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Created Taipy Dashboard to display the PSD's based on user input
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‎.gitignore‎

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@@ -11,4 +11,7 @@ __pycache__/
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temp.py
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.DS_Store
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Icon?
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Icon[[:cntrl:]]
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Icon[[:cntrl:]]
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taipy_visualization/__pycache__/
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taipy_visualization/.env
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M2_Ship_Data/

‎3.9‎

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Channels:
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- conda-forge
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- defaults
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Platform: win-64
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Collecting package metadata (repodata.json): ...working... done
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Solving environment: ...working... done
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## Package Plan ##
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environment location: C:\Users\srima\anaconda3\envs\orca_env
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added / updated specs:
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- ffmpeg
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- python
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The following packages will be downloaded:
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package | build
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---------------------------|-----------------
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libmpdec-4.0.0 | hfd05255_1 87 KB conda-forge
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pip-26.0.1 | pyh145f28c_0 1.1 MB conda-forge
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python-3.14.3 |h4b44e0e_101_cp314 17.4 MB conda-forge
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python_abi-3.14 | 8_cp314 7 KB conda-forge
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------------------------------------------------------------
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Total: 18.6 MB
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The following NEW packages will be INSTALLED:
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aom conda-forge/win-64::aom-3.9.1-he0c23c2_0
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bzip2 conda-forge/win-64::bzip2-1.0.8-h0ad9c76_8
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ca-certificates conda-forge/noarch::ca-certificates-2026.1.4-h4c7d964_0
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cairo conda-forge/win-64::cairo-1.18.4-h477c42c_1
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dav1d conda-forge/win-64::dav1d-1.2.1-hcfcfb64_0
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ffmpeg conda-forge/win-64::ffmpeg-8.0.1-gpl_h15a8bbd_712
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font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0
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font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0
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font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0
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font-ttf-ubuntu conda-forge/noarch::font-ttf-ubuntu-0.83-h77eed37_3
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fontconfig conda-forge/win-64::fontconfig-2.15.0-h765892d_1
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fonts-conda-ecosy~ conda-forge/noarch::fonts-conda-ecosystem-1-0
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fonts-conda-forge conda-forge/noarch::fonts-conda-forge-1-hc364b38_1
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freetype conda-forge/win-64::freetype-2.14.1-h57928b3_0
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fribidi conda-forge/win-64::fribidi-1.0.16-hfd05255_0
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gdk-pixbuf conda-forge/win-64::gdk-pixbuf-2.44.5-h1f5b9c4_0
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glslang conda-forge/win-64::glslang-16.2.0-h294ba9c_1
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graphite2 conda-forge/win-64::graphite2-1.3.14-hac47afa_2
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harfbuzz conda-forge/win-64::harfbuzz-12.3.2-h5a1b470_0
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icu conda-forge/win-64::icu-78.2-h637d24d_0
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lame conda-forge/win-64::lame-3.100-hcfcfb64_1003
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lerc conda-forge/win-64::lerc-4.0.0-h6470a55_1
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libbrotlicommon conda-forge/win-64::libbrotlicommon-1.2.0-hfd05255_1
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libbrotlidec conda-forge/win-64::libbrotlidec-1.2.0-hfd05255_1
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libbrotlienc conda-forge/win-64::libbrotlienc-1.2.0-hfd05255_1
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libdeflate conda-forge/win-64::libdeflate-1.25-h51727cc_0
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libexpat conda-forge/win-64::libexpat-2.7.4-hac47afa_0
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libffi conda-forge/win-64::libffi-3.5.2-h3d046cb_0
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libfreetype conda-forge/win-64::libfreetype-2.14.1-h57928b3_0
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libfreetype6 conda-forge/win-64::libfreetype6-2.14.1-hdbac1cb_0
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libglib conda-forge/win-64::libglib-2.86.4-h0c9aed9_0
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libhwy conda-forge/win-64::libhwy-1.3.0-ha71e874_1
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libiconv conda-forge/win-64::libiconv-1.18-hc1393d2_2
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libintl conda-forge/win-64::libintl-0.22.5-h5728263_3
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libjpeg-turbo conda-forge/win-64::libjpeg-turbo-3.1.2-hfd05255_0
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libjxl conda-forge/win-64::libjxl-0.11.2-hf3f85d1_0
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liblzma conda-forge/win-64::liblzma-5.8.2-hfd05255_0
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libmpdec conda-forge/win-64::libmpdec-4.0.0-hfd05255_1
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libogg conda-forge/win-64::libogg-1.3.5-h2466b09_1
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libopus conda-forge/win-64::libopus-1.6.1-h6a83c73_0
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libpng conda-forge/win-64::libpng-1.6.55-h7351971_0
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librsvg conda-forge/win-64::librsvg-2.60.0-hd5e4115_1
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libsqlite conda-forge/win-64::libsqlite-3.51.2-hf5d6505_0
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libtiff conda-forge/win-64::libtiff-4.7.1-h8f73337_1
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libusb conda-forge/win-64::libusb-1.0.29-h1839187_0
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libvorbis conda-forge/win-64::libvorbis-1.3.7-h5112557_2
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libvulkan-loader conda-forge/win-64::libvulkan-loader-1.4.341.0-h477610d_0
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libwebp-base conda-forge/win-64::libwebp-base-1.6.0-h4d5522a_0
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libxml2 conda-forge/win-64::libxml2-2.15.1-h779ef1b_1
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libxml2-16 conda-forge/win-64::libxml2-16-2.15.1-h3cfd58e_1
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libzlib conda-forge/win-64::libzlib-1.3.1-h2466b09_2
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openh264 conda-forge/win-64::openh264-2.6.0-hb17fa0b_0
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openssl conda-forge/win-64::openssl-3.6.1-hf411b9b_1
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pango conda-forge/win-64::pango-1.56.4-h03d888a_0
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pcre2 conda-forge/win-64::pcre2-10.47-hd2b5f0e_0
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pip conda-forge/noarch::pip-26.0.1-pyh145f28c_0
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pixman conda-forge/win-64::pixman-0.46.4-h5112557_1
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python conda-forge/win-64::python-3.14.3-h4b44e0e_101_cp314
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python_abi conda-forge/noarch::python_abi-3.14-8_cp314
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sdl2 conda-forge/win-64::sdl2-2.32.56-h5112557_0
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sdl3 conda-forge/win-64::sdl3-3.4.0-h5112557_0
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shaderc conda-forge/win-64::shaderc-2025.5-h8fa7867_1
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spirv-tools conda-forge/win-64::spirv-tools-2026.1-h49e36cd_0
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svt-av1 conda-forge/win-64::svt-av1-4.0.1-hac47afa_0
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tk conda-forge/win-64::tk-8.6.13-h6ed50ae_3
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tzdata conda-forge/noarch::tzdata-2025c-hc9c84f9_1
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ucrt conda-forge/win-64::ucrt-10.0.26100.0-h57928b3_0
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vc conda-forge/win-64::vc-14.3-h41ae7f8_34
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vc14_runtime conda-forge/win-64::vc14_runtime-14.44.35208-h818238b_34
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vcomp14 conda-forge/win-64::vcomp14-14.44.35208-h818238b_34
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vs2015_runtime conda-forge/win-64::vs2015_runtime-14.44.35208-h38c0c73_34
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x264 conda-forge/win-64::x264-1!164.3095-h8ffe710_2
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x265 conda-forge/win-64::x265-3.5-h2d74725_3
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zstd conda-forge/win-64::zstd-1.5.7-h534d264_6
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Downloading and Extracting Packages: ...working...python-3.14.3 | 17.4 MB | | 0%
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pip-26.0.1 | 1.1 MB | | 0% 
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libmpdec-4.0.0 | 87 KB | | 0% 
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python_abi-3.14 | 7 KB | | 0% 
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libmpdec-4.0.0 | 87 KB | #8 | 18% 
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python_abi-3.14 | 7 KB | ########## | 100% python-3.14.3 | 17.4 MB | | 0%
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python_abi-3.14 | 7 KB | ########## | 100% 
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pip-26.0.1 | 1.1 MB | 1 | 1% 
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libmpdec-4.0.0 | 87 KB | ########## | 100% 
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python_abi-3.14 | 7 KB | ########## | 100% 
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libmpdec-4.0.0 | 87 KB | ########## | 100% 
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libmpdec-4.0.0 | 87 KB | ########## | 100% python-3.14.3 | 17.4 MB | 1 | 1%
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pip-26.0.1 | 1.1 MB | ##3 | 24% python-3.14.3 | 17.4 MB | 4 | 4%
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pip-26.0.1 | 1.1 MB | #########4 | 94% 
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pip-26.0.1 | 1.1 MB | ########## | 100% python-3.14.3 | 17.4 MB | #8 | 18% python-3.14.3 | 17.4 MB | ###6 | 37% python-3.14.3 | 17.4 MB | #####4 | 55% python-3.14.3 | 17.4 MB | #######3 | 73% python-3.14.3 | 17.4 MB | #########4 | 95% python-3.14.3 | 17.4 MB | ########## | 100%
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pip-26.0.1 | 1.1 MB | ########## | 100% python-3.14.3 | 17.4 MB | ########## | 100%
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 done
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Preparing transaction: - \ | / - done
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Verifying transaction: | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done
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Executing transaction: / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \
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| / - \ | / - \ | / - \ | / - \
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| / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | done
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#
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# To activate this environment, use
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#
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# $ conda activate orca_env
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#
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# To deactivate an active environment, use
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#
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# $ conda deactivate
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‎requirements.txt‎

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plotly==6.5.0
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altair<5
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pytest
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pytest-asyncio
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pytest-asyncio
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taipy
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polars
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geopandas

‎taipy_visualization/data_utils.py‎

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import pandas as pd
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import polars as pl
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import datetime as dt
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import geopandas as gpd
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from datetime import datetime
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from dotenv import load_dotenv
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load_dotenv()
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s3_path = "s3://acoustic-sandbox/ambient-sound-analysis/data_2.0/psd/"
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def _day_prefix(hydrophone, year, month, day):
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return (
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"s3://acoustic-sandbox/ambient-sound-analysis/data_2.0/psd/"
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f"hydrophone={hydrophone}/year={year}/month={int(month):02d}/day={int(day):02d}/"
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)
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def get_available_hours_map(month_str, day_str, hydrophone, year):
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month = int(month_str)
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day = int(day_str)
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year = int(year)
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day_prefix = _day_prefix(hydrophone, year, month, day)
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try:
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lf = pl.scan_parquet(
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day_prefix,
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hive_partitioning=True,
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storage_options={'aws_region': 'us-west-2'}
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)
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start_time = dt.datetime(year, month, day, 0, 0, 0)
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end_time = dt.datetime(year, month, day, 23, 0, 0)
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hours = (
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lf.select(
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pl.col("__index_level_0__").dt.hour().alias("hour")
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)
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.filter(
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pl.col("hour").is_between(start_time.hour, end_time.hour)
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)
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.unique()
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.sort("hour")
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.collect()
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.get_column("hour")
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.to_list()
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)
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formatted_hours = [f"{h:02d}:00 to {h+1:02d}:00" for h in hours]
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return formatted_hours
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except Exception as e:
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print(f"An error occured\n: {e}")
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return []
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def load_ship_data(shp_path):
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"""
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Loads Radar ship tracks, filters by validity, and formats columns
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for the Plotly spectrogram overlay.
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"""
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try:
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# Load the shapefile
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gdf = gpd.read_file(shp_path)
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gdf['entry_time'] = pd.to_datetime(gdf['sdate'] + ' ' + gdf['stime'])
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gdf['exit_time'] = pd.to_datetime(gdf['ldate'] + ' ' + gdf['ltime'])
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# Filter for valid tracks (Confidence >= 0.5 or has AIS association)
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gdf['valid'] = (gdf['confidence'] >= 0.5) | (gdf['assoc_id'].notna())
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gdf = gdf[gdf['valid'] & gdf['geometry'].notna()].sort_values('entry_time')
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# Rename distance to closest_approach_km
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if 'distance' in gdf.columns:
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gdf = gdf.rename(columns={'distance': 'closest_approach_km'})
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# Convert to standard Pandas DataFrame (drop geometry)
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df = pd.DataFrame(gdf.drop(columns=['geometry'], errors='ignore'))
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return df
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except Exception as e:
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print(f"Error loading Shapefile: {e}")
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return pd.DataFrame()
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def fetch_acoustic_data(date, start_time, end_time, hydrophone="orcasound_lab"):
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"""
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This function will fetch all the parquet files for a given day within the start and end time range.
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These will then be sorted by index, concatenated, and sent as a dictionary
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"""
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year = date.year
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month = date.month
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day = date.day
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prefix = _day_prefix(hydrophone, year, month, day)
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psd_df = pl.scan_parquet(
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prefix,
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hive_partitioning=True,
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storage_options={'aws_region': 'us-west-2'}
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)
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try:
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pq_files = (
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psd_df
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.filter(
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(pl.col("__index_level_0__").is_between(start_time, end_time))
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).collect()
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)
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return pq_files
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except Exception:
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print("Could not find data in the selected time range")
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return pl.DataFrame()
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# print(fetch_acoustic_data(datetime(2026, 2, 7), datetime(2026, 2, 7, 0, 0, 0), datetime(2026, 2, 7, 23, 0, 0)))

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