diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/README.md b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/README.md new file mode 100644 index 000000000..c871ab9bf --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/README.md @@ -0,0 +1,176 @@ +# Real-Time Bitcoin Price Analysis with InfluxDB and PyFlink + +This project demonstrates a real-time Bitcoin analytics pipeline that fetches live prices, stores them in InfluxDB, and uses NeuralProphet to forecast future prices. + +--- + +## Folder Structure + +``` +bitcoin-analytics-project/ +│ +├── Dockerfile # Dockerfile to create the container environment for the app +├── docker-compose.yml # Defines and runs multi-container Docker apps (InfluxDB + App) +├── .env # Stores sensitive env variables like InfluxDB token +│ +├── bitcoin_utils.py # Core API module: fetches live BTC price and computes metrics +│ +├── bitcoin.API.ipynb # Interactive notebook demonstrating usage of the API class +├── bitcoin.Fetch.ipynb # Notebook showcasing the full pipeline: streaming + forecast +│ +├── bitcoin.API.md # Markdown documentation for the API class and its usage +├── bitcoin.fetch.md # Full end-to-end markdown doc showing real-time + forecast demo +│ +└── README.md # Instructions to build, run, and test the entire project +``` + +--- + +## Prerequisites + +- Install **Docker** and **Docker Compose**. +- Ensure the following ports are free: + - `8086` (for InfluxDB) + - `8888` (for Jupyter Notebook) +- Clone the repository: + +```bash +git clone https://github.com/yourusername/bitcoin-price-analysis.git +cd bitcoin-price-analysis +``` + +--- + +## 🔧 Step 1: Start InfluxDB (Initial Setup Only) + +```bash +docker-compose up influxdb +``` + +Then, open your browser and go to: + http://localhost:8086 + +Complete the setup form using the following: + +- **Username**: `admin` +- **Password**: `admin123` +- **Organization**: `crypto` +- **Bucket**: `bitcoin_prices` + +### Generate Token + +1. Go to the **"Data"** section in the left sidebar. +2. Navigate to the **"Tokens"** tab. +3. Click **"Generate Token" → "All-Access Token"**. +4. **Copy the generated token**. + +--- + +## Step 2: Save the Token + +Paste the token inside your `.env` file: + +``` +INFLUXDB_TOKEN= +``` + +> Do not commit `.env` to GitHub. + +--- + +## 🔄 Step 3: Restart with Full Application + +First, shut down InfluxDB: + +```bash +Ctrl + C +``` + +Then clean and restart everything: + +```bash +docker-compose down -v +docker-compose up --build +``` + +--- + +## 📓 Access Jupyter Notebook + +Once up, visit: + http://localhost:8888 + +Open and run the following notebooks: + +- `bitcoin.API.ipynb` +- `bitcoin.Fetch.ipynb` + +--- + +## Notebook Summaries + +### `bitcoin.API.ipynb` + +**Purpose**: Demonstrates how to use the `BitcoinPriceSource` API class. + +**What it does**: +- Initializes the price source. +- Iterates over 10 fetches from the CoinGecko API. +- Prints: + - Timestamped Bitcoin prices + - Moving Average (MA) + - Standard Deviation + - Exponential Moving Average (EMA) + - Trend and Cumulative Return + +--- + +### `bitcoin.Fetch.ipynb` + +**Purpose**: Complete forecast pipeline. + +**What it does**: +- Uses Pyflink a apache flink framework to extract real time data and print it +- Fetches historical BTC data using `yfinance`. +- Trains a `NeuralProphet` model. +- Forecasts prices for the next 365 days. +- Visualizes: + - Forecasted prices + - Trend, weekly, and yearly seasonality components +- Prints forecast for the next 7 days. + + +**Why we use two Docker containers for clean separation of concerns:** + +- **influxdb_container**: Runs the InfluxDB service to store time-series data. +- **umd_data605_app**: Runs the application (Python + Jupyter + PyFlink) that fetches Bitcoin prices and sends metrics to InfluxDB. + +Keeping them separate ensures: + +- Each container has a single responsibility. +- Easier debugging, scaling, and maintenance. +- Flexibility to replace or upgrade one service without touching the other. + + +**Why Docker Network** + +Docker containers are isolated by default. To allow them to communicate (e.g., the app pushing data into InfluxDB), we connect them using a custom bridge network (flink_influx_network): +This makes sure: + +- The app can reach InfluxDB at http://influxdb_container:8086 (container name acts like a hostname). +- Both services remain discoverable to each other but isolated from the host unless explicitly exposed. + + + +**Why Set Up InfluxDB and Generate Tokens** + +- InfluxDB 2.x uses token-based authentication for secure access. +- On first-time setup, we must: +- Run only the InfluxDB container. +- Open http://localhost:8086 and manually: +- Create an admin user, org, and bucket. +- Generate an All-Access Token. +- This token is needed so the app container can authenticate and write metrics to the InfluxDB service securely. +- Once the token is created: +- We store it in a .env file. +- It is automatically injected into the app via docker-compose.yml \ No newline at end of file diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/__init__.py b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/Dockerfile b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/Dockerfile new file mode 100644 index 000000000..e0abf2d97 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/Dockerfile @@ -0,0 +1,50 @@ +FROM ubuntu:20.04 + +# Set environment variables to avoid interactive prompts +ENV DEBIAN_FRONTEND=noninteractive +ENV TZ=Etc/UTC + +# Install system dependencies +RUN apt-get update && apt-get upgrade -y && \ + apt-get install -y --no-install-recommends \ + openjdk-11-jdk \ + python3 \ + python3-pip \ + python3-dev \ + curl \ + git \ + build-essential \ + tzdata && \ + apt-get clean && rm -rf /var/lib/apt/lists/* + +# Set JAVA path for Flink (needed on ARM64) +ENV JAVA_HOME=/usr/lib/jvm/java-11-openjdk-arm64 +ENV PATH=$JAVA_HOME/bin:$PATH + +# Upgrade pip and install base Python packages (excluding prophet/pystan) +RUN python3 -m pip install --upgrade pip setuptools wheel && \ + pip install \ + apache-flink==1.17.1 \ + ipython \ + tornado==6.1 \ + jupyter-client==7.3.2 \ + jupyter-contrib-core \ + jupyter-contrib-nbextensions \ + notebook \ + influxdb-client \ + requests \ + psycopg2-binary \ + yapf \ + numpy \ + pandas \ + matplotlib \ + yfinance + +# Create working directory +WORKDIR /data + +# Expose Jupyter port +EXPOSE 8888 + +# Start Jupyter on container boot +CMD ["jupyter", "notebook", "--ip=0.0.0.0", "--port=8888", "--allow-root", "--NotebookApp.token=''"] diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bashrc b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bashrc new file mode 120000 index 000000000..6fbd519b9 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bashrc @@ -0,0 +1 @@ +../../../docker_common/bashrc \ No newline at end of file diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin.API.ipynb b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin.API.ipynb new file mode 100644 index 000000000..eba099863 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin.API.ipynb @@ -0,0 +1,107 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "469b4c2b", + "metadata": {}, + "source": [ + "### Real-Time Bitcoin Price Streaming: Sample API Usage" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "03626841", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2025-05-18 01:28:25.250286] Price: $103324.00\n", + "[1] Timestamp: 1747531705250, Price: $103324.00\n", + "[2025-05-18 01:28:55.354586] Price: $103319.00\n", + "[2] Timestamp: 1747531735354, Price: $103319.00\n", + "[2025-05-18 01:29:25.476993] Price: $103319.00\n", + "[3] Timestamp: 1747531765476, Price: $103319.00\n", + "[2025-05-18 01:29:55.613666] Price: $103313.00\n", + "[4] Timestamp: 1747531795613, Price: $103313.00\n", + "[2025-05-18 01:30:25.956787] Price: $103313.00\n", + "[5] Timestamp: 1747531825956, Price: $103313.00\n", + "[2025-05-18 01:30:56.115765] Price: $103302.00\n", + "[6] Timestamp: 1747531856115, Price: $103302.00\n", + "[2025-05-18 01:31:26.224400] Price: $103302.00\n", + "[7] Timestamp: 1747531886224, Price: $103302.00\n", + "[2025-05-18 01:31:56.332820] Price: $103302.00\n", + "[8] Timestamp: 1747531916332, Price: $103302.00\n", + "[2025-05-18 01:32:26.440963] Price: $103297.00\n", + "[9] Timestamp: 1747531946440, Price: $103297.00\n", + "[2025-05-18 01:32:56.540052] Price: $103297.00\n", + "-----> MA: $103308.80, StdDev: 9.44, EMA: $103297.00, Max: $103324.00, Min: $103297.00\n", + " Trend: -1, Cumulative Return: -0.03%, 24h Change: 0.35%\n", + "\n", + "[10] Timestamp: 1747531976540, Price: $103297.00\n" + ] + } + ], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "from bitcoin_utils import BitcoinPriceSource\n", + "import itertools\n", + "\n", + "# Initialize source with window\n", + "source = BitcoinPriceSource()\n", + "\n", + "# Simulate just one fetch loop iteration for demonstration\n", + "for i, (timestamp, price) in zip(range(10), source):\n", + " print(f\"[{i+1}] Timestamp: {timestamp}, Price: ${price:.2f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "1a8b3ccd", + "metadata": {}, + "source": [ + "The output shows Bitcoin price fetches every 30 seconds along with computed statistics once enough data points are collected## Price Fetch Logs\n", + "Each fetch prints the current UTC timestamp and Bitcoin price. It also prints a count of the fetch number, the timestamp in milliseconds, and the price.\n", + "\n", + "## Computed Metrics\n", + "After collecting 10 price points (the window size), the script calculates and displays:\n", + "\n", + "- **Moving Average (MA)** of prices in the window\n", + "- **Standard Deviation (StdDev)**, measuring price volatility\n", + "- **Exponential Moving Average (EMA)**, which weights recent prices more\n", + "- **Maximum and Minimum** prices in the window\n", + "- **Trend indicator**: -1 means the price is trending down, 1 means up\n", + "- **Cumulative Return**: percentage price change over the window\n", + "- **24h Change**: price change in last 24 hours reported by the API\n", + "\n", + "## Summary\n", + "The script fetches prices repeatedly, and after every 10 samples, it summarizes recent price behavior to help analyze short-term market trends and volatility.\n" + ] + } + ], + "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": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin.API.md b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin.API.md new file mode 100644 index 000000000..cd24ca0d0 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin.API.md @@ -0,0 +1,63 @@ +# BitcoinPriceSource API Documentation + +This markdown file documents the native API class `BitcoinPriceSource` and the software layer written on top of it, as demonstrated in `XYZ.API.ipynb`. + +--- + +## Overview + +`BitcoinPriceSource` is a Python iterable class designed to fetch real-time Bitcoin prices from the CoinGecko API at fixed intervals (default 30 seconds). It maintains a rolling window of recent prices and calculates various statistical metrics such as moving average, standard deviation, exponential moving average, max/min prices, trend, cumulative return, and 24-hour price change. + +Additionally, it writes raw price data and computed statistics to an InfluxDB time-series database for further analysis. + +--- + +## How to Use + +The class can be instantiated and iterated over in a Python environment. Each iteration yields a tuple `(timestamp, price)` representing the current Unix timestamp (milliseconds) and the latest Bitcoin price in USD. + +### Sample usage: + +```python +from bitcoin_utils import BitcoinPriceSource + +# Create an instance of the source +source = BitcoinPriceSource() + +# Iterate to fetch Bitcoin price updates +for i, (timestamp, price) in zip(range(10), source): + print(f"[{i+1}] Timestamp: {timestamp}, Price: ${price:.2f}") + +## What Happens Internally + +- **Fetching Data:** On each iteration, the API queries CoinGecko for the latest Bitcoin price and 24h percent change. + +- **Rolling Window:** Maintains a fixed-size window (default 10 samples) of recent prices. + +- **Metrics Calculation:** Once enough data points are collected, calculates: + + - Moving Average (MA) + + - Standard Deviation (StdDev) + + - Exponential Moving Average (EMA) + + - Max and Min prices + + - Trend indicator (`1` for upward trend, `-1` for downward) + + - Cumulative return over the window + + - 24-hour percent price change + +- **Data Storage:** Writes raw and computed data to InfluxDB for persistent storage. + +- **Logging:** Prints the fetched prices and metrics to the console. + +--- + +## Summary + +`BitcoinPriceSource` abstracts real-time Bitcoin price streaming with integrated statistics and time-series storage. The API is easy to integrate with Python iterables and provides valuable insights for downstream analysis or visualization. + +The companion Jupyter notebook `XYZ.API.ipynb` demonstrates minimal, clean usage of this API layer. diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin.Fetch.ipynb b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin.Fetch.ipynb new file mode 100644 index 000000000..c88383a5c --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin.Fetch.ipynb @@ -0,0 +1,1133 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a77c646a", + "metadata": {}, + "source": [ + "### Import Libraries and API Functions\n", + "\n", + "This cell imports necessary libraries, suppresses warnings for cleaner output, and loads the key classes and functions from the `bitcoin_utils` module.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "943a039e", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "eae02f4f", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "!pip install --quiet --root-user-action=ignore neuralprophet\n", + "\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "markdown", + "id": "65eb92ac", + "metadata": {}, + "source": [ + "### Import Forecasting and Streaming Utilities from `bitcoin_utils.py`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "30c581f1", + "metadata": {}, + "outputs": [], + "source": [ + "from bitcoin_utils import (\n", + " BitcoinPriceSource,\n", + " fetch_historical_data,\n", + " train_neural_prophet_model,\n", + " make_forecast,\n", + " plot_forecast,\n", + " plot_components\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "e7c426f5", + "metadata": {}, + "source": [ + "## Real-Time Bitcoin Price Streaming with PyFlink\n", + "\n", + "This section defines and runs a streaming job that fetches live Bitcoin prices every 30 seconds and prints timestamped price updates in real-time using Apache Flink’s Python API (`pyflink`).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf88ae02", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2025-05-18 01:18:44.224023] Price: $103315.00\n", + "[2025-05-18 01:19:14.355741] Price: $103315.00\n", + "[2025-05-18 01:19:44.437101] Price: $103315.00\n", + "[2025-05-18 01:20:14.528791] Price: $103315.00\n", + "[2025-05-18 01:20:44.671413] Price: $103314.00\n", + "[2025-05-18 01:21:14.809197] Price: $103314.00\n", + "[2025-05-18 01:21:44.909195] Price: $103314.00\n", + "[2025-05-18 01:22:14.986067] Price: $103314.00\n", + "[2025-05-18 01:22:45.051645] Price: $103314.00\n", + "[2025-05-18 01:23:15.205554] Price: $103317.00\n", + "-----> MA: $103314.70, StdDev: 0.90, EMA: $103317.00, Max: $103317.00, Min: $103314.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.29%\n", + "\n", + "[2025-05-18 01:23:45.330494] Price: $103317.00\n", + "-----> MA: $103314.90, StdDev: 1.14, EMA: $103317.00, Max: $103317.00, Min: $103314.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.29%\n", + "\n", + "[2025-05-18 01:24:15.458510] Price: $103317.00\n", + "-----> MA: $103315.10, StdDev: 1.30, EMA: $103317.00, Max: $103317.00, Min: $103314.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.29%\n", + "\n", + "[2025-05-18 01:24:45.616571] Price: $103315.00\n", + "-----> MA: $103315.10, StdDev: 1.30, EMA: $103316.60, Max: $103317.00, Min: $103314.00\n", + " Trend: -1, Cumulative Return: 0.00%, 24h Change: 0.28%\n", + "\n", + "[2025-05-18 01:25:15.719052] Price: $103315.00\n", + "-----> MA: $103315.10, StdDev: 1.30, EMA: $103316.28, Max: $103317.00, Min: $103314.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.28%\n", + "\n", + "[2025-05-18 01:25:45.866458] Price: $103315.00\n", + "-----> MA: $103315.20, StdDev: 1.25, EMA: $103316.02, Max: $103317.00, Min: $103314.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.28%\n", + "\n", + "[2025-05-18 01:26:16.212863] Price: $103321.00\n", + "-----> MA: $103315.90, StdDev: 2.07, EMA: $103317.02, Max: $103321.00, Min: $103314.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.29%\n", + "\n", + "[2025-05-18 01:26:46.340794] Price: $103321.00\n", + "-----> MA: $103316.60, StdDev: 2.46, EMA: $103317.82, Max: $103321.00, Min: $103314.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.29%\n", + "\n", + "[2025-05-18 01:27:16.689451] Price: $103321.00\n", + "-----> MA: $103317.30, StdDev: 2.61, EMA: $103318.45, Max: $103321.00, Min: $103314.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.29%\n", + "\n", + "[2025-05-18 01:27:46.796247] Price: $103324.00\n", + "-----> MA: $103318.30, StdDev: 3.03, EMA: $103319.56, Max: $103324.00, Min: $103315.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 01:28:16.912675] Price: $103324.00\n", + "-----> MA: $103319.00, StdDev: 3.44, EMA: $103320.45, Max: $103324.00, Min: $103315.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 01:28:47.058928] Price: $103319.00\n", + "-----> MA: $103319.20, StdDev: 3.37, EMA: $103320.16, Max: $103324.00, Min: $103315.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 01:29:17.167983] Price: $103319.00\n", + "-----> MA: $103319.40, StdDev: 3.29, EMA: $103319.93, Max: $103324.00, Min: $103315.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 01:29:47.274665] Price: $103319.00\n", + "-----> MA: $103319.80, StdDev: 2.96, EMA: $103319.74, Max: $103324.00, Min: $103315.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 01:30:17.618726] Price: $103313.00\n", + "-----> MA: $103319.60, StdDev: 3.32, EMA: $103318.39, Max: $103324.00, Min: $103313.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.34%\n", + "\n", + "[2025-05-18 01:30:47.727099] Price: $103313.00\n", + "-----> MA: $103319.40, StdDev: 3.64, EMA: $103317.31, Max: $103324.00, Min: $103313.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.34%\n", + "\n", + "[2025-05-18 01:31:17.838244] Price: $103302.00\n", + "-----> MA: $103317.50, StdDev: 6.30, EMA: $103314.25, Max: $103324.00, Min: $103302.00\n", + " Trend: -1, Cumulative Return: -0.02%, 24h Change: 0.33%\n", + "\n", + "[2025-05-18 01:31:47.925928] Price: $103302.00\n", + "-----> MA: $103315.60, StdDev: 7.67, EMA: $103311.80, Max: $103324.00, Min: $103302.00\n", + " Trend: -1, Cumulative Return: -0.02%, 24h Change: 0.33%\n", + "\n", + "[2025-05-18 01:32:18.054674] Price: $103297.00\n", + "-----> MA: $103313.20, StdDev: 9.21, EMA: $103308.84, Max: $103324.00, Min: $103297.00\n", + " Trend: -1, Cumulative Return: -0.03%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 01:32:48.160247] Price: $103297.00\n", + "-----> MA: $103310.50, StdDev: 9.59, EMA: $103306.47, Max: $103324.00, Min: $103297.00\n", + " Trend: -1, Cumulative Return: -0.03%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 01:33:18.272438] Price: $103297.00\n", + "-----> MA: $103307.80, StdDev: 9.21, EMA: $103304.58, Max: $103319.00, Min: $103297.00\n", + " Trend: -1, Cumulative Return: -0.02%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 01:33:48.421497] Price: $103294.00\n", + "-----> MA: $103305.30, StdDev: 9.22, EMA: $103302.46, Max: $103319.00, Min: $103294.00\n", + " Trend: -1, Cumulative Return: -0.02%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 01:34:18.551462] Price: $103294.00\n", + "-----> MA: $103302.80, StdDev: 8.53, EMA: $103300.77, Max: $103319.00, Min: $103294.00\n", + " Trend: -1, Cumulative Return: -0.02%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 01:34:48.669773] Price: $103280.00\n", + "-----> MA: $103298.90, StdDev: 9.13, EMA: $103296.62, Max: $103313.00, Min: $103280.00\n", + " Trend: -1, Cumulative Return: -0.03%, 24h Change: 0.33%\n", + "\n", + "[2025-05-18 01:35:18.773106] Price: $103280.00\n", + "-----> MA: $103295.60, StdDev: 9.39, EMA: $103293.29, Max: $103313.00, Min: $103280.00\n", + " Trend: -1, Cumulative Return: -0.03%, 24h Change: 0.33%\n", + "\n", + "[2025-05-18 01:35:48.865850] Price: $103280.00\n", + "-----> MA: $103292.30, StdDev: 8.45, EMA: $103290.63, Max: $103302.00, Min: $103280.00\n", + " Trend: -1, Cumulative Return: -0.02%, 24h Change: 0.33%\n", + "\n", + "[2025-05-18 01:36:19.004678] Price: $103283.00\n", + "-----> MA: $103290.40, StdDev: 8.19, EMA: $103289.11, Max: $103302.00, Min: $103280.00\n", + " Trend: -1, Cumulative Return: -0.02%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:36:49.114144] Price: $103283.00\n", + "-----> MA: $103288.50, StdDev: 7.45, EMA: $103287.89, Max: $103297.00, Min: $103280.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:37:19.200348] Price: $103283.00\n", + "-----> MA: $103287.10, StdDev: 7.02, EMA: $103286.91, Max: $103297.00, Min: $103280.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:37:49.352976] Price: $103283.00\n", + "-----> MA: $103285.70, StdDev: 6.26, EMA: $103286.13, Max: $103297.00, Min: $103280.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:38:19.505451] Price: $103283.00\n", + "-----> MA: $103284.30, StdDev: 5.02, EMA: $103285.50, Max: $103294.00, Min: $103280.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:38:49.601990] Price: $103283.00\n", + "-----> MA: $103283.20, StdDev: 3.84, EMA: $103285.00, Max: $103294.00, Min: $103280.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:39:19.697091] Price: $103284.00\n", + "-----> MA: $103282.20, StdDev: 1.47, EMA: $103284.80, Max: $103284.00, Min: $103280.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:39:50.045484] Price: $103284.00\n", + "-----> MA: $103282.60, StdDev: 1.36, EMA: $103284.64, Max: $103284.00, Min: $103280.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:40:20.195804] Price: $103284.00\n", + "-----> MA: $103283.00, StdDev: 1.10, EMA: $103284.51, Max: $103284.00, Min: $103280.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:40:50.307666] Price: $103284.00\n", + "-----> MA: $103283.40, StdDev: 0.49, EMA: $103284.41, Max: $103284.00, Min: $103283.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:41:20.444146] Price: $103284.00\n", + "-----> MA: $103283.50, StdDev: 0.50, EMA: $103284.33, Max: $103284.00, Min: $103283.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:41:50.533528] Price: $103287.00\n", + "-----> MA: $103283.90, StdDev: 1.14, EMA: $103284.86, Max: $103287.00, Min: $103283.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:42:20.659900] Price: $103287.00\n", + "-----> MA: $103284.30, StdDev: 1.42, EMA: $103285.29, Max: $103287.00, Min: $103283.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:42:50.842041] Price: $103283.00\n", + "-----> MA: $103284.30, StdDev: 1.42, EMA: $103284.83, Max: $103287.00, Min: $103283.00\n", + " Trend: -1, Cumulative Return: 0.00%, 24h Change: 0.37%\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2025-05-18 01:43:20.974792] Price: $103283.00\n", + "-----> MA: $103284.30, StdDev: 1.42, EMA: $103284.47, Max: $103287.00, Min: $103283.00\n", + " Trend: -1, Cumulative Return: 0.00%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 01:43:51.136380] Price: $103279.00\n", + "-----> MA: $103283.90, StdDev: 2.12, EMA: $103283.37, Max: $103287.00, Min: $103279.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 01:44:21.286792] Price: $103279.00\n", + "-----> MA: $103283.40, StdDev: 2.58, EMA: $103282.50, Max: $103287.00, Min: $103279.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 01:44:51.635388] Price: $103279.00\n", + "-----> MA: $103282.90, StdDev: 2.88, EMA: $103281.80, Max: $103287.00, Min: $103279.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 01:45:21.814200] Price: $103277.00\n", + "-----> MA: $103282.20, StdDev: 3.34, EMA: $103280.84, Max: $103287.00, Min: $103277.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 01:45:51.931533] Price: $103277.00\n", + "-----> MA: $103281.50, StdDev: 3.61, EMA: $103280.07, Max: $103287.00, Min: $103277.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 01:46:22.087107] Price: $103273.00\n", + "-----> MA: $103280.40, StdDev: 4.29, EMA: $103278.66, Max: $103287.00, Min: $103273.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 01:46:52.212799] Price: $103273.00\n", + "-----> MA: $103279.00, StdDev: 4.20, EMA: $103277.53, Max: $103287.00, Min: $103273.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 01:47:22.332824] Price: $103273.00\n", + "-----> MA: $103277.60, StdDev: 3.58, EMA: $103276.62, Max: $103283.00, Min: $103273.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 01:47:52.515157] Price: $103268.00\n", + "-----> MA: $103276.10, StdDev: 4.11, EMA: $103274.90, Max: $103283.00, Min: $103268.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.44%\n", + "\n", + "[2025-05-18 01:48:22.837428] Price: $103268.00\n", + "-----> MA: $103274.60, StdDev: 4.05, EMA: $103273.52, Max: $103279.00, Min: $103268.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.44%\n", + "\n", + "[2025-05-18 01:48:52.978199] Price: $103268.00\n", + "-----> MA: $103273.50, StdDev: 4.20, EMA: $103272.41, Max: $103279.00, Min: $103268.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.44%\n", + "\n", + "[2025-05-18 01:49:23.116567] Price: $103267.00\n", + "-----> MA: $103272.30, StdDev: 4.17, EMA: $103271.33, Max: $103279.00, Min: $103267.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.44%\n", + "\n", + "[2025-05-18 01:49:53.241410] Price: $103267.00\n", + "-----> MA: $103271.10, StdDev: 3.78, EMA: $103270.46, Max: $103277.00, Min: $103267.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.44%\n", + "\n", + "[2025-05-18 01:50:23.424229] Price: $103266.00\n", + "-----> MA: $103270.00, StdDev: 3.49, EMA: $103269.57, Max: $103277.00, Min: $103266.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.44%\n", + "\n", + "[2025-05-18 01:50:53.518701] Price: $103266.00\n", + "-----> MA: $103268.90, StdDev: 2.77, EMA: $103268.86, Max: $103273.00, Min: $103266.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.44%\n", + "\n", + "[2025-05-18 01:51:23.618683] Price: $103266.00\n", + "-----> MA: $103268.20, StdDev: 2.52, EMA: $103268.29, Max: $103273.00, Min: $103266.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.44%\n", + "\n", + "[2025-05-18 01:51:53.770762] Price: $103267.00\n", + "-----> MA: $103267.60, StdDev: 1.96, EMA: $103268.03, Max: $103273.00, Min: $103266.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.45%\n", + "\n", + "[2025-05-18 01:52:23.894459] Price: $103267.00\n", + "-----> MA: $103267.00, StdDev: 0.77, EMA: $103267.82, Max: $103268.00, Min: $103266.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.45%\n", + "\n", + "[2025-05-18 01:52:54.102450] Price: $103265.00\n", + "-----> MA: $103266.70, StdDev: 0.90, EMA: $103267.26, Max: $103268.00, Min: $103265.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.45%\n", + "\n", + "[2025-05-18 01:53:24.204609] Price: $103265.00\n", + "-----> MA: $103266.40, StdDev: 0.92, EMA: $103266.81, Max: $103268.00, Min: $103265.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.45%\n", + "\n", + "[2025-05-18 01:53:54.325381] Price: $103265.00\n", + "-----> MA: $103266.10, StdDev: 0.83, EMA: $103266.45, Max: $103267.00, Min: $103265.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.45%\n", + "\n", + "[2025-05-18 01:54:24.485884] Price: $103267.00\n", + "-----> MA: $103266.10, StdDev: 0.83, EMA: $103266.56, Max: $103267.00, Min: $103265.00\n", + " Trend: -1, Cumulative Return: 0.00%, 24h Change: 0.45%\n", + "\n", + "[2025-05-18 01:54:54.610909] Price: $103267.00\n", + "-----> MA: $103266.10, StdDev: 0.83, EMA: $103266.65, Max: $103267.00, Min: $103265.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.45%\n", + "\n", + "[2025-05-18 01:55:24.732856] Price: $103267.00\n", + "-----> MA: $103266.20, StdDev: 0.87, EMA: $103266.72, Max: $103267.00, Min: $103265.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.45%\n", + "\n", + "[2025-05-18 01:55:54.848745] Price: $103262.00\n", + "-----> MA: $103265.80, StdDev: 1.54, EMA: $103265.77, Max: $103267.00, Min: $103262.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.45%\n", + "\n", + "[2025-05-18 01:56:24.970873] Price: $103262.00\n", + "-----> MA: $103265.40, StdDev: 1.91, EMA: $103265.02, Max: $103267.00, Min: $103262.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.45%\n", + "\n", + "[2025-05-18 01:56:55.131372] Price: $103264.00\n", + "-----> MA: $103265.10, StdDev: 1.87, EMA: $103264.81, Max: $103267.00, Min: $103262.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.45%\n", + "\n", + "[2025-05-18 01:57:25.236481] Price: $103264.00\n", + "-----> MA: $103264.80, StdDev: 1.78, EMA: $103264.65, Max: $103267.00, Min: $103262.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.45%\n", + "\n", + "[2025-05-18 01:57:55.357535] Price: $103264.00\n", + "-----> MA: $103264.70, StdDev: 1.79, EMA: $103264.52, Max: $103267.00, Min: $103262.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.45%\n", + "\n", + "[2025-05-18 01:58:25.509780] Price: $103270.00\n", + "-----> MA: $103265.20, StdDev: 2.40, EMA: $103265.62, Max: $103270.00, Min: $103262.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.42%\n", + "\n", + "[2025-05-18 01:58:55.621288] Price: $103270.00\n", + "-----> MA: $103265.70, StdDev: 2.79, EMA: $103266.49, Max: $103270.00, Min: $103262.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.42%\n", + "\n", + "[2025-05-18 01:59:25.699802] Price: $103270.00\n", + "-----> MA: $103266.00, StdDev: 3.07, EMA: $103267.19, Max: $103270.00, Min: $103262.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.42%\n", + "\n", + "[2025-05-18 01:59:55.824817] Price: $103269.00\n", + "-----> MA: $103266.20, StdDev: 3.19, EMA: $103267.56, Max: $103270.00, Min: $103262.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.42%\n", + "\n", + "[2025-05-18 02:00:26.159521] Price: $103269.00\n", + "-----> MA: $103266.40, StdDev: 3.29, EMA: $103267.84, Max: $103270.00, Min: $103262.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.42%\n", + "\n", + "[2025-05-18 02:00:56.292351] Price: $103261.00\n", + "-----> MA: $103266.30, StdDev: 3.44, EMA: $103266.48, Max: $103270.00, Min: $103261.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.42%\n", + "\n", + "[2025-05-18 02:01:26.423084] Price: $103261.00\n", + "-----> MA: $103266.20, StdDev: 3.57, EMA: $103265.38, Max: $103270.00, Min: $103261.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.42%\n", + "\n", + "[2025-05-18 02:01:56.573946] Price: $103262.00\n", + "-----> MA: $103266.00, StdDev: 3.74, EMA: $103264.70, Max: $103270.00, Min: $103261.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.42%\n", + "\n", + "[2025-05-18 02:02:26.684044] Price: $103262.00\n", + "-----> MA: $103265.80, StdDev: 3.89, EMA: $103264.16, Max: $103270.00, Min: $103261.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.42%\n", + "\n", + "[2025-05-18 02:02:56.818777] Price: $103262.00\n", + "-----> MA: $103265.60, StdDev: 4.03, EMA: $103263.73, Max: $103270.00, Min: $103261.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.42%\n", + "\n", + "[2025-05-18 02:03:26.937585] Price: $103268.00\n", + "-----> MA: $103265.40, StdDev: 3.85, EMA: $103264.58, Max: $103270.00, Min: $103261.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.34%\n", + "\n", + "[2025-05-18 02:03:57.062200] Price: $103268.00\n", + "-----> MA: $103265.20, StdDev: 3.66, EMA: $103265.27, Max: $103270.00, Min: $103261.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.34%\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2025-05-18 02:04:27.200623] Price: $103289.00\n", + "-----> MA: $103267.10, StdDev: 8.01, EMA: $103270.01, Max: $103289.00, Min: $103261.00\n", + " Trend: 1, Cumulative Return: 0.02%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 02:04:57.314459] Price: $103289.00\n", + "-----> MA: $103269.10, StdDev: 10.38, EMA: $103273.81, Max: $103289.00, Min: $103261.00\n", + " Trend: 1, Cumulative Return: 0.02%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 02:05:27.450960] Price: $103290.00\n", + "-----> MA: $103271.20, StdDev: 12.12, EMA: $103277.05, Max: $103290.00, Min: $103261.00\n", + " Trend: 1, Cumulative Return: 0.03%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 02:05:57.558957] Price: $103290.00\n", + "-----> MA: $103274.10, StdDev: 12.79, EMA: $103279.64, Max: $103290.00, Min: $103261.00\n", + " Trend: 1, Cumulative Return: 0.03%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 02:06:27.657295] Price: $103290.00\n", + "-----> MA: $103277.00, StdDev: 12.77, EMA: $103281.71, Max: $103290.00, Min: $103262.00\n", + " Trend: 1, Cumulative Return: 0.03%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 02:06:57.804067] Price: $103291.00\n", + "-----> MA: $103279.90, StdDev: 12.32, EMA: $103283.57, Max: $103291.00, Min: $103262.00\n", + " Trend: 1, Cumulative Return: 0.03%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 02:07:28.175955] Price: $103291.00\n", + "-----> MA: $103282.80, StdDev: 11.12, EMA: $103285.06, Max: $103291.00, Min: $103262.00\n", + " Trend: 1, Cumulative Return: 0.03%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 02:07:58.346865] Price: $103293.00\n", + "-----> MA: $103285.90, StdDev: 9.02, EMA: $103286.64, Max: $103293.00, Min: $103268.00\n", + " Trend: 1, Cumulative Return: 0.02%, 24h Change: 0.33%\n", + "\n", + "[2025-05-18 02:08:28.682805] Price: $103293.00\n", + "-----> MA: $103288.40, StdDev: 6.93, EMA: $103287.92, Max: $103293.00, Min: $103268.00\n", + " Trend: 1, Cumulative Return: 0.02%, 24h Change: 0.33%\n", + "\n", + "[2025-05-18 02:08:58.799386] Price: $103293.00\n", + "-----> MA: $103290.90, StdDev: 1.51, EMA: $103288.93, Max: $103293.00, Min: $103289.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.33%\n", + "\n", + "[2025-05-18 02:09:28.925966] Price: $103293.00\n", + "-----> MA: $103291.30, StdDev: 1.49, EMA: $103289.75, Max: $103293.00, Min: $103289.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.33%\n", + "\n", + "[2025-05-18 02:09:59.047418] Price: $103293.00\n", + "-----> MA: $103291.70, StdDev: 1.35, EMA: $103290.40, Max: $103293.00, Min: $103290.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.33%\n", + "\n", + "[2025-05-18 02:10:29.230076] Price: $103289.00\n", + "-----> MA: $103291.60, StdDev: 1.50, EMA: $103290.12, Max: $103293.00, Min: $103289.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.33%\n", + "\n", + "[2025-05-18 02:10:59.350317] Price: $103289.00\n", + "-----> MA: $103291.50, StdDev: 1.63, EMA: $103289.89, Max: $103293.00, Min: $103289.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.33%\n", + "\n", + "[2025-05-18 02:11:29.466438] Price: $103289.00\n", + "-----> MA: $103291.40, StdDev: 1.74, EMA: $103289.72, Max: $103293.00, Min: $103289.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.33%\n", + "\n", + "[2025-05-18 02:11:59.850710] Price: $103293.00\n", + "-----> MA: $103291.60, StdDev: 1.80, EMA: $103290.37, Max: $103293.00, Min: $103289.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.34%\n", + "\n", + "[2025-05-18 02:12:29.989576] Price: $103293.00\n", + "-----> MA: $103291.80, StdDev: 1.83, EMA: $103290.90, Max: $103293.00, Min: $103289.00\n", + " Trend: -1, Cumulative Return: 0.00%, 24h Change: 0.34%\n", + "\n", + "[2025-05-18 02:13:00.142126] Price: $103297.00\n", + "-----> MA: $103292.20, StdDev: 2.40, EMA: $103292.12, Max: $103297.00, Min: $103289.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.38%\n", + "\n", + "[2025-05-18 02:13:30.283349] Price: $103297.00\n", + "-----> MA: $103292.60, StdDev: 2.80, EMA: $103293.09, Max: $103297.00, Min: $103289.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.38%\n", + "\n", + "[2025-05-18 02:14:00.420409] Price: $103297.00\n", + "-----> MA: $103293.00, StdDev: 3.10, EMA: $103293.88, Max: $103297.00, Min: $103289.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.38%\n", + "\n", + "[2025-05-18 02:14:30.647633] Price: $103297.00\n", + "-----> MA: $103293.40, StdDev: 3.32, EMA: $103294.50, Max: $103297.00, Min: $103289.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.39%\n", + "\n", + "[2025-05-18 02:15:00.763392] Price: $103297.00\n", + "-----> MA: $103293.80, StdDev: 3.49, EMA: $103295.00, Max: $103297.00, Min: $103289.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.39%\n", + "\n", + "[2025-05-18 02:15:30.888868] Price: $103297.00\n", + "-----> MA: $103294.60, StdDev: 3.20, EMA: $103295.40, Max: $103297.00, Min: $103289.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.39%\n", + "\n", + "[2025-05-18 02:16:01.040441] Price: $103299.00\n", + "-----> MA: $103295.60, StdDev: 2.84, EMA: $103296.12, Max: $103299.00, Min: $103289.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 02:16:31.144097] Price: $103299.00\n", + "-----> MA: $103296.60, StdDev: 1.96, EMA: $103296.70, Max: $103299.00, Min: $103293.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 02:17:01.267907] Price: $103299.00\n", + "-----> MA: $103297.20, StdDev: 1.66, EMA: $103297.16, Max: $103299.00, Min: $103293.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 02:17:31.397973] Price: $103302.00\n", + "-----> MA: $103298.10, StdDev: 1.58, EMA: $103298.13, Max: $103302.00, Min: $103297.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 02:18:01.537668] Price: $103302.00\n", + "-----> MA: $103298.60, StdDev: 1.91, EMA: $103298.90, Max: $103302.00, Min: $103297.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.35%\n", + "\n", + "[2025-05-18 02:18:31.708685] Price: $103308.00\n", + "-----> MA: $103299.70, StdDev: 3.32, EMA: $103300.72, Max: $103308.00, Min: $103297.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 02:19:02.056649] Price: $103308.00\n", + "-----> MA: $103300.80, StdDev: 3.99, EMA: $103302.18, Max: $103308.00, Min: $103297.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 02:19:32.194675] Price: $103310.00\n", + "-----> MA: $103302.10, StdDev: 4.61, EMA: $103303.74, Max: $103310.00, Min: $103297.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 02:20:02.331092] Price: $103310.00\n", + "-----> MA: $103303.40, StdDev: 4.82, EMA: $103304.99, Max: $103310.00, Min: $103297.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 02:20:32.450018] Price: $103310.00\n", + "-----> MA: $103304.70, StdDev: 4.67, EMA: $103305.99, Max: $103310.00, Min: $103299.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.36%\n", + "\n", + "[2025-05-18 02:21:02.589200] Price: $103315.00\n", + "-----> MA: $103306.30, StdDev: 5.16, EMA: $103307.80, Max: $103315.00, Min: $103299.00\n", + " Trend: 1, Cumulative Return: 0.02%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 02:21:32.706517] Price: $103315.00\n", + "-----> MA: $103307.90, StdDev: 5.13, EMA: $103309.24, Max: $103315.00, Min: $103299.00\n", + " Trend: 1, Cumulative Return: 0.02%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 02:22:02.829386] Price: $103315.00\n", + "-----> MA: $103309.50, StdDev: 4.57, EMA: $103310.39, Max: $103315.00, Min: $103302.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 02:22:32.934080] Price: $103320.00\n", + "-----> MA: $103311.30, StdDev: 4.80, EMA: $103312.31, Max: $103320.00, Min: $103302.00\n", + " Trend: 1, Cumulative Return: 0.02%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 02:23:03.045689] Price: $103320.00\n", + "-----> MA: $103313.10, StdDev: 4.32, EMA: $103313.85, Max: $103320.00, Min: $103308.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.37%\n", + "\n", + "[2025-05-18 02:23:33.180313] Price: $103321.00\n", + "-----> MA: $103314.40, StdDev: 4.54, EMA: $103315.28, Max: $103321.00, Min: $103308.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.30%\n", + "\n", + "[2025-05-18 02:24:03.313428] Price: $103321.00\n", + "-----> MA: $103315.70, StdDev: 4.38, EMA: $103316.42, Max: $103321.00, Min: $103310.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.30%\n", + "\n", + "[2025-05-18 02:24:33.418249] Price: $103322.00\n", + "-----> MA: $103316.90, StdDev: 4.30, EMA: $103317.54, Max: $103322.00, Min: $103310.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.30%\n", + "\n", + "[2025-05-18 02:25:03.498222] Price: $103322.00\n", + "-----> MA: $103318.10, StdDev: 3.86, EMA: $103318.43, Max: $103322.00, Min: $103310.00\n", + " Trend: 1, Cumulative Return: 0.01%, 24h Change: 0.30%\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2025-05-18 02:25:33.641155] Price: $103319.00\n", + "-----> MA: $103319.00, StdDev: 2.76, EMA: $103318.54, Max: $103322.00, Min: $103315.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.30%\n", + "\n", + "[2025-05-18 02:26:03.766564] Price: $103319.00\n", + "-----> MA: $103319.40, StdDev: 2.42, EMA: $103318.64, Max: $103322.00, Min: $103315.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.30%\n", + "\n", + "[2025-05-18 02:26:33.867138] Price: $103319.00\n", + "-----> MA: $103319.80, StdDev: 1.94, EMA: $103318.71, Max: $103322.00, Min: $103315.00\n", + " Trend: 1, Cumulative Return: 0.00%, 24h Change: 0.30%\n", + "\n", + "[2025-05-18 02:27:03.935233] Price: $103317.00\n", + "-----> MA: $103320.00, StdDev: 1.48, EMA: $103318.37, Max: $103322.00, Min: $103317.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.22%\n", + "\n", + "[2025-05-18 02:27:34.258467] Price: $103317.00\n", + "-----> MA: $103319.70, StdDev: 1.73, EMA: $103318.09, Max: $103322.00, Min: $103317.00\n", + " Trend: -1, Cumulative Return: -0.00%, 24h Change: 0.22%\n", + "\n", + "[2025-05-18 02:28:04.370339] Price: $103315.00\n", + "-----> MA: $103319.20, StdDev: 2.23, EMA: $103317.47, Max: $103322.00, Min: $103315.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.22%\n", + "\n", + "[2025-05-18 02:28:34.486479] Price: $103315.00\n", + "-----> MA: $103318.60, StdDev: 2.46, EMA: $103316.98, Max: $103322.00, Min: $103315.00\n", + " Trend: -1, Cumulative Return: -0.01%, 24h Change: 0.22%\n", + "\n" + ] + } + ], + "source": [ + "\n", + "\n", + "from bitcoin_utils import BitcoinPriceSource\n", + "from pyflink.datastream import StreamExecutionEnvironment\n", + "from pyflink.common.typeinfo import Types\n", + "\n", + "def main():\n", + " env = StreamExecutionEnvironment.get_execution_environment()\n", + " env.set_parallelism(1)\n", + "\n", + " source = BitcoinPriceSource(interval_sec=30, window_size=10)\n", + "\n", + " ds = env.from_collection(\n", + " collection=source,\n", + " type_info=Types.TUPLE([Types.LONG(), Types.FLOAT()])\n", + " )\n", + "\n", + " ds.print()\n", + " env.execute(\"Bitcoin Stats Streaming Job\")\n", + "\n", + "main()\n" + ] + }, + { + "cell_type": "markdown", + "id": "f6c1ea81", + "metadata": {}, + "source": [ + "# Fetch and Preview Historical Bitcoin Price Data\n", + "\n", + "This section downloads historical Bitcoin price data from Yahoo Finance and displays the first few records for verification.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "aba8d5ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "YF.download() has changed argument auto_adjust default to True\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ds y\n", + "0 2014-09-17 457.334015\n", + "1 2014-09-18 424.440002\n", + "2 2014-09-19 394.795990\n", + "3 2014-09-20 408.903992\n", + "4 2014-09-21 398.821014" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "btc_data = fetch_historical_data()\n", + "btc_data.head() # Display first few rows to confirm\n" + ] + }, + { + "cell_type": "markdown", + "id": "17010d51", + "metadata": {}, + "source": [ + "# Train Prophet Forecasting Model\n", + "\n", + "This section trains a Prophet time series forecasting model using the historical Bitcoin price data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "27c89441", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING - (NP.forecaster.fit) - When Global modeling with local normalization, metrics are displayed in normalized scale.\n", + "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.949% of the data.\n", + "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n", + "INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.\n", + "INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 64\n", + "INFO - (NP.config.set_auto_batch_epoch) - Auto-set epochs to 70\n", + "WARNING - (NP.config.set_lr_finder_args) - Learning rate finder: The number of batches (61) is too small than the required number for the learning rate finder (240). The results might not be optimal.\n", + "Finding best initial lr: 100%|██████████| 240/240 [00:00<00:00, 771.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 70: 100%|██████████| 70/70 [00:00<00:00, 947.56it/s, loss=0.0146, v_num=3, MAE=5.64e+3, RMSE=7.2e+3, Loss=0.0148, RegLoss=0.000] \n", + "Model training complete.\n" + ] + } + ], + "source": [ + "model = train_neural_prophet_model(btc_data)\n", + "\n", + "print(\"Model training complete.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c78120d3", + "metadata": {}, + "source": [ + "# Generate Forecast with Prophet Model\n", + "\n", + "This section generates future Bitcoin price predictions using the trained Prophet model and displays the last 10 forecasted values including confidence intervals.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "211da112", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.949% of the data.\n", + "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n", + "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n", + "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.726% of the data.\n", + "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n", + "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.726% of the data.\n", + "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 734.94it/s] " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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dsyhat1
3552026-05-09115678.531250
3562026-05-10115493.906250
3572026-05-11115404.296875
3582026-05-12115686.335938
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3632026-05-17115156.273438
3642026-05-18115327.703125
\n", + "
" + ], + "text/plain": [ + " ds yhat1\n", + "355 2026-05-09 115678.531250\n", + "356 2026-05-10 115493.906250\n", + "357 2026-05-11 115404.296875\n", + "358 2026-05-12 115686.335938\n", + "359 2026-05-13 115632.804688\n", + "360 2026-05-14 115389.648438\n", + "361 2026-05-15 115584.296875\n", + "362 2026-05-16 115355.468750\n", + "363 2026-05-17 115156.273438\n", + "364 2026-05-18 115327.703125" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecast = make_forecast(model, btc_data)\n", + "forecast[['ds', 'yhat1']].tail(10)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "841f99bf", + "metadata": {}, + "source": [ + "### Displaying Bitcoin Price Forecast for the Next 7 Days\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ca8f0514", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.949% of the data.\n", + "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n", + "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n", + "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.726% of the data.\n", + "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n", + "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.726% of the data.\n", + "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 624.62it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bitcoin price forecast for next 7 days:\n", + " ds yhat1\n", + "0 2025-05-19 89362.687500\n", + "1 2025-05-20 89329.046875\n", + "2 2025-05-21 89453.546875\n", + "3 2025-05-22 89260.140625\n", + "4 2025-05-23 89231.664062\n", + "5 2025-05-24 89188.734375\n", + "6 2025-05-25 88968.937500\n" + ] + } + ], + "source": [ + "forecast = make_forecast(model, btc_data)\n", + "\n", + "last_date = btc_data['ds'].max()\n", + "future_forecast = forecast[forecast['ds'] > last_date]\n", + "\n", + "print(\"Bitcoin price forecast for next 7 days:\")\n", + "print(future_forecast[['ds', 'yhat1']].head(7))\n" + ] + }, + { + "cell_type": "markdown", + "id": "7971311a", + "metadata": {}, + "source": [ + "### Visualizing Bitcoin Price Forecast (Next 1 Year)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8e1805e7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING - (NP.plotting.log_warning_deprecation_plotly) - DeprecationWarning: default plotting_backend will be changed to plotly in a future version. Switch to plotly by calling `m.set_plotting_backend('plotly')`.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_forecast(model, forecast)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "5d14c05e", + "metadata": {}, + "source": [ + "### Visualizing Forecast Components: Trend and Seasonality\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "79c1d79e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING - (NP.plotting.log_warning_deprecation_plotly) - DeprecationWarning: default plotting_backend will be changed to plotly in a future version. Switch to plotly by calling `m.set_plotting_backend('plotly')`.\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_components(model, forecast)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c32946e", + "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": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin.fetch.md b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin.fetch.md new file mode 100644 index 000000000..d0aa0a91b --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin.fetch.md @@ -0,0 +1,129 @@ +# bitcoin.fetch.md — Example Application Using the API Layer + +This example demonstrates a complete end-to-end use case of the Bitcoin API layer defined in `bitcoin_utils.py`. It integrates live Bitcoin streaming, historical data fetching, forecasting, and visualization. + +--- + +## 🔁Step 1: Real-Time Bitcoin Price Streaming + +We create a PyFlink streaming job that consumes Bitcoin prices from the BitcoinPriceSource generator. The class tracks a rolling price window, computes real-time metrics, and logs both raw prices and computed statistics to InfluxDB. + +```python +from pyflink.datastream import StreamExecutionEnvironment +from bitcoin_utils import BitcoinPriceSource +from pyflink.common.typeinfo import Types + +env = StreamExecutionEnvironment.get_execution_environment() +env.set_parallelism(1) + +source = BitcoinPriceSource(interval_sec=30, window_size=10) + +ds = env.from_collection( + collection=source, + type_info=Types.TUPLE([Types.LONG(), Types.FLOAT()]) +) + +ds.print() +env.execute("Bitcoin Stats Streaming Job") +``` + +Each streaming record prints: +- Current Bitcoin price +- Moving average (MA) +- Standard deviation +- Exponential moving average (EMA) +- Max/Min in window +- 24h percent change +- InfluxDB logs with timestamp + +--- + +## Step 2: Fetch Historical Data + +Use the Yahoo Finance API via `yfinance` to fetch daily Bitcoin price history for training the forecasting model. + +```python +from bitcoin_utils import fetch_historical_data + +btc_data = fetch_historical_data() +btc_data.head() +``` + +This produces a DataFrame with: +- `ds`: Date +- `y`: Closing price + +--- + +## Step 3: Train NeuralProphet Forecasting Model + +```python +from bitcoin_utils import train_neural_prophet_model + +model = train_neural_prophet_model(btc_data) +``` + +This creates a Prophet model trained on historical price data, using daily and yearly seasonality components. + +--- + +## Step 4: Generate Forecast + +Forecast Bitcoin prices for the next 365 days: + +```python +from bitcoin_utils import make_forecast + +forecast = make_forecast(model, btc_data, periods=365) + +``` + +This returns a DataFrame containing forecasted values (`yhat`) and confidence intervals (`yhat_lower`, `yhat_upper`). + +--- + +## Step 5: Plot Forecast & Components + +```python +from bitcoin_utils import plot_forecast, plot_components + +plot_forecast(model, forecast) +plot_components(model, forecast) +``` + +- `plot_forecast` plots the full time series including forecast +- `plot_components` shows trend, weekly, and yearly seasonality + +--- + +## Step 6: View Forecast for the Next 7 Days + +Filter and print only the next week of forecasted prices beyond the historical date range: + +```python +last_date = btc_data['ds'].max() +future_forecast = forecast[forecast['ds'] > last_date] + +print("Bitcoin price forecast for next 7 days:") +print(future_forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].head(7)) +``` + +--- + +## Summary + +-This example demonstrated how to: + +-Stream real-time Bitcoin prices using BitcoinPriceSource + +-Store metrics in InfluxDB + +--Fetch historical data via Yahoo Finance + +Train a NeuralProphet model on closing prices + +--Forecast future Bitcoin prices with daily/yearly seasonality + +-Visualize full forecasts and seasonal trends + +All logic is modularized in `bitcoin_utils.py` and can be reused for larger crypto analytics pipelines or dashboards. diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin_prices.csv b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin_prices.csv new file mode 100644 index 000000000..24c234dbd --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin_prices.csv @@ -0,0 +1,61 @@ +timestamp,price +2025-04-21T04:35:39.368771,87144 +2025-04-21T04:35:44.443206,87144 +2025-04-21T04:35:49.524304,87144 +2025-04-21T04:35:54.589439,87144 +2025-04-21T04:35:59.666758,87144 +2025-04-21T04:36:04.742355,87144 +2025-04-21T04:36:09.812802,87144 +2025-04-21T04:36:14.895465,87144 +2025-04-21T04:36:19.971010,87144 +2025-04-21T04:36:25.039725,87144 +2025-04-21T04:36:30.109888,87144 +2025-04-21T04:36:35.169754,87144 +2025-04-21T04:36:40.302753,87158 +2025-04-21T04:36:45.382042,87158 +2025-04-21T04:36:50.464224,87158 +2025-04-21T04:36:55.530175,87158 +2025-04-21T04:37:00.626086,87158 +2025-04-21T04:37:05.705197,87158 +2025-04-21T04:37:10.792267,87158 +2025-04-21T04:37:15.861608,87158 +2025-04-21T04:37:20.958403,87158 +2025-04-21T04:37:26.032692,87158 +2025-04-21T04:37:31.111748,87158 +2025-04-21T04:37:36.184836,87158 +2025-04-21T04:37:41.340143,87158 +2025-04-21T04:37:46.447787,87158 +2025-04-21T04:37:51.520644,87158 +2025-04-21T04:37:56.589797,87158 +2025-04-21T04:38:01.674341,87158 +2025-04-21T04:38:06.747552,87158 +2025-04-21T04:38:11.809748,87158 +2025-04-21T04:38:16.878086,87158 +2025-04-21T04:38:21.941667,87158 +2025-04-21T04:38:27.002556,87158 +2025-04-21T04:38:32.088501,87158 +2025-04-21T04:38:37.162130,87158 +2025-04-21T04:38:42.288790,87163 +2025-04-21T04:38:47.361299,87163 +2025-04-21T04:38:52.432006,87163 +2025-04-21T04:38:57.515028,87163 +2025-04-21T04:39:02.584737,87163 +2025-04-21T04:39:07.672142,87163 +2025-04-21T04:39:12.739958,87163 +2025-04-21T04:39:17.814217,87163 +2025-04-21T04:39:22.883159,87163 +2025-04-21T04:39:27.958171,87163 +2025-04-21T04:39:33.028941,87163 +2025-04-21T04:39:38.122709,87163 +2025-04-21T04:39:43.248302,87168 +2025-04-21T04:39:48.329557,87168 +2025-04-21T04:39:53.400313,87168 +2025-04-21T04:39:58.471711,87168 +2025-04-21T04:40:03.548517,87168 +2025-04-21T04:40:08.614751,87168 +2025-04-21T04:40:13.685755,87168 +2025-04-21T04:40:18.784002,87168 +2025-04-21T04:40:23.851282,87168 +2025-04-21T04:40:28.918726,87168 +2025-04-21T04:40:33.988877,87168 +2025-04-21T04:40:39.054569,87168 diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin_utils.py b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin_utils.py new file mode 100644 index 000000000..799ca5f8b --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/bitcoin_utils.py @@ -0,0 +1,149 @@ +# --- bitcoin_utils.py --- +import os +import time +import requests +from collections import deque +from datetime import datetime + +from influxdb_client import InfluxDBClient, Point +from influxdb_client.client.write_api import SYNCHRONOUS + +import yfinance as yf +import pandas as pd +from neuralprophet import NeuralProphet +import matplotlib.pyplot as plt + +# === General Settings === +FETCH_INTERVAL = 30 # Seconds between fetches +WINDOW_SIZE = 10 # Number of points in rolling window +EMA_ALPHA = 0.2 # EMA smoothing factor + +# === Runtime Environment === +RUN_ENV = os.getenv("RUN_ENV", "local") + +# === InfluxDB Configuration === +INFLUXDB_URL = os.getenv("INFLUXDB_URL") or ( + "http://influxdb_container:8086" if os.getenv("RUN_ENV") == "docker" else "http://localhost:8086" +) + +INFLUXDB_TOKEN = os.getenv("INFLUXDB_TOKEN") +INFLUXDB_ORG = os.getenv("INFLUXDB_ORG", "crypto") +INFLUXDB_BUCKET = os.getenv("INFLUXDB_BUCKET", "bitcoin_prices") + + +class BitcoinPriceSource: + def __init__(self, interval_sec=FETCH_INTERVAL, window_size=WINDOW_SIZE): + self.interval = interval_sec + self.window_size = window_size + self.api_url = "https://api.coingecko.com/api/v3/simple/price" + self.params = { + "ids": "bitcoin", + "vs_currencies": "usd", + "include_24hr_change": "true" + } + self.price_window = deque(maxlen=window_size) + self.ema = None + + self.client = InfluxDBClient( + url=INFLUXDB_URL, + token=INFLUXDB_TOKEN, + org=INFLUXDB_ORG, + timeout=30000 + ) + self.write_api = self.client.write_api(write_options=SYNCHRONOUS) + + def __iter__(self): + while True: + try: + response = requests.get(self.api_url, params=self.params, timeout=10) + data = response.json() + + if "bitcoin" in data and "usd" in data["bitcoin"]: + price = float(data["bitcoin"]["usd"]) + change_pct = float(data["bitcoin"].get("usd_24h_change", 0)) + timestamp = int(time.time() * 1000) + current_time = datetime.utcnow() + self.price_window.append(price) + + # Write price point + self.write_api.write(bucket=INFLUXDB_BUCKET, org=INFLUXDB_ORG, + record=Point("bitcoin_price").field("price", price).time(current_time)) + print(f"[{current_time}] Price: ${price:.2f}") + + # If window is full, compute metrics + if len(self.price_window) == self.window_size: + ma = sum(self.price_window) / self.window_size + variance = sum((x - ma) ** 2 for x in self.price_window) / self.window_size + std_dev = variance ** 0.5 + self.ema = price if self.ema is None else EMA_ALPHA * price + (1 - EMA_ALPHA) * self.ema + max_price = max(self.price_window) + min_price = min(self.price_window) + trend = 1 if self.price_window[-1] > self.price_window[0] else -1 + cumulative_return = ((self.price_window[-1] - self.price_window[0]) / self.price_window[0]) * 100 + + print(f"-----> MA: ${ma:.2f}, StdDev: {std_dev:.2f}, EMA: ${self.ema:.2f}, " + f"Max: ${max_price:.2f}, Min: ${min_price:.2f}") + print(f" Trend: {trend}, Cumulative Return: {cumulative_return:.2f}%, " + f"24h Change: {change_pct:.2f}%\n") + + # Write metrics + self.write_api.write(bucket=INFLUXDB_BUCKET, org=INFLUXDB_ORG, + record=Point("bitcoin_stats") + .field("moving_avg", ma) + .field("std_dev", std_dev) + .field("ema", self.ema) + .field("max", max_price) + .field("min", min_price) + .field("trend", trend) + .field("cumulative_return", cumulative_return) + .field("percent_change_24h", change_pct) + .time(current_time)) + + yield (timestamp, price) + else: + print("API error: unexpected response ->", data) + + except Exception as e: + print("Error:", e) + + time.sleep(self.interval) + + +# === Forecasting Utilities === + +# Fetch historical BTC data from Yahoo Finance +def fetch_historical_data(ticker="BTC-USD", start="2013-01-01"): + df = yf.download(ticker, start=start, progress=False) + df = df.reset_index() + df = df[['Date', 'Close']] + df.columns = ['ds', 'y'] # Required format for NeuralProphet + df['ds'] = pd.to_datetime(df['ds']) + df['y'] = pd.to_numeric(df['y'], errors='coerce') + return df.dropna() + +# Train NeuralProphet model on daily data +def train_neural_prophet_model(df): + model = NeuralProphet(daily_seasonality=True, yearly_seasonality=True) + model.fit(df, freq='D') + return model + +# Generate 1-year forecast using trained model +def make_forecast(model, df, periods=365): + future = model.make_future_dataframe(df, periods=periods) + forecast = model.predict(future) + return forecast + +# Plot forecasted BTC prices +def plot_forecast(model, forecast): + fig = model.plot(forecast, plotting_backend="matplotlib") + plt.suptitle("Bitcoin Price Forecast (Next 1 Year)") + plt.xlabel("Date") + plt.ylabel("Price (USD)") + plt.grid(True) + plt.show() + +# Plot trend and seasonality components +def plot_components(model, forecast): + fig = model.plot_components(forecast, plotting_backend="matplotlib") + plt.tight_layout() + plt.show() diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker-compose.yaml b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker-compose.yaml new file mode 100644 index 000000000..edccca201 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker-compose.yaml @@ -0,0 +1,45 @@ +version: '3.8' + +services: + influxdb: + image: influxdb:2.0 + container_name: influxdb_container + ports: + - "8086:8086" + volumes: + - influxdb_data:/var/lib/influxdb2 + environment: + - DOCKER_INFLUXDB_INIT_MODE=setup + - DOCKER_INFLUXDB_INIT_USERNAME=admin + - DOCKER_INFLUXDB_INIT_PASSWORD=admin123 + - DOCKER_INFLUXDB_INIT_ORG=crypto + - DOCKER_INFLUXDB_INIT_BUCKET=bitcoin_prices + - DOCKER_INFLUXDB_INIT_ADMIN_TOKEN=${INFLUXDB_TOKEN} + networks: + - flink_influx_network # <-- Add this line! + + app: + build: . + container_name: umd_data605_app + ports: + - "8888:8888" + volumes: + - ./:/data + env_file: + - .env + environment: + - INFLUXDB_URL=http://influxdb_container:8086 + - INFLUXDB_TOKEN=${INFLUXDB_TOKEN} + - INFLUXDB_ORG=crypto + - INFLUXDB_BUCKET=bitcoin_prices + networks: + - flink_influx_network + depends_on: + - influxdb + +networks: + flink_influx_network: + driver: bridge + +volumes: + influxdb_data: diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_bash.sh b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_bash.sh new file mode 100755 index 000000000..475a3f3c9 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_bash.sh @@ -0,0 +1,16 @@ +#!/bin/bash -xe + +REPO_NAME=umd_data605 +IMAGE_NAME=umd_data605_template +FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME + +docker image ls $FULL_IMAGE_NAME + +CONTAINER_NAME=$IMAGE_NAME + +docker run --rm -ti \ + --name $CONTAINER_NAME \ + --network=flink_influx_network \ + -p 8888:8888 \ + -v $(pwd):/data \ + $FULL_IMAGE_NAME diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_build.log b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_build.log new file mode 100644 index 000000000..761b3493d --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_build.log @@ -0,0 +1,1212 @@ +#0 building with "default" instance using docker driver + +#1 [internal] load build definition from Dockerfile +#1 transferring dockerfile: 1.39kB done +#1 DONE 0.0s + +#2 [internal] load metadata for docker.io/library/ubuntu:20.04 +#2 DONE 0.0s + +#3 [internal] load .dockerignore +#3 transferring context: 2B done +#3 DONE 0.0s + +#4 [internal] load build context +#4 DONE 0.0s + +#5 [auth] library/ubuntu:pull token for registry-1.docker.io +#5 DONE 0.0s + +#6 [ 1/10] FROM docker.io/library/ubuntu:20.04@sha256:8feb4d8ca5354def3d8fce243717141ce31e2c428701f6682bd2fafe15388214 +#6 resolve docker.io/library/ubuntu:20.04@sha256:8feb4d8ca5354def3d8fce243717141ce31e2c428701f6682bd2fafe15388214 0.3s done +#6 DONE 0.3s + +#6 [ 1/10] FROM docker.io/library/ubuntu:20.04@sha256:8feb4d8ca5354def3d8fce243717141ce31e2c428701f6682bd2fafe15388214 +#6 CACHED + +#4 [internal] load build context +#4 transferring context: 253B done +#4 DONE 0.0s + +#7 [ 2/10] RUN apt-get -y update +#7 0.404 Get:1 http://ports.ubuntu.com/ubuntu-ports focal InRelease [265 kB] +#7 0.862 Get:2 http://ports.ubuntu.com/ubuntu-ports focal-updates InRelease [128 kB] +#7 0.970 Get:3 http://ports.ubuntu.com/ubuntu-ports focal-backports InRelease [128 kB] +#7 1.084 Get:4 http://ports.ubuntu.com/ubuntu-ports focal-security InRelease [128 kB] +#7 1.193 Get:5 http://ports.ubuntu.com/ubuntu-ports focal/multiverse arm64 Packages [139 kB] +#7 1.232 Get:6 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 Packages [1234 kB] +#7 1.497 Get:7 http://ports.ubuntu.com/ubuntu-ports focal/restricted arm64 Packages [1317 B] +#7 1.498 Get:8 http://ports.ubuntu.com/ubuntu-ports focal/universe arm64 Packages [11.1 MB] +#7 2.902 Get:9 http://ports.ubuntu.com/ubuntu-ports focal-updates/multiverse arm64 Packages [14.9 kB] +#7 2.905 Get:10 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 Packages [3744 kB] +#7 3.450 Get:11 http://ports.ubuntu.com/ubuntu-ports focal-updates/restricted arm64 Packages [74.5 kB] +#7 3.628 Get:12 http://ports.ubuntu.com/ubuntu-ports focal-updates/universe arm64 Packages [1511 kB] +#7 4.331 Get:13 http://ports.ubuntu.com/ubuntu-ports focal-backports/main arm64 Packages [54.8 kB] +#7 4.339 Get:14 http://ports.ubuntu.com/ubuntu-ports focal-backports/universe arm64 Packages [27.8 kB] +#7 4.343 Get:15 http://ports.ubuntu.com/ubuntu-ports focal-security/universe arm64 Packages [1212 kB] +#7 4.584 Get:16 http://ports.ubuntu.com/ubuntu-ports focal-security/restricted arm64 Packages [69.0 kB] +#7 4.596 Get:17 http://ports.ubuntu.com/ubuntu-ports focal-security/multiverse arm64 Packages [8098 B] +#7 4.598 Get:18 http://ports.ubuntu.com/ubuntu-ports focal-security/main arm64 Packages [3331 kB] +#7 5.191 Fetched 23.2 MB in 5s (4599 kB/s) +#7 5.191 Reading package lists... +#7 DONE 5.6s + +#8 [ 3/10] RUN apt-get -y upgrade +#8 0.124 Reading package lists... +#8 0.447 Building dependency tree... +#8 0.509 Reading state information... +#8 0.516 Calculating upgrade... +#8 0.578 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. +#8 DONE 0.6s + +#9 [ 4/10] RUN apt install -y --no-install-recommends sudo curl systemctl gnupg git vim +#9 0.098 +#9 0.098 WARNING: apt does not have a stable CLI interface. Use with caution in scripts. +#9 0.098 +#9 0.121 Reading package lists... +#9 0.430 Building dependency tree... +#9 0.489 Reading state information... +#9 0.547 The following additional packages will be installed: +#9 0.547 dirmngr git-man gnupg-l10n gnupg-utils gpg gpg-agent gpg-wks-client +#9 0.547 gpg-wks-server gpgconf gpgsm libasn1-8-heimdal libasound2 libasound2-data +#9 0.547 libassuan0 libbrotli1 libcanberra0 libcurl3-gnutls libcurl4 liberror-perl +#9 0.547 libexpat1 libgdbm-compat4 libgdbm6 libgpm2 libgssapi-krb5-2 +#9 0.547 libgssapi3-heimdal libhcrypto4-heimdal libheimbase1-heimdal +#9 0.547 libheimntlm0-heimdal libhx509-5-heimdal libk5crypto3 libkeyutils1 +#9 0.547 libkrb5-26-heimdal libkrb5-3 libkrb5support0 libksba8 libldap-2.4-2 +#9 0.547 libldap-common libltdl7 libmpdec2 libnghttp2-14 libnpth0 libogg0 libperl5.30 +#9 0.547 libpsl5 libpython3-stdlib libpython3.8 libpython3.8-minimal +#9 0.547 libpython3.8-stdlib libreadline8 libroken18-heimdal librtmp1 libsasl2-2 +#9 0.547 libsasl2-modules-db libsqlite3-0 libssh-4 libssl1.1 libtdb1 libvorbis0a +#9 0.547 libvorbisfile3 libwind0-heimdal mime-support perl perl-modules-5.30 +#9 0.547 pinentry-curses python3 python3-minimal python3.8 python3.8-minimal +#9 0.547 readline-common sound-theme-freedesktop vim-common vim-runtime xxd +#9 0.548 Suggested packages: +#9 0.548 dbus-user-session libpam-systemd pinentry-gnome3 tor gettext-base +#9 0.548 git-daemon-run | git-daemon-sysvinit git-doc git-el git-email git-gui gitk +#9 0.548 gitweb git-cvs git-mediawiki git-svn parcimonie xloadimage scdaemon +#9 0.548 libasound2-plugins alsa-utils libcanberra-gtk0 libcanberra-pulse gdbm-l10n +#9 0.548 gpm krb5-doc krb5-user perl-doc libterm-readline-gnu-perl +#9 0.548 | libterm-readline-perl-perl make libb-debug-perl liblocale-codes-perl +#9 0.548 pinentry-doc python3-doc python3-tk python3-venv python3.8-venv +#9 0.548 python3.8-doc binutils binfmt-support readline-doc tini | dumb-init ctags +#9 0.548 vim-doc vim-scripts +#9 0.548 Recommended packages: +#9 0.548 ca-certificates patch less ssh-client alsa-ucm-conf alsa-topology-conf +#9 0.548 krb5-locales publicsuffix libsasl2-modules file xz-utils netbase +#9 0.630 The following NEW packages will be installed: +#9 0.630 curl dirmngr git git-man gnupg gnupg-l10n gnupg-utils gpg gpg-agent +#9 0.630 gpg-wks-client gpg-wks-server gpgconf gpgsm libasn1-8-heimdal libasound2 +#9 0.630 libasound2-data libassuan0 libbrotli1 libcanberra0 libcurl3-gnutls libcurl4 +#9 0.630 liberror-perl libexpat1 libgdbm-compat4 libgdbm6 libgpm2 libgssapi-krb5-2 +#9 0.630 libgssapi3-heimdal libhcrypto4-heimdal libheimbase1-heimdal +#9 0.630 libheimntlm0-heimdal libhx509-5-heimdal libk5crypto3 libkeyutils1 +#9 0.630 libkrb5-26-heimdal libkrb5-3 libkrb5support0 libksba8 libldap-2.4-2 +#9 0.630 libldap-common libltdl7 libmpdec2 libnghttp2-14 libnpth0 libogg0 libperl5.30 +#9 0.630 libpsl5 libpython3-stdlib libpython3.8 libpython3.8-minimal +#9 0.630 libpython3.8-stdlib libreadline8 libroken18-heimdal librtmp1 libsasl2-2 +#9 0.630 libsasl2-modules-db libsqlite3-0 libssh-4 libssl1.1 libtdb1 libvorbis0a +#9 0.631 libvorbisfile3 libwind0-heimdal mime-support perl perl-modules-5.30 +#9 0.631 pinentry-curses python3 python3-minimal python3.8 python3.8-minimal +#9 0.631 readline-common sound-theme-freedesktop sudo systemctl vim vim-common +#9 0.631 vim-runtime xxd +#9 0.803 0 upgraded, 79 newly installed, 0 to remove and 0 not upgraded. +#9 0.803 Need to get 34.0 MB of archives. +#9 0.803 After this operation, 172 MB of additional disk space will be used. +#9 0.803 Get:1 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libssl1.1 arm64 1.1.1f-1ubuntu2.24 [1159 kB] +#9 1.447 Get:2 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libpython3.8-minimal arm64 3.8.10-0ubuntu1~20.04.18 [717 kB] +#9 1.624 Get:3 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libexpat1 arm64 2.2.9-1ubuntu0.8 [63.0 kB] +#9 1.627 Get:4 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 python3.8-minimal arm64 3.8.10-0ubuntu1~20.04.18 [1829 kB] +#9 1.879 Get:5 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 python3-minimal arm64 3.8.2-0ubuntu2 [23.6 kB] +#9 1.879 Get:6 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 mime-support all 3.64ubuntu1 [30.6 kB] +#9 1.880 Get:7 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libmpdec2 arm64 2.4.2-3 [79.6 kB] +#9 1.883 Get:8 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 readline-common all 8.0-4 [53.5 kB] +#9 1.887 Get:9 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libreadline8 arm64 8.0-4 [123 kB] +#9 1.893 Get:10 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libsqlite3-0 arm64 3.31.1-4ubuntu0.6 [507 kB] +#9 1.953 Get:11 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libpython3.8-stdlib arm64 3.8.10-0ubuntu1~20.04.18 [1650 kB] +#9 2.211 Get:12 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 python3.8 arm64 3.8.10-0ubuntu1~20.04.18 [387 kB] +#9 2.715 Get:13 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libpython3-stdlib arm64 3.8.2-0ubuntu2 [7068 B] +#9 2.717 Get:14 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 python3 arm64 3.8.2-0ubuntu2 [47.6 kB] +#9 2.724 Get:15 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 perl-modules-5.30 all 5.30.0-9ubuntu0.5 [2739 kB] +#9 3.093 Get:16 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libgdbm6 arm64 1.18.1-5 [26.4 kB] +#9 3.093 Get:17 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libgdbm-compat4 arm64 1.18.1-5 [6040 B] +#9 3.093 Get:18 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libperl5.30 arm64 5.30.0-9ubuntu0.5 [3763 kB] +#9 3.236 Get:19 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 perl arm64 5.30.0-9ubuntu0.5 [224 kB] +#9 3.239 Get:20 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 sudo arm64 1.8.31-1ubuntu1.5 [474 kB] +#9 3.248 Get:21 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 xxd arm64 2:8.1.2269-1ubuntu5.32 [49.4 kB] +#9 3.249 Get:22 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 vim-common all 2:8.1.2269-1ubuntu5.32 [84.9 kB] +#9 3.416 Get:23 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libkrb5support0 arm64 1.17-6ubuntu4.9 [30.8 kB] +#9 3.610 Get:24 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libk5crypto3 arm64 1.17-6ubuntu4.9 [80.5 kB] +#9 3.770 Get:25 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libkeyutils1 arm64 1.6-6ubuntu1.1 [10.1 kB] +#9 3.784 Get:26 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libkrb5-3 arm64 1.17-6ubuntu4.9 [312 kB] +#9 3.954 Get:27 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libgssapi-krb5-2 arm64 1.17-6ubuntu4.9 [114 kB] +#9 3.984 Get:28 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libpsl5 arm64 0.21.0-1ubuntu1 [51.3 kB] +#9 4.002 Get:29 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libbrotli1 arm64 1.0.7-6ubuntu0.1 [257 kB] +#9 4.035 Get:30 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libroken18-heimdal arm64 7.7.0+dfsg-1ubuntu1.4 [40.1 kB] +#9 4.043 Get:31 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libasn1-8-heimdal arm64 7.7.0+dfsg-1ubuntu1.4 [150 kB] +#9 4.069 Get:32 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libheimbase1-heimdal arm64 7.7.0+dfsg-1ubuntu1.4 [28.7 kB] +#9 4.077 Get:33 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libhcrypto4-heimdal arm64 7.7.0+dfsg-1ubuntu1.4 [84.7 kB] +#9 4.257 Get:34 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libwind0-heimdal arm64 7.7.0+dfsg-1ubuntu1.4 [47.5 kB] +#9 4.508 Get:35 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libhx509-5-heimdal arm64 7.7.0+dfsg-1ubuntu1.4 [98.9 kB] +#9 4.621 Get:36 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libkrb5-26-heimdal arm64 7.7.0+dfsg-1ubuntu1.4 [192 kB] +#9 4.731 Get:37 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libheimntlm0-heimdal arm64 7.7.0+dfsg-1ubuntu1.4 [14.7 kB] +#9 4.737 Get:38 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libgssapi3-heimdal arm64 7.7.0+dfsg-1ubuntu1.4 [88.4 kB] +#9 4.768 Get:39 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libsasl2-modules-db arm64 2.1.27+dfsg-2ubuntu0.1 [14.9 kB] +#9 4.773 Get:40 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libsasl2-2 arm64 2.1.27+dfsg-2ubuntu0.1 [48.4 kB] +#9 4.782 Get:41 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libldap-common all 2.4.49+dfsg-2ubuntu1.10 [16.5 kB] +#9 4.788 Get:42 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libldap-2.4-2 arm64 2.4.49+dfsg-2ubuntu1.10 [145 kB] +#9 4.814 Get:43 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libnghttp2-14 arm64 1.40.0-1ubuntu0.3 [75.5 kB] +#9 4.828 Get:44 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 librtmp1 arm64 2.4+20151223.gitfa8646d.1-2build1 [53.3 kB] +#9 5.006 Get:45 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libssh-4 arm64 0.9.3-2ubuntu2.5 [160 kB] +#9 5.409 Get:46 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libcurl4 arm64 7.68.0-1ubuntu2.25 [216 kB] +#9 5.516 Get:47 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 curl arm64 7.68.0-1ubuntu2.25 [157 kB] +#9 5.576 Get:48 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libassuan0 arm64 2.5.3-7ubuntu2 [33.1 kB] +#9 5.581 Get:49 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 gpgconf arm64 2.2.19-3ubuntu2.4 [117 kB] +#9 5.605 Get:50 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libksba8 arm64 1.3.5-2ubuntu0.20.04.2 [89.1 kB] +#9 5.621 Get:51 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libnpth0 arm64 1.6-1 [7440 B] +#9 5.622 Get:52 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 dirmngr arm64 2.2.19-3ubuntu2.4 [311 kB] +#9 5.671 Get:53 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libcurl3-gnutls arm64 7.68.0-1ubuntu2.25 [214 kB] +#9 5.699 Get:54 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 liberror-perl all 0.17029-1 [26.5 kB] +#9 5.702 Get:55 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 git-man all 1:2.25.1-1ubuntu3.14 [887 kB] +#9 5.941 Get:56 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 git arm64 1:2.25.1-1ubuntu3.14 [4460 kB] +#9 6.744 Get:57 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 gnupg-l10n all 2.2.19-3ubuntu2.4 [51.9 kB] +#9 6.747 Get:58 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 gnupg-utils arm64 2.2.19-3ubuntu2.4 [442 kB] +#9 6.836 Get:59 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 gpg arm64 2.2.19-3ubuntu2.4 [442 kB] +#9 6.846 Get:60 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 pinentry-curses arm64 1.1.0-3build1 [34.3 kB] +#9 6.848 Get:61 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 gpg-agent arm64 2.2.19-3ubuntu2.4 [216 kB] +#9 6.852 Get:62 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 gpg-wks-client arm64 2.2.19-3ubuntu2.4 [89.4 kB] +#9 6.920 Get:63 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 gpg-wks-server arm64 2.2.19-3ubuntu2.4 [83.2 kB] +#9 6.923 Get:64 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 gpgsm arm64 2.2.19-3ubuntu2.4 [198 kB] +#9 6.928 Get:65 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 gnupg all 2.2.19-3ubuntu2.4 [259 kB] +#9 6.935 Get:66 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libasound2-data all 1.2.2-2.1ubuntu2.5 [20.1 kB] +#9 7.100 Get:67 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libasound2 arm64 1.2.2-2.1ubuntu2.5 [304 kB] +#9 7.578 Get:68 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libltdl7 arm64 2.4.6-14 [37.5 kB] +#9 7.587 Get:69 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libtdb1 arm64 1.4.5-0ubuntu0.20.04.1 [43.2 kB] +#9 7.594 Get:70 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libogg0 arm64 1.3.4-0ubuntu1 [22.9 kB] +#9 7.598 Get:71 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libvorbis0a arm64 1.3.6-2ubuntu1 [79.5 kB] +#9 7.616 Get:72 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libvorbisfile3 arm64 1.3.6-2ubuntu1 [15.3 kB] +#9 7.658 Get:73 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 sound-theme-freedesktop all 0.8-2ubuntu1 [384 kB] +#9 7.759 Get:74 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libcanberra0 arm64 0.30-7ubuntu1 [34.6 kB] +#9 7.761 Get:75 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libgpm2 arm64 1.20.7-5 [14.4 kB] +#9 7.761 Get:76 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libpython3.8 arm64 3.8.10-0ubuntu1~20.04.18 [1492 kB] +#9 7.918 Get:77 http://ports.ubuntu.com/ubuntu-ports focal/universe arm64 systemctl all 1.4.3424-2 [75.5 kB] +#9 8.087 Get:78 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 vim-runtime all 2:8.1.2269-1ubuntu5.32 [5876 kB] +#9 8.925 Get:79 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 vim arm64 2:8.1.2269-1ubuntu5.32 [1138 kB] +#9 9.124 debconf: delaying package configuration, since apt-utils is not installed +#9 9.134 Fetched 34.0 MB in 8s (4053 kB/s) +#9 9.148 Selecting previously unselected package libssl1.1:arm64. +#9 9.148 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 4117 files and directories currently installed.) +#9 9.156 Preparing to unpack .../libssl1.1_1.1.1f-1ubuntu2.24_arm64.deb ... +#9 9.158 Unpacking libssl1.1:arm64 (1.1.1f-1ubuntu2.24) ... +#9 9.214 Selecting previously unselected package libpython3.8-minimal:arm64. +#9 9.214 Preparing to unpack .../libpython3.8-minimal_3.8.10-0ubuntu1~20.04.18_arm64.deb ... +#9 9.215 Unpacking libpython3.8-minimal:arm64 (3.8.10-0ubuntu1~20.04.18) ... +#9 9.258 Selecting previously unselected package libexpat1:arm64. +#9 9.259 Preparing to unpack .../libexpat1_2.2.9-1ubuntu0.8_arm64.deb ... +#9 9.259 Unpacking libexpat1:arm64 (2.2.9-1ubuntu0.8) ... +#9 9.270 Selecting previously unselected package python3.8-minimal. +#9 9.270 Preparing to unpack .../python3.8-minimal_3.8.10-0ubuntu1~20.04.18_arm64.deb ... +#9 9.272 Unpacking python3.8-minimal (3.8.10-0ubuntu1~20.04.18) ... +#9 9.360 Setting up libssl1.1:arm64 (1.1.1f-1ubuntu2.24) ... +#9 9.385 Setting up libpython3.8-minimal:arm64 (3.8.10-0ubuntu1~20.04.18) ... +#9 9.388 Setting up libexpat1:arm64 (2.2.9-1ubuntu0.8) ... +#9 9.390 Setting up python3.8-minimal (3.8.10-0ubuntu1~20.04.18) ... +#9 9.599 Selecting previously unselected package python3-minimal. +#9 9.599 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 4418 files and directories currently installed.) +#9 9.601 Preparing to unpack .../0-python3-minimal_3.8.2-0ubuntu2_arm64.deb ... +#9 9.602 Unpacking python3-minimal (3.8.2-0ubuntu2) ... +#9 9.610 Selecting previously unselected package mime-support. +#9 9.611 Preparing to unpack .../1-mime-support_3.64ubuntu1_all.deb ... +#9 9.611 Unpacking mime-support (3.64ubuntu1) ... +#9 9.621 Selecting previously unselected package libmpdec2:arm64. +#9 9.622 Preparing to unpack .../2-libmpdec2_2.4.2-3_arm64.deb ... +#9 9.623 Unpacking libmpdec2:arm64 (2.4.2-3) ... +#9 9.632 Selecting previously unselected package readline-common. +#9 9.633 Preparing to unpack .../3-readline-common_8.0-4_all.deb ... +#9 9.633 Unpacking readline-common (8.0-4) ... +#9 9.642 Selecting previously unselected package libreadline8:arm64. +#9 9.643 Preparing to unpack .../4-libreadline8_8.0-4_arm64.deb ... +#9 9.643 Unpacking libreadline8:arm64 (8.0-4) ... +#9 9.655 Selecting previously unselected package libsqlite3-0:arm64. +#9 9.656 Preparing to unpack .../5-libsqlite3-0_3.31.1-4ubuntu0.6_arm64.deb ... +#9 9.657 Unpacking libsqlite3-0:arm64 (3.31.1-4ubuntu0.6) ... +#9 9.684 Selecting previously unselected package libpython3.8-stdlib:arm64. +#9 9.684 Preparing to unpack .../6-libpython3.8-stdlib_3.8.10-0ubuntu1~20.04.18_arm64.deb ... +#9 9.685 Unpacking libpython3.8-stdlib:arm64 (3.8.10-0ubuntu1~20.04.18) ... +#9 9.767 Selecting previously unselected package python3.8. +#9 9.767 Preparing to unpack .../7-python3.8_3.8.10-0ubuntu1~20.04.18_arm64.deb ... +#9 9.768 Unpacking python3.8 (3.8.10-0ubuntu1~20.04.18) ... +#9 9.781 Selecting previously unselected package libpython3-stdlib:arm64. +#9 9.781 Preparing to unpack .../8-libpython3-stdlib_3.8.2-0ubuntu2_arm64.deb ... +#9 9.782 Unpacking libpython3-stdlib:arm64 (3.8.2-0ubuntu2) ... +#9 9.789 Setting up python3-minimal (3.8.2-0ubuntu2) ... +#9 9.847 Selecting previously unselected package python3. +#9 9.847 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 4848 files and directories currently installed.) +#9 9.848 Preparing to unpack .../00-python3_3.8.2-0ubuntu2_arm64.deb ... +#9 9.850 Unpacking python3 (3.8.2-0ubuntu2) ... +#9 9.860 Selecting previously unselected package perl-modules-5.30. +#9 9.860 Preparing to unpack .../01-perl-modules-5.30_5.30.0-9ubuntu0.5_all.deb ... +#9 9.861 Unpacking perl-modules-5.30 (5.30.0-9ubuntu0.5) ... +#9 10.01 Selecting previously unselected package libgdbm6:arm64. +#9 10.01 Preparing to unpack .../02-libgdbm6_1.18.1-5_arm64.deb ... +#9 10.02 Unpacking libgdbm6:arm64 (1.18.1-5) ... +#9 10.03 Selecting previously unselected package libgdbm-compat4:arm64. +#9 10.03 Preparing to unpack .../03-libgdbm-compat4_1.18.1-5_arm64.deb ... +#9 10.03 Unpacking libgdbm-compat4:arm64 (1.18.1-5) ... +#9 10.04 Selecting previously unselected package libperl5.30:arm64. +#9 10.04 Preparing to unpack .../04-libperl5.30_5.30.0-9ubuntu0.5_arm64.deb ... +#9 10.04 Unpacking libperl5.30:arm64 (5.30.0-9ubuntu0.5) ... +#9 10.25 Selecting previously unselected package perl. +#9 10.25 Preparing to unpack .../05-perl_5.30.0-9ubuntu0.5_arm64.deb ... +#9 10.25 Unpacking perl (5.30.0-9ubuntu0.5) ... +#9 10.27 Selecting previously unselected package sudo. +#9 10.27 Preparing to unpack .../06-sudo_1.8.31-1ubuntu1.5_arm64.deb ... +#9 10.27 Unpacking sudo (1.8.31-1ubuntu1.5) ... +#9 10.30 Selecting previously unselected package xxd. +#9 10.30 Preparing to unpack .../07-xxd_2%3a8.1.2269-1ubuntu5.32_arm64.deb ... +#9 10.30 Unpacking xxd (2:8.1.2269-1ubuntu5.32) ... +#9 10.31 Selecting previously unselected package vim-common. +#9 10.31 Preparing to unpack .../08-vim-common_2%3a8.1.2269-1ubuntu5.32_all.deb ... +#9 10.31 Unpacking vim-common (2:8.1.2269-1ubuntu5.32) ... +#9 10.32 Selecting previously unselected package libkrb5support0:arm64. +#9 10.32 Preparing to unpack .../09-libkrb5support0_1.17-6ubuntu4.9_arm64.deb ... +#9 10.32 Unpacking libkrb5support0:arm64 (1.17-6ubuntu4.9) ... +#9 10.33 Selecting previously unselected package libk5crypto3:arm64. +#9 10.33 Preparing to unpack .../10-libk5crypto3_1.17-6ubuntu4.9_arm64.deb ... +#9 10.33 Unpacking libk5crypto3:arm64 (1.17-6ubuntu4.9) ... +#9 10.34 Selecting previously unselected package libkeyutils1:arm64. +#9 10.34 Preparing to unpack .../11-libkeyutils1_1.6-6ubuntu1.1_arm64.deb ... +#9 10.34 Unpacking libkeyutils1:arm64 (1.6-6ubuntu1.1) ... +#9 10.35 Selecting previously unselected package libkrb5-3:arm64. +#9 10.35 Preparing to unpack .../12-libkrb5-3_1.17-6ubuntu4.9_arm64.deb ... +#9 10.35 Unpacking libkrb5-3:arm64 (1.17-6ubuntu4.9) ... +#9 10.37 Selecting previously unselected package libgssapi-krb5-2:arm64. +#9 10.37 Preparing to unpack .../13-libgssapi-krb5-2_1.17-6ubuntu4.9_arm64.deb ... +#9 10.37 Unpacking libgssapi-krb5-2:arm64 (1.17-6ubuntu4.9) ... +#9 10.39 Selecting previously unselected package libpsl5:arm64. +#9 10.39 Preparing to unpack .../14-libpsl5_0.21.0-1ubuntu1_arm64.deb ... +#9 10.39 Unpacking libpsl5:arm64 (0.21.0-1ubuntu1) ... +#9 10.40 Selecting previously unselected package libbrotli1:arm64. +#9 10.40 Preparing to unpack .../15-libbrotli1_1.0.7-6ubuntu0.1_arm64.deb ... +#9 10.40 Unpacking libbrotli1:arm64 (1.0.7-6ubuntu0.1) ... +#9 10.42 Selecting previously unselected package libroken18-heimdal:arm64. +#9 10.42 Preparing to unpack .../16-libroken18-heimdal_7.7.0+dfsg-1ubuntu1.4_arm64.deb ... +#9 10.42 Unpacking libroken18-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 10.43 Selecting previously unselected package libasn1-8-heimdal:arm64. +#9 10.43 Preparing to unpack .../17-libasn1-8-heimdal_7.7.0+dfsg-1ubuntu1.4_arm64.deb ... +#9 10.43 Unpacking libasn1-8-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 10.44 Selecting previously unselected package libheimbase1-heimdal:arm64. +#9 10.44 Preparing to unpack .../18-libheimbase1-heimdal_7.7.0+dfsg-1ubuntu1.4_arm64.deb ... +#9 10.44 Unpacking libheimbase1-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 10.45 Selecting previously unselected package libhcrypto4-heimdal:arm64. +#9 10.46 Preparing to unpack .../19-libhcrypto4-heimdal_7.7.0+dfsg-1ubuntu1.4_arm64.deb ... +#9 10.46 Unpacking libhcrypto4-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 10.47 Selecting previously unselected package libwind0-heimdal:arm64. +#9 10.47 Preparing to unpack .../20-libwind0-heimdal_7.7.0+dfsg-1ubuntu1.4_arm64.deb ... +#9 10.47 Unpacking libwind0-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 10.48 Selecting previously unselected package libhx509-5-heimdal:arm64. +#9 10.48 Preparing to unpack .../21-libhx509-5-heimdal_7.7.0+dfsg-1ubuntu1.4_arm64.deb ... +#9 10.48 Unpacking libhx509-5-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 10.49 Selecting previously unselected package libkrb5-26-heimdal:arm64. +#9 10.49 Preparing to unpack .../22-libkrb5-26-heimdal_7.7.0+dfsg-1ubuntu1.4_arm64.deb ... +#9 10.49 Unpacking libkrb5-26-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 10.51 Selecting previously unselected package libheimntlm0-heimdal:arm64. +#9 10.51 Preparing to unpack .../23-libheimntlm0-heimdal_7.7.0+dfsg-1ubuntu1.4_arm64.deb ... +#9 10.51 Unpacking libheimntlm0-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 10.52 Selecting previously unselected package libgssapi3-heimdal:arm64. +#9 10.52 Preparing to unpack .../24-libgssapi3-heimdal_7.7.0+dfsg-1ubuntu1.4_arm64.deb ... +#9 10.52 Unpacking libgssapi3-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 10.53 Selecting previously unselected package libsasl2-modules-db:arm64. +#9 10.53 Preparing to unpack .../25-libsasl2-modules-db_2.1.27+dfsg-2ubuntu0.1_arm64.deb ... +#9 10.53 Unpacking libsasl2-modules-db:arm64 (2.1.27+dfsg-2ubuntu0.1) ... +#9 10.54 Selecting previously unselected package libsasl2-2:arm64. +#9 10.54 Preparing to unpack .../26-libsasl2-2_2.1.27+dfsg-2ubuntu0.1_arm64.deb ... +#9 10.54 Unpacking libsasl2-2:arm64 (2.1.27+dfsg-2ubuntu0.1) ... +#9 10.54 Selecting previously unselected package libldap-common. +#9 10.54 Preparing to unpack .../27-libldap-common_2.4.49+dfsg-2ubuntu1.10_all.deb ... +#9 10.55 Unpacking libldap-common (2.4.49+dfsg-2ubuntu1.10) ... +#9 10.55 Selecting previously unselected package libldap-2.4-2:arm64. +#9 10.55 Preparing to unpack .../28-libldap-2.4-2_2.4.49+dfsg-2ubuntu1.10_arm64.deb ... +#9 10.55 Unpacking libldap-2.4-2:arm64 (2.4.49+dfsg-2ubuntu1.10) ... +#9 10.57 Selecting previously unselected package libnghttp2-14:arm64. +#9 10.57 Preparing to unpack .../29-libnghttp2-14_1.40.0-1ubuntu0.3_arm64.deb ... +#9 10.57 Unpacking libnghttp2-14:arm64 (1.40.0-1ubuntu0.3) ... +#9 10.58 Selecting previously unselected package librtmp1:arm64. +#9 10.58 Preparing to unpack .../30-librtmp1_2.4+20151223.gitfa8646d.1-2build1_arm64.deb ... +#9 10.58 Unpacking librtmp1:arm64 (2.4+20151223.gitfa8646d.1-2build1) ... +#9 10.59 Selecting previously unselected package libssh-4:arm64. +#9 10.59 Preparing to unpack .../31-libssh-4_0.9.3-2ubuntu2.5_arm64.deb ... +#9 10.59 Unpacking libssh-4:arm64 (0.9.3-2ubuntu2.5) ... +#9 10.60 Selecting previously unselected package libcurl4:arm64. +#9 10.60 Preparing to unpack .../32-libcurl4_7.68.0-1ubuntu2.25_arm64.deb ... +#9 10.61 Unpacking libcurl4:arm64 (7.68.0-1ubuntu2.25) ... +#9 10.62 Selecting previously unselected package curl. +#9 10.62 Preparing to unpack .../33-curl_7.68.0-1ubuntu2.25_arm64.deb ... +#9 10.62 Unpacking curl (7.68.0-1ubuntu2.25) ... +#9 10.64 Selecting previously unselected package libassuan0:arm64. +#9 10.64 Preparing to unpack .../34-libassuan0_2.5.3-7ubuntu2_arm64.deb ... +#9 10.64 Unpacking libassuan0:arm64 (2.5.3-7ubuntu2) ... +#9 10.65 Selecting previously unselected package gpgconf. +#9 10.65 Preparing to unpack .../35-gpgconf_2.2.19-3ubuntu2.4_arm64.deb ... +#9 10.65 Unpacking gpgconf (2.2.19-3ubuntu2.4) ... +#9 10.66 Selecting previously unselected package libksba8:arm64. +#9 10.66 Preparing to unpack .../36-libksba8_1.3.5-2ubuntu0.20.04.2_arm64.deb ... +#9 10.66 Unpacking libksba8:arm64 (1.3.5-2ubuntu0.20.04.2) ... +#9 10.67 Selecting previously unselected package libnpth0:arm64. +#9 10.67 Preparing to unpack .../37-libnpth0_1.6-1_arm64.deb ... +#9 10.67 Unpacking libnpth0:arm64 (1.6-1) ... +#9 10.68 Selecting previously unselected package dirmngr. +#9 10.68 Preparing to unpack .../38-dirmngr_2.2.19-3ubuntu2.4_arm64.deb ... +#9 10.69 Unpacking dirmngr (2.2.19-3ubuntu2.4) ... +#9 10.71 Selecting previously unselected package libcurl3-gnutls:arm64. +#9 10.71 Preparing to unpack .../39-libcurl3-gnutls_7.68.0-1ubuntu2.25_arm64.deb ... +#9 10.71 Unpacking libcurl3-gnutls:arm64 (7.68.0-1ubuntu2.25) ... +#9 10.72 Selecting previously unselected package liberror-perl. +#9 10.73 Preparing to unpack .../40-liberror-perl_0.17029-1_all.deb ... +#9 10.73 Unpacking liberror-perl (0.17029-1) ... +#9 10.73 Selecting previously unselected package git-man. +#9 10.73 Preparing to unpack .../41-git-man_1%3a2.25.1-1ubuntu3.14_all.deb ... +#9 10.73 Unpacking git-man (1:2.25.1-1ubuntu3.14) ... +#9 10.78 Selecting previously unselected package git. +#9 10.78 Preparing to unpack .../42-git_1%3a2.25.1-1ubuntu3.14_arm64.deb ... +#9 10.79 Unpacking git (1:2.25.1-1ubuntu3.14) ... +#9 11.03 Selecting previously unselected package gnupg-l10n. +#9 11.03 Preparing to unpack .../43-gnupg-l10n_2.2.19-3ubuntu2.4_all.deb ... +#9 11.03 Unpacking gnupg-l10n (2.2.19-3ubuntu2.4) ... +#9 11.04 Selecting previously unselected package gnupg-utils. +#9 11.04 Preparing to unpack .../44-gnupg-utils_2.2.19-3ubuntu2.4_arm64.deb ... +#9 11.04 Unpacking gnupg-utils (2.2.19-3ubuntu2.4) ... +#9 11.06 Selecting previously unselected package gpg. +#9 11.07 Preparing to unpack .../45-gpg_2.2.19-3ubuntu2.4_arm64.deb ... +#9 11.07 Unpacking gpg (2.2.19-3ubuntu2.4) ... +#9 11.09 Selecting previously unselected package pinentry-curses. +#9 11.09 Preparing to unpack .../46-pinentry-curses_1.1.0-3build1_arm64.deb ... +#9 11.09 Unpacking pinentry-curses (1.1.0-3build1) ... +#9 11.10 Selecting previously unselected package gpg-agent. +#9 11.10 Preparing to unpack .../47-gpg-agent_2.2.19-3ubuntu2.4_arm64.deb ... +#9 11.10 Unpacking gpg-agent (2.2.19-3ubuntu2.4) ... +#9 11.12 Selecting previously unselected package gpg-wks-client. +#9 11.12 Preparing to unpack .../48-gpg-wks-client_2.2.19-3ubuntu2.4_arm64.deb ... +#9 11.12 Unpacking gpg-wks-client (2.2.19-3ubuntu2.4) ... +#9 11.13 Selecting previously unselected package gpg-wks-server. +#9 11.13 Preparing to unpack .../49-gpg-wks-server_2.2.19-3ubuntu2.4_arm64.deb ... +#9 11.13 Unpacking gpg-wks-server (2.2.19-3ubuntu2.4) ... +#9 11.14 Selecting previously unselected package gpgsm. +#9 11.14 Preparing to unpack .../50-gpgsm_2.2.19-3ubuntu2.4_arm64.deb ... +#9 11.14 Unpacking gpgsm (2.2.19-3ubuntu2.4) ... +#9 11.16 Selecting previously unselected package gnupg. +#9 11.16 Preparing to unpack .../51-gnupg_2.2.19-3ubuntu2.4_all.deb ... +#9 11.16 Unpacking gnupg (2.2.19-3ubuntu2.4) ... +#9 11.17 Selecting previously unselected package libasound2-data. +#9 11.17 Preparing to unpack .../52-libasound2-data_1.2.2-2.1ubuntu2.5_all.deb ... +#9 11.17 Unpacking libasound2-data (1.2.2-2.1ubuntu2.5) ... +#9 11.18 Selecting previously unselected package libasound2:arm64. +#9 11.18 Preparing to unpack .../53-libasound2_1.2.2-2.1ubuntu2.5_arm64.deb ... +#9 11.18 Unpacking libasound2:arm64 (1.2.2-2.1ubuntu2.5) ... +#9 11.20 Selecting previously unselected package libltdl7:arm64. +#9 11.20 Preparing to unpack .../54-libltdl7_2.4.6-14_arm64.deb ... +#9 11.21 Unpacking libltdl7:arm64 (2.4.6-14) ... +#9 11.21 Selecting previously unselected package libtdb1:arm64. +#9 11.21 Preparing to unpack .../55-libtdb1_1.4.5-0ubuntu0.20.04.1_arm64.deb ... +#9 11.22 Unpacking libtdb1:arm64 (1.4.5-0ubuntu0.20.04.1) ... +#9 11.22 Selecting previously unselected package libogg0:arm64. +#9 11.22 Preparing to unpack .../56-libogg0_1.3.4-0ubuntu1_arm64.deb ... +#9 11.22 Unpacking libogg0:arm64 (1.3.4-0ubuntu1) ... +#9 11.23 Selecting previously unselected package libvorbis0a:arm64. +#9 11.23 Preparing to unpack .../57-libvorbis0a_1.3.6-2ubuntu1_arm64.deb ... +#9 11.23 Unpacking libvorbis0a:arm64 (1.3.6-2ubuntu1) ... +#9 11.25 Selecting previously unselected package libvorbisfile3:arm64. +#9 11.25 Preparing to unpack .../58-libvorbisfile3_1.3.6-2ubuntu1_arm64.deb ... +#9 11.25 Unpacking libvorbisfile3:arm64 (1.3.6-2ubuntu1) ... +#9 11.25 Selecting previously unselected package sound-theme-freedesktop. +#9 11.25 Preparing to unpack .../59-sound-theme-freedesktop_0.8-2ubuntu1_all.deb ... +#9 11.25 Unpacking sound-theme-freedesktop (0.8-2ubuntu1) ... +#9 11.28 Selecting previously unselected package libcanberra0:arm64. +#9 11.28 Preparing to unpack .../60-libcanberra0_0.30-7ubuntu1_arm64.deb ... +#9 11.28 Unpacking libcanberra0:arm64 (0.30-7ubuntu1) ... +#9 11.29 Selecting previously unselected package libgpm2:arm64. +#9 11.29 Preparing to unpack .../61-libgpm2_1.20.7-5_arm64.deb ... +#9 11.29 Unpacking libgpm2:arm64 (1.20.7-5) ... +#9 11.29 Selecting previously unselected package libpython3.8:arm64. +#9 11.29 Preparing to unpack .../62-libpython3.8_3.8.10-0ubuntu1~20.04.18_arm64.deb ... +#9 11.30 Unpacking libpython3.8:arm64 (3.8.10-0ubuntu1~20.04.18) ... +#9 11.37 Selecting previously unselected package systemctl. +#9 11.37 Preparing to unpack .../63-systemctl_1.4.3424-2_all.deb ... +#9 11.37 Unpacking systemctl (1.4.3424-2) ... +#9 11.38 Selecting previously unselected package vim-runtime. +#9 11.38 Preparing to unpack .../64-vim-runtime_2%3a8.1.2269-1ubuntu5.32_all.deb ... +#9 11.39 Adding 'diversion of /usr/share/vim/vim81/doc/help.txt to /usr/share/vim/vim81/doc/help.txt.vim-tiny by vim-runtime' +#9 11.39 Adding 'diversion of /usr/share/vim/vim81/doc/tags to /usr/share/vim/vim81/doc/tags.vim-tiny by vim-runtime' +#9 11.39 Unpacking vim-runtime (2:8.1.2269-1ubuntu5.32) ... +#9 11.68 Selecting previously unselected package vim. +#9 11.68 Preparing to unpack .../65-vim_2%3a8.1.2269-1ubuntu5.32_arm64.deb ... +#9 11.68 Unpacking vim (2:8.1.2269-1ubuntu5.32) ... +#9 11.74 Setting up libksba8:arm64 (1.3.5-2ubuntu0.20.04.2) ... +#9 11.74 Setting up libkeyutils1:arm64 (1.6-6ubuntu1.1) ... +#9 11.74 Setting up libpsl5:arm64 (0.21.0-1ubuntu1) ... +#9 11.75 Setting up libgpm2:arm64 (1.20.7-5) ... +#9 11.75 Setting up libogg0:arm64 (1.3.4-0ubuntu1) ... +#9 11.75 Setting up perl-modules-5.30 (5.30.0-9ubuntu0.5) ... +#9 11.75 Setting up mime-support (3.64ubuntu1) ... +#9 11.76 Setting up libtdb1:arm64 (1.4.5-0ubuntu0.20.04.1) ... +#9 11.76 Setting up libbrotli1:arm64 (1.0.7-6ubuntu0.1) ... +#9 11.77 Setting up libsqlite3-0:arm64 (3.31.1-4ubuntu0.6) ... +#9 11.77 Setting up libnghttp2-14:arm64 (1.40.0-1ubuntu0.3) ... +#9 11.77 Setting up libnpth0:arm64 (1.6-1) ... +#9 11.77 Setting up libassuan0:arm64 (2.5.3-7ubuntu2) ... +#9 11.77 Setting up libldap-common (2.4.49+dfsg-2ubuntu1.10) ... +#9 11.78 Setting up xxd (2:8.1.2269-1ubuntu5.32) ... +#9 11.78 Setting up libkrb5support0:arm64 (1.17-6ubuntu4.9) ... +#9 11.78 Setting up libsasl2-modules-db:arm64 (2.1.27+dfsg-2ubuntu0.1) ... +#9 11.78 Setting up libasound2-data (1.2.2-2.1ubuntu2.5) ... +#9 11.79 Setting up vim-common (2:8.1.2269-1ubuntu5.32) ... +#9 11.79 Setting up gnupg-l10n (2.2.19-3ubuntu2.4) ... +#9 11.79 Setting up librtmp1:arm64 (2.4+20151223.gitfa8646d.1-2build1) ... +#9 11.79 Setting up libvorbis0a:arm64 (1.3.6-2ubuntu1) ... +#9 11.80 Setting up sudo (1.8.31-1ubuntu1.5) ... +#9 11.80 Setting up libk5crypto3:arm64 (1.17-6ubuntu4.9) ... +#9 11.80 Setting up libltdl7:arm64 (2.4.6-14) ... +#9 11.81 Setting up libsasl2-2:arm64 (2.1.27+dfsg-2ubuntu0.1) ... +#9 11.81 Setting up libroken18-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 11.81 Setting up sound-theme-freedesktop (0.8-2ubuntu1) ... +#9 11.81 Setting up libasound2:arm64 (1.2.2-2.1ubuntu2.5) ... +#9 11.82 Setting up git-man (1:2.25.1-1ubuntu3.14) ... +#9 11.82 Setting up libkrb5-3:arm64 (1.17-6ubuntu4.9) ... +#9 11.82 Setting up libmpdec2:arm64 (2.4.2-3) ... +#9 11.82 Setting up vim-runtime (2:8.1.2269-1ubuntu5.32) ... +#9 11.85 Setting up readline-common (8.0-4) ... +#9 11.85 Setting up libheimbase1-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 11.85 Setting up libgdbm6:arm64 (1.18.1-5) ... +#9 11.86 Setting up pinentry-curses (1.1.0-3build1) ... +#9 11.86 Setting up libasn1-8-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 11.86 Setting up libreadline8:arm64 (8.0-4) ... +#9 11.87 Setting up libhcrypto4-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 11.87 Setting up libvorbisfile3:arm64 (1.3.6-2ubuntu1) ... +#9 11.87 Setting up libwind0-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 11.87 Setting up libgssapi-krb5-2:arm64 (1.17-6ubuntu4.9) ... +#9 11.87 Setting up libgdbm-compat4:arm64 (1.18.1-5) ... +#9 11.88 Setting up libssh-4:arm64 (0.9.3-2ubuntu2.5) ... +#9 11.88 Setting up gpgconf (2.2.19-3ubuntu2.4) ... +#9 11.88 Setting up libperl5.30:arm64 (5.30.0-9ubuntu0.5) ... +#9 11.89 Setting up libpython3.8-stdlib:arm64 (3.8.10-0ubuntu1~20.04.18) ... +#9 11.89 Setting up python3.8 (3.8.10-0ubuntu1~20.04.18) ... +#9 12.14 Setting up gpg (2.2.19-3ubuntu2.4) ... +#9 12.14 Setting up libpython3-stdlib:arm64 (3.8.2-0ubuntu2) ... +#9 12.14 Setting up gnupg-utils (2.2.19-3ubuntu2.4) ... +#9 12.14 Setting up libcanberra0:arm64 (0.30-7ubuntu1) ... +#9 12.15 Setting up gpg-agent (2.2.19-3ubuntu2.4) ... +#9 12.21 Usage: systemctl [options] command [name...] +#9 12.21 +#9 12.21 systemctl: error: no such option: --global +#9 12.21 /usr/bin/deb-systemd-helper: error: systemctl preset failed on gpg-agent-browser.socket: No such file or directory +#9 12.28 Usage: systemctl [options] command [name...] +#9 12.28 +#9 12.28 systemctl: error: no such option: --global +#9 12.28 /usr/bin/deb-systemd-helper: error: systemctl preset failed on gpg-agent-extra.socket: No such file or directory +#9 12.34 Usage: systemctl [options] command [name...] +#9 12.34 +#9 12.34 systemctl: error: no such option: --global +#9 12.34 /usr/bin/deb-systemd-helper: error: systemctl preset failed on gpg-agent-ssh.socket: No such file or directory +#9 12.40 Usage: systemctl [options] command [name...] +#9 12.40 +#9 12.40 systemctl: error: no such option: --global +#9 12.40 /usr/bin/deb-systemd-helper: error: systemctl preset failed on gpg-agent.socket: No such file or directory +#9 12.40 Setting up libhx509-5-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 12.41 Setting up gpgsm (2.2.19-3ubuntu2.4) ... +#9 12.41 Setting up python3 (3.8.2-0ubuntu2) ... +#9 12.44 Setting up perl (5.30.0-9ubuntu0.5) ... +#9 12.45 Setting up libpython3.8:arm64 (3.8.10-0ubuntu1~20.04.18) ... +#9 12.45 Setting up gpg-wks-server (2.2.19-3ubuntu2.4) ... +#9 12.45 Setting up systemctl (1.4.3424-2) ... +#9 12.45 Setting up libkrb5-26-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 12.45 Setting up vim (2:8.1.2269-1ubuntu5.32) ... +#9 12.46 update-alternatives: using /usr/bin/vim.basic to provide /usr/bin/vim (vim) in auto mode +#9 12.46 update-alternatives: using /usr/bin/vim.basic to provide /usr/bin/vimdiff (vimdiff) in auto mode +#9 12.46 update-alternatives: using /usr/bin/vim.basic to provide /usr/bin/rvim (rvim) in auto mode +#9 12.46 update-alternatives: using /usr/bin/vim.basic to provide /usr/bin/rview (rview) in auto mode +#9 12.46 update-alternatives: using /usr/bin/vim.basic to provide /usr/bin/vi (vi) in auto mode +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/da/man1/vi.1.gz because associated file /usr/share/man/da/man1/vim.1.gz (of link group vi) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/de/man1/vi.1.gz because associated file /usr/share/man/de/man1/vim.1.gz (of link group vi) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/fr/man1/vi.1.gz because associated file /usr/share/man/fr/man1/vim.1.gz (of link group vi) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/it/man1/vi.1.gz because associated file /usr/share/man/it/man1/vim.1.gz (of link group vi) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/ja/man1/vi.1.gz because associated file /usr/share/man/ja/man1/vim.1.gz (of link group vi) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/pl/man1/vi.1.gz because associated file /usr/share/man/pl/man1/vim.1.gz (of link group vi) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/ru/man1/vi.1.gz because associated file /usr/share/man/ru/man1/vim.1.gz (of link group vi) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/man1/vi.1.gz because associated file /usr/share/man/man1/vim.1.gz (of link group vi) doesn't exist +#9 12.46 update-alternatives: using /usr/bin/vim.basic to provide /usr/bin/view (view) in auto mode +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/da/man1/view.1.gz because associated file /usr/share/man/da/man1/vim.1.gz (of link group view) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/de/man1/view.1.gz because associated file /usr/share/man/de/man1/vim.1.gz (of link group view) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/fr/man1/view.1.gz because associated file /usr/share/man/fr/man1/vim.1.gz (of link group view) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/it/man1/view.1.gz because associated file /usr/share/man/it/man1/vim.1.gz (of link group view) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/ja/man1/view.1.gz because associated file /usr/share/man/ja/man1/vim.1.gz (of link group view) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/pl/man1/view.1.gz because associated file /usr/share/man/pl/man1/vim.1.gz (of link group view) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/ru/man1/view.1.gz because associated file /usr/share/man/ru/man1/vim.1.gz (of link group view) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/man1/view.1.gz because associated file /usr/share/man/man1/vim.1.gz (of link group view) doesn't exist +#9 12.46 update-alternatives: using /usr/bin/vim.basic to provide /usr/bin/ex (ex) in auto mode +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/da/man1/ex.1.gz because associated file /usr/share/man/da/man1/vim.1.gz (of link group ex) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/de/man1/ex.1.gz because associated file /usr/share/man/de/man1/vim.1.gz (of link group ex) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/fr/man1/ex.1.gz because associated file /usr/share/man/fr/man1/vim.1.gz (of link group ex) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/it/man1/ex.1.gz because associated file /usr/share/man/it/man1/vim.1.gz (of link group ex) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/ja/man1/ex.1.gz because associated file /usr/share/man/ja/man1/vim.1.gz (of link group ex) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/pl/man1/ex.1.gz because associated file /usr/share/man/pl/man1/vim.1.gz (of link group ex) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/ru/man1/ex.1.gz because associated file /usr/share/man/ru/man1/vim.1.gz (of link group ex) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/man1/ex.1.gz because associated file /usr/share/man/man1/vim.1.gz (of link group ex) doesn't exist +#9 12.46 update-alternatives: using /usr/bin/vim.basic to provide /usr/bin/editor (editor) in auto mode +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/da/man1/editor.1.gz because associated file /usr/share/man/da/man1/vim.1.gz (of link group editor) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/de/man1/editor.1.gz because associated file /usr/share/man/de/man1/vim.1.gz (of link group editor) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/fr/man1/editor.1.gz because associated file /usr/share/man/fr/man1/vim.1.gz (of link group editor) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/it/man1/editor.1.gz because associated file /usr/share/man/it/man1/vim.1.gz (of link group editor) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/ja/man1/editor.1.gz because associated file /usr/share/man/ja/man1/vim.1.gz (of link group editor) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/pl/man1/editor.1.gz because associated file /usr/share/man/pl/man1/vim.1.gz (of link group editor) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/ru/man1/editor.1.gz because associated file /usr/share/man/ru/man1/vim.1.gz (of link group editor) doesn't exist +#9 12.46 update-alternatives: warning: skip creation of /usr/share/man/man1/editor.1.gz because associated file /usr/share/man/man1/vim.1.gz (of link group editor) doesn't exist +#9 12.46 Setting up libheimntlm0-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 12.47 Setting up liberror-perl (0.17029-1) ... +#9 12.47 Setting up libgssapi3-heimdal:arm64 (7.7.0+dfsg-1ubuntu1.4) ... +#9 12.47 Setting up libldap-2.4-2:arm64 (2.4.49+dfsg-2ubuntu1.10) ... +#9 12.47 Setting up libcurl3-gnutls:arm64 (7.68.0-1ubuntu2.25) ... +#9 12.48 Setting up dirmngr (2.2.19-3ubuntu2.4) ... +#9 12.54 Usage: systemctl [options] command [name...] +#9 12.54 +#9 12.54 systemctl: error: no such option: --global +#9 12.54 /usr/bin/deb-systemd-helper: error: systemctl preset failed on dirmngr.socket: No such file or directory +#9 12.54 Setting up git (1:2.25.1-1ubuntu3.14) ... +#9 12.55 Setting up libcurl4:arm64 (7.68.0-1ubuntu2.25) ... +#9 12.55 Setting up curl (7.68.0-1ubuntu2.25) ... +#9 12.56 Setting up gpg-wks-client (2.2.19-3ubuntu2.4) ... +#9 12.56 Setting up gnupg (2.2.19-3ubuntu2.4) ... +#9 12.56 Processing triggers for libc-bin (2.31-0ubuntu9.17) ... +#9 DONE 12.7s + +#10 [ 5/10] RUN apt install -y --no-install-recommends python3 python3-pip python3-dev +#10 0.119 +#10 0.119 WARNING: apt does not have a stable CLI interface. Use with caution in scripts. +#10 0.119 +#10 0.137 Reading package lists... +#10 0.451 Building dependency tree... +#10 0.510 Reading state information... +#10 0.566 python3 is already the newest version (3.8.2-0ubuntu2). +#10 0.566 python3 set to manually installed. +#10 0.566 The following additional packages will be installed: +#10 0.566 ca-certificates libc-dev-bin libc6-dev libcrypt-dev libexpat1-dev +#10 0.566 libpython3-dev libpython3.8-dev linux-libc-dev openssl python-pip-whl +#10 0.566 python3-distutils python3-lib2to3 python3-pkg-resources python3-setuptools +#10 0.566 python3-wheel python3.8-dev zlib1g-dev +#10 0.566 Suggested packages: +#10 0.566 glibc-doc manpages-dev python-setuptools-doc +#10 0.566 Recommended packages: +#10 0.566 manpages manpages-dev build-essential +#10 0.613 The following NEW packages will be installed: +#10 0.613 ca-certificates libc-dev-bin libc6-dev libcrypt-dev libexpat1-dev +#10 0.614 libpython3-dev libpython3.8-dev linux-libc-dev openssl python-pip-whl +#10 0.614 python3-dev python3-distutils python3-lib2to3 python3-pip +#10 0.614 python3-pkg-resources python3-setuptools python3-wheel python3.8-dev +#10 0.614 zlib1g-dev +#10 0.784 0 upgraded, 19 newly installed, 0 to remove and 0 not upgraded. +#10 0.784 Need to get 11.4 MB of archives. +#10 0.784 After this operation, 52.7 MB of additional disk space will be used. +#10 0.784 Get:1 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 openssl arm64 1.1.1f-1ubuntu2.24 [600 kB] +#10 1.352 Get:2 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 ca-certificates all 20240203~20.04.1 [159 kB] +#10 1.364 Get:3 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 python3-pkg-resources all 45.2.0-1ubuntu0.2 [130 kB] +#10 1.437 Get:4 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libc-dev-bin arm64 2.31-0ubuntu9.17 [64.2 kB] +#10 1.442 Get:5 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 linux-libc-dev arm64 5.4.0-214.234 [1097 kB] +#10 1.633 Get:6 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libcrypt-dev arm64 1:4.4.10-10ubuntu4 [111 kB] +#10 1.642 Get:7 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libc6-dev arm64 2.31-0ubuntu9.17 [2069 kB] +#10 1.803 Get:8 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libexpat1-dev arm64 2.2.9-1ubuntu0.8 [104 kB] +#10 1.807 Get:9 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 libpython3.8-dev arm64 3.8.10-0ubuntu1~20.04.18 [3764 kB] +#10 1.950 Get:10 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 libpython3-dev arm64 3.8.2-0ubuntu2 [7236 B] +#10 1.950 Get:11 http://ports.ubuntu.com/ubuntu-ports focal-updates/universe arm64 python-pip-whl all 20.0.2-5ubuntu1.11 [1808 kB] +#10 2.151 Get:12 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 zlib1g-dev arm64 1:1.2.11.dfsg-2ubuntu1.5 [154 kB] +#10 2.547 Get:13 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 python3.8-dev arm64 3.8.10-0ubuntu1~20.04.18 [514 kB] +#10 2.729 Get:14 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 python3-lib2to3 all 3.8.10-0ubuntu1~20.04 [76.3 kB] +#10 2.746 Get:15 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 python3-distutils all 3.8.10-0ubuntu1~20.04 [141 kB] +#10 2.765 Get:16 http://ports.ubuntu.com/ubuntu-ports focal/main arm64 python3-dev arm64 3.8.2-0ubuntu2 [1212 B] +#10 2.765 Get:17 http://ports.ubuntu.com/ubuntu-ports focal-updates/main arm64 python3-setuptools all 45.2.0-1ubuntu0.2 [330 kB] +#10 2.812 Get:18 http://ports.ubuntu.com/ubuntu-ports focal-updates/universe arm64 python3-wheel all 0.34.2-1ubuntu0.1 [23.9 kB] +#10 2.814 Get:19 http://ports.ubuntu.com/ubuntu-ports focal-updates/universe arm64 python3-pip all 20.0.2-5ubuntu1.11 [231 kB] +#10 2.910 debconf: delaying package configuration, since apt-utils is not installed +#10 2.920 Fetched 11.4 MB in 2s (5133 kB/s) +#10 2.928 Selecting previously unselected package openssl. +#10 2.928 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 10345 files and directories currently installed.) +#10 2.931 Preparing to unpack .../00-openssl_1.1.1f-1ubuntu2.24_arm64.deb ... +#10 2.932 Unpacking openssl (1.1.1f-1ubuntu2.24) ... +#10 2.968 Selecting previously unselected package ca-certificates. +#10 2.968 Preparing to unpack .../01-ca-certificates_20240203~20.04.1_all.deb ... +#10 2.969 Unpacking ca-certificates (20240203~20.04.1) ... +#10 2.984 Selecting previously unselected package python3-pkg-resources. +#10 2.984 Preparing to unpack .../02-python3-pkg-resources_45.2.0-1ubuntu0.2_all.deb ... +#10 2.985 Unpacking python3-pkg-resources (45.2.0-1ubuntu0.2) ... +#10 2.996 Selecting previously unselected package libc-dev-bin. +#10 2.997 Preparing to unpack .../03-libc-dev-bin_2.31-0ubuntu9.17_arm64.deb ... +#10 2.998 Unpacking libc-dev-bin (2.31-0ubuntu9.17) ... +#10 3.007 Selecting previously unselected package linux-libc-dev:arm64. +#10 3.008 Preparing to unpack .../04-linux-libc-dev_5.4.0-214.234_arm64.deb ... +#10 3.008 Unpacking linux-libc-dev:arm64 (5.4.0-214.234) ... +#10 3.067 Selecting previously unselected package libcrypt-dev:arm64. +#10 3.068 Preparing to unpack .../05-libcrypt-dev_1%3a4.4.10-10ubuntu4_arm64.deb ... +#10 3.069 Unpacking libcrypt-dev:arm64 (1:4.4.10-10ubuntu4) ... +#10 3.080 Selecting previously unselected package libc6-dev:arm64. +#10 3.081 Preparing to unpack .../06-libc6-dev_2.31-0ubuntu9.17_arm64.deb ... +#10 3.081 Unpacking libc6-dev:arm64 (2.31-0ubuntu9.17) ... +#10 3.197 Selecting previously unselected package libexpat1-dev:arm64. +#10 3.198 Preparing to unpack .../07-libexpat1-dev_2.2.9-1ubuntu0.8_arm64.deb ... +#10 3.199 Unpacking libexpat1-dev:arm64 (2.2.9-1ubuntu0.8) ... +#10 3.211 Selecting previously unselected package libpython3.8-dev:arm64. +#10 3.211 Preparing to unpack .../08-libpython3.8-dev_3.8.10-0ubuntu1~20.04.18_arm64.deb ... +#10 3.213 Unpacking libpython3.8-dev:arm64 (3.8.10-0ubuntu1~20.04.18) ... +#10 3.399 Selecting previously unselected package libpython3-dev:arm64. +#10 3.400 Preparing to unpack .../09-libpython3-dev_3.8.2-0ubuntu2_arm64.deb ... +#10 3.400 Unpacking libpython3-dev:arm64 (3.8.2-0ubuntu2) ... +#10 3.406 Selecting previously unselected package python-pip-whl. +#10 3.407 Preparing to unpack .../10-python-pip-whl_20.0.2-5ubuntu1.11_all.deb ... +#10 3.407 Unpacking python-pip-whl (20.0.2-5ubuntu1.11) ... +#10 3.476 Selecting previously unselected package zlib1g-dev:arm64. +#10 3.476 Preparing to unpack .../11-zlib1g-dev_1%3a1.2.11.dfsg-2ubuntu1.5_arm64.deb ... +#10 3.477 Unpacking zlib1g-dev:arm64 (1:1.2.11.dfsg-2ubuntu1.5) ... +#10 3.489 Selecting previously unselected package python3.8-dev. +#10 3.490 Preparing to unpack .../12-python3.8-dev_3.8.10-0ubuntu1~20.04.18_arm64.deb ... +#10 3.491 Unpacking python3.8-dev (3.8.10-0ubuntu1~20.04.18) ... +#10 3.501 Selecting previously unselected package python3-lib2to3. +#10 3.501 Preparing to unpack .../13-python3-lib2to3_3.8.10-0ubuntu1~20.04_all.deb ... +#10 3.502 Unpacking python3-lib2to3 (3.8.10-0ubuntu1~20.04) ... +#10 3.513 Selecting previously unselected package python3-distutils. +#10 3.514 Preparing to unpack .../14-python3-distutils_3.8.10-0ubuntu1~20.04_all.deb ... +#10 3.515 Unpacking python3-distutils (3.8.10-0ubuntu1~20.04) ... +#10 3.547 Selecting previously unselected package python3-dev. +#10 3.548 Preparing to unpack .../15-python3-dev_3.8.2-0ubuntu2_arm64.deb ... +#10 3.549 Unpacking python3-dev (3.8.2-0ubuntu2) ... +#10 3.556 Selecting previously unselected package python3-setuptools. +#10 3.556 Preparing to unpack .../16-python3-setuptools_45.2.0-1ubuntu0.2_all.deb ... +#10 3.557 Unpacking python3-setuptools (45.2.0-1ubuntu0.2) ... +#10 3.579 Selecting previously unselected package python3-wheel. +#10 3.579 Preparing to unpack .../17-python3-wheel_0.34.2-1ubuntu0.1_all.deb ... +#10 3.580 Unpacking python3-wheel (0.34.2-1ubuntu0.1) ... +#10 3.588 Selecting previously unselected package python3-pip. +#10 3.589 Preparing to unpack .../18-python3-pip_20.0.2-5ubuntu1.11_all.deb ... +#10 3.590 Unpacking python3-pip (20.0.2-5ubuntu1.11) ... +#10 3.608 Setting up python3-pkg-resources (45.2.0-1ubuntu0.2) ... +#10 3.674 Setting up linux-libc-dev:arm64 (5.4.0-214.234) ... +#10 3.677 Setting up python3-wheel (0.34.2-1ubuntu0.1) ... +#10 3.722 Setting up libcrypt-dev:arm64 (1:4.4.10-10ubuntu4) ... +#10 3.724 Setting up libc-dev-bin (2.31-0ubuntu9.17) ... +#10 3.726 Setting up openssl (1.1.1f-1ubuntu2.24) ... +#10 3.729 Setting up python3-lib2to3 (3.8.10-0ubuntu1~20.04) ... +#10 3.760 Setting up python3-distutils (3.8.10-0ubuntu1~20.04) ... +#10 3.800 Setting up python3-setuptools (45.2.0-1ubuntu0.2) ... +#10 3.880 Setting up ca-certificates (20240203~20.04.1) ... +#10 4.004 Updating certificates in /etc/ssl/certs... +#10 4.153 146 added, 0 removed; done. +#10 4.161 Setting up libc6-dev:arm64 (2.31-0ubuntu9.17) ... +#10 4.163 Setting up python-pip-whl (20.0.2-5ubuntu1.11) ... +#10 4.165 Setting up libexpat1-dev:arm64 (2.2.9-1ubuntu0.8) ... +#10 4.167 Setting up libpython3.8-dev:arm64 (3.8.10-0ubuntu1~20.04.18) ... +#10 4.170 Setting up python3-pip (20.0.2-5ubuntu1.11) ... +#10 4.243 Setting up zlib1g-dev:arm64 (1:1.2.11.dfsg-2ubuntu1.5) ... +#10 4.245 Setting up libpython3-dev:arm64 (3.8.2-0ubuntu2) ... +#10 4.248 Setting up python3.8-dev (3.8.10-0ubuntu1~20.04.18) ... +#10 4.250 Setting up python3-dev (3.8.2-0ubuntu2) ... +#10 4.252 Processing triggers for ca-certificates (20240203~20.04.1) ... +#10 4.254 Updating certificates in /etc/ssl/certs... +#10 4.368 0 added, 0 removed; done. +#10 4.368 Running hooks in /etc/ca-certificates/update.d... +#10 4.369 done. +#10 DONE 4.4s + +#11 [ 6/10] RUN python3 -m pip install --upgrade pip +#11 0.603 Collecting pip +#11 0.672 Downloading pip-25.0.1-py3-none-any.whl (1.8 MB) +#11 0.789 Installing collected packages: pip +#11 0.789 Attempting uninstall: pip +#11 0.790 Found existing installation: pip 20.0.2 +#11 0.790 Not uninstalling pip at /usr/lib/python3/dist-packages, outside environment /usr +#11 0.790 Can't uninstall 'pip'. No files were found to uninstall. +#11 1.095 Successfully installed pip-25.0.1 +#11 DONE 1.1s + +#12 [ 7/10] RUN pip3 install ipython tornado==6.1 jupyter-client==7.3.2 jupyter-contrib-core jupyter-contrib-nbextensions psycopg2-binary yapf +#12 0.258 Collecting ipython +#12 0.293 Downloading ipython-8.12.3-py3-none-any.whl.metadata (5.7 kB) +#12 0.329 Collecting tornado==6.1 +#12 0.337 Downloading tornado-6.1-cp38-cp38-manylinux2014_aarch64.whl.metadata (2.4 kB) +#12 0.358 Collecting jupyter-client==7.3.2 +#12 0.368 Downloading jupyter_client-7.3.2-py3-none-any.whl.metadata (8.5 kB) +#12 0.384 Collecting jupyter-contrib-core +#12 0.393 Downloading jupyter_contrib_core-0.4.2.tar.gz (17 kB) +#12 0.399 Preparing metadata (setup.py): started +#12 0.465 Preparing metadata (setup.py): finished with status 'done' +#12 0.480 Collecting jupyter-contrib-nbextensions +#12 0.490 Downloading jupyter_contrib_nbextensions-0.7.0.tar.gz (23.5 MB) +#12 1.166 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 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filename=jupyter_contrib_nbextensions-0.7.0-py2.py3-none-any.whl size=23428787 sha256=f35bbbca18cc23b5b38c517e7b4ede7b95e297dfdd51c7ad4286f5c322be51a0 +#12 7.832 Stored in directory: /root/.cache/pip/wheels/1a/60/9f/043697d3cd00df43b23bbf4e722366a2abfa68700b6d8411f4 +#12 7.834 Successfully built jupyter-contrib-core jupyter-contrib-nbextensions +#12 7.908 Installing collected packages: webencodings, wcwidth, pure-eval, ptyprocess, pickleshare, jupyter_highlight_selected_word, ipython_genutils, fastjsonschema, backcall, zipp, websocket-client, typing-extensions, traitlets, tornado, tomli, tinycss2, soupsieve, sniffio, six, Send2Trash, rpds-py, pyzmq, pyyaml, pygments, pycparser, psycopg2-binary, psutil, prompt-toolkit, prometheus-client, platformdirs, pkgutil-resolve-name, pexpect, parso, pandocfilters, packaging, nest-asyncio, markupsafe, lxml, jupyterlab-pygments, idna, executing, exceptiongroup, entrypoints, defusedxml, decorator, debugpy, attrs, asttokens, yapf, terminado, stack-data, referencing, python-dateutil, mistune, matplotlib-inline, jupyter-core, jinja2, jedi, importlib-resources, importlib-metadata, comm, cffi, bleach, beautifulsoup4, anyio, jupyter-client, jsonschema-specifications, ipython, argon2-cffi-bindings, jsonschema, ipykernel, argon2-cffi, nbformat, nbclient, nbconvert, jupyter-server, notebook-shim, nbclassic, notebook, jupyter-contrib-core, jupyter_nbextensions_configurator, jupyter-contrib-nbextensions +#12 10.20 Successfully installed Send2Trash-1.8.3 anyio-3.7.1 argon2-cffi-23.1.0 argon2-cffi-bindings-21.2.0 asttokens-3.0.0 attrs-25.3.0 backcall-0.2.0 beautifulsoup4-4.13.4 bleach-6.1.0 cffi-1.17.1 comm-0.2.2 debugpy-1.8.14 decorator-5.2.1 defusedxml-0.7.1 entrypoints-0.4 exceptiongroup-1.2.2 executing-2.2.0 fastjsonschema-2.21.1 idna-3.10 importlib-metadata-8.5.0 importlib-resources-6.4.5 ipykernel-6.29.5 ipython-8.12.3 ipython_genutils-0.2.0 jedi-0.19.2 jinja2-3.1.6 jsonschema-4.23.0 jsonschema-specifications-2023.12.1 jupyter-client-7.3.2 jupyter-contrib-core-0.4.2 jupyter-contrib-nbextensions-0.7.0 jupyter-core-5.7.2 jupyter-server-1.24.0 jupyter_highlight_selected_word-0.2.0 jupyter_nbextensions_configurator-0.6.4 jupyterlab-pygments-0.3.0 lxml-5.3.2 markupsafe-2.1.5 matplotlib-inline-0.1.7 mistune-3.1.3 nbclassic-1.2.0 nbclient-0.10.1 nbconvert-7.16.6 nbformat-5.10.4 nest-asyncio-1.6.0 notebook-6.5.7 notebook-shim-0.2.4 packaging-25.0 pandocfilters-1.5.1 parso-0.8.4 pexpect-4.9.0 pickleshare-0.7.5 pkgutil-resolve-name-1.3.10 platformdirs-4.3.6 prometheus-client-0.21.1 prompt-toolkit-3.0.51 psutil-7.0.0 psycopg2-binary-2.9.10 ptyprocess-0.7.0 pure-eval-0.2.3 pycparser-2.22 pygments-2.19.1 python-dateutil-2.9.0.post0 pyyaml-6.0.2 pyzmq-26.4.0 referencing-0.35.1 rpds-py-0.20.1 six-1.17.0 sniffio-1.3.1 soupsieve-2.7 stack-data-0.6.3 terminado-0.18.1 tinycss2-1.2.1 tomli-2.2.1 tornado-6.1 traitlets-5.14.3 typing-extensions-4.13.2 wcwidth-0.2.13 webencodings-0.5.1 websocket-client-1.8.0 yapf-0.43.0 zipp-3.20.2 +#12 10.20 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning. +#12 DONE 10.5s + +#13 [ 8/10] RUN mkdir /install +#13 DONE 0.1s + +#14 [ 9/10] ADD version.sh /install/ +#14 DONE 0.0s + +#15 [10/10] RUN /install/version.sh 2>&1 | tee version.log +#15 0.101 # Pytho3 +#15 0.101 Python 3.8.10 +#15 0.102 # pip3 +#15 0.161 pip 25.0.1 from /usr/local/lib/python3.8/dist-packages/pip (python 3.8) +#15 0.170 # jupyter +#15 0.368 Selected Jupyter core packages... +#15 0.368 IPython : 8.12.3 +#15 0.368 ipykernel : 6.29.5 +#15 0.368 ipywidgets : not installed +#15 0.368 jupyter_client : 7.3.2 +#15 0.368 jupyter_core : 5.7.2 +#15 0.368 jupyter_server : 1.24.0 +#15 0.368 jupyterlab : not installed +#15 0.368 nbclient : 0.10.1 +#15 0.368 nbconvert : 7.16.6 +#15 0.368 nbformat : 5.10.4 +#15 0.368 notebook : 6.5.7 +#15 0.368 qtconsole : not installed +#15 0.368 traitlets : 5.14.3 +#15 0.398 # Python packages +#15 0.477 Package Version +#15 0.477 --------------------------------- ----------- +#15 0.478 anyio 3.7.1 +#15 0.478 argon2-cffi 23.1.0 +#15 0.478 argon2-cffi-bindings 21.2.0 +#15 0.478 asttokens 3.0.0 +#15 0.478 attrs 25.3.0 +#15 0.478 backcall 0.2.0 +#15 0.478 beautifulsoup4 4.13.4 +#15 0.478 bleach 6.1.0 +#15 0.478 cffi 1.17.1 +#15 0.479 comm 0.2.2 +#15 0.479 debugpy 1.8.14 +#15 0.479 decorator 5.2.1 +#15 0.479 defusedxml 0.7.1 +#15 0.479 entrypoints 0.4 +#15 0.479 exceptiongroup 1.2.2 +#15 0.479 executing 2.2.0 +#15 0.479 fastjsonschema 2.21.1 +#15 0.480 idna 3.10 +#15 0.480 importlib_metadata 8.5.0 +#15 0.480 importlib_resources 6.4.5 +#15 0.480 ipykernel 6.29.5 +#15 0.480 ipython 8.12.3 +#15 0.480 ipython-genutils 0.2.0 +#15 0.480 jedi 0.19.2 +#15 0.480 Jinja2 3.1.6 +#15 0.480 jsonschema 4.23.0 +#15 0.480 jsonschema-specifications 2023.12.1 +#15 0.481 jupyter-client 7.3.2 +#15 0.481 jupyter-contrib-core 0.4.2 +#15 0.481 jupyter-contrib-nbextensions 0.7.0 +#15 0.481 jupyter_core 5.7.2 +#15 0.481 jupyter-highlight-selected-word 0.2.0 +#15 0.481 jupyter_nbextensions_configurator 0.6.4 +#15 0.481 jupyter-server 1.24.0 +#15 0.481 jupyterlab_pygments 0.3.0 +#15 0.481 lxml 5.3.2 +#15 0.481 MarkupSafe 2.1.5 +#15 0.481 matplotlib-inline 0.1.7 +#15 0.481 mistune 3.1.3 +#15 0.482 nbclassic 1.2.0 +#15 0.482 nbclient 0.10.1 +#15 0.482 nbconvert 7.16.6 +#15 0.482 nbformat 5.10.4 +#15 0.482 nest-asyncio 1.6.0 +#15 0.482 notebook 6.5.7 +#15 0.482 notebook_shim 0.2.4 +#15 0.482 packaging 25.0 +#15 0.482 pandocfilters 1.5.1 +#15 0.482 parso 0.8.4 +#15 0.482 pexpect 4.9.0 +#15 0.482 pickleshare 0.7.5 +#15 0.482 pip 25.0.1 +#15 0.482 pkgutil_resolve_name 1.3.10 +#15 0.483 platformdirs 4.3.6 +#15 0.483 prometheus_client 0.21.1 +#15 0.483 prompt_toolkit 3.0.51 +#15 0.483 psutil 7.0.0 +#15 0.483 psycopg2-binary 2.9.10 +#15 0.483 ptyprocess 0.7.0 +#15 0.483 pure_eval 0.2.3 +#15 0.483 pycparser 2.22 +#15 0.483 Pygments 2.19.1 +#15 0.483 python-dateutil 2.9.0.post0 +#15 0.483 PyYAML 6.0.2 +#15 0.483 pyzmq 26.4.0 +#15 0.484 referencing 0.35.1 +#15 0.484 rpds-py 0.20.1 +#15 0.484 Send2Trash 1.8.3 +#15 0.484 setuptools 45.2.0 +#15 0.484 six 1.17.0 +#15 0.484 sniffio 1.3.1 +#15 0.484 soupsieve 2.7 +#15 0.484 stack-data 0.6.3 +#15 0.484 terminado 0.18.1 +#15 0.484 tinycss2 1.2.1 +#15 0.484 tomli 2.2.1 +#15 0.485 tornado 6.1 +#15 0.485 traitlets 5.14.3 +#15 0.485 typing_extensions 4.13.2 +#15 0.485 wcwidth 0.2.13 +#15 0.485 webencodings 0.5.1 +#15 0.485 websocket-client 1.8.0 +#15 0.485 wheel 0.34.2 +#15 0.485 yapf 0.43.0 +#15 0.485 zipp 3.20.2 +#15 0.496 # mongo +#15 0.496 /install/version.sh: line 11: mongod: command not found +#15 DONE 0.5s + +#16 exporting to image +#16 exporting layers +#16 exporting layers 4.9s done +#16 exporting manifest sha256:9d0c35d21e86a7b8bf527ed9f11b157a62fb3f404fe1b0909612e6bf780fb883 done +#16 exporting config sha256:e587452d8f36e829e66a4fd1ff3115ebb10889ce95888da5a1250194a3c5b6ff done +#16 exporting attestation manifest sha256:bc96a53458543800003ed2e16e61b0253ad4f338ca270764a3a6225ffa16db48 done +#16 exporting manifest list sha256:3ab6e503c598474f4486cd5f1d5e0871c28265a38595730f1c3775fbd13f9e3f done +#16 naming to docker.io/umd_data605/umd_data605_template:latest done +#16 unpacking to docker.io/umd_data605/umd_data605_template:latest +#16 unpacking to docker.io/umd_data605/umd_data605_template:latest 2.1s done +#16 DONE 7.0s diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_build.sh b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_build.sh new file mode 100755 index 000000000..384494217 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_build.sh @@ -0,0 +1,12 @@ +#!/bin/bash -e + +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/docker_common/utils.sh + +REPO_NAME=umd_data605 +IMAGE_NAME=umd_data605_template + +# Build container. +export DOCKER_BUILDKIT=1 +#export DOCKER_BUILDKIT=0 +build_container_image diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_build.version.log b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_build.version.log new file mode 100644 index 000000000..a14ada9f3 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_build.version.log @@ -0,0 +1,109 @@ +# Pytho3 +Python 3.8.10 +# pip3 +pip 25.0.1 from /usr/local/lib/python3.8/dist-packages/pip (python 3.8) +# jupyter +Selected Jupyter core packages... +IPython : 8.12.3 +ipykernel : 6.29.5 +ipywidgets : not installed +jupyter_client : 7.3.2 +jupyter_core : 5.7.2 +jupyter_server : 1.24.0 +jupyterlab : not installed +nbclient : 0.10.1 +nbconvert : 7.16.6 +nbformat : 5.10.4 +notebook : 6.5.7 +qtconsole : not installed +traitlets : 5.14.3 +# Python packages +Package Version +--------------------------------- ----------- +anyio 3.7.1 +argon2-cffi 23.1.0 +argon2-cffi-bindings 21.2.0 +asttokens 3.0.0 +attrs 25.3.0 +backcall 0.2.0 +beautifulsoup4 4.13.4 +bleach 6.1.0 +cffi 1.17.1 +comm 0.2.2 +debugpy 1.8.14 +decorator 5.2.1 +defusedxml 0.7.1 +entrypoints 0.4 +exceptiongroup 1.2.2 +executing 2.2.0 +fastjsonschema 2.21.1 +idna 3.10 +importlib_metadata 8.5.0 +importlib_resources 6.4.5 +ipykernel 6.29.5 +ipython 8.12.3 +ipython-genutils 0.2.0 +jedi 0.19.2 +Jinja2 3.1.6 +jsonschema 4.23.0 +jsonschema-specifications 2023.12.1 +jupyter-client 7.3.2 +jupyter-contrib-core 0.4.2 +jupyter-contrib-nbextensions 0.7.0 +jupyter_core 5.7.2 +jupyter-highlight-selected-word 0.2.0 +jupyter_nbextensions_configurator 0.6.4 +jupyter-server 1.24.0 +jupyterlab_pygments 0.3.0 +lxml 5.3.2 +MarkupSafe 2.1.5 +matplotlib-inline 0.1.7 +mistune 3.1.3 +nbclassic 1.2.0 +nbclient 0.10.1 +nbconvert 7.16.6 +nbformat 5.10.4 +nest-asyncio 1.6.0 +notebook 6.5.7 +notebook_shim 0.2.4 +packaging 25.0 +pandocfilters 1.5.1 +parso 0.8.4 +pexpect 4.9.0 +pickleshare 0.7.5 +pip 25.0.1 +pkgutil_resolve_name 1.3.10 +platformdirs 4.3.6 +prometheus_client 0.21.1 +prompt_toolkit 3.0.51 +psutil 7.0.0 +psycopg2-binary 2.9.10 +ptyprocess 0.7.0 +pure_eval 0.2.3 +pycparser 2.22 +Pygments 2.19.1 +python-dateutil 2.9.0.post0 +PyYAML 6.0.2 +pyzmq 26.4.0 +referencing 0.35.1 +rpds-py 0.20.1 +Send2Trash 1.8.3 +setuptools 45.2.0 +six 1.17.0 +sniffio 1.3.1 +soupsieve 2.7 +stack-data 0.6.3 +terminado 0.18.1 +tinycss2 1.2.1 +tomli 2.2.1 +tornado 6.1 +traitlets 5.14.3 +typing_extensions 4.13.2 +wcwidth 0.2.13 +webencodings 0.5.1 +websocket-client 1.8.0 +wheel 0.34.2 +yapf 0.43.0 +zipp 3.20.2 +# mongo +/data/version.sh: line 11: mongod: command not found diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_clean.sh b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_clean.sh new file mode 100755 index 000000000..42b67ae09 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_clean.sh @@ -0,0 +1,9 @@ +#!/bin/bash -e + +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/tutorial_github_simple/docker_common/utils.sh + +REPO_NAME=umd_data605 +IMAGE_NAME=umd_data605_template + +remove_container_image diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_exec.sh b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_exec.sh new file mode 100755 index 000000000..34c661a96 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_exec.sh @@ -0,0 +1,9 @@ +#!/bin/bash -e + +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/tutorial_github_simple/docker_common/utils.sh + +REPO_NAME=umd_data605 +IMAGE_NAME=umd_data605_template + +exec_container diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_push.sh b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_push.sh new file mode 100755 index 000000000..9078d3a94 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/docker_push.sh @@ -0,0 +1,9 @@ +#!/bin/bash -e + +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/tutorial_github_simple/docker_common/utils.sh + +REPO_NAME=umd_data605 +IMAGE_NAME=umd_data605_template + +push_container_image diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/etc_sudoers b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/etc_sudoers new file mode 120000 index 000000000..37cf95d1d --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/etc_sudoers @@ -0,0 +1 @@ +../../../docker_common/etc_sudoers \ No newline at end of file diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/install_jupyter_extensions.sh b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/install_jupyter_extensions.sh new file mode 120000 index 000000000..6fbea6f28 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/install_jupyter_extensions.sh @@ -0,0 +1 @@ +../../../docker_common/install_jupyter_extensions.sh \ No newline at end of file diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/run_jupyter.sh b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/run_jupyter.sh new file mode 100755 index 000000000..65a765e08 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/run_jupyter.sh @@ -0,0 +1,7 @@ +#!/bin/bash -xe + +jupyter-notebook \ + --port=8888 \ + --no-browser --ip=0.0.0.0 \ + --allow-root \ + --NotebookApp.token='' --NotebookApp.password='' diff --git a/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/version.sh b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/version.sh new file mode 100755 index 000000000..534d5a694 --- /dev/null +++ b/DATA605/Spring2025/projects/TutorTask172_Spring2025_Ingest_and_Analyze_Bitcoin_Prices_Using_Apache_Flink/tutorial_template/docker_data605_style/version.sh @@ -0,0 +1,11 @@ +#!/bin/bash +echo "# Pytho3" +python3 --version +echo "# pip3" +pip3 --version +echo "# jupyter" +jupyter --version +echo "# Python packages" +pip3 list +echo "# mongo" +mongod --version