SOMplots.ipynb

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    "# Soil Organic Matter Plotting in Python\n",
    "This script reads soil organic matter data (see the first table below) from a csv file using the Pandas package, and plots it using Plotly.\n",
    "\n",
    "If nothing but the code is visible, choose `Run All` from the `Cell` menu above. Contents of the cells may be modified, and you may run an individual cell by typing `Shift` + `Enter` while the cursor is in the cell. Changes made in the public notebook cannot be saved, so do anything you want here, but know that once you leave the page all changes will be lost.\n",
    "\n",
    "More in-code comments and notes to come..."
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    "# Required imports from Plotly and Pandas\n",
    "import plotly.offline as py\n",
    "import pandas as pd\n",
    "import plotly.figure_factory as ff\n",
    "from plotly import tools\n",
    "import plotly.graph_objs as go\n",
    "py.init_notebook_mode(connected=True)\n"
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   ],
   "source": [
    "# Using Pandas, load data from columns labeled \"depth\", \"Location\",\n",
    "# \"Horizon\", and \"%SOM\" in an Excel file exported as csv.\n",
    "df = pd.read_csv('SOM.csv', usecols=['depth', 'Location', 'Horizon', '%SOM'])\n",
    "# Replace any missing data with numbers\n",
    "for i in range(len(df['%SOM'])):\n",
    "    if df.loc[i,'%SOM'] == '#DIV/0!':\n",
    "        df.loc[i,'%SOM'] = 99\n",
    "\n",
    "# Make a table of the data with plotly\n",
    "table = ff.create_table(df)\n",
    "py.iplot(table, filename='jupyter/table1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "The process of creating a plot with Plotly isn't that transparent unless you want to do really simple plots. See the [Plotly documentation for Python](https://plot.ly/python/) for details and examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "This is the format of your plot grid:\n",
      "[ (1,1) x1,y1 ]  [ (1,2) x2,y1 ]  [ (1,3) x3,y1 ]\n",
      "\n"
     ]
    },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data using Plotly\n",
    "grouped = df.groupby('Location')\n",
    "plots = []\n",
    "titles = []\n",
    "for i in grouped.groups:\n",
    "    titles.append(\"Pit {}\".format(i))\n",
    "    xx = list(grouped.get_group(i)['%SOM'])\n",
    "    yy = list(grouped.get_group(i)['depth'])\n",
    "    xx.append(xx[-1])\n",
    "    yy.append(24)\n",
    "    # The line shape option 'vh' gives the SoilWeb-style line shape. On an interval between data points, it\n",
    "    # sets the line height to the height of the 'next' vertex.\n",
    "    plots.append(go.Scatter(x=xx,\n",
    "                            y=yy,\n",
    "                            line=dict(shape='vh', color='#a004ac'),\n",
    "                            mode='lines'))\n",
    "\n",
    "fig = tools.make_subplots(rows=1, cols=len(plots), subplot_titles=titles, shared_yaxes=True)\n",
    "\n",
    "for i in range(len(plots)):\n",
    "    fig.append_trace(plots[i], 1, i+1)\n",
    "\n",
    "\n",
    "fig['layout'].update(showlegend=False)\n",
    "for i in range(len(plots)):\n",
    "    xaxis = 'xaxis{}'.format(i+1)\n",
    "    yaxis = 'yaxis{}'.format(i+1)\n",
    "    fig['layout'][xaxis].update(title='%SOM', range=[0,100])\n",
    "    fig['layout'][yaxis].update(title='Depth', autorange='reversed')\n",
    "py.iplot(fig, filename='jupyter/plot1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notes\n",
    "You can also work on Python notebooks in Google Drive with Google Collaboratory, which is nice if you have all your stuff in Drive. It also doesn't require that you install anything on your own computer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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