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1 import os
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2 import sys
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3 if sys.version_info <= (2, 8):
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4 from builtins import super
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5
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6 # from abc import ABC, abstractmethod
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7 import abc
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8 import copy
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9
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10 import pandas as pd
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11 import six
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12 import sfa.utils
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13
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14 __all__ = ['Algorithm', 'Data', 'Result']
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15
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16
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17 @six.add_metaclass(abc.ABCMeta)
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18 class ContainerItem():
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19 """
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20 The base class that defines the item object of
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21 ``sfa.containers.Container``.
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22
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23 """
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24 def __init__(self, abbr=None, name=None):
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25 self._abbr = abbr
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26 self._name = name
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27
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28 def __str__(self):
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29 return self._abbr
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30
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31 def __repr__(self):
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32 class_name = self.__class__.__name__
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33 return "%s object" % (class_name)
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34
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35 @property
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36 def abbr(self):
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37 """Abbreviation or symbol representing this item.
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38 """
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39 return self._abbr
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40
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41 @abbr.setter
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42 def abbr(self, val):
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43 self._abbr =val
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44
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45 @property
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46 def name(self):
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47 """Full name or description of this item.
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48 """
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49 return self._name
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50
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51 @name.setter
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52 def name(self, val):
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53 self._name = val
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54
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55
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56 class ParameterSet(sfa.utils.FrozenClass):
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57 """The base class of ParameterSet objects.
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58 """
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59
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60 def __init__(self, abbr):
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61 """
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62 """
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63 super().__init__(abbr)
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64
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65
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66 class Algorithm(ContainerItem):
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67 """The base class of Algorithm classes.
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68
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69 Attributes
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70 ----------
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71 abbr : str
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72 name : str
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73 data : sfa.base.Data
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74 params : sfa.base.ParameterSet
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75 result : sfa.base.Result
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76
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77 Examples
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78 --------
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79 >>> class AnAlgorithm(sfa.base.Algorithm):
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80 # Definition of algorithm ...
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81 ...
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82
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83 >>> alg = AnAlgorithm()
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84 >>> alg.params = params_obj # Parameters of the algorithm
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85 >>> alg.data = data_obj # Data to be analyzed by the algorithm
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86 >>> alg.initialize()
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87 >>> res = alg.compute()
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88
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89 """
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90 def __init__(self, abbr):
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91 super().__init__(abbr)
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92 self._data = None
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93 self._params = None
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94 self._result = None
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95
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96 def copy(self, is_deep=False):
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97 """
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98
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99 """
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100 if is_deep:
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101 copy.deepcopy(self)
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102 else:
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103 return copy.copy(self)
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104
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105 # Read-only properties
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106 @property
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107 def result(self):
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108 """The object of ``sfa.base.Result``.
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109 The result of computing the batch.
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110 """
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111 return self._result
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112
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113 # Read & write properties
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114 @property
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115 def params(self):
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116 """The object of ``sfa.base.ParameterSet``.
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117 Parameters of the algorithm can accessed
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118 through this member.
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119 """
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120 return self._params
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121
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122 @params.setter
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123 def params(self, obj):
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124 self._params = obj
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125
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126 @property
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127 def data(self):
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128 """The object of ``sfa.base.Data``.
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129 Data to be processed based on the algorithm
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130 can accessed through this member.
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131 """
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132 return self._data
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133
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134 @data.setter
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135 def data(self, obj):
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136 self._data = obj
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137
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138 def initialize(self, network=True, ba=True):
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139 """
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140 """
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141 if network:
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142 self.initialize_network()
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143
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144 if ba:
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145 self.initialize_basal_activity()
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146
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147 def initialize_network(self):
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148 """Initialize the data structures related to network.
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149 """
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150 pass
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151
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152 def initialize_basal_activity(self):
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153 """Initialize the basal activity, :math:`b`.
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154 """
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155 pass
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156
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157 @abc.abstractmethod
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158 def compute(self, b,pi):
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159 r"""Process the assigned data
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160 with the given basal activity, :math:`b`.
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161
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162 Parameters
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163 ----------
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164 b : numpy.ndarray
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165 1D array of basal activity.
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166
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167 pi: list
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168 list of fixed node perturbations
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169 Returns
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170 -------
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171 x : numpy.ndarray
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172 1D-array object of activity at steady-state.
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173 """
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174 raise NotImplementedError("compute() should be implemented")
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175
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176 @abc.abstractmethod
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177 def compute_batch(self):
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178 """Process the assigned data that contains a batch data.
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179 The result is stored in ``result`` member.
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180 """
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181 raise NotImplementedError("compute_batch() should be implemented")
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182
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183 # end of class Algorithm
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184
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185
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186 class Data(ContainerItem):
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187 def __init__(self):
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188 super().__init__()
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189 self._A = None
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190 self._n2i = None
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191 self._i2n = None
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192 self._dg = None
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193 self._inputs= None
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194 self._df_conds = None
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195 self._df_exp = None
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196 self._df_ptb = None
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197 self._names_ptb = None
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198 self._iadj_to_idf = None
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199 self._has_link_perturb = None
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200
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201 def initialize(self,
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202 fpath,
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203 fname_network="network.sif",
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204 fname_ptb="ptb.tsv",
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205 fname_conds="conds.tsv",
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206 fname_exp="exp.tsv",
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207 inputs={}):
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208
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209 dpath = os.path.dirname(fpath)
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210 fpath_network = os.path.join(dpath, fname_network)
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211 fpath_ptb = os.path.join(dpath, fname_ptb)
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212
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213 A, n2i, dg = sfa.read_sif(fpath_network, as_nx=True)
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214 self._A = A
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215 self._n2i = n2i
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216 self._dg = dg
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217 self._df_conds = pd.read_table(os.path.join(dpath, fname_conds),
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218 header=0, index_col=0)
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219 self._df_exp = pd.read_table(os.path.join(dpath, fname_exp),
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220 header=0, index_col=0)
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221
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222 self._inputs = inputs
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223 self._df_ptb = pd.read_table(fpath_ptb, index_col=0)
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224 if any(self._df_ptb.Type == 'link'):
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225 self._has_link_perturb = True
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226 else:
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227 self._has_link_perturb = False
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228
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229 self._names_ptb = []
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230 for i, row in enumerate(self._df_conds.iterrows()):
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231 row = row[1]
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232 list_name = [] # Target names
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233 for target in self._df_conds.columns[row.nonzero()]:
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234 list_name.append(target)
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235 # end of for
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236 self._names_ptb.append(list_name)
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237 # end of for
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238
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239 # For mapping from the indices of adj. matrix to those of DataFrame
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240 # (arrange the indices of adj. matrix according to df_exp.columns)
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241 self._iadj_to_idf = [n2i[x] for x in self._df_exp.columns]
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242
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243 self._i2n = {idx: name for name, idx in n2i.items()}
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244
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245 # end of def
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246
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247 # Read-only members
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248 @property
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249 def A(self): # Adjacency matrix (numpy.ndarray)
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250 return self._A
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251
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252 @property
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253 def n2i(self): # Name to index mapping (hashable)
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254 return self._n2i
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255
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256 @property
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257 def i2n(self): # Index to name mapping (hashable)
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258 return self._i2n
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259
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260 @property
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261 def dg(self): # Directed graph object of NetworkX
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262 return self._dg
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263
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264 @property # List of perturbation targets
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265 def names_ptb(self):
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266 return self._names_ptb
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267
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268 @property # List of values for perturbation
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269 def vals_ptb(self):
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270 return self._vals_ptb
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271
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272 # @property # List of perturbation types
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273 # def types_ptb(self):
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274 # return self._types_ptb
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275
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276 @property
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277 def iadj_to_idf(self):
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278 return self._iadj_to_idf
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279
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280 @iadj_to_idf.setter
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281 def iadj_to_idf(self, arr):
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282 self._iadj_to_idf = arr
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283
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284 # Replaceable (assignable) members
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285 @property
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286 def inputs(self): # Input conditions
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287 return self._inputs
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288
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289 @inputs.setter
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290 def inputs(self, obj_dict):
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291 self._inputs = obj_dict
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292
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293 @property # DataFrame of experimental conditions
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294 def df_conds(self):
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295 return self._df_conds
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296
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297 @df_conds.setter
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298 def df_conds(self, df):
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299 self._df_conds = df
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300
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301 @property # DataFrame of experimental results
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302 def df_exp(self):
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303 return self._df_exp
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304
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305 @df_exp.setter
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306 def df_exp(self, df):
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307 self._df_exp = df
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308
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309 @property # DataFrame of perturbation information
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310 def df_ptb(self):
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311 return self._df_ptb
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312
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313 @df_ptb.setter
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314 def df_ptb(self, df):
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315 self._df_ptb = df
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316
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317 @property
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318 def has_link_perturb(self):
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319 return self._has_link_perturb
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320
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321 @has_link_perturb.setter
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322 def has_link_perturb(self, val):
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323 if not isinstance(val, bool):
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324 raise TypeError("has_link_perturb should be boolean.")
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325 self._has_link_perturb = val
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326
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327 # end of class Data
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328
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329
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330 class Result(sfa.utils.FrozenClass):
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331
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332 def __init__(self):
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333 self._df_sim = None
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334 self._freeze()
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335
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336 @property
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337 def df_sim(self):
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338 return self._df_sim
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339
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340 @df_sim.setter
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341 def df_sim(self, val):
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342 self._df_sim = val
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343
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344 # end of def class Result
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