various cosmits

[agramfort]

result on my we procrastination...

[agramfort]

just a remark but you certainly already know. the options for hrf_model parameter are not consistent in the package.

[agramfort]

I am not sure I understand the rationale for the check_design_matrix that is called in user code just to extract the numpy array from a design_matrix.

why not passing a design_matrix to FirstLevelGLM ?

I think that would simplify examples.

[bthirion]

On 22/11/2015 09:12, Alexandre Gramfort wrote:

just a remark but you certainly already know. the options for
hrf_model parameter are not consistent in the package.

—
Reply to this email directly or view it on GitHub
#39 (comment).

Yes, please see #38...

[agramfort]

great

[bthirion]

@GaelVaroquaux laughs at me when I do this, so I tend to avoid it now...

[agramfort]

@GaelVaroquaux https://github.com/GaelVaroquaux laughs at me when I do
this, so I tend to avoid it now...

let's team up to laugh at him :)

[lesteve]

I am not going to laugh at anyone but since you are using it once below, you may as well remove this import and use the more verbose os.path.

[bthirion]

I'll have a closer look tomorrow, but this looks fine. Thanks a lot for taking time to improve nistats !

[bthirion]

+1

In examples, I like to give people the concrete sense of where the
design matrix values are, but in terms of API, we should only use the
high-level objects. IIRC, this should work.

[agramfort]

one idea I add also would be to use the index of a DataFrame rather than using the frame_times (don't like the name much btw)

In [4]: df = pd.DataFrame(dict(reg1=np.random.randn(10), reg2=np.random.randn(10)))

In [5]: df
Out[5]:
       reg1      reg2
0  0.435177 -1.034931
1 -0.059424  2.223872
2  0.070459 -0.838222
3 -0.777904 -0.690715
4 -0.782690 -0.045528
5  0.278254  0.388872
6  0.867945  0.242028
7 -2.214093  0.866546
8 -0.186639 -0.626605
9 -0.635547  1.209869

In [6]: tr = 2.4

In [7]: frame_times = np.arange(10) * 2.4

In [8]: df.index = frame_times

In [9]: df
Out[9]:
          reg1      reg2
0.0   0.435177 -1.034931
2.4  -0.059424  2.223872
4.8   0.070459 -0.838222
7.2  -0.777904 -0.690715
9.6  -0.782690 -0.045528
12.0  0.278254  0.388872
14.4  0.867945  0.242028
16.8 -2.214093  0.866546
19.2 -0.186639 -0.626605
21.6 -0.635547  1.209869

In [10]: matrix = df.values

In [11]: matrix
Out[11]:
array([[ 0.43517668, -1.03493107],
       [-0.05942358,  2.22387234],
       [ 0.07045882, -0.83822237],
       [-0.77790391, -0.69071475],
       [-0.78269037, -0.04552835],
       [ 0.27825373,  0.38887175],
       [ 0.8679447 ,  0.2420283 ],
       [-2.21409337,  0.8665459 ],
       [-0.18663918, -0.62660464],
       [-0.63554651,  1.20986888]])

[bthirion]

Unless import os.path as op I agree with all changes.
Can anybody else review ?

[agramfort]

let me know if I should revert the op...

[bthirion]

Let's say it's a matter of taste ;-) ... I'm not a purist...

[lesteve]

Use frame_times = np.arange(n_scans) * tr as above.

[lesteve]

Other than the two minor comments, this looks fine to me.

[agramfort]

comments addressed

[lesteve]

Merging, thanks a lot!