slightly tweaked mocapnet alt container

2026-10-15 00:03:06 +00:00 · 2024-01-18 19:23:00 +05:30
parent 881c2fc840
commit 0d662c5b9d
294 changed files with 99862 additions and 2 deletions
@@ -0,0 +1,783 @@
+#!/usr/bin/python3
+""" 
+Author : "Ammar Qammaz"
+Copyright : "2022 Foundation of Research and Technology, Computer Science Department Greece, See license.txt"
+License : "FORTH" 
+"""
+
+import sys
+import os
+import numpy as np
+
+#python3 principleComponentAnalysisTester.py --config dataset/body_configuration.json --mem 1000 --highlight 4 --mode 3
+#python3 principleComponentAnalysisTool.py --config dataset/body_configuration.json --all body --mem 1000 --show --type sparsepca
+#python3 principleComponentAnalysisTool.py --config dataset/upperbody_configuration.json --all upperbody --mem 1000  --show --type factoranalysis
+
+class EmptyDecomposition:
+  def __init__(self,):
+               self.decompositionType = ""
+  def ok(self):
+               return True
+  def fit(self,data):
+               pass
+  def transform(self,data,selectedPCADimensions=0):
+               return data
+  def save(self,filename):
+               return True
+  def load(self,filename):
+               return True
+
+
+#------------------------------------------------------------------
+#------------------------------------------------------------------
+#------------------------------------------------------------------
+def getSKPCA_Decomposer(numberOfDimensions=20):
+     from sklearn.decomposition import PCA 
+     engine = PCA(n_components=numberOfDimensions)
+     return engine
+
+def getTruncatedSVD_Decomposer(numberOfDimensions=20):
+     from sklearn.decomposition import TruncatedSVD
+     engine = TruncatedSVD(n_components=numberOfDimensions)
+     return engine
+
+def getLatentDirichletAllocation_Decomposer(numberOfDimensions=20):
+     from sklearn.decomposition import LatentDirichletAllocation
+     engine = LatentDirichletAllocation(n_components=numberOfDimensions)
+     return engine
+
+def getFactorAnalysis_Decomposer(numberOfDimensions=20):
+     from sklearn.decomposition import FactorAnalysis
+     engine = FactorAnalysis(n_components=numberOfDimensions)
+     return engine
+
+def getDictionaryLearning_Decomposer(numberOfDimensions=20):
+     from sklearn.decomposition import DictionaryLearning
+     engine = DictionaryLearning(n_components=numberOfDimensions)
+     return engine
+
+def getFastICA_Decomposer(numberOfDimensions=20):
+     from sklearn.decomposition import FastICA
+     engine = FastICA(n_components=numberOfDimensions)
+     return engine
+
+def getNMF_Decomposer(numberOfDimensions=20):
+     from sklearn.decomposition import NMF
+     engine = NMF(n_components=numberOfDimensions)
+     return engine
+
+def getIncrementalPCA_Decomposer(numberOfDimensions=20):
+     from sklearn.decomposition import IncrementalPCA
+     engine = IncrementalPCA(n_components=numberOfDimensions)
+     return engine
+
+def getSparsePCA_Decomposer(numberOfDimensions=20):
+     from sklearn.decomposition import SparsePCA
+     engine = SparsePCA(n_components=numberOfDimensions)
+     return engine
+
+def getTSNE_Decomposer(numberOfDimensions=20):
+     from sklearn.manifold import TSNE
+     engine = TSNE(n_components=numberOfDimensions,perplexity=100, learning_rate=100, verbose=2, n_iter=350)
+     return engine
+#------------------------------------------------------------------
+#------------------------------------------------------------------
+#------------------------------------------------------------------
+class Decomposition():
+  def __init__(self,
+               inputData:np.array=np.array([]),
+               savedFile:str="",
+               decompositionType:str="pca",
+               selectedPCADimensions:int=0
+              ):
+               self.disabledDecomposition   = False
+               self.numberOfSamplesFittedOn = 0
+               self.decompositionType       = decompositionType
+               self.noTransformFunction     = 0 #Some methods dont have a transform function
+               self.trackedFiles            = list() #<- all needed files should be tracked here
+              
+               if (selectedPCADimensions==0):
+                 self.numberOfSamplesFittedOn = inputData.shape[0]
+               else:
+                 self.numberOfSamplesFittedOn = selectedPCADimensions
+
+
+               import sklearn
+               print("Decomposition code using Sk-Learn : ",sklearn.__version__)
+               print("Will try to perform : ",self.decompositionType)
+
+               #self.engine = getSKPCA_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
+               from sklearn.preprocessing import StandardScaler
+               self.scaler = StandardScaler()
+
+               if (self.decompositionType==""):
+                          print("Not Performing any decomposition")
+                          self.disabledDecomposition = True
+                          return;
+               elif (self.decompositionType=="skpca"):
+                          self.engine = getSKPCA_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
+               elif (self.decompositionType=="sparsepca"):
+                          self.engine = getSparsePCA_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
+               elif (self.decompositionType=="incrementalpca"):
+                          self.engine = getIncrementalPCA_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
+               elif (self.decompositionType=="fastica"):
+                          self.engine = getFastICA_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
+               elif (self.decompositionType=="nmf"):
+                          self.engine = getNMF_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
+               elif (self.decompositionType=="dictionary"):
+                          self.engine = getDictionaryLearning_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
+               elif (self.decompositionType=="factoranalysis"):
+                          self.engine = getFactorAnalysis_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
+               elif (self.decompositionType=="dirichlet"):
+                          self.engine = getLatentDirichletAllocation_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
+               elif (self.decompositionType=="svd"):
+                          self.engine = getTruncatedSVD_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
+               elif (self.decompositionType=="tsne"):
+                          self.engine = getTSNE_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
+                          self.noTransformFunction = 1
+               elif (self.decompositionType!="pca"):
+                          print("Could not understand decomposition Type ",self.decompositionType)
+                          sys.exit(1)
+               else:
+                          print("Using SK code for decomposition Type ",self.decompositionType)
+                          self.engine = getSKPCA_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
+
+               if (savedFile!=""):
+                  print("Loading existing fitted decomposition from ",savedFile,"..!")
+                  self.load(savedFile) 
+               elif (inputData.size!=0):
+                  print("Generating new fit..!")
+                  self.fit(inputData)
+               else:
+                  print("No Decomposition input given..!")
+
+  def ok(self):
+    return self.numberOfSamplesFittedOn!=0
+
+  def getNumberOfExpectedSamples(self):
+    return self.numberOfSamplesFittedOn
+
+  def fit(self,data):
+    import time
+    startAt = time.time()
+    #----------------------------------------------------------------------------------------
+    self.numberOfSamplesFittedOn = data.shape[0]
+    self.scaler.fit(data)
+    dataTransformed = self.scaler.transform(data)
+    outputData = self.engine.fit_transform(dataTransformed)
+    #----------------------------------------------------------------------------------------
+    endAt = time.time()
+    print("Time required to fit ",self.decompositionType," was ",(endAt-startAt)/60," mins")
+    return outputData
+
+  def transform(self,data,selectedPCADimensions=0):
+    if (self.disabledDecomposition):
+       return data
+    #if (selectedPCADimensions!=self.numberOfSamplesFittedOn):
+    #    print("INCONSISTENT TRANSFORM")
+
+    if (selectedPCADimensions==0):
+      if (self.noTransformFunction):
+         dataTransformed = self.scaler.fit_transform(data)
+         return self.engine.fit_transform(dataTransformed)
+      else:
+         dataTransformed = self.scaler.transform(data)
+         return self.engine.transform(dataTransformed) 
+    else:
+      if (self.noTransformFunction):
+         dataTransformed = self.scaler.fit_transform(data)
+         return self.engine.fit_transform(dataTransformed)[:,:selectedPCADimensions] # ,n_components=selectedPCADimensions
+      else:
+         dataTransformed = self.scaler.transform(data)
+         return self.engine.transform(dataTransformed)[:,:selectedPCADimensions] # ,n_components=selectedPCADimensions
+      
+  def save(self,filename):
+       print("Saving Decomposition to ",filename) 
+       OKGREEN = '\033[92m'
+       FAIL    = '\033[91m'
+       ENDC    = '\033[0m'
+       import pickle
+       self.trackedFiles = list()
+       successfulSaves = 0
+       #---------------------------------------------------------------
+       pathToEngine = "%s.%s" % (filename,self.decompositionType) 
+       with open(pathToEngine,'wb') as engineFile:
+          self.trackedFiles.append(pathToEngine)
+          print(OKGREEN,"Saving Decomposition Engine to ",pathToEngine,ENDC) 
+          pickle.dump(self.engine,engineFile)
+          successfulSaves = successfulSaves + 1
+       #---------------------------------------------------------------
+       pathToScaler = "%s.scaler.%s" % (filename,self.decompositionType)
+       with open(pathToScaler,'wb') as scalerFile:
+          self.trackedFiles.append(pathToScaler)
+          print(OKGREEN,"Saving Decomposition Scaler to ",pathToScaler,ENDC) 
+          pickle.dump(self.scaler,scalerFile)
+          successfulSaves = successfulSaves + 1
+       #---------------------------------------------------------------
+       return (successfulSaves==2)
+
+  def load(self,filename):
+       print("Loading Decomposition from ",filename)
+       OKGREEN = '\033[92m'
+       FAIL    = '\033[91m'
+       ENDC    = '\033[0m'
+       import pickle
+       self.trackedFiles = list()
+       successfulLoads = 0
+       #---------------------------------------------------------------
+       pathToEngine = "%s.%s" % (filename,self.decompositionType) 
+       if (os.path.isfile(pathToEngine)):
+          self.trackedFiles.append(pathToEngine)
+          unpickleEngine = open(pathToEngine, 'rb')
+          print(OKGREEN,"Loaded Decomposition Engine from ",pathToEngine,ENDC) 
+          self.engine = pickle.load(unpickleEngine) #, encoding='bytes'
+          successfulLoads = successfulLoads + 1
+       else:
+          print(FAIL,"Failed Loading Decomposition Engine from ",pathToEngine,ENDC) 
+       #---------------------------------------------------------------           
+       pathToScaler = "%s.scaler.%s" % (filename,self.decompositionType)
+       if (os.path.isfile(pathToEngine)):
+          self.trackedFiles.append(pathToScaler)
+          unpickleScaler = open(pathToScaler, 'rb')
+          print(OKGREEN,"Loading Decomposition Scaler from ",pathToScaler,ENDC) 
+          self.scaler = pickle.load(unpickleScaler) #, encoding='bytes'
+          successfulLoads = successfulLoads + 1
+       else:
+          print(FAIL,"Failed Loading Decomposition Scaler from ",pathToScaler,ENDC)
+       #---------------------------------------------------------------
+       return (successfulLoads==2)
+
+  def visualize(self,data,saveToFile="",onlyScreePlotNDimensions=0,label="PCA",colors=list(),colorLabel="Highlighting PC-4",viewAzimuth=45,viewElevation=45,showScree=0): 
+    import matplotlib
+    import matplotlib.pyplot as plt
+    font = {'family' : 'normal',
+            'weight' : 'bold',
+            'size'   : 28 }
+    plt.rc('font', **font)
+    plt.rc('xtick', labelsize=15) 
+    plt.rc('ytick', labelsize=15) 
+    # === Plot =========================================================================
+    fig = plt.figure()
+    fig.set_size_inches(19.2, 10.8, forward=True)
+    ax1 = fig.add_subplot(1, 1, 1,projection='3d')
+    #=================================================================================== 
+
+    #Number of PCA components to plot on first plot (our plot is 3D so max is 4 if we dont have a color ..! )
+    keepNDimensions = 3
+    if (len(colors)==0):
+       keepNDimensions = 4
+
+    #Do transform of our input using the PCA dimensions as new basis
+    #=================================================================================== 
+    transformedData = self.transform(data,selectedPCADimensions=keepNDimensions).real
+    #print("TRANSFORMED DATA : ",transformedData)
+    print("TRANSFORMED DATA")
+    print("Number of Samples    : ",transformedData.shape[0])
+    print("Number of Dimensions : ",transformedData.shape[1])
+    #=================================================================================== 
+
+    if (len(colors)==0):
+       if (transformedData.shape[1]<4):
+         print("Not enough dimensions to plot color")
+         keepNDimensions = 3
+       else:
+         colors = transformedData[:,3]
+         colorLabel = "highlighting PC-4"
+    else:
+       print("Using provided set of colorValues")
+       keepNDimensions = 3
+
+    ax1.view_init(viewAzimuth,viewElevation) 
+    #===================================================================================
+    if (len(colors)==0):
+      ax1.scatter(transformedData[:,0],transformedData[:,1],transformedData[:,2],c='red')
+    else:
+      scatter = ax1.scatter(transformedData[:,0],transformedData[:,1],transformedData[:,2],c=colors,label=colors)
+      ax1.legend(*scatter.legend_elements(num=5),loc="upper right", title="Values", fontsize="x-small", framealpha=0.4)
+    #=================================================================================== 
+
+    # Adding title, xlabel and ylabel
+    ax1.set_title('%s %s %s '%(self.decompositionType,label,colorLabel)) # Title of the plot
+    ax1.set_xlabel('PC-1',labelpad=30) # X-Label
+    ax1.set_ylabel('PC-2',labelpad=30) # Y-Label
+    ax1.set_zlabel('PC-3',labelpad=30) # Z-Label
+    #ax1.tick_params(axis='x', pad=5) #fine tune numbers of plot
+    plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.08)
+
+    if (saveToFile!=""):
+      print("Saving Figure to output file %s" % saveToFile)
+      fig.savefig(saveToFile)
+    else:
+      plt.show() 
+
+
+if __name__ == '__main__':
+  print("principleComponentAnalysis.py is a library")
+  decomp = Decomposition(savedFile='dataset/combinedModel/mocapnet4/mode1/1.0/step1_upperbody_all/upperbody_all.pca',decompositionType='pca')
+  sample = list()
+  sample.append(0.470052) #0
+  sample.append(0.452315) #1
+  sample.append(1.000000) #2
+  sample.append(0.479688) #3
+  sample.append(0.239352) #4
+  sample.append(1.000000) #5
+  sample.append(0.520312) #6
+  sample.append(0.185185) #7
+  sample.append(1.000000) #8
+  sample.append(0.519271) #9
+  sample.append(0.173148) #10
+  sample.append(1.000000) #11
+  sample.append(0.000000) #12
+  sample.append(0.000000) #13
+  sample.append(0.000000) #14
+  sample.append(0.450000) #15
+  sample.append(0.238889) #16
+  sample.append(1.000000) #17
+  sample.append(0.422917) #18
+  sample.append(0.337037) #19
+  sample.append(1.000000) #20
+  sample.append(0.000000) #21
+  sample.append(0.000000) #22
+  sample.append(0.000000) #23
+  sample.append(0.509375) #24
+  sample.append(0.239815) #25
+  sample.append(1.000000) #26
+  sample.append(0.500000) #27
+  sample.append(0.362963) #28
+  sample.append(1.000000) #29
+  sample.append(0.000000) #30
+  sample.append(0.000000) #31
+  sample.append(0.000000) #32
+  sample.append(0.652332) #33
+  sample.append(0.283472) #34
+  sample.append(0.547378) #35
+  sample.append(0.213181) #36
+  sample.append(0.536087) #37
+  sample.append(0.077135) #38
+  sample.append(0.214366) #39
+  sample.append(0.509478) #40
+  sample.append(0.095500) #41
+  sample.append(0.029691) #42
+  sample.append(0.167750) #43
+  sample.append(0.522894) #44
+  sample.append(0.141564) #45
+  sample.append(0.065052) #46
+  sample.append(0.050908) #47
+  sample.append(0.124542) #48
+  sample.append(0.540789) #49
+  sample.append(0.190115) #50
+  sample.append(0.112984) #51
+  sample.append(0.101816) #52
+  sample.append(0.050908) #53
+  sample.append(0.383942) #54
+  sample.append(0.270394) #55
+  sample.append(0.307847) #56
+  sample.append(0.277424) #57
+  sample.append(0.248705) #58
+  sample.append(0.254739) #59
+  sample.append(0.270394) #60
+  sample.append(0.652332) #61
+  sample.append(0.000000) #62
+  sample.append(0.547378) #63
+  sample.append(0.536087) #64
+  sample.append(0.509478) #65
+  sample.append(0.522894) #66
+  sample.append(0.540789) #67
+  sample.append(0.270394) #68
+  sample.append(0.212132) #69
+  sample.append(0.440391) #70
+  sample.append(0.248585) #71
+  sample.append(0.182055) #72
+  sample.append(0.155387) #73
+  sample.append(0.126341) #74
+  sample.append(0.113851) #75
+  sample.append(0.174184) #76
+  sample.append(0.440391) #77
+  sample.append(0.212132) #78
+  sample.append(0.678899) #79
+  sample.append(0.152507) #80
+  sample.append(0.143071) #81
+  sample.append(0.171002) #82
+  sample.append(0.175392) #83
+  sample.append(0.193561) #84
+  sample.append(0.419609) #85
+  sample.append(0.678899) #86
+  sample.append(0.300000) #87
+  sample.append(0.216108) #88
+  sample.append(0.563004) #89
+  sample.append(0.067397) #90
+  sample.append(0.029691) #91
+  sample.append(0.059382) #92
+  sample.append(0.087379) #93
+  sample.append(0.130105) #94
+  sample.append(0.306329) #95
+  sample.append(0.563004) #96
+  sample.append(0.209537) #97
+  sample.append(0.116016) #98
+  sample.append(0.154849) #99
+  sample.append(0.587831) #100
+  sample.append(0.129067) #101
+  sample.append(0.066885) #102
+  sample.append(0.083048) #103
+  sample.append(0.069538) #104
+  sample.append(0.089203) #105
+  sample.append(0.321928) #106
+  sample.append(0.587831) #107
+  sample.append(0.192741) #108
+  sample.append(0.107269) #109
+  sample.append(0.061752) #110
+  sample.append(0.094237) #111
+  sample.append(0.617853) #112
+  sample.append(0.190791) #113
+  sample.append(0.125269) #114
+  sample.append(0.133770) #115
+  sample.append(0.098298) #116
+  sample.append(0.081326) #117
+  sample.append(0.347944) #118
+  sample.append(0.617853) #119
+  sample.append(0.194984) #120
+  sample.append(0.131074) #121
+  sample.append(0.123504) #122
+  sample.append(0.061752) #123
+  sample.append(0.348961) #124
+  sample.append(0.308926) #125
+  sample.append(0.269400) #126
+  sample.append(0.236866) #127
+  sample.append(0.208076) #128
+  sample.append(0.214721) #129
+  sample.append(0.232589) #130
+  sample.append(0.040663) #131
+  sample.append(0.308926) #132
+  sample.append(0.143338) #133
+  sample.append(0.378955) #134
+  sample.append(0.265854) #135
+  sample.append(0.281502) #136
+  sample.append(0.308926) #137
+  sample.append(0.652332) #138
+  sample.append(0.000000) #139
+  sample.append(0.547378) #140
+  sample.append(0.536087) #141
+  sample.append(0.509478) #142
+  sample.append(0.522894) #143
+  sample.append(0.540789) #144
+  sample.append(0.270394) #145
+  sample.append(0.000000) #146
+  sample.append(0.440391) #147
+  sample.append(0.678899) #148
+  sample.append(0.563004) #149
+  sample.append(0.587831) #150
+  sample.append(0.617853) #151
+  sample.append(0.308926) #152
+  sample.append(0.106590) #153
+  sample.append(0.587454) #154
+  sample.append(0.178302) #155
+  sample.append(0.106590) #156
+  sample.append(0.109798) #157
+  sample.append(0.069458) #158
+  sample.append(0.052693) #159
+  sample.append(0.317531) #160
+  sample.append(0.587454) #161
+  sample.append(0.166465) #162
+  sample.append(0.146195) #163
+  sample.append(0.111492) #164
+  sample.append(0.053520) #165
+  sample.append(0.030413) #166
+  sample.append(0.278528) #167
+  sample.append(0.587454) #168
+  sample.append(3.096379) #169
+  sample.append(0.651328) #170
+  sample.append(5.955375) #171
+  sample.append(6.084674) #172
+  sample.append(-0.059619) #173
+  sample.append(0.048326) #174
+  sample.append(0.234845) #175
+  sample.append(0.585668) #176
+  sample.append(0.651328) #177
+  sample.append(0.686142) #178
+  sample.append(5.398531) #179
+  sample.append(5.946909) #180
+  sample.append(5.904308) #181
+  sample.append(5.806487) #182
+  sample.append(0.529019) #183
+  sample.append(0.651328) #184
+  sample.append(6.084674) #185
+  sample.append(3.792921) #186
+  sample.append(3.694669) #187
+  sample.append(4.237240) #188
+  sample.append(4.096264) #189
+  sample.append(4.071058) #190
+  sample.append(3.975877) #191
+  sample.append(3.886222) #192
+  sample.append(3.886222) #193
+  sample.append(0.000000) #194
+  sample.append(3.776154) #195
+  sample.append(4.110508) #196
+  sample.append(4.119073) #197
+  sample.append(4.020748) #198
+  sample.append(3.931176) #199
+  sample.append(3.931177) #200
+  sample.append(0.000000) #201
+  sample.append(3.929643) #202
+  sample.append(2.813782) #203
+  sample.append(1.095648) #204
+  sample.append(3.325809) #205
+  sample.append(2.449419) #206
+  sample.append(2.176756) #207
+  sample.append(2.363436) #208
+  sample.append(2.456802) #209
+  sample.append(1.402459) #210
+  sample.append(1.095648) #211
+  sample.append(2.003560) #212
+  sample.append(3.639651) #213
+  sample.append(2.840933) #214
+  sample.append(2.875062) #215
+  sample.append(2.887117) #216
+  sample.append(1.448436) #217
+  sample.append(1.095648) #218
+  sample.append(2.736625) #219
+  sample.append(2.943081) #220
+  sample.append(0.954672) #221
+  sample.append(5.591012) #222
+  sample.append(3.255518) #223
+  sample.append(1.401905) #224
+  sample.append(2.261431) #225
+  sample.append(2.461843) #226
+  sample.append(1.159315) #227
+  sample.append(0.954672) #228
+  sample.append(1.819818) #229
+  sample.append(4.163903) #230
+  sample.append(4.543498) #231
+  sample.append(3.371371) #232
+  sample.append(3.151165) #233
+  sample.append(1.170683) #234
+  sample.append(0.954672) #235
+  sample.append(2.943081) #236
+  sample.append(3.081973) #237
+  sample.append(0.929466) #238
+  sample.append(5.318349) #239
+  sample.append(4.543498) #240
+  sample.append(3.256703) #241
+  sample.append(2.719052) #242
+  sample.append(2.719052) #243
+  sample.append(1.130633) #244
+  sample.append(0.929466) #245
+  sample.append(1.897446) #246
+  sample.append(4.228285) #247
+  sample.append(4.543498) #248
+  sample.append(3.707124) #249
+  sample.append(3.371372) #250
+  sample.append(1.137976) #251
+  sample.append(0.929466) #252
+  sample.append(3.216783) #253
+  sample.append(3.189919) #254
+  sample.append(0.834284) #255
+  sample.append(5.505028) #256
+  sample.append(5.403023) #257
+  sample.append(5.860644) #258
+  sample.append(3.256703) #259
+  sample.append(2.719052) #260
+  sample.append(0.929466) #261
+  sample.append(0.834284) #262
+  sample.append(1.597937) #263
+  sample.append(4.522202) #264
+  sample.append(5.142685) #265
+  sample.append(4.364796) #266
+  sample.append(3.691174) #267
+  sample.append(0.898620) #268
+  sample.append(0.834284) #269
+  sample.append(3.574276) #270
+  sample.append(3.376438) #271
+  sample.append(0.744629) #272
+  sample.append(5.598395) #273
+  sample.append(5.603435) #274
+  sample.append(5.860644) #275
+  sample.append(5.860644) #276
+  sample.append(3.166879) #277
+  sample.append(0.744629) #278
+  sample.append(0.744629) #279
+  sample.append(1.183479) #280
+  sample.append(4.781023) #281
+  sample.append(5.403024) #282
+  sample.append(4.970201) #283
+  sample.append(4.234641) #284
+  sample.append(0.684905) #285
+  sample.append(0.744629) #286
+  sample.append(4.391370) #287
+  sample.append(3.727261) #288
+  sample.append(0.744629) #289
+  sample.append(4.544052) #290
+  sample.append(4.300908) #291
+  sample.append(4.272226) #292
+  sample.append(4.071058) #293
+  sample.append(3.886222) #294
+  sample.append(3.166879) #295
+  sample.append(0.744629) #296
+  sample.append(3.604797) #297
+  sample.append(4.254323) #298
+  sample.append(4.324193) #299
+  sample.append(4.133639) #300
+  sample.append(3.966107) #301
+  sample.append(4.234641) #302
+  sample.append(0.744629) #303
+  sample.append(3.966615) #304
+  sample.append(3.792921) #305
+  sample.append(0.000000) #306
+  sample.append(4.237240) #307
+  sample.append(4.096264) #308
+  sample.append(4.071058) #309
+  sample.append(3.975877) #310
+  sample.append(3.886222) #311
+  sample.append(3.886222) #312
+  sample.append(3.694669) #313
+  sample.append(3.776154) #314
+  sample.append(4.110508) #315
+  sample.append(4.119073) #316
+  sample.append(4.020748) #317
+  sample.append(3.931176) #318
+  sample.append(3.931177) #319
+  sample.append(0.000000) #320
+  sample.append(3.929643) #321
+  sample.append(3.827735) #322
+  sample.append(0.634561) #323
+  sample.append(5.145152) #324
+  sample.append(4.961411) #325
+  sample.append(5.039039) #326
+  sample.append(4.739530) #327
+  sample.append(4.325072) #328
+  sample.append(0.463204) #329
+  sample.append(0.634561) #330
+  sample.append(0.000000) #331
+  sample.append(4.613133) #332
+  sample.append(4.903871) #333
+  sample.append(4.607087) #334
+  sample.append(4.287396) #335
+  sample.append(0.295320) #336
+  sample.append(0.634561) #337
+  sample.append(4.346044) #338
+  sample.append(2.256939) #339
+  sample.append(0.968915) #340
+  sample.append(0.498058) #341
+  sample.append(1.022310) #342
+  sample.append(1.086692) #343
+  sample.append(1.380610) #344
+  sample.append(1.639430) #345
+  sample.append(1.112731) #346
+  sample.append(0.968915) #347
+  sample.append(1.471541) #348
+  sample.append(0.000000) #349
+  sample.append(0.927337) #350
+  sample.append(1.482404) #351
+  sample.append(1.967686) #352
+  sample.append(1.114843) #353
+  sample.append(0.968915) #354
+  sample.append(1.776775) #355
+  sample.append(2.805316) #356
+  sample.append(0.977481) #357
+  sample.append(5.982525) #358
+  sample.append(1.401905) #359
+  sample.append(1.401905) #360
+  sample.append(2.001093) #361
+  sample.append(2.261431) #362
+  sample.append(1.182600) #363
+  sample.append(0.977481) #364
+  sample.append(1.762278) #365
+  sample.append(4.068930) #366
+  sample.append(3.258445) #367
+  sample.append(2.912313) #368
+  sample.append(2.912314) #369
+  sample.append(1.196280) #370
+  sample.append(0.977481) #371
+  sample.append(2.673644) #372
+  sample.append(2.762715) #373
+  sample.append(0.879156) #374
+  sample.append(6.016655) #375
+  sample.append(0.229778) #376
+  sample.append(0.565532) #377
+  sample.append(1.223204) #378
+  sample.append(1.828609) #379
+  sample.append(0.992046) #380
+  sample.append(0.879156) #381
+  sample.append(1.465495) #382
+  sample.append(4.623997) #383
+  sample.append(6.053906) #384
+  sample.append(3.258445) #385
+  sample.append(2.912314) #386
+  sample.append(0.977481) #387
+  sample.append(0.879156) #388
+  sample.append(2.397369) #389
+  sample.append(2.664894) #390
+  sample.append(0.789584) #391
+  sample.append(6.028709) #392
+  sample.append(0.009572) #393
+  sample.append(0.229779) #394
+  sample.append(0.549582) #395
+  sample.append(1.093048) #396
+  sample.append(0.824514) #397
+  sample.append(0.789584) #398
+  sample.append(1.145804) #399
+  sample.append(5.109279) #400
+  sample.append(6.053906) #401
+  sample.append(6.053907) #402
+  sample.append(3.136574) #403
+  sample.append(0.789584) #404
+  sample.append(0.789584) #405
+  sample.append(0.819205) #406
+  sample.append(3.670611) #407
+  sample.append(0.789584) #408
+  sample.append(4.590029) #409
+  sample.append(4.312276) #410
+  sample.append(4.279569) #411
+  sample.append(4.040213) #412
+  sample.append(3.826498) #413
+  sample.append(1.093048) #414
+  sample.append(0.789584) #415
+  sample.append(3.436912) #416
+  sample.append(4.256435) #417
+  sample.append(4.337873) #418
+  sample.append(4.119073) #419
+  sample.append(3.931176) #420
+  sample.append(3.136574) #421
+  sample.append(0.789584) #422
+  sample.append(3.927943) #423
+  sample.append(3.792921) #424
+  sample.append(0.000000) #425
+  sample.append(4.237240) #426
+  sample.append(4.096264) #427
+  sample.append(4.071058) #428
+  sample.append(3.975877) #429
+  sample.append(3.886222) #430
+  sample.append(3.886222) #431
+  sample.append(0.000000) #432
+  sample.append(3.776154) #433
+  sample.append(4.110508) #434
+  sample.append(4.119073) #435
+  sample.append(4.020748) #436
+  sample.append(3.931176) #437
+  sample.append(3.931177) #438
+  sample.append(3.694669) #439
+  sample.append(3.929643) #440
+  sample.append(2.943082) #441
+  sample.append(0.788050) #442
+  sample.append(5.878218) #443
+  sample.append(6.084674) #444
+  sample.append(0.075190) #445
+  sample.append(0.432683) #446
+  sample.append(1.249777) #447
+  sample.append(0.825022) #448
+  sample.append(0.788050) #449
+  sample.append(1.204451) #450
+  sample.append(4.918368) #451
+  sample.append(5.815236) #452
+  sample.append(5.538962) #453
+  sample.append(3.960798) #454
+  sample.append(0.786350) #455
+  sample.append(0.788050) #456
+  sample.append(3.255518) #457
+
+
+
+  out = decomp.transform(np.asarray(sample),selectedPCADimensions=210)
+  for i in range(0,len(out)): 
+    print(i," = ",out[i])
+
+