{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hämta temperatur från SMHI (enklare demo)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import requests\n", "import datetime\n", "\n", "# Koordinater (Halmstad)\n", "lat = 56.6745\n", "lon = 12.8570\n", "\n", "url = f\"https://opendata-download-metfcst.smhi.se/api/category/pmp3g/version/2/geotype/point/lon/{lon}/lat/{lat}/data.json\"\n", "\n", "response = requests.get(url)\n", "\n", "if response.status_code == 200:\n", " data = response.json()\n", " print(\"Första prognosen:\")\n", " first_time = data[\"timeSeries\"][0]\n", " valid_time = first_time[\"validTime\"]\n", " temperature = next(p for p in first_time[\"parameters\"] if p[\"name\"] == \"t\")[\"values\"][0]\n", " print(f\"Tidpunkt: {valid_time}\")\n", " print(f\"Temperatur: {temperature} °C\")\n", "else:\n", " print(f\"Fel vid API-anrop: {response.status_code}\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Hämta temperatur från SMHI (demo)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import requests\n", "import pandas as pd\n", "from datetime import datetime, timedelta\n", "from geopy.distance import distance\n", "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", "\n", "lat = 58.51\n", "lon = 12.49\n", "\n", "station_url = \"https://opendata-download-metobs.smhi.se/api/version/latest/parameter/1.json\"\n", "r = requests.get(station_url)\n", "if r.status_code != 200:\n", " raise Exception(f\"Fel vid hämtning av stationsdata: {r.status_code}\")\n", "stations = r.json()[\"station\"]\n", "\n", "def find_working_station(lat, lon, stations, max_candidates=10):\n", " sorted_stations = sorted(\n", " stations, key=lambda s: distance((lat, lon), (s[\"latitude\"], s[\"longitude\"])).km\n", " )\n", " for station in sorted_stations[:max_candidates]:\n", " station_id = station[\"id\"]\n", " url = f\"https://opendata-download-metobs.smhi.se/api/version/latest/parameter/1/station/{station_id}/period/latest-months/data.json\"\n", " r = requests.get(url)\n", " if r.status_code == 200:\n", " try:\n", " r.json() # Kontrollera att det verkligen är JSON\n", " return station\n", " except ValueError:\n", " continue\n", " raise Exception(\"Hittade ingen närliggande station med fungerande observationsdata.\")\n", "\n", "closest_station = find_working_station(lat, lon, stations)\n", "print(\"Närmaste fungerande station:\", closest_station[\"name\"])\n", "\n", "station_id = closest_station[\"id\"]\n", "\n", "obs_url = f\"https://opendata-download-metobs.smhi.se/api/version/latest/parameter/1/station/{station_id}/period/latest-months/data.json\"\n", "obs_response = requests.get(obs_url)\n", "if obs_response.status_code != 200:\n", " raise Exception(f\"Fel vid hämtning av observationsdata: {obs_response.status_code}\")\n", "try:\n", " obs_data = obs_response.json()\n", "except ValueError:\n", " raise Exception(\"Kunde inte tolka observationssvar som JSON.\")\n", "\n", "temps = []\n", "for item in obs_data.get(\"value\", []):\n", " if item[\"value\"] is not None:\n", " timestamp = pd.to_datetime(item[\"date\"], unit=\"ms\")\n", " temps.append((timestamp, item[\"value\"]))\n", "\n", "df_hist = pd.DataFrame(temps, columns=[\"time\", \"temperature\"])\n", "df_hist[\"typ\"] = \"Observation\"\n", "\n", "forecast_url = f\"https://opendata-download-metfcst.smhi.se/api/category/pmp3g/version/2/geotype/point/lon/{lon}/lat/{lat}/data.json\"\n", "forecast_response = requests.get(forecast_url)\n", "if forecast_response.status_code != 200:\n", " raise Exception(f\"Fel vid hämtning av prognos: {forecast_response.status_code}\")\n", "forecast = forecast_response.json()\n", "\n", "forecast_data = []\n", "for item in forecast[\"timeSeries\"]:\n", " timestamp = pd.to_datetime(item[\"validTime\"])\n", " temp = next((p for p in item[\"parameters\"] if p[\"name\"] == \"t\"), None)\n", " if temp:\n", " forecast_data.append((timestamp, temp[\"values\"][0]))\n", "\n", "df_forecast = pd.DataFrame(forecast_data, columns=[\"time\", \"temperature\"])\n", "df_forecast[\"typ\"] = \"Prognos\"\n", "\n", "nn = 100 # nn senaste datapunkterna\n", "df_hist = df_hist.sort_values(\"time\").tail(nn)\n", "df_forecast = df_forecast.sort_values(\"time\").head(nn)\n", "\n", "df_all = pd.concat([df_hist, df_forecast])\n", "\n", "# konvertera temperaturvärdena till float\n", "df_all[\"temperature\"] = pd.to_numeric(df_all[\"temperature\"], errors='coerce')\n", "\n", "# Ta bort rader med NaN-värden (ogiltiga temperaturvärden)\n", "df_all = df_all.dropna(subset=[\"temperature\"])\n", "\n", "plt.figure(figsize=(12, 6))\n", "for typ, grp in df_all.groupby(\"typ\"):\n", " plt.plot(grp[\"time\"], grp[\"temperature\"], '-*', label=typ)\n", "\n", "min_temp = df_all[\"temperature\"].values.min()\n", "max_temp = df_all[\"temperature\"].values.max()\n", "min_time = df_hist[\"time\"].min()\n", "max_time = df_forecast[\"time\"].max()\n", "\n", "plt.ylim(min_temp - 2, max_temp + 2)\n", "plt.xlim(min_time, max_time)\n", "plt.yticks(range(int(min_temp) - 2, int(max_temp) + 3, 2))\n", "plt.title(f\"Temperatur i {closest_station['name']} – senaste tiden + prognos\")\n", "plt.ylabel(\"Temperatur (°C)\")\n", "plt.grid(True)\n", "plt.legend()\n", "\n", "ax = plt.gca()\n", "ax.xaxis.set_major_locator(mdates.AutoDateLocator())\n", "ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(ax.xaxis.get_major_locator()))\n", "\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import requests\n", "\n", "url = \"https://api.thecatapi.com/v1/images/search\"\n", "response = requests.get(url)\n", "\n", "if response.status_code == 200:\n", " data = response.json()\n", " print(\"Här är en kattbild:\", data[0][\"url\"])\n", "else:\n", " print(\"Något gick fel:\", response.status_code)\n", "\n", "# print(data[0][\"url\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Demo - hämta många bilder för att skapa kollage" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import requests\n", "from PIL import Image\n", "from io import BytesIO\n", "import matplotlib.pyplot as plt\n", "import random\n", "import numpy as np\n", "\n", "# Hämta 50 kattbilder från The Cat API\n", "\n", "all_data = []\n", "for _ in range(5): # 5 requests med 10 bilder vardera\n", " response = requests.get(\"https://api.thecatapi.com/v1/images/search?limit=10\")\n", " if response.status_code == 200:\n", " all_data.extend(response.json())\n", " else:\n", " print(\"Något gick fel med API-förfrågan:\", response.status_code)\n", "data = all_data\n", "\n", "images = []\n", "for item in data:\n", " image_url = item['url']\n", " img_response = requests.get(image_url)\n", " try:\n", " img = Image.open(BytesIO(img_response.content)).convert(\"RGBA\")\n", " img.thumbnail((150, 150)) # Gör bilderna mindre enligt önskemål\n", " images.append(img)\n", " except:\n", " print(\"Kunde inte läsa en bild:\", image_url)\n", "\n", "# Skapa en figur med svart bakgrund\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "ax.set_facecolor(\"black\")\n", "ax.set_xlim(0, 800)\n", "ax.set_ylim(0, 500)\n", "ax.axis('off')\n", "\n", "# Lägg ut bilder slumpmässigt och med rotation\n", "for img in images:\n", " x = random.randint(0, 650)\n", " y = random.randint(0, 350)\n", " angle = random.uniform(-30, 30)\n", "\n", " # Rotera bild runt centrum (expand=True så att den inte kapas)\n", " rotated = img.rotate(angle, expand=True)\n", "\n", " # Omvandla till numpy-array för att kunna visa i matplotlib\n", " img_array = np.array(rotated)\n", "\n", " ax.imshow(img_array, extent=(x, x + rotated.width, y, y + rotated.height), zorder=1)\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"cats\", dpi=500)\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import json\n", "\n", "# Läs JSON från fil\n", "with open('exempel.json', 'r', encoding='utf-8') as f:\n", " data = json.load(f)\n", "\n", "# Hämta ägarinformation\n", "ägare = data.get('owner', {})\n", "namn = ägare.get('name')\n", "plats = ägare.get('location')\n", "\n", "print(f\"Ägare: {namn}, Plats: {plats}\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# LLM via API\n", " (i denna kodsnutt behöver ni någon betalvariant av OpenAI så ni får tillgång till en API-nyckel)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import json\n", "from openai import OpenAI\n", "\n", "with open(\"config.json\", \"r\", encoding=\"utf-8\") as f:\n", " api_key = json.load(f).get(\"openai_api_key\")\n", "\n", "client = OpenAI(api_key=api_key)\n", "\n", "# Systemprompt/instruktion och användarfråga\n", "system_prompt = \"Svara så att en nioåring förstår.\"\n", "user_question = \"Hur fungerar en 3D-skrivare?\"\n", "\n", "# Skicka API-förfrågan\n", "response = client.chat.completions.create(\n", " model=\"gpt-4o-mini\",\n", " messages=[\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\": user_question}\n", " ],\n", " temperature=0.1 # Högre temp -> mer slumpmässig/kreativ\n", ")\n", "\n", "print(\"\\nSvar från GPT:\\n\")\n", "print(response.choices[0].message.content.strip())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**config.json - json-fil med namn config.json och API-nyckel. Ska se ut så här:**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "{\n", " \"openai_api_key\": \"sk-asdflökjasdflkjasdflsdökjflöaskjdflökasjdf\" # klistra in API-nyckel mellan citations-tecknen\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Primtalsskript med olika abstraktionsgrad" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Funktion för att kontrollera om ett tal är ett primtal\n", "def är_primtal(n):\n", " if n < 2:\n", " return False\n", " for i in range(2, int(n**0.5) + 1):\n", " if n % i == 0:\n", " return False\n", " return True\n", "\n", "# Lista för att spara primtalen\n", "primtal = []\n", "\n", "# Gå igenom alla tal från 0 till 100\n", "for tal in range(101):\n", " if är_primtal(tal):\n", " primtal.append(tal)\n", "\n", "# Skriv ut resultatet\n", "print(\"Primtal mellan 0 och 100:\")\n", "print(primtal)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Kompakt motsv. skript**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print([n for n in range(2, 101) if all(n % d != 0 for d in range(2, int(n**0.5)+1))])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Inför medveten bugg**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print([n for n in range(2, 101) if All(n % d != 0 for d in range(2, int(n**0.5)+1))])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Anropa lokal LLM i ipynb-fil\n", "\n", "(se ollama.com för instruktioner kring att komma igång med ex.vis Llama-modellerna)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import requests\n", "\n", "response = requests.post(\n", " 'http://localhost:11434/api/chat',\n", " json={\n", " 'model': 'llama3.1',\n", " 'messages': [\n", " {\"role\": \"system\", \"content\": \"Answer like Arnold in Terminator 2.\"},\n", " {\"role\": \"user\", \"content\": \"What's the point of life?\"}\n", " ],\n", " 'stream': False\n", " }\n", ")\n", "\n", "result = response.json()\n", "print(result['message']['content'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python (venv)", "language": "python", "name": "venv" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 2 }