deploy: ae2fdf9

core/allocation.html

@@ -548,7 +548,7 @@ <h2 data-number="4.3" class="anchored" data-anchor-id="the-optimization-constrai
 <section id="example" class="level2" data-number="4.4">
 <h2 data-number="4.4" class="anchored" data-anchor-id="example"><span class="header-section-number">4.4</span> Example</h2>
 <p>The following is an example of an optimization problem for the example shown <a href="../python/examples.html#model-with-allocation">here</a>:</p>
-<div id="2081b65f" class="cell" data-execution_count="1">
+<div id="2eb4c2f5" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">using</span> <span class="bu">Ribasim</span></span>
@@ -573,34 +573,34 @@ <h2 data-number="4.4" class="anchored" data-anchor-id="example"><span class="hea
 <span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a><span class="fu">println</span>(p.allocation.allocation_models[<span class="fl">1</span>].problem)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
-<pre><code>Min F_abs_user_demand[UserDemand #13] + F_abs_user_demand[UserDemand #6] + F_abs_user_demand[UserDemand #3]
+<pre><code>Min F_abs_user_demand[UserDemand #3] + F_abs_user_demand[UserDemand #6] + F_abs_user_demand[UserDemand #13]
 Subject to
- flow_conservation_basin[Basin #12] : F[(Basin #12, UserDemand #13)] - F[(TabulatedRatingCurve #7, Basin #12)] = 0
- flow_conservation_basin[Basin #2] : F[(Basin #2, Basin #5)] - F[(UserDemand #3, Basin #2)] - F[(FlowBoundary #1, Basin #2)] - F[(Basin #5, Basin #2)] + F[(Basin #2, UserDemand #3)] = 0
- flow_conservation_basin[Basin #5] : -F[(Basin #2, Basin #5)] - F[(UserDemand #6, Basin #5)] + F[(Basin #5, Basin #2)] + F[(Basin #5, UserDemand #6)] + F[(Basin #5, TabulatedRatingCurve #7)] = 0
- abs_positive_user_demand[UserDemand #13] : -F[(Basin #12, UserDemand #13)] + F_abs_user_demand[UserDemand #13] ≥ 0
- abs_positive_user_demand[UserDemand #6] : -F[(Basin #5, UserDemand #6)] + F_abs_user_demand[UserDemand #6] ≥ 0
+ flow_conservation_basin[Basin #12] : -F[(TabulatedRatingCurve #7, Basin #12)] + F[(Basin #12, UserDemand #13)] = 0
+ flow_conservation_basin[Basin #2] : F[(Basin #2, Basin #5)] - F[(Basin #5, Basin #2)] - F[(UserDemand #3, Basin #2)] - F[(FlowBoundary #1, Basin #2)] + F[(Basin #2, UserDemand #3)] = 0
+ flow_conservation_basin[Basin #5] : -F[(Basin #2, Basin #5)] + F[(Basin #5, Basin #2)] + F[(Basin #5, TabulatedRatingCurve #7)] - F[(UserDemand #6, Basin #5)] + F[(Basin #5, UserDemand #6)] = 0
  abs_positive_user_demand[UserDemand #3] : -F[(Basin #2, UserDemand #3)] + F_abs_user_demand[UserDemand #3] ≥ -1.5
- abs_negative_user_demand[UserDemand #13] : F[(Basin #12, UserDemand #13)] + F_abs_user_demand[UserDemand #13] ≥ 0
- abs_negative_user_demand[UserDemand #6] : F[(Basin #5, UserDemand #6)] + F_abs_user_demand[UserDemand #6] ≥ 0
+ abs_positive_user_demand[UserDemand #6] : -F[(Basin #5, UserDemand #6)] + F_abs_user_demand[UserDemand #6] ≥ 0
+ abs_positive_user_demand[UserDemand #13] : -F[(Basin #12, UserDemand #13)] + F_abs_user_demand[UserDemand #13] ≥ 0
  abs_negative_user_demand[UserDemand #3] : F[(Basin #2, UserDemand #3)] + F_abs_user_demand[UserDemand #3] ≥ 1.5
+ abs_negative_user_demand[UserDemand #6] : F[(Basin #5, UserDemand #6)] + F_abs_user_demand[UserDemand #6] ≥ 0
+ abs_negative_user_demand[UserDemand #13] : F[(Basin #12, UserDemand #13)] + F_abs_user_demand[UserDemand #13] ≥ 0
  source[(FlowBoundary #1, Basin #2)] : F[(FlowBoundary #1, Basin #2)] ≤ 1
- F[(UserDemand #13, Terminal #10)] ≤ 0
- F[(UserDemand #6, Basin #5)] ≤ 0
  F[(UserDemand #3, Basin #2)] ≤ 0
+ F[(UserDemand #6, Basin #5)] ≤ 0
+ F[(UserDemand #13, Terminal #10)] ≤ 0
  fractional_flow[(TabulatedRatingCurve #7, Basin #12)] : F[(TabulatedRatingCurve #7, Basin #12)] - 0.4 F[(Basin #5, TabulatedRatingCurve #7)] ≤ 0
+ F[(TabulatedRatingCurve #7, Basin #12)] ≥ 0
  F[(Basin #2, Basin #5)] ≥ 0
- F[(UserDemand #3, Basin #2)] ≥ 0
- F[(FlowBoundary #1, Basin #2)] ≥ 0
- F[(UserDemand #6, Basin #5)] ≥ 0
  F[(Basin #12, UserDemand #13)] ≥ 0
  F[(Basin #5, Basin #2)] ≥ 0
- F[(Basin #5, UserDemand #6)] ≥ 0
  F[(TabulatedRatingCurve #7, Terminal #10)] ≥ 0
- F[(TabulatedRatingCurve #7, Basin #12)] ≥ 0
  F[(Basin #5, TabulatedRatingCurve #7)] ≥ 0
- F[(Basin #2, UserDemand #3)] ≥ 0
+ F[(UserDemand #6, Basin #5)] ≥ 0
+ F[(UserDemand #3, Basin #2)] ≥ 0
+ F[(Basin #5, UserDemand #6)] ≥ 0
+ F[(FlowBoundary #1, Basin #2)] ≥ 0
  F[(UserDemand #13, Terminal #10)] ≥ 0
+ F[(Basin #2, UserDemand #3)] ≥ 0
 </code></pre>
 </div>
 </div>

core/equations.html

@@ -427,7 +427,7 @@ <h2 data-number="2.1" class="anchored" data-anchor-id="sec-reduction_factor"><sp
     \end{cases}
 \end{align}\]</span></p>
 <p>Here <span class="math inline">\(p &gt; 0\)</span> is the threshold value which determines the interval <span class="math inline">\([0,p]\)</span> of the smooth transition between <span class="math inline">\(0\)</span> and <span class="math inline">\(1\)</span>, see the plot below.</p>
-<div id="f9223d80" class="cell" data-execution_count="1">
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 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
@@ -475,7 +475,7 @@ <h2 data-number="2.1" class="anchored" data-anchor-id="sec-reduction_factor"><sp
 
 invalid escape sequence '\p'
 
-/tmp/ipykernel_5013/665069857.py:31: SyntaxWarning:
+/tmp/ipykernel_5084/665069857.py:31: SyntaxWarning:
 
 invalid escape sequence '\p'
 </code></pre>

core/validation.html

@@ -262,7 +262,7 @@ <h1 class="title">Validation</h1>
 <section id="connectivity" class="level1" data-number="1">
 <h1 data-number="1"><span class="header-section-number">1</span> Connectivity</h1>
 <p>In the table below, each column shows which node types are allowed to be downstream (or ‘down-control’) of the node type at the top of the column.</p>
-<div id="da56efbd" class="cell" data-execution_count="1">
+<div id="4f53fca7" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">using</span> <span class="bu">Ribasim</span></span>
@@ -546,7 +546,7 @@ <h1 data-number="1"><span class="header-section-number">1</span> Connectivity</h
 <section id="neighbor-amounts" class="level1" data-number="2">
 <h1 data-number="2"><span class="header-section-number">2</span> Neighbor amounts</h1>
 <p>The table below shows for each node type between which bounds the amount of in- and outneighbors must be, for both flow and control edges.</p>
-<div id="fed3f8ce" class="cell" data-execution_count="2">
+<div id="a180a204" class="cell" data-execution_count="2">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>flow_in_min <span class="op">=</span> <span class="fu">Vector</span><span class="dt">{String}</span>()</span>

python/test-models.html

@@ -227,7 +227,7 @@ <h1 class="title">Test models</h1>
 
 
 <p>Ribasim developers use the following models in their testbench and in order to test new features.</p>
-<div id="06c15a09" class="cell" data-execution_count="1">
+<div id="63ea55c4" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> ribasim_testmodels</span>

search.json

@@ -482,7 +482,7 @@
     "href": "core/equations.html#sec-reduction_factor",
     "title": "Equations",
     "section": "2.1 The reduction factor",
-    "text": "2.1 The reduction factor\nAt several points in the equations below a reduction factor is used. This is a term that makes certain transitions more smooth, for instance when a pump stops providing water when its source basin dries up. The reduction factor is given by\n\\[\\begin{align}\n    \\phi(x; p) =\n    \\begin{cases}\n    0 &\\text{if}\\quad x < 0 \\\\\n        -2 \\left(\\frac{x}{p}\\right)^3 + 3\\left(\\frac{x}{p}\\right)^2 &\\text{if}\\quad 0 \\le x \\le p \\\\\n        1 &\\text{if}\\quad x > p\n    \\end{cases}\n\\end{align}\\]\nHere \\(p > 0\\) is the threshold value which determines the interval \\([0,p]\\) of the smooth transition between \\(0\\) and \\(1\\), see the plot below.\n\n\nCode\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef f(x, p = 3):\n    x_scaled = x / p\n    phi = (-2 * x_scaled + 3) * x_scaled**2\n    phi = np.where(x < 0, 0, phi)\n    phi = np.where(x > p, 1, phi)\n\n    return phi\n\nfontsize = 15\np = 3\nN = 100\nx_min = -1\nx_max = 4\nx = np.linspace(x_min,x_max,N)\nphi = f(x,p)\n\nfig,ax = plt.subplots(dpi=80)\nax.plot(x,phi)\n\ny_lim = ax.get_ylim()\n\nax.set_xticks([0,p], [0,\"$p$\"], fontsize=fontsize)\nax.set_yticks([0,1], [0,1], fontsize=fontsize)\nax.hlines([0,1],x_min,x_max, color = \"k\", ls = \":\", zorder=-1)\nax.vlines([0,p], *y_lim, color = \"k\", ls = \":\")\nax.set_xlim(x_min,x_max)\nax.set_xlabel(\"$x$\", fontsize=fontsize)\nax.set_ylabel(\"$\\phi(x;p)$\", fontsize=fontsize)\nax.set_ylim(y_lim)\n\nfig.tight_layout()\nplt.show()\n\n\n<>:31: SyntaxWarning:\n\ninvalid escape sequence '\\p'\n\n<>:31: SyntaxWarning:\n\ninvalid escape sequence '\\p'\n\n/tmp/ipykernel_5013/665069857.py:31: SyntaxWarning:\n\ninvalid escape sequence '\\p'",
+    "text": "2.1 The reduction factor\nAt several points in the equations below a reduction factor is used. This is a term that makes certain transitions more smooth, for instance when a pump stops providing water when its source basin dries up. The reduction factor is given by\n\\[\\begin{align}\n    \\phi(x; p) =\n    \\begin{cases}\n    0 &\\text{if}\\quad x < 0 \\\\\n        -2 \\left(\\frac{x}{p}\\right)^3 + 3\\left(\\frac{x}{p}\\right)^2 &\\text{if}\\quad 0 \\le x \\le p \\\\\n        1 &\\text{if}\\quad x > p\n    \\end{cases}\n\\end{align}\\]\nHere \\(p > 0\\) is the threshold value which determines the interval \\([0,p]\\) of the smooth transition between \\(0\\) and \\(1\\), see the plot below.\n\n\nCode\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef f(x, p = 3):\n    x_scaled = x / p\n    phi = (-2 * x_scaled + 3) * x_scaled**2\n    phi = np.where(x < 0, 0, phi)\n    phi = np.where(x > p, 1, phi)\n\n    return phi\n\nfontsize = 15\np = 3\nN = 100\nx_min = -1\nx_max = 4\nx = np.linspace(x_min,x_max,N)\nphi = f(x,p)\n\nfig,ax = plt.subplots(dpi=80)\nax.plot(x,phi)\n\ny_lim = ax.get_ylim()\n\nax.set_xticks([0,p], [0,\"$p$\"], fontsize=fontsize)\nax.set_yticks([0,1], [0,1], fontsize=fontsize)\nax.hlines([0,1],x_min,x_max, color = \"k\", ls = \":\", zorder=-1)\nax.vlines([0,p], *y_lim, color = \"k\", ls = \":\")\nax.set_xlim(x_min,x_max)\nax.set_xlabel(\"$x$\", fontsize=fontsize)\nax.set_ylabel(\"$\\phi(x;p)$\", fontsize=fontsize)\nax.set_ylim(y_lim)\n\nfig.tight_layout()\nplt.show()\n\n\n<>:31: SyntaxWarning:\n\ninvalid escape sequence '\\p'\n\n<>:31: SyntaxWarning:\n\ninvalid escape sequence '\\p'\n\n/tmp/ipykernel_5084/665069857.py:31: SyntaxWarning:\n\ninvalid escape sequence '\\p'",
     "crumbs": [
       "Julia core",
       "Equations"
@@ -977,7 +977,7 @@
     "href": "core/allocation.html#example",
     "title": "Allocation",
     "section": "4.4 Example",
-    "text": "4.4 Example\nThe following is an example of an optimization problem for the example shown here:\n\n\nCode\nusing Ribasim\nusing Ribasim: NodeID\nusing SQLite\nusing ComponentArrays: ComponentVector\n\ntoml_path = normpath(@__DIR__, \"../../generated_testmodels/allocation_example/ribasim.toml\")\np = Ribasim.Model(toml_path).integrator.p\nu = ComponentVector(; storage = zeros(length(p.basin.node_id)))\n\nallocation_model = p.allocation.allocation_models[1]\nt = 0.0\npriority_idx = 1\n\nRibasim.set_flow!(p.graph, NodeID(:FlowBoundary, 1), NodeID(:Basin, 2), 1.0)\n\nRibasim.adjust_capacities_edge!(allocation_model, p, priority_idx)\nRibasim.adjust_capacities_source!(allocation_model, p, priority_idx)\nRibasim.set_objective_priority!(allocation_model, p, u, t, priority_idx)\n\nprintln(p.allocation.allocation_models[1].problem)\n\n\nMin F_abs_user_demand[UserDemand #13] + F_abs_user_demand[UserDemand #6] + F_abs_user_demand[UserDemand #3]\nSubject to\n flow_conservation_basin[Basin #12] : F[(Basin #12, UserDemand #13)] - F[(TabulatedRatingCurve #7, Basin #12)] = 0\n flow_conservation_basin[Basin #2] : F[(Basin #2, Basin #5)] - F[(UserDemand #3, Basin #2)] - F[(FlowBoundary #1, Basin #2)] - F[(Basin #5, Basin #2)] + F[(Basin #2, UserDemand #3)] = 0\n flow_conservation_basin[Basin #5] : -F[(Basin #2, Basin #5)] - F[(UserDemand #6, Basin #5)] + F[(Basin #5, Basin #2)] + F[(Basin #5, UserDemand #6)] + F[(Basin #5, TabulatedRatingCurve #7)] = 0\n abs_positive_user_demand[UserDemand #13] : -F[(Basin #12, UserDemand #13)] + F_abs_user_demand[UserDemand #13] ≥ 0\n abs_positive_user_demand[UserDemand #6] : -F[(Basin #5, UserDemand #6)] + F_abs_user_demand[UserDemand #6] ≥ 0\n abs_positive_user_demand[UserDemand #3] : -F[(Basin #2, UserDemand #3)] + F_abs_user_demand[UserDemand #3] ≥ -1.5\n abs_negative_user_demand[UserDemand #13] : F[(Basin #12, UserDemand #13)] + F_abs_user_demand[UserDemand #13] ≥ 0\n abs_negative_user_demand[UserDemand #6] : F[(Basin #5, UserDemand #6)] + F_abs_user_demand[UserDemand #6] ≥ 0\n abs_negative_user_demand[UserDemand #3] : F[(Basin #2, UserDemand #3)] + F_abs_user_demand[UserDemand #3] ≥ 1.5\n source[(FlowBoundary #1, Basin #2)] : F[(FlowBoundary #1, Basin #2)] ≤ 1\n F[(UserDemand #13, Terminal #10)] ≤ 0\n F[(UserDemand #6, Basin #5)] ≤ 0\n F[(UserDemand #3, Basin #2)] ≤ 0\n fractional_flow[(TabulatedRatingCurve #7, Basin #12)] : F[(TabulatedRatingCurve #7, Basin #12)] - 0.4 F[(Basin #5, TabulatedRatingCurve #7)] ≤ 0\n F[(Basin #2, Basin #5)] ≥ 0\n F[(UserDemand #3, Basin #2)] ≥ 0\n F[(FlowBoundary #1, Basin #2)] ≥ 0\n F[(UserDemand #6, Basin #5)] ≥ 0\n F[(Basin #12, UserDemand #13)] ≥ 0\n F[(Basin #5, Basin #2)] ≥ 0\n F[(Basin #5, UserDemand #6)] ≥ 0\n F[(TabulatedRatingCurve #7, Terminal #10)] ≥ 0\n F[(TabulatedRatingCurve #7, Basin #12)] ≥ 0\n F[(Basin #5, TabulatedRatingCurve #7)] ≥ 0\n F[(Basin #2, UserDemand #3)] ≥ 0\n F[(UserDemand #13, Terminal #10)] ≥ 0",
+    "text": "4.4 Example\nThe following is an example of an optimization problem for the example shown here:\n\n\nCode\nusing Ribasim\nusing Ribasim: NodeID\nusing SQLite\nusing ComponentArrays: ComponentVector\n\ntoml_path = normpath(@__DIR__, \"../../generated_testmodels/allocation_example/ribasim.toml\")\np = Ribasim.Model(toml_path).integrator.p\nu = ComponentVector(; storage = zeros(length(p.basin.node_id)))\n\nallocation_model = p.allocation.allocation_models[1]\nt = 0.0\npriority_idx = 1\n\nRibasim.set_flow!(p.graph, NodeID(:FlowBoundary, 1), NodeID(:Basin, 2), 1.0)\n\nRibasim.adjust_capacities_edge!(allocation_model, p, priority_idx)\nRibasim.adjust_capacities_source!(allocation_model, p, priority_idx)\nRibasim.set_objective_priority!(allocation_model, p, u, t, priority_idx)\n\nprintln(p.allocation.allocation_models[1].problem)\n\n\nMin F_abs_user_demand[UserDemand #3] + F_abs_user_demand[UserDemand #6] + F_abs_user_demand[UserDemand #13]\nSubject to\n flow_conservation_basin[Basin #12] : -F[(TabulatedRatingCurve #7, Basin #12)] + F[(Basin #12, UserDemand #13)] = 0\n flow_conservation_basin[Basin #2] : F[(Basin #2, Basin #5)] - F[(Basin #5, Basin #2)] - F[(UserDemand #3, Basin #2)] - F[(FlowBoundary #1, Basin #2)] + F[(Basin #2, UserDemand #3)] = 0\n flow_conservation_basin[Basin #5] : -F[(Basin #2, Basin #5)] + F[(Basin #5, Basin #2)] + F[(Basin #5, TabulatedRatingCurve #7)] - F[(UserDemand #6, Basin #5)] + F[(Basin #5, UserDemand #6)] = 0\n abs_positive_user_demand[UserDemand #3] : -F[(Basin #2, UserDemand #3)] + F_abs_user_demand[UserDemand #3] ≥ -1.5\n abs_positive_user_demand[UserDemand #6] : -F[(Basin #5, UserDemand #6)] + F_abs_user_demand[UserDemand #6] ≥ 0\n abs_positive_user_demand[UserDemand #13] : -F[(Basin #12, UserDemand #13)] + F_abs_user_demand[UserDemand #13] ≥ 0\n abs_negative_user_demand[UserDemand #3] : F[(Basin #2, UserDemand #3)] + F_abs_user_demand[UserDemand #3] ≥ 1.5\n abs_negative_user_demand[UserDemand #6] : F[(Basin #5, UserDemand #6)] + F_abs_user_demand[UserDemand #6] ≥ 0\n abs_negative_user_demand[UserDemand #13] : F[(Basin #12, UserDemand #13)] + F_abs_user_demand[UserDemand #13] ≥ 0\n source[(FlowBoundary #1, Basin #2)] : F[(FlowBoundary #1, Basin #2)] ≤ 1\n F[(UserDemand #3, Basin #2)] ≤ 0\n F[(UserDemand #6, Basin #5)] ≤ 0\n F[(UserDemand #13, Terminal #10)] ≤ 0\n fractional_flow[(TabulatedRatingCurve #7, Basin #12)] : F[(TabulatedRatingCurve #7, Basin #12)] - 0.4 F[(Basin #5, TabulatedRatingCurve #7)] ≤ 0\n F[(TabulatedRatingCurve #7, Basin #12)] ≥ 0\n F[(Basin #2, Basin #5)] ≥ 0\n F[(Basin #12, UserDemand #13)] ≥ 0\n F[(Basin #5, Basin #2)] ≥ 0\n F[(TabulatedRatingCurve #7, Terminal #10)] ≥ 0\n F[(Basin #5, TabulatedRatingCurve #7)] ≥ 0\n F[(UserDemand #6, Basin #5)] ≥ 0\n F[(UserDemand #3, Basin #2)] ≥ 0\n F[(Basin #5, UserDemand #6)] ≥ 0\n F[(FlowBoundary #1, Basin #2)] ≥ 0\n F[(UserDemand #13, Terminal #10)] ≥ 0\n F[(Basin #2, UserDemand #3)] ≥ 0",
     "crumbs": [
       "Julia core",
       "Allocation"