# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
#
# Copyright 2021 The NiPreps Developers <nipreps@gmail.com>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# We support and encourage derived works from this project, please read
# about our expectations at
#
# https://www.nipreps.org/community/licensing/
#
"""Encapsulates report generation functions."""
from builtins import object # pylint: disable=W0622
from io import open
from sys import version_info
import pandas as pd
from .. import config
from ..utils.misc import BIDS_COMP
[docs]def gen_html(csv_file, mod, csv_failed=None, out_file=None):
import datetime
import os.path as op
from pkg_resources import resource_filename as pkgrf
from .. import __version__ as ver
from mriqc.data.config import GroupTemplate
if version_info[0] > 2:
from io import StringIO as TextIO
else:
from io import BytesIO as TextIO
QCGROUPS = {
"T1w": [
(["cjv"], None),
(["cnr"], None),
(["efc"], None),
(["fber"], None),
(["wm2max"], None),
(["snr_csf", "snr_gm", "snr_wm"], None),
(["snrd_csf", "snrd_gm", "snrd_wm"], None),
(["fwhm_avg", "fwhm_x", "fwhm_y", "fwhm_z"], "vox"),
(["qi_1", "qi_2"], None),
(["inu_range", "inu_med"], None),
(["icvs_csf", "icvs_gm", "icvs_wm"], None),
(["rpve_csf", "rpve_gm", "rpve_wm"], None),
(["tpm_overlap_csf", "tpm_overlap_gm", "tpm_overlap_wm"], None),
(
[
"summary_bg_mean",
"summary_bg_median",
"summary_bg_stdv",
"summary_bg_mad",
"summary_bg_k",
"summary_bg_p05",
"summary_bg_p95",
],
None,
),
(
[
"summary_csf_mean",
"summary_csf_median",
"summary_csf_stdv",
"summary_csf_mad",
"summary_csf_k",
"summary_csf_p05",
"summary_csf_p95",
],
None,
),
(
[
"summary_gm_mean",
"summary_gm_median",
"summary_gm_stdv",
"summary_gm_mad",
"summary_gm_k",
"summary_gm_p05",
"summary_gm_p95",
],
None,
),
(
[
"summary_wm_mean",
"summary_wm_median",
"summary_wm_stdv",
"summary_wm_mad",
"summary_wm_k",
"summary_wm_p05",
"summary_wm_p95",
],
None,
),
],
"T2w": [
(["cjv"], None),
(["cnr"], None),
(["efc"], None),
(["fber"], None),
(["wm2max"], None),
(["snr_csf", "snr_gm", "snr_wm"], None),
(["snrd_csf", "snrd_gm", "snrd_wm"], None),
(["fwhm_avg", "fwhm_x", "fwhm_y", "fwhm_z"], "mm"),
(["qi_1", "qi_2"], None),
(["inu_range", "inu_med"], None),
(["icvs_csf", "icvs_gm", "icvs_wm"], None),
(["rpve_csf", "rpve_gm", "rpve_wm"], None),
(["tpm_overlap_csf", "tpm_overlap_gm", "tpm_overlap_wm"], None),
(
[
"summary_bg_mean",
"summary_bg_stdv",
"summary_bg_k",
"summary_bg_p05",
"summary_bg_p95",
],
None,
),
(
[
"summary_csf_mean",
"summary_csf_stdv",
"summary_csf_k",
"summary_csf_p05",
"summary_csf_p95",
],
None,
),
(
[
"summary_gm_mean",
"summary_gm_stdv",
"summary_gm_k",
"summary_gm_p05",
"summary_gm_p95",
],
None,
),
(
[
"summary_wm_mean",
"summary_wm_stdv",
"summary_wm_k",
"summary_wm_p05",
"summary_wm_p95",
],
None,
),
],
"bold": [
(["efc"], None),
(["fber"], None),
(["fwhm", "fwhm_x", "fwhm_y", "fwhm_z"], "mm"),
(["gsr_%s" % a for a in ["x", "y"]], None),
(["snr"], None),
(["dvars_std", "dvars_vstd"], None),
(["dvars_nstd"], None),
(["fd_mean"], "mm"),
(["fd_num"], "# timepoints"),
(["fd_perc"], "% timepoints"),
(["spikes_num"], "# slices"),
(["dummy_trs"], "# TRs"),
(["gcor"], None),
(["tsnr"], None),
(["aor"], None),
(["aqi"], None),
(
[
"summary_bg_mean",
"summary_bg_stdv",
"summary_bg_k",
"summary_bg_p05",
"summary_bg_p95",
],
None,
),
(
[
"summary_fg_mean",
"summary_fg_stdv",
"summary_fg_k",
"summary_fg_p05",
"summary_fg_p95",
],
None,
),
],
}
if csv_file.suffix == ".csv":
def_comps = list(BIDS_COMP.keys())
dataframe = pd.read_csv(
csv_file, index_col=False, dtype={comp: object for comp in def_comps}
)
id_labels = list(set(def_comps) & set(dataframe.columns.ravel().tolist()))
dataframe["label"] = dataframe[id_labels].apply(
_format_labels, args=(id_labels,), axis=1
)
else:
dataframe = pd.read_csv(
csv_file, index_col=False, sep="\t", dtype={"bids_name": object}
)
dataframe = dataframe.rename(index=str, columns={"bids_name": "label"})
nPart = len(dataframe)
failed = None
if csv_failed is not None and op.isfile(csv_failed):
config.loggers.cli.warning(f'Found failed-workflows table "{csv_failed}"')
failed_df = pd.read_csv(csv_failed, index_col=False)
cols = list(set(id_labels) & set(failed_df.columns.ravel().tolist()))
try:
failed_df = failed_df.sort_values(by=cols)
except AttributeError:
failed_df = failed_df.sort(columns=cols)
# myfmt not defined
# failed = failed_df[cols].apply(myfmt, args=(cols,), axis=1).ravel().tolist()
csv_groups = []
datacols = dataframe.columns.ravel().tolist()
for group, units in QCGROUPS[mod]:
dfdict = {"iqm": [], "value": [], "label": [], "units": []}
for iqm in group:
if iqm in datacols:
values = dataframe[[iqm]].values.ravel().tolist()
if values:
dfdict["iqm"] += [iqm] * nPart
dfdict["units"] += [units] * nPart
dfdict["value"] += values
dfdict["label"] += dataframe[["label"]].values.ravel().tolist()
# Save only if there are values
if dfdict["value"]:
csv_df = pd.DataFrame(dfdict)
csv_str = TextIO()
csv_df[["iqm", "value", "label", "units"]].to_csv(csv_str, index=False)
csv_groups.append(csv_str.getvalue())
if out_file is None:
out_file = op.abspath("group.html")
tpl = GroupTemplate()
tpl.generate_conf(
{
"modality": mod,
"timestamp": datetime.datetime.now().strftime("%Y-%m-%d, %H:%M"),
"version": ver,
"csv_groups": csv_groups,
"failed": failed,
"boxplots_js": open(
pkgrf(
"mriqc",
op.join("data", "reports", "embed_resources", "boxplots.js"),
)
).read(),
"d3_js": open(
pkgrf(
"mriqc", op.join("data", "reports", "embed_resources", "d3.min.js")
)
).read(),
"boxplots_css": open(
pkgrf(
"mriqc",
op.join("data", "reports", "embed_resources", "boxplots.css"),
)
).read(),
},
out_file,
)
return out_file
def _format_labels(row, id_labels):
"""format participant labels"""
crow = []
for col_id, prefix in list(BIDS_COMP.items()):
if col_id in id_labels:
crow.append("%s-%s" % (prefix, row[[col_id]].values[0]))
return "_".join(crow)