# 7. Generate the Q-transform of a `TimeSeries`

¶

One of the most useful tools for filtering and visualising short-duration
features in a `TimeSeries`

is the
Q-transform.
This is regularly used by the Detector Characterization working groups of
the LIGO Scientific Collaboration and the Virgo Collaboration to produce
high-resolution time-frequency maps of transient noise (glitches) and
potential gravitational-wave signals.

This algorithm was used to visualise the first ever gravitational-wave detection GW150914, so we can reproduce that result (bottom panel of figure 1) here.

First, we need to download the `TimeSeries`

record for the H1 strain
measurement from GWOSC:

```
from gwpy.timeseries import TimeSeries
data = TimeSeries.fetch_open_data('H1', 1126259446, 1126259478)
```

Next, we generate the `q_transform`

of these data:

```
qspecgram = data.q_transform(outseg=(1126259462.2, 1126259462.5))
```

Note

We can save memory by focusing on a specific window around the
interesting time. The `outseg`

keyword argument returns a `Spectrogram`

that is only as long as we need it to be.

Now, we can plot the resulting `Spectrogram`

:

```
plot = qspecgram.plot(figsize=[8, 4])
ax = plot.gca()
ax.set_xscale('seconds')
ax.set_yscale('log')
ax.set_ylim(20, 500)
ax.set_ylabel('Frequency [Hz]')
ax.grid(True, axis='y', which='both')
ax.colorbar(cmap='viridis', label='Normalized energy')
plot.show()
```

(`png`

)

Here we can clearly see the trace of a compact binary coalescence, specifically a binary black hole merger! For more details on this result, please see http://www.ligo.org/science/Publication-GW150914/.