# 2. Plotting a normalised `Spectrogram`

¶

The previous example showed how to
generate and display a `Spectrogram`

of the LIGO-Hanford
strain data around the GW150914 event.

However, because of the shape of the LIGO sensitivity curve, picking out features in the most sensitive frequency band (a few hundred Hertz) is very hard.

We can normalise our `Spectrogram`

to highligh those
features.

Again, we import the `TimeSeries`

and call
`TimeSeries.fetch_open_data()`

the download the strain
data for the LIGO-Hanford interferometer

```
from gwpy.timeseries import TimeSeries
data = TimeSeries.fetch_open_data(
'H1', 'Sep 14 2015 09:45', 'Sep 14 2015 09:55')
```

Next, we can calculate a `Spectrogram`

using the
`spectrogram()`

method of the `TimeSeries`

over a 2-second stride
with a 1-second FFT and # .5-second overlap (50%):

```
specgram = data.spectrogram(2, fftlength=1, overlap=.5) ** (1/2.)
```

and can normalise it against the overall median ASD by calling the
`ratio()`

method:

```
normalised = specgram.ratio('median')
```

Finally, we can make a plot using the
`plot()`

method

```
plot = normalised.plot(norm='log', vmin=.1, vmax=10, cmap='Spectral_r')
ax = plot.gca()
ax.set_yscale('log')
ax.set_ylim(10, 2000)
ax.colorbar(label='Relative amplitude')
plot.show()
```

(`png`

)

Even with a normalised spectrogram, the resolution is such that a signal as short as that of GW150914 is impossible to see. The next example uses a high-resolution spectrogram method to zoom in around the exact time of the signal.