diff --git a/examples/speed_tests_numba_and_cython.ipynb b/examples/speed_tests_numba_and_cython.ipynb
index 05c3e20..665ad7f 100644
--- a/examples/speed_tests_numba_and_cython.ipynb
+++ b/examples/speed_tests_numba_and_cython.ipynb
@@ -51,7 +51,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 50,
"metadata": {
"cellView": "form",
"id": "xKirGWMNdvAs"
@@ -61,6 +61,9 @@
"#@title Imports and Definitions\n",
"import time\n",
"import numpy as np\n",
+ "import plotly.graph_objects as go\n",
+ "\n",
+ "\n",
"import matplotlib.pyplot as plt\n",
"from plotly.subplots import make_subplots\n",
"import plotly.graph_objects as go\n",
@@ -84,6 +87,9 @@
"psi_n_sfmp_Cython = int_array_cache_Cython(wc.psi_n_single_fock_multiple_position)\n",
"psi_n_sfmp_Numba = int_array_cache_Numba_single_fock(wn.psi_n_single_fock_multiple_position)\n",
"\n",
+ "psi_n_mfmp_Cython = int_array_cache_Cython(wc.psi_n_multiple_fock_multiple_position)\n",
+ "psi_n_mfmp_Numba = int_array_cache_Numba_multiple_fock(wn.psi_n_multiple_fock_multiple_position)\n",
+ "\n",
"%matplotlib inline"
]
},
@@ -93,7 +99,7 @@
"## Performance Comparison: Mr Mustard vs. Fast Wave for Single Fock & Single Position Wavefunctions\n",
"---\n",
"$$$$\n",
- "**Explanation:** This analysis evaluates the computational efficiency of three approaches—Mr Mustard, Fast Wave (Numba), and Fast Wave (Cython)—for calculating the squared amplitude $|\\psi_n(x)|^2$ of a Single Fock & Single Position Wavefunction $\\psi_n(x)$. The Single Fock & Single Position wavefunction, $\\psi_{i}(x)$, maps a scalar input $x$ to a scalar output. Execution times for each method are plotted to facilitate comparison.\n",
+ "**Explanation:** This analysis evaluates the computational efficiency of three approaches — Mr Mustard, Fast Wave (Numba), and Fast Wave (Cython)—for calculating the squared amplitude $|\\psi_n(x)|^2$ of a Single Fock & Single Position Wavefunction $\\psi_n(x)$. The Single Fock & Single Position wavefunction, $\\psi_{i}(x)$, maps a scalar input $x$ to a scalar output. Execution times for each aproach are plotted to facilitate comparison.\n",
"$$$$"
],
"metadata": {
@@ -185,7 +191,7 @@
"$$\\psi_{i}(X_{m}) = [\\psi_{i}(x_{1}), \\psi_{i}(x_{2}), ..., \\psi_{i}(x_{m})]_{\\, m}$$\n",
"$$$$\n",
"\n",
- "Execution times for each method are plotted to facilitate comparison.\n",
+ "Execution times for each aproach method are plotted to facilitate comparison.\n",
"$$$$"
]
},
@@ -267,7 +273,7 @@
"## Performance Comparison: Mr Mustard vs. Fast Wave for Multiple Fock & Multiple Position Wavefunctions\n",
"---\n",
"$$$$\n",
- "**Explanation:** This analysis evaluates the computational efficiency of three approaches—Mr Mustard, Fast Wave (Numba), and Fast Wave (Cython) — for calculating of a Multiple Fock & Multiple Position Wavefunction $\\psi_{\\,0\\rightarrow n}\\big(X_{m}\\big)$. The Single Fock & Multiple Position wavefunction, $\\psi_{\\,0\\rightarrow i}\\big(X_{m}\\big)$, maps an array of position inputs $X_{m}$ to an matrix of corresponding outputs, this implies that:\n",
+ "**Explanation:** This analysis evaluates the computational efficiency of three approaches — Mr Mustard, Fast Wave (Numba), and Fast Wave (Cython) — for calculating the squared amplitude $|\\psi_{\\,0\\rightarrow n}\\big(X_{m}\\big)|^2$ of a Multiple Fock & Multiple Position Wavefunction $\\psi_{\\,0\\rightarrow n}\\big(X_{m}\\big)$. The Multiple Fock & Multiple Position Wavefunction, $\\psi_{\\,0\\rightarrow i}\\big(X_{m}\\big)$, maps an array of position inputs $X_{m}$ to an matrix of corresponding outputs, this implies that:\n",
"\n",
"$$$$\n",
"$$ \\psi_{\\,0\\rightarrow i}\\big(X_{m}\\big) = \\begin{bmatrix}\n",
@@ -276,18 +282,111 @@
" \\psi_{i}(x_{1}) & \\cdots & \\psi_{i}(x_{m}) \\\\\n",
" \\end{bmatrix}_{(i+1) \\, \\times \\, m}$$\n",
"$$$$\n",
- "...\n",
+ "Execution times for each aproach are plotted to facilitate comparison.\n",
"$$$$"
]
},
{
"cell_type": "code",
- "source": [],
+ "source": [
+ "n = 5 #@param\n",
+ "x_max = 5.0 #@param\n",
+ "x_min = -5.0 #@param\n",
+ "number_of_points = 5000 #@param\n",
+ "\n",
+ "x_values = np.linspace(x_min, x_max, number_of_points)\n",
+ "\n",
+ "start_time = time.time()\n",
+ "y_Psi_n_x_Fast_Wave_Cython = (abs(psi_n_mfmp_Cython(n,x_values))**2).tolist()\n",
+ "Fast_Wave_Cython_time = time.time() - start_time\n",
+ "\n",
+ "start_time = time.time()\n",
+ "y_Psi_n_x_Fast_Wave_Numba = (abs(psi_n_mfmp_Numba(n,x_values))**2).tolist()\n",
+ "Fast_Wave_Numba_time = time.time() - start_time\n",
+ "\n",
+ "start_time = time.time()\n",
+ "y_Psi_n_x_Mr_Mustard = (abs((oscillator_eigenstate(x_values*h_bar_12,n+1)*h_bar_14))**2).tolist()\n",
+ "Mr_Mustard_time = time.time() - start_time\n",
+ "\n",
+ "\n",
+ "approaches = [\"Mr Mustard\", \"Fast Wave (Numba)\", \"Fast Wave (Cython)\"]\n",
+ "execution_times = [Mr_Mustard_time, Fast_Wave_Numba_time, Fast_Wave_Cython_time]\n",
+ "\n",
+ "fig = go.Figure(data=[go.Bar(\n",
+ " x=approaches,\n",
+ " y=execution_times,\n",
+ " marker=dict(\n",
+ " color=['#800080', '#008000', '#FFFF00'],\n",
+ " )\n",
+ ")])\n",
+ "\n",
+ "fig.update_layout(\n",
+ " title=\"Runtime Comparison\",\n",
+ " xaxis_title=\"Approach\",\n",
+ " yaxis_title=\"Runtime (s)\",\n",
+ " bargap=0.6,\n",
+ " plot_bgcolor='rgba(0,0,0,0)',\n",
+ " paper_bgcolor='rgba(255,255,255,0.8)',\n",
+ " font=dict(\n",
+ " family=\"Courier New, monospace\",\n",
+ " size=18,\n",
+ " color=\"#7f7f7f\"\n",
+ " ),\n",
+ " width=800,\n",
+ " height=400\n",
+ ")"
+ ],
"metadata": {
- "id": "WaYYDVs6PLWq"
+ "id": "WaYYDVs6PLWq",
+ "cellView": "form",
+ "outputId": "d37e2558-0e0c-41a5-d849-e5fee91c8e1b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 437
+ }
},
- "execution_count": null,
- "outputs": []
+ "execution_count": 60,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ ""
+ ]
+ },
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+ }
+ ]
}
],
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