Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Refactor metrics.dependence module #1088

Merged
merged 7 commits into from
Aug 7, 2024
Merged

Refactor metrics.dependence module #1088

Show file tree
Hide file tree
Changes from 6 commits
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
a1d7faf
Add type hints to metric functions.
tsalo Apr 22, 2024
fcd9c77
Use keyword arguments.
tsalo Apr 22, 2024
8f62c6b
Update tests.
tsalo Apr 22, 2024
0fdb81d
Update dependence.py
tsalo Apr 22, 2024
288db78
Update collect.py
tsalo Apr 22, 2024
9a1d536
Fix other stuff.
tsalo Apr 22, 2024
9162c40
Merge remote-tracking branch 'upstream/main' into refactor-metrics
tsalo Aug 5, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
122 changes: 74 additions & 48 deletions tedana/metrics/collect.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -119,7 +119,10 @@ def generate_metrics(
metric_maps = {}
if "map weight" in required_metrics:
LGR.info("Calculating weight maps")
metric_maps["map weight"] = dependence.calculate_weights(data_optcom, mixing)
metric_maps["map weight"] = dependence.calculate_weights(
data_optcom=data_optcom,
Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Related to #1086 (comment) we use data_oc in other places. I'd like to commit to one or the other and make the variable names consistent between this PR and #1086.

Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I personally like data_optcom better.

Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

That works for me. @handwerkerd do you have an opinion on this?

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

No opinion.

mixing=mixing,
)
signs = determine_signs(metric_maps["map weight"], axis=0)
comptable["optimal sign"] = signs
metric_maps["map weight"], mixing = flip_components(
Expand All @@ -128,31 +131,42 @@ def generate_metrics(

if "map optcom betas" in required_metrics:
LGR.info("Calculating parameter estimate maps for optimally combined data")
metric_maps["map optcom betas"] = dependence.calculate_betas(data_optcom, mixing)
metric_maps["map optcom betas"] = dependence.calculate_betas(
data=data_optcom,
mixing=mixing,
)
if io_generator.verbose:
metric_maps["map echo betas"] = dependence.calculate_betas(data_cat, mixing)
metric_maps["map echo betas"] = dependence.calculate_betas(
data=data_cat,
mixing=mixing,
)

if "map percent signal change" in required_metrics:
LGR.info("Calculating percent signal change maps")
# used in kundu v3.2 tree
metric_maps["map percent signal change"] = dependence.calculate_psc(
data_optcom, metric_maps["map optcom betas"]
data_optcom=data_optcom,
optcom_betas=metric_maps["map optcom betas"],
)

if "map Z" in required_metrics:
LGR.info("Calculating z-statistic maps")
metric_maps["map Z"] = dependence.calculate_z_maps(metric_maps["map weight"])
metric_maps["map Z"] = dependence.calculate_z_maps(weights=metric_maps["map weight"])

if io_generator.verbose:
io_generator.save_file(
utils.unmask(metric_maps["map Z"] ** 2, mask),
label + " component weights img",
f"{label} component weights img",
)

if ("map FT2" in required_metrics) or ("map FS0" in required_metrics):
LGR.info("Calculating F-statistic maps")
m_t2, m_s0, p_m_t2, p_m_s0 = dependence.calculate_f_maps(
data_cat, metric_maps["map Z"], mixing, adaptive_mask, tes
data_cat=data_cat,
z_maps=metric_maps["map Z"],
mixing=mixing,
adaptive_mask=adaptive_mask,
tes=tes,
)
metric_maps["map FT2"] = m_t2
metric_maps["map FS0"] = m_s0
Expand All @@ -162,58 +176,73 @@ def generate_metrics(
if io_generator.verbose:
io_generator.save_file(
utils.unmask(metric_maps["map FT2"], mask),
label + " component F-T2 img",
f"{label} component F-T2 img",
)
io_generator.save_file(
utils.unmask(metric_maps["map FS0"], mask),
label + " component F-S0 img",
f"{label} component F-S0 img",
)

if "map Z clusterized" in required_metrics:
LGR.info("Thresholding z-statistic maps")
z_thresh = 1.95
metric_maps["map Z clusterized"] = dependence.threshold_map(
metric_maps["map Z"], mask, ref_img, z_thresh
maps=metric_maps["map Z"],
mask=mask,
ref_img=ref_img,
threshold=z_thresh,
)

if "map FT2 clusterized" in required_metrics:
LGR.info("Calculating T2* F-statistic maps")
f_thresh, _, _ = getfbounds(len(tes))
metric_maps["map FT2 clusterized"] = dependence.threshold_map(
metric_maps["map FT2"], mask, ref_img, f_thresh
maps=metric_maps["map FT2"],
mask=mask,
ref_img=ref_img,
threshold=f_thresh,
)

if "map FS0 clusterized" in required_metrics:
LGR.info("Calculating S0 F-statistic maps")
f_thresh, _, _ = getfbounds(len(tes))
metric_maps["map FS0 clusterized"] = dependence.threshold_map(
metric_maps["map FS0"], mask, ref_img, f_thresh
maps=metric_maps["map FS0"],
mask=mask,
ref_img=ref_img,
threshold=f_thresh,
)

# Intermediate metrics
if "countsigFT2" in required_metrics:
LGR.info("Counting significant voxels in T2* F-statistic maps")
comptable["countsigFT2"] = dependence.compute_countsignal(
metric_maps["map FT2 clusterized"]
stat_cl_maps=metric_maps["map FT2 clusterized"],
)

if "countsigFS0" in required_metrics:
LGR.info("Counting significant voxels in S0 F-statistic maps")
comptable["countsigFS0"] = dependence.compute_countsignal(
metric_maps["map FS0 clusterized"]
stat_cl_maps=metric_maps["map FS0 clusterized"],
)

# Back to maps
if "map beta T2 clusterized" in required_metrics:
LGR.info("Thresholding optimal combination beta maps to match T2* F-statistic maps")
metric_maps["map beta T2 clusterized"] = dependence.threshold_to_match(
metric_maps["map optcom betas"], comptable["countsigFT2"], mask, ref_img
maps=metric_maps["map optcom betas"],
n_sig_voxels=comptable["countsigFT2"],
mask=mask,
ref_img=ref_img,
)

if "map beta S0 clusterized" in required_metrics:
LGR.info("Thresholding optimal combination beta maps to match S0 F-statistic maps")
metric_maps["map beta S0 clusterized"] = dependence.threshold_to_match(
metric_maps["map optcom betas"], comptable["countsigFS0"], mask, ref_img
maps=metric_maps["map optcom betas"],
n_sig_voxels=comptable["countsigFS0"],
mask=mask,
ref_img=ref_img,
)

# Dependence metrics
Expand All @@ -229,24 +258,23 @@ def generate_metrics(
if "variance explained" in required_metrics:
LGR.info("Calculating variance explained")
comptable["variance explained"] = dependence.calculate_varex(
metric_maps["map optcom betas"]
optcom_betas=metric_maps["map optcom betas"],
)

if "normalized variance explained" in required_metrics:
LGR.info("Calculating normalized variance explained")
comptable["normalized variance explained"] = dependence.calculate_varex_norm(
metric_maps["map weight"]
weights=metric_maps["map weight"],
)

# Spatial metrics
if "dice_FT2" in required_metrics:
LGR.info(
"Calculating DSI between thresholded T2* F-statistic and "
"optimal combination beta maps"
"Calculating DSI between thresholded T2* F-statistic and optimal combination beta maps"
)
comptable["dice_FT2"] = dependence.compute_dice(
metric_maps["map beta T2 clusterized"],
metric_maps["map FT2 clusterized"],
clmaps1=metric_maps["map beta T2 clusterized"],
clmaps2=metric_maps["map FT2 clusterized"],
axis=0,
)

Expand All @@ -256,20 +284,18 @@ def generate_metrics(
"optimal combination beta maps"
)
comptable["dice_FS0"] = dependence.compute_dice(
metric_maps["map beta S0 clusterized"],
metric_maps["map FS0 clusterized"],
clmaps1=metric_maps["map beta S0 clusterized"],
clmaps2=metric_maps["map FS0 clusterized"],
axis=0,
)

if "signal-noise_t" in required_metrics:
LGR.info("Calculating signal-noise t-statistics")
RepLGR.info(
"A t-test was performed between the distributions of T2*-model "
"F-statistics associated with clusters (i.e., signal) and "
"non-cluster voxels (i.e., noise) to generate a t-statistic "
"(metric signal-noise_z) and p-value (metric signal-noise_p) "
"measuring relative association of the component to signal "
"over noise."
"A t-test was performed between the distributions of T2*-model F-statistics "
"associated with clusters (i.e., signal) and non-cluster voxels (i.e., noise) to "
"generate a t-statistic (metric signal-noise_z) and p-value (metric signal-noise_p) "
"measuring relative association of the component to signal over noise."
)
(
comptable["signal-noise_t"],
Expand All @@ -283,20 +309,18 @@ def generate_metrics(
if "signal-noise_z" in required_metrics:
LGR.info("Calculating signal-noise z-statistics")
RepLGR.info(
"A t-test was performed between the distributions of T2*-model "
"F-statistics associated with clusters (i.e., signal) and "
"non-cluster voxels (i.e., noise) to generate a z-statistic "
"(metric signal-noise_z) and p-value (metric signal-noise_p) "
"measuring relative association of the component to signal "
"over noise."
"A t-test was performed between the distributions of T2*-model F-statistics "
"associated with clusters (i.e., signal) and non-cluster voxels (i.e., noise) to "
"generate a z-statistic (metric signal-noise_z) and p-value (metric signal-noise_p) "
"measuring relative association of the component to signal over noise."
)
(
comptable["signal-noise_z"],
comptable["signal-noise_p"],
) = dependence.compute_signal_minus_noise_z(
Z_maps=metric_maps["map Z"],
Z_clmaps=metric_maps["map Z clusterized"],
F_T2_maps=metric_maps["map FT2"],
z_maps=metric_maps["map Z"],
z_clmaps=metric_maps["map Z clusterized"],
f_t2_maps=metric_maps["map FT2"],
)

if "countnoise" in required_metrics:
Expand All @@ -306,18 +330,19 @@ def generate_metrics(
"calculated for each component."
)
comptable["countnoise"] = dependence.compute_countnoise(
metric_maps["map Z"], metric_maps["map Z clusterized"]
stat_maps=metric_maps["map Z"],
stat_cl_maps=metric_maps["map Z clusterized"],
)

# Composite metrics
if "d_table_score" in required_metrics:
LGR.info("Calculating decision table score")
comptable["d_table_score"] = dependence.generate_decision_table_score(
comptable["kappa"],
comptable["dice_FT2"],
comptable["signal-noise_t"],
comptable["countnoise"],
comptable["countsigFT2"],
kappa=comptable["kappa"],
dice_ft2=comptable["dice_FT2"],
signal_minus_noise_t=comptable["signal-noise_t"],
countnoise=comptable["countnoise"],
countsig_ft2=comptable["countsigFT2"],
)

# Write verbose metrics if needed
Expand All @@ -326,6 +351,7 @@ def generate_metrics(
write_t2s0 = "map predicted T2" in metric_maps
if write_betas:
betas = metric_maps["map echo betas"]

if write_t2s0:
pred_t2_maps = metric_maps["map predicted T2"]
pred_s0_maps = metric_maps["map predicted S0"]
Expand All @@ -335,22 +361,22 @@ def generate_metrics(
echo_betas = betas[:, i_echo, :]
io_generator.save_file(
utils.unmask(echo_betas, mask),
"echo weight " + label + " map split img",
f"echo weight {label} map split img",
echo=(i_echo + 1),
)

if write_t2s0:
echo_pred_t2_maps = pred_t2_maps[:, i_echo, :]
io_generator.save_file(
utils.unmask(echo_pred_t2_maps, mask),
"echo T2 " + label + " split img",
f"echo T2 {label} split img",
echo=(i_echo + 1),
)

echo_pred_s0_maps = pred_s0_maps[:, i_echo, :]
io_generator.save_file(
utils.unmask(echo_pred_s0_maps, mask),
"echo S0 " + label + " split img",
f"echo S0 {label} split img",
echo=(i_echo + 1),
)

Expand Down
Loading