Py_lectures_01_basicElements_b.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/gf/src/Python/Python-in-the-lab/Bk'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.chdir(\"/home/gf/src/Python/Python-in-the-lab/Bk\") # Insert here your directory. Windows should have \"\\\" instead of \"/\"\n",
    "os.path.abspath(\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"F64ac_0.02_T.dat\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, I did lecture 1 so...\n",
    "Can we now open the file, please?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method 1: hard rock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(filename) # We need to be in the directory containing the file\n",
    "data = f.readlines()\n",
    "f.close() # MANDATORY, do not forget!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['5.01924E-006 0\\n',\n",
       " '6.31885E-006 0\\n',\n",
       " '7.95496E-006 0\\n',\n",
       " '1.00147E-005 0\\n',\n",
       " '1.26078E-005 0\\n',\n",
       " '1.58722E-005 0\\n',\n",
       " '1.9982E-005 0\\n',\n",
       " '2.51558E-005 0\\n',\n",
       " '3.16693E-005 0\\n',\n",
       " '3.98693E-005 509496\\n']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:10] # Let's printthe first 10 lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like a list of two columns of numbers, with boaring elements like \"\\\\n\". Not very efficient!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How can we get the numbers as two columns x and y?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "not enough values to unpack (expected 2, got 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-290e63ce2a10>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mcol0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m# Empty list\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mc0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Get rid of spaces, and return characters, etc...\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mc0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Att. c0,c1 = float(line.split()) does not work\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mcol0\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: not enough values to unpack (expected 2, got 0)"
     ]
    }
   ],
   "source": [
    "col0, col1 = [], [] # Empty list\n",
    "for row in data:\n",
    "    c0, c1 = row.split() # Get rid of spaces, and return characters, etc...\n",
    "    c0, c1 = float(c0), float(c1) # Att. c0,c1 = float(line.split()) does not work\n",
    "    col0.append(c0)\n",
    "    col1.append(c1)\n",
    "# Ohhhh, what's wrong????"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row\n",
    "# Eh?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0.19982 0\\n', '0.251558 0\\n', '0.316693 0\\n', '0.398693 0\\n', '\\n']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "col0, col1 = [], [] # Empty list\n",
    "for row in data[:-1]:\n",
    "    c0, c1 = row.split() # Get rid of spaces, and return characters, etc...\n",
    "    c0, c1 = float(c0), float(c1) # Att. c0,c1 = float(line.split()) does not work\n",
    "    col0.append(c0)\n",
    "    col1.append(c1)\n",
    "# Cool..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(float, 5.01924e-06)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(col0[0]), col0[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the list comprehension\n",
    "\n",
    "https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['5.01924E-006', '0'],\n",
       " ['6.31885E-006', '0'],\n",
       " ['7.95496E-006', '0'],\n",
       " ['1.00147E-005', '0'],\n",
       " ['1.26078E-005', '0'],\n",
       " ['1.58722E-005', '0'],\n",
       " ['1.9982E-005', '0'],\n",
       " ['2.51558E-005', '0'],\n",
       " ['3.16693E-005', '0'],\n",
       " ['3.98693E-005', '509496']]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I make a loop of the rows first, splitting the two numbers\n",
    "[row.split() for row in data[:-1]][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.01924e-06, 0.0, 6.31885e-06, 0.0, 7.95496e-06, 0.0, 1.00147e-05, 0.0, 1.26078e-05, 0.0]\n"
     ]
    }
   ],
   "source": [
    "# Now I use the two numbers from the row.split()\n",
    "q = [float(c) for row in data[:-1] for c in row.split()] # This is a long list\n",
    "print(q[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ah, ah, we are close: wouldn't it be nice to make a two cols array just reshaping?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Welcome numpy and numpy array!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.01924e-06, 0.00000e+00],\n",
       "       [6.31885e-06, 0.00000e+00],\n",
       "       [7.95496e-06, 0.00000e+00],\n",
       "       [1.00147e-05, 0.00000e+00],\n",
       "       [1.26078e-05, 0.00000e+00],\n",
       "       [1.58722e-05, 0.00000e+00],\n",
       "       [1.99820e-05, 0.00000e+00],\n",
       "       [2.51558e-05, 0.00000e+00],\n",
       "       [3.16693e-05, 0.00000e+00],\n",
       "       [3.98693e-05, 5.09496e+05]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Everything can be done in a single line \n",
    "import numpy as np\n",
    "d = np.array([float(c) for row in data[:-1] for c in row.split()]).reshape((-1,2))\n",
    "d[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Man, you are too fast, I did not understand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.01924e-06, 0.00000e+00],\n",
       "       [6.31885e-06, 0.00000e+00],\n",
       "       [7.95496e-06, 0.00000e+00],\n",
       "       [1.00147e-05, 0.00000e+00],\n",
       "       [1.26078e-05, 0.00000e+00],\n",
       "       [1.58722e-05, 0.00000e+00],\n",
       "       [1.99820e-05, 0.00000e+00],\n",
       "       [2.51558e-05, 0.00000e+00],\n",
       "       [3.16693e-05, 0.00000e+00],\n",
       "       [3.98693e-05, 5.09496e+05]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As a list cannot be reshaped, we do it using numpy array\n",
    "q = [float(c) for row in data[:-1] for c in row.split()] # This is a long list\n",
    "q = np.array(q)\n",
    "# Now I can reshape using two columns. I do not know how many rows are present, so I put -1\n",
    "q = q.reshape(-1,2)\n",
    "q[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.01924e-06, 0.00000e+00],\n",
       "       [6.31885e-06, 0.00000e+00],\n",
       "       [7.95496e-06, 0.00000e+00],\n",
       "       [1.00147e-05, 0.00000e+00],\n",
       "       [1.26078e-05, 0.00000e+00],\n",
       "       [1.58722e-05, 0.00000e+00],\n",
       "       [1.99820e-05, 0.00000e+00],\n",
       "       [2.51558e-05, 0.00000e+00],\n",
       "       [3.16693e-05, 0.00000e+00],\n",
       "       [3.98693e-05, 5.09496e+05]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In a single line\n",
    "q = np.array([float(c) for row in data[:-1] for c in row.split()]).reshape(-1,2)\n",
    "q[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clear now?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now I need to horizontally split the array in two\n",
    "x, y = np.hsplit(q,2)\n",
    "# or simply call the two columns\n",
    "x, y = q[:,0], q[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method 2: a little less hard rock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(filename) as f:\n",
    "    data = f.readlines()\n",
    "# NO NEED TO CLOSE THE FILE, IT IS AUTOMATIC\n",
    "# data as above\n",
    "q = np.array([float(c) for row in data[:-1] for c in row.split()]).reshape((-1,2))\n",
    "x, y = np.hsplit(q,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method 3: the pythonic way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.loadtxt(filename) # Exercise: explore the loadtxt help or check on the numpy website"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 2)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.01924e-06, 0.00000e+00],\n",
       "       [6.31885e-06, 0.00000e+00],\n",
       "       [7.95496e-06, 0.00000e+00],\n",
       "       [1.00147e-05, 0.00000e+00],\n",
       "       [1.26078e-05, 0.00000e+00],\n",
       "       [1.58722e-05, 0.00000e+00],\n",
       "       [1.99820e-05, 0.00000e+00],\n",
       "       [2.51558e-05, 0.00000e+00],\n",
       "       [3.16693e-05, 0.00000e+00],\n",
       "       [3.98693e-05, 5.09496e+05]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:10] # it's a 2D array: where is the other axis???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.01924e-06"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0,0] # sounds familiar?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = data[:,0], data[:,1] #or\n",
    "x, y = np.hsplit(data,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method 4: the real best pythonic way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Isn't a better and faster way to do it? It's python!\n",
    "x, y = np.loadtxt(filename, unpack=True) #Uauu, isn't it nice?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the *hard rock* case we use many lines (it was an excuse to learn something else, of course). Here everything is in one line: this happens very often and is called refactoring: find the best, clearest and shortest code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y = np.genfromtxt(filename, unpack=True) # This works too. Check the documentation\n",
    "# https://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Homework: explore the differences between loadtxt and genfromtxt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ok, stop: plot, please!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd37cee1908>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh, man! You are joking!\n",
    "\n",
    "I try myself... let me think... a little more... ok, found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(x,y); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEACAYAAAC6d6FnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAEbBJREFUeJzt3V+IXOd5x/Hvk63TKIZaoU4KXjlIJUKpiAuii1MQFBucWiGmFmpoLPfGjbBxQbnoRYhCAwmUIhXTQlyrTdVEqIHUjjBGlWMZBaIE9cIXWkeFWHVUhKB4V1Ct66jQRsRW/PRid5PVas5oR2fOmTPzfj8g0HnnzJz31aL57Xn/nchMJEnlec+oKyBJGg0DQJIKZQBIUqEMAEkqlAEgSYUyACSpUAaAJBXKAJCkQhkAklSoX2nrQhHxHuAvgF8DZjPzn9q6tiTpRrUCICIOAw8BlzPzYyvKdwBfBaaAr2fmAeBhYBp4C5hby+ffeeeduXHjxjpVlKSivPrqq29m5gfXcm7dO4AjwDPAN5cLImIKOAh8gsUv+jMRcRzYArySmf8QEc8D37vZh2/cuJHZ2dmaVZSkckTEf6713FpjAJl5msXf6Fe6F7iQmRcz823gORZ/+58DfrJ0zs/rXFeSVF8Tg8DTwBsrjueWyl4AHoyIvwVOV705Ip6IiNmImF1YWGigepIkaGYQOHqUZWb+FNhzszdn5iHgEMDMzIx7VUtSQ5q4A5gD7l5xvAG41MB1JEk1NHEHcAbYHBGbgHngEeDRBq5TpGNn53nq5HkuXbnKXevX8fkHt7Bz2/SoqyVpDNW6A4iIZ4FXgC0RMRcRezLzGrAXOAm8DhzNzHP1q6pjZ+f54gs/Yv7KVRKYv3KVL77wI46dnR911SSNoVp3AJm5u6L8BHCizmfrRk+dPM/Vd66fQHX1nZ/z1Mnz3gVIGphbQYyRS1euDlQuSf0YAGPkrvXrBiqXpH4MgDHy+Qe3sO62qevK1t02xecf3DKiGkkaZ61tBqf6lvv5nQUkaRgMgDGzc9u0X/iShsIuIEkqlAEgSYUyACSpUAaAJBXKAJCkQhkAklQoA0CSCmUASFKhDABJKpQBIEmFciuICecTxCRVMQAm2PITxJYfIrP8BDHAEJBkF9Ak6/cEMUkyACaYTxCT1I8BMMF8gpikfgyACeYTxCT14yDwBPMJYpL6MQAmnE8Qk1TFLiBJKpQBIEmFsguoUK4QlmQAFMgVwpLALqAiuUJYEhgARXKFsCQwAIrkCmFJYAAUyRXCksBB4CK5QlgSGADFcoWwJANAN3CNgFQGA0DX6bdGAOw2kiZJqwEQEbcDp4EvZ+Z32ry21qZqjcBXjp/jZ9fedfGYNEFqzQKKiMMRcTkiXltVviMizkfEhYjYt+KlLwBH61xTzapaC3Dl6jsuHpMmTN1poEeAHSsLImIKOAh8EtgK7I6IrRHxAPDvwH/VvKYaNOhaABePSeOrVgBk5mngrVXF9wIXMvNiZr4NPAc8DNwP/C7wKPB4RLgGoYOq1gh84P239TzfxWPS+GpiDGAaeGPF8Rzw8czcCxARjwFvZua7vd4cEU8ATwB8+MMfbqB66qdqjQBw3eAwuHhMGndNBED0KMtf/CXzSL83Z+Yh4BDAzMxM9jtXzei3RsBZQNLkaCIA5oC7VxxvAC41cB21zMVj0mRpoh/+DLA5IjZFxHuBR4DjDVxHklRD3WmgzwKvAFsiYi4i9mTmNWAvcBJ4HTiamefqV1WSNEy1uoAyc3dF+QngRJ3P1vhzSwmp29wKQo3wsZNS9zkXX43wsZNS9xkAaoSPnZS6zy4g1darr/+u9euY7/Fl78phqTu8A1Aty33981eukvyyr//+j37Qx05KHWcAqJaqvv7v/3iB/bvuYXr9OgKYXr+O/bvucQBY6hC7gFRLv75+Vw5L3WYAqJZb6et3fYDUDXYBqZaq7aOr+vqrxgyOnZ1vobaSVjIAVMvObdMD9fW7PkDqDruAVNsgff2uD5C6wzsAtapqbMD1AVL7DAC1atAxA0nNsQtIrap65ORyuTOEpPYYAGpd1ZhBvx1EwcdRSsNmAKgzqmYIfeX4OX527V23lpaGzDEAdUbVTKArV99x6qjUAANAnTHoTCCnjkr1GADqjKoZQh94/209z79r/TqOnZ1n+4FTbNr3EtsPnHJFsTQAxwDUGVUzhIDrBodhMRju/+gHfeykVIMBoE7pt6p4dTD021bCAJBuzgDQWOgVDH/27X/rea5jA9LaGAAaW/22onZBmXRzDgJrbFUNGi+PDbjltNSfAaCxVbUV9fd/vOC6AWkN7ALSWBt0bMCuIemXvAPQxKlaUHbHutvsGpJWMAA0carGBiKwa0hawQDQxKkaG7jy03d6nu+0UZXKMQBNpF5jA0+dPF85bRR8FoHK4x2AitHvaWTLzyJwfEAlMQBUjKquoZ3bpvtuKyFNKruAVJSqvYaqxgEcH9AkMwAk3FZCZbILSMJtJVQmA0DCbSVUpta6gCJiJ/Ap4EPAwcz8blvXltbCbSVUmlp3ABFxOCIuR8Rrq8p3RMT5iLgQEfsAMvNYZj4OPAZ8ps51pba4rYQmWd0uoCPAjpUFETEFHAQ+CWwFdkfE1hWnfGnpdanz3FZCk6xWAGTmaeCtVcX3Ahcy82Jmvg08Bzwci/4KeDkzf1j1mRHxRETMRsTswsJCnepJtbmthCZZE2MA08AbK47ngI8DnwMeAO6IiI9k5td6vTkzDwGHAGZmZrKB+kkDGXRbCccGNC6amAUUPcoyM5/OzN/JzCervvylceG0UU2CJgJgDrh7xfEG4FID15FGxmmjmgRNdAGdATZHxCZgHngEeLSB60gjNei0UalragVARDwL3AfcGRFzwJcz8xsRsRc4CUwBhzPzXO2aSmOg35YS4JbT6pbI7O4468zMTM7Ozo66GtKaLW8rvbIbaN1tU+zfdQ9A5WuGgIYlIl7NzJm1nOtWENIQueW0xom7gUpD5pbTGhfeAUgtqdpWoqpcapoBILXkZo+k3H7gFJv2vcT2A6dcN6BW2AUktWS5W2j1LCC4fnB4efHYyvdITTAApBb1Gh/YfuBU5eCwAaAm2QUkjZiDwxoVA0AaMQeHNSoGgDRiDg5rVBwDkEbMwWGNigEgdYCDwxoFu4CkjnJwWE0zAKSOcnBYTTMApI7qNzgsDYNjAFJHVQ0O79w27XMFNBQGgNRhvQaHVz9zwNlBulV2AUljxucKaFgMAGnMODtIw2IASGPG2UEaFgNAGjPODtKwOAgsjRlnB2lYDABpDDk7SMNgF5A0IZwdpEEZANKEcHaQBmUASBPC2UEalAEgTQhnB2lQDgJLE6Lf7CCpFwNAmiC9ZgdJVewCkqRCeQcgFcJFYlrNAJAK4CIx9WIXkFQAF4mpFwNAKoCLxNSLASAVwEVi6sUAkArgIjH10togcETcDvwd8Dbwg8z8VlvXlkrnIjH1UisAIuIw8BBwOTM/tqJ8B/BVYAr4emYeAHYBz2fmixHxbcAAkFrkIjGtVrcL6AiwY2VBREwBB4FPAluB3RGxFdgAvLF02vXTESRJrasVAJl5GnhrVfG9wIXMvJiZbwPPAQ8DcyyGQO3rSpLqa+KLeJpf/qYPi1/808ALwB9GxN8DL1a9OSKeiIjZiJhdWFhooHqSJGhmEDh6lGVm/h/wJzd7c2YeAg4BzMzM5JDrJkla0kQAzAF3rzjeAFxq4DqShsA9gsrVRACcATZHxCZgHngEeLSB60iqyT2CylZrDCAingVeAbZExFxE7MnMa8Be4CTwOnA0M8/Vr6qkYXOPoLLVugPIzN0V5SeAE3U+W1Lz3COobE7HlArmHkFlMwCkgrlHUNl8IIxUMPcIKpsBIBWuao8gp4dOPgNA0g2cHloGxwAk3cDpoWUwACTdwOmhZTAAJN3A6aFlMAAk3cDpoWVwEFjSDZweWgYDQFJPPkJy8tkFJEmFMgAkqVAGgCQVyjEASQNxi4jJYQBIWjO3iJgsdgFJWjO3iJgsBoCkNXOLiMliAEhaM7eImCwGgKQ1c4uIyeIgsKQ1c4uIyWIASBqIW0RMDgNA0lC4PmD8GACSanN9wHhyEFhSba4PGE8GgKTaXB8wngwASbW5PmA8GQCSanN9wHhyEFhSbTdbH+AMoW4yACQNRdX6AGcIdZddQJIa5Qyh7jIAJDXKGULdZQBIapQzhLrLAJDUKGcIdZeDwJIa5Q6i3dVaAETETuBTwIeAg5n53bauLWm03EG0m9bUBRQRhyPickS8tqp8R0Scj4gLEbGv32dk5rHMfBx4DPjMLddYkjQUa70DOAI8A3xzuSAipoCDwCeAOeBMRBwHpoD9q97/2cy8vPT3Ly29T1LhXCA2WmsKgMw8HREbVxXfC1zIzIsAEfEc8HBm7gceWv0ZERHAAeDlzPxhnUpLGn8uEBu9OrOApoE3VhzPLZVV+RzwAPDpiHiy6qSIeCIiZiNidmFhoUb1JHXZrSwQO3Z2nu0HTrFp30tsP3CKY2fnm67mRKszCBw9yrLq5Mx8Gnj6Zh+amYeAQwAzMzOVnydpvA26QMw7huGrcwcwB9y94ngDcKledSSVYtAFYm4pMXx1AuAMsDkiNkXEe4FHgOPDqZakSTfoAjG3lBi+tU4DfRZ4BdgSEXMRsSczrwF7gZPA68DRzDzXXFUlTZKd26bZv+septevI4Dp9evYv+ueyu4ct5QYvsjsbjf7zMxMzs7Ojroakjpg9RgALN4x9AuNEkXEq5k5s5Zz3QpC0lhwS4nhMwAkjQ23lBgudwOVpEIZAJJUKANAkgrlGICkznGTuHYYAJI6xS0f2mMXkKROccuH9hgAkjrFLR/aYwBI6hS3fGiPASCpUwbdJE63zkFgSZ3ilg/tMQAkdY5bPrTDLiBJKpR3AJKK5GIzA0BSgVxstsguIEnFcbHZIgNAUnFcbLbIAJBUHBebLTIAJBXHxWaLHASWVBwXmy0yACQVycVmdgFJUrEMAEkqlAEgSYUyACSpUAaAJBUqMnPUdagUEQvAFeB/+px2R8XrvcpXl608rnrtTuDNtde6r6q63sq5Tbcbhtf2LrZ79bHttt3DsNa2N9nuzZl5xxrqAJnZ6T/AoVt5vVf56rKVx1WvAbNttaVL7R5m27vY7n7/Drbbdjfd9jbb3e/POHQBvXiLr/cqX1324hpfG5ZBPtN2D/b6oO1efWy7h6fUdg/yuW22u1Knu4C6ICJmM3Nm1PUYhVLbbrvLUmq7wUHgtTg06gqMUKltt91lKbXd3gFIUqm8A5CkQhkAklQoA0CSCmUA1BAR90XEv0bE1yLivlHXp00RcXtEvBoRD426Lm2JiN9a+lk/HxF/Our6tCkidkbEP0bEv0TE74+6Pm2JiN+MiG9ExPOjrksTig2AiDgcEZcj4rVV5Tsi4nxEXIiIfTf5mAT+F3gfMNdUXYdpSO0G+AJwtJlaDt8w2p2Zr2fmk8AfAWMzbXBIbT+WmY8DjwGfabC6QzOkdl/MzD3N1nR0ip0FFBG/x+KX9zcz82NLZVPAfwCfYPEL/QywG5gC9q/6iM8Cb2bmuxHxG8DfZOYft1X/WzWkdv82i8vn38fiv8F32qn9rRtGuzPzckT8AbAPeCYz/7mt+tcxrLYvve+vgW9l5g9bqv4tG3K7n8/MT7dV97YU+0SwzDwdERtXFd8LXMjMiwAR8RzwcGbuB/p1dfwE+NUm6jlsw2h3RNwP3A5sBa5GxInMfLfRitc0rJ93Zh4HjkfES8BYBMCQfuYBHABeHocvfxj6//GJVGwAVJgG3lhxPAd8vOrkiNgFPAisB55ptmqNGqjdmfnnABHxGEt3QY3WrjmD/rzvA3axGPYnGq1Z8wZqO/A54AHgjoj4SGZ+rcnKNWjQn/mvA38JbIuILy4FxcQwAK4XPcoq+8gy8wXgheaq05qB2v2LEzKPDL8qrRr05/0D4AdNVaZlg7b9aeDp5qrTmkHb/d/Ak81VZ7SKHQSuMAfcveJ4A3BpRHVpk+1eVEq7ody2l9rungyA650BNkfEpoh4L/AIcHzEdWqD7S6r3VBu20ttd0/FBkBEPAu8AmyJiLmI2JOZ14C9wEngdeBoZp4bZT2HzXaX1W4ot+2ltnsQxU4DlaTSFXsHIEmlMwAkqVAGgCQVygCQpEIZAJJUKANAkgplAEhSoQwASSqUASBJhfp/1Duxcb/e1kcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I prefer dots, please\n",
    "plt.loglog(x,y,'o'); # suppress the output ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem n. 2\n",
    "### How can we plot all the files F64ac_someFreq_T.dat at different frequencies with a nice label etc etc???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F64ac_0.03_time_V_T.dat',\n",
       " 'F64ac_0.02_ave_S_vs_T.dat',\n",
       " 'F64ac_0.02_T.dat',\n",
       " 'F64ac_0.03_T.dat',\n",
       " 'F64ac_0.01_time_V_T.dat',\n",
       " 'F64ac_0.03_ave_S_vs_T.dat',\n",
       " 'F64ac_0.02_time_V_T.dat',\n",
       " 'F64ac_0.01_T.dat',\n",
       " 'F64ac_0.01_ave_S_vs_T.dat']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find a pattern in the filename we are interested in\n",
    "import glob\n",
    "glob.glob(\"F64ac_*_T.dat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh, too many! \n",
    "\n",
    "The \"*\" takes an arbitrary number of data\n",
    "One or two only?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F64ac_0.02_T.dat', 'F64ac_0.03_T.dat', 'F64ac_0.01_T.dat']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.glob(\"F64ac_0.??_T.dat\") # cool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An unordered list, by the way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F64ac_0.01_T.dat', 'F64ac_0.02_T.dat', 'F64ac_0.03_T.dat']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filenames = sorted(glob.glob(\"F64ac_0.0?_T.dat\")) # nice\n",
    "filenames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can I plot them all together?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd37cd98b70>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sure\n",
    "for filename in filenames:\n",
    "    x, y = np.loadtxt(filename,unpack=True)\n",
    "    # Let's extract the frequency value to make a legend\n",
    "    material, freq, meas = filename.split(\"_\")\n",
    "    lb = \"{0} Hz\".format(freq)\n",
    "    plt.loglog(x,y,'o',label=lb) # I use the same plot\n",
    "plt.legend(numpoints=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stop, man, I do not understand the logic!\n",
    "How does it know to put the new data in the same plot?\n",
    "\n",
    "(teacher) Hem, it uses the same figure...\n",
    "\n",
    "Oh, I see, but we did not tell him to make a figure, at least, explicity... Does it do something behind the curtain?\n",
    "\n",
    "(teacher) Very good point! *Explicit is better than implicit!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd37c1be2b0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's declare the figure, then add a (sub)plot\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111) # 111 stands for: 1 row, 1 colums, fig n. 1\n",
    "for filename in filenames:\n",
    "    x, y = np.loadtxt(filename,unpack=True)\n",
    "    # Let's extract the frequency value to make a legend\n",
    "    material, freq, meas = filename.split(\"_\")\n",
    "    lb = \"{0} Hz\".format(freq)\n",
    "    ax.loglog(x,y,'o',label=lb) # I use the same plot, explicitly\n",
    "# plt.legend(numpoints=1);\n",
    "ax.legend(numpoints=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# My boss wants the error bars... :(\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "for filename in filenames:\n",
    "    x, y = np.loadtxt(filename,unpack=True)\n",
    "    yerr = y * 0.6\n",
    "    material, freq, meas = filename.split(\"_\")\n",
    "    lb = \"{0} Hz\".format(freq)\n",
    "    ax.loglog(x,y,'o',label=lb) \n",
    "    ax.errorbar(x,y,yerr,fmt=\"\",ecolor='k')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh, the autoscale does not work! Or does it?\n",
    "Better to check the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0.,      0.,      0.,      0.,      0.,      0.,      0.,\n",
       "            0.,      0., 281709.])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh, it is full of zero values. Can we get rid of them?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True, False, False, False, False, False, False,\n",
       "       False, False, False, False, False])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_not_zero = y!=0 # Breath first\n",
    "is_not_zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.81709e+05, 4.79296e+04, 1.81690e+04, 8.39005e+03, 4.32740e+03,\n",
       "       4.32954e+03, 2.10176e+03, 1.58280e+03, 8.39195e+02, 7.36124e+02,\n",
       "       3.91619e+02, 2.96808e+02, 2.19558e+02, 1.38495e+02, 1.17083e+02,\n",
       "       7.62096e+01, 5.55879e+01, 3.80288e+01, 2.62899e+01, 1.52914e+01,\n",
       "       9.44287e+00, 4.95448e+00, 2.13223e+00, 8.80718e-01, 2.38318e-01,\n",
       "       1.09918e-01, 3.88048e-02, 1.54119e-02, 6.12104e-03])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ok let me redefine x and y\n",
    "x, y = x[is_not_zero], y[is_not_zero]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "for filename in filenames:\n",
    "    x, y = np.loadtxt(filename,unpack=True)\n",
    "    is_not_zero = y!=0\n",
    "    x, y = x[is_not_zero], y[is_not_zero]\n",
    "    yerr = y * 0.5\n",
    "    material, freq, meas = filename.split(\"_\")\n",
    "    lb = \"{0} Hz\".format(freq)\n",
    "    ax.loglog(x,y,'o',label=freq)\n",
    "    ax.errorbar(x,y,yerr,fmt=\"none\",ecolor='k')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Test 1')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4ym6XyeOJ3/UboES6ArVUaB21VOhiAk3z//Pq/2x6cmua0qlrqoiEWJcJPmaWamabzCw023c7UTD2AmVZ+Yl0bHVNFZEQi9rgY2ZPmtk+M9ve6PhVZrbDzHaamXfz8ZlAzBQ+yxuSR/pz6WQ8lcHq61cHXF/eW+n/tp5PU7rWBCgRkSCK2uADPAVc5X3AzOKBR4CrgbOAG83sLDO7EvgA+CzUgwyn4tJivv21fn73Avk0pVPXVJEmdD+8eR29PlGb7eace83MBjU6fD6w0zlXCmBmhcC3gR5AKp6AVGFmK5xzTcpEm9mtwK0AAwcO7LzBh0Bze4FKj9/o25Tuijl+kxLUNVW6qqSkJMrLy8nIyGhXaZpY55yjvLycpKSklk8OIGqDTwD9Ae96M2XABc65OwDMbCpwwF/gAXDOPQY8Bp5st84dasc1lxIdaC/Qf6cm85d7GjWly8mnGzTJdlPXVOmqsrOzKSsrY//+/eEeSsRKSkoiO9v/qklrxFrw8fe/KA1BxDn3VOiGEl5t7guUk8+NP1rE1q1bGTlyACUlgbumaj+QxLqEhAQGDx4c7mHEtGi+5+NPGTDA63E2sDtMYwmrNvUFaoOlW3bx6ahb+fiCn3Lx/HVKxRaRdom14LMBOM3MBptZIjAFWBbmMYVFa0t+eLfgLikpadjN7O+cpVt2MfvF96jpngZm7DpUwewX3/MNQNuK2Dsrk9pfpMGvRqizqoj4FbXLbma2BMgFMs2sDPiFc+4JM7sDWAXEA086594P4zDDpj71+t6V91KbWku/Hv2alPwoLi3m4OSD1KbWMu75ccw4d0aTpTTvc+ZsvpGq5HFQNarh+YqqGh5YtcNzb8irsyrgafew/Eee73PyO/XnFZHoovI6AURyeZ22CHR/piEbzispISk+yWezqr9zXG0ClXsmUf3liQBkwEfz8zwzHT/9hUgbAHdtb3pcRGKOyutIs5prgNXcORZXRfc+q3yOtbRZNeBxEemyFHxiXElJid+stNY0wAp0TnzCQd5I/BET4t4gOSG+xc2qAY+LSJel4NNFBcqG8z4e8JzqGrLjDvBfiU/w9Hmf+G5WbZTkUB2fBFfMCc6gRSRmKPh0Ua3JhvN7Tm0tMw4eAiCZY5z39980PLe05mJmVU2nrDaTWmeU1WYyq2o6S2su7sSfRESiUdRmu0nHtCYbzvscl1pD3+oaZhw8RN6Royfe6IsyikuLuXflvdSk1uIG9GLp/n/xSUh4uz4brs6Y627js74XEt8jk/7pKdw9fpgqKYh0Mcp2CyBWst1a0ppqBbm5uRReuP1ECrWX4j4DKEhLbjYjriEbDtiw7Lf02/hfnorZLpMF1fmsiR/rt/W3iEQfZbtJUP2uNAsSGlW4TkhmYXqvFjPi6rPh7s/PYfimn5EdV97QtG5+wuN8o+Z/1TdIpItR8OniAmXDNT7n50Xb4JqH2FuZQK3Ds3fnmofYW/Wl39dYQt19obpsuOLSYkq+UcWFg0/xae+QYse5p1uR375B3tUXRCS2KPhI6+XkM+WdEVz+v6M8m0Zz8gNmxLmqXvTvlcy8SWeTkLaVgrcK2JfYDWcn2jvUB6AsK2/SN0g15ERim4KPdIi/jDiqoedbVbw563ImjuofsL3DwvReAOwhw6dv0NItu7hr8Ybma8iJSFRTtpu0SeMlOn9Zc18Wf0nSRycCUsANrd3iqaA7u0ff45NssLX4MV5P/hNZdqAhKWFZ1SUnasjVUWsHkeilmY90WN6QPNKfSyfjqQxWX7/aJ/BA4M2qfapqSJ70MOdN+MGJg9uKuKdqEdlxB3ySEibEveF7X2hbEYUXbmfd2C2qni0ShRR8JOgaJzH4W5qrPV7LkbfSmlS73rv4dlLsuM+x+qSEhvtCXtWz44wT1bMVgESihoKPdLq8IXkUXFRA3OE4cBB3OI6DhQebzJCKS4v53tA+5Awa4JMRB56khPr7QnsX3w5VjbLjqio4ulJlfESihTaZBtBVNpmGUnP3aPy2eKitpeDA5+QdOcrR5H6kzPx/FJcWs3DdT9nbLb5JxYVaZyyb+H6Tzaq6NyQSOtpkKlGl2Yy4hGRSrp7bEKD2JPhP2d7tMppsVh1z3W38ffhUpWyLRBhlu0nINDfzaC4jjmsegpx8Fj4/LmCAuuxwNQuq832SEjYs+y1Lh68my5Z4sua+zGf2i577SSrlIxJemvlIRAjYvqFHVkNSQnMBalbVdJbVXuKTlDBi889bXcpH1RREQkvBRyJCoM2q3i0eAgWomqp0ltVe4tvYbu1ckjnmc16gUj6qpiASego+EhEaZ8T1S+3H/Mvm+7R48BegXE03ju0b11DKp345rfbQp34/p3Epnw3LfsuYl/6Jv530r7zRfQajv1yjagoiIaB7PhIx8obk8cBzDwCwumS13+ehaTWFzI/WUlLy84bzikuL+WV2FvsTmmbE+ZTyqVuaS47zzJCyzbM0RxU8sCpR94VEOpFmPhJVWqqmUJ8R56+IaZNSPm1YmsvNzaVXr166LyQSJJr5SETp6F6cQCnbv+zVi7zx93FeXfJCcWkxC3vWsDd9QJPZUZMq29uKWHLRDk4Z69jtPqXg33/ByLxbNTMS6QAFH4k63gGqcbAKlBG3L6FbQ9Zcw4bWBM9f//rZEUDekaNNluaqX76TfomegJZtB7inahFzXqoGblcAEmknLbtJTAmUEdevR7+G75vb0Oq9NJebm8vexbfTrdG5KXacf6NQKdsiHaDgIzHFX0ZcUnyST8p2c/uF6qtsF5cWc3DyQcad0bdJnTnwLM01rrK9595+rBu7hT9d6lmaU8acSGCtCj5mlt3ZAxEJBn8p2wUXFfikbAeaHdmReMjJb1iWq+1R67eMD3hK+XhvaPUszR1t2NB6T9Ui3nhpkQKQSACtnfn8w8ze79SRiARJ44w478ADgVs8JG/0BJOWOq8edYn8mik+G1pbuzSnZTkRj7Ysu6V32ihEQshfi4eeb/Xknd+/AzS/LFdWm8mChNu55FqvZIMvyvye33hpbumWXfx9+FQ+UiUFEWW7SWxqKWXbe0Nr43P7pvZlz5E9TV5TdbCG7742gJKS+wBP1ty9K+/FDcpukq4NvktzS7fs4o2XFvF2RqGnPfjRTH790hSUMSddlRIORBoJVGfui1e+aHjY0n0h76W53NxcSpY8wFx7zKc9+Fx7jK3Fj4XyRxOJGJr5iDTir4zPjHNnkDftxL2j5u4LnfNVCo8nfpdL8m4lIW0rBycfZF3qn5lYnekzO0qx40w//ifgvob3GHPdbXzW90Lie2TSPz2Fu8cP08xIYlKXCD5mNhHIA04GHnHONS0cJl1Oc0tzLdWZC3RfaHe3+IalOe/ZEViTzawAWXHlDa9V/yHpStqy7HaSmc0ys6vNLKvTRtRKZvakme0zs+2Njl9lZjvMbKeZzQJwzi11zt0CTAVuCMNwJcYEbO9wsKbh+5ay5gAqk+vep439h0SiXVuCTzLwH8ArwKd1//CvNrMFZvYdMxtuZqG8h/QUcJX3ATOLBx4BrgbOAm40s7O8TvlZ3fMiHRLovlDatrSGGVWz3VmB6vgkUq6e6zm2+PZWFzkViQVtWXarAg4A9bOeTOBK4Aqvc46Z2QfAVq+vbc65L4MwVh/OudfMbFCjw+cDO51zpQBmVgh828w+BOYDK51zmwO9p5ndCtwKMHDgwGAPWaJMS8ty0Px9oUBZc32rayBtAN2umNOwqXXh0D7s7da0BUSTIqd49gpt3bqVkSNHdrgQq0i4mHOu5ZPMaoG9zrksM8sARnp9nQOcgW8ga/ymHwNbnHPXB2PQXuMaBLzinBtR9/h64Crn3PS6xzcBFwB/Bb4PbAC2Oucebem9x4wZ4zZu3BjM4UoMqt8w6i8INBQw9Vp6qz1ey8HCg+xeuzvgOUm1tRQc+Jy8I0fZ5TLZMPG1E/d8thWxp3AGpyQcZbfL5PHE76rCtkQUM9vknBvT0nltTjhwzpUDa+u+6j8sERiBJxDVB6UcIK3ulMHAoLZ+VjuYn2POOfcQ8FAIPl+kgb/Z0fant3N004m9QM3dF7r8SI1P/6H783P46Rll9Ev03FdShW2JZkG5R+OcO+6c2+yc+71zboZzbqxzLh0YAkwC5gLLgvFZLSgDBng9zgZ2h+BzRfxqXOrn9JrTGTlyZMPzrSlyCp4ZUsk3qrhgSJZPodPmyvio+Z1Esk5NtXbOfYxnyW1pZ36Olw3AaWY2GNgFTAH+OUSfLV1QW++5tLaaQn2RU/Bamkv033/IX4VtNb+TSBe1FQ7MbAnwNjDMzMrMbJpzrhq4A1gFfAgUOedUEFXCqqSkJGCQaqnIKbScsq0K2xKNWjvzeRmIqP9tcs7dGOD4CmBFiIcj0i5+s+b+aQZ5t5zImmtuac67jE9xaTG/XP8L9g/o45M1l2LH+TdXyA2rrtDsRyJGq4KPc+7azh6ISFfVUjWFQEtzfaqdp8J2XRmfNi3N1WkuW0+kM0XtsptIVxFoae7Imz0p+Nl9TBzVv21Lc3XGXHcbfx8+lY/V4kHCoEvUdhOJdG3Z0Bp3JI7yF8vpU9On4ZzWLs2BZ7Zz8ZAklg7/q+rISdj4nfnU1UfbaGZHzewfdSV0evg57ztmVuPvPUQkeLxTtv/yw7+we+1un4AVqNZcw9JcXfO74tJiKm4oZ/mlZdw8sDsre6SojpyERZPgY2aj8dRv6we8COwEfgJsNbMzQzs8EWmN1izN1adsH02hSQ8i1ZGTUPO37FYA/B24wDl3CMDMLgYWA6+bWZ5zbn3ohigi0PGluebuC9UnJTS+L7R0yy5+8vT/UpN4kvoLSVD5W3Y7F3isPvAAOOfeBMbg2TD6ZzP7RmiGJyKt5b00l/5cOqfXnO4TsFqqsr2HjIb7QuAJPHct3kBN9zQwY9ehCma/+J4SEyQo/M18TgIONT7onNtvZrl4luSW1xXtFJEI5G+W1FyV7Qq6+9SRy83NZeT5I3k9eTlZdsCTlFCdz7KqS3hg1Q7NfqTD/M18PsJTFLQJ59xhPD101uJZhlMAEokS/u4LJdXW8v19XzWpI1dxQznrzvozNw9M9ElKmBD3hu4LSVD4Cz4lwGQz85uG7ZyrBL4NvASM67yhiUhbNVfKJ29IHgUXFRB3OA4cxB2OY9efDvK71UOb1JFrLimh8X0hkfbwF3yeBurv8fhVV0PtBuDXwGudMzQRCbbG94W82ztAy3Xksqzc576QSHs1md045zYCk1t6ofN0oftxZwxKRDqfvxlSS0kJlSl9fe4LARzOOIPP+l5IfI9MZcRJq6nCgUgX01zKdrOtvxOSSbl6LuBZnjs4+SC1qbWcXP06v/j8ZUYdTlGlBGk11XYTkQbNJSVwzUOQk99wX6i2Ry0Y7EuIY26f3vyl51FVSpBWa9fMx8x6ARfhaZ09GOgB1ODpJLoZeN05tz9YgxSR0PC3WXXXi+X8ruZ0vnOvJymh+c2qu7mnWxH/dOiSkI9dokt7l90OANbM87Vm9gbwG+fci+38DBEJA+8WDwDppLdps6q/SgkijbU3+NQv15XjaVd9HEgEBgDpQDwwFrjUzF4HbnTONV1IFpGI1tbNqtC0UoKIP+2953Ml0Ns518c5N9I5d37dnxnAQGAqsLLu3H8C1ppZaseHKyKh0NbW30m1tcw4eMinUkJubm5DRpz39yLQzuDjnFvnXfut0XNlzrmnnXN5wNfx3AcaBsxs/zBFJFI03qx68vFq5uz/nNHlVQ2VEuqz4cqnljNu8SXcMm4n68ZuYe+sTNhWFO4fQSKAebbrdOIHeFo0bAD+6pw7o1M/LIjGjBnjNm7cGO5hiEQs75nM1q1bGTlyJCUlJQ3ZcN5JCUm1tRQc+Jy8I0epoDvbz72/oZyPxBYz2+ScC1ikoF6np1o75zYBX+JZjhORGNdSlYRkjpG1aYGqY3dxQdtkamZZQJxzrszrmAHfx1Mp+6NgfZaIRI7G94ZayoYD6Ee5qmN3ccGscHAjsMDMjgGfAVVAXzx7gAB+G8TPEpEwC5SQ0FI2HMBul6Hq2F1cMJfdPsUTdJKArwFD8Sy3PQTkOecWBPGzRCRCNZcNB3DUJbKgOl97gbq4oAUf51yRcy59vPCKAAAUcUlEQVQLGALcBrwOZAETgCPB+hwRiWyNs+Ey4npy5/7jXH24grLaTGZVTWdN/FjtBeriOjXbzcwuAf6IJwiNdc6902kfFmTKdhPpmPpsuJKSEsZcd5vfytfe50hsaG22W3tru60BZjjnPmjuPOfcG2Z2FfABcD/wjfZ8nohEtx7l/4+dawsb0rHBtzL2uOfHse+1HpTXTFBrhi6ivQkHVwBbzawQmN9cEHLO7TCzwzTTnE5EYo/3bKakpMRnX5BPZWxgz5E9dB/lWLR/tlozdBHtvefz27rXfgd4z8xeM7PbzOz0xiea2R1AT6Bzd7OKSETzLtnjby/QsTjjN717kR13QK0ZuoD2ltf5Vzylc17HU936EuAR4EMzO2xmH5rZFjPbDyzEE3heD9KYRSTKtbQXKMWOc0+3IqVjx7B2Z7s55zY453KBy4GlePr5GJCCp5bbOUBG3bG9wE86OlgRiQ19U/v6P+61F0itGWJbhzeZOudKgBIz64mnjUIOngZzqcBhPM3lljjnvujoZ4lIbJhx7gy/9d/q9wKBWjPEuqBVOHDOfQW8UvcVUeraOSzC03eoxDn3TJiH1KIvv/ySG264gdLSUm6++Wby8/PJz8+nurqa2bNnk5+fH+4hirRb446p/WpqmPH5QfKOHAXwac0gsanTC4t2FjN70sz2mdn2RsevMrMdZrbTzGbVHZ4EPO+cuwXPpteI99hjj3HPPfewfft2SkpKmDRpEo8++iibNm1iyZIl1NTUtPwmIhEsb0ge6c+lk/FUBqvPK2B0eRW1DvZWJjS0ZgD1AopVURt8gKeAq7wPmFk8nsSHq4GzgBvN7CwgG0/5H/Dcm4p4paWlfP3rXychIYF77rmHlJQURo8eTVxcHKeffjrl5eXhHqJI8OTkM+WdEfReaEx5ZwTkeGb2Pn2Bnh9HcWlxmAcqwRK1wcc59xrweaPD5wM7nXOlzrnjQCHwbTwN7bLrzomKn3nUqFEsXryY8vJyFixYQGJiIo8//jgHDx5k+/bt9OnTJ9xDFOmw5jqm+uwFMs9eoIK3ChSAYkRU/EPcBv05McMBT9DpD7wIXGdm/xdYHujFZnarmW00s4379+/v3JG2YOrUqbz33nvk5eXxve99j6VLl/Laa69xzTXXMGvWLDzdKkRiR0lJCYcOHWp2L1BlTSULNy8Mw+gk2Dq9k2lnMrNBwCvOuRF1jycD451z0+se3wSc75y7s63vrdpuIuGV84ccnJ+96c5B2t6FKr8ToSKmk2mIlQEDvB5nA7vDNBYR6YBAe4Gyqqt59ugtvPHSInVDjWKxFnw2AKeZ2WAzSwSmAMvCPCYRaYfm+gJlxx1grj3G1uLHwjQ66aioDT5mtgR4GxhmZmVmNs05Vw3cAawCPgSKnHPvh3OcItI+3n2BzDn6VVVTcODzhr1AKXac6cf/FOZRSntF9T2fzhTKez67d+/m3Xffbffrv/71r3PKKacEcUQikSM3N5d1Y7cQ5yfHphYjruBQ0yckbDq1n48E11tvvcXkyZPb/frly5fzrW99K4gjEoksn1Wl0C/xaJPjlcl9SQnDeKTjonbZLZbExek/g0ggJSUl9JuykOpG93+q45NIuXquzzFVQ4gemvlEgLy8PK688kr+/Oc/AzB69GheeOEFunfv3qrXp6end+bwRMIvJ59uwD+euoXsnhDXawDdrpjTUAkBgG1FFF64nZO7V8GvRkDj5yWi6J5PAKHe53PkyBGuvPJK3nnnHQCuvfZannvuOeLj40M2BpFI16tXLwAOHfK9z3N/fg53D/sHSfFe/54lJMM1DykAhVhX3ecTtVJTU1mxYgVnn302AC+99BK33HIL+p8DkeYVlxZT8o0qzj81m3HZWRSn1t0FqqqAtXObf7GEjYJPBElPT2f16tUMHToUgN///vf85CfqwSdS79ChQz6znvr6b/sSu+HM2JPQjYLM3icC0BdlYRqptETBJ8L07duXNWvW0L+/p2zIr371K+6///4wj0okMvmt/xYXx8J0z/Icadl+XiWRQMEnAg0aNIg1a9aQmZkJwJw5c3jkkUfCPCqRyLP3yF7/x7vFe+75XDEnxCOS1lLwiVBnnnkmr776KieddBIAd955J888E/ENWEVCKlD9t761KNkgwin4RLDRo0ezfPlykpOTcc4xdepUXnkl4rqUi4SNv/pvrqYbpXtu4OIVmSo8GsEUfCLcpZdeynPPPUdCQgLV1dXk5+fz2muvhXtYIhHBu/4bDmqPp1G59zqqvzqXXYcqmP3iew0BSBtQI4s2mUaB4cOHk56ezr59+6isrGT3bnWJEKmXNySPB557gE9H3UpN9zSf5yqqanhg1Q4S0rZycPJBalNrOeeRc0jemMw7v38nTCMW0Mwn4h04cIDx48ezb98+wJP9NmXKlCbnVVRUkJ2dzcCBAzl27JjPc9OnTyc+Pp7CwsKQjFkkHGoST/J7fF/tWz7tuGt71FJz6VcUPzICthWFeJRST8Engh05coS8vDz++te/AjBr1ixmzJjh99zk5GTuu+8+Pv30UxYtWtRwfPbs2TzxxBP85je/8Ru0RGJBSUkJ/dP9lxhNPmW1/3Ts7jWw/EcKQGGi4BOhqqurmTx5ckOrhalTpzJv3rxmXzN16lSGDx/OvHnzOHz4ML/+9a+ZP38+9913H7fffnsohi0SNnePH0Zygm85quSEeFw3/y0X9naLVxWEMFLwiVDTp09n5cqVAHzzm9/kd7/7XYuviY+PZ/78+ezfv5+JEyfy4x//mDvvvJM5c5rudTj//PNZtWpV0MctEi4TR/Vn3qSziT/2BThH/17JzJt0Nv0CpWNX13i+URWEsFBh0QBCXVjU28yZM1mwYAEAF154IWvXriUlpfVdS0aPHs3mzZuZMmUKixcvxsxPFy6RGFWf0VZSUgKcKMHjvfSWVFt7oitq2gC4a3sYRhqb1EwuSi1cuLAh8Jxxxhm88sorbQo8RUVFbN26FYCePXsq8EiXlzckD4C5q2ZRkezoW13DjIOHPIFHVRDCRstuEeTZZ5/lrrvuAiArK4tVq1aRkZHR6tevXr2am266iWuvvZYpU6bw5JNP8uGHHzY5b/ny5eTk5ARt3CKRLm9IHsnPZjB5RRxP79zP1YfrZjyqghA2mvlEiLVr1/K9730P5xy9evXi1VdfZeDAga1+/fr165k0aRIXX3wxzzzzDGVlZbzwwgvMnj2bpUuX+py7efNmRo0aFewfQSQi1C+3+bN2X2/W7uvd4nmNl+4k+BR8IsCWLVu49tprOX78OAATJ05k/fr1rF+/vlWvP+2007juuus4/fTTWbp0Kd27d+fUU09l2rRpPProo7z55ptcfPHFDedv3ryZyy67rFN+FpFolpubyxUnf07hhbvVEbWTKeEggFAmHOTl5bFixYp2vz47O5vExETeeustTjnllIbje/bs4dRTT2XUqFG8+eabDccHDhzIH//4R8aOHduhcYtEo0CzmuLS4sD3hbQ812pKOIgiNTU1HXr9FVdcwVNPPdXkeL9+/Th69KjPsQMHDlBWVsbIkSM79Jki0crfUlpDRlwKwImmdIAnAK2dq+ATZAo+EeDVV18N2Wdt3ryZwYMHk5aW1vLJIl1Ec03p8o4c1V6gTqBsty5my5YtSjYQaaTZpnSgjqidQDOfLmbmzJnhHoJI2DW+79M3tS97juxpcl7f6pqAe4GUEdcxmvlECZXDEQmOpVt28emoW/n4gp9y8fx1LN2yy29TuqTaWmYci/efbLCtiMILt7Nu7BZPRpyKk7aZst0CCGd5HRHpHEu37GL2i+9RUXUiySc5IZ55k84mIW0r9668l9rUWuKOxAXu+bOtyFMNu6rixDFlxDVobbabZj4i0mU8sGqHT+CBEw3n8obkkf5cOhlPZZD+XDpJHyX5f5O1c30DD6g6djso+EQBlcMRCY7dhypadbykpCRgSva4njXkDBrAuOwsilO96i4qI65NlHAQBVQORyQ4snols8tPAMrqlQw0nzzQsBcowfPPZpO9QMqIaxPNfKKAgo9IcARqOHf3+GE+x5Zu2cWpdy1m0MxXGpISmtsLpOrYbafgEwW0N0ckOAI1nJs4qn/DOfVJCTXd08CMXYcqmP3ie+xpbi+Qkg3arEssu5nZRCAPOBl4xDm3OsxDajWVwxEJromj+vPrux4D/C+zBUpKOKm6F67bwSbn9+2RFTDwaC9QYBE/8zGzJ81sn5ltb3T8KjPbYWY7zWxWc+/hnFvqnLsFmArc0InDDTqVwxEJrUBJCRWfjWuyF4hqmHHuDP9vpL1AzYr44AM8BVzlfcDM4oFHgKuBs4AbzewsMzvbzF5p9HWy10t/Vve6qKElN5HgC5TNBieSDxo7Oe4iCi4qIO5wHDjol9qP+ZfNb+iU6qNuL1DfpCriDPjiU8/eoHYGoNzc3IZZVKyIik2mZjYIeMU5N6Lu8deBAufc+LrHswGcc/MCvN6A+cAa59yfm/mcW4FbAQYOHDj6k08+CeJPISLRoLmNqBNH9W/dUtqvRngCTmNpA+Cu7U2PN2dbEXsX387J3auI6zUg4vsLxfom0/6A93/ZsrpjgdwJXAlcb2a3BTrJOfeYc26Mc25Mnz59gjNSEYkqrUlKaEntIT+BB9q+FyjIM6hIEq0JB+bnWMApnHPuIeChzhuOiMSSlpISmlNcWswvs7PYnxDv25QO2r4XqLlqChE8+2mNaA0+ZcAAr8fZwO4wjUVEupBWbURN9LMR9bhr+16gQDOlGKimEK3LbhuA08xssJklAlOAZWEek4h0cQE3omZktG8vUKCZUgxUU4j44GNmS4C3gWFmVmZm05xz1cAdwCrgQ6DIOfd+OMcpIrGluYy4QAI2pYuPa98y2RVzPNUTvMVINYWIX3Zzzt0Y4PgKYEWIhyMiElDApnSpfdv3hnUBK5qy3Vor4mc+IiLRwm9TuvikwBtRWyMnnynvjODy/x3lSdOOgcADUTDzERGJFvUbTuub0vXr0Y8Z587wvxG1i4uKTabhoE6mItJeXbmmW6xvMhURiX7bitg7K5PaX6R1ufpvWnYTEQmyVs14vKoXACeqF0DM3NdpjmY+IiLh0Fz1gi5AwUdEJBxiuHpBayj4iIiEQwxXL2gNBR8RkXCI4eoFraHgIyISDjn5cM1D7K1MoNbh6fXTnvpvUUrZbiIi4ZKTz5QfLQK63p4gzXxERAQIbbtuBR8REYFtRRReuJ11Y7eEZMOrlt1ERMIoIpbbwrDhVTMfEZGuLgwbXhV8RES6ujBseFXwERHp6sKw4VXBR0SkqwvDhlcFHxGRri4MG16V7SYiIiHf8KqZj4iIhJxmPiIiAoR2z5FmPiIiEnIKPiIiEnIKPiIiEnIKPiIiEnIKPiIiEnIKPiIiEnIKPiIiEnIKPiIiEnIKPiIiEnLmnAv3GCKSme0HPvHzVBrwRSvfpjXnNndOoOf8HW/NsUzgQAvjCZa2XKeOvLarXuOOXN+2vj6c1zha/w639fWxdI2/5pzr0+JZzjl9teELeCyY5zZ3TqDn/B1vzTFgYyReJ13j0F7faLrG0fp3WNe45S8tu7Xd8iCf29w5gZ7zd7y1x0KlI5+ta9yyjn5utFzjaP073NbXd7lrrGW3LsTMNjrnxoR7HLFM17hz6fp2vlBdY818upbHwj2ALkDXuHPp+na+kFxjzXxERCTkNPMREZGQU/AREZGQU/AREZGQU/ARAMws1cw2mdm3wj2WWGRmZ5rZo2b2vJn9a7jHE4vMbKKZ/c7MXjazceEeTywysyFm9oSZPd/R91LwiXJm9qSZ7TOz7Y2OX2VmO8xsp5nNasVbzQSKOmeU0S0Y19g596Fz7jYgH1CqcCNBusZLnXO3AFOBGzpxuFEpSNe41Dk3LSjjUbZbdDOzS4HDwNPOuRF1x+KBvwLfAMqADcCNQDwwr9Fb3Azk4CmpkQQccM69EprRR4dgXGPn3D4zmwDMAh52zi0O1fijQbCucd3r/ht4xjm3OUTDjwpBvsbPO+eu78h4unXkxRJ+zrnXzGxQo8PnAzudc6UAZlYIfNs5Nw9osqxmZpcBqcBZQIWZrXDO1XbqwKNIMK5x3fssA5aZWTGg4OMlSH+PDZgPrFTgaSpYf4+DRcEnNvUHPvV6XAZcEOhk59z/ATCzqXhmPgo8LWvTNTazXGAS0B1Y0akjix1tusbAncCVQJqZDXXOPdqZg4sRbf17nAH8BzDKzGbXBal2UfCJTebnWIvrq865p4I/lJjVpmvsnCsBSjprMDGqrdf4IeChzhtOTGrrNS4HbgvGByvhIDaVAQO8HmcDu8M0llila9z5dI07X9iusYJPbNoAnGZmg80sEZgCLAvzmGKNrnHn0zXufGG7xgo+Uc7MlgBvA8PMrMzMpjnnqoE7gFXAh0CRc+79cI4zmukadz5d484XaddYqdYiIhJymvmIiEjIKfiIiEjIKfiIiEjIKfiIiEjIKfiIiEjIKfiIiEjIKfiIiEjIKfiIRCAze8nMXDu/BoZ7/CItUWFRkciUDXzm53hvIAGoAL708/wh59w/OnNgIsGgCgciUcTMdgFZwM+cc/8R7vGItJeW3USihJll4gk8AFvDORaRjlLwEYkeI72+/0vYRiESBAo+ItGjPviUO+fKwjoSkQ5S8BGJHvXBR0tuEvUUfESixzl1fyr4SNRT8BGJAmbWHTij7qHu90jUU/ARiQ4jOLEvTzMfiXoKPiLRoX7J7RiedsciUU3BRyQ61CcbfOCcqw7rSESCQMFHJDoo001iioKPSHTIqftTwUdigoKPSIQzs8FAWt1DBR+JCQo+IpHPu6zOtrCNQiSIFHxEIl998PnYOXcorCMRCRIFH5HIp8oGEnPUz0dEREJOMx8REQk5BR8REQk5BR8REQk5BR8REQk5BR8REQk5BR8REQk5BR8REQk5BR8REQk5BR8REQk5BR8REQk5BR8REQm5/w/9BYo6WG2Y3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ok, boss, hold on, let me finish....\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "for filename in filenames:\n",
    "    x, y = np.loadtxt(filename,unpack=True)\n",
    "    is_not_zero = y!=0\n",
    "    x, y = x[is_not_zero], y[is_not_zero]\n",
    "    yerr = y * 0.5  \n",
    "    material, freq, meas = filename.split(\"_\")\n",
    "    lb = \"{0} Hz\".format(freq)\n",
    "    ax.loglog(x,y,'o',label=lb) \n",
    "    ax.errorbar(x,y,yerr,fmt=\"none\",ecolor='k')\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"$T$\", size=24)\n",
    "ax.set_ylabel(\"$\\omega^2 T$\", size=24)\n",
    "ax.annotate(\"$\\sum_i^\\infty x_i$\", (5e-5,.05), size=18)\n",
    "ax.set_title(\"Test 1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a little problem here, small, but it is better to solve it\n",
    "\n",
    "The error bar has a different color than the dots....\n",
    "here a solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Test 1')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wWipErZ1KComkpYKW3VJQa/YChZSOvWURNW/cQwd3lAyDwoxyHjj5FGtef0r7hURCNP+6+W0q8ERKwScFtWYvUEjp2KtnkdXoGVIHq+ZHLNR+IRGJCQWfFBXKXqCQ07EPlQT8jp5Wof1CIhITSjhIUw3p2MepA5907Ap3Ousrb2JV5teY3ZCOnVcY8LnQPtdVjeukzXLOhVUZoK2INF9AwSdJRbp23Fw6dqe6a/37Al09g5o37vFbejvmcvhPJgTcL9SQiq32DZKu2rdvT0VFBV27dlUACsA5R0VFBe3bt2/55CAUfNJUq9KxB48nC5pku10ebrabmttJiissLKSkpISysrJEDyVptW/fnsLCwrCvV/BJU907dg+cjh2gNYM3RbT3UE9Tuq6PNJnVNJzzzbN+wabPKqmureOyOe9wf6DOqiIpLjs7m759+yZ6GGlNCQfpYFIxs7o+4neoVa0ZQvT5F72Y/tpWqmvrANhbWcX017YqHVtEWk0znzTReKbSZNe1y+Smyhze6eCfjt3Qh6S6rppRlslNuYGz5raUbeGEW09G7z+TVTaamsNDAag6WcsjK3f4bValZB3UnvBscL16BgweH4PfWERSmWY+aayldOwmfUislmc6VVGZsTbgOWaQkVNJ+x6vkXXGJu853nRsn1I+gCeDbtm9au0tIk0o+LRhgTLiqjOgNOv1Zs+xjJO067bS+9qbjh2gsyonqzzHRUR8KPi0YcEy4mrsQIvnZGYfZEzGGnKzM0+lYwfZrBr0uIi0WQo+KSzSWlLB+o34ZsQF7VVSU8uvc55jwYhPGTu0gEkrJlGeEzjn/1hugM+YX8T2X12u9g0ibZSCTxsWSkZcwF4ldXVMPVhJLicY8f9+5z3+aq+B1DQ695jLYcbRm5QRJyJ+FHzaigDp2KEUKG04p8fJGsw5epysYWb5AYqOHvOccKjEmw33RF0pX+l5Ngs6nEWdM0rq8pl2cgqLq7/iX6B0yyKOfbaJ805s5Td7v8PMX/xcwUmkjVGqdRsSqBxOUb8iFv9jMQDz95eyveoQ73Roes7IhZPJr25aZLS4W6FfxlxVVhVzu53GrJrbvOnY4MmIm7RiEpeUfcbk3dvo4I6DQaF52jfMeL0G+KHfhlWV8RFJX5r5yCmTihn40zUB/2X/aq+BnMjI9D+YncvjnTu1mA0HpzLibtqzXe0bRETBR0Lz9269eaHvUMhshwPKMs6Eb/yWz08eDni+ZVd6/5ybncmoi/eypWwLV/XM92vv0KBx+4Ylm/aya/cnfPhJBZfNeUfLciJpRsFHQvb3br2hcAQf5VzA3WctgMHjg2bDWc0ZABR0ymXClWW8ue+3VNdV43zaO/gGIN/2DUs27WX6a1spc3k4LHgZn/lFp4qYikhKUfBJc7Fu7RswG84Zdx85yci+XXh/2lW8f+C/ArZ3eLxzJ6Bp+4ZHVu6g6mSt3/kNZXxEJD20ieBjZueZ2dNmttjMfpDo8aSTJhlzHXsw03X2K+UTbKPq51mZlNTlMzf7h1x+4w+9+4X2Vh5jTMYa1uTcy652/8qanHsZk7FGXVVF0kjKBh8ze97MSs1sW6Pj15nZDjPbaWbTAJxzHzvn7gLGA8MTMd505ldD7ua3KeI0v/eDLc11rc3gJwX/zcyfPeSX5XZTu3eYk/0shRnlZBgUZpQzJ/tZvnvah4CnvcO8/7oaStbhPl1D2axzVT9OJMWkbPABXgCu8z1gZpnAk8D1wPnArWZ2fv17Y4A1wOr4DlOCLc1NOOx/rGG/0Kq+bzO2V77fM6EOVs0D2X8E4JKyz7j9k01QewIDutWVNlvA9JZ5H6iSgkiSSdng45x7DzjQ6PDFwE7n3C7nXDWwEPhm/flLnXNfAb4d35Gmn4E98lq19yaUpTnf6tnBkhI6VH1O8a5iXqnez4jePf2z5k5WceytGU2/fH4RMyruD+8XFZGYSdngE0QBsMfndQlQYGZXmNlvzWwesDzYxWZ2h5mtN7P1ap8bXS0tzQWqnu2blACnNrR+kZUZMEC1P/Z5k4y4JYf6c/uRH7L2kwNK2RZJIulW4cACHHPOuXeBd1u62Dn3DPAMwPDhw11UR5bEQsmG854TSmrzpGJmtXKZq7mkBODUhtaTh/zebwhQRUePsc919Wtst27pPEYcXMbanBfY5/KZe3g801/zVGJo3Ppb1RRE4ivdZj4lQC+f14XAvgSNRVqhuerZ5PVqdkPr51mZHHM5zK0Z79fYbtDGf6fAKvySFq6t/UvTlG0tzYnEXboFn3XAuWbW18xygAnA0gSPSUIQrML2TUdOh/u2NbuhtVuNY9rJKSytu9y7UbX8zXvJ5YTfeR2smgeyFjVJ2dbSnEj8pWzwMbOXgQ+AAWZWYmaTnXM1wN3ASuBjYJFzbnsix5l2JhV7fqIsUFLCHZW5fkkJgQKUq8vm09JbWVp3uV9ju64BiqCCp4yPt/MqvktzP2BNzr0MO7wqcDUFEYmqlH3m45y7Ncjx5TSTVCDxEc6zE78K29fNZ/uvLm/yPsCM92dQXVdNXm07jn4+mn8eHkpBp1zuHz2AsUMLKN5VzGO9CijNzKB7TS1TD1Z6W0Dsp6s3QM37r6u5becWcs3zHKjQPEtznIRHVub4PxeaX8T2/YeY1fURPRcSiYKUDT7SNjVpAcFfmNX3Bm9AaEjZPl6fqNCQEQdw1dFa9g17wBtUbtqz3Rt4GjQszf1LpX/gE5HoUvCRqGh1/bhJxQwE/hjlcQRL2f6PLl0oGv1zRgweD3iC1ONndubzrPwms6PGS3Pe5nd1R+ub332HIUV3NMmYA2XNiYQqZZ/5iAQSLGW7NDMDfALPzL/NZH92VsD9Qo2X5o6//gM6uKPerLkHTj7FmtefClhlW1lzIqFR8JG0EiwjLst18f65uQ2tVbRrsjTX3tX4nRus+Z2y5kRCp2U3iZtYtnZoMPWiqZ5nPj7BJacO8mtu9L5ubkNr7rgnQl6a803Zbs2GVhFR8JEkNrBHXquvaZwR18NlclNlDu90GOk9p3vH7uw/ur/JtZmua5OluePZnv+L+CYuNFRT8D4Xqt/QmmuefUXNZs2h50IioGU3SUN+deRcgd9eIQi+ofVMn9lRc0tzjZvftWZDq54LiXiEFHzMrDDWAxFpIl4bWl0md1Tm0qnu1OyouaU53+Z30PKG1kkrJjFpxSRAz4VEGoQ68/nMzFQpQJLLpGJmdX0krEtbmh0FS1zIdF39mt8V7yrm2l4FDO7Ty7/FA/5Zc9CKagrzi9j+q8vVg0jSWmue+XSO2ShE6sUjKSEUoSQuhLKhNTtvM1u2baG6rpqfnlzLj06rouBoy8+FRNKdEg4k5fgGqFg9tA8lcaGlDa2lp3X0NsgD+Dw70y9pIWA1hVZsaBVJZUo4kLTX2s6rDVpammtpQ2soDfJ8nwvN+6+rqXnjntA2tIqkOAUfSW8xSlqAlje0ttggD//nQjft2U5Wo2AVbEPr9l9d3qTwqkgqUfARCVNLKdvNNsgDbzWF7LzNbCnbwlU985skLUDTDa0i6aA1wecMM5tmZtebWc+YjUgkAcJZmmspZTtQcGpfV8e9ByspyziT3HFPUDqo0PtcKFCdOcC7obUhZXvd0nl0ObGX805s5fOZ/Vm3dF6Ev71I/LUm+OQCvwTeBPaYWamZvW1mc83s22Y20Mw0k5LUE8HSXHPPhQI1yJtJPn1PnsPdZy0I6blQ4w2tX955lEEbfkYPO0CGQXfKGLThZ00DkNK1Jcm1JtvtJFAONMx68oFrgKt9zjlhZh8Bm31+tjjnDkdhrCIhS5aU7cYN8phfxHYOed9v7rlQSV0+z+Z8h8uL7vCmbG847QSr2+f71ZnLtWp6bXwExtwZ+19IJEpaE3wOOOcKzawrMMTn50Lgy/Wf1R64CBjqe6GZ7QY2OedujsagRdJFsDpzXWsz+EnBf/PHOy/17ieqrqsGn6U5wBuAznTlAExaMYlLyj5j4mcfK11bklqr9/k45yqA1fU/AJhZDjAITyBqCEqDgYbKkH2BPhGOVSSqWjU7ilHzu0CbWds7Y8Lh9rxT/9inuaW5huBTavl0By4p+4zv7trsaQNhns2sD5x8ihmv1wA/VACSpBGVTabOuWpgY/2Pl5n1wX+GJCI+mmxm7diDqUdO0Keq1ht8WkrZrnI57Bl2Pxt2FfNK9X6ePLuHXwuIDlbNj9xCbll5tSpsS9KIaYUD59xuYDewJJbfI5IsBvbI44+TWp8119xzoWBLc91ravmcbuwZdr83ay5QqZ+io8f80rUbipzO31/KjIpDYdfHE4mEyuuINKO1S3OxEKzO3E1HTqf7zA/pDoxaPKrZpTm//kP1lhzqzy+PjKTssKfC9v2jB2hZTuIm1ODzBqC/lSIJEGhp7qY9lX5p3c0tzTVO1wZPyvaIg39R51VJmJD25TjnbnTOXRzrwYhIYH77iW5+O+QWEN1qnF//oeJdxez4YiMvnfYRk3q1463TOlCY4amwfW3tX5qU8QHPcyHtF5Jo06ZQkTgLt9Bpc4KV+sly3/f2H2pI2T5CXZNqCuq8KvGmZz4iEUqG50KRtIBoeC7UUGEbTiUlfFPPhSRGAs58zOw6M1tvZsfM7LP6EjqnBTjv22ZWG/thikhLwm0B0ZCy3bjzque5UAidV0XC0GTmY2bD8NRv+wJ4DU85nZ8A48zsG865j+M7RJHU1upSPzHa0NpcynZDhW3f50IbTqtldft2TD3YgaKj6rwq0RVo5jMT+H/AQOfcd5xzVwFfBbKBv5rZyADXiEiSC1Zle1JlHbnjnmDEmDvDey4kEoZAweci4BnnXGXDAefc+8BwPBtG/2Rm18ZneCISLYFaQEyp7MiS01+AweOB5p8LAU2eC01aMUkVtCUsgRIOzgAqGx90zpWZ2RV4luSWmdnEGI9NpM2IVxVuv2oK+0vZXnXIW8YHWv9cSCRcgWY+n+ApCtqEc+6fwHV4ior+AUiJAGRm/czsOTNbnOixiETDwJ+uYeBP10T9c5vrvur7XKjBJWWfceyzTZx3Ymt9Be2fKyFBQhIo+LwLfMvMAqZhO+eOA98EXgdGxW5ozTOz5+sb2m1rdPw6M9thZjvNbBqAc26Xc25yYkYqkjpCeS4EeJMSnqz9grEFed7Nqg+cfIo1rz+lACQtChR8FgANz3gCcs7VALcA/wm8F5uhtegFPLMwLzPLBJ4ErgfOB241s/PjPzSR6Jt/3fyYL8+F8lyopaSEH7EwYKUEEV9NZjfOufXAt1q60DnngB/HYlChcM69V9+ywdfFwE7n3C4AM1uIZ5b2USifaWZ3AHcA9O7dO2pjFYm7CNK1W3ouFMpmVWXESUvSrcJBAbDH53UJMLK+++ovgaFmNt05NzvQxc65Z4BnAIYPH+5iPViRVNRSUoJvBW3f9g2eA7Gp8CCpJ92CjwU45uq7r94V78GIxFs8suaa26waqIJ2g+37DzFr3gdqXCdAmMHHzDoBX8HTOrsvcBpQi2emsRH4q3OuLFqDbIUSoJfP60JgXwLGIZK2Arb+rqvjOwdqPRW0i+7wVkrYUraF6rpqRtXVcm/2QZ744jbY8kvv8yNpu8Kd+ZQTeJbRoM7M1gC/c869FuZ3hGMdcK6Z9QX2AhOAf43j94uklHA7r0LjIqa5FOc97p3VNCQlVNd5egTtz8rkofwuzCw/wFWv3c223Qe9mXPSNoXbUiEDT/A5AGwFNtT/s7L+eCbwNeAVM3vXzHpEYax+zOxl4ANggJmVmNnk+iy8u4GVwMfAIufc9mh/t0hamFQc9jOYloqYNpeUkMsJem6Yq3TsNi7cmc81wEbfEjwNzKwQuApPKvZ1wL8Aq81shHPuaNgjbcQ5d2uQ48uB5dH6HpF0E4/nQi0lJfSggkdW7lCB0jYsrJmPc+6dQIGn/r0S59wC51wRcCme5zADgAfDH6aIpJLmKiWAJyNO6dhtW0w7mTrnPgTG4VmK0xNGkRQUTufVYJUSph6s5JjLYW7N+KYFSqVNiXmqtXNug5kdBrRrUyTVRPBMCOqTEmqrObOmjh8dOMiF/+zAtJrxrMr8GrMbp2PPL/KkY3d9ROnYbUDUgo+Z9QQynHMlPscM+C6eStkC7Xk2AAAUt0lEQVSfROu7RCS2ovFcyLdSwjf/7xnMOjiSMpdHQadcZqsdd5sXzZnPrcBcMzuBpwvqSaA7nj1AAPOi+F0ikkLG5u3k3GMbmsxqfPcCXVtXy71ZB+urY3+HIfX7hSQ9RfOZzx48Qac9cDbQHzgM/BYocs7NjeJ3iUiKa7wX6POsTGZ168L/Of2YqmO3AVELPs65Rc65nkA/PKVs/gr0BMYAUUuxFpH00NxeIFXHTn9Rz3Zzzu12zj3jnPsa8FU8mW6rzOySaH+XiCSHcDLiWtoLpOrY6S2s4GNmq0Lpk+OcW4Nno2kW8HA439VWHT58mOuvv54BAwbw61//mk8++YQRI0YwdOhQFi1alOjhiZzqLxRmpYRQ9gI1pGNL+gl35nM1sNnMFrQUhJxzO4B/0kxzOmnqmWee4YEHHmDbtm28++67jBs3jqeffpoNGzbw8ssvU1tbm+ghikSkpb1ADdWxtQ8oPYUbfObVX/ttYKuZvWdmd5nZlxqfaGZ3A6cD6o/TCrt27eLSSy8lOzubBx54gA4dOjBs2DAyMjL40pe+REVFRaKHKBKRxl1Tu9fUMqPsABce6eCpjn3jD5XtlsbCLa/zAzylc/6K55nO5XjaV39sZv80s4/NbJOZlQGP4wk8f43SmNuEoUOH8oc//IGKigrmzp1LTk4Ozz77LAcPHmTbtm1069Yt0UMUiZhvgdJVGX3pV3MOPyn4b2b+7CG/tgzrv1jPqMWjKN6lZnTpIuyEA+fcOufcFXiKiC7B08/HgA54arldCHStP/Y58JNIB9uW3H777WzdupWioiJuu+02lixZwnvvvcc3vvENpk2bhmf/rkj6atKW4eh+Zv5tJqN//5/cMu+DBI9OIhXxJlPn3LvAu2Z2Op42CoPxNJjriOdZz0bgZefcoUi/qy3Jzs7mscce8zu2YMGCBI1GJDy+lRJmtTJgBEzFrj1OdcbzUDcyKuOTxIlahQPn3BHgzfofERE/0UrFrsh0nFkTjRFJIsW0qrWISLiCp2LX1Jfg+bkqIKQwBR8RSUrNpWIXZpSrBE+Ki3lLBWnZvn37+PDDD8O+/tJLL+Wss86K4ohEEs+3LcPJ2hN0r6ll6sFKio4eA/CU4HELuWXl1UrJTkEKPkngb3/7G9/61rfCvn7ZsmXccMMNURyRSHJoaMvw/N9fI1B+p0rwpC4tuyWBjAz9zyDSnIqcwGV2ApbgmV/k+ZGkpplPEigqKuKaa67hT3/6EwDDhg3j1VdfpV27diFd37lz51gOTyThXu01kMm7t5Hlk3rtW4JHUo85p6o3gQwfPtytX78+bt939OhRrrnmGv7+978DcOONN/LKK6+QmZkZtzGIJKOGum7ze17Psdd/RPu6o+xz+Tybc6rhnO85vHE31J6AvF5w9QwYPD6Rw29zzGyDc67FWp5a70kSHTt2ZPny5VxwwQUAvP7663z/+99H/3EgUm/weDr0HsrH7S7wK8HT4JKyz2DZvZ7AA3Boj+f1FlWBT0YKPkmkc+fOvP322/Tv3x+A+fPn85OfqCqRSHMa6r89WfsFo87qTHHHDqfePFkFq2clbnASlIJPkunevTurVq2ioMDzX3SPPfYYDz+sVkgigfjWf3Nm7M/OYmZ+F/8AdKgkcQOUoBR8klCfPn1YtWoV+fn5AMyYMYMnn3wywaMSST7NteL2yiuM86gkFAo+Seq8885jxYoVnHHGGQDcc889vPTSSwkelUhyaakVN9m5nqQDSToKPkls2LBhLFu2jNzcXJxz3H777bz5puq2ijRorhV3WcaZ8I3ferPd1BE1uSj4JLmvfvWrvPLKK2RnZ1NTU8P48eN57733Ej0skbiZf918v9YMvgLVf8t2GZSVjmPEsf/ksuX5gWu/aSNqwmmTaQoYOHAgnTt3prS0lOPHj7Nv375ED0kkKfjWf6uuqyYv+0wO7P4qxw57tpnsraxi+mtbEzlECaLNzHzMrJ+ZPWdmixM9ltYoLy9n9OjRlJaWAp7stwkTJjQ5r6qqisLCQnr37s2JEyf83psyZQqZmZksXLgwLmMWiSffVtzus//lDTwNqk7W8su/vOTfjpt/Jmi00iAlgo+ZPW9mpWa2rdHx68xsh5ntNLNpzX2Gc26Xc25ybEcaXUePHqWoqIh//OMfAEybNo2pU6cGPDc3N5eHHnqIPXv28NRTT3mPT58+neeee47f/e53AYOWSDoJVGQ064xNVOUt9G/HTTnF5RvhsUHahJogKVFex8y+iqcl9wLn3KD6Y5nAP4BrgRJgHXArkAnMbvQR33POldZft9g5d3NL3xnv8jqN1dTUMGbMGN566y0Abr/9dubPD7zu3aC2tpYLL7yQ0tJSdu3axbPPPst9993HQw89xIwZyviR9NWQSPCPzRPZ2ygAdTxnDhk5lU2u6XGyhrdL9nky4nwSEyQyaVVexzn3HnCg0eGLgZ31M5pqYCHwTefcVufcDY1+SkP5HjO7w8zWm9n6srKyKP8WrTNlyhRv4Pn617/O73//+xavyczMZM6cOZSVlTF27Fh+/OMfc8899wQMPBdffDErV66M+rhFEun+0QPItZN+xyy7aeABn3RsVUFIiJQIPkEUAHt8XpfUHwvIzLqa2dPAUDObHugc59wzzrnhzrnh3bp1i+5oW+HBBx/kxRdfBOCSSy7hlVdeISsrtNyQG264gYsuuojVq1dzyy238Pjjjwc878MPP2T06NFRG7NIMhg7tIDZZ/2ZbnYIgIJOuXTKOTPgud1rak+9UBWEuEvl4BOot1TQNUTnXIVz7i7n3DnOucbLcknj8ccfZ+7cuQB8+ctf5s0336RDhw4tXHXKokWL2Lx5MwCnn346ZoFuk0j6Gpu3kxdOf4qRfbvw/rSrmH7Jj4O24/ZqXAVBqdgxl8rBpwTo5fO6EEjpHOQ//vGP3HfffQD07NmTlStX0rVr15Cvf/vtt5k4cSI33ngjEyZM4Pnnn+fjjz9uct6yZcsYPHhw1MYtksyK+hUx8yszycnIAaBHTS0zyw9423GrCkJipHLwWQeca2Z9zSwHmAAsTfCYwrZ69Wpuu+02nHN06tSJFStW0Lt375CvX7t2LePGjeOyyy7jpZde4he/+AUZGRlMn950hXHjxo0MHTo0msMXSWq+6dhvj5hJ0fH6Jbe8XqqCkCApscnUzF4GrgDyzawE+Llz7jkzuxtYiSfD7Xnn3PYEDjNsmzZt4sYbb6S62pMKOnbsWNauXcvatWtDuv7cc8/lpptu4ktf+hJLliyhXbt2nHPOOUyePJmnn36a999/n8suu8x7/saNG7nyyitj8ruIJNykYmbN+yD4+4PHw4YXvec2dknZZ1Cy1dMX6LFBakgXIykRfJxztwY5vhxYHufhRN3PfvYzjhw54n39wgsv8MILL4R8fWFhIXl5ebz11lveQqTgqYb94osv8sADD/D+++97j2/atIkf//jHURm7SLoo3lXMji82ssHV8mqPrkw9WElRQ0M6UACKspQIPumutra25ZOacfXVVwcMVj169ODYsWN+x8rLyykpKWHIkCERfadIMglW+y1UDX2BjlMHPn2BAM+zodWzFHyiTMEnCaxYsSJu37Vx40b69u1LXl5e3L5TJNls3+9JxR5Y/7q5vkBFR48FTsVuyIYLsHQnLUvlhAMJw6ZNm5RsINJIi32B1JAu6jTzaWMefPDBRA9BJKGWbNrLL4/8kHJ3Bj3nvMP9owfQvWN39h/d3+Tc7jW1gVOxtyyCknVKSoiAZj4pQuVwRCL3+Re9mP7aVspcHg7ztly4rMvEwBtRT2Q2rfu2ZZEnCaG2vnp8Q1KCCpS2SkoUFk2ERBcWFZHomrRiEn/74HpOnOjY5L2CTrn8dHyVty9QD5fJVJdH0aS/NDm3/Ffdya9uWj2bvF5w37amx9uYUAuLatlNRNqMEycCl6raV1lFUb8iFv/D0+5r/v7gtYi7Bgo8oPpwraRltxSgcjgikZt/3XwKOgUOPj075TY5tn3/IW5ptFm1eFcx1/YqYHCfXowq7ElxR5/PU1JCqyj4pACVwxGJjvtHDyA3O9PvWG52JvePHtDitQ17gb7IysT57AUq7thB9eHCoOCTAhR8RKJj7NACZo+7gJxMz7/6CjrlMnvcBYwd6t+NZcmh/tx+5Ies/eQAl815hyWb9gbfC9S1a/BmdKqOHZSe+aQAlcMRiZ6xQwt4+cPPAPjjnZc2ef/zL3ox/YuLqHLZAN6MuKz+QfYCZWYozToMmvkkOZXDEYmvXbsGeQNPg6qTtVhNp4Dnd+/YPfAHNewF+nSNZy9QJKnYaTiDUvBJciqHIxJfwTLiqr4Y1XQvkDOmXjS16cnaC9QiBZ8kp3I4IvHVrt2xgMfPzPiKf1M6l8lM15mifk1nJOVv3gsnG6Vkn6zyFChtrWjOoJKInvkkOZXDEYmvfv22sfv/XuS39NaQEVfUryC+e4GCzaAg5Z8zaeYjIuKj+1l7mD1+RIsZccFEdS/Q6lnRm0ElGc18RETq+fYFai4jLhhvX6D6ath+fYGqXev3AgWbKaVBNQXNfEREoiSsvUDNCTZTSoNqCgo+IiJRErQvULh7ga6e4ame4CtNqiko+IiIREmwPT9B9wK1ZPB4z4wps53ndV6v8GZQSUjPfESkzWnNc5ygArTPnnrRVM8zH5+lt6B7gUI1eDxseDHod6YqBR8RkShp2PPTpC9QgL1AXg2VC9IosIRCwUdEJIpC7QvU1umZj4hIoqRp9YJQaOYjItIKvnuBIpLG1QtCoZmPiEgipHH1glBo5iMiEkBUMuKak8bVC0KhmY+ISCKkcfWCUCj4iIjEyqTi4CnUaVy9IBQKPiIiiZCs1Qvi1DW1TTzzMbPzgKlAPrDaOfe/EzwkEZHQqxek4QbUpJ/5mNnzZlZqZtsaHb/OzHaY2U4zm9bcZzjnPnbO3QWMB4bHcrwiIikrjvuOUmHm8wLwBLCg4YCZZQJPAtcCJcA6M1sKZAKzG13/PedcqZmNAabVf5aISMxEbS9QPMV531HSz3ycc+8BBxodvhjY6Zzb5ZyrBhYC33TObXXO3dDop7T+c5Y6574CfDu+v4GISAqI876jVJj5BFIA7PF5XQKMDHaymV0BjAPaAcubOe8O4A6A3r17R2OcIiKpIc77jlI1+FiAYy7Yyc65d4F3W/pQ59wzwDMAw4cPD/p5IiJpJ6/Qs9QW6HgMJP2yWxAlQC+f14XAvgSNRUQk9cV531GqBp91wLlm1tfMcoAJwNIEj0lEJHXFed9R0i+7mdnLwBVAvpmVAD93zj1nZncDK/FkuD3vnNuewGGKiKS+OHZNTfrg45y7Ncjx5TSTPCAiIskr6YOPiEhaS8PqBaFI1Wc+IiKSwhR8REQk7hR8REQk7hR8REQk7hR8REQk7hR8REQk7hR8REQk7rTPR0RETonTviPNfEREJO4UfEREJO4UfEREJO4UfEREJO4UfEREJO4UfEREJO4UfEREJO4UfEREJO4UfEREJO7MOZfoMSQlMysDPg3ydh5wKMSPaunc5t4P9l6g46EcywfKmxlLNLXmHkV6re5xbK8N5dx0vMeR3N/WXp/IexzonEju8dnOuW4tnuWc008rf4BnonVuc+8Hey/Q8VCOAeuT8R5Feq3ucWyvDeXcdLzHkdzfVLrHQc6J+T3Wslt4lkXx3ObeD/ZeoOOhHouXSL67tdfqHsf22lDOTcd7HOn3pso9Tsj91bJbG2Jm651zwxM9jnSmexx7usexF497rJlP2/JMogfQBugex57ucezF/B5r5iMiInGnmY+IiMSdgo+IiMSdgo+IiMSdgo94mVlHM9tgZjckeizpyMzOM7OnzWyxmf0g0eNJR2Y21sx+b2ZvmNmoRI8n3ZhZPzN7zswWR/pZCj5pwMyeN7NSM9vW6Ph1ZrbDzHaa2bQQPupBYFFsRpnaonGPnXMfO+fuAsYDShVuJEr3eIlz7vvA7cAtMRxuyonS/d3lnJsclfEo2y31mdlXgX8CC5xzg+qPZQL/AK4FSoB1wK1AJjC70Ud8DxiMp6RGe6DcOfdmfEafGqJxj51zpWY2BpgGPOGc+0O8xp8KonWP66/7DfCSc25jnIaf9KJ8fxc7526OZDxZkVwsycE5956Z9Wl0+GJgp3NuF4CZLQS+6ZybDTRZVjOzK4GOwPlAlZktd87VxXTgKSQa97j+c5YCS82sGFDw8RGlv8cGzAHeUuDxF62/w9Gi4JO+CoA9Pq9LgJHBTnbO/S8AM7sdz8xHgadlrbrHZnYFMA5oByyP6cjSR6vuMXAPcA2QZ2b9nXNPx3JwaaC1f4e7Ar8EhprZ9PogFRYFn/RlAY61uMbqnHsh+kNJW626x865d4F3YzWYNNXae/xb4LexG07aae39rQDuisYXK+EgfZUAvXxeFwL7EjSWdKV7HHu6x7GVsPur4JO+1gHnmllfM8sBJgBLEzymdKN7HHu6x7GVsPur4JMGzOxl4ANggJmVmNlk51wNcDewEvgYWOSc257IcaYy3ePY0z2OrWS7v0q1FhGRuNPMR0RE4k7BR0RE4k7BR0RE4k7BR0RE4k7BR0RE4k7BR0RE4k7BR0RE4k7BRyQJmdnrZubC/Omd6PGLtESFRUWSUyHwRYDjXYBsoAo4HOD9SufcZ7EcmEg0qMKBSAoxs71AT+BnzrlfJno8IuHSsptIijCzfDyBB2BzIsciEikFH5HUMcTnz/8nYaMQiQIFH5HU0RB8KpxzJQkdiUiEFHxEUkdD8NGSm6Q8BR+R1HFh/T8VfCTlKfiIpAAzawd8uf6lnvdIylPwEUkNgzi1L08zH0l5Cj4iqaFhye0EnnbHIilNwUckNTQkG3zknKtJ6EhEokDBRyQ1KNNN0oqCj0hqGFz/TwUfSQsKPiJJzsz6Ann1LxV8JC0o+IgkP9+yOlsSNgqRKFLwEUl+DcFnt3OuMqEjEYkSBR+R5KfKBpJ21M9HRETiTjMfERGJOwUfERGJOwUfERGJOwUfERGJOwUfERGJOwUfERGJOwUfERGJOwUfERGJOwUfERGJOwUfERGJOwUfERGJu/8PGLbCXHgzY5EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Error bar with the right color\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_yscale(\"log\")\n",
    "for filename in filenames:\n",
    "    x, y = np.loadtxt(filename,unpack=True)\n",
    "    is_not_zero = y!=0\n",
    "    x, y = x[is_not_zero], y[is_not_zero]\n",
    "    yerr = y * 0.8 \n",
    "    material, freq, meas = filename.split(\"_\")\n",
    "    lb = \"{0} Hz\".format(freq)\n",
    "    # ax.loglog(x,y,'o',label=lb) \n",
    "    ax.errorbar(x,y,yerr,fmt=\"o\", label=lb)\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"$T$\", size=24)\n",
    "ax.set_ylabel(\"$\\omega^2 T$\", size=24)\n",
    "ax.annotate(\"$\\sum_i^\\infty x_i$\", (5e-5,.05), size=18)\n",
    "ax.set_title(\"Test 1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How can we add different markers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Further points can be found here: http://matplotlib.org/api/markers_api.html\n",
    "# Here: http://matplotlib.org/1.3.1/examples/pylab_examples/line_styles.html\n",
    "# and here: http://matplotlib.org/examples/lines_bars_and_markers/marker_reference.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "markers = ['o','p','s','<','>','8','v','d','D']*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Test 1')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Problem: how can we use this list of markers in our plot? \n",
    "# There are different options\n",
    "# This is not too bad. Let's use enumerate which gives us an index automatically\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_yscale(\"log\")\n",
    "for i,filename in enumerate(filenames):\n",
    "    x, y = np.loadtxt(filename,unpack=True)\n",
    "    is_not_zero = y!=0\n",
    "    x, y = x[is_not_zero], y[is_not_zero]\n",
    "    yerr = y * 0.8\n",
    "    material, freq, meas = filename.split(\"_\")\n",
    "    lb = \"{0} Hz\".format(freq)\n",
    "    ax.errorbar(x,y,yerr,fmt=markers[i],label=lb)    \n",
    "ax.legend()\n",
    "ax.set_xlabel(\"$T$\", size=24)\n",
    "ax.set_ylabel(\"$\\omega^2 T$\", size=24)\n",
    "ax.annotate(\"$\\sum_i^\\infty x_i$\", (5e-5,.05), size=18)\n",
    "ax.set_title(\"Test 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Test 1')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is also nice, and it is MUCH safer. Why?\n",
    "mks = markers[:len(filenames)] \n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_yscale(\"log\")\n",
    "for filename,marker in zip(filenames,mks):\n",
    "    x, y = np.loadtxt(filename,unpack=True)\n",
    "    is_not_zero = y!=0\n",
    "    x, y = x[is_not_zero], y[is_not_zero]\n",
    "    yerr = y * 0.8\n",
    "    material, freq, meas = filename.split(\"_\")\n",
    "    lb = \"{0} Hz\".format(freq)\n",
    "    ax.errorbar(x,y,yerr,fmt=markers[i],label=lb)    \n",
    "ax.legend()\n",
    "ax.set_xlabel(\"$T$\", size=24)\n",
    "ax.set_ylabel(\"$\\omega^2 T$\", size=24)\n",
    "ax.annotate(\"$\\sum_i^\\infty x_i$\", (5e-5,.05), size=18)\n",
    "ax.set_title(\"Test 1\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x7fd3700ed1c8>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Zip is a nice tool to join to arrays, or list\n",
    "zip(filenames, mks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F64ac_0.01_T.dat o\n",
      "F64ac_0.02_T.dat p\n",
      "F64ac_0.03_T.dat s\n"
     ]
    }
   ],
   "source": [
    "for filename, marker in zip(filenames, mks):\n",
    "    print(filename, marker)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Satisfied?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Excercise\n",
    "=========\n",
    "The three files F64ac_freq_sp.dat are the power spectra $S$ of magnetic noise signals taken at three different frequencies $f_H$ of an applied magnetic field .\n",
    "\n",
    "I remember that the *amplitude* of the power spectra rescales, i.e. it is proportional, with the frequency $f_H$, but I do not remember if it is directly or inversely proportional.\n",
    "\n",
    "In other words, if $S / f_H$ or $S f_H$ shows a good collapse of the data.\n",
    "\n",
    "Would you please check it for me?\n",
    "\n",
    "ps. Or are they already rescaled?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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