IFT_Step10_ffc_results_hydrographs.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def str_to_path(path_str):\n",
    "    '''Converts windows path string to os.path'''\n",
    "    path_list = path_str.split('\\\\')\n",
    "    path = os.path.join(path_list[0], os.sep, *path_list[1:])\n",
    "    return path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate the water year day\n",
    "def calculate_water_year_day(row):\n",
    "    # Water year starts on October 1st\n",
    "    water_year_start = pd.Timestamp(year=row['date'].year if row['date'].month >= 10 else row['date'].year - 1, month=10, day=1)\n",
    "    # Calculate the difference in days from the water year start\n",
    "    return (row['date'] - water_year_start).days + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paths, input, and output file names\n",
    "\n",
    "outer_dir = os.path.abspath(os.path.join(os.getcwd() ,\"../..\"))\n",
    "path = os.path.join(outer_dir, 'IFT_files', 'IFT Results')\n",
    "plot_path = os.path.join(path, 'plots')\n",
    "\n",
    "\n",
    "version = 'V1_0_0'\n",
    "\n",
    "in_csv = os.path.join(path, 'All_hydrographs_{}.csv'.format(version))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "238605\n"
     ]
    },
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       "      <th>date</th>\n",
       "      <th>flow</th>\n",
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       "      <th>version</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>water_year</th>\n",
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       "      <td>indian</td>\n",
       "      <td>V1_0_0</td>\n",
       "      <td>10</td>\n",
       "      <td>1950</td>\n",
       "      <td>1951</td>\n",
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       "         date       flow    ift  stream version  month  year  water_year\n",
       "0  1950-10-01  33.839921  unimp  indian  V1_0_0     10  1950        1951\n",
       "1  1950-10-02  33.839921  unimp  indian  V1_0_0     10  1950        1951\n",
       "2  1950-10-03  33.839921  unimp  indian  V1_0_0     10  1950        1951\n",
       "3  1950-10-04  33.839921  unimp  indian  V1_0_0     10  1950        1951\n",
       "4  1950-10-05  33.839921  unimp  indian  V1_0_0     10  1950        1951"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import data\n",
    "df = pd.read_csv(in_csv)\n",
    "print(len(df))\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert 'date' column to datetime if it's not already\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "\n",
    "\n",
    "\n",
    "# Apply the function to each row in the DataFrame\n",
    "df['water_year_day'] = df.apply(calculate_water_year_day, axis=1)\n",
    "\n",
    "# Now df has a new column 'water_year_day' with the calculated day of the water year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>flow</th>\n",
       "      <th>ift</th>\n",
       "      <th>stream</th>\n",
       "      <th>version</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>water_year</th>\n",
       "      <th>water_year_day</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>1950-10-01</td>\n",
       "      <td>33.839921</td>\n",
       "      <td>unimp</td>\n",
       "      <td>indian</td>\n",
       "      <td>V1_0_0</td>\n",
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       "      <td>1950</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>33.839921</td>\n",
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       "      <td>V1_0_0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>33.839921</td>\n",
       "      <td>unimp</td>\n",
       "      <td>indian</td>\n",
       "      <td>V1_0_0</td>\n",
       "      <td>10</td>\n",
       "      <td>1950</td>\n",
       "      <td>1951</td>\n",
       "      <td>4</td>\n",
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       "      <td>33.839921</td>\n",
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       "      <td>indian</td>\n",
       "      <td>V1_0_0</td>\n",
       "      <td>10</td>\n",
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       "      <th>295</th>\n",
       "      <td>1951-07-23</td>\n",
       "      <td>2.781339</td>\n",
       "      <td>unimp</td>\n",
       "      <td>indian</td>\n",
       "      <td>V1_0_0</td>\n",
       "      <td>7</td>\n",
       "      <td>1951</td>\n",
       "      <td>1951</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>1951-07-24</td>\n",
       "      <td>2.651339</td>\n",
       "      <td>unimp</td>\n",
       "      <td>indian</td>\n",
       "      <td>V1_0_0</td>\n",
       "      <td>7</td>\n",
       "      <td>1951</td>\n",
       "      <td>1951</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>1951-07-25</td>\n",
       "      <td>2.781339</td>\n",
       "      <td>unimp</td>\n",
       "      <td>indian</td>\n",
       "      <td>V1_0_0</td>\n",
       "      <td>7</td>\n",
       "      <td>1951</td>\n",
       "      <td>1951</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1951-07-26</td>\n",
       "      <td>2.651339</td>\n",
       "      <td>unimp</td>\n",
       "      <td>indian</td>\n",
       "      <td>V1_0_0</td>\n",
       "      <td>7</td>\n",
       "      <td>1951</td>\n",
       "      <td>1951</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1951-07-27</td>\n",
       "      <td>2.781339</td>\n",
       "      <td>unimp</td>\n",
       "      <td>indian</td>\n",
       "      <td>V1_0_0</td>\n",
       "      <td>7</td>\n",
       "      <td>1951</td>\n",
       "      <td>1951</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 9 columns</p>\n",
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      ],
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       "          date       flow    ift  stream version  month  year  water_year  \\\n",
       "0   1950-10-01  33.839921  unimp  indian  V1_0_0     10  1950        1951   \n",
       "1   1950-10-02  33.839921  unimp  indian  V1_0_0     10  1950        1951   \n",
       "2   1950-10-03  33.839921  unimp  indian  V1_0_0     10  1950        1951   \n",
       "3   1950-10-04  33.839921  unimp  indian  V1_0_0     10  1950        1951   \n",
       "4   1950-10-05  33.839921  unimp  indian  V1_0_0     10  1950        1951   \n",
       "..         ...        ...    ...     ...     ...    ...   ...         ...   \n",
       "295 1951-07-23   2.781339  unimp  indian  V1_0_0      7  1951        1951   \n",
       "296 1951-07-24   2.651339  unimp  indian  V1_0_0      7  1951        1951   \n",
       "297 1951-07-25   2.781339  unimp  indian  V1_0_0      7  1951        1951   \n",
       "298 1951-07-26   2.651339  unimp  indian  V1_0_0      7  1951        1951   \n",
       "299 1951-07-27   2.781339  unimp  indian  V1_0_0      7  1951        1951   \n",
       "\n",
       "     water_year_day  \n",
       "0                 1  \n",
       "1                 2  \n",
       "2                 3  \n",
       "3                 4  \n",
       "4                 5  \n",
       "..              ...  \n",
       "295             296  \n",
       "296             297  \n",
       "297             298  \n",
       "298             299  \n",
       "299             300  \n",
       "\n",
       "[300 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(300)"
   ]
  },
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   "source": [
    "# Filter the DataFrame based on your criteria\n",
    "filtered_df = df[(df['water_year'] == 1957) &\n",
    "                 (df['stream'] == 'indian') &\n",
    "                 (df['ift'].isin(['unimp', 'ncifp']))]\n",
    "\n",
    "fig = px.line(filtered_df,\n",
    "              x='water_year_day',\n",
    "              y='flow',\n",
    "              color='ift', # Differentiates lines by 'ift' category\n",
    "              labels={'flow': 'Flow', 'date': 'Date'},\n",
    "              title='Hydrograph for Indian Stream in 1957')\n",
    "\n",
    "fig.update_layout(xaxis_title='Date',\n",
    "                  yaxis_title='Flow',\n",
    "                  legend_title='IFT Category')\n",
    "fig.show()"
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   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
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   "source": [
    "# Filter the DataFrame based on your criteria\n",
    "filtered_df = df[(df['water_year'] == 1958) &\n",
    "                 (df['stream'] == 'indian') &\n",
    "                 (df['ift'].isin(['unimp', 'ncifp']))]\n",
    "\n",
    "fig = px.line(filtered_df,\n",
    "              x='water_year_day',\n",
    "              y='flow',\n",
    "              color='ift', # Differentiates lines by 'ift' category\n",
    "              labels={'flow': 'Flow', 'date': 'Date'},\n",
    "              title='Hydrograph for Indian Stream in 1957')\n",
    "\n",
    "fig.update_layout(xaxis_title='Date',\n",
    "                  yaxis_title='Flow',\n",
    "                  legend_title='IFT Category')\n",
    "fig.show()"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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