Examples¶

Simplest Possible Example¶

This examples contains a detailed explanation of the simplest possible example. Many of the following examples will build of this one.

from cinfdata import Cinfdata
cinfdb = Cinfdata('stm312')
data = cinfdb.get_data(6688)
metadata = cinfdb.get_metadata(6688)

Line 1 imports the cinfdata module. For this to be possible, it must be in your Python path (which is the list of folders that Python can import from). The simplest way to do that is to simple drop the cinfdata.py file in the same folder as your data treatment script.
Line 2 makes a Cinfdata database client object, abbreviated cinfdb[1]. The only mandatory argument to the Cinfdata class is the codename of the setup in this case ‘stm312’.

Line 3 fetches the data …

array([[  1.56250000e-02,   2.09650000e-13],
       [  3.12500000e-02,   1.86725000e-13],
       [  4.68750000e-02,   1.58958000e-13],
       ...,
       [  9.99687500e+01,   7.48633000e-14],
       [  9.99843750e+01,   6.37814000e-14],
       [  1.00000000e+02,   7.38152000e-14]])

Line 4 fetches the metadata as a dictionary …

{'Comment': 'Chamber background,P=9.7E-11torr', 'pass_energy': None, 'pre_wait_time': None,
 'timestep': None, 'year': None, 'file_name': None, 'preamp_range': 0, 'project': None,
 'number_of_scans': None, 'mass_label': 'Mass Scan', 'actual_mass': None,
 'SEM_voltage': 2200.0, u'unixtime': 1418807588L,
 'time': datetime.datetime(2014, 12, 17, 10, 13, 8), 'excitation_energy': None, 'type': 4L,
 'id': 6688L, 'name': None}

Simple Example with Plot¶

Code

To make a plot that uses e.g. the comment fielde of the metadata as the title, Simplest Possible Example can be expanded into:

from cinfdata import Cinfdata
from matplotlib import pyplot as plt

db = Cinfdata('stm312')
spectrum = db.get_data(6688)
metadata = db.get_metadata(6688)

plt.plot(spectrum[:, 0], spectrum[:, 1])
plt.title(metadata['Comment'])
plt.show()

Note

The data comes out the same way in would if it was fetched directly from the database i.e. with x and y being two columns in a numpy array, so they are retrieved individually with the [:, 0] syntax

Simple Example Using the Cache¶

Code

To enable caching of the database results (which is disabled by default) simply instantiate the Cinfdata object with use_caching=True:

from cinfdata import Cinfdata

db = Cinfdata('stm312', use_caching=True)
spectrum = db.get_data(6688)
metadata = db.get_metadata(6688)

Except from the instantiation argument, the usage is exactly the same. If the folder of the cinfdata.py file, there will now be a cache folder with the following content:

cache
└── stm312
    ├── data
    │   └── 6688.npy
    └── infoitem.pickle

A folder for each of the setups being used (stm312 in this case). Under that, there is the data folder, that contains one file (named meaurement_id.npy[2]) for each data set and there is the infoitem.pickle ([#pickle]) that contains all the metadata.

Important

Due to the use of native data saving functionality and the use of pickle, the cache cannot be used across different operating systems or Pythons versions. Only use on one machine and one Python version. If you need to switch machines or Python version you shoule reset the cache.

To reset the cache simply delete the cache folder.

Footnotes

[1]

In general, Python users are encouraged to make descriptive variable names, which often means that they should be written fully out to make the code easier to read. However, it is “allowed” to make a few very short variables, if they are used extremely often (like it is often done with Numpy as np, Pyplot as plt etc.). Besides, cinfdb, is close to readable, ‘db’ is a common abbreaviation for database and all readers should know what Cinf is.

[2]	npy is numpys own save format for arrays. It it very efficient because it contains just a small header, that contains the array dimensions and the data type and then just the raw bytes that describes the numbers.

[3]	pickle is Python serialization format for serialization of (almost) arbitrary arguments. The format is not guarantied to be preserved across Python version, which is one of the reasons that the cache should not be used across Python versions.