Source code for ccpn.util.traits.CcpNmrDataFrame

#=========================================================================================
# Licence, Reference and Credits
#=========================================================================================
__copyright__ = "Copyright (C) CCPN project (http://www.ccpn.ac.uk) 2014 - 2021"
__credits__ = ("Ed Brooksbank, Joanna Fox, Victoria A Higman, Luca Mureddu, Eliza Płoskoń",
               "Timothy J Ragan, Brian O Smith, Gary S Thompson & Geerten W Vuister")
__licence__ = ("CCPN licence. See http://www.ccpn.ac.uk/v3-software/downloads/license",
               )
__reference__ = ("Skinner, S.P., Fogh, R.H., Boucher, W., Ragan, T.J., Mureddu, L.G., & Vuister, G.W.",
                 "CcpNmr AnalysisAssign: a flexible platform for integrated NMR analysis",
                 "J.Biomol.Nmr (2016), 66, 111-124, http://doi.org/10.1007/s10858-016-0060-y"
                )
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# Last code modification
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__modifiedBy__ = "$modifiedBy: Geerten Vuister $"
__dateModified__ = "$dateModified: 2021-12-23 11:27:19 +0000 (Thu, December 23, 2021) $"
__version__ = "$Revision: 3.0.4 $"
#=========================================================================================
# Created
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__author__ = "$Author: geertenv $"
__date__ = "$Date: 2018-05-14 10:28:41 +0000 (Fri, April 07, 2017) $"
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# Start of code
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import pandas as pd

from ccpn.util.AttributeDict import AttributeDict
from ccpn.util.traits.TraitJsonHandlerBase import TraitJsonHandlerBase
from ccpn.util.traits.CcpNmrJson import fileHandler, CcpNmrJson
from ccpn.util.traits.CcpNmrTraits import Adict, Instance, default, List


[docs]class DataFrameTrait(Instance): """A trait that defines a json serialisable Pandas DataFrame """ default_value = pd.DataFrame() info_text = 'A json serialisable DataFrame' def __init__(self, *args, **kwargs): kwargs.setdefault('default_value', pd.DataFrame()) kwargs['klass'] = pd.DataFrame Instance.__init__(self, *args, **kwargs) # trait-specific json handler
[docs] class jsonHandler(TraitJsonHandlerBase): """Serialise DataFrame instance to be json compatible. Needs some complicated encoding/decoding as result of int64 rows encoding """
[docs] def encode(self, obj, trait): df = getattr(obj, trait) value = dict( columns=list(df.columns), nrows=int(df.shape[0]), data=[(int(r), v) for r, v in df.to_dict(orient='index').items()], dtypes=dict([(c, str(d)) for c, d in dict(df.dtypes).items()]) ) return value
[docs] def decode(self, obj, trait, value): # restore the DataFrame; if nrows=0 create one with the known columns. This assures # the columns of an empty table (i.e. no rows, but columns defined) to be restored # Otherwise, create from the data tuples using Pandas from_dict() method nrows = value['nrows'] columns = value['columns'] data = value['data'] dtypes = value['dtypes'] if nrows == 0: df = pd.DataFrame(columns=columns) else: df = pd.DataFrame.from_dict(dict(data), orient='index') setattr(obj, trait, df)
# end class # end class # we are subclassing fromJson, so need to redefine the .json fileHandler
[docs]@fileHandler('.json', 'toJson', 'fromJson') class CcpNmrDataFrame(CcpNmrJson): """Class for json serialisable and easy Pandas DataFrame """ classVersion = 3.0 # -------------------------------------------------------------------------------------------- _state = Adict().tag(saveToJson=True) # uses Adict json handler @default('_state') def _state_default(self): return AttributeDict( sortColumn=None, # sorted on column; None indicated row-sorted (default) sortAscending=True, # sorted ascending ) # -------------------------------------------------------------------------------------------- # actual table data (Pandas DataFrame) dataFrame = DataFrameTrait().tag(saveToJson=True) # uses DataFrameTrait json handler # dropped collumn names, retained for later _droppedColumns = List(default_value=[]) @property def sizes(self): "Return (numberOfRows, numberOfColumns) tuple" return tuple(self.dataFrame.shape) @property def rows(self): "Return rows of dataFrame as a list" return list(self.dataFrame.index) @property def columns(self): "Return columns of dataFrame as a list" return list(self.dataFrame.columns) def _dropColumns(self, drops): "Remove drops from dataFrame" # print('>>> drops:', drops) if len(drops) > 0: self.dataFrame.drop(drops, axis=1, inplace=True) self._droppedColumns += drops for d in drops: if d in self._formats: del (self._formats[d]) self._sortDataFrame()
[docs] def deleteColumns(self, *columns): "Delete columns, retaining others" for c in columns: if c not in self.columns: raise KeyError('invalid column "%s" to delete' % c) self._dropColumns(list(columns)) # with one argument, collumns is a tuple ('name',) return self
[docs] def selectColumns(self, *columns): "Select columns, deleting others" for c in columns: if c not in self.columns: raise KeyError('invalid column "%s" to select' % c) drops = [] for c in self.columns: if c not in columns: drops.append(c) self._dropColumns(drops) return self
[docs] def addColumn(self, column, fmt=None, values=None, beforeColumn=None): "Add a column, optional beforeColumn (default at end), optionally setting values" if column in self.columns: raise KeyError('column "%s" already exists' % column) if beforeColumn is None: loc = len(self.columns) else: if beforeColumn not in self.columns: raise KeyError('invalid beforeColumn "%s"' % beforeColumn) loc = self.columns.index(beforeColumn) self.dataFrame.insert(loc, column, values) self.setFormat(column, fmt) return self
[docs] def getRow(self, row): "Return row as a AttributeDict" # NB 'into' keyword not functioning in the pandas version used during development row = AttributeDict(**self.dataFrame.loc[row].to_dict()) return row
[docs] def setRow(self, row, **kwds): "For each (key,value) of kwds, set (row,key) to value" for key, value in kwds.items(): if key not in self.columns: raise KeyError('invalid key "%s"' % key) self.dataFrame.loc[row, key] = value self._sortDataFrame()
[docs] def appendRow(self, **kwds): "For append each (key,value) of kwds as new row" if len(self.rows) > 0: row = max(self.rows) + 1 else: row = 0 self.setRow(row, **kwds)
[docs] def insertRow(self, row, **kwds): """Insert (key, values) as row This will re-index (+1) all current rows >= row Also replaces dataFrame with new instance """ newRows = [r if r < row else r + 1 for r in self.rows] self.dataFrame = self.dataFrame.reindex(index=newRows) self.setRow(row, **kwds)
def _sortDataFrame(self): "Sort dataFrame according to settings" if self._state.sortColumn is not None and self._state.sortColumn not in self.columns: # The sortColumn may have been deleted; revert to row sorting self._state.sortColumn = None self._state.sortAscending = True if self._state.sortColumn is not None: self.dataFrame.sort_values(by=self._state.sortColumn, ascending=self._state.sortAscending, inplace=True) else: self.dataFrame.sort_index(ascending=self._state.sortAscending, inplace=True)
[docs] def sort(self, sortColumn, ascending=True): "Sort dataFrame by sortColumn; use None to revert to row-sorted" if sortColumn not in self.columns: raise KeyError('invalid sortColumn "%s"' % sortColumn) self._state.sortColumn = sortColumn self._state.sortAscending = ascending self._sortDataFrame() return self
[docs] def sortRows(self): "Convience method to revert to ascending sorted rows" return self.sort(None, ascending=True)
[docs] def allRows(self, sorted=True): "Iterate over each row, maintain the currently sorted order if sorted=True" # rows are returned in dataFrame sorted order rows = self.rows # sort the rows back in index order if sorted is False if not sorted: rows.sort() for row in rows: yield self.getRow(row)
# -------------------------------------------------------------------------------------------- def __init__(self, columns=[], **metadata): CcpNmrJson.__init__(self, **metadata) self.dataFrame = pd.DataFrame(columns=columns) def __str__(self): return '<%s: sizes=%s>' % (self.__class__.__name__, self.sizes) # --------------------------------------------------------------------------------------------
[docs] def fromJson(self, string, **kwds): """Subclassed to execute _sortDataFrame""" CcpNmrJson.fromJson(self, string, **kwds) self._sortDataFrame() return self
# -------------------------------------------------------------------------------------------- # def save(self, path, **kwds): defined by CcpNmrJson # def restore(self, path, **kwds): defined by CcpNmrJson # end class