Mercurial > repos > computational-metabolomics > dimspy_hdf5_to_txt
changeset 0:5c545e1f0fa2 draft
"planemo upload for repository https://github.com/computational-metabolomics/dimspy-galaxy commit 6321871098b2c4bc9e321d20b7e66fff3d641839"
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.rst Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,66 @@ +Galaxy tool wrappers for DIMSpy +======================== +|Build Status (Travis)| |Git| |Bioconda| |License| + +Galaxy tool wrappers for Python package DIMSpy: data processing of direct-infusion mass spectrometry (DIMS)-based metabolomics and lipidomics data + +Source code: https://github.com/computational-metabolomics/dimspy + +PyPI (The Python Package Index): https://pypi.org/project/dimspy + + +Version +-------- +v1.4.0+Galaxy0 (`DIMSpy v1.4.0 <https://pypi.org/project/dimspy/1.4.0/>`_) + + +Galaxy +------- +`Galaxy <https://galaxyproject.org>`_ is an open, web-based platform for data intensive biomedical research. Whether on the free public server or your own instance, you can perform, reproduce, and share complete analyses. + + +Authors, contributors & Contacts +---------------------------------- + - Ralf J. M. Weber (r.j.weber@bham.ac.uk) - `University of Birmingham (UK) <http://www.birmingham.ac.uk/index.aspx>`_ + - Thomas N. Lawson (tnl495@bham.ac.uk) - `University of Birmingham (UK) <http://www.birmingham.ac.uk/index.aspx>`_ + - Martin R. Jones (m.r.jones.1@bham.ac.uk) - `University of Birmingham (UK) <http://www.birmingham.ac.uk/index.aspx>`_ + + +Bugs +---- +Please report any bugs that you find `here <https://github.com/computational-metabolomics/dimspy-galaxy/issues>`_. +Or fork the repository on `GitHub <https://github.com/computational-metabolomics/dimspy-galaxy/>`_ +and create a pull request (PR). We welcome all contributions, and we +will help you to make the PR if you are new to `git`. + + +Changes +------- +v1.4.0+galaxy0 + - First stable release + - Final release (Python 2-based version `DIMSpy <https://pypi.org/project/dimspy/1.4.0/>`_) + + +Licenses +------- +DIMSpy and Galaxy tools are released under the GNU General Public License v3.0 (see `LICENSE file <https://github.com/computational-metabolomics/dimspy-galaxy/blob/master/LICENSE>`_) + +**Third-party licenses and copyright** + +RawFileReader reading tool. Copyright © 2016 by Thermo Fisher Scientific, Inc. All rights reserved. See `RawFileReaderLicense <https://github.com/computational-metabolomics/dimspy/blob/master/RawFileReaderLicense.rst>`_ for licensing information. +Using DIMSpy software for processing Thermo Fisher Scientific *.raw files implies the acceptance of the RawFileReader license terms. +Anyone receiving RawFileReader as part of a larger software distribution (in the current context, as part of DIMSpy) is considered an "end user" under +section 3.3 of the RawFileReader License, and is not granted rights to redistribute RawFileReader. + + +.. |Build Status (Travis)| image:: https://img.shields.io/travis/computational-metabolomics/dimspy-galaxy.svg?style=flat&maxAge=3600&label=Travis-CI + :target: https://travis-ci.com/computational-metabolomics/dimspy-galaxy + +.. |Git| image:: https://img.shields.io/badge/repository-GitHub-blue.svg?style=flat&maxAge=3600 + :target: https://github.com/computational-metabolomics/dimspy + +.. |Bioconda| image:: https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat&maxAge=3600 + :target: http://bioconda.github.io/recipes/dimspy/README.html + +.. |License| image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg + :target: https://www.gnu.org/licenses/gpl-3.0.html
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/hdf5_to_txt.xml Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,174 @@ +<tool id="dimspy_hdf5_to_txt" name="Convert DIMSpy-based HDF5 to tsv" version="@TOOL_VERSION@+galaxy@GALAXY_TOOL_VERSION@"> + <description></description> + <macros> + <import>macros.xml</import> + </macros> + <expand macro="requirements" /> + <command detect_errors="exit_code"> + <![CDATA[ + #if $input.object_type == 'peak_lists' + dimspy hdf5-pls-to-txt + --input '$input.hdf5_file_in' + --output . + --delimiter $delimiter + #elif $input.object_type == 'peak_matrix' + #if $input.matrix_format == 'standard' or $input.matrix_format == 'standard_comprehensive' + dimspy hdf5-pm-to-txt + --input '$input.hdf5_file_in' + --output '$matrix_file_out' + --delimiter $delimiter + --attribute_name $input.matrix_attr + --representation-samples $input.representation_samples + #end if + #if $input.matrix_format == "standard_comprehensive" + && + #end if + #if $input.matrix_format == 'comprehensive' or $input.matrix_format == 'standard_comprehensive' + dimspy hdf5-pm-to-txt + --input '$input.hdf5_file_in' + --output '$matrix_comprehensive_file_out' + --delimiter $delimiter + --comprehensive + --attribute_name '$input.matrix_attr' + --representation-samples $input.representation_samples + #end if + #end if + ]]> + </command> + <inputs> + <param name="delimiter" argument="--delimiter" type="hidden" value="tab" /> + <conditional name="input"> + <param name="object_type" type="select" label="Content HDF5 file" help=""> + <option value="peak_matrix" selected="true">Peak Intensity Matrix</option> + <option value="peak_lists">Peak list(s)</option> + </param> + <when value="peak_lists"> + <param name="hdf5_file_in" argument="--input" type="data" format="h5" label="Peaklist(s) (HDF5 file)" help="" /> + </when> + <when value="peak_matrix"> + <param name="hdf5_file_in" argument="--input" type="data" format="h5" label="Peak intensity matrix (HDF5 file)" help="" /> + <param name="matrix_format" type="select" label="Select format(s) tsv formatted peak instensity matrix" help=""> + <option value="standard" selected="true">Standard Peak Matrix (i.e. m/z and intensity).</option> + <option value="comprehensive">Comprehensive Peak Matrix (e.g. m/z and intensity, rsd, missing values).</option> + <option value="standard_comprehensive">Standard and comprehensive</option> + </param> + <param name="representation_samples" type="select" label="Should the rows or columns represent the samples?" help=""> + <option value="rows" selected="true">Rows</option> + <option value="columns">Columns</option> + </param> + <param name="matrix_attr" type="select" label="The Peak Matrix should contain ... values" help=""> + <option value="intensity" selected="true">Intensity</option> + <option value="mz">m/z</option> + <option value="snr">Signal-to-noise ratio (SNR)</option> + </param> + </when> + </conditional> + </inputs> + <outputs> + <collection name="peaklists_txt" type="list" label="${tool.name} on ${on_string}: Peaklists"> + <filter>input["object_type"] == "peak_lists"</filter> + <discover_datasets pattern="(?P<designation>.+)\.txt" format="tsv" directory="." visible="false" /> + </collection> + <data name="matrix_file_out" format="tsv" label="${tool.name} on ${on_string}: Peak Matrix"> + <filter>input["object_type"] == "peak_matrix" and (input["matrix_format"] == "standard" or input["matrix_format"] == "standard_comprehensive")</filter> + </data> + <data name="matrix_comprehensive_file_out" format="tsv" label="${tool.name} on ${on_string}: Peak Matrix (comprehensive)" > + <filter>input["object_type"] == "peak_matrix" and (input["matrix_format"] == "comprehensive" or input["matrix_format"] == "standard_comprehensive")</filter> + </data> + </outputs> + <tests> + <test> + <conditional name="input"> + <param name="object_type" value="peak_lists"/> + <param name="hdf5_file_in" value="pls.h5" ftype="h5"/> + </conditional> + <output_collection name="peaklists_txt" type="list"> + <element name="batch04_QC17_rep01_262" file="batch04_QC17_rep01_262.txt" ftype="tsv"/> + <element name="batch04_QC17_rep02_263" file="batch04_QC17_rep02_263.txt" ftype="tsv"/> + <element name="batch04_QC17_rep03_264" file="batch04_QC17_rep03_264.txt" ftype="tsv"/> + </output_collection> + </test> + <test> + <conditional name="input"> + <param name="object_type" value="peak_matrix"/> + <param name="hdf5_file_in" value="pm_as_bf_sf.h5" ftype="h5"/> + <param name="matrix_format" value="comprehensive"/> + <param name="representation_samples" value="columns"/> + <param name="matrix_attr" value="intensity"/> + </conditional> + <output name="matrix_comprehensive_file_out" file="peak_matrix_hdf5_to_txt_intensity.txt" ftype="tsv"/> + </test> + <test> + <conditional name="input"> + <param name="object_type" value="peak_matrix"/> + <param name="hdf5_file_in" value="pm_as_bf_sf.h5" ftype="h5"/> + <param name="matrix_format" value="standard"/> + <param name="representation_samples" value="rows"/> + <param name="matrix_attr" value="mz"/> + </conditional> + <output name="matrix_file_out" file="peak_matrix_hdf5_to_txt_mz.txt" ftype="tsv"/> + </test> + <test> + <conditional name="input"> + <param name="object_type" value="peak_matrix"/> + <param name="hdf5_file_in" value="pm_as_bf_sf.h5" ftype="h5"/> + <param name="matrix_format" value="standard_comprehensive"/> + <param name="representation_samples" value="rows"/> + <param name="matrix_attr" value="snr"/> + </conditional> + <output name="matrix_file_out" file="peak_matrix_hdf5_to_txt_snr.txt" ftype="tsv"/> + <output name="matrix_comprehensive_file_out" file="peak_matrix_hdf5_to_txt_comprehensive_snr.txt" ftype="tsv"/> + </test> + </tests> + <help> +-------------------------------- +Convert DIMSpy-based HDF5 to tsv +-------------------------------- + +.. + +---------------------------------- + +Description +----------- + +This tool converts a .hdf5 file, containing either peaklist(s) or a peak intensity matrix, to user-friendly .tsv file(s). + +---------------------------------- + +Parameters +---------- + +**Contents of hdf5 file** (REQUIRED; default = **peak intensity matrix**) - selection menu in which the user defines whether the input .hdf5 file contains: + +- **a peak intensity matrix** + +- **peaklist(s)** + +| + +**Peak intensity matrix or peaklists (HDF5 file)** (REQUIRED) - selection menu in which the user chooses the .hdf5 file (from the active Galaxy history) to be converted to .tsv formatted output(s). + +| + +**Select format(s) tsv formatted peak intensity matrix** (REQUIRED; default = **Standard Peak Matrix**) - selection menu in which the user chooses from the following export options: + +@help_options_addtional_output@ + +---------------------------------- + +Output file(s) +-------------- + +**Important** - the metric recorded output peak matrix/matrices is adjusted using the "The peak matrix should contain intensity | m/z | SNR values" parameter. By default, samples are presented row-wise and peak mass-to-charge ratio values are presented column-wise in each peak matrix, though this may be adjusted using the "Should rows or columns represent samples" parameter toggle. + +@help_outputs_matrix@ + +---------------------------------------------- + +@github_developers_contributors@ +@license@ + </help> + <expand macro="citations" /> +</tool> +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,310 @@ +<macros> + <token name="@TOOL_VERSION@">1.4.0</token> + <token name="@GALAXY_TOOL_VERSION@">0</token> + + <xml name="requirements"> + <requirements> + <requirement type="package" version="@TOOL_VERSION@">dimspy</requirement> + </requirements> + </xml> + + <token name="@HDF5_PM_TO_TXT@"> + dimspy hdf5-pm-to-txt + --input '$hdf5_file_out' + --output '$matrix_file_out' + --delimiter $delimiter + --attribute_name $hdf5_to_txt.matrix_attr + --representation-samples $hdf5_to_txt.representation_samples + </token> + + <token name="@HDF5_PM_TO_TXT_COMPREHENSIVE@"> + dimspy hdf5-pm-to-txt + --input '$hdf5_file_out' + --output '$matrix_comprehensive_file_out' + --delimiter $delimiter + --comprehensive + --attribute_name $hdf5_to_txt.matrix_attr + --representation-samples $hdf5_to_txt.representation_samples + </token> + + <xml name="hdf5_pm_to_txt"> + <section name="hdf5_to_txt" title="Show options for addtional output (*.tsv files)" expanded="False"> + <param name="standard" type="boolean" checked="false" truevalue="true" falsevalue="false" label="Standard output?" help="Set to "yes" to produce a "standard" Peak Matrix (i.e. m/z and intensity)."/> + <param name="comprehensive" type="boolean" checked="false" truevalue="true" falsevalue="false" label="Comprehensive output?" help="Set to "yes" to produce a Peak Matrix with additional information (e.g. m/z and intensity, rsd, missing values)."/> + <param name="representation_samples" type="select" label="Should the rows or columns represent the samples?" help=""> + <option value="rows" selected="true">Rows</option> + <option value="columns">Columns</option> + </param> + <param name="matrix_attr" type="select" label="The Peak Matrix should contain ... values" help=""> + <option value="intensity" selected="true">Intensity</option> + <option value="mz">m/z</option> + <option value="snr">Signal-to-noise ratio (SNR)</option> + </param> + </section> + </xml> + + <xml name="outputs_peak_intensity_matrix"> + <data name="hdf5_file_out" format="h5" label="${tool.name} on ${on_string}: Peak Intensity Matrix (HDF5 file)"/> + <data name="matrix_file_out" format="tsv" label="${tool.name} on ${on_string}: Peak Intensity Matrix"> + <filter>hdf5_to_txt["standard"] is True</filter> + </data> + <data name="matrix_comprehensive_file_out" format="tsv" label="${tool.name} on ${on_string}: Peak Intensity Matrix (comprehensive)" > + <filter>hdf5_to_txt["comprehensive"] is True</filter> + </data> + </xml> + + <xml name="citations"> + <citations> + <citation type="doi">10.1038/nprot.2016.156</citation> + <citation type="doi">10.1038/sdata.2014.12</citation> + <citation type="doi">10.1021/ac062446p</citation> + <citation type="doi">10.1021/ac2001803</citation> + <yield /> + </citations> + </xml> + + <token name="@example_filelist@"><![CDATA[ + + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | **filename** | **classLabel** | **replicate** | **batch** | **injectionOrder** | **[...]** | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | sample_rep1.raw | sample | 1 | 1 | 1 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | sample_rep2.raw | sample | 2 | 1 | 2 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | sample_rep3.raw | sample | 3 | 1 | 3 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | sample_rep4.raw | sample | 4 | 1 | 4 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | blank_rep1.raw | blank | 1 | 1 | 5 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | blank_rep2.raw | blank | 2 | 1 | 6 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | blank_rep3.raw | blank | 3 | 1 | 7 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | blank_rep4.raw | blank | 4 | 1 | 8 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | ... | ... | ... | ... | ... | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + + ]]></token> + + + <token name="@help_columns_peaklist@"> + + - **mz** - the mass-to-charge ratio of the extracted mass spectral peak + + - **intensity** - the intensity of the extracted mass spectral peak + + - **snr** - the signal-to-noise ratio of the extracted peak, which is defined as the ratio of the peak’s intensity value to that of the background noise intensity value + + - **present** - a numeric value greater than 0 that indicates the total number of scans at least one peak was detected in a given file + + - **fraction** - a proportion ranging from 0 to 1 that indicates the total number of times a peak was detected in a given scan event type, divided by the total number of occurrences of that scan event type recorded in a given file. + + - **purity** - a numeric value ranging from 0 to 1 that indicates the proportion of scans, for a given scan event type, that contained a single mass spectral peak following hierarchical clustering. A purity score less-than 1 indicates that in some proportion of scans, multiple peaks within a single scan were grouped together during the hierarchical clustering process. + + - **occurrence** - a numeric value greater than 0 that indicates the total number of peaks that were observed across scans within the user-defined ppm error tolerance. + + - **snr_flag** - a boolean value indicating whether to keep (“1”) or discard (“0”) a peak according to its signal-to-noise ratio value. + + - **fraction_flag** - a boolean value indicating whether a peak should be kept or discarded according to the ratio of the number of scans in which it was detected, to the the number of scans in which it was not detected. + + - **flags** - a boolean value indicating whether a peak should be retained or discarded based upon both its ‘snr_flag’ and ‘fraction_flag’ boolean values (if either is set to ‘0’ i.e. discard peak, then the ‘flags’ boolean should also be 0). + + </token> + + <token name="@example_peaklist@"> + + +**Example of an processed and filtered peaklist**: + + ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| mz | intensity | snr | present | fraction | rsd | occurrence | purity | snr_flag | fraction_flag | flags | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 90.44000 | 4744.0 | 3.06 | 1 | 0.063 | nan | 1 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 97.07380 | 5423.6 | 3.52 | 1 | 0.063 | nan | 1 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 99.04180 | 4105.8 | 3.60 | 1 | 0.063 | nan | 1 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 99.49800 | 4775.7 | 3.05 | 1 | 0.063 | nan | 1 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 99.95020 | 5657.8 | 3.63 | 1 | 0.063 | nan | 1 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 100.40660 | 5489.5 | 3.57 | 3 | 0.188 | 14.51 | 3 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 100.8672 | 4841.18 | 3.27 | 7 | 0.4375 | 16.36 | 7 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 101.0027 | 9047.79 | 5.99 | 16 | 1 | 21.53 | 19 | 0.8125 | 1 | 1 | 1 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 101.0033 | 271893.9 | 182 | 16 | 1 | 4.17 | 16 | 1 | 1 | 1 | 1 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 101.0038 | 8738.03 | 5.9 | 14 | 0.875 | 9.71 | 14 | 1 | 1 | 1 | 1 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 101.004 | 5166.67 | 3.5 | 5 | 0.3125 | 18.02 | 6 | 0.8 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 101.0599 | 5894.69 | 3.88 | 2 | 0.125 | 15.06 | 2 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 101.2728 | 6846.28 | 4.44 | 1 | 0.0625 | nan | 1 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ + + </token> + + <token name="@help_options_addtional_output@"> + +**Show options for additional output(s)** (OPTIONAL): + +- **Standard output** (default = **No**) - boolean toggle where selection of: + + - **No** - prevent the export of a .txt formatted peak matrix to the active Galaxy history. + + - **Yes** - export a .txt formatted peak matrix to the active Galaxy history that includes only those peaks from the input peak intensity matrix that passed the filtering procedure. + +- **Comprehensive output** (default = "No") - boolean toggle where selection of: + + - **No** - prevents export of a .txt formatted comprehensive peak matrix. + + - **Yes** - exports a .txt formatted comprehensive peak matrix to the active Galaxy history that contains the m/z, missing values and other metrics associated with all peaks included in the input peak intensity matrix, including the metric defined by the "The peak matrix should contain intensity | m/z | SNR values" parameter. + +- **Should rows or columns represent the samples?** (default = **rows**) - binary toggle where selection of: + + - **rows** - sample information is presented in the rows and m/z values (for aligned mass spectral peaks) in the columns of any output peak matrix. + + - **columns** - sample information is presented in the columns and m/z values (for aligned mass spectral peaks) in the rows of any output peak matrix. + +- **The peak matrix should contain intensity | m/z | SNR values** - use this option to define which peak metric is inserted in to the cells of any optionally-output peak matrix: + + - **Intensity** - writes the absolute peak intensity to the cells of the peak matrix + + - **m/z** - writes the mass-to-charge ratio to the cells of the peak matrix + + - **signal-to-noise ratio (SNR)** - writes the signal-to-noise ratio to the cells of the peak matrix + + </token> + + <token name="@help_outputs_matrix@"> + +**Default output** - a HDF5 file containing the aligned peak intensity matrix. + +| + +**Optional outputs** - the metric recorded in any optionally output peak matrix/matrices is defined using the parameter "The peak matrix should contain intensity | m/z | SNR values". By default, study samples are listed row-wise, while mass-to-charge ratios of the aligned mass spectral peaks are presented in columns (to adjust, users must adjust the "Should rows or columns represent samples" toggle to “columns”). + + +- **Standard output** - an aligned peak matrix in tab-delimited format (“.” as decimal and NA for missing values). + + **Example of a standard peak intensity matrix**: + + +------------+----------+----------+----------+----------+---------+ + | mz | 96.04216 | 99.08062 | 100.0759 | 100.8672 | ... | + +------------+----------+----------+----------+----------+---------+ + | QC_1 | 0 | 0 | 0 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Blank_1 | 3342.626 | 0 | 0 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Control_10 | 0 | 0 | 45432.2 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Sample_2 | 0 | 3423.3 | 0 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Control_5 | 0 | 0 | 49759 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Control_10 | 0 | | 39890.5 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Sample_20 | 0 | 14563.7 | 0 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Sample_2 | 0 | 34676.4 | 0 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Sample_14 | 0 | 13134.9 | 0 | 521.4 | ... | + +------------+----------+----------+----------+----------+---------+ + | ... | ... | ... | ... | ... | ... | + +------------+----------+----------+----------+----------+---------+ + + | + +- **Comprehensive output** - an aligned peak matrix, as described for the "standard output" (above), including all metadata from the "Process Scans" Filelist/samplelist and the following additional mass spectral peak metrics: + + - **present** - a positive integer value (0 <![CDATA[ < ]]> value <![CDATA[ < ]]> total number of study samples in the filelist / samplelist) that indicates the total number of study samples in which a peak was detected with the specified mass-to-charge ratio, plus or minus the user-defined ppm error tolerance. + + - **occurrence** - a positive integer value indicating the number of peaks that were grouped together during the alignment procedure and thus, that were used to calculate the average mass-to-charge ratio indicated for the aligned peak. A value greater than given in the “Present” metric indicates that one or more peaklists contained more-than one mass spectral peak with the specified mass-to-charge, plus or minus the user-defined ppm error tolerance. + + - **purity** - a proportion ranging from 0 to 1 that indicates the number of scans in which only a single peak was detected during the peaklist alignment process. If the value in the “occurrence” metric is greater than the “present” metric, purity will be <![CDATA[ < ]]> 1. A purity <![CDATA[ < ]]> 1 means that in at least one peaklist there was more-than one mass spectral peak with the specified mass-to-charge, plus or minus the user-defined ppm error tolerance. + + - **rsd_all** - a numeric value indicating the percent relative standard deviation (otherwise termed the percent coefficient of variation) of peak intensities for peaks aligned together using the Align Samples tool. If fewer than 2 peaks were aligned across samples, then the rsd_all column will be filled in with ‘nan’ + + - **blank_flag** (may be absent if "Blank filter” tool was not applied) - a boolean value where 0 = reject peak, 1 = accept peak. A peak is accepted during blank filtering if a user-defined minimum proportion of study samples had peak intensity values greater-than the product of the average of “reference” sample peak intensities and the “min_fold_change” parameter. + + - **fraction_flag** (may be absent if "Sample filter” tool was not applied)- a boolean value where 0 = reject peak, 1 = accept peak. If greater-than a user-defined minimum fraction of samples (whether checked across ALL experimental classes, or within ANY of the individual experimental classes) had recorded intensity values for a given peak, then this peak is accepted, i.e. it is considered in downstream processing procedures, while rejected peaks are not. + + - **flags** - a boolean value indicating whether a peak should be included (“1”) or excluded (“0”) from downstream processing procedures. Exclusion of a peak occurs if the thresholds for “relative standard deviation” and/or “minimum number of technical replicates a peak has to be present in” were not met. + + | + + **Example of a comprehensive peak intensity matrix**: + + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | mz | missing values | tags_batch | tags_replicates | tags_replicate | tags_injectionOrder | tags_classLabel | tags_untyped | 96.04216 | 99.08062 | 100.0759 | 100.8672 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | present* | | | | | | | | 1 | 4 | 3 | 1 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | occurrence* | | | | | | | | 1 | 4 | 4 | 1 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | purity* | | | | | | | | 1 | 1 | 1 | 1 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | rsd_all* | | | | | | | | nan | nan | 10.98 | nan | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | flags* | | | | | | | | 1 | 1 | 1 | 1 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | QC_1 | 2901 | 1 | 2_3_4 | 2 | 2 | QC | | 0 | 0 | 0 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Blank_1 | 2948 | 1 | 1_2_4 | 1 | 5 | Blank | | 3342.626 | 0 | 0 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Control_10 | 2921 | 1 | 2_3_4 | 2 | 10 | Control | | 0 | 0 | 45432.2 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Sample_2 | 2819 | 1 | 1_2_4 | 1 | 13 | Exposed | | 0 | 3423.3 | 0 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Control_5 | 2877 | 1 | 2_3_4 | 2 | 18 | Control | | 0 | 0 | 49759 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Control_10 | 2856 | 1 | 1_2_3 | 1 | 21 | Control | | 0 | | 39890.5 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Sample_20 | 2855 | 1 | 1_2_4 | 1 | 25 | Exposed | | 0 | 14563.7 | 0 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Sample_2 | 2814 | 1 | 1_2_4 | 1 | 29 | Exposed | | 0 | 34676.4 | 0 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Sample_14 | 2870 | 1 | 1_2_3 | 1 | 33 | Exposed | | 0 | 13134.9 | 0 | 521.4 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + + </token> + + + <token name="@github_developers_contributors@"> +Github respository (source code) +-------------------------------- + +Galaxy Tool Wrappers: https://github.com/computational-metabolomics/dimspy-galaxy/ +DIMSpy package: https://github.com/computational-metabolomics/dimspy/ + + +Developers and contributors +--------------------------- + +- Ralf Weber (r.j.weber@bham.ac.uk) - University of Birmingham (UK) +- Martin R. Jones (m.r.jones.1@bham.ac.uk) - University of Birmingham (UK) +- Thomas Lawson (tnl495@bham.ac.uk) - University of Birmingham (UK) + + </token> + + <token name="@license@"> +License +------- +DIMSpy is released under the GNU General Public License v3.0 (see `LICENSE file`_) + +.. _`LICENSE file`: https://github.com/computational-metabolomics/dimspy-galaxy/blob/master/LICENSE + + </token> + + +</macros>
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F4/kux3YKVbYFdq5ncDXYn/gu/mSdQYfQw6TBOJfNZ5H/CQBu8f9+g981sEE/mp1kPceyXleug/6JolDLhRerLTK361MntRIKM14xbCDba9F6INqG3m/ajT4MPMLXzhPFhDfapsOPL/OD38UxV9xl7fHT9W+0O5HLhxFH2Mo+Ruj5VXpbyy9BZ4r5YEP+sWDx/gr6OWqudH3U4pLXEvLM4Px5p+X7+2Ehqzr+hb29weh7AuHpvL7mAjwlqoq+9c3T+hx64387ryaQb9/YtgPj4APZ04d7B0P8gnuy5bQ6/Hf8Gmcl8BjmwOQ2wP4g/rES8Sj9+M3atXaIv9pP8KHWVsQB11UE/xV8BA+cq6/lPHMN1Owh9rhh9i15gHXlJS87xYofqsz+QF4zNSZfj2SSnzXuFmbfS7bE+Rk7h7oV3id+AXOimCJz2ovq0g8RvtchXjsQP2AwJUhVMaxg0oqvvbC/6tdCrw8mSXjaBv/wM/In1daff0wgcvaslT6mcGXgHfBRPTNRg1+/f4v8SW/efjfMeH41xPQC7ZF3tlN/rf0M+a+VHnMKeDBLyt8EYYf6DzVweNk8rL2hhfky1elZ/9/iX+hNyIvYq9fgH/1cCh85DUZu+ww8QxndBL8loSH0l+//xu+Cf/KumouBL7wdfJs3fDFHt62GDv8yXb4JtIb+aqC/W+tgP8tfxU3mJ8b/hiXn32vQUX4qrKKI/5KhR7YSDzUyt6H3wtiF2m1Vf74GfuJtY/4jbuyNfLuT1xd70c805jTVPBpRar9ecBk4H8/Bn913RLW54VfoNUvRHyrDvEMZ3wr+DQfcQsnC/k6c/qf6LncBvjP+QP9UL8M9kTqGPTGRaXf7bzynfG7iXrGbzIOLmZ/v38MOmX1gh558UvszveAI1Nz7K4u57EvRnujP6pexo+eXZTnUPwNu46LnXL3Ffy49SFy/NplPbM7QicDu0Cvh/9lLHqr8s/v2RcaqLxXn3LA2Rx94DRtDH1uNQQfn6exrvf4E+Zs4pv2olmMV0ThKTf+qXniOnKZbwJxjxKj4PeUJVQ8lPim8XwZbcVz6JOZ45CHZ+z/eifyVFbQROjhz/7i7i/KPhjBPmXeqc9+cg//y3EyQ/8oF77YPhD+/nUUfdUPP9WaR72MW/8rfuql1sD5MjPyN419zf19CT/aK4L17DgGfmZ0F/xoCehv60R94qlziR+ai96xjmMjlf+ejXGDPqD/U2ZaBV6I+xsHKsP3k6tBv6Fr4ctZU4H/Rhr44GF6xs1/Tb63U5dAPg+3Y9+truL5p4i7aQPKgqeclcFvPHEy99Rb6NVxH/5hhQDg8e+xinHII+tNp7O+CB8V761H/vUb9ps2ejLrS3UfvpxIvlzvl5L1LDxF/5vK/n+AH6j75Wf+vuw/9vbZiu+qAP8Y8upWkunEQ4oMYj8fFwwcN4gHu/VVHv3eGuyFVCuge/Jn+KvxjdA7jYKVfdEcO+LEJIV3ZU+GkD8wijdj/W12wR+DM4L/oK7kD8vngx+6VkNu4j9A70s54I8kr+jfnP3a/Y1d6pb25HkmfGucIc+jTf8CfhMCwXcQ/Gam4jv7xQn2Rx/F794vZH8zvpy5Lm3+Ssy7ivWYXvjF2vkm2OWZyA84gQOhV7vcrCt+Ls97qbOwqlUCzqxd5NlcmxY5MkYSH/M6CR3rKH+qTBnw3vIXdDyA/jdmted9LHFS60xhGc85HA3f/0zHegcchY/bEF+3qvZn3qfK7yjC/qRp5NusSWHMu7kacGRlvzSDoZf5FT9afxXNer+3Zf9q54NezdQRP6DwS+yJZuRd9X7PGSc6kH6X0gGvdQL526byeZM3Ac/l7dhHcauQ05Tko5zUF9mnOlL35czeAh7mPGa+AQtY56EZ1Pd4zYMOjVMA/0vsTzeUuKIRRb7fuEudjPG2BXg5VQD+eLvntIzfNuknwUvjCdiTqWPke3s/eXuzTEn26+OB8HmYqi8rsBW5S8Qv1FPfQH6nIk9OKkv4zq4wj/eR8Lez7SP2xvlO+P+/ljNumUrQNRI/2NhN/Y398BP2kF9v8OUHv9l/DQJf3ckTmY/Ss/6BFyQPb27BH7S+fmfcj9S/mAm70WP7DPDRl/oII+sh+D4X8THrOfukE7eX/rVGMO9sFe8prOoTuwWiJ4tRt2hVrYG9m+UY9nuBK+zn7x5Rh9BL4a1gTvRSKex8sxf2reGkRh7ubmbefw7DZ2lqgdc7jbE/+41gPT6qXqmwsldyhcBfj2qD18Cb4CE98Uf7WBKxd91aPsh9O4v97xt2iD2sxhHpf7Qz9tiKu9gr0fCftoX4uTZ+N88Fq5+RceLnEVdNSl2BG+EH3VsRLzZK49fp73vDv37V0KPLq8GnX/DzdX2MslvHgIdY4m7O/Ufou2zRUu9h3sN+0jKspP/p1DHyvTNA/Akte/oo+a6fl/ChNvgi63tHftDIWxC+ClB5k2KX0F9/zQVfJalzNPKhX+x8z/l9wxH4ow12hjHRF/nc3Rl/P+M/6IF+7Kf69LnYWVeygOdtNuPlrbsF+q2jX96CArdTYQJ0LdEXfjpEPZFlAIf7DX3jllH1AveSsY5irVjX5HNi19pNyLO6yS7L+vU/L1IHMQx723pFnlA7FMN4D6vL9/riV9ClYxD28UmXfPutzdBt9C5V10cdrd0Tv9EstY94fMQ/8EuOPsTdxlFHYOgjlVz8CX+WVfn6tuTZnY7e4KfdQupuAqpAt+f4N9aiUuB7VEP4acytXfJ79AXgGuqj8t3jZZ2G2wH793xL4hJD34OHTD8Fz3ZiTqkTciP9qQc6c51+3d4Kf+nv/0YfRi2QVo86I3Lzf3oz313ypXpEHep7jhqHZFz/XewTM19SB1RwDfrl5L8Cr3nYm/qqskfw97bEMo53UuKdtTpHy/s2Fw5K/wJ3xQ81YobJ+ObG8iKfZtKH1CnNnCJ+q5VgCL8b/ugHO2AE/m/hoTKec7WbfGfPHizr0+KKXpLvdnzdyby3pF7KaB8fJ/B4npHxnSYP5Hu78VvqqqZelHGdS80FLmdyHvjso4fMr6fQ5Hsn3S2By+69+bw8v/sl72179jFpYyfFStuoscDl3Govz0bfvUdlfTt6npLnR4vle31iCXlvDcpxW+Bv/8WR54fLgLdvRdnPrE7N5FkLOsV8Gd8I3u3MAbJOK10jwasT3Bf8vJ3Gukcul/G0Hi+hY3Dpi7Ku+7dkXGfggZPyvLWfQ3tG3tvPMkp/fd5V6a+F3Lom75e0l2ezw0RXntcME/jtYiFnpX/RIeekf3nvE/Kcs4rgQe/UDbyYeaB/W0/qwvTmoi/s2gOlvx3VlDhFPuIoxvikglcnKpXAaXmESz+37A3hb+eGKe+1UmmA62UofH/o03Fppza9I/0q3rwqcPwzSOhuzun5UL6bECX8ou14I/OYe/3BzzZ/oZO7qJSsW68+Xt47Vj7ppz2qLuNrR/wFT9r7DAKX1b0Y42TZJfNp0ybLuq2I4ezfZWfEMO7OK/Kc+F3mt89F8vyrCXzhujfk+djqp/KczXkk7dyhl2VdcYMey3fdpgo/mgkNBO/Oqivyndt3E+vNnfSuPF+dJnC4A3LBR2/eyXfOpA1CD73WBVmPObE4+97fJ+DvHY1Y79Wwo/L+YBLWl2EDfHh4JP1LbINvc/WR342oWdLfyFJMxre/b5bx3deN4uU5yw/BrzOtJ/wXs1PgN+ypMo5TPa3gU7sVJ+MbgXeEn637r0R+rUGf5Vnbux++upQEufT+hJ5wNmHnR6YXOXHulCNf2n07euHvv5CXi7WxW7ZMpa4xbAPykuyr+Ov2uGTyvb5wGPiaaLJ/NG+OXq1VEv12tLrUd9rnx1L/UO4f9OfD1uwPXxcSr3+nCfzGnXPyu/mjMXG6DD2wX76mR78m9aduNHe0yIcVegi707sR+08Q5wXMbe1F71n3huBvrUeujCdTad+fYx1TdrDvfKmD3A37Alxj1zJfTT/25bEpGD9/Gey4v7axnxQjHqXX53yGNaIn+/9T6n/dNWfZ5y6UQC8drkUdrudN5jmJXWpnrafyzvip1h+XBC5zIvk87exK9nmP8iqepeo2K1H3Yr4sjp1drQ39Nh4njjcgiv15STL6x33A37z6GTx1oc7dikyNPfwznPfNqwl+rALd0fcvsIeNTh7Y8X6qHjzgsLx31qvzLivV+ZMZ13n/dyf2ZU/im26jvdhrBSeL/jPqEx+095dgvw8ZAB4zYL+a1/oyr+cFae2lTbFzG9rEL3a6gie3Bv66W5R6N9d7KHbL2y2sNx/1UMZyzjO5G2PVuaUg4DKGgPcOY3nfs7mMo2WZyfMr7C0nxTzotCg7fPcef1rrRz5Mf1VY+unbq9EvL+cE3OM+Qhez2xLsoKXB+Knh2JXmZ+pY9PXUXVpZemBPpyRuYkc+xC7J/UL0oWPj35lbqENxWxE/MlMfVXinfkU7R/7IvOBL22UAbQh1aPoN6hSN2GHwVU/kRe9GPMyaEQl+jpHf176WRY/McuGvtP3x8+ZT/2LmIm9qDaOeyz75Df8xvQE+u5XC7/+1TNUjXAD+ScpO3xij6l6o69VXjMaOST+cfS//X9g/qxLYZ9vyvTGTuJV+9yXj5U5knOkVoMu/K6irKOkHn2efyfhbJhEvv/0Hdn66buAhtgLr6NqX76OJhxrdNiJfP4kj6OHkz6wiicDf6S3rzkXcQl/TlviZ7wlFD3XOpIzyi5/VhC+yxwBPbeIg+i/qD02PZOwPGaJ4X28x+iRlWvB3XMX5w6iT0ULhdyuI83ZOg7vgoTNxVH3PcOhxWJ13ShUB36cOge6b58O37TPx3XXiPu40zsXYefdj91chv+p8CoeP3jG+3Y1zBNbwB8y3Kpa8ziT2G7fmLeT1KHU7pkW9g33/A/mkE03ZH9KsJ/+xFb1gdj3L/qHq1R1jLfzoA/21OsWhnxvLutMQ53Zjn4O3Qu3B21fyZs4fqt66OfkDJ3Vuxik5njyQJ/W4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Gb/g9UbPStz7uIh2mJVQuKpJ0OkQQLJAvaU+7jWvwXPmVX3xfXQYhtoW0RPa41optSSImNGbnEMV133aNlJHgrS8EPU/9di//9pLixtI8DX/jbRuE94vunzTN7RJLcCD7z0J6mozaH+bMb7qLl9ft+C5NkpTO9qK6zEJflXzEeuEXT2whDqu4owutOP6HobKUHtI8Hp1zvXwXqxb2YnFYTT5zKbjfP0kOEAWig9C5crrKiQGMJ+x/zIlB/EcOHVA9xLqUbk6+OVnvK+sopzVEMbrvOiaejTae+XZoBESLPByyg2/Y58/GV99bQzzynvy684pPB/OjCgtnyZBfhklzvkXjluzfn3mHxKcF9zclYveMJM9s3sG673yqmbYLPazotwBxl8SVFADUjXnSDAmY5BOXCCBdeQGptgirleIxyJzORmGSWcObeEmw6pM481recjQO151MBpdJn1U4R4vGUL1o5TCVpLhZ9Cm7kV0q0fdvgU+Mhg3WT5tESSDqP+3nCV0tUvagsMaMlxf/t61ALWu8n5yfB0Zok1XiGeKk+FU6q1umgQZdv6cuGMqSYbqKpqhohQZqvQPPd8pTQbP3NI6T7QsTMFxywYy3NS50c2RI4NeUir1hgIZXEZ4fqahaRciGkvQ9ame4KlEhtz3MqUNaPyFEUNeZTIcPHwkpAt1rVuhvkmFDHsaTvGZoE+dTHWXqeK+xneS9NEDDemx0ltxnMb3zUroegUuUzc0Uc9vLHobGVTmFY6BFhkuDqntHUI7pL/Z/tIlg1aXfE6yPhn266958w4NF+N2bkG7HtYZ799Nhkkz2m4dQ9x/bvG1IjS5wk7Qbg8ZKgXbeRyADEJHjQ41ohHSA5suG5NhyV5x90v0etFpI0UTMrzpuXE5Gd0s+Lan3JQMC8XN0hWoBFdOVNY+MkTFrDZ/vx/jI7JFYeUBMmg03cm+ZE4GSdchgrg1Gd7ed3mgjH5j3B55h/qvq4+aQb8X8YmdtMHf+6Zf/I4Wp3LfGyCSgap5VPIYhQzurV/Lp6lkYLDdvZ1pZGDz57Wy6GRwujcsu5NBhpevN/feQWfzD4nMoT6LjCFPJsZByK2kDX3ieHh6Cwu/P/blkS5KLHxNdkQbKVe/HUH3m0xsq0Y3pt1/bsnGuD8U8DqBVm4h8AlzsA6CL6tsQgciKOui7HHdjXu2GBwhgzJf0F4eZzJ8VOHKDEZrMt4lPDyO9fI+f+0rFzL0DdYfy3Ulw7YQz+QtJ8igqP40ZoMbGURe9/TqnyID+bKRjs5pMjwv/5gb7U6GF4Wav7acwbrc3W609iwZnhWY/FBC26Q0YoPQxVwzec1zZOBZ5cC8j5q8/qnm54nxOr8tJ9CLDA5zLf1z6C2Cbce98xi/9rbYfd5kEBuIbp7ywzqvfHD900UypKeMntgaiP0y5tzUgkqt4u/nBJHh0ZVKQf5LZPhk3Qk7UdvZlX6uqGEm3+9bqIVhhldcKBmMak0v5aG/vX0FbS/jeqSy58fCsR/PxLwXjCADJH59z75ChmOm3lX9kWTI+a7BZkeRQV343VknVEJ8b/oIurbldohpNNZT/LtEGvqz66tbOPqB3XhOLQbzSOC3Ko8nA9eH13yfE8ggK7pp9xd0mztrRug6Gc7fdhi3R/dYX9A+jrr94uI9kIR9FBWeVYteHHZdPp2M691LK7G5SQbpAfULk6iG+eeghdtkmFohMJ10lwzCjYOaofewX7+euK3/EPt8rDGs9BEZzkgq9JahTv+cPrWjrKhMkZTHeD8RpUlSBtbVyPOdCU/I0FxYeSk7C/PTvdasD40sv/L3bDb26aVs9+inZODbOS51sIAMTYbiBlNoZbprVUYhGeYPXHTnPMN6+RK1qPKCDCu//pkNKCND2GbOfcZrMmxYVmZYhua+Tu/7UkUGj0OtdzWqyWDaJah4uYYMf1yiCI9RTkXyy/tvsX97fd8pvyNDgWOOQvF77F/tw+dqP2B+iDU/DFrJYPBl/B93BxkSdE4as1HrVya7dHrI0Hr/k+ou9MLPx9rf0aCPkcG7+siwfX7wvDP6O1mBXoIagptYG0r2rVG83U8G3qGpJadBjLvWqKfRZ6w7Kwaz/isZ/pa2K8yh/gYrWF3fyGAjrO0pOUEG0rszG8MnyXBa5IlpJ/qgWfSq+F8yeG3NLNuE7lG1avNB3w+W7pWcI4PfG+EOObRHTkZjCU3YzWX85h/+XkdK7so8Ge52O1t2LOL5OXl7YmSJDBmicgmsZRRoKw8f7llOAZI1hxm2ggL/lLnZbei1WFOBT+gooVOxmJsC1XVb2jfwUkDMseO9Enq6I2dbwioKtFg4Oz3lo4AinJuj8VNgLoxo4iyI1yKjwwto5ZlvbqfWUOBOV739XfTlp8jlZuso8KbYi8knTAGbg6ES4yIUkBKIepsvSoFtR5mypPUU2HxBqCQb5W/bXfYMzc36lz6N1krubOWRoOD7ObNVV4oCmwaP7TDZQIF8ku3UUXkKhEvGVzSgfznuzBm0SZBS3adAgZuUAy3qihSI+Tz6M1iZApYtXRc+oo8b86PbNlPg6grGHbktFBh/WFoTo0oBA/dIoT50Z0trcboaBU7a95v1alGAGnNhTH8nBda/SHl1Gj2mLK/VpINxUfsWK7KLAnSb35bjujgf/6tta/UokKaUlGmNnhVR2NuhTwH791IrxHdTIGBDpAOgaV1Gj17soQD4cCWdMKKAhrvOwAFjCqzs8VhRZ0KBFYKeZYqmFOjLuxPosR/zovXg6poDFNiRtclvqxWuo8E06Y0tBcLWWVmsIVDgSKJxy1X0l624SB862e++5gSZArG9ltq1aLByS5kNhQIbVU4U9aEvdbNzx1GjFKsPjnQKRJ3YKJqO/tvovVGXgfdp91PxZVHgWUVasBWHArOmvteN7CmgefpyYRx6Q/zrRlVHCmzNppPNDlPgk8qF/UeOYJwdrZ9OofNxv9eVH8V4nBd1Ou1EAeHx7Va5qJtD7etvzjiv7wGbF8coUOj3M5LiiuPbKzOtTlHgSkzW7RWnKSCUdk7sLtqRYEcleeDv3K+4rD2L8Xt9yZfLE7/frThjhsYf6gBxLwoc6nTxfYxq+W97nH+eAmeuvgic86ZAIDvLe7sPBfaDrnrQBRxnHpZYgwa15ol2+FIgZXHIc8oP73PgXtqUPwV2y4WGyV+kQELCliXjAApc71o9WxBEgcWueW3LEJxfJ+iQYigFkuIjz8yh9qYNicsuU+DE20V+0/99HXSHgj4XMzAWCMP6zc4MKrhCgUi7qZEbURQY/Mt+/S2aAha0HTukruK8jS1K/AkYd7nfG9yuU6A1Q2mraBIFJO/a93EnU0BiRidi5Bbu42bhKo07eF8uobc+dyngwKvA3HiPArw2UxKK9ymwd25E1C6dAl8vEqYVsyiwJljwBgvd65hunY0e474ZEJSNeRLoXHEnhwL9bT6qH1Gn5KDfcbkUcN6WW/ECdVCPF1TNp8C9V5rMzgIK/OgJ5fV+jnWaG95DLqYAUZ3+d/AF9kmJVIrJS1wX0yQivowCvV1av26WU0DtfP+c+iusn7PqZw0qcbzCBT6hKgqENH00vllNAYGcW61F6OnQgxetayjQxb5hEop2/1Io9q+jwB83nfBSNPPalVThenTFwi3bBgo4HvCwS0Q3G4ZVRDZT4FI5fTawhQKvzIOf8rdS4EXdkzbpLgpwUR8qaqALKtKx79GGF53TXajaxjv7Sj5hPjzWSq7uwX7tK7J3RPd/z3/4qhfrVJH/glofBcwSy73H+3F99RecIgaw71dYHJ4bpMCqccO6oc8UWJe5aCDwhQJWy3u2nB6mQIGO2JXf6Kn9zn2bv1Fg3/FVssZoeGnNq8ARCsR5V8i8/E6BiLgqBfiJdZYYu75vCuv5lXSHzC8KyH2eOvcK5dncpTTymwLfvh0O8vtDAf1l+7gezmKcl5rWKP3FuLj+/m6CdmVp8qSiNQthDy3n8Fon/dnQPzwPTb+M75/H86rX08wW9eg76F6K1oaeeF61iPe3tbX3WKLAhOL+c+YrqLD5V9a+UvRR5s9HHC4qvFflqcjnp0JRprlvO/pphL55rRAVTsxOSTagZeF3rzxfRwWffWfaR9Fh7e/qj0SpoG5GifUTp8JyVzmTVRJU8OdRGT4gRYWsn1utBlDlOs5Hf2kqSMb41EzJUCHg573bUXJU0Pwb2V2NRj/0YZzbSIVD+m9rL6PzGonndBSpoHQwte2tEhUOyk1GqytToetS640bm6kwHS9NWbeVClyqXyMzNND5Wzwj26mQ8q2zxUIL76fYy7moQ4WKt2ba5/Vxf/yRuoKGVPAs5mlej7r9i41Q2kMFJzX9FblGVLjqsWOmEDWxtB6pNqZC0zbFI+r7qOBB25RhjW5KVXD3QinW0oMn9+PnYpun8w9QIQH+KUZaUOGD57DeLksq5Hyz+W5mhfd5eesTEd2VeJd/FtXn/KzcYE0F/oDizOyDVMjlWHxuQNPqNbypNlRYw9BQ5ydTofDb6yO8FCrc3rtTdgh9nqD6zZiK8cryXveRjvG85p8sy6CCmX2HtB0T8zXD2eXFpsJ65v43GRwqRH6NXp50iAr/PCRG7Oxx36Ott1sdqGBEkKdmHqMC+e3kmya09sVHwoXjVGjVCdufhXLdJ9euO0mFi368vEJuVBD1O696/zQVtm2pzJByp0Lw6TTbZx6Yv0m9M8FeVIh77ltahOrIuGja+VIhm8Ca70K3X/1qvskf90lWqVxCv+vt7zwegHFS+WD+Al08q3D10iUqxDIuFIWhanwUv5EoKlw+MK1HuUoF4xlNow70skLHrZAYKvTSzj9NQs+MS8gMx1HhUpbOm/xrVFjwepoNN6nwTdFo70G0o6VirhTVOL3hYh+64DJ+6w86bqJ8nusW5uvXojwNZYsefeyEKnTJE8+jJCGzO0q3sZ7/vc9URWdv9583QDUHX/UdRNd0s7jOoLxKacV30H7tp/WpaHGL0c9H6L1s7ptZaN9DQ9U8dERV9nEBmkm1iSxDR1cqCFeiE3pbs2tQvZvqWS3oDWMe3Ta0J3vVhT70uKt66WdU1dBubBwNijzYMok+tJv0W0Cd8tQEVtzBPrsRc2ctmmHFMJBHz9w+OroVbfn5jLoDfXVmPUUfzd4gqEdDUzKXdXmiQ7lx57xRkVeDlsFo5tKoXxj6Y/fx4AxUbufl91Wo89PgNWvvYv34mN/QRnNTnzhGoQK/u1kv0dmMy4R3qFLpEYW+FCrsdTrnKH+PCipTmecU0Naz26VoqXguCEyJVtynwtjoOaVHD6ngu9nfTj8N57+z77XMIyq0c1HKPDKp8De0wP8u+nXc42Mp6r1qqsfxCRUmR37z+RRQoWHnkvPOZ1SgEY6vykFXDO1t1SvC84HZXnOyBK/3rxoaQgfyz6dZv6DCSRnHm+tKqWCR/LXqYDkV6q+3zXx5RYU6EyX155VYX8vzu168xrpjmW2fraHCHxOyHbOOClUGXTKrG6gge4dfKeAt1snMeoM/KEGPfmoWNXNZOi7ciOdbwYbsw+8w793dwUtNWG++vmZ/31PBmhPgKtVCBb4+uTe7WrEfyeyxHR+x3nmtZYTaqZC0ZYVccgcV3N9JvKnqxL5Q5xkR6ML+Zn9avxPlFjDa7d2H63NZMb2mH69rG0XjBnCd88dGJr9gfeyV0bg2hHl3LTKcR79NN1fnfsW6Xu0ct2kY6/HYHcINVCreqntuhAoHghuVW75TwfHPvaWSMSoE/tocJzGOv1O7kBg9QYX9ZQZT9EnM38hTrRG0oID7q+YUnsPC59ZLT1Nh9XSlpuNvKvyKTnnkMoN1z73Vb+MsFeQvTtXrohyNfUceoY4Jb7r65qlgsL5+NH+JCjzqRJ4ZbhpkzVTEqPDQ4OJjX2UaLw2eplCbj6Heaa27FlbS4ELog+gxPhpQDayy1dbSQHnSy6sUPXUuq09SGK+PX/uycj0Nfq4rupWPbl76y3tNggY1F3vFFKRosLZ5NS0NFS7gMjgpS4PK2ZH6Uxtx3skj9l830aA41iLmrBINNrgfketAHawragJVaGDZ0SmwYQsN1qwVHolVpcE3haGrJWjV/pipZ2o0UPTZbtuOdreKrtHQpIH1fIrc2+00mPJZp39/Bw1efzMq5NbG7xsKmbtQ4YgDn58a0qDU66/hWWMa/JN6pKVkQoNRszVekvtoIK3c83EPOuiUPueJvrcpcarcTwPDc3pC+w/g/dnJ46Fojiz/U6IVDUo6H/2wt6bBRCnfhLctDf8WHE9VItHgQFa9exzKXy6unE6hwb3Eo0UNaFDgpnxJKg1aTHO37afRwOrxwsw+OxroHOzTLEDT4s3JNw7RgD79N6DNgQbPHQxCFRxpoE/nUC3RU6zslYZHMA+sdRleR3F+6Zetz51pwNuWbepynAaitPta91xp4Cnu2jF4Avc7Z+K83J0GWz/HfJM/SwPXGYXCznM0UDotOH/WiwZiWxU+yXnTQMTUweQ3mtxq+4LhQ4P6A9Y3XqObDncqKF+gwV9SIu91X8zTujraADote9FU248GCqrXj5j602D1zBPNDYE0WKr1YB8MosGbzLfSLy9jPPW+BpwJo0FdeSJRPJwG4RPdiToRNIg0Yru9vUKDgMwnB2WvYp1dk5K3QZ/JbI1LRE+NbYsWTKRBhcSV9Wq3MK9Rnim+d3FfwauPbnqI+S094liPZqX08ax9RIOODrmtguk0eCfSFB7xmAZyW0uUYjJo4N8dx7qBbhcQObeUSYOVlrd6OFkY94QlxwJ03G7ltulsGoj3e70QzsX5DnNXS6MFMhddU5/S4JN4u/qLZzTwU1EX3fOcBqeFc0QT0Wih7vTnpTTQvhlyqKUc1xc5TpJ9RYOP6avu0ytosNHp3jrpNzSwuVpioYjueGKqcL2aBgP3lcr06mjQs7LAVaeRBgeX77cpb6HBXa4d2mMfsN6vqbz78ZEGlESplAudNBgqKxFN68J62e93mPiJBqHXRRr4e2jgu1LwLNcA1uP2og6rL9h/1YJxHFT39067gSEaPIlL6iF9pYHjiRz1NLTUey/z8ggN9va8jbAdx758TTW5+JMGgtfaOlOnsX49JReKUIX+2EjV3/j7MtFGoT9YhxFGXjwzNLi9tV+x5i/2IfGFk9Q85jNC7IPNAg3O1jXJ2C3SYKa+dhlhiQa1o96zR5bTocZTXnYU/bXutVjrCjrEGSjtFeKmA6/uRXEeXjqoOYlLNqIFIclXfVbSIWSlUUoRHx3eF7mE1grQoXd9DSFRkA4tpy6cAyE6lN0p98xFr9TSh8OF6bBuamh+gxgdBmM+/TuMSqzUqTYXp8Nwqib5G6q8UUf4gwQdRPVvbs6TpANzdFL6zAY6bF1FWBcqRwenwohVXBvpYKP7+fYyRVzXBzHrYhU6HItsbajaQgeLF1sWjVTpkKcqt3B+Kx3s8oZEMjXpcKqz3OjZdjr0ax1I8NpBBx/Cpdkk9AAhICZdiw4ZHx3L57XpsNNoWsdZhw7Potki4bvowC+1KF6K7ldNX+msS4fFXwHjU+izMBkpQQM6fJCf+LhgSAfjMKkQK8DPD+cFEdDP/ae/HNxHh/W9N2tS9tOBJrMuao8ZHS6E/FNfZ06Ht7xJ697+70krerglHb4VZPh1oXpNMx1breiwer58JgSNtiOejbOmw5xfUvfcQTpE/RaWG7PF/cavHBgl0GGX2dc3wiQ6lGRWTwmQ6bDlgn+hDSooaJHXQMPrSJZMoh0dYg8F7LSzp8O+61dV3zriPnvO3tjljPH3WtrxwI0OKdRej4On6DD+eUWGP5pd9NO2xwPjNdLVOHeWDhN/1V698qaDuWnhlQgfOkSK97qoXsR9tX/ZNRpAB7ONzOiWQLyv0TLJ20G4ryNBb0jhdFheaG35Bz1uo8ifG43js5fJmMfQweuXUgEllg5y+86qboijQ6rCj2el1+jAeLvu1KsEOihW73lx4wYdkv/6xLsk02E+NeSoSQodrq7uHHuIpvAeMfuEkrP13d/ep8Om2UqRl2l08HTTEG54TAcuRl/43iw6UKM+Zk4+pUPT77wTrS/pkJ5M/C5Xjvu8v9k2F7VXENB/huZ4rf+n9YoORmTl7hcVWKfnwrlXVNLhQUy0g+prrDv2/Op0dHPbdFYeujf1ecL7WjpUx7i5TqHtZUn7D7zFfiFGBzk20sF7OrbNtwnjMrE6KfIDHUKpfRO7PmJ9xE1GkFGdoefV3O10OH9se1wDSibQlE520CEhkbH4oAvnZbVwVaHbLJke7B46PJFy2dLZR8dzyq2/sp8OBP5KVZFBOmiD64/Hw3Qw9Sy+5v6NDv+s55R6R3Ff08vmt43RoXzlgtdbNPxR4tj9H3SIWLkjJ2eCDpbjBXyzqJ/SizzrSTqs9Mvmjpiiw5HAtXkXf+E5EHRf4MhvzMtbtgnxLx2GdEPpbvN0cLdgfK9Hs2m0D0LLGTBh/2xTHzp4tWqkjosByqck+b7zMuBGbZnXCT4GfCiaanESZMAY95BvpBADnuu66Y2gXp0MhY61DFC8Y15EEWaA1NOzIofXM2Dtxvw7PBIMSGQdSBNCv499vCQgyYATy+Xj96CrRsS8lkkxgCDu+eaYNAO0dRfIuTIM6A15VDmApo1cVtspy4CRhztvP1ZgwJfeNzSBLQxIXrYsb5kqA2bFs2seqjFAcFbw1Dc0/uXU5DkNBgiwPnz312RAZ7qSxasdDJjfd9fUZxcDXh950tWjywCFgOLYy/o4fuKHaz+62T7pvZsBA4jOKr+D/ldhlDmKxtmGrLA2YsB2n5ak62jPcV2JRdQ2WmyTFjDg5NcY0RljBoiWqdIW0I7WPGK0Ce4zW/RqsykD9lc26p88gOur4e/xQtf3DK4cNmfAE//oCzMWDNjUtVlT1ZIBlEB/UqUVA6zFDxEErRlwTmfqhB6RAQGPcwOJZAbc/KxGpaEuK7icj6DT83b3fVG39cfnJtAYMamwaTTd5eW25RQGyAdJ3uJGm/OsX+mjjdz3U6+gl5SX1cegfb8YdXdRGYkrGepUBlAbFbdGo4md9EfONAb8+Rlz5Sw6+bL17QCdAUfUy3cIMhlgJD3JuXaIAWLUp4dDHBnw1ZvwTPAwA/r9m19YoIci96teOsKAKHeR4Dw0k/dx/CYnzEdKtWUEWuC0tSsd1dD8ZObgzAB9c9crOei9ejsz3mO435V6WyfRByliXyOP43omZmZi0UYBjZFc1GFwDbUQXeqy5xN1wfU18OseQHn1n4VcRAXezh1b64r7K382543aHDLYko4eGjw49RSFWG/RAfRloGHdLJrvtdZe7ATWn/HpzfvQdi5pljUavp+xPwLN/CkwlIEuBnLr56K7bybfK0I/8FOu1KN2f6YTRlE+Iit8Bv23Q8d33UkGvn/KvJVHrQkzvWroF40SS330eVIWrzH6R1M4ZT9qGxsbTEZ/enrtZKLBe7afckCPV1h1nULPEuvVvFEPl0/zQejTHcqqIej5fyJ6sajd9mHBDJQjaP4lG20ZkQgtRDd+/HGuGM0k8l9/jZ4idMxVoXwn9Nvq0XN6liYtaM9Nc4eP6FBJ7q7hk//HP2/5NLr05YHnIur5I/uukBueC3wMX2HUTHipWRqdfaPzUg59xxssr4jGjJsP6vw/bq3tHgM0fMeZQwTURdXRnoke2aHRZo9ekohxPYFSusX/nkHPXVfmCUTHjglORKLLRT+euYb2dY6uSUbN9wq8uY9uEv6u/hgd1pVqykXnL73cVIxGBZTJvUHXJn8OfocWkp/ItKDuSlcM29DVtu6hXahPxAyxD328+OHrF/THYSPD76gFuVh7Al3nlGb4By3qt5qeQ7UL+QaWUIaV9hj/KQaork36vA5trdmdKo2yGrcmbECjDin/3IiaXD8Wp4RaBA2834r+3VCwVRfdtkbmtCFao/1j1ARdMfx70gx1X/liFwH1CpVfoKJXxoorWKj31o9P7VG1/HyRw+he8ZSGE6jsy5pGD7Rgsei+N3pXQRYC0Fe3G1ND0OquMdF4VF5E+XMS6mB+Ivw22mOavCwVHX7loJqB9m32+56DNqc2xT9HBWyXcZWhdsJap16jkZY6ctXo1V+szw0oJe3So2Z0qGmNRfv/n4fapX76f9/5mpeH0LrHV06MoaXLPRYm0GNdu4Z/odc/Vv2ZQz98k3ywiBptfj/AdxrP8TgehhB6VJB9ShKV3ZSosBEd95a6uxkVXal5YzsKnVqXddAL8n92GqBpFueVTdDbzacUDqA3NTc/OIjusAgKo6Jzy8XobHTZyEylPUpp0QpwRoNDBoxO/z+/WMzwWVRa87exD+pSEjMajH568W/1FXRrssdEHNorm6ufhF7lailKRifEN/bdQ1MMVUXSUGGrayZP0HbJX3F5qDbThK/k/3nHqqLK0ajgTq5atFLNZOAdWjTwzPIDairukvkJ/RxaMtD3/zxCRj6f/78vjW37FRV4nNo7gTJiDPKn/x//s3JouTvGbRdtgQ8tdrIRX4POD8bpiKDtfFqnpFDtnlvNCv+P+3Q8eiu61s2gWBM9ffWnjz4660eO3ot2NQT7HUDtb3RgSzCALXxUlIpWdVZEcVB/8flEBzSw7mzLUdRA0GfcDW2KNF/ngZY35C15odkx90190Xt197UD0aLc03wh6JRoxa1I1NSxDm6gbiqXZ++iCrsGph+6//8ca1+RhapMLxjmo3n+O3xeoDejdWTK/99HS7jkG1TDYIdUHdp3/BCtHv3bn3C4BTXeF7StFf06XHmhC63J07IfRE1KS+S/o6MrPQ5NoN1PuP1m0DUBy6r/oVn+19W4zzDA6aG5JD+aO19lvhbNN5j8K4Y2igoEyKJaalMnNqG3TJKj1NHx4ks/dqFhYytW7kHfVicv7kX9RV9rWKLbjRffEtDm6D49Bir0ZqaTg7JeXvNwRHnWqfQ7oXMLd9tOoYFzM0e8UQVz8o6LqOMKoYAwdNnFt55X0Rsbq/IT/r9eahm7ge7Z98rgAfr07KRENury55B13v/3qUs7UIau5ov5WPH/vJbWX6pQycp61SZUN+P824/os5mRmUE03O1n2RSqetZz9Rzavf79siXU6IRNHY8HnjuPgm7yof7cbJ11KGFqy9B61OZExkoZNM97G7cyermx+p8BemTB4qwxOkuBVAtUf9OnKzYoh9qVQPj/82uMDA5af3rknTM61FEd7YZea6V5e6LbInm1/VGXvnbnS2jd7ee1YehPs6HByP/Hj1y6GY8myhWtyEZfC83ZPkPHSDlry1FKXNuvJvQGqA5+QHs8BdR70IgqKcEv/69fKyZ2DN0YN3ltAhVrO2u/+iyeb4kJASIo15dgAW10/tOIgAnasn7DBuv/fZveZItGlJ9wJKPG271fHELDDVOen0enKLFN/ujXmOqAy6iHuktNFHre2PT6S3Stlb1qE8r+0Z7UhmZNlBf0/D9+WyhpAnUODLjPfQ773uEYrwhqOjytLo2u22NrsQFtCLT9aoJKWq++dOD/z4s0i23Q0TOXzo6gd3fYPB1FT9zJE1LxxPdI3dv39dGODUWSDFR4eRnnFMpP6D/6GD31Q+nGM3Tcw/f3Ni+MS3VF3gz6+Hth0qrzeA4pH/knhNo+GleXR2tqrnzSR0/IpFsHoLztPfaZqOF+9w3N6BUvw0BRb3yPirnts9EH4xjb26qC7u3irgL00/PX5S0X8P0g01JjClXkO6yp7Yvnx97X4u5o6g5VzyDUjc/DPN2PAXouDsck/PE82N3QG4XKX1Mpo17E55SPvzkxEM+FiHyL4EvYt5m/TJcHM2ALQ5hHAt2/pyZTPATPiXG5LR0obdZF1SoUnz+UO8zwy/ieIKQ+qhqGeXnZsToMfV8TEZYQju9tJ94fTkd74xcFsiLwvX5iQrAQ3ah5zqAPJZHe/ZhEhaOMahSu4PtXjM3hBJTKNaZ8NQr7qvWYWUg0/i7djHfNVQYcdGoTYKPN448nTqFB7mbsFJTrQqK7aQy+t4zM/niAbpzWvDUSi+fNA0cdkWv4v6jU21sRJcgxvyTHY/80X91hnIDvUZv+GDxCbU24qF3XGXCfM/ljVxLGSX0uYDYVn4suTBW7h/h/plJ+t3kaA3ZtLY8ipOP3ew7mP0GrSvoNGjIYYHW1NOFzNp4/pz6GK+bieW13NFQ5H8/91KjaFvRa34t7PejWwZSo5wVYh91Xw6oKMS65O238XuL7apmLv1cl1lXUChV/9LOX0cKjavyf9FS+vaMGz8+8Wn/NOqyTeSP+iUY8150btbmb8PM0nzT5FnxvfNTurIYydl1X2I9+XD1lVd6G7ycpOTnrOvB/QvLPrq+92O8G59lP+vG9zBxGOQMMSFoXsYPzmQFtI04jt4bwf4ypZMSbYXz+r+n+KTPCgIQv/x7/+o7/q4KWPfkxis8z5e5e0hi+P8Bdcc9xfB76/gy2msT30Onywqto+q3oat8prKPD1Sfq0WP3TPiXTWOf2TN6FNEQ/7VZWr/xf9TMovPULOZdSsFscBHjv59lKrqEz79dxp42y5lw1PL8sWBuJhyjxlU1one+3slv5mWC/ha34g50WdH3nw4rmeB3v8lm+yomfFj7tu3Laia0xxfo+/MzwfdEsNpDQSb01cv+KkTv/puxsxJigsqPNaXJ6AaKbVb2WiY0XnyfrijKxOeAq2PSeiYQLBVlysSYcPKbyBozcSa47luwt5VgQqzlgpOsJBN07bSG5GSY0OQ/WhCygQkr7wtYxqNau9e4fkW3LAXUJsozIdRyeG58IxNCRNqY9UpMmG+7MpehzITl73/JrNdmwuj2qUJtXSa81KtLWq6H49MyrxijPP/EFiLQBbOSnk+GTGDZ+rqRjZhAlvXcqQ1MePwhrv0Wui5DQt/QjAmbhmy+jpszQS2pkGfIggmvNilanLPGfW+/W2x4kAn9Or4rUtHrieZxj0hM6NiduaBFZUJDumf2N3SL2cOEYDoTjkxab69nMOGJsNhdbxYT+7xDQ/IQ7itR9ass+utdUZYymnosMUQLPXA52/0surKCNHQRHe5+KXsXFZnvVVKwZ4KdjmtxKfrgJcm5A615ltz8Gf2Q4Fc4jE48Pxm3hIY9G2ngd2BCetktLQL6vlU2+xe66ufh0QeOTHi36Nyl6swExblA8ms3JhTcZHVLnsI4SZzfRkE1l1tIw2kmLCXbL/B4oisCVhMvYp4ehCp2op1xXRKnA/DzS1rih4NwnqzST5qXmOAwn536KpgJpLGwRmYoE05f4NxWuoz1kdsosDKMCWkjl6RzrjFhP5eArloSEzyKL8/+S2ZCnb7xubmbTBAw5oqtfYh5ElbX0EhjQll+uwMTjT1tN/fjERMOpz9KUk5nQgat9l0/+t7UPZ7wHPPPt2azXCkTzIU+tu9FBz8xJNvQ9OR47gX0VyarXOAVE2q5K5POoKO+/3YWo5KhN0mfKplwK5rzYWMDxuV9y/xHtFG1z3rbW7zPqJEGVyPWZe3b+HMo31zTzwctTNh3cb2T50cmqNeOP/7VxoTCoewJ7y4mPFcoEbvSh/UwGOIW8IUJQxMuu+qGmBBfFncnaBTjcS9c7NQP7BtuxycJqNHkjqPVqL31HivjCSbI/iSSQieZ8Enph1w9ul5PIezET8y/V7qE3R/MX1nZktQME7qVfTzsZ5nw2iUlJuN/13uvTVpgQu8V4zm1Rfw90cD+wgoWaLGlQ0N5WLBaW33uxkoWQPBCvRwfC6IGEg54rGFhXm++vC3MgonJPT+6RFnwR/6W/koxvI6od6VLsGDZ279BryVZcD9SXSpSlgXueW1Hd21gQccpQ49hORZ83EZtCpFnwbUvg9YXVVhw8+50o7gaCyw8NPQy1VlQuzOsYUGXBfzlPd3N+ixI223+PHwfztP+zE7WjAUF+8I8fSzxPrV7RZhWLKjiOWieYsOC5RrRN/htWdCfsjPxJ1orq1JTT8J9FI9+qaKw4Jic6nQ7qme8jl+QygKdxKe/uGksuLIzpbuNxYKUClm9djYLxnf9XBFmxwLrYzRxC2cc9+vLo5uuLAiVn3Y7dIIFQu9rebTOssB56+38IH8WSK1P6Ci9yIIj/L/8fqDnHj4KvxrMgqxGrejtISwIn28fuYxmPn15c99l3BdRV6P0CgvUfer070axIDbgyPNVcSzYJ+Py7weqw+dQ0pHAAnLgqOaWJBb4SiTMNaPPGh9m2NxhgVjzX9eMuyyIoaxUXkR9+2xezt7HPNxnFf1D36jbO5GfsMBtyzopSi4LtstuvGBcwALhVbEd9YUsoPmAPfczjMM9Tx6HEozf4Z87qS9YELBGeczhJQvav+e2mZWjvzY3GFSwoIaYv/IhmhI9e3VrJQumBmUXyFWYx7KUQPFazJ9TVcNTVIHQXalcxwK/u709M6iodmVgWj0LWAH1Po+6WUANbX58tI8Fe07YBL0YwPqw7BpZNohxXF56lP6ZBZOfe01Lv7CAy+PifNswCxq+8s+t/MOCk9w7WG2zWJeiW8SPLbAg//vcua1LOG5Zyq/Xy9jw7NC9GK7lbLh6f78eewUblBIuNnFzsWFXju0pYfQzT+0XC/RSVMCIDeob//4sCaU1Ffyjo/uND969ysOGpQcflF1XsuFFrqFY6Wo2ZNSe75DhZ4PoTsI9K0E2hBx8zbggwoZTQWseeYvjPBtz2yrR7fF3TORlcFxRGnePPBu67YnHdBXZ4L+l+US3MhvwmVvF0mSDlnq4Wx5azvVEhleLDW0qFrF82mx45bwpz1iHDb3zdcpx6K9c3z/xemxY9fRWUzmq1HzvDb8+Gw5Zu7E2GbDBjPUlJQzYcD0BkqnGbGhxg60RJmzQXDaTW4zKNGSuijFlg30Lg6sZlW/MOjdixcb++KQUaMuGhF+dRRvobIiXZvwZZLIh7lB/QwibDSv4dl/4ZscGB0vXLiV7Nuh/X0g9c4QNSZ6Np/3Rgxm7B1+gukOBB7qPsoFks6HR6hgbGD+PCNifYEO0FI+E52k2/KtfydJ3Z0NdX43cIU82TMrfDSz2YkNO2Ow00xvXHXk85LYfG2KqGrZY+bOhqLX7Uu5FNljlNPHqX2JDJEV7VxaqONI1tTMY9xPLp2GA3r50XGxDCBse1ibelIxmQ2ppnRoxjg1GG077vkG7jYyPhl5jg+cp30DReDY8qWlUlEOt29TlHyeyIZBuPP48iQ1Da1vHM2+w4RzxxrO5m5jH6K/hV1LYcM38x0ne+2y4mce9ivyADVFfHBJ2pOF9B/Y+/YPuqRm/K/QI68lSvuJsOhtsze5KCDxmQ8ry6FtT6Iar3XHGGRinl95uZ7LY0K8bn/Y+hw2SMrv3uOTiulSGz9Tns+GD9TruMXRka33gpmdskKt88X19CRvyPrk4b3zBBpXggiEb9H1++Si1kg3DOSYaHDTFe+9rjzf4uciBNR9QwX+e+UM1OH7igndRHRtWd9SGOH5gg7PMXq6Mdsxj23Wf3i426HkfEynoYcMEkX8DoRfje1RtLniQDdXn1C73fsb95Wgqxn5jg8K7GrkD37HOnkroHvrJBp8NT9SDfrOB5eXVav+HDRYPbU/r/MV+ozzo8l3E9ZIUt79axoHwpKWAxOUcmFM5NeDHzYGWVeetgtGY5uVzEajb6nptXl4ODFhv6png40DbZZdXi0Ic6Gq55a25lgND0iudN6/jgPE2saPXUYf74TPZqFBnnIOVMAe4l+5MnUM9qZ1Db9BDsud5dopwIFa+sGAv2r7Pt8Eb9flQxR+PmnPduXMfDfhpcWEKve64JotbFMdLl3UpoM9+BAnvRu9XtBlT0O3ndwo5o8PWM/GX0SadguvR6Cg11boenW9xHphEzZjqB6XXc0BPyEN/UowDdznb2sckOOCVnr6UJsWBU8/Z0jOognmoHksa92tkezlOhgOLFMKx7+hJ13iZl7IckNaT+VGBaqqdhIvyHDCKz9kVsJEDb2U2yPkrc+BdWHKHxBYOPJT/4lyiwYFlIuWfdbZxQGNTgpTudg6knWjY1r2DA9c+Zj6dQl8P3chI2sWBEnvte890OSDVPs2nsZsD6l+ejJsackDlYsuxsD0cIL5/fLcXHT/VqbTThAPpl7WFnQ9wwKL0bM9F9L2Xw20LCw7cvr7TPf4gB/iV/B6oUnA/R3ztO+gcqAtZqm5jcWDWkqTMz+bAzIONojdRwzVTox8PceATpaJK6CgHBt9UxMU7caDHrGui1ZkD7PGn/K9cOHCn+ij9NTrv2j3n68qB3Tq63bonOBCyQvoK9xmMX2ZqlZYXBz56OwetOM+B38s/nFsbhPGuFSSdDuHAh5rThzzRx2WPVS6gJK6pTSHoiWsXv0ygh710tdyjMF78PIuv0CQxG2F2HAey7vTzHL3GAYOITxquiRg/bvm6B0kYF95Dy96jpIjmyo23OPBgZU7iLZT6N15lz10OpLx9dccvhQM/9Nqzb97jQB9TSezEAxw3L3XrXjoHIPRaSBqqODus8Rvt35KhdCcb97dac8fpfA58z+NZp/2cA1qph5MulWL+Qv13eJVxIPJpWu7fcg4ctxAv+fUK644sm6hTgfflEBRlKzkg8ErDKhkdTeSxK63igLBeXczvdziP4HfC02asQ9s8T8UPHCjQ8LkR38oB24efD5t1Yh267L9S0MUBXVN6iP4nHC9QF7iilwObfNf9WPzMgaDnYjJeQ7ju6nw7pWEOPPr5hpWGzth3SNaMcSDe20HacgrzfK9Fz3IG43S/+dfnJQ74l14qMl5uB3/vyx7l57KD/XZaOS/QqZ2Pk7Zz28EtoWSeYTE70NH9ef8vSv3+3EdIzg4qTyXfOL/RDuaSDjT7qdhBRUe3x6ktdlDT92vk+lY7SLDWEFPXsIPiZ9zHXmjawThNdvet7Xitk9V0ZocdFGRVWKzfZQdRx59r/VdRfcdT9YYBAJcKSaiUUEZJQiqJJDzZ2bNQ7ln3RhIpKStJUWYkW1kVUdEgUrYmUpmJ/GQUlZGkkt/TX9/Pveec97zPeJ9Tp82CzTwRg+/Rm/c0ma9ovmvP9FJgwW/BdgHBnSxQjvWqEtVjwcWbT54R+ixQvKmjtHkX3j9PMijClAXy+QLfW8xZcGzHOZUEKxbkGDy/Qzqx4Om77G2vSRYovBnz/MFhwby1hSusvVhQ+0Y5Qs+HBYXJNy5qHGeB/8N12rW+LPhOr6zMPsmC2+Elc58GsaB57NKV4rMsmFzpXawaygJfUlmkDV08dOpERQQL+jZmCSyNxLhPbrk0L4YFIbU9RiQ66d/UMR3HApHMyi7feBac4m8e1r7EAqPJ6U7hRBaETYU58qZgvg0Ulo1cZkG4xsXp3iss2OR13dA0iwXUs8rLmeg6LhAPy2bBS903ZRI5LGg3c8hdep0F279/vOOJprk/CUzPZUHFUGEgO48FG394bbhzmwUFIp0XFR+yIHbK7s7Bcoy/dPZqxmMWWD1ZPtexggU+nE1ERyULTN7zF/6HPvMPVxGtwesax24EoX21O6rO/7NPSN77KQv81B336j9nwZTx2XCdJhZ06m/jM3/NgisjYnMSWzGvMb4vPqNQ2sz1roMFoekyOnwDLPjP5WfVsUEW6ByM6gkZYkF6/+QajS8sCNjCY9D4iwUHBBc0VPxlgU1Sku8oFwGptv0PWTwEmJGZjkm8BMS+MRc3FCbATnEin1lGwNkdZ+90ixHg8NsxkbWSgOLa8ycDUQmhTLCWJECofoBPbh0BKZz5VV1bCGim2rObVQlY4K56+o8aAVfH4lYKaBLw3+KvC620CJj0yuX81SHgjcaA6UMg4Os7/W5ZIwKWbDmtNWxMQJy8vcH4LgLkBJZqhlgT4KeXmfsXNTrfaZNkS8CJifNVs3YEnCqvdOexJ+BBood7IVrDz3+gwJGAOdL7+G04BKQZ/bpTeICAsidFK23cCKj7YdjX7U7Arg2pLnaeBCjIy8jEoC1pR6YsvAn4Hp4rPHOcgO1tJzfxnyBgsNMjshGVd91XutWXgDPtu/889SdgLX/5zAgqvG/FkYEAApLW2SjXnyQgMSgz6cApAm65j/eQwQQcebWh0AUt9c+2tw4lQNB+u4doLAF9a5+IdaG5U4U7MhIIuLRXNCE4lYDDrPyfrGz0o7d9Qg4B5jnar9sKCPBPOOS4pJAA+8wrTtpFBNxvCXFi3yPgG309tes+AVLBN7xdi/G55Ldb1R7jPpmB0NlKAtLrbRN2VxPAD/5urTUEfFnprlr+lADjdanh6s0EJBxuEJ1ox+ekerdUdxAgM163JLCLgKl0H7ZWLwGb9paLOaO/kp/6pPcTYBldrvZugIDyb+Lhg18IiBB+4cLzlYBPX5Ps9qCGDsco628ECPydv+noKAGH9EoG7o0R4PVqlcqeCcwjj8Tx/u8EcD9U/BT9l4Dn6eKzwrPoBUNTWy4S5q5lvXBATX3q7OfPIUEx6IDqTtQ1NcvBCFX6MNA8OpeEfWKhrrLzSMjvuisahIa0ennMmU9C6peYum38JATLJlN3UW/l24FaC0lYQa09XS1Igl7rgj03RUn4ESY7JSpGwqmDXa88JEkoE+k1/41urhvripQi4cyHw89eoYnDbT9a0LE0od5D0iSQDjZLctAbfyyWcMmQ8D3z+h5xtCNmha06+q2c9JBYTUJsyQv5T2jEhhcbeteQwF3ZdvGELAkJQrsNJ1Aj2VXuU3IkdCrkcR4okCAav+G/05tIcAklLy7eTEKFil5bihoJDePPb6Wh4RR78zD6YF1+t8M2Ejjq6+1q0RLzU820BgmlJdFF+7RI+JmcwauoQ8KEc7pkzE4SavlULC+ifUPNSUmocIaMaTqapPN7aK4uCRqftu7djRav3lz7CJ2eMo+qRjUdLVz36pOQduSpYwTKY/V2c6kJvm/m5MsFpiR8PDsix2tGQpSflz+vOeZpbB1/FzraqGpqZ02C+880L1f01mBuyVW0oHreugE7EuKk+ZXc9mC+ZdyMFjiS4JXuqMtBC35GNKmzSHg/mlLUh7rZ53ypIjCe7nVb61GPvdOxZiTuY5FLvhX6XObbwt3oS5L/JoFe2f83+xg6EnAtMxZd93z9pUvozeI38qmoR8Rg9V2KhG0J5cFbaRIMmz4H6qDVp+0MP6Jf52jXH2BwPV1K5gGHBIj/zBO/H+tV0vDsqAtejxvTzj1Awp7CAFMlNxK2Bo4v2HGQBJN5XxJ3oimdb/qfHsa83rfyH/YmYcuBcfmnPiQIiMgnN6GdhscWvUV72hcJqh/Hfg7J4+I9QYJEWpPzcV8SFGZWiMsHknBYgtJVQBe8a/DyOkXC+WUfjbPQI4yzsEcwCYu+l8/1RJWq2lIj0Dux5xc+RMtPRx2dQSe69icvPU2Cn3nm0RWog2KRsDIqJcmrsRlNYxsPb0Gdtkh+UEMvap4W246+5e611UKjx3de0UG7s4af66KGsu/D9NEP6weajVB5g9S3u9Cf5cQac1TOvf6tBVqy7U2n9b/13Ybc7dBe/xcie9DE3hJw+nef9xOFfWhHg6Q8gYpo6U7S6JWX+Yf2ozmaGr/c/u3LL9/GE/UXkpfyRtPb8k+dQNXeqc8LQkOWFS4KRk8ePdx/Gr1563vxGXR15gr9UHRR8XWJc2h1yPU359FjoX+SIlC+wQyNKHRlioVkDHpkUdHdC//2t6QsMQ49lJgiFY+eWnBrfgK60HjgeiK6KrbdLRmt8PxhnorqV/lXpqGisS+eXEZ5TbqWZaCaELMpC5VcJPA4+1+ear+GXkX3V372u44ev1BdnXv6X99Hlxb8W/fbSrlb6O3kAc1CVHDMO78IDXWyCb777/drA6776F7TVSUP0NchaQvL0OTItVcr0JqF8yWr0N/vVh/RCME6zs/U10J1LEfGdNGNQ3OOGaE5oCl5+SyeR6/eHzqhJAjV1T5NjCAhi938Z3U09gmZo6CAChcffnkmBv8XkKdCUYf7wv25qCCfZatKPLpEjCftEgmTO4UMrJKwf/Wi1lUnk2DXzbGsQfMf6l/7hjr9XcB/J4UEWb4+i2VXSKB9Ax6Ioz5xmVlj6Jtxb/6dGSRUdgfdMUSrZ4esw1BObJPPvEwSWtOfwmJURfWo5zJ08m9YhgTq6n4p7xKaX5bEO46Kau5rt8ki4ZxW8dhutGuRy419aMWlslUUumRCfNQVddVt3xSCBqY0Foai8XTLRDjKqzFtcwEdMNL1jUeNsrSlclERTcEHFeiW/TE+K7NJ+EUGRDuilZozJiw0wOEZvTyHhPV1O93lr2KfHF2eaYXyTulGX0P7GuJdv6LCi03ndORhHlcau3JuYt+Upee9vYV1yA5yTriNc2L9d6Gb6JtSC36hQhIeft67RrkIv1+J00upuyT0U45F3ejito3eX+6RwBWn9V2qGOfT/UYNyxISNn2+HCNQSsKXLa/k0spJiLxsMXr0EfaZnkKz+WOcwysMy5MrSRheefucUBXWNSS4SQaNNt9qoo4WSc5ZdgDtyHze54GKbHqy8Rd69T9qLLYa5+iPvacS0bKwt135qFj823U16NE9eVHVNZiPxOg/hXVY30MJ5kL1GNe2g0WvUeOM8FcTT/F7yfdzUrCRhEvK2g/Cm0l4Ur5+tgQdHRPNv/OahLau6Kkjb0iIKdqe2/MWz2GGxPaTLfi9XHI+XbKVhKEHTxc4oL/5wjp/oqRrTbpAG57PyK0Tve0kmD8eWlXWhfG+y1Iy6sG5rKRcdwJ9d6/C+ANaxxLz6UPDxkWiF33AfB+xJBaj6oILE0VRz7DMSQl0l4lgdB66/ZxaPW8vzn1zXb9wVExou1fcf9i3otNm0Id9GOG19TGarbI/S+8jCdfv7TtHodvUIk/692O9RiJ1G9Apuf1flAZIuOeYoXXnK57nvtqC2W8451W3RA+OkrD0TIet/hh+V7qWXZSawL43O9Th+Z0EmxhrCPxBgu1mAXHjaRL+nMzuUfyD80P2hIDRLNZNVOuLChcF0UfrZLTQwZPFBTbo8pQvJoXo+b/myQFzKHgpvtT4FOrv+S34DOq+N8MxHK2qevkzGr3bU779C8ofUDEygc51B4lfaOj7CclZdCzu7TxTbgrEuyTUHNDtC2SrZPgo+N3qN7EapRee+CYiQEHyZbEhE2EK/N5v1apfTEGIQX+PuAgFO6/+tf2JPhJPszddRkF326TSIGodYG2bt5wC3/RnTx+gi+t05ZZJUFB0zX5OBto0W64muZICZrfaBX5JCtjGQ2uF0KyOxAmRf/o3moih/vxWY3fRFUvbEudJYTzS5ddmUWENTzs1aQoKrOKfDaMBwaKFYyj/+Gej9TIUjIio8B1CL5hLKXihvwePrjiOVufGhPqjt3ZcFstAywfEG6+idqUGzU3/7ssrt29BZcr6Gj6hwfztDSKrKXiXKSc4i+pb/XAQWUPB9Sn3YB05CgQmX8TNlcd4ZIPd45UogLmO00qbKKjwHvQ9gvYl7vMoRM+/r+k4pErBcHTn/jdbKcje22hzRZMCl5SpvBDAdXl1tvxE1c7YbOHeiXVayBaPQxuOzd68hR5cWh5fhS4ueuDHbYDPn9fSMTCk4NCTuEDHXVifWG6+aVMKJO10p+PMKPgAKSHzLSmQlaZuhqPLhHkl5WwoUKpNnBe9G5+75isd7UBBm2nFD14nCrh2lUmE7KWAzJEhDJ1x/XP9e+azKPiabqbXiQ48jhM5RmK8mv5hvuiGGlZjEPpktjuxDt35qjpfkcLrV6cU7GkKpAN9xC8yFBheeFuzh43xs45P7ztAgYk9h5tEe7QEUycPUqBtPtuvd4iC5rJhu2l0pp3VMnaYAh/HBdukvSh4/EA3ltsbz8E9YeVlxyiQmhGDAh/Mk+YVUe4T2B/aQq0OvhSE+cVb1PpR8DZC7KlpAAU/oq1bBgMxPmml49wnKZitlluYEYTxzIxYpZ7CvnY+IuIcTEH8r9oLIegzbVlP6dMUqCt47pJFV7eG9cqjBZn6x5TRoF8l27egZWf5nzuGUPBL1+P9Z7Th26N31mcpcDtq8L72HAX1O+Um9kZiHrkyuRLRm/FTTjcvULC/w9WDicX4ggYilsZR4NVT+ygFvf3tZhg7AfNzqt8pJwldsjJxfzKe66SwUIM0Cq7taXlVhd6OLxttukKB/KO6Va2oxxhfj3gGxs+XHnYEzfros/w4KqHw3yV/dHbNp95g1Pv7pZ5GVFU7ks2Vif3Y+6spHz3rIy23PAvXeVnKaKEWUNajh+brlBAm6BfVy8WW6JCLEqcVtVpMlX5C/+bHJ9zJxvmRU96pn0PBQ1dbnivoqoLiv+R1Cv40zdgGoeKUh3dXLgUap63+3MijYOvrB/1L83F+OJRWvS2loIMTLs5VRsF7pxcLvMuxr39d50uvpIC1W/5YMzpu/1CtHe15uELrbBXmO2dJWTh6OmUsLg8tkXFVCa6h4MA9HocH6J2YDttHtXj+2pWvrq/Hcz7/rmv3Swo6SxxL6t5QkDGyO+pPC65f65+c0Yl1KyiUCHhHwRzDLgvtboxzTnnPbrTYPnp8YQ8FaZMXOQs+YH5/f9ayRJPNnb13o7/uczFRvThnah4fkO+jIHAzv4kL2i+krW3wEdcz8BjU/0wBIew7SwxTECuxR2XeV+xXd70XbqMU7L08xpMxhv1+xLXTZZyCmHMrV5xCPa0oYd4JCu6X1hdmo6LSpOn9aQqOzXW6KPsb139RKfroD8YbV241ih6P3sBDz1CQSA2NZsxi3+TL22zkosGsuGXUBSXD3BRFuWlYcfvQHvW5NDyo02d2oPc/6z7XRVvcry/vQa95aw/HzKehq3hFUCgvDVlOJx+K89OQ4lzxUgEdVRluc11Ig13v5wZzQRqWNl3T8VxMg9Wf+wl3xHGdCrfwJmka9HvUb7eiq2+TxSIyNLz/XX5UAqWe9VkqomOCv7ZvRsO2OgpYoa9UzJovobWSVcLTqCLr03zWGhrOr3eyzpSj4Z3S7NzAdTSoZJ+NC1tPQ58yT+ktJRpSh1a2zKK0WULm0AYaWheuXFO/EeNO3+w7bxMN/KlNt2bRM+pSvqMqNBwxk9iYq0rDja/ODTfVaRCuUrEgd9AQyBmf+x/KfnKvrwNoSF47Q0/q0rCzNqi1RI+Gfg+e1nI0ZbMw1KCsKXb2DgMalo0UmuxCN/E32b5AFx5saBQyxPflvIf1qEfy8pA01Et569rVxjQoPFG1c9tFw/ZFbIcwUxoeSYgm21nS4GPkaOxiS8PicY4Svx0NavlLUy870FB0WO/DO0ca4s7udlrnjPvPiD6ykUXDcAvlwk1gHC1EcBaaernt+R4S6ypV9sELVfUR2ZGIavQ+qfCiaLBwCfKjaRp26cOz/WjoFb8rB9G1tc+avdD66Divkygt2Pv6CbpMuS3+AoN5vtNocgmNjjvDSkNdLqS9yERv6g5czkMFQ59LtKNcgtkJ3Wh8hsX1iANY35vUUSt3GtpZ3mF5XjT4F+9a0YZKZRiU+R+jId1PxuewDw2Htc9eU/ejIbZ2g+yxABoSV1svFT6F/fXQcpcISprYmIqhzx5vmpZCS+psDNeiG11XLesJxrxvmBfVh9oYq14bQrXM/TS/obdqjMnv6PexrqxpNHFO5AfH01jn+z+UT6MDZM6NUNTLP7QyCjXzFDj5Cm2/eOSwWAgNz9ufNZegu2+cuzV2hgaHv9wjBedoqCz2PpMch33Lq+F++RLuR/HKibJU7PMC/WG+DBosz1/4Lo5eSHFmNNHyU07fKlDNjFVi46ialxbPwiwaQoTFDwmjQlKak/Gok1gxCGfTEFD5NGJHDg1uR96bpuXTsNxCVfhdAeZtzLD0D9p0ovk3zy3sy6vSB60Kadjy2yey/x46k8rvdx/zxp8xaVFMw49Mt0V1FTQIdCzJnUHpC9ltupV4TjPXLfFC55gY8HdXYbxmJdae1TR8kbaRKKuhYXJGbO5j1G53yrxGNKrmzPnm53jufh5xmdtAw89AvlY9lJa+1FWPukjsLP+viYbgz1W+oc3YN1nkh87XNNzlN+zOfYP93HOwRvMt1oNn87RbKw1lEp6Xj3fT8GE/w6XWg/NiIyGliXL62zV10eW5s4uMUXJL6HztXhq85WdmTqHtUj9ehaEtcSeu5X+kQWw28ERjPw224kW8DwfxXHkcIz9/pqFjRhbOjWFfx2lzzL7TsGBHoZzhDxoenhCN4p2i4at9Qc9ytMIgSEcc3ZYu1bHkLw2F24q1b3ExcKLsPjeOdLg77fKxFE0v+jo5bw4Dahrtz1q4Gdiz8PGkkiADbdd/zAaIMCC8efO3g6IM2D5JFJiQYKDI1s0/fCUDOz0bPJ+jHanEtoxVDAQeHdUWl2TAIFv4o4oUAweuCvx0RF9WHlBUlWbgalT5tXwZBr6N5D4cRO9vsLgovZoBky0L7BXXMNApIdRuu5YB45iNWeHolfC7ipmobcFgezGa3LlDC9Yx8PeenehRdAP7SUPuBgaYtqpuy80MTHyqcSLUGDg9vjphUIOBTR/CTEd2MNDVvCPwjy4DpfoO4Ur6DNjX31oeijq4VYo/M2Jg9ne52bFdDEh+3iQRgqqn8u+qs2JgTN+0+b49AzcuO4U47mHg97lPpblonmO4etReBhzz/c7WogvGlCxM9jGwPVJQS9GZgTI11uwEycBMd2z7FM2AzoX/GhezMQ/55cruaOKOzoPnXBigNFKk9A4wkDTv6zXLgwyk7r0tNXkIf08LGz/2YsDcxlpIwQffF3sp2QRdR00rjB9nQNZkrYSDLwMHVYXn96MR7aY6dX4MmFmcCl1/koHemE/njIIZ4FpEhZmhS7KChWzQWl41D0eUdV55N4FeVnbWMDjNwKXr6+9LnmFgJCHOeCoM75fV6TeNZqDOqMrYIw7X12ZFbUjEvqn4WpOYzICz/Xz1nFQGmp86XPfOZMBqrbayUBbmq8TT1DaXgdzQgaSTaMMmU2H7AnxP/d+ss+iagLhhm1sMWEY6qTvdZkD+PD3gUMTA9Rv18tJ3sG9uO5ZX32OgnVf86uNiBkQH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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/filelist_merge.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,14 @@ +filename classLabel +batch04_B02_rep01_301.mzML blank +batch04_B02_rep02_302.mzML blank +batch04_B02_rep03_303.mzML blank +batch04_S01_rep01_247.mzML sample +batch04_S01_rep02_248.mzML sample +batch04_S01_rep03_249.mzML sample +batch04_QC17_rep01_262.mzML QC +batch04_QC17_rep02_263.mzML QC +batch04_QC17_rep03_264.mzML QC +batch04_QC18_rep01_280.mzML QC +batch04_QC18_rep02_281.mzML QC +batch04_QC18_rep03_282.mzML QC +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/filelist_mzml_QC17_triplicate.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,5 @@ +filename replicate injectionOrder classLabel +batch04_QC17_rep01_262.mzML 1 1 QC +batch04_QC17_rep02_263.mzML 2 2 QC +batch04_QC17_rep03_264.mzML 3 3 QC +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/filelist_mzml_triplicates.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,10 @@ +filename replicate injectionOrder classLabel +batch04_B02_rep01_301.mzML 1 1 blank +batch04_B02_rep02_302.mzML 2 2 blank +batch04_B02_rep03_303.mzML 3 3 blank +batch04_QC17_rep01_262.mzML 1 4 QC +batch04_QC17_rep02_263.mzML 2 5 QC +batch04_QC17_rep03_264.mzML 3 6 QC +batch04_S01_rep01_247.mzML 1 7 sample +batch04_S01_rep02_248.mzML 2 8 sample +batch04_S01_rep03_249.mzML 3 9 sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/peak_matrix_as.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,4 @@ +mz 95.0854867136 96.0477230462 97.0647529114 97.1011384065 98.9954998691 99.0804011767 101.003118017 101.059284262 101.059667337 101.777300408 101.781524007 102.127686063 102.970223555 103.038951206 103.075318834 104.106972509 105.003234195 105.090964311 107.070230246 107.085062328 107.085488315 109.100703053 109.101137314 111.08040236 111.116786045 112.052301283 113.059666808 113.096051485 114.065677969 114.066170487 114.091302909 115.036557299 115.075318293 115.111705216 116.070599012 116.985382141 116.98587154 117.054583395 117.090968646 118.086253653 119.085485311 120.980267261 120.980786531 121.00756219 121.064754009 123.013845316 123.055288913 123.116788199 125.095487802 125.096051948 126.021940468 127.075321735 127.111706998 128.010843476 128.994858336 129.090369007 129.090974735 129.127358602 130.049222294 130.049863468 130.964513436 130.965138358 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/peak_matrix_as_bf_compr.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,10 @@ +mz missing values tags_injectionOrder tags_classLabel tags_replicates tags_untyped 95.0854867136 96.0477230462 97.0647529114 97.1011384065 98.9954998691 99.0804011767 101.003118017 101.059284262 101.059667337 101.777300408 101.781524007 102.127686063 102.970223555 103.038951206 103.075318834 104.106972509 105.003234195 105.090964311 107.070230246 107.085062328 107.085488315 109.100703053 109.101137314 111.08040236 111.116786045 112.052301283 113.059666808 113.096051485 114.065677969 114.066170487 114.091302909 115.036557299 115.075318293 115.111705216 116.070599012 116.985382141 116.98587154 117.054583395 117.090968646 118.086253653 119.085485311 120.980267261 120.980786531 121.00756219 121.064754009 123.013845316 123.055288913 123.116788199 125.095487802 125.096051948 126.021940468 127.075321735 127.111706998 128.010843476 128.994858336 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/peak_matrix_as_bf_sf_compr.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,11 @@ +mz missing values tags_classLabel tags_untyped 95.0854867136 96.0477230462 97.0647529114 97.1011384065 98.9954998691 99.0804011767 101.003118017 101.059284262 101.059667337 101.777300408 101.781524007 102.127686063 102.970223555 103.038951206 103.075318834 104.106972509 105.003234195 105.090964311 107.070230246 107.085062328 107.085488315 109.100703053 109.101137314 111.08040236 111.116786045 112.052301283 113.059666808 113.096051485 114.065677969 114.066170487 114.091302909 115.036557299 115.075318293 115.111705216 116.070599012 116.985382141 116.98587154 117.054583395 117.090968646 118.086253653 119.085485311 120.980267261 120.980786531 121.00756219 121.064754009 123.013845316 123.055288913 123.116788199 125.095487802 125.096051948 126.021940468 127.075321735 127.111706998 128.010843476 128.994858336 129.090369007 129.090974735 129.127358602 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/peak_matrix_as_bf_sf_mz.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,238 @@ +mz batch04_B02_rep01_301_2_302_3_303 batch04_QC17_rep01_262_2_263_3_264 batch04_S01_rep01_247_2_248_3_249 +98.9954998691 0.0 98.9955011572 98.9954985809 +104.106972509 0.0 104.106975455 104.106969562 +112.052301283 0.0 112.052301296 112.05230127 +118.086253653 0.0 118.086252982 118.086254325 +123.013845316 0.0 123.013844104 123.013846528 +128.010843476 0.0 128.010844488 128.010842465 +128.994858336 0.0 128.994863945 128.994852728 +131.009836808 0.0 131.009832759 131.009840858 +131.010517825 0.0 131.010516364 131.010519286 +131.018942036 0.0 131.018942282 131.018941789 +132.074583988 0.0 132.074579642 132.074588333 +132.075008963 0.0 132.075002532 132.075015393 +132.075567318 0.0 132.075557576 132.075577059 +132.076101717 0.0 132.076094335 132.076109099 +132.076671348 0.0 132.076676827 132.07666587 +132.077202011 0.0 132.077211554 132.077192467 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/peak_matrix_as_bf_sf_mz_compr.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,897 @@ +mz present occurrence purity rsd_all blank_flag fraction_flag flags batch04_B02_rep01_301_2_302_3_303 batch04_QC17_rep01_262_2_263_3_264 batch04_S01_rep01_247_2_248_3_249 +missing values 231 0 0 +tags_classLabel blank QC sample +tags_untyped +95.0854867136 1 1.0 1.0 nan 0 0 0 95.0854867136 0.0 0.0 +96.0477230462 1 1.0 1.0 nan 0 0 0 96.0477230462 0.0 0.0 +97.0647529114 1 1.0 1.0 nan 0 0 0 97.0647529114 0.0 0.0 +97.1011384065 1 1.0 1.0 nan 0 0 0 97.1011384065 0.0 0.0 +98.9954998691 2 2.0 1.0 7.44464804268 1 1 1 0.0 98.9955011572 98.9954985809 +99.0804011767 1 1.0 1.0 nan 0 0 0 99.0804011767 0.0 0.0 +101.003118017 1 1.0 1.0 nan 0 0 0 101.003118017 0.0 0.0 +101.059284262 1 1.0 1.0 nan 0 0 0 101.059284262 0.0 0.0 +101.059667337 1 1.0 1.0 nan 0 0 0 101.059667337 0.0 0.0 +101.777300408 1 1.0 1.0 nan 1 0 0 0.0 0.0 101.777300408 +101.781524007 1 1.0 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/peak_matrix_as_mz_compr.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,9 @@ +mz missing values tags_injectionOrder tags_classLabel tags_replicates tags_untyped 95.0854867136 96.0477230462 97.0647529114 97.1011384065 98.9954998691 99.0804011767 101.003118017 101.059284262 101.059667337 101.777300408 101.781524007 102.127686063 102.970223555 103.038951206 103.075318834 104.106972509 105.003234195 105.090964311 107.070230246 107.085062328 107.085488315 109.100703053 109.101137314 111.08040236 111.116786045 112.052301283 113.059666808 113.096051485 114.065677969 114.066170487 114.091302909 115.036557299 115.075318293 115.111705216 116.070599012 116.985382141 116.98587154 117.054583395 117.090968646 118.086253653 119.085485311 120.980267261 120.980786531 121.00756219 121.064754009 123.013845316 123.055288913 123.116788199 125.095487802 125.096051948 126.021940468 127.075321735 127.111706998 128.010843476 128.994858336 129.090369007 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17.5236947432 14.5521229306 33.7015165238 172.921630576 783.074503416 7890.53014739 908.060331335 19.7999598644 31.0065089557 106.320383274 14.1627966488 48.5067629241 15.4655233304 57.0005637827 220.002640213 20.2645705597 23.6425345661 15.7389691415 22.4742009609 153.800490893 12.9021969764 13.5289343462 35.1791189379 17.8027145046 22.9101033887 29.0717840023 10.4351781971 26.0012626069 162.171595168 20.5234147547 11.7347068919 68.0448555761 15.209643774 35.7072823486 141.614224422 995.488692629 15.3596100852 19.626216186 54.8042362232 332.410853955 27.5707172754 23.0818703511 132.401997754 11.352292313 34.1915179905 52.7082207875 32.6922781816 16.7298237531 129.373547053 12.7332443974 37.6588394362 20.1752219396 32.6255898833 1649.89025387 144.575605326 11.8182139938 11.4577517246 95.4530369747 45.9819102429 392.376498483 28.6812937849 19.6600308029 45.5710384955 65.0245470428 27.6596723274 34.4479945792 26.2370255633 259.109789326 16.9909428614 24.5757517546 45.0510309736 19.7665266095 12.0902301177 29.6325758677 64.4142199996 28.2921152043 23.6056160166 155.109908421 117.512065403 12.7732910828 14.759327 67.4040483492 26.6529754807 176.988021752 17.5671512792 11.8161616815 222.972124841 3525.53976256 263.377489596 22.0258326279 40.9965299845 249.834282356 11.4210228614 18.5953076191 35.3365523063 14.2361444326 11.2876774111 20.9367772161 11.5275722745 14.1466575878 113.357580204 30.4918200742 358.677860537 41.2184943593 44.2327665617 30.9008074552 18.2433951854 32.3166525292 11.7615071479 17.9647800696 161.862502292 19.539131141 16.4229752553 70.8119997843 646.791207011 71.6622317938 11.6676296228 49.6346364686 36.9962397464 42.8220250099 11.5269539356 22.9558739192 27.4410772485 290.575242551 31.6793065631 62.6602968912 11.4656015113 121.121197186 19.1667556434 23.2561335574 223.913527883 19.1146764412 13.0070937558 12.5300643331 84.5048169726 11.9952981643 23.3776659038 21.702191176 54.4286459454 32.5792303817 27.277565931 13.2493745891 14.5218996643 23.3774057332 20.3142241481 14.5305001268 16.8158418665 220.080362075 22.0781658101 30.006752663 16.159203984 15.8490953533 42.2571291115 13.514541111 51.2980983968 395.004885103 68.7928077379 28.6226900936 3628.29618503 31.9664090314 55.4092030284 545.672030518 181.948983687 41.5621679602 31.7855620533 32.1722225497 11.5835913921 11.3845749449 45.2281762965 58.4538694971 33.4367058724 12.9887217159 13.0354007449 13.4972177977 21.05986485 18.2935739723 89.8372157561 106.444029387 23.3553817068 76.6293564426 200.365390026 47.7423326559 66.5581176078 24.915301286 11.3751403225 42.5010621501 15.2219615383 13.6225768138 93.170038069 649.769086575 18.7622178523 137.910560001 38.047406797 12.1897174967 131.518907352 39.1682927832 14.9572361498 25.9559281077 19.2532003976 21.1105985765 54.7118976534 15.1767110975 16.1073993052 12.9750653887 13.6749271877 22.4408723712 24.5162391824 16.3589347091 55.7023528125 16.274560588 12.4047150391 11.2490564201 551.885046616 247.444133255 153.680848731 46.6732912371 37.7174316228 2496.18228347 82.0966312828 17.4906830242 777.190722473 181.653269998 95.7877629621 46.7377488266 14.4233930202 11.5941650138 305.739047439 83.6481389716 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/report_pls_01.xt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,73 @@ +filename event scans peaks median_rsd +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 125 8.16474487133 +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [140.00-240.00] 10 118 7.91689453929 +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 66 7.68607124086 +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 59 7.3571823389 +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [350.00-450.00] 14 38 7.81073302387 +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [420.00-520.00] 12 31 7.11304069203 +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [490.00-590.00] 14 45 6.30005138853 +batch04_B02_rep01_301.mzML SIM-Stitch NA 482 7.76858437374 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 128 8.07600991574 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [140.00-240.00] 10 125 8.03864926779 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 65 7.44888873374 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 61 6.29657260707 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [350.00-450.00] 14 47 7.01271931319 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [420.00-520.00] 12 31 7.3500419991 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [490.00-590.00] 14 43 7.37566447385 +batch04_B02_rep02_302.mzML SIM-Stitch NA 500 7.68715459068 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 123 9.16935154458 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [140.00-240.00] 10 118 7.55444375891 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [210.00-310.00] 15 66 7.78351619427 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 57 6.27056997549 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [350.00-450.00] 13 44 8.20380544323 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [420.00-520.00] 12 32 8.26327800306 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [490.00-590.00] 14 45 8.21592686599 +batch04_B02_rep03_303.mzML SIM-Stitch NA 485 8.03552677192 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 79 7.03196696794 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 80 7.76766561352 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 61 8.15363335888 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 54 6.06465919921 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [350.00-450.00] 13 38 6.24970496714 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 19 7.42363265295 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 38 7.08865209918 +batch04_QC17_rep01_262.mzML SIM-Stitch NA 369 7.18546096997 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 77 7.67971252743 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 76 7.40959870023 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 61 8.07007716689 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [280.00-380.00] 15 53 6.56434715809 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [350.00-450.00] 13 43 6.01206175751 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 19 6.89479622473 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 41 6.82339923038 +batch04_QC17_rep02_263.mzML SIM-Stitch NA 370 7.32153403689 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 76 7.06684475755 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 77 7.70812724744 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 62 7.27381932537 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 56 6.85168250471 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [350.00-450.00] 13 44 8.1369518449 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 22 9.04310387911 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 44 8.17119973043 +batch04_QC17_rep03_264.mzML SIM-Stitch NA 381 7.63853989932 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 67 6.9449339373 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [140.00-240.00] 12 74 7.56562141338 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 63 7.25018452732 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [280.00-380.00] 15 51 6.83402648161 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [350.00-450.00] 13 46 6.86311688031 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 30 7.56433662729 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [490.00-590.00] 14 47 7.38328588939 +batch04_S01_rep01_247.mzML SIM-Stitch NA 378 7.20481827147 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 68 7.61402326633 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 74 6.91382809333 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [210.00-310.00] 15 63 7.81020674999 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 52 6.67116706795 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [350.00-450.00] 14 49 7.47235757299 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 29 9.18092932673 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 50 7.49743321145 +batch04_S01_rep02_248.mzML SIM-Stitch NA 385 7.44772817369 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 70 8.14059659419 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 72 6.70234634239 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 60 8.31220999647 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [280.00-380.00] 15 51 6.64301911594 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [350.00-450.00] 14 48 6.5996960655 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 30 7.38454762189 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 50 9.08654743835 +batch04_S01_rep03_249.mzML SIM-Stitch NA 381 7.56306270833
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/report_pls_02.xt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,25 @@ +filename event scans peaks median_rsd +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 62 9.10679576511 +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 66 8.37633825047 +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 30 13.8609519301 +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 43 11.9950166441 +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [350.00-450.00] 13 32 10.1868114169 +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [420.00-520.00] 12 15 11.2326559481 +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 24 14.1641211658 +batch04_QC18_rep01_280.mzML SIM-Stitch NA 272 10.8339327639 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 56 10.022274693 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 47 16.5074895025 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 47 10.32312253 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 46 7.96958465718 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [350.00-450.00] 14 30 8.39097430939 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [420.00-520.00] 12 16 9.67349503755 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [490.00-590.00] 12 27 9.46664767552 +batch04_QC18_rep02_281.mzML SIM-Stitch NA 269 10.0625229083 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 69 7.5232876329 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 71 7.71867980771 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 50 7.01093076419 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 49 7.24728641458 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [350.00-450.00] 14 39 8.26255528557 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 20 8.48394726745 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 41 5.46125101547 +batch04_QC18_rep03_282.mzML SIM-Stitch NA 339 7.33323994646
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/report_pls_rf_01.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,4 @@ +name peaks peaks_3oo3 median_rsd_3oo3 +batch04_B02_rep01_301_2_302_3_303 487 421 12.3353334905 +batch04_QC17_rep01_262_2_263_3_264 364 321 7.90853836608 +batch04_S01_rep01_247_2_248_3_249 383 331 5.34421558583
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/report_pls_rf_02.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,4 @@ +name peaks peaks_3oo3 median_rsd_3oo3 +batch04_B02_rep01_301_2_302_3_303 487 421 12.3353334905 +batch04_QC17_rep01_262_2_263_3_264 364 321 7.90853836608 +batch04_S01_rep01_247_2_248_3_249 383 331 5.34421558583
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/report_pls_rf_03.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,2 @@ +name peaks peaks_3oo3 median_rsd_3oo3 +batch04_QC17_rep01_262_2_263_3_264 15 15 8.60246934744
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/report_pls_scan5.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,4 @@ +filename event scans peaks median_rsd +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [70.00-170.00] 1 15 nan +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [70.00-170.00] 1 15 nan +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [70.00-170.00] 1 15 nan
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/sample_list_after_bf.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,3 @@ +filename injectionOrder replicates classLabel +batch04_QC17_rep01_262_2_263_3_264 4 1_2_3 QC +batch04_S01_rep01_247_2_248_3_249 7 1_2_3 sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/sample_list_after_mv_filter.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,3 @@ +filename classLabel +batch04_QC17_rep01_262_2_263_3_264 QC +batch04_S01_rep01_247_2_248_3_249 sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/samplelist_1.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,4 @@ +filename injectionOrder replicates classLabel +batch04_B02_rep01_301_2_302_3_303 1 1_2_3 blank +batch04_QC17_rep01_262_2_263_3_264 4 1_2_3 QC +batch04_S01_rep01_247_2_248_3_249 7 1_2_3 sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/samplelist_2.txt Sat Apr 11 16:45:45 2020 -0400 @@ -0,0 +1,4 @@ +filename injectionOrder replicates classLabel +batch04_B02_rep01_301_2_302_3_303 1 1_2_3 blank +batch04_QC17_rep01_262_2_263_3_264 4 1_2_3 QC +batch04_S01_rep01_247_2_248_3_249 7 1_2_3 sample