98-DA_Numpy_Exercises.ipynb

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    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
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   "source": [
    "# Exercice Numpy"
   ]
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    "## 1. Array creation"
   ]
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    "- Create a 1D array with values from 0 to 10 and in steps of 0.1. Check the shape of the array:"
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    "- Create an array of normally distributed numbers with mean $\\mu=0$ and standard deviation $\\sigma=0.5$. It should have 20 rows and as many columns as there are elements in ```xarray```. Call it ```normal_array```:"
   ]
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    "- Check the type of ```normal_array```:"
   ]
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   "source": [
    "## 2. Array mathematics"
   ]
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   "source": [
    "- Using ```xarray``` as x-variable, create a new array ```yarray``` as y-variable using the function $y = 10* cos(x) * e^{-0.1x}$:"
   ]
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    "- Create ```array_abs``` by taking the absolute value of ```array_mul```:"
   ]
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    "- Create a boolan array (logical array) where all positions $>0.3$ in ```array_abs``` are ```True``` and the others ```False```"
   ]
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    "- Create a standard deviation projection along the second dimension (columns) of ```array_abs```. Check that the dimensions are the ones you expected. Also are the values around the value you expect?"
   ]
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   "source": [
    "## 3. Plotting\n",
    "\n",
    "- Use a line plot to plot ```yarray``` vs ```xarray```:"
   ]
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    "- Try to change the color of the plot to red and to have markers on top of the line as squares:"
   ]
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    "- Plot the ```normal_array``` as an imagage and change the colormap to 'gray':"
   ]
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    "- Assemble the two above plots in a figure with one row and two columns grid:"
   ]
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   "source": [
    "## 4. Indexing\n",
    "\n",
    "- Create new arrays where you select every second element from xarray and yarray. Plot them on top of ```xarray``` and ```yarray```."
   ]
  },
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    "- Select all values of ```yarray``` that are larger than 0. Plot those on top of the regular ```xarray``` and ```yarray```plot."
   ]
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    "- Flip the order of ```xarray``` use it to plot ```yarray```:"
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   "source": [
    "## 5. Combining arrays\n",
    "\n",
    "- Create an array filled with ones with the same shape as ```normal_array```. Concatenate it to ```normal_array``` along the first dimensions and plot the result:"
   ]
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    "- ```yarray``` represents a signal. Each line of ```normal_array``` represents a possible random noise for that signal. Using broadcasting, try to create an array of noisy versions of ```yarray``` using ```normal_array```. Finally, plot it:"
   ]
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