# This is a version of DockerfileLeanFoundation for ARM
# Some packages from the AMD image are excluded because they are not available on ARM or take too long to build

# Use base system for cleaning up wayward processes
FROM phusion/baseimage:focal-1.0.0

MAINTAINER QuantConnect <contact@quantconnect.com>

# Use baseimage-docker's init system.
CMD ["/sbin/my_init"]

# Install OS Packages:
# Misc tools for running Python.NET and IB inside a headless container.
RUN add-apt-repository ppa:ubuntu-toolchain-r/test && apt-get update \
    && apt-get install -y git libgtk2.0.0 cmake bzip2 curl unzip wget python3-pip python-opengl zlib1g-dev \
    xvfb libxrender1 libxtst6 libxi6 libglib2.0-dev libopenmpi-dev libstdc++6 openmpi-bin \
    r-base pandoc libcurl4-openssl-dev \
    openjdk-11-jdk openjdk-11-jre bbe \
    && apt-get clean && apt-get autoclean && apt-get autoremove --purge -y \
    && rm -rf /var/lib/apt/lists/*

# Install dotnet 6 sdk & runtime
# The .deb packages don't support ARM, the install script does
ENV PATH="/root/.dotnet:${PATH}"
RUN wget https://dot.net/v1/dotnet-install.sh && \
    chmod 777 dotnet-install.sh && \
    ./dotnet-install.sh -c 6.0 && \
    rm dotnet-install.sh
ENV DOTNET_ROOT="/root/.dotnet"

# Set PythonDLL variable for PythonNet
ENV PYTHONNET_PYDLL="/opt/miniconda3/lib/libpython3.8.so"

# Install miniconda
ENV CONDA="Miniconda3-py38_23.1.0-1-Linux-aarch64.sh"
ENV PATH="/opt/miniconda3/bin:${PATH}"
RUN wget -q https://cdn.quantconnect.com/miniconda/${CONDA} && \
    bash ${CONDA} -b -p /opt/miniconda3 && rm -rf ${CONDA}

# Avoid pip install read timeouts
ENV PIP_DEFAULT_TIMEOUT=120

# Install numpy first to avoid it not being resolved when installing libraries that depend on it next
RUN pip install --no-cache-dir numpy==1.23.5

# The list of packages in this image is shorter than the list in the AMD images
# This list only includes packages that can be installed within 2 minutes on ARM
RUN pip install --no-cache-dir      \
    cython==0.29.35                 \
    pandas==1.5.3                   \
    scipy==1.10.1                   \
    numpy==1.23.5                   \
    wrapt==1.14.1                   \
    astropy==5.2.2                  \
    beautifulsoup4==4.12.2          \
    dill==0.3.6                     \
    jsonschema==4.17.3              \
    lxml==4.9.2                     \
    msgpack==1.0.5                  \
    numba==0.56.4                   \
    xarray==2023.1.0                \
    plotly==5.15.0                  \
    jupyterlab==3.4.4               \
    tensorflow==2.12.0              \
    docutils==0.20.1                \
    gensim==4.3.1                   \
    keras==2.12.0                   \
    lightgbm==3.3.5                 \
    mpi4py==3.1.4                   \
    nltk==3.8.1                     \
    graphviz==0.20.1                \
    cmdstanpy==1.1.0                \
    copulae==0.7.8                  \
    featuretools==1.26.0            \
    PuLP==2.7.0                     \
    pymc==5.5.0                     \
    rauth==0.7.3                    \
    scikit-learn==1.2.2             \
    scikit-multiflow==0.5.3         \
    scikit-optimize==0.9.0          \
    aesara==2.9.0                   \
    tsfresh==0.20.1                 \
    tslearn==0.5.3.2                \
    tweepy==4.14.0                  \
    PyWavelets==1.4.1               \
    umap-learn==0.5.3               \
    fastai==2.7.12                  \
    arch==5.6.0                     \
    copulas==0.9.0                  \
    creme==0.6.1                    \
    cufflinks==0.17.3               \
    gym==0.21                       \
    ipywidgets==8.0.6               \
    deap==1.3.3                     \
    cvxpy==1.3.2                    \
    pykalman==0.9.5                 \
    pyro-ppl==1.8.5                 \
    sklearn-json==0.1.0             \
    dtw-python==1.3.0               \
    gluonts==0.13.2                 \
    gplearn==0.4.2                  \
    jax==0.4.12                     \
    pennylane==0.30.0               \
    PennyLane-Lightning==0.31.0     \
    pennylane-qiskit==0.29.0        \
    mplfinance==0.12.9b7            \
    hmmlearn==0.3.0                 \
    ta==0.10.2                      \
    seaborn==0.12.2                 \
    optuna==3.2.0                   \
    findiff==0.9.2                  \
    sktime==0.20.0                  \
    hyperopt==0.2.7                 \
    bayesian-optimization==1.4.3    \
    matplotlib==3.7.1               \
    sdeint==0.3.0                   \
    pandas_market_calendars==4.1.4  \
    ruptures==1.1.8                 \
    simpy==4.0.1                    \
    scikit-learn-extra==0.3.0       \
    ray==2.5.0                      \
    "ray[tune]"==2.5.0              \
    "ray[rllib]"==2.5.0             \
    fastText==0.9.2                 \
    h2o==3.40.0.4                   \
    prophet==1.1.4                  \
    Riskfolio-Lib==4.0.3            \
    torch==2.0.1                    \
    torchvision==0.15.2             \
    ax-platform==0.3.3              \
    alphalens-reloaded==0.4.3       \
    pyfolio-reloaded==0.9.5         \
    altair==5.0.1                   \
    stellargraph==1.2.1             \
    modin==0.22.2                   \
    persim==0.3.1                   \
    ripser==0.6.4                   \
    pydmd==0.4.1.post2306           \
    EMD-signal==1.5.1               \
    spacy==3.5.3                    \
    pandas-ta==0.3.14b              \
    pytorch-ignite==0.4.12          \
    tensorly==0.8.1                 \
    mlxtend==0.22.0                 \
    shap==0.41.0                    \
    lime==0.2.0.1                   \
    mpmath==1.3.0                   \
    polars==0.18.4                  \
    stockstats==0.5.4               \
    QuantStats==0.0.61              \
    hurst==0.0.5                    \
    numerapi==2.14.0                \
    pymdptoolbox==4.0-b3            \
    panel==1.1.1                    \
    hvplot==0.8.4                   \
    py-heat==0.0.6                  \
    py-heat-magic==0.0.2            \
    bokeh==3.1.1                    \
    river==0.14.0                   \
    stumpy==1.11.1                  \
    pyvinecopulib==0.6.2            \
    ijson==3.2.2                    \
    jupyter-resource-usage==0.7.2   \
    injector==0.20.1                \
    openpyxl==3.1.2                 \
    xlrd==2.0.1                     \
    mljar-supervised==1.0.0         \
    dm-tree==0.1.8                  \
    lz4==4.3.2                      \
    ortools==9.6.2534               \
    py_vollib==1.0.1                \
    thundergbm==0.3.17              \
    yellowbrick==1.5                \
    livelossplot==0.5.5             \
    gymnasium==0.26.3               \
    interpret==0.4.2                \
    DoubleML==0.6.3                 \
    jupyter-bokeh==3.0.7            \
    imbalanced-learn==0.10.1        \
    scikeras==0.11.0                \
    openai==0.27.8                  \
    openai[embeddings]==0.27.8      \
    openai[wandb]==0.27.8           \
    lazypredict==0.2.12             \
    fracdiff==0.9.0                 \
    darts==0.24.0                   \
    fastparquet==2023.4.0           \
    tables==3.8.0                   \
    dimod==0.12.3                   \
    dwave-samplers==1.0.0           \
    python-statemachine==2.1.0      \
    pymannkendall==1.4.3            \
    Pyomo==6.6.1                    \
    gpflow==2.8.1                   \
    pyarrow==12.0.1                 \
    dwave-ocean-sdk==6.1.1          \
    chardet==5.1.0                  \
    stable-baselines3==1.8.0        \
    FixedEffectModel==0.0.5         \
    transformers==4.30.2            \
    langchain==0.0.218              \
    tensorflow-ranking==0.5.1       \
    pomegranate==1.0.0              \
    tigramite==5.2.1.8

# Install dwave tool
RUN dwave install --all -y

# Install 'ipopt' solver for 'Pyomo'
RUN conda install -c conda-forge ipopt==3.14.12 \
    && conda clean -y --all

# We install need to install separately else fails to find numpy
RUN pip install --no-cache-dir Riskfolio-Lib==4.4.0 iisignature==0.24

# Install spacy models
RUN python -m spacy download en_core_web_md && python -m spacy download en_core_web_sm

RUN conda install -y -c conda-forge \
    openmpi=4.1.5                   \
    && conda clean -y --all

# Install nltk data
RUN python -m nltk.downloader -d /usr/share/nltk_data punkt && \
    python -m nltk.downloader -d /usr/share/nltk_data vader_lexicon && \
    python -m nltk.downloader -d /usr/share/nltk_data stopwords && \
    python -m nltk.downloader -d /usr/share/nltk_data wordnet

# Install ppscore
RUN wget -q https://cdn.quantconnect.com/ppscore/ppscore-master-ce93fa3.zip && \
    unzip -q ppscore-master-ce93fa3.zip && cd ppscore-master && \
    pip install . && cd .. && rm -rf ppscore-master && rm ppscore-master-ce93fa3.zip

# Install DX Analytics
RUN wget -q https://cdn.quantconnect.com/dx/dx-master-69922c0.zip && \
    unzip -q dx-master-69922c0.zip && cd dx-master && \
    pip install . && cd .. && rm -rf dx-master && rm dx-master-69922c0.zip

# Install Pyrb
RUN wget -q https://cdn.quantconnect.com/pyrb/pyrb-master-250054e.zip && \
    unzip -q pyrb-master-250054e.zip && cd pyrb-master && \
    pip install . && cd .. && rm -rf pyrb-master && rm pyrb-master-250054e.zip

# Install SSM
RUN wget -q https://cdn.quantconnect.com/ssm/ssm-master-646e188.zip && \
    unzip -q ssm-master-646e188.zip && cd ssm-master && \
    pip install . && cd .. && rm -rf ssm-master && rm ssm-master-646e188.zip

# Due to conflicts install 'pomegranate' virtual environment package
RUN python -m venv /Foundation-Pomegranate --system-site-packages && . /Foundation-Pomegranate/bin/activate \
    && pip install --no-cache-dir       \
    pomegranate==0.14.8                 \
    mxnet==1.9.1                        \
    nbeats-keras==1.8.0                 \
    nbeats-pytorch==1.8.0               \
    neuralprophet[live]==0.6.2          \
    && python -m ipykernel install --name=Foundation-Pomegranate \
    && deactivate

RUN echo "{\"argv\":[\"python\",\"-m\",\"ipykernel_launcher\",\"-f\",\"{connection_file}\"],\"display_name\":\"Foundation-Py-Default\",\"language\":\"python\",\"metadata\":{\"debugger\":true}}" > /opt/miniconda3/share/jupyter/kernels/python3/kernel.json

# Install wkhtmltopdf and xvfb to support HTML to PDF conversion of reports
RUN apt-get update && apt install -y xvfb wkhtmltopdf && \
    apt-get clean && apt-get autoclean && apt-get autoremove --purge -y && rm -rf /var/lib/apt/lists/*

# Install fonts for matplotlib
RUN wget -q https://cdn.quantconnect.com/fonts/foundation.zip && unzip -q foundation.zip && rm foundation.zip \
    && mv "lean fonts/"* /usr/share/fonts/truetype/ && rm -rf "lean fonts/" "__MACOSX/"