IoT-For-Beginners/translations/et/2-farm/lessons/1-predict-plant-growth/code-notebook/gdd.ipynb

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   "source": [
    "# Kasvupäevade kraadid\n",
    "\n",
    "See märkmik laadib CSV-faili salvestatud temperatuuride andmed ja analüüsib neid. See joonistab temperatuurid, näitab iga päeva kõrgeimat ja madalaimat väärtust ning arvutab GDD.\n",
    "\n",
    "Selle märkmiku kasutamiseks:\n",
    "\n",
    "* Kopeeri fail `temperature.csv` samasse kausta, kus see märkmik asub\n",
    "* Käivita kõik lahtrid, kasutades ülal asuvat nuppu **▶︎ Run**. See käivitab valitud lahtri ja liigub seejärel järgmise juurde.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seadke allolevas lahtris `base_temperature` taime baastemperatuuriks.\n"
   ]
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   "execution_count": 2,
   "metadata": {},
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   "source": [
    "base_temperature = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CSV-fail tuleb nüüd laadida, kasutades pandas't\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read the temperature CSV file\n",
    "df = pd.read_csv('temperature.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temperatuuri saab nüüd graafikul kujutada.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(df['date'], df['temperature'])\n",
    "plt.xticks(rotation='vertical');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kui andmed on loetud, saab need rühmitada `date` veeru järgi ning iga kuupäeva jaoks saab välja võtta minimaalse ja maksimaalse temperatuuri.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert datetimes to pure dates so we can group by the date\n",
    "df['date'] = pd.to_datetime(df['date']).dt.date\n",
    "\n",
    "# Group the data by date so it can be analyzed by date\n",
    "data_by_date = df.groupby('date')\n",
    "\n",
    "# Get the minimum and maximum temperatures for each date\n",
    "min_by_date = data_by_date.min()\n",
    "max_by_date = data_by_date.max()\n",
    "\n",
    "# Join the min and max temperatures into one dataframe and flatten it\n",
    "min_max_by_date = min_by_date.join(max_by_date, on='date', lsuffix='_min', rsuffix='_max')\n",
    "min_max_by_date = min_max_by_date.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GDD-d saab arvutada, kasutades standardset GDD valemit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_gdd(row):\n",
    "    return ((row['temperature_max'] + row['temperature_min']) / 2) - base_temperature\n",
    "\n",
    "# Calculate the GDD for each row\n",
    "min_max_by_date['gdd'] = min_max_by_date.apply (lambda row: calculate_gdd(row), axis=1)\n",
    "\n",
    "# Print the results\n",
    "print(min_max_by_date[['date', 'gdd']].to_string(index=False))"
   ]
  },
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   "execution_count": null,
   "metadata": {},
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   "source": []
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   "source": [
    "\n---\n\n**Lahtiütlus**:  \nSee dokument on tõlgitud AI tõlketeenuse [Co-op Translator](https://github.com/Azure/co-op-translator) abil. Kuigi püüame tagada täpsust, palume arvestada, et automaatsed tõlked võivad sisaldada vigu või ebatäpsusi. Algne dokument selle algses keeles tuleks pidada autoriteetseks allikaks. Olulise teabe puhul soovitame kasutada professionaalset inimtõlget. Me ei vastuta selle tõlke kasutamisest tulenevate arusaamatuste või valesti tõlgenduste eest.\n"
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