update README

README.md

@@ -1,5 +1,22 @@
 # Training of AI Surrogate Models
 
-- run `git lfs pull` to get the data from the large file storage
-- create conda environment with `conda env create -f environment.yml`
+This repository contains the current experiments for training AI models that attempt to predict the chemistry component of POET and is structued as follows:
+
+```
+└── dataset
+    └── Barite_50_Data_training.h5
+    └── barite_50_4_corner.h5
+└── doc
+└── results
+└── src
+    └── POET_Training.ipynb
+    └── convert_data.jl
+    └── optuna_runs.py
+    └── preprocessing.py
+```
+
+The datasets in `datasets` must first be pulled via `git lfs pull` to get the data from the large file storage.
+A conda environment can then be set up with the packages contained in environment.yml with `conda env create -f environment.yml`
+
+The `preprocessing.py` file defines all the necessary steps for preprocessing as well as the keras models used. The actual training and additional explanations then take place in `POET_Training.ipynb`.
 

convert_barite.jl

@@ -1,88 +0,0 @@
-#!/usr/bin/env julia
-
-# qs_read = []
-
-
-# # find all the files in 'barite_out'
-# files = readdir("barite_out"; join=true)
-
-# # remove files which do not have the extension '.qs2' and contains 'iter'
-# files = filter(x -> occursin(r".*\.qs2", x) && occursin(r"iter", x), files)
-
-# # remove first entry as it is iteration 0
-# files = files[2:end]
-
-# test1 = qs_read(files[1])
-
-# @rput test1
-
-# R"test1 <- test1$C"
-
-# @rget test1
-
-# check if ARGS contains 2 elements
-if length(ARGS) != 2
-    println("Usage: julia convert.jl <directory> <output_file_name>.h5")
-    exit(1)
-end
-
-to_read_dir = ARGS[1]
-output_file_name = ARGS[2] * ".h5"
-
-# check if the directory exists
-if !isdir(to_read_dir)
-    println("The directory \"$to_read_dir\" does not exist")
-    exit(1)
-end
-
-using HDF5, RCall, DataFrames
-@rlibrary qs2
-
-# List all .rds files starting with "iter" in a given directory
-qs_files = filter(x -> occursin(r".*\.qs2", x) && occursin(r"iter", x), readdir(to_read_dir; join=true))[2:end]
-
-df_design = DataFrame()
-df_result = DataFrame()
-
-for file in qs_files
-    # Load the RDS file
-    data = qs_read(file)
-
-    # get basename of the file
-    basename = split(file, "/")[end]
-
-    # get the iteration number by splitting the basename and parse the second element
-    iteration = parse(Int, split(split(basename, "_")[2], ".")[1])
-
-    @rput data
-
-    R"transport <- data$T"
-    R"chemistry <- data$C"
-
-    @rget transport
-    @rget chemistry
-
-    # Add iteration number to the DataFrame
-    transport.iteration = fill(iteration, size(transport, 1))
-    chemistry.iteration = fill(iteration, size(chemistry, 1))
-
-    # Append the DataFrame to the big DataFrame
-    append!(df_design, transport)
-    append!(df_result, chemistry)
-end
-
-# remove ID, Barite_p1, Celestite_p1 columns
-df_design = df_design[:, Not([:ID, :Barite_p1, :Celestite_p1])]
-df_result = df_result[:, Not([:ID, :Barite_p1, :Celestite_p1])]
-
-
-h5open(output_file_name, "w") do fid
-    group_in = create_group(fid, "design")
-    group_out = create_group(fid, "result")
-
-    group_in["names"] = names(df_design)
-    group_in["data", compress=9] = Matrix(df_design)
-
-    group_out["names"] = names(df_result)
-    group_out["data", compress=9] = Matrix(df_result)
-end

convert_data.jl

@@ -1,60 +0,0 @@
-using HDF5
-using RData
-
-using DataFrames
-
-# Load Training Data 
-# train_data = load("Barite_50_Data.rds")
-
-# training_h5_name = "Barite_50_Data.h5"
-# h5open(training_h5_name, "w") do fid
-#     for key in keys(train_data)
-#         group = create_group(fid, key)
-#         group["names"] = names(train_data[key])
-#         group["data", compress=3] = Matrix(train_data[key])
-#         # group = create_group(fid, key)
-#         # grou["names"] = coln
-#     end
-# end
-
-# List all .rds files starting with "iter" in a given directory
-rds_files = filter(x -> startswith(x, "iter"), readdir("barite_out/"))
-
-# remove "iter_0.rds" from the list 
-rds_files = rds_files[2:end]
-
-big_df_in = DataFrame()
-big_df_out = DataFrame()
-
-for rds_file in rds_files
-    # Load the RDS file
-    data = load("barite_out/$rds_file")
-    # Convert the data to a DataFrame
-    df_T = DataFrame(data["T"])
-    df_C = DataFrame(data["C"])
-    # Append the DataFrame to the big DataFrame
-    append!(big_df_in, df_T)
-    append!(big_df_out, df_C)
-end
-
-# remove ID, Barite_p1, Celestite_p1 columns
-big_df_in = big_df_in[:, Not([:ID, :Barite_p1, :Celestite_p1])]
-big_df_out = big_df_out[:, Not([:ID, :Barite_p1, :Celestite_p1])]
-
-inference_h5_name = "Barite_50_Data_inference.h5"
-h5open(inference_h5_name, "w") do fid
-    fid["names"] = names(big_df_in)
-    fid["data", compress=9] = Matrix(big_df_in)
-end
-
-training_h5_name = "Barite_50_Data_training.h5"
-h5open(training_h5_name, "w") do fid
-    group_in = create_group(fid, "design")
-    group_out = create_group(fid, "result")
-
-    group_in["names"] = names(big_df_in)
-    group_in["data", compress=9] = Matrix(big_df_in)
-
-    group_out["names"] = names(big_df_out)
-    group_out["data", compress=9] = Matrix(big_df_out)
-end

doc/measurement_plan.md

@@ -3,19 +3,11 @@
 ### Parameters to optimize
 
 
-
-
-
-
-
 ### Saved models
 
 `./results/model_large_standardization.keras`: Trained on `barite_50_4_corner.h5` dataset with extended Loss function (Huber loss with mass balance) and **standardized data**
 
 
-
-
-
 ### Experiments
 
 | **Experiment** | **Dataset**           | **Model** | **Lossfunction** | **Activation** | **Preprocessing** |

environment.yaml

@@ -0,0 +1,248 @@
+name: training
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=5.1=1_gnu
+  - absl-py=2.1.0=py311h06a4308_0
+  - alembic=1.13.3=py311h06a4308_0
+  - anyio=4.6.2=py311h06a4308_0
+  - argon2-cffi=21.3.0=pyhd3eb1b0_0
+  - argon2-cffi-bindings=21.2.0=py311h5eee18b_1
+  - asttokens=2.0.5=pyhd3eb1b0_0
+  - astunparse=1.6.3=py_0
+  - async-lru=2.0.4=py311h06a4308_0
+  - attrs=24.3.0=py311h06a4308_0
+  - babel=2.11.0=py311h06a4308_0
+  - beautifulsoup4=4.12.3=py311h06a4308_0
+  - blas=1.0=mkl
+  - bleach=6.2.0=py311h06a4308_0
+  - bottleneck=1.4.2=py311hf4808d0_0
+  - brotli=1.0.9=h5eee18b_9
+  - brotli-bin=1.0.9=h5eee18b_9
+  - brotli-python=1.0.9=py311h6a678d5_9
+  - bzip2=1.0.8=h5eee18b_6
+  - c-ares=1.19.1=h5eee18b_0
+  - ca-certificates=2024.12.31=h06a4308_0
+  - certifi=2025.1.31=py311h06a4308_0
+  - cffi=1.17.1=py311h1fdaa30_1
+  - charset-normalizer=3.3.2=pyhd3eb1b0_0
+  - colorlog=5.0.1=py311h06a4308_1
+  - comm=0.2.1=py311h06a4308_0
+  - contourpy=1.3.1=py311hdb19cb5_0
+  - cycler=0.11.0=pyhd3eb1b0_0
+  - cyrus-sasl=2.1.28=h52b45da_1
+  - dbus=1.13.18=hb2f20db_0
+  - debugpy=1.8.11=py311h6a678d5_0
+  - decorator=5.1.1=pyhd3eb1b0_0
+  - defusedxml=0.7.1=pyhd3eb1b0_0
+  - executing=0.8.3=pyhd3eb1b0_0
+  - expat=2.6.4=h6a678d5_0
+  - flatbuffers=24.3.25=h6a678d5_0
+  - fontconfig=2.14.1=h55d465d_3
+  - fonttools=4.51.0=py311h5eee18b_0
+  - freetype=2.12.1=h4a9f257_0
+  - gast=0.5.3=pyhd3eb1b0_0
+  - giflib=5.2.2=h5eee18b_0
+  - glib=2.78.4=h6a678d5_0
+  - glib-tools=2.78.4=h6a678d5_0
+  - google-pasta=0.2.0=pyhd3eb1b0_0
+  - greenlet=3.1.1=py311h6a678d5_0
+  - grpcio=1.62.2=py311h6a678d5_0
+  - gst-plugins-base=1.14.1=h6a678d5_1
+  - gstreamer=1.14.1=h5eee18b_1
+  - h11=0.14.0=py311h06a4308_0
+  - h5py=3.12.1=py311hc0802c4_0
+  - hdf5=1.12.1=h2b7332f_3
+  - httpcore=1.0.2=py311h06a4308_0
+  - httpx=0.27.0=py311h06a4308_0
+  - icu=73.1=h6a678d5_0
+  - idna=3.7=py311h06a4308_0
+  - imbalanced-learn=0.12.3=py311h06a4308_1
+  - intel-openmp=2023.1.0=hdb19cb5_46306
+  - ipykernel=6.29.5=py311h06a4308_0
+  - ipython=8.30.0=py311h06a4308_0
+  - ipywidgets=8.1.5=py311h06a4308_0
+  - jedi=0.19.2=py311h06a4308_0
+  - jinja2=3.1.4=py311h06a4308_1
+  - joblib=1.4.2=py311h06a4308_0
+  - jpeg=9e=h5eee18b_3
+  - json5=0.9.25=py311h06a4308_0
+  - jsonschema=4.23.0=py311h06a4308_0
+  - jsonschema-specifications=2023.7.1=py311h06a4308_0
+  - jupyter=1.0.0=py311h06a4308_9
+  - jupyter-lsp=2.2.0=py311h06a4308_0
+  - jupyter_client=8.6.0=py311h06a4308_0
+  - jupyter_console=6.6.3=py311h06a4308_0
+  - jupyter_core=5.7.2=py311h06a4308_0
+  - jupyter_events=0.10.0=py311h06a4308_0
+  - jupyter_server=2.14.1=py311h06a4308_0
+  - jupyter_server_terminals=0.4.4=py311h06a4308_1
+  - jupyterlab=4.2.5=py311h06a4308_0
+  - jupyterlab_pygments=0.1.2=py_0
+  - jupyterlab_server=2.27.3=py311h06a4308_0
+  - jupyterlab_widgets=3.0.13=py311h06a4308_0
+  - keras=3.6.0=py311h06a4308_0
+  - kiwisolver=1.4.4=py311h6a678d5_0
+  - krb5=1.20.1=h143b758_1
+  - lcms2=2.16=hb9589c4_0
+  - ld_impl_linux-64=2.40=h12ee557_0
+  - lerc=4.0.0=h6a678d5_0
+  - libabseil=20240116.2=cxx17_h6a678d5_0
+  - libbrotlicommon=1.0.9=h5eee18b_9
+  - libbrotlidec=1.0.9=h5eee18b_9
+  - libbrotlienc=1.0.9=h5eee18b_9
+  - libclang=14.0.6=default_hc6dbbc7_2
+  - libclang13=14.0.6=default_he11475f_2
+  - libcups=2.4.2=h2d74bed_1
+  - libcurl=8.11.1=hc9e6f67_0
+  - libdeflate=1.22=h5eee18b_0
+  - libedit=3.1.20230828=h5eee18b_0
+  - libev=4.33=h7f8727e_1
+  - libffi=3.4.4=h6a678d5_1
+  - libgcc-ng=11.2.0=h1234567_1
+  - libgfortran-ng=11.2.0=h00389a5_1
+  - libgfortran5=11.2.0=h1234567_1
+  - libglib=2.78.4=hdc74915_0
+  - libgomp=11.2.0=h1234567_1
+  - libgrpc=1.62.2=h2d74bed_0
+  - libiconv=1.16=h5eee18b_3
+  - libllvm14=14.0.6=hecde1de_4
+  - libnghttp2=1.57.0=h2d74bed_0
+  - libpng=1.6.39=h5eee18b_0
+  - libpq=17.2=hdbd6064_0
+  - libprotobuf=4.25.3=he621ea3_0
+  - libsodium=1.0.18=h7b6447c_0
+  - libssh2=1.11.1=h251f7ec_0
+  - libstdcxx-ng=11.2.0=h1234567_1
+  - libtiff=4.5.1=hffd6297_1
+  - libuuid=1.41.5=h5eee18b_0
+  - libwebp-base=1.3.2=h5eee18b_1
+  - libxcb=1.15=h7f8727e_0
+  - libxkbcommon=1.0.1=h097e994_2
+  - libxml2=2.13.5=hfdd30dd_0
+  - lz4-c=1.9.4=h6a678d5_1
+  - mako=1.2.3=py311h06a4308_0
+  - markdown=3.4.1=py311h06a4308_0
+  - markdown-it-py=2.2.0=py311h06a4308_1
+  - markupsafe=2.1.3=py311h5eee18b_1
+  - matplotlib=3.10.0=py311h06a4308_0
+  - matplotlib-base=3.10.0=py311hbfdbfaf_0
+  - matplotlib-inline=0.1.6=py311h06a4308_0
+  - mdurl=0.1.0=py311h06a4308_0
+  - mistune=2.0.4=py311h06a4308_0
+  - mkl=2023.1.0=h213fc3f_46344
+  - mkl-service=2.4.0=py311h5eee18b_2
+  - mkl_fft=1.3.11=py311h5eee18b_0
+  - mkl_random=1.2.8=py311ha02d727_0
+  - ml_dtypes=0.4.0=py311ha02d727_0
+  - mysql=8.4.0=h29a9f33_1
+  - namex=0.0.7=py311h06a4308_0
+  - nbclient=0.8.0=py311h06a4308_0
+  - nbconvert=7.16.4=py311h06a4308_0
+  - nbformat=5.10.4=py311h06a4308_0
+  - ncurses=6.4=h6a678d5_0
+  - nest-asyncio=1.6.0=py311h06a4308_0
+  - notebook=7.2.2=py311h06a4308_1
+  - notebook-shim=0.2.3=py311h06a4308_0
+  - numexpr=2.10.1=py311h3c60e43_0
+  - numpy=1.26.4=py311h08b1b3b_0
+  - numpy-base=1.26.4=py311hf175353_0
+  - openjpeg=2.5.2=he7f1fd0_0
+  - openldap=2.6.4=h42fbc30_0
+  - openssl=3.0.15=h5eee18b_0
+  - opt_einsum=3.3.0=pyhd3eb1b0_1
+  - optree=0.12.1=py311hdb19cb5_0
+  - optuna=4.2.1=pyhd8ed1ab_0
+  - overrides=7.4.0=py311h06a4308_0
+  - packaging=24.2=py311h06a4308_0
+  - pandas=2.2.3=py311h6a678d5_0
+  - pandocfilters=1.5.0=pyhd3eb1b0_0
+  - parso=0.8.4=py311h06a4308_0
+  - pcre2=10.42=hebb0a14_1
+  - pexpect=4.8.0=pyhd3eb1b0_3
+  - pillow=11.0.0=py311hcea889d_1
+  - pip=24.2=py311h06a4308_0
+  - platformdirs=3.10.0=py311h06a4308_0
+  - ply=3.11=py311h06a4308_0
+  - prometheus_client=0.21.0=py311h06a4308_0
+  - prompt-toolkit=3.0.43=py311h06a4308_0
+  - prompt_toolkit=3.0.43=hd3eb1b0_0
+  - protobuf=4.25.3=py311he36ed58_1
+  - psutil=5.9.0=py311h5eee18b_1
+  - ptyprocess=0.7.0=pyhd3eb1b0_2
+  - pure_eval=0.2.2=pyhd3eb1b0_0
+  - pycparser=2.21=pyhd3eb1b0_0
+  - pygments=2.15.1=py311h06a4308_1
+  - pyparsing=3.2.0=py311h06a4308_0
+  - pyqt=5.15.10=py311h6a678d5_0
+  - pyqt5-sip=12.13.0=py311h5eee18b_0
+  - pysocks=1.7.1=py311h06a4308_0
+  - python=3.11.11=he870216_0
+  - python-dateutil=2.9.0post0=py311h06a4308_2
+  - python-fastjsonschema=2.20.0=py311h06a4308_0
+  - python-flatbuffers=24.3.25=py311h06a4308_0
+  - python-json-logger=3.2.1=py311h06a4308_0
+  - python-tzdata=2023.3=pyhd3eb1b0_0
+  - pytz=2024.1=py311h06a4308_0
+  - pyyaml=6.0.2=py311h5eee18b_0
+  - pyzmq=26.2.0=py311h6a678d5_0
+  - qt-main=5.15.2=hb6262e9_11
+  - qtconsole=5.6.0=py311h06a4308_0
+  - qtpy=2.4.1=py311h06a4308_0
+  - re2=2022.04.01=h295c915_0
+  - readline=8.2=h5eee18b_0
+  - referencing=0.30.2=py311h06a4308_0
+  - requests=2.32.3=py311h06a4308_1
+  - rfc3339-validator=0.1.4=py311h06a4308_0
+  - rfc3986-validator=0.1.1=py311h06a4308_0
+  - rich=13.9.4=py311h06a4308_0
+  - rpds-py=0.22.3=py311h4aa5aa6_0
+  - scikit-learn=1.5.2=py311h6a678d5_0
+  - scipy=1.14.1=py311h08b1b3b_0
+  - seaborn=0.13.2=py311h06a4308_1
+  - send2trash=1.8.2=py311h06a4308_1
+  - setuptools=75.1.0=py311h06a4308_0
+  - sip=6.7.12=py311h6a678d5_1
+  - six=1.16.0=pyhd3eb1b0_1
+  - snappy=1.2.1=h6a678d5_0
+  - sniffio=1.3.0=py311h06a4308_0
+  - soupsieve=2.5=py311h06a4308_0
+  - sqlalchemy=2.0.37=py311h00e1ef3_0
+  - sqlite=3.45.3=h5eee18b_0
+  - stack_data=0.2.0=pyhd3eb1b0_0
+  - tbb=2021.8.0=hdb19cb5_0
+  - tensorboard=2.17.0=py311h06a4308_0
+  - tensorboard-data-server=0.7.0=py311h52d8a92_1
+  - tensorflow=2.17.0=cpu_py311hbca4264_0
+  - tensorflow-base=2.17.0=cpu_py311hb07566e_0
+  - tensorflow-estimator=2.17.0=cpu_py311hfedf350_0
+  - termcolor=2.1.0=py311h06a4308_0
+  - terminado=0.17.1=py311h06a4308_0
+  - threadpoolctl=3.5.0=py311h92b7b1e_0
+  - tinycss2=1.2.1=py311h06a4308_0
+  - tk=8.6.14=h39e8969_0
+  - tornado=6.4.2=py311h5eee18b_0
+  - tqdm=4.67.1=py311h92b7b1e_0
+  - traitlets=5.14.3=py311h06a4308_0
+  - typing-extensions=4.12.2=py311h06a4308_0
+  - typing_extensions=4.12.2=py311h06a4308_0
+  - tzdata=2024b=h04d1e81_0
+  - unicodedata2=15.1.0=py311h5eee18b_1
+  - urllib3=2.2.3=py311h06a4308_0
+  - wcwidth=0.2.5=pyhd3eb1b0_0
+  - webencodings=0.5.1=py311h06a4308_1
+  - websocket-client=1.8.0=py311h06a4308_0
+  - werkzeug=3.0.6=py311h06a4308_0
+  - wheel=0.44.0=py311h06a4308_0
+  - widgetsnbextension=4.0.13=py311h06a4308_0
+  - wrapt=1.17.0=py311h5eee18b_0
+  - xz=5.4.6=h5eee18b_1
+  - yaml=0.2.5=h7b6447c_0
+  - zeromq=4.3.5=h6a678d5_0
+  - zlib=1.2.13=h5eee18b_1
+  - zstd=1.5.6=hc292b87_0
+  - pip:
+      - smogn==0.1.2
+prefix: /home/signer/bin/miniconda3/envs/training

environment.yml

@@ -1,162 +0,0 @@
-name: ai
-channels:
-  - conda-forge
-  - defaults
-  - https://repo.anaconda.com/pkgs/main
-  - https://repo.anaconda.com/pkgs/r
-dependencies:
-  - absl-py=2.1.0=py311hca03da5_0
-  - appnope=0.1.4=pyhd8ed1ab_1
-  - asttokens=3.0.0=pyhd8ed1ab_1
-  - astunparse=1.6.3=py_0
-  - blas=1.0=openblas
-  - bottleneck=1.4.2=py311hb9f6ed7_0
-  - brotli=1.0.9=h80987f9_9
-  - brotli-bin=1.0.9=h80987f9_9
-  - brotli-python=1.0.9=py311h313beb8_9
-  - bzip2=1.0.8=h80987f9_6
-  - c-ares=1.34.4=h5505292_0
-  - ca-certificates=2024.12.31=hca03da5_0
-  - cached-property=1.5.2=py_0
-  - certifi=2024.12.14=py311hca03da5_0
-  - charset-normalizer=3.3.2=pyhd3eb1b0_0
-  - comm=0.2.2=pyhd8ed1ab_1
-  - contourpy=1.3.1=py311h48ca7d4_0
-  - cycler=0.11.0=pyhd3eb1b0_0
-  - debugpy=1.8.11=py311h155a34a_0
-  - decorator=5.1.1=pyhd8ed1ab_1
-  - exceptiongroup=1.2.2=pyhd8ed1ab_1
-  - executing=2.1.0=pyhd8ed1ab_1
-  - flatbuffers=24.3.25=h313beb8_0
-  - fonttools=4.51.0=py311h80987f9_0
-  - freetype=2.12.1=hadb7bae_2
-  - gast=0.5.3=pyhd3eb1b0_0
-  - giflib=5.2.2=h80987f9_0
-  - google-pasta=0.2.0=pyhd3eb1b0_0
-  - grpcio=1.65.5=py311hc367efa_0
-  - h5py=3.12.1=nompi_py311h5dd25b7_103
-  - hdf5=1.14.4=nompi_ha698983_105
-  - icu=75.1=hfee45f7_0
-  - idna=3.7=py311hca03da5_0
-  - importlib-metadata=8.5.0=pyha770c72_1
-  - ipykernel=6.29.5=pyh57ce528_0
-  - ipython=8.31.0=pyh707e725_0
-  - jedi=0.19.2=pyhd8ed1ab_1
-  - joblib=1.4.2=py311hca03da5_0
-  - jupyter_client=8.6.3=pyhd8ed1ab_1
-  - jupyter_core=5.7.2=pyh31011fe_1
-  - keras=3.8.0=pyh753f3f9_0
-  - kiwisolver=1.4.4=py311h313beb8_0
-  - krb5=1.21.3=hf3e1bf2_0
-  - lcms2=2.16=ha0e7c42_0
-  - lerc=4.0.0=h313beb8_0
-  - libabseil=20240722.0=cxx17_h07bc746_4
-  - libaec=1.1.3=h313beb8_0
-  - libbrotlicommon=1.0.9=h80987f9_9
-  - libbrotlidec=1.0.9=h80987f9_9
-  - libbrotlienc=1.0.9=h80987f9_9
-  - libcurl=8.11.1=h73640d1_0
-  - libcxx=19.1.6=ha82da77_1
-  - libdeflate=1.23=hec38601_0
-  - libedit=3.1.20230828=h80987f9_0
-  - libev=4.33=h1a28f6b_1
-  - libexpat=2.6.4=h286801f_0
-  - libffi=3.4.4=hca03da5_1
-  - libgfortran=5.0.0=13_2_0_hd922786_3
-  - libgfortran5=13.2.0=hf226fd6_3
-  - libgrpc=1.65.5=h3d9cf25_0
-  - libjpeg-turbo=3.0.3=h80987f9_0
-  - liblzma=5.6.3=h39f12f2_1
-  - libnghttp2=1.64.0=h6d7220d_0
-  - libopenblas=0.3.21=h269037a_0
-  - libpng=1.6.45=h3783ad8_0
-  - libprotobuf=5.27.5=h53f8970_2
-  - libre2-11=2024.07.02=h07bc746_2
-  - libsodium=1.0.20=h99b78c6_0
-  - libsqlite=3.47.2=h3f77e49_0
-  - libssh2=1.11.1=h9cc3647_0
-  - libtiff=4.7.0=h551f018_3
-  - libwebp-base=1.5.0=h2471fea_0
-  - libxcb=1.17.0=hdb1d25a_0
-  - libzlib=1.3.1=h8359307_2
-  - llvm-openmp=14.0.6=hc6e5704_0
-  - lz4-c=1.9.4=h313beb8_1
-  - markdown=3.4.1=py311hca03da5_0
-  - markdown-it-py=2.2.0=py311hca03da5_1
-  - markupsafe=2.1.3=py311h80987f9_1
-  - matplotlib=3.10.0=py311hca03da5_0
-  - matplotlib-base=3.10.0=py311h7ef442a_0
-  - matplotlib-inline=0.1.7=pyhd8ed1ab_1
-  - mdurl=0.1.0=py311hca03da5_0
-  - ml_dtypes=0.4.0=py311h7aedaa7_0
-  - namex=0.0.7=py311hca03da5_0
-  - ncurses=6.5=h5e97a16_2
-  - nest-asyncio=1.6.0=pyhd8ed1ab_1
-  - numexpr=2.10.1=py311h5d9532f_0
-  - numpy=1.26.4=py311he598dae_0
-  - numpy-base=1.26.4=py311hfbfe69c_0
-  - openjpeg=2.5.3=h8a3d83b_0
-  - openssl=3.4.0=h81ee809_1
-  - opt_einsum=3.3.0=pyhd3eb1b0_1
-  - optree=0.12.1=py311h48ca7d4_0
-  - packaging=24.2=py311hca03da5_0
-  - pandas=2.2.3=py311hcf29cfe_0
-  - parso=0.8.4=pyhd8ed1ab_1
-  - pexpect=4.9.0=pyhd8ed1ab_1
-  - pickleshare=0.7.5=pyhd8ed1ab_1004
-  - pillow=11.1.0=py311hb9ba9e9_0
-  - pip=24.2=py311hca03da5_0
-  - platformdirs=4.3.6=pyhd8ed1ab_1
-  - prompt-toolkit=3.0.48=pyha770c72_1
-  - protobuf=5.27.5=py311h3f08180_0
-  - psutil=6.1.1=py311h917b07b_0
-  - pthread-stubs=0.3=h1a28f6b_1
-  - ptyprocess=0.7.0=pyhd8ed1ab_1
-  - pure_eval=0.2.3=pyhd8ed1ab_1
-  - pygments=2.15.1=py311hca03da5_1
-  - pyparsing=3.2.0=py311hca03da5_0
-  - pysocks=1.7.1=py311hca03da5_0
-  - python=3.11.11=hc22306f_1_cpython
-  - python-dateutil=2.9.0.post0=pyhff2d567_1
-  - python-flatbuffers=24.3.25=py311hca03da5_0
-  - python-tzdata=2023.3=pyhd3eb1b0_0
-  - python_abi=3.11=5_cp311
-  - pytz=2024.1=py311hca03da5_0
-  - pyzmq=26.2.0=py311h730b646_3
-  - re2=2024.07.02=h6589ca4_2
-  - readline=8.2=h1a28f6b_0
-  - requests=2.32.3=py311hca03da5_1
-  - rich=13.9.4=py311hca03da5_0
-  - scikit-learn=1.5.2=py311h313beb8_0
-  - scipy=1.14.1=py311hac8794a_0
-  - setuptools=75.1.0=py311hca03da5_0
-  - six=1.16.0=pyhd3eb1b0_1
-  - snappy=1.2.1=h313beb8_0
-  - sqlite=3.47.2=hd7222ec_0
-  - stack_data=0.6.3=pyhd8ed1ab_1
-  - tensorboard=2.17.1=pyhd8ed1ab_0
-  - tensorboard-data-server=0.7.0=py311ha6e5c4f_1
-  - tensorflow=2.17.0=cpu_py311h9d3d1e9_3
-  - tensorflow-base=2.17.0=cpu_py311ha270cad_3
-  - tensorflow-estimator=2.17.0=cpu_py311h935fadc_3
-  - termcolor=2.1.0=py311hca03da5_0
-  - threadpoolctl=3.5.0=py311hb6e6a13_0
-  - tk=8.6.13=h5083fa2_1
-  - tornado=6.4.2=py311h917b07b_0
-  - traitlets=5.14.3=pyhd8ed1ab_1
-  - typing-extensions=4.12.2=py311hca03da5_0
-  - typing_extensions=4.12.2=py311hca03da5_0
-  - tzdata=2024b=h04d1e81_0
-  - unicodedata2=15.1.0=py311h80987f9_1
-  - urllib3=2.2.3=py311hca03da5_0
-  - wcwidth=0.2.13=pyhd8ed1ab_1
-  - werkzeug=3.0.6=py311hca03da5_0
-  - wheel=0.44.0=py311hca03da5_0
-  - wrapt=1.17.0=py311h80987f9_0
-  - xorg-libxau=1.0.12=h5505292_0
-  - xorg-libxdmcp=1.1.5=hd74edd7_0
-  - xz=5.4.6=h80987f9_1
-  - zeromq=4.3.5=hc1bb282_7
-  - zipp=3.21.0=pyhd8ed1ab_1
-  - zlib=1.3.1=h8359307_2
-  - zstd=1.5.6=hb46c0d2_0