Inspecting cross-sections¶

If deeper analysis of cross-section is required, you can enable the option ExportCSSData in the fm2prof project configuration file. This notebooks shows some techniques to visualise this data.

Loading the data¶

Example output is provided in the tests directory of the fm2prof repository. This is pickled data, so we'll use pickle to read it.

In [8]:

import pickle

# Cross-sections test cases
cases = ["waal_1_40147.826", "reevediep_2888.800", "veessenwapenveld_3519.948", "ketelmeer02_0.000", "twentekanaal_941.714", "nederrijn_20011.855"]

case = cases[0]

with open(fr"../../tests/test_data/cross_sections/{case}.pickle", "rb") as f:
    data = pickle.load(f)

import pickle # Cross-sections test cases cases = ["waal_1_40147.826", "reevediep_2888.800", "veessenwapenveld_3519.948", "ketelmeer02_0.000", "twentekanaal_941.714", "nederrijn_20011.855"] case = cases[0] with open(fr"../../tests/test_data/cross_sections/{case}.pickle", "rb") as f: data = pickle.load(f)

Visualise the cross-section input data¶

When unpickled, this data is a dict of pandas DataFrames. The variable data holds all information that the fm2prof.CrossSection class uses to build a 1D cross-section. Let's visualise this data:

In [9]:

import matplotlib.pyplot as plt

variable = 'bedlevel'  # other variables to plot: bedlevel, section, region, area
x = data.get('fm_data').get('x')
y = data.get('fm_data').get('y')
z = data.get('fm_data').get(variable)

fig, ax = plt.subplots(1)
sc = ax.scatter(x/1000, y/1000, c=z)
ax.set_aspect('equal', adjustable='box')
ax.set_title(case)
cb = plt.colorbar(sc)
cb.set_label(variable)

import matplotlib.pyplot as plt variable = 'bedlevel' # other variables to plot: bedlevel, section, region, area x = data.get('fm_data').get('x') y = data.get('fm_data').get('y') z = data.get('fm_data').get(variable) fig, ax = plt.subplots(1) sc = ax.scatter(x/1000, y/1000, c=z) ax.set_aspect('equal', adjustable='box') ax.set_title(case) cb = plt.colorbar(sc) cb.set_label(variable)

Figure

Visualising velocity¶

In [10]:

%matplotlib widget
from ipywidgets import *
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

variable = 'velocity' # try also: waterlevel, waterdepth

z = data.get('fm_data').get(variable)
fig, ax = plt.subplots(1)

sc = ax.scatter(x/1000, y/1000, c=z.iloc[6])
ax.set_aspect('equal', adjustable='box')
ax.set_title(case)
cb = plt.colorbar(sc)
cb.set_label(variable)
plt.show(fig)

def update(i):
    plt.gca()
    zi = z.iloc[i]
    ax.scatter(x/1000, y/1000, c=zi)
    fig.canvas.draw()

interact(update, i=widgets.IntSlider(min=0, max=46, step=1, value=6))

%matplotlib widget from ipywidgets import * import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap variable = 'velocity' # try also: waterlevel, waterdepth z = data.get('fm_data').get(variable) fig, ax = plt.subplots(1) sc = ax.scatter(x/1000, y/1000, c=z.iloc[6]) ax.set_aspect('equal', adjustable='box') ax.set_title(case) cb = plt.colorbar(sc) cb.set_label(variable) plt.show(fig) def update(i): plt.gca() zi = z.iloc[i] ax.scatter(x/1000, y/1000, c=zi) fig.canvas.draw() interact(update, i=widgets.IntSlider(min=0, max=46, step=1, value=6))

Figure

interactive(children=(IntSlider(value=6, description='i', max=46), Output()), _dom_classes=('widget-interact',…

Out[10]:

<function __main__.update(i)>

Visualising conveyance¶

Another provided data file is the flowmask.pickle file. This file contains information generated during the cross-section production. The flowmask encodes information on which cells meet the conveyance criterium (stroomvoeringscriterium).

In [7]:

with open(fr"../../tests/test_data/cross_sections/{case}_flowmask.pickle", "rb") as f:
    flowmask = pickle.load(f)

with open(fr"../../tests/test_data/cross_sections/{case}_flowmask.pickle", "rb") as f: flowmask = pickle.load(f)

The code below visualises how this criterium is met during the 2D simulation. You'll see that as you progress in time, more cells will mee the criterium.

In [8]:

%matplotlib widget
from ipywidgets import *
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

fig, ax = plt.subplots(1)
fm = flowmask.T.iloc[0]
sc = ax.scatter(x/1000, y/1000, c=fm)
ax.set_aspect('equal', adjustable='box')
ax.set_title(case)
cb = plt.colorbar(sc)
cb.set_label("meets conveyance criterium")
plt.show(fig)

def update(i):
    plt.gca()
    fm = flowmask.T.iloc[i]
    ax.scatter(x/1000, y/1000, c=fm)
    fig.canvas.draw()

interact(update, i=widgets.IntSlider(min=0, max=46, step=1, value=6))

%matplotlib widget from ipywidgets import * import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap fig, ax = plt.subplots(1) fm = flowmask.T.iloc[0] sc = ax.scatter(x/1000, y/1000, c=fm) ax.set_aspect('equal', adjustable='box') ax.set_title(case) cb = plt.colorbar(sc) cb.set_label("meets conveyance criterium") plt.show(fig) def update(i): plt.gca() fm = flowmask.T.iloc[i] ax.scatter(x/1000, y/1000, c=fm) fig.canvas.draw() interact(update, i=widgets.IntSlider(min=0, max=46, step=1, value=6))

Figure

interactive(children=(IntSlider(value=6, description='i', max=46), Output()), _dom_classes=('widget-interact',…

Out[8]:

<function __main__.update(i)>