{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c05dc143-1f20-4644-9ab6-3406ed11fdd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import kstest, expon\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0df18e10-968c-4bdd-80b2-c0428ec7ed2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We reach that point here ...\n",
      "And here...\n",
      "Here\n",
      "(13020, 8)\n"
     ]
    }
   ],
   "source": [
    "import snowflake.connector\n",
    "import os\n",
    "from cryptography.hazmat.backends import default_backend\n",
    "from cryptography.hazmat.primitives.asymmetric import rsa\n",
    "from cryptography.hazmat.primitives.asymmetric import dsa\n",
    "from cryptography.hazmat.primitives import serialization\n",
    "\n",
    "from password import PRIVATE_KEY_PASSPHRASE\n",
    "\n",
    "with open(\"/Users/impr001/Keys_snowflake/rsa_key.p8\", \"rb\") as key: \n",
    "    p_key= serialization.load_pem_private_key(\n",
    "        key.read(),\n",
    "        password=PRIVATE_KEY_PASSPHRASE.encode(),\n",
    "        backend=default_backend()\n",
    "    )\n",
    "\n",
    "pkb = p_key.private_bytes(\n",
    "    encoding=serialization.Encoding.DER,\n",
    "    format=serialization.PrivateFormat.PKCS8,\n",
    "    encryption_algorithm=serialization.NoEncryption())\n",
    "\n",
    "print(\"We reach that point here ...\")\n",
    "ctx = snowflake.connector.connect(\n",
    "    user='eimpara@theorchard.com', \n",
    "    account='orchard',\n",
    "    private_key=pkb,\n",
    "    role= 'PROD_DATALYTICS_ROLE',\n",
    "    warehouse = \"DEV_ANALYTICS_ORCHARD\"\n",
    "    )\n",
    "print(\"And here...\")\n",
    "\n",
    "try:\n",
    "    cs = ctx.cursor()\n",
    "    print(\"Here\")\n",
    "    sql = \"\"\"\n",
    "    select * from intelligence.dbt_prod_project_moments.fourier_table\n",
    "where isrc_key in ('GBGBQCP1400035',\n",
    "'GBDKUCA0700015',\n",
    "'GBGBBQY0202057',\n",
    "'GBGBAAW9400140',\n",
    "'GBGBSXS1400096',\n",
    "'GBUSD8D1634003',\n",
    "'GBGBAAW9500189',\n",
    "'USGBUM72308738',\n",
    "'USQM4TW2376714',\n",
    "'USQM6N22355980',\n",
    "'USUSK110416513',\n",
    "'USUKELY2300051',\n",
    "'USQMFME2361818',\n",
    "'USQMQYB1709724',\n",
    "'USUSB270900011',\n",
    "'USUKLQ62200014',\n",
    "'USBRJ9B0500005',\n",
    "'USQM4TW2376714',\n",
    "'USUSLS51683401',\n",
    "'USQMDA72188449',\n",
    "'USUSFDB0613253',\n",
    "'USGBUM71801202',\n",
    "'USUSA4D0922205',\n",
    "'USQM6N21986488',\n",
    "'USJPR652100061',\n",
    "'USUS4DG2300493',\n",
    "'USQM4TX2342919',\n",
    "'USUSI4R1130483',\n",
    "'USUSPRZ1200090',\n",
    "'USUSUM71108871',\n",
    "'USQM4TW2328614',\n",
    "'USUSFV71201673',\n",
    "'USQM6N22266016',\n",
    "'ITQZ45A1600041',\n",
    "'ITQZ45A1600040',\n",
    "'ITQM4TW2040256',\n",
    "'ITUSXDR2000586',\n",
    "'GBGBSYA1500102',\n",
    "'GBGBCAD0707135',\n",
    "'GBGBBHS7400063',\n",
    "'GBUKLQ62200014',\n",
    "'GBUSJPL2200212',\n",
    "'GBGBCAD0707114',\n",
    "'GBGBAAW9400140',\n",
    "'GBQMDA72355707',\n",
    "'GBGBQCP1400030',\n",
    "'GBQM6XS1807314',\n",
    "'GBGBAAW9400146',\n",
    "'GBGBQCP1400017',\n",
    "'GBGBSYA1500113',\n",
    "'GBQM6N22266016',\n",
    "'GBGBAAW9400021',\n",
    "'GBUSCGJ2004694',\n",
    "'FRQZTBB2250572',\n",
    "'FRES7311000083',\n",
    "'FRQZ22B1791372',\n",
    "'ESQMDA62376665',\n",
    "'ESQMDA61886536',\n",
    "'ESDEQ321200132',\n",
    "'ESQMDA62141597',\n",
    "'ESQM6N21901829',\n",
    "'ESQMDA62182560',\n",
    "'ESUSLZJ1715784',\n",
    "'ESQMFME2326142',\n",
    "'ESQMFMG2304963',\n",
    "'ESQM6N22010810',\n",
    "'ESQM6N21986488',\n",
    "'ESQM4TX2314301',\n",
    "'ESQM4TX2320032',\n",
    "'ESQM4TX2276359',\n",
    "'ESUYB282374004',\n",
    "'ESFR2X41874268',\n",
    "'ESES71G2302885',\n",
    "'ESQMFME1900021',\n",
    "'ESQM6MZ2272606',\n",
    "'DEQM7281886206',\n",
    "'DEQMBZ92014857',\n",
    "'DENOUJW1719003',\n",
    "'DENOUJW1719003',\n",
    "'DETR1290601381',\n",
    "'DEDEKF22001554',\n",
    "'DEQMDA62109750',\n",
    "'DENOUJW1719003',\n",
    "'DEUSA561215539',\n",
    "'DEQM6P42099227',\n",
    "'DEQM6N21997825',\n",
    "'GBARA820601538',\n",
    "'GBARA820601539',\n",
    "'GBARA820601540',\n",
    "'GBARA820601541',\n",
    "'GBARA820601556',\n",
    "'GBARA820601557',\n",
    "'GBARA820601558',\n",
    "'GBARA820601559',\n",
    "'GBARA820601560',\n",
    "'GBARA820601561',\n",
    "'GBARA820601562',\n",
    "'GBARA820601568',\n",
    "'GBARA820800164',\n",
    "'GBARA820800167',\n",
    "'GBARA820800168',\n",
    "'GBARA820800330',\n",
    "'GBARA820800332',\n",
    "'GBARA820800335',\n",
    "'GBARA820900372',\n",
    "'GBARA820900373',\n",
    "'GBARA820900374',\n",
    "'GBARA820900375',\n",
    "'GBARA820900376',\n",
    "'GBARA820900377',\n",
    "'GBARA820900378',\n",
    "'GBARA820900379',\n",
    "'GBARA820900380',\n",
    "'GBARA821800118',\n",
    "'GBARA821900442',\n",
    "'GBARA821900681',\n",
    "'GBARA940600003',\n",
    "'GBARA940600010',\n",
    "'GBARA940600017',\n",
    "'GBARA947200001',\n",
    "'GBARA947200015',\n",
    "'GBARACN2200009',\n",
    "'GBARACN2200010',\n",
    "'GBARACN2200014',\n",
    "'GBARACN2200018',\n",
    "'GBARBAA1600001',\n",
    "'GBARBAA1600002',\n",
    "'GBARBAA1600003',\n",
    "'GBARBAA1600004',\n",
    "'GBARBEC1800067',\n",
    "'GBARBEC1800075',\n",
    "'GBARBEC1800076',\n",
    "'GBARBEC1900001',\n",
    "'GBARBEC1900003',\n",
    "'GBAREZZ2100001',\n",
    "'GBAREZZ2100002',\n",
    "'GBAREZZ2100003',\n",
    "'GBAREZZ2100004',\n",
    "'GBAREZZ2100005',\n",
    "'GBAREZZ2100007',\n",
    "'GBAREZZ2100009',\n",
    "'GBDKUCA0700015',\n",
    "'GBAREZZ2100015',\n",
    "'GBAREZZ2100016',\n",
    "'GBAREZZ2100043',\n",
    "'GBAREZZ2100044',\n",
    "'GBAREZZ2300001',\n",
    "'GBAREZZ2300002',\n",
    "'GBAREZZ2300004',\n",
    "'GBAREZZ2300005',\n",
    "'GBAREZZ2300006',\n",
    "'GBAREZZ2300007',\n",
    "'GBAREZZ2300008',\n",
    "'GBAREZZ2300009',\n",
    "'GBAREZZ2300010',\n",
    "'GBAREZZ2300011',\n",
    "'GBAREZZ2300012',\n",
    "'GBAREZZ2300013',\n",
    "'GBARF059800010',\n",
    "'GBARF070700181',\n",
    "'GBARF079401408',\n",
    "'GBARF111800135',\n",
    "'GBARF220500542',\n",
    "'GBARF220500605',\n",
    "'GBARF310500025',\n",
    "'GBARF310500209',\n",
    "'GBARF310500232',\n",
    "'GBARF410090807',\n",
    "'GBARF410200030',\n",
    "'GBARF410300001',\n",
    "'GBARF410300049',\n",
    "'GBARF410300064',\n",
    "'GBARF410300070',\n",
    "'GBARF410300090',\n",
    "'GBARF410400013',\n",
    "'GBARF410400015',\n",
    "'GBARF410400059',\n",
    "'GBARF410400191',\n",
    "'GBARF410400214',\n",
    "'GBARF411200382',\n",
    "'GBARF411200383',\n",
    "'GBARF411200389',\n",
    "'GBARF411200659',\n",
    "'GBARF411200660',\n",
    "'GBARF411200661',\n",
    "'GBARF411200662',\n",
    "'GBARF411200663',\n",
    "'GBARF411200664',\n",
    "'GBARF411200667',\n",
    "'GBARF411200669',\n",
    "'GBARF411200670',\n",
    "'GBARF411200710',\n",
    "'GBARF411200711',\n",
    "'GBARF411200713',\n",
    "'GBARF411200716',\n",
    "'GBARF411301133',\n",
    "'GBARF411301138',\n",
    "'GBARF411301139',\n",
    "'GBARF411400121',\n",
    "'GBARF411400340',\n",
    "'GBARF411500258',\n",
    "'GBARF411500260',\n",
    "'GBARF411500262',\n",
    "'GBARF411500264',\n",
    "'GBARF411500265',\n",
    "'GBARF411600148',\n",
    "'GBARF411600232',\n",
    "'GBARF411600234',\n",
    "'GBARF411600358',\n",
    "'GBARF411600378',\n",
    "'GBARF411600379',\n",
    "'GBARF411600380',\n",
    "'GBARF411600382',\n",
    "'GBARF411800005',\n",
    "'GBARF411800007',\n",
    "'GBARF411800008',\n",
    "'GBARF411800009',\n",
    "'GBARF411800054',\n",
    "'GBARF411800055'\n",
    ");\n",
    "    \"\"\"\n",
    "    cs.execute(sql)\n",
    "    original = cs.fetch_pandas_all()\n",
    "    \n",
    "    print(original.shape)\n",
    "\n",
    "finally:\n",
    "    cs.close()\n",
    "\n",
    "ctx.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b2e3932-0b79-427b-aea3-bcc38b4c11c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13020 entries, 0 to 13019\n",
      "Data columns (total 8 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   ACTIVITY_DATE             13020 non-null  datetime64[ns]\n",
      " 1   FOURIER                   13020 non-null  object        \n",
      " 2   FOURIER_REAL_PART         13020 non-null  float64       \n",
      " 3   ISRC                      13020 non-null  object        \n",
      " 4   ISRC_KEY                  13020 non-null  object        \n",
      " 5   INFLECTION_POINT          13020 non-null  int8          \n",
      " 6   STREAMS                   13020 non-null  int32         \n",
      " 7   TRANSACTION_COUNTRY_CODE  13020 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(1), int32(1), int8(1), object(4)\n",
      "memory usage: 674.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df = original.copy()\n",
    "df['ACTIVITY_DATE'] = pd.to_datetime(df['ACTIVITY_DATE'])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "05fb94ab-526b-4825-9bfd-0cda301eca42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Artist_Name</th>\n",
       "      <th>ACTIVITY_DATE</th>\n",
       "      <th>ISRC</th>\n",
       "      <th>\\n     Country Name</th>\n",
       "      <th>Track_Name</th>\n",
       "      <th>STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pitbull</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>USNPW1000027</td>\n",
       "      <td>USA</td>\n",
       "      <td>Guantanamera (She's Hot)</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pitbull</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>USNPW1000027</td>\n",
       "      <td>USA</td>\n",
       "      <td>Guantanamera (She's Hot)</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pitbull</td>\n",
       "      <td>2023-12-02</td>\n",
       "      <td>USNPW1000027</td>\n",
       "      <td>USA</td>\n",
       "      <td>Guantanamera (She's Hot)</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pitbull</td>\n",
       "      <td>2023-12-02</td>\n",
       "      <td>USNPW1000027</td>\n",
       "      <td>USA</td>\n",
       "      <td>Guantanamera (She's Hot)</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pitbull</td>\n",
       "      <td>2023-12-03</td>\n",
       "      <td>USNPW1000027</td>\n",
       "      <td>USA</td>\n",
       "      <td>Guantanamera (She's Hot)</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Artist_Name ACTIVITY_DATE          ISRC \\n     Country Name   \\\n",
       "0     Pitbull    2023-12-01  USNPW1000027                  USA   \n",
       "1     Pitbull    2023-12-01  USNPW1000027                  USA   \n",
       "2     Pitbull    2023-12-02  USNPW1000027                  USA   \n",
       "3     Pitbull    2023-12-02  USNPW1000027                  USA   \n",
       "4     Pitbull    2023-12-03  USNPW1000027                  USA   \n",
       "\n",
       "                 Track_Name  STREAMS  \n",
       "0  Guantanamera (She's Hot)       47  \n",
       "1  Guantanamera (She's Hot)       65  \n",
       "2  Guantanamera (She's Hot)       62  \n",
       "3  Guantanamera (She's Hot)       76  \n",
       "4  Guantanamera (She's Hot)       27  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example = pd.read_csv('~/Desktop/pitbull_example.csv')\n",
    "example['ACTIVITY_DATE'] = pd.to_datetime(example['ACTIVITY_DATE'])\n",
    "example = example.sort_values(by='ACTIVITY_DATE', ascending =True)\n",
    "example.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "00a1554a-94fa-4705-9821-23a8941c68c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "grp = example.groupby('ACTIVITY_DATE')['STREAMS'].sum().reset_index()\n",
    "grp['time'] = grp.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "da0d67ea-5288-4af9-a0b4-9c8e718473c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACTIVITY_DATE</th>\n",
       "      <th>STREAMS</th>\n",
       "      <th>time</th>\n",
       "      <th>MA7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-12-02</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-12-03</td>\n",
       "      <td>81</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-12-04</td>\n",
       "      <td>95</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-12-05</td>\n",
       "      <td>105</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2024-02-06</td>\n",
       "      <td>17800</td>\n",
       "      <td>67</td>\n",
       "      <td>17276.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2024-02-07</td>\n",
       "      <td>17957</td>\n",
       "      <td>68</td>\n",
       "      <td>17746.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2024-02-08</td>\n",
       "      <td>17905</td>\n",
       "      <td>69</td>\n",
       "      <td>17928.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2024-02-09</td>\n",
       "      <td>18718</td>\n",
       "      <td>70</td>\n",
       "      <td>17807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2024-02-10</td>\n",
       "      <td>17318</td>\n",
       "      <td>71</td>\n",
       "      <td>17670.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ACTIVITY_DATE  STREAMS  time           MA7\n",
       "0     2023-12-01      112     0           NaN\n",
       "1     2023-12-02      138     1           NaN\n",
       "2     2023-12-03       81     2           NaN\n",
       "3     2023-12-04       95     3           NaN\n",
       "4     2023-12-05      105     4           NaN\n",
       "..           ...      ...   ...           ...\n",
       "67    2024-02-06    17800    67  17276.285714\n",
       "68    2024-02-07    17957    68  17746.000000\n",
       "69    2024-02-08    17905    69  17928.428571\n",
       "70    2024-02-09    18718    70  17807.000000\n",
       "71    2024-02-10    17318    71  17670.571429\n",
       "\n",
       "[72 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp['MA7'] =  grp['STREAMS'].rolling(window=7).mean()\n",
    "grp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "defa44ee-e507-402e-9de8-39d0838e6059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x309b9e890>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(30, 15))\n",
    "ax.plot(grp['ACTIVITY_DATE'], grp['MA7'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d2a95724-ac33-4f50-ba84-d52faa3969f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACTIVITY_DATE</th>\n",
       "      <th>STREAMS</th>\n",
       "      <th>time</th>\n",
       "      <th>MA7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023-12-08</td>\n",
       "      <td>117</td>\n",
       "      <td>7</td>\n",
       "      <td>112.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023-12-09</td>\n",
       "      <td>121</td>\n",
       "      <td>8</td>\n",
       "      <td>110.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023-12-10</td>\n",
       "      <td>128</td>\n",
       "      <td>9</td>\n",
       "      <td>116.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023-12-11</td>\n",
       "      <td>92</td>\n",
       "      <td>10</td>\n",
       "      <td>116.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2023-12-12</td>\n",
       "      <td>117</td>\n",
       "      <td>11</td>\n",
       "      <td>118.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2024-02-06</td>\n",
       "      <td>17800</td>\n",
       "      <td>67</td>\n",
       "      <td>17276.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2024-02-07</td>\n",
       "      <td>17957</td>\n",
       "      <td>68</td>\n",
       "      <td>17746.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2024-02-08</td>\n",
       "      <td>17905</td>\n",
       "      <td>69</td>\n",
       "      <td>17928.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2024-02-09</td>\n",
       "      <td>18718</td>\n",
       "      <td>70</td>\n",
       "      <td>17807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2024-02-10</td>\n",
       "      <td>17318</td>\n",
       "      <td>71</td>\n",
       "      <td>17670.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ACTIVITY_DATE  STREAMS  time           MA7\n",
       "7     2023-12-08      117     7    112.571429\n",
       "8     2023-12-09      121     8    110.142857\n",
       "9     2023-12-10      128     9    116.857143\n",
       "10    2023-12-11       92    10    116.428571\n",
       "11    2023-12-12      117    11    118.142857\n",
       "..           ...      ...   ...           ...\n",
       "67    2024-02-06    17800    67  17276.285714\n",
       "68    2024-02-07    17957    68  17746.000000\n",
       "69    2024-02-08    17905    69  17928.428571\n",
       "70    2024-02-09    18718    70  17807.000000\n",
       "71    2024-02-10    17318    71  17670.571429\n",
       "\n",
       "[65 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub = grp.iloc[7:].copy()\n",
    "sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bc0f4aec-a457-4a32-895c-4fc144303abd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACTIVITY_DATE</th>\n",
       "      <th>STREAMS</th>\n",
       "      <th>time</th>\n",
       "      <th>MA7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2024-01-05</td>\n",
       "      <td>139</td>\n",
       "      <td>35</td>\n",
       "      <td>121.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2024-01-06</td>\n",
       "      <td>153</td>\n",
       "      <td>36</td>\n",
       "      <td>128.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2024-01-07</td>\n",
       "      <td>191</td>\n",
       "      <td>37</td>\n",
       "      <td>139.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2024-01-08</td>\n",
       "      <td>256</td>\n",
       "      <td>38</td>\n",
       "      <td>160.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2024-01-09</td>\n",
       "      <td>305</td>\n",
       "      <td>39</td>\n",
       "      <td>187.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2024-01-10</td>\n",
       "      <td>432</td>\n",
       "      <td>40</td>\n",
       "      <td>231.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2024-01-11</td>\n",
       "      <td>997</td>\n",
       "      <td>41</td>\n",
       "      <td>353.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2024-01-12</td>\n",
       "      <td>2374</td>\n",
       "      <td>42</td>\n",
       "      <td>672.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2024-01-13</td>\n",
       "      <td>3053</td>\n",
       "      <td>43</td>\n",
       "      <td>1086.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2024-01-14</td>\n",
       "      <td>3474</td>\n",
       "      <td>44</td>\n",
       "      <td>1555.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2024-01-15</td>\n",
       "      <td>4063</td>\n",
       "      <td>45</td>\n",
       "      <td>2099.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2024-01-16</td>\n",
       "      <td>4818</td>\n",
       "      <td>46</td>\n",
       "      <td>2744.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2024-01-17</td>\n",
       "      <td>6072</td>\n",
       "      <td>47</td>\n",
       "      <td>3550.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2024-01-18</td>\n",
       "      <td>6727</td>\n",
       "      <td>48</td>\n",
       "      <td>4368.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2024-01-19</td>\n",
       "      <td>8426</td>\n",
       "      <td>49</td>\n",
       "      <td>5233.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2024-01-20</td>\n",
       "      <td>9329</td>\n",
       "      <td>50</td>\n",
       "      <td>6129.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2024-01-21</td>\n",
       "      <td>9084</td>\n",
       "      <td>51</td>\n",
       "      <td>6931.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>11405</td>\n",
       "      <td>52</td>\n",
       "      <td>7980.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2024-01-23</td>\n",
       "      <td>11100</td>\n",
       "      <td>53</td>\n",
       "      <td>8877.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2024-01-24</td>\n",
       "      <td>11957</td>\n",
       "      <td>54</td>\n",
       "      <td>9718.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2024-01-25</td>\n",
       "      <td>13170</td>\n",
       "      <td>55</td>\n",
       "      <td>10638.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2024-01-26</td>\n",
       "      <td>14921</td>\n",
       "      <td>56</td>\n",
       "      <td>11566.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2024-01-27</td>\n",
       "      <td>13709</td>\n",
       "      <td>57</td>\n",
       "      <td>12192.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2024-01-28</td>\n",
       "      <td>12998</td>\n",
       "      <td>58</td>\n",
       "      <td>12751.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2024-01-29</td>\n",
       "      <td>13679</td>\n",
       "      <td>59</td>\n",
       "      <td>13076.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2024-01-30</td>\n",
       "      <td>14277</td>\n",
       "      <td>60</td>\n",
       "      <td>13530.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2024-01-31</td>\n",
       "      <td>14669</td>\n",
       "      <td>61</td>\n",
       "      <td>13917.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2024-02-01</td>\n",
       "      <td>16628</td>\n",
       "      <td>62</td>\n",
       "      <td>14411.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2024-02-02</td>\n",
       "      <td>19568</td>\n",
       "      <td>63</td>\n",
       "      <td>15075.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2024-02-03</td>\n",
       "      <td>18273</td>\n",
       "      <td>64</td>\n",
       "      <td>15727.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2024-02-04</td>\n",
       "      <td>16424</td>\n",
       "      <td>65</td>\n",
       "      <td>16216.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2024-02-05</td>\n",
       "      <td>17572</td>\n",
       "      <td>66</td>\n",
       "      <td>16773.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2024-02-06</td>\n",
       "      <td>17800</td>\n",
       "      <td>67</td>\n",
       "      <td>17276.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2024-02-07</td>\n",
       "      <td>17957</td>\n",
       "      <td>68</td>\n",
       "      <td>17746.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2024-02-08</td>\n",
       "      <td>17905</td>\n",
       "      <td>69</td>\n",
       "      <td>17928.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2024-02-09</td>\n",
       "      <td>18718</td>\n",
       "      <td>70</td>\n",
       "      <td>17807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2024-02-10</td>\n",
       "      <td>17318</td>\n",
       "      <td>71</td>\n",
       "      <td>17670.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ACTIVITY_DATE  STREAMS  time           MA7\n",
       "35    2024-01-05      139    35    121.571429\n",
       "36    2024-01-06      153    36    128.857143\n",
       "37    2024-01-07      191    37    139.571429\n",
       "38    2024-01-08      256    38    160.857143\n",
       "39    2024-01-09      305    39    187.571429\n",
       "40    2024-01-10      432    40    231.428571\n",
       "41    2024-01-11      997    41    353.285714\n",
       "42    2024-01-12     2374    42    672.571429\n",
       "43    2024-01-13     3053    43   1086.857143\n",
       "44    2024-01-14     3474    44   1555.857143\n",
       "45    2024-01-15     4063    45   2099.714286\n",
       "46    2024-01-16     4818    46   2744.428571\n",
       "47    2024-01-17     6072    47   3550.142857\n",
       "48    2024-01-18     6727    48   4368.714286\n",
       "49    2024-01-19     8426    49   5233.285714\n",
       "50    2024-01-20     9329    50   6129.857143\n",
       "51    2024-01-21     9084    51   6931.285714\n",
       "52    2024-01-22    11405    52   7980.142857\n",
       "53    2024-01-23    11100    53   8877.571429\n",
       "54    2024-01-24    11957    54   9718.285714\n",
       "55    2024-01-25    13170    55  10638.714286\n",
       "56    2024-01-26    14921    56  11566.571429\n",
       "57    2024-01-27    13709    57  12192.285714\n",
       "58    2024-01-28    12998    58  12751.428571\n",
       "59    2024-01-29    13679    59  13076.285714\n",
       "60    2024-01-30    14277    60  13530.142857\n",
       "61    2024-01-31    14669    61  13917.571429\n",
       "62    2024-02-01    16628    62  14411.571429\n",
       "63    2024-02-02    19568    63  15075.428571\n",
       "64    2024-02-03    18273    64  15727.428571\n",
       "65    2024-02-04    16424    65  16216.857143\n",
       "66    2024-02-05    17572    66  16773.000000\n",
       "67    2024-02-06    17800    67  17276.285714\n",
       "68    2024-02-07    17957    68  17746.000000\n",
       "69    2024-02-08    17905    69  17928.428571\n",
       "70    2024-02-09    18718    70  17807.000000\n",
       "71    2024-02-10    17318    71  17670.571429"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub2 = grp.iloc[35:].copy()\n",
    "sub2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0ed301d9-5dd8-4fc9-9974-7c51ecf3e1c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>MA7</td>       <th>  R-squared:         </th> <td>   0.894</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   260.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 07 Sep 2024</td> <th>  Prob (F-statistic):</th> <td>6.57e-31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:49:51</td>     <th>  Log-Likelihood:    </th> <td> -70.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    65</td>      <th>  AIC:               </th> <td>   146.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    62</td>      <th>  BIC:               </th> <td>   153.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td>    4.2469</td> <td>    0.396</td> <td>   10.717</td> <td> 0.000</td> <td>    3.455</td> <td>    5.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>      <td>   -0.0071</td> <td>    0.023</td> <td>   -0.309</td> <td> 0.759</td> <td>   -0.053</td> <td>    0.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time_sq</th>   <td>    0.0015</td> <td>    0.000</td> <td>    5.103</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>14.898</td> <th>  Durbin-Watson:     </th> <td>   0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>   3.650</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.018</td> <th>  Prob(JB):          </th> <td>   0.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.840</td> <th>  Cond. No.          </th> <td>1.05e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.05e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    MA7   R-squared:                       0.894\n",
       "Model:                            OLS   Adj. R-squared:                  0.890\n",
       "Method:                 Least Squares   F-statistic:                     260.7\n",
       "Date:                Sat, 07 Sep 2024   Prob (F-statistic):           6.57e-31\n",
       "Time:                        13:49:51   Log-Likelihood:                -70.303\n",
       "No. Observations:                  65   AIC:                             146.6\n",
       "Df Residuals:                      62   BIC:                             153.1\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept      4.2469      0.396     10.717      0.000       3.455       5.039\n",
       "time          -0.0071      0.023     -0.309      0.759      -0.053       0.039\n",
       "time_sq        0.0015      0.000      5.103      0.000       0.001       0.002\n",
       "==============================================================================\n",
       "Omnibus:                       14.898   Durbin-Watson:                   0.034\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):                3.650\n",
       "Skew:                          -0.018   Prob(JB):                        0.161\n",
       "Kurtosis:                       1.840   Cond. No.                     1.05e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.05e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sub2['intercept'] = 1\n",
    "sub['time_sq'] = sub['time']**2\n",
    "features2 = ['intercept', 'time', 'time_sq']\n",
    "model2= sm.OLS(np.log(sub['MA7']), sub[features2]).fit()\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4b6172c8-ea81-49c2-bf9c-d44c149e0c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred2 = model2.predict(sub[features2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "74e56d8d-bfe8-4379-b589-1e68a667d166",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(30, 15))\n",
    "\n",
    "ax.plot(sub['time'], sub['STREAMS'], label='Streams', linewidth=3)\n",
    "ax.plot(sub['time'], sub['MA7'], label='MA7', linewidth=3)\n",
    "ax.plot(sub['time'], np.exp(pred2), label='pred exp', linewidth=3)\n",
    "\n",
    "plt.ylabel('Streams')\n",
    "plt.title('Distribution of Guantanamera (Shes Hot)', fontsize=20)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d8f68e56-0b8b-4e89-b10f-81a244339d17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>intercept</th>\n",
       "      <td>3.454780</td>\n",
       "      <td>5.039110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>-0.053021</td>\n",
       "      <td>0.038836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_sq</th>\n",
       "      <td>0.000894</td>\n",
       "      <td>0.002045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1\n",
       "intercept  3.454780  5.039110\n",
       "time      -0.053021  0.038836\n",
       "time_sq    0.000894  0.002045"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c21f1262-8e70-476a-8bb6-d2d1a87d3095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.05302108311681954"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con_int = pd.DataFrame(model2.conf_int())\n",
    "con_int.iloc[1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a81f8b9f-a8e7-4f09-a45a-16e48a8d3cfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8937385830018513"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "41164464-8136-454d-9215-d8486727e981",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.007092633360375122"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.params['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "121fbc32-2541-489e-a799-d987a5e7295a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7585869695305936"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.pvalues['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "66a7b0fd-6d09-4667-b90e-8d12c9774116",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def iterate_group_by_key(df, col_for_key, sort_data=True):\n",
    "    if sort_data:\n",
    "        df = df.sort_values(by=col_for_key)\n",
    "    # helper function that returns the value in the column specified by col_for_key at the index i.\n",
    "    def key_at(i): return np.array(df[col_for_key].iloc[i])\n",
    "    index, size = (0, df.shape[0])\n",
    "    while index < size:\n",
    "        current_key = key_at(index)\n",
    "        res = []\n",
    "        while index < size and list(current_key) == list(key_at(index)):\n",
    "            res.append(df.iloc[index])\n",
    "            index =  index + 1\n",
    "        resdf = pd.DataFrame(res, columns=df.columns)\n",
    "        yield resdf\n",
    "\n",
    "def detect_pattern(df, window=7):\n",
    "    df['ACTIVITY_DATE'] = pd.to_datetime(df['ACTIVITY_DATE'])\n",
    "    df = df.sort_values(by='ACTIVITY_DATE', ascending =True)\n",
    "    df['MA7'] =  df['STREAMS'].rolling(window=window).mean()\n",
    "    df['time'] = df.index\n",
    "    sub = df.iloc[7:].copy()\n",
    "    sub['intercept'] = 1\n",
    "    sub['time_sq'] = sub['time']**2\n",
    "    features = ['intercept', 'time', 'time_sq']\n",
    "    model= sm.OLS(np.log(sub['MA7']), sub[features]).fit()\n",
    "    return (model.params['time'], model.params['time_sq'], model.rsquared)\n",
    "\n",
    "def compile_coeff_table_ols(df,  window=7):\n",
    "    res = pd.DataFrame(columns=['ISRC_KEY', 'COEFF_time', 'COEFF_time_sq','R-Sq'])\n",
    "    unique_keys = df['ISRC_KEY'].unique()\n",
    "    for subdf in iterate_group_by_key(df, ['ISRC_KEY']):  \n",
    "        df_isrc = subdf\n",
    "        isrckey    = df_isrc['ISRC_KEY'].iloc[0]\n",
    "        df_isrc = df_isrc.sort_values(by=['ACTIVITY_DATE'])\n",
    "        try:\n",
    "            (coeff_time, coeff_time_sq, r_sq) = detect_pattern(df_isrc, window)\n",
    "            res = pd.concat([res, pd.DataFrame([{\n",
    "                'ISRC_KEY': isrckey, \n",
    "                'COEFF_time': coeff_time,\n",
    "                'COEFF_time_sq': coeff_time_sq,\n",
    "                'R-Sq': r_sq\n",
    "                }], columns=res.columns)])\n",
    "        except Exception as e:\n",
    "            print(f'Error: {e}')\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "772a3e79-af43-4e86-950c-1159a0669231",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s6/ddfqcfms7t595md8ctg1bs7r0000gq/T/ipykernel_3731/2888445574.py:37: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  res = pd.concat([res, pd.DataFrame([{\n"
     ]
    }
   ],
   "source": [
    "res = compile_coeff_table_ols(original,  window=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4c26a128-401a-49aa-bb13-3945455c6412",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ISRC_KEY</th>\n",
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      "text/plain": [
       "         ISRC_KEY  COEFF_time  COEFF_time_sq      R-Sq\n",
       "0  DEDEKF22001554   -0.030957       0.000017  0.010044\n",
       "0  DENOUJW1719003   -0.131600       0.000011  0.049216\n",
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       "0  DEQM6P42099227   -0.067742       0.000003  0.006287\n",
       "0  DEQM7281886206    0.126900      -0.000082  0.268743"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7dac7362-ea01-4f43-9084-a319a41cdf78",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>intercept</th>\n",
       "      <td>3.454780</td>\n",
       "      <td>5.039110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>-0.053021</td>\n",
       "      <td>0.038836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_sq</th>\n",
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       "      <td>0.002045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                  0         1\n",
       "intercept  3.454780  5.039110\n",
       "time      -0.053021  0.038836\n",
       "time_sq    0.000894  0.002045"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a6ca74fe-6f15-4d42-a197-b8ad7eacdd14",
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_rows(df):\n",
    "    sub = df[(df['COEFF_time'] >= -0.06) & \n",
    "             (df['COEFF_time'] <= 0.04) & \n",
    "             (df['COEFF_time_sq'] >= -0.001)  &  \n",
    "             (df['COEFF_time_sq'] <= 0.002)] # & \n",
    "             # (df['R-Sq'] >= 0.80) & \n",
    "             # (df['R-Sq'] <= 1)]\n",
    "    return sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b2654ee9-2081-43c9-93c3-f4dfd3e43dc8",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "         ISRC_KEY  COEFF_time  COEFF_time_sq      R-Sq\n",
       "0  DEDEKF22001554   -0.030957   1.725875e-05  0.010044\n",
       "0  DEQMBZ92014857   -0.000063   2.492665e-06  0.194173\n",
       "0  ESES71G2302885    0.007422  -9.567246e-07  0.632778\n",
       "0  ESFR2X41874268    0.018383  -8.292697e-06  0.024791\n",
       "0  ESQM6MZ2272606   -0.022565   6.257649e-06  0.055545\n",
       "0  ESQMDA62141597    0.016100  -9.823032e-07  0.859283\n",
       "0  GBGBCAD0707114   -0.036739   2.400371e-06  0.305021\n",
       "0  GBUSCGJ2004694    0.035519  -4.983193e-05  0.068252\n",
       "0  USGBUM71801202   -0.000218   5.441129e-08  0.003505"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_rows(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "051073cf-4ce4-4aa5-b295-12134bbf00de",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = original[original['ISRC_KEY']=='USUSK110416513'].copy()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(30, 15))\n",
    "test['ACTIVITY_DATE'] = pd.to_datetime(test['ACTIVITY_DATE'])\n",
    "test = test.sort_values(by='ACTIVITY_DATE', ascending =True)\n",
    "test['MA7'] =  test['STREAMS'].rolling(window=7).mean()\n",
    "ax.plot(test['ACTIVITY_DATE'], test['STREAMS'], label='Streams', linewidth=3)\n",
    "ax.plot(test['ACTIVITY_DATE'], test['MA7'], label='MA7', linewidth=3)\n",
    "\n",
    "plt.ylabel('Streams')\n",
    "plt.title('Distribution of Streams using model with log(streams) = time + time sq', fontsize=20)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "74c9da0d-46f6-4c1b-a1c9-62e05c5b0dbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140, 9)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ddd9b415-7c6e-45f2-88aa-6ffcc54d767b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_test = test.iloc[7:].copy()\n",
    "sub_test = sub_test.sort_values(by='ACTIVITY_DATE', ascending =True).reset_index()\n",
    "sub_test['time'] = (sub_test.index)+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a4d418ac-23ad-4ac7-8c70-b5e781b93f38",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ACTIVITY_DATE</th>\n",
       "      <th>FOURIER</th>\n",
       "      <th>FOURIER_REAL_PART</th>\n",
       "      <th>ISRC</th>\n",
       "      <th>ISRC_KEY</th>\n",
       "      <th>INFLECTION_POINT</th>\n",
       "      <th>STREAMS</th>\n",
       "      <th>TRANSACTION_COUNTRY_CODE</th>\n",
       "      <th>MA7</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4067</td>\n",
       "      <td>2024-04-24</td>\n",
       "      <td>(1803.5121909679588+0j)</td>\n",
       "      <td>1803.512191</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>314</td>\n",
       "      <td>US</td>\n",
       "      <td>346.142857</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4068</td>\n",
       "      <td>2024-04-25</td>\n",
       "      <td>(1639.4755216684828-2.078845032623836e-13j)</td>\n",
       "      <td>1639.475522</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>354</td>\n",
       "      <td>US</td>\n",
       "      <td>376.857143</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4069</td>\n",
       "      <td>2024-04-26</td>\n",
       "      <td>(1480.3282288757127+0j)</td>\n",
       "      <td>1480.328229</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>671</td>\n",
       "      <td>US</td>\n",
       "      <td>418.714286</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4070</td>\n",
       "      <td>2024-04-27</td>\n",
       "      <td>(1326.7655871346326+0j)</td>\n",
       "      <td>1326.765587</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>315</td>\n",
       "      <td>US</td>\n",
       "      <td>432.428571</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4071</td>\n",
       "      <td>2024-04-28</td>\n",
       "      <td>(1179.4364116563204-1.559133774467877e-13j)</td>\n",
       "      <td>1179.436412</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>699</td>\n",
       "      <td>US</td>\n",
       "      <td>500.142857</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>4195</td>\n",
       "      <td>2024-08-30</td>\n",
       "      <td>(3858.436746064109+8.977153891502806e-14j)</td>\n",
       "      <td>3858.436746</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>5383</td>\n",
       "      <td>US</td>\n",
       "      <td>5648.000000</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>4196</td>\n",
       "      <td>2024-08-31</td>\n",
       "      <td>(3700.0014534809557-2.5006627184247836e-13j)</td>\n",
       "      <td>3700.001453</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>6189</td>\n",
       "      <td>US</td>\n",
       "      <td>5524.285714</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>4197</td>\n",
       "      <td>2024-09-01</td>\n",
       "      <td>(3536.3622523928234-5.974886406557783e-14j)</td>\n",
       "      <td>3536.362252</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>6042</td>\n",
       "      <td>US</td>\n",
       "      <td>5547.857143</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>4198</td>\n",
       "      <td>2024-09-02</td>\n",
       "      <td>(3368.357382436247+2.7736506418840395e-13j)</td>\n",
       "      <td>3368.357382</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>5694</td>\n",
       "      <td>US</td>\n",
       "      <td>5561.000000</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>4199</td>\n",
       "      <td>2024-09-03</td>\n",
       "      <td>(3196.8454391525725-1.482421034089872e-13j)</td>\n",
       "      <td>3196.845439</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>5103</td>\n",
       "      <td>US</td>\n",
       "      <td>5502.714286</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index ACTIVITY_DATE                                       FOURIER  \\\n",
       "0     4067    2024-04-24                       (1803.5121909679588+0j)   \n",
       "1     4068    2024-04-25   (1639.4755216684828-2.078845032623836e-13j)   \n",
       "2     4069    2024-04-26                       (1480.3282288757127+0j)   \n",
       "3     4070    2024-04-27                       (1326.7655871346326+0j)   \n",
       "4     4071    2024-04-28   (1179.4364116563204-1.559133774467877e-13j)   \n",
       "..     ...           ...                                           ...   \n",
       "128   4195    2024-08-30    (3858.436746064109+8.977153891502806e-14j)   \n",
       "129   4196    2024-08-31  (3700.0014534809557-2.5006627184247836e-13j)   \n",
       "130   4197    2024-09-01   (3536.3622523928234-5.974886406557783e-14j)   \n",
       "131   4198    2024-09-02   (3368.357382436247+2.7736506418840395e-13j)   \n",
       "132   4199    2024-09-03   (3196.8454391525725-1.482421034089872e-13j)   \n",
       "\n",
       "     FOURIER_REAL_PART          ISRC        ISRC_KEY  INFLECTION_POINT  \\\n",
       "0          1803.512191  USK110416513  USUSK110416513                 0   \n",
       "1          1639.475522  USK110416513  USUSK110416513                 0   \n",
       "2          1480.328229  USK110416513  USUSK110416513                 0   \n",
       "3          1326.765587  USK110416513  USUSK110416513                 0   \n",
       "4          1179.436412  USK110416513  USUSK110416513                 0   \n",
       "..                 ...           ...             ...               ...   \n",
       "128        3858.436746  USK110416513  USUSK110416513                 0   \n",
       "129        3700.001453  USK110416513  USUSK110416513                 0   \n",
       "130        3536.362252  USK110416513  USUSK110416513                 0   \n",
       "131        3368.357382  USK110416513  USUSK110416513                 0   \n",
       "132        3196.845439  USK110416513  USUSK110416513                 0   \n",
       "\n",
       "     STREAMS TRANSACTION_COUNTRY_CODE          MA7  time  \n",
       "0        314                       US   346.142857     1  \n",
       "1        354                       US   376.857143     2  \n",
       "2        671                       US   418.714286     3  \n",
       "3        315                       US   432.428571     4  \n",
       "4        699                       US   500.142857     5  \n",
       "..       ...                      ...          ...   ...  \n",
       "128     5383                       US  5648.000000   129  \n",
       "129     6189                       US  5524.285714   130  \n",
       "130     6042                       US  5547.857143   131  \n",
       "131     5694                       US  5561.000000   132  \n",
       "132     5103                       US  5502.714286   133  \n",
       "\n",
       "[133 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48ccc17f-1806-4ec9-88b4-64cf84936ffd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d78692b7-d615-46ab-a6ae-7bb8842dc1b1",
   "metadata": {},
   "source": [
    "# On Fourier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8fabbf92-6bb0-46d4-bca2-ced7015383cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "test2 = test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c0232c4a-8242-4924-8f95-8d0f5dc3d811",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACTIVITY_DATE</th>\n",
       "      <th>FOURIER</th>\n",
       "      <th>FOURIER_REAL_PART</th>\n",
       "      <th>ISRC</th>\n",
       "      <th>ISRC_KEY</th>\n",
       "      <th>INFLECTION_POINT</th>\n",
       "      <th>STREAMS</th>\n",
       "      <th>TRANSACTION_COUNTRY_CODE</th>\n",
       "      <th>MA7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4060</th>\n",
       "      <td>2024-04-17</td>\n",
       "      <td>(3022.699456094658+0j)</td>\n",
       "      <td>3022.699456</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4061</th>\n",
       "      <td>2024-04-18</td>\n",
       "      <td>(2846.800898476302+0j)</td>\n",
       "      <td>2846.800898</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>139</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4062</th>\n",
       "      <td>2024-04-19</td>\n",
       "      <td>(2670.033614664217+2.078845032623836e-13j)</td>\n",
       "      <td>2670.033615</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>1</td>\n",
       "      <td>378</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4063</th>\n",
       "      <td>2024-04-20</td>\n",
       "      <td>(2493.2777920999774+0j)</td>\n",
       "      <td>2493.277792</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>219</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4064</th>\n",
       "      <td>2024-04-21</td>\n",
       "      <td>(2317.4039641561358+0j)</td>\n",
       "      <td>2317.403964</td>\n",
       "      <td>USK110416513</td>\n",
       "      <td>USUSK110416513</td>\n",
       "      <td>0</td>\n",
       "      <td>225</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ACTIVITY_DATE                                     FOURIER  \\\n",
       "4060    2024-04-17                      (3022.699456094658+0j)   \n",
       "4061    2024-04-18                      (2846.800898476302+0j)   \n",
       "4062    2024-04-19  (2670.033614664217+2.078845032623836e-13j)   \n",
       "4063    2024-04-20                     (2493.2777920999774+0j)   \n",
       "4064    2024-04-21                     (2317.4039641561358+0j)   \n",
       "\n",
       "      FOURIER_REAL_PART          ISRC        ISRC_KEY  INFLECTION_POINT  \\\n",
       "4060        3022.699456  USK110416513  USUSK110416513                 0   \n",
       "4061        2846.800898  USK110416513  USUSK110416513                 0   \n",
       "4062        2670.033615  USK110416513  USUSK110416513                 1   \n",
       "4063        2493.277792  USK110416513  USUSK110416513                 0   \n",
       "4064        2317.403964  USK110416513  USUSK110416513                 0   \n",
       "\n",
       "      STREAMS TRANSACTION_COUNTRY_CODE  MA7  \n",
       "4060      156                       US  NaN  \n",
       "4061      139                       US  NaN  \n",
       "4062      378                       US  NaN  \n",
       "4063      219                       US  NaN  \n",
       "4064      225                       US  NaN  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ea8e44b3-96f6-4a4b-965c-c48ac5c792a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/impr001/opt/anaconda/anaconda3/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>FOURIER_REAL_PART</td> <th>  R-squared:         </th> <td>     nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>     nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th> <td>     nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 07 Sep 2024</td>  <th>  Prob (F-statistic):</th>  <td>   nan</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:52:33</td>      <th>  Log-Likelihood:    </th> <td>     nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   140</td>       <th>  AIC:               </th> <td>     nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   137</td>       <th>  BIC:               </th> <td>     nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td>       nan</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>      <td>       nan</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time_sq</th>   <td>       nan</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>   nan</td> <th>  Durbin-Watson:     </th> <td>     nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td>   nan</td> <th>  Jarque-Bera (JB):  </th> <td>     nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>   nan</td> <th>  Prob(JB):          </th> <td>     nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>   nan</td> <th>  Cond. No.          </th> <td>1.99e+11</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.99e+11. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:      FOURIER_REAL_PART   R-squared:                         nan\n",
       "Model:                            OLS   Adj. R-squared:                    nan\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Sat, 07 Sep 2024   Prob (F-statistic):                nan\n",
       "Time:                        13:52:33   Log-Likelihood:                    nan\n",
       "No. Observations:                 140   AIC:                               nan\n",
       "Df Residuals:                     137   BIC:                               nan\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept         nan        nan        nan        nan         nan         nan\n",
       "time              nan        nan        nan        nan         nan         nan\n",
       "time_sq           nan        nan        nan        nan         nan         nan\n",
       "==============================================================================\n",
       "Omnibus:                          nan   Durbin-Watson:                     nan\n",
       "Prob(Omnibus):                    nan   Jarque-Bera (JB):                  nan\n",
       "Skew:                             nan   Prob(JB):                          nan\n",
       "Kurtosis:                         nan   Cond. No.                     1.99e+11\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.99e+11. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sub2['intercept'] = 1\n",
    "test2 = test.copy()\n",
    "test2['ACTIVITY_DATE'] = pd.to_datetime(test2['ACTIVITY_DATE'])\n",
    "test2 = test2.sort_values(by='ACTIVITY_DATE', ascending =True)\n",
    "test2['time'] = test2.index\n",
    "test2['time_sq'] = test2['time']**2\n",
    "test2['intercept'] = 1\n",
    "features2 = ['intercept', 'time', 'time_sq']\n",
    "m= sm.OLS(np.log(test2['FOURIER_REAL_PART']), test2[features2]).fit()\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ca99133a-6ec0-4b45-9ca6-3a1e6b83a9ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = m.predict(test2[features2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8b1a02f5-c7a9-4358-8962-69b91e50f040",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(30, 15))\n",
    "\n",
    "ax.plot(test2['time'],test2['STREAMS'], label='Streams', linewidth=3)\n",
    "ax.plot(test2['time'], test2['FOURIER_REAL_PART'], label='FOURIER_REAL_PART', linewidth=3)\n",
    "#ax.plot(sub['time'], np.log(sub['MA7']), label='MA7- log', linewidth=3)\n",
    "ax.plot(test2['time'], np.exp(p), label='pred', linewidth=3)\n",
    "\n",
    "plt.ylabel('Streams')\n",
    "plt.title('Distribution of Guantanamera (Shes Hot)', fontsize=20)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ebedf461-e576-4231-9b9d-1718dc22eb28",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def detect_pattern2(df):\n",
    "    df['ACTIVITY_DATE'] = pd.to_datetime(df['ACTIVITY_DATE'])\n",
    "    df = df.sort_values(by='ACTIVITY_DATE', ascending =True)\n",
    "    df['time'] = df.index\n",
    "    sub = df.iloc[7:].copy()\n",
    "    sub['intercept'] = 1\n",
    "    sub['time_sq'] = sub['time']**2\n",
    "    features = ['intercept', 'time', 'time_sq']\n",
    "    model= sm.OLS(np.log(sub['FOURIER_REAL_PART']), sub[features]).fit()\n",
    "    return (model.params['time'], model.params['time_sq'], model.rsquared)\n",
    "\n",
    "def compile_coeff_table_ols2(df,  window=7):\n",
    "    res = pd.DataFrame(columns=['ISRC_KEY', 'COEFF_time', 'COEFF_time_sq','R-Sq'])\n",
    "    unique_keys = df['ISRC_KEY'].unique()\n",
    "    for subdf in iterate_group_by_key(df, ['ISRC_KEY']):  \n",
    "        df_isrc = subdf\n",
    "        isrckey    = df_isrc['ISRC_KEY'].iloc[0]\n",
    "        df_isrc = df_isrc.sort_values(by=['ACTIVITY_DATE'])\n",
    "        try:\n",
    "            (coeff_time, coeff_time_sq, r_sq) = detect_pattern(df_isrc, window)\n",
    "            res = pd.concat([res, pd.DataFrame([{\n",
    "                'ISRC_KEY': isrckey, \n",
    "                'COEFF_time': coeff_time,\n",
    "                'COEFF_time_sq': coeff_time_sq,\n",
    "                'R-Sq': r_sq\n",
    "                }], columns=res.columns)])\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(f'Error: {e}')\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7bf2b557-e799-477d-8041-e2fe44e333fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s6/ddfqcfms7t595md8ctg1bs7r0000gq/T/ipykernel_3731/2144906567.py:21: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  res = pd.concat([res, pd.DataFrame([{\n"
     ]
    }
   ],
   "source": [
    "res2 = compile_coeff_table_ols2(original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c23d4733-28bc-48df-ad30-b30d18343fd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93, 4)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d18865a-961b-4eaf-bc76-051ea08841ac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}