diff --git a/good_standard_metagv0.csv b/good_standard_metagv0.csv
new file mode 100644
index 00000000..3e5e7bba
--- /dev/null
+++ b/good_standard_metagv0.csv
@@ -0,0 +1,38 @@
+[Header],,,,,,,,
+IEMFileVersion,4,,,,,,,
+SheetType,tellseq_metag,,,,,,,
+SheetVersion,10,,,,,,,
+Investigator Name,Knight,,,,,,,
+Experiment Name,RKL_experiment,,,,,,,
+Date,2025-08-14,,,,,,,
+Workflow,GenerateFASTQ,,,,,,,
+Application,FASTQ Only,,,,,,,
+Assay,Metagenomic,,,,,,,
+Description,,,,,,,,
+Chemistry,Default,,,,,,,
+,,,,,,,,
+[Reads],,,,,,,,
+151,,,,,,,,
+151,,,,,,,,
+,,,,,,,,
+[Settings],,,,,,,,
+ReverseComplement,0,,,,,,,
+,,,,,,,,
+[Data],,,,,,,,
+Sample_ID,Sample_Name,Sample_Plate,well_id_384,barcode_id,Sample_Project,Well_description,Lane,
+sample_1,sample.1,A1,False,C501,MyProject_99999,A1.sample.1.False,1,
+sample_2,sample.2,A2,False,C509,MyProject_99999,A2.sample.2.False,1,
+sample_3,sample.3,A3,False,C520,MyProject_99999,A3.sample.3.False,1,
+,,,,,,,,
+[Bioinformatics],,,,,,,,
+Sample_Project,QiitaID,BarcodesAreRC,ForwardAdapter,ReverseAdapter,HumanFiltering,library_construction_protocol,experiment_design_description,contains_replicates
+MyProject_99999,99999,True,AACC,GGTT,False,some protocol,some description,False
+,,,,,,,,
+[Contact],,,,,,,,
+Sample_Project,Email,,,,,,,
+MyProject_99999,foo@bar.org,,,,,,,
+,,,,,,,,
+[SampleContext],,,,,,,,
+sample_name,sample_type,primary_qiita_study,secondary_qiita_studies,,,,,
+sample.3,control blank,99999,,,,,,
+,,,,,,,,
diff --git a/notebooks/tellseq_C_equal_volume_pooling.ipynb b/notebooks/tellseq_C_equal_volume_pooling.ipynb
index 875c6ffa..df6a74a6 100644
--- a/notebooks/tellseq_C_equal_volume_pooling.ipynb
+++ b/notebooks/tellseq_C_equal_volume_pooling.ipynb
@@ -2,10 +2,48 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: 2026-02-02T18:10:04.968935+09:00\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.9.23\n",
+      "IPython version      : 8.18.1\n",
+      "\n",
+      "metapool    : 0+untagged.232.g2b2eaec\n",
+      "sample_sheet: 0.13.0\n",
+      "openpyxl    : 3.1.5\n",
+      "\n",
+      "Compiler    : Clang 18.1.8 \n",
+      "OS          : Darwin\n",
+      "Release     : 23.4.0\n",
+      "Machine     : arm64\n",
+      "Processor   : arm\n",
+      "CPU cores   : 8\n",
+      "Architecture: 64bit\n",
+      "\n",
+      "Hostname: seosonghuis-MacBook-Air.local\n",
+      "\n",
+      "qiita_client: 0.1.0.dev0\n",
+      "yaml        : 6.0.2\n",
+      "pandas      : 2.3.1\n",
+      "metapool    : 0+untagged.232.g2b2eaec\n",
+      "re          : 2.2.1\n",
+      "seaborn     : 0.13.2\n",
+      "numpy       : 2.0.2\n",
+      "json        : 2.0.9\n",
+      "matplotlib  : 3.9.4\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
+    "### This cell has `parameters` tag\n",
     "import pandas as pd\n",
     "%reload_ext watermark\n",
     "%matplotlib inline\n",
@@ -27,13 +65,266 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# packages in environment at /opt/anaconda3/envs/metapool:\n",
+      "#\n",
+      "# Name                    Version                   Build  Channel\n",
+      "ansicolors                1.1.8                    pypi_0    pypi\n",
+      "anyio                     4.10.0             pyhe01879c_0    conda-forge\n",
+      "appnope                   0.1.4              pyhd8ed1ab_1    conda-forge\n",
+      "argon2-cffi               25.1.0             pyhd8ed1ab_0    conda-forge\n",
+      "argon2-cffi-bindings      25.1.0           py39he7485ab_0    conda-forge\n",
+      "arrow                     1.3.0              pyhd8ed1ab_1    conda-forge\n",
+      "asttokens                 3.0.0              pyhd8ed1ab_1    conda-forge\n",
+      "async-lru                 2.0.5              pyh29332c3_0    conda-forge\n",
+      "attrs                     25.3.0             pyh71513ae_0    conda-forge\n",
+      "babel                     2.17.0             pyhd8ed1ab_0    conda-forge\n",
+      "beautifulsoup4            4.13.4             pyha770c72_0    conda-forge\n",
+      "biom-format               2.1.16                   pypi_0    pypi\n",
+      "bleach                    6.2.0              pyh29332c3_4    conda-forge\n",
+      "bleach-with-css           6.2.0                h82add2a_4    conda-forge\n",
+      "brotli                    1.1.0                h5505292_3    conda-forge\n",
+      "brotli-bin                1.1.0                h5505292_3    conda-forge\n",
+      "brotli-python             1.1.0            py39h941272d_3    conda-forge\n",
+      "bzip2                     1.0.8                h99b78c6_7    conda-forge\n",
+      "ca-certificates           2025.8.3             hbd8a1cb_0    conda-forge\n",
+      "cached-property           1.5.2                hd8ed1ab_1    conda-forge\n",
+      "cached_property           1.5.2              pyha770c72_1    conda-forge\n",
+      "certifi                   2025.8.3           pyhd8ed1ab_0    conda-forge\n",
+      "cffi                      1.17.1           py39h7f933ea_0    conda-forge\n",
+      "charset-normalizer        3.4.3              pyhd8ed1ab_0    conda-forge\n",
+      "click                     8.1.8                    pypi_0    pypi\n",
+      "comm                      0.2.3              pyhe01879c_0    conda-forge\n",
+      "contourpy                 1.3.0            py39h85b62ae_2    conda-forge\n",
+      "coverage                  7.10.3                   pypi_0    pypi\n",
+      "cycler                    0.12.1             pyhd8ed1ab_1    conda-forge\n",
+      "debugpy                   1.8.16           py39hd866990_0    conda-forge\n",
+      "decorator                 5.2.1              pyhd8ed1ab_0    conda-forge\n",
+      "defusedxml                0.7.1              pyhd8ed1ab_0    conda-forge\n",
+      "entrypoints               0.4                      pypi_0    pypi\n",
+      "et_xmlfile                2.0.0              pyhd8ed1ab_1    conda-forge\n",
+      "exceptiongroup            1.3.0              pyhd8ed1ab_0    conda-forge\n",
+      "executing                 2.2.0              pyhd8ed1ab_0    conda-forge\n",
+      "flake8                    7.3.0              pyhd8ed1ab_0    conda-forge\n",
+      "fonttools                 4.59.0           py39hb270ea8_0    conda-forge\n",
+      "fqdn                      1.5.1              pyhd8ed1ab_1    conda-forge\n",
+      "freetype                  2.13.3               hce30654_1    conda-forge\n",
+      "future                    1.0.0                    pypi_0    pypi\n",
+      "h11                       0.16.0             pyhd8ed1ab_0    conda-forge\n",
+      "h2                        4.2.0              pyhd8ed1ab_0    conda-forge\n",
+      "h5py                      3.14.0                   pypi_0    pypi\n",
+      "hpack                     4.1.0              pyhd8ed1ab_0    conda-forge\n",
+      "httpcore                  1.0.9              pyh29332c3_0    conda-forge\n",
+      "httpx                     0.28.1             pyhd8ed1ab_0    conda-forge\n",
+      "hyperframe                6.1.0              pyhd8ed1ab_0    conda-forge\n",
+      "icu                       75.1                 hfee45f7_0    conda-forge\n",
+      "idna                      3.10               pyhd8ed1ab_1    conda-forge\n",
+      "importlib-metadata        8.7.0              pyhe01879c_1    conda-forge\n",
+      "importlib-resources       6.5.2              pyhd8ed1ab_0    conda-forge\n",
+      "importlib_resources       6.5.2              pyhd8ed1ab_0    conda-forge\n",
+      "ipykernel                 6.30.1             pyh92f572d_0    conda-forge\n",
+      "ipython                   8.18.1             pyh707e725_3    conda-forge\n",
+      "ipywidgets                8.1.7              pyhd8ed1ab_0    conda-forge\n",
+      "isoduration               20.11.0            pyhd8ed1ab_1    conda-forge\n",
+      "jedi                      0.19.2             pyhd8ed1ab_1    conda-forge\n",
+      "jinja2                    3.1.6              pyhd8ed1ab_0    conda-forge\n",
+      "joblib                    1.5.1              pyhd8ed1ab_0    conda-forge\n",
+      "json5                     0.12.0             pyhd8ed1ab_0    conda-forge\n",
+      "jsonpointer               3.0.0            py39h2804cbe_1    conda-forge\n",
+      "jsonschema                4.25.0             pyhe01879c_0    conda-forge\n",
+      "jsonschema-specifications 2025.4.1           pyh29332c3_0    conda-forge\n",
+      "jsonschema-with-format-nongpl 4.25.0               he01879c_0    conda-forge\n",
+      "jupyter                   1.1.1              pyhd8ed1ab_1    conda-forge\n",
+      "jupyter-lsp               2.2.6              pyhe01879c_0    conda-forge\n",
+      "jupyter_client            8.6.3              pyhd8ed1ab_1    conda-forge\n",
+      "jupyter_console           6.6.3              pyhd8ed1ab_1    conda-forge\n",
+      "jupyter_core              5.8.1              pyh31011fe_0    conda-forge\n",
+      "jupyter_events            0.12.0             pyh29332c3_0    conda-forge\n",
+      "jupyter_server            2.16.0             pyhe01879c_0    conda-forge\n",
+      "jupyter_server_terminals  0.5.3              pyhd8ed1ab_1    conda-forge\n",
+      "jupyterlab                4.4.5              pyhd8ed1ab_0    conda-forge\n",
+      "jupyterlab_pygments       0.3.0              pyhd8ed1ab_2    conda-forge\n",
+      "jupyterlab_server         2.27.3             pyhd8ed1ab_1    conda-forge\n",
+      "jupyterlab_widgets        3.0.15             pyhd8ed1ab_0    conda-forge\n",
+      "kiwisolver                1.4.7            py39h157d57c_0    conda-forge\n",
+      "krb5                      1.21.3               h237132a_0    conda-forge\n",
+      "lark                      1.2.2              pyhd8ed1ab_1    conda-forge\n",
+      "lcms2                     2.17                 h7eeda09_0    conda-forge\n",
+      "lerc                      4.0.0                hd64df32_1    conda-forge\n",
+      "libblas                   3.9.0           34_h10e41b3_openblas    conda-forge\n",
+      "libbrotlicommon           1.1.0                h5505292_3    conda-forge\n",
+      "libbrotlidec              1.1.0                h5505292_3    conda-forge\n",
+      "libbrotlienc              1.1.0                h5505292_3    conda-forge\n",
+      "libcblas                  3.9.0           34_hb3479ef_openblas    conda-forge\n",
+      "libcxx                    20.1.8               hf598326_1    conda-forge\n",
+      "libdeflate                1.24                 h5773f1b_0    conda-forge\n",
+      "libedit                   3.1.20250104    pl5321hafb1f1b_0    conda-forge\n",
+      "libexpat                  2.7.1                hec049ff_0    conda-forge\n",
+      "libffi                    3.4.6                h1da3d7d_1    conda-forge\n",
+      "libfreetype               2.13.3               hce30654_1    conda-forge\n",
+      "libfreetype6              2.13.3               h1d14073_1    conda-forge\n",
+      "libgfortran               15.1.0               hfdf1602_0    conda-forge\n",
+      "libgfortran5              15.1.0               hb74de2c_0    conda-forge\n",
+      "libjpeg-turbo             3.1.0                h5505292_0    conda-forge\n",
+      "liblapack                 3.9.0           34_hc9a63f6_openblas    conda-forge\n",
+      "liblzma                   5.8.1                h39f12f2_2    conda-forge\n",
+      "libopenblas               0.3.30          openmp_h60d53f8_1    conda-forge\n",
+      "libpng                    1.6.50               h280e0eb_1    conda-forge\n",
+      "libsodium                 1.0.20               h99b78c6_0    conda-forge\n",
+      "libsqlite                 3.50.4               h4237e3c_0    conda-forge\n",
+      "libtiff                   4.7.0                h2f21f7c_5    conda-forge\n",
+      "libwebp-base              1.6.0                h07db88b_0    conda-forge\n",
+      "libxcb                    1.17.0               hdb1d25a_0    conda-forge\n",
+      "libzlib                   1.3.1                h8359307_2    conda-forge\n",
+      "llvm-openmp               20.1.8               hbb9b287_1    conda-forge\n",
+      "markupsafe                3.0.2            py39hefdd603_1    conda-forge\n",
+      "matplotlib                3.9.4            py39hdf13c20_0    conda-forge\n",
+      "matplotlib-base           3.9.4            py39h7251d6c_0    conda-forge\n",
+      "matplotlib-inline         0.1.7              pyhd8ed1ab_1    conda-forge\n",
+      "mccabe                    0.7.0              pyhd8ed1ab_1    conda-forge\n",
+      "metapool                  0+untagged.232.g2b2eaec          pypi_0    pypi\n",
+      "mistune                   3.1.3              pyh29332c3_0    conda-forge\n",
+      "munkres                   1.1.4              pyhd8ed1ab_1    conda-forge\n",
+      "nbclient                  0.10.2             pyhd8ed1ab_0    conda-forge\n",
+      "nbconvert-core            7.16.6             pyh29332c3_0    conda-forge\n",
+      "nbformat                  5.10.4             pyhd8ed1ab_1    conda-forge\n",
+      "ncurses                   6.5                  h5e97a16_3    conda-forge\n",
+      "nest-asyncio              1.6.0              pyhd8ed1ab_1    conda-forge\n",
+      "nose                      1.3.7                   py_1006    conda-forge\n",
+      "notebook                  7.4.5              pyhd8ed1ab_0    conda-forge\n",
+      "notebook-shim             0.2.4              pyhd8ed1ab_1    conda-forge\n",
+      "numpy                     2.0.2            py39h3ba1154_1    conda-forge\n",
+      "openjpeg                  2.5.3                h889cd5d_1    conda-forge\n",
+      "openpyxl                  3.1.5            py39h5f73de6_1    conda-forge\n",
+      "openssl                   3.5.2                he92f556_0    conda-forge\n",
+      "overrides                 7.7.0              pyhd8ed1ab_1    conda-forge\n",
+      "packaging                 25.0               pyh29332c3_1    conda-forge\n",
+      "pandas                    2.3.1            py39h6aaa60c_0    conda-forge\n",
+      "pandocfilters             1.5.0              pyhd8ed1ab_0    conda-forge\n",
+      "papermill                 2.6.0                    pypi_0    pypi\n",
+      "parso                     0.8.4              pyhd8ed1ab_1    conda-forge\n",
+      "patsy                     1.0.1              pyhd8ed1ab_1    conda-forge\n",
+      "pep8                      1.7.1                      py_0    conda-forge\n",
+      "pexpect                   4.9.0              pyhd8ed1ab_1    conda-forge\n",
+      "pickleshare               0.7.5           pyhd8ed1ab_1004    conda-forge\n",
+      "pillow                    11.3.0           py39hfea3036_0    conda-forge\n",
+      "pip                       25.2               pyh8b19718_0    conda-forge\n",
+      "platformdirs              4.3.8              pyhe01879c_0    conda-forge\n",
+      "prometheus_client         0.22.1             pyhd8ed1ab_0    conda-forge\n",
+      "prompt-toolkit            3.0.51             pyha770c72_0    conda-forge\n",
+      "prompt_toolkit            3.0.51               hd8ed1ab_0    conda-forge\n",
+      "psutil                    7.0.0            py39hf3bc14e_0    conda-forge\n",
+      "pthread-stubs             0.4               hd74edd7_1002    conda-forge\n",
+      "ptyprocess                0.7.0              pyhd8ed1ab_1    conda-forge\n",
+      "pure_eval                 0.2.3              pyhd8ed1ab_1    conda-forge\n",
+      "pycodestyle               2.14.0             pyhd8ed1ab_0    conda-forge\n",
+      "pycparser                 2.22               pyh29332c3_1    conda-forge\n",
+      "pyflakes                  3.4.0              pyhd8ed1ab_0    conda-forge\n",
+      "pygments                  2.19.2             pyhd8ed1ab_0    conda-forge\n",
+      "pyobjc-core               11.1             py39h65d0b63_0    conda-forge\n",
+      "pyobjc-framework-cocoa    11.1             py39hebff0d6_0    conda-forge\n",
+      "pyparsing                 3.2.3              pyhe01879c_2    conda-forge\n",
+      "pysocks                   1.7.1              pyha55dd90_7    conda-forge\n",
+      "python                    3.9.23          h7139b31_0_cpython    conda-forge\n",
+      "python-dateutil           2.9.0.post0        pyhe01879c_2    conda-forge\n",
+      "python-fastjsonschema     2.21.1             pyhd8ed1ab_0    conda-forge\n",
+      "python-json-logger        2.0.7              pyhd8ed1ab_0    conda-forge\n",
+      "python-tzdata             2025.2             pyhd8ed1ab_0    conda-forge\n",
+      "python_abi                3.9                      8_cp39    conda-forge\n",
+      "pytz                      2025.2             pyhd8ed1ab_0    conda-forge\n",
+      "pyyaml                    6.0.2            py39hefdd603_2    conda-forge\n",
+      "pyzmq                     27.0.1           py39h6c7bd39_0    conda-forge\n",
+      "qhull                     2020.2               h420ef59_5    conda-forge\n",
+      "qiita-client              0.1.0.dev0               pypi_0    pypi\n",
+      "readline                  8.2                  h1d1bf99_2    conda-forge\n",
+      "referencing               0.36.2             pyh29332c3_0    conda-forge\n",
+      "requests                  2.32.4             pyhd8ed1ab_0    conda-forge\n",
+      "rfc3339-validator         0.1.4              pyhd8ed1ab_1    conda-forge\n",
+      "rfc3986-validator         0.1.1              pyh9f0ad1d_0    conda-forge\n",
+      "rfc3987-syntax            1.1.0              pyhe01879c_1    conda-forge\n",
+      "rpds-py                   0.27.0           py39hf64921a_0    conda-forge\n",
+      "sample-sheet              0.13.0                   pypi_0    pypi\n",
+      "scikit-learn              1.6.1            py39h451895d_0    conda-forge\n",
+      "scipy                     1.13.1           py39h3d5391c_0    conda-forge\n",
+      "seaborn                   0.13.2               hd8ed1ab_3    conda-forge\n",
+      "seaborn-base              0.13.2             pyhd8ed1ab_3    conda-forge\n",
+      "send2trash                1.8.3              pyh31c8845_1    conda-forge\n",
+      "setuptools                80.9.0             pyhff2d567_0    conda-forge\n",
+      "six                       1.17.0             pyhe01879c_1    conda-forge\n",
+      "sniffio                   1.3.1              pyhd8ed1ab_1    conda-forge\n",
+      "soupsieve                 2.7                pyhd8ed1ab_0    conda-forge\n",
+      "stack_data                0.6.3              pyhd8ed1ab_1    conda-forge\n",
+      "statsmodels               0.14.5           py39hbd04bc9_0    conda-forge\n",
+      "tabulate                  0.9.0                    pypi_0    pypi\n",
+      "tenacity                  9.1.2                    pypi_0    pypi\n",
+      "terminado                 0.18.1             pyh31c8845_0    conda-forge\n",
+      "terminaltables            3.1.10                   pypi_0    pypi\n",
+      "threadpoolctl             3.6.0              pyhecae5ae_0    conda-forge\n",
+      "tinycss2                  1.4.0              pyhd8ed1ab_0    conda-forge\n",
+      "tk                        8.6.13               h892fb3f_2    conda-forge\n",
+      "tomli                     2.2.1              pyhe01879c_2    conda-forge\n",
+      "tornado                   6.5.2            py39he7485ab_0    conda-forge\n",
+      "tqdm                      4.67.1                   pypi_0    pypi\n",
+      "traitlets                 5.14.3             pyhd8ed1ab_1    conda-forge\n",
+      "types-python-dateutil     2.9.0.20250809     pyhd8ed1ab_0    conda-forge\n",
+      "typing-extensions         4.14.1               h4440ef1_0    conda-forge\n",
+      "typing_extensions         4.14.1             pyhe01879c_0    conda-forge\n",
+      "typing_utils              0.1.0              pyhd8ed1ab_1    conda-forge\n",
+      "tzdata                    2025b                h78e105d_0    conda-forge\n",
+      "unicodedata2              16.0.0           py39hf3bc14e_0    conda-forge\n",
+      "uri-template              1.3.0              pyhd8ed1ab_1    conda-forge\n",
+      "urllib3                   2.5.0              pyhd8ed1ab_0    conda-forge\n",
+      "watermark                 2.5.0                    pypi_0    pypi\n",
+      "wcwidth                   0.2.13             pyhd8ed1ab_1    conda-forge\n",
+      "webcolors                 24.11.1            pyhd8ed1ab_0    conda-forge\n",
+      "webencodings              0.5.1              pyhd8ed1ab_3    conda-forge\n",
+      "websocket-client          1.8.0              pyhd8ed1ab_1    conda-forge\n",
+      "wheel                     0.45.1             pyhd8ed1ab_1    conda-forge\n",
+      "widgetsnbextension        4.0.14             pyhd8ed1ab_0    conda-forge\n",
+      "xorg-libxau               1.0.12               h5505292_0    conda-forge\n",
+      "xorg-libxdmcp             1.1.5                hd74edd7_0    conda-forge\n",
+      "yaml                      0.2.5                h925e9cb_3    conda-forge\n",
+      "zeromq                    4.3.5                hc1bb282_7    conda-forge\n",
+      "zipp                      3.23.0             pyhd8ed1ab_0    conda-forge\n",
+      "zstandard                 0.23.0           py39hf3bc14e_2    conda-forge\n",
+      "zstd                      1.5.7                h6491c7d_2    conda-forge\n"
+     ]
+    }
+   ],
    "source": [
     "! conda list"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "current_set_id = \"col19to24\"\n",
+    "total_vol = 190\n",
+    "iseq_sequencer = \"iSeq\"\n",
+    "novaseq_sequencer = \"NovaSeqXPlus\"\n",
+    "\n",
+    "full_plate_fp = './test_output/QC/Tellseq_plate_df_B.txt'\n",
+    "expt_config_fp = './test_output/QC/Tellseq_expt_info.yml'\n",
+    "evp_picklist_fbase = './test_output/Indices/Tellseq_evp'\n",
+    "machine_samplesheet_fbase = './test_output/SampleSheets/Tellseq_samplesheet_instrument_iseq'\n",
+    "spp_samplesheet_fbase = './test_output/SampleSheets/Tellseq_samplesheet_spp'\n",
+    "\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -69,18 +360,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "## INPUT\n",
-    "full_plate_fp = './test_output/QC/Tellseq_plate_df_B.txt'\n",
-    "expt_config_fp = './test_output/QC/Tellseq_expt_info.yml'"
+    "#full_plate_fp = './test_output/QC/Tellseq_plate_df_B.txt'\n",
+    "#expt_config_fp = './test_output/QC/Tellseq_expt_info.yml'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,9 +394,237 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sample</th>\n",
+       "      <th>Col</th>\n",
+       "      <th>Compressed Plate Name</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Plate Position</th>\n",
+       "      <th>Project Abbreviation</th>\n",
+       "      <th>Project Name</th>\n",
+       "      <th>Project Plate</th>\n",
+       "      <th>RackID</th>\n",
+       "      <th>Row</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Barcode_96_Well_Position</th>\n",
+       "      <th>i5 name</th>\n",
+       "      <th>i5 plate</th>\n",
+       "      <th>i5 well_row</th>\n",
+       "      <th>i5 well_col</th>\n",
+       "      <th>barcode_set_id</th>\n",
+       "      <th>barcode_id</th>\n",
+       "      <th>MiniPico Library DNA Concentration</th>\n",
+       "      <th>MiniPico Library Concentration</th>\n",
+       "      <th>sample sheet Sample_ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01LJ00482.V5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>A1</td>\n",
+       "      <td>C501</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col1to6</td>\n",
+       "      <td>C501</td>\n",
+       "      <td>0.612</td>\n",
+       "      <td>1.854545</td>\n",
+       "      <td>01LJ00482_V5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>01LJ01100.V5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_10to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>3</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_3_10to1dilution</td>\n",
+       "      <td>TestProjA_Plate_3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>A2</td>\n",
+       "      <td>C509</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col1to6</td>\n",
+       "      <td>C509</td>\n",
+       "      <td>0.623</td>\n",
+       "      <td>1.887879</td>\n",
+       "      <td>01LJ01100_V5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>01LJ01597.V8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>...</td>\n",
+       "      <td>B1</td>\n",
+       "      <td>C502</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col1to6</td>\n",
+       "      <td>C502</td>\n",
+       "      <td>0.632</td>\n",
+       "      <td>1.915152</td>\n",
+       "      <td>01LJ01597_V8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>01LJ01315.V5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_10to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>3</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_3_10to1dilution</td>\n",
+       "      <td>TestProjA_Plate_3</td>\n",
+       "      <td>B</td>\n",
+       "      <td>...</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>C510</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>D</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col1to6</td>\n",
+       "      <td>C510</td>\n",
+       "      <td>0.606</td>\n",
+       "      <td>1.836364</td>\n",
+       "      <td>01LJ01315_V5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>01LJ02862.V5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>C503</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>E</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col1to6</td>\n",
+       "      <td>C503</td>\n",
+       "      <td>1.001</td>\n",
+       "      <td>3.033333</td>\n",
+       "      <td>01LJ02862_V5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 45 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Sample  Col                  Compressed Plate Name      Date  \\\n",
+       "0  01LJ00482.V5    1   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "1  01LJ01100.V5    1  TestProjA_10002_1_2_3_4_10to1dilution  20240911   \n",
+       "2  01LJ01597.V8    1   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "3  01LJ01315.V5    1  TestProjA_10002_1_2_3_4_10to1dilution  20240911   \n",
+       "4  01LJ02862.V5    1   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "\n",
+       "   Plate Position Project Abbreviation     Project Name  \\\n",
+       "0               1            TestProjA  TestProjA_10002   \n",
+       "1               3            TestProjA  TestProjA_10002   \n",
+       "2               1            TestProjA  TestProjA_10002   \n",
+       "3               3            TestProjA  TestProjA_10002   \n",
+       "4               1            TestProjA  TestProjA_10002   \n",
+       "\n",
+       "                           Project Plate             RackID Row  ...  \\\n",
+       "0   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   A  ...   \n",
+       "1  TestProjA_10002_Plate_3_10to1dilution  TestProjA_Plate_3   A  ...   \n",
+       "2   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   B  ...   \n",
+       "3  TestProjA_10002_Plate_3_10to1dilution  TestProjA_Plate_3   B  ...   \n",
+       "4   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   C  ...   \n",
+       "\n",
+       "   Barcode_96_Well_Position  i5 name  \\\n",
+       "0                        A1     C501   \n",
+       "1                        A2     C509   \n",
+       "2                        B1     C502   \n",
+       "3                        B2     C510   \n",
+       "4                        C1     C503   \n",
+       "\n",
+       "                                      i5 plate  i5 well_row i5 well_col  \\\n",
+       "0  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            A           1   \n",
+       "1  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            B           1   \n",
+       "2  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            C           1   \n",
+       "3  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            D           1   \n",
+       "4  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            E           1   \n",
+       "\n",
+       "  barcode_set_id  barcode_id MiniPico Library DNA Concentration  \\\n",
+       "0        col1to6        C501                              0.612   \n",
+       "1        col1to6        C509                              0.623   \n",
+       "2        col1to6        C502                              0.632   \n",
+       "3        col1to6        C510                              0.606   \n",
+       "4        col1to6        C503                              1.001   \n",
+       "\n",
+       "  MiniPico Library Concentration  sample sheet Sample_ID  \n",
+       "0                       1.854545            01LJ00482_V5  \n",
+       "1                       1.887879            01LJ01100_V5  \n",
+       "2                       1.915152            01LJ01597_V8  \n",
+       "3                       1.836364            01LJ01315_V5  \n",
+       "4                       3.033333            01LJ02862_V5  \n",
+       "\n",
+       "[5 rows x 45 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "full_plate_df = pd.read_csv(full_plate_fp, sep='\\t')\n",
     "full_plate_df.head()"
@@ -113,16 +632,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "is_absquant(full_plate_df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,9 +662,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'RKLtest'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "expt_name = expt_config['experiment_name']\n",
     "expt_name"
@@ -142,9 +683,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'Email': 'r@gmail.com',\n",
+       "  'HumanFiltering': 'True',\n",
+       "  'Project Abbreviation': 'TestProjA',\n",
+       "  'Project Name': 'TestProjA_10002',\n",
+       "  'experiment_design_description': 'plasma sequencing',\n",
+       "  'qiita_id': '10002',\n",
+       "  'qiita_metadata_fp': './test_data/Plate_Maps/10002_20241004-110731.txt',\n",
+       "  'sample_accession_fp': './test_data/Plate_Maps/Tellseq_TestProjA - 10002 - Sample Accession.csv'}]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "full_studies_info = expt_config['studies']\n",
     "full_studies_info"
@@ -152,9 +711,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['col1to6', 'col7to12', 'col13to18', 'col19to24'], dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "set_ids = full_plate_df[TELLSEQ_BARCODE_SET_ID_KEY].unique()\n",
     "set_ids"
@@ -176,19 +746,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "## INPUT\n",
-    "current_set_id = \"col19to24\""
+    "#current_set_id = \"col19to24\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(96, 45)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "plate_df = full_plate_df[full_plate_df[TELLSEQ_BARCODE_SET_ID_KEY] == current_set_id].copy()\n",
     "plate_df.shape"
@@ -196,9 +777,237 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sample</th>\n",
+       "      <th>Col</th>\n",
+       "      <th>Compressed Plate Name</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Plate Position</th>\n",
+       "      <th>Project Abbreviation</th>\n",
+       "      <th>Project Name</th>\n",
+       "      <th>Project Plate</th>\n",
+       "      <th>RackID</th>\n",
+       "      <th>Row</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Barcode_96_Well_Position</th>\n",
+       "      <th>i5 name</th>\n",
+       "      <th>i5 plate</th>\n",
+       "      <th>i5 well_row</th>\n",
+       "      <th>i5 well_col</th>\n",
+       "      <th>barcode_set_id</th>\n",
+       "      <th>barcode_id</th>\n",
+       "      <th>MiniPico Library DNA Concentration</th>\n",
+       "      <th>MiniPico Library Concentration</th>\n",
+       "      <th>sample sheet Sample_ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>01LJ01593.V8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>A1</td>\n",
+       "      <td>C501</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C501</td>\n",
+       "      <td>0.635</td>\n",
+       "      <td>1.924242</td>\n",
+       "      <td>01LJ01593_V8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>01LJ01300.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_10to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>3</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_3_10to1dilution</td>\n",
+       "      <td>TestProjA_Plate_3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>A2</td>\n",
+       "      <td>C509</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C509</td>\n",
+       "      <td>5.113</td>\n",
+       "      <td>15.493939</td>\n",
+       "      <td>01LJ01300_V5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>01LJ02603.V8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>...</td>\n",
+       "      <td>B1</td>\n",
+       "      <td>C502</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C502</td>\n",
+       "      <td>0.677</td>\n",
+       "      <td>2.051515</td>\n",
+       "      <td>01LJ02603_V8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>01LJ01344.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>3</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_3_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_3</td>\n",
+       "      <td>B</td>\n",
+       "      <td>...</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>C510</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>D</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C510</td>\n",
+       "      <td>1.047</td>\n",
+       "      <td>3.172727</td>\n",
+       "      <td>01LJ01344_V5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>01LJ04170.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>C503</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>E</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C503</td>\n",
+       "      <td>3.777</td>\n",
+       "      <td>11.445455</td>\n",
+       "      <td>01LJ04170_V5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 45 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Sample  Col                  Compressed Plate Name      Date  \\\n",
+       "288  01LJ01593.V8   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "289  01LJ01300.V5   10  TestProjA_10002_1_2_3_4_10to1dilution  20240911   \n",
+       "290  01LJ02603.V8   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "291  01LJ01344.V5   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "292  01LJ04170.V5   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "\n",
+       "     Plate Position Project Abbreviation     Project Name  \\\n",
+       "288               1            TestProjA  TestProjA_10002   \n",
+       "289               3            TestProjA  TestProjA_10002   \n",
+       "290               1            TestProjA  TestProjA_10002   \n",
+       "291               3            TestProjA  TestProjA_10002   \n",
+       "292               1            TestProjA  TestProjA_10002   \n",
+       "\n",
+       "                             Project Plate             RackID Row  ...  \\\n",
+       "288   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   A  ...   \n",
+       "289  TestProjA_10002_Plate_3_10to1dilution  TestProjA_Plate_3   A  ...   \n",
+       "290   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   B  ...   \n",
+       "291   TestProjA_10002_Plate_3_2to1dilution  TestProjA_Plate_3   B  ...   \n",
+       "292   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   C  ...   \n",
+       "\n",
+       "     Barcode_96_Well_Position  i5 name  \\\n",
+       "288                        A1     C501   \n",
+       "289                        A2     C509   \n",
+       "290                        B1     C502   \n",
+       "291                        B2     C510   \n",
+       "292                        C1     C503   \n",
+       "\n",
+       "                                        i5 plate  i5 well_row i5 well_col  \\\n",
+       "288  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            A           1   \n",
+       "289  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            B           1   \n",
+       "290  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            C           1   \n",
+       "291  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            D           1   \n",
+       "292  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            E           1   \n",
+       "\n",
+       "    barcode_set_id  barcode_id MiniPico Library DNA Concentration  \\\n",
+       "288      col19to24        C501                              0.635   \n",
+       "289      col19to24        C509                              5.113   \n",
+       "290      col19to24        C502                              0.677   \n",
+       "291      col19to24        C510                              1.047   \n",
+       "292      col19to24        C503                              3.777   \n",
+       "\n",
+       "    MiniPico Library Concentration  sample sheet Sample_ID  \n",
+       "288                       1.924242            01LJ01593_V8  \n",
+       "289                      15.493939            01LJ01300_V5  \n",
+       "290                       2.051515            01LJ02603_V8  \n",
+       "291                       3.172727            01LJ01344_V5  \n",
+       "292                      11.445455            01LJ04170_V5  \n",
+       "\n",
+       "[5 rows x 45 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "plate_df.head()"
    ]
@@ -212,9 +1021,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "## DECISION -- verify no duplicate barcodes\n",
     "plate_df[TELLSEQ_BARCODE_ID_KEY].value_counts().nunique() == 1"
@@ -222,7 +1042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,9 +1052,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['A19', 'A20', 'A21', 'A22', 'A23', 'A24'],\n",
+       "       ['B19', 'B20', 'B21', 'B22', 'B23', 'B24'],\n",
+       "       ['C19', 'C20', 'C21', 'C22', 'C23', 'C24'],\n",
+       "       ['D19', 'D20', 'D21', 'D22', 'D23', 'D24'],\n",
+       "       ['E19', 'E20', 'E21', 'E22', 'E23', 'E24'],\n",
+       "       ['F19', 'F20', 'F21', 'F22', 'F23', 'F24'],\n",
+       "       ['G19', 'G20', 'G21', 'G22', 'G23', 'G24'],\n",
+       "       ['H19', 'H20', 'H21', 'H22', 'H23', 'H24'],\n",
+       "       ['I19', 'I20', 'I21', 'I22', 'I23', 'I24'],\n",
+       "       ['J19', 'J20', 'J21', 'J22', 'J23', 'J24'],\n",
+       "       ['K19', 'K20', 'K21', 'K22', 'K23', 'K24'],\n",
+       "       ['L19', 'L20', 'L21', 'L22', 'L23', 'L24'],\n",
+       "       ['M19', 'M20', 'M21', 'M22', 'M23', 'M24'],\n",
+       "       ['N19', 'N20', 'N21', 'N22', 'N23', 'N24'],\n",
+       "       ['O19', 'O20', 'O21', 'O22', 'O23', 'O24'],\n",
+       "       ['P19', 'P20', 'P21', 'P22', 'P23', 'P24']], dtype=object)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "source_well_names = make_compressed_2d_array(\n",
     "    plate_df, data_col=PM_LIB_WELL_KEY, \n",
@@ -244,9 +1090,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'Email': 'r@gmail.com',\n",
+       "  'HumanFiltering': 'True',\n",
+       "  'Project Abbreviation': 'TestProjA',\n",
+       "  'Project Name': 'TestProjA_10002',\n",
+       "  'experiment_design_description': 'plasma sequencing',\n",
+       "  'qiita_id': '10002',\n",
+       "  'qiita_metadata_fp': './test_data/Plate_Maps/10002_20241004-110731.txt',\n",
+       "  'sample_accession_fp': './test_data/Plate_Maps/Tellseq_TestProjA - 10002 - Sample Accession.csv'}]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "unique_projects = plate_df[PM_PROJECT_NAME_KEY].unique()\n",
     "studies_info = []\n",
@@ -292,19 +1156,247 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
     "## INPUT -- verify default\n",
-    "total_vol = 190"
+    "#total_vol = 190"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sample</th>\n",
+       "      <th>Col</th>\n",
+       "      <th>Compressed Plate Name</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Plate Position</th>\n",
+       "      <th>Project Abbreviation</th>\n",
+       "      <th>Project Name</th>\n",
+       "      <th>Project Plate</th>\n",
+       "      <th>RackID</th>\n",
+       "      <th>Row</th>\n",
+       "      <th>...</th>\n",
+       "      <th>i5 name</th>\n",
+       "      <th>i5 plate</th>\n",
+       "      <th>i5 well_row</th>\n",
+       "      <th>i5 well_col</th>\n",
+       "      <th>barcode_set_id</th>\n",
+       "      <th>barcode_id</th>\n",
+       "      <th>MiniPico Library DNA Concentration</th>\n",
+       "      <th>MiniPico Library Concentration</th>\n",
+       "      <th>sample sheet Sample_ID</th>\n",
+       "      <th>MiniPico Pooled Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>01LJ01593.V8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C501</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C501</td>\n",
+       "      <td>0.635</td>\n",
+       "      <td>1.924242</td>\n",
+       "      <td>01LJ01593_V8</td>\n",
+       "      <td>1979.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>01LJ01300.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_10to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>3</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_3_10to1dilution</td>\n",
+       "      <td>TestProjA_Plate_3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C509</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C509</td>\n",
+       "      <td>5.113</td>\n",
+       "      <td>15.493939</td>\n",
+       "      <td>01LJ01300_V5</td>\n",
+       "      <td>1979.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>01LJ02603.V8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C502</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C502</td>\n",
+       "      <td>0.677</td>\n",
+       "      <td>2.051515</td>\n",
+       "      <td>01LJ02603_V8</td>\n",
+       "      <td>1979.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>01LJ01344.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>3</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_3_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_3</td>\n",
+       "      <td>B</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C510</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>D</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C510</td>\n",
+       "      <td>1.047</td>\n",
+       "      <td>3.172727</td>\n",
+       "      <td>01LJ01344_V5</td>\n",
+       "      <td>1979.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>01LJ04170.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C503</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>E</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C503</td>\n",
+       "      <td>3.777</td>\n",
+       "      <td>11.445455</td>\n",
+       "      <td>01LJ04170_V5</td>\n",
+       "      <td>1979.166667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Sample  Col                  Compressed Plate Name      Date  \\\n",
+       "288  01LJ01593.V8   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "289  01LJ01300.V5   10  TestProjA_10002_1_2_3_4_10to1dilution  20240911   \n",
+       "290  01LJ02603.V8   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "291  01LJ01344.V5   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "292  01LJ04170.V5   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "\n",
+       "     Plate Position Project Abbreviation     Project Name  \\\n",
+       "288               1            TestProjA  TestProjA_10002   \n",
+       "289               3            TestProjA  TestProjA_10002   \n",
+       "290               1            TestProjA  TestProjA_10002   \n",
+       "291               3            TestProjA  TestProjA_10002   \n",
+       "292               1            TestProjA  TestProjA_10002   \n",
+       "\n",
+       "                             Project Plate             RackID Row  ...  \\\n",
+       "288   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   A  ...   \n",
+       "289  TestProjA_10002_Plate_3_10to1dilution  TestProjA_Plate_3   A  ...   \n",
+       "290   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   B  ...   \n",
+       "291   TestProjA_10002_Plate_3_2to1dilution  TestProjA_Plate_3   B  ...   \n",
+       "292   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   C  ...   \n",
+       "\n",
+       "     i5 name                                     i5 plate i5 well_row  \\\n",
+       "288     C501  TellSeq_Barcode_Plate_1_LN2409001_EXP052026           A   \n",
+       "289     C509  TellSeq_Barcode_Plate_1_LN2409001_EXP052026           B   \n",
+       "290     C502  TellSeq_Barcode_Plate_1_LN2409001_EXP052026           C   \n",
+       "291     C510  TellSeq_Barcode_Plate_1_LN2409001_EXP052026           D   \n",
+       "292     C503  TellSeq_Barcode_Plate_1_LN2409001_EXP052026           E   \n",
+       "\n",
+       "     i5 well_col barcode_set_id barcode_id  \\\n",
+       "288            1      col19to24       C501   \n",
+       "289            1      col19to24       C509   \n",
+       "290            1      col19to24       C502   \n",
+       "291            1      col19to24       C510   \n",
+       "292            1      col19to24       C503   \n",
+       "\n",
+       "     MiniPico Library DNA Concentration MiniPico Library Concentration  \\\n",
+       "288                               0.635                       1.924242   \n",
+       "289                               5.113                      15.493939   \n",
+       "290                               0.677                       2.051515   \n",
+       "291                               1.047                       3.172727   \n",
+       "292                               3.777                      11.445455   \n",
+       "\n",
+       "    sample sheet Sample_ID  MiniPico Pooled Volume  \n",
+       "288           01LJ01593_V8             1979.166667  \n",
+       "289           01LJ01300_V5             1979.166667  \n",
+       "290           01LJ02603_V8             1979.166667  \n",
+       "291           01LJ01344_V5             1979.166667  \n",
+       "292           01LJ04170_V5             1979.166667  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "plate_df = autopool(plate_df,method='evp',total_vol=total_vol)\n",
     "plate_df.head()"
@@ -312,9 +1404,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667],\n",
+       "       [1979.16666667, 1979.16666667, 1979.16666667, 1979.16666667,\n",
+       "        1979.16666667, 1979.16666667]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "MINIPICO_POOLED_VOL_KEY = 'MiniPico Pooled Volume'\n",
     "\n",
@@ -327,9 +1461,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(nan)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#threshold = find_threshold(plate_df[MINIPICO_LIB_CONC_KEY], plate_df[PM_BLANK_KEY])\n",
     "threshold = find_threshold(plate_df['MiniPico Library Concentration'], plate_df['Blank'])\n",
@@ -338,9 +1483,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Floor concentration: nan\n",
+      "Pool concentration: 4.16\n",
+      "Pool volume: 190000.00\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x2000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# visualize\n",
     "print(\"Floor concentration: {}\".format(threshold))\n",
@@ -353,9 +1518,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='MiniPico Library Concentration', ylabel='MiniPico Pooled Volume'>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sns.scatterplot(x=MINIPICO_LIB_CONC_KEY, y=MINIPICO_POOLED_VOL_KEY,data=plate_df)"
    ]
@@ -369,17 +1555,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
     "## INPUT\n",
-    "evp_picklist_fbase = './test_output/Indices/Tellseq_evp'"
+    "#evp_picklist_fbase = './test_output/Indices/Tellseq_evp'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -389,9 +1575,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'./test_output/Indices/Tellseq_evp_set_col19to24.txt'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "evp_picklist_fp = get_set_fp(evp_picklist_fbase, current_set_id)\n",
     "evp_picklist_fp"
@@ -399,18 +1596,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/seosonghee/Documents/KnightLab/kl-metapool/metapool/util.py:174: UserWarning: Warning! This file exists already: ./test_output/Indices/Tellseq_evp_set_col19to24.txt.\n",
+      "  warnings.warn(f\"Warning! This file exists already: {fp}.\")\n"
+     ]
+    }
+   ],
    "source": [
     "warn_if_fp_exists(evp_picklist_fp)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Source Plate Name,Source Plate Type,Source Well,Concentration,Transfer Volume,Destination Plate Name,Destination Well\n",
+      "1,384LDV_AQ_B2,A19,,1979.17,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,A20,,1979.17,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,A21,,1979.17,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,A22,,1979.17,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,A23,,1979.17,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,A24,,1979.17,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,B19,,1979.17,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,B20,,1979.17,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,B21,,1979.17,NormalizedDNA,A1\n"
+     ]
+    }
+   ],
    "source": [
     "with open(evp_picklist_fp,'w') as f:\n",
     "    f.write(evp_picklist)\n",
@@ -427,12 +1650,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
     "## INPUT\n",
-    "machine_samplesheet_fbase = './test_output/SampleSheets/Tellseq_samplesheet_instrument_iseq'"
+    "#machine_samplesheet_fbase = './test_output/SampleSheets/Tellseq_samplesheet_instrument_iseq'"
    ]
   },
   {
@@ -444,7 +1667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -472,7 +1695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -494,7 +1717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -504,9 +1727,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/seosonghee/Documents/KnightLab/kl-metapool/metapool/util.py:174: UserWarning: Warning! This file exists already: ./test_output/SampleSheets/Tellseq_samplesheet_instrument_iseq_set_col19to24.csv.\n",
+      "  warnings.warn(f\"Warning! This file exists already: {fp}.\")\n"
+     ]
+    }
+   ],
    "source": [
     "machine_samplesheet_fp = get_set_fp(\n",
     "    machine_samplesheet_fbase, current_set_id, extension=\"csv\")\n",
@@ -515,9 +1747,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Header],,,,,,,,,,,\n",
+      "Experiment Name,RKLtest_col19to24,,,,,,,,,,\n",
+      "Investigator Name,Enter the investigator name (optional),,,,,,,,,,\n",
+      "Project Name,TestProjA_10002_1_2_3_4_10to1dilution,,,,,,,,,,\n",
+      "Date,2026-02-02,,,,,,,,,,\n",
+      "Workflow,GenerateFASTQ,,,,,,,,,,\n",
+      "Library Prep Kit,TELLSEQ,,,,,,,,,,\n",
+      "[Manifest],,,,,,,,,,,\n",
+      "Enter the manifest files used to align to targeted reference regions of the genome. Use the following format.,,,,,,,,,,,\n",
+      "ManifestKey, ManifestFile,,,,,,,,,,\n"
+     ]
+    }
+   ],
    "source": [
     "with open(machine_samplesheet_fp,'w') as f:\n",
     "    f.write(machine_sheet_str)\n",
@@ -534,19 +1783,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
     "## INPUT\n",
-    "spp_samplesheet_fbase = './test_output/SampleSheets/Tellseq_samplesheet_spp'\n",
-    "iseq_sequencer = 'iSeq'\n",
-    "novaseq_sequencer = 'NovaSeqXPlus'"
+    "#spp_samplesheet_fbase = './test_output/SampleSheets/Tellseq_samplesheet_spp'\n",
+    "#iseq_sequencer = 'iSeq'\n",
+    "#novaseq_sequencer = 'NovaSeqXPlus'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -555,7 +1804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -578,9 +1827,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'tellseq_metag'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Determine the sample sheet type to make\n",
     "expt_type = TELLSEQ_ABSQUANT_SHEET_TYPE if is_absquant(plate_df) \\\n",
@@ -590,7 +1850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -602,7 +1862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -612,9 +1872,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/seosonghee/Documents/KnightLab/kl-metapool/metapool/util.py:174: UserWarning: Warning! This file exists already: ./test_output/SampleSheets/Tellseq_samplesheet_spp_iseq_set_col19to24.csv.\n",
+      "  warnings.warn(f\"Warning! This file exists already: {fp}.\")\n"
+     ]
+    }
+   ],
    "source": [
     "iseq_spp_samplesheet_fp = get_set_fp(\n",
     "    f\"{spp_samplesheet_fbase}_{iseq_sequencer.lower()}\", current_set_id, \n",
@@ -624,9 +1893,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Header],,,,,,,,\n",
+      "IEMFileVersion,4,,,,,,,\n",
+      "SheetType,tellseq_metag,,,,,,,\n",
+      "SheetVersion,10,,,,,,,\n",
+      "Investigator Name,Knight,,,,,,,\n",
+      "Experiment Name,RKLtest,,,,,,,\n",
+      "Date,2026-02-02,,,,,,,\n",
+      "Workflow,GenerateFASTQ,,,,,,,\n",
+      "Application,FASTQ Only,,,,,,,\n",
+      "Assay,Metagenomic,,,,,,,\n"
+     ]
+    }
+   ],
    "source": [
     "with open(iseq_spp_samplesheet_fp,'w') as f:\n",
     "    iseq_spp_sheet.write(f)\n",
@@ -636,7 +1922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -646,9 +1932,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/seosonghee/Documents/KnightLab/kl-metapool/metapool/util.py:174: UserWarning: Warning! This file exists already: ./test_output/SampleSheets/Tellseq_samplesheet_spp_novaseqxplus_set_col19to24.csv.\n",
+      "  warnings.warn(f\"Warning! This file exists already: {fp}.\")\n"
+     ]
+    }
+   ],
    "source": [
     "novaseq_spp_samplesheet_fp = get_set_fp(\n",
     "    f\"{spp_samplesheet_fbase}_{novaseq_sequencer.lower()}\", current_set_id, \n",
@@ -658,9 +1953,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Header],,,,,,,,\n",
+      "IEMFileVersion,4,,,,,,,\n",
+      "SheetType,tellseq_metag,,,,,,,\n",
+      "SheetVersion,10,,,,,,,\n",
+      "Investigator Name,Knight,,,,,,,\n",
+      "Experiment Name,RKLtest,,,,,,,\n",
+      "Date,2026-02-02,,,,,,,\n",
+      "Workflow,GenerateFASTQ,,,,,,,\n",
+      "Application,FASTQ Only,,,,,,,\n",
+      "Assay,Metagenomic,,,,,,,\n"
+     ]
+    }
+   ],
    "source": [
     "with open(novaseq_spp_samplesheet_fp,'w') as f:\n",
     "    novaseq_spp_sheet.write(f)\n",
@@ -679,9 +1991,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 47,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'./test_output/QC/Tellseq_plate_df_C_set_col19to24.txt'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "plate_set_fbase = full_plate_fp.replace(\"B.txt\", f\"C\")\n",
     "plate_set_fp = get_set_fp(plate_set_fbase, current_set_id)\n",
@@ -690,16 +2015,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/seosonghee/Documents/KnightLab/kl-metapool/metapool/util.py:174: UserWarning: Warning! This file exists already: ./test_output/QC/Tellseq_plate_df_C_set_col19to24.txt.\n",
+      "  warnings.warn(f\"Warning! This file exists already: {fp}.\")\n"
+     ]
+    }
+   ],
    "source": [
     "warn_if_fp_exists(plate_set_fp)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -730,7 +2064,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.23"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/notebooks/tellseq_D_variable_volume_pooling.ipynb b/notebooks/tellseq_D_variable_volume_pooling.ipynb
index 7120c99d..90af9966 100644
--- a/notebooks/tellseq_D_variable_volume_pooling.ipynb
+++ b/notebooks/tellseq_D_variable_volume_pooling.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -12,7 +12,43 @@
      "parameters"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: 2026-02-17T19:20:31.192780+09:00\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.9.23\n",
+      "IPython version      : 8.18.1\n",
+      "\n",
+      "metapool    : 0+untagged.232.g2b2eaec\n",
+      "sample_sheet: 0.13.0\n",
+      "openpyxl    : 3.1.5\n",
+      "\n",
+      "Compiler    : Clang 18.1.8 \n",
+      "OS          : Darwin\n",
+      "Release     : 23.4.0\n",
+      "Machine     : arm64\n",
+      "Processor   : arm\n",
+      "CPU cores   : 8\n",
+      "Architecture: 64bit\n",
+      "\n",
+      "Hostname: seosonghuis-MacBook-Air.local\n",
+      "\n",
+      "pandas      : 2.3.1\n",
+      "seaborn     : 0.13.2\n",
+      "matplotlib  : 3.9.4\n",
+      "metapool    : 0+untagged.232.g2b2eaec\n",
+      "json        : 2.0.9\n",
+      "numpy       : 2.0.2\n",
+      "qiita_client: 0.1.0.dev0\n",
+      "re          : 2.2.1\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "### This cell has `parameters` tag\n",
     "%reload_ext watermark\n",
@@ -28,15 +64,243 @@
     "    PM_BLANK_KEY, MINIPICO_LIB_CONC_KEY, PM_LIB_WELL_KEY, \n",
     "    TELLSEQ_BARCODE_ID_KEY)\n",
     "%watermark -i -v -iv -m -h -p metapool,sample_sheet,openpyxl -u\n",
-    "\n",
     "test_dict = None"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# packages in environment at /opt/anaconda3/envs/metapool:\n",
+      "#\n",
+      "# Name                    Version                   Build  Channel\n",
+      "ansicolors                1.1.8                    pypi_0    pypi\n",
+      "anyio                     4.10.0             pyhe01879c_0    conda-forge\n",
+      "appnope                   0.1.4              pyhd8ed1ab_1    conda-forge\n",
+      "argon2-cffi               25.1.0             pyhd8ed1ab_0    conda-forge\n",
+      "argon2-cffi-bindings      25.1.0           py39he7485ab_0    conda-forge\n",
+      "arrow                     1.3.0              pyhd8ed1ab_1    conda-forge\n",
+      "asttokens                 3.0.0              pyhd8ed1ab_1    conda-forge\n",
+      "async-lru                 2.0.5              pyh29332c3_0    conda-forge\n",
+      "attrs                     25.3.0             pyh71513ae_0    conda-forge\n",
+      "babel                     2.17.0             pyhd8ed1ab_0    conda-forge\n",
+      "beautifulsoup4            4.13.4             pyha770c72_0    conda-forge\n",
+      "biom-format               2.1.16                   pypi_0    pypi\n",
+      "bleach                    6.2.0              pyh29332c3_4    conda-forge\n",
+      "bleach-with-css           6.2.0                h82add2a_4    conda-forge\n",
+      "brotli                    1.1.0                h5505292_3    conda-forge\n",
+      "brotli-bin                1.1.0                h5505292_3    conda-forge\n",
+      "brotli-python             1.1.0            py39h941272d_3    conda-forge\n",
+      "bzip2                     1.0.8                h99b78c6_7    conda-forge\n",
+      "ca-certificates           2025.8.3             hbd8a1cb_0    conda-forge\n",
+      "cached-property           1.5.2                hd8ed1ab_1    conda-forge\n",
+      "cached_property           1.5.2              pyha770c72_1    conda-forge\n",
+      "certifi                   2025.8.3           pyhd8ed1ab_0    conda-forge\n",
+      "cffi                      1.17.1           py39h7f933ea_0    conda-forge\n",
+      "charset-normalizer        3.4.3              pyhd8ed1ab_0    conda-forge\n",
+      "click                     8.1.8                    pypi_0    pypi\n",
+      "comm                      0.2.3              pyhe01879c_0    conda-forge\n",
+      "contourpy                 1.3.0            py39h85b62ae_2    conda-forge\n",
+      "coverage                  7.10.3                   pypi_0    pypi\n",
+      "cycler                    0.12.1             pyhd8ed1ab_1    conda-forge\n",
+      "debugpy                   1.8.16           py39hd866990_0    conda-forge\n",
+      "decorator                 5.2.1              pyhd8ed1ab_0    conda-forge\n",
+      "defusedxml                0.7.1              pyhd8ed1ab_0    conda-forge\n",
+      "entrypoints               0.4                      pypi_0    pypi\n",
+      "et_xmlfile                2.0.0              pyhd8ed1ab_1    conda-forge\n",
+      "exceptiongroup            1.3.0              pyhd8ed1ab_0    conda-forge\n",
+      "executing                 2.2.0              pyhd8ed1ab_0    conda-forge\n",
+      "flake8                    7.3.0              pyhd8ed1ab_0    conda-forge\n",
+      "fonttools                 4.59.0           py39hb270ea8_0    conda-forge\n",
+      "fqdn                      1.5.1              pyhd8ed1ab_1    conda-forge\n",
+      "freetype                  2.13.3               hce30654_1    conda-forge\n",
+      "future                    1.0.0                    pypi_0    pypi\n",
+      "h11                       0.16.0             pyhd8ed1ab_0    conda-forge\n",
+      "h2                        4.2.0              pyhd8ed1ab_0    conda-forge\n",
+      "h5py                      3.14.0                   pypi_0    pypi\n",
+      "hpack                     4.1.0              pyhd8ed1ab_0    conda-forge\n",
+      "httpcore                  1.0.9              pyh29332c3_0    conda-forge\n",
+      "httpx                     0.28.1             pyhd8ed1ab_0    conda-forge\n",
+      "hyperframe                6.1.0              pyhd8ed1ab_0    conda-forge\n",
+      "icu                       75.1                 hfee45f7_0    conda-forge\n",
+      "idna                      3.10               pyhd8ed1ab_1    conda-forge\n",
+      "importlib-metadata        8.7.0              pyhe01879c_1    conda-forge\n",
+      "importlib-resources       6.5.2              pyhd8ed1ab_0    conda-forge\n",
+      "importlib_resources       6.5.2              pyhd8ed1ab_0    conda-forge\n",
+      "ipykernel                 6.30.1             pyh92f572d_0    conda-forge\n",
+      "ipython                   8.18.1             pyh707e725_3    conda-forge\n",
+      "ipywidgets                8.1.7              pyhd8ed1ab_0    conda-forge\n",
+      "isoduration               20.11.0            pyhd8ed1ab_1    conda-forge\n",
+      "jedi                      0.19.2             pyhd8ed1ab_1    conda-forge\n",
+      "jinja2                    3.1.6              pyhd8ed1ab_0    conda-forge\n",
+      "joblib                    1.5.1              pyhd8ed1ab_0    conda-forge\n",
+      "json5                     0.12.0             pyhd8ed1ab_0    conda-forge\n",
+      "jsonpointer               3.0.0            py39h2804cbe_1    conda-forge\n",
+      "jsonschema                4.25.0             pyhe01879c_0    conda-forge\n",
+      "jsonschema-specifications 2025.4.1           pyh29332c3_0    conda-forge\n",
+      "jsonschema-with-format-nongpl 4.25.0               he01879c_0    conda-forge\n",
+      "jupyter                   1.1.1              pyhd8ed1ab_1    conda-forge\n",
+      "jupyter-lsp               2.2.6              pyhe01879c_0    conda-forge\n",
+      "jupyter_client            8.6.3              pyhd8ed1ab_1    conda-forge\n",
+      "jupyter_console           6.6.3              pyhd8ed1ab_1    conda-forge\n",
+      "jupyter_core              5.8.1              pyh31011fe_0    conda-forge\n",
+      "jupyter_events            0.12.0             pyh29332c3_0    conda-forge\n",
+      "jupyter_server            2.16.0             pyhe01879c_0    conda-forge\n",
+      "jupyter_server_terminals  0.5.3              pyhd8ed1ab_1    conda-forge\n",
+      "jupyterlab                4.4.5              pyhd8ed1ab_0    conda-forge\n",
+      "jupyterlab_pygments       0.3.0              pyhd8ed1ab_2    conda-forge\n",
+      "jupyterlab_server         2.27.3             pyhd8ed1ab_1    conda-forge\n",
+      "jupyterlab_widgets        3.0.15             pyhd8ed1ab_0    conda-forge\n",
+      "kiwisolver                1.4.7            py39h157d57c_0    conda-forge\n",
+      "krb5                      1.21.3               h237132a_0    conda-forge\n",
+      "lark                      1.2.2              pyhd8ed1ab_1    conda-forge\n",
+      "lcms2                     2.17                 h7eeda09_0    conda-forge\n",
+      "lerc                      4.0.0                hd64df32_1    conda-forge\n",
+      "libblas                   3.9.0           34_h10e41b3_openblas    conda-forge\n",
+      "libbrotlicommon           1.1.0                h5505292_3    conda-forge\n",
+      "libbrotlidec              1.1.0                h5505292_3    conda-forge\n",
+      "libbrotlienc              1.1.0                h5505292_3    conda-forge\n",
+      "libcblas                  3.9.0           34_hb3479ef_openblas    conda-forge\n",
+      "libcxx                    20.1.8               hf598326_1    conda-forge\n",
+      "libdeflate                1.24                 h5773f1b_0    conda-forge\n",
+      "libedit                   3.1.20250104    pl5321hafb1f1b_0    conda-forge\n",
+      "libexpat                  2.7.1                hec049ff_0    conda-forge\n",
+      "libffi                    3.4.6                h1da3d7d_1    conda-forge\n",
+      "libfreetype               2.13.3               hce30654_1    conda-forge\n",
+      "libfreetype6              2.13.3               h1d14073_1    conda-forge\n",
+      "libgfortran               15.1.0               hfdf1602_0    conda-forge\n",
+      "libgfortran5              15.1.0               hb74de2c_0    conda-forge\n",
+      "libjpeg-turbo             3.1.0                h5505292_0    conda-forge\n",
+      "liblapack                 3.9.0           34_hc9a63f6_openblas    conda-forge\n",
+      "liblzma                   5.8.1                h39f12f2_2    conda-forge\n",
+      "libopenblas               0.3.30          openmp_h60d53f8_1    conda-forge\n",
+      "libpng                    1.6.50               h280e0eb_1    conda-forge\n",
+      "libsodium                 1.0.20               h99b78c6_0    conda-forge\n",
+      "libsqlite                 3.50.4               h4237e3c_0    conda-forge\n",
+      "libtiff                   4.7.0                h2f21f7c_5    conda-forge\n",
+      "libwebp-base              1.6.0                h07db88b_0    conda-forge\n",
+      "libxcb                    1.17.0               hdb1d25a_0    conda-forge\n",
+      "libzlib                   1.3.1                h8359307_2    conda-forge\n",
+      "llvm-openmp               20.1.8               hbb9b287_1    conda-forge\n",
+      "markupsafe                3.0.2            py39hefdd603_1    conda-forge\n",
+      "matplotlib                3.9.4            py39hdf13c20_0    conda-forge\n",
+      "matplotlib-base           3.9.4            py39h7251d6c_0    conda-forge\n",
+      "matplotlib-inline         0.1.7              pyhd8ed1ab_1    conda-forge\n",
+      "mccabe                    0.7.0              pyhd8ed1ab_1    conda-forge\n",
+      "metapool                  0+untagged.232.g2b2eaec          pypi_0    pypi\n",
+      "mistune                   3.1.3              pyh29332c3_0    conda-forge\n",
+      "munkres                   1.1.4              pyhd8ed1ab_1    conda-forge\n",
+      "nbclient                  0.10.2             pyhd8ed1ab_0    conda-forge\n",
+      "nbconvert-core            7.16.6             pyh29332c3_0    conda-forge\n",
+      "nbformat                  5.10.4             pyhd8ed1ab_1    conda-forge\n",
+      "ncurses                   6.5                  h5e97a16_3    conda-forge\n",
+      "nest-asyncio              1.6.0              pyhd8ed1ab_1    conda-forge\n",
+      "nose                      1.3.7                   py_1006    conda-forge\n",
+      "notebook                  7.4.5              pyhd8ed1ab_0    conda-forge\n",
+      "notebook-shim             0.2.4              pyhd8ed1ab_1    conda-forge\n",
+      "numpy                     2.0.2            py39h3ba1154_1    conda-forge\n",
+      "openjpeg                  2.5.3                h889cd5d_1    conda-forge\n",
+      "openpyxl                  3.1.5            py39h5f73de6_1    conda-forge\n",
+      "openssl                   3.5.2                he92f556_0    conda-forge\n",
+      "overrides                 7.7.0              pyhd8ed1ab_1    conda-forge\n",
+      "packaging                 25.0               pyh29332c3_1    conda-forge\n",
+      "pandas                    2.3.1            py39h6aaa60c_0    conda-forge\n",
+      "pandocfilters             1.5.0              pyhd8ed1ab_0    conda-forge\n",
+      "papermill                 2.6.0                    pypi_0    pypi\n",
+      "parso                     0.8.4              pyhd8ed1ab_1    conda-forge\n",
+      "patsy                     1.0.1              pyhd8ed1ab_1    conda-forge\n",
+      "pep8                      1.7.1                      py_0    conda-forge\n",
+      "pexpect                   4.9.0              pyhd8ed1ab_1    conda-forge\n",
+      "pickleshare               0.7.5           pyhd8ed1ab_1004    conda-forge\n",
+      "pillow                    11.3.0           py39hfea3036_0    conda-forge\n",
+      "pip                       25.2               pyh8b19718_0    conda-forge\n",
+      "platformdirs              4.3.8              pyhe01879c_0    conda-forge\n",
+      "prometheus_client         0.22.1             pyhd8ed1ab_0    conda-forge\n",
+      "prompt-toolkit            3.0.51             pyha770c72_0    conda-forge\n",
+      "prompt_toolkit            3.0.51               hd8ed1ab_0    conda-forge\n",
+      "psutil                    7.0.0            py39hf3bc14e_0    conda-forge\n",
+      "pthread-stubs             0.4               hd74edd7_1002    conda-forge\n",
+      "ptyprocess                0.7.0              pyhd8ed1ab_1    conda-forge\n",
+      "pure_eval                 0.2.3              pyhd8ed1ab_1    conda-forge\n",
+      "pycodestyle               2.14.0             pyhd8ed1ab_0    conda-forge\n",
+      "pycparser                 2.22               pyh29332c3_1    conda-forge\n",
+      "pyflakes                  3.4.0              pyhd8ed1ab_0    conda-forge\n",
+      "pygments                  2.19.2             pyhd8ed1ab_0    conda-forge\n",
+      "pyobjc-core               11.1             py39h65d0b63_0    conda-forge\n",
+      "pyobjc-framework-cocoa    11.1             py39hebff0d6_0    conda-forge\n",
+      "pyparsing                 3.2.3              pyhe01879c_2    conda-forge\n",
+      "pysocks                   1.7.1              pyha55dd90_7    conda-forge\n",
+      "python                    3.9.23          h7139b31_0_cpython    conda-forge\n",
+      "python-dateutil           2.9.0.post0        pyhe01879c_2    conda-forge\n",
+      "python-fastjsonschema     2.21.1             pyhd8ed1ab_0    conda-forge\n",
+      "python-json-logger        2.0.7              pyhd8ed1ab_0    conda-forge\n",
+      "python-tzdata             2025.2             pyhd8ed1ab_0    conda-forge\n",
+      "python_abi                3.9                      8_cp39    conda-forge\n",
+      "pytz                      2025.2             pyhd8ed1ab_0    conda-forge\n",
+      "pyyaml                    6.0.2            py39hefdd603_2    conda-forge\n",
+      "pyzmq                     27.0.1           py39h6c7bd39_0    conda-forge\n",
+      "qhull                     2020.2               h420ef59_5    conda-forge\n",
+      "qiita-client              0.1.0.dev0               pypi_0    pypi\n",
+      "readline                  8.2                  h1d1bf99_2    conda-forge\n",
+      "referencing               0.36.2             pyh29332c3_0    conda-forge\n",
+      "requests                  2.32.4             pyhd8ed1ab_0    conda-forge\n",
+      "rfc3339-validator         0.1.4              pyhd8ed1ab_1    conda-forge\n",
+      "rfc3986-validator         0.1.1              pyh9f0ad1d_0    conda-forge\n",
+      "rfc3987-syntax            1.1.0              pyhe01879c_1    conda-forge\n",
+      "rpds-py                   0.27.0           py39hf64921a_0    conda-forge\n",
+      "sample-sheet              0.13.0                   pypi_0    pypi\n",
+      "scikit-learn              1.6.1            py39h451895d_0    conda-forge\n",
+      "scipy                     1.13.1           py39h3d5391c_0    conda-forge\n",
+      "seaborn                   0.13.2               hd8ed1ab_3    conda-forge\n",
+      "seaborn-base              0.13.2             pyhd8ed1ab_3    conda-forge\n",
+      "send2trash                1.8.3              pyh31c8845_1    conda-forge\n",
+      "setuptools                80.9.0             pyhff2d567_0    conda-forge\n",
+      "six                       1.17.0             pyhe01879c_1    conda-forge\n",
+      "sniffio                   1.3.1              pyhd8ed1ab_1    conda-forge\n",
+      "soupsieve                 2.7                pyhd8ed1ab_0    conda-forge\n",
+      "stack_data                0.6.3              pyhd8ed1ab_1    conda-forge\n",
+      "statsmodels               0.14.5           py39hbd04bc9_0    conda-forge\n",
+      "tabulate                  0.9.0                    pypi_0    pypi\n",
+      "tenacity                  9.1.2                    pypi_0    pypi\n",
+      "terminado                 0.18.1             pyh31c8845_0    conda-forge\n",
+      "terminaltables            3.1.10                   pypi_0    pypi\n",
+      "threadpoolctl             3.6.0              pyhecae5ae_0    conda-forge\n",
+      "tinycss2                  1.4.0              pyhd8ed1ab_0    conda-forge\n",
+      "tk                        8.6.13               h892fb3f_2    conda-forge\n",
+      "tomli                     2.2.1              pyhe01879c_2    conda-forge\n",
+      "tornado                   6.5.2            py39he7485ab_0    conda-forge\n",
+      "tqdm                      4.67.1                   pypi_0    pypi\n",
+      "traitlets                 5.14.3             pyhd8ed1ab_1    conda-forge\n",
+      "types-python-dateutil     2.9.0.20250809     pyhd8ed1ab_0    conda-forge\n",
+      "typing-extensions         4.14.1               h4440ef1_0    conda-forge\n",
+      "typing_extensions         4.14.1             pyhe01879c_0    conda-forge\n",
+      "typing_utils              0.1.0              pyhd8ed1ab_1    conda-forge\n",
+      "tzdata                    2025b                h78e105d_0    conda-forge\n",
+      "unicodedata2              16.0.0           py39hf3bc14e_0    conda-forge\n",
+      "uri-template              1.3.0              pyhd8ed1ab_1    conda-forge\n",
+      "urllib3                   2.5.0              pyhd8ed1ab_0    conda-forge\n",
+      "watermark                 2.5.0                    pypi_0    pypi\n",
+      "wcwidth                   0.2.13             pyhd8ed1ab_1    conda-forge\n",
+      "webcolors                 24.11.1            pyhd8ed1ab_0    conda-forge\n",
+      "webencodings              0.5.1              pyhd8ed1ab_3    conda-forge\n",
+      "websocket-client          1.8.0              pyhd8ed1ab_1    conda-forge\n",
+      "wheel                     0.45.1             pyhd8ed1ab_1    conda-forge\n",
+      "widgetsnbextension        4.0.14             pyhd8ed1ab_0    conda-forge\n",
+      "xorg-libxau               1.0.12               h5505292_0    conda-forge\n",
+      "xorg-libxdmcp             1.1.5                hd74edd7_0    conda-forge\n",
+      "yaml                      0.2.5                h925e9cb_3    conda-forge\n",
+      "zeromq                    4.3.5                hc1bb282_7    conda-forge\n",
+      "zipp                      3.23.0             pyhd8ed1ab_0    conda-forge\n",
+      "zstandard                 0.23.0           py39hf3bc14e_2    conda-forge\n",
+      "zstd                      1.5.7                h6491c7d_2    conda-forge\n"
+     ]
+    }
+   ],
    "source": [
     "! conda list"
    ]
@@ -50,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,9 +382,237 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sample</th>\n",
+       "      <th>Col</th>\n",
+       "      <th>Compressed Plate Name</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Plate Position</th>\n",
+       "      <th>Project Abbreviation</th>\n",
+       "      <th>Project Name</th>\n",
+       "      <th>Project Plate</th>\n",
+       "      <th>RackID</th>\n",
+       "      <th>Row</th>\n",
+       "      <th>...</th>\n",
+       "      <th>i5 plate</th>\n",
+       "      <th>i5 well_row</th>\n",
+       "      <th>i5 well_col</th>\n",
+       "      <th>barcode_set_id</th>\n",
+       "      <th>barcode_id</th>\n",
+       "      <th>MiniPico Library DNA Concentration</th>\n",
+       "      <th>MiniPico Library Concentration</th>\n",
+       "      <th>sample sheet Sample_ID</th>\n",
+       "      <th>MiniPico Pooled Volume</th>\n",
+       "      <th>Well_description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01LJ01593.V8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C501</td>\n",
+       "      <td>0.635</td>\n",
+       "      <td>1.924242</td>\n",
+       "      <td>01LJ01593_V8</td>\n",
+       "      <td>1979.166667</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution.01LJ01593...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>01LJ01300.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_10to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>3</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_3_10to1dilution</td>\n",
+       "      <td>TestProjA_Plate_3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C509</td>\n",
+       "      <td>5.113</td>\n",
+       "      <td>15.493939</td>\n",
+       "      <td>01LJ01300_V5</td>\n",
+       "      <td>1979.166667</td>\n",
+       "      <td>TestProjA_10002_Plate_3_10to1dilution.01LJ0130...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>01LJ02603.V8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>...</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C502</td>\n",
+       "      <td>0.677</td>\n",
+       "      <td>2.051515</td>\n",
+       "      <td>01LJ02603_V8</td>\n",
+       "      <td>1979.166667</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution.01LJ02603...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>01LJ01344.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>3</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_3_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_3</td>\n",
+       "      <td>B</td>\n",
+       "      <td>...</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>D</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C510</td>\n",
+       "      <td>1.047</td>\n",
+       "      <td>3.172727</td>\n",
+       "      <td>01LJ01344_V5</td>\n",
+       "      <td>1979.166667</td>\n",
+       "      <td>TestProjA_10002_Plate_3_2to1dilution.01LJ01344...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>01LJ04170.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>TellSeq_Barcode_Plate_1_LN2409001_EXP052026</td>\n",
+       "      <td>E</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C503</td>\n",
+       "      <td>3.777</td>\n",
+       "      <td>11.445455</td>\n",
+       "      <td>01LJ04170_V5</td>\n",
+       "      <td>1979.166667</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution.01LJ04170...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 47 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Sample  Col                  Compressed Plate Name      Date  \\\n",
+       "0  01LJ01593.V8   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "1  01LJ01300.V5   10  TestProjA_10002_1_2_3_4_10to1dilution  20240911   \n",
+       "2  01LJ02603.V8   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "3  01LJ01344.V5   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "4  01LJ04170.V5   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "\n",
+       "   Plate Position Project Abbreviation     Project Name  \\\n",
+       "0               1            TestProjA  TestProjA_10002   \n",
+       "1               3            TestProjA  TestProjA_10002   \n",
+       "2               1            TestProjA  TestProjA_10002   \n",
+       "3               3            TestProjA  TestProjA_10002   \n",
+       "4               1            TestProjA  TestProjA_10002   \n",
+       "\n",
+       "                           Project Plate             RackID Row  ...  \\\n",
+       "0   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   A  ...   \n",
+       "1  TestProjA_10002_Plate_3_10to1dilution  TestProjA_Plate_3   A  ...   \n",
+       "2   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   B  ...   \n",
+       "3   TestProjA_10002_Plate_3_2to1dilution  TestProjA_Plate_3   B  ...   \n",
+       "4   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   C  ...   \n",
+       "\n",
+       "                                      i5 plate  i5 well_row i5 well_col  \\\n",
+       "0  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            A           1   \n",
+       "1  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            B           1   \n",
+       "2  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            C           1   \n",
+       "3  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            D           1   \n",
+       "4  TellSeq_Barcode_Plate_1_LN2409001_EXP052026            E           1   \n",
+       "\n",
+       "   barcode_set_id barcode_id MiniPico Library DNA Concentration  \\\n",
+       "0       col19to24       C501                              0.635   \n",
+       "1       col19to24       C509                              5.113   \n",
+       "2       col19to24       C502                              0.677   \n",
+       "3       col19to24       C510                              1.047   \n",
+       "4       col19to24       C503                              3.777   \n",
+       "\n",
+       "   MiniPico Library Concentration sample sheet Sample_ID  \\\n",
+       "0                        1.924242           01LJ01593_V8   \n",
+       "1                       15.493939           01LJ01300_V5   \n",
+       "2                        2.051515           01LJ02603_V8   \n",
+       "3                        3.172727           01LJ01344_V5   \n",
+       "4                       11.445455           01LJ04170_V5   \n",
+       "\n",
+       "  MiniPico Pooled Volume                                   Well_description  \n",
+       "0            1979.166667  TestProjA_10002_Plate_1_2to1dilution.01LJ01593...  \n",
+       "1            1979.166667  TestProjA_10002_Plate_3_10to1dilution.01LJ0130...  \n",
+       "2            1979.166667  TestProjA_10002_Plate_1_2to1dilution.01LJ02603...  \n",
+       "3            1979.166667  TestProjA_10002_Plate_3_2to1dilution.01LJ01344...  \n",
+       "4            1979.166667  TestProjA_10002_Plate_1_2to1dilution.01LJ04170...  \n",
+       "\n",
+       "[5 rows x 47 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "plate_df = pd.read_csv(plate_df_set_fp, sep='\\t')\n",
     "plate_df.head()"
@@ -135,9 +627,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "## DECISION -- verify no duplicate barcodes\n",
     "plate_df[TELLSEQ_BARCODE_ID_KEY].value_counts().nunique() == 1"
@@ -145,9 +648,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'col19to24'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# split the evp_plate_df_set_fp to extract the set id\n",
     "_, set_str = os.path.splitext(plate_df_set_fp)[0].rsplit(SET_SUFFIX, 1)\n",
@@ -157,7 +671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,9 +681,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['A19', 'A20', 'A21', 'A22', 'A23', 'A24'],\n",
+       "       ['B19', 'B20', 'B21', 'B22', 'B23', 'B24'],\n",
+       "       ['C19', 'C20', 'C21', 'C22', 'C23', 'C24'],\n",
+       "       ['D19', 'D20', 'D21', 'D22', 'D23', 'D24'],\n",
+       "       ['E19', 'E20', 'E21', 'E22', 'E23', 'E24'],\n",
+       "       ['F19', 'F20', 'F21', 'F22', 'F23', 'F24'],\n",
+       "       ['G19', 'G20', 'G21', 'G22', 'G23', 'G24'],\n",
+       "       ['H19', 'H20', 'H21', 'H22', 'H23', 'H24'],\n",
+       "       ['I19', 'I20', 'I21', 'I22', 'I23', 'I24'],\n",
+       "       ['J19', 'J20', 'J21', 'J22', 'J23', 'J24'],\n",
+       "       ['K19', 'K20', 'K21', 'K22', 'K23', 'K24'],\n",
+       "       ['L19', 'L20', 'L21', 'L22', 'L23', 'L24'],\n",
+       "       ['M19', 'M20', 'M21', 'M22', 'M23', 'M24'],\n",
+       "       ['N19', 'N20', 'N21', 'N22', 'N23', 'N24'],\n",
+       "       ['O19', 'O20', 'O21', 'O22', 'O23', 'O24'],\n",
+       "       ['P19', 'P20', 'P21', 'P22', 'P23', 'P24']], dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "source_well_names = make_compressed_2d_array(\n",
     "    plate_df, data_col=PM_LIB_WELL_KEY, \n",
@@ -188,7 +728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,7 +742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,11 +766,239 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sample</th>\n",
+       "      <th>Col</th>\n",
+       "      <th>Compressed Plate Name</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Plate Position</th>\n",
+       "      <th>Project Abbreviation</th>\n",
+       "      <th>Project Name</th>\n",
+       "      <th>Project Plate</th>\n",
+       "      <th>RackID</th>\n",
+       "      <th>Row</th>\n",
+       "      <th>...</th>\n",
+       "      <th>i5 well_col</th>\n",
+       "      <th>barcode_set_id</th>\n",
+       "      <th>barcode_id</th>\n",
+       "      <th>MiniPico Library DNA Concentration</th>\n",
+       "      <th>MiniPico Library Concentration</th>\n",
+       "      <th>sample sheet Sample_ID</th>\n",
+       "      <th>MiniPico Pooled Volume</th>\n",
+       "      <th>Well_description</th>\n",
+       "      <th>Raw Reads</th>\n",
+       "      <th>Filtered Reads</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01LJ01593.V8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C501</td>\n",
+       "      <td>0.635</td>\n",
+       "      <td>1.924242</td>\n",
+       "      <td>01LJ01593_V8</td>\n",
+       "      <td>1979.166667</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution.01LJ01593...</td>\n",
+       "      <td>710</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>01LJ01300.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_10to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>3</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_3_10to1dilution</td>\n",
+       "      <td>TestProjA_Plate_3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C509</td>\n",
+       "      <td>5.113</td>\n",
+       "      <td>15.493939</td>\n",
+       "      <td>01LJ01300_V5</td>\n",
+       "      <td>1979.166667</td>\n",
+       "      <td>TestProjA_10002_Plate_3_10to1dilution.01LJ0130...</td>\n",
+       "      <td>40280</td>\n",
+       "      <td>1064.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>01LJ02603.V8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C502</td>\n",
+       "      <td>0.677</td>\n",
+       "      <td>2.051515</td>\n",
+       "      <td>01LJ02603_V8</td>\n",
+       "      <td>1979.166667</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution.01LJ02603...</td>\n",
+       "      <td>1374</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>01LJ01344.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>3</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_3_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_3</td>\n",
+       "      <td>B</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C510</td>\n",
+       "      <td>1.047</td>\n",
+       "      <td>3.172727</td>\n",
+       "      <td>01LJ01344_V5</td>\n",
+       "      <td>1979.166667</td>\n",
+       "      <td>TestProjA_10002_Plate_3_2to1dilution.01LJ01344...</td>\n",
+       "      <td>6802</td>\n",
+       "      <td>378.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>01LJ04170.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_2to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution</td>\n",
+       "      <td>TestProjA_Plate_1</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>col19to24</td>\n",
+       "      <td>C503</td>\n",
+       "      <td>3.777</td>\n",
+       "      <td>11.445455</td>\n",
+       "      <td>01LJ04170_V5</td>\n",
+       "      <td>1979.166667</td>\n",
+       "      <td>TestProjA_10002_Plate_1_2to1dilution.01LJ04170...</td>\n",
+       "      <td>65646</td>\n",
+       "      <td>768.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 49 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Sample  Col                  Compressed Plate Name      Date  \\\n",
+       "0  01LJ01593.V8   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "1  01LJ01300.V5   10  TestProjA_10002_1_2_3_4_10to1dilution  20240911   \n",
+       "2  01LJ02603.V8   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "3  01LJ01344.V5   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "4  01LJ04170.V5   10   TestProjA_10002_1_2_3_4_2to1dilution  20240911   \n",
+       "\n",
+       "   Plate Position Project Abbreviation     Project Name  \\\n",
+       "0               1            TestProjA  TestProjA_10002   \n",
+       "1               3            TestProjA  TestProjA_10002   \n",
+       "2               1            TestProjA  TestProjA_10002   \n",
+       "3               3            TestProjA  TestProjA_10002   \n",
+       "4               1            TestProjA  TestProjA_10002   \n",
+       "\n",
+       "                           Project Plate             RackID Row  ...  \\\n",
+       "0   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   A  ...   \n",
+       "1  TestProjA_10002_Plate_3_10to1dilution  TestProjA_Plate_3   A  ...   \n",
+       "2   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   B  ...   \n",
+       "3   TestProjA_10002_Plate_3_2to1dilution  TestProjA_Plate_3   B  ...   \n",
+       "4   TestProjA_10002_Plate_1_2to1dilution  TestProjA_Plate_1   C  ...   \n",
+       "\n",
+       "   i5 well_col  barcode_set_id barcode_id  MiniPico Library DNA Concentration  \\\n",
+       "0            1       col19to24       C501                               0.635   \n",
+       "1            1       col19to24       C509                               5.113   \n",
+       "2            1       col19to24       C502                               0.677   \n",
+       "3            1       col19to24       C510                               1.047   \n",
+       "4            1       col19to24       C503                               3.777   \n",
+       "\n",
+       "  MiniPico Library Concentration sample sheet Sample_ID  \\\n",
+       "0                       1.924242           01LJ01593_V8   \n",
+       "1                      15.493939           01LJ01300_V5   \n",
+       "2                       2.051515           01LJ02603_V8   \n",
+       "3                       3.172727           01LJ01344_V5   \n",
+       "4                      11.445455           01LJ04170_V5   \n",
+       "\n",
+       "   MiniPico Pooled Volume                                   Well_description  \\\n",
+       "0             1979.166667  TestProjA_10002_Plate_1_2to1dilution.01LJ01593...   \n",
+       "1             1979.166667  TestProjA_10002_Plate_3_10to1dilution.01LJ0130...   \n",
+       "2             1979.166667  TestProjA_10002_Plate_1_2to1dilution.01LJ02603...   \n",
+       "3             1979.166667  TestProjA_10002_Plate_3_2to1dilution.01LJ01344...   \n",
+       "4             1979.166667  TestProjA_10002_Plate_1_2to1dilution.01LJ04170...   \n",
+       "\n",
+       "  Raw Reads  Filtered Reads  \n",
+       "0       710             NaN  \n",
+       "1     40280          1064.0  \n",
+       "2      1374             NaN  \n",
+       "3      6802           378.0  \n",
+       "4     65646           768.0  \n",
+       "\n",
+       "[5 rows x 49 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Merge read_counts_df with plate_df \n",
     "plate_df_w_reads = merge_read_counts(\n",
@@ -249,9 +1017,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "reads_column = 'Raw Reads'\n",
     "\n",
@@ -293,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,9 +1084,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plate_df_normalized = calculate_iseqnorm_pooling_volumes(\n",
     "    plate_df_w_reads,dynamic_range=dynamic_range, normalization_column='Raw Reads')"
@@ -315,9 +1105,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[500.        , 500.        , 100.        , 100.        ,\n",
+       "         65.1612368 ,  52.30061098],\n",
+       "       [155.70233366, 395.75430598, 100.        , 100.        ,\n",
+       "         46.17844196, 163.10315671],\n",
+       "       [500.        , 372.53852822, 100.        , 100.        ,\n",
+       "        157.75949304, 297.36295881],\n",
+       "       [500.        , 500.        , 100.        , 100.        ,\n",
+       "        170.89040008, 500.        ],\n",
+       "       [ 66.55759681, 500.        , 100.        , 100.        ,\n",
+       "        500.        , 372.62475915],\n",
+       "       [500.        , 500.        , 100.        , 100.        ,\n",
+       "        500.        , 500.        ],\n",
+       "       [500.        ,  71.84570652, 100.        , 100.        ,\n",
+       "        500.        , 500.        ],\n",
+       "       [500.        , 213.91586867, 100.        , 100.        ,\n",
+       "        500.        , 500.        ],\n",
+       "       [500.        , 464.33197911, 100.        , 100.        ,\n",
+       "        500.        , 500.        ],\n",
+       "       [500.        ,  40.        , 100.        , 100.        ,\n",
+       "        500.        , 500.        ],\n",
+       "       [500.        , 500.        , 100.        , 100.        ,\n",
+       "        500.        , 127.79974685],\n",
+       "       [387.87557282, 500.        , 100.        , 100.        ,\n",
+       "        500.        , 500.        ],\n",
+       "       [500.        , 500.        , 100.        , 100.        ,\n",
+       "        500.        , 379.76607615],\n",
+       "       [500.        , 500.        , 100.        , 100.        ,\n",
+       "        500.        , 500.        ],\n",
+       "       [500.        , 175.59840354, 100.        , 100.        ,\n",
+       "        500.        , 500.        ],\n",
+       "       [500.        , 500.        , 100.        , 100.        ,\n",
+       "        500.        , 500.        ]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ISEQ_NORM_VOL_KEY = 'iSeq normpool volume'\n",
     "\n",
@@ -329,9 +1161,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pool concentration: 3.29\n",
+      "Pool volume: 29377.07\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x2000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# visualize\n",
     "conc, vol = estimate_pool_conc_vol(\n",
@@ -352,9 +1203,240 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sample</th>\n",
+       "      <th>Col</th>\n",
+       "      <th>Compressed Plate Name</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Plate Position</th>\n",
+       "      <th>Project Abbreviation</th>\n",
+       "      <th>Project Name</th>\n",
+       "      <th>Project Plate</th>\n",
+       "      <th>RackID</th>\n",
+       "      <th>Row</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Filtered Reads</th>\n",
+       "      <th>proportion</th>\n",
+       "      <th>LoadingFactor</th>\n",
+       "      <th>iSeq normpool volume</th>\n",
+       "      <th>projected_reads</th>\n",
+       "      <th>projected_proportion</th>\n",
+       "      <th>projected_HO_reads</th>\n",
+       "      <th>on_target_proportion</th>\n",
+       "      <th>projected_off_target_reads</th>\n",
+       "      <th>projected_on_target_reads</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>01LJ02814.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_undiluted</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>4</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_4_undiluted</td>\n",
+       "      <td>TestProjA_Plate_4</td>\n",
+       "      <td>E</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1830.0</td>\n",
+       "      <td>0.083158</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>80806.0</td>\n",
+       "      <td>0.035662</td>\n",
+       "      <td>1.426483e+08</td>\n",
+       "      <td>0.022647</td>\n",
+       "      <td>1.394178e+08</td>\n",
+       "      <td>3.230533e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>01LJ00438.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_undiluted</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>2</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_2_undiluted</td>\n",
+       "      <td>TestProjA_Plate_2</td>\n",
+       "      <td>B</td>\n",
+       "      <td>...</td>\n",
+       "      <td>206.0</td>\n",
+       "      <td>0.021368</td>\n",
+       "      <td>3.891639</td>\n",
+       "      <td>372.538528</td>\n",
+       "      <td>80806.0</td>\n",
+       "      <td>0.035662</td>\n",
+       "      <td>1.426483e+08</td>\n",
+       "      <td>0.009921</td>\n",
+       "      <td>1.412331e+08</td>\n",
+       "      <td>1.415217e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>01LJ00544.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_10to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>2</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_2_10to1dilution</td>\n",
+       "      <td>TestProjA_Plate_2</td>\n",
+       "      <td>D</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4788.0</td>\n",
+       "      <td>0.065124</td>\n",
+       "      <td>1.276919</td>\n",
+       "      <td>71.845707</td>\n",
+       "      <td>80806.0</td>\n",
+       "      <td>0.035662</td>\n",
+       "      <td>1.426483e+08</td>\n",
+       "      <td>0.075661</td>\n",
+       "      <td>1.318554e+08</td>\n",
+       "      <td>1.079296e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>01LJ00586.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_10to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>2</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_2_10to1dilution</td>\n",
+       "      <td>TestProjA_Plate_2</td>\n",
+       "      <td>E</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.017731</td>\n",
+       "      <td>4.689843</td>\n",
+       "      <td>464.331979</td>\n",
+       "      <td>80806.0</td>\n",
+       "      <td>0.035662</td>\n",
+       "      <td>1.426483e+08</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>01LJ01024.V5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TestProjA_10002_1_2_3_4_10to1dilution</td>\n",
+       "      <td>20240911</td>\n",
+       "      <td>2</td>\n",
+       "      <td>TestProjA</td>\n",
+       "      <td>TestProjA_10002</td>\n",
+       "      <td>TestProjA_10002_Plate_2_10to1dilution</td>\n",
+       "      <td>TestProjA_Plate_2</td>\n",
+       "      <td>H</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.038161</td>\n",
+       "      <td>2.179117</td>\n",
+       "      <td>175.598404</td>\n",
+       "      <td>80806.0</td>\n",
+       "      <td>0.035662</td>\n",
+       "      <td>1.426483e+08</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 58 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Sample  Col                  Compressed Plate Name      Date  \\\n",
+       "25  01LJ02814.V5   10      TestProjA_10002_1_2_3_4_undiluted  20240911   \n",
+       "18  01LJ00438.V5   10      TestProjA_10002_1_2_3_4_undiluted  20240911   \n",
+       "22  01LJ00544.V5   10  TestProjA_10002_1_2_3_4_10to1dilution  20240911   \n",
+       "24  01LJ00586.V5   10  TestProjA_10002_1_2_3_4_10to1dilution  20240911   \n",
+       "30  01LJ01024.V5   10  TestProjA_10002_1_2_3_4_10to1dilution  20240911   \n",
+       "\n",
+       "    Plate Position Project Abbreviation     Project Name  \\\n",
+       "25               4            TestProjA  TestProjA_10002   \n",
+       "18               2            TestProjA  TestProjA_10002   \n",
+       "22               2            TestProjA  TestProjA_10002   \n",
+       "24               2            TestProjA  TestProjA_10002   \n",
+       "30               2            TestProjA  TestProjA_10002   \n",
+       "\n",
+       "                            Project Plate             RackID Row  ...  \\\n",
+       "25      TestProjA_10002_Plate_4_undiluted  TestProjA_Plate_4   E  ...   \n",
+       "18      TestProjA_10002_Plate_2_undiluted  TestProjA_Plate_2   B  ...   \n",
+       "22  TestProjA_10002_Plate_2_10to1dilution  TestProjA_Plate_2   D  ...   \n",
+       "24  TestProjA_10002_Plate_2_10to1dilution  TestProjA_Plate_2   E  ...   \n",
+       "30  TestProjA_10002_Plate_2_10to1dilution  TestProjA_Plate_2   H  ...   \n",
+       "\n",
+       "    Filtered Reads  proportion LoadingFactor  iSeq normpool volume  \\\n",
+       "25          1830.0    0.083158      1.000000             40.000000   \n",
+       "18           206.0    0.021368      3.891639            372.538528   \n",
+       "22          4788.0    0.065124      1.276919             71.845707   \n",
+       "24             NaN    0.017731      4.689843            464.331979   \n",
+       "30             NaN    0.038161      2.179117            175.598404   \n",
+       "\n",
+       "   projected_reads projected_proportion  projected_HO_reads  \\\n",
+       "25         80806.0             0.035662        1.426483e+08   \n",
+       "18         80806.0             0.035662        1.426483e+08   \n",
+       "22         80806.0             0.035662        1.426483e+08   \n",
+       "24         80806.0             0.035662        1.426483e+08   \n",
+       "30         80806.0             0.035662        1.426483e+08   \n",
+       "\n",
+       "   on_target_proportion projected_off_target_reads  projected_on_target_reads  \n",
+       "25             0.022647               1.394178e+08               3.230533e+06  \n",
+       "18             0.009921               1.412331e+08               1.415217e+06  \n",
+       "22             0.075661               1.318554e+08               1.079296e+07  \n",
+       "24                  NaN                        NaN                        NaN  \n",
+       "30                  NaN                        NaN                        NaN  \n",
+       "\n",
+       "[5 rows x 58 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Plots estimate of read depth proportion, and returns a df with estimates. \n",
     "plate_df_normalized_with_estimates = estimate_read_depth(plate_df_normalized)\n",
@@ -370,11 +1452,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
     "## INPUT\n",
+    "# When parameters do not exist, use the variable value; if they do exist, use the key in the test dictionary.\n",
     "iseqnormed_picklist_fbase = assign_input(variable_value='./test_output/Pooling/Tellseq_iSeqnormpool',\n",
     "                               test_dict=test_dict,\n",
     "                               key='iseqnormed_picklist_fbase')"
@@ -382,7 +1465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -392,9 +1475,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/seosonghee/Documents/KnightLab/kl-metapool/metapool/util.py:174: UserWarning: Warning! This file exists already: ./test_output/Pooling/Tellseq_iSeqnormpool_set_col19to24.txt.\n",
+      "  warnings.warn(f\"Warning! This file exists already: {fp}.\")\n"
+     ]
+    }
+   ],
    "source": [
     "iseqnormed_picklist_fp = get_set_fp(iseqnormed_picklist_fbase, current_set_id)\n",
     "warn_if_fp_exists(iseqnormed_picklist_fp)"
@@ -402,9 +1494,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Source Plate Name,Source Plate Type,Source Well,Concentration,Transfer Volume,Destination Plate Name,Destination Well\n",
+      "1,384LDV_AQ_B2,A19,,500.00,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,A20,,500.00,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,A21,,100.00,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,A22,,100.00,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,A23,,65.16,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,A24,,52.30,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,B19,,155.70,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,B20,,395.75,NormalizedDNA,A1\n",
+      "1,384LDV_AQ_B2,B21,,100.00,NormalizedDNA,A1\n"
+     ]
+    }
+   ],
    "source": [
     "with open(iseqnormed_picklist_fp,'w') as fh:\n",
     "    fh.write(iseqnormed_picklist)\n",
@@ -429,7 +1538,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.23"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/notebooks/test_output/QC/Tellseq_plate_df_C_set_col19to24.txt b/notebooks/test_output/QC/Tellseq_plate_df_C_set_col19to24.txt
index d3155b94..fd245ead 100644
--- a/notebooks/test_output/QC/Tellseq_plate_df_C_set_col19to24.txt
+++ b/notebooks/test_output/QC/Tellseq_plate_df_C_set_col19to24.txt
@@ -1,7 +1,7 @@
 Sample	Col	Compressed Plate Name	Date	Plate Position	Project Abbreviation	Project Name	Project Plate	RackID	Row	Time	TubeCode	Well	vol_extracted_elution_ul	well_id_96	LocationCell	LocationColumn	LocationRow	description	Blank	Sample DNA Concentration_10to1dilution	Sample DNA Concentration_2to1dilution	Sample DNA Concentration_undiluted	Sample DNA Concentration	Diluted	dilution_factor_10to1dilution	Library Well	contains_replicates	Normalized DNA volume	Normalized water volume	Input DNA	syndna_pool_number	Library Well_row	Library Well_col	i5 well	Barcode_96_Well_Position	i5 name	i5 plate	i5 well_row	i5 well_col	barcode_set_id	barcode_id	MiniPico Library DNA Concentration	MiniPico Library Concentration	sample sheet Sample_ID	MiniPico Pooled Volume	Well_description
 01LJ01593.V8	10	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	A	93522	359190605	A19	70	A10					False	0.6333	3.1665	6.333	3.1665	True	0.0999999999999999	A19	False	2367.5	2632.5	7.496688750000001		A	19	A1	A1	C501	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	A	1	col19to24	C501	0.635	1.924242424242424	01LJ01593_V8	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.01LJ01593.V8.A19
 01LJ01300.V5	10	TestProjA_10002_1_2_3_4_10to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_10to1dilution	TestProjA_Plate_3	A	93654	359138235	B19	70	A10					False	6.5469	32.7345	65.469	6.5469	True	0.1	B19	False	1145.0	3855.0	7.4962005		B	19	B1	A2	C509	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	B	1	col19to24	C509	5.113	15.493939393939396	01LJ01300_V5	1979.1666666666667	TestProjA_10002_Plate_3_10to1dilution.01LJ01300.V5.B19
-01LJ02603.V8	10	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	B	93522	359190229	C19	70	B10					False	1.3558	6.779	13.558	6.779	True	0.0999999999999999	C19	False	1107.5	3892.5	7.5077425		C	19	C1	B1	C502	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	C	1	col19to24	C502	0.677	2.051515151515152	01LJ02603_V8	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.01LJ02603.V8.C19
+01LJ02603.V8	10	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	B	93522	359190229	C19	70	B10					False	1.3558	6.779	13.558	6.779	True	0.0999999999999999	C19	False	1107.5	3892.5	7.5077425		C	19	C1	B1	C502	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	C	1	col19to24	C502	0.677	2.0515151515151517	01LJ02603_V8	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.01LJ02603.V8.C19
 01LJ01344.V5	10	TestProjA_10002_1_2_3_4_2to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_2to1dilution	TestProjA_Plate_3	B	93654	359138236	D19	70	B10					False	0.5004	2.502	5.004	2.502	True	0.1	D19	False	2997.5	2002.5	7.499744999999999		D	19	D1	B2	C510	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	D	1	col19to24	C510	1.047	3.1727272727272724	01LJ01344_V5	1979.1666666666667	TestProjA_10002_Plate_3_2to1dilution.01LJ01344.V5.D19
 01LJ04170.V5	10	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	C	93522	359190242	E19	70	C10					False	1.0447	5.2235	10.447	5.2235	True	0.1	E19	False	1435.0	3565.0	7.4957225		E	19	E1	C1	C503	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	E	1	col19to24	C503	3.777	11.445454545454544	01LJ04170_V5	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.01LJ04170.V5.E19
 01LJ01518.V8	10	TestProjA_10002_1_2_3_4_10to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_10to1dilution	TestProjA_Plate_3	C	93654	359138227	F19	70	C10					False	1.5422	7.711	15.422	1.5422	True	0.0999999999999999	F19	False	4862.5	137.5	7.4989475		F	19	F1	C2	C511	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	F	1	col19to24	C511	0.665	2.015151515151515	01LJ01518_V8	1979.1666666666667	TestProjA_10002_Plate_3_10to1dilution.01LJ01518.V8.F19
@@ -16,11 +16,11 @@ Sample	Col	Compressed Plate Name	Date	Plate Position	Project Abbreviation	Projec
 01LJ00176.V5	10	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	H	93522	359190580	O19	70	H10					False	0.6626	3.313	6.626	3.313	True	0.0999999999999999	O19	False	2265.0	2735.0	7.503945000000001		O	19	O1	H1	C508	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	O	1	col19to24	C508	0.746	2.2606060606060607	01LJ00176_V5	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.01LJ00176.V5.O19
 01LJ02258.V8	10	TestProjA_10002_1_2_3_4_10to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_10to1dilution	TestProjA_Plate_3	H	93654	359138203	P19	70	H10					False	6.5583	32.7915	65.583	6.5583	True	0.1	P19	False	1142.5	3857.5	7.49285775		P	19	P1	H2	C516	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	P	1	col19to24	C516	0.766	2.3212121212121213	01LJ02258_V8	1979.1666666666667	TestProjA_10002_Plate_3_10to1dilution.01LJ02258.V8.P19
 01LJ00310.V8	10	TestProjA_10002_1_2_3_4_10to1dilution	20240911	2	TestProjA	TestProjA_10002	TestProjA_10002_Plate_2_10to1dilution	TestProjA_Plate_2	A	93614	359190829	A20	70	A10					False	6.4684	32.342	64.684	6.4684	True	0.1	A20	False	1160.0	3840.0	7.503344		A	20	A2	A3	C517	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	A	2	col19to24	C517	0.962	2.915151515151515	01LJ00310_V8	1979.1666666666667	TestProjA_10002_Plate_2_10to1dilution.01LJ00310.V8.A20
-01LJ02338.V8	10	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	A	93726	359126265	B20	70	A10					False	0.0269	0.1345	0.269	0.269	False	0.0999999999999999	B20	False	5000.0	0.0	1.345		B	20	B2	A4	C525	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	B	2	col19to24	C525	1.437	4.354545454545455	01LJ02338_V8	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.01LJ02338.V8.B20
+01LJ02338.V8	10	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	A	93726	359126265	B20	70	A10					False	0.0269	0.1345	0.269	0.269	False	0.0999999999999999	B20	False	5000.0	0.0	1.345		B	20	B2	A4	C525	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	B	2	col19to24	C525	1.437	4.3545454545454545	01LJ02338_V8	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.01LJ02338.V8.B20
 01LJ00438.V5	10	TestProjA_10002_1_2_3_4_undiluted	20240911	2	TestProjA	TestProjA_10002	TestProjA_10002_Plate_2_undiluted	TestProjA_Plate_2	B	93614	359190831	C20	70	B10					False	0.2618	1.309	2.618	2.618	False	0.0999999999999999	C20	False	2865.0	2135.0	7.50057		C	20	C2	B3	C518	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	C	2	col19to24	C518	1.597	4.83939393939394	01LJ00438_V5	1979.1666666666667	TestProjA_10002_Plate_2_undiluted.01LJ00438.V5.C20
 01LJ02358.V5	10	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	B	93726	359126266	D20	70	B10					False	0.0266	0.133	0.266	0.266	False	0.0999999999999999	D20	False	5000.0	0.0	1.33		D	20	D2	B4	C526	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	D	2	col19to24	C526	0.761	2.3060606060606057	01LJ02358_V5	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.01LJ02358.V5.D20
 01LJ00503.V8	10	TestProjA_10002_1_2_3_4_10to1dilution	20240911	2	TestProjA	TestProjA_10002	TestProjA_10002_Plate_2_10to1dilution	TestProjA_Plate_2	C	93614	359190830	E20	70	C10					False	7.1602	35.801	71.602	7.1602	True	0.0999999999999999	E20	False	1047.5	3952.5	7.500309499999999		E	20	E2	C3	C519	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	E	2	col19to24	C519	0.823	2.4939393939393937	01LJ00503_V8	1979.1666666666667	TestProjA_10002_Plate_2_10to1dilution.01LJ00503.V8.E20
-01LJ02503.V5	10	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	C	93726	359126267	F20	70	C10					False	0.0267	0.1335	0.267	0.267	False	0.1	F20	False	5000.0	0.0	1.335		F	20	F2	C4	C527	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	F	2	col19to24	C527	1.013	3.0696969696969694	01LJ02503_V5	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.01LJ02503.V5.F20
+01LJ02503.V5	10	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	C	93726	359126267	F20	70	C10					False	0.0267	0.1335	0.267	0.267	False	0.1	F20	False	5000.0	0.0	1.335		F	20	F2	C4	C527	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	F	2	col19to24	C527	1.013	3.06969696969697	01LJ02503_V5	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.01LJ02503.V5.F20
 01LJ00544.V5	10	TestProjA_10002_1_2_3_4_10to1dilution	20240911	2	TestProjA	TestProjA_10002	TestProjA_10002_Plate_2_10to1dilution	TestProjA_Plate_2	D	93614	359190828	G20	70	D10					False	1.8579	9.2895	18.579	1.8579	True	0.1	G20	False	4037.5	962.5	7.50127125		G	20	G2	D3	C520	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	G	2	col19to24	C520	3.724	11.284848484848483	01LJ00544_V5	1979.1666666666667	TestProjA_10002_Plate_2_10to1dilution.01LJ00544.V5.G20
 01LJ02691.V5	10	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	D	93726	359126268	H20	70	D10					False	0.0267	0.1335	0.267	0.267	False	0.1	H20	False	5000.0	0.0	1.335		H	20	H2	D4	C528	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	H	2	col19to24	C528	2.352	7.127272727272727	01LJ02691_V5	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.01LJ02691.V5.H20
 01LJ00586.V5	10	TestProjA_10002_1_2_3_4_10to1dilution	20240911	2	TestProjA	TestProjA_10002	TestProjA_10002_Plate_2_10to1dilution	TestProjA_Plate_2	E	93614	359190822	I20	70	E10					False	6.1305	30.6525	61.305	6.1305	True	0.0999999999999999	I20	False	1222.5	3777.5	7.494536249999999		I	20	I2	E3	C521	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	I	2	col19to24	C521	1.785	5.409090909090909	01LJ00586_V5	1979.1666666666667	TestProjA_10002_Plate_2_10to1dilution.01LJ00586.V5.I20
@@ -28,27 +28,27 @@ Sample	Col	Compressed Plate Name	Date	Plate Position	Project Abbreviation	Projec
 01LJ00619.V8	10	TestProjA_10002_1_2_3_4_10to1dilution	20240911	2	TestProjA	TestProjA_10002	TestProjA_10002_Plate_2_10to1dilution	TestProjA_Plate_2	F	93614	359190821	K20	70	F10					False	6.2316	31.158	62.316	6.2316	True	0.1	K20	False	1202.5	3797.5	7.493499000000001		K	20	K2	F3	C522	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	K	2	col19to24	C522	1.114	3.375757575757576	01LJ00619_V8	1979.1666666666667	TestProjA_10002_Plate_2_10to1dilution.01LJ00619.V8.K20
 01LJ02927.V5	10	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	F	93726	359126270	L20	70	F10					False	0.0267	0.1335	0.267	0.267	False	0.1	L20	False	5000.0	0.0	1.335		L	20	L2	F4	C530	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	L	2	col19to24	C530	0.631	1.9121212121212123	01LJ02927_V5	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.01LJ02927.V5.L20
 01LJ00845.V5	10	TestProjA_10002_1_2_3_4_10to1dilution	20240911	2	TestProjA	TestProjA_10002	TestProjA_10002_Plate_2_10to1dilution	TestProjA_Plate_2	G	93614	359190818	M20	70	G10					False	6.8762	34.381	68.762	6.8762	True	0.0999999999999999	M20	False	1090.0	3910.0	7.495058		M	20	M2	G3	C523	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	M	2	col19to24	C523	0.928	2.812121212121212	01LJ00845_V5	1979.1666666666667	TestProjA_10002_Plate_2_10to1dilution.01LJ00845.V5.M20
-01LJ03225.V5	10	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	G	93726	359126271	N20	70	G10					False	0.0267	0.1335	0.267	0.267	False	0.1	N20	False	5000.0	0.0	1.335		N	20	N2	G4	C531	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	N	2	col19to24	C531	0.755	2.287878787878788	01LJ03225_V5	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.01LJ03225.V5.N20
+01LJ03225.V5	10	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	G	93726	359126271	N20	70	G10					False	0.0267	0.1335	0.267	0.267	False	0.1	N20	False	5000.0	0.0	1.335		N	20	N2	G4	C531	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	N	2	col19to24	C531	0.755	2.2878787878787876	01LJ03225_V5	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.01LJ03225.V5.N20
 01LJ01024.V5	10	TestProjA_10002_1_2_3_4_10to1dilution	20240911	2	TestProjA	TestProjA_10002	TestProjA_10002_Plate_2_10to1dilution	TestProjA_Plate_2	H	93614	359190496	O20	70	H10					False	3.3973	16.9865	33.973	3.3973	True	0.1	O20	False	2207.5	2792.5	7.49953975		O	20	O2	H3	C524	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	O	2	col19to24	C524	2.382	7.218181818181819	01LJ01024_V5	1979.1666666666667	TestProjA_10002_Plate_2_10to1dilution.01LJ01024.V5.O20
-01LJ03357.V5	10	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	H	93726	359126744	P20	70	H10					False	0.0362	0.181	0.362	0.362	False	0.1	P20	False	5000.0	0.0	1.81		P	20	P2	H4	C532	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	P	2	col19to24	C532	1.18	3.575757575757576	01LJ03357_V5	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.01LJ03357.V5.P20
+01LJ03357.V5	10	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	H	93726	359126744	P20	70	H10					False	0.0362	0.181	0.362	0.362	False	0.1	P20	False	5000.0	0.0	1.81		P	20	P2	H4	C532	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	P	2	col19to24	C532	1.18	3.5757575757575757	01LJ03357_V5	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.01LJ03357.V5.P20
 BLANK.TestProjA.1.A11	11	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	A	93522	364366604	A21	70	A11	G10	10.0	G	negative_control	True	1.0968	5.484	10.968	5.484	True	0.1	A21	False	1367.5	3632.5	7.49937		A	21	A3	A5	C533	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	A	3	col19to24	C533	0.602	1.824242424242424	BLANK_TestProjA_1_A11	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.BLANK.TestProjA.1.A11.A21
 BLANK.TestProjA.3.A11	11	TestProjA_10002_1_2_3_4_10to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_10to1dilution	TestProjA_Plate_3	A	93654	364366587	B21	70	A11	F9	9.0	F	negative_control	True	6.3035	31.5175	63.035	6.3035	True	0.1	B21	False	1190.0	3810.0	7.501165		B	21	B3	A6	C541	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	B	3	col19to24	C541	0.61	1.8484848484848484	BLANK_TestProjA_3_A11	1979.1666666666667	TestProjA_10002_Plate_3_10to1dilution.BLANK.TestProjA.3.A11.B21
-BLANK.TestProjA.1.B11	11	TestProjA_10002_1_2_3_4_10to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_10to1dilution	TestProjA_Plate_1	B	93522	364366525	C21	70	B11	H10	10.0	H	negative_control	True	3.3363	16.6815	33.363	3.3363	True	0.1	C21	False	2247.5	2752.5	7.49833425		C	21	C3	B5	C534	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	C	3	col19to24	C534	0.607	1.8393939393939396	BLANK_TestProjA_1_B11	1979.1666666666667	TestProjA_10002_Plate_1_10to1dilution.BLANK.TestProjA.1.B11.C21
+BLANK.TestProjA.1.B11	11	TestProjA_10002_1_2_3_4_10to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_10to1dilution	TestProjA_Plate_1	B	93522	364366525	C21	70	B11	H10	10.0	H	negative_control	True	3.3363	16.6815	33.363	3.3363	True	0.1	C21	False	2247.5	2752.5	7.49833425		C	21	C3	B5	C534	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	C	3	col19to24	C534	0.607	1.8393939393939391	BLANK_TestProjA_1_B11	1979.1666666666667	TestProjA_10002_Plate_1_10to1dilution.BLANK.TestProjA.1.B11.C21
 BLANK.TestProjA.3.B11	11	TestProjA_10002_1_2_3_4_10to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_10to1dilution	TestProjA_Plate_3	B	93654	364366586	D21	70	B11	E9	9.0	E	negative_control	True	3.231	16.155	32.31	3.231	True	0.0999999999999999	D21	False	2322.5	2677.5	7.5039975		D	21	D3	B6	C542	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	D	3	col19to24	C542	0.601	1.8212121212121213	BLANK_TestProjA_3_B11	1979.1666666666667	TestProjA_10002_Plate_3_10to1dilution.BLANK.TestProjA.3.B11.D21
 BLANK.TestProjA.1.C11	11	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	C	93522	364365934	E21	70	C11	A11	11.0	A	negative_control	True	1.2217	6.1085	12.217	6.1085	True	0.0999999999999999	E21	False	1227.5	3772.5	7.49818375		E	21	E3	C5	C535	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	E	3	col19to24	C535	0.592	1.7939393939393935	BLANK_TestProjA_1_C11	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.BLANK.TestProjA.1.C11.E21
 BLANK.TestProjA.3.C11	11	TestProjA_10002_1_2_3_4_10to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_10to1dilution	TestProjA_Plate_3	C	93654	364366585	F21	70	C11	D9	9.0	D	negative_control	True	7.9985	39.9925	79.985	7.9985	True	0.1	F21	False	937.5	4062.5	7.49859375		F	21	F3	C6	C543	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	F	3	col19to24	C543	0.595	1.803030303030303	BLANK_TestProjA_3_C11	1979.1666666666667	TestProjA_10002_Plate_3_10to1dilution.BLANK.TestProjA.3.C11.F21
 BLANK.TestProjA.1.D11	11	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	D	93522	364366599	G21	70	D11	B11	11.0	B	negative_control	True	0.6279	3.1395	6.279	3.1395	True	0.1	G21	False	2390.0	2610.0	7.503405		G	21	G3	D5	C536	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	G	3	col19to24	C536	0.599	1.8151515151515152	BLANK_TestProjA_1_D11	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.BLANK.TestProjA.1.D11.G21
 BLANK.TestProjA.3.D11	11	TestProjA_10002_1_2_3_4_10to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_10to1dilution	TestProjA_Plate_3	D	93654	364366584	H21	70	D11	C9	9.0	C	negative_control	True	7.7112	38.556	77.112	7.7112	True	0.1	H21	False	972.5	4027.5	7.499142		H	21	H3	D6	C544	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	H	3	col19to24	C544	0.617	1.8696969696969696	BLANK_TestProjA_3_D11	1979.1666666666667	TestProjA_10002_Plate_3_10to1dilution.BLANK.TestProjA.3.D11.H21
 BLANK.TestProjA.1.E11	11	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	E	93522	364366600	I21	70	E11	C11	11.0	C	negative_control	True	1.4723	7.3615	14.723	7.3615	True	0.0999999999999999	I21	False	1020.0	3980.0	7.508730000000001		I	21	I3	E5	C537	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	I	3	col19to24	C537	0.602	1.824242424242424	BLANK_TestProjA_1_E11	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.BLANK.TestProjA.1.E11.I21
-BLANK.TestProjA.3.E11	11	TestProjA_10002_1_2_3_4_2to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_2to1dilution	TestProjA_Plate_3	E	93654	364366583	J21	70	E11	B9	9.0	B	negative_control	True	0.9685	4.8425	9.685	4.8425	True	0.0999999999999999	J21	False	1550.0	3450.0	7.505875		J	21	J3	E6	C545	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	J	3	col19to24	C545	0.607	1.8393939393939396	BLANK_TestProjA_3_E11	1979.1666666666667	TestProjA_10002_Plate_3_2to1dilution.BLANK.TestProjA.3.E11.J21
+BLANK.TestProjA.3.E11	11	TestProjA_10002_1_2_3_4_2to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_2to1dilution	TestProjA_Plate_3	E	93654	364366583	J21	70	E11	B9	9.0	B	negative_control	True	0.9685	4.8425	9.685	4.8425	True	0.0999999999999999	J21	False	1550.0	3450.0	7.505875		J	21	J3	E6	C545	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	J	3	col19to24	C545	0.607	1.8393939393939391	BLANK_TestProjA_3_E11	1979.1666666666667	TestProjA_10002_Plate_3_2to1dilution.BLANK.TestProjA.3.E11.J21
 BLANK.TestProjA.1.F11	11	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	F	93522	364366601	K21	70	F11	D11	11.0	D	negative_control	True	0.9396	4.698	9.396	4.698	True	0.0999999999999999	K21	False	1597.5	3402.5	7.505055		K	21	K3	F5	C538	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	K	3	col19to24	C538	0.613	1.8575757575757577	BLANK_TestProjA_1_F11	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.BLANK.TestProjA.1.F11.K21
 BLANK.TestProjA.3.F11	11	TestProjA_10002_1_2_3_4_2to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_2to1dilution	TestProjA_Plate_3	F	93654	364365925	L21	70	F11	A9	9.0	A	negative_control	True	1.137	5.685	11.37	5.685	True	0.1	L21	False	1320.0	3680.0	7.5042		L	21	L3	F6	C546	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	L	3	col19to24	C546	0.6	1.818181818181818	BLANK_TestProjA_3_F11	1979.1666666666667	TestProjA_10002_Plate_3_2to1dilution.BLANK.TestProjA.3.F11.L21
 BLANK.TestProjA.1.G11	11	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	G	93522	364366602	M21	70	G11	E11	11.0	E	negative_control	True	0.9455	4.7275	9.455	4.7275	True	0.1	M21	False	1587.5	3412.5	7.50490625		M	21	M3	G5	C539	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	M	3	col19to24	C539	0.599	1.8151515151515152	BLANK_TestProjA_1_G11	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.BLANK.TestProjA.1.G11.M21
 BLANK.TestProjA.3.G11	11	TestProjA_10002_1_2_3_4_10to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_10to1dilution	TestProjA_Plate_3	G	93654	364366605	N21	70	G11	H8	8.0	H	negative_control	True	6.749	33.745	67.49	6.749	True	0.1	N21	False	1112.5	3887.5	7.5082625		N	21	N3	G6	C547	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	N	3	col19to24	C547	0.601	1.8212121212121213	BLANK_TestProjA_3_G11	1979.1666666666667	TestProjA_10002_Plate_3_10to1dilution.BLANK.TestProjA.3.G11.N21
 BLANK.TestProjA.1.H11	11	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	H	93522	364366603	O21	70	H11	F11	11.0	F	negative_control	True	0.7154	3.577	7.154	3.577	True	0.1	O21	False	2097.5	2902.5	7.502757499999999		O	21	O3	H5	C540	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	O	3	col19to24	C540	0.6	1.818181818181818	BLANK_TestProjA_1_H11	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.BLANK.TestProjA.1.H11.O21
-BLANK.TestProjA.3.H11	11	TestProjA_10002_1_2_3_4_10to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_10to1dilution	TestProjA_Plate_3	H	93654	364366580	P21	70	H11	G8	8.0	G	negative_control	True	6.1245	30.6225	61.245	6.1245	True	0.1	P21	False	1225.0	3775.0	7.502512500000001		P	21	P3	H6	C548	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	P	3	col19to24	C548	0.611	1.851515151515152	BLANK_TestProjA_3_H11	1979.1666666666667	TestProjA_10002_Plate_3_10to1dilution.BLANK.TestProjA.3.H11.P21
+BLANK.TestProjA.3.H11	11	TestProjA_10002_1_2_3_4_10to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_10to1dilution	TestProjA_Plate_3	H	93654	364366580	P21	70	H11	G8	8.0	G	negative_control	True	6.1245	30.6225	61.245	6.1245	True	0.1	P21	False	1225.0	3775.0	7.502512500000001		P	21	P3	H6	C548	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	P	3	col19to24	C548	0.611	1.8515151515151516	BLANK_TestProjA_3_H11	1979.1666666666667	TestProjA_10002_Plate_3_10to1dilution.BLANK.TestProjA.3.H11.P21
 BLANK.TestProjA.2.A11	11	TestProjA_10002_1_2_3_4_10to1dilution	20240911	2	TestProjA	TestProjA_10002	TestProjA_10002_Plate_2_10to1dilution	TestProjA_Plate_2	A	93614	364366588	A22	70	A11	G9	9.0	G	negative_control	True	4.1226	20.613	41.226	4.1226	True	0.1	A22	False	1820.0	3180.0	7.503132000000001		A	22	A4	A7	C549	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	A	4	col19to24	C549	0.629	1.9060606060606065	BLANK_TestProjA_2_A11	1979.1666666666667	TestProjA_10002_Plate_2_10to1dilution.BLANK.TestProjA.2.A11.A22
-BLANK.TestProjA.4.A11	11	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	A	93726	364366572	B22	70	A11	G7	7.0	G	negative_control	True	0.0264	0.132	0.264	0.264	False	0.0999999999999999	B22	False	5000.0	0.0	1.32		B	22	B4	A8	C557	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	B	4	col19to24	C557	0.638	1.933333333333333	BLANK_TestProjA_4_A11	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.BLANK.TestProjA.4.A11.B22
+BLANK.TestProjA.4.A11	11	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	A	93726	364366572	B22	70	A11	G7	7.0	G	negative_control	True	0.0264	0.132	0.264	0.264	False	0.0999999999999999	B22	False	5000.0	0.0	1.32		B	22	B4	A8	C557	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	B	4	col19to24	C557	0.638	1.9333333333333331	BLANK_TestProjA_4_A11	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.BLANK.TestProjA.4.A11.B22
 BLANK.TestProjA.2.B11	11	TestProjA_10002_1_2_3_4_2to1dilution	20240911	2	TestProjA	TestProjA_10002	TestProjA_10002_Plate_2_2to1dilution	TestProjA_Plate_2	B	93614	364366541	C22	70	B11	H9	9.0	H	negative_control	True	1.4325	7.1625	14.325	7.1625	True	0.1	C22	False	1047.5	3952.5	7.50271875		C	22	C4	B7	C550	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	C	4	col19to24	C550	0.603	1.8272727272727272	BLANK_TestProjA_2_B11	1979.1666666666667	TestProjA_10002_Plate_2_2to1dilution.BLANK.TestProjA.2.B11.C22
 BLANK.TestProjA.4.B11	11	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	B	93726	364366564	D22	70	B11	G6	6.0	G	negative_control	True	0.0268	0.134	0.268	0.268	False	0.0999999999999999	D22	False	5000.0	0.0	1.34		D	22	D4	B8	C558	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	D	4	col19to24	C558	0.596	1.806060606060606	BLANK_TestProjA_4_B11	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.BLANK.TestProjA.4.B11.D22
 BLANK.TestProjA.2.C11	11	TestProjA_10002_1_2_3_4_undiluted	20240911	2	TestProjA	TestProjA_10002	TestProjA_10002_Plate_2_undiluted	TestProjA_Plate_2	C	93614	364365854	E22	70	C11	A10	10.0	A	negative_control	True	0.0315	0.1575	0.315	0.315	False	0.1	E22	False	5000.0	0.0	1.575		E	22	E4	C7	C551	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	E	4	col19to24	C551	0.601	1.8212121212121213	BLANK_TestProjA_2_C11	1979.1666666666667	TestProjA_10002_Plate_2_undiluted.BLANK.TestProjA.2.C11.E22
@@ -72,13 +72,13 @@ KATHARO.TestProjA.3.12C.24000	12	TestProjA_10002_1_2_3_4_2to1dilution	20240911	3
 KATHARO.TestProjA.1.12D.4800	12	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	D	93522	364346325	G23	70	D12					False	1.068	5.34	10.68	5.34	True	0.1	G23	False	1405.0	3595.0	7.5027		G	23	G5	D9	C568	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	G	5	col19to24	C568	0.665	2.015151515151515	KATHARO_TestProjA_1_12D_4800	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.KATHARO.TestProjA.1.12D.4800.G23
 KATHARO.TestProjA.3.12D.4800	12	TestProjA_10002_1_2_3_4_10to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_10to1dilution	TestProjA_Plate_3	D	93654	364346333	H23	70	D12					False	8.0313	40.1565	80.313	8.0313	True	0.0999999999999999	H23	False	935.0	4065.0	7.5092655		H	23	H5	D10	C576	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	H	5	col19to24	C576	0.72	2.1818181818181817	KATHARO_TestProjA_3_12D_4800	1979.1666666666667	TestProjA_10002_Plate_3_10to1dilution.KATHARO.TestProjA.3.12D.4800.H23
 KATHARO.TestProjA.1.12E.960	12	TestProjA_10002_1_2_3_4_10to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_10to1dilution	TestProjA_Plate_1	E	93522	364346326	I23	70	E12					False	3.0792	15.396	30.792	3.0792	True	0.1	I23	False	2435.0	2565.0	7.497852000000001		I	23	I5	E9	C569	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	I	5	col19to24	C569	0.601	1.8212121212121213	KATHARO_TestProjA_1_12E_960	1979.1666666666667	TestProjA_10002_Plate_1_10to1dilution.KATHARO.TestProjA.1.12E.960.I23
-KATHARO.TestProjA.3.12E.960	12	TestProjA_10002_1_2_3_4_undiluted	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_undiluted	TestProjA_Plate_3	E	93654	364346358	J23	70	E12					False	0.0268	0.134	0.268	0.268	False	0.0999999999999999	J23	False	5000.0	0.0	1.34		J	23	J5	E10	C577	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	J	5	col19to24	C577	0.619	1.875757575757576	KATHARO_TestProjA_3_12E_960	1979.1666666666667	TestProjA_10002_Plate_3_undiluted.KATHARO.TestProjA.3.12E.960.J23
-KATHARO.TestProjA.1.12F.192	12	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	F	93522	364342855	K23	70	F12					False	0.7205	3.6025	7.205	3.6025	True	0.1	K23	False	2082.5	2917.5	7.50220625		K	23	K5	F9	C570	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	K	5	col19to24	C570	0.609	1.8454545454545448	KATHARO_TestProjA_1_12F_192	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.KATHARO.TestProjA.1.12F.192.K23
+KATHARO.TestProjA.3.12E.960	12	TestProjA_10002_1_2_3_4_undiluted	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_undiluted	TestProjA_Plate_3	E	93654	364346358	J23	70	E12					False	0.0268	0.134	0.268	0.268	False	0.0999999999999999	J23	False	5000.0	0.0	1.34		J	23	J5	E10	C577	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	J	5	col19to24	C577	0.619	1.8757575757575755	KATHARO_TestProjA_3_12E_960	1979.1666666666667	TestProjA_10002_Plate_3_undiluted.KATHARO.TestProjA.3.12E.960.J23
+KATHARO.TestProjA.1.12F.192	12	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	F	93522	364342855	K23	70	F12					False	0.7205	3.6025	7.205	3.6025	True	0.1	K23	False	2082.5	2917.5	7.50220625		K	23	K5	F9	C570	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	K	5	col19to24	C570	0.609	1.8454545454545452	KATHARO_TestProjA_1_12F_192	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.KATHARO.TestProjA.1.12F.192.K23
 KATHARO.TestProjA.3.12F.192	12	TestProjA_10002_1_2_3_4_undiluted	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_undiluted	TestProjA_Plate_3	F	93654	364342863	L23	70	F12					False	0.0266	0.133	0.266	0.266	False	0.0999999999999999	L23	False	5000.0	0.0	1.33		L	23	L5	F10	C578	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	L	5	col19to24	C578	0.604	1.8303030303030303	KATHARO_TestProjA_3_12F_192	1979.1666666666667	TestProjA_10002_Plate_3_undiluted.KATHARO.TestProjA.3.12F.192.L23
 KATHARO.TestProjA.1.12G.38	12	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	G	93522	364342848	M23	70	G12					False	1.1975	5.9875	11.975	5.9875	True	0.1	M23	False	1252.5	3747.5	7.49934375		M	23	M5	G9	C571	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	M	5	col19to24	C571	0.61	1.8484848484848484	KATHARO_TestProjA_1_12G_38	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.KATHARO.TestProjA.1.12G.38.M23
 KATHARO.TestProjA.3.12G.38	12	TestProjA_10002_1_2_3_4_undiluted	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_undiluted	TestProjA_Plate_3	G	93654	364342856	N23	70	G12					False	0.0266	0.133	0.266	0.266	False	0.0999999999999999	N23	False	5000.0	0.0	1.33		N	23	N5	G10	C579	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	N	5	col19to24	C579	0.601	1.8212121212121213	KATHARO_TestProjA_3_12G_38	1979.1666666666667	TestProjA_10002_Plate_3_undiluted.KATHARO.TestProjA.3.12G.38.N23
 KATHARO.TestProjA.1.12H.7	12	TestProjA_10002_1_2_3_4_2to1dilution	20240911	1	TestProjA	TestProjA_10002	TestProjA_10002_Plate_1_2to1dilution	TestProjA_Plate_1	H	93522	364342857	O23	70	H12					False	0.5676	2.838	5.676	2.838	True	0.0999999999999999	O23	False	2642.5	2357.5	7.499415		O	23	O5	H9	C572	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	O	5	col19to24	C572	0.597	1.809090909090909	KATHARO_TestProjA_1_12H_7	1979.1666666666667	TestProjA_10002_Plate_1_2to1dilution.KATHARO.TestProjA.1.12H.7.O23
-KATHARO.TestProjA.3.12H.7	12	TestProjA_10002_1_2_3_4_2to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_2to1dilution	TestProjA_Plate_3	H	93654	364342865	P23	70	H12					False	0.3906	1.953	3.906	1.953	True	0.0999999999999999	P23	False	3840.0	1160.0	7.49952		P	23	P5	H10	C580	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	P	5	col19to24	C580	0.609	1.8454545454545448	KATHARO_TestProjA_3_12H_7	1979.1666666666667	TestProjA_10002_Plate_3_2to1dilution.KATHARO.TestProjA.3.12H.7.P23
+KATHARO.TestProjA.3.12H.7	12	TestProjA_10002_1_2_3_4_2to1dilution	20240911	3	TestProjA	TestProjA_10002	TestProjA_10002_Plate_3_2to1dilution	TestProjA_Plate_3	H	93654	364342865	P23	70	H12					False	0.3906	1.953	3.906	1.953	True	0.0999999999999999	P23	False	3840.0	1160.0	7.49952		P	23	P5	H10	C580	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	P	5	col19to24	C580	0.609	1.8454545454545452	KATHARO_TestProjA_3_12H_7	1979.1666666666667	TestProjA_10002_Plate_3_2to1dilution.KATHARO.TestProjA.3.12H.7.P23
 KATHARO.TestProjA.2.12A.600000	12	TestProjA_10002_1_2_3_4_10to1dilution	20240911	2	TestProjA	TestProjA_10002	TestProjA_10002_Plate_2_10to1dilution	TestProjA_Plate_2	A	93614	364346354	A24	70	A12					False	6.7124	33.562	67.124	6.7124	True	0.1	A24	False	1117.5	3882.5	7.501107		A	24	A6	A11	C581	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	A	6	col19to24	C581	7.281	22.063636363636363	KATHARO_TestProjA_2_12A_600000	1979.1666666666667	TestProjA_10002_Plate_2_10to1dilution.KATHARO.TestProjA.2.12A.600000.A24
 KATHARO.TestProjA.4.12A.600000	12	TestProjA_10002_1_2_3_4_undiluted	20240911	4	TestProjA	TestProjA_10002	TestProjA_10002_Plate_4_undiluted	TestProjA_Plate_4	A	93726	364346338	B24	70	A12					False	0.0269	0.1345	0.269	0.269	False	0.0999999999999999	B24	False	5000.0	0.0	1.345		B	24	B6	A12	C589	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	B	6	col19to24	C589	5.406	16.381818181818183	KATHARO_TestProjA_4_12A_600000	1979.1666666666667	TestProjA_10002_Plate_4_undiluted.KATHARO.TestProjA.4.12A.600000.B24
 KATHARO.TestProjA.2.12B.120000	12	TestProjA_10002_1_2_3_4_10to1dilution	20240911	2	TestProjA	TestProjA_10002	TestProjA_10002_Plate_2_10to1dilution	TestProjA_Plate_2	B	93614	364346347	C24	70	B12					False	7.2194	36.097	72.194	7.2194	True	0.1	C24	False	1040.0	3960.0	7.508176000000001		C	24	C6	B11	C582	TellSeq_Barcode_Plate_1_LN2409001_EXP052026	C	6	col19to24	C582	3.551	10.760606060606062	KATHARO_TestProjA_2_12B_120000	1979.1666666666667	TestProjA_10002_Plate_2_10to1dilution.KATHARO.TestProjA.2.12B.120000.C24
diff --git a/notebooks/test_output/SampleSheets/Tellseq_samplesheet_instrument_iseq_set_col19to24.csv b/notebooks/test_output/SampleSheets/Tellseq_samplesheet_instrument_iseq_set_col19to24.csv
index 76e17b9f..dcc16a0a 100644
--- a/notebooks/test_output/SampleSheets/Tellseq_samplesheet_instrument_iseq_set_col19to24.csv
+++ b/notebooks/test_output/SampleSheets/Tellseq_samplesheet_instrument_iseq_set_col19to24.csv
@@ -2,7 +2,7 @@
 Experiment Name,RKLtest_col19to24,,,,,,,,,,
 Investigator Name,Enter the investigator name (optional),,,,,,,,,,
 Project Name,TestProjA_10002_1_2_3_4_10to1dilution,,,,,,,,,,
-Date,2025-04-01,,,,,,,,,,
+Date,2026-02-02,,,,,,,,,,
 Workflow,GenerateFASTQ,,,,,,,,,,
 Library Prep Kit,TELLSEQ,,,,,,,,,,
 [Manifest],,,,,,,,,,,
diff --git a/notebooks/test_output/SampleSheets/Tellseq_samplesheet_spp_iseq_set_col19to24.csv b/notebooks/test_output/SampleSheets/Tellseq_samplesheet_spp_iseq_set_col19to24.csv
index a98c7a5c..bf4de3ff 100644
--- a/notebooks/test_output/SampleSheets/Tellseq_samplesheet_spp_iseq_set_col19to24.csv
+++ b/notebooks/test_output/SampleSheets/Tellseq_samplesheet_spp_iseq_set_col19to24.csv
@@ -1,162 +1,162 @@
-[Header],,,,,,,,
-IEMFileVersion,4,,,,,,,
-SheetType,tellseq_metag,,,,,,,
-SheetVersion,10,,,,,,,
-Investigator Name,Knight,,,,,,,
-Experiment Name,RKLtest,,,,,,,
-Date,2025-04-01,,,,,,,
-Workflow,GenerateFASTQ,,,,,,,
-Application,FASTQ Only,,,,,,,
-Assay,Metagenomic,,,,,,,
-Description,,,,,,,,
-Chemistry,Default,,,,,,,
-,,,,,,,,
-[Reads],,,,,,,,
-151,,,,,,,,
-151,,,,,,,,
-,,,,,,,,
-[Settings],,,,,,,,
-ReverseComplement,0,,,,,,,
-,,,,,,,,
-[Data],,,,,,,,
-Sample_ID,Sample_Name,Sample_Plate,well_id_384,barcode_id,Sample_Project,Well_description,Lane,
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-,,,,,,,,
-[Bioinformatics],,,,,,,,
-Sample_Project,QiitaID,BarcodesAreRC,ForwardAdapter,ReverseAdapter,HumanFiltering,library_construction_protocol,experiment_design_description,contains_replicates
-TestProjA_10002,10002,True,GATCGGAAGAGCACACGTCTGAACTCCAGTCAC,GATCGGAAGAGCGTCGTGTAGGGAAAGGAGTGT,True,Knight Lab Kapa HyperPlus,plasma sequencing,False
-,,,,,,,,
-[Contact],,,,,,,,
-Sample_Project,Email,,,,,,,
-TestProjA_10002,r@gmail.com,,,,,,,
-,,,,,,,,
-[SampleContext],,,,,,,,
-sample_name,sample_type,primary_qiita_study,secondary_qiita_studies,,,,,
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-,,,,,,,,
+[Header],,,,,,,,
+IEMFileVersion,4,,,,,,,
+SheetType,tellseq_metag,,,,,,,
+SheetVersion,10,,,,,,,
+Investigator Name,Knight,,,,,,,
+Experiment Name,RKLtest,,,,,,,
+Date,2026-02-02,,,,,,,
+Workflow,GenerateFASTQ,,,,,,,
+Application,FASTQ Only,,,,,,,
+Assay,Metagenomic,,,,,,,
+Description,,,,,,,,
+Chemistry,Default,,,,,,,
+,,,,,,,,
+[Reads],,,,,,,,
+151,,,,,,,,
+151,,,,,,,,
+,,,,,,,,
+[Settings],,,,,,,,
+ReverseComplement,0,,,,,,,
+,,,,,,,,
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+,,,,,,,,
+[Bioinformatics],,,,,,,,
+Sample_Project,QiitaID,BarcodesAreRC,ForwardAdapter,ReverseAdapter,HumanFiltering,library_construction_protocol,experiment_design_description,contains_replicates
+TestProjA_10002,10002,True,GATCGGAAGAGCACACGTCTGAACTCCAGTCAC,GATCGGAAGAGCGTCGTGTAGGGAAAGGAGTGT,True,Knight Lab Kapa HyperPlus,plasma sequencing,False
+,,,,,,,,
+[Contact],,,,,,,,
+Sample_Project,Email,,,,,,,
+TestProjA_10002,r@gmail.com,,,,,,,
+,,,,,,,,
+[SampleContext],,,,,,,,
+sample_name,sample_type,primary_qiita_study,secondary_qiita_studies,,,,,
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diff --git a/notebooks/test_output/SampleSheets/Tellseq_samplesheet_spp_novaseqxplus_set_col19to24.csv b/notebooks/test_output/SampleSheets/Tellseq_samplesheet_spp_novaseqxplus_set_col19to24.csv
index 5bc707dc..6ebe78fe 100644
--- a/notebooks/test_output/SampleSheets/Tellseq_samplesheet_spp_novaseqxplus_set_col19to24.csv
+++ b/notebooks/test_output/SampleSheets/Tellseq_samplesheet_spp_novaseqxplus_set_col19to24.csv
@@ -1,164 +1,164 @@
-[Header],,,,,,,,
-IEMFileVersion,4,,,,,,,
-SheetType,tellseq_metag,,,,,,,
-SheetVersion,10,,,,,,,
-Investigator Name,Knight,,,,,,,
-Experiment Name,RKLtest,,,,,,,
-Date,2025-04-01,,,,,,,
-Workflow,GenerateFASTQ,,,,,,,
-Application,FASTQ Only,,,,,,,
-Assay,Metagenomic,,,,,,,
-Description,,,,,,,,
-Chemistry,Default,,,,,,,
-,,,,,,,,
-[Reads],,,,,,,,
-151,,,,,,,,
-151,,,,,,,,
-,,,,,,,,
-[Settings],,,,,,,,
-ReverseComplement,0,,,,,,,
-MaskShortReads,1,,,,,,,
-OverrideCycles,Y151;I8N2;I8N2;Y151,,,,,,,
-,,,,,,,,
-[Data],,,,,,,,
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-01LJ01300_V5,01LJ01300.V5,TestProjA_10002_Plate_3_10to1dilution,B19,C509,TestProjA_10002,TestProjA_10002_Plate_3_10to1dilution.01LJ01300.V5.B19,1,
-01LJ02603_V8,01LJ02603.V8,TestProjA_10002_Plate_1_2to1dilution,C19,C502,TestProjA_10002,TestProjA_10002_Plate_1_2to1dilution.01LJ02603.V8.C19,1,
-01LJ01344_V5,01LJ01344.V5,TestProjA_10002_Plate_3_2to1dilution,D19,C510,TestProjA_10002,TestProjA_10002_Plate_3_2to1dilution.01LJ01344.V5.D19,1,
-01LJ04170_V5,01LJ04170.V5,TestProjA_10002_Plate_1_2to1dilution,E19,C503,TestProjA_10002,TestProjA_10002_Plate_1_2to1dilution.01LJ04170.V5.E19,1,
-01LJ01518_V8,01LJ01518.V8,TestProjA_10002_Plate_3_10to1dilution,F19,C511,TestProjA_10002,TestProjA_10002_Plate_3_10to1dilution.01LJ01518.V8.F19,1,
-01LJ04621_V8,01LJ04621.V8,TestProjA_10002_Plate_1_10to1dilution,G19,C504,TestProjA_10002,TestProjA_10002_Plate_1_10to1dilution.01LJ04621.V8.G19,1,
-01LJ01714_V5,01LJ01714.V5,TestProjA_10002_Plate_3_10to1dilution,H19,C512,TestProjA_10002,TestProjA_10002_Plate_3_10to1dilution.01LJ01714.V5.H19,1,
-01LJ05491_V5,01LJ05491.V5,TestProjA_10002_Plate_1_2to1dilution,I19,C505,TestProjA_10002,TestProjA_10002_Plate_1_2to1dilution.01LJ05491.V5.I19,1,
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-01LJ05719_V8,01LJ05719.V8,TestProjA_10002_Plate_1_2to1dilution,K19,C506,TestProjA_10002,TestProjA_10002_Plate_1_2to1dilution.01LJ05719.V8.K19,1,
-01LJ01941_V5,01LJ01941.V5,TestProjA_10002_Plate_3_2to1dilution,L19,C514,TestProjA_10002,TestProjA_10002_Plate_3_2to1dilution.01LJ01941.V5.L19,1,
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-01LJ00176_V5,01LJ00176.V5,TestProjA_10002_Plate_1_2to1dilution,O19,C508,TestProjA_10002,TestProjA_10002_Plate_1_2to1dilution.01LJ00176.V5.O19,1,
-01LJ02258_V8,01LJ02258.V8,TestProjA_10002_Plate_3_10to1dilution,P19,C516,TestProjA_10002,TestProjA_10002_Plate_3_10to1dilution.01LJ02258.V8.P19,1,
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-01LJ02338_V8,01LJ02338.V8,TestProjA_10002_Plate_4_undiluted,B20,C525,TestProjA_10002,TestProjA_10002_Plate_4_undiluted.01LJ02338.V8.B20,1,
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-,,,,,,,,
-[Bioinformatics],,,,,,,,
-Sample_Project,QiitaID,BarcodesAreRC,ForwardAdapter,ReverseAdapter,HumanFiltering,library_construction_protocol,experiment_design_description,contains_replicates
-TestProjA_10002,10002,False,GATCGGAAGAGCACACGTCTGAACTCCAGTCAC,GATCGGAAGAGCGTCGTGTAGGGAAAGGAGTGT,True,Knight Lab Kapa HyperPlus,plasma sequencing,False
-,,,,,,,,
-[Contact],,,,,,,,
-Sample_Project,Email,,,,,,,
-TestProjA_10002,r@gmail.com,,,,,,,
-,,,,,,,,
-[SampleContext],,,,,,,,
-sample_name,sample_type,primary_qiita_study,secondary_qiita_studies,,,,,
-BLANK.TestProjA.1.A11,control blank,10002,,,,,,
-BLANK.TestProjA.3.A11,control blank,10002,,,,,,
-BLANK.TestProjA.1.B11,control blank,10002,,,,,,
-BLANK.TestProjA.3.B11,control blank,10002,,,,,,
-BLANK.TestProjA.1.C11,control blank,10002,,,,,,
-BLANK.TestProjA.3.C11,control blank,10002,,,,,,
-BLANK.TestProjA.1.D11,control blank,10002,,,,,,
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+[Header],,,,,,,,
+IEMFileVersion,4,,,,,,,
+SheetType,tellseq_metag,,,,,,,
+SheetVersion,10,,,,,,,
+Investigator Name,Knight,,,,,,,
+Experiment Name,RKLtest,,,,,,,
+Date,2026-02-02,,,,,,,
+Workflow,GenerateFASTQ,,,,,,,
+Application,FASTQ Only,,,,,,,
+Assay,Metagenomic,,,,,,,
+Description,,,,,,,,
+Chemistry,Default,,,,,,,
+,,,,,,,,
+[Reads],,,,,,,,
+151,,,,,,,,
+151,,,,,,,,
+,,,,,,,,
+[Settings],,,,,,,,
+ReverseComplement,0,,,,,,,
+MaskShortReads,1,,,,,,,
+OverrideCycles,Y151;I8N2;I8N2;Y151,,,,,,,
+,,,,,,,,
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+,,,,,,,,
+[Bioinformatics],,,,,,,,
+Sample_Project,QiitaID,BarcodesAreRC,ForwardAdapter,ReverseAdapter,HumanFiltering,library_construction_protocol,experiment_design_description,contains_replicates
+TestProjA_10002,10002,False,GATCGGAAGAGCACACGTCTGAACTCCAGTCAC,GATCGGAAGAGCGTCGTGTAGGGAAAGGAGTGT,True,Knight Lab Kapa HyperPlus,plasma sequencing,False
+,,,,,,,,
+[Contact],,,,,,,,
+Sample_Project,Email,,,,,,,
+TestProjA_10002,r@gmail.com,,,,,,,
+,,,,,,,,
+[SampleContext],,,,,,,,
+sample_name,sample_type,primary_qiita_study,secondary_qiita_studies,,,,,
+BLANK.TestProjA.1.A11,control blank,10002,,,,,,
+BLANK.TestProjA.3.A11,control blank,10002,,,,,,
+BLANK.TestProjA.1.B11,control blank,10002,,,,,,
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+BLANK.TestProjA.3.D11,control blank,10002,,,,,,
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+BLANK.TestProjA.3.E11,control blank,10002,,,,,,
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+,,,,,,,,
diff --git a/notebooks/tests/test2_tellseq_C_equal_volume_pooling.py b/notebooks/tests/test2_tellseq_C_equal_volume_pooling.py
new file mode 100644
index 00000000..2c7de373
--- /dev/null
+++ b/notebooks/tests/test2_tellseq_C_equal_volume_pooling.py
@@ -0,0 +1,52 @@
+import unittest
+import papermill as pm
+import tempfile
+from pathlib import Path
+import os
+
+# ==========================================
+# 1. Configuration & Parameters
+# ==========================================
+NOTEBOOK = "tellseq_D_variable_volume_pooling.ipynb"
+CURRENT_SET_ID = "col19to24"  # Change this ID for different test sets
+EXPECTED_PICKLIST = f"Tellseq_iSeqnormpool_set_{CURRENT_SET_ID}.txt"
+
+class TestTellseqD_Simple(unittest.TestCase):
+    def setUp(self):
+        """Set up standard paths"""
+        self.base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+        self.test_output_dir = os.path.join(self.base_dir, 'test_output')
+        self.test_data_dir = os.path.join(self.base_dir, 'test_data')
+
+    def test_variable_volume_pooling(self):
+        """Execute notebook and verify picklist generation"""
+        
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tmp_root = Path(tmp_dir)
+            
+            # 2. Define Parameters for the Notebook
+        
+            test_values = {
+                "plate_df_set_fp": os.path.join(self.test_output_dir, "QC", f"Tellseq_plate_df_C_set_{CURRENT_SET_ID}.txt"),
+                "read_counts_fps": [os.path.join(self.test_data_dir, "Demux", "Tellseq_fastqc_sequence_counts.tsv")],
+                "dynamic_range": 5,
+                "iseqnormed_picklist_fbase": str(tmp_root / "Tellseq_iSeqnormpool")
+            }
+            
+            # 3. Run Notebook
+            pm.execute_notebook(
+                input_path=os.path.join(self.base_dir, NOTEBOOK),
+                output_path=str(tmp_root / "executed_test.ipynb"),
+                parameters={"test_dict": test_values},
+                log_output=True,
+            )
+
+            # 4. Verify Output File
+            produced_file = tmp_root / EXPECTED_PICKLIST
+            
+            # Check if file exists
+            self.assertTrue(produced_file.exists(), f"Missing output file: {EXPECTED_PICKLIST}")
+
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file
diff --git a/notebooks/tests/test2_tellseq_D_variable_volume_pooling.py b/notebooks/tests/test2_tellseq_D_variable_volume_pooling.py
new file mode 100644
index 00000000..4cdda2fb
--- /dev/null
+++ b/notebooks/tests/test2_tellseq_D_variable_volume_pooling.py
@@ -0,0 +1,86 @@
+import unittest
+import papermill as pm
+import tempfile
+from pathlib import Path
+import os
+import glob
+
+NOTEBOOK_NAME = "tellseq_D_variable_volume_pooling.ipynb"
+
+class TestTellseqD(unittest.TestCase):
+   
+    def setUp(self):
+        """Set up paths before starting tests"""
+        self.test_file_path = os.path.abspath(__file__)
+        self.notebooks_dir = os.path.dirname(os.path.dirname(self.test_file_path)) 
+        self.test_output_dir = os.path.join(self.notebooks_dir, 'test_output')
+        self.test_data_dir = os.path.join(self.notebooks_dir, 'test_data')
+
+    def test_iseqnorm_picklist_dynamic(self):
+        """Test Notebook D dynamically by extracting the ID from the input filename"""
+        
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tmp_path = Path(tmp_dir)
+            
+
+            search_pattern = os.path.join(self.test_output_dir, "QC", "Tellseq_plate_df_C_set_*.txt")
+            matching_files = glob.glob(search_pattern)
+            
+            if not matching_files:
+                self.fail(f"Could not find input file for testing: {search_pattern}")
+            
+         
+            input_plate_c_fp = matching_files[0]
+            
+           
+            filename = os.path.basename(input_plate_c_fp)
+            extracted_id = filename.split('_set_')[-1].replace('.txt', '')
+            
+          
+            test_values = {
+                'plate_df_set_fp': input_plate_c_fp,
+                'read_counts_fps': [
+                    os.path.join(self.test_data_dir, "Demux", "Tellseq_fastqc_sequence_counts.tsv")
+                ],
+                'dynamic_range': 5,
+                'iseqnormed_picklist_fbase': str(tmp_path / "Tellseq_iSeqnormpool")
+            }
+
+            # 5. Execute Notebook (Overwrites the top-level 'test_dict = None')
+            pm.execute_notebook(
+                input_path=os.path.join(self.notebooks_dir, NOTEBOOK_NAME),
+                output_path=str(tmp_path / "executed_test_D.ipynb"),
+                parameters={'test_dict': test_values},
+                log_output=True,
+            )
+
+            # 6. Dynamically verify the output filename
+            # Based on notebook logic: {{fbase}}_set_{{extracted_id}}.txt is generated
+            expected_filename = f"Tellseq_iSeqnormpool_set_{extracted_id}.txt"
+            produced_picklist_fp = tmp_path / expected_filename
+
+            # Check if the file exists
+            self.assertTrue(produced_picklist_fp.exists(),
+                            msg=f"Notebook failed to produce the output file: {expected_filename}")
+
+            # 7. Compare the actual result with the Golden File (expected value)
+            golden_picklist_fp = os.path.join(self.test_output_dir, "Pooling", expected_filename)
+            
+            if os.path.exists(golden_picklist_fp):
+                with open(produced_picklist_fp, 'r') as out, open(golden_picklist_fp, 'r') as gold:
+                    out_lines = out.readlines()
+                    gold_lines = gold.readlines()
+
+                    # Compare line count and content
+                    self.assertEqual(len(out_lines), len(gold_lines), 
+                                     msg=f"Line count mismatch: {expected_filename}")
+                    
+                    for i, (o_line, g_line) in enumerate(zip(out_lines, gold_lines), 1):
+                        self.assertEqual(o_line.strip(), g_line.strip(),
+                                         msg=f"Content mismatch in {expected_filename} at line {i}")
+                print(f"Test Passed: {expected_filename} verified successfully (Extracted ID: {extracted_id})")
+            else:
+                print(f"Warning: Golden file for comparison is missing: {golden_picklist_fp}")
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file
diff --git a/notebooks/tests/test_tellseq_C_equal_volume_pooling_1.py b/notebooks/tests/test_tellseq_C_equal_volume_pooling_1.py
new file mode 100644
index 00000000..b87557c4
--- /dev/null
+++ b/notebooks/tests/test_tellseq_C_equal_volume_pooling_1.py
@@ -0,0 +1,70 @@
+import unittest
+import papermill as pm
+import tempfile
+from pathlib import Path
+import os
+
+NOTEBOOK = "tellseq_C_equal_volume_pooling.ipynb"
+PICKLIST_FNAME = "Tellseq_iSeqnormpool_set_col19to24.txt"
+
+
+class TestTellseqD(unittest.TestCase):
+    def setUp(self):
+        self.notebooks_dir = os.path.dirname(os.path.dirname(__file__))
+        self.test_output_dir = os.path.join(self.notebooks_dir, 'test_output')
+
+    def test_iseqnorm_picklist(self):
+        """Verify notebook produces expected output for iSeqnormed picklist."""
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tmp_path = Path(tmp_dir)
+
+            run_params = {
+                    """
+                    'plate_df_set_fp': f"{self.test_output_dir}/QC/"
+                                       f"Tellseq_plate_df_C_set_col19to24.txt",
+                    'read_counts_fps': [
+                        f"{self.notebooks_dir}/test_data/Demux/"
+                        f"Tellseq_fastqc_sequence_counts.tsv"],
+                    'dynamic_range': 5,
+                    'iseqnormed_picklist_fbase':
+                        f"{tmp_path}/Tellseq_iSeqnormpool"
+                    """
+                    'full_plate_fp' : f"{self.test_output_dir}/QC/Tellseq_plate_df_B.txt",
+                    'expt_config_fp' : f"{self.test_output_dir}/QC/Tellseq_expt_info.yml",
+                    'current_set_id' : "col19to24",
+                    'total_vol' : 190,
+                    'evp_picklist_fbase' : f"{self.test_output_dir}/Indices/Tellseq_evp",
+                    'machine_samplesheet_fbase' : f"{self.test_output_dir}/SampleSheets/Tellseq_samplesheet_instrument_iseq",
+                    'spp_samplesheet_fbase' : f"{self.test_output_dir}/SampleSheets/Tellseq_samplesheet_spp",
+                    'iseq_sequencer' : 'iSeq',
+                    'novaseq_sequencer' : 'NovaSeqXPlus'
+
+            }
+
+            pm.execute_notebook(
+                input_path=f"{self.notebooks_dir}/{NOTEBOOK}",
+                output_path=f"{tmp_path}/test_iseqnorm_picklist.ipynb",
+                parameters=run_params,
+                log_output=True,
+            )
+
+            out_iseqnormed_picklist_fp = f"{tmp_path}/{PICKLIST_FNAME}"
+            self.assertTrue(os.path.exists(out_iseqnormed_picklist_fp),
+                            msg="Notebook did not produce desired file.")
+
+            exp_iseqnormed_fp = \
+                f"{self.test_output_dir}/Pooling/{PICKLIST_FNAME}"
+            with open(out_iseqnormed_picklist_fp, 'r') as out:
+                with open(exp_iseqnormed_fp, 'r') as test:
+                    out_lines = out.readlines()
+                    test_lines = test.readlines()
+                    for out_line, test_line in zip(out_lines,
+                                                   test_lines):
+                        self.assertEqual(out_line, test_line,
+                                         msg=("Lines of output" +
+                                              "and test don't match"))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/notebooks/tests/test_tellseq_D_variable_volume_pooling.py b/notebooks/tests/test_tellseq_D_variable_volume_pooling.py
index eb338f7d..139b4f9b 100644
--- a/notebooks/tests/test_tellseq_D_variable_volume_pooling.py
+++ b/notebooks/tests/test_tellseq_D_variable_volume_pooling.py
@@ -7,20 +7,17 @@
 NOTEBOOK = "tellseq_D_variable_volume_pooling.ipynb"
 PICKLIST_FNAME = "Tellseq_iSeqnormpool_set_col19to24.txt"
 
-
 class TestTellseqD(unittest.TestCase):
+   
     def setUp(self):
-        self.notebooks_dir = os.path.dirname(os.path.dirname(__file__))
+        self.notebooks_dir = os.path.dirname(os.path.dirname(__file__)) 
         self.test_output_dir = os.path.join(self.notebooks_dir, 'test_output')
 
-    def test_iseqnorm_picklist(self):
-        """Verify notebook produces expected output for iSeqnormed picklist."""
 
+    def test_iseqnorm_picklist(self):
         with tempfile.TemporaryDirectory() as tmp_dir:
             tmp_path = Path(tmp_dir)
-
             run_params = {
-                'test_dict': {
                     'plate_df_set_fp': f"{self.test_output_dir}/QC/"
                                        f"Tellseq_plate_df_C_set_col19to24.txt",
                     'read_counts_fps': [
@@ -29,7 +26,6 @@ def test_iseqnorm_picklist(self):
                     'dynamic_range': 5,
                     'iseqnormed_picklist_fbase':
                         f"{tmp_path}/Tellseq_iSeqnormpool"
-                }
             }
 
             pm.execute_notebook(
@@ -55,6 +51,5 @@ def test_iseqnorm_picklist(self):
                                          msg=("Lines of output" +
                                               "and test don't match"))
 
-
 if __name__ == "__main__":
     unittest.main()