refraction2_pres.ipynb

{"cells":[{"metadata":{"deletable":false,"editable":false,"hide_input":true,"hide_output":true,"init_cell":true,"run_control":{"frozen":true,"marked":false},"slideshow":{"slide_type":"skip"},"trusted":true},"cell_type":"code","source":"from IPython.display import Javascript\n\nJavascript(\"\"\"let timerId = setInterval(() => Jupyter.notebook.execute_cells([1]), 60000);\nsetTimeout(() => { clearInterval(timerId); alert('ATTENTION! fin de cession. Sauvegardez votre travail.'); },7200000);\n    IPython.OutputArea.prototype._should_scroll = function(lines) {\n    return false;\n};\n    require(\n        ['base/js/namespace', 'jquery'], \n        function(jupyter, $) {\n            \n                \n                jupyter.actions.call('jupyter-notebook:run-all-cells-below');\n                jupyter.actions.call('jupyter-notebook:save-notebook');\n\n        }\n    );\"\"\")","execution_count":1,"outputs":[{"data":{"application/javascript":"let timerId = setInterval(() => Jupyter.notebook.execute_cells([1]), 60000);\nsetTimeout(() => { clearInterval(timerId); alert('ATTENTION! fin de cession. Sauvegardez votre travail.'); },7200000);\n    IPython.OutputArea.prototype._should_scroll = function(lines) {\n    return false;\n};\n    require(\n        ['base/js/namespace', 'jquery'], \n        function(jupyter, $) {\n            \n                \n                jupyter.actions.call('jupyter-notebook:run-all-cells-below');\n                jupyter.actions.call('jupyter-notebook:save-notebook');\n\n        }\n    );","text/plain":"<IPython.core.display.Javascript object>"},"execution_count":1,"metadata":{},"output_type":"execute_result"}]},{"metadata":{"deletable":false,"editable":false,"hide_input":true,"hide_output":true,"run_control":{"frozen":true,"marked":false},"slideshow":{"slide_type":"skip"},"trusted":true},"cell_type":"code","source":"A = 2+3","execution_count":2,"outputs":[]},{"metadata":{"rise":{"autolaunch":true,"theme":"sky"},"slideshow":{"slide_type":"slide"}},"cell_type":"markdown","source":"<h1 style='text-align:center; width:900px;'>  TP réfraction </h1>"},{"metadata":{"slideshow":{"slide_type":"subslide"}},"cell_type":"markdown","source":"Regardez la vidéo d'expérience ci-dessous."},{"metadata":{"hide_input":true,"hide_output":false,"run_control":{"marked":false},"scrolled":true,"slideshow":{"slide_type":"-"},"trusted":true},"cell_type":"code","source":"from IPython.display import YouTubeVideo\n\nYouTubeVideo('2lboRG60DoA', width=900, height=500)\n","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"<IPython.lib.display.YouTubeVideo at 0x7f4dd7153d10>","text/html":"\n        <iframe\n            width=\"900\"\n            height=\"500\"\n            src=\"https://www.youtube.com/embed/2lboRG60DoA\"\n            frameborder=\"0\"\n            allowfullscreen\n        ></iframe>\n        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expérience est reproduite dans la simulaton ci-dessous. Attention, dans la vidéo le changement de milieu se fait de gauche à droite alors que dans la simulation elle se fait de haut en bas.\n\nCe que vous avez à faire:\n* Si vous êtes en salle de TP, utilisez le matériel mis à votre disposition afin de réaliser cette expérience, si vous êtes à la maison, utilisez la simulation\n* Relevez des valeurs d'angle d'incidence et d'angle de réfraction correspondant sur votre feuille. Il faudra au moins 6 couples de valeurs différentes bien réparties entre 0 et 90°.\n* Une fois terminé, cliquez sur \"exploiter\" afin de modéliser vos données en python\n* Suivez les consignes du document de modélisation afin de le rendre à l'enseignant "},{"metadata":{"hide_input":true,"hide_output":false,"init_cell":true,"rise":{"enable_chalkboard":true,"scroll":true},"run_control":{"marked":false},"slideshow":{"slide_type":"slide"},"trusted":true},"cell_type":"code","source":"from math import cos,pi,sin,asin\nfrom ipywidgets import widgets\nfrom ipywidgets.widgets import FloatSlider, Layout, Button, HBox, VBox\nfrom IPython.display import display\nimport ipyvuetify as v\nfrom bqplot import *\n\n# parametres de la simulation\nn1 = 1.\nn2 = 1.33\n\n# actions lorsque lors des événemets sur le slider\ndef augmente(a,b,c):\n    temp = test.v_model\n    temp += test.step\n    test.v_model = temp\n    setparam()\n\ndef diminue(a,b,c):\n    temp = test.v_model\n    temp -= test.step\n    test.v_model = temp\n    setparam()\n    \ndef maj_slider(a,b,c):\n    setparam()\n    \n    \n# slider    \ntest = v.Slider(v_model = 45, min = 0, max = 90, step = 0.1, append_icon = 'mdi-plus', prepend_icon = 'mdi-minus')\ntest.on_event(\"end\",maj_slider)\ntest.on_event(\"click\",maj_slider)\ntest.on_event(\"click:append\",augmente)\ntest.on_event(\"click:prepend\",diminue)\ntest.layout.height='150px'\ntest.layout.width = '900px'\n\n# 3 figures\nx_inc = [0,0]\ny_inc = [0,0]\nx_refr = [[1,1],[2,2]]\ny_refr = [[-1,1],[-1,1]]\nx_refl = []\ny_refl = []\nx_regle = [[i,i] for i in range(0,91)]\ny_regle = [[0,1] for i in range(0,91)]\ni1_x = [45,45]\ni1_y = [0,2]\ni2_x = [39,39]\ni2_y = [0,2]\nvect_lum = [0,1]\nvect_perp = [-1,0]\n\n# ajustement des paramètres des figures\ndef setparam():\n    global n1,n2\n    i1 = test.v_model\n    imin = 5*int(i1/5)-5\n    R1=1/cos(i1*pi/180)\n    x1=-R1*sin(i1*pi/180)\n    vect_lum = [-x1/(1+x1**2)**0.5,-1/(1+x1**2)**0.5]\n    vect_perp = [-vect_lum[1],vect_lum[0]]         \n    x_sc_regle1.min=imin\n    x_sc_regle1.max=imin+10\n    if -1<= n1/n2*sin(i1*pi/180) <= 1:\n        i2 = 180/pi*asin(n1/n2*sin(i1*pi/180))\n        imin2 = 5*int(i2/5)-5\n        R2=-1/cos(i2*pi/180)\n        x2=abs(R2*sin(i2*pi/180))\n        x_inc = [x1,0,x2]\n        y_inc = [1,0,-1]\n        \n\n        line.x = x_inc\n        line.y = y_inc\n        x_refl = [0,-x1]\n        y_refl = [0,1]\n        line_refl.x = x_refl\n        line_refl.y = y_refl\n        i1_x = [i1,i1]\n        i2_x = [i2,i2]\n        line_inc.x=i1_x\n        i2_y = [0,2]\n        line_refr.y=i2_y\n        line_refr.x=i2_x\n        rt = False\n        x_sc_regle2.min=imin2\n        x_sc_regle2.max=imin2+10\n    else:\n        x_inc = [x1,0,-x1]\n        y_inc = [1,0,1]\n        line.x = x_inc\n        line.y = y_inc\n        x_refl = [0,-x1]\n        y_refl = [0,1]\n        line_refl.x = x_refl\n        line_refl.y = y_refl\n        i1_x = [i1,i1]\n        i2_x = []\n        i2_y = []\n        line_refr.y=i2_y\n        line_inc.x=i1_x\n        line_refr.x=i2_x\n    sens.x = [-0.5*vect_lum[0],-0.55*vect_lum[0]+0.05*vect_perp[0],-0.5*vect_lum[0],-0.55*vect_lum[0]-0.05*vect_perp[0]],\n    sens.y = [-0.5*vect_lum[1],-0.55*vect_lum[1]+0.05*vect_perp[1],-0.5*vect_lum[1],-0.55*vect_lum[1]-0.05*vect_perp[1]],\n    \n        \n\n\nx_sc = LinearScale(min=-1,max=1)\ny_sc = LinearScale(min=-1,max=1)\nx_sc_regle1 = LinearScale()\ny_sc_regle1 = LinearScale(min=0.1,max=3)\nx_sc_regle2 = LinearScale()\ny_sc_regle2 = LinearScale(min=0.1,max=3)\n\nax_x = Axis(label='', visible=False, scale=x_sc, tick_format='0.0f')\nax_y = Axis(label='', visible=False, scale=y_sc,\n            orientation='vertical', tick_format='0.0f')\nax_xregle1 = Axis(label=\"angle d'incidence i1\", scale=x_sc_regle1, tick_format='d',num_ticks=3,color=\"black\",grid_lines='none')\nax_yregle1 = Axis(label='', visible=False, scale=y_sc_regle1,\n            orientation='vertical', tick_format='d')\nax_xregle2 = Axis(label=\"angle de réfraction i2\", scale=x_sc_regle2, tick_format='d',num_ticks=3,color=\"black\",grid_lines='none')\nax_yregle2 = Axis(label='', visible=False, scale=y_sc_regle2,\n            orientation='vertical', tick_format='d')\n\n# figure principale\nline = Lines(x=x_inc,\n             y=y_inc,\n             scales={'x': x_sc, 'y': y_sc},\n             colors=['red'])\nsens = Lines(x=x_inc,\n             y=y_inc,\n             scales={'x': x_sc, 'y': y_sc},\n             colors=['red'])\nline_refl = Lines(x=x_refl,\n             y=y_refl,\n             scales={'x': x_sc, 'y': y_sc},\n             colors=['red'],\n             line_style='dashed')\nline_axes = Lines(x=[-1,1], y = [0,0], scales={'x':x_sc, 'y':y_sc},colors=['black'])\nline_axes2 = Lines(x=[0,0], y = [-1,1], scales={'x':x_sc, 'y':y_sc},colors=['black'],line_style='dashed')\nangles = Label(x = [0.45,0.52,0.5], y = [0.55, 0.55,0.45], text =  [\"i1\",\"r\",\"i2\"],colors=['blue'])\nfig = Figure(axes=[ax_x, ax_y], marks=[line_axes,line_axes2,line,sens,line_refl,angles])\nfig.layout.height = '580px'\n\n# graduations angle d'incidence\nline2 = Lines(x=x_regle,\n             y=y_regle,\n             scales={'x': x_sc_regle1, 'y': y_sc_regle1},\n             colors=['black'],stroke_width=2)\nline_inc = Lines(x = i1_x, y= i1_y, scales={'x': x_sc_regle1, 'y': y_sc_regle1},\n             colors=['red'], stroke_width=5)\n\nfig2 = Figure(axes=[ax_xregle1, ax_yregle1], marks=[line_inc, line2],linewidth=[4,2])\nfig2.layout.height = '280px'\n\n# graduations angle de réfraction\nline3 = Lines(x=x_regle,\n             y=y_regle,\n             scales={'x': x_sc_regle2, 'y': y_sc_regle2},\n             colors=['black'],stroke_width=2)\nline_refr = Lines(x = i2_x, y= i2_y, scales={'x': x_sc_regle2, 'y': y_sc_regle2},\n             colors=['red'], stroke_width=10)\n\nfig3 = Figure(axes=[ax_xregle2, ax_yregle2], marks=[line_refr, line3],linewidth=[8,2])\nfig3.layout.height = '280px'\n\n# affichage des différents objets\ndisplay(VBox([HBox([VBox([fig2,fig3],layout = Layout(align_content = 'stretch', justify_content = 'center',height = '600px',min_width='300px',width = '300px')),\n                         fig],layout = Layout(align_content = 'stretch',align_items='stretch',height='600px',min_width='600px',width='900px')),test],\n             layout = Layout(align_content = 'stretch',align_items='stretch',height='800px',justify_content='center')))\n\n\n# une première mise à jour à l'initialisation\nsetparam()\n\n\n","execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"VBox(children=(HBox(children=(VBox(children=(Figure(axes=[Axis(color='black', grid_lines='none', label=\"angle …","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fe09667f4ee445a986a5bb66803b69f5"}},"metadata":{}}]},{"metadata":{"slideshow":{"slide_type":"subslide"}},"cell_type":"markdown","source":"Un petit bout de code"},{"metadata":{"trusted":true,"run_control":{"marked":false},"hide_input":false,"hide_output":false},"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.plot([1,2,3,5],[4,5,6,8],\"+-\")\nplt.show()","execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"slideshow":{"slide_type":"-"}},"cell_type":"markdown","source":"<a 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