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Criado usando o Colab
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fobos123deimos committed Nov 18, 2024
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117 changes: 108 additions & 9 deletions examples/speed_tests_numba_and_cython.ipynb
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Expand All @@ -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",
Expand All @@ -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"
]
},
Expand All @@ -93,7 +99,7 @@
"## Performance Comparison: Mr Mustard vs. Fast Wave for Single Fock & Single Position Wavefunctions\n",
"---\n",
"$$$$\n",
"**Explanation:** This analysis evaluates the computational efficiency of three approaches—Mr Mustard, Fast Wave (Numba), and Fast Wave (Cython)—for calculating the squared amplitude $|\\psi_n(x)|^2$ of a Single Fock & Single Position Wavefunction $\\psi_n(x)$. The Single Fock & Single Position wavefunction, $\\psi_{i}(x)$, maps a scalar input $x$ to a scalar output. Execution times for each method are plotted to facilitate comparison.\n",
"**Explanation:** This analysis evaluates the computational efficiency of three approaches — Mr Mustard, Fast Wave (Numba), and Fast Wave (Cython)—for calculating the squared amplitude $|\\psi_n(x)|^2$ of a Single Fock & Single Position Wavefunction $\\psi_n(x)$. The Single Fock & Single Position wavefunction, $\\psi_{i}(x)$, maps a scalar input $x$ to a scalar output. Execution times for each aproach are plotted to facilitate comparison.\n",
"$$$$"
],
"metadata": {
Expand Down Expand Up @@ -185,7 +191,7 @@
"$$\\psi_{i}(X_{m}) = [\\psi_{i}(x_{1}), \\psi_{i}(x_{2}), ..., \\psi_{i}(x_{m})]_{\\, m}$$\n",
"$$$$\n",
"\n",
"Execution times for each method are plotted to facilitate comparison.\n",
"Execution times for each aproach method are plotted to facilitate comparison.\n",
"$$$$"
]
},
Expand Down Expand Up @@ -267,7 +273,7 @@
"## Performance Comparison: Mr Mustard vs. Fast Wave for Multiple Fock & Multiple Position Wavefunctions\n",
"---\n",
"$$$$\n",
"**Explanation:** This analysis evaluates the computational efficiency of three approaches—Mr Mustard, Fast Wave (Numba), and Fast Wave (Cython) — for calculating of a Multiple Fock & Multiple Position Wavefunction $\\psi_{\\,0\\rightarrow n}\\big(X_{m}\\big)$. The Single Fock & Multiple Position wavefunction, $\\psi_{\\,0\\rightarrow i}\\big(X_{m}\\big)$, maps an array of position inputs $X_{m}$ to an matrix of corresponding outputs, this implies that:\n",
"**Explanation:** This analysis evaluates the computational efficiency of three approaches — Mr Mustard, Fast Wave (Numba), and Fast Wave (Cython) — for calculating the squared amplitude $|\\psi_{\\,0\\rightarrow n}\\big(X_{m}\\big)|^2$ of a Multiple Fock & Multiple Position Wavefunction $\\psi_{\\,0\\rightarrow n}\\big(X_{m}\\big)$. The Multiple Fock & Multiple Position Wavefunction, $\\psi_{\\,0\\rightarrow i}\\big(X_{m}\\big)$, maps an array of position inputs $X_{m}$ to an matrix of corresponding outputs, this implies that:\n",
"\n",
"$$$$\n",
"$$ \\psi_{\\,0\\rightarrow i}\\big(X_{m}\\big) = \\begin{bmatrix}\n",
Expand All @@ -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",
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" font=dict(\n",
" family=\"Courier New, monospace\",\n",
" size=18,\n",
" color=\"#7f7f7f\"\n",
" ),\n",
" width=800,\n",
" height=400\n",
")"
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"outputs": []
"execution_count": 60,
"outputs": [
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Expand Down

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