flopy
dython
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flopy | dython | |
---|---|---|
1 | 2 | |
480 | 492 | |
2.5% | - | |
9.3 | 7.7 | |
6 days ago | 3 months ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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flopy
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Using PEST in flopy
Ps. I've checked the notebooks regarding PEST utilities and how to write the template file (https://github.com/modflowpy/flopy/blob/develop/examples/Notebooks/flopy3_PEST.ipynb) but other than that I couldn't find how to use it in order to estimate the best parameter using some observation data
dython
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How to interpret scatterplot regarding customer purchasing habits
Make a categorical heatmap instead (example see https://github.com/shakedzy/dython/issues/2)
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Time series prediction problem
to answer question one try just running a simple correlation matrix among your yearly and the average of your daily figures For years 2012+ when you have all four inputs. I frequently use the small convenience library Dython Dython in Github. If your features are very independent then you will not be able to fill in missing values and will need to find other surrogates such as “is my crop largely a fixed percentage of overall exports and are overall exports available for missing years?” If your features are highly dependent then essentially you don’t need them all - both XGBoost and LightGBM have simple fill-in-with-the-mean type imputation of missing values - run across all your data with imputation on and removing low impact features will remove all but one highly interdependent features.
What are some alternatives?
pymadcad - Simple yet powerful CAD (Computer Aided Design) library, written with Python.
RocketPy - Next generation High-Power Rocketry 6-DOF Trajectory Simulation
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
dash - Data Apps & Dashboards for Python. No JavaScript Required.
bokeh - Interactive Data Visualization in the browser, from Python
Machine-Learning-for-Asset-Managers - Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.
viper - Simple, expressive pipeline syntax to transform and manipulate data with ease
cadquery - A python parametric CAD scripting framework based on OCCT