rendering tests updated

tests/test_rendering_example1_doc.py

@@ -0,0 +1,176 @@
+import pyladoc
+import matplotlib.pyplot as plt
+import pandas as pd
+import document_validation
+import numpy as np
+
+VALIDATE_HTML_CODE_ONLINE = False
+WRITE_RESULT_FILES = True
+
+
+def mason_saxena_k_mixture(x_h2):
+    """
+    Calculate the thermal conductivity of a H2/CO2 gas mixture using the Mason and Saxena mixing rule.
+
+    The mixture thermal conductivity is computed as:
+        k_mix = Σ_i [ X_i * k_i / (Σ_j X_j * φ_ij) ]
+    with:
+        φ_ij = 1/√8 * (1 + M_i/M_j)^(-0.5) * [ 1 + ( (k_i/k_j)**0.5 * (M_j/M_i)**0.25 ) ]^2
+
+    Parameters:
+        x_h2 (float or array-like): Mole fraction of H2 (from 0 to 1). The CO2 mole fraction is 1 - x_h2.
+
+    Returns:
+        float or array-like: Thermal conductivity of the mixture in W/m·K.
+    """
+    # Pure gas properties (at room temperature, approx.)
+    k_h2 = 0.1805   # Thermal conductivity of H2 in W/mK
+    k_co2 = 0.0166  # Thermal conductivity of CO2 in W/mK
+
+    M_h2 = 2.016    # Molar mass of H2 in g/mol
+    M_co2 = 44.01   # Molar mass of CO2 in g/mol
+
+    # Define the phi_ij function according to Mason and Saxena.
+    def phi(k_i, k_j, M_i, M_j):
+        return (1 / np.sqrt(8)) * (1 + M_i / M_j) ** (-0.5) * (1 + ((k_i / k_j) ** 0.5 * (M_j / M_i) ** 0.25))**2
+
+    # Compute phi terms for the two species.
+    # For i = j the phi terms should be 1.
+    phi_h2_h2 = phi(k_h2, k_h2, M_h2, M_h2)       # Should be 1
+    phi_h2_co2 = phi(k_h2, k_co2, M_h2, M_co2)
+    phi_co2_h2 = phi(k_co2, k_h2, M_co2, M_h2)
+    phi_co2_co2 = phi(k_co2, k_co2, M_co2, M_co2)   # Should be 1
+
+    # Ensure we can perform vectorized operations.
+    x_h2 = np.array(x_h2)
+    x_co2 = 1 - x_h2
+
+    # Use the Mason-Saxena mixing rule:
+    # k_mix = X_H2 * k_H2 / (X_H2*φ_H2_H2 + X_CO2*φ_H2_CO2) +
+    #         X_CO2 * k_CO2 / (X_CO2*φ_CO2_CO2 + X_H2*φ_CO2_H2)
+    k_mix = (x_h2 * k_h2 / (x_h2 * phi_h2_h2 + x_co2 * phi_h2_co2)) + \
+            (x_co2 * k_co2 / (x_co2 * phi_co2_co2 + x_h2 * phi_co2_h2))
+
+    return k_mix
+
+
+def make_document():
+    doc = pyladoc.DocumentWriter()
+
+    doc.add_markdown("""
+    # Thermal Conductivity of Mixtures
+
+    The determination of the thermal conductivity of gas mixtures is a central aspect of modeling
+    transport phenomena, particularly in high-temperature and high-pressure processes. Among the
+    most established approaches is the empirical equation introduced by Wassiljewa, which was
+    subsequently refined by Mason and Saxena to improve its applicability to multicomponent systems.
+    This model offers a reliable means of estimating the thermal conductivity of gas mixtures based
+    on the properties of the pure components and their molar interactions.
+
+    The thermal conductivity of a gas mixture, denoted by $$\\lambda_{\\text{mix}}$$, can expressed as
+    shown in equation @eq:lambda_mixture.
+
+    $$
+    \\label{eq:lambda_mixture}
+    \\lambda_{\text{mix}} = \\sum_{i=1}^{n} \\frac{x_i \\lambda_i}{\\sum_{j=1}^{n} x_j \\Phi_{ij}}
+    $$
+
+    In this equation, $$x_i$$ represents the molar fraction of component $$i$$ within the mixture,
+    while $$\\lambda_i$$ denotes the thermal conductivity of the pure substance $$i$$. The denominator
+    contains the interaction parameter $$\\Phi_{ij}$$, which describes the influence of component
+    $$j$$ on the transport properties of component $$i$$.
+
+    The interaction parameter $$\\Phi_{ij}$$ is given by the relation shown in equation @eq:interaction_parameter.
+
+    $$
+    \\label{eq:interaction_parameter}
+    \\Phi_{ij} = \\frac{1}{\\sqrt{8}} \\left(1 + \\frac{M_i}{M_j} \\right)^{-1/2} \\left[ 1 + \\left( \\frac{\\lambda_i}{\\lambda_j} \\right)^{1/2} \\left( \\frac{M_j}{M_i} \\right)^{1/4} \\right]^2
+    $$
+
+    Here, $$M_i$$ and $$M_j$$ are the molar masses of the components $$i$$ and $$j$$, respectively.
+    Molar masses and thermal conductivity of the pure substances are listed in table @table:gas_probs.
+    The structure of this expression illustrates the nonlinear dependence of the interaction term on
+    both the molar mass ratio and the square root of the conductivity ratio of the involved species.
+    """)
+
+    # Table
+    data = {
+        "Gas": ["H2", "O2", "N2", "CO2", "CH4", "Ar", "He"],
+        "Molar mass in g/mol": ["2.016", "32.00", "28.02", "44.01", "16.04", "39.95", "4.0026"],
+        "Thermal conductivity in W/m/K": ["0.1805", "0.0263", "0.0258", "0.0166", "0.0341", "0.0177", "0.1513"]
+    }
+    df = pd.DataFrame(data)
+    doc.add_table(df.style.hide(axis="index"), 'Properties of some gases', 'gas_probs')
+
+    doc.add_markdown("""
+    This formulation acknowledges that the transport properties of a gas mixture are not a simple
+    linear combination of the individual conductivities. Rather, they are governed by intermolecular
+    interactions, which affect the energy exchange and diffusion behavior of each component. These
+    interactions are particularly significant at elevated pressures or in cases where the gas components
+    exhibit widely differing molecular masses or transport properties.
+
+    The equation proposed by Wassiljewa and refined by Mason and Saxena assumes that binary interactions
+    dominate the behavior of the mixture, while higher-order (three-body or more) interactions are
+    neglected. It also presumes that the gases approximate ideal behavior, although in practical
+    applications, moderate deviations from ideality are tolerated without significant loss of accuracy.
+    In figure @fig:mixture the resulting thermal conductivity of an H2/CO2-mixture is shown.""")
+
+    # Figure
+    x_h2_values = np.linspace(0, 1, 100)
+    k_mixture_values = mason_saxena_k_mixture(x_h2_values)
+
+    fig, ax = plt.subplots()
+    ax.set_xlabel("H2 molar fraction / %")
+    ax.set_ylabel("Thermal Conductivity / (W/m·K)")
+    ax.plot(x_h2_values * 100, k_mixture_values, color='black')
+
+    doc.add_diagram(fig, 'Thermal Conductivity of H2/CO2 mixtures', 'mixture')
+
+    doc.add_markdown("""
+    In engineering practice, the accurate determination of $$\\lambda_{\\text{mix}}$$ is essential
+    for the prediction of heat transfer in systems such as membrane modules, chemical reactors, and
+    combustion chambers. In the context of membrane-based gas separation, for instance, the thermal
+    conductivity of the gas mixture influences the local temperature distribution, which in turn affects
+    both the permeation behavior and the structural stability of the membrane.
+
+    It is important to note that the calculated mixture conductivity reflects only the gas phase
+    behavior. In porous systems such as carbon membranes, additional effects must be considered.
+    These include the solid-phase thermal conduction through the membrane matrix, radiative transport
+    in pore channels at high temperatures, and transport in the Knudsen regime for narrow pores.
+    To account for these complexities, models based on effective medium theory, such as those of
+    Maxwell-Eucken or Bruggeman, are frequently employed. These models combine the conductivities of
+    individual phases (gas and solid) with geometrical factors that reflect the morphology of the
+    porous structure.
+
+    ---
+
+    Expanded by more or less sensible AI jabbering; based on: [doi:10.14279/depositonce-7390](https://doi.org/10.14279/depositonce-7390)
+    """)
+
+    return doc
+
+
+def test_html_render():
+    doc = make_document()
+    html_code = doc.to_html()
+
+    document_validation.validate_html(html_code, VALIDATE_HTML_CODE_ONLINE)
+
+    if WRITE_RESULT_FILES:
+        with open('tests/out/test_html_render1.html', 'w', encoding='utf-8') as f:
+            f.write(pyladoc.inject_to_template(html_code, internal_template='templates/test_template.html'))
+
+
+def test_latex_render():
+    doc = make_document()
+
+    if WRITE_RESULT_FILES:
+        with open('tests/out/test_html_render1.tex', 'w', encoding='utf-8') as f:
+            f.write(doc.to_latex())
+
+    assert doc.to_pdf('tests/out/test_latex_render1.pdf', font_family='serif')
+
+
+if __name__ == '__main__':
+    test_html_render()
+    test_latex_render()

tests/test_rendering_example2_doc.py

@@ -8,9 +8,9 @@ WRITE_RESULT_FILES = True
 
 
 def make_document():
-    dw = pyladoc.DocumentWriter()
+    doc = pyladoc.DocumentWriter()
 
-    dw.add_markdown("""
+    doc.add_markdown("""
     # Special characters
 
     ö ä ü Ö Ä Ü ß @ ∆
@@ -20,9 +20,9 @@ def make_document():
     £ ¥ $ €
 
     Œ
-                    
+
     # Link
-                    
+
     This is a hyperlink: [nonan.net](https://www.nonan.net)
 
     # Table
@@ -42,15 +42,15 @@ def make_document():
     |  3  | KL3202   | PT100 2 Temperatureingänge (3-Leiter)
     |  1  | KL2404   | 4 Digitalausgänge
     |  2  | KL9010   | Endklemme
-    
+
     ---
 
     # Equations
-                    
+
     This line represents a reference to the equation @eq:test1.
     """)
 
-    dw.add_equation(r'y = a + b * \sum_{i=0}^{\infty} a_i x^i', 'test1')
+    doc.add_equation(r'y = a + b * \sum_{i=0}^{\infty} a_i x^i', 'test1')
 
     # Figure
     fig, ax = plt.subplots()
@@ -65,7 +65,7 @@ def make_document():
     ax.set_title('Fruit supply by kind and color')
     ax.legend(title='Fruit color')
 
-    dw.add_diagram(fig, 'Bar chart with individual bar colors')
+    doc.add_diagram(fig, 'Bar chart with individual bar colors')
 
     # Table
     mydataset = {
@@ -79,9 +79,9 @@ def make_document():
     }
     df = pd.DataFrame(mydataset)
 
-    dw.add_table(df.style.hide(axis="index"), 'This is a example table', 'example1')
+    doc.add_table(df.style.hide(axis="index"), 'This is a example table', 'example1')
 
-    return dw
+    return doc
 
 
 def test_html_render():
@@ -91,7 +91,7 @@ def test_html_render():
     document_validation.validate_html(html_code, VALIDATE_HTML_CODE_ONLINE)
 
     if WRITE_RESULT_FILES:
-        with open('tests/out/test_html_render.html', 'w', encoding='utf-8') as f:
+        with open('tests/out/test_html_render2.html', 'w', encoding='utf-8') as f:
             f.write(pyladoc.inject_to_template(html_code, internal_template='templates/test_template.html'))
 
 
@@ -100,7 +100,7 @@ def test_latex_render():
 
     # print(doc.to_latex())
 
-    assert doc.to_pdf('tests/out/test_latex_render.pdf', font_family='serif')
+    assert doc.to_pdf('tests/out/test_latex_render2.pdf', font_family='serif')
 
 
 if __name__ == '__main__':