1. Files and channels

Download this page as a Jupyter notebook

Opening a Bluelake HDF5 file is very simple:

from lumicks import pylake

file = pylake.File("example.h5")

1.1. Contents

To see a textual representation of the contents of a file:

>>> print(file)
File root metadata:
- Bluelake version: 1.3.1
- Experiment: Example
- Description: Collecting example data for Pylake
- GUID: {1A8024D2-C49B-48FF-B183-2FDF0065F26D}
- Export time (ns): 1531162366497820300
- File format version: 1

Calibration:
  1:
    Force 1x
    Force 1y
    Force 2x
    Force 2y
    JSON:
    - Data type: object
    - Size: 1
Force HF:
  Force 1x:
  - Data type: float64
  - Size: 706251
  Force 1y:
  - Data type: float64
  - Size: 706251
  Force 2x:
  - Data type: float64
  - Size: 706251
  Force 2y:
  - Data type: float64
  - Size: 706251
Info wave:
  Info wave:
  - Data type: uint8
  - Size: 706251
Marker:
  FRAP 3:
  - Data type: object
  - Size: 1
Photon count:
  Blue:
  - Data type: uint32
  - Size: 706251
  Green:
  - Data type: uint32
  - Size: 706251
  Red:
  - Data type: uint32
  - Size: 706251
Scan:
  reference:
  - Data type: object
  - Size: 1
  bleach:
  - Data type: object
  - Size: 1
  imaging:
  - Data type: object
  - Size: 1

For a listing of more specific timeline items:

>> list(file.fdcurves)
['baseline', '1', '2']

>>> list(file.scans)
['reference', 'bleach', 'imaging']

>>> list(file.kymos)
['5', '6', '7']

They can also be printed to get more information:

>>> print(file.scans)
{'reference': Scan(pixels=(67, 195)),
 'bleach': Scan(pixels=(20, 19)),
 'imaging': Scan(pixels=(20, 19))}

1.2. Channels

Just like the Bluelake timeline, exported HDF5 files contain multiple channels of data. They can be easily accessed as shown below:

file.force1x.plot()
plt.savefig("force1x.png")

The channels have a few convenient methods, like .plot() which make it easy to preview the contents, but you can also always access the raw data directly:

f1x_data = file.force1x.data
f1x_timestamps = file.force1x.timestamps
plt.plot(f1x_timestamps, f1x_data)

The above examples use the force1x channel. A full list of available channels can be found on the File reference page.

By default, entire channels are returned from a file:

everything = file.force1x
everything.plot()

But channels can easily be sliced:

# Get the data between 1 and 1.5 seconds
part = file.force1x['1s':'1.5s']
part.plot()
# Or manually
f1x_data = part.data
f1x_timestamps = part.timestamps
plt.plot(f1x_timestamps, f1x_data)

# More slicing examples
a = file.force1x[:'-5s']  # everything except the last 5 seconds
b = file.force1x['-1m':]  # take the last minute
c = file.force1x['-1m':'-500ms']  # last minute except the last 0.5 seconds
d = file.force1x['1.2s':'-4s']  # between 1.2 seconds and 4 seconds from the end
e = file.force1x['5.7m':'1h 40m']  # 5.7 minutes to an hour and 40 minutes

# Subslicing is also possible
a = file.force1x['1s':]  # from 1 second to the end of the file
b = a['1s':]  # 1 second relative to the start of slice `a`
              # --> `b` starts at 2 seconds relative to the beginning of the file

Note that channels are indexed in time units using numbers with suffixes. The possible suffixes are d, h, m, s, ms, us, ns, corresponding to day, hour, minute, second, millisecond, microsecond and nanosecond. This indexing only applies to channels slices. Once you access the raw data, those are regular arrays which use regular array indexing:

channel_slice = file.force1x['1.5s':'20s']  # timestamps
data_slice = file.force1x.data[20:40]  # indices into the array