dimspy.models package¶

Submodules¶

dimspy.models.peak_matrix module¶

The PeakMatrix data object class.

New in version 0.1.

class dimspy.models.peak_matrix.PeakMatrix(peaklist_ids, peaklist_tags, peaklist_attributes)¶

Bases: object

The PeakMatrix class.

Stores aligned mass spectrometry peaks matrix data. It requires IDs, tags, and attributes from the source peak lists. It uses tags based mask to “hide” the unrelated samples for convenient processing. It utilises the automatically managed flags to “remove” peaks without actually delete them. Therefore the filterings on the peaks are traceable. Normally, PeakMatrix object is created by functions e.g. align_peaks() rather than manual.

Parameters:

peaklist_ids – the IDs of the source peak lists
peaklist_tags – the tags (PeakList_Tags) of the source peak lists
peaklist_attributes – the attributes of the source peak lists. Must be a list or tuple in the format of [(attr_name, attr_matrix), …], where attr_name is name of the attribute, and attr_matrix is the vertically stacked arrtibute values in the shape of samples x peaks. The order of the attributes will be kept in the PeakMatrix. The first two attributes must be “mz” and “intensity”.

>>> pids = [pl.ID for pl in peaklists]
>>> tags = [pl.tags for pl in peaklists]
>>> attrs = [(attr_name, np.vstack([pl[attr_name] for pl in peaklists]))                  for attr_name in peaklists[0].attributes]
>>> pm = PeakMatrix(pids, tags, attrs)

Internally the attribute data is stored in OrderedDict as a list of matrix. An attribute matrix can be illustrated as follows, in which the mask and flags are the same for all attributes. The final row “flags” is automatically calculated based on the manually added flags. It decides which peaks are “removed” i.e. unflagged. Particularly, the “–” indicates no peak in that sample can be aligned into the mz value.

attribute: “mz”

mask	peak_1	peak_2	peak_3	…
False	12.7	14.9	21.0	…
True	–	15.1	21.1
False	12.1	14.7	–
False	12.9	14.8	20.9
…
flag_1	True	False	True	…
flag_2	True	True	False
flags*	True	False	False

Warning

Removing a flag may change the overall “flags”, and cause the unflagged peaks to be flagged again. As most the processes are applied only on the flagged peaks, these peaks, if the others have gone through such process, may have incorrect values.

In principle, setting a flag attribute should be considered as an irreversible process.

Different from the flags, mask should be considered as a more temporary way to hide the unrelated samples. A masked sample (row) will not be used for processing, but its data is still in the attribute matrix. For this reason, the mask_peakmatrix, unmask_peakmatrix, and unmask_all_peakmatrix statements are provided as a more flexible way to set / unset the mask.

mask¶

Property of the mask.

Getter:	returns a deep copy of the mask array
Setter:	sets the mask array. Provide None to unmask all samples
Type:	numpy array

flag_names¶

Property of the flag names.

Getter:	returns a tuple including the names of the manually set flags
Type:	tuple

flags¶

Property of the flags.

Getter:	returns a deep copy of the flags array
Type:	numpy array

attributes¶

Property of the attribute names.

Getter:	returns a tuple including the names of the attribute matrix
Type:	tuple

peaklist_ids¶

Property of the source peaklist IDs.

Getter:	returns a tuple including the IDs of the source peaklists
Type:	tuple

peaklist_tags¶

Property of the source peaklist tags.

Getter:	returns a tuple including the Peaklist_Tags objects of the source peaklists
Type:	tuple

peaklist_tag_types¶

Property of the source peaklist tag types.

Getter:	returns a tuple including the types of the typed tags of the source peaklists
Type:	tuple

peaklist_tag_values¶

Property of the source peaklist tag values.

Getter:	returns a tuple including the values of the source peaklists tags, both typed and untyped
Type:	tuple

shape¶

Property of the peak matrix shape.

Getter:	returns the shape of the attribute matrix
Type:	tuple

full_shape¶

Property of the peak matrix full shape.

Getter:	returns the full shape of the attribute matrix, i.e., ignore mask and flags
Type:	tuple

present¶

Property of the present array.

Getter:	returns the present array, indicating how many peaks are aligned in each mz value
Type:	numpy array

present_matrix¶

Property of the present matrix.

Getter:	returns the present matrix, indicating whether a sample has peak(s) aligned in each mz value
Type:	numpy array

>>> print pm.present_matrix
array([[ True,  True,  True,  True, False],
       [ True,  True, False, False,  True],
       [ True,  True,  True,  True,  True],
       [False,  True, False,  True,  True],])
>>> print pm.present
array([3, 4, 2, 3, 3])

fraction¶

Property of the fraction array.

Getter:	returns the fraction array, indicating the ratio of present peaks on each mz value
Type:	numpy array

>>> print pm.present
array([3, 4, 2, 3, 3])
>>> print pm.shape[0]
4
>>> print pm.fraction
array([0.75, 1.0, 0.5, 0.75, 0.75])

missing_values¶

Property of the missing values array.

Getter:	returns the missing values array, indicating the number of unaligned peaks on each sample
Type:	numpy array

>>> print pm.present_matrix
array([[ True,  True,  True,  True, False],
       [ True,  True, False, False,  True],
       [ True,  True,  True,  True,  True],
       [False,  True, False,  True,  True],])
>>> print pm.missing_values
array([1, 2, 0, 2])

occurrence¶

Property of the occurrence array.

Getter:	returns the occurrence array, indicating the total number of peaks (including peaks in the same sample) aliged in each mz value. This property is valid only when the intra_count attribute matrix is available
Type:	numpy array

>>> print pm.attr_matrix('intra_count')
array([[ 2,  1,  1,  1,  0],
       [ 1,  1,  0,  0,  1],
       [ 1,  3,  1,  2,  1],
       [ 0,  1,  0,  1,  1],])
>>> print pm.occurrence
array([ 4,  6,  2,  4,  3])

purity¶

Property of the purity level array.

Getter:	returns the purity array, indicating the ratio of only one peak in each sample being aligned in each mz value. This property is valid only when the intra_count attribute matrix is available
Type:	numpy array

>>> print pm.attr_matrix('intra_count')
array([[ 2,  1,  1,  1,  0],
       [ 1,  1,  0,  0,  1],
       [ 1,  3,  1,  2,  1],
       [ 0,  1,  0,  1,  1],])
>>> print pm.purity
array([ 0.667,  0.75,  1.0,  0.667,  1.0])

mz_matrix¶

Property of the mz matrix.

Getter:	returns the mz attribute matrix, unmasked and flagged values only
Type:	numpy array

intensity_matrix¶

Property of the intensity matrix.

Getter:	returns the intensity attribute matrix, unmasked and flagged values only
Type:	numpy array

mz_mean_vector¶

Property of the mz mean values array.

Getter:	returns the mean values array of the mz attribute matrix, unmasked and flagged values only
Type:	numpy array

intensity_mean_vector¶

Property of the intensity mean values array.

Getter:	returns the mean values array of the intensity attribute matrix, unmasked and flagged values only
Type:	numpy array

tags_of(tag_type=None)¶

Obtains tags of the peaklist_tags with particular tag type.

Parameters:	tag_type – the type of the returning tags. Provide None to obtain untyped tags
Return type:	tuple

mask_tags(*args, **kwargs)¶

Masks samples with particular tags.

Parameters:	args – target tag values, both typed and untyped kwargs – target typed tag types and values override – whether to override the current mask, default = False
Return type:	PeakMatrix object (self)

This function will mask samples with ALL the tags. To match ANY of the tags, use cascade form instead.

>>> pm.mask_tags('qc', plate = 1)
(will mask all QC samples on plate 1)
>>> pm.mask_tags('qc').mask_tags(plate = 1)
(will mask QC samples and all samples on plate 1)

unmask_tags(*args, **kwargs)¶

Unmasks samples with particular tags.

Parameters:	args – target tag values, both typed and untyped kwargs – target typed tag types and values override – whether to override the current mask, default = False
Return type:	PeakMatrix object (self)

This function will unmask samples with ALL the tags. To unmask ANY of the tags, use cascade form instead.

>>> pm.mask = [True] * pm.full_shape[0]
>>> pm.unmask_tags('qc', plate = 1)
(will unmask all QC samples on plate 1)
>>> pm.unmask_tags('qc').unmask_tags(plate = 1)
(will unmask QC samples and all samples on plate 1)

add_flag(flag_name, flag_values, flagged_only=True)¶

Adds a flag to the peak matrix peaks.

Parameters:	flag_name – name of the flag, it must be unique and not equal to “flags” flag_values – values of the flag. It must have a length of pm.shape[1] if flagged_only = True, or pm.full_shape[1] if flagged_only = False flagged_only – whether to set the flagged peaks only. Default = True, and the values of the unflagged peaks are set to False

The overall flags property will be automatically recalculated.

drop_flag(flag_name)¶

Drops a existing flag from the peak matrix.

Parameters:	flag_name – name of the flag to drop. It must exist and not equal to “flags”

The overall flags property will be automatically recalculated.

flag_values(flag_name)¶

Obtains values of an existing flag.

Parameters:	flag_name – name of the target flag. It must exist and not equal to “flags”
Return type:	numpy array

property(prop_name, flagged_only=True)¶

Obtains an existing attribute matrix.

Parameters:	prop_name – name of the target property. Valid properties include ‘present’, ‘present_matrix’, ‘fraction’, ‘missing_values’, ‘occurrence’, and ‘purity’ flagged_only – whether to return the flagged values only. Default = True
Return type:	numpy array

attr_matrix(attr_name, flagged_only=True)¶

Obtains an existing attribute matrix.

Parameters:	attr_name – name of the target attribute flagged_only – whether to return the flagged values only. Default = True
Return type:	numpy array

attr_mean_vector(attr_name, flagged_only=True)¶

Obtains the mean array of an existing attribute matrix.

Parameters:	attr_name – name of the target attribute flagged_only – whether to return the mean array of the flagged values only. Default = True
Return type:	numpy array

Noting that only the “present” peaks will be used for mean values calculation. If the attribute matrix has a string / unicode data type, the values in each column will be concatenated.

remove_samples(sample_ids, masked_only=True)¶

Removes samples from the peak matrix.

Parameters:	sample_ids – the indices of the samples to remove masked_only – whether the indices are for unmasked samples or all samples. Default = True
Return type:	PeakMatrix object (self)

remove_peaks(peak_ids, flagged_only=True)¶

Removes peaks from the peak matrix.

Parameters:	peak_ids – the indices of the peaks to remove flagged_only – whether the indices are for flagged peaks or all peaks. Default = True
Return type:	PeakMatrix object (self)

is_empty()¶

Checks whether the peak matrix is empty under the current mask and flags.

Return type:	bool

extract_peaklist(peaklist_id)¶

Extracts one peaklist from the peak matrix.

Parameters:	peaklist_id – ID of the peaklist to extract
Return type:	PeakList object

Only the “present” peaks will be included in the result peaklist.

extract_peaklists()¶

Extracts all peaklists from the peak matrix.

Return type:	list

to_peaklist(ID)¶

Averages the peak matrix into a single peaklist.

Parameters:	ID – ID of the merged peaklist
Return type:	PeakList object

Only the “present” peaks will be included in the result peaklist. The new peaklist will only contain the following attributes: mz, intensity, present, fraction, rsd, occurence, and purity.

to_str(attr_name='intensity', delimiter='\t', transpose=False, comprehensive=True)¶

Exports the peak matrix to a string.

Parameters:	attr_name – name of the attribute matrix for exporting. Default = ‘intensity’ delimiter – delimiter to separate the matrix. Default = ‘ ‘, i.e., TSV format transpose – whether to transpose the matrix. Default = False comprehensive – whether to include comprehensive info, e.g., mask, flags, present, rsd etc. Default = True
Return type:	str

class dimspy.models.peak_matrix.mask_peakmatrix(pm, *args, **kwargs)¶

The mask_peakmatrix statement.

Temporary mask the peak matrix with particular tags. Within the statement the samples can be motified or removed. After leaving the statement the original mask will be recoverd.

Parameters:	pm – the target peak matrix override – whether to override the current mask, default = True args – target tag values, both typed and untyped kwargs – target typed tag types and values
Return type:	PeakMatrix object

>>> print pm.peaklist_ids
('sample_1', 'sample_2', 'qc_1', 'sample_3', 'sample_4', 'qc_2')
>>> with mask_peakmatrix(pm., 'qc') as m: print m.peaklist_ids
('sample_1', 'sample_2', 'sample_3', 'sample_4')
>>> print pm.peaklist_ids
('sample_1', 'sample_2', 'qc_1', 'sample_3', 'sample_4', 'qc_2')

class dimspy.models.peak_matrix.unmask_peakmatrix(pm, *args, **kwargs)¶

The unmask_peakmatrix statement.

Temporary unmask the peak matrix with particular tags. Within the statement the samples can be motified or removed. After leaving the statement the original mask will be recoverd.

Parameters:	pm – the target peak matrix override – whether to override the current mask, default = True args – target tag values, both typed and untyped kwargs – target typed tag types and values
Return type:	PeakMatrix object

>>> print pm.peaklist_ids
('sample_1', 'sample_2', 'qc_1', 'sample_3', 'sample_4', 'qc_2')
>>> with unmask_peakmatrix(pm, 'qc') as m: print m.peaklist_ids
('qc_1', 'qc_2') # no need to set pm.mask to True
>>> print pm.peaklist_ids
('sample_1', 'sample_2', 'qc_1', 'sample_3', 'sample_4', 'qc_2')

class dimspy.models.peak_matrix.mask_all_peakmatrix(pm)¶

The mask_all_peakmatrix statement.

Temporary mask all the peak matrix samples. Within the statement the samples can be motified or removed. After leaving the statement the original mask will be recoverd.

Parameters:	pm – the target peak matrix
Return type:	PeakMatrix object

>>> print pm.peaklist_ids
('sample_1', 'sample_2', 'qc_1', 'sample_3', 'sample_4', 'qc_2')
>>> with mask_all_peakmatrix(pm) as m: print m.peaklist_ids
()
>>> print pm.peaklist_ids
('sample_1', 'sample_2', 'qc_1', 'sample_3', 'sample_4', 'qc_2')

class dimspy.models.peak_matrix.unmask_all_peakmatrix(pm)¶

The unmask_all_peakmatrix statement.

Temporary unmask all the peak matrix samples. Within the statement the samples can be motified or removed. After leaving the statement the original mask will be recoverd.

Parameters:	pm – the target peak matrix
Return type:	PeakMatrix object

>>> print pm.peaklist_ids
('sample_1', 'sample_2', 'qc_1', 'sample_3', 'sample_4', 'qc_2')
>>> with unmask_all_peakmatrix(pm) as m: print m.peaklist_ids
('sample_1', 'sample_2', 'qc_1', 'sample_3', 'sample_4', 'qc_2')
>>> print pm.peaklist_ids
('sample_1', 'sample_2', 'qc_1', 'sample_3', 'sample_4', 'qc_2')

dimspy.models.peak_matrix.peak_matrix_rsd(pm, *args, **kwargs)¶

Calculate the QC samples’ relative standard deviation (RSD) array for PeakMatrix.

Parameters:	pm – target peak matrix object args – untyped tag label for RSD calculation, no value = calculate over all samples kwargs – typed tag label for RSD calculation, , no value = calculate over all samples flagged_only – whether to calculate on flagged peaks only. Default = True
Type:	numpy array

The RSD is calculated as:

>>> rsd = std(pm.intensity_matrix, axis = 0, ddof = 1) / mean(pm.intensity_matrix, axis = 0) * 100

Noting that the means delta degrees of freedom (ddof) is set to 1 for standard deviation calculation. Moreover, only the “present” peaks will be used for calculation. If a column has less than 2 peaks, the corresponding rsd value will be set to np.nan.

dimspy.models.peaklist module¶

The PeakList data object class.

New in version 0.1.

class dimspy.models.peaklist.PeakList(ID, mz, intensity, **metadata)¶

Bases: object

The PeakList class.

Stores mass spectrometry peaks list data. It requires an ID, mz values, and intensities. It can store extra peak attributes e.g. SNRs, and peaklist tags and metadata. It utilises the automatically managed flags to “remove” or “retain” peaks without actually delete them. Therefore the filterings on the peaks are traceable.

Parameters:	ID – the ID of the peaklist data, unique string or integer value is recommended mz – mz values of all the peaks. Must in the ascending order intensity – intensities of all the peaks. Must have the same size as mz kwargs – key-value pairs of the peaklist metadata

>>> mz_values = np.random.uniform(100, 1200, size = 100)
>>> int_values = np.random.normal(60, 10, size = 100)
>>> peaks = PeakList('dummy', mz_values, int_values, description = 'a dummy peaklist')

Internally the peaklist data is stored by using numpy structured array namely the attribute talbe (this may change in the future):

mz	intensity	snr	snr_flag	…	flags*
102.5	21.7	10.5	True	…	True
111.7	12.3	5.1	False		False
126.3	98.1	31.7	True		True
133.1	68.9	12.6	True		True
…

Each column is called an attribute. The first two attributes are fixed as “mz” and “intensity”. They cannot be added or removed as the others. The last “attribute” is the “flags”, which is fact stored separately. The “flags” column is calculated automatically according to all the manually set flag attributes, e.g., the “snr_flag”. It can only be changed by the class itself. The unflagged peaks are considered as “removed”. They are kept internally mainly for visualization and tracing purposes.

Warning

Removing a flag attribute may change the “flags” column, and cause the unflagged peaks to be flagged again. As most the processes are applied only on the flagged peaks, these peaks, if the others have gone through such process, may have incorrect values.

In principle, setting a flag attribute should be considered as an irreversible process.

ID¶

Property of the peaklist ID.

Getter:	returns the peaklist ID
Type:	same as input ID

size¶

Property of the peaklist size.

Getter:	returns the flagged peaklist size
Type:	int

full_size¶

Property of the peaklist full size.

Getter:	returns the full peaklist size, i.e., including the unflagged peaks
Type:	int

shape¶

Property of the peaklist attributes table shape.

Getter:	returns the attibutes table shape, i.e., peaks number x attributes number. The “flags” column does not count
Type:	tuple

full_shape¶

Property of the peaklist full attributes table shape.

Getter:	returns the full attibutes table shape, including the unflagged peaks
Type:	tuple

metadata¶

Property of the peaklist metadata.

Getter:	returns an access interface to the peaklist metadata object
Type:	PeakList_Metadata object

tags¶

Property of the peaklist tags.

Getter:	returns an access interface to the peaklist tags object
Type:	PeakList_Tags object

attributes¶

Property of the attribute names.

Getter:	returns a tuple of the attribute names
Type:	tuple

flag_attributes¶

Property of the flag attribute names.

Getter:	returns a tuple of the flag attribute names
Type:	tuple

peaks¶

Property of the attribute table.

Getter:	returns a deep copy of the flagged attribute table
Type:	numpy structured array

flags¶

Property of the flags.

Getter:	returns a deep copy of the flags array
Type:	numpy array

dtable¶

Property of the overall attribute table.

Getter:	returns the original attribute table
Type:	numpy structured array

Warning

This property directly accesses the internal attribute table. Be careful when manipulating the data, particularly pay attention to the potential side-effects.

sort_peaks_order()¶: Sorts peaklist mz values into ascending order.

Note

This method will be called automatically every time the mz values are changed.

calculate_flags()¶

Re-calculates the flags according to the flag attributes.

Return type:	numpy array

Note

This method will be called automatically every time a flag attribute is added, removed, or changed.

has_attribute(attr_name)¶

Checks whether there exists an attribute in the table.

Parameters:	attr_name – the attribute name for checking
Return type:	bool

add_attribute(attr_name, attr_value, attr_dtype=None, is_flag=False, on_index=None, flagged_only=True, invalid_value=nan)¶

Adds an new attribute to the PeakList attribute table.

Parameters:

attr_name – the name of the new attribute, must be a string
attr_value – the values of the new attribute. It’s size must equals to PeakList.size (if flagged_only == True), or PeakList.full_size (if flagged_only == False)
attr_dtype – the data type of the new attribute. If it is set to None, the PeakList will try to detect the data type based on attr_value. If the detection failed it will take the “object” type. Default = None
is_flag – whether the new attribute is a flag attribute, i.e., will be used in flags calculation. Default = False
on_index – insert the new attribute on a specific column. It can’t be 0 or 1, as the first two attributes are fixed as mz and intensity. Setting to None means to put it to the last column. Default = None
flagged_only – whether the attr_value is set to the flagged peaks or all peaks. Default = True
invalid_value – if flagged_only is set to True, this value will be assigned to the unflagged peaks. The actual value depends on the attribute data type. For instance, on a boolean attribute invalid_value = 0 will be converted to False. Default = numpy.nan

Return type:

PeakList object (self)

drop_attribute(attr_name)¶

Drops an existing attribute.

Parameters:	attr_name – the attribute name to drop. It cannot be mz, intensity, or flags
Return type:	PeakList object (self)

set_attribute(attr_name, attr_value, flagged_only=True, unsorted_mz=False)¶

Sets values to an existing attribute.

Parameters:

attr_name – the attribute to set values
attr_value – the new attribute values, It’s size must equals to PeakList.size (if flagged_only == True), or PeakList.full_size (if flagged_only == False)
flagged_only – whether the attr_value is set to the flagged peaks or all peaks. Default = True
unsorted_mz – whether the attr_value contains unsorted mz values. This parameter is valid only when attr_name == “mz”. Default = False

Return type:

PeakList object (self)

get_attribute(attr_name, flagged_only=True)¶

Gets values of an existing attribute.

Parameters:	attr_name – the attribute to get values flagged_only – whether to return the values of flagged peaks or all peaks. Default = True
Return type:	numpy array

set_peak(peak_index, peak_value, flagged_only=True)¶

Sets values to a peak.

Parameters:	peak_index – the index of the peak to set values peak_value – the new peak values. Must contain values for all the attributes (not including flags) flagged_only – whether the peak_value is set to the index of flagged peaks or all peaks. Default = True
Return type:	PeakList object (self)

>>> print peaks
mz, intensity, snr, flags
10, 10, 10, True
20, 20, 20, True
30, 30, 30, False
40, 40, 40, True
>>> print peaks.set_peak(2, [50, 50, 50], flagged_only = True)
mz, intensity, snr, flags
10, 10, 10, True
20, 20, 20, True
30, 30, 30, False
50, 50, 50, True
>>> print peaks.set_peak(2, [40, 40, 40], flagged_only = False)
mz, intensity, snr, flags
10, 10, 10, True
20, 20, 20, True
40, 40, 40, False
50, 50, 50, True

get_peak(peak_index, flagged_only=True)¶

Gets values of a peak.

Parameters:	peak_index – the index of the peak to get values flagged_only – whether the values are taken from the index of flagged peaks or all peaks. Default = True
Return type:	numpy array

insert_peak(peak_value)¶

Insert a new peak.

Parameters:	peak_value – the values of the new peak. Must contain values for all the attributes. It’s position depends on the mz value, i.e., the 1st value of the input
Return type:	PeakList object (self)

remove_peak(peak_index, flagged_only=True)¶

Remove an existing peak.

Parameters:	peak_index – the index of the peak to remove flagged_only – whether the index is for flagged peaks or all peaks. Default = True
Return type:	PeakList object (self)

cleanup_unflagged_peaks(flag_name=None)¶

Remove unflagged peaks.

Parameters:	flag_name – remove peaks unflagged by this flag attribute. Setting None means to remove peaks unflagged by the overall flags. Default = None
Return type:	PeakList object (self)

>>> print peaks
mz, intensity, intensity_flag, snr, snr_flag, flags
10, 70, True, 10, False, False
20, 60, True, 20, True, True
30, 50, False, 30, True, False
40, 40, False, 40, True, False
>>> print peaks.cleanup_unflagged_peaks('snr_flag')
mz, intensity, intensity_flag, snr, snr_flag, flags
20, 60, True, 20, True, True
30, 50, False, 30, True, False
40, 40, False, 40, True, False
>>> print peaks.cleanup_unflagged_peaks()
mz, intensity, intensity_flag, snr, snr_flag, flags
20, 60, True, 20, True, True

to_list()¶

Exports peaklist attribute table to a list, including the flags.

Return type:	list

to_dict(dict_type=<class 'collections.OrderedDict'>)¶

Exports peaklist attribute table to a dictionary (mappable object), including the flags.

Parameters:	dict_type – result dictionary type, Default = OrderedDict
Return type:	list

to_str(delimiter=', ')¶

Exports peaklist attribute table to a string, including the flags. It can also be used inexplicitly.

Return type:	str

copy()¶

Returns a deep copy of the peaklist.

Return type:	PeakList object

dimspy.models.peaklist_metadata module¶

The PeakList metadata class.

New in version 0.1.

Warning

This class is designed for PeakList internal use only.

class dimspy.models.peaklist_metadata.PeakList_Metadata(*args, **kwargs)¶

Bases: dict

The PeakList_Metadata class.

Dictionary-like container for PeakList metadata storage.

Parameters:	args – iterable object of key-value pairs kwargs – metadata key-value pairs

>>> PeakList_Metadata([('name', 'sample_1'), ('qc', False)])
>>> PeakList_Metadata(name = 'sample_1', qc = False)

metadata attributes can be accessed in both dictionary-like and property-like manners.

>>> meta = PeakList_Metadata(name = 'sample_1', qc = False)
>>> meta['name']
sample_1
>>> meta.qc
False
>>> del meta.qc
>>> meta.has_key('qc')
False

Warning

The __getattr__, __setattr__, and __delattr__ methods are overrided. DO NOT assign a metadata object to another metadata object, e.g., metadata.metadata.attr = value.

dimspy.models.peaklist_tags module¶

The PeakList tags class.

New in version 0.1.

Warning

This class is designed for PeakList and PeakMatrix internal use only.

class dimspy.models.peaklist_tags.PeakList_Tags(*args, **kwargs)¶

Bases: object

The PeakList_Tags class.

Container for both typed and untyped tags. This class is mainly used in PeakList and PeakMatrix classes for sample filtering.

Parameters:	args – list of untyped tags kwargs – list of typed tags. Only one tag value can be assigned to a specific tag type

>>> PeakList_Tags('untyped_tag1', 'untyped_tag2')
>>> PeakList_Tags(tag_type1 = 'tag_value1', tag_type2 = 'tag_value2')

tag_types¶

Property of included tag types.

Getter:	returns a tuple containing all the tag types of the typed tags
Type:	tuple

tag_values¶

Property of included tag values.

Getter:	returns a tuple containing all the tag values, both typed and untyped tags
Type:	tuple

typed_tags¶

Property of included typed tags.

Getter:	returns a tuple containing all the typed tags, each in the format of (tag_type, tag_value)
Type:	tuple

untyped_tags¶

Property of included untyped tags.

Getter:	returns a tuple containing all the untyped tags
Type:	tuple

has_tag_type(tag_type)¶

Checks whether there exists a specific tag type.

Parameters:	tag_type – the tag type for checking
Return type:	bool

has_tag(*args, **kwargs)¶

Checks whether there exists a specific tag.

Parameters:	args – one tag value, either typed or untyped kwargs – one tag_type = tag_value
Return type:	bool

>>> tags = PeakList_Tags('untyped_tag1', tag_type1 = 'tag_value1')
>>> tags.has_tag('untyped_tag1')
True
>>> tags.has_tag(tag_type1 = 'untyped_tag1')
False
>>> tags.has_tag('untyped_tag1', 'tag_value1')
...
ValueError: searching multiple tags is not allowded

tag_of(tag_type=None)¶

Returns tag value of the given tag type, or tuple of untyped tags if tag_type is None.

Parameters:	tag_type – valid tag type, or None for untyped tags
Return type:	same as tag_value (tag_type is not None), or tupe (tag_type is None)

add_tags(*args, **kwargs)¶

Adds multiple typed and untyped tags.

Parameters:	args – list of untyped tags kwargs – list of typed tags. Only one tag value can be assigned to a specific tag type

>>> PeakList_Tags('untyped_tag1', tag_type1 = 'tag_value1')

drop_tags(*args)¶

Drops multiple typed and untyped tags.

Parameters:	args – list of tag values, both typed and untyped

>>> tags = PeakList_Tags('untyped_tag1', tag_type1 = 'tag_value1')
>>> tags.drop_tags('tag_value1')
>>> print tags
untyped_tag1

drop_tag_types(*args)¶

Drops multiple tag types and their tag values.

Parameters:	args – list of tag types

drop_all_tags()¶: Drops all tags, both typed and untyped.

to_list()¶

Exports tags to a list.

Return type:	list

to_str()¶

Exports tags to a string. It can also be used inexplicitly as

>>> tags = PeakList_Tags('untyped_tag1', tag_type1 = 'tag_value1')
>>> print tags
untyped_tag1, tag_type1:tag_value1

Return type:	str

dimspy.models package¶

Submodules¶

dimspy.models.peak_matrix module¶

dimspy.models.peaklist module¶

dimspy.models.peaklist_metadata module¶

dimspy.models.peaklist_tags module¶

Module contents¶