ColdTOFU package¶
Subpackages¶
Submodules¶
ColdTOFU.AndorSifReader module¶
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class
ColdTOFU.AndorSifReader.AndorSifFile(filename)[source]¶ Bases:
objectThis is main class to handle the reading of sif files. All the work is done in the constructor, the sif file is opened, data is read and the file is closed. This is adopted from https://github.com/ardiloot/AndorSifReader with modifications.
- Parameters
filename – str, Path to .sif file to read
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signal¶ _SifFrameInstance of the_SifFramehelper class to store signal information.
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data¶ ndarray, Convenience property linking to signal.data.
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props¶ dict, Convenience property linking to signal.props.
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ANTSIF_PROPS= ['Type', 'Active', 'Version', 'Time', 'FormattedTime', 'FileName', 'Temperature', 'UnstabalizedTemperature', 'Head', 'HeadModel', 'StoreType', 'DataType', 'SIDisplacement', 'SINumberSubFrames', 'PixelReadOutTime', 'TrackHeight', 'ReadPattern', 'ReadPatternFullName', 'ShutterDelay', 'CentreRow', 'RowOffset', 'Operation', 'Mode', 'ModeFullName', 'TriggerSource', 'TriggerSourceFullName', 'TriggerLevel', 'ExposureTime', 'Delay', 'IntegrationCycleTime', 'NumberIntegrations', 'KineticCycleTime', 'FlipX', 'FlipY', 'Clock', 'AClock', 'IOC', 'Frequency', 'NumberPulses', 'FrameTransferAcquisitionMode', 'BaselineClamp', 'PreScan', 'EMRealGain', 'BaselineOffset', 'SWVersion', 'SWVersionEx', 'MCP', 'Gain', 'VerticalClockAmp', 'VerticalShiftSpeed', 'OutputAmplifier', 'PreAmplifierGain', 'Serial', 'DetectorFormatX', 'DetectorFormatZ', 'NumberImages', 'NumberSubImages', 'SubImageHBin', 'SubImageVBin', 'SubImageLeft', 'SubImageRight', 'SubImageTop', 'SubImageBottom', 'Baseline', 'CCDLeft', 'CCDRight', 'CCDTop', 'CCDBottom', 'Sensitivity', 'DetectionWavelength', 'CountConvertMode', 'IsCountConvert', 'XAxisType', 'XAxisUnit', 'YAxisType', 'YAxisUnit', 'ZAxisType', 'ZAxisUnit', 'UserText', 'IsPhotonCountingEnabled', 'NumberThresholds', 'Threshold1', 'Threshold2', 'Threshold3', 'Threshold4', 'AveragingFilterMode', 'AveragingFactor', 'FrameCount', 'NoiseFilter', 'Threshold', 'TimeStamp']¶
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ATSIF_AT_32= 1073741826¶
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ATSIF_AT_64= 1073741828¶
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ATSIF_AT_8= 1073741824¶
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ATSIF_AT_U32= 1073741827¶
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ATSIF_AT_U64= 1073741829¶
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ATSIF_AT_U8= 1¶
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ATSIF_Background= 1073741826¶
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ATSIF_CONV= {1: <class 'numpy.uint8'>, 1073741824: <class 'numpy.int8'>, 1073741826: <class 'numpy.int32'>, 1073741827: <class 'numpy.uint32'>, 1073741828: <class 'numpy.int64'>, 1073741829: <class 'numpy.uint64'>, 1073741830: <class 'float'>, 1073741831: <class 'numpy.float64'>, 1073741832: <function AndorSifFile.<lambda>>}¶
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ATSIF_CalibX= 1073741824¶
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ATSIF_CalibY= 1073741825¶
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ATSIF_CalibZ= 1073741826¶
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ATSIF_Double= 1073741831¶
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ATSIF_Float= 1073741830¶
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ATSIF_Live= 1073741827¶
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ATSIF_ReadAll= 1073741824¶
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ATSIF_ReadHeaderOnly= 1073741825¶
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ATSIF_Reference= 1073741825¶
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ATSIF_Signal= 1073741824¶
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ATSIF_Source= 1073741828¶
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ATSIF_String= 1073741832¶
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property
data¶
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property
props¶
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class
ColdTOFU.AndorSifReader.SifError[source]¶ Bases:
objectThis is a helper class to handle error codes produced by SIFReaderSDK.
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ATSIF_DATA_NOT_PRESENT= 'ATSIF_DATA_NOT_PRESENT'¶
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ATSIF_FILE_ACCESS_ERROR= 'ATSIF_FILE_ACCESS_ERROR'¶
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ATSIF_FILE_NOT_FOUND= 'ATSIF_FILE_NOT_FOUND'¶
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ATSIF_NO_SIF_LOADED= 'ATSIF_NO_SIF_LOADED'¶
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ATSIF_P1INVALID= 'ATSIF_P1INVALID'¶
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ATSIF_P2INVALID= 'ATSIF_P2INVALID'¶
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ATSIF_P3INVALID= 'ATSIF_P3INVALID'¶
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ATSIF_P4INVALID= 'ATSIF_P4INVALID'¶
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ATSIF_P5INVALID= 'ATSIF_P5INVALID'¶
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ATSIF_P6INVALID= 'ATSIF_P6INVALID'¶
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ATSIF_P7INVALID= 'ATSIF_P7INVALID'¶
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ATSIF_P8INVALID= 'ATSIF_P8INVALID'¶
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ATSIF_SIF_FORMAT_ERROR= 'ATSIF_SIF_FORMAT_ERROR'¶
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ATSIF_SUCCESS= 'ATSIF_SUCCESS'¶
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ERROR_STR= {22002: 'ATSIF_SUCCESS', 22003: 'ATSIF_SIF_FORMAT_ERROR', 22004: 'ATSIF_NO_SIF_LOADED', 22005: 'ATSIF_FILE_NOT_FOUND', 22006: 'ATSIF_FILE_ACCESS_ERROR', 22007: 'ATSIF_DATA_NOT_PRESENT', 22101: 'ATSIF_P1INVALID', 22102: 'ATSIF_P2INVALID', 22103: 'ATSIF_P3INVALID', 22104: 'ATSIF_P4INVALID', 22105: 'ATSIF_P5INVALID', 22106: 'ATSIF_P6INVALID', 22107: 'ATSIF_P7INVALID', 22108: 'ATSIF_P8INVALID'}¶
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ColdTOFU.Images module¶
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class
ColdTOFU.Images.FluorescenceImage(filePath)[source]¶ Bases:
objectThe main class to extract relevant information from the fluorescence image sequences that are obtained in the experiment. The multiple image sequence consists of fluorescence signal image and a background image acquired immediately after the signal from the atomic cloud for as many runs of the experiment.
- Parameters
filePath – str, Path to the multiple-image file
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filePath¶ str, Path to the multiple-image file
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im¶ PIL.TiffImagePlugin.TiffImageFile or AndorSifFile._SifFrames
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n¶ int, total number of data = number of images/3
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frames¶ ndarray, all the frames in the image file
dict, the named tag dictionary of the TiffImage or properties of sif file
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averagedSignal(nAveraging, truncate=())[source]¶ Calculates the average signal with nAveraging being the Superloop in the experiment and finds the fluorescence after the averaging.
- Parameters
nAveraging –
- type
int, the repetitions of the experiment
truncate –
- type
tuple or list, the super loops to be ignored.
- Returns
an ndarray of length equal to Serie in the experiment.
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class
ColdTOFU.Images.ShadowImage(filePath)[source]¶ Bases:
objectThe main class to extract relevant information from the shadow imaging sequences that are obtained in the experiment. The multiple image sequence consists of the shadow or the absorption image, image of the incident probe and the image of the background for as many runs of the experiment.
- Parameters
filePath – str, Path to the multiple-image file
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filePath¶ str, Path to the multiple-image file
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ext¶ str, extension of the image file passed.
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im¶ PIL.TiffImagePlugin.TiffImageFile or AndorSifFile._SifFrames
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n¶ int, total number of data = number of images/3
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frames¶ ndarray, all the frames in the image file
dict, the named tag dictionary of the TiffImage or properties of sif file
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transmission¶ ndarray, All the transmission images after subtracting the background
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incidence¶ ndarray, All the images of incident probe after subtracting the background
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OD¶ ndarray, optical depth all the runs of the experiment calculated from the transmission and incidence
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ODaveraged¶ ndarray, OD averaged with averaging like a superloop
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averagedOD¶ ndarray, OD of the averaged signal with averaging on superloop
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averagedOD2¶ ndarray, OD of the averaged signal with averaging on loop
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averagedSignalOD(nAveraging, truncate=())[source]¶ Calculates the average signal with nAveraging being the Superloop in the experiment and finds the optical depth after the averaging.
- Parameters
nAveraging – int, number of averaging to be used.
truncate – tuple or list, the superloops to be ignored while averaging. Default is ().
- Returns
an ndarray of length equal to Serie in the experiment.
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averagedSignalOD2(nAveraging, truncate=())[source]¶ Calculates the average signal with nAveraging being the loops in the experiment and finds the optical depth after the averaging.
- Parameters
nAveraging – int, number of averaging to be used.
truncate – tuple or list, the superloops to be ignored while averaging. Default is ().
- Returns
an ndarray of length equal to Serie in the experiment.
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blueProbeIntensity(ROI)[source]¶ Estimates blue probe intensity and power from the reference image using the losses in imaging system and quantum effeiciency of the camera for red light. rcParams used in calculating the intensity are
quantum efficiency,pixelSize,binningandmagnification.- Parameters
ROI – list, ROI in which to estimate the intensity.
- Returns
intensity (\(\mu W/cm^2\)) and power (\(\mu W\))
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comment(comment)[source]¶ Adds comment to the tif image under the ImageDescription tag and replaces it silently with the new image, if the user has the permissions.
- Parameters
comment – str, comment to be added.
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opticalDepth(xSpan, ySpan)[source]¶ Returns the optical depth calculated from the ShadowImage as an ndarray.
- Parameters
xSpan – list, containing the range of pixels of the image in x direction.
ySpan – list, containing the range of pixels of the image in y direction.
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opticalDepthAveraged(nAveraging)[source]¶ Calculates the optical depth from every triad of the images and the take average with nAveraging as the Superloop in the experiment.
- Parameters
nAveraging – int, number of averaging to be used.
- Returns
an ndarray of length equal to Serie in the experiment.
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plotAveragedSignalOD(nAveraging, ROI, truncate=())[source]¶ Calculates and plots the average signal with nAveraging being the Superloop in the experiment and finds the optical depth after the averaging.
- Parameters
nAveraging – int, number of averaging to be used.
ROI – list, ROI in the images to be plotted.
truncate – tuple or list, the superloops to be ignored while averaging. Default is ().
Returns None.
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redProbeIntensity(ROI)[source]¶ Estimates red probe intensity and power from the reference image using the losses in imaging system and quantum effeiciency of the camera for red light. rcParams used in calculating the intensity are
quantum efficiency,pixelSize,binningandmagnification.- Parameters
ROI – list, ROI in which to estimate the intensity.
- Returns
intensity (\(\mu W/cm^2\)) and power (\(\mu W\))
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class
ColdTOFU.Images.rcParams[source]¶ Bases:
objectA class that is designed to read and update resource parameters of the package. These parameters are usually the hardware parameters that are used while converting certain digital values to real units. Currently these are imaging parameters like magnification, pixel size etc. The idea behind this is that once these parameters are set, these are used by many function and classes throughout the package without having to explicitly pass them. Open the file rcParams.json using the notepad to see the current parameters used throughout the library.
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params¶ dict, current parameters used by the package
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update(key, value)[source]¶ A method to update the rcParams.
- Parameters
key – string, key of the parameter. Careful about the spelling of the key. If its wrong, then you will add another parameter with the wrong spelling instead of updating the desired parameter.
value – value of the parameter to update.
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ColdTOFU.fits module¶
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ColdTOFU.fits.gaussian(x, amplitude, x_0, sigma_x, offset)[source]¶ 1D Gaussian function, \(f(x) = e^{\frac{-(x-x_0)^2}{2\sigma_x^2}}+offset\)
- Parameters
x – 1D array, x variable of the function
amplitude – float, amplitude of the gaussian
xo – float, center of the gaussian
sigma_x – float, width of the gaussian
offset – float, constant offset.
- Returns
1D array, returns f(x)
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ColdTOFU.fits.gaussian2D(X, amplitude, xo, yo, sigma_x, sigma_y, theta, offset)[source]¶ 2D Gaussian function
- Parameters
X – np.meshgrid,
amplitude – float, amplitude
xo – float, x-center
yo – float, y-center
sigma_x – float, \(sigma_1\)
sigma_y – float, \(sigma_2\)
theta – float, angle of tilt
offset – float, offset
- Returns
1d array, flattened 2D array. Reshape it like (X[0], X[0][0]) to get a matrix representation of the gaussian.
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ColdTOFU.fits.gaussian2DFit(image, p0=None, bounds=[(), ()], plot=True, title='')[source]¶ Fits an image with a 2D gaussian.
- Parameters
image – numpy ndarray
p0 – ndarray, initial guess for the fit params in the form of [amplitude, xo, yo, sigma_x, sigma_y, theta, offset]. Default None (fits for OD images).
bounds – tuple of lower bound and upper bound for the fit. Default None (fits for OD images)
plot – bool to show the plot of the fit. Default True.
- Returns
optimized parameters in the same order as p0 pCov: covarience parameters of the fit. Read scipy.optimize.curve_fit for details.
- Return type
pOpt
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ColdTOFU.fits.gaussianFit(x, array, p0=[], bounds=[(), ()], plot=True)[source]¶ Fits the given array to an 1D-gaussian.
- Parameters
x – 1darray, the argument values of the gaussian
array – 1darray, the data to fit to the gaussian
p0 – ndarray, initial guess for the fit params in the form of [amplitude, xo, sigma, offset]. Default is None.
bounds – tuple of lower bound and upper bound for the fit. Default is None.
plot – bool, Default is True
- Returns
optimized parameters in the same order as p0 pCov: covarience parameters of the fit. Read scipy.optimize.curve_fit for details.
- Return type
pOpt
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ColdTOFU.fits.lorentzianFit(x, array, p0=[], bounds=[(), ()], plot=True)[source]¶ Fits the given array to a Lorentzian.
- Parameters
array – 1darray, the data to fit to the lorentzian
p0 – ndarray, initial guess for the fit params in the form of [amplitude, xo, gamma, offset]. Default is [].
bounds – tuple of lower bound and upper bound for the fit. Default is [(), ()].
plot – bool, Default is True
- Returns
optimized parameters in the same order as p0 pCov: covarience parameters of the fit. Read scipy.optimize.curve_fit for details.
- Return type
pOpt
ColdTOFU.jupyterTools module¶
ColdTOFU.numberOfAtoms module¶
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ColdTOFU.numberOfAtoms.numAtomsBlue(image, delta, s=0, plot=True, p0=None, bounds=[(), ()])[source]¶ Calculates number of atoms from blue shadow imaging.
- Parameters
image – a numpy.ndarray, OD from the experiment
delta – a float, detuning of the probe, 2*(AOMFreq-69) MHz
s (optional) – a float, saturation parameter of the probe. Default is 0.
plot (optional) – a bool, flag to plot the gaussian fits if True. Default is True.
p0 (optional) – a list, initial guess parameters corresponding to gaussian2D function
bounds (optional) – list of lists, bounds for parameters in the form [[lower bounds], [upper bounds]].
- Returns
a tuple, (number of atoms from 2D gaussian fit, number of atoms from pixel sum, number density from gaussian fit, sigma_x, sigma_y, amplitude, x0, y0)
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ColdTOFU.numberOfAtoms.numAtomsGeneral(image, delta, wLen, Gamma, s=0, plot=True, p0=None, bounds=[(), ()])[source]¶ Calculates number of atoms from blue shadow imaging.
- Parameters
image – a numpy.ndarray, OD from the experiment
delta – a float, detuning of the probe in MHz
wLen – float, wavelength of the probe in nm
Gamma – flaot, linewidth of the excited state in MHz.
s (optional) – a float, saturation parameter of the probe. Default is 0.
plot (optional) – a bool, flag to plot the gaussian fits if True. Default is True.
p0 (optional) – a list, initial guess parameters corresponding to gaussian2D function
bounds (optional) – list of lists, bounds for parameters in the form [[lower bounds], [upper bounds]].
- Returns
a tuple, (number of atoms from 2D gaussian fit, number of atoms from pixel sum, number density from gaussian fit, sigma_x, sigma_y, amplitude, x0, y0)
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ColdTOFU.numberOfAtoms.numAtomsRed(image, delta, s=0, plot=True, p0=None, bounds=[(), ()])[source]¶ Calculates number of atoms from red shadow imaging.
- Parameters
image – a numpy.ndarray, OD from the experiment
delta – float, detuning of the probe in kHz
s (optional) – a float, saturation parameter of the probe. Default is 0.
plot (optional) – a bool, a flag to plot the gaussian fits if True. Default is True.
p0 (optional) – a list, initial guess parameters corresponding to gaussian2D function
bounds (optional) – list of lists, bounds for parameters in the form [[lower bounds], [upper bounds]].
- Returns
a tuple, (number of atoms from 2D gaussian fit, number of atoms from pixel sum, number density from gaussian fit, sigma_x, sigma_y, amplitude, x0, y0)
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ColdTOFU.numberOfAtoms.temperature(sizes, timeStamps, plot=True)[source]¶ Calculates temperature of the cold gas given size of the cloud at various times of flight.
- Parameters
sizes – 2D array, sizes of the cloud extracted from gaussian2D fit in pixel units. Eg: ([[x1, y1], [x2, y2], [x3, y3]])
timeStamps – array, corresponding times of flight in s.
plot – bool, to plot \(v^2\) vs \(TOF^2\). Default:True.
- Returns
a tuple, (temperature in K along x, temperature in K along y)
ColdTOFU.picoMatTools module¶
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ColdTOFU.picoMatTools.PSD(path, avg=30, channels=['A'])[source]¶ - Reads multiple waveforms .mat files folder exported using picoscope software and
alculates power spectral density (PSD).
- Parameters
path – string, path of the folder to read.
avg – int, number of waveforms in the folder to average.
channels – list of strings, channels of the file to read. Ex: [‘A’, ‘B’]
- Returns
a touple, (frequencies array, list of PSD arrays in the order of channels)
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ColdTOFU.picoMatTools.RIN(path, avg=30, channel='A')[source]¶ Reads multiple waveforms .mat files folder exported using picoscope software and calculates relative intensity noise (RIN).
- Parameters
path – string, path of the folder to read.
avg – int, number of waveforms in the folder to average.
channels – list of strings, channels of the file to read. Ex: [‘A’, ‘B’]
- Returns
a touple, (frequencies array, list of RIN arrays in the order of channels)
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ColdTOFU.picoMatTools.picoMatRead(filePath, channels=['A'])[source]¶ Reads .mat files exported using picoscope software.
- Parameters
filePath – string, path of the file to read.
channels – list of strings, channels of the file to read. Ex: [‘A’, ‘B’]
- Returns
a touple, (time array, list of data arrays)
ColdTOFU.sigma module¶
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ColdTOFU.sigma.sigmaBlue(delta, A, s=0)[source]¶ Calculates the scattering cross-section of Sr for \(^1S_0 \rightarrow ^1P_1\) taking into account the isotope shift w.r.t. A=88 isotope and hyperfine levels present in the excited state.
- Parameters
delta – float, detuning of the probe w.r.t the reference in MHz.
s – float, saturation parameter of the probe, \(I/I_s\).
A – int, mass number of the isotope.
- Returns
A float, Scattering cross-section.
- Comment:
All frequencies are in MHz.
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ColdTOFU.sigma.sigmaGeneral(delta, wLen, Gamma, s=0)[source]¶ Calculates the scattering cross-section of a two-level atom with a weak probe.
- Parameters
delta – float, detuning of the probe w.r.t the reference in MHz.
wLen – float, wavelength of the probe in nm
Gamma – flaot, linewidth of the excited state in MHz.
s – float, saturation parameter of the probe, \(I/I_s\).
- Returns
A float, Scattering cross-section.
- Comment:
All frequencies are in MHz.
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ColdTOFU.sigma.sigmaRed(delta, s=0)[source]¶ Calculates the scattering cross-section of Sr for \(^1S_0 \rightarrow ^3P_1\) taking saturation into account.
- Parameters
delta – float, detuning of the probe w.r.t the reference in kHz.
s – float, saturation parameter of the probe, \(I/I_s\).
- Returns
A float, Scattering cross-section.
- Comment:
All frequencies are in kHz.
ColdTOFU.spectrum module¶
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ColdTOFU.spectrum.bv(f, f0, b0, T, s)[source]¶ Function representing convolution of the lorentzian line shape of the red transition and gaussian maxwell distribution. This is taken from 5.13 from Chang chi’s thesis and added the effect of saturation parameter.
- Parameters
f – numpy.array, frequency vector
f0 – float, resonance frequency or centre of the spectrum
b0 – float, optical depth at resonance at zero temperature
T – float, temperature in micro K
s – float, saturation parameter of the probe, \(I/I_s\).
- Returns
optical depth for frequencies f in the shape f.
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ColdTOFU.spectrum.bvFit(f, array, p0=None, bounds=None)[source]¶ Function to fit spectroscopy data to real line shape of the transition.
- Parameters
f – numpy.array, frequency vector
array – float, optical depth at scan frequencies f
p0 – initial guess for the fit as [f_0, b_0, T (in \(\mu K\)), s]
bounds – bounds for the fit as ([lower bounds], [upper bounds])
- Returns
(pOpt, pCov)
- Return type
a tuple with optimized parameters and covariance ex
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ColdTOFU.spectrum.spectroscopy(ODimages, f, d=4, loss=False, plot=True, fileNum='', savefig=False)[source]¶ Adds the OD of the pixels around the centre and uses the sum to plot the spectrum of the scan corresponding to the given frequencies. This is then fit to a lorentzian to find the center and linewidth.
- Parameters
ODimages – ODimages extracted from ShadowImaging sequences
f – array of frequencies for which the scan is done
d – int, to specify size of the image area to consider around the centre of OD
plot – bool, default is True to specify if the data has to be plotted
fileNum – string, file number (plus any additional description) of the image file for which the analysis is done.
savefig – bool, default is False. Change it to true if you want to save the spectrum as .png
- Returns
a tuple, (amp, centre, gamma, offset) of the lorentzian fit
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ColdTOFU.spectrum.spectroscopyFaddeva(ODimages, f, imaging_params, plot=True, atom_loss=False, fileNum='', savefig=False)[source]¶ Fits od images to a gaussian and uses its amplitude to plot the spectrum of the scan corresponding to the given frequencies. This is fit to \(b_v(\delta)\) from Chang Chi’s thesis to extract temperature in addition to center.
- Parameters
ODimages – ODimages extracted from ShadowImaging sequences
f – array of frequencies for which the scan is done
imaging_params – a dictionary with keys as follows ex: {‘binning’:2, ‘magnification’: 2.2, ‘pixelSize’: 16*micro, ‘saturation’: 1 }
plot – bool, default is True to specify if the data has to be plotted
fileNum – string, file number (plus any additional description) of the image file for which the analysis is done.
savefig – bool, default is False. Change it to true if you want to save the spectrum as .png
- Returns
a tuple, (centre, \(b_0\), T, s) of fit to \(b_v(\delta)\)