You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
IoT-For-Beginners/translations/nl/2-farm/lessons/1-predict-plant-growth/code-notebook/gdd.ipynb

167 lines
4.8 KiB

{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Groeigraden Dagen\n",
"\n",
"Dit notebook laadt temperatuurgegevens uit een CSV-bestand en analyseert deze. Het maakt grafieken van de temperaturen, toont de hoogste en laagste waarde per dag, en berekent de GDD.\n",
"\n",
"Om dit notebook te gebruiken:\n",
"\n",
"* Kopieer het bestand `temperature.csv` naar dezelfde map als dit notebook\n",
"* Voer alle cellen uit met de knop **▶︎ Run** hierboven. Dit zal de geselecteerde cel uitvoeren en vervolgens doorgaan naar de volgende.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In de cel hieronder stel je `base_temperature` in op de basistemperatuur van de plant.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"base_temperature = 10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Het CSV-bestand moet nu worden geladen met behulp van pandas\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": []
},
{
"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": [
"Zodra de gegevens zijn gelezen, kunnen ze worden gegroepeerd op de `datum`-kolom, en de minimum- en maximumtemperaturen worden geëxtraheerd voor elke datum.\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": [
"De GDD kan worden berekend met behulp van de standaard GDD-vergelijking\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))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n---\n\n**Disclaimer**: \nDit document is vertaald met behulp van de AI-vertalingsservice [Co-op Translator](https://github.com/Azure/co-op-translator). Hoewel we streven naar nauwkeurigheid, dient u zich ervan bewust te zijn dat geautomatiseerde vertalingen fouten of onnauwkeurigheden kunnen bevatten. Het originele document in de oorspronkelijke taal moet worden beschouwd als de gezaghebbende bron. Voor cruciale informatie wordt professionele menselijke vertaling aanbevolen. Wij zijn niet aansprakelijk voor misverstanden of verkeerde interpretaties die voortvloeien uit het gebruik van deze vertaling.\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.9.1"
},
"metadata": {
"interpreter": {
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
}
},
"coopTranslator": {
"original_hash": "8fcf954f6042f0bf3601a2c836a09574",
"translation_date": "2025-08-27T23:06:14+00:00",
"source_file": "2-farm/lessons/1-predict-plant-growth/code-notebook/gdd.ipynb",
"language_code": "nl"
}
},
"nbformat": 4,
"nbformat_minor": 2
}