- Calculate lake inloop
- Dynamically update the snow layer
- Load a setclone file
- Check if netcdf file exists in netCDF file
- Dynamically dump dynamic data
- Determine the variables of the variable
- Resume the simulation
- Writes tss file
- Dynamic dynamic model
- Return an Xarray reader
- Read the meteogical data
lisflood-code Key Features
lisflood-code Examples and Code Snippets
Lines of Code : 13License : Strong Copyleft (EUPL-1.2)
Copyconda create --name lisflood python=3.7 -c conda-forge
conda activate lisflood
conda install -c conda-forge pcraster
pip install lisflood-model
lisflood /absolute_path/to/my/local/folder/TestCatchment/settings/cold_day_base.xml
git clone --single- docker pull efas/lisflood:latest
docker run -v /absolute_path/to/my/local/folder:/usecases efas/lisflood:latest usecases
docker run -v /absolute_path/to/my/local/folder/TestCatchment:/input efas/lisflood /input/settings/cold_day_base.xml
pip install lisflood-model
lisflood /absolute_path/to/my/local/folder/TestCatchment/settings/cold_day_base.xml
Community Discussions
Trending Discussions on Predictive Analytics
QUESTION
GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?
note: predictive Analysis requires no graphics processing.
...ANSWER
Answered 2020-Nov-21 at 21:35The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.
The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.
If you want to know more about this, I'd recommend you start here or here.
some things to consider:- how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
- Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
- Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.
QUESTION
I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.
The current structure is as below.
What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):
Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.
...ANSWER
Answered 2020-Nov-01 at 19:39You might want try pivoting your table:
QUESTION
I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files
...ANSWER
Answered 2020-May-17 at 23:55The new object to get params in React Navigation 5 is:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install lisflood-code
Install miniconda
Create a conda env named "lisflood" and install dependencies:
Install lisflood-model pypi package
Clone the master branch of this repository (you need to have git installed on your machine).
Install requirements into a python 3 conda env
Compile the cython module kinematic_wave_parallel_tool
Delete the files *.so (if any) in directory hydrological-modules
Inside the hydrological_modules folder, execute "python compile_kinematic_wave_parallel_tools.py build_ext --inplace"
Run a cold run for the test catchment
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