diff --git a/.github/workflows/lint_r.yml b/.github/workflows/lint_r.yml
deleted file mode 100644
index cc999fe..0000000
--- a/.github/workflows/lint_r.yml
+++ /dev/null
@@ -1,31 +0,0 @@
-name: Lint R code
-
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - main
-
-defaults:
-  run:
-    shell: bash
-
-jobs:
-  lint:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - uses: r-lib/actions/setup-r@v2
-        with:
-          use-public-rspm: true
-      - uses: r-lib/actions/setup-r-dependencies@v2
-        with:
-          extra-packages: |
-            r-lib/lintr
-            local::.
-          needs: lint
-      - name: Lint
-        run: lintr::lint_package()
-        shell: Rscript {0}
diff --git a/.vscode/settings.json b/.vscode/settings.json
index 1f5c709..1d8ab3d 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -1,4 +1,8 @@
 {
-    "python.linting.flake8Enabled": true,
-    "python.linting.enabled": true
+    "[python]": {
+        "editor.rulers": [99]
+    },
+    "flake8.args": [
+        "--max-line-length=100"
+    ],
 }
diff --git a/00_download_data.sh b/00_download_data.sh
new file mode 100644
index 0000000..b8cc797
--- /dev/null
+++ b/00_download_data.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+# Flywheel project name
+project="bbl/MEBOLD"
+
+# List any subjects you want to download here
+subjects="ID1 ID2"
+
+# Include a path to your flywheel API token here
+token=$(</cbica/home/salot/tokens/flywheel.txt)
+fw login "$token"
+
+# Navigate to the folder to which you want to download the data
+cd /cbica/projects/mebold/sourcedata || exit
+
+for subject in $subjects; do
+    fw download --yes --zip "fw://${project}/${subject}"
+done
diff --git a/01_unzip_dicoms.py b/01_unzip_dicoms.py
new file mode 100644
index 0000000..4dca8d5
--- /dev/null
+++ b/01_unzip_dicoms.py
@@ -0,0 +1,13 @@
+"""Expand dicom zip files in order to run heudiconv."""
+
+import os
+import zipfile
+from glob import glob
+
+if __name__ == "__main__":
+    zip_files = sorted(glob("/cbica/projects/mebold/sourcedata/*_*/*/*/*.dicom.zip"))
+    for zip_file in zip_files:
+        with zipfile.ZipFile(zip_file, "r") as zip_ref:
+            zip_ref.extractall(os.path.dirname(zip_file))
+
+        os.remove(zip_file)
diff --git a/02_run_heudiconv.sh b/02_run_heudiconv.sh
new file mode 100644
index 0000000..5c6dccd
--- /dev/null
+++ b/02_run_heudiconv.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+# Loop over subjects and run heudiconv on each.
+# Make sure to activate the conda environment with heudiconv installed before running this.
+
+declare -a subjects=("ID1" "ID2")
+for sub in "${subjects[@]}"
+do
+    echo "$sub"
+    heudiconv \
+        -f reproin \
+        -o /cbica/projects/mebold/dset \
+        -d "/cbica/projects/mebold/sourcedata/{subject}_{session}/*/*/*/*.dcm" \
+        -s "$sub" \
+        -ss 1 \
+        --bids
+done
diff --git a/03_fix_bids.py b/03_fix_bids.py
new file mode 100644
index 0000000..b5074fc
--- /dev/null
+++ b/03_fix_bids.py
@@ -0,0 +1,145 @@
+"""Fix BIDS files after heudiconv conversion.
+
+This script should deal with steps 1-6 below.
+
+The necessary steps are:
+
+1.  Deal with duplicates.
+2.  Rename multi-echo magnitude BOLD files to part-mag_bold.
+3.  Rename phase files to part-phase_bold.
+4.  Split out noRF noise scans from multi-echo BOLD scans.
+    -   Also copy the JSON.
+5.  Copy first echo of each multi-echo field map without echo entity.
+6.  Update filenames in the scans.tsv files.
+7.  Remove events files.
+"""
+
+import os
+import shutil
+from glob import glob
+
+import nibabel as nb
+import pandas as pd
+
+# Number of EPI noise scans to split out of the BOLD scans.
+N_NOISE_VOLS = 3
+
+FULL_RUN_LENGTHS = (240, 204, 200)
+
+
+if __name__ == "__main__":
+    dset_dir = "/cbica/projects/mebold/dset/"
+    subject_dirs = sorted(glob(os.path.join(dset_dir, "sub-*")))
+    for subject_dir in subject_dirs:
+        sub_id = os.path.basename(subject_dir)
+        session_dirs = sorted(glob(os.path.join(subject_dir, "ses-*")))
+        for session_dir in session_dirs:
+            ses_id = os.path.basename(session_dir)
+            anat_dir = os.path.join(session_dir, "anat")
+            fmap_dir = os.path.join(session_dir, "fmap")
+            func_dir = os.path.join(session_dir, "func")
+
+            # Remove empty events files created by heudiconv
+            events_files = sorted(glob(os.path.join(func_dir, "*_events.tsv")))
+            for events_file in events_files:
+                os.remove(events_file)
+
+            # Load scans file
+            scans_file = os.path.join(session_dir, f"{sub_id}_{ses_id}_scans.tsv")
+            assert os.path.isfile(scans_file), f"Scans file DNE: {scans_file}"
+            scans_df = pd.read_table(scans_file)
+
+            # Heudiconv's reproin heuristic currently (as of v1.2.0) names magnitude and phase
+            # files as _bold and _phase, respectively.
+            # The better way to do it is to call them part-mag_bold and part-phase_bold.
+            mag_files = sorted(glob(os.path.join(func_dir, "*echo-*_bold.*")))
+            for mag_file in mag_files:
+                if "part-" in mag_file:
+                    print(f"Skipping {mag_file}")
+                    continue
+
+                new_mag_file = mag_file.replace("_bold.", "_part-mag_bold.")
+                os.rename(mag_file, new_mag_file)
+
+                mag_filename = os.path.join("func", os.path.basename(mag_file))
+                new_mag_filename = os.path.join("func", os.path.basename(new_mag_file))
+
+                # Replace the filename in the scans.tsv file
+                scans_df = scans_df.replace({"filename": {mag_filename: new_mag_filename}})
+
+            # Rename phase files from _phase to _part-phase_bold.
+            phase_files = sorted(glob(os.path.join(func_dir, "*_phase.*")))
+            for phase_file in phase_files:
+                new_phase_file = phase_file.replace("_phase.", "_part-phase_bold.")
+                os.rename(phase_file, new_phase_file)
+
+                phase_filename = os.path.join("func", os.path.basename(phase_file))
+                new_phase_filename = os.path.join("func", os.path.basename(new_phase_file))
+
+                # Replace the filename in the scans.tsv file
+                scans_df = scans_df.replace({"filename": {phase_filename: new_phase_filename}})
+
+            # Split out noise scans from all multi-echo BOLD files.
+            # There is no metadata to distinguish noise scans from BOLD scans,
+            # so we need to hardcode the number of noise scans to split out.
+            # In order to handle partial scans where the last N volumes aren't noise scans,
+            # we also need to hardcode valid scan lengths.
+            me_bolds = sorted(glob(os.path.join(func_dir, "*acq-MBME*_bold.nii.gz")))
+            for me_bold in me_bolds:
+                noise_scan = me_bold.replace("_bold.nii.gz", "_noRF.nii.gz")
+                if os.path.isfile(noise_scan):
+                    print(f"File exists: {os.path.basename(noise_scan)}")
+                    continue
+
+                img = nb.load(me_bold)
+                n_vols = img.shape[-1]
+                if n_vols not in FULL_RUN_LENGTHS:
+                    print(f"File is a partial scan: {os.path.basename(me_bold)}")
+                    continue
+
+                noise_img = img.slicer[..., -N_NOISE_VOLS:]
+                bold_img = img.slicer[..., :-N_NOISE_VOLS]
+
+                # Overwrite the BOLD scan
+                os.remove(me_bold)
+                bold_img.to_filename(me_bold)
+                noise_img.to_filename(noise_scan)
+
+                # Copy the JSON as well
+                shutil.copyfile(
+                    me_bold.replace(".nii.gz", ".json"),
+                    noise_scan.replace(".nii.gz", ".json"),
+                )
+
+                # Add noise scans to scans DataFrame
+                i_row = len(scans_df.index)
+                me_bold_fname = os.path.join("func", os.path.basename(me_bold))
+                noise_fname = os.path.join("func", os.path.basename(noise_scan))
+                scans_df.loc[i_row] = scans_df.loc[scans_df["filename"] == me_bold_fname].iloc[0]
+                scans_df.loc[i_row, "filename"] = noise_fname
+
+            # In this protocol, we have multi-echo field maps.
+            # In practice, multi-echo field maps aren't useful, so we just grab the first echo's
+            # data and label it as a single-echo field map.
+            # Copy first echo's sbref of multi-echo field maps without echo entity.
+            me_fmaps = sorted(glob(os.path.join(fmap_dir, "*_acq-ME*_echo-1_sbref.*")))
+            for me_fmap in me_fmaps:
+                out_fmap = me_fmap.replace("_echo-1_", "_").replace("_sbref", "_epi")
+                if os.path.isfile(out_fmap):
+                    print(f"File exists: {os.path.basename(out_fmap)}")
+                    continue
+
+                me_fmap_fname = os.path.join("fmap", os.path.basename(me_fmap))
+                out_fmap_fname = os.path.join("fmap", os.path.basename(out_fmap))
+                shutil.copyfile(me_fmap, out_fmap)
+                if me_fmap.endswith(".nii.gz"):
+                    i_row = len(scans_df.index)
+                    scans_df.loc[i_row] = scans_df.loc[
+                        scans_df["filename"] == me_fmap_fname
+                    ].iloc[0]
+                    scans_df.loc[i_row, "filename"] = out_fmap_fname
+
+            # Save out the modified scans.tsv file.
+            scans_df = scans_df.sort_values(by=["acq_time", "filename"])
+            os.remove(scans_file)
+            scans_df.to_csv(scans_file, sep="\t", na_rep="n/a", index=False)
diff --git a/04_reface_t1ws.sh b/04_reface_t1ws.sh
new file mode 100644
index 0000000..cbb14b3
--- /dev/null
+++ b/04_reface_t1ws.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+# Reface T1w images using afni_refacer_run.
+module load afni/2022_05_03
+
+t1w_files=$(find /cbica/projects/mebold/dset/sub-*/ses-*/anat/*T1w.nii.gz)
+for t1w_file in $t1w_files
+do
+    echo "$t1w_file"
+    @afni_refacer_run \
+        -input "${t1w_file}" \
+        -mode_reface \
+        -no_images \
+        -overwrite \
+        -prefix "${t1w_file}"
+done
diff --git a/05_anonymize_acqtimes.py b/05_anonymize_acqtimes.py
new file mode 100644
index 0000000..27b53f7
--- /dev/null
+++ b/05_anonymize_acqtimes.py
@@ -0,0 +1,55 @@
+"""Anonymize acquisition datetimes for a dataset.
+
+Anonymize acquisition datetimes for a dataset. Works for both longitudinal
+and cross-sectional studies. The time of day is preserved, but the first
+scan is set to January 1st, 1800. In a longitudinal study, each session is
+anonymized relative to the first session, so that time between sessions is
+preserved.
+
+Overwrites scan tsv files in dataset. Only run this *after* data collection
+is complete for the study, especially if it's longitudinal.
+"""
+
+import os
+from glob import glob
+
+import pandas as pd
+from dateutil import parser
+
+if __name__ == "__main__":
+    dset_dir = "/cbica/projects/mebold/dset"
+
+    bl_dt = parser.parse("1800-01-01")
+
+    subject_dirs = sorted(glob(os.path.join(dset_dir, "sub-*")))
+    for subject_dir in subject_dirs:
+        sub_id = os.path.basename(subject_dir)
+        print(f"Processing {sub_id}")
+
+        scans_files = sorted(glob(os.path.join(subject_dir, "ses-*/*_scans.tsv")))
+
+        for i_ses, scans_file in enumerate(scans_files):
+            ses_dir = os.path.dirname(scans_file)
+            ses_name = os.path.basename(ses_dir)
+            print(f"\t{ses_name}")
+
+            df = pd.read_table(scans_file)
+            if i_ses == 0:
+                # Anonymize in terms of first scan for subject.
+                first_scan = df["acq_time"].min()
+                first_dt = parser.parse(first_scan.split("T")[0])
+                diff = first_dt - bl_dt
+
+            acq_times = df["acq_time"].apply(parser.parse)
+            acq_times = (acq_times - diff).astype(str)
+            df["acq_time"] = acq_times
+            df["acq_time"] = df["acq_time"].str.replace(" ", "T")
+
+            os.remove(scans_file)
+            df.to_csv(
+                scans_file,
+                sep="\t",
+                lineterminator="\n",
+                na_rep="n/a",
+                index=False,
+            )
diff --git a/06_clean_jsons.py b/06_clean_jsons.py
new file mode 100644
index 0000000..177722c
--- /dev/null
+++ b/06_clean_jsons.py
@@ -0,0 +1,28 @@
+"""Remove unneeded fields from bottom-level JSON files."""
+
+import json
+import os
+from glob import glob
+
+if __name__ == "__main__":
+    dset_dir = "/cbica/projects/mebold/dset/"
+    drop_keys = [
+        "AcquisitionTime",
+        "CogAtlasID",
+        "InstitutionAddress",
+        "TaskName",
+        "ImageComments",
+    ]
+
+    json_files = sorted(glob(os.path.join(dset_dir, "sub-*/ses-*/*/*.json")))
+    for json_file in json_files:
+        with open(json_file, "r") as fo:
+            json_data = json.load(fo)
+
+        for drop_key in drop_keys:
+            if drop_key in json_data.keys():
+                json_data.pop(drop_key)
+
+        os.remove(json_file)
+        with open(json_file, "w") as fo:
+            json.dump(json_data, fo, indent=4, sort_keys=True)
diff --git a/07_assign_intendedfor.py b/07_assign_intendedfor.py
new file mode 100644
index 0000000..c310cd3
--- /dev/null
+++ b/07_assign_intendedfor.py
@@ -0,0 +1,96 @@
+"""Assign IntendedFor and related metadata fields."""
+
+import json
+import os
+from glob import glob
+
+if __name__ == "__main__":
+    dset_dir = "/cbica/projects/mebold/dset/"
+    subject_dirs = sorted(glob(os.path.join(dset_dir, "sub-*")))
+    for subject_dir in subject_dirs:
+        subject = os.path.basename(subject_dir)
+
+        session_dirs = sorted(glob(os.path.join(subject_dir, "ses-*")))
+        for session_dir in session_dirs:
+            session = os.path.basename(session_dir)
+            prefix = f"{subject}_{session}"
+
+            fmap_dir = os.path.join(session_dir, "fmap")
+            func_dir = os.path.join(session_dir, "func")
+
+            # Remove intendedfor-related fields from multi-echo field maps.
+            me_fmaps = sorted(
+                glob(os.path.join(fmap_dir, "*_acq-ME*_echo-*_sbref.json"))
+                + glob(os.path.join(fmap_dir, "*_acq-ME*_echo-*_epi.json"))
+            )
+            for me_fmap in me_fmaps:
+                with open(me_fmap, "r") as fo:
+                    json_metadata = json.load(fo)
+
+                if "B0FieldIdentifier" in json_metadata.keys():
+                    json_metadata.pop("B0FieldIdentifier")
+
+                if "B0FieldSource" in json_metadata.keys():
+                    json_metadata.pop("B0FieldSource")
+
+                if "IntendedFor" in json_metadata.keys():
+                    json_metadata.pop("IntendedFor")
+
+                os.remove(me_fmap)
+                with open(me_fmap, "w") as fo:
+                    json.dump(json_metadata, fo, sort_keys=True, indent=4)
+
+            # Add intendedfor-related fields to single-echo field maps.
+            se_fmaps = sorted(glob(os.path.join(fmap_dir, "*_dir-AP_epi.json")))
+            for ap_fmap in se_fmaps:
+                pa_fmap = ap_fmap.replace("_dir-AP_", "_dir-PA_")
+                with open(ap_fmap, "r") as fo:
+                    ap_metadata = json.load(fo)
+
+                with open(pa_fmap, "r") as fo:
+                    pa_metadata = json.load(fo)
+
+                b0fieldname = f"{prefix}_{ap_metadata['ProtocolName'].replace('_dir-AP', '')}"
+                b0fieldname = b0fieldname.replace(":", "_").replace("-", "_")
+
+                if "acq-ME" in ap_fmap:
+                    acq = "MBME"
+                elif "acq-SESE" in ap_fmap:
+                    acq = "MBSE"
+                else:
+                    raise Exception(f"What is {ap_fmap}?")
+
+                target_files = sorted(
+                    glob(os.path.join(func_dir, f"*acq-{acq}*bold.nii.gz"))
+                )
+                target_jsons = [f.replace(".nii.gz", ".json") for f in target_files]
+                ap_metadata["B0FieldIdentifier"] = [b0fieldname]
+                pa_metadata["B0FieldIdentifier"] = [b0fieldname]
+                # TODO: Fix paths
+                # target_filenames = [
+                #     "bids::" + tf.replace(dset_dir, "") for tf in target_files
+                # ]
+                # ap_metadata["IntendedFor"] = target_filenames
+                # pa_metadata["IntendedFor"] = target_filenames
+
+                os.remove(ap_fmap)
+                with open(ap_fmap, "w") as fo:
+                    json.dump(ap_metadata, fo, sort_keys=True, indent=4)
+
+                os.remove(pa_fmap)
+                with open(pa_fmap, "w") as fo:
+                    json.dump(pa_metadata, fo, sort_keys=True, indent=4)
+
+                for target_json in target_jsons:
+                    with open(target_json, "r") as fo:
+                        target_metadata = json.load(fo)
+
+                    # if "B0FieldSource" not in target_metadata.keys():
+                    target_metadata["B0FieldSource"] = []
+
+                    if "MESE" in b0fieldname or "SESE" in b0fieldname:
+                        target_metadata["B0FieldSource"].append(b0fieldname)
+
+                    os.remove(target_json)
+                    with open(target_json, "w") as fo:
+                        json.dump(target_metadata, fo, sort_keys=True, indent=4)
diff --git a/README.md b/README.md
index 000e59d..3bb67f3 100644
--- a/README.md
+++ b/README.md
@@ -1,17 +1,19 @@
-# paper-template
-A template repository for scientific projects.
+# mebold-curation
 
-## File organization on CUBIC
+Code to curate complex-valued, multi-echo BOLD data acquired with the CMRR sequence.
 
-```
-/path/to/project/
-    |-  code/           # Local clone of the GitHub repository
-        |-  figures/    # Any figures for the manuscript
-        |_  data/       # Tabular data that may be shared on GitHub
-    |-  data/
-    |-  results/        # Results that cannot be shared on GitHub
-    |_  reproduction/
-        |-  code/       # Local clone of reproducibilibuddy's fork of GitHub repository
-        |-  data/       # Any data that must be copied and not referenced
-        |_  results/
-```
\ No newline at end of file
+The code in this repo assumes the following:
+
+1. The protocol was named according to ReproIn convention.
+    - BOLD: `func_task-rest_acq-MBME_run-01`
+    - BOLD field maps: `fmap-epi_acq-func_dir-AP`
+        -   This assumes the BOLD field maps are multi-echo,
+            even though we don't need multiple echoes for field maps.
+    - T1w: `anat-T1w`
+2. You are planning to download the DICOMs to CUBIC and convert them into a BIDS dataset with heudiconv.
+    - DICOMs will go in `sourcedata/`.
+    - BIDS-format data will go in `dset/`.
+3. Raw data are on Flywheel, in the project `bbl/MEBOLD`.
+4. Your CUBIC project directory is at `/cbica/projects/mebold`.
+
+TODO: Create conda environment config file.
diff --git a/analysis/generate_figure_01.ipynb b/analysis/generate_figure_01.ipynb
deleted file mode 100644
index 342e1f3..0000000
--- a/analysis/generate_figure_01.ipynb
+++ /dev/null
@@ -1,72 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Generate Figure 1\n",
-    "Figure 1 shows the distributions of `measure_a` and `measure_b`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "\n",
-    "sns.set_style(\"whitegrid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Read in the data\n",
-    "in_file = \"../data/demographics.tsv\"\n",
-    "df = pd.read_table(in_file)\n",
-    "\n",
-    "# Generate the plot\n",
-    "plot = sns.jointplot(data=df, x=\"measure_a\", y=\"measure_b\", kind=\"reg\", truncate=False)\n",
-    "plot.figure.savefig(\"../figures/figure_01.svg\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/check_multiecho.py b/check_multiecho.py
new file mode 100644
index 0000000..791a75a
--- /dev/null
+++ b/check_multiecho.py
@@ -0,0 +1,52 @@
+"""Check that multi-echo scans are the right length."""
+
+from bids import BIDSLayout
+from bids.layout import Query
+import nibabel as nb
+
+layout = BIDSLayout("/cbica/projects/mebold/dset", validate=False)
+files = layout.get(
+    echo=1,
+    reconstruction=Query.NONE,
+    part="mag",
+    suffix=["noRF", "bold"],
+    extension=["nii.gz"],
+)
+for f in files:
+    print(f.filename)
+    size_check = {}
+    file_entities = f.get_entities(metadata=False)
+    file_entities["reconstruction"] = Query.NONE
+    for i_echo in range(1, 6):
+        file_entities["echo"] = i_echo
+        echo_file = layout.get(**file_entities)
+        if len(echo_file) != 1:
+            raise ValueError(
+                f"Something's wrong with {file_entities}\n{len(echo_file)} files found:"
+                f"\n{echo_file}"
+            )
+
+        img_size = nb.load(echo_file[0].path).shape
+        size_check[f"mag_{i_echo}"] = img_size
+
+    file_entities["part"] = "phase"
+
+    for i_echo in range(1, 6):
+        file_entities["echo"] = i_echo
+        echo_file = layout.get(**file_entities)
+        if len(echo_file) != 1:
+            raise ValueError(
+                f"Something's wrong with {file_entities}\n{len(echo_file)} files found:"
+                f"\n{echo_file}"
+            )
+
+        img_size = nb.load(echo_file[0].path).shape
+        size_check[f"phase_{i_echo}"] = img_size
+
+    test_size = size_check["mag_1"]
+    if (len(test_size) != 4) or (test_size[3] == 0):
+        print(f"Size of {f.filename} is bad: ({test_size})")
+
+    for k, v in size_check.items():
+        if test_size != v:
+            print(f"Size of {k} ({v}) != {f.filename} ({test_size})")
diff --git a/data/demographics.tsv b/data/demographics.tsv
deleted file mode 100644
index 82bb775..0000000
--- a/data/demographics.tsv
+++ /dev/null
@@ -1,101 +0,0 @@
-participant_id	measure_a	measure_b	measure_c	measure_d	measure_e
-0	0.6066589316459093	0.03512852663750968	1.7143769992818914	-1.2744142167907762	-1.0425431670249434
-1	-0.2594029181096712	0.06125909183705339	0.5720684861060996	0.11815569356317517	-0.39037807361007937
-2	-0.5518843910225449	0.5968064875767328	-0.852376470274402	-1.149600732482422	0.2809333892132978
-3	-0.7004496150533241	1.8806421686291188	0.9379311468018007	1.2017352159594303	-0.7207741774940307
-4	-1.2722428575604066	0.39428149770262105	-0.959872840984791	2.56526097051042	-2.3742920166408292
-5	0.3669853679692641	-1.3134427156315651	1.2774605311387488	-0.3054671727320336	0.6672220155487646
-6	1.0969408717913307	-0.19023138558787225	-1.486444712936383	0.8026794839295996	0.2775380558313201
-7	-1.2439012020650257	1.2121633727521652	0.8357070275741608	-1.8667447452397732	-1.9317395941445124
-8	0.5743460423565054	-0.3061288360966874	-0.02150115206108712	-1.2939504466066754	-0.6190383873078469
-9	0.5349590780741268	0.3039880083120335	-0.37293843039743446	0.12118836707942034	-0.8535569142419104
-10	-1.6685155623898125	0.33769035990391655	-0.7871503028263989	-0.05275644213343285	-2.033667793904189
-11	-1.7261219340781218	0.33744733579176844	-1.3345598703479573	0.29683516274392463	0.38452730882185476
-12	-0.9837126695959447	-1.7269404435778346	-0.12051188072693265	0.1807865041125031	2.0406342457712072
-13	-0.5857197577702211	-0.5697032354545003	0.40033279217098494	0.9429245499445972	-0.49378198345706453
-14	1.5140794411974163	-1.9999758360231976	0.9370074459659825	1.7434658723834142	1.9732555512110224
-15	-0.5678404746855369	-0.05747594935780584	-0.33478789706427153	-1.8309503180656543	-0.32725242749010935
-16	-0.029517933083049872	-0.27286675645033653	0.7686131338307126	-1.7287098050561736	-0.17325812405070465
-17	0.6572974592864085	1.5784006488719684	-0.40545712539088835	2.823181638665233	1.1851396763053945
-18	0.7665220619200834	-1.584193842775645	1.0951055443032627	0.042100370535288846	1.4570040187212034
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