diff --git a/Physics.ipynb b/Physics.ipynb index d13d735..5ed1802 100644 --- a/Physics.ipynb +++ b/Physics.ipynb @@ -1,682 +1,693 @@ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", - " Teaching and Learning differently with Jupyter Notebooks - LEARN Hour of Discovery, January 28, 2020
\n", + " Jupyter Notebooks for Teaching and Learning - LEARN Hour of Discovery, January 28, 2020
\n", " C. Hardebolle, CC BY-NC-SA 4.0 Int.

\n", " How to use this notebook?
\n", " This notebook is made of text cells and code cells. The code cells have to be executed to see the result of the program. To execute a cell, simply select it and click on the \"play\" button (►) in the tool bar just above the notebook, or type shift + enter. It is important to execute the code cells in their order of appearance in the notebook.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learning goals\n", "\n", "After using this notebook, you should be able to:\n", "* Describe how the counterweight influences the position of the cable in the jeans problem\n", - "* Analyze how the tension force is affected by the position of the cable in the jeans problem\n", + "* Analyze how the the counterweight and the position of the cable influence the tension force in the jeans problem\n", "* Illustrate what effects can high tension have on a cable\n", "* Use Python to make arithmetic calculations\n", "\n", "
\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Activity 1: Experimenting with the virtual lab\n", "\n", "The \"Suspended object\" virtual lab below allows you to **experiment with different counterweights** to see how it affects the position of the object suspended on the cable. \n", "The **4 questions in the quiz below the virtual lab** are designed to guide you in your experimentation by making you observe what happens at specific moments.\n", "\n", "**Execute the code cell below** (click on it then click on the \"play\" button in the tool bar above) to launch the virtual lab, then **answer the 4 questions in the quiz below**.\n", "\n", "" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from lib.suspendedobjects import *\n", "SuspendedObjectsLab();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*If you wonder how the virtual lab works, you can have a look at the code in [this python file](lib/suspendedobjects.py).*\n", "\n", - "---\n", "\n", - "# What have you learned so far?\n", + "## What have you learned so far?\n", "\n", - "**Summarize for yourself:** what did you understand from experimenting with the virtual lab above?" + "**Summarize for yourself** one thing that you have understood from experimenting with the virtual lab above." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Type your answer here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Think back to the suspended jeans question:** from what you have observed in the virtual lab, which counterweight would allow to suspend the wet jeans (3kg) on the cable in the desired position?" + "Think back to the suspended jeans question and **compare your previous answer to what you observe in the virtual lab:** which counterweight would allow to suspend the wet jeans (3kg) on the cable in the desired position?" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Type your answer here." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "toc-hr-collapsed": false + }, "source": [ "---\n", "\n", "# Activity 2: Looking more closely at the tension in the cable\n", "\n", - "The goal of this activity is to **analyze what happens to the cable** in the jeans problem and **why**.
\n", + "The goal of this activity is to **analyze more in depth what happens to the cable** in the jeans problem and try to understand **why it is impossible to pull the cable taut completely horizontally** as observed with the virtual lab above.
\n", "In the mini-lecture, we have seen that, in this situation, the value of the tension in the cable (represented by the plain red arrows in the virtual lab above) is defined by:\n", "\n", "$\n", "\\begin{align}\n", "\\lvert\\vec{T}\\rvert = \\frac{\\frac{1}{2}.m.g}{sin(\\alpha)}\n", "\\end{align}\n", "$, where $m$ is the mass of the suspended object, $\\alpha$ the angle that the cable makes with the horizon and $g$ the gravity of earth.\n", "\n", "In the following we are going to use Python to **compute the tension in the cable** for **different values of the angle $\\alpha$**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Defining the constants of our physics problem\n", "\n", "In the above equation we have two constants:\n", "- $g$ the gravity of earth, which is 9.81 m/s²\n", "- $m$ the mass of the suspended object, which is 3 kg in the case of the jeans\n", "\n", "**Execute the code cell below** so that these two constants get defined in Python." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Let's define the value of gravity\n", "gravity = 9.81\n", "\n", "# And the mass of the jeans\n", "jeans_mass = 3\n", "\n", "# Display the value of the constants to check they are well defined\n", "print(\"gravity:\", gravity, \", jeans_mass:\", jeans_mass)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating a function to compute the tension in the cable\n", "\n", "Now let's define a Python function that represents the equation of the tension and in which we are going to vary $\\alpha$. \n", "The function takes one input parameters, `alpha`, which is the angle that the cable makes with the horizon. \n", "It returns the value of the tension in the cable as computed with the equation: \n", "$\n", "\\begin{align}\n", "\\lvert\\vec{T}\\rvert = \\frac{\\frac{1}{2}.m.g}{sin(\\alpha)}\n", "\\end{align}\n", "$\n", "\n", "**Execute the code cell below** so that this function gets defined in Python." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Let's define the function\n", "def tension_norm(alpha):\n", " return (.5 * jeans_mass * gravity) / np.sin(alpha)\n", "\n", "# And display it to check it is well defined\n", "tension_norm??" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing tension in the cable using our function\n", "\n", "Now let's use this Python function to compute the norm of the tension in the cable for an angle of 1.5$^\\circ = \\frac{\\pi}{120}$.\n", "\n", "**Execute the code cell below** to see the result, expressed in Newtons." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# First choose an angle alpha\n", "alpha = np.pi/120\n", "\n", "# Then compute the tension using our equation\n", "T = tension_norm(alpha)\n", "\n", "# And print the result with a pretty format\n", "display(Math(r'$\\lvert\\vec{T}\\rvert = $'+' {:8.0f} $N $'.format(T)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get an idea of how big this value is, you can think about our jeans which weight 3 kg: the force exerted by earth gravity on the jeans is $\\lvert\\vec{F}\\rvert = $ 29 $N$ only!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Question 1 \n", "What is the value of the tension for an angle of 0.5$^\\circ = \\frac{\\pi}{360}$? \n", "In the code cell above, change the value of `alpha` and re-execute the cell to see the result." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Type the value you obtain here:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*You can check your answers with the solution available [in this file](solution/Physics-solution.ipynb).*\n", "\n", "## Looking at how the tension in the cable evolves with the angle $\\alpha$\n", "\n", "Now we want to look at **how the tension evolves** when the angle $\\alpha$ changes. \n", "Thanks to our previously defined Python function, we are going to compute the tension in the cable for 100 different values of $\\alpha$ and plot the result on a graph. \n", "**Execute the code cell below** to see the resulting graph.\n", "\n", "*Note:* \n", "Python code for plotting can be quite verbose and not particularly interesting to look at unless you want to learn how to generate plots in Python. \n", "The good news is that you can **hide** a code cell from the notebook by selecting it and clicking on the blue bar which appears on its left. To make the cell visible again, just click again on the blue bar, or on the three \"dots\" which represent the collapsed cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Let's define the boundaries of the graph\n", "a_start = 21.8 # start the angle at 21.8° which is the initial angle that our cable makes with the horizon\n", "a_stop = 0 # stop at O°\n", "\n", "# Let's define how fine we want the grid on the graph\n", - "a_step = 1 # we want the grid to be shown every 1°\n", - "t_step = 500 # we want the grid to be shown every 500N\n", + "a_step = 1 # for the angle, we want the grid to be shown every 2°\n", + "t_step = 500 # for the tention, we want the grid to be shown every 500N\n", "\n", "# Now we generate 100 possible values for the angle alpha between a_start and a_stop (excluded)\n", "a_deg = np.linspace(start=a_start, stop=a_stop, num=100, endpoint = False)\n", "\n", "# Then we compute the value of the tension for all the 100 possible angles alpha using our previously defined function\n", "# But since our previously defined function works with angles expressed in radians, we first convert our list of angles from degrees to radians\n", "a_rad = degrees_to_radians(a_deg)\n", "t = tension_norm(a_rad)\n", "\n", "# Finally we generate the plot and customize its appearance\n", - "fig = figure(title='Tension in the cable', x_range=(a_deg[0],0), width=500, height=400)\n", + "fig = figure(title='Tension in the cable', x_axis_label = 'Angle ⍺ (°)', y_axis_label = 'Tension T (N)', x_range=(a_deg[0],0), width=500, height=400, toolbar_location=None)\n", "fig.title.align = \"center\"\n", - "fig.yaxis.axis_label = 'Tension T (N)'\n", - "fig.xaxis.axis_label = 'Angle ⍺ (°)'\n", "fig.grid.grid_line_color = 'darkgray'\n", "fig.ygrid.minor_grid_line_color = 'lightgray'\n", "fig.xaxis.ticker.desired_num_ticks = int(round(a_deg[0]/a_step))\n", "fig.yaxis.ticker.desired_num_ticks = int(round(t[-1]/t_step))\n", "fig.line(a_deg, t, color=\"red\", line_width=2)\n", "show(fig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Question 2 \n", "For which angle does the tension reach 500 Newtons, approximately? And 1000 Newtons?" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Type your answer here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*You can check your answers with the solution available [in this file](solution/Physics-solution.ipynb).*\n", "\n", "## What are the consequences?\n", "\n", - "As you can see, **the tension increases very very fast when the cable approaches the horizon**: what the graph shows is that the value of the tension **tends towards $+\\infty$** when the angle $\\alpha$ approaches 0. This is what makes it impossible to pull the cable taut completely horizontally.\n", + "As you can see, **the tension increases very very fast when the cable approaches the horizon**: what the graph shows is that the value of the tension **tends towards $+\\infty$** when the angle $\\alpha$ approaches 0. \n", + "**This is what makes it impossible to pull the cable taut completely horizontally.**\n", "\n", "It is important to know that high tension in a cable is very dangerous and can lead to serious accidents, especially when the cable is not well adapted for its intended use (i.e. not strong enough) or when the cable is progressively deteriorating with use. \n", "\n", "Question 3 \n", "Execute the cell below to see the video and watch the first minute (sound is not necessary). \n", "What happens at time 0:53? Why?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('KIbd5zBek5s', 600, 337)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Type your answer here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "*You can check your answers with the solution available [in this file](solution/Physics-solution.ipynb).*\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# What have you learned so far?\n", "\n", "Write **3 things you have learned** about the **tension force** in a cable:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "- \n", "- \n", "- " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Can you identify **other real-life situations** in which cables are used to suspend objects or in which cables are taut between poles? \n", "Write 2 or 3 ideas:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "- \n", "- \n", "- " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you are finished with the above questions, **check-in with the instructor**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "# Additional exercises (optional)\n", "\n", "## Expressing the tension in 'kilogram-force'\n", "To have a better intuition about how big the tension is, people like to express it in kilograms. The idea is to make a parallel with the force exerted by gravity i.e. the weight, $\\vec{F} = m.\\vec{g}$, and to compute which mass $m$ would create an equivalent force. \n", "To get the value in 'kilogram-force' of a tension, you divide its value in Newtons by the value of gravity: \n", "\n", "$\n", "\\begin{align}\n", "T' = \\frac{T}{g}\n", "\\end{align}\n", "$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Question 1 \n", "Complete the code in the cell below to compute the value in 'kilogram-force' of the tension we have computed above. Then execute the cell to see the result." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# TODO: replace the \"0\" in the line below with the expression of the equation above\n", "T_prime = 0\n", "\n", "# pretty printing of the result\n", "display(Math(r'$\\lvert\\vec{T}\\rvert = $'+' {:8.0f} kg$_F$'.format(T_prime)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To go further, you could transform your code above into a function, which would take `T` as an input parameter and return the value of `T` in 'kilogram-force'." + "To go further, you could transform your code above into a function, which would take `T` as an input parameter and return the value of `T` in 'kilogram-force'.\n", + "\n", + "
\n", + "\n", + "*You can check your answers with the solution available [in this file](solution/Physics-solution.ipynb).*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What happens to the tension when $\\alpha$ = 0?\n", "\n", "What happens with our piece of Python code when we say that the cable it taut completely horizontal, i.e. the angle $\\alpha$ = 0$^\\circ$, which is actually impossible in real life? \n", "\n", "Question 2 \n", "In the cell where we have computed $T$ above, change the value of `alpha` to $0$ and re-execute the cell. \n", "What happens? " ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Type your answer here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "*You can check your answers for this activity with the solution available [in this file](solution/Physics-solution.ipynb).*\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Is it possible to pull the cable taut completely horizontally?\n", "\n", "In this section we demonstrate the physical and mathematical reasoning to answer this question.\n", "\n", "### Goal\n", "\n", "For the cable to be taut completely horizontally means that the angle $\\alpha$ that the cable makes with the horizon is 0$^\\circ$. \n", "In other words, the question could be reframed as: which mass $m_{cw}$ should we put as a counterweight to get $\\alpha$ = 0$^\\circ$?\n", "If we can find a 'reasonable' value for the counterweight then it will mean that it is possible to pull the cable taut completely horizontally.\n", "\n", "To answer this question, we therefore look for an expression relating the **mass of the counterweight $m_{cw}$** and the **angle $\\alpha$** that the cable makes with the horizon.\n", "\n", "\n", "### Method\n", "\n", "Given that the system is in static equilibrium, the sum of external forces exerted on the system will be zero, so using Newton's second law should be easy. The force that the counterweight exerts on the system will involve the mass of the counterweight so we should be able to rewrite Newton's second law to get an expression of the form $m_{cw} = ...$.\n", "\n", "### Hypotheses and simplifications\n", "\n", "We make the following assumptions and simplifications:\n", "* the jeans are considered as positioned exactly mid-way between the poles so the tension is equal on both sides of the cable\n", "* we represent the jeans by the point at which they are suspended\n", "* the cable is considered as rigid (not bended), with a negligible mass\n", "* the pulley is considered as perfect, without mass nor friction\n", "* we consider the static equilibrium obtained after changing the weight, once the system is stabilized\n", "\n", "### Resolution\n", "\n", "First, let's draw a diagram and represent the different forces involved.\n", "\n", "\n", "The *forces applied on the jeans* are:\n", "* the weight: $\\vec F_j = m_j \\vec g$ \n", - "* the force exerted by the cable on each side of the jeans: assuming the jeans are suspended at the exact center of the cable, then the tension applied on each of the two sides is is equally distributed $\\vec T$, which combine into a vertical resulting tension $\\vec T_r = 2.\\vec T$\n", + "* the force exerted by the cable on each side of the jeans: assuming the jeans are suspended at the exact center of the cable, then the tension applied on each of the two sides is equally distributed with two tensions of equal magnitude $T$ on each side but with opposite directions on the $x$ axis, which combine into a vertical resulting tension $\\vec T_r$\n", "\n", "From Newton's second law in a static equilibrium we can write: $\\sum \\vec F_j = \\vec 0$ \n", "With the forces on the jeans we get: $\\vec F_j + \\vec T_r = 0$ \n", - "Using the fact that the tension is equal on both sides of the jeans we get: $\\vec F_j + 2.\\vec T = 0$\n", "\n", "If we project on $x$ and $y$ axes, we get: \n", - "$\\left\\{\\begin{matrix} F_{jx} + 2.T_x = 0 \\\\ F_{jy} + 2.T_y = 0\\end{matrix}\\right. $\n", + "$\\left\\{\\begin{matrix} F_{jx} + T_{rx} = 0 \\\\ F_{jy} + T_{ry} = 0\\end{matrix}\\right. $\n", + "\n", + "Using the fact that $\\vec T_r$ is the result of two tensions of same magnitude $T$ on both sides of the jeans but of opposite directions on the $x$ axis we can decompose $T_{rx}$ and $T_{ry}$ into the $x$ and $y$ components of $T$: \n", + "$\\left\\{\\begin{matrix} F_{jx} + T_{x} - T_{x} = 0 \\\\ F_{jy} + 2.T_{y} = 0\\end{matrix}\\right. $\n", "\n", - "Since the weight does not have a component on the x axis, it simplifies into: \n", - "$\\left\\{\\begin{matrix} T_x = 0 \\\\ F_{jy} + 2.T_y = 0\\end{matrix}\\right. $\n", + "\n", + "Since the two $T_{x}$ cancel out we cannot know their value yet and so we focus on the second equation: \n", + "$\\begin{align} F_{jy} + 2.T_y = 0 \\end{align}$\n", "\n", "The component of the weight on the y axis is $F_{jy} = - m_j.g$, which gives us: \n", - "$\\left\\{\\begin{matrix} T_x = 0 \\\\ - m_j.g + 2.T_y = 0\\end{matrix}\\right. $\n", + "$\\begin{align} - m_j.g + 2.T_y = 0 \\end{align}$\n", "\n", "Using the angle $\\alpha$ we can get the tension $T_y$ expressed as a function of T since $sin(\\alpha) = \\frac{T_y}{T}$, therefore $T_y = T.sin(\\alpha)$\n", "\n", "By replacing $T_y$ by this expression in the above equation we get: \n", - "$\\left\\{\\begin{matrix} T_x = 0 \\\\ - m_j.g + 2.T.sin(\\alpha) = 0\\end{matrix}\\right. $\n", + "$\\begin{align} - m_j.g + 2.T.sin(\\alpha) = 0 \\end{align}$\n", "\n", "From there we can get $T$, and this is equation number $(1)$: \n", "\n", "$\n", "\\begin{align}\n", "T = \\frac{m_j.g}{2.sin(\\alpha)}\n", "\\end{align}\n", "$\n", "\n", " \n", "\n", "We now want to make the mass of the counterweight appear in this expression. \n", "So we will now look at the forces applied on the *counterweight*.\n", "\n", " \n", "\n", "The forces applied on the *counterweight* are:\n", "* the weight: $\\vec F_{cw} = m_{cw} \\vec g$ \n", - "* the force exerted by the cable: a simple pulley simply changes the direction of the tension so the tension applied on the counterweight is therefore $\\vec T$\n", + "* the force exerted by the cable: a simple pulley simply changes the direction of the tension so the tension applied on the counterweight is therefore of the same magnitude $T$ as before but with a different direction - we will note it $\\vec T$\n", "\n", "From Newton's second law in a static equilibrium we can write: $\\sum \\vec F_{cw} = \\vec 0$ \n", "With the forces involved in our problem : $\\vec F_{cw} + \\vec T = \\vec 0$ \n", "\n", "All forces being vertical, there is no need to project on $x$ so we get: $- F_{cw} + T = 0$ \n", "We replace the weight by its detailed expression: $-m_{cw}.g + T = 0$ \n", "Now we can express $T$ as a function of the other parameters, which is equation number $(2)$: $T = m_{cw}.g$ \n", "\n", " \n", "\n", "Let's now summarize what we have so far with equations $(1)$ and $(2)$: \n", "\n", "$\n", "\\begin{align}\n", "\\left\\{\\begin{matrix}T = \\frac{m_j.g}{2.sin(\\alpha)} \\\\ T = m_{cw}.g\\end{matrix}\\right. \n", "\\end{align}\n", "$\n", "\n", "These two equations combined give us:\n", "\n", "$\n", "\\begin{align}\n", "\\frac{m_j.g}{2.sin(\\alpha)} = m_{cw}.g\n", "\\end{align}\n", "$\n", "\n", "This allow us to find the mass of the counterweight as a function of the *mass of the jeans* and of the *angle that the cable makes with the horizon*: \n", "\n", "
\n", "\n", "$$m_{cw} = \\frac{m_j}{2.sin(\\alpha)}$$\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conclusion\n", "\n", "When the cable approaches the horizon, $\\alpha$ is really small i.e. really close to zero. \n", "This means that $sin(\\alpha)$ is also close to zero, which means in turn that $m_{cw}$ is very big.\n", "Therefore, **the more we want the cable to be close to the horizon, the bigger $m_{cw}$ we will need!**\n", "\n", "Mathematically speaking, the limit of the expression defining the mass of the counterweight when alpha tends to 0 is:\n", "\n", "$\n", "\\begin{align}\n", "\\lim_{\\alpha\\to0}m_{cw} = \\lim_{\\alpha\\to0}\\frac{m_j}{2.sin(\\alpha)} = +\\infty\n", "\\end{align}\n", "$\n", "\n", "So basically, we would need to put an **infinitely heavy counterweight** to make the angle null and that is why it is impossible to get the cable taut so that it is absolutely straight (the cable would break before that! :-p)...\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " \n", "\n", "### Numerical application\n", "\n", "We have already defined above the constant `jeans_mass` which represents the mass of our jeans (3 kg).\n", "\n", "Let's define a Python function that represents the above equation. \n", "The function takes one input parameters, `alpha`, the angle that the cable makes with the horizon. \n", "It returns the mass of the counterweight as computed with the equation above." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "# Define the function\n", "def counterweight_mass(alpha):\n", - " return jeans_mass / (2 * np.sin(alpha))" + " return jeans_mass / (2 * np.sin(alpha))\n", + "\n", + "# Display the function\n", + "counterweight_mass??" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For an angle of $1.5^\\circ = \\frac{\\pi}{120}$, we need to put a counterweight of:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Choose a value for angle alpha\n", "alpha = np.pi / 120\n", "\n", "# Computes the mass of the counterweight using our function\n", "mcw = counterweight_mass(alpha)\n", "\n", "# And print the result\n", "print('{:.02f} kg'.format(mcw))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can check that you get a similar result with the virtual lab above.\n", "\n", "You can also check what happens when you put `alpha = 0`...\n", "\n", " " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 4 } diff --git a/examples/ExampleA-FluidDynamics.ipynb b/examples/ExampleA-FluidDynamics.ipynb new file mode 100644 index 0000000..2104b62 --- /dev/null +++ b/examples/ExampleA-FluidDynamics.ipynb @@ -0,0 +1,573 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Doublet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Welcome to the third lesson of *XXX*! We created some very interesting potential flows in lessons 1 and 2, with our [Source & Sink](01_Lesson01_sourceSink.ipynb) notebook, and our [Source & Sink in a Freestream](02_Lesson02_sourceSinkFreestream.ipynb) notebook.\n", + "\n", + "Think about the Source & Sink again, and now imagine that you are looking at this flow pattern from very far away. The streamlines that are between the source and the sink will be very short, from this vantage point. And the other streamlines will start looking like two groups of circles, tangent at the origin. If you look from far enough away, the distance between source and sink approaches zero, and the pattern you see is called a *doublet*.\n", + "\n", + "Let's see what this looks like. First, load our favorite libraries." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "import numpy\n", + "from matplotlib import pyplot\n", + "# embed figures into the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous notebook, we saw that a source-sink pair in a uniform flow can be used to represent the streamlines around a particular shape, named a Rankine oval. In this notebook, we will turn that source-sink pair into a doublet.\n", + "\n", + "First, consider a source of strength $\\sigma$ at $\\left(-\\frac{l}{2},0\\right)$ and a sink of opposite strength located at $\\left(\\frac{l}{2},0\\right)$. Here is a sketch to help you visualize the situation:\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The stream-function associated to the source-sink pair, evaluated at point $\\text{P}\\left(x,y\\right)$, is\n", + "\n", + "$$\\psi\\left(x,y\\right) = \\frac{\\sigma}{2\\pi}\\left(\\theta_1-\\theta_2\\right) = -\\frac{\\sigma}{2\\pi}\\Delta\\theta$$\n", + "\n", + "Let the distance $l$ between the two singularities approach zero while the strength magnitude is increasing so that the product $\\sigma l$ remains constant. In the limit, this flow pattern is a *doublet* and we define its strength by $\\kappa = \\sigma l$.\n", + "\n", + "The stream-function of a doublet, evaluated at point $\\text{P}\\left(x,y\\right)$, is given by\n", + "\n", + "$$\\psi\\left(x,y\\right) = \\lim \\limits_{l \\to 0} \\left(-\\frac{\\sigma}{2\\pi}d\\theta\\right) \\quad \\text{and} \\quad \\sigma l = \\text{constant}$$\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Considering the case where $d\\theta$ is infinitesimal, we deduce from the figure above that\n", + "\n", + "$$a = l\\sin\\theta$$\n", + "\n", + "$$b = r-l\\cos\\theta$$\n", + "\n", + "$$d\\theta = \\frac{a}{b} = \\frac{l\\sin\\theta}{r-l\\cos\\theta}$$\n", + "\n", + "so the stream function becomes\n", + "\n", + "$$\\psi\\left(r,\\theta\\right) = \\lim \\limits_{l \\to 0} \\left(-\\frac{\\sigma l}{2\\pi}\\frac{\\sin\\theta}{r-l\\cos\\theta}\\right) \\quad \\text{and} \\quad \\sigma l = \\text{constant}$$\n", + "\n", + "i.e.\n", + "\n", + "$$\\psi\\left(r,\\theta\\right) = -\\frac{\\kappa}{2\\pi}\\frac{\\sin\\theta}{r}$$\n", + "\n", + "In Cartesian coordinates, a doublet located at the origin has the stream function\n", + "\n", + "$$\\psi\\left(x,y\\right) = -\\frac{\\kappa}{2\\pi}\\frac{y}{x^2+y^2}$$\n", + "\n", + "from which we can derive the velocity components\n", + "\n", + "$$u\\left(x,y\\right) = \\frac{\\partial\\psi}{\\partial y} = -\\frac{\\kappa}{2\\pi}\\frac{x^2-y^2}{\\left(x^2+y^2\\right)^2}$$\n", + "\n", + "$$v\\left(x,y\\right) = -\\frac{\\partial\\psi}{\\partial x} = -\\frac{\\kappa}{2\\pi}\\frac{2xy}{\\left(x^2+y^2\\right)^2}$$\n", + "\n", + "Now we have done the math, it is time to code and visualize what the streamlines look like. We start by creating a mesh grid." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "N = 50 # Number of points in each direction\n", + "x_start, x_end = -2.0, 2.0 # x-direction boundaries\n", + "y_start, y_end = -1.0, 1.0 # y-direction boundaries\n", + "x = numpy.linspace(x_start, x_end, N) # creates a 1D-array for x\n", + "y = numpy.linspace(y_start, y_end, N) # creates a 1D-array for y\n", + "X, Y = numpy.meshgrid(x, y) # generates a mesh grid" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We consider a doublet of strength $\\kappa=1.0$ located at the origin." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "kappa = 1.0 # strength of the doublet\n", + "x_doublet, y_doublet = 0.0, 0.0 # location of the doublet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As seen in the previous notebook, we play smart by defining functions to calculate the stream function and the velocity components that could be re-used if we decide to insert more than one doublet in our domain." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def get_velocity_doublet(strength, xd, yd, X, Y):\n", + " \"\"\"\n", + " Returns the velocity field generated by a doublet.\n", + " \n", + " Parameters\n", + " ----------\n", + " strength: float\n", + " Strength of the doublet.\n", + " xd: float\n", + " x-coordinate of the doublet.\n", + " yd: float\n", + " y-coordinate of the doublet.\n", + " X: 2D Numpy array of floats\n", + " x-coordinate of the mesh points.\n", + " Y: 2D Numpy array of floats\n", + " y-coordinate of the mesh points.\n", + " \n", + " Returns\n", + " -------\n", + " u: 2D Numpy array of floats\n", + " x-component of the velocity vector field.\n", + " v: 2D Numpy array of floats\n", + " y-component of the velocity vector field.\n", + " \"\"\"\n", + " u = (- strength / (2 * math.pi) *\n", + " ((X - xd)**2 - (Y - yd)**2) /\n", + " ((X - xd)**2 + (Y - yd)**2)**2)\n", + " v = (- strength / (2 * math.pi) *\n", + " 2 * (X - xd) * (Y - yd) /\n", + " ((X - xd)**2 + (Y - yd)**2)**2)\n", + " \n", + " return u, v\n", + "\n", + "def get_stream_function_doublet(strength, xd, yd, X, Y):\n", + " \"\"\"\n", + " Returns the stream-function generated by a doublet.\n", + " \n", + " Parameters\n", + " ----------\n", + " strength: float\n", + " Strength of the doublet.\n", + " xd: float\n", + " x-coordinate of the doublet.\n", + " yd: float\n", + " y-coordinate of the doublet.\n", + " X: 2D Numpy array of floats\n", + " x-coordinate of the mesh points.\n", + " Y: 2D Numpy array of floats\n", + " y-coordinate of the mesh points.\n", + " \n", + " Returns\n", + " -------\n", + " psi: 2D Numpy array of floats\n", + " The stream-function.\n", + " \"\"\"\n", + " psi = - strength / (2 * math.pi) * (Y - yd) / ((X - xd)**2 + (Y - yd)**2)\n", + " \n", + " return psi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the functions have been defined, we call them using the parameters of the doublet: its strength `kappa` and its location `x_doublet`, `y_doublet`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# compute the velocity field on the mesh grid\n", + "u_doublet, v_doublet = get_velocity_doublet(kappa, x_doublet, y_doublet, X, Y)\n", + "\n", + "# compute the stream-function on the mesh grid\n", + "psi_doublet = get_stream_function_doublet(kappa, x_doublet, y_doublet, X, Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are ready to do a nice visualization." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the streamlines\n", + "width = 10\n", + "height = (y_end - y_start) / (x_end - x_start) * width\n", + "pyplot.figure(figsize=(width, height))\n", + "pyplot.xlabel('x', fontsize=16)\n", + "pyplot.ylabel('y', fontsize=16)\n", + "pyplot.xlim(x_start, x_end)\n", + "pyplot.ylim(y_start, y_end)\n", + "pyplot.streamplot(X, Y, u_doublet, v_doublet,\n", + " density=2, linewidth=1, arrowsize=1, arrowstyle='->')\n", + "pyplot.scatter(x_doublet, y_doublet, color='#CD2305', s=80, marker='o');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just like we imagined that the streamlines of a source-sink pair would look from very far away. What is this good for, you might ask? It does not look like any streamline pattern that has a practical use in aerodynamics. If that is what you think, you would be wrong!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Uniform flow past a doublet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A doublet alone does not give so much information about how it can be used to represent a practical flow pattern in aerodynamics. But let's use our superposition powers: our doublet in a uniform flow turns out to be a very interesting flow pattern. Let's first define a uniform horizontal flow." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "u_inf = 1.0 # freestream speed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember from our previous lessons that the Cartesian velocity components of a uniform flow in the $x$-direction are given by $u=U_\\infty$ and $v=0$. Integrating, we find the stream-function, $\\psi = U_\\infty y$.\n", + "\n", + "So let's calculate velocities and stream function values for all points in our grid. And as we now know, we can calculate them all together with one line of code per array." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "u_freestream = u_inf * numpy.ones((N, N), dtype=float)\n", + "v_freestream = numpy.zeros((N, N), dtype=float)\n", + "\n", + "psi_freestream = u_inf * Y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, the stream function of the flow created by superposition of a doublet in a free stream is obtained by simple addition. Like we did before in the [Source & Sink in a Freestream](02_Lesson02_sourceSinkFreestream.ipynb) notebook, we find the *dividing streamline* and plot it separately in red. \n", + "\n", + "The plot shows that this pattern can represent the flow around a cylinder with center at the location of the doublet. All the streamlines remaining outside the cylinder originated from the uniform flow. All the streamlines inside the cylinder can be ignored and this area assumed to be a solid object. This will turn out to be more useful than you may think." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# superposition of the doublet on the freestream flow\n", + "u = u_freestream + u_doublet\n", + "v = v_freestream + v_doublet\n", + "psi = psi_freestream + psi_doublet\n", + "\n", + "# plot the streamlines\n", + "width = 10\n", + "height = (y_end - y_start) / (x_end - x_start) * width\n", + "pyplot.figure(figsize=(width, height))\n", + "pyplot.xlabel('x', fontsize=16)\n", + "pyplot.ylabel('y', fontsize=16)\n", + "pyplot.xlim(x_start, x_end)\n", + "pyplot.ylim(y_start, y_end)\n", + "pyplot.streamplot(X, Y, u, v,\n", + " density=2, linewidth=1, arrowsize=1, arrowstyle='->')\n", + "pyplot.contour(X, Y, psi,\n", + " levels=[0.], colors='#CD2305', linewidths=2, linestyles='solid')\n", + "pyplot.scatter(x_doublet, y_doublet, color='#CD2305', s=80, marker='o')\n", + "\n", + "# calculate the stagnation points\n", + "x_stagn1, y_stagn1 = +math.sqrt(kappa / (2 * math.pi * u_inf)), 0.0\n", + "x_stagn2, y_stagn2 = -math.sqrt(kappa / (2 * math.pi * u_inf)), 0.0\n", + "\n", + "# display the stagnation points\n", + "pyplot.scatter([x_stagn1, x_stagn2], [y_stagn1, y_stagn2],\n", + " color='g', s=80, marker='o');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Challenge question" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What is the radius of the circular cylinder created when a doublet of strength $\\kappa$ is added to a uniform flow $U_\\infty$ in the $x$-direction?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Challenge task" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You have the streamfunction of the doublet in cylindrical coordinates above. Add the streamfunction of the free stream in those coordinates, and study it. You will see that $\\psi=0$ at $r=a$ for all values of $\\theta$. The line $\\psi=0$ represents the circular cylinder of radius $a$. Now write the velocity components in cylindrical coordinates, find the speed of the flow at the surface. What does this tell you?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bernoulli's equation and the pressure coefficient" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A very useful measurement of a flow around a body is the *coefficient of pressure* $C_p$. To evaluate the pressure coefficient, we apply *Bernoulli's equation* for ideal flow, which says that along a streamline we can apply the following between two points:\n", + "\n", + "$$p_\\infty + \\frac{1}{2}\\rho U_\\infty^2 = p + \\frac{1}{2}\\rho U^2$$\n", + "\n", + "We define the pressure coefficient as the ratio between the pressure difference with the free stream, and the dynamic pressure:\n", + "\n", + "$$C_p = \\frac{p-p_\\infty}{\\frac{1}{2}\\rho U_\\infty^2}$$\n", + "\n", + "i.e.,\n", + "\n", + "$$C_p = 1 - \\left(\\frac{U}{U_\\infty}\\right)^2$$\n", + "\n", + "In an incompressible flow, $C_p=1$ at a stagnation point. Let's plot the pressure coefficient in the whole domain." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XspQNJ09z3GKmq6hbt4RQzAs7ZzmI2mNV+FolQ8hZwlEnGpOCMVimhpbF22eFozPGLs6SqiHoOoXiJESi6WOnyzKFqduqvFRyFzTBZTHfVYMw9v1sqfvzd2yv9I1unHC0/tiTCUUPZEIny8gUzUdclNRSQiimw7pJEZcXJi4113OKrO10xxn4f6iNmvdVJhyta9OEQs51zxXeZKFo+tjZYj7/5aBxzuKiYL48zaLuX8FlXcZRw9FNmQJwCG19xPzC24VCi2yhpls3b708BhzEjExo8ROuoj+8TVZYGijOkC4opdOEAt11i8w0dQlF0782j0V2GrsVnEVvQT04IxZrxnRizabOEHVdgrFOyorMQtcwgdb90gm6jPCzbj9lhGIdAjFr2zzhmAxFF4WllaUKxy9qs6OLQtE1F+dOj1es4lLX5SrWIRRN/9p8Flk0LjOLKYFngLHi5486zlkdIemsNuhcnvRNvs4ZOUahbPg5EopFYeesdYbCxN5oj7x9Jtub1bbhdWTotXiZ7pRrx2PmC/esgul1l9QZxVWcllA0/ev8Yc7XYmGcxRowX4r5ZtxfwnUkvjRtKkCt85jjRuaFn3ViqJwQy95n3jpZKEsffk47jslM6LTLqHMYk21TNuXdxTkgr2+bhlA0fet8syguY6U6ixVme5knjFgcA9ORLRbjdGxlBOMo4ehGjl2MHcBsQakTiv3X+qsn3cT0a+ntspaXJas8DgwLx0g0Jl9Ph6Wj5XnjF3PHLmpE5ajjFu2csHKZEHRVV7GMUDQi0RCxKKJxmTFicQRMR7bYjNqxlZnxJU8wlnUX09P/1VZCR+sAFgvV2FXMcx5TQrHITSwjEEeZKzrpFkJeXUW9y5hOfkkLxrxyOtNyF6sKv1FL5eQxqlA0feti07QISll6VRJcFrR0jhGLFTGd2fIwasdW5DJWdRiruosTcSMjIVjC1dONUwyeD/5NLhtYrnEgdevrnueh7OxkFigQh97wa8k2aAVjhXqKWndyhowTfh5FKJp+dXkwLuN8spgSeAKYAdbLyajnveiGmXXDrfsaS4Yn60yIiELQaVdxFKGYTDTRhqpTCSrj1lnM3G+JdmS9l8pE21bISh+FohB03eHnqkLR9KvLiznv84VxFktgLmrDKL+GR3UYdY5m2i2cWCiaamV1qpA3PjFPnOmeazONSxCEgxPPo7CzxlFMuokw/Foy6aXIXRwpFF1z+ZyyZPV3kxCKhuVmXlzGtl8lwWUxPTgjFgswHZohSdXOre6QdF2IrQpFYdZ4xVFdxeRrydeHHDtPv/7AMTXLilD24PY64Zi1zywRmScYm04VV7FOoWj6VEOaeR3LuEwYsZiB6dDqxZlwPUB3SnMkR1Tp3EYprTOKuzgxqoxXLBF+Hng9QyjmuYhF5XXKkCccI9GYKxAzBGO6naO4i+PM5JIUgKMU4tb1e3UJxVn0qYvW7ywyTRaMlUrn0Mz3MC5GLGowQnE8Jt1BVz3mpDr0Ki5jnmCsEo7OIxmKnlrJnZSrmCQtFMUrGYr2ygvEsu6iLpw8sCw8XiQaRxGMUXtKu4upEjqjOpJ5ZXOy0LmK8yoUZ9HfFB3biMjRmJew9DJixGIKIxSrMcuOuizpNtbdkZcveVNdMKZpSt1FbRFuzWwmeaHlLDcx2nfZ2ovjJJqkhWNsClQUjFE7hl4bITN6EhS5ipNI4hp1v4XHNX3OwtNkl3FZMWIxgRGK5ZiHzjoPXfvH7czL/iKuKhiLOs0yoWjb9vFqClfrCnHnuYrJ5eJXF4pVZnfJbXeJ9QfEoAI0ZXYyHUVdGFrjFGqLdI9AUWZ7HdNA5rmK0xSK897fgBGPo9AkwdirMCuLV8cXvIEYsRhihGI+i9Bh5xG9v2mIxqpjGNOdZp67WGdW9AC605+cAzrHVcz6XycUc5NdRgg/55E1tV/8XDGUBJPcbiC0nZE1HbV1eGrA+gt0ZyWsFJXLqRJ+npZIXJb+BoxwzKNJgnHZWXqxKGKEYhaL3mHrqKsTL3YE9YJxlPGLWe6iTlSKpVBVwtg6J7EgIzldrzBvzGJyfKLOgYyep49neaMJLd+WXKGZFo3Yw+MYIV8QDjqOCXdRF4qOxi3OiCYJxWXsb8AIxyKaIBg7FUrneGZuaMOysKyddppxO/EyghGGpwcsM35x0mMXdWVztHNBDyzPCRvrxiymhKJOVKa3jUTiqOMULfpCyCf//cTOo2Yc48By9OHoTDFZUyga9MktyRD0uEW4+/vJF4GjikTT1wxSV4Rj0TCGzuwxYtEQYzrubEYVjqOGpauOX0y6i7pQdJ3jFgFtCLr/fPD/tGOoG5+Y5z4mXcThkjvlxI+yhtVZlnBMi8D0OMa0YEy2S+cu1sWo5XQiRg0/T0Iomr4mHyMaDU3DiEWD6bgrMkpHPkoopchhnHVmdG7x7PTzdGg5RyimRaI+WSZYZrllW6vwnUjEZQvHPLcxLRiTbUtGntJCMSsUPTBusco80hpBp3MM8zKgZyEUTT9THSMam0GlBJcFnUXZiMUlxnTe41G1I893BsuNYaw6drFuQTlU/9BPOox6V3FonueUUEw7j2mRmCcQpcLYRSsUgr4TbaMXjWnBOJTIksiUTovE5GtDzzWJLnWTlQVdFH6uKhSNSJwuRjQaZo0Ri0uG6bjrp0pHnheWrpr0AtliMC8rOkpyKTPlHzCcCZ3aRucq6uoilhWKRSJRRhy3KL5CWWB7oGwhchvDpfF6OpcxLRh173tonuksd5Fq4xazyuTkFeOu4ipOSiiavqZ+jGicDWZuaCMWlwrTeU+WKuMasxzCMoJxlMxoqLveYuq5nyEQ/XyhmBSGkVAsKxJHyYjuC8Bw3zluo85ljLEHxd5A2Z2saf8ickLOo8ziUuQa5i2vIhSNSGwORjQapo0Ri0vAsnbeK4mbW3fKpRfKdOZZLmOZOoxJwVgUaq4jFK0txq3NmB4en5i1PEsoiq8KBWK8rVteWClHsEN3ES8so5NyGwPRKLGTmBSM2tleNOHo+L0m6i5OgzKFuJOCr26hOIt+ZiXVrml/z2eNY/lGMBqmghGLC86iCcX0zWFS29V10ynTmeucwrIZ0hFJQRi5i2ULdFeuvZjcNudjTbuKVYRinkiMBKL4qrRYVI4gXRUktzjBex0ONytsD7xVyHIY04W88z4DrYDUjVusUGuxaOYWGAxBJ13FIsE3qlCsu48Z9TteZdtFEpXGZZw8Hb+8VPIWNAzduHclIq8QkZtE5FYReadm+ZtF5EERuTZ8/Gpi2ZtE5Jbw8abptrxZOJa/EEJxxfIGHrM67jjHLnMudDdk3c3brnDzz6OM6BjaJmOTKARdNKtKFaEoPtg9heUp7F7w3OoG4lB8hdX1g/89v9TD6niIG2wXbWt19cexOwq7Gxwnaq/lqYG2R+3P+kyG3rund2JHJRqvmBdirhJ+noVQ1H3HpvUdn9VxJ8ki9PeG5tIoZ1FEbODDwEuBXcBVInKRUuqG1KqfUkq9LbXt4cCfAE8nGJD0vXDbR6bQ9EYxz51GkzvtcUNeRQ5AWYcxaxtduDlyF6NlY49bLCrMnRCEaVdxYL0CoRgvS7iIQCwQo9cqOYu+F5bNsRAUyhGIMqsTbmPgIios+mHpwWUpEuHozONnfOS50/4lE2IKyupEIeisxJa88PMoQnHUPmaRv99NwLiMk6Ha3NCL+dk3SiwC5wC3KqVuBxCRC4HXAGmxqOPlwFeUUnvCbb8CvAL45ITa2jjmVSQ2+QaSx6hjIvNC01mCEfozvZQJR+cluoxL5JLlhqBzws9lHEVdqFk8f1A0+uWvd+mCsiKRGISjg5CwNRSirioYRy2Ho8uIrprckkXZ8HNVoThKH7Ns3+8mYMYyGuqmaWLxWOCexPNdwDM16/2MiDwfuBl4h1Lqnoxtj51UQ5vGvAnFeb2BZFHVlcjLnC5TwLtsdrR224S7WLp8TgbJELSurmK8TgmhGIWBo1BznkgUN3QX3fLXvXIslGPFwjF2Gz1B2YGQ9LFKC8bktH5pdzFPQIoHivJFuOPtQgGYDkEXuYpJkq6iTijWIRIX7bsN/fc0T6LRuIyGOmmaWCzDvwOfVEp1ROTXgI8DL6qyAxF5K/BWgNUjt9TfwikzT0JxEW8kOqrcXHQugC5TOh2SzhKMaXcxHYqOGEpssRTUNK5OF36uIhStTrDhgGhMi8TE81L4FuKGghEf5VtDbqPVBeUL/oqVKxixh0WxSvSm2a5rtdI4o4wtzXIVJy0Ul+G7PY9uo3EZx8ckuDRPLN4LHJ94flz4WoxS6uHE078H/jyx7bmpbb+mO4hS6gLgAoAtpz2mnrjPjJgHobgMN5EsyorGrA497RrmjWEcZUrBuhma1i+d1FJSKEYiMQo1i+vHAjF6jueDV/7aEs8G2wLfAstCXF/jNip8bCz8ICy9glYwWlYiQzrKik5NBTgJyiS2RBSVyclaN0lR/2K+2/MhGo1gNIxL08TiVcApInISgfg7H/jF5AoicrRS6r7w6XnAjeH/lwDvFZHDwucvA941+SbPBiMS54syjsQogjFrOsAidzGd6DJu+Rzd3M/a2VlKCkWr6+ldxKRAdN3guVtigmjHCYSi48SiMRCJfuw24gCuhYWHjw0OWF20glH8IFTt2YNjN5XTf991ica85JZ0CDoSkWXHKRqROD7zIhqNYBwdt8K59ctOzTRnNEosKqVcEXkbgfCzgY8ppa4XkfcAVyulLgL+q4icR5DHuAd4c7jtHhH57wSCE+A9UbLLomGEYn2U+Szr7mDzbi5Z44yqCMY6UJbKLfUSC0TdrC0pkQiJ8HMqmSUSilY3LHHT9eNws1YkJgWi76N6JYQiQQgbywq2TYpGiN3GSDSq8LNXnuCv2lrBODBeMf7MBt3F9LKyFGU+VyUr/DyKUJzE93oW38FJMA+i0QhGw6g0SiwCKKUuBi5OvfbuxP/vIsMxVEp9DPjYRBs4Y5ouFJsoEsf9zCZ1MysSjaMKxrLuYkSVJJe8eoFxqFnjKsbuoibrOS0UrbZbTiR6HqqMqwjBPmy7n0FtWVq3Ed8CHCzAX7GxOp5WMAbvtR+OVlb//Ufu4uDc0OGyUTOnE4IvK7FF5ypWEYqTEol19FlF+2iSAGq6aDSC0TAKjROLhmyaLBSbJBJn8Tnpjlm2Q866uehcxrodRl34eZQM6VgcpkViKvxcWih23EKRqHouyvNAlTjfYiG2jbQcxHH6wjHtNtIiCFrkC0bLZSAcrezEDC8qKPQq/rA4TIvITDRjDO0S2c4D6xcktCTJ+s5U+V7Psn9KH7sJYmjF8oxgXBCqJLi4JsHFMEuMUMyniZ9P1RtY1s0lLRrLCMYy7iJoCnRXzIjWlspJCMbkX8ulX3A7Ko8TlsUZEoqdTr5IdHul2wiA8lGuj/I8xHaRljPoNkai0XWR1dXok40Fo7gKHPri0AIcicVf/DclEEuLQw26TOiyrmKeUEy6iuOKxCZ+7yC/NNU0abLLaASjoQpGLM4BTe2QZy0Sm/q5ZFHmBlY2NF0lS7osVZNcskRi8v+0qzgwdjGRzDIkFDtdVKcbJ7Mo18Vvd8q5iHmUEY2QKLvtIFY/6UUsFYhEnwF3MSqlE41VFElkSacFpK+ZtSVjnKLYqrB8zrSF4iJ+7yZNU11GIxjLUcUt1M7ytAAYsdhwmtoxz0ooNvXzqErRDSwvNF0kGMu6i0lKhZ5zlg+MV4z+j5NgVOwqBnM8+4k5nlNC8dA6dLuodqd6qLkKkWh0e4jTGhSN0XsK/1qAj4NYPlYXfKxIU0I4XjEauygWA8kvuv+HCF/Pq79o237lItxVhWLed3pZvneTpKkuoxGMhjIYsWiozCyE4qLcrHTkzbSgu8FUEYx5xKForMplc5KuYnK8YjIEHbmKUVJLHH52FVbPi4WidF3o9qDTjYWiv96ejEjUoNxeENYWC2stCEHrBCM4+CthKDocvxiFnpPuovj6cYviF4ekq84BneUqVhGKyyASdcxqhpMmuoxGMBqKMGKxwTSto15GkTjOe656QygSjWUFY5Iq7mIaZalALOWV0NGcnnheaH8wqaX/PHQWoyLbOqFYdUxiHSgfv92JjL5MwQjgr9rB/Pfn328AACAASURBVNhhODpyFz07kWWeKKGTFonRMi1WcehZh04oprOek9+nukXiuP3DrATULESjEYzzRderMIPLgn6GRiw2lFmLpDTTForTfP+Tem+6/Zad/g+Gb15plzEtGCGYHrBsOFqHWIpoUrsqGdHZ09sFRK6i1fGwuj5WN3AUpeMGSSyuO1uhGJElGG0LLB9x/EAkuoGgs6xBd1E8ECt4qDF712i8YjIEDeULcI/iJs5yar+sfU5LVE1bLBnBaJgnjFhsIMssFKf13mc15rKKgMwTjTrBCH0nMUswJglCmsGIPI9+IeohLMAjSMLIG7eoEuMSkyHo5FjFMPM5LrqdSGapLhQVG60eQcA3H1dZdFWr5G5TgtFxoNtDiIcXoiwJncVBd5GMQ8TJL6lkl6FElxwsyy8dfq4iFMt852aZzDbN+Zin7TI2cRyjEYzDVDk/Zm5ow1QwQnFyzDp7O4uiG4buBpYWjMnleWHpMu5iEbqaiunlyRC05SXrKQ6PU1SdLv6Bg4VC0cbjuNW9nLD2MCes7eGE1UfY4nRKt/uB7mbuah/OnZ3DubN9OA/1NkNW5mJCMEor7CZtOy4WbnV9lG0NuIsgWF5QczEat1i2dE5ecksRkxKKTfy+TEs4zkI0GsFoaDJGLDaIZRWKk3zfTbzhZVF0I0x34HnjGKE4O7pO4nGKCSEZ11XsJuoqpsYp5gnFNelxwtoeTlp7mBM3PMxxK4/SSl0rbd8p9Ut+RVyOXDnAkSsHeAZ3A3DQW+HO9uHc0d7BHe0d/LizDZ/EvkLBCGBtBmwrkJaWhTg+Vs9DOX13MXBNw7miEw6jrji3bryi2Coer5gOQee5iknKCsV5E4k6piEcpymamiYYDYYkRiwatMy7UJz2DW/Fzp92rsoAaci+EaYdj6xxjEXhaMtSOPgErR4MRQP9cYuA5IWeUx9zHIIeqKuoYldR3KiWYhd18NCQUGyJy7O23snZm3dx9MperNShd3e3BO5g+3DubO9gj7uRTHcwgY3P0at7OXFtDyeuBs7kVqfDmZt2c+am3QB0fJs72zv49t6TuWn9yGC/ysdfX48/JQjGL1qAsiysjoeyBMsCy5U4FJ0WjPmNqxKKLpf9nJfIUrVsThF5137V637kNkwwnDtNl7FJgtG4i32qfA6+MnUWDROkSa6iEYqafRaIwXG3z7upZpXPgUHRWFUwVkHZCnEz5oTOmLElzoCOpvVz/SD83OkEmc+dTiwUV8Tl2Vvv4Pnbb2Wz3QXAU8Jd7e3c3t7Bnes7uKtzOOv+SqV2R3hY7Oocxq7OYXyLxwGKw51DnLj2MCeFjyNWDnLaxgc4beMD7Ops4z8eOY0bDz0GEPx2B3GCAt04DjhWMPbSl4SjGEhsy1P4LRkuzj3GbC5JisYpRtdGkUgs+z0Z59qv+0dU4fEmLBqNYDQsK0YsNgAjFOuh7raPKxDHOZbuJlpUczG5vEgwQuhSlfnI7PJTAMYzt7iJaf08P05qicPPvaDg9qr0eM62O3jettvYFIrEu9vb+eqjp3LL+hH0xk0pzm4pe9xN7Dmwie8feCwAm+02Z2/exQu23cpxq3t582Ou5MedrVz66Glcf/BolBs4ouI4iG1jWRbKsuLMaGkFwhFEW1IoTdKAKBOCtiw1klCs6iTO6rqvUzhOSjQuo2A0VE1wMc6iYcGZhlBsukic5o0yj7ybqC70DMMuY5ZghGTWdHEouoxQlEQkNRi7qBLj+FTfVQznel7t7ufZm2/gJ7bdxkY7cBfvbB/OpY+cxs3rR1AmtFw3B7w1vrn38Vyx7yTO2XIn526/lWNW9/HGo65id3cLlz5yGjd4pwbvwfOCZBffD8Vw4CharuCXDT8DWIl6liUZRyjqvitNvOahHvE4CdG4bILRuIsGMGJx5jTFVZxHoVhXm5tys8wiSzjqElwgEI15N5oys7uMgniJ8YpR+LkTzNQSZT+vdfbzXOsannPkjaxZwfu6bX0Hlz5yGre1dzILkZjGVTaX73scV+4/kadvuZsXbr+Zx6zs5w1HXc0DvZv4aucsruucAQl3UdlW8J49hXgSZ4QDce3FKiHoLFexrFCcJ5GYRZ2uY93Ca9kE1LK9X8MwRizOECMUR6eONtd9s3TKxB4LKJqwfsV2hwQj5JfdyQpHg0cPW5vokqy3qCyFWBKETe1gBr6it9oXjSquqfjE3g2ct+EbbLKCDONbDu3k0kdP4472zvydzQhX2Xxn30lcte+xPG3LPbxw+80c2drPL7S+yXO9G/lX75U86B/ddxddC2kNit1o2r9CUiHoPMYVimWu+zquZSi+nssStXkc0Vi3yziNxJemuIvLjklwMWLRMAXqFIpNEol13VDL7DN509U5Lunxilmkw9FZRHMUK8gtxD20nZ8oxB3OA+102ry6+xWe2boOCJzES/aczl2dHaX3O0s8bK7cfyJX738sT91yDy877Ecc5zzEb6pP8rn1l3HtylmxMIYg9G554JVxSW2VOR+0zlUsKxSrOImTuI6z9j2ueEz/WBppH3PmMjZFMBp3cbkxYnFGLIuruEhCcZI31SrH1gnHLNGYDEkPnwubLHfRrXBzSs4FHTwPRZPns6P3MG9of5pjuR9XWXz+4Sdxxb4TaEK4uSo+FlfvP4HrDhzDzzzmhzxlw938grqYEw/8mItar8Jv2fG4RW81u3yOshTYamD2Fl1iSxZVhaLump/VtVyHeKzLZTSCsTrLKhirVI8wM7gYasMIxeqM29YmicRVK7stHb/4Kxm1p0g0ZgnGKCwdYNPTvL2BUKitwFfQK5HoEpXRcRVPPHgjP//I59hAhz3+Fj6x9ye4Z99opW+aREe1+MTup3LHkcfw6o1X8kz3Wo7bs5v/Zf889288CsgO02vvIxphmOUqlhWK4wrEvGsUyl2nZdBdy2UZVzQ2caq9PJoiGA31ICKvAP6K4Ff73yul3jfjJuVixOKSsixCcRYisehGO+q26Ru0zm1M3kCLBGNEyyrnLipbBVnPBaLR9jzOe+CLnLv3cgBu8E/ifx98LgcePQg044fS2CjFd/edyL3W0Zy/4TKO9Xbz2/d/hAvldXx/9UmUdU6jTOgyruKoQjHrOp7WdVqGcUVjE1zGZXHdluV9ThIRsYEPAy8FdgFXichFSqkbZtuybIxYnDJNcRUnybwLxVFE4jg33lGOkSUck6KxSDD2yXcXlV9uhhHxYHPnAL956z9x0vrdeFh8yX4+l3eegGofCDJjFgi/3eHe9a38devneN3q1znTu4Vfue+THO/ey6dPfSVpwZic4k8J8ZQw6YSWvLGKZYVi3jU86Ws17zotYlTRWIfLOA+C0biLs6FojHeSkgku5wC3KqVuBxCRC4HXAI0Vi+bnwRIyj3O/Vt52BKHoiF9ZKK5a7lSEYtZx08dOvofoMyiT/NCyvGAKQNvHsgKhYtv9z0JZKu4tVIbzteJ1+Y2bA6H4qL2VCzb/Epd7T4FuD+/AwfHecBNRPqrTod0RPmGdx79veikeFi958Bu8+q5Ls0PRiftOlNyS5ypG4WfH8lmxPFbC/6sIxazrZdKMetxRvosw5mwzNfWLkzYEmtB/L4PpMWGOBe5JPN8VvtZYjLM4RZrwBZuX8PM0heI0nUSnps8/GSJOt6XjOzji4yprwGHMZzDZpRu6IxKWdIkkjJJwzujUz0zL9/g/bvgkJx+4m0ecbXx4x3/h0P4W4h5EtTsL5ypG+O0O1uZN0HO5fNMzeHjHDn754U/x6l3/wcNbtvLVU54JDArEmEgo2tmztdgJ8VjFTcy7PidxDZYhalMVtzG6jqswTlh6XhxGw3SpUpdWBc7iy0TkjxMvX6CUuqDudk0TczUbamPRheIoLoljeQOPusjbb9S+yJ1ZsV2tyxg5VC3Li8OcEUl3UaLyLlE5nfS9VCneeP3nOOuhGzhob+Ajx76J/WpLML2f5wfT5C0qyg/EsOsirs/1G57Ap48+D4A33vhZzto9GFWKPrs4E1qb3DKc1BJ9t0YRirO4BvOo/B0awWVcdIfRuIuN58tKqacnHmmheC9wfOL5ceFrjcWIxSnRhC/WJDuYWQvFpCAqS53ZoQP7HfPGnBSl6UfZYybXT4alV2x3IJSZxA5FSjocDWhFjbIBpXjdTV/m3LuvpGs5fOSkX+ZB5wjED4Qi3S5+uzPS5zAvKNcN3qvvI77iiu3ncPGxL8JC8Rvf+wSnPnRHsF7U20ai2+qHoJOuYkQ6/JwMQyev9+j8Dpzzgmsw7xobxzWvct2PIhqr0ATBaDBkcBVwioicJCIrwPnARTNuUy6NE4si8goRuUlEbhWRd2qW/66I3CAiPxCRS0XkhMQyT0SuDR+N/uANw4wjFKtQxamockMb5UZZ9UZdZptkG9IuIwx+Xml3MSkYI+JEF6tfGzASij933SW85qbL8BH+/gmv547NwdcxcBU9VM9d2BB0hN/uQLcbzlQTFCL//LEv4ZvHPINVr8fvf/ujnHH/bUBivGciucUOhXlW+Bn6TjAMuolJoRisp3eaq4rBcYVk1e9CWaYpGOvAuIuLQc+3Sz+8EgkuSikXeBtwCXAj8C9Kqesn/DbGolFiMZFO/krgDOD1InJGarVrgKcrpZ4MfBr488SydaXUWeHjvKk0ugRN+EI13VWcplAsS903xkkmGaT3XcplTLlW6XB05C7att/P2LWInbGfvP4bvObGr+KJxd+e/Xr+c+eZiZlbfOh0UZ3FdhWBIBTdC+a+Fj/4fC1P+KfTX8e3jn8aq16P3/vaP/DYPfcFq4f3kii8HwnzrPBz8rtR5CYmX5vWtZZH1e9GGaYlGE042jBJlFIXK6VOVUo9Tin1p7NuTxGNEosk0smVUl0gSiePUUp9VSl1KHz6HYJYv2FGNEEIl6VuoTiJG2FLvJEeWcfTicaBsYwpwQi6cLTqj1sMOe3+O3j9978EwN8843yuOuYp8TIJQ7K47sKHoCNUpz9uMZr2T4nFR8/6Wb712LNZ9Xr8zlf/mQ3ddrCBreL5oIE4/KwTinH42e6fO9051l1ndVxPeUxCNJZh3gTjIjNP94BR8X0p/VALOjd008Ri1XTytwBfTDxfE5GrReQ7IvLaSTSwKk34IjW9w5uGqzgJoVi0j6Ib6Tg36aJ9pduQJRqTghHIDEeLFYgbbMWW9QO84yufwFY+nz/9BXz3hKcMloPxQ2fR9xc+BB3htzvg+XEoOn7dsvjoM36Gu7YfzTH7HuLXvvEZlOUHxbhjR7Effi4SiunzmRaJk7qeivZVRTQWsYiC0biLhnlnbkvniMgvAU8HXpB4+QSl1L0icjJwmYhcp5S6TbPtW4G3AqweuWUq7V1E5iX8XGV8YuG+arjZVXVuqtLxnYFj9JQ9tB/XD15zvf70e8PnIiin07Xs8H8QPP7rl/+Fww/t48ajTuRTZ78cev0trHBeaLwwS3hZUIGTij98rfWcFh849w289wsf5Lm3/oDrf3AiXzznHABsOxirmBynmCcUdeNRYfiaGvW6ySM6Rk9bB2jwuHn7ciyvsOzOquWWnvqySmmdcWd7MegxpYIWn6ad3VLp5CLyEuD/As5TSsV3JKXUveHf24GvAWfrDqKUuiBKaW9t31hf6xvIIv7qnMSg9TrdxCyqODTjjDdLb6tzG6P3khWOTjqMjt0ft/gzl3+ds++6hb1rm/irF/4inmWjrP44PAjGLOJ5i10yR4PqhEkuno94aqAW5e5tR/C3z/9ZAH7la1/g8ffviscrWpYamvsZyBSKOicxoo7rpsyPnaJruWz2flF7ylD33O06jLu43LieVfrhV6jJOE80TSwWppOLyNnARwiE4gOJ1w8TkdXw/53Ac5nx1DlNCEFPilm5ipNIaBlXKI4rEotu0mtWr9Ijq11p0RiFMIsEYyRonnTXbZz/zSDz+YMv/3n2bN2mLTYt4XhF1VsusYjnBSV0Eig7fFhw+SlP5otPfhYtz+P3PnMhmzrrWInPOR1+1qETienrZ9TrJX2cKkMpsij6bs1CMC5qONpgmCSN8uOVUq6IROnkNvAxpdT1IvIe4Gql1EXA+4HNwL+KCMDdYebz6cBHRMQnEMHva/Kk3NNgUr82Zxl+rsK0hGIWZVzELLJu4mVIbtv2WwPHGQhRJ38q+g5k3ERblkcP4Vc/fzEWin95zgu59sRTsToKZUksiHxb+lPceT7KHf09zCP+ehvb8+IElwhlhQ8b/uHcV/H4B+7hlN338rpvfpPPvvb58fjQovDzquUOCMQkdV4vOorCyy3xMsPTRSHlorD0JELSixiOnvW80YscivYrvK9FTXBp3LdFKXUxcHHqtXcn/n9JxnaXA0+abOsMs2RSCS25+5mAUBxXJJYd89hTdv7+op9V8XO9YLQtxXOuu4HjHnyI+7dv519/4gUoV6Fsfacobjh+b8lQnjfwviMRHWMr3BWHv3vlq/nzf/gIL7/iKi572dNQG+zSQjF57ejObZVrQ0eRcMwTjXljGhdFMNYhyBZZVBkWF3PFTohZhxuaPIZl0uHnuhJapikUs0KC42RM67ZLhh+TAiQvJN2SHq/58rcA+MwLnoe3Ysc9R9I1G0CT6LHwhJnfyWzo6LOJPx9bcevxx/L9U09hrdvjVV/9Di3LY6PTY6PTY3Orw0anyya7yyanMyQU0+HjOq+NNEVh6rx9V90G6gtJGwyG+mmcs2hoNrMWwXUxbaGYJxKr7Ke/v/Jhx07oEA3tM/qpWOAwPuuaGznm/j08cNg2vv60p4AbzhNtB+5iNCYvEkTi+ss3XjEiMWYxFopWOPuNBH8tS/G/X/o8nnrzLZz7jWv4/qvPgDVH6yYCA0JRd12Mci2kyXMFo2s07TQWuYyzchiX2V00oejJUCVpRQ3PjLoQLN5ZNTSaJriKTRCKRU7i4Pa9oUfWdjq3KGv7lni5DuNGp8sWe52X/PtVAHz+Zc+BFelP/xeKn6H7sh9M9beUuG48iwsMOq/RVIliK+444Rj+84zHsdp1ef4l11QSinnXQpFjWHQt5W2bdc1mfSdm6TBOOkO6yZEbg2ESGGdxAszafWtyYsskmZVQrMNNrOoYlQk5Zq0T1F0M9510FdMOI/DY7+ziiN2P8vDOrVzxzDOx3GAmF99KOIuaMLS/3i5s30KSFIp24q+twAqn+LMUluXzhVc9i6fccBvPuPQGrvvpJ8BWO1coJq+HsiFn3Xppxy/ab9p1zHIb16ye1mWs02GcJrNKdllUd3ER8TyT4NKMb6thKZhGqZymMAmhmCcQ1qQ4FNlWmnC0xZBgbNkePcum4zs84Tt3APCtlzyZ1qrC8oN6i9LyoWcNCsZwH+L6QbLHEqJ6wZR/EDqvdj8Eja0Qx48Lce9+/BHc/oRjOPlHP+aUa+/mjnMfOzQ+Mekmat2+Cuc9Il2wPSJPNE5bMM5DONqgZ1FD0cuOOaOGUhhXsbyrWFYo5oUKy6y3Jr34UYbk+tEjcqxa4rHFbrPNWY8Fy8Zeh+P/8z4Abn76iTi2x0rLiwt0q5Yfh6L9VspdXJJp/oYIRXKUKe47iRB0y48Lca86Li3L40dPPxGAU66+WysUo1BxSzzt+StD3rpZwx506w3td8Ih6TKzJc0SE4o2LBPmp9SCsUgd2DRdxTrDzzp0QnH4OCVv0pob/yh19tp+q78vTSh6x3UP0up4PHDy4RzauZFW14vnixY7eKiEsxjjLalQhIFZa+Is6DAEjdUPQduWwrF8bn/a8fDPcMy197PNPURrQzhHd0LEQyj4RqylGDmAyetG5zamXcYmOIx51O0uzgLjws0HqlKCiwlDG0rQdAduVkxSxNZVfLvKdlWTWYq21bmJA/soKRBbUuF9JgVi8rUwFH3yd+8B4O5zjmFzq8Mht4VtKVZaLr2eDZaClgJXIxiXFNVziW4VyRC0avnBtH62z0ormLFlxfLwjlrh4ZO3s+P2Rznu2vvY85ydgwlI4Xnvl8opf357yhnYNinqov0mRWN6jKIuLD2uYKxKXfUXyzCrzOhJ0NR2GeYX070bCpmFAG6Kq1iFaQnFdFZqS9z4kcdwCDrYZs0aDktvv3cvx3x9N8qCXc89JsyO7sXT/7VaXhCKTpSEiTXEko5XhKAwt7h+EH6OsqBbfSfWcQJ3NqqtuGq53P28YwE45cLbWFXdODScFIrxuaoQhk5fE7op/nT7KQpLjxOSHjVDehymMXe0YZBFM02UJ6UfLNZbjzHO4gLR1BD0MriKkxCKOpE4uG5qyreSY9hWdKIy5TI+9jN3Y/mKXa84FnVci9VOUKh7g9Oj03PoWjZWy0e5FqqlUK6EorFUExYXNZgNrZywJqWlcFoejh2EoDc6vaBcjuXxwE8+hvYXb2XLnQc48uoHWX/2VqAvEqF/blfEpav63XbeOe8nNAX76CW3S7mBa9IbchnzwtJlHUYdVcPRTXcXx8WEog3zgLlCDUvNpAbRlxlfVsaxydpfWaG4Iu7QI71sTXpssdZjl/Ew7wA7vv0wAA/99JGsWT02OZ14ZpfVlhuIHttHHD/IiHYSzuISTvWXRllRSaHAfbXCMLRlKTa0ujiWjyM+m+0O9ho88MrHAHDk1x9gzeqxxV4fcBKT5053TnU/ANKuYZbTmFw/ySgOY5qmzLoySXdxGX+kG5YPIxYNjaPOEPSsXMWi7aqEnssKxSIBsSbu0CO5XSQat135CPYhj/VTNsDxrTgsuskORE7L8lh13DjRhZYfJroUfgxLhbIJaiu2/DixZc1x42kUNzkdVi2XVavH/nO3A7Dtu4+wst4ZEom6c7eWOr9Z535YBKauiwqCMc3QdTtmODqLpmdGGxYb5Uv5x4ImuBixWCOLNk4Dmv2exnULpuUqTkooJp0jvbM0LCyGy6/0l0ciY9tX9wBw8EXbWbN6bLbb8cwim1sdnDCbd6Xl4rS8ONFFWeDbi9lRVkXZYdmcUCi2Wt5AYstGpxuXyNlitVk7xmf9iRuxOortlz8yJBJh+NwFrxWLx+S2EVUEY5IqUwuOQt0/7kZhXmu7GgyTxIhFw0SZx1BI3a5iFbKE4lAIMbHeoIs0LBzSAiNZW29IeBzosHblAZRA79yNbLHWA8cxEoypRBcrDLEOJrg09wfGNFF2MGNLEIIOHNkNTi9ObGmJx2a7HScYtV+0BYDNlz2qFYnpqfh0iS6685/lMpYVjJV/7DTUXWxyokuTf5SPykK9J1/KP8zc0IYmM4+ibNJMuuDvKEktZRNa0kIxuU467By8Xk4g9oWGXni0vnUI6YF31hrOTgncrbAw9GY7CJtGiS5rjotl9Wd0WfrklgTBjDaDM7astoLPLkps2eash+cgOI/eCzaiHGhds4484g2JRAjOG+jOZ7ZwhGGXMaIuwZhm1JqQwb7nz9kz/a9h0THdu6FRlA0BTdslqOqCVN1n2UzmiLSbmNxP+iY/eBwvFhzR/1usfrmWlWsPASDPW43FRuBqekOJLkMzutgmGxqI54eOnEXH8eIZW5KJLYOzsrisblOop6wiPmz+4f5ECR1Pe94ihs9xcVmdPME4CtNKdhmleP4yY0SsoS5M127IZNwwwjx2VOPccKq6imX2kxV67r+Wl8igF4ktUfEjvdy6sQuA/URnaIycLtGlZXk4jheGoku9vaXAtwXswXI5usSWNasXJxetSQ/rzKBsi31jRyMKB8/bWur6yhr7WjUkXZe7WIZJ/AirAzNu0ZCkUp1Fk+BiMBgixnUxytwky4xR1C3XuYn9ZYMDapICxN7rwY89WIPVkyXez1arHYeik4kuG51eIILCMjqxWDSlcwDwHRWP63TsoAh3MrElCkGvhc7tFqtL64xg4Kd/Q2/g3OjOGxS7jBFZgrFOyoxdLMukXMImj1s0GJqMEYs1sVCDeReAOscr1uF+5LkwednPaXTh5yz3R+cmBq+roQeAf0OwP/s0hxWnv31/thcvFI1Bootj+WxodeMyOsFcyIv5q7oqUdmclZbLmuOywekFNSrDxJZkCHqL1Y7P0+rpgbOobnZR7vB5Sp6vMi5jegxjmnHdxaoscqh43EiKuYc0F/Gl9MMkuBgMC84kQ9Bllo0aftbtN08kArQYFHVropAfhYkzpzusDbhaHlvDrOhI6ESJLskyOspa0F5yBJQF2H1XMZnYkgxBbw2FYvRZb9gu2MdZ0AXn9sFz3ELi85YWjcFrwy5j8PqwYMyrw1iFOhJd6syKXmQxajDMEiMWF4B5HBuoo6nJLZOkyo06mfmc3j5LKPZf64vESHBE/0cP76Zgv63Q3eo7WaHoSIWik4kukbNY06xrC4GyVH9cZ5jYkg5BRyTFnxO5izd5A+cnIvl/WjAGr+nC0MPXmS4cneUuDq5T/YeQYTkxbuniYLp2gyGHOmorjpMIUNX10QuFco6f90DQsa8e42icx8Gs6GQoOhJEhj5RJnQysUUXgg7cWjUoBo+x6QDegzmiDKFH3zFuK6Elip6KnEePtrLRzuEsLm1VretPzx09Deqc89lgGAuvwvAavzlDcUTkSOA3gZ3A1cAnlFLdUfZlnEWDoWGULWWicxX7+xgWiP0Qpq19+A8HYtE6whraly4rOh2KxjZh6AEKQtArkfhOnSt7ZyDu5CGlPU8RaUEP+vMekffDY5Kh6HnFZEQbFoDPAKcB9wA/BVwjIseNsiPzs80wEWYZGp90Me40VbI+y4xX1FEmgzXPVdQJiyR+R+HvU+DAxu0OIv319ysvbkNeKHpBK0aMRZkQNDAgAr0jHPYC7sP6EN6a2LRVukxO4C5q26BxF3PbLC69is5jFXSOYdU2joMjPq6p82SoQpVoerN+Mx+mlPqJ6ImIvBr4W+DVVXdkvjEGQ82UHbdVpXBy/7XhDOjh4/fDk8Pb62/I++4L9ntoi/CpWw+xv+sntlGZ4fhkKBpAWRIXpZ42K9LjqZvv5gXbJ1uBpQAAIABJREFUbuGpm+9mZQy3bFyUBZIKQacJip57A0J+f9fn0kMdAB7e5Q6ch8Ftg/OY5y7qfjykKfoRMsq4xbL7MEyHRRnTbhiJh0Vka/REKfV54MRRdtQ4sSgirxCRm0TkVhF5p2b5qoh8Klz+XRE5MbHsXeHrN4nIy6fZ7kXDDEyeD9LTwGnXSYSfAVbFiR8r2PzP7+3nvR94EIDbbI/f//ajnPbP9/HBaw+wgk0LCTNtB8ctJnEsHzWzMLTi3G0388cnXMJrd/6Alx9+I6/d+QP++IRLOHfbzczqp77Y6dqI+vGKa2Kzgs0Hrz3Aaf98H3908/5gg/s9fvb/7Z+H6JxFpAVjmbGpukLd6WXzjsmINhhi2sDFIvJUCXgC8OgoOyotFkXkchF5o4isjnKgksewgQ8DrwTOAF4vImekVnsL8IhS6vHAXwJ/Fm57BnA+cCbwCuCvw/0ZDHNDHVOvlUW5igs/8hBnvfsgv/794Kty3U7FAVex7in+7Jq9/I9r9+buIxq3OEvO3XYLLz7s5tDB87BFsWoFYwVffNjNnLvtlpm1LRqvWMT/uHYvf3bNXtY9xS5HsWuzYqMr/M0XHY58337+/rN74nWTgnGalBWT08iInsRwEoMhC/Gk/KNZPstXgQeAfwO6wPXA9aEpt7PKjqr0Ol3g48AHROTjwAVKqR9VOVgJzgFuVUrdDiAiFwKvAW5IrPMa4L+F/38a+JAEA6xeA1yolOoAd4jIreH+rsg7oO15bH30wNgNt2dYY25lAp2zPYOp/lp2uW2KSufkhdbyHNMVjSPhaMOvqWnPUjfRdPtWEi5ccn9Oop1OeGw78ZqV2K9FsJ1Ey6P9hH9VYr9+6DBFCXzxxCrxc+HQ9zo88I/7OOteDxDu3Kr4uyd7fPmE/nV8qKe44Nt7ef2RLZRtIZ6L7fdouR1WXZsNro1yPXo9hdfzOOzAPrZ1LbbIQVx7nWmwIi4vPuwmVjLOayAYb+K6g8fQndKYONvfz3q7hbNvnS0bD7Jptc1Wp82a3WHF6eA4XSynB5aHb3s86vp8+Iq9HHKCz14J/NKrXN5wo8Xrf2Rxzo8t+Ms2d3z7IY568xZaRzu44ZhFL/obHtsPT5+Kxi+Gz1v0xwRKuCzqsqyE82rH2/dfc/z+/y3V/5wl8Xmq1NADKz0mMDXGUvzhc+Fozo+dkw3t+tleh1Mw5rLsmMUVr9o109W8ryp4Oe9pHKZ17evwasgO9q3GBUEbg4hsUEppO1yl1P+TWO8xwDPCx28DTwOOLHuc0mJRKXVuaGG+Ffhl4LdF5JvA3wCfUUrVEcM4liBrJ2IX8MysdZRSrojsBXaEr38nte2xRQc8ZtfD/Mkf/MM4bTYYZk5H8//BEtvt2qL4yJM9LjlR4SX64yc+KLzrSpsn7BH2fyqIWmxmD5spMeBlc/hoCCuWzx889tLpHbANXBo+CjgYPr6Iw7+f7PNXT/V4ZAPsX4W/Pcvnk6f7/NINFuf/yIIrO9x1Zadol5m0Un8bdIoMhlwe2rmVz8/w+CWrj00dEXkRcImIvFEpdWHeukqp3cC/h4/KVIpnhE7i74rIu4CfJxCOnwAeEpF/IHAbbx+lIdNERN5K0HZOXbPYu23T+PtsWArUuMgMMlvr+gzz2559DN1mZdqU3k5yepbk/ga3U0OvaZfL4H70bU4fS1Kvg73d4qpn2fy6HKCT2MnWDrztGoufvsXCQjjYUlgbhA0tCx/BVxL+DZ6r8DWlwHVtrC5YHQ/86YQJVyyPVXFzz7lS0FHO2K5PaVoO3oqFWgkSXCxRiCgsFFb0F4UlPhaw3lPYB3x+6naLF+wSPnS2z2cf7+NbsHcVPny2z6fO8PmbPRs57Yc+qqfiM9u/mkJXMjzLuiuwv024TrySaNZJMrw8uR8dSumu3nx0+8vfLu/4JQ5Ygrz3OAnqaneTqOMzPLB1I/Dw+I1ZPH4LuCJPKIrIM4BTgYuUUvtHPdBIg1/CUO//EpHrgb8Ang/8AfB7IvJZ4O2hiq3KvcDxiefHha/p1tklIg6wjeAqKrNt1P4LgAsAtpz2GPWe979hhKYOMsuEkEmMGRv3/YzSprpmcBm1dI5uuzJFuYum+lsdmMYvOdPK8JzQWdP8pWdvSddYHJzmbTAbWpfgAnDFzQdofesgHTdY7ydvE97xPZvDOoIrio+f4XHh2Yp3n7uVFz9uEw96m3jA28KD7lYecTfxUG8zB91VHulu5NHuGrfccCxHXWFx+JdvwX3woaHPbRI8dfPdvHbnD1jNGYrRUTafe+jJXHPg+Mx16sR+4mnc98Id7H/WOqcfu5vtq+tsa62z2e6ws3WAnc5+jnD2caS9n8PtDt+9tc0Hvvgob7vC4jk/tvjD79qcd6vwfz/L45bDg322Nwl7X76Rx5+6OXxPwXUQldCJCnRH5XOi4tztRPjRDf+PimtHhbm7iZBttCxZOqftt4aWB+v0993xBwt2p8vgtFPLdcW2daVz8opyuzniv6iYd9kwdNerdosc9wdJXmh9HKb2Q0lDbe/pK39Rz34Wi+cC/2fBOj8kcBN3AB8c9UCVz6KIbBCR/yIiVwJXEcS8fxs4BvgN4DnA/zdie64CThGRk0RkhSBh5aLUOhcBbwr//1ngMhUMsLkIOD/Mlj4JOAW4csR2GAwLz3knbsQLnYwzHxLec7nDYR3h6qN8zn+1y4ee6nOwBa86cW22Dc3hhwePLvQtLOD6g0dPozmZ5AmbnzxxA3dvgbe/yOMPnu/ywAbFEx+2+NBlfaHiAeedtHHk40+rhqHBsJB4Uv4x3RlcDgNyo7nheMaPM0JtxSRVsqGfJCIfAn5MUNTxLuAlSqkzlFL/Uym1Wyn1d8CvE6jdyiilXOBtwCXAjcC/KKWuF5H3iMh54WofBXaECSy/C7wz3PZ64F8IkmG+BPyWUmo6sTCDoSZGLYbcHkEMbFmxeOdTt7HRkXhMzm3bFL/2Uo87tsNGR3jn2dvY1MruJjp+EN6dlCNSRFe1uPSRUzOdk65vB8snWGQ6j65vF7pTrZYKzkNLuPQExa+9bNC5js7D5vA8pF3FKuRN2Vc0nV/Z6f6mIUzzxLfBsEQ8BBxVYr1vEczkMjJVetD/JBCKHyAYm3hfxnq3UpCBnIdS6mLg4tRr70783wZ+LmPbPwX+dNRjG/q4vmVqLc6YtmplliuJlvXC+X/16whrougRzD3cVh5rYsdiA+DtT9mEp3z+8UAwlGVrFza3BE/B7521hbc/ZRMH8Ggrh7Zy6CmHnrJp+y06voOrrFgoSpX5U2vka3tPAeDFh92MT3+GDgu49JFT4+XTRnmC61t0fZuOH3xuPWXTVq340VNdEBWfh/dfs5+jwtP50EbYYEt8HpLnLUlWCDqPsnNDp8PHo1DHPqpg5pMeZJYh6EWhSsGRKSfDXAn8DEFlmDz2Uk5UZlLlW/WzwL8VuXVKqRuBF47TKINhnskTcEnyxGD2No62oPLg8SVzjuBIMEaICO84eytvOW0TD376IXa2hfc/exuvftzG2Mlq+0JP2XSVE4rE4Zt/z7cRBeIrmHqZC+Fre0/l8n0nceam3Wy12+zz1rj+4NEzcxTFB5UKR3X84PMLRKMTCnCbHj3WwvPwq2du5vLP7gfaHHWcw81vPDI+D0nSYxV1lHGbR/18TFjbYGgEHwU+JyL/qpT6TM56JwP7xjlQ6V5dKfUZE9Y1lGWWv2ZHHRQ/KlWck6ybbNE+8m7qvThxYXjfsdtUkFe6daONfVgQjv6pw1dwHEVbeZmhzo7v4IYh1q5v41asRzcJuqrFNQeO5+t7T+GaA8fPTCgm6YUh+qKEiuizbrUUT3eCz/LYE1q5QnHwtWxXsZdKbClsc8XPTffjIX/9csktk8LMC21YFMLp+z4JXBgO19uSXifM//gd4NvjHGv2vanBYBigp5xSM7nkOZM6dzEZjtZh77Tw93h4D/nYR9jxNj1lpUKnQTg1OV7R88PB3QtWQmosfMH17HjcomsHn1nHb9H2W3StMKSvXBAVZ617DwXDP+ydVu64xHT4OUmeq6gLQZcVkstE1Uxow+IiVZJWpt8FvhlYB/4IeIeIfIYgA/p+gqowbwJOIiwXOCrm27AAdH175lOuTRNXWYXlc+pCF1Lu+E6l+Wc7fmugfE4VdIIwLxTdVvbQPNHR2MUi5Cgbbvbo3OUhpyfLqdi54xV7vk3PjIsaQLxgzKLnlxu3GI0tBejeE5w/+6hsB0znFOe5iuMyCzFpxh4aDMWEEd+3isingHcBb2Awarwb+Dml1FjVYcy30dAYup5TutbiopAUg22/NVSzMUlXOUNTGeoSXSLBGImHlqiBZJcsWqc7dL/ZpXdjD17eL5eTHq+oC0H7vmD1BMs1zmKE+BIL6SgUnTVusZWoxuzeEFwDcrpTMCaxLw6TQlE/HGGwtiL0hzYM1k7U11dMkidAi+orGpaLWVVJqJtKCS4zygtVSl0KXCoi24EnE9Sgvh/4flhpZiyMWDQYQqo6hknSQi8vyaVMAkwyFF3kLmYJRiAWjZFgDF5TqX0Fr/uhm9i9wQsmKI6X54eg265Dt+dU6lAXHfEBT+j2bDqWQ9fJD0X3wnCz/aCL/5CCzULvOBs3wxXWjVFMi8S8sYpVxnPmuYqzGK84TjHuJrMowsowW5RSjwLfqHu/5uqsCfNFbxZ1JrlUnVlC355y9e2STowu0WBwto3iG28kIHpKErN6yMAjwj+1BRb4t7motgrDz9kh6ENui/XeCr4v+L4gPohnnEUInAjpCX4Ypl93WxxyW5mh6Oiz7t4QiEbrdAdXwmz01PnSzdKSFIrR/oNlydlXsmdsCbYbz1VcdkyJGsMiYxSOIZNxBfCyd55VwnVFJG/qOsGYnoYtuf+kmOilQpfJh2y04EQHPDh4U38/6RD0QXeVA73VOLHF9Szcnt0Pvzjz6+7UiSjo9YIQfZTocshd4aC7OhSKjj7r3vWBW+yfvgIMnqOIQeGvdxODZcNCMYtRisFXneKv2r4ncw2ZTGjDKIhf/rGoOX7mm2OYSybV6Y9zkyq6OY7rLmYJRt3yYNtilzF6PXp4oUjxrnNTzlcgbg54qwOJLR3XwXVt1IwKcjcVy1PgCcoXuj2HtusMJLoc8Fb7oWjlxJ+x/8NguIH7hLWB8wLDIjHLTQyW64VilquYpMxc0HVRZwi6TkwmtMEwiBGLhkYxrU56FqHoMvssQneDb6cEQBmXMfnoJR7dp28CwPr6OulZW/a6GwYSWzo9B9ez8DwL5VpB6NVM+hMXJY8yoqMw/XpvZSDRZb+3lhDjDt37FNaNPdSqsP7ETQPnJX3OInQisapQLBN+LqKMqzjp8YTzPF5xUix7dKcuqjiLU57BZWoYsbggmE5hmFncPNI3yfRNNC90l+cu6hzGrnISGa2OVjRG6yaFR/LYybFtbdXiwDlb8DdZtG7t4N/lsc/fQNtvccBbG0hsWXdb9Hwb37fwexbKE+L8Htt0K8GNQ1BuEKJ3vSBkf8htBYkuscO4Ri88j85lhwDoPHsTbLS05y/5f/rcpX9IjCsU81zFqoktOrJ+LM2j6DP9r2HRMb26YaLMYyeadbMa1V2scqPNEozBfopD0pFoTIpHXTh5OMEiXH/FYv15wSQAK5ceoKcc9vtrcWLLIXeFQ24rLpfT69koX5AwwcUyCS5A4CpavcBZVJ7gunZcRueQ2+KgtxKfh0e9jez31thwWTAb18EXbU+dx0HRCHqBqDv3gz8o0q5zPUJxWq7iuCHoJo9XXMQEyUV8T8uMOZs1sohfjia/pzKd/7Sn/oPqobzhsYXlBaPOZRzc16B4SG8XLYu23+dvYO8LDwdg41f30facgcSWdLkc5QuqZ0FPTOmcFOKD9CyUL3ieFZTR6TnxjC7JRBduc1m5q4O71ebRp23XusZpgagTh0nyxidOSijqqCIURxWVdbqRZryiIY145R+LmuBivhWGxtGE4txZNRfLzuhSVHcxPatLenlese70dIDJdXWCcSWux5gtKPrOlcP+M3ayc8cuVnd34IYuvVMHE1uS5XL8nhW4ip4Ri0nEV4gn4IPqWfiWwm/1y+hsdHpxossWu83Wrz4CwKPP20HPbtHzWwPneEXc3IzmrLqJeSIR8oXiKJT9odTUxJZRaGr0pKntMswnzbWNDAtDkzutad6EKofzUg5j0TjGrBt9N+Ecph/90LTDfi8Yn9i2Vrj/BUcBcNyndg0kthxyWwPlcgjnhBY3kQ1tSucAgbNouUGIXoUh+6iMziG3FSe6dB4WjvzibgB2v/AoHvU2ho6jw35/A23Vyj2HkxKKi+QqNjkEbWg+JsHFOIsLxbLNEQ3l5okedWaWut1F/THKO4zRPoF4v5EASM72UkTsWIYiMdpvclzc7a89iaO/dB87r36YbTft5f4Tt3IoTGrp+XZcLidIbAlcRVEmGxrpixLxAE+gJ9Dql9FZdVxc3+JAb5VNdpeT/u1OnEMeDz1tB4884bB+GCv6LBM6Z016ld2/IpEI9QjFRXAVZxGCbvJQH4MhwlylhkJm0ZlNs9MeZfqwsskuuuzoURzGoUzXDKcx+cgi6SZG+0kKxY7f4tC2DdzzkuMAOPaK+zjkrsTzQHd7dlAuxxekZ4UiMRGGtpvrJE8asW2UY2G5ocvgEYSjw4xx3xc6rhPPF33QbXHcd+4D4LbXnRSOD20NJiCFLmMvdILzyLsO0tcMFJdaitozeIxyQrHK1H7z6io2OWoyS4wAXjyMs1gzrm/hWMturwwzSdezDnfR9W2cGtpX1mHUjWEEYpcxuqmmXUYg12kcPEb+1zvaNsqQjtoRTet3+9OP46SL7uKxV/6Y7uuseMYW3w+EIn4wJo9wvOLSu4qAtPqfefCZCKJU4MKGiS6uZ8Uzumy9ez9bdx+kvXWFO085hhXPC86lz+BPeY3LWIYyLmJEmaz9aQvFRXcVJ4URsfVSaTz2gvaDi/PtMACLFYpuQqILVAtHZ61fRjAG2+aHpWHwZp9MgqlK2k2My7KE2c/3nXYk61tW2X7/fg7ftZeHj9hEx3Xw/SD8TM+KE1sG3u4S11kUx4mjyFH5HL8l4CtUz0K1fPwwm3xDy+JxV98DwF1PO5auFWSXA4EoTAnENemNNYVeWZEI9QvFSTBrV7EOjANnmBfMlWooRdM7tXHL6EB94eis9XU31jI3al1B7XifGQWZs9bR1VtMuol73Q0c8FY54K3SlhZ3Pu1YAE7/7u1DM7bgyUAIekDTSrOvl4kRhuDFU/EYzigULWH2eDyjS7fFGVfeDsDtTz+eju/Ehc/3uhvYHxdCT4WlNY8s8tbJGhIxCaG4iK6ice+WB5PgYsSiYYqM0rnOQ2hoUoIx66ZdRjjqREJy20iERDOzRG5iJFZc3+agu8oPnnsKAM++7Dqcg24cglZePwRt9fodJRCM2Vtb1X4mi460HJQzmORi9QJRjReE7qNQ9Jk/uIPH7NrDgW0buO3M4xKzuqzG5yGqwxgJxqzzX1ZE5l0/eddbmlkLReMqZjNrEdt0Y8EwGuasToBZf1km1VnM+n0VMSt3EeoRjFWcnvQ2umn8spYnkyii9qSFYsd3cJXF7aceyx2nHs2mQx1e/PXvxSHodG3FdDRelrV8jjUoFONxnFHNxXDsou8JP3nx5QB861Vn0Wm14s8++vx1gjF5PRSd57xlSbKSWLJE4jwIxSoYV9FgKKbZd3/DwjFpd3GeBGPW+LM80ZgWDKNuV0Yodr2ght8Xf+pZALz8a1exerAbjKtLhqDTA7ota3kzoh0HFQrG6HNJh6KVJ5x9/S2cuOt+9m7dxDef92QO9Fb7dRcT5yEtGJOJSFnXQp5zWHQd5YnKLJFYp1Csi6a7iob5wszgYhJcDBWZVbZ3lWSXSWdH522bl/QCaBNfICubeTgjenCf5ZMdhsKW4ba5QjEs7/KjU07gR48/nifceg+v/PZ3+ZfnvHgwC9rrCyIIw9CtJe1aUsk98WeTzIr24Ge+8g0AvvTSc2g7K1h+jwO9VVYsL7jOk4IqkeyyZvWGzmVLvErXgo4qAjGirh9OSaYdfp6Vq2hC0IZ5w5zZCTHrL82sO408mtK2aTuMedvF9Q4LQtRFIcWsbXTHKCMUo9qKn3358wD4qW9/hw0H20BSCGlKS1hL2LWEST3KCTKakyI6LtDtwzk/+hEn37ebR7Zs5rJnnU3Ptwc+767nxC6jzmFMn8ey10R63dzxrznOd5GbuMhC0bCcVHEWF7WEWGN6dBE5XES+IiK3hH8P06xzlohcISLXi8gPROQXEsv+UUTuEJFrw8dZ030Hy8OshHDd4ehS+xlDMObdNPO2zbtR646Rd/PXCcToUUoohrUVrz/pRH548ols6rT59S/9O9G0xQNiCFBW4Cwu45R/1trq0PtOCmqArYcO8Ktf+gIAnzv3uXScFVxv8PMuEozJ6ydPOFb9gZHcXxZ1/0iKmPY4xVFpsqtoMEySJl217wQuVUqdAlwaPk9zCPhlpdSZwCuAD4jI9sTy31dKnRU+rp18k5tNUxw8HaO2bdrjF6FYMI5zAy0jGnVuUh55AjFqU1mhGE3v5/sWH/mpV7O+ssLzbvwBr/jBdxBfBo5recFgHWVZYFuIM15odN4Qx4GVFsoS7XLL9fmdi/+Vnfv3cePxj+WSZzwjKEXkSzyjSxnB2B+3mC8c86hybaWPk6ZIiE5LKC67qzjr/t6I4MWmSWf3NcDHw/8/Drw2vYJS6mal1C3h/z8GHgCOmFoLK7LIX5463ts0OreygnGSN7TibFSn8IacJOtGn77h6wRi0p0qIxRdz47rAv74sCP48Ct/GoA3f+MLPG73rr5zlh7U7TgDs5ksA7K2OlA2BwZDU6/73tc46+5b2LthI3/x0z+PKw5+4nMuKxhBNy3f4LVT9hrJosz1WEYkLpJQNK7i8iKq/GNRadKVe5RS6r7w/93AUXkri8g5wApwW+LlPw3D038pIv9/e+ceNMtZ1/nPr2femffkek6uJ4EkJyEB5BrwSIGwCElAZNlcEFEsFTRUSBVarusFqNTuH5arqLvi7qKrKVCiclMwJi4gCSGApYQQYsiNJCd3Eg+5nJycEHJuM/3sH909b0/P091P93T3dM/8PlVvvTPTPT1Pz3Q//e3v7/n9ntUs9JZg0XebdVC0s3e9kLhc2OZxGSH/Ahvfjqt4TK5vE4nJ9rkKxbEvG4W4feFfn/8SPv/SV7Lmj/m1L/8Nh+5/Zqotfk+CMXu9HjJcsVMwHKdpeh6mN50l/sKdd/P2b12Nj/DHP/HTPH7o5slc0b4v4fc8KxiTRL+b7SYA7MeCC0Xe43oMZ5F3LrlsY7KtBjKf2y4Ul7GfV9pFo2JRRL4kIrda/s6Lr2eMMWQkoIvICcBfA79ojIm65A8Azwd+BDgKeF/G+y8SkRtE5IaDTz6TtlolLPOd5CLdxUUJRnBzQ6q44Ca3lyUKs7afFIlxhypPKEYhaBMLOV/2mv/IPcc+i+Oe3s17vv53YKZPVeNJEIZeIWdR+mvBeEVLYs/mHzzFr3ztk3jG8JntZ/HtbUGh8/hc0Qf93pRgjEi6izD9G6bdGMTJO35cRVmRG50sXGZmqUsoLmP4uQ0s83UONMEFGhaLxphzjDEvsvxdATwSisBIDD5q24aIHAF8DrjEGHNdbNs7TcB+4C+BV2S041JjzHZjzPa1zYdUuYutpO13nU0JRleqEIzRdlxFY9FEhLxtJD8/KTBg+kKbLhQD5wuAsL7iuNfnj87+OX4wWGf7Q7fztluuQkYJwdj3AuG0ItP+yVofeh6m702yoQE2HdjHr/7rX3Pkvqe55YTn8NmXnz2ptwiBYPT9YArASDACmeHo5G8K6UMOqjqeqhKJXRSK6ioqSrvC0FcC7wwfvxO4IrmCiAyAy4G/MsZ8JrEsEppCMN7x1lpbqwDVdYJtSXiJcBWMVYnGJHlZrVkXcReRmAw/A1ahOBqHodVxbIo/4LEjjuJPX/PT+CKcd+eX+ck7vwjGBNnQvVAoDgcrM+2fDINMaNP3Jgkum0Z7+Y3rPsoZTzzI44ccyYd/7B34kfMYTv0HTL7nSDDmjV+Mi8ZoLKNNNMaZ53hKo4gz6TrP8zIKxWVn2V1FJaBNv/IHgTeIyA7gnPA5IrJdRD4SrvN24LXAuywlcj4uIrcAtwDHAL/TbPPTacPJVGent+j9KyoYi4Sk6xCNRYVjke2muU4wve+28HOcDQGzMV5xwjh4fONJL+DDr/pZxuLxlnuu5U3f/QoQ1BmMyuesxLhF8ZDhIMyEDr7HnneQX7npMk7f/SCPHbKF3znnYvZsOjwszh28bSLAE2SNX4yO9fgxnOU0Vn28Fd1mHTdUXROK6ip2n0k9WYe/eWdwEZGfCssD+iKyvZIdqIDWDOAwxuwCzra8fgPw7vDx3wB/k/L+s2ptoJJJFTO7HPB7DFJmTcl9b4EZXsBtlpeI/X72bC+TbYYdd9rML8ltuhD/3CIX6OT6cZEI5I5TjPB9mQjFaD5o2KireP1JL0FG8N7rP8EF93+Rp085nJv6Lw3EYq8XhGfFA7OkA3nYqK84CUH3DL9496d57pP38cT6Efzua9/DY4dtmR7LFD6Oxi16nmEE9PE5SA8YA9HxFKwcnR/RsR79pn3xp45R2+xDtmMneUxXKSqrdhIn21WhOKENQnHRRsGScivwVuDPF92QOK0Ri8vOoqbJizOPGGuKNgtGmL3AWreb6MRdxGPe5xb5vPj7psYlxoTizDYs4Wff3whBR+MVYUMoRv9vOOElfOKFT/Nzt13Bzz7w9+w7cRN3e6dihn1kEISi/b173Xe6Y8hwGEzz53kYgQse+X+cufs2ftDfxP985YXsOiQxv8BYoG8S4xYF8CaCMR7zybogR6IxEowQHKMux+AixGHZzy+T8bzoZBYVUsvRlZ+6AAAgAElEQVRDyoyr9nXndBaNMd8BCEbUtQc9mleMLoSj52ljmSzpomMZC7shYSjONSQ3z/ZsCSwHxv1J2DlyEpOuYkRcKPqh6wVMwqVpHeE1236Uz5/8enr4/MLOT3Gy/1AwbrHnLXcoWsKs7+EQ0xPOefpr/Ifd3+Cg9PnwS3+Bh4/YOr36RGjHkoZg8j1HJLOjZ8LR4W8WD0tHv7ftGG3yGMyj6DnUtFBsg2OXRxvaqGJ4tVBnsUHa4C7WTVX72KTDCMVcRijmNM58VoUdve2im+UmxsVGVvgZmIxVnIxXjH09tq/q8tN+nC3P7OFVj9/Iex6/jH/c9AZuGPwQstZH+muY0cF5drWVeOtDGAwYeCPO3fdFXrb/NnyEvzjjZ9ix5bTJejIG1jYemz6Bw7gWCHHxzIy7GA9Hr4XnQpZIiLuMMH1sJI/TKo9BF8q4mGXrJ7ZBKKqQWnneKCL/Nfb8UmPMpdETEfkSsHX2bVwSVodpHSoWV5C6w9FdFoxAKdEI5YRjGbIuvMmxiWAXinFs4Wffnw6BxKf2S07zt7FA+Pipb8Uf93n17uu5YO8/cZp3P/8weDX7N60z/v6SiUUJXNPjert5x+gqjjNPsF/W+MTJb+XfjnsRWVpHTDAO3owFvECQj/EIFLkHveljMBLyfc+fnBfReZY1ljFiEcdp8nOLsIiwc1eEorqKzVOodmIQfbnKGPPe1FWMOWfeNjWNisWGaYu72IXxizC/YAQaEY0we2Gs+qKcduFNXlhtYxOTQjHuKtqYuIqxEi/BgvRxNKYH436fT590PvetncxPPXYFL/Xv4MThI3zykNfx73uXy1301oe8/ND7Obf/LwzMiO/1j+Vjz3oHD2+xGQahuxj/CTMOr9HYsya7gJtghPTjuE7hOM84yHlmYlGhqCj1omJxAbRFMNZJlfs4r7At4zJCedEYkXXhrCIT1XZxdRWKES6uYoRkCMU4xoMbjzyTneZ4fn7Xp9nqP87Fh/wjV45/hG8+fpzbzrWcNc/n/KNuYPvwbgC+NXwxnz3uPPYesgnTk0xXMY74gRiPxoTG3cURWI/7pEC0CUZgRjRCtnC0UWfGdMQyiERYHaG4aq4iFExwmfOyJyIXAP8HOBb4nIjcZIz58fm2Oj8qFleYroSjYaOjbNplhOyLbVmqDM8lL5hpYed48W3AyVWM/jsLxXCTpi88OjyWP9lyIec/+Tl+eHwrbzvi62zzTuHKx17IfrPmtL02cvzaU7xj642csLaHg/S4cvhGrj9iO6PBIFckFj18fF9m3MU1i0BMPgf7MV/0BqgOcZhsS1lWSSgqq4Mx5nKCyUdahYrFBdEWd7FLghGqcRmhnGiEeoRjkc+MY7tYFhGKRVzFLEwvmB46ambgrBlMz+NAb8hnN72F+5/eyn8y17L9sAd47vr3+MqTZ/CN729jVHAqukWyufcMr9u8g1cc8QA9MTzuH8kn+ueyc9NJmLVg5/2eEOmrtF0TXzBjEAm+M+MF4xYj8tzFg34vVzASe1+WaIxo4lieVxxGVFESp2tCUV1FZdGoWFRqp22CEcqHpuNUfcF1vZhmiUQo7yhGxF1F/HAeY9+tfpjxwO+Homng4R0MZnO5cXAmDz1zDBfINZzcf4xzj7mV12/ewVf3nM51T23joGlvV7Sl/wNev3kHP3z4g/TF4Bu4/uDz+ae117Fv/YjJFH/BXyD+/F51NdJs7qKNuGCEWTc+ftwkj/20Y2+eY7oqcRhRVd1EFYrlWGWh6I3diyfOW2exrbS3h14BVsVdhHoEI5QPS0P2xbMMVV8ck+SJRHAXimmuYrLeXxIZ54/fCUSTwfQFf62HDAwy8nl0fCJ//tR5PG+0g7PWv82zh0/ylqNv43Wbd/C1J0/n609t40CLwtNH95/m9Vt28PLDvksvFIn/9oOT+OrBl/PYphNhcChm0Mcf9PCHPfzBrEA0ku4wTuHL1CxhkbvoJX6OsS/0PPvVKOkwxl+H6XPF9Wap7mPahTaKRFgtoagoKhYXzKoJRqB1LiNULxyrIu1CaZ2FJXHxyhKKdWB6gaPoHQzEjPGieaIDd1F6HgyH3Omfznee2MoZPMA5W+7g5PUnefPRt/Njm3fwz3uew9f3nMa+BYrGY9a+z1mb7+Jlhz2EJzA2wre+fxJf3vM8dg+3IodsguEgnN5vw1UMvgMJwvI9psYvZglG8YMpAm34/nTdxaje4mjco98bZ4akgRnRmBSMEW065qHa2VfqEFyrJhRX2VWEUqVzlg4Vi8qEpsrptNFlnNregi+ieRfKNDcxbTnMCsU0VzEZgjZjwUVamnCliVDqCb4PXiik/EEfGfnIeAhjHxmuc9fTz+bOfz+OMzY9xjlb7mTb+hO86ag7eO2R9/D1p07ljmeO56H9m/EbmGhq3TvItuEuzjz8IV566MMTkfjNp07i2ifPYNfoMLxNm4Ii3MMBDNYwgz7G8zC9YE7oaLxiITPOJyuybCVyF/MEI8zeoKWdK8ljro3HfaltqlCcm1UXikqAisUW0BZ3EborGKF60QjNCEfXi2QRkZh0FePYXsskJxPa9MJZSWLN8fvgHQydxZ6H6RtM34NhHxkPkNEIbzxm/PSYHXuPY8feY3nO+uOcveVOnrNpF2dvuYuzt9zFAb/Hg/u3cN++o7l/31E8uG9LJaHqI3p72bb+BNvWd3Hq+i62Dp6a5JiMjHD9U6fwlSfPYPfoEACkvxZMWzgYQL8/cRX9QSAUg7GKbDiMDtfXqDA3MBHltlB0claXNW/sJBiBKdGYF5qO09QNU13zN9cltlQ4KauKisWWoIKxOuoQjeB2YUteWOsq7xG/aKWJyKzwc9GsZ9fSObAhmoxnNkLR4zAUPfKg14PhAPF9vNEIf+9eQLhn37Hcs/NYTl1/nJcd9hCnru/iuMHTnL7pcU7f9HjQbgM7DxzJ/fuO4oF9R/G9A0ew3yE55hDvACev72bb8AlOWd/FUWt7p5aPjMcD+zZz795j+MZT29gz3hTbeQ9v0zoyHNhdxTChxYRJLVFyS+SyTr6XivRLJAzzBCOQ6TJGyyNchGMW8WO/LhGYxzKIRHUV20eTdRbbiopFxUqTghGqHccYUZdozPzMmst65IWc84Ri3FV0CkHniErTm+4cjbcxbjEKRcvAQ0YGf9BDRn7gLgaNwjsseF8gGAPu23cM9+07BoBDvf1sW9/FaZt2ccrwCU4c7uFZ4d+rj7wvs21Z7PP7PBAKznv3Hc1392+xl/ERj95hhyLJ8HPcVQydRb8fqzMZF4l519uxQErSCjAzZ7TnmcKCEcgUjbb1irIogQj1CiwVioqiYrFVtMldhGanBKxz313ckzaQd6FwCTtDvlAsU0sxj0mtRR84GCa79DfC0/7AQ8Y+/qA/GYEo40HwfzgKAq57985s9wf+kNueOZHbnjkRgDUZcdJwN6esP8G29ScYyojN/dn3JTloejy0fzMP7DuK+/cfxSMHjsDkjcaMC8XBAIbDWPi5N3EVjRfVlowe539fMp5eT8JsaFsoupeYJ9r3xSoYk9hCz/HjP+2cW5bzpQqaEkxtEomgQjGJ+AWyVsxyZrioWGwZqy4YoR6XMaJtF0KXi4Srm2gjTSjOW4Rb/Nh/bzpMY3pgxmEoukfgLvbBH/YQ32D8MBw9CMYeyigIX6YJxjgHTZ979x3LvfuOLdzuQoiHtz6MCcUBZtgPS+X0g30LXcVJYkuYDT4ZvxgrmzMpWD7nNXg09uj37OdH0l0EN8EI6efcItz5LJoSVavqJipKGioWW0gbBSM0d8FoQjRC88Kx6EXBdsHKEorTIeaMUHailuJMCDpJ9DPkJbpMZiQJRJP4ZspdNF5Yd3EUJrv4HjIOxy+G2/AAf9/+0KJcEKFQ9A47dCIUGayBF5bKiZJ2vA1hWPqjxsHYzjyiULQXnhNp7mKaYASs4xgjXEVjnKb6gybFVNOOWhuForqKig0Viy2lbYIRmnUZodnvIKvTLrPPZS8CWR11UaFYOvwcCsciRBnR9mUycReNH8zsArFwdOL/QgVj5CgOh+B50AsScjZqKnr4a73JeMUosSUegq4qsSX6DZKh6MhdzAtHR7+7zWVMuwEscqOWdowXPV/aIJgWIZDasN9JVCja8QoUBNAEF6VxVDA25zJmseixUXlh5yJCMXIVXULQ4ku+m2gLQfvhXxiK9vtmyl00PQ9/AK0SjOIhvR6y1keGw43M5+FwKvycTGqJJ7ZEIej4d5FG2rJ4CR2xOI6+H7iLLuHoiLywNKSLRih+7rVRBKWxKHHUxu9IhaKShYrFlqOCMaANorEOiohE2/quoec0ohB0UYy3cQc9NYZxtLE8CkVDYuwiHt4BgtqLfuAywqxg7PX7mNEIc3CEGY/rEY5xkdjvT41RZLAWuIkxoegPexMXcbw26ypG+z4pUh4XjznXYvGzQ9KRk2h7LS0cHZElGCH7nG5jHzQvixRGKhS7h+jc0CoWu0AbO+tFDXyfx/FoCy4d8zxC0cVVTOIUdu6ZKacx7ipGj6Nxi1G2rwmXx8cu0g/GRvrh1CXhbQCQEIy+Dwd7SB2iMSESCR9PCcVJiZzQUYzGKcbCz3FXEaZD0M6h6JxZXIwvU6HopLtoE5FQzGGE7HM6eex18dxbtCBqo0iExX8vSjdQsdgR2igYYTEuY0SXhKNrh+xShBvKO4qRULSFoKP6ikWI11mczOQShqLFD0RUzzdTmdEewXP6gUj1Bz2rYGTsI55XrWiMxiSGAhEIRKLnQb9vFYobs9BYws/eRug5nuySVWfR6h6O7fNDm7EgKfNGJ7GNXSwqGMHtRrAr515bhJAKRaXrqFjsEG0WjLDY8hptdD6KdMRFCnFnCcU8V3FqXctrZZhyGMNQ9JRwirmLEyk4AO9AUE7H2z+2C8bxOEgwsYhGAH/vvkA0OmB1ESOB2Av/g1Uo+mu94P/Am4TXJ+HnaKxi3FGM6e15Zm0xvsyMW4xcxDR3sYxgBPv54noj2Dbh2CYB1FaRCO36ntpOoaQVDUMrSjaLdBmT2DrCui5k83a6rm4izCcUba6ibbxiVkg6OWOL7XUjYXHp3qy7GCW7+AjSB0YZgjGaGjASjYAMfBiNwPfxomWupInEXg96QbZzVB4nKRQjB9HvCf6ahMJ3tn5i3swtzuLRn53VZTyeLdCdhatghOpmdUk7F9p67tVFm0UitPd7U9pLa8SiiBwFfBrYBtwPvN0Ys9uy3hi4JXz6oDHm3PD1U4FPAUcD3wJ+3hhzoP6WN0vbEz3a4DKm0aYOsoiTGOEiFMtQuEyOZ6xzRUfiKApFRwW7g8+I1pJQMBnGaxIM1UsTjJ4fuIn0kdEajH272+hKjkgEch3FSCjaXMW4YEyOV5y3HmPcZXR1F6GYYIT5QtNZtOncqxsVisuHVyTBpchsLx2iTUfN+4FrjDFnANeEz23sNcacGf6dG3v994EPGWNOB3YDF9bb3MXS9hP+gN9rfae5CPK+FxehmCQuFF3Czy64zAk9mamkN7sMNpZNQrdhU4KkkFhGcejcRZnG/qAXJJWsByVr8IKMZDPsB8Wxh+E8zcNBkJDi+hcrh8NgbVISZxJ2Xu9nh57j4xR70/s3+U5k9jsqSlHxnkf8+EjeZNiOq5HvpR6H0fGr5/YsXfhe2n7dUNpLm46c84DLwseXAee7vlFEBDgL+EyZ93eVLpz4XehAm8Dle3AVirbM5zySIejkeMUy5XOm3p8QjdFUd3EhNXHhPJkRjP5gQzBuhH69DdEYlrBJFY1Of3aROHETPS8oj5MiFE0849lLZEDHS+SUFImp361FPEa/Y/S7bgw1mB6KYMNFMEJ+/6LndkBXvocuXC+U9tKaMDRwvDFmZ/j4e8DxKeuti8gNwAj4oDHmHwhCz08aY6I66w8Bz6q1tS2hrUkvSdocnq4T14tI3vjEyXoZQjFLINiYVyBatxnbjUnNxbiIjIWj/b7BC0tQ+8gk6YU++PQQzw/mkqaHeB7i+5i+h4z8SYhaRm4XQNMP15tM2ReGnb0gyzl4HBUM9yZZz3GhGLmKEBe+G/sYnwu6LoqMW0wLR4NbSBrc+pdVPLe7IA7jqFCcjyIJLlpnsQJE5EvAVsuiS+JPjDFGJPUrP8UY87CInAZ8WURuAfYUbMdFwEUAw+MOL/LWVtIVwQjNz8e8CIpcSIqEnbNK5MTXz0tsSc2Crlg8GmFSPzDKlvZ7gjdJF8wWjKYfzCMtYx8zEMQXZGTCZTHR6NKWSByGU/YFj2Ui/ialcTzJFIrJ8HOaiziz3uRxsSuJLSM6DVvdxSzBmCQ5PeBku47jpPXcbh8qEpWqaFQsGmPOSVsmIo+IyAnGmJ0icgLwaMo2Hg7/3ysiXwFeBnwW2Cwi/dBdfDbwcEY7LgUuBTj8eVuX4j6g7YkvNpbFkShzAcnqxF2Eom2cYhnmGR+X56IlpwGM8MkXjNPCMBCNMC0azcBD+u7Hu81FhIRodBCKUfg52M60q+gSgjY9rDUVIXsWl0g4Ru5iMtElDdeEF9v6ccrOG72K53cbUKFYHUH/40h3LsGFaNPRdCXwzvDxO4ErkiuIyBYRGYaPjwFeDdxujDHAtcDbst6/CnSxg+jioPmybc5KHoDiQjGJy2wt8+LqkE2JqIQbtyHGZscwTpZNxg3KxlR7vWBMYRQqDpJhXP/Cba31gm3Fth8vuJ0nFOP7Mdf3KGAcC24XJWve76yEl4jMMY8Fj6n4udKVc7xr7U3SxeuA0m7aNGbxg8DfisiFwAPA2wFEZDtwsTHm3cAPAX8uIj6B0P2gMeb28P3vAz4lIr8D/Bvw0aZ3oC100WWMsHXObXAmqrho5HXgZYSiLfxsIzcE7Upiyr84qeJpjHU6u1yHsReUrAjcRGBE+NhMhahdiULNwEQgBo/D9oSOYbI8TrROUiimuYq2EHQpYrUW82ZySZbRSZLmFkJxhxHm62Pa6Dp2VRQmUZGo1EVrxKIxZhdwtuX1G4B3h4//FXhxyvvvBV5RZxu7RpfGMmaR7MjrvsBUfeFw6cCLjlFMe0/wvvzPK5PcYsQ+eHtSX7HAoTYRaAnBOB5A7wAQvuaHM754bIhGw3SI2v0zN0LNU23obTy3uYnROnlCMfOzs9zYEtf3ZCjaRtYyW8ILMCMaswQjzH9jmnau1XmOL4swTKJCsT6K1E4UsxQj22ZojVhU6qHLLmMaXensXTtvV6HoEn6efs0xU7hkYksgfmRqbOJkWbL5CXdRxnbBKGMYh6LQGxHON20Ye6G7OOU2ivNYoiyBGLQ3dBUdhGL690HuenOHrh0SXuLuYlqyC9gTXmwuY55ghOpvTLtyjrcBFYlKE6hYXBGWxWXsAvOIRHATiq7hZ5flLpiefdaWqXXiZXNsUwLGsqMhXTCGS/H7wXMvmk86xW0sQpZIDF7PF4qurmJqCLq2cYrZiS7gKPxSBCPMZkpPvW8Jb0zbjgrFZtAEFxWLK4V25vUyr0iE4kJx9v3pbZh7vGKMwPGLPfdmBWJSREXrp4krm2AEJqKRvtjdRtc2ZwjEeJttBbfThGJ822muYhE3sWhpHbCHm9PcxSRp5XTKjGOcvDd2HmhfUw8qEpWmUbG4gqhorJYiHXdRoZhHmmuYFYKOws7OZXM8pu+WY0kuyVC0zV2cctfGwbjHSECJPy2m4q7hpL29je2nuY0upInEpOiL3MToucuUfjMCMeEqJkWgKWCIpoWeixToLhqOhmzBCNku42Qb2tdUiopEZVGoWFxhtCOfj6IddxmhOK+rWEUIOk5akgvMuouTxJeko5gjGMHmMkZMu40bojGbNBcx3k5ImZnFIhRtNRXzXMWqZnfJGrcYD0WnZUaDu2DMwsVljNC+Zj5UJC6WIlUXiiTDdAkVi4p25AWpUiSCu1BMUrUQLEo8FD31OEUkTpzGHjOC0YYfE4JJ4Ri5jeNB8TbH2wiJItsFhKLtPcltB89jbe/FHVP762XICjW7ruOa8BKniGAE7WuKoiJRaQsqFpUJ2pGnU7bTLju7ik0ourqKthB00fGKxjNIog1TSS6JUHS0rs1dTIajZTwrGOPJLvHH0fNI0NndxiL7FfxPCsT48qSgTApF27q2ZVWTV2vRRhF3EcoLRnALS0+2qeMaM1GR2C4KuYVaOmc5WdLfdS60Iw+Yt8Ou0lFMbqsxVzGjCDe4uYs2pzEuGIPPyRaMEVluows2kTjlCibGKoLdUUxzEm3bmnlc4U9nG7eYlRXt4kCWEYxQ3GWcbFv7G0AFYhbzTGmqVMPKi0Uo38mtAqvmNlbRYbt0bGVDz/Zt2dtsE5RlaypObcMybjHNXUyKSdgYoxgJRhkzKauTJhJt+I7jFafameL+pbqLGUIxNWSdlthicwUb0AdF3cX07dgLd5fdnvUzVkw4qkDMR4ViO1CxGKKCMZtkp7ZMHXmVHfY8QtF1m9nT+tV08UlmRIPVdUy6i/HkFVsiS0RcME69nvKerG1lkSUQk6/Hy+PYhGLWdvISW+qYE9rFNbSt5xqOjqgjLG39nCUVjioQ3WmLUCxSZ7FIOa8uoWIxhgpGd7rckdfRWbt2allCseg4xWB7+ftSbX3F2eLcG2MOZ93F5JjFKVfRs4i+lHGMUF4gJts69TxFJE49TxGKtvGN08tnLzBVhaDzZnJxKdAdp2rBGG0T5heN0O2bVRWHxWmLSFQ2ULGYQAVjcWydYVs68yY66iaF4qIzoCMyS+gkxKDttdTniXGMEUXC07ltT3EXo/2Kv54lFJPbs7Vvsv0sJ7GmGV3iJEPRri7kvIIR6ulT2yweVRzORxuFovhFqv8vZyKEikULVd4RrypZHWbVHfuiOucinVpRoei2zen9joegm06AsbmLEbkC0SIYJ2FpZkvs2BzHPGbK2aQknNjCx2lC0R5ejtZxuGBkldgpQFpx7ircxTyKCEaor0/N6wOWpc9ZdtooFJUAFYsZqMtYD13vaIt2aGVmZmmDqzgRftHYxHDcotM80UkhWEIwwrRohHzhmNemqecFRaLttayklqSruOFaZovCouVx4mQ5hlmJLhFFw9HBdt0EY9r2m6Drfc4qoEKx3ahYzEEFoxKnaqFYZpyijaKJLc5T/WVtIwpFp7iLRQSjjbhonDzPEI6ubZ56nhSQLmHnFKFY1QwtTeEaiobqBSNo5EYJ6IJIlJHO4KK3Ww4c9HudOKCV+ihzDJQRimkkXUWXxJayzONsRSTdtbQwcHwOZWtpmnjJmuRzKf7ntN05hKKzq+jy85UMS8cpkx2fdpznHa9lsvy1X11t9PfvDuosFkBdxtWjbGdWVihW0XnGhWU8E7qKGovJUHSau5jGpOh2zGGMXk+6jtF6YHEYE8/zSHMQbW3LLamTIxQLEYnJCoShC2UTXSKqdBgj1GlcPbomEos4iyyps6hisSDasa0GdYlEKJ7Q0ooM6JyZXOLkhaNhVhwmX4P0RJYs4ZjXrpnXshJfssYsZhh2aa5iW7EJxqwb4zoEY/SZoH3rstM1oagEqFgsibqMy8k8Hdm8QtG9BE96FvRceAZmMplns5sny1LcRdv78wQj2MvtRK9H60fYhKMrWQIxuTxPKKaGn22fWyQEXZB4RnRS/CWzol0SXWB+wQjZs72U+Vylu3RaJGrpHBWL86B3wstFG4ViK1zFJLbZXCIywtG2EHNaLcZoefR6hE04liXPacyqqTi93HJxcHEVLWNDqxgvWoai4WhX1GVUoONCUQE0waUSdKB2d4l+u0UKxTRsQrHOxJY4VtFiec1kiCKbmEpz6pJJLfHXkwItvk5RbO9Pfo4t2WXmcda+ZXxP1jbliLSsmVrqJuu8cD2my5SOin++9q3dRX+/5UGdxQrR8El3qKoDm+dCGDFPW2qbCzqGNRTt4C7C7PjF+GvBtmfD0sl1omUR84Sgbdubej0te9tVKMbfY9NSFf5ceVP+ZWELRZdxF/PC0RufV85hjIifI9rHtp9lE4haOkedxcrRO6l2U9XvMxr3nIViVa5ile+rMtxpdRdt7lqOwxg9Tiudk1ynaCg6z6lMcxOzpgecec3VVcz7/it2E+e5qcg7X5pwGONoH9te9LdZXtRZrAkdc9MuquzAilz08i6kxaYMbMG9XU5W9NSc0Zbxi1kOI9hdxmjdiDTH0ZU0R9LFTdxYZkloiQnAMq5ilQK+7BjEtPflRU2achiTbQLtY9vA0gvEsSa4qFisGe3QFkcdHVgTQrHppBbxzEYNxlhGtPRM9kwvsVB0fArAKcFItDxdMEJ6WHrSRgfhmIWrQLS9Nu2CplwIUoRinqvYVH1FG65Z0dV/bvlMaRsaol4cSy8SlQktsCoCROQoEblaRHaE/7dY1nm9iNwU+9snIueHyz4mIvfFlp3Z/F6kU0UihZJPnd9zVWG0qmhivGLElKiJC6C8JvTSQs8mMyxtc/msr/fc/ly2FS1Lbjve5uRnz+xjmlBsTU+bT9rNSlXh6Ig6ziftY5tBv+fVo01d2PuBa4wxZwDXhM+nMMZca4w50xhzJnAW8AxwVWyV34yWG2NuaqTVJdATrXrq/k6LXtiqDD+XZdxw2NqkhWFTBCPMjmPMylLOez2zbTkCMWtsYlLYTi0vGjpOcRWbKJlTxc1FFwQj6M15Haz0dzoeu/8VqcnYIdokFs8DLgsfXwacn7P+24AvGGOeqbVVNbLSJ18FNPH9FUlkiZhHKKa5Oi7jFesIX0tayDTFNSsrGHMzix2EY9af8/Yc3MTUkHZVrmKDoem042qeY6ktgjFC+9j50O9PgXaJxeONMTvDx98Djs9Z/2eATyZe++8icrOIfEhEhpW3sEZUOLrR5PdU5iJWJvO5dVQoVrIEo4vLWEQ4ZrYjTzzmtCdaZ4oGws+LrLGYhsu5V0YwNiUatZ/NR7+nBKOR+9+SOouNJriIyJeArZZFl8SfGGOMSGSGwwMAABHYSURBVHKI/NR2TgBeDHwx9vIHCETmALgUeB/w2ynvvwi4CGB43BEF9qAZNClmg0V1VnUJxapcxSbHK6YSz4xOSXYBrBnSk2Uz9RRnazpmJbTMM5NLetJLjkiEYgkttvdUEILOE5J1zcoCbjVlXTOk41SZLZ1H8lxc9f5WhaGSRaNi0RhzTtoyEXlERE4wxuwMxeCjGZt6O3C5MeZgbNuRK7lfRP4S+I2MdlxKICg57Llb23frHrKqndkiO62y7sa8QnHRTGVEJ5fFsqKz5oouJBhhqnA3TGdLA9bPKZMJnfb+mWUWYZPlJgLZ0/nFhayrIMwQV3WNa0zLiq5CbLZdMMZZtazqNvdHSvtoU+mcK4F3Ah8M/1+Rse47CJzECTGhKQTjHW+tq6GLYhnFY5s6rDqFYh6tmwM6VkInyZRgzKi7mCkYLe+1uYxxshzHsqSVrslzE2FWKLqGnxdZLsf3PTyvmjBZnTNWLUowRtj6pa73t23qazuH1llslVj8IPC3InIh8ACBe4iIbAcuNsa8O3y+DTgJ+Gri/R8XkWMBAW4CLm6m2Ysj7eRva6fW5s6qbqFYdt/rLMSdW0fRdd2UcDTMCsbM9zLrMsaZCt3OIa7zxFqemwgFhWKGI5jlFrZhvOKi3EWovh7jvHRJQLa5r1W6SWvEojFmF3C25fUbgHfHnt8PPMuy3ll1tq9LLEpEdrWDalv9xLrICjUXxTUcDdOCMRJZMw4jOIvG6POrJNWlLCoU8z4nq90dLNDt6i6WFYyweJcxi7w+r84+t6v9bScZjdzX1QQXpeto5zLNvCKxKlexaAi6ruSWGTGZEYqeIRmOznEYbbO82ELaSRFXZpxiGplhbAeRGGwjsV5FrmKXaEowQntcRle0z1WWhRakVCpK8zQlFOehFXNBx0iKmxmXLEc4JYWVEYsA65lMgZVVUscFp/fXJBQXOVZxHqocTzvvebMqUQBFaRvqLCorRRUXmyIXvKachdYlyEQ4jGFMdRkhNXkm2FY1TZz6vORnpHz8vEJxxlVMLs9bfw7SklzmmSu6zmSXJG0OSytLSpHQ8pImuLTLulCUGmmjK9E2kTeTVJEjWnLdRch1GCHFZYy2F/+rkpztprmJ84Seu0qb3EVopoi3oigbqLOorARVXVja6CrOQ5GM6DRmkl1s5XQsDiNgdRnB4jTGtx1Rpt0OQs7ZTQSn2+2irmJXKeIuzjN+MY66jEoTmIPuCS6mSJmdDqFiUVlqqnQfqhaKWW5Nq8YrJhJdnASmg2CE9NI6uaIx+owKKSQSwS4UKxin6FIypw1ldeZFBaOidIcWXZEUpVoWJRTbQq/k+DMnseIiihydOGtoN1omGSHqOYlvuw6haKPtGdBZNykuoeiibnpV55WGpRWlXtRZVJaOqi8aRS9o87qK2e+b7/6udK1FhzI61tqLjg4jpIemJ8sTL2e6jmltdNz1QiIR7BnULkkqJYR52yma7FKVwwjdLbGjtJxxgeNJE1wUpf0sWih2EZsYKeMuQkGHMaX3cS1ynXQGXf5yt5nhcs4jFO3bK/e9L5K6ErKqPs/UZVSUalFnUVkK6rg4lLmAVZHU0qrxinEs7qJt/KKzwwgbAswylnHyGXMm4OSRK06zfg5Hodg1d3BeypTSqdJhBB3LqFSH0Rlc1FlUuk/XhGLbyuXUgbPDGJHRE0VuX6brVxDn7dUlFB1dxbKubx5Zwxnybla6dPzqWEZFqQYVi0pnqetCsAqh5yTOoqSAa1ZKMLqUoikhHgu/J6stKbUZuzpDS12UcdnrOvdUMCrKfGgYWukkbev8u+Qqlk5yiXAMR0NGSBrS6ySmJMCkUZXbOPnsLIoIY+ZzFZeBNoSjIzT5RSmL1llUZ1HpGHWHlRbtKrZtvGIREVPIYYTsWVm8xF+duHxO1kwvcwrFNIqMcyxbJqksdd/01HkeamhaaTMi8ocicoeI3Cwil4vI5kW3CVQsKh2hiQ6+7AWqCzO1uFAoCaOIQCInRFskLFxFj1VEiOZMM1hIKKZQSJB30IEse37UfeOmglFxxYzHzn+YuW/crgZeZIx5CXAX8IG5d6ACVCwqraeJTr0JR3ERIeg0x6kSgVK1YHQVWEmxV/TPlZz2FBaKNbmKddA2h7su1GVU2oYx5ipjTBT3vg549iLbE7EaPYLSSZrqyOcRisviKkZUJVJKCUYoJhrrwqENVQnFLjqFEUVuftrqLkaoaFQyOHDA7Hdeeb/ZC/BaEbkh9ndRyc/+JeALJd9bKZrgorSOJjttFYpupCbFZMzskpX0AswmvsSJi66a6yzOfF4GWWJ30W7gslJXwosNrc2oWPizB80drzqdF+euuN/s5fvsBvhNY9Lj0SLyJWCrZdElxpgrwnUuAUbAx0u1umJULCqtoitCsSguLkx+fbv05Z5nKgtzp4m8KgUjOIpGqEc4FhR2eY5oplAs6CqmbavLLiSUy4yOaFowgmZNKxM+sYtHLjvZPJeBDDNXvJ87OYXncbP5eubARWPMOVnLReRdwFuAs41px/yBGoZWWkHTYaB5heIyuIqVio85HLdC9QmjEHGZcHWJ9xnPNCoUy9B0JnScJsfhNl2pQEPTCoAxZnQSZ/Agd2Wut9/sZQ+7uIXr5jpoRORNwG8B5xpjnplnW1WiYlFZKNoh108ZMVG1u+UiGEsVtU6Kx6y/gri0p4xQLL29hqkjyWXem6xFlLbSPkq5nW+u7eIRssYuRq5iVvjZkQ8DhwNXi8hNIvJnc26vEjQMrSyERXa+TbuKbSjEXSVlwtGQHZKOcA5N14iraC0rFMsI7q6HoKukyZB0HB3PuLoYY0YvlFfwIHdhG7sYuYrf5e65L2zGmNPn3UYdqLOoNMqi79IXXXS7bVQuQuYJ2caInMamptAr8nnSM7UIxTa5ikUoejNUxRCORZ3Hi+6/lMWR5S5W6Cq2FhWLSmMsupOt4gKzDGMVXckSL5ki0zO5YxiLCKO4kKtKPJbZplO7axC3Wd/1IscrLppF3vipaFw90sYuVjVWse2oWFRqJepUF92xLurC0pYQdJaoKOt25bqSDi5jGTetjNCbV3A6tTNvfxt2Fb0Wh66X5aarDX2b0hw2d3EVXEVokVgUkZ8SkdtExBeR7RnrvUlE7hSRu0Xk/bHXTxWRb4Svf1pEBs20XLHRpk60KqFY5wWuqzNmzCsYobxojEgKQdtfWZzb1jKh2DSLuilqy7CSNvV3Sn0k3cVVcRWhRWIRuBV4K/C1tBVEpAf8CfATwAuAd4jIC8LFvw98KBwcuhu4sN7mKja001wc8zhJ84iZKgRj9DltEU6F2jKHUMxtR4vdwXmp6uarLYIRtP9bBeLu4qq4itAisWiM+Y4x5s6c1V4B3G2MudcYcwD4FHCeiAhwFvCZcL3LgPPra62SpK2dZBdcxS4wt4grOC/yokRjYZE4p1CcZz+bGK/YFce7TYIR2tsfKvMTuYs7uHllXEVokVh05FnAd2PPHwpfOxp4Mjb5dvS6UjNt7hQXfQFpy3jFiDrFhZMDVtAli4Rb3cKx1Oc04Pgts6sYUeVN2KLPdxtt7h+V8tzON9f28MTKuIrQcJ1Fl/kQG2rHRUA0sff+f3nDH97a1Ge3hGOAxxfdiAWwivut+7w6rOJ+6z6vDs9bdAMiQmOqfXcnNdKoWMybD9GBh4GTYs+fHb62C9gsIv3wR4xeT2vHpcClACJygzEmNaFmGVnFfYbV3G/d59VhFfdb93l1EJEbFt2GVaZrYehvAmeEmc8D4GeAK8OJtq8F3hau906gMadSURRFURRlWWmNWBSRC0TkIeBVwOdE5Ivh6yeKyOdhYv3+MvBF4DvA3xpjbgs38T7gv4jI3QRjGD/a9D4oiqIoiqIsG62ZG9oYczlwueX1fwfeHHv+eeDzlvXuJciWLsqlJd7TdVZxn2E191v3eXVYxf3WfV4dVnW/W4EEEVxFURRFURRFmaU1YWhFURRFURSlfaycWBSRPxSRO0TkZhG5XEQ2p6xnnVawixSYSvF+EblFRG5ahsyzeaeQ7CIicpSIXC0iO8L/W1LWG4e/800icmXT7ayCvN9NRIbh1J93h1OBbmu+ldXisM/vEpHHYr/tuxfRzioRkb8QkUdFxFriTAL+d/id3CwiL2+6jXXgsN+vE5E9sd/6vzXdxqoRkZNE5FoRuT3su3/Vss5S/t5tZ+XEInA18CJjzEuAu4APJFfImVawi+ROpRjj9caYM5ekNMO8U0h2kfcD1xhjzgCuCZ/b2Bv+zmcaY85trnnV4Pi7XQjsDqcA/RDBlKCdpcCx+unYb/uRRhtZDx8D3pSx/CeAM8K/i4D/20CbmuBjZO83wD/HfuvfbqBNdTMCft0Y8wLglcB7Lcf4sv7erWblxKIx5qrYTC/XEdRkTGKdVrCpNlaN41SKS8c8U0jW37raOI9guktY7mkvXX63+HfxGeDscGrQrrJsx6oTxpivAU9krHIe8Fcm4DqCmrsnNNO6+nDY76XDGLPTGHNj+Pj7BFVPkrOxLeXv3XZWTiwm+CXgC5bX06YVXHYMcJWIfCuc5WYVWLbf+nhjzM7w8feA41PWWxeRG0TkOhHpoqB0+d0m64Q3iHsIymp1Fddj9SfD8NxnROQky/JlY9nO4SK8SkS+LSJfEJEXLroxVRIOG3kZ8I3EolX+vRdGa0rnVIk4TCsoIpcQWN4fb7JtdeGyzw68xhjzsIgcB1wtIneEd7etpaL97hRZ+xx/YowxIpJW7uCU8Lc+DfiyiNxijLmn6rYqjfOPwCeNMftF5D0EzupZC26TUg83EpzHT4vIm4F/IAjNdh4ROQz4LPCfjTFPLbo9ypKKxbxpBUXkXcBbgLONvXZQ2rSCraWCqRQxxjwc/n9URC4nCHu1WizWOIVka8naZxF5REROMMbsDEMzj6ZsI/qt7xWRrxDcwXdJLLr8btE6D4lIHziSYGrQrpK7z8aY+P59BPiDBtq1aDp3DldBXEQZYz4vIn8qIscYYzo9b7SIrBEIxY8bY/7esspK/t6LZuXC0CLyJuC3gHONMc+krGadVrCpNi4CETlURA6PHgNvJEgQWXaW7be+kmC6S0iZ9lJEtojIMHx8DPBq4PbGWlgNLr9b/Lt4G/DllJvDrpC7z4mxW+cSjPladq4EfiHMkn0lsCc2FGNpEZGt0RhcEXkFwfW8yzdDhPvzUeA7xpg/SlltJX/vhWOMWak/4G6C8Q43hX9/Fr5+IvD52HpvJsiWvocgpLnwts+xzxcQjOvYDzwCfDG5z8BpwLfDv9u6vs+u+72Ev/XRBFnQO4AvAUeFr28HPhI+/lHglvC3vgW4cNHtLrmvM78b8NsEN4IA68Dfhef89cBpi25zA/v8e+H5+23gWuD5i25zBfv8SWAncDA8ny8ELgYuDpcLQZb4PeHxvH3RbW5ov3859ltfB/zoottcwT6/hmDs/M2xa/SbV+H3bvufzuCiKIqiKIqipLJyYWhFURRFURTFHRWLiqIoiqIoSioqFhVFURRFUZRUVCwqiqIoiqIoqahYVBRFURRFUVJRsagoiqIoiqKkomJRURRFURRFSUXFoqIoiqIoipKKikVFUTpNOFXlHSJyfTivbPT6G0XEF5H3LrJ9iqIoXUdncFEUpfOIyMsIpjz7kDHm/SJyPME0aN8wxpy32NYpiqJ0GxWLiqIsBSLya8D/AH4c+A3gxcBLjTGPL7RhiqIoHUfFoqIoS4GICPA54CxgALzBGHPNYlulKIrSfXTMoqIoS4EJ7nz/GhgC31ahqCiKUg0qFhVFWQpEZCvwv4AbgZeKyK8uuEmKoihLgYpFRVE6TxiCvgzYD5wD/DHw+yLykoU2TFEUZQnQMYuKonQeEfl14A+As4wxXxWRAUF29BDYbozZu9AGKoqidBh1FhVF6TQi8nLgd4HfM8Z8FcAYcwB4B7AN+KPFtU5RFKX7qLOoKIqiKIqipKLOoqIoiqIoipKKikVFURRFURQlFRWLiqIoiqIoSioqFhVFURRFUZRUVCwqiqIoiqIoqahYVBRFURRFUVJRsagoiqIoiqKkomJRURRFURRFSUXFoqIoiqIoipLK/wcEYjPq3/hWpAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# compute the pressure coefficient field\n", + "cp = 1.0 - (u**2 + v**2) / u_inf**2\n", + "\n", + "# plot the pressure coefficient field\n", + "width = 10\n", + "height = (y_end - y_start) / (x_end - x_start) * width\n", + "pyplot.figure(figsize=(1.1 * width, height))\n", + "pyplot.xlabel('x', fontsize=16)\n", + "pyplot.ylabel('y', fontsize=16)\n", + "pyplot.xlim(x_start, x_end)\n", + "pyplot.ylim(y_start, y_end)\n", + "contf = pyplot.contourf(X, Y, cp,\n", + " levels=numpy.linspace(-2.0, 1.0, 100), extend='both')\n", + "cbar = pyplot.colorbar(contf)\n", + "cbar.set_label('$C_p$', fontsize=16)\n", + "cbar.set_ticks([-2.0, -1.0, 0.0, 1.0])\n", + "pyplot.scatter(x_doublet, y_doublet,\n", + " color='#CD2305', s=80, marker='o')\n", + "pyplot.contour(X,Y,psi,\n", + " levels=[0.], colors='#CD2305', linewidths=2, linestyles='solid')\n", + "pyplot.scatter([x_stagn1, x_stagn2], [y_stagn1, y_stagn2],\n", + " color='g', s=80, marker='o');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Challenge task" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Show that the pressure coefficient distribution on the surface of the circular cylinder is given by\n", + "\n", + "$$C_p = 1-4\\sin^2\\theta$$\n", + "\n", + "and plot the coefficient of pressure versus the angle." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Think" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Don't you find it a bit fishy that the pressure coefficient (and the surface distribution of pressure) is symmetric about the vertical axis?\n", + "\n", + "That means that the pressure in the front of the cylinder is the same as the pressure in the *back* of the cylinder. In turn, this means that the horizontal components of forces are zero.\n", + "\n", + "We know that, even at very low Reynolds number (creeping flow), there *is* in fact a drag force. The theory is unable to reflect that experimentally observed fact! This discrepancy is known as *d'Alembert's paradox*. \n", + "\n", + "Here's how creeping flow around a cylinder *really* looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('Ekd8czwELOc')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you look carefully, there is a slight asymmetry in the flow pattern. Can you explain it? What is the consequence of that?\n", + "\n", + "Here's a famous visualization of actual flow around a cylinder at a Reynolds number of 1.54. This image was obtained by S. Taneda and it appears in the \"Album of Fluid Motion\", by Milton Van Dyke. A treasure of a book.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/ExampleB-Stats.ipynb b/examples/ExampleB-Stats.ipynb new file mode 100644 index 0000000..bb836a7 --- /dev/null +++ b/examples/ExampleB-Stats.ipynb @@ -0,0 +1,5586 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to hypothesis testing\n", + "\n", + "In this notebook we look at data on a type of flower called Iris and we analyze it using a programming language called Python." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning goals\n", + "\n", + "This notebook is designed for you to learn:\n", + "* How to distinguish between \"population\" datasets and \"sample\" datasets when dealing with experimental data\n", + "* How to compare a sample to a population using a statistical test called the \"t-test\" and interpret its results\n", + "* How to evaluate the magnitude of the difference between a sample and a population using a measure of the effect size called \"Cohen's d\" and interpret the results\n", + "* How to use Python scripts to make statistical analyses on a dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "
\n", + " \"iris\n", + "\n", + "###### Iris virginica (Credit: Frank Mayfield CC BY-SA 2.0)\n", + "\n", + "
\n", + "\n", + "In 1935, an american botanist called Edgar Anderson worked on quantifying the morphologic variation of Iris flowers of three related species, Iris setosa, Iris virginica and Iris versicolor [[1]](#Bibliography). He realized a series of measures of the petal length, petal width, sepal length, sepal width and species.\n", + "Based on the combination of these four features, a British statistician and biologist named Ronald Fisher developed a model to distinguish the species from each other [[2]](#Bibliography).\n", + "\n", + "\n", + "### Question\n", + "A recent series of measurements has been carried out at the [Iris Garden of the Vullierens Castle](https://chateauvullierens.ch/en/) near Lausanne, on a sample of $n=50$ flowers of the Iris virginica species. \n", + "**How similar (or different) is the Iris sample from the Vullierens Castle compared to the Iris virginica population documented by Edgar Anderson?**\n", + "\n", + "\n", + "### Instructions \n", + "\n", + "**1. Read the notebook and execute the code cells**. \n", + "To check your understanding, ask yourself the following questions:\n", + "* Can I explain how the concept of sample differs from the concept of population?\n", + "* What does a t-test tell me on my sample?\n", + "* What does Cohen's d tell me on my sample?\n", + "\n", + "**2. Do the exercise** at the end of the notebook. \n", + "\n", + " \n", + "\n", + "--- \n", + "\n", + "## Getting started\n", + "\n", + "### Python tools for stats\n", + "Python comes with a number of libraries for processing data and computing statistics.\n", + "To use these tool you first have to load them using the `import` keyword. \n", + "The role of the code cell just below is to load the tools that we use in the rest of the notebook. It is important to execute this cell *prior to executing any other cell in the notebook*." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# plotting and display tools\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt \n", + "plt.style.use('seaborn-whitegrid') # global style for plotting\n", + "\n", + "from IPython.display import display, set_matplotlib_formats\n", + "set_matplotlib_formats('svg') # vector format for graphs\n", + "\n", + "# data computation tools\n", + "import numpy as np \n", + "import pandas as pan\n", + "import math\n", + "\n", + "# statistics tools\n", + "import scipy.stats as stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data available on the Anderson dataset\n", + "\n", + "Anderson has published summary statistics of his dataset. \n", + "You have the mean petal length of the Iris virginica species: $\\mu = 5.552$ cm." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.552" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define mu as mean petal length of iris virginica species from Anderson\n", + "mu = 5.552\n", + "\n", + "# Display mu\n", + "mu" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data available on the Vullierens sample\n", + "\n", + "You have the raw data collected on the petal length of the Vullierens sample, which is stored in the CSV file `iris-sample1-vullierens.csv` in the resource folder accessible through the file explorer in the left pane. \n", + "If you double click on the file it will open in a new tab and you can look at what is inside.\n", + "\n", + "Now to analyze the data using Python you have to read the file:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " petal_length\n", + "0 6.0\n", + "1 5.9\n", + "2 5.5\n", + "3 5.7\n", + "4 5.4" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read the Vullierens sample data from the CSV file\n", + "sample_data = pan.read_csv('resources/iris-sample1-vullierens.csv')\n", + "\n", + "# Display the first few lines of the dataset\n", + "sample_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After reading the CSV file, its content is stored in the variable `sample_data`, which is a kind of table. Each line of the table is given an index number to identify it. We see above that, appart from the index, the table contains only one column, called `\"petal_length\"`, which contains all the measurements made on the Vullierens Irises. To get the list of all the values stored in that column, you can use the following syntax: `sample_data[\"petal_length\"]`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 6.0\n", + "1 5.9\n", + "2 5.5\n", + "3 5.7\n", + "4 5.4\n", + "5 5.9\n", + "6 6.7\n", + "7 5.8\n", + "8 6.1\n", + "9 6.2\n", + "10 6.8\n", + "11 5.2\n", + "12 5.0\n", + "13 4.4\n", + "14 4.4\n", + "15 5.9\n", + "16 6.4\n", + "17 4.9\n", + "18 6.0\n", + "19 5.3\n", + "20 5.8\n", + "21 6.1\n", + "22 4.6\n", + "23 6.0\n", + "24 5.4\n", + "25 5.7\n", + "26 5.1\n", + "27 5.1\n", + "28 5.6\n", + "29 5.6\n", + "30 6.7\n", + "31 6.5\n", + "32 5.7\n", + "33 5.3\n", + "34 6.2\n", + "35 6.1\n", + "36 5.6\n", + "37 5.9\n", + "38 5.1\n", + "39 4.9\n", + "40 6.0\n", + "41 5.8\n", + "42 6.5\n", + "43 6.6\n", + "44 4.8\n", + "45 4.8\n", + "46 6.0\n", + "47 5.5\n", + "48 5.0\n", + "49 5.4\n", + "Name: petal_length, dtype: float64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# All values stored in the \"petal_length\" column of the \"sample_data\" table\n", + "sample_data[\"petal_length\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## First look at the data\n", + "\n", + "### Descriptive statistics\n", + "\n", + "Let's compute some simple descriptive statistics on this sample data. The `describe()` function gives us right away a number of useful descriptive stats:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " petal_length\n", + "count 50.000000\n", + "mean 5.658000\n", + "std 0.603084\n", + "min 4.400000\n", + "25% 5.225000\n", + "50% 5.700000\n", + "75% 6.000000\n", + "max 6.800000" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compute the descriptive stats\n", + "sample_stats = sample_data.describe()\n", + "\n", + "# Display the result\n", + "sample_stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can access individual elements of the `sample_stats` table using the corresponding names for the line and column of the value. \n", + "The following cell illustrates how to get the mean value of the petal length in the sample:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.6579999999999995" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Extract the sample mean from the descriptive stats\n", + "sample_mean = sample_stats.loc[\"mean\",\"petal_length\"]\n", + "\n", + "# Display the result\n", + "sample_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizations\n", + "\n", + "Now let's make some simple visualisations of this data:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create visualisation\n", + "fig = plt.figure(figsize=(16, 4))\n", + "\n", + "# Plot the sample values\n", + "ax1 = plt.subplot(131)\n", + "ax1.set_xlabel('index of sample')\n", + "ax1.set_ylabel('petal length')\n", + "ax1.set_title(\"Samples\")\n", + "ax1.plot(sample_data[\"petal_length\"], 'go')\n", + "# Add the means\n", + "ax1.axhline(y=sample_mean, color='black', linestyle='-.', linewidth=1, label=\"sample mean $m$\")\n", + "ax1.axhline(y=mu, color='black', linestyle=':', linewidth=1, label=\"$\\mu$\")\n", + "ax1.legend()\n", + "\n", + "# Plot the distribution of the samples\n", + "ax2 = plt.subplot(132)\n", + "ax2.set_xlabel('petal length')\n", + "ax2.set_ylabel('number of samples')\n", + "ax2.set_title(\"Distribution\")\n", + "ax2.hist(sample_data[\"petal_length\"], color='green')\n", + "# Add the means\n", + "ax2.axvline(x=sample_mean, color='black', linestyle='-.', linewidth=1, label=\"sample mean $m$\")\n", + "ax2.axvline(x=mu, color='black', linestyle=':', linewidth=1, label=\"$\\mu$\")\n", + "ax2.legend()\n", + "\n", + "# Box plot with quartiles\n", + "ax3 = plt.subplot(133, sharey = ax1)\n", + "box = ax3.boxplot(sample_data[\"petal_length\"], sym='k+', patch_artist=True)\n", + "ax3.set_ylabel('petal length')\n", + "ax3.set_title(\"Quartiles\")\n", + "ax3.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n", + "plt.setp(box['medians'], color='black')\n", + "for patch in box['boxes']:\n", + " patch.set(facecolor='green')\n", + "# Add the means\n", + "ax3.axhline(y=sample_mean, color='black', linestyle='-.', linewidth=1, label=\"sample mean $m$\")\n", + "ax3.axhline(y=mu, color='black', linestyle=':', linewidth=1, label=\"$\\mu$\")\n", + "ax3.legend()\n", + "\n", + "# Display the graph\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the Python code necessary to generate the graphs above is quite long. Hiding this particular code cell can improve the readability of the notebook. To hide it, select it and then click on the blue bar at its left. If you want to make the code visible again, simply click on the three \"dots\" or on the blue bar.\n", + "\n", + "\n", + "### Interpretation and hypothesis\n", + "\n", + "The simple analyses we have made so far allow us to have a preliminary idea about how the Irises from Vullierens compare to those observed by Anderson. One feature to look at for the comparison is their respective mean petal length. We see above that the mean petal length $m$ of the Vullierens sample is quite close to the mean $\\mu$ reported by Anderson.\n", + "\n", + "Let's formulate this as an **hypothesis** which we state as: the sample mean $m$ is similar to the mean of the reference population $\\mu$, i.e. $m = \\mu$. This hypothesis is noted $H_0$ and called the \"null\" hypothesis because it states that there is no difference between the sample and the population. \n", + "The \"alternate\" hypothesis $H_a$ is that the sample mean is not similar to the mean of the reference population, $m \\neq \\mu$.\n", + "\n", + "How can we test our hypothesis? In the following, we use a **statistical test** to answer this question." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "---\n", + "\n", + "## Testing our hypothesis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In our hypothesis we compare the mean of one sample to a reference value. To test this hypothesis we can use a statistical test called a **one-sample t-test**. \n", + "\n", + "But what does it mean when we test the hypothesis that a sample mean is potentially equal to a given value? \n", + "\n", + "### Sample versus population\n", + "\n", + "
\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "To understand this, it is useful to start by thinking about a population, in this case our population of Irises which has a mean petal length of $\\mu = 5.552$ cm.\n", + "\n", + "Now imagine you take a sample of, say, 50 flowers from this population. The mean petal length of this sample is $m_1 = 6.234$ cm. You then take a second sample of 50, which ends up having a mean petal length of $m_2 = 5.874$ cm. You then take a third sample of 50 which gives you a mean petal length of $m_3 = 5.349$ cm.\n", + "\n", + "If you keep taking samples from this population, you will start to notice a pattern: while some of the samples will give a mean average length which is not at all close to the population mean, most of the mean petal lengths are reasonably close to the population mean of 5.552 cm. Furthermore, the mean average of the mean average of the samples will be the same as that of the population as a whole i.e. 5.552 cm. \n", + "\n", + "In fact, if we keep taking samples from this population, it turns out that the distribution of the average of these samples will take a very particular pattern that looks like a normal curve. In fact if you take bigger sample sizes (say 130 instead of 50) the distribution will get closer and closer to being a normal curve for which the mean average is equal to the mean average of the population. For these smaller samples, the distribution is called the **[Student's t-distribution](https://en.wikipedia.org/wiki/Student%27s_t-distribution)** (actually it is a family of distributions, which depend on the sample size).\n", + "\n", + "\n", + "This is useful because it allows us to rephrase our question as to how similar or different our sample from Vullierens Castle is to the population of Irises as described by Edgar Anderson. \n", + "**What we have from the Vullierens Castle is a sample**. We want to know if it is a sample that might have come from a population like that described by Edgar Anderson. We now know the shape (more or less a normal distribution) and the mean (5.552 cm) of all of the samples that could be taken from the population described by Edgar Anderson. **So our question becomes \"where does our sample fall on the distribution of all such sample means?\"**. \n", + "If our mean is in position A on the figure on the right, then it is plausible that our sample came from a population like that of Edgar Anderson. If our mean is in position B, then it is less plausible to believe that our sample came from a population like Anderson’s.\n", + "\n", + "### Cut-off point\n", + "\n", + "You might be wondering, how far away is far enough away for us to think it is implausible that our sample comes from a population like Anderson’s. The answer is, it depends on how sure you want to be. \n", + "\n", + "One common answer to this question is to be 95% sure - meaning that a sample mean would need to be in the most extreme 5% of cases before we would think it is implausible that our sample comes from a population like Anderson’s ($\\alpha=0.05$). These most extreme 5% cases are represented by the zones in light blue on the figure on the right. If the sample mean falls into these most extreme zones, we say that *the difference is \"statistically significant\"*.\n", + "\n", + "A second, common answer is 99% sure meaning that a sample mean would need to be in the most extreme 1% of cases before we would think it is implausible that our sample comes from a population like Anderson’s ($\\alpha=0.01$). \n", + "\n", + "In the following, **we will work on the basis of being 95% sure**. Let's define our $\\alpha=0.05$:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.05" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define alpha\n", + "alpha = 0.05\n", + "\n", + "# Display alpha\n", + "alpha" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If our distribution of sample means is a normal curve then we know that the most extreme 5% of sample means are found above or below ±1.96 standard deviations above and below the mean. In our case, because our sample size is less than 130 (it is 50), our distribution is close to normal but not quite normal. \n", + "Still we can find out the relevant cut off point from looking it up in statistical tables: for a sample size of 50, the most extreme 5% of cases are found above or below 2.01 standard deviations from the mean. Let's define our cutoff point:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.01" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define the cutoff point\n", + "cutoff = 2.01\n", + "\n", + "# Display cutoff\n", + "cutoff" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Error in the distribution of means\n", + "\n", + "So far we know a lot that will help us to test the hypothesis that our sample mean is similar to Anderson’s population mean. We know:\n", + "* Our sample mean $m$\n", + "* The population mean $\\mu$\n", + "* The shape of the distribution of the mean of all samples that would come from this population (a normal curve, centred on the population mean)\n", + "* Our cut off point defined by $\\alpha$ (the most extreme 5% of cases, above or below 2.01 standard deviations from the mean)\n", + "\n", + "The last piece of information missing that would enable us to test this hypothesis is the size of the standard deviation of the distribution of sample means from Anderson’s population. \n", + "It turns out that a good guess for the size of this standard deviation can be obtained from knowing the standard deviation of our sample.\n", + "If $s$ is the sample standard deviation of our sample and $n$ is the sample size, then the standard deviation of the distribution of sample means is:\n", + "\n", + "$\n", + "\\begin{align}\n", + "\\sigma_{\\overline{X}} = \\frac{s}{\\sqrt{n}}\n", + "\\end{align}\n", + "$ \n", + "\n", + "This standard deviation of the distribution of sample means is called the \"standard error of the mean\" (also noted SEM). \n", + "We can compute it by retrieving the sample size and standard deviation from the descriptive stats we have computed earlier: " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.08528894466243941" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Extract the sample size from the descriptive stats generated earlier\n", + "sample_size = sample_stats.loc[\"count\",\"petal_length\"]\n", + "\n", + "# Extract the sample standard deviation from the descriptive stats\n", + "sample_std = sample_stats.loc[\"std\",\"petal_length\"]\n", + "\n", + "# Compute the estimation of the standard deviation of sample means from Anderson's population (standard error)\n", + "sem = sample_std / math.sqrt(sample_size)\n", + "\n", + "# Display the standard error\n", + "sem" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now restate our question in more precise terms: **\"is our sample mean in the most extreme 5% of samples that would be drawn from a population with the same mean as Anderson’s population?\"**. \n", + "Or to be even more precise, **\"is the gap between our sample mean and Anderson’s population mean greater than 2.01 times the standard error?\"**. \n", + "\n", + "This would be equivalent to compare\n", + "$\n", + "\\begin{align}\n", + "\\frac{m - \\mu}{\\sigma_{\\overline{X}}}\n", + "\\end{align}\n", + "$\n", + "to our cutoff of 2.01. That is the definition of the **t** statistics: the value $t = $\n", + "$\n", + "\\begin{align}\n", + "\\frac{m - \\mu}{\\sigma_{\\overline{X}}}\n", + "\\end{align}\n", + "$ \n", + " has to be compared to the cutoff point we have chosen to determine if the sample mean falls into the most extreme zones and to be able to say whether the difference is statistically significant or not.\n", + "Let's compute $t$:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.2428339970617721\n", + "The difference is not statistically significant.\n" + ] + } + ], + "source": [ + "# Compute the t statistics:\n", + "t = (sample_mean - mu) / sem\n", + "\n", + "# Display t\n", + "print(t)\n", + "\n", + "# Compare t with our cutoff point\n", + "if t > cutoff: \n", + " print(\"The difference is statistically significant.\")\n", + "else: \n", + " print(\"The difference is not statistically significant.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see in the results above that for our Vullierens sample $t < 2.01$, therefore the difference between the two means is not greater than 2.01 times the standard error. In other words, our sample mean **is not in the most extremes 5%** of samples that would be drawn from a population with the same mean as Anderson's population. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "What can we conclude from there? What the one sample t-test tells us is that we don't have evidence which would lead us to think that the sample doesn't come from an Anderson like population. Therefore we **cannot reject our hypothesis $H_0$**. However this is not the same to say that *it is* the same as the Anderson population. This is one of the limits of the t-test: like many other statistical tests, **it can be used only to reject an hypothesis**, not to confirm it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "---\n", + "\n", + "## Testing our hypothesis using a predefined Python function\n", + "\n", + "So far we have made the computations by hand but Python comes with a number of libraries with interesting statistical tools. \n", + "In particular, the `stats` library includes a function for doing a **one-sample t-test** as we have done above. \n", + "\n", + "### Computation of the test\n", + "\n", + "Let's now use it and then look at what information it gives us." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "t = 1.243\n", + "p = 0.220\n" + ] + } + ], + "source": [ + "# Compute the t-test\n", + "t, p = stats.ttest_1samp(sample_data[\"petal_length\"], mu)\n", + "\n", + "# Display the result\n", + "print(\"t = {:.3f}\".format(t))\n", + "print(\"p = {:.3f}\".format(p))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function returns two values `t` and `p` which say the same thing but in two different ways:\n", + "* `t` tells us where our sample mean falls on the distribution of all the possible sample means for the Anderson population ; `t` has to be compared to the `cutoff` value (2.01) to know if our sample mean is in the most extremes 5%.\n", + "* `p` (called the \"p-value\") is the probability to get a more extreme sample mean than the one we observe ; `p` has to be compared to `alpha` (0.05) to know if our sample mean is in the most extremes 5%.\n", + "\n", + "### Interpretation of the results\n", + "\n", + "We see above that `t < cutoff`, which means that the difference between the two means is smaller than 2.01 times the standard error and `p > alpha`, which means that the probability of getting more extreme sample mean than the one we observe is higher than 5% so it cannot be considered as one of the extreme possible values. Because they convey the same information, you can use either `t` or `p` to interpret the result of the t-test. In practice, most people use the p-value. \n", + "\n", + "As expected from the calculations we have made above, the test confirms that the difference between the mean petal length of the Vullierens sample and the mean petal length of Anderson's population is **not statistically significant**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualization\n", + "\n", + "Using Python we can visualize what that means graphically by plotting the t-distribution of all the possible sample means that would be drawn from a population with the same mean as Anderson's population and showing where `t` is in the distribution compared to the zone defined by our $\\alpha$ of 5%:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create the t-test visualization\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "ax.set_title(\"Probability distribution of all possible sample means if $H_0$ is true\")\n", + "\n", + "# Let's plot the T distribution for this sample size\n", + "tdist = stats.t(df=sample_size, loc=0, scale=1)\n", + "x = np.linspace(tdist.ppf(0.0001), tdist.ppf(0.9999), 100)\n", + "y = tdist.pdf(x) \n", + "ax.plot(x, y, color='blue', linewidth=1)\n", + "\n", + "# Polish the look of the graph\n", + "ax.get_yaxis().set_visible(False) # hide the y axis\n", + "ax.set_ylim(bottom=0) \n", + "ax.grid(False) # hide the grid \n", + "ax.spines['top'].set_visible(False) # hide the frame except bottom line\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['left'].set_visible(False)\n", + "\n", + "# Plot the rejection zone two tailed\n", + "x_zone_1 = np.linspace(tdist.ppf(0.0001), tdist.ppf(alpha/2), 100)\n", + "x_zone_2 = np.linspace(tdist.ppf(1-alpha/2), tdist.ppf(0.9999), 100)\n", + "y_zone_1 = tdist.pdf(x_zone_1) \n", + "y_zone_2 = tdist.pdf(x_zone_2) \n", + "ax.fill_between(x_zone_1, y_zone_1, 0, alpha=0.3, color='blue', label = r'rejection of $H_0$ with $\\alpha={}$'.format(alpha))\n", + "ax.fill_between(x_zone_2, y_zone_2, 0, alpha=0.3, color='blue')\n", + "\n", + "# Plot the t-test stats\n", + "ax.axvline(x=t, color='green', linestyle='dashed', linewidth=1)\n", + "ax.annotate('t-test $t$={:.3f}'.format(t), xy=(t, 0), xytext=(-10, 130), textcoords='offset points', bbox=dict(boxstyle=\"round\", facecolor = \"white\", edgecolor = \"green\", alpha = 0.8))\n", + "\n", + "# Plot the p-value\n", + "if t >= 0: x_t = np.linspace(t, tdist.ppf(0.9999), 100)\n", + "else: x_t = np.linspace(tdist.ppf(0.0001), t, 100)\n", + "y_t = tdist.pdf(x_t) \n", + "ax.fill_between(x_t, y_t, 0, facecolor=\"none\", edgecolor=\"green\", hatch=\"///\", linewidth=0.0, label = r'p-value $p$={:.3f}'.format(p))\n", + "\n", + "# Add a legend\n", + "ax.legend()\n", + "\n", + "# Display the graph\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "---\n", + "\n", + "## Evaluating the magnitude of the difference between two means: effect size\n", + "\n", + "So far we have tested our hypothesis regarding the similarity of means between our sample and the population and the results show us that there is probably no difference.\n", + "However whether this statistical test finds a difference depends on the sample size: with small samples it is harder to find a statistically significant difference.\n", + "We need therefore to **distinguish between a difference being statistically significant and a difference being large**.\n", + "The t-test is used to assess whether the difference is statistically significant. To assess whether the difference is large we use a measure called the effect size.\n", + "\n", + "### Computation of the effect size\n", + "\n", + "The effect size represents the size of the difference between the sample mean and the population mean taking into account the variation inside the sample (and inside the population, if known). \n", + "Cohen's d is one of the existing measures of effect size. \n", + "With $m$ and $s$ respectively the mean and standard deviation of the sample and $\\mu$ the population mean, Cohen's d for one sample (when the standard deviation of the population $\\sigma$ is not known) can be calculated as follows: \n", + "\n", + "$\n", + "\\begin{align}\n", + "d = \\frac{m - \\mu}{s}\n", + "\\end{align}\n", + "$\n", + "\n", + "Let's compute the effect size of the difference between the Vullierens sample and Anderson's population:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "d = 0.176\n" + ] + } + ], + "source": [ + "# Compute cohen's d\n", + "d = (sample_mean - mu)/sample_std\n", + "\n", + "# Display the result\n", + "print(\"d = {:.3f}\".format(d))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpretation of the results\n", + "\n", + "To interpret this result we have to **compare it to thresholds from the litterature**. Cohen suggested that $d=0.2$ was a small effect size, $0.5$ represents a medium effect size and $0.8$ represents a large effect size.\n", + "For our Vullierens sample the effect size is therefore small. In this case, the difference is also not statistically significant. However, with a larger sample size, it would be possible to have a statistically significant difference which nonetheless would be so small as to be trivial.\n", + "\n", + "\n", + "### Visualization\n", + "\n", + "Let's visualize the result graphically (again, you can hide the lengthy code by clicking on the blue bar at its left). \n", + "The graph below represents the theoretical distributions of the population in blue and of the sample in green. We see below that the two groups largely overlap, which is representative of the size of the difference being trivial." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create the vizualisation for the effect size\n", + "fig, ax = plt.subplots(figsize=(8, 4))\n", + "\n", + "# Plot the normal distribution\n", + "norm = stats.norm(loc=0, scale=1)\n", + "x = np.linspace(norm.ppf(0.0001), norm.ppf(0.9999), 100)\n", + "y = norm.pdf(x) \n", + "ax.plot(x, y, color='blue', linewidth=1)\n", + "ax.axvline(x=0, color='blue', linestyle='dashed', linewidth=1)\n", + "\n", + "# Plot the distribution of the sample\n", + "norm_d = stats.norm(loc=d, scale=1)\n", + "x_d = np.linspace(norm_d.ppf(0.0001), norm_d.ppf(0.9999), 100)\n", + "y_d = norm_d.pdf(x_d) \n", + "ax.plot(x_d, y_d, color='green', linewidth=1)\n", + "ax.axvline(x=d, color='green', linestyle='dashed', linewidth=1)\n", + "\n", + "# Display the value of Cohen's d\n", + "max_y = np.max(y)+.02\n", + "ax.plot([0,d], [max_y, max_y], color='red', linewidth=1, marker=\".\")\n", + "ax.annotate(\"effect size $d$={:.3f}\".format(d), xy=(d, max_y), xytext=(15, -5), textcoords='offset points', bbox=dict(boxstyle=\"round\", facecolor = \"white\", edgecolor = \"red\", alpha = 0.8))\n", + "\n", + "# Display the graph\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "---\n", + "\n", + "## Summary\n", + "\n", + "To summarize, to compare the mean of a sample to a reference value from a population, you have to proceed in four main steps:\n", + "1. formulate the hypothese you want to test: the null hypothesis $H_0: m = \\mu$ and its alternate $H_a: m \\neq \\mu$ \n", + "1. choose a cut-off point for being sure, usually $\\alpha = .05$, $\\alpha = .01$ or $\\alpha = .001$ \n", + "1. compute the result of the t-test and interpret the result - in particular if the p-value is *below* the significance level you have chosen, $p \\lt \\alpha$, then it means $H_0$ should probably be rejected\n", + "1. compute the effect size and interpret the result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "---\n", + "\n", + "## Exercise\n", + "\n", + "**A. Getting familiar with the code.**\n", + "1. In the code cells above, where is the t-test computed using the predefined Python function?\n", + "1. What are the two parameters that the t-test function takes as input?\n", + "1. If you wanted to change the population mean to a different value, like $\\mu = 5.4$ cm for instance, in which cell would you change it? \n", + "Then what would you need to do to update the result of the analysis in the notebook?\n", + "1. What is the result of the t-test if you compare the Vullierens sample to a population mean of $\\mu = 5.4$ cm? And what is the effect size?\n", + "\n", + "*Change the value of $\\mu$ back to 5.552 before working on the following questions.*\n", + "\n", + "**B. Analyzing another dataset.**\n", + "\n", + "A researcher from Tokyo sends you the results of a series of measurements she has done on the Irises of the [Meiji Jingu Imperial Gardens](http://www.meijijingu.or.jp/english/nature/2.html). The dataset can be found in the `iris-sample2-meiji.csv` file. \n", + "How similar (or different) is the Meiji sample compared to the Iris virginica population documented by Edgar Anderson? \n", + "The following questions are designed to guide you in analyzing this new dataset using this notebook.\n", + "\n", + "1. Which of the code cells above loads the data from the file containing the Vullierens dataset? Modify it to load the Meiji dataset.\n", + "1. Do you need to modify anything else in the code to analyze this new dataset?\n", + "1. What can you conclude about the Meiji sample from this analysis?\n", + "\n", + "**C. Going a bit further in the interpretation of the t-test.**\n", + "1. In the code cells above, where is the cut-off point $\\alpha$ defined? Change its value to 0.01 and re-execute the notebook. \n", + "1. How does this affect the result of the t-test for the Meiji sample?\n", + "\n", + " \n", + "\n", + "*A solution of the exercise is available [in this file](solution/ExampleB-solution.ipynb).*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "---\n", + "\n", + "

Bibliography

\n", + "\n", + "[1] E. Anderson (1935). \"The Irises of the Gaspe Peninsula.\" Bulletin of the American Iris Society 59: 2–5.\n", + "\n", + "[2] R. A. Fisher (1936). \"The use of multiple measurements in taxonomic problems\". Annals of Eugenics. 7 (2): 179–188. doi:10.1111/j.1469-1809.1936.tb02137.x\n", + "\n", + "More about the Iris Dataset on Wikipedia: https://en.wikipedia.org/wiki/Iris_flower_data_set\n", + "\n", + "*Please note that the datasets used in this notebook have been generated using a random generator, they do not come from real measurement and cannot be used for any research purpose.*" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/ExampleC-Physics-FR.ipynb b/examples/ExampleC-Physics-FR.ipynb new file mode 100644 index 0000000..a85a433 --- /dev/null +++ b/examples/ExampleC-Physics-FR.ipynb @@ -0,0 +1,1382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comment utiliser le notebook.\n", + "\n", + "Vous trouverez des cellules contenant du texte et des blocs interactifs. Pour exécuter les fonctions interactives, cliquez sur la cellule, puis cliquez sur le button Run il se trouve sur la parti supérieure du notebook). Il est important de suivre l'ordre des blocs et exécuter tous les blocs précédents pour utiliser les blocs interactifs. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(\"1001\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };var element = document.getElementById(\"1001\");\n", + " if (element == null) {\n", + " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n", + " return false;\n", + " }\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " \n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n", + " var css_urls = [];\n", + " \n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " function(Bokeh) {\n", + " \n", + " \n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if (root.Bokeh !== undefined || force === true) {\n", + " \n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + " if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1001\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"1001\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from ipywidgets import interact, interactive, fixed, interact_manual\n", + "import ipywidgets as widgets\n", + "from IPython.display import HTML\n", + "\n", + "from bokeh.io import push_notebook, show, output_notebook\n", + "from bokeh.plotting import figure\n", + "from bokeh.models import Arrow, OpenHead, NormalHead, VeeHead, BoxAnnotation, Legend\n", + "from bokeh.layouts import gridplot\n", + "output_notebook()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# L'agent Logan : exercice de cinématique\n", + "\n", + "Lancé à la poursuite d'un criminel, l'agent Logan du FBI doit traverser une rivière d'une largeur de 1600 m qui coule à 0.80 m$\\cdot$s$^{-1}$ en un minimum de temps et se rendre directement en face de son point de départ. Sachant qu'il peut ramer à 1.50 m$\\cdot$s$^{-1}$ et courir à 3.00 m$\\cdot$s$^{-1}$, décrivez la route qu'il devrait suivre (en bateau et à pied le long de la rive) pour traverser ce cours d'eau le plus rapidement possible.\n", + "Déterminez le temps minimal requis pour cette traversée.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## On commence par un schéma..." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(\"resources/Logan.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## On analyse le problème et on affine la marche à suivre\n", + "\n", + "Logan doit aller de A à B le plus vite possible (pas forcément en ligne droite), \n", + "sa vitesse par rapport à la rivière est $\\vec v'$, la vitesse du courant $\\vec v_c$, donc sa vitesse par rapport à la rive est $\\vec v = \\vec v' + \\vec v_c$. \n", + "\n", + "Il choisit l'angle $\\beta$. Si il ne choisit pas un beta tel que $\\alpha$ vaille 90°, il ne va pas débarquer en B, et devra courir un peu sur la rive. Mais comme il court plus vite qu'il ne rame, cela peut être une bonne idée si il choisit un beta qui va lui permettre de gagner du temps sur la traversée. \n", + "\n", + "Le but est de choisir l'angle qui lui donne le meilleur temps. C'est un problème d'optimisation. On exprime le temps total en fonction du paramètre libre ($\\beta$) et on cherche la valeur de $\\beta$ qui donne le minimum." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fonctions et paramètres" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Définition des paramètres initiaux\n", + "l = 1600 #largeur de la rivière (m)\n", + "v_courant = 0.8 # vitesse du courant (m/s)\n", + "v_ramer = 1.5 # vitesse à la rame (m/s)\n", + "v_course = 3.0 # vitesse à la course (m/s)\n", + "\n", + "beta = 90.0 # angle de départ" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calcul analytique 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le temps de traversée à la rame peut être calculé dans le référentiel de l'eau de la rivière.\n", + "\n", + "$$t_r=\\frac{l}{v_\\text{ramer} \\sin \\beta}$$\n", + "\n", + "Le temps de course nécessite de calculer d'abord la distance à parcourir sur la rive" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "vitesse du courant: $\\vec v_c$\n", + "\n", + "vitesse par rapport au courant: $\\vec v'$ avec $|\\vec v'|=v'$ connu ( appelé $v_{ramer}$ dans le programme)\n", + "\n", + "vitesse par rapport à la rive: $\\vec v=\\vec v'+\\vec v_c$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Composantes des vitesses:\n", + "\n", + "\\begin{align*}\n", + "\\vec v_c&=\\begin{pmatrix}v_c\\\\0 \\end{pmatrix} & \\vec v&=\\begin{pmatrix}v_x=v\\cos\\alpha\\\\v_y=v\\sin\\alpha \\end{pmatrix} & \\vec v'&=\\begin{pmatrix}v'\\cos\\beta\\\\v'\\sin\\beta \\end{pmatrix}\n", + "\\end{align*}\n", + "Il vient:\n", + "\n", + "$$\\vec v=\\begin{pmatrix}v_c+v'\\cos\\beta\\\\v'\\sin\\beta \\end{pmatrix}=\\begin{pmatrix}v\\cos\\alpha\\\\v\\sin\\alpha \\end{pmatrix}$$\n", + "\n", + "Ainsi: $$\\tan\\alpha=\\frac{v\\sin\\alpha}{v\\cos\\alpha}=\\frac{v'\\sin\\beta}{v_c+v'\\cos\\beta}=\\frac ld$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Donc:\n", + "$$\\boxed{d=l\\frac{v_c+v'\\cos\\beta}{v'\\sin\\beta}}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le temps de course sur la rive est donc donné par \n", + "\n", + "$$t_c=l\\frac{v_c+v'\\cos\\beta}{v'\\sin\\beta}\\frac{1}{v_{course}}=l\\frac{v_\\text{courant}+v_\\text{ramer}\\cos\\beta}{v_\\text{ramer}\\sin\\beta}\\frac{1}{v_\\text{course}} $$\n", + "\n", + "Le temps total est $t_c+t_r$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Vitesses" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Angle entre la trajectoire dans l'eau et la rive: 0.7044 [rad]\n" + ] + } + ], + "source": [ + "# vecteur vitesse du rameur par rapport au courant (v') en fonction de l'angle beta (variable) et \n", + "# de la norme de la vitesse à la rame (v_ramer)\n", + "\n", + "v_ramer_ = lambda beta, v_ramer: [np.linspace(0, 200*np.cos(beta)*v_ramer, 10), \\\n", + " np.linspace(0, 200*np.sin(beta)*v_ramer, 10)]\n", + "\n", + "# vecteur vitesse par raport au courant\n", + "v_ramer_x, v_ramer_y = v_ramer_(beta, v_ramer)\n", + "\n", + "# vecteur vitesse courant\n", + "v_courant_x = v_ramer_x[-1] + np.linspace(0, 300*v_ramer, 10)\n", + "v_courant_y = [v_ramer_y[-1]]*10\n", + "\n", + "# vitesse (vectorielle) initiale en x et y \n", + "v_init_x = v_ramer*np.cos(beta)\n", + "v_init_y = v_ramer*np.sin(beta)\n", + "\n", + "# angle alpha\n", + "alpha = np.arctan2(v_courant_y[-1],v_courant_x[-1])\n", + "print(\"Angle entre la trajectoire dans l'eau et la rive: {:0.4f} [rad]\".format(alpha))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Temps" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temps de traversée à la rame 1066.67[s]\n", + "Temps de course sur la rive 284.44[s]\n", + "Temps total 1351.11[s]\n" + ] + } + ], + "source": [ + "# conversion degres radians\n", + "beta_rad = np.pi*beta/180\n", + "\n", + "# fonction pour calculer le temps de traversée à la rame \n", + "t_ramer = lambda v_ramer, beta: l/(v_ramer*np.sin(beta))\n", + "# calcul du temps à la rame\n", + "t_r = t_ramer(v_ramer, beta_rad)\n", + "\n", + "# fonction pour calculer le temps de course\n", + "t_course = lambda v_courant, v_ramer, beta: abs(l*(v_courant + v_ramer*np.cos(beta))/(v_ramer*np.sin(beta) * v_course))\n", + "# calcul du temps de course\n", + "t_c = t_course(v_courant, v_ramer, beta_rad)\n", + "\n", + "# calcul du temps total \n", + "t_total = t_r + t_c\n", + "\n", + "print('Temps de traversée à la rame {:0.2f}[s]'.format(t_r))\n", + "print('Temps de course sur la rive {:0.2f}[s]'.format(t_c))\n", + "print('Temps total {:0.2f}[s]'.format(t_total))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Point d'arrivée" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Distance de course 136.42[m]\n" + ] + } + ], + "source": [ + "# distance de course (donne le point d'arrivée)\n", + "d = (v_courant + v_ramer*np.cos(beta))*t_r\n", + "print('Distance de course {:0.2f}[m]'.format(d))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Représentation graphique \n", + "\n", + "Dans cette partie du Notebook, on définira les différents vecteurs qu'on veut représenter ainsi comme la figure globale. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Définition de la Figure" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# figure\n", + "p = figure(title=\"Logan\", plot_height=500, plot_width=980, y_range=(-100,1800), x_range=(-2000,3000), \n", + " background_fill_color='#ffffff', toolbar_location=\"below\")\n", + "\n", + "# rives\n", + "r_down = p.line(np.linspace(-5000, 5000, 100), [0]*100, color=\"#5A2806\", alpha=0.6, line_width=5)\n", + "r_down = p.line(np.linspace(-5000, 5000, 100), [l]*100, color=\"#5A2806\", alpha=0.6, line_width=5)\n", + "\n", + "# fons de l'image\n", + "mid_box = BoxAnnotation(bottom=0, top=l, fill_alpha=0.1, fill_color='blue')\n", + "low_box = BoxAnnotation(bottom=-100, top=0, fill_alpha=0.1, fill_color='green')\n", + "top_box = BoxAnnotation(bottom=l, top=1800, fill_alpha=0.1, fill_color='green')\n", + "p.add_layout(mid_box)\n", + "p.add_layout(low_box)\n", + "p.add_layout(top_box)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Point initial, final et d'arrivée" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# point initial\n", + "A = p.circle([0], [0], size=10, fill_color='#e32020', line_color='#e32020',)\n", + "\n", + "# point final\n", + "B = p.circle([0], [l], size=10, fill_color='#e32020', line_color='#e32020')\n", + "\n", + "# point d'arrivé\n", + "p_arrive = p.circle([d], [l], size=10, fill_color='#0A0451', line_color='#0A0451')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Vecteurs de différentes vitesses" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# vecteur vitesse par raport au courant\n", + "vit_ramer= p.line(v_ramer_x, v_ramer_y, color='#D94D31', line_width=1.5)\n", + "\n", + "\n", + "# vecteur vitesse courant\n", + "vit_courant = p.line(v_courant_x, v_courant_y, color='#130f78', line_width=1.5)\n", + "\n", + "\n", + "# vecteur vitesse par raport à la rive\n", + "vit_rive = p.line(np.linspace(0 ,v_courant_x[-1], 10), np.linspace(0, v_courant_y[-1], 10), color='#40ba2d',\\\n", + " line_width=1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Fonctions pour dessiner des flèches pour les vecteurs" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# fonctions pour dessiner des flèches \n", + "\n", + "# angle omega\n", + "omega= np.pi*20/180 # Cet angle sert à dessiner les têtes des flèches\n", + "\n", + "def fleches_ramer(v_ramer_x, v_ramer_y, beta):\n", + " haut_x = v_ramer_x[-1] + np.linspace(0, -100*np.cos(beta + omega), 10)\n", + " haut_y = v_ramer_y[-1] + np.linspace(0, -100*np.sin(beta + omega), 10)\n", + " \n", + " bas_x = v_ramer_x[-1] + np.linspace(0, -100*np.cos(beta - omega), 10)\n", + " bas_y = v_ramer_y[-1] + np.linspace(0, -100*np.sin(beta - omega), 10)\n", + " \n", + " return haut_x, haut_y, bas_x, bas_y\n", + "\n", + "\n", + "def fleches_courant(v_courant_x, v_courant_y):\n", + " haut_x = v_courant_x[-1] + np.linspace(0, +100*np.cos(np.pi + omega), 10)\n", + " haut_y = v_courant_y[-1] + np.linspace(0, +100*np.sin(np.pi + omega), 10)\n", + " \n", + " bas_x = v_courant_x[-1] + np.linspace(0, +100*np.cos(np.pi - omega), 10)\n", + " bas_y = v_courant_y[-1] + np.linspace(0, +100*np.sin(np.pi - omega), 10)\n", + " \n", + " return haut_x, haut_y, bas_x, bas_y\n", + "\n", + "\n", + "def fleches_rive(v_courant_x, v_courant_y, alpha):\n", + " haut_x = v_courant_x[-1] - np.linspace(0, 100*np.cos(alpha + omega), 10)\n", + " haut_y = v_courant_y[-1] - np.linspace(0, 100*np.sin(alpha + omega), 10)\n", + " \n", + " bas_x = v_courant_x[-1] - np.linspace(0, 100*np.cos(alpha - omega), 10)\n", + " bas_y = v_courant_y[-1] - np.linspace(0, 100*np.sin(alpha - omega), 10)\n", + " \n", + " return haut_x, haut_y, bas_x, bas_y\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Flèches des vecteurs " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# flèches vecteur vitesse ramer\n", + "r_haut_x, r_haut_y, r_bas_x, r_bas_y = fleches_ramer(v_ramer_x, v_ramer_y, beta)\n", + "\n", + "fleche_haut_ramer = p.line(r_haut_x, r_haut_y, color='#D94D31', line_width=1.5)\n", + "fleche_bas_ramer = p.line(r_bas_x, r_bas_y, color='#D94D31', line_width=1.5)\n", + "\n", + "\n", + "# flèches vecteur vitesse courant\n", + "c_haut_x, c_haut_y, c_bas_x, c_bas_y = fleches_courant(v_courant_x, v_courant_y)\n", + "\n", + "fleche_haut_courant = p.line(c_haut_x, c_haut_y, color='#130f78', line_width=1.5)\n", + "fleche_bas_courant = p.line(c_bas_x, c_bas_y, color='#130f78', line_width=1.5)\n", + "\n", + "# flèches vecteur vitesse par raport à la rive\n", + "riv_haut_x, riv_haut_y, riv_bas_x, riv_bas_y = fleches_rive(v_courant_x, v_courant_y, alpha)\n", + "\n", + "fleche_haut_v_rive = p.line(riv_haut_x, riv_haut_y, color='#40ba2d', line_width=1.5)\n", + "fleche_bas_v_rive = p.line(riv_bas_x, riv_bas_y, color='#40ba2d', line_width=1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Trajectoires" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# trajectoire dans l'eau\n", + "trajectoire_eau = p.line(np.linspace(0, d*100, 10), np.linspace(0, l, 10),\\\n", + " color='#ccce2b', line_width=6, alpha=0.5, line_dash='dashed')\n", + "\n", + "# trajectoire course\n", + "trajectoire_course = p.line(np.linspace(0, d*100, 10), np.linspace(l, l, 10),\\\n", + " color='#2f4fec', line_width=6, alpha=0.9, line_dash='dotdash')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# légende\n", + "legend1 = Legend(items=[(\"Vitesse par rapport au courant\", [vit_ramer, fleche_haut_ramer, fleche_bas_ramer]),\n", + " (\"Vitesse du courant\", [vit_courant, fleche_haut_courant, fleche_bas_courant]),\n", + " (\"Vitesse par rapport à la rive\", [vit_rive, fleche_haut_v_rive, fleche_bas_v_rive])])\n", + "legend2 = Legend(items=[('Trajectoire Eau', [trajectoire_eau]),('Trajectoire Course', [trajectoire_course]), ('Point Initial', [A]), \n", + " ('Point Final', [B]), (\"Point d'Arrivé\", [p_arrive])])\n", + "\n", + "p.add_layout(legend1, 'below')\n", + "p.add_layout(legend2, 'below')\n", + "p.legend.orientation = 'horizontal'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Temps" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "p_temps = figure(title=\"Temps\", plot_height=200, plot_width=980, y_range=(-10, 10), x_range=(0,3000), \n", + " background_fill_color='#ffffff', toolbar_location=None)\n", + "\n", + "p_temps.yaxis.visible = None\n", + "p_temps.xaxis.axis_label = 'Temps [s]'" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# calculer temps \n", + "temps_ramer = p_temps.line(np.linspace(0, t_r, 10), [0]*10,color='#6DD3DB', line_width=25, name='Temps ramer')\n", + "temps_course = p_temps.line(np.linspace(t_r, t_total, 10), [0]*10,color='#4a8d5c', line_width=25, name='Temps course')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# légend\n", + "l_temps = Legend(items=[('Temps navigation [s]', [temps_ramer]), ('Temps courant [s]', [temps_course])])\n", + "\n", + "p_temps.add_layout(l_temps)\n", + "p.ygrid.visible = False\n", + "p_temps.legend.orientation = 'horizontal'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fonction pour mettre à jour la figure" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# mettre à jour \n", + "def update(beta):\n", + " beta = np.pi*beta/180\n", + " \n", + " # mettre à jour vitesse\n", + " v_ramer_x, v_ramer_y = v_ramer_(beta, v_ramer)\n", + " v_courant_x = v_ramer_x[-1] + np.linspace(0, 200*v_courant, 10)\n", + " v_courant_y = [v_ramer_y[-1]]*10\n", + " alpha = np.arctan2(v_courant_y[-1],v_courant_x[-1])\n", + "\n", + " \n", + " # mettre à jour temps\n", + " t = t_course(v_courant, v_ramer, beta) + t_ramer(v_ramer, beta)\n", + "\n", + " point_arrive = (v_courant + v_ramer*np.cos(beta))*t_ramer(v_ramer,beta)\n", + " \n", + " temps_ramer.data_source.data['x'] = np.linspace(0,t_ramer(v_ramer,beta), 10)\n", + " temps_course.data_source.data['x'] = np.linspace(t_ramer(v_ramer,beta), t, 10)\n", + " \n", + " # vecteur vitesse ramer\n", + " vit_ramer.data_source.data['x'] = v_ramer_x\n", + " vit_ramer.data_source.data['y'] = v_ramer_y\n", + " \n", + " # flèches du vecteur vitesse ramer\n", + " r_haut_x, r_haut_y, r_bas_x, r_bas_y = fleches_ramer(v_ramer_x, v_ramer_y, beta)\n", + " \n", + " fleche_haut_ramer.data_source.data['x'] = r_haut_x\n", + " fleche_haut_ramer.data_source.data['y'] = r_haut_y\n", + " fleche_bas_ramer.data_source.data['x'] = r_bas_x\n", + " fleche_bas_ramer.data_source.data['y'] = r_bas_y\n", + " \n", + " # vecteur vitesse courant\n", + " vit_courant.data_source.data['x'] = v_courant_x\n", + " vit_courant.data_source.data['y'] = v_courant_y\n", + " \n", + " # flèches de vectuer vitesse courant \n", + " c_haut_x, c_haut_y, c_bas_x, c_bas_y = fleches_courant(v_courant_x, v_courant_y)\n", + " \n", + " fleche_haut_courant.data_source.data['x'] = c_haut_x\n", + " fleche_haut_courant.data_source.data['y'] = c_haut_y\n", + " fleche_bas_courant.data_source.data['x'] = c_bas_x\n", + " fleche_bas_courant.data_source.data['y'] = c_bas_y\n", + " \n", + " # vecteur vitesse rive\n", + " vit_rive.data_source.data['x'] = np.linspace(0, v_courant_x[-1], 10)\n", + " vit_rive.data_source.data['y'] = np.linspace(0, v_courant_y[-1], 10)\n", + " \n", + " riv_haut_x, riv_haut_y, riv_bas_x, riv_bas_y = fleches_rive(v_courant_x, v_courant_y, alpha)\n", + " \n", + " fleche_haut_v_rive.data_source.data['x'] = riv_haut_x\n", + " fleche_haut_v_rive.data_source.data['y'] = riv_haut_y\n", + " fleche_bas_v_rive.data_source.data['x'] = riv_bas_x\n", + " fleche_bas_v_rive.data_source.data['y'] = riv_bas_y\n", + " \n", + " p_arrive.data_source.data['x'] = [point_arrive]\n", + " \n", + " trajectoire_eau.data_source.data['x'] = np.linspace(0, point_arrive, 10)\n", + " trajectoire_course.data_source.data['x'] = np.linspace(0, point_arrive, 10)\n", + " \n", + " print('Temps de navigation:\\t\\t{:0.1f} [s]'.format(t_ramer(v_ramer,beta)))\n", + " print('Temps de course sur la rive:\\t{:0.1f} [s]'.format(abs(t_course(v_courant, v_ramer, beta))))\n", + " print('Temps total:\\t\\t\\t{:0.1f} [s]'.format(t))\n", + " \n", + " push_notebook()\n", + " \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tracé" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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Application\",\"version\":\"1.4.0\"}};\n", + " var render_items = [{\"docid\":\"a84f45b4-e011-477f-a64b-380024f85ad6\",\"notebook_comms_target\":\"1278\",\"roots\":{\"1229\":\"5eaac64d-7416-480e-99eb-71155857311f\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + "\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " var attempts = 0;\n", + " var timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1229" + } + }, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4865de74b21d40a78c6be9167c58b92f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=90.0, description='$Beta$:', max=160.0, min=70.0, step=1.0), Output())…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f_plot = gridplot([[p], [p_temps]])\n", + "show(f_plot, notebook_handle=True)\n", + "interact(update,\\\n", + " beta=widgets.FloatSlider(min=70,max=160,step=1,value=90, description='$Beta$:')); " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analyse du temps en fonction de l'angle beta" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beta pour le temps minimum à la rame 89.6[º]. \n", + "Temps ramer: 1066.7[s].\tTemps course: 288.3[s].\tTemps total: 1354.9[s]\n", + "\n", + "Beta pour le temps minimum de course 122.2[º]. \n", + "Temps ramer: 1261.2[s].\tTemps course: 0.1[s].\tTemps total: 1261.3[s]\n", + "\n", + "Beta pour le temps minimum total 113.1[º]. \n", + "Temps ramer: 1159.3[s].\tTemps course: 82.1[s].\tTemps total: 1241.4[s]\n", + "\n" + ] + } + ], + "source": [ + "v_ramer = 1.5 # vitesse à la rame (m/s)\n", + "v_course = 3.0 # vitesse à la course (m/s)\n", + "\n", + "angle_beta_grad = np.linspace(60, 160, 99)\n", + "\n", + "temps_ramer_beta = t_ramer(v_ramer, angle_beta_grad*np.pi/180)\n", + "temps_course_beta = abs(t_course(v_courant, v_ramer, angle_beta_grad*np.pi/180) )\n", + "\n", + "temps_total_beta = temps_ramer_beta + temps_course_beta\n", + "\n", + "t_ramer_idx = np.argmin(temps_ramer_beta)\n", + "t_course_idx = np.argmin(temps_course_beta)\n", + "t_total_idx = np.argmin(temps_total_beta)\n", + "\n", + "# Graphique temps vs. angle beta\n", + "plt.figure(figsize=(14,7))\n", + "plt.plot(angle_beta_grad, temps_ramer_beta, color='green', linestyle='dashed', linewidth=2)\n", + "plt.plot(angle_beta_grad, temps_course_beta, color='blue', linestyle='dashed', linewidth=2)\n", + "plt.plot(angle_beta_grad, temps_total_beta, color='red', linewidth=2)\n", + "\n", + "plt.plot(angle_beta_grad[t_ramer_idx], temps_ramer_beta[t_ramer_idx], color='green', marker='o', ms=8)\n", + "plt.plot(angle_beta_grad[t_course_idx], temps_course_beta[t_course_idx], color='blue', marker='o', ms=8)\n", + "plt.plot(angle_beta_grad[t_total_idx], temps_total_beta[t_total_idx], color='red', marker='o', ms=8)\n", + "\n", + "\n", + "plt.legend(['Temps traversée à la rame', 'Temps de course', 'Temps total'])\n", + "plt.grid(True)\n", + "plt.xlabel('Angle beta [º]')\n", + "plt.ylabel('Temps [s]')\n", + "plt.title('Temps vs Beta.\\n (Vitesse Ramer = 1.5[m/s]. Vitesse Course = 3.0 [m/s])')\n", + "plt.show()\n", + "\n", + "print(\"Beta pour le temps minimum à la rame {:0.1f}[º]. \\n\\\n", + "Temps ramer: {:0.1f}[s].\\tTemps course: {:0.1f}[s].\\tTemps total: {:0.1f}[s]\\n\".format(angle_beta_grad[t_ramer_idx], \n", + "temps_ramer_beta[t_ramer_idx], temps_course_beta[t_ramer_idx], temps_total_beta[t_ramer_idx]))\n", + "\n", + "print(\"Beta pour le temps minimum de course {:0.1f}[º]. \\n\\\n", + "Temps ramer: {:0.1f}[s].\\tTemps course: {:0.1f}[s].\\tTemps total: {:0.1f}[s]\\n\".format(angle_beta_grad[t_course_idx], \n", + "temps_ramer_beta[t_course_idx], temps_course_beta[t_course_idx], temps_total_beta[t_course_idx]))\n", + "\n", + "print(\"Beta pour le temps minimum total {:0.1f}[º]. \\n\\\n", + "Temps ramer: {:0.1f}[s].\\tTemps course: {:0.1f}[s].\\tTemps total: {:0.1f}[s]\\n\".format(angle_beta_grad[t_total_idx], \n", + "temps_ramer_beta[t_total_idx], temps_course_beta[t_total_idx], temps_total_beta[t_total_idx]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calcul analytique 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le temps total est une fonction de $\\beta$:\n", + "\n", + "$$\\boxed{t_{tot}=\\frac l{v'\\sin\\beta}+l\\frac{v_c+v'\\cos\\beta}{v'\\sin\\beta}\\frac1{v_{course}}=l\\left[\\frac{v_{course}+v_c+v'\\cos\\beta}{v_{course}\\cdot v'\\sin\\beta}\\right]}$$\n", + "\n", + "On cherche à la minimiser en fonction de $\\beta$:\n", + "\n", + "$$\\frac{\\text dt_{tot}}{\\text d\\beta}=l\\cdot\\frac{-v_{course}v'\\sin\\beta\\cdot v'\\sin\\beta-v_{course}v'\\cos\\beta\\left[v_{course}+v_c+v'\\cos\\beta\\right]}{\\left[v_{course}\\cdot v'\\sin\\beta\\right]^2}=f(\\beta)$$\n", + "\n", + "et on cherche $f(\\beta)=0$.\n", + "\n", + "\\begin{align*}\n", + "-v'^2v_{course}\\sin^2\\beta-v'v_{course}^2\\cos\\beta\n", + "-v_cv'v_{course}\\cos\\beta-v_{course}v'^2\\cos^2\\beta&=0\\\\\n", + "\\end{align*}\n", + "\n", + "$$\\boxed{\\cos\\beta=\\frac{-v'}{v_c+v_{course}}}$$\n", + "A.N.: $\\beta=1.98$ (équivalent à 113.2°, soit 23.2° vers l'amont). Le temps de course sera alors de 80.5 s et la traversée de 1160 s, soit un temps total de 1241.5 s (20.7 min)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Demo: Variation des vitesses " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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Application\",\"version\":\"1.4.0\"}};\n", + " var render_items = [{\"docid\":\"da739baa-c4d4-4019-8b4b-d258e31520e6\",\"notebook_comms_target\":\"1739\",\"roots\":{\"1642\":\"870e71e9-e186-401a-8605-22c3f18f519b\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + "\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " var attempts = 0;\n", + " var timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1642" + } + }, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "feb3fd8f52d84eada8c6495e32ab4179", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=113.2, description='$Beta$:', max=179.0, min=90.0, step=1.0), FloatSli…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def update2(beta, v_course, v_ramer, v_courant):\n", + " beta = np.pi*beta/180\n", + " \n", + " # mettre à jour vitesse\n", + " v_ramer_x = np.linspace(0, 200*np.cos(beta)*v_ramer, 10)\n", + " v_ramer_y = np.linspace(0, 200*np.sin(beta)*v_ramer, 10)\n", + " v_courant_x = v_ramer_x[-1] + np.linspace(0, 200*v_courant, 10)\n", + " v_courant_y = [v_ramer_y[-1]]*10\n", + " \n", + " alpha = np.arctan2(v_courant_y[-1],v_courant_x[-1])\n", + "\n", + " \n", + " # mettre à jour temps\n", + " t_r = t_ramer(v_ramer, beta)\n", + " t_c = t_course(v_courant, v_ramer, beta)\n", + " t = t_c + t_r\n", + "\n", + " point_arrive = (v_courant + v_ramer*np.cos(beta))*t_r\n", + " \n", + " temps_ramer.data_source.data['x'] = np.linspace(0,t_r, 10)\n", + " temps_course.data_source.data['x'] = np.linspace(t_r, t, 10)\n", + " \n", + " # vecteur vitesse ramer\n", + " vit_ramer.data_source.data['x'] = v_ramer_x\n", + " vit_ramer.data_source.data['y'] = v_ramer_y\n", + " \n", + " # flèches du vecteur vitesse ramer\n", + " r_haut_x, r_haut_y, r_bas_x, r_bas_y = fleches_ramer(v_ramer_x, v_ramer_y, beta)\n", + " \n", + " fleche_haut_ramer.data_source.data['x'] = r_haut_x\n", + " fleche_haut_ramer.data_source.data['y'] = r_haut_y\n", + " fleche_bas_ramer.data_source.data['x'] = r_bas_x\n", + " fleche_bas_ramer.data_source.data['y'] = r_bas_y\n", + " \n", + " # vecteur vitesse courant\n", + " vit_courant.data_source.data['x'] = v_courant_x\n", + " vit_courant.data_source.data['y'] = v_courant_y\n", + " \n", + " # flèches de vectuer vitesse courant \n", + " c_haut_x, c_haut_y, c_bas_x, c_bas_y = fleches_courant(v_courant_x, v_courant_y)\n", + " \n", + " fleche_haut_courant.data_source.data['x'] = c_haut_x\n", + " fleche_haut_courant.data_source.data['y'] = c_haut_y\n", + " fleche_bas_courant.data_source.data['x'] = c_bas_x\n", + " fleche_bas_courant.data_source.data['y'] = c_bas_y\n", + " \n", + " # vecteur vitesse rive\n", + " vit_rive.data_source.data['x'] = np.linspace(0, v_courant_x[-1], 10)\n", + " vit_rive.data_source.data['y'] = np.linspace(0, v_courant_y[-1], 10)\n", + " \n", + " riv_haut_x, riv_haut_y, riv_bas_x, riv_bas_y = fleches_rive(v_courant_x, v_courant_y, alpha)\n", + " \n", + " fleche_haut_v_rive.data_source.data['x'] = riv_haut_x\n", + " fleche_haut_v_rive.data_source.data['y'] = riv_haut_y\n", + " fleche_bas_v_rive.data_source.data['x'] = riv_bas_x\n", + " fleche_bas_v_rive.data_source.data['y'] = riv_bas_y\n", + " \n", + " p_arrive.data_source.data['x'] = [point_arrive]\n", + " \n", + " trajectoire_eau.data_source.data['x'] = np.linspace(0, point_arrive, 10)\n", + " trajectoire_course.data_source.data['x'] = np.linspace(0, point_arrive, 10)\n", + " \n", + " print('Temps de navigation:\\t\\t{:0.1f} [s]'.format(t_r))\n", + " print('Temps de course sur la rive:\\t{:0.1f} [s]'.format(abs(t_c)))\n", + " print('Temps total:\\t\\t\\t{:0.1f} [s]'.format(t))\n", + " \n", + " push_notebook()\n", + " \n", + " \n", + " \n", + "f_plot = gridplot([[p], [p_temps]])\n", + "show(f_plot, notebook_handle=True)\n", + "interact(update2,\\\n", + " beta=widgets.FloatSlider(min=90,max=179,step=1,value=113.2, description='$Beta$:'), \\\n", + " v_course=widgets.FloatSlider(min=0.5,max=5.0,step=0.05,value=3.0, description='$v_{course} [m/s]$:'),\\\n", + " v_ramer=widgets.FloatSlider(min=0.5,max=5,step=0.05,value=1.5, description='$v_{rame} [m/s]$:'),\\\n", + " v_courant=widgets.FloatSlider(min=0.1,max=3,step=0.05,value=0.8, description='$v_{c} [m/s]$:') ); \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + "# Feedback" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import IFrame\n", + "IFrame('https://www.surveymonkey.com/r/NOTOSURVEY?notebook_set=MecaDRIL¬ebook_id=01-Logan', 600, 800)" + ] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/ExampleD-SolidStatePhysics.ipynb b/examples/ExampleD-SolidStatePhysics.ipynb new file mode 100644 index 0000000..a52b489 --- /dev/null +++ b/examples/ExampleD-SolidStatePhysics.ipynb @@ -0,0 +1,289 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from bokeh.plotting import figure, show\n", + "from bokeh.layouts import grid, column, row\n", + "from bokeh.io import output_notebook\n", + "from bokeh.models import Span, Arrow, VeeHead\n", + "output_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Constructing real space lattice\n", + "a1 = np.array([0,2]);\n", + "a2 = np.array([2,0]);\n", + "N = 3;\n", + "x = range(-N, N+1)*np.linalg.norm(a1);\n", + "y = range(-N, N+1)*np.linalg.norm(a2);\n", + "xg,yg = np.meshgrid(x,y);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Constructing reciprocal space lattice\n", + "b1 = np.array([0,2*np.pi / a1[1]]);\n", + "b2 = np.array([2*np.pi / a2[0],0]);\n", + "N = 3;\n", + "xx = range(-N, N+1)*np.linalg.norm(b1);\n", + "yy = range(-N, N+1)*np.linalg.norm(b2);\n", + "xxg,yyg = np.meshgrid(xx,yy);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "real = figure(title = \"Real space lattice\", tools = \"\", x_range = [-10, 10], y_range = [-10, 10], plot_width = 400, plot_height = 400);\n", + "real.circle(xg.flatten(),yg.flatten(),size = 10,fill_color = \"blue\",line_color = None,line_width = 3);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "img = figure(title = \"Reciprocal space lattice\", tools = \"\", x_range = [-10, 10], y_range = [-10, 10], plot_width = 400, plot_height = 400);\n", + "img.circle(xxg.flatten(),yyg.flatten(),size = 10,fill_color = \"red\", line_color = None,line_width = 3);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the real and reciprocal lattice\n", + "show(row(real,img))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# draw the reciprocal lattice vectors from the origin to the neearest neighbour \n", + "vec1 = img.multi_line([[b1[1], -b1[1]], [0, 0]],[[0, 0], [b2[0], -b2[0]]], line_width = 2, line_color = \"black\");\n", + "arrow1 = Arrow(end = VeeHead(size = 10), x_start=0, y_start=0, x_end=b2[0], y_end=0);\n", + "arrow2 = Arrow(end = VeeHead(size = 10), x_start=0, y_start=0, x_end=-b2[0], y_end=0);\n", + "arrow3 = Arrow(end = VeeHead(size = 10), x_start=0, y_start=0, x_end=0, y_end=b1[1]);\n", + "arrow4 = Arrow(end = VeeHead(size = 10), x_start=0, y_start=0, x_end=0, y_end=-b1[1]);\n", + "img.add_layout(arrow1);\n", + "img.add_layout(arrow2);\n", + "img.add_layout(arrow3);\n", + "img.add_layout(arrow4);\n", + "show(row(real, img))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# constructing bragg planes (bisecting the reciprocal vectors drawn in the previous step)\n", + "bgp1 = Span(location = b2[0]/2, dimension = \"height\", line_dash=\"dashed\");\n", + "bgp2 = Span(location = -b2[0]/2, dimension = \"height\", line_dash=\"dashed\");\n", + "bgp3 = Span(location = b1[1]/2, dimension = \"width\", line_dash=\"dashed\");\n", + "bgp4 = Span(location = -b1[1]/2, dimension = \"width\", line_dash=\"dashed\");\n", + "bragg1 = img.renderers.extend([bgp1,bgp2,bgp3,bgp4]);\n", + "show(row(real, img))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Color the first Brillouin zone, which is the Wigner-Seitz unit cell in the reciprocal lattice\n", + "bz1 = img.patch([b2[0]/2, b2[0]/2, -b2[0]/2, -b2[0]/2, b2[0]/2],[b1[1]/2, -b1[1]/2, -b1[1]/2, b1[1]/2, b1[1]/2],line_width = 2, color = \"red\");\n", + "show(row(real,img))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# hide the first brillouin zone elements\n", + "vec1.visible = False;\n", + "arrow1.visible = False;\n", + "arrow2.visible = False;\n", + "arrow3.visible = False;\n", + "arrow4.visible = False;\n", + "bz1.visible = False;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# draw the reciprocal lattice vectors from the origin to the next nearst neighbours\n", + "vec2 = img.multi_line([[-b2[0], b2[0]], [-b1[1], b1[1]]],[[b2[0], -b2[0]], [-b1[1], b1[1]]], line_width = 2, line_color = \"black\");\n", + "arrow1 = Arrow(end = VeeHead(size = 10), x_start=0, y_start=0, x_end=b2[0], y_end=b1[1]);\n", + "arrow2 = Arrow(end = VeeHead(size = 10), x_start=0, y_start=0, x_end=-b2[0], y_end=-b1[1]);\n", + "arrow3 = Arrow(end = VeeHead(size = 10), x_start=0, y_start=0, x_end=-b2[0], y_end=b1[1]);\n", + "arrow4 = Arrow(end = VeeHead(size = 10), x_start=0, y_start=0, x_end=b2[0], y_end=-b1[1]);\n", + "img.add_layout(arrow1);\n", + "img.add_layout(arrow2);\n", + "img.add_layout(arrow3);\n", + "img.add_layout(arrow4);\n", + "show(row(real, img))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# construct bragg planes for the second brillouin zone\n", + "bragg2 = img.ray(x = [-b2[0], b2[0], b2[1], b2[1], -b2[0], b2[0], b2[1], b2[1]], y = [b1[0], b1[0], -b1[1], b1[1], b1[0], b1[0], -b1[1], b1[1]], length = 0, angle = [45, -135, 135, -45, -135, 45, -45, 135], angle_units = \"deg\", color = \"black\", line_width = 1, line_dash = \"dashed\");\n", + "bragg2.level = \"annotation\";\n", + "show(row(real, img))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Color both the first and second brillouin zones\n", + "bz2 = img.patch([b2[1], b2[0], b2[1], -b2[0], b2[1]],[b1[1], b1[0], -b1[1], b1[0], b1[1]], line_width = 2);\n", + "show(row(real, img))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# hide the elements of the first and second brillouin zones\n", + "bz2.visible = False;\n", + "arrow1.visible = False;\n", + "arrow2.visible = False;\n", + "arrow3.visible = False;\n", + "arrow4.visible = False;\n", + "vec2.visible = False;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# draw the reciprocal lattice vectors from the origin to the next next nearst neighbours\n", + "vec3 = img.multi_line([[2*b1[1], -2*b1[1]], [0, 0]],[[0, 0], [2*b2[0], -2*b2[0]]], line_width = 2, line_color = \"black\");\n", + "arrow1 = Arrow(end = VeeHead(size = 10), x_start=0, y_start=0, x_end=2*b2[0], y_end=0);\n", + "arrow2 = Arrow(end = VeeHead(size = 10), x_start=0, y_start=0, x_end=-2*b2[0], y_end=0);\n", + "arrow3 = Arrow(end = VeeHead(size = 10), x_start=0, y_start=0, x_end=0, y_end=2*b1[1]);\n", + "arrow4 = Arrow(end = VeeHead(size = 10), x_start=0, y_start=0, x_end=0, y_end=-2*b1[1]);\n", + "img.add_layout(arrow1);\n", + "img.add_layout(arrow2);\n", + "img.add_layout(arrow3);\n", + "img.add_layout(arrow4);\n", + "show(row(real,img))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# construct bragg planes for the third brillouin zone\n", + "bgp1 = Span(location = b2[0], dimension = \"height\", line_dash=\"dashed\");\n", + "bgp2 = Span(location = -b2[0], dimension = \"height\", line_dash=\"dashed\");\n", + "bgp3 = Span(location = b1[1], dimension = \"width\", line_dash=\"dashed\");\n", + "bgp4 = Span(location = -b1[1], dimension = \"width\", line_dash=\"dashed\");\n", + "bragg3 = img.renderers.extend([bgp1,bgp2,bgp3,bgp4]);\n", + "show(row(real, img))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# color the third (along with the first and second) brillouin zones\n", + "bz3 = img.patches([[-b2[0], -b2[0], b2[0], b2[0]],[-b2[0]/2, -b2[0]/2, b2[0]/2, b2[0]/2]], [[-b1[1]/2, b1[1]/2, b1[1]/2, -b1[1]/2], [-b1[1], b1[1], b1[1], -b1[1]]], line_width = 2, color = \"indigo\");\n", + "show(row(real,img))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# overlay all three Brillouin zones along with the bragg planes\n", + "bz3.visible = True;\n", + "bz3.level = \"underlay\";\n", + "bz2.visible = True;\n", + "bz2.level = \"glyph\";\n", + "bz1.visible = True;\n", + "bz1.level = \"overlay\";\n", + "vec3.visible = False;\n", + "arrow1.visible = False;\n", + "arrow2.visible = False;\n", + "arrow3.visible = False;\n", + "arrow4.visible = False;\n", + "show(row(real, img))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/figs/diagram-normalcurve.png b/examples/figs/diagram-normalcurve.png new file mode 100644 index 0000000..4211516 Binary files /dev/null and b/examples/figs/diagram-normalcurve.png differ diff --git a/examples/figs/diagram-normalcurveAB.png b/examples/figs/diagram-normalcurveAB.png new file mode 100644 index 0000000..b3b92b5 Binary files /dev/null and b/examples/figs/diagram-normalcurveAB.png differ diff --git a/examples/figs/diagram-normalcurveAlpha.png b/examples/figs/diagram-normalcurveAlpha.png new file mode 100644 index 0000000..ff72970 Binary files /dev/null and b/examples/figs/diagram-normalcurveAlpha.png differ diff --git a/examples/figs/diagram-samples.png b/examples/figs/diagram-samples.png new file mode 100644 index 0000000..5fc20f3 Binary files /dev/null and b/examples/figs/diagram-samples.png differ diff --git a/examples/figs/iris-picture.jpg b/examples/figs/iris-picture.jpg new file mode 100644 index 0000000..9e9bf50 Binary files /dev/null and b/examples/figs/iris-picture.jpg differ diff --git a/examples/resources/Cylinder-Re=1dot54.png b/examples/resources/Cylinder-Re=1dot54.png new file mode 100644 index 0000000..a6ea0de Binary files /dev/null and b/examples/resources/Cylinder-Re=1dot54.png differ diff --git a/examples/resources/Logan.png b/examples/resources/Logan.png new file mode 100644 index 0000000..3f956ef Binary files /dev/null and b/examples/resources/Logan.png differ diff --git a/examples/resources/doubletSketch1.png b/examples/resources/doubletSketch1.png new file mode 100644 index 0000000..7609706 Binary files /dev/null and b/examples/resources/doubletSketch1.png differ diff --git a/examples/resources/doubletSketch2.png b/examples/resources/doubletSketch2.png new file mode 100644 index 0000000..3a388e1 Binary files /dev/null and b/examples/resources/doubletSketch2.png differ diff --git a/examples/resources/iris-sample1-vullierens.csv b/examples/resources/iris-sample1-vullierens.csv new file mode 100644 index 0000000..7928165 --- /dev/null +++ b/examples/resources/iris-sample1-vullierens.csv @@ -0,0 +1,51 @@ +petal_length +6.0 +5.9 +5.5 +5.7 +5.4 +5.9 +6.7 +5.8 +6.1 +6.2 +6.8 +5.2 +5.0 +4.4 +4.4 +5.9 +6.4 +4.9 +6.0 +5.3 +5.8 +6.1 +4.6 +6.0 +5.4 +5.7 +5.1 +5.1 +5.6 +5.6 +6.7 +6.5 +5.7 +5.3 +6.2 +6.1 +5.6 +5.9 +5.1 +4.9 +6.0 +5.8 +6.5 +6.6 +4.8 +4.8 +6.0 +5.5 +5.0 +5.4 diff --git a/examples/resources/iris-sample2-meiji.csv b/examples/resources/iris-sample2-meiji.csv new file mode 100644 index 0000000..3919d65 --- /dev/null +++ b/examples/resources/iris-sample2-meiji.csv @@ -0,0 +1,51 @@ +petal_length +5.7 +4.5 +5.8 +5.1 +6.2 +6.1 +5.3 +5.7 +6.4 +5.2 +5.7 +6.7 +5.8 +5.6 +5.6 +5.3 +6.1 +5.7 +5.4 +5.7 +5.0 +5.4 +5.6 +5.8 +5.6 +5.1 +5.7 +4.9 +5.5 +6.2 +5.6 +6.8 +6.1 +4.5 +6.5 +5.8 +5.8 +5.9 +5.5 +5.7 +6.6 +6.0 +5.7 +6.3 +5.8 +6.0 +5.3 +5.4 +6.2 +5.9 diff --git a/examples/solution/ExampleB-solution.ipynb b/examples/solution/ExampleB-solution.ipynb new file mode 100644 index 0000000..d499e8f --- /dev/null +++ b/examples/solution/ExampleB-solution.ipynb @@ -0,0 +1,81 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solution of the exercise\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**A. Getting familiar with the code.**\n", + "1. In the code cells above, where is the t-test computed using the predefined Python function? \n", + " 🡆 The predefined Python function for computing a one-sample t-test is `stats.ttest_1samp`. You can search for it in the notebook by typing `ctrl + F` or using the top menu `Edit` > `Find`.\n", + "\n", + "1. What are the two parameters that the t-test function takes as input? \n", + " 🡆 The two input parameters of the `stats.ttest_1samp` function are: a) the list of all the sample values, in our case `sample_data[\"petal_length\"]`, and b) the population reference value for comparison, in our case `mu`.\n", + "\n", + "1. If you wanted to change the population mean to a different value, like $\\mu = 5.4$ cm for instance, in which cell would you change it? \n", + "Then what would you need to do to update the result of the analysis in the notebook? \n", + " 🡆 You could change it in two different places: in the cell where `mu` is defined, at the top of the notebook, or you could replace `mu` with the value `5.4` in the cell where the function `stats.ttest_1samp` is called. \n", + "     Then you would have to *re-execute the cells* of the notebook which do the analysis to update the results.\n", + "\n", + "1. What is the result of the t-test if you compare the Vullierens sample to a population mean of $\\mu = 5.4$ cm? \n", + " 🡆 The difference becomes statistically significant with $t=3.025$ and $p=0.004 \\lt \\alpha$, in which case we would reject our null hypothesis and conclude that our sample is probably from a different population. \n", + "     However, the effect size remains below the medium threshold, $d=0.428$.\n", + "\n", + "*Change the value of $\\mu$ back to 5.552 before working on the following questions.*\n", + "\n", + "**B. Analyzing another dataset.**\n", + "\n", + "A researcher from Tokyo sends you the results of a series of measurements she has done on the Irises of the [Meiji Jingu Imperial Gardens](http://www.meijijingu.or.jp/english/nature/2.html). The dataset can be found in the `iris-sample2-meiji.csv` file. \n", + "How similar (or different) is the Meiji sample compared to the Iris virginica population documented by Edgar Anderson? \n", + "The following questions are designed to guide you in analyzing this new dataset using this notebook.\n", + "\n", + "1. Which of the code cells above loads the data from the file containing the Vullierens dataset? Modify it to load the Meiji dataset. \n", + " 🡆 The function to read a CSV file in Python is `pan.read_csv`. To load the Meiji dataset, you have to replace its input parameter with the name of the file containing the second dataset, `'iris-sample2-meiji.csv'`.\n", + "\n", + "1. Do you need to modify anything else in the code to analyze this new dataset? \n", + " 🡆 There is nothing else to change in the code to analyse this new dataset since the new sample values are simply stored in the `sample_data` variable, in replacement of the previous values. \n", + "     However, you have to *re-execute all the code cells* which compute the analysis so that all the calculations are made with the new values. \n", + "     Note that you can use the top menu `Run` > `Run All Cells` to execute all the code cells of the notebook all at once.\n", + "\n", + "1. What can you conclude about the Meiji sample from this analysis? \n", + " 🡆 The difference in petal length mean between the Meiji sample and the Anderson population is statistically significant at the 5% level with $t=2.352$ and $p=0.023$, but a relatively small effect size $d=0.333$. \n", + "     In this case we could reject our null hypothesis and conclude that the Meiji sample is probably from a different population.\n", + "\n", + "**C. Going a bit further in the interpretation of the t-test.**\n", + "1. In the code cells above, where is the cut-off point $\\alpha$ defined? Change its value to 0.01 and re-execute the notebook. \n", + " 🡆 Search for the variable `alpha` in the notebook and modify its assignment in `alpha = 0.01`. Then re-execute the notebook.\n", + "\n", + "1. How does this affect the result of the t-test for the Meiji sample? \n", + " 🡆 When choosing a significance level of 1% ($\\alpha = .01$), the difference in petal length mean between the Meiji sample and the Anderson population cannot be considered statistically significant. \n", + "     This means that the evidence we have is not strong enough if we want to be 99% sure. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lib/suspendedobjects.py b/lib/suspendedobjects.py index 2eea9ef..390c85b 100644 --- a/lib/suspendedobjects.py +++ b/lib/suspendedobjects.py @@ -1,377 +1,389 @@ import numpy as np from operator import add from ipywidgets import interact, interactive, fixed, interact_manual from ipywidgets import HBox, VBox, Label, Layout import ipywidgets as widgets from IPython.display import IFrame from IPython.display import set_matplotlib_formats, display, Math, Markdown, Latex, HTML set_matplotlib_formats('svg') from bokeh.io import push_notebook, show, output_notebook, curdoc from bokeh.plotting import figure from bokeh.models import Legend, ColumnDataSource, Slider, Span, LegendItem from bokeh.models import Arrow, OpenHead, NormalHead, VeeHead, LabelSet from bokeh.models.glyphs import Wedge, Bezier from bokeh.layouts import gridplot, row, column output_notebook() class SuspendedObjectsLab: """ This class embeds all the necessary code to create a virtual lab to study the static equilibrium of an object suspended on a clothesline with a counterweight. """ def __init__(self, m_object = 3, distance = 5, height = 1, x_origin = 0, y_origin = 0): ''' Initiates and displays the virtual lab on suspended objects. :m_object: mass of the suspended object :distance: horizontal distance between the two poles :height: height of the poles (same height for both) :x_origin: x coordinate of the bottom of the left pole (origin of the coordinate system) :y_origin: y coordinate of the bottom of the left pole (origin of the coordinate system) ''' ###--- Static parameters of the situation self.m_object = m_object # mass of the wet object, in kg self.distance = distance # distance between the poles, in m self.height = height # height of the poles, in m self.x_origin = x_origin # x coordinate of point of origin of the figure = x position of the left pole, in m self.y_origin = y_origin # y coordinate of point of origin of the figure = y position of the lower point (ground), in m - ###--- Then we define the elements of the ihm: + ###--- Elements of the ihm: # parameters for sliders self.m_counterweight_min = 0.0 self.m_counterweight_max = 100.0 self.m_counterweight = self.m_counterweight_min # initial mass of the counterweight (0 by default, no counterweight at the beginning) # IHM input elements self.m_counterweight_label = Label('Mass of the counterweight ($kg$):', layout=Layout(margin='0px 5px 0px 0px')) self.m_counterweight_widget = widgets.FloatSlider(min=self.m_counterweight_min,max=self.m_counterweight_max,step=0.5,value=self.m_counterweight, layout=Layout(margin='0px')) self.m_counterweight_note = Label('[Note: once you have clicked on the slider (the circle becomes blue), you can use the arrows from your keyboard to make it move.]', layout=Layout(margin='0px 0px 15px 0px')) self.m_counterweight_input = VBox([HBox([self.m_counterweight_label, self.m_counterweight_widget], layout=Layout(margin='0px')), self.m_counterweight_note]) - # IHM output elements - self.quiz_output = widgets.Output() - # Linking widgets to handlers self.m_counterweight_widget.observe(self.m_counterweight_event_handler, names='value') + # IHM output element for moodle quiz + self.quiz_output = widgets.Output() ###--- Compute variables dependent with the counterweight selected by the user alpha = self.get_angle(self.m_counterweight) alpha_degrees = alpha*180/np.pi alpha_text = '⍺ = {:.2f} °'.format(alpha_degrees) coord_object = self.get_object_coords(alpha) height_text = 'h = {:.2f} m'.format(coord_object[1]) - ###--- Create the figure ---### # LIMITATIONS of Bokeh (BokehJS 1.4.0) # - labels: impossible to use LaTeX formatting in labels # - arc: impossible to take into account figure ratio when dynamic rescaling is activated # - forces/vectors: impossible to adjust the line_color and line_dash of OpenHead according to datasource ###--- First display the clothesline # Fix graph to problem boundaries ymargin = .05 xmargin = .2 fig_object = figure(title='Suspended object ({} kg)'.format(self.m_object), #plot_width=600, plot_height=400, sizing_mode='stretch_height', y_range=(self.y_origin-ymargin,self.y_origin+self.height+ymargin), x_range=(self.x_origin-xmargin,self.x_origin+self.distance+xmargin), background_fill_color='#ffffff', toolbar_location=None) fig_object.title.align = "center" fig_object.yaxis.axis_label = 'Height (m)' # Customize graph style so that it doesn't look too much like a graph fig_object.ygrid.visible = False fig_object.xgrid.visible = False fig_object.outline_line_color = None # trick to keep the three graphs aligned, while having auto scaling fig_object.xaxis.axis_label = " " fig_object.xaxis.axis_line_color = "white" fig_object.xaxis.axis_label_text_color = "white" fig_object.xaxis.major_label_text_color = "white" fig_object.xaxis.major_tick_line_color = "white" fig_object.xaxis.minor_tick_line_color = "white" # Draw the horizon line fig_object.add_layout(Span(location=height, dimension='width', line_color='gray', line_dash='dashed', line_width=1)) # Draw the poles fig_object.line([x_origin, x_origin], [y_origin, y_origin+height], color="black", line_width=8, line_cap="round") fig_object.line([x_origin+distance, x_origin+distance], [y_origin, y_origin+height], color="black", line_width=8, line_cap="round") # Draw the ground fig_object.add_layout(Span(location=x_origin, dimension='width', line_color='black', line_width=1)) fig_object.hbar(y=y_origin-ymargin, height=ymargin*2, left=x_origin-xmargin, right=x_origin+distance+xmargin, color="white", line_color="white", hatch_pattern="right_diagonal_line", hatch_color="gray") # --DYN-- Draw the point at which the object is suspended (this data source also used for the other graphs) self.object_source = ColumnDataSource(data=dict( x=[coord_object[0]], y=[coord_object[1]], m_counterweight=[self.m_counterweight], alpha_degrees=[alpha_degrees], height_text=[height_text], alpha_text=[alpha_text] )) fig_object.circle(source=self.object_source, x='x', y='y', size=8, fill_color="black", line_color='black', line_width=2) fig_object.add_layout(LabelSet(source=self.object_source, x='x', y='y', text='height_text', level='glyph', x_offset=8, y_offset=-20)) # --DYN-- Draw the hanging cable self.cable_source = ColumnDataSource(data=dict( x=[self.x_origin, coord_object[0], self.x_origin+self.distance], y=[self.y_origin+self.height, coord_object[1], self.y_origin+self.height] )) fig_object.line(source=self.cable_source, x='x', y='y', color="black", line_width=2, line_cap="round") # --DYN-- Draw the angle between the hanging cable and horizonline # Trick here: we use a straight line because the Arc glyph doesn't support dynamic figure ratio self.radius=0.3 ratio=1.5 x0=self.x_origin+ratio*self.radius y0=self.y_origin+self.height x1=(self.x_origin+self.radius*np.cos(alpha)) y1=(self.y_origin+self.height-self.radius*np.sin(alpha)) self.alpha_arc = fig_object.line([x0, x1], [y0, y1], color="gray", line_width=1, line_dash=[2,2]) fig_object.add_layout(LabelSet(source=self.object_source, x=self.x_origin, y=self.y_origin+self.height, text='alpha_text', level='glyph', x_offset=50, y_offset=-20)) # --DYN-- Draw the force vectors # Parameters for drawing forces self.gravity = 9.81 self.force_scaling = .01 self.forces_nb = 4 # Weight Fy = self.m_object*self.gravity*self.force_scaling # Tension Tx = ((self.m_object*self.gravity) / (2*np.tan(alpha)))*self.force_scaling Ty = .5*self.m_object*self.gravity*self.force_scaling self.forces_x_start = [coord_object[0]]*self.forces_nb self.forces_y_start = [coord_object[1]]*self.forces_nb - self.forces_x_end = [0, Tx, -Tx, 0] - self.forces_y_end = [-Fy, Ty, Ty, Fy] + self.forces_x_mag = [0, Tx, 0, -Tx] + self.forces_y_mag = [-Fy, Ty, Fy, Ty] self.forces_source = ColumnDataSource(data=dict( x_start=self.forces_x_start, y_start=self.forces_y_start, - x_end=list(map(add, self.forces_x_start, self.forces_x_end)), - y_end=list(map(add, self.forces_y_start, self.forces_y_end)), - name=["F", "T", "T", "Tr"], - color=["blue", "red", "red", "red"], - dash=["solid", "solid", "solid", [2,2]], - x_offset=[8, 25, -35, 8], - y_offset=[-45, 6, 6, 45] + x_end=list(map(add, self.forces_x_start, self.forces_x_mag)), + y_end=list(map(add, self.forces_y_start, self.forces_y_mag)), + name=["F", "T", "Tr", "T"], + color=["blue", "red", "gray", "red"], + dash=["solid", "solid", [2,2], "solid"], + x_offset=[8, 25, 8, -35], + y_offset=[-45, 6, 45, 6] )) # Draw the arrows forces_arrows = Arrow(source=self.forces_source, x_start='x_start', y_start='y_start', x_end='x_end', y_end='y_end', - line_color='color', line_width=2, end=OpenHead(line_width=2, size=12, line_color='red')) + line_color='color', line_width=2, end=OpenHead(line_width=2, size=12, line_color='black')) fig_object.add_layout(forces_arrows) # Add the labels forces_labels = LabelSet(source=self.forces_source, x='x_start', y='y_start', text='name', text_color='color', level='glyph', x_offset='x_offset', y_offset='y_offset', render_mode='canvas') fig_object.add_layout(forces_labels) + # --DYN-- Draw the tension projection lines + self.proj_source = ColumnDataSource(data=dict( + x=self.forces_source.data["x_end"][1:4], + y=self.forces_source.data["y_end"][1:4] + )) + fig_object.line(source=self.proj_source, x='x', y='y', color="gray", line_width=1, line_dash="dashed") + + ###--- Then display the angle and the height as functions from the mass of the counterweight # Create all possible values of the mass of the counterweight m_cw_list = np.linspace(self.m_counterweight_min, self.m_counterweight_max, 100) # Compute the angle (in degrees) and height for all these values angle_list = [] height_list = [] for m in m_cw_list: a = self.get_angle(m) angle_list.append(a*180/np.pi) c = self.get_object_coords(a) height_list.append(c[1]) ### Create the graph for height fig_height = figure(title='Height (m)', #plot_height=400, plot_width=200, y_range=(-ymargin,self.height+ymargin), x_range=(0,102), background_fill_color='#ffffff', toolbar_location=None) fig_height.title.align = "center" fig_height.xaxis.axis_label = 'Mass of the counterweight (kg)' fig_height.grid.grid_line_color = 'lightgray' fig_height.grid.minor_grid_line_color = 'gainsboro' # Draw the curve of the function fig_height.line(m_cw_list, height_list, color="green", line_width=1) # Draw the horizon line fig_height.add_layout(Span(location=1, dimension='width', line_color='gray', line_dash='dashed', line_width=1)) # --DYN-- Add the current height from the counterweight selected by the user fig_height.circle(source=self.object_source, x='m_counterweight', y='y', size=8, fill_color="black", line_color='black', line_width=2, legend_field="height_text") fig_height.legend.location = "bottom_right" fig_height.legend.label_text_font_size = '12pt' ### Create the graph for angle fig_alpha = figure(title='Angle ⍺ (°)', #plot_height=400, plot_width=400, y_range=(-1,23), x_range=(0,102), background_fill_color='#ffffff', toolbar_location=None) fig_alpha.title.align = "center" fig_alpha.xaxis.axis_label = 'Mass of the counterweight (kg)' fig_alpha.grid.grid_line_color = 'lightgray' fig_alpha.grid.minor_grid_line_color = 'gainsboro' # Draw the curve of the function c = fig_alpha.line(m_cw_list, angle_list, color="green", line_width=1) # Draw the horizon line fig_alpha.add_layout(Span(location=0, dimension='width', line_color='gray', line_dash='dashed', line_width=1)) # --DYN-- Add the current angle from the counterweight selected by the user fig_alpha.circle(source=self.object_source, x='m_counterweight', y='alpha_degrees', size=8, fill_color="black", line_color='black', line_width=2, legend_field="alpha_text") fig_alpha.legend.location = "top_right" fig_alpha.legend.label_text_font_size = '12pt' ###--- Add a quiz from Moodle iframe_quiz = ''' ''' widget_quiz = widgets.HTML(value=iframe_quiz) ###--- Display the whole interface show(row(column(children=[fig_object], sizing_mode='stretch_height'), fig_alpha, fig_height, sizing_mode='scale_both'), notebook_handle=True) display(VBox([self.m_counterweight_input, widget_quiz])) # Event handlers def m_counterweight_event_handler(self, change): # get new value of counterweight mass self.m_counterweight = change.new # compute all problem values alpha = self.get_angle(self.m_counterweight) alpha_degrees = alpha*180/np.pi alpha_text = '⍺ = {:.2f} °'.format(alpha_degrees) coord_object = self.get_object_coords(alpha) height_text = 'h = {:.2f} m'.format(coord_object[1]) self.forces_y_start = [coord_object[1]]*self.forces_nb Tx = ((self.m_object*self.gravity) / (2*np.tan(alpha)))*self.force_scaling - self.forces_x_end = [0, Tx, -Tx, 0] + self.forces_x_mag = [0, Tx, 0, -Tx] # update the object representation on all graphs (coordinates+labels) self.object_source.data = dict( x=[coord_object[0]], y=[coord_object[1]], m_counterweight=[self.m_counterweight], alpha_degrees=[alpha_degrees], height_text=[height_text], alpha_text=[alpha_text] ) # update line representing the angle alpha self.alpha_arc.data_source.patch({ 'x' : [(1, self.x_origin+self.radius*np.cos(alpha))], 'y' : [(1, self.y_origin+self.height-self.radius*np.sin(alpha))] }) # update the point where the object is attached on cable self.cable_source.patch({ 'x' : [(1, coord_object[0])], 'y' : [(1, coord_object[1])] }) - # update point of start for F and Tr, update Tx and Ty for T + # update point of start for all forces, update Tx and Ty for T self.forces_source.patch({ 'y_start' : [(slice(self.forces_nb), self.forces_y_start)], - 'x_end' : [(slice(self.forces_nb), list(map(add, self.forces_x_start, self.forces_x_end)))], - 'y_end' : [(slice(self.forces_nb), list(map(add, self.forces_y_start, self.forces_y_end)))], + 'x_end' : [(slice(self.forces_nb), list(map(add, self.forces_x_start, self.forces_x_mag)))], + 'y_end' : [(slice(self.forces_nb), list(map(add, self.forces_y_start, self.forces_y_mag)))], + }) + + # update the tension projection lines + self.proj_source.patch({ + 'x' : [(slice(3), self.forces_source.data["x_end"][1:4])], + 'y' : [(slice(3), self.forces_source.data["y_end"][1:4])] }) push_notebook() # Utility functions def get_angle(self, m_counterweight): """ Computes the angle that the cable makes with the horizon depending on the counterweight chosen: - if the counterweight is sufficient: angle = arcsin(1/2 * m_object / m_counterweight) - else (object on the ground): alpha = arctan(height / (distance / 2)) :m_counterweight: mass of the chosen counterweight :returns: angle that the cable makes with the horizon (in rad) """ # Default alpha value i.e. object is on the ground alpha_default = np.arctan(self.height / (self.distance / 2)) alpha = alpha_default # Let's check that there is actually a counterweight if m_counterweight > 0: # Then we compute the ratio of masses ratio = 0.5 * self.m_object / m_counterweight # Check that the ratio of masses is in the domain of validity of arcsin ([-1;1]) if abs(ratio) < 1: alpha = np.arcsin(ratio) return min(alpha_default, alpha) def get_object_coords(self, angle): """ Computes the position of the object on the cable taking into account the angle determined by the counterweight and the dimensions of the hanging system. By default: - the object is supposed to be suspended exactly in the middle of the cable - the object is considered on the ground for all values of the angle which give a height smaller than the height of the poles :angle: angle that the cable makes with the horizon :returns: coordinates of the point at which the object are hanged """ # the jean is midway between the poles x_object = self.x_origin + 0.5 * self.distance # default y value: the jean is on the ground y_object = self.y_origin # we check that the angle is comprised between horizontal (greater than 0) and vertical (smaller than pi/2) if angle > 0 and angle < (np.pi / 2): # we compute the delta between the horizon and the point given by the angle delta = (0.5 * self.distance * np.tan(angle)) # we check that the delta is smaller than the height of the poles (otherwise it just means the jean is on the ground) if delta <= self.height: y_object = self.y_origin + self.height - delta return [x_object, y_object] def degrees_to_radians(angle_degrees): return angle_degrees * np.pi / 180 def radians_to_degrees(angle_radians): return angle_radians * 180 / np.pi # EOF diff --git a/solution/Physics-solution.ipynb b/solution/Physics-solution.ipynb index 7dd31b6..554b811 100644 --- a/solution/Physics-solution.ipynb +++ b/solution/Physics-solution.ipynb @@ -1,112 +1,112 @@ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", - " Teaching and Learning differently with Jupyter Notebooks - HEG, December 12, 2019
\n", + " Jupyter Notebooks for Teaching and Learning - LEARN Hour of Discovery, January 28, 2020
\n", " C. Hardebolle, CC BY-NC-SA 4.0 Int.
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Solutions\n", "\n", "---\n", "\n", "## Activity 2: Looking more closely at the tension in the cable\n", "\n", "\n", "Question 1 \n", "\n", "$\\lvert\\vec{T}\\rvert$ = 1686 $N$\n", "\n", "Question 2 \n", "\n", "The tension reaches 500 N for approximately 1.5°. \n", "The tension reaches 1000 N for approximately 1°.\n", "\n", "Question 3 \n", "\n", "At time 0:53, the cable desintegrates completely. This is probably because it has grown weaker and weaker after repeated use until it cannot withstand the high tension anymore.\n", "\n", "
\n", "\n", "---\n", "\n", "## Additional exercises (optional)\n", "\n", "Question 1 \n", "\n", "A simple solution is:\n", "```python\n", "T_prime = T / gravity \n", "``` \n", "\n", "This would give: $\\lvert\\vec{T}\\rvert = $ 172 kg$_F$ for a tension of 1686 $N$.\n", "\n", "A better designed option would be to first define a function so that you can reuse it at any time, then use the function to compute the value:\n", "```python\n", "def newtons_to_kgf(tension):\n", " return tension / gravity\n", "\n", "T_prime = newtons_to_kgf(T)\n", "```\n", "\n", "\n", "Question 2 \n", "\n", "Two things happen:\n", "* You get a warning message which says `RuntimeWarning: divide by zero encountered in double_scalars`\n", "* The function nonetheless returns a value which is `inf`\n", "\n", "When the angle $\\alpha$ = 0 then $sin(\\alpha)$ = 0 and therefore we divide `(.5 * jeans_mass * gravity)` by 0 so on one hand, mathematically speaking, we know that the result should be $+\\infty$. On the other hand, we also know that usually division by 0 is not well supported by computers.\n", "\n", "Actually, division by 0 is not supported in standard Python. Try to execute the cell below to see what happens." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(0.5 * 3 * 9.81)/0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, because in the calculation we use the function `np.sin()` from the Numpy library, our data is automatically converted to Numpy types, which support division by zero and returns the \"real\" result which is $+\\infty$. \n", "By convention, Numpy also generates a warning message but this can be deactivated when not necessary. \n", "If you are curious, you can take a look at [the errors generated by Numpy for floating-point calculations](https://docs.scipy.org/doc/numpy/reference/generated/numpy.seterr.html#numpy.seterr)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 4 }