SKN Statystyki - Python 6.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wizualizacja danych 2 - Środowisko aplikacji, Skrypty, Dash\n",
    "---\n",
    "#### *Michał Korzycki*  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zadanie 1\n",
    "\n",
    "Narysować okrąg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "circle=plt.Circle((0,0),2)\n",
    "# here must be something like circle.plot() or not?\n",
    "ax=plt.gca()\n",
    "ax.add_patch(circle)\n",
    "plt.axis('scaled')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "u = np.linspace(-1, 1, 100)\n",
    "\n",
    "x = 10 * np.outer(np.cos(u), np.sin(v))\n",
    "plt.plot(u,np.sqrt(1-u*u))\n",
    "plt.plot(u,-np.sqrt(1-u*u))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.linspace(0, 5)\n",
    "fig = plt.figure()\n",
    "\n",
    "fig.add_subplot(221)   #top left\n",
    "plt.plot(np.cos(x))\n",
    "fig.add_subplot(222)   #top right\n",
    "plt.plot(np.sin(x))\n",
    "fig.add_subplot(223)   #bottom left\n",
    "plt.plot(np.tan(x))\n",
    "fig.add_subplot(224)   #bottom right \n",
    "plt.plot(x)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Plot circle or radius 3\n",
    "\n",
    "an = np.linspace(0, 2*np.pi, 100)\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.plot(3*np.cos(an), 3*np.sin(an))\n",
    "plt.title('not equal, looks like ellipse', fontsize=10)\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.plot(3*np.cos(an), 3*np.sin(an))\n",
    "plt.axis('equal')\n",
    "plt.title('equal, looks like circle', fontsize=10)\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.plot(3*np.cos(an), 3*np.sin(an))\n",
    "plt.axis('equal')\n",
    "plt.axis([-3, 3, -3, 3])\n",
    "plt.title('looks like circle, even after changing limits', fontsize=10)\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.plot(3*np.cos(an), 3*np.sin(an))\n",
    "plt.axis('equal')\n",
    "plt.axis([-3, 3, -3, 3])\n",
    "plt.plot([0, 4], [0, 4])\n",
    "plt.title('still equal after adding line', fontsize=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zadanie 2\n",
    "\n",
    "Narysować kulę w 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Make data\n",
    "u = np.linspace(0, 2 * np.pi, 100)\n",
    "v = np.linspace(0, np.pi, 100)\n",
    "x = 10 * np.outer(np.cos(u), np.sin(v))\n",
    "y = 10 * np.outer(np.sin(u), np.sin(v))\n",
    "z = 10 * np.outer(np.ones(np.size(u)), np.cos(v))\n",
    "\n",
    "# Plot the surface\n",
    "ax.plot_surface(x, y, z, color='b')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "fig2 = plt.figure()\n",
    "plt.plot(x, np.sin(x), '-')\n",
    "plt.plot(x, np.cos(x), '--');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "fig2 = plt.figure()\n",
    "plt.plot(x, np.sin(x), '-', label='Pierwsze')\n",
    "plt.plot(x, np.cos(x), '--', label='Drugie');\n",
    "plt.legend()\n",
    "plt.xlabel('Iksy'); plt.ylabel('Igreki');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "labels = 'Algorytm', 'Decyzje biznesowe', 'Jakość Danych', 'Prezentacja'\n",
    "sizes = [5, 20, 25, 50]\n",
    "explode = (0.4, 0, 0, 0) \n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mu, sigma = 100, 15\n",
    "x = mu + sigma*np.random.randn(10000)\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75, rwidth=0.60)\n",
    "\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability')\n",
    "plt.title(r'$\\mathrm{Histogram\\ of\\ IQ:}\\ \\mu=100,\\ \\sigma=15$')\n",
    "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
    "plt.axis([40, 160, 0, 0.03])\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anatomia Skryptu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Środowisko"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "virtualenv dashplotly\n",
    "\n",
    "source Scripts/activate\n",
    "\n",
    "pip install dash==0.32.2  # The core dash backend\n",
    "\n",
    "pip install dash-html-components==0.13.2  # HTML components\n",
    "\n",
    "pip install dash-core-components==0.40.3  # Supercharged components\n",
    "\n",
    "pip install dash-table==3.1.11  # Interactive DataTable component\n",
    "\n",
    "pip freeze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import dash\n",
    "import dash_core_components as dcc\n",
    "import dash_html_components as html\n",
    "\n",
    "external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n",
    "\n",
    "app = dash.Dash(__name__, external_stylesheets=external_stylesheets)\n",
    "\n",
    "app.layout = html.Div(children=[\n",
    "    html.H1(children='Hello Dash'),\n",
    "\n",
    "    html.Div(children='''\n",
    "        Dash: A web application framework for Python.\n",
    "    '''),\n",
    "\n",
    "    dcc.Graph(\n",
    "        id='example-graph',\n",
    "        figure={\n",
    "            'data': [\n",
    "                {'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'},\n",
    "                {'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'},\n",
    "            ],\n",
    "            'layout': {\n",
    "                'title': 'Dash Data Visualization'\n",
    "            }\n",
    "        }\n",
    "    )\n",
    "])\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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