From eeaadc13f446337d1939f017ded24e9adcf90566 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Thu, 22 Aug 2019 09:35:48 -0700 Subject: [PATCH 01/56] implement FA module --- xicam/BSISB/__init__.py | 33 ++--- .../BSISB/__pycache__/__init__.cpython-37.pyc | Bin 10599 -> 10464 bytes xicam/BSISB/widgets/factorizationwidget.py | 139 ++++++++++-------- 3 files changed, 94 insertions(+), 78 deletions(-) diff --git a/xicam/BSISB/__init__.py b/xicam/BSISB/__init__.py index 0a41803..5d1aca1 100644 --- a/xicam/BSISB/__init__.py +++ b/xicam/BSISB/__init__.py @@ -95,8 +95,6 @@ def __init__(self, header: NonDBHeader = None, field: str = 'primary', ): self.imageview.setHeader(header, field='image') self.spectra.setHeader(header, field='spectra') self.header = header - # init pixel selection dict - self.pixSelection = {'ROI': None, 'Mask': None} #setup ROI item sideLen = 10 @@ -107,6 +105,7 @@ def __init__(self, header: NonDBHeader = None, field: str = 'primary', ): #constants self.path = os.path.expanduser('~/') + self.pixSelection = {'ROI': None, 'Mask': None} # init pixel selection dict # Connect signals self.imageview.sigShowSpectra.connect(self.spectra.showSpectra) @@ -274,12 +273,12 @@ def __init__(self, *args, **kwargs): # Selection model self.selectionmodel = QItemSelectionModel(self.headermodel) - self.PCA_widget = FactorizationWidget(self.headermodel, self.selectionmodel) - self.NMF_widget = FactorizationWidget(self.headermodel, self.selectionmodel) + self.FA_widget = FactorizationWidget(self.headermodel, self.selectionmodel) + # self.NMF_widget = FactorizationWidget(self.headermodel, self.selectionmodel) # update headers list when a tab window is closed - self.headermodel.rowsRemoved.connect(partial(self.PCA_widget.setHeader, 'spectra')) - self.headermodel.rowsRemoved.connect(partial(self.NMF_widget.setHeader, 'volume')) + self.headermodel.rowsRemoved.connect(partial(self.FA_widget.setHeader, 'spectra')) + # self.headermodel.rowsRemoved.connect(partial(self.NMF_widget.setHeader, 'volume')) # Setup tabviews and update map selection self.imageview = TabView(self.headermodel, self.selectionmodel, MapView, 'image') @@ -287,8 +286,8 @@ def __init__(self, *args, **kwargs): self.stages = {"MapToH5": GUILayout(self.mapToH5), "Image View": GUILayout(self.imageview), - "PCA": GUILayout(self.PCA_widget), - "NMF": GUILayout(self.NMF_widget)} + "Factor Analysis": GUILayout(self.FA_widget)} + # "NMF": GUILayout(self.NMF_widget)} super(BSISB, self).__init__(*args, **kwargs) def appendHeader(self, header: NonDBHeader, **kwargs): @@ -314,10 +313,10 @@ def appendHeader(self, header: NonDBHeader, **kwargs): currentMapView.sigAutoMaskState.connect(partial(self.appendSelection, 'autoMask')) currentMapView.sigSelectMaskState.connect(partial(self.appendSelection, 'select')) - self.PCA_widget.setHeader(field='spectra') - self.NMF_widget.setHeader(field='volume') + self.FA_widget.setHeader(field='spectra') + # self.NMF_widget.setHeader(field='volume') for i in range(4): - self.PCA_widget.roiList[i].sigRegionChangeFinished.connect(self.updateROI) + self.FA_widget.roiList[i].sigRegionChangeFinished.connect(self.updateROI) def appendSelection(self, sigCase, sigContent): # get current widget and append selectedPixels to item @@ -326,16 +325,16 @@ def appendSelection(self, sigCase, sigContent): self.headermodel.item(currentItemIdx).selectedPixels = sigContent elif sigCase == 'ROI': self.headermodel.item(currentItemIdx).roiState = sigContent - self.PCA_widget.updateRoiMask() - self.NMF_widget.updateRoiMask() + self.FA_widget.updateRoiMask() + # self.NMF_widget.updateRoiMask() elif sigCase == 'autoMask': self.headermodel.item(currentItemIdx).maskState = sigContent - self.PCA_widget.updateRoiMask() - self.NMF_widget.updateRoiMask() + self.FA_widget.updateRoiMask() + # self.NMF_widget.updateRoiMask() elif sigCase == 'select': self.headermodel.item(currentItemIdx).selectState = sigContent - self.PCA_widget.updateRoiMask() - self.NMF_widget.updateRoiMask() + self.FA_widget.updateRoiMask() + # self.NMF_widget.updateRoiMask() def updateROI(self, roi): diff --git a/xicam/BSISB/__pycache__/__init__.cpython-37.pyc b/xicam/BSISB/__pycache__/__init__.cpython-37.pyc index 87b763d4b89ce49d5f64a55030365fb12c42990a..d4721f824129c2612641a6b6d73394f6a616181e 100644 GIT binary patch delta 1193 zcmY*YZ)jUp6u;-am*g!?(lkxmr2q0i^)>7MbTUQVSjQII&9t;BY*T7Y`{K-Nm##Oj ztm>-+1A?N1dG{E{zt^(`|CpIMb;-Nta7EW-Q!@tfJxAY@v9~0P&1NiSxQwl}A+nh| z028*htxk4!d(+=hXTZ#z#&B(y9_JIdy zJikumNIf4VXegBzmQFQVDy8F7OQpP7FpCYPc%o$T!(vyh6wPLkKSfIo__HGMx&NUc zylH}0?6cl|0B_(AzHj`Za5JAfg*Sm8x0>*}9r0TL6S&@%f>FHDHV25Qz=8$4GkBY510i(Fw znTNypb?0Mn?d`5Rxa{q59HE_?wi@aL$Y3P=D&+8dSc9MJTj9Il6jgWzH+qI(67TkW z2)FI?y-|QU{Ic&HypKowx1Xj#NQSGXnp?tW&eK9!ia^q2+!yI+ zf2lQFm^YVrbfjEZe6dohXdPIHOv+obBX}cnF46*wRXQnwNvfpE9?7qSlpy#e6;wie zIXVO?UWop@Z8dFn>m=p?o9SM2k+6`_W4Xf2=&c24)hC26-#)S_#fM+TcEKS26YF*T zu26NYAEWUkT*ia(cbuP4fFHy=@f~mt!-+%Alu!}HQX&cxzM0rc-^OQ&zmIhgk>t&YDZyi1L17$i#U50fuPgeMXcR3Nc=LfYBVvW{qa+9OL|RA*wmq zGs63VvAs-l^PRM4c;;6)FP6=*vR+)}m&N{ufOiEf2oSIIw0GsCPM3l%hq!2XJvp7c zN%0Z!b|G?@mv*m0G@&)|>gz5ee7L7c=zCclcYfkeM5PW8}Q1x507`+C)O)x|GTKuhv*oEykN L*1kCKDL~~vHTEp9 delta 1307 zcmZ8gTWl0%6rMA)x9;w2r)Ar2>1FRrr*5HAu||s#l;UD{Dcyp$SZiS>b*Gmtvs2rU zZAe(3R7l|T!Dt_L2_)hhnOaFe9v~)an#N1w!)l`O1$ptoTVle4=bus(cara%|2voe zoIU^f>B!w7ala_KIr{zZ`_Zx4YvPi98>eteXoW9w&^gulyu=?A?ko_c}H} zl=-SKbU0PDr6&qg=y3(jUe`?!y>q$6tU9fpG;;Y_{HP*hi2bk4WPz{UJ({z+Ue~ksc{tzaZ zHcW8FydXXY@D6_C`&?p)t$5wRv_br+)_}Lopi~4H#Fe@@r0{m#30TH>{Y(?P$f`x3 zBuy4J??~a*`Vd?(zpMuy4&u5$;yJ_|Ut*BKzx^X{)yy`y!M}r;;eJ`tpJJvH3{rTi zF$)9uS>w}i^J3F4kimOR?XYgT0*wG27;HXE)TL$_zBAXG?}LNo&^vIWr3?D;e#;fO zYhG#%0i47;ZOgEMue9$vW7ugBG)~P{VncPZljbVd7;e%Wnn!^W|6ol2@=oIUP2TWs z#T%j`lx&p4M~qLaE!U8gSgcV;8dm~K`W{G!C6Ch^DuH5KVS)9=Hp5@!O1#!kgd%^Q zLKQB{$@Yw%pjN7mH}li;GpZh^gKQghrYGix^Dpi>?M}^1Osb({Id!2b3_ibaRLG?J zzl^^~oK=;ir|)2|WSvk}Na{30BKRs1#lp63&0I>6vMz8mR=Tli_@kR!oL zkDwS03E%_V8BM~6csv^0bG8TyKgH{rBAX^(7K%az#@TcoWhYxi(p;O`>@o5c$oDj^ zM+4da7&^&dBg5rKl2}jS|4FtcbDLH8us{6{Yr}&O^CwrC`7=M-8= zT1~d;`gP*4FE-q{LCSvCUppxr@3zL^qBh;M09Q-?$cSMegof6M4<+6C0gMyexFFg LK~vtb24MUHy0A#i diff --git a/xicam/BSISB/widgets/factorizationwidget.py b/xicam/BSISB/widgets/factorizationwidget.py index 84f45dd..08f2734 100644 --- a/xicam/BSISB/widgets/factorizationwidget.py +++ b/xicam/BSISB/widgets/factorizationwidget.py @@ -6,7 +6,7 @@ from qtpy.QtGui import QStandardItemModel from functools import partial from qtpy.QtCore import Signal -from sklearn.decomposition import PCA, NMF +from sklearn.decomposition import PCA, NMF, FastICA from sklearn.preprocessing import StandardScaler, Normalizer import numpy as np import pandas as pd @@ -31,7 +31,11 @@ def __init__(self, headermodel: QStandardItemModel, selectionmodel: QItemSelecti self.selectionmodel = selectionmodel self.parameter = Parameter(name='params', type='group', - children=[{'name': "# of Components", + children=[{'name': "Method", + 'values': ['PCA', 'NMF', 'MCR'], + 'value': 'PCA', + 'type': 'list'}, + {'name': "# of Components", 'value': 4, 'type': 'int'}, {'name': "Calculate", @@ -65,10 +69,26 @@ def __init__(self, headermodel: QStandardItemModel, selectionmodel: QItemSelecti self.setParameters(self.parameter, showTop=False) self.setIndentation(0) + #constants + self.method = 'PCA' self.parameter.child('Calculate').sigActivated.connect(self.calculate) self.parameter.child('Save results').sigActivated.connect(self.saveResults) self.parameter.child('# of Components').sigValueChanged.connect(self.setNumComponents) + self.parameter.child('Method').sigValueChanged.connect(self.setMethod) + + def setMethod(self): + if self.parameter['Method'] == 'PCA': + self.method = 'PCA' + self.field = 'spectra' + elif self.parameter['Method'] == 'NMF': + self.method = 'NMF' + self.field = 'volume' + self.parameter.child('Normalization').setValue('None') + elif self.parameter['Method'] == 'MCR': + self.method = 'MCR' + self.field = 'spectra' + self.parameter.child('Normalization').setValue('None') def setHeader(self, wavenumbers, imgShapes, rc2indList, ind2rcList, field: str): # get all headers selected @@ -81,25 +101,20 @@ def setHeader(self, wavenumbers, imgShapes, rc2indList, ind2rcList, field: str): self.imgShapes = imgShapes self.rc2indList = rc2indList self.ind2rcList = ind2rcList - self._dataSets = [] + self._dataSets = {'spectra': [], 'volume': []} - if field == 'spectra': # PCA workflow - for header in self.headers: - data = None - try: - data = header.meta_array(self.field) - except IndexError: - msg.logMessage('Header object contained no frames with field ''{field}''.', msg.ERROR) - - if data is not None: - self._dataSets.append(data) - elif field == 'volume': # NMF workflow - self.parameter.child('Normalization').setValue('None') - for header in self.headers: - volumeEvent = next(header.events(fields=['volume'])) - # readin filepath - path = volumeEvent['path'] - self._dataSets.append(path) + for header in self.headers: + data = None + try: # spectra datasets + data = header.meta_array('spectra') + except IndexError: + msg.logMessage('Header object contained no frames with field ''{field}''.', msg.ERROR) + if data is not None: + self._dataSets['spectra'].append(data) + # NMF path sets + volumeEvent = next(header.events(fields=['volume'])) + path = volumeEvent['path'] # readin filepath + self._dataSets['volume'].append(path) def setNumComponents(self): N = self.parameter['# of Components'] @@ -143,7 +158,7 @@ def calculate(self): self._allData = np.empty((0, self.N_w)) print(self.imgShapes) - for i, data in enumerate(self._dataSets): # i: map idx + for i, data in enumerate(self._dataSets['spectra']): # i: map idx if self.selectedPixelsList[i] is None: n_spectra = len(data) tmp = np.zeros((n_spectra, self.N_w)) @@ -161,32 +176,41 @@ def calculate(self): self.dataRowSplit.append(self.dataRowSplit[-1] + n_spectra) self._allData = np.append(self._allData, tmp, axis=0) - # define pop up plots labels - self.fac_method_name = 'PCA' - self.data_fac_name = 'data_PCA' - if len(self._allData) > 0: - # normalize and mean center - if self.parameter['Normalization'] == 'L1':# normalize - data_norm = Normalizer(norm='l1').fit_transform(self._allData) - elif self.parameter['Normalization'] == 'L2': - data_norm = Normalizer(norm='l2').fit_transform(self._allData) - else: - data_norm = self._allData - #subtract mean - data_centered = StandardScaler(with_std=False).fit_transform(data_norm) - # Do PCA - self.PCA = PCA(n_components=N) - self.PCA.fit(data_centered) - self.data_PCA = self.PCA.transform(data_centered) - # pop up plots - self.popup_plots() + if self.method == 'PCA': + self.data_fac_name = 'data_PCA' # define pop up plots labels + # normalize and mean center + if self.parameter['Normalization'] == 'L1':# normalize + data_norm = Normalizer(norm='l1').fit_transform(self._allData) + elif self.parameter['Normalization'] == 'L2': + data_norm = Normalizer(norm='l2').fit_transform(self._allData) + else: + data_norm = self._allData + #subtract mean + data_centered = StandardScaler(with_std=False).fit_transform(data_norm) + # Do PCA + self.PCA = PCA(n_components=N) + self.PCA.fit(data_centered) + self.data_PCA = self.PCA.transform(data_centered) + # pop up plots + self.popup_plots() + elif self.method == 'MCR': + self.data_fac_name = 'data_MCR' # define pop up plots labels + # Do MCR + self.MCR = FastICA(n_components=N) + self.data_MCR = self.MCR.fit_transform(self._allData) + # pop up plots + self.popup_plots() else: msg.logMessage('The data matrix is empty. No PCA is performed.', msg.ERROR) MsgBox('The data matrix is empty. No PCA is performed.', 'error') self.PCA, self.data_PCA = None, None - # emit PCA and transformed data : data_PCA - self.sigPCA.emit((self.wavenumbers_select, self.PCA, self.data_PCA, self.dataRowSplit)) + self.MCR, self.data_MCR = None, None + # emit PCA and transformed data + if self.method == 'PCA': + self.sigPCA.emit((self.wavenumbers_select, self.PCA, self.data_PCA, self.dataRowSplit)) + elif self.method == 'MCR': + self.sigPCA.emit((self.wavenumbers_select, self.MCR, self.data_MCR, self.dataRowSplit)) elif self.field == 'volume': # NMF workflow data_files = [] @@ -195,7 +219,7 @@ def calculate(self): self.allDataRowSplit = [0] # row split for complete datasets print(self.imgShapes) - for i, file in enumerate(self._dataSets): + for i, file in enumerate(self._dataSets['volume']): ir_data, fmt = read_map.read_all_formats(file) n_spectra = ir_data.data.shape[0] self.allDataRowSplit.append(self.allDataRowSplit[-1] + n_spectra) @@ -218,20 +242,18 @@ def calculate(self): self.dataRowSplit.append(self.dataRowSplit[-1] + n_spectra) # row split for ROI selected rows # define pop up plots labels - self.fac_method_name = 'NMF' self.data_fac_name = 'data_NMF' if len(self.df_row_idx) > 0: # aggregate datasets - ir_data_agg = aggregate_data(self._dataSets, data_files, wav_masks) + ir_data_agg = aggregate_data(self._dataSets['volume'], data_files, wav_masks) col_idx = list(set(wavROIidx) & set(ir_data_agg.master_wmask)) self.wavenumbers_select = self.wavenumbers[col_idx] ir_data_agg.data = ir_data_agg.data[:, col_idx] ir_data_agg.data = ir_data_agg.data[row_idx, :] # perform NMF - NMF_obj = NMF(n_components=N) - self.data_NMF = NMF_obj.fit_transform(ir_data_agg.data) - self.NMF = NMF_obj + self.NMF = NMF(n_components=N) + self.data_NMF = self.NMF.fit_transform(ir_data_agg.data) # pop up plots self.popup_plots() else: @@ -243,9 +265,9 @@ def calculate(self): def popup_plots(self): labels = [] - for i in range(getattr(self, self.fac_method_name).components_.shape[0]): - labels.append(self.fac_method_name + str(i + 1)) - plt.plot(self.wavenumbers_select, getattr(self, self.fac_method_name).components_[i, :], '-', + for i in range(getattr(self, self.method).components_.shape[0]): + labels.append(self.method + str(i + 1)) + plt.plot(self.wavenumbers_select, getattr(self, self.method).components_[i, :], '-', label=labels[i]) loadings_legend = plt.legend(loc='best') plt.setp(loadings_legend, draggable=True) @@ -271,8 +293,9 @@ def popup_plots(self): plt.show() def saveResults(self): - if (hasattr(self, 'PCA') and self.PCA is not None) or (hasattr(self, 'NMF') and self.NMF is not None): - name = self.fac_method_name + if (hasattr(self, 'PCA') and self.PCA is not None) or (hasattr(self, 'NMF') and self.NMF is not None)\ + or (hasattr(self, 'MCR') and self.MCR is not None): + name = self.method df_fac_components = pd.DataFrame(getattr(self, name).components_, columns=self.wavenumbers_select) df_data_fac = pd.DataFrame(getattr(self, self.data_fac_name), index=self.df_row_idx) df_fac_components.to_csv(name + '_components.csv') @@ -426,10 +449,7 @@ def updateComponents(self, i): # update PCA components if hasattr(self, '_plots'): # update plots - if self.field == 'spectra': - name = 'PCA' + str(component_index) - elif self.field == 'volume': - name = 'NMF' + str(component_index) + name = self.parameter['Method'] + str(component_index) self._plots[i].setData(self.wavenumbers, self._fac.components_[component_index - 1, :], name=name) # update legend label sample, label = self._plotLegends.items[i] @@ -471,10 +491,7 @@ def showComponents(self, fac_obj): self._plots = [] for i in range(4): component_index = self.parameter[f'Map {i + 1} Component'] - if self.field == 'spectra': - name = 'PCA' + str(component_index) - elif self.field == 'volume': - name = 'NMF' + str(component_index) + name = self.parameter['Method'] + str(component_index) # show loading plots tmp = self.componentSpectra.plot(self.wavenumbers, self._fac.components_[component_index - 1, :], name=name, pen=mkPen(self._colors[i], width=2)) From 3578f164270c468cb3ec375fd0e54068106ea5cd Mon Sep 17 00:00:00 2001 From: lchen23 Date: Fri, 23 Aug 2019 17:17:33 -0700 Subject: [PATCH 02/56] implement MCR --- xicam/BSISB/__init__.py | 8 +---- .../BSISB/__pycache__/__init__.cpython-37.pyc | Bin 10464 -> 0 bytes xicam/BSISB/widgets/factorizationwidget.py | 33 +++++++++++++----- 3 files changed, 26 insertions(+), 15 deletions(-) delete mode 100644 xicam/BSISB/__pycache__/__init__.cpython-37.pyc diff --git a/xicam/BSISB/__init__.py b/xicam/BSISB/__init__.py index 5d1aca1..b350cd3 100644 --- a/xicam/BSISB/__init__.py +++ b/xicam/BSISB/__init__.py @@ -155,6 +155,7 @@ def loadRoi(self): self.roi.show() self.autoMaskBtn.setChecked(roiStates['maskBtn']) self.selectMaskBtn.setChecked(True) + self.showSelectMask() for k, v in roiStates['parameter'].items(): self.parameter[k] = v MsgBox(f'ROI states were loaded from: \n{filePath + fileName}') @@ -274,11 +275,9 @@ def __init__(self, *args, **kwargs): self.selectionmodel = QItemSelectionModel(self.headermodel) self.FA_widget = FactorizationWidget(self.headermodel, self.selectionmodel) - # self.NMF_widget = FactorizationWidget(self.headermodel, self.selectionmodel) # update headers list when a tab window is closed self.headermodel.rowsRemoved.connect(partial(self.FA_widget.setHeader, 'spectra')) - # self.headermodel.rowsRemoved.connect(partial(self.NMF_widget.setHeader, 'volume')) # Setup tabviews and update map selection self.imageview = TabView(self.headermodel, self.selectionmodel, MapView, 'image') @@ -314,7 +313,6 @@ def appendHeader(self, header: NonDBHeader, **kwargs): currentMapView.sigSelectMaskState.connect(partial(self.appendSelection, 'select')) self.FA_widget.setHeader(field='spectra') - # self.NMF_widget.setHeader(field='volume') for i in range(4): self.FA_widget.roiList[i].sigRegionChangeFinished.connect(self.updateROI) @@ -326,16 +324,12 @@ def appendSelection(self, sigCase, sigContent): elif sigCase == 'ROI': self.headermodel.item(currentItemIdx).roiState = sigContent self.FA_widget.updateRoiMask() - # self.NMF_widget.updateRoiMask() elif sigCase == 'autoMask': self.headermodel.item(currentItemIdx).maskState = sigContent self.FA_widget.updateRoiMask() - # self.NMF_widget.updateRoiMask() elif sigCase == 'select': self.headermodel.item(currentItemIdx).selectState = sigContent self.FA_widget.updateRoiMask() - # self.NMF_widget.updateRoiMask() - def updateROI(self, roi): if self.selectionmodel.hasSelection(): diff --git a/xicam/BSISB/__pycache__/__init__.cpython-37.pyc b/xicam/BSISB/__pycache__/__init__.cpython-37.pyc deleted file mode 100644 index d4721f824129c2612641a6b6d73394f6a616181e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 10464 zcmZ`<>2Dm#b?<9tdQJ{0ikG|2Gn02s1J~%B{%k zS)OH5*^caWojYlsSYU(R^>gTi|jjTI?-(OT9DR8LpS3<=zwC6I`xDXM0b2 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z^k1isC-n(09-g~{SeE|Sbm_0HW}ivcA#>QDM}8Ybbj7WmCF&&qjS>>Sq-BsaqWlX=?o5)2rSgeDMvV*$pITkhu_{ZoWw<~E zZfQO%*TA5Pn>&5{SJfbj*_NvEyUB2GRZ&_3Iw`&ZrT_o{ diff --git a/xicam/BSISB/widgets/factorizationwidget.py b/xicam/BSISB/widgets/factorizationwidget.py index 08f2734..8c4a7dd 100644 --- a/xicam/BSISB/widgets/factorizationwidget.py +++ b/xicam/BSISB/widgets/factorizationwidget.py @@ -6,6 +6,7 @@ from qtpy.QtGui import QStandardItemModel from functools import partial from qtpy.QtCore import Signal +from pymcr.mcr import McrAR from sklearn.decomposition import PCA, NMF, FastICA from sklearn.preprocessing import StandardScaler, Normalizer import numpy as np @@ -63,6 +64,10 @@ def __init__(self, headermodel: QStandardItemModel, selectionmodel: QItemSelecti 'values': ['L2', 'L1', 'None'], 'value': 'L2', 'type': 'list'}, + {'name': "C regressor", + 'values': ['OLS', 'NNLS'], + 'value': 'OLS', + 'type': 'list'}, {'name': "Save results", 'type': 'action'} ]) @@ -71,31 +76,37 @@ def __init__(self, headermodel: QStandardItemModel, selectionmodel: QItemSelecti self.setIndentation(0) #constants self.method = 'PCA' + self.field = 'spectra' self.parameter.child('Calculate').sigActivated.connect(self.calculate) self.parameter.child('Save results').sigActivated.connect(self.saveResults) self.parameter.child('# of Components').sigValueChanged.connect(self.setNumComponents) self.parameter.child('Method').sigValueChanged.connect(self.setMethod) + self.parameter.child('C regressor').hide() def setMethod(self): if self.parameter['Method'] == 'PCA': self.method = 'PCA' self.field = 'spectra' + self.parameter.child('Normalization').setToDefault() + self.parameter.child('Normalization').show() + self.parameter.child('C regressor').hide() elif self.parameter['Method'] == 'NMF': self.method = 'NMF' self.field = 'volume' - self.parameter.child('Normalization').setValue('None') + self.parameter.child('Normalization').hide() + self.parameter.child('C regressor').hide() elif self.parameter['Method'] == 'MCR': self.method = 'MCR' self.field = 'spectra' - self.parameter.child('Normalization').setValue('None') + self.parameter.child('Normalization').hide() + self.parameter.child('C regressor').show() - def setHeader(self, wavenumbers, imgShapes, rc2indList, ind2rcList, field: str): + def setHeader(self, wavenumbers, imgShapes, rc2indList, ind2rcList): # get all headers selected # headers = [self.headermodel.itemFromIndex(i).header for i in self.selectionmodel.selectedRows()] self.headers = [self.headermodel.item(i).header for i in range(self.headermodel.rowCount())] - self.field = field self.wavenumbers = wavenumbers self.N_w = len(self.wavenumbers) self.imgShapes = imgShapes @@ -113,7 +124,7 @@ def setHeader(self, wavenumbers, imgShapes, rc2indList, ind2rcList, field: str): self._dataSets['spectra'].append(data) # NMF path sets volumeEvent = next(header.events(fields=['volume'])) - path = volumeEvent['path'] # readin filepath + path = volumeEvent['path'] # readin filepath self._dataSets['volume'].append(path) def setNumComponents(self): @@ -196,9 +207,15 @@ def calculate(self): self.popup_plots() elif self.method == 'MCR': self.data_fac_name = 'data_MCR' # define pop up plots labels + # Do ICA to find initial estimate of ST matrix + self.ICA = FastICA(n_components=N) + self.ICA.fit(self._allData) # Do MCR - self.MCR = FastICA(n_components=N) - self.data_MCR = self.MCR.fit_transform(self._allData) + self.MCR = McrAR(max_iter=100, c_regr=self.parameter['C regressor'], st_regr='NNLS', + tol_err_change=1e-6, tol_increase=0.5) + self.MCR.fit(self._allData, ST=self.ICA.components_) + self.MCR.components_ = self.MCR.ST_opt_ + self.data_MCR = self.MCR.C_opt_ # pop up plots self.popup_plots() else: @@ -553,7 +570,7 @@ def setHeader(self, field: str): MsgBox('Length of wavenumber arrays of displayed maps are not equal. \n' 'Perform PCA or NMF on these maps will lead to error.','warn') - self.parametertree.setHeader(self.wavenumbers, self.imgShapes, self.rc2indList, self.ind2rcList, field=field) + self.parametertree.setHeader(self.wavenumbers, self.imgShapes, self.rc2indList, self.ind2rcList) From e12f305f8a637ddd84b72d41fce5a0eabac2ade5 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Wed, 28 Aug 2019 16:50:03 -0700 Subject: [PATCH 03/56] add interactivity to FA components --- requirements.txt | 2 +- .../__pycache__/mapviewwidget.cpython-37.pyc | Bin 3583 -> 3583 bytes .../spectraplotwidget.cpython-37.pyc | Bin 4450 -> 4689 bytes xicam/BSISB/widgets/factorizationwidget.py | 91 +++++++++++++++--- xicam/BSISB/widgets/mapviewwidget.py | 4 - xicam/BSISB/widgets/spectraplotwidget.py | 9 +- 6 files changed, 88 insertions(+), 18 deletions(-) diff --git a/requirements.txt b/requirements.txt index 2429bca..934e790 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,4 +10,4 @@ matplotlib seaborn scikit-learn umap-learn - +pyMCR diff --git a/xicam/BSISB/widgets/__pycache__/mapviewwidget.cpython-37.pyc b/xicam/BSISB/widgets/__pycache__/mapviewwidget.cpython-37.pyc index ef8c62c3d35deb1eceae558ea68bf28bc4e1f539..31a4b7545006ba850337e7b47e90c564baafbfc7 100644 GIT binary patch delta 80 zcmew_{a>2biIr^T9Yp&6UNZV?mWjCc_;JpPGyXmyp%Tz0Ea3UZ~y=R delta 80 zcmew_{a>2biI1R?0EgKa00000 diff --git a/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc b/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc index 0a5d9fa954a9babfb05a4241a593976675fc23c2..612cb47db31a43988c4797f42d168abccbf4aa43 100644 GIT binary patch delta 1329 zcmZ8h&2Jk;6yKTs@a}s3ku-7Q6rr?Ycl}f&WzqLO14exETTH+og#59pD;(Ho5!aWTpoHH~Ajv227zImf52tl#8@0Sx1WCIq-?TEo9= z=D`RFELBW*S|4sEbFh`JDR00miMDbEr1A&*^7^iL5R=KcO!zg+O+_Yu;TOli!b4@R zz-F4Fy~wV<8zoypj+$TjH58R_rPJ~!^Qc{AJ`3bM^IZ9JJjA2<4m3so7Jcq2V-9 z#}H_x;|K=k;IaVGXAZBLWzN|+X7#=E8>`DN3Jdj@Pzhc}P*z)PXUY-RMLSEOF8?mf zUmin2Sb&g}9%{l#VrLMp25CpEWDs2-BW1;y*C3UL`RGTwD56-vto+2iy+AJU0TU4c zOpv2z>A>nFo0-0B$aOfy*8lIRn67PpygUdvFPq+bPn05APHrrERmM%T{mm~C*Fk6G7g 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*args, **kwargs) + self.addItem(self.line) + self.addItem(self.cross) + x_val = self.line.value() + idx = val2ind(x_val, x) + x_val = x[idx] + y_val = y[idx] + txt_html = f'
\ + X = {x_val: .2f}, Y = {y_val: .4f}
' + self.txt.setHtml(txt_html) + self.txt.setZValue(self.zmax - 1) + self.cross.setData([x_val], [y_val]) + self.cross.setZValue(self.zmax) + ymax = max(y) + if ymax > self.ymax: + self.ymax = ymax + self._x, self._y = x, y + self.txt.setPos(1200, 0.95 * self.ymax) + self.addItem(self.txt) + return plot_item class FactorizationWidget(QSplitter): def __init__(self, headermodel, selectionmodel): @@ -339,10 +388,10 @@ def __init__(self, headermodel, selectionmodel): self.gridwidget.setLayout(self.gridlayout) self.display = QSplitter() - self.componentSpectra = PlotWidget() + # self.componentSpectra = PlotWidget() + self.componentSpectra = ComponentPlotWidget() self._plotLegends = self.componentSpectra.addLegend() self._colors = ['r', 'g', 'b', 'y', 'c', 'm', 'w'] # color for plots - self.componentSpectra.getViewBox().invertX(True) # self.spectraROI = PlotWidget() self.NWimage = BetterButtons() @@ -361,22 +410,25 @@ def __init__(self, headermodel, selectionmodel): getattr(self, self._imageDict[i]).view.invertY(True) getattr(self, self._imageDict[i]).imageItem.setOpts(axisOrder="row-major") # set up roi item - roi = pg.PolyLineROI(positions=[[0, 0], [sideLen, 0], [sideLen, sideLen], [0, sideLen]], closed=True) + roi = PolyLineROI(positions=[[0, 0], [sideLen, 0], [sideLen, sideLen], [0, sideLen]], closed=True) roi.hide() self.roiInitState = roi.getState() self.roiList.append(roi) # set up mask item - maskItem = pg.ImageItem(np.ones((1,1)), axisOrder="row-major", autoLevels=True, opacity=0.3) + maskItem = ImageItem(np.ones((1,1)), axisOrder="row-major", autoLevels=True, opacity=0.3) maskItem.hide() self.maskList.append(maskItem) # set up select mask item - selectMaskItem = pg.ImageItem(np.ones((1, 1)), axisOrder="row-major", autoLevels=True, opacity=0.3, + selectMaskItem = ImageItem(np.ones((1, 1)), axisOrder="row-major", autoLevels=True, opacity=0.3, lut = np.array([[0, 0, 0], [255, 0, 0]])) selectMaskItem.hide() self.selectMaskList.append(selectMaskItem) + # set up image title + getattr(self, self._imageDict[i]).imageTitle = TextItem() getattr(self, self._imageDict[i]).view.addItem(roi) getattr(self, self._imageDict[i]).view.addItem(maskItem) getattr(self, self._imageDict[i]).view.addItem(selectMaskItem) + getattr(self, self._imageDict[i]).view.addItem(getattr(self, self._imageDict[i]).imageTitle) self.parametertree = FactorizationParameters(headermodel, selectionmodel) self.parameter = self.parametertree.parameter @@ -453,7 +505,6 @@ def updateRoiMask(self): except Exception: pass - def updateComponents(self, i): # i is imageview/window number # component_index is the PCA component index @@ -498,7 +549,8 @@ def showComponents(self, fac_obj): # get map ROI selected region self.selectedPixelsList = [self.headermodel.item(i).selectedPixels for i in range(self.headermodel.rowCount())] # clear plots and legends - self.componentSpectra.clear() + self.componentSpectra.plotItem.clearPlots() + self.componentSpectra.ymax = 0 for sample, label in self._plotLegends.items[:]: self._plotLegends.removeItem(label.text) @@ -512,9 +564,14 @@ def showComponents(self, fac_obj): # show loading plots tmp = self.componentSpectra.plot(self.wavenumbers, self._fac.components_[component_index - 1, :], name=name, pen=mkPen(self._colors[i], width=2)) + tmp.curve.setClickable(True) + tmp.curve.sigClicked.connect(partial(self.curveHighLight, i)) self._plots.append(tmp) # show score plots self.drawMap(component_index, i) + # update the last image and loading plots as a recalculation complete signal + N = self.parameter['# of Components'] + self.parameter.child(f'Map 4 Component').setValue(N) # clear maps else: tab_idx = self.headermodel.rowCount() - 1 @@ -523,10 +580,6 @@ def showComponents(self, fac_obj): img = np.zeros((self.imgShapes[tab_idx][0], self.imgShapes[tab_idx][1])) getattr(self, self._imageDict[i]).setImage(img=img) - # update the last image and loading plots as a recalculation complete signal - N = self.parameter['# of Components'] - self.parameter.child(f'Map 4 Component').setValue(N) - def drawMap(self, component_index, i): # i is imageview/window number data_slice = self._data_fac[self._dataRowSplit[self.selectMapIdx]:self._dataRowSplit[self.selectMapIdx + 1], @@ -542,6 +595,20 @@ def drawMap(self, component_index, i): 1]] = data_slice img = np.flipud(img) getattr(self, self._imageDict[i]).setImage(img=img) + # set imageTitle + imageTitle = getattr(self, self._imageDict[i]).imageTitle + title = self.parameter['Method'] + str(component_index) + imageTitle.setHtml(f'
{title}
') + imageTitle.setPos(0, -5) + + def curveHighLight(self, k): + for i in range(4): + if i == k: + self._plots[i].setPen(mkPen(self._colors[k], width=6)) + else: + self._plots[i].setPen(mkPen(self._colors[i], width=2)) + self.componentSpectra._x, self.componentSpectra._y = self._plots[k].getData() + self.componentSpectra.getEnergy() def setHeader(self, field: str): diff --git a/xicam/BSISB/widgets/mapviewwidget.py b/xicam/BSISB/widgets/mapviewwidget.py index 1051bec..a136d0a 100644 --- a/xicam/BSISB/widgets/mapviewwidget.py +++ b/xicam/BSISB/widgets/mapviewwidget.py @@ -14,12 +14,8 @@ def __init__(self, *args, **kwargs): # self.scene.sigMouseMoved.connect(self.showSpectra) self.scene.sigMouseClicked.connect(self.showSpectra) self.view.invertY(True) - # add arrow - # self.arrow = ArrowItem(angle=60, headLen=15, tipAngle=45, baseAngle=30, brush = (200, 80, 20)) - # self.arrow.setPos(0, 0) self.cross = PlotDataItem([0], [0], symbolBrush=(200, 0, 0), symbolPen=(200, 0, 0), symbol='+', symbolSize=16) - self.view.addItem(self.cross) self.cross.hide() #add txt diff --git a/xicam/BSISB/widgets/spectraplotwidget.py b/xicam/BSISB/widgets/spectraplotwidget.py index 9a27aa1..a9af72d 100644 --- a/xicam/BSISB/widgets/spectraplotwidget.py +++ b/xicam/BSISB/widgets/spectraplotwidget.py @@ -18,10 +18,14 @@ def __init__(self, *args, **kwargs): self._meanSpec = True # whether current spectrum is a mean spectrum self.line = InfiniteLine(movable=True) self.line.setPen((255, 255, 0, 200)) - self.line.setZValue(100) + self.line.setValue(650) self.line.sigPositionChanged.connect(self.sigEnergyChanged) self.line.sigPositionChanged.connect(self.getEnergy) self.addItem(self.line) + self.cross = PlotDataItem([650], [0], symbolBrush=(255, 0, 0), symbolPen=(255, 0, 0), symbol='+', + symbolSize=20) + self.cross.setZValue(100) + self.addItem(self.cross) self.getViewBox().invertX(True) self.selectedPixels = None self._y = None @@ -42,6 +46,7 @@ def getEnergy(self, lineobject): txt_html += f'
\ X = {x_val: .2f}, Y = {y_val: .4f}
' self.txt.setHtml(txt_html) + self.cross.setData([x_val], [y_val]) def setHeader(self, header: NonDBHeader, field: str, *args, **kwargs): self.header = header @@ -99,6 +104,7 @@ def plot(self, x, y, *args, **kwargs): # set up infinity line and get its position self.plotItem.plot(x, y, *args, **kwargs) self.addItem(self.line) + self.addItem(self.cross) x_val = self.line.value() if x_val == 0: y_val = 0 @@ -120,4 +126,5 @@ def plot(self, x, y, *args, **kwargs): ymax = max(y) self._y = y self.txt.setPos(1500, 0.95 * ymax) + self.cross.setData([x_val], [y_val]) self.addItem(self.txt) From 13d98d5ef2e0cbe63e95d11064b496ec8fcf1748 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Fri, 30 Aug 2019 14:03:03 -0700 Subject: [PATCH 04/56] add pix value reader --- .../__pycache__/mapviewwidget.cpython-37.pyc | Bin 3583 -> 3769 bytes xicam/BSISB/widgets/mapviewwidget.py | 16 ++++++++-------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/xicam/BSISB/widgets/__pycache__/mapviewwidget.cpython-37.pyc b/xicam/BSISB/widgets/__pycache__/mapviewwidget.cpython-37.pyc index 31a4b7545006ba850337e7b47e90c564baafbfc7..0950263ec8fef3f022e4313e8a581d14ba6c2427 100644 GIT binary patch delta 711 zcmXAnPe>F|9LL{p=FOX#byn2XT-VG+`?GU!CH%7`mYR48)NsqPf6Ks(vgMBQb`+H- z9SVd9BOVkafd-vAbttHV2QS^a1O*dB*Dm6rbM<@rhTmtt@5k@W_xI+#f2|+el9!Un z1i|rnqkQM*yW|JGkjI&TZL&kwwFrbJ$UW_$wyxt_7kUd3q(lYTHiW(dKzvdpE=;&g z?(3OaqvU&$7qe2i;>`upq&cdW{S|qZw%1t71}x11M;_RXvO-e{2UDXM)j#qv&A@?t zL;K*7{6WVbur1GC-^=P z^?3P)}{vi@^ZEsthk90I2(qN%(2^Y z&j`R%s%hGrAIPrJY`%APjSY6=s*7%;e9kru?&7Cpo7vvnjpAmavanPQt}K+pi1%WG z_sODpIt>=ofdd(+1r5W|vyjl3J|Lf&W3VT`n=jAoV#{?x_ie$-Am|Hi6E`Ms^Ch+U zdfz-efCl-*%0XVfw=TD)_^@&qLBuq~?qe1#2Vqp@1(~srL%%H8qiLQ~MXF2J`q{KHlLs<-zG$1QdL}4xTY|TaP_0bV=B?162Lo^KB;g@VN%|$ z*=$gDG4t@JE(Wm{F7h7vx8`+EkE%GQ(4^3%(4s&Qv0jZhpHkDJ!lC~y`PCU}K33OU XOC8G9U3KeYBV4L1uJRe_Ccpg$7p|k= delta 571 zcmXw%&ubGw6vyYyPG+;)-O@&CYEs)^vAR@|T5GXLgN7m$68b}FEs6xQYAW4^*=;eh z1wqi0UfxX+60e^0Q0%=3o&*t*^&)r@{0pi@-xz$u`!MhEzTY>8**W|t?_P9Wn^7$N zx^;c>wYzPM9-%6R7HhLD9zo1ww#py!EhA-8W~r>UXF@wYkB&(qKY4&N_|P{ zMy(oEJyR9*bVq`h>~nIG>ac*1>|uOm@4y23UGYF!4=rk3*0@6OjE=N~`&~IGW54q> zQ=xt$t6|h^_z4dhY7E~xFUQBU=B!4a#;C?24M9kZFj8t>mkS#IR;}O@x0ucL9PAd3 VQ2Kv(y-6zASZg%ZGJbbI{{@o>hd%%S diff --git a/xicam/BSISB/widgets/mapviewwidget.py b/xicam/BSISB/widgets/mapviewwidget.py index a136d0a..d936a53 100644 --- a/xicam/BSISB/widgets/mapviewwidget.py +++ b/xicam/BSISB/widgets/mapviewwidget.py @@ -25,12 +25,11 @@ def __init__(self, *args, **kwargs): def setEnergy(self, lineobject): E = lineobject.value() # map E to index - i = val2ind(E, self.wavenumbers) - # print('E:', E, 'wav:', self.wavenumbers[i]) - self.setCurrentIndex(i) + idx = val2ind(E, self.wavenumbers) + self._image = self._data[idx] + self.setCurrentIndex(idx) def showSpectra(self, event): - pos = event.pos() if self.view.sceneBoundingRect().contains(pos): # Note, when axes are added, you must get the view with self.view.getViewBox() mousePoint = self.view.mapSceneToView(pos) @@ -40,14 +39,14 @@ def showSpectra(self, event): ind = self.rc2ind[(y,x)] self.sigShowSpectra.emit(ind) # print(x, y, ind, x + y * self.n_col) - - #update arrow + #update crosshair self.cross.setData([x + 0.5], [self.row - y - 0.5]) self.cross.show() # update text - self.txt.setHtml(f'
X: {x}
\ + self.txt.setHtml(f'
Point: #{ind}
\ +
X: {x}
\
Y: {y}
\ -
Point: #{ind}
') +
Val: {self._image[self.row - y -1, x]: .4f}
') except Exception: self.cross.hide() @@ -73,6 +72,7 @@ def setHeader(self, header: NonDBHeader, field: str, *args, **kwargs): # kwargs['transform'] = QTransform(1, 0, 0, -1, 0, data.shape[-2]) self.setImage(img=data, *args, **kwargs) self._data = data + self._image = self._data[0] def updateImage(self, autoHistogramRange=True): super(MapViewWidget, self).updateImage(autoHistogramRange) From 53a4e63c001e4dd597dc40da98b201485d1a0720 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Fri, 30 Aug 2019 17:04:11 -0700 Subject: [PATCH 05/56] fix the crosshair bug in mapconvertwidget --- xicam/BSISB/__init__.py | 5 +---- .../spectraplotwidget.cpython-37.pyc | Bin 4689 -> 4702 bytes xicam/BSISB/widgets/mapconvertwidget.py | 3 ++- xicam/BSISB/widgets/spectraplotwidget.py | 1 + 4 files changed, 4 insertions(+), 5 deletions(-) diff --git a/xicam/BSISB/__init__.py b/xicam/BSISB/__init__.py index b350cd3..ccb4877 100644 --- a/xicam/BSISB/__init__.py +++ b/xicam/BSISB/__init__.py @@ -5,6 +5,7 @@ from qtpy.QtWidgets import * import pickle import pyqtgraph as pg +from pyqtgraph.parametertree import ParameterTree, Parameter import numpy as np from xicam.core.data import NonDBHeader from xicam.BSISB.widgets.uiwidget import MsgBox, uiSaveFile, uiGetFile @@ -12,12 +13,8 @@ from xicam.BSISB.widgets.mapviewwidget import MapViewWidget from xicam.BSISB.widgets.spectraplotwidget import SpectraPlotWidget from xicam.BSISB.widgets.factorizationwidget import FactorizationWidget - from xicam.plugins import GUIPlugin, GUILayout from xicam.gui.widgets.tabview import TabView -from pyqtgraph.parametertree import ParameterTree, Parameter - - class MapView(QSplitter): sigRoiPixels = Signal(object) diff --git a/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc b/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc index 612cb47db31a43988c4797f42d168abccbf4aa43..3b0efefc1ead6da0c6ef33a6a654b270f709ef6b 100644 GIT binary patch delta 579 zcmcbpa!-ZViIB6*O-AUy~R?HlV4IK31n(A6@g5@#h8h%k-In}zdRW1iA49w zx?Iu58VL18AU3)V!SvLU;M5$Tw^LIBGAmMZiv2e4=32nWs4+Q)Cj{urjXYWksJ^_# z53Qh)!-4oFf2N4KZJTvaXN{0IfWMh5!Hn delta 545 zcmcboa#4lXiIY%5lM-FaQu9h~v7{uH zBtlGp8*_`ZIJLwhH8CZ%$ar!D=NdBupuSry1v&X8U>9jJ6@g5@#h8h%k-In}zdRW1 z(nPn(R$S3C>Oh51^+g~yvX03cxx^TKH=p2Iz{sdRIg=*@=&5}?S_-J1y2TGN)HgLT z4{C7WW)5Ca<{*8LX{e!81afGRA&6iG5k^4b7ISW5MG-h;*?`^($S*E30Saq!LH$s1 zi?I?KI+ODSlvY@Qh)nJioFf2N L4UL`2RzfNOBH4KI diff --git a/xicam/BSISB/widgets/mapconvertwidget.py b/xicam/BSISB/widgets/mapconvertwidget.py index 3753a54..3f732cb 100644 --- a/xicam/BSISB/widgets/mapconvertwidget.py +++ b/xicam/BSISB/widgets/mapconvertwidget.py @@ -7,7 +7,6 @@ from xicam.BSISB.widgets.uiwidget import MsgBox, YesNoDialog, uiGetFile, uiGetDir, uiSaveFile from xicam.BSISB.widgets.mapviewwidget import MapViewWidget from xicam.BSISB.widgets.spectraplotwidget import SpectraPlotWidget - from lbl_ir.data_objects import ir_map from lbl_ir.io_tools.read_omnic import read_and_convert @@ -129,6 +128,8 @@ def updateImage(self, row, col, wavenumbers, rc2ind, dataCube): self.imageview.row, self.imageview.col = row, col self.imageview.wavenumbers = wavenumbers self.imageview.rc2ind = rc2ind + self.imageview._data = dataCube + self.imageview._image = self.imageview._data[0] self.imageview.setImage(img=dataCube) def saveBtnClicked(self): diff --git a/xicam/BSISB/widgets/spectraplotwidget.py b/xicam/BSISB/widgets/spectraplotwidget.py index a9af72d..afed59e 100644 --- a/xicam/BSISB/widgets/spectraplotwidget.py +++ b/xicam/BSISB/widgets/spectraplotwidget.py @@ -27,6 +27,7 @@ def __init__(self, *args, **kwargs): self.cross.setZValue(100) self.addItem(self.cross) self.getViewBox().invertX(True) + self.spectrumInd = 0 self.selectedPixels = None self._y = None From 81a544c2a9f2bf43b2e0d05a3674fbe628f7f57f Mon Sep 17 00:00:00 2001 From: lchen23 Date: Mon, 9 Sep 2019 12:26:24 -0700 Subject: [PATCH 06/56] show file open, FA calculate message in status bar --- xicam/BSISB/__init__.py | 6 +++++- .../spectraplotwidget.cpython-37.pyc | Bin 4702 -> 4705 bytes xicam/BSISB/widgets/factorizationwidget.py | 4 +--- xicam/BSISB/widgets/spectraplotwidget.py | 2 +- 4 files changed, 7 insertions(+), 5 deletions(-) diff --git a/xicam/BSISB/__init__.py b/xicam/BSISB/__init__.py index ccb4877..9e6ec88 100644 --- a/xicam/BSISB/__init__.py +++ b/xicam/BSISB/__init__.py @@ -7,6 +7,7 @@ import pyqtgraph as pg from pyqtgraph.parametertree import ParameterTree, Parameter import numpy as np +from xicam.core import msg from xicam.core.data import NonDBHeader from xicam.BSISB.widgets.uiwidget import MsgBox, uiSaveFile, uiGetFile from xicam.BSISB.widgets.mapconvertwidget import mapToH5 @@ -287,8 +288,11 @@ def __init__(self, *args, **kwargs): super(BSISB, self).__init__(*args, **kwargs) def appendHeader(self, header: NonDBHeader, **kwargs): + # get fileName and update status bar + fileName = header.startdoc.get('sample_name', '????') + msg.showMessage(f'Opening {fileName}.h5') # init item - item = QStandardItem(header.startdoc.get('sample_name', '????') + '_' + str(self.headermodel.rowCount())) + item = QStandardItem(fileName + '_' + str(self.headermodel.rowCount())) item.header = header item.selectedPixels = None diff --git a/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc b/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc index 3b0efefc1ead6da0c6ef33a6a654b270f709ef6b..f78d7e98126611424a29013d54dff6e94c5b6935 100644 GIT binary patch delta 205 zcmcbo@=%4>iI Date: Tue, 10 Sep 2019 15:45:51 -0700 Subject: [PATCH 07/56] allow npy format import --- .../__pycache__/mapviewwidget.cpython-37.pyc | Bin 3769 -> 3769 bytes .../spectraplotwidget.cpython-37.pyc | Bin 4705 -> 4705 bytes xicam/BSISB/widgets/mapconvertwidget.py | 69 +++++++++++------- 3 files changed, 43 insertions(+), 26 deletions(-) diff --git a/xicam/BSISB/widgets/__pycache__/mapviewwidget.cpython-37.pyc b/xicam/BSISB/widgets/__pycache__/mapviewwidget.cpython-37.pyc index 0950263ec8fef3f022e4313e8a581d14ba6c2427..3dac6ffda2f2ca08009b93267a931c694fb1bec8 100644 GIT binary patch delta 20 acmdlfyHl3iiIA') self.T2AConvert.setChecked(True) # Assemble widgets - self.toollayout.addWidget(self.openBtn) + self.toollayout.addWidget(self.openMapBtn) + self.toollayout.addWidget(self.openNpyBtn) self.toollayout.addWidget(self.saveBtn) self.toollayout.addWidget(self.batchBtn) self.toollayout.addWidget(QLabel('Sample Name:')) @@ -66,19 +69,29 @@ def __init__(self): # Connect signals self.imageview.sigShowSpectra.connect(self.spectra.showSpectra) self.spectra.sigEnergyChanged.connect(self.imageview.setEnergy) - self.openBtn.clicked.connect(self.openBtnClicked) + self.openMapBtn.clicked.connect(self.openBtnClicked) + self.openNpyBtn.clicked.connect(self.openNpy) self.saveBtn.clicked.connect(self.saveBtnClicked) self.batchBtn.clicked.connect(self.batchBtnClicked) # Constants self.path = os.path.dirname(sys.path[1]) self.minYLimit = 5 self.epsilon = 1e-10 + self.fileFormat = 'map' + + def openNpy(self): + self.fileFormat = 'npy' + self.T2AConvert.setChecked(True) + self.openBtnClicked() def openBtnClicked(self): # open omnic map file - self.filePath, self.fileName, canceled = uiGetFile('Open map file', self.path, "Omnic Map Files (*.map)") + if self.fileFormat == 'map': + self.filePath, self.fileName, canceled = uiGetFile('Open map file', self.path, "Omnic Map Files (*.map)") + elif self.fileFormat == 'npy': + self.filePath, self.fileName, canceled = uiGetFile('Open npy file', self.path, "Numpy array Files (*.npy)") if canceled: - self.infoBox.setText('Open map canceled.') + self.infoBox.setText('Open file canceled.') return # set sample_id if self.sampleName.text() == 'None': @@ -87,31 +100,35 @@ def openBtnClicked(self): sample_info = ir_map.sample_info(sample_id=self.sampleName.text()) #try to open omnic map file try: - self.irMap = read_and_convert(self.filePath + self.fileName, sample_info=sample_info) + if self.fileFormat == 'map': + self.irMap = read_and_convert(self.filePath + self.fileName, sample_info=sample_info) + elif self.fileFormat == 'npy': + self.irMap = read_npy(self.filePath + self.fileName, sample_info=sample_info) except Exception as error: self.infoBox.setText(error.args[0] + f'\nFailed to open file: {self.fileName}.') else: - # check whether to perform T->A conversion - spec0 = self.irMap.imageCube[0, 0, :] - maxSpecY = np.max(spec0) - if (not self.T2AConvert.isChecked()) and (maxSpecY >= self.minYLimit): - userMsg = YesNoDialog(f'max(Y) of the first spectrum is greater than {self.minYLimit}, \ - while the "Auto T->A" box is not checked. \nPlease make sure data format is in absorbance.\ - \nDo you want to perform "Auto T->A" conversion?') - userChoice = userMsg.choice() - if userChoice == QMessageBox.Yes: - self.T2AConvert.setChecked(True) + if self.fileFormat == 'map': + # check whether to perform T->A conversion + spec0 = self.irMap.imageCube[0, 0, :] + maxSpecY = np.max(spec0) + if (not self.T2AConvert.isChecked()) and (maxSpecY >= self.minYLimit): + userMsg = YesNoDialog(f'max(Y) of the first spectrum is greater than {self.minYLimit}, \ + while the "Auto T->A" box is not checked. \nPlease make sure data format is in absorbance.\ + \nDo you want to perform "Auto T->A" conversion?') + userChoice = userMsg.choice() + if userChoice == QMessageBox.Yes: + self.T2AConvert.setChecked(True) + self.irMap.imageCube = -np.log10(self.irMap.imageCube / 100 + self.epsilon) + self.irMap.data = -np.log10(self.irMap.data / 100 + self.epsilon) + self.infoBox.setText(f'User chooses to perform T->A conversion in {self.fileName}.') + else: + self.infoBox.setText(f'User chooses not to perform T->A conversion in {self.fileName}.') + elif maxSpecY >= self.minYLimit: self.irMap.imageCube = -np.log10(self.irMap.imageCube / 100 + self.epsilon) self.irMap.data = -np.log10(self.irMap.data / 100 + self.epsilon) - self.infoBox.setText(f'User chooses to perform T->A conversion in {self.fileName}.') + self.infoBox.setText(f'T->A conversion is performed in {self.fileName}.') else: - self.infoBox.setText(f'User chooses not to perform T->A conversion in {self.fileName}.') - elif maxSpecY >= self.minYLimit: - self.irMap.imageCube = -np.log10(self.irMap.imageCube / 100 + self.epsilon) - self.irMap.data = -np.log10(self.irMap.data / 100 + self.epsilon) - self.infoBox.setText(f'T->A conversion is performed in {self.fileName}.') - else: - self.infoBox.setText(f"{self.fileName}'s datatype is absorbance. \nT->A conversion is not performed.") + self.infoBox.setText(f"{self.fileName}'s datatype is absorbance. \nT->A conversion is not performed.") self.dataCube = np.moveaxis(np.flipud(self.irMap.imageCube), -1, 0) # set up required data/properties in self.imageview From 005814d67a985429f57d0126ef4a8614676549ed Mon Sep 17 00:00:00 2001 From: lchen23 Date: Tue, 10 Sep 2019 22:24:47 -0700 Subject: [PATCH 08/56] fix open npy bug --- xicam/BSISB/widgets/mapconvertwidget.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/xicam/BSISB/widgets/mapconvertwidget.py b/xicam/BSISB/widgets/mapconvertwidget.py index f41ba91..1a3a94d 100644 --- a/xicam/BSISB/widgets/mapconvertwidget.py +++ b/xicam/BSISB/widgets/mapconvertwidget.py @@ -81,8 +81,9 @@ def __init__(self): def openNpy(self): self.fileFormat = 'npy' - self.T2AConvert.setChecked(True) + self.T2AConvert.setChecked(False) self.openBtnClicked() + self.fileFormat = 'map' def openBtnClicked(self): # open omnic map file @@ -90,6 +91,7 @@ def openBtnClicked(self): self.filePath, self.fileName, canceled = uiGetFile('Open map file', self.path, "Omnic Map Files (*.map)") elif self.fileFormat == 'npy': self.filePath, self.fileName, canceled = uiGetFile('Open npy file', self.path, "Numpy array Files (*.npy)") + if canceled: self.infoBox.setText('Open file canceled.') return From f21d5aa6ae24743b57d52f6fbb3d2e68b7be3543 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Fri, 13 Sep 2019 16:55:51 -0700 Subject: [PATCH 09/56] fix bugs in mapconvert and spectraview --- xicam/BSISB/widgets/mapconvertwidget.py | 3 ++- xicam/BSISB/widgets/spectraplotwidget.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/xicam/BSISB/widgets/mapconvertwidget.py b/xicam/BSISB/widgets/mapconvertwidget.py index 1a3a94d..5b10d24 100644 --- a/xicam/BSISB/widgets/mapconvertwidget.py +++ b/xicam/BSISB/widgets/mapconvertwidget.py @@ -8,7 +8,8 @@ from xicam.BSISB.widgets.mapviewwidget import MapViewWidget from xicam.BSISB.widgets.spectraplotwidget import SpectraPlotWidget from lbl_ir.data_objects import ir_map -from lbl_ir.io_tools.read_omnic import read_and_convert, read_npy +from lbl_ir.io_tools.read_omnic import read_and_convert +from lbl_ir.io_tools.read_numpy import read_npy class mapToH5(QSplitter): def __init__(self): diff --git a/xicam/BSISB/widgets/spectraplotwidget.py b/xicam/BSISB/widgets/spectraplotwidget.py index 0cac753..cdb0fbf 100644 --- a/xicam/BSISB/widgets/spectraplotwidget.py +++ b/xicam/BSISB/widgets/spectraplotwidget.py @@ -69,7 +69,7 @@ def setHeader(self, header: NonDBHeader, field: str, *args, **kwargs): self._data = data def showSpectra(self, i=0): - if self._data is not None: + if (self._data is not None) and (i < len(self._data)): self.clear() self._meanSpec = False self.spectrumInd = i From b7449d8203c107e3ae938a975bb5bea9fecbb101 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Fri, 13 Sep 2019 16:57:55 -0700 Subject: [PATCH 10/56] implement ROI for sparse image --- xicam/BSISB/__init__.py | 44 +++++++++++++++++++++++++++++++---------- 1 file changed, 34 insertions(+), 10 deletions(-) diff --git a/xicam/BSISB/__init__.py b/xicam/BSISB/__init__.py index 9e6ec88..af52f2c 100644 --- a/xicam/BSISB/__init__.py +++ b/xicam/BSISB/__init__.py @@ -11,8 +11,9 @@ from xicam.core.data import NonDBHeader from xicam.BSISB.widgets.uiwidget import MsgBox, uiSaveFile, uiGetFile from xicam.BSISB.widgets.mapconvertwidget import mapToH5 +from xicam.BSISB.widgets.xasimagewidget import xasImageView from xicam.BSISB.widgets.mapviewwidget import MapViewWidget -from xicam.BSISB.widgets.spectraplotwidget import SpectraPlotWidget +from xicam.BSISB.widgets.xasimagewidget import xasSpectraWidget from xicam.BSISB.widgets.factorizationwidget import FactorizationWidget from xicam.plugins import GUIPlugin, GUILayout from xicam.gui.widgets.tabview import TabView @@ -28,7 +29,7 @@ def __init__(self, header: NonDBHeader = None, field: str = 'primary', ): # layout set up self.setOrientation(Qt.Vertical) self.imageview = MapViewWidget() - self.spectra = SpectraPlotWidget() + self.spectra = xasSpectraWidget() self.imageview_and_toolbar = QSplitter() self.imageview_and_toolbar.setOrientation(Qt.Horizontal) @@ -164,13 +165,22 @@ def roiMove(self, roi): roiState = roi.getState() self.roi.setState(roiState) - def getImgShape(self, imgShape): + def getImgShape(self, imgShape, rc2ind): self.row, self.col = imgShape[0], imgShape[1] + self.rc2ind = rc2ind + # determine whether spectra data is sparse + if len(rc2ind) == self.row * self.col: + self.isDenseImage = True + else: + self.isDenseImage = False #set up X,Y grid x = np.linspace(0, self.col - 1, self.col) y = np.linspace(self.row - 1, 0, self.row) self.X, self.Y = np.meshgrid(x, y) - self.fullMap = list(zip(self.Y.ravel(), self.X.ravel())) + if self.isDenseImage: + self.fullMap = list(zip(self.Y.ravel(), self.X.ravel())) + else: + self.fullMap = list(rc2ind.keys()) # setup automask item self.autoMask = np.ones((self.row, self.col)) self.autoMaskItem = pg.ImageItem(self.autoMask, axisOrder="row-major", autoLevels=True, opacity=0.3) @@ -237,8 +247,15 @@ def intersectSelection(self, selector, selectedPixels): self.pixSelection['ROI'] = list(reverseROI) if (self.pixSelection['ROI'] is None) and (self.pixSelection['Mask'] is None): - self.sigRoiPixels.emit(None) # no ROI, select all pixels - self.selectMask = np.ones((self.row, self.col)) + if self.isDenseImage: + self.sigRoiPixels.emit(None) # no ROI, select all pixels + self.selectMask = np.ones((self.row, self.col)) + else: + allSelected = np.array(list(self.rc2ind.keys()), dtype='int') + self.sigRoiPixels.emit(allSelected) # no ROI, select all sparse rc2ind + self.selectMask = np.zeros((self.row, self.col)) + self.selectMask[allSelected[:, 0], allSelected[:, 1]] = 1 + self.selectMask = np.flipud(self.selectMask) return elif self.pixSelection['ROI'] is None: allSelected = set(self.pixSelection['Mask']) #de-duplication of pixels @@ -247,7 +264,12 @@ def intersectSelection(self, selector, selectedPixels): else: allSelected = set(self.pixSelection['ROI']) & set(self.pixSelection['Mask']) - allSelected = np.array(list(allSelected), dtype='int') # convert to array + if self.isDenseImage: + allSelected = np.array(list(allSelected), dtype='int') # convert to array + else: + allSelected &= set(self.rc2ind.keys()) + allSelected = np.array(list(allSelected), dtype='int') + self.selectMask = np.zeros((self.row, self.col)) if len(allSelected) > 0: self.selectMask[allSelected[:, 0], allSelected[:, 1]] = 1 @@ -265,6 +287,7 @@ class BSISB(GUIPlugin): def __init__(self, *args, **kwargs): + self.xas = xasImageView() self.mapToH5 = mapToH5() # Data model self.headermodel = QStandardItemModel() @@ -281,8 +304,8 @@ def __init__(self, *args, **kwargs): self.imageview = TabView(self.headermodel, self.selectionmodel, MapView, 'image') self.imageview.currentChanged.connect(self.updateTab) - self.stages = {"MapToH5": GUILayout(self.mapToH5), - "Image View": GUILayout(self.imageview), + self.stages = {'Xas View': GUILayout(self.xas), + "ROI View": GUILayout(self.imageview), "Factor Analysis": GUILayout(self.FA_widget)} # "NMF": GUILayout(self.NMF_widget)} super(BSISB, self).__init__(*args, **kwargs) @@ -302,11 +325,12 @@ def appendHeader(self, header: NonDBHeader, **kwargs): # read out image shape imageEvent = next(header.events(fields=['image'])) imgShape = imageEvent['imgShape'] + rc2ind = imageEvent['rc_index'] # get current MapView widget currentMapView = self.imageview.currentWidget() # transmit imgshape to currentMapView - currentMapView.getImgShape(imgShape) + currentMapView.getImgShape(imgShape, rc2ind) # get xy coordinates of ROI selected pixels currentMapView.sigRoiPixels.connect(partial(self.appendSelection, 'pixel')) currentMapView.sigRoiState.connect(partial(self.appendSelection, 'ROI')) From 6caff125e80ac353eace906e3e4efe805c6a9f51 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Fri, 13 Sep 2019 17:06:55 -0700 Subject: [PATCH 11/56] change back from XAS view to maptoH5 --- xicam/BSISB/__init__.py | 10 ++++------ .../spectraplotwidget.cpython-37.pyc | Bin 4705 -> 4724 bytes 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/xicam/BSISB/__init__.py b/xicam/BSISB/__init__.py index af52f2c..068d08d 100644 --- a/xicam/BSISB/__init__.py +++ b/xicam/BSISB/__init__.py @@ -11,9 +11,8 @@ from xicam.core.data import NonDBHeader from xicam.BSISB.widgets.uiwidget import MsgBox, uiSaveFile, uiGetFile from xicam.BSISB.widgets.mapconvertwidget import mapToH5 -from xicam.BSISB.widgets.xasimagewidget import xasImageView from xicam.BSISB.widgets.mapviewwidget import MapViewWidget -from xicam.BSISB.widgets.xasimagewidget import xasSpectraWidget +from xicam.BSISB.widgets.spectraplotwidget import SpectraPlotWidget from xicam.BSISB.widgets.factorizationwidget import FactorizationWidget from xicam.plugins import GUIPlugin, GUILayout from xicam.gui.widgets.tabview import TabView @@ -29,7 +28,7 @@ def __init__(self, header: NonDBHeader = None, field: str = 'primary', ): # layout set up self.setOrientation(Qt.Vertical) self.imageview = MapViewWidget() - self.spectra = xasSpectraWidget() + self.spectra = SpectraPlotWidget() self.imageview_and_toolbar = QSplitter() self.imageview_and_toolbar.setOrientation(Qt.Horizontal) @@ -287,7 +286,6 @@ class BSISB(GUIPlugin): def __init__(self, *args, **kwargs): - self.xas = xasImageView() self.mapToH5 = mapToH5() # Data model self.headermodel = QStandardItemModel() @@ -304,8 +302,8 @@ def __init__(self, *args, **kwargs): self.imageview = TabView(self.headermodel, self.selectionmodel, MapView, 'image') self.imageview.currentChanged.connect(self.updateTab) - self.stages = {'Xas View': GUILayout(self.xas), - "ROI View": GUILayout(self.imageview), + self.stages = {'MapToH5': GUILayout(self.mapToH5), + "Image View": GUILayout(self.imageview), "Factor Analysis": GUILayout(self.FA_widget)} # "NMF": GUILayout(self.NMF_widget)} super(BSISB, self).__init__(*args, **kwargs) diff --git a/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc b/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc index cbbf303fc545a93b69c7a8a16314263a06b9f2e4..b3b813dc0124e8f32093bd047ae7307b20bfe9f2 100644 GIT binary patch delta 103 zcmaE;@W5r ug$#@gDL}D!<{A*m0u*Bfs$iRZpHq~By+|CWy~t_ucg_GtiOn8dk{kdxHyI-U delta 100 zcmeyO@=%4>iIU|h(+$dJNV!w}C@ u10tD$Vk|%vtRPi2jFJosnSk;P!3>&Aer}p Date: Sat, 14 Sep 2019 21:41:05 -0700 Subject: [PATCH 12/56] add 3 initial params to make spectraplot more customizable --- .../spectraplotwidget.cpython-37.pyc | Bin 4724 -> 5201 bytes xicam/BSISB/widgets/spectraplotwidget.py | 23 ++++++++++++------ 2 files changed, 16 insertions(+), 7 deletions(-) diff --git a/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc b/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc index b3b813dc0124e8f32093bd047ae7307b20bfe9f2..5ce648b3c3dea68f7a2ea2d3553edee7545442e4 100644 GIT binary patch delta 1907 zcmZ8iO>7%Q6rNe{+UuY7UrgGRHeHDVbs+trrHG`Zv}&Odr8Oifm8;flJ>$gf`bRUH zzp|Aj8dQN)B(xGI4$cvALg9kMfg2n_;=&xbq8va7aVESsag*Y)=G*sX-pqXS&71K* zhkibsJ?}Uv0%iL9`!^r%`7HZy&qNoBd>??5DrKZ<@EA8A(5l5s4@rYElUZxYYMjMc zVvST2+~!GUe_^mBORbsJ6nlzgn6pN!X_jR_cLbYHpU4M^$0A z%j<#gDt;$;ugQAzJlOoiAmn!>LC8;M=jx9s#lg3s=Ag>I00I)wkUTK%7@-+jpHfN~ z__bJQFypQz4#PKgm&#*-$$s3gQn4d=om>W)|4VAjK>ee?-F`sii}5C*9Am z#5zo!jD;6UNYG$aSygIHqwQqac{$S(K2JJ1FPe%o92 zSgYRQjYhNHdfa+eo4 zuoSqr?Kb!Nq_@(PJB+Rf(1L6t+?%ZU+>_juD8?>Dcggb?xqF)j%bd5}W3Ja`?(rjY zWvUXb&XRZA=Vfzq3uX|QR>ILU{s?9{CUVhN#?187%94v+E(G2bW!GvbO|7P^8uJ2A zWzR_od_lg8LUe>mKe2jLjQzSVu1v}OMl~_eUhKz(UI2*xj@>Pdf#E=v696Ppq;a}; zqh&H<{}J$&r8gjwV2g2tg9tB1f5Z?Y|nYxW~9S9oQLTfhdh3)f!nt8C~s6>SbrmzE2i z<-sd)HL9d;oS1~6gLr}V@Q4E3h*|E#yTVzexx#&UZR5Ap=k$!$u7V@Zsu-Nsv(A;Nm#Hr}(%&4P};2rR_+`nWd-2cn1a>({=?zIPutI@b~ zJ(q?N_ruc<4VUBi2Cx delta 1398 zcmZ8hOK%)S5bmDonVp%P*_R(-c{+{-9)lAe5h3J&FyRpr9D|X7L17uT+ne2t*Q=h1 zajZoavZW}Nf`mpwIdE7XIXjo|3%J55YWWXXLL5*eBo07Tdy^2)s=ltSuIhSp)&4qt zZ>lsG1RjBJf4YCEGxmMypA8eY@TFe>2oh0FB14#hZPLiPPL?RA+}Ls=o12_%k*FXX z;d1LogWJ5Y#UhVaxy!vR8Wnkw`=H_TfR|tmc$rsVEnO#!MCC;i)>L(|Cz_dz7m}6i zlQwUOjjTzve?l-42dE5`{sxedjP}W<(J}h8Z+u57;iOL*X2v#gwe-&BpwlR14#s&C zdACnG?!e>b4p?Zu$k~0q;|~ImmG191hJx~eU{FB=HoeGN2f zgAwli&!RTuM|a3zj8oik3v|bIh(|O&#tD?5IR9}-RbhE$HU26QA)DO)7c!MT0v2Nb z7l8J*G2Dix^z#vRj@I)_?CJa#yIU|7OGUDjZ(CO?rOyHoTHAGPZaf(RudLNL zxhkX!ysRTUj__Q5!+EB93>6Bg$j-)D9NKawzvIk~9!CWYCy+Y|FuD7;6HsvOcysmF zQF;bIv94Illr2_8H%nzbZ+h>{AAy6gAZJkNfu?*Ow=V!hetPioZ=;c796TR5d*D&I z5Yc-g=0cv#|MGsifLF>n0A)235zAo;C}2HkAe~oeXrGF<4_{YIFRy$(?6-IG-;1BW zG7Y9feTn>Gi)u?`iz0yz6?~z+CX#f1cisPieyMAB9xb$)j>+iul(jD8N-E#NkSrN@ zTS8gc)m|c`j{PiJyo85rS;SrCY4t|Zz9J6ZE#IE%8=LeSw{Jkhm7qEOK^Z5p?~alkLr(x3RPuV|DH`mas@$L( z?LCW-){_4Icq6PT2djB56U!0;!Eb&1 z@PA2m9P`NcYG*nVcqLAUZexiRQ)Ze;`98|#5wL>tGXzXp1&u~CiPN+xlBCgi7(+= 0) and (txtPosRatio <= 1), 'Please set txtPosRatio value between 0 and 1.' + self.txtPosRatio = txtPosRatio self.positionmap = dict() self.wavenumbers = None - self._meanSpec = True # whether current spectrum is a mean spectrum + self._meanSpec = True # whether current spectrum is a mean spectrum self.line = InfiniteLine(movable=True) self.line.setPen((255, 255, 0, 200)) - self.line.setValue(650) + self.line.setValue(linePos) self.line.sigPositionChanged.connect(self.sigEnergyChanged) self.line.sigPositionChanged.connect(self.getEnergy) self.addItem(self.line) - self.cross = PlotDataItem([650], [0], symbolBrush=(255, 0, 0), symbolPen=(255, 0, 0), symbol='+', + self.cross = PlotDataItem([linePos], [0], symbolBrush=(255, 0, 0), symbolPen=(255, 0, 0), symbol='+', symbolSize=20) self.cross.setZValue(100) self.addItem(self.cross) - self.getViewBox().invertX(True) + self.getViewBox().invertX(invertX) self.spectrumInd = 0 self.selectedPixels = None self._y = None @@ -124,8 +132,9 @@ def plot(self, x, y, *args, **kwargs): txt_html += f'
\ X = {x_val: .2f}, Y = {y_val: .4f}
' self.txt = TextItem(html=txt_html, anchor=(0, 0)) - ymax = max(y) + ymax = np.max(y) self._y = y - self.txt.setPos(1500, 0.95 * ymax) + r = self.txtPosRatio + self.txt.setPos(r * x[-1] + (1 - r) * x[0], 0.95 * ymax) self.cross.setData([x_val], [y_val]) self.addItem(self.txt) From 1e3c4c21420d9284e0828e5b56d3296de8060678 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Sun, 15 Sep 2019 21:41:44 -0700 Subject: [PATCH 13/56] add a splitter to spectraplot panel, make markselect control all mask --- xicam/BSISB/__init__.py | 31 +++++++++++++++++++--- xicam/BSISB/widgets/factorizationwidget.py | 8 +++--- 2 files changed, 30 insertions(+), 9 deletions(-) diff --git a/xicam/BSISB/__init__.py b/xicam/BSISB/__init__.py index 068d08d..fc66833 100644 --- a/xicam/BSISB/__init__.py +++ b/xicam/BSISB/__init__.py @@ -9,6 +9,7 @@ import numpy as np from xicam.core import msg from xicam.core.data import NonDBHeader +from xicam.gui.widgets.imageviewmixins import BetterButtons from xicam.BSISB.widgets.uiwidget import MsgBox, uiSaveFile, uiGetFile from xicam.BSISB.widgets.mapconvertwidget import mapToH5 from xicam.BSISB.widgets.mapviewwidget import MapViewWidget @@ -24,11 +25,20 @@ class MapView(QSplitter): sigSelectMaskState = Signal(object) def __init__(self, header: NonDBHeader = None, field: str = 'primary', ): + """ + A widget to display imageCube like dataset with ROI buttons + :param header: Xi-cam datahandler header + :param field: header's field param + """ super(MapView, self).__init__() # layout set up self.setOrientation(Qt.Vertical) self.imageview = MapViewWidget() self.spectra = SpectraPlotWidget() + self.spectraSplitter = QSplitter() + self.spectraSplitter.addWidget(self.spectra) + # self.spectraSplitter.insertWidget(1, BetterButtons()) # add a 2D spectrum window + # self.spectra.getViewBox().setXRange(0, 4000) # set xrange self.imageview_and_toolbar = QSplitter() self.imageview_and_toolbar.setOrientation(Qt.Horizontal) @@ -86,7 +96,7 @@ def __init__(self, header: NonDBHeader = None, field: str = 'primary', ): self.imageview_and_toolbar.addWidget(self.imageview) self.imageview_and_toolbar.setSizes([1, 1000])#adjust initial splitter size self.addWidget(self.imageview_and_toolbar) - self.addWidget(self.spectra) + self.addWidget(self.spectraSplitter) self.setSizes([1000, 1000]) # adjust initial splitter size # readin header @@ -153,7 +163,7 @@ def loadRoi(self): self.roi.show() self.autoMaskBtn.setChecked(roiStates['maskBtn']) self.selectMaskBtn.setChecked(True) - self.showSelectMask() + self.showSelectMask(True) for k, v in roiStates['parameter'].items(): self.parameter[k] = v MsgBox(f'ROI states were loaded from: \n{filePath + fileName}') @@ -208,8 +218,15 @@ def roiSelectPixel(self): self.intersectSelection('ROI', None) # no ROI, select all pixels self.sigRoiState.emit((False, self.roi.getState())) - def showSelectMask(self): + def showSelectMask(self, signalReceived): if self.selectMaskBtn.isChecked(): + # show roi and autoMask + if self.roiBtn.isChecked(): + self.roi.show() + self.sigRoiState.emit((True, self.roi.getState())) + if self.autoMaskBtn.isChecked(): + self.autoMaskItem.show() + self.sigAutoMaskState.emit((True, self.autoMask)) # update and show mask self.selectMaskItem.setImage(self.selectMask) self.selectMaskItem.show() @@ -217,6 +234,12 @@ def showSelectMask(self): else: self.selectMaskItem.hide() self.sigSelectMaskState.emit((False, self.selectMask)) + if signalReceived == False: + self.roi.hide() + self.autoMaskItem.hide() + self.sigRoiState.emit((False, self.roi.getState())) + self.sigAutoMaskState.emit((False, self.autoMask)) + def showAutoMask(self): if self.autoMaskBtn.isChecked(): @@ -275,7 +298,7 @@ def intersectSelection(self, selector, selectedPixels): self.selectMask = np.flipud(self.selectMask) self.sigRoiPixels.emit(allSelected) # show SelectMask - self.showSelectMask() + self.showSelectMask(selector) #recover ROI selection if (self.parameter['ROI type'] == '-') and (self.pixSelection['ROI'] is not None): self.pixSelection['ROI'] = roi_copy diff --git a/xicam/BSISB/widgets/factorizationwidget.py b/xicam/BSISB/widgets/factorizationwidget.py index dfc0d06..2f0f21e 100644 --- a/xicam/BSISB/widgets/factorizationwidget.py +++ b/xicam/BSISB/widgets/factorizationwidget.py @@ -475,17 +475,15 @@ def updateRoiMask(self): self.roiList[i].blockSignals(True) self.roiList[i].setState(roiState[1]) self.roiList[i].blockSignals(False) - except Exception: for i in range(4): self.roiList[i].hide() - # self.roiList[i].setState(self.roiInitState) - # update mask + # update automask try: maskState = self.headermodel.item(self.selectMapIdx).maskState for i in range(4): self.maskList[i].setImage(maskState[1]) - if maskState[0]: # roi on + if maskState[0]: # automask on self.maskList[i].show() else: self.maskList[i].hide() @@ -496,7 +494,7 @@ def updateRoiMask(self): selectMaskState = self.headermodel.item(self.selectMapIdx).selectState for i in range(4): self.selectMaskList[i].setImage(selectMaskState[1]) - if selectMaskState[0]: # roi on + if selectMaskState[0]: # selectmask on self.selectMaskList[i].show() else: self.selectMaskList[i].hide() From c2275d73a047183e064b7b35bd76e99ce6ced5ac Mon Sep 17 00:00:00 2001 From: lchen23 Date: Sun, 15 Sep 2019 22:00:43 -0700 Subject: [PATCH 14/56] make spectramaproi widget --- xicam/BSISB/widgets/spectramaproiwidget.py | 298 +++++++++++++++++++++ 1 file changed, 298 insertions(+) create mode 100644 xicam/BSISB/widgets/spectramaproiwidget.py diff --git a/xicam/BSISB/widgets/spectramaproiwidget.py b/xicam/BSISB/widgets/spectramaproiwidget.py new file mode 100644 index 0000000..74cc46e --- /dev/null +++ b/xicam/BSISB/widgets/spectramaproiwidget.py @@ -0,0 +1,298 @@ +import os +from qtpy.QtCore import * +from qtpy.QtGui import * +from qtpy.QtWidgets import * +import pickle +import pyqtgraph as pg +from pyqtgraph.parametertree import ParameterTree, Parameter +import numpy as np +from xicam.core.data import NonDBHeader +from xicam.gui.widgets.imageviewmixins import BetterButtons +from xicam.BSISB.widgets.uiwidget import MsgBox, uiSaveFile, uiGetFile +from xicam.BSISB.widgets.mapviewwidget import MapViewWidget +from xicam.BSISB.widgets.spectraplotwidget import SpectraPlotWidget + +class MapView(QSplitter): + sigRoiPixels = Signal(object) + sigRoiState = Signal(object) + sigAutoMaskState = Signal(object) + sigSelectMaskState = Signal(object) + + def __init__(self, header: NonDBHeader = None, field: str = 'primary', ): + """ + A widget to display imageCube like dataset with ROI buttons + :param header: Xi-cam datahandler header + :param field: header's field param + """ + super(MapView, self).__init__() + # layout set up + self.setOrientation(Qt.Vertical) + self.imageview = MapViewWidget() + self.spectra = SpectraPlotWidget() + self.spectraSplitter = QSplitter() + self.spectraSplitter.addWidget(self.spectra) + # self.spectraSplitter.insertWidget(1, BetterButtons()) # add a 2D spectrum window + # self.spectra.getViewBox().setXRange(0, 4000) # set xrange + + self.imageview_and_toolbar = QSplitter() + self.imageview_and_toolbar.setOrientation(Qt.Horizontal) + self.toolbar_and_param = QSplitter() + self.toolbar_and_param.setOrientation(Qt.Vertical) + #define tool bar + self.toolBar = QWidget() + self.gridlayout = QGridLayout() + self.toolBar.setLayout(self.gridlayout) + #add tool bar buttons + self.roiBtn = QToolButton() + self.roiBtn.setText('Manual ROI') + self.roiBtn.setCheckable(True) + self.roiMeanBtn = QToolButton() + self.roiMeanBtn.setText('ROI Mean') + self.autoMaskBtn = QToolButton() + self.autoMaskBtn.setText('Auto ROI') + self.autoMaskBtn.setCheckable(True) + self.selectMaskBtn = QToolButton() + self.selectMaskBtn.setText('Mark Select') + self.selectMaskBtn.setCheckable(True) + self.saveRoiBtn = QToolButton() + self.saveRoiBtn.setText('Save ROI') + self.saveRoiBtn.setCheckable(False) + self.loadRoiBtn = QToolButton() + self.loadRoiBtn.setText('Load ROI') + self.loadRoiBtn.setCheckable(False) + self.gridlayout.addWidget(self.roiBtn, 0, 0, 1, 1) + self.gridlayout.addWidget(self.autoMaskBtn, 0, 1, 1, 1) + self.gridlayout.addWidget(self.selectMaskBtn, 1, 0, 1, 1) + self.gridlayout.addWidget(self.roiMeanBtn, 1, 1, 1, 1) + self.gridlayout.addWidget(self.saveRoiBtn, 2, 0, 1, 1) + self.gridlayout.addWidget(self.loadRoiBtn, 2, 1, 1, 1) + + self.parameterTree = ParameterTree() + self.parameter = Parameter(name='Threshhold', type='group', + children=[{'name': 'Amide II', + 'value': 0, + 'type': 'float'}, + {'name': "ROI type", + 'values': ['+', '-'], + 'value': '+', + 'type': 'list'}, + ]) + self.parameter.child('Amide II').setOpts(step=0.1) + self.parameterTree.setParameters(self.parameter, showTop=False) + self.parameterTree.setHeaderLabels(['Params','Value']) + self.parameterTree.setIndentation(0) + + # Assemble widgets + self.toolbar_and_param.addWidget(self.toolBar) + self.toolbar_and_param.addWidget(self.parameterTree) + self.toolbar_and_param.setSizes([1000, 1]) #adjust initial splitter size + self.imageview_and_toolbar.addWidget(self.toolbar_and_param) + self.imageview_and_toolbar.addWidget(self.imageview) + self.imageview_and_toolbar.setSizes([1, 1000])#adjust initial splitter size + self.addWidget(self.imageview_and_toolbar) + self.addWidget(self.spectraSplitter) + self.setSizes([1000, 1000]) # adjust initial splitter size + + # readin header + self.imageview.setHeader(header, field='image') + self.spectra.setHeader(header, field='spectra') + self.header = header + + #setup ROI item + sideLen = 10 + self.roi = pg.PolyLineROI(positions=[[0, 0], [sideLen, 0], [sideLen, sideLen], [0, sideLen]], closed=True) + self.imageview.view.addItem(self.roi) + self.roiInitState = self.roi.getState() + self.roi.hide() + + #constants + self.path = os.path.expanduser('~/') + self.pixSelection = {'ROI': None, 'Mask': None} # init pixel selection dict + + # Connect signals + self.imageview.sigShowSpectra.connect(self.spectra.showSpectra) + self.spectra.sigEnergyChanged.connect(self.imageview.setEnergy) + self.roiBtn.clicked.connect(self.roiBtnClicked) + self.roi.sigRegionChangeFinished.connect(self.roiSelectPixel) + self.roi.sigRegionChangeFinished.connect(self.showSelectMask) + self.sigRoiPixels.connect(self.spectra.getSelectedPixels) + self.roiMeanBtn.clicked.connect(self.spectra.showMeanSpectra) + self.autoMaskBtn.clicked.connect(self.showAutoMask) + self.selectMaskBtn.clicked.connect(self.showSelectMask) + self.saveRoiBtn.clicked.connect(self.saveRoi) + self.loadRoiBtn.clicked.connect(self.loadRoi) + self.parameter.child('Amide II').sigValueChanged.connect(self.showAutoMask) + self.parameter.child('Amide II').sigValueChanged.connect(self.intersectSelection) + self.parameter.child('ROI type').sigValueChanged.connect(self.intersectSelection) + + def roiBtnClicked(self): + self.roiSelectPixel() + if self.roiBtn.isChecked(): + self.imageview.cross.hide() + self.roi.show() + self.sigRoiState.emit((True, self.roi.getState())) + else: + self.roi.hide() + self.roi.setState(self.roiInitState) + self.sigRoiState.emit((False, self.roi.getState())) + + def saveRoi(self): + parameterDict = {name: self.parameter[name] for name in self.parameter.names.keys()} + roiStates = {'roiBtn': self.roiBtn.isChecked(), 'maskBtn': self.autoMaskBtn.isChecked(), + 'roiState': self.roi.getState(), 'parameter': parameterDict} + filePath, fileName, canceled = uiSaveFile('Save ROI state', self.path, "Pickle Files (*.pkl)") + if not canceled: + with open(filePath + fileName, 'wb') as f: + pickle.dump(roiStates, f) + MsgBox(f'ROI state file was saved! \nFile Location: {filePath + fileName}') + + def loadRoi(self): + filePath, fileName, canceled = uiGetFile('Open ROI state file', self.path, "Pickle Files (*.pkl)") + if not canceled: + with open(filePath + fileName, 'rb') as f: + roiStates = pickle.load(f) + self.roiBtn.setChecked(roiStates['roiBtn']) + self.roi.setState(roiStates['roiState']) + if roiStates['roiBtn']: + self.roi.show() + self.autoMaskBtn.setChecked(roiStates['maskBtn']) + self.selectMaskBtn.setChecked(True) + self.showSelectMask(True) + for k, v in roiStates['parameter'].items(): + self.parameter[k] = v + MsgBox(f'ROI states were loaded from: \n{filePath + fileName}') + else: + return + + def roiMove(self, roi): + roiState = roi.getState() + self.roi.setState(roiState) + + def getImgShape(self, imgShape, rc2ind): + self.row, self.col = imgShape[0], imgShape[1] + self.rc2ind = rc2ind + # determine whether spectra data is sparse + if len(rc2ind) == self.row * self.col: + self.isDenseImage = True + else: + self.isDenseImage = False + #set up X,Y grid + x = np.linspace(0, self.col - 1, self.col) + y = np.linspace(self.row - 1, 0, self.row) + self.X, self.Y = np.meshgrid(x, y) + if self.isDenseImage: + self.fullMap = list(zip(self.Y.ravel(), self.X.ravel())) + else: + self.fullMap = list(rc2ind.keys()) + # setup automask item + self.autoMask = np.ones((self.row, self.col)) + self.autoMaskItem = pg.ImageItem(self.autoMask, axisOrder="row-major", autoLevels=True, opacity=0.3) + self.imageview.view.addItem(self.autoMaskItem) + self.autoMaskItem.hide() + # setup selctmask item to mark selected pixels + self.selectMask = np.ones((self.row, self.col)) + self.selectMaskItem = pg.ImageItem(self.selectMask, axisOrder="row-major", autoLevels=True, opacity=0.3, + lut = np.array([[0, 0, 0], [255, 0, 0]])) + self.imageview.view.addItem(self.selectMaskItem) + self.selectMaskItem.hide() + + def roiSelectPixel(self): + if self.roiBtn.isChecked(): + #get x,y positions list + xPos = self.roi.getArrayRegion(self.X, self.imageview.imageItem) + xPos = np.round(xPos[xPos > 0]) + yPos = self.roi.getArrayRegion(self.Y, self.imageview.imageItem) + yPos = np.round(yPos[yPos > 0]) + + # extract x,y coordinate from selected region + selectedPixels = list(zip(yPos, xPos)) + self.intersectSelection('ROI', selectedPixels) + self.sigRoiState.emit((True, self.roi.getState())) + else: + self.intersectSelection('ROI', None) # no ROI, select all pixels + self.sigRoiState.emit((False, self.roi.getState())) + + def showSelectMask(self, signalReceived): + if self.selectMaskBtn.isChecked(): + # show roi and autoMask + if self.roiBtn.isChecked(): + self.roi.show() + self.sigRoiState.emit((True, self.roi.getState())) + if self.autoMaskBtn.isChecked(): + self.autoMaskItem.show() + self.sigAutoMaskState.emit((True, self.autoMask)) + # update and show mask + self.selectMaskItem.setImage(self.selectMask) + self.selectMaskItem.show() + self.sigSelectMaskState.emit((True, self.selectMask)) + else: + self.selectMaskItem.hide() + self.sigSelectMaskState.emit((False, self.selectMask)) + if signalReceived == False: + self.roi.hide() + self.autoMaskItem.hide() + self.sigRoiState.emit((False, self.roi.getState())) + self.sigAutoMaskState.emit((False, self.autoMask)) + + + def showAutoMask(self): + if self.autoMaskBtn.isChecked(): + # update and show mask + self.autoMask = self.imageview.makeMask([self.parameter['Amide II']]) + self.autoMaskItem.setImage(self.autoMask) + self.autoMaskItem.show() + # select pixels + mask = self.autoMask.astype(np.bool) + selectedPixels = list(zip(self.Y[mask], self.X[mask])) + self.intersectSelection('Mask', selectedPixels) + self.sigAutoMaskState.emit((True, self.autoMask)) + else: + self.autoMaskItem.hide() + self.autoMask[:, :] = 1 + self.intersectSelection('Mask', None) # no mask, select all pixels + self.sigAutoMaskState.emit((False, self.autoMask)) + + def intersectSelection(self, selector, selectedPixels): + # update pixel selection dict + if (selector == 'ROI') or (selector == 'Mask'): + self.pixSelection[selector] = selectedPixels + # reverse ROI selection + if (self.parameter['ROI type'] == '-') and (self.pixSelection['ROI'] is not None): + roi_copy = self.pixSelection['ROI'] + reverseROI = set(self.fullMap) - set(self.pixSelection['ROI']) + self.pixSelection['ROI'] = list(reverseROI) + + if (self.pixSelection['ROI'] is None) and (self.pixSelection['Mask'] is None): + if self.isDenseImage: + self.sigRoiPixels.emit(None) # no ROI, select all pixels + self.selectMask = np.ones((self.row, self.col)) + else: + allSelected = np.array(list(self.rc2ind.keys()), dtype='int') + self.sigRoiPixels.emit(allSelected) # no ROI, select all sparse rc2ind + self.selectMask = np.zeros((self.row, self.col)) + self.selectMask[allSelected[:, 0], allSelected[:, 1]] = 1 + self.selectMask = np.flipud(self.selectMask) + return + elif self.pixSelection['ROI'] is None: + allSelected = set(self.pixSelection['Mask']) #de-duplication of pixels + elif self.pixSelection['Mask'] is None: + allSelected = set(self.pixSelection['ROI']) #de-duplication of pixels + else: + allSelected = set(self.pixSelection['ROI']) & set(self.pixSelection['Mask']) + + if self.isDenseImage: + allSelected = np.array(list(allSelected), dtype='int') # convert to array + else: + allSelected &= set(self.rc2ind.keys()) + allSelected = np.array(list(allSelected), dtype='int') + + self.selectMask = np.zeros((self.row, self.col)) + if len(allSelected) > 0: + self.selectMask[allSelected[:, 0], allSelected[:, 1]] = 1 + self.selectMask = np.flipud(self.selectMask) + self.sigRoiPixels.emit(allSelected) + # show SelectMask + self.showSelectMask(selector) + #recover ROI selection + if (self.parameter['ROI type'] == '-') and (self.pixSelection['ROI'] is not None): + self.pixSelection['ROI'] = roi_copy \ No newline at end of file From cb5d497f2ead86a8be374c97a2efeb5d20723efb Mon Sep 17 00:00:00 2001 From: lchen23 Date: Sun, 15 Sep 2019 22:14:05 -0700 Subject: [PATCH 15/56] pull out spectramaproi widget, reformat init file --- xicam/BSISB/__init__.py | 294 +--------------------------------------- 1 file changed, 1 insertion(+), 293 deletions(-) diff --git a/xicam/BSISB/__init__.py b/xicam/BSISB/__init__.py index fc66833..e38e94a 100644 --- a/xicam/BSISB/__init__.py +++ b/xicam/BSISB/__init__.py @@ -1,308 +1,16 @@ -import os from functools import partial from qtpy.QtCore import * from qtpy.QtGui import * -from qtpy.QtWidgets import * -import pickle -import pyqtgraph as pg -from pyqtgraph.parametertree import ParameterTree, Parameter -import numpy as np from xicam.core import msg from xicam.core.data import NonDBHeader -from xicam.gui.widgets.imageviewmixins import BetterButtons -from xicam.BSISB.widgets.uiwidget import MsgBox, uiSaveFile, uiGetFile from xicam.BSISB.widgets.mapconvertwidget import mapToH5 from xicam.BSISB.widgets.mapviewwidget import MapViewWidget +from xicam.BSISB.widgets.spectramaproiwidget import MapView from xicam.BSISB.widgets.spectraplotwidget import SpectraPlotWidget from xicam.BSISB.widgets.factorizationwidget import FactorizationWidget from xicam.plugins import GUIPlugin, GUILayout from xicam.gui.widgets.tabview import TabView -class MapView(QSplitter): - sigRoiPixels = Signal(object) - sigRoiState = Signal(object) - sigAutoMaskState = Signal(object) - sigSelectMaskState = Signal(object) - - def __init__(self, header: NonDBHeader = None, field: str = 'primary', ): - """ - A widget to display imageCube like dataset with ROI buttons - :param header: Xi-cam datahandler header - :param field: header's field param - """ - super(MapView, self).__init__() - # layout set up - self.setOrientation(Qt.Vertical) - self.imageview = MapViewWidget() - self.spectra = SpectraPlotWidget() - self.spectraSplitter = QSplitter() - self.spectraSplitter.addWidget(self.spectra) - # self.spectraSplitter.insertWidget(1, BetterButtons()) # add a 2D spectrum window - # self.spectra.getViewBox().setXRange(0, 4000) # set xrange - - self.imageview_and_toolbar = QSplitter() - self.imageview_and_toolbar.setOrientation(Qt.Horizontal) - self.toolbar_and_param = QSplitter() - self.toolbar_and_param.setOrientation(Qt.Vertical) - #define tool bar - self.toolBar = QWidget() - self.gridlayout = QGridLayout() - self.toolBar.setLayout(self.gridlayout) - #add tool bar buttons - self.roiBtn = QToolButton() - self.roiBtn.setText('Manual ROI') - self.roiBtn.setCheckable(True) - self.roiMeanBtn = QToolButton() - self.roiMeanBtn.setText('ROI Mean') - self.autoMaskBtn = QToolButton() - self.autoMaskBtn.setText('Auto ROI') - self.autoMaskBtn.setCheckable(True) - self.selectMaskBtn = QToolButton() - self.selectMaskBtn.setText('Mark Select') - self.selectMaskBtn.setCheckable(True) - self.saveRoiBtn = QToolButton() - self.saveRoiBtn.setText('Save ROI') - self.saveRoiBtn.setCheckable(False) - self.loadRoiBtn = QToolButton() - self.loadRoiBtn.setText('Load ROI') - self.loadRoiBtn.setCheckable(False) - self.gridlayout.addWidget(self.roiBtn, 0, 0, 1, 1) - self.gridlayout.addWidget(self.autoMaskBtn, 0, 1, 1, 1) - self.gridlayout.addWidget(self.selectMaskBtn, 1, 0, 1, 1) - self.gridlayout.addWidget(self.roiMeanBtn, 1, 1, 1, 1) - self.gridlayout.addWidget(self.saveRoiBtn, 2, 0, 1, 1) - self.gridlayout.addWidget(self.loadRoiBtn, 2, 1, 1, 1) - - self.parameterTree = ParameterTree() - self.parameter = Parameter(name='Threshhold', type='group', - children=[{'name': 'Amide II', - 'value': 0, - 'type': 'float'}, - {'name': "ROI type", - 'values': ['+', '-'], - 'value': '+', - 'type': 'list'}, - ]) - self.parameter.child('Amide II').setOpts(step=0.1) - self.parameterTree.setParameters(self.parameter, showTop=False) - self.parameterTree.setHeaderLabels(['Params','Value']) - self.parameterTree.setIndentation(0) - - # Assemble widgets - self.toolbar_and_param.addWidget(self.toolBar) - self.toolbar_and_param.addWidget(self.parameterTree) - self.toolbar_and_param.setSizes([1000, 1]) #adjust initial splitter size - self.imageview_and_toolbar.addWidget(self.toolbar_and_param) - self.imageview_and_toolbar.addWidget(self.imageview) - self.imageview_and_toolbar.setSizes([1, 1000])#adjust initial splitter size - self.addWidget(self.imageview_and_toolbar) - self.addWidget(self.spectraSplitter) - self.setSizes([1000, 1000]) # adjust initial splitter size - - # readin header - self.imageview.setHeader(header, field='image') - self.spectra.setHeader(header, field='spectra') - self.header = header - - #setup ROI item - sideLen = 10 - self.roi = pg.PolyLineROI(positions=[[0, 0], [sideLen, 0], [sideLen, sideLen], [0, sideLen]], closed=True) - self.imageview.view.addItem(self.roi) - self.roiInitState = self.roi.getState() - self.roi.hide() - - #constants - self.path = os.path.expanduser('~/') - self.pixSelection = {'ROI': None, 'Mask': None} # init pixel selection dict - - # Connect signals - self.imageview.sigShowSpectra.connect(self.spectra.showSpectra) - self.spectra.sigEnergyChanged.connect(self.imageview.setEnergy) - self.roiBtn.clicked.connect(self.roiBtnClicked) - self.roi.sigRegionChangeFinished.connect(self.roiSelectPixel) - self.roi.sigRegionChangeFinished.connect(self.showSelectMask) - self.sigRoiPixels.connect(self.spectra.getSelectedPixels) - self.roiMeanBtn.clicked.connect(self.spectra.showMeanSpectra) - self.autoMaskBtn.clicked.connect(self.showAutoMask) - self.selectMaskBtn.clicked.connect(self.showSelectMask) - self.saveRoiBtn.clicked.connect(self.saveRoi) - self.loadRoiBtn.clicked.connect(self.loadRoi) - self.parameter.child('Amide II').sigValueChanged.connect(self.showAutoMask) - self.parameter.child('Amide II').sigValueChanged.connect(self.intersectSelection) - self.parameter.child('ROI type').sigValueChanged.connect(self.intersectSelection) - - def roiBtnClicked(self): - self.roiSelectPixel() - if self.roiBtn.isChecked(): - self.imageview.cross.hide() - self.roi.show() - self.sigRoiState.emit((True, self.roi.getState())) - else: - self.roi.hide() - self.roi.setState(self.roiInitState) - self.sigRoiState.emit((False, self.roi.getState())) - - def saveRoi(self): - parameterDict = {name: self.parameter[name] for name in self.parameter.names.keys()} - roiStates = {'roiBtn': self.roiBtn.isChecked(), 'maskBtn': self.autoMaskBtn.isChecked(), - 'roiState': self.roi.getState(), 'parameter': parameterDict} - filePath, fileName, canceled = uiSaveFile('Save ROI state', self.path, "Pickle Files (*.pkl)") - if not canceled: - with open(filePath + fileName, 'wb') as f: - pickle.dump(roiStates, f) - MsgBox(f'ROI state file was saved! \nFile Location: {filePath + fileName}') - - def loadRoi(self): - filePath, fileName, canceled = uiGetFile('Open ROI state file', self.path, "Pickle Files (*.pkl)") - if not canceled: - with open(filePath + fileName, 'rb') as f: - roiStates = pickle.load(f) - self.roiBtn.setChecked(roiStates['roiBtn']) - self.roi.setState(roiStates['roiState']) - if roiStates['roiBtn']: - self.roi.show() - self.autoMaskBtn.setChecked(roiStates['maskBtn']) - self.selectMaskBtn.setChecked(True) - self.showSelectMask(True) - for k, v in roiStates['parameter'].items(): - self.parameter[k] = v - MsgBox(f'ROI states were loaded from: \n{filePath + fileName}') - else: - return - - def roiMove(self, roi): - roiState = roi.getState() - self.roi.setState(roiState) - - def getImgShape(self, imgShape, rc2ind): - self.row, self.col = imgShape[0], imgShape[1] - self.rc2ind = rc2ind - # determine whether spectra data is sparse - if len(rc2ind) == self.row * self.col: - self.isDenseImage = True - else: - self.isDenseImage = False - #set up X,Y grid - x = np.linspace(0, self.col - 1, self.col) - y = np.linspace(self.row - 1, 0, self.row) - self.X, self.Y = np.meshgrid(x, y) - if self.isDenseImage: - self.fullMap = list(zip(self.Y.ravel(), self.X.ravel())) - else: - self.fullMap = list(rc2ind.keys()) - # setup automask item - self.autoMask = np.ones((self.row, self.col)) - self.autoMaskItem = pg.ImageItem(self.autoMask, axisOrder="row-major", autoLevels=True, opacity=0.3) - self.imageview.view.addItem(self.autoMaskItem) - self.autoMaskItem.hide() - # setup selctmask item to mark selected pixels - self.selectMask = np.ones((self.row, self.col)) - self.selectMaskItem = pg.ImageItem(self.selectMask, axisOrder="row-major", autoLevels=True, opacity=0.3, - lut = np.array([[0, 0, 0], [255, 0, 0]])) - self.imageview.view.addItem(self.selectMaskItem) - self.selectMaskItem.hide() - - def roiSelectPixel(self): - if self.roiBtn.isChecked(): - #get x,y positions list - xPos = self.roi.getArrayRegion(self.X, self.imageview.imageItem) - xPos = np.round(xPos[xPos > 0]) - yPos = self.roi.getArrayRegion(self.Y, self.imageview.imageItem) - yPos = np.round(yPos[yPos > 0]) - - # extract x,y coordinate from selected region - selectedPixels = list(zip(yPos, xPos)) - self.intersectSelection('ROI', selectedPixels) - self.sigRoiState.emit((True, self.roi.getState())) - else: - self.intersectSelection('ROI', None) # no ROI, select all pixels - self.sigRoiState.emit((False, self.roi.getState())) - - def showSelectMask(self, signalReceived): - if self.selectMaskBtn.isChecked(): - # show roi and autoMask - if self.roiBtn.isChecked(): - self.roi.show() - self.sigRoiState.emit((True, self.roi.getState())) - if self.autoMaskBtn.isChecked(): - self.autoMaskItem.show() - self.sigAutoMaskState.emit((True, self.autoMask)) - # update and show mask - self.selectMaskItem.setImage(self.selectMask) - self.selectMaskItem.show() - self.sigSelectMaskState.emit((True, self.selectMask)) - else: - self.selectMaskItem.hide() - self.sigSelectMaskState.emit((False, self.selectMask)) - if signalReceived == False: - self.roi.hide() - self.autoMaskItem.hide() - self.sigRoiState.emit((False, self.roi.getState())) - self.sigAutoMaskState.emit((False, self.autoMask)) - - - def showAutoMask(self): - if self.autoMaskBtn.isChecked(): - # update and show mask - self.autoMask = self.imageview.makeMask([self.parameter['Amide II']]) - self.autoMaskItem.setImage(self.autoMask) - self.autoMaskItem.show() - # select pixels - mask = self.autoMask.astype(np.bool) - selectedPixels = list(zip(self.Y[mask], self.X[mask])) - self.intersectSelection('Mask', selectedPixels) - self.sigAutoMaskState.emit((True, self.autoMask)) - else: - self.autoMaskItem.hide() - self.autoMask[:, :] = 1 - self.intersectSelection('Mask', None) # no mask, select all pixels - self.sigAutoMaskState.emit((False, self.autoMask)) - - def intersectSelection(self, selector, selectedPixels): - # update pixel selection dict - if (selector == 'ROI') or (selector == 'Mask'): - self.pixSelection[selector] = selectedPixels - # reverse ROI selection - if (self.parameter['ROI type'] == '-') and (self.pixSelection['ROI'] is not None): - roi_copy = self.pixSelection['ROI'] - reverseROI = set(self.fullMap) - set(self.pixSelection['ROI']) - self.pixSelection['ROI'] = list(reverseROI) - - if (self.pixSelection['ROI'] is None) and (self.pixSelection['Mask'] is None): - if self.isDenseImage: - self.sigRoiPixels.emit(None) # no ROI, select all pixels - self.selectMask = np.ones((self.row, self.col)) - else: - allSelected = np.array(list(self.rc2ind.keys()), dtype='int') - self.sigRoiPixels.emit(allSelected) # no ROI, select all sparse rc2ind - self.selectMask = np.zeros((self.row, self.col)) - self.selectMask[allSelected[:, 0], allSelected[:, 1]] = 1 - self.selectMask = np.flipud(self.selectMask) - return - elif self.pixSelection['ROI'] is None: - allSelected = set(self.pixSelection['Mask']) #de-duplication of pixels - elif self.pixSelection['Mask'] is None: - allSelected = set(self.pixSelection['ROI']) #de-duplication of pixels - else: - allSelected = set(self.pixSelection['ROI']) & set(self.pixSelection['Mask']) - - if self.isDenseImage: - allSelected = np.array(list(allSelected), dtype='int') # convert to array - else: - allSelected &= set(self.rc2ind.keys()) - allSelected = np.array(list(allSelected), dtype='int') - - self.selectMask = np.zeros((self.row, self.col)) - if len(allSelected) > 0: - self.selectMask[allSelected[:, 0], allSelected[:, 1]] = 1 - self.selectMask = np.flipud(self.selectMask) - self.sigRoiPixels.emit(allSelected) - # show SelectMask - self.showSelectMask(selector) - #recover ROI selection - if (self.parameter['ROI type'] == '-') and (self.pixSelection['ROI'] is not None): - self.pixSelection['ROI'] = roi_copy - class BSISB(GUIPlugin): name = 'BSISB' From 9d2948227da131df6dc39df4d068a5a34ed2da14 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Mon, 23 Sep 2019 15:56:29 -0700 Subject: [PATCH 16/56] fix datahandler import --- .../__pycache__/mapfile.cpython-37.pyc | Bin 5658 -> 5658 bytes xicam/BSISB/formats/mapfile.py | 2 +- .../__pycache__/mapviewwidget.cpython-37.pyc | Bin 3769 -> 3769 bytes .../spectraplotwidget.cpython-37.pyc | Bin 5201 -> 5201 bytes 4 files changed, 1 insertion(+), 1 deletion(-) diff --git a/xicam/BSISB/formats/__pycache__/mapfile.cpython-37.pyc b/xicam/BSISB/formats/__pycache__/mapfile.cpython-37.pyc index ff5c4d838f82e4a223de1bab83de6670aebba28d..0df2008a696519093495b22b612ee78abcdebd68 100644 GIT binary patch delta 37 rcmbQGGfRitiI6yQ3JWG+$w mv0W$s<(6Xf-z>qC$|w*75(x$oU>(tudwD|{BPT!Ry#@dxW)?UA diff --git a/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc b/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc index 5ce648b3c3dea68f7a2ea2d3553edee7545442e4..1219cd3dde53cbcbdfac3cd94fd10e58e261b8b9 100644 GIT binary patch delta 20 acmcbpaZ!WYiIj|B1n delta 20 acmcbpaZ!WYiIG6eDf From c6805efae1f7f61851e3221a10ab5faa32f93635 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Mon, 7 Oct 2019 10:55:30 -0700 Subject: [PATCH 17/56] add new imageview widget --- xicam/BSISB/widgets/imshowwidget.py | 33 +++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 xicam/BSISB/widgets/imshowwidget.py diff --git a/xicam/BSISB/widgets/imshowwidget.py b/xicam/BSISB/widgets/imshowwidget.py new file mode 100644 index 0000000..7b8a06b --- /dev/null +++ b/xicam/BSISB/widgets/imshowwidget.py @@ -0,0 +1,33 @@ +from xicam.gui.widgets.imageviewmixins import BetterButtons +import numpy as np + + +class SlimImageView(BetterButtons): + def __init__(self, invertY=True): + super(SlimImageView, self).__init__() + # Shrink LUT + self.getHistogramWidget().setMinimumWidth(1) + # set up layout + self.ui.gridLayout.addWidget(self.resetAxesBtn, 2, 2, 1, 1) + self.ui.gridLayout.addWidget(self.resetLUTBtn, 3, 2, 1, 1) + self.ui.gridLayout.addWidget(self.ui.graphicsView, 0, 0, 4, 1) + # set up colorbar + self.setPredefinedGradient("viridis") + self.view.invertY(invertY) + self.imageItem.setOpts(axisOrder="row-major") + # Setup late signal + self.sigTimeChangeFinished = self.timeLine.sigPositionChangeFinished + + def quickMinMax(self, data): + """ + Estimate the min/max values of *data* by subsampling. MODIFIED TO USE THE 99TH PERCENTILE instead of max. + """ + if data is None: + return 0, 0 + ax = np.argmax(data.shape) + sl = [slice(None)] * data.ndim + sl[ax] = slice(None, None, max(1, int(data.size // 1e4))) + data = data[sl] + return (np.nanmin(data), np.nanpercentile(np.where(data < np.nanmax(data), data, np.nanmin(data)), 99)) + + From 61bf38fbfa30bf06a652cf79419893e03dcfba7a Mon Sep 17 00:00:00 2001 From: lchen23 Date: Mon, 7 Oct 2019 11:40:49 -0700 Subject: [PATCH 18/56] add slim window widget and toHtml func --- .../__pycache__/mapfile.cpython-37.pyc | Bin 5658 -> 5658 bytes .../__pycache__/mapviewwidget.cpython-37.pyc | Bin 3769 -> 3695 bytes .../spectraplotwidget.cpython-37.pyc | Bin 5201 -> 4621 bytes xicam/BSISB/widgets/factorizationwidget.py | 24 +++++++++++++----- xicam/BSISB/widgets/mapviewwidget.py | 16 +++++++----- xicam/BSISB/widgets/spectraplotwidget.py | 19 +++++--------- 6 files changed, 34 insertions(+), 25 deletions(-) diff --git a/xicam/BSISB/formats/__pycache__/mapfile.cpython-37.pyc b/xicam/BSISB/formats/__pycache__/mapfile.cpython-37.pyc index 0df2008a696519093495b22b612ee78abcdebd68..0c1067dead972ef633b786ffa41eef8b5505141e 100644 GIT binary patch delta 20 acmbQGGfRitiIhyuA2t(15@0DG&K|hDg;QQP(m%*2%=U28oL6sc&6zl_O3O% zNfXN@LKJaABAOdkrTD-BRYF3Xdg_J$0cm^TniDq;91!nqng(U8`DWfX@4b2N&09Zw z{;#6D;JP+}@!+feXMgOvV{~(O`euc@Jj?BSv|jjt@EmvU5$*`~4XdBzbeWX%`)IOM zrir`U^4q&V)n@h`V4528FmN?sz7I1R0^qbq`-E#f0?6(g;6z{QR@Gy(dbX=~;)(gL zIrl2}H|KvQWi4UxR-EXO-xV?k)(Gkqwm6!Yad;(ex3cIry^P8eu>bYnKBWcqqqb&9 z3`AwwWZ}RxkM_?2oKSb!!wXZetJ^I<5aE|=q8TT&nlPVlJ-vrWv`Dg%h^v8+tJ_88 z>oe09kZCp(l#m|XBLi|5vRR{h+JNk_o;ILAPzOB*9R+t&|0jgSGQarSu z@u}B>MK4v}xK(~8VVy9NCG}pWK_}E8b7}`moMfOv_!j8{$n!{Lt6NRciTyB;FQVg& z#BwKw*c3r5FTlQTx5JG{r1#6WfspT@2J-`c>;>cta4-ooDgqE{(IM*4ahj)E9>8HG zYHKFrTH&`j>l`&-0wwvnTFZWT=`wDs0Er$5Xe2YeKav-bN4>;ui`Z*;QhM8o5#9DW zLe3)3nqf;i>hJ9AnU_%+vwD(+eCk!6#iMC;-F|lbusi?PHCKek!JYcr{>~gn&4SA3 z&eJJ%F*iPZ4vEw#?aLJquPZ+nR1Y-q^kak?LLK2IK$)e5l#57ABSGacPiXatDmveu zP78t2C#Dz0+Z~Z;K}WuYR)(BXkDT(@v8b)Kw=O>NqV<}hZfyru}r zBrJ-hyn!MHe-P~#c;Ge~&6XEMP0?yK8b_~r7;Bnv0W`6DBHUQi(j3>ySR;jzyzb}{ z$?^=9Hm*a75p1+O+wyae>ZL8e>9s2h%U72dDtCOoD&nZ(x1-zPodZdk`R*nBFG#%{ eU4K;V#F}e|e4{1i@oRVzfdftD&|yT)X8!@F7!f7_ delta 1227 zcmaJ>&2QX96rb_e+O9WjA^q4WP2GelwM4gvHff1M8lVeRprzk!mR2iwXS#Lb^{&Rw z$3}~Cpa^k{<`|H&5>kN^QsK}mH~xh=An2`^mIHqP?|G?$BE(ql=QqFk&CGjm#(p{a z^hoi&V$mhgzWwN{d%tZJ%QW7a{B+_&!ffU|BFy3X=X$Ng=yejf&rx_epvt?@O}2hq zeBx=_l)CaeeaTdMww}pD#svLd{y-NjaR9WQTri%V_Q9*Uag^}({UzSW6s;;fV(a2f z7#P5D@RXDCteSAq?3U%UIepv%@_n%MFMuB1A${_h)+0-FN9&Uvy{Gl5_)|)AiawIR znhTpu?^)Y~89f8g?3sWT$oGTUJ@YVO^f2ie54192g>8e`-#C3r$!(I186?sh<`TuJ2JgtuAsyj3O{|gpD5dQZHgMDs=F-Q!*a^}H?H4p~FO7FB& zF(RK?_03TTRTZGD7s4!5M*6V5CXT-1E@rJ*1lV(`>kZz?qIM$2pdS>3kF?2Gd6J1! z;Md$%dnM(0V&V)CVhR;{l(0RgD9SM4gQcV72)w9GOVpzybckwTp6*juv-BA{^yFFL z1hpnXBPz1wd^|so?z;fWNT8m|%J)yjS>#ct+!oKmdMHHLRc87y>~JxSyxwTX;;8)E znYnrb#hBGUS%`Tga8+>(VH`lYQ7dfnDyF9l2DRCfM1B=7@{qy$a?<_M8pf%v{K${bC6>FX_{xck~v;<&NTl0!`!9G|zG-P;& zz|r93eq|;rtxi|epj#VTj~ZdC(p-ruYY}VmEUmC^k{`rs#Mk7{#f`Ud-tLCG^;Pz4 WtIbwoK1V^34guJ3sqh&_)cywdh8QOR diff --git a/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc b/xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc index 1219cd3dde53cbcbdfac3cd94fd10e58e261b8b9..0078a648b1ce2cbc0a36fe083cccb6e59307a027 100644 GIT binary patch delta 1501 zcmZux-ESL35WhX&o%hb)j!9Zd6_+BKgC-3vP!yV`7z#;6LuiUx5|BB0H?G_BhxRUs zV_g(PkPs4;h_0%TqDamoyrDk9U%)GGXvIH(gb;5?yui#lG-||I`}54s%?a16G1Xp$XsYtJr$f%QjoxmP&0BgSIEOvM;6syhh>7H@B zJYDV?<%#nvJ?m`w!}4^zSD>fU;v6I<%{9+o<-!J8j3Sf~uyA5L{*sOsTo7e4;Ne?d z=((1djDMix=Uzq!1+SoV2H-^)!7zYE970JN&>S_I%c*OpbjKS-Mq$$d%=C+q6A@+~*GkZCk#kG~ji5H7Rah#rOBxawT#f!vWCEqF z0i`3YkEvyl@QU8)dDV$eayAT{Xl?E4)iGL~1Y(0-6zC5ZAg$0sK z1fdWMICm4_1B5C9mO^H#)tc`GL5=%KvO%HR2lF=QNP5$E%!-? 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{txt}
' + +class MapViewWidget(SlimImageView): sigShowSpectra = Signal(int) def __init__(self, *args, **kwargs): @@ -43,10 +46,11 @@ def showSpectra(self, event): self.cross.setData([x + 0.5], [self.row - y - 0.5]) self.cross.show() # update text - self.txt.setHtml(f'
Point: #{ind}
\ -
X: {x}
\ -
Y: {y}
\ -
Val: {self._image[self.row - y -1, x]: .4f}
') + self.txt.setHtml(toHtml(f'Point: #{ind}', size=8) + + toHtml(f'X: {x}', size=8) + + toHtml(f'Y: {y}', size=8) + + toHtml(f'Val: {self._image[self.row - y -1, x]: .4f}', size=8) + ) except Exception: self.cross.hide() diff --git a/xicam/BSISB/widgets/spectraplotwidget.py b/xicam/BSISB/widgets/spectraplotwidget.py index 5bf0b7c..9fc03d0 100644 --- a/xicam/BSISB/widgets/spectraplotwidget.py +++ b/xicam/BSISB/widgets/spectraplotwidget.py @@ -5,6 +5,7 @@ from pyqtgraph import InfiniteLine from qtpy.QtCore import Signal from lbl_ir.data_objects.ir_map import val2ind +from xicam.BSISB.widgets.mapviewwidget import toHtml class SpectraPlotWidget(PlotWidget): @@ -46,14 +47,11 @@ def getEnergy(self, lineobject): x_val = self.wavenumbers[idx] y_val = self._y[idx] if not self._meanSpec: - txt_html = f'
\ - Spectrum #{self.spectrumInd}
' + txt_html = toHtml(f'Spectrum #{self.spectrumInd}') else: - txt_html = f'
\ - {self._mean_title}
' + txt_html = toHtml(f'{self._mean_title}') - txt_html += f'
\ - X = {x_val: .2f}, Y = {y_val: .4f}
' + txt_html += toHtml(f'X = {x_val: .2f}, Y = {y_val: .4f}') self.txt.setHtml(txt_html) self.cross.setData([x_val], [y_val]) @@ -123,14 +121,11 @@ def plot(self, x, y, *args, **kwargs): y_val = y[idx] if not self._meanSpec: - txt_html = f'
\ - Spectrum #{self.spectrumInd}
' + txt_html = toHtml(f'Spectrum #{self.spectrumInd}') else: - txt_html = f'
\ - {self._mean_title}
' + txt_html = toHtml(f'{self._mean_title}') - txt_html += f'
\ - X = {x_val: .2f}, Y = {y_val: .4f}
' + txt_html += toHtml(f'X = {x_val: .2f}, Y = {y_val: .4f}') self.txt = TextItem(html=txt_html, anchor=(0, 0)) ymax = np.max(y) self._y = y From 852639b6bfbfbbe71122025399e8cdfe10b5aab4 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Wed, 11 Mar 2020 10:35:56 -0700 Subject: [PATCH 19/56] Update MapView with stream arg (see xi-cam.gui #44) --- xicam/BSISB/widgets/spectramaproiwidget.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/xicam/BSISB/widgets/spectramaproiwidget.py b/xicam/BSISB/widgets/spectramaproiwidget.py index 74cc46e..2f427e6 100644 --- a/xicam/BSISB/widgets/spectramaproiwidget.py +++ b/xicam/BSISB/widgets/spectramaproiwidget.py @@ -18,7 +18,7 @@ class MapView(QSplitter): sigAutoMaskState = Signal(object) sigSelectMaskState = Signal(object) - def __init__(self, header: NonDBHeader = None, field: str = 'primary', ): + def __init__(self, header: NonDBHeader = None, stream: str = 'primary', field: str = 'primary' ): """ A widget to display imageCube like dataset with ROI buttons :param header: Xi-cam datahandler header From 0e6e80e27cb21a7995ee1709efabe0dcaa8578ba Mon Sep 17 00:00:00 2001 From: lchen23 Date: Wed, 11 Mar 2020 10:38:58 -0700 Subject: [PATCH 20/56] Replaced yapsy plugin file with entry points --- setup.py | 7 ++++++- xicam/BSISB/formats/mapfile.yapsy-plugin | 9 --------- 2 files changed, 6 insertions(+), 10 deletions(-) delete mode 100644 xicam/BSISB/formats/mapfile.yapsy-plugin diff --git a/setup.py b/setup.py index d44db80..1ef8e3f 100644 --- a/setup.py +++ b/setup.py @@ -35,5 +35,10 @@ author='Liang Chen', install_requires=install_requires, dependency_links=dependency_links, - author_email='lchen2@lbl.gov' + author_email='lchen2@lbl.gov', + entry_points={"xicam.plugins.GUIPlugin": + ["BSISB = xicam.BSISB:BSISB"], + "xicam.plugins.DataHandlerPlugin": + ["mapfile = xicam.BSISB.formats.mapfile:MapFilePlugin"] + } ) diff --git a/xicam/BSISB/formats/mapfile.yapsy-plugin b/xicam/BSISB/formats/mapfile.yapsy-plugin deleted file mode 100644 index 39da3f9..0000000 --- a/xicam/BSISB/formats/mapfile.yapsy-plugin +++ /dev/null @@ -1,9 +0,0 @@ -[Core] -Name = BSISB Map File Format -Module = mapfile.py - -[Documentation] -Author = Liang Chen -Version = 0.1 -Website = -Description = From 931a63250fd141cb18f2c2a1dddc3c8c125faf3c Mon Sep 17 00:00:00 2001 From: lchen23 Date: Wed, 11 Mar 2020 10:39:53 -0700 Subject: [PATCH 21/56] Remove BSISB yapsy file --- xicam/BSISB/BSISB.yapsy-plugin | 9 --------- 1 file changed, 9 deletions(-) delete mode 100644 xicam/BSISB/BSISB.yapsy-plugin diff --git a/xicam/BSISB/BSISB.yapsy-plugin b/xicam/BSISB/BSISB.yapsy-plugin deleted file mode 100644 index 4d507f8..0000000 --- a/xicam/BSISB/BSISB.yapsy-plugin +++ /dev/null @@ -1,9 +0,0 @@ -[Core] -Name = xicam.BSISB -Module = __init__.py - -[Documentation] -Author = Liang Chen -Version = 0.1 -Website = -Description = From 7888cb653ed3e551f4379447862d021ca3570bbd Mon Sep 17 00:00:00 2001 From: lchen23 Date: Wed, 11 Mar 2020 10:46:44 -0700 Subject: [PATCH 22/56] Removed tracked files that should be ignored --- xicam.BSISB.egg-info/PKG-INFO | 46 ------------------ xicam.BSISB.egg-info/SOURCES.txt | 16 ------ xicam.BSISB.egg-info/dependency_links.txt | 1 - xicam.BSISB.egg-info/requires.txt | 3 -- xicam.BSISB.egg-info/top_level.txt | 1 - .../__pycache__/mapfile.cpython-37.pyc | Bin 5658 -> 0 bytes .../__pycache__/__init__.cpython-37.pyc | Bin 153 -> 0 bytes .../__pycache__/mapviewwidget.cpython-37.pyc | Bin 3695 -> 0 bytes .../spectraplotwidget.cpython-37.pyc | Bin 4621 -> 0 bytes 9 files changed, 67 deletions(-) delete mode 100644 xicam.BSISB.egg-info/PKG-INFO delete mode 100644 xicam.BSISB.egg-info/SOURCES.txt delete mode 100644 xicam.BSISB.egg-info/dependency_links.txt delete mode 100644 xicam.BSISB.egg-info/requires.txt delete mode 100644 xicam.BSISB.egg-info/top_level.txt delete mode 100644 xicam/BSISB/formats/__pycache__/mapfile.cpython-37.pyc delete mode 100644 xicam/BSISB/widgets/__pycache__/__init__.cpython-37.pyc delete mode 100644 xicam/BSISB/widgets/__pycache__/mapviewwidget.cpython-37.pyc delete mode 100644 xicam/BSISB/widgets/__pycache__/spectraplotwidget.cpython-37.pyc diff --git a/xicam.BSISB.egg-info/PKG-INFO b/xicam.BSISB.egg-info/PKG-INFO deleted file mode 100644 index eed8d82..0000000 --- a/xicam.BSISB.egg-info/PKG-INFO +++ /dev/null @@ -1,46 +0,0 @@ -Metadata-Version: 1.1 -Name: xicam.BSISB -Version: 0.1 -Summary: UNKNOWN -Home-page: UNKNOWN -Author: Liang Chen -Author-email: lchen2@lbl.gov -License: BSD -Description: Bsisb (Xi-cam PluginMaker) - =============================== - - version number: 0.1 - author: Liang Chen - - Overview - -------- - - - - Installation / Usage - -------------------- - - To install use pip: - - $ pip install Xi-cam.plugins.BSISB - - - Or clone the repo: - - $ git clone - $ python setup.py install - - Contributing - ------------ - - TBD - - Example - ------- - - TBD - -Platform: UNKNOWN -Classifier: Development Status :: 3 - Alpha -Classifier: Intended Audience :: Developers -Classifier: Programming Language :: Python :: 3 diff --git a/xicam.BSISB.egg-info/SOURCES.txt b/xicam.BSISB.egg-info/SOURCES.txt deleted file mode 100644 index bf7fb28..0000000 --- a/xicam.BSISB.egg-info/SOURCES.txt +++ /dev/null @@ -1,16 +0,0 @@ -MANIFEST.in -README.md -requirements.txt -setup.cfg -setup.py -xicam.BSISB.egg-info/PKG-INFO -xicam.BSISB.egg-info/SOURCES.txt -xicam.BSISB.egg-info/dependency_links.txt -xicam.BSISB.egg-info/requires.txt -xicam.BSISB.egg-info/top_level.txt -xicam/BSISB/__init__.py -xicam/BSISB/formats/__init__.py -xicam/BSISB/formats/mapfile.py -xicam/BSISB/widgets/__init__.py -xicam/BSISB/widgets/mapviewwidget.py -xicam/BSISB/widgets/spectraplotwidget.py \ No newline at end of file diff --git a/xicam.BSISB.egg-info/dependency_links.txt b/xicam.BSISB.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/xicam.BSISB.egg-info/dependency_links.txt +++ 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zRENF*_&U38cZX7xv3~Zjp{=8~-l&5M3Nma`UU@O8-Ofi_?T_+zX`+_%OtO-MvDA~o zQs%53%Mf=QZ5-;klCw_a+tWn8jov%BZIsnA^CW2wg)K`Ys`Ad2=lnLs9xd;@)+{B9 H`nCQG40e?t From 1a924b34272cb4c0eaed279c25e4a1ddeff3f758 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Wed, 11 Mar 2020 10:51:36 -0700 Subject: [PATCH 23/56] Ignore egg-info dir --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index f40e32a..de24304 100644 --- a/.gitignore +++ b/.gitignore @@ -7,4 +7,4 @@ *.tif build/ dist/ -*.egg_info/ +*.egg-info/ From 19caf7732efcd6fc643cd7510e9875aff5420b61 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Sat, 4 Apr 2020 20:10:00 -0700 Subject: [PATCH 24/56] adjust cross size and change txt position from fixed to x[0] --- xicam/BSISB/widgets/factorizationwidget.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/xicam/BSISB/widgets/factorizationwidget.py b/xicam/BSISB/widgets/factorizationwidget.py index 1ac4ab7..2f35f2f 100644 --- a/xicam/BSISB/widgets/factorizationwidget.py +++ b/xicam/BSISB/widgets/factorizationwidget.py @@ -339,7 +339,7 @@ def __init__(self, *args, **kwargs): self.line = InfiniteLine(pos=800, movable=True) self.line.sigPositionChanged.connect(self.getEnergy) self.addItem(self.line) - self.cross = PlotDataItem([800], [0], symbolBrush=(255, 255, 255), symbolPen=(255, 255, 255), symbol='+', symbolSize=20) + self.cross = PlotDataItem([800], [0], symbolBrush=(255, 255, 255), symbolPen=(255, 255, 255), symbol='+', symbolSize=25) self.addItem(self.cross) self.txt = TextItem() self.getViewBox().invertX(True) @@ -376,7 +376,7 @@ def plot(self, x, y, *args, **kwargs): if ymax > self.ymax: self.ymax = ymax self._x, self._y = x, y - self.txt.setPos(1200, 0.95 * self.ymax) + self.txt.setPos(x[0], 0.95 * self.ymax) self.addItem(self.txt) return plot_item From bb4e9ed5c02045397a277fd5c3bdd3530e67769d Mon Sep 17 00:00:00 2001 From: lchen23 Date: Mon, 6 Apr 2020 16:20:31 -0700 Subject: [PATCH 25/56] add lbl_ir install --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index 6e5f50f..c2aec49 100644 --- a/README.md +++ b/README.md @@ -15,6 +15,11 @@ Install Xi-CAM: Follow the guide [here](https://xi-cam2.readthedocs.io/en/latest/install.html) +Install lbl_ir: + + $ git clone https://lchen2ir@bitbucket.org/berkeleylab/lbl_ir.git + $ cd lbl_ir + $ pip install -e . Clone the repo: @@ -23,6 +28,7 @@ Clone the repo: $ pip install -e . $ cd .. $ xicam + Contributing ------------ From 783025cedb141e4f69e2cf3c8adba74eece9beb8 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Mon, 4 May 2020 21:25:53 -0700 Subject: [PATCH 26/56] bring highlighted curve to front in PCA loadings --- xicam/BSISB/widgets/factorizationwidget.py | 4 +++- xicam/BSISB/widgets/preprocesswidget.py | 0 2 files changed, 3 insertions(+), 1 deletion(-) create mode 100644 xicam/BSISB/widgets/preprocesswidget.py diff --git a/xicam/BSISB/widgets/factorizationwidget.py b/xicam/BSISB/widgets/factorizationwidget.py index 2f35f2f..bb9ebc1 100644 --- a/xicam/BSISB/widgets/factorizationwidget.py +++ b/xicam/BSISB/widgets/factorizationwidget.py @@ -399,7 +399,7 @@ def __init__(self, headermodel, selectionmodel): # self.componentSpectra = PlotWidget() self.componentSpectra = ComponentPlotWidget() self._plotLegends = self.componentSpectra.addLegend() - self._colors = ['r', 'g', 'b', 'y', 'c', 'm', 'w'] # color for plots + self._colors = ['r', 'g', 'm', 'y', 'c', 'b', 'w'] # color for plots # self.spectraROI = PlotWidget() self.NWimage = SlimImageView() @@ -611,8 +611,10 @@ def curveHighLight(self, k): for i in range(4): if i == k: self._plots[i].setPen(mkPen(self._colors[k], width=6)) + self._plots[i].setZValue(50) else: self._plots[i].setPen(mkPen(self._colors[i], width=2)) + self._plots[i].setZValue(0) self.componentSpectra._x, self.componentSpectra._y = self._plots[k].getData() self.componentSpectra.getEnergy() diff --git a/xicam/BSISB/widgets/preprocesswidget.py b/xicam/BSISB/widgets/preprocesswidget.py new file mode 100644 index 0000000..e69de29 From 63e5f20aca4fb4eb7e78225526b9d80bb70afc49 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Mon, 4 May 2020 21:26:27 -0700 Subject: [PATCH 27/56] add preprocess stage --- xicam/BSISB/__init__.py | 35 +- xicam/BSISB/formats/mapfile.py | 4 +- xicam/BSISB/widgets/preprocesswidget.py | 572 +++++++++++++++++++++++ xicam/BSISB/widgets/spectraplotwidget.py | 131 +++++- 4 files changed, 731 insertions(+), 11 deletions(-) diff --git a/xicam/BSISB/__init__.py b/xicam/BSISB/__init__.py index d3713a1..0557f5e 100644 --- a/xicam/BSISB/__init__.py +++ b/xicam/BSISB/__init__.py @@ -8,9 +8,25 @@ from xicam.BSISB.widgets.spectramaproiwidget import MapView from xicam.BSISB.widgets.spectraplotwidget import SpectraPlotWidget from xicam.BSISB.widgets.factorizationwidget import FactorizationWidget +from xicam.BSISB.widgets.preprocesswidget import PreprocessWidget from xicam.plugins import GUIPlugin, GUILayout from xicam.gui.widgets.tabview import TabView +class BSISBTabview(TabView): + + def __init__(self, *args, **kwargs): + super(BSISBTabview, self).__init__(*args, **kwargs) + + def closeTab(self, i): + newindex = self.currentIndex() + if (i <= self.currentIndex()) and (newindex > 0): + newindex -= 1 + + self.removeTab(i) + self.catalogmodel.removeRow(i) + self.setCurrentIndex(newindex) + self.selectionmodel.setCurrentIndex(self.catalogmodel.index(newindex, 0), QItemSelectionModel.Rows + | QItemSelectionModel.ClearAndSelect) class BSISB(GUIPlugin): name = 'BSISB' @@ -23,17 +39,19 @@ def __init__(self, *args, **kwargs): # Selection model self.selectionmodel = QItemSelectionModel(self.headermodel) - + self.preprocess = PreprocessWidget(self.headermodel, self.selectionmodel) self.FA_widget = FactorizationWidget(self.headermodel, self.selectionmodel) # update headers list when a tab window is closed self.headermodel.rowsRemoved.connect(partial(self.FA_widget.setHeader, 'spectra')) # Setup tabviews and update map selection - self.imageview = TabView(self.headermodel, self.selectionmodel, MapView, 'image') + self.imageview = BSISBTabview(self.headermodel, self.selectionmodel, MapView, 'image') + self.imageview.currentChanged.connect(self.updateTab) - self.stages = {'MapToH5': GUILayout(self.mapToH5), + self.stages = {"MapToH5": GUILayout(self.mapToH5), "Image View": GUILayout(self.imageview), + "Preprocess": GUILayout(self.preprocess), "Factor Analysis": GUILayout(self.FA_widget)} # "NMF": GUILayout(self.NMF_widget)} super(BSISB, self).__init__(*args, **kwargs) @@ -65,6 +83,7 @@ def appendHeader(self, header: NonDBHeader, **kwargs): currentMapView.sigAutoMaskState.connect(partial(self.appendSelection, 'autoMask')) currentMapView.sigSelectMaskState.connect(partial(self.appendSelection, 'select')) + self.preprocess.setHeader(field='spectra') self.FA_widget.setHeader(field='spectra') for i in range(4): self.FA_widget.roiList[i].sigRegionChangeFinished.connect(self.updateROI) @@ -91,7 +110,9 @@ def updateROI(self, roi): selectMapIdx = 0 self.imageview.widget(selectMapIdx).roiMove(roi) - def updateTab(self, tabIdx): - if tabIdx >= 0: - self.selectionmodel.select(self.headermodel.index(tabIdx, 0), QItemSelectionModel.ClearAndSelect) - + def updateTab(self): + # clear specItemModel + self.preprocess.specItemModel.clear() + self.preprocess.rawSpectra.clearAll() + self.preprocess.resultSpectra.clearAll() + self.preprocess.infoBox.setText('') \ No newline at end of file diff --git a/xicam/BSISB/formats/mapfile.py b/xicam/BSISB/formats/mapfile.py index f41731b..b47073f 100644 --- a/xicam/BSISB/formats/mapfile.py +++ b/xicam/BSISB/formats/mapfile.py @@ -86,7 +86,7 @@ def getImageEvents(cls, path, descriptor_uid): for i in range(n): yield embedded_local_event_doc(descriptor_uid, 'image', cls, (path,), resource_kwargs={'E': i}, - metadata={'wavenumbers': wavenumbers, 'rc_index': rc2ind, 'index_rc': ind2rc, 'imgShape': imgShape}) + metadata={'path': path, 'wavenumbers': wavenumbers, 'rc_index': rc2ind, 'index_rc': ind2rc, 'imgShape': imgShape}) @classmethod def getSpectraDescriptor(cls, path, start_uid): @@ -112,7 +112,7 @@ def getSpectraEvents(cls, path, descriptor_uid): for i in range(n): yield embedded_local_event_doc(descriptor_uid, 'spectra', cls, (path,), resource_kwargs={'i': i}, - metadata={'wavenumbers':wavenumbers, 'rc_index': rc2ind, 'index_rc': ind2rc, 'imgShape':imgShape}) + metadata={'path': path, 'wavenumbers':wavenumbers, 'rc_index': rc2ind, 'index_rc': ind2rc, 'imgShape':imgShape}) @classmethod def ingest(cls, paths): diff --git a/xicam/BSISB/widgets/preprocesswidget.py b/xicam/BSISB/widgets/preprocesswidget.py index e69de29..2bddf7a 100644 --- a/xicam/BSISB/widgets/preprocesswidget.py +++ b/xicam/BSISB/widgets/preprocesswidget.py @@ -0,0 +1,572 @@ +import os +import numpy as np +import pandas as pd +from scipy.interpolate import interp1d +from functools import partial +from qtpy.QtWidgets import * +from qtpy.QtCore import Qt, QItemSelectionModel, Signal +from qtpy.QtGui import QStandardItemModel, QStandardItem, QFont +from pyqtgraph.parametertree import ParameterTree, Parameter +from xicam.core import msg +from lbl_ir.data_objects.ir_map import ir_map, val2ind +from xicam.BSISB.widgets.spectraplotwidget import baselinePlotWidget +from xicam.BSISB.widgets.uiwidget import MsgBox, YesNoDialog + + +class Preprocessor: + def __init__(self, wavenumbers, spectrum): + self.wavenumbers = wavenumbers + self.spectrum = spectrum + self.preprocess_method = None + self.interp_method = None + self.wav_anchor = None + + def parse_anchors(self, anchors): + """ + parse anchor points str to real valued wavenumbers and confine energy range + :return: None + """ + anchor_idx = [] + for entry in anchors.split(','): + try: + anchor_idx.append(val2ind(int(entry.strip()), self.wavenumbers)) + except: + continue + anchor_idx = sorted(anchor_idx) + + self.energy = self.wavenumbers[anchor_idx[0]: anchor_idx[-1] + 1] + self.specTrim = self.spectrum[anchor_idx[0]: anchor_idx[-1] + 1] + + self.wav_anchor = self.wavenumbers[anchor_idx] + self.spec_anchor = self.spectrum[anchor_idx] + return None + + def isBaseFitOK(self, anchors, kind): + """ + Check is there is enough anchor points to fit higher order baseline + :param anchors: anchor points + :param kind: fitting method + :return: decide if there is enough anchor points for baseline fit + """ + # parse anchor points + self.parse_anchors(anchors) + # decide if num of anchor points is enough for 'quadratic' or 'cubic' fit + if len(self.wav_anchor) < 2: + MsgBox('Baseline fitting needs at least 2 anchor points.\n' + + 'Please add more "anchor points" to correctly fit the baseline.') + return False + elif len(self.wav_anchor) < 3 and kind == 'quadratic': + MsgBox('Quadratic baseline needs more than 2 anchor points.\n' + + 'Please add more "anchor points" to correctly fit the baseline.') + return False + elif len(self.wav_anchor) < 4 and kind == 'cubic': + MsgBox('Cubic baseline needs more than 3 anchor points.\n' + + 'Please add more "anchor points" to correctly fit the baseline.') + return False + else: + return True + + def rubber_band(self, anchors, kind='linear'): + """ + Calculate rubberBaseline, debased spectrum and 2nd, 4th order derivative of the spectrum + :param anchors: rubberband anchor points + :param kind: spline fit curve order + :return: baseline fit success (bool) + """ + self.preprocess_method = 'rubberband' + self.interp_method = kind + # get rubberBaseline and debased spectrum + if not self.isBaseFitOK(anchors, kind): + return False + + f = interp1d(self.wav_anchor, self.spec_anchor, kind=kind) + self.rubberBaseline = f(self.energy) + self.rubberDebased = self.specTrim - self.rubberBaseline + + # get 2nd and 4th order derivatives + self.get_derivative() + return True + + def get_derivative(self): + """ + Calculate 2nd and 4th order derivative of the spectrum + :param n: the derivative order + :return: None + """ + dx = self.energy[1] - self.energy[0] + self.deriv2 = self.nthOrderGradient(dx, self.specTrim, n=2) + self.deriv4 = self.nthOrderGradient(dx, self.specTrim, n=4) + return None + + def nthOrderGradient(self, dx, y, n=1): + """ + Calculate nth order derivative of array y + :param dx: spacing of x + :param y: array + :param n: order of derivative + :return: nth order derivative + """ + for i in range(n): + z = np.gradient(y, dx) + y = z + y = np.where(np.abs(y) == np.inf, 0, y) # fix infinity values + return y + + +class PreprocessParameters(ParameterTree): + sigParamChanged = Signal(object) + + def __init__(self): + super(PreprocessParameters, self).__init__() + + self.parameter = Parameter(name='params', type='group', + children=[{'name': "Preprocess method", + 'values': ['Rubberband'], + 'value': 'Rubberband', + 'type': 'list'}, + {'name': "Anchor points", + 'value': '400, 4000', + 'type': 'str'}, + {'name': "Interp method", + 'value': 'linear', + 'values': ['linear', 'quadratic', 'cubic'], + 'type': 'list'} + ]) + self.setParameters(self.parameter, showTop=False) + self.setIndentation(0) + # change Fonts + self.fontSize = 12 + font = QFont("Helvetica [Cronyx]", self.fontSize) + boldFont = QFont("Helvetica [Cronyx]", self.fontSize, QFont.Bold) + self.header().setFont(font) + for item in self.listAllItems(): + if hasattr(item, 'widget'): + item.setFont(0, boldFont) + item.widget.setFont(font) + item.displayLabel.setFont(font) + item.widget.setMaximumHeight(40) + # init params dict + self.argMap = {"Anchor points": 'anchors', + "Interp method": 'kind' + } + # set self.processArgs to default value + self.processArgs = {} + for child in self.parameter.childs: + if child.name() == "Anchor points": + self.processArgs['anchors'] = '400, 4000' + elif child.name() == "Interp method": + self.processArgs['kind'] = 'linear' + # elif child.name() not in ["Normalization method"]: + # self.processArgs[self.argMap[child.name()]] = None + + # connect signals + for name in self.argMap.keys(): + self.parameter.child(name).sigValueChanged.connect(partial(self.updateParam, name)) + + # self.parameter.child('Preprocess method').sigValueChanged.connect(self.updateMethod) + + def updateParam(self, name): + """ + get latest parameter values + :param name: parameter name + :return: None + """ + self.processArgs[self.argMap[name]] = self.parameter[name] + self.sigParamChanged.emit(self.processArgs) + + # def updateMethod(self): + # """ + # Toggle parameter menu based on fit method + # :return: + # """ + # if self.parameter["Normalization method"] == 'mback': + # self.parameter.child('Z number').show() + # self.parameter.child('Edge').show() + # self.parameter.child('Edge step').hide() + # else: + # self.parameter.child('Z number').hide() + # self.parameter.child('Edge').hide() + # self.parameter.child('Edge step').show() + + +class PreprocessWidget(QSplitter): + def __init__(self, headermodel, selectionmodel): + super(PreprocessWidget, self).__init__() + self.headermodel = headermodel + self.mapselectmodel = selectionmodel + self.selectMapidx = 0 + self.resultDict = {} + self.isBatchProcessOn = False + self.out = None + self.reportList = ['preprocess_method', 'wav_anchor', 'interp_method'] + self.arrayList = ['rubberDebased', 'deriv2', 'deriv4'] + + # split between spectrum parameters and viewwindow, vertical split + self.params_and_specview = QSplitter() + self.params_and_specview.setOrientation(Qt.Vertical) + # split between buttons and parameters + self.buttons_and_params = QSplitter() + self.buttons_and_params.setOrientation(Qt.Horizontal) + # split between speclist and report + self.speclist_and_report = QSplitter() + self.speclist_and_report.setOrientation(Qt.Vertical) + + # buttons layout + self.buttons = QWidget() + self.buttonlayout = QGridLayout() + self.buttons.setLayout(self.buttonlayout) + # set up buttons + self.fontSize = 12 + font = QFont("Helvetica [Cronyx]", self.fontSize) + self.loadBtn = QPushButton() + self.loadBtn.setText('Load spectra') + self.loadBtn.setFont(font) + self.removeBtn = QPushButton() + self.removeBtn.setText('Remove spectrum') + self.removeBtn.setFont(font) + self.normBox = QComboBox() + self.normBox.addItems(['Raw spectrum', + 'Rubberband baseline', + 'Rubberband + 2nd derivative', + 'Rubberband + 4th derivative', + ]) + self.normBox.setFont(font) + self.batchBtn = QPushButton() + self.batchBtn.setText('Batch process') + self.batchBtn.setFont(font) + self.saveResultBox = QComboBox() + self.saveResultBox.addItems(['Save rubberband', + 'Save 2nd derivative', + 'Save 4th derivative', + 'Save all', + ]) + self.saveResultBox.setFont(font) + # add all buttons + self.buttonlayout.addWidget(self.loadBtn) + self.buttonlayout.addWidget(self.removeBtn) + self.buttonlayout.addWidget(self.normBox) + self.buttonlayout.addWidget(self.batchBtn) + self.buttonlayout.addWidget(self.saveResultBox) + # define report + self.reportWidget = QWidget() + self.reportWidget.setLayout(QVBoxLayout()) + self.infoBox = QTextEdit() + reportTitle = QLabel('Preprocess results') + reportTitle.setFont(font) + self.reportWidget.layout().addWidget(reportTitle) + self.reportWidget.layout().addWidget(self.infoBox) + # spectrum list view + self.specItemModel = QStandardItemModel() + self.specSelectModel = QItemSelectionModel(self.specItemModel) + self.speclistview = QListView() + self.speclistview.setModel(self.specItemModel) + self.speclistview.setSelectionModel(self.specSelectModel) + # add title to list view + self.specListWidget = QWidget() + self.listLayout = QVBoxLayout() + self.specListWidget.setLayout(self.listLayout) + specListTitle = QLabel('Spectrum List') + specListTitle.setFont(font) + self.listLayout.addWidget(specListTitle) + self.listLayout.addWidget(self.speclistview) + + # spectrum plot + self.rawSpectra = baselinePlotWidget() + self.resultSpectra = baselinePlotWidget() + # ParameterTree + self.parametertree = PreprocessParameters() + self.processArgs = self.parametertree.processArgs + self.argMap = self.parametertree.argMap + + # assemble widgets + self.buttons_and_params.addWidget(self.parametertree) + self.buttons_and_params.addWidget(self.buttons) + self.buttons_and_params.setSizes([1000, 100]) + self.params_and_specview.addWidget(self.buttons_and_params) + self.params_and_specview.addWidget(self.rawSpectra) + self.params_and_specview.addWidget(self.resultSpectra) + self.params_and_specview.setSizes([150, 50, 50]) + self.speclist_and_report.addWidget(self.specListWidget) + self.speclist_and_report.addWidget(self.reportWidget) + self.speclist_and_report.setSizes([150, 100]) + self.addWidget(self.params_and_specview) + self.addWidget(self.speclist_and_report) + self.setSizes([1000, 200]) + + # Connect signals + self.loadBtn.clicked.connect(self.loadData) + self.removeBtn.clicked.connect(self.removeSpec) + self.batchBtn.clicked.connect(self.batchProcess) + self.specSelectModel.selectionChanged.connect(self.updateSpecPlot) + self.normBox.currentIndexChanged.connect(self.updateSpecPlot) + self.parametertree.sigParamChanged.connect(self.updateSpecPlot) + + def setHeader(self, field: str): + self.headers = [self.headermodel.item(i).header for i in range(self.headermodel.rowCount())] + self.field = field + self.wavenumberList = [] + self.rc2indList = [] + self.ind2rcList = [] + self.pathList = [] + self.dataSets = [] + + # get wavenumbers, rc2ind + for header in self.headers: + dataEvent = next(header.events(fields=[field])) + self.wavenumberList.append(dataEvent['wavenumbers']) + self.rc2indList.append(dataEvent['rc_index']) + self.ind2rcList.append(dataEvent['index_rc']) + self.pathList.append(dataEvent['path']) + # get raw spectra + data = None + try: # spectra datasets + data = header.meta_array('spectra') + except IndexError: + msg.logMessage('Header object contained no frames with field ''{field}''.', msg.ERROR) + if data is not None: + self.dataSets.append(data) + + def isMapOpen(self): + if not self.mapselectmodel.selectedIndexes(): # no map is open + return False + else: + self.selectMapidx = self.mapselectmodel.selectedIndexes()[0].row() + return True + + def getCurrentSpecid(self): + # get selected spectrum idx + specidx = None # default value + if self.specSelectModel.selectedIndexes(): + selectedSpecRow = self.specSelectModel.selectedIndexes()[0].row() + currentSpecItem = self.specItemModel.item(selectedSpecRow) + specidx = currentSpecItem.idx + return specidx + + + + def updateSpecPlot(self): + # get current map idx and selected spectrum idx + specidx = self.getCurrentSpecid() + if not self.isMapOpen(): + return + elif self.specItemModel.rowCount() == 0: + MsgBox('No spectrum is loaded.\nPlease click "Load spectra" to import data.') + return + elif specidx is None: + return + + # get plotchoice + plotChoice = self.normBox.currentIndex() + + # create Preprocessor object + self.out = Preprocessor(self.wavenumberList[self.selectMapidx], self.dataSets[self.selectMapidx][specidx]) + # calculate rubberband baseline + baselineOK = self.out.rubber_band(**self.processArgs) + if not baselineOK: + return + + # make results report + if plotChoice != 0: + self.getReport(self.out) + + # if not batch processing, show plots + if not self.isBatchProcessOn: + # clean up plots + self.rawSpectra.clearAll() + self.resultSpectra.clearAll() + if plotChoice == 0: # plot raw spectrum + self.infoBox.setText('') # clear txt + self.rawSpectra.plotBase(self.out, plotType='raw') + elif plotChoice == 1: # plot raw, edges, norm + self.rawSpectra.plotBase(self.out, plotType='base') + self.resultSpectra.plotBase(self.out, plotType='rubberband') + elif plotChoice == 2: # plot raw, edges, flattened + self.rawSpectra.plotBase(self.out, plotType='base') + self.resultSpectra.plotBase(self.out, plotType='deriv2') + elif plotChoice == 3: # plot raw, edges, Mback + poly normalized + self.rawSpectra.plotBase(self.out, plotType='base') + self.resultSpectra.plotBase(self.out, plotType='deriv4') + + def getReport(self, output): + resultTxt = '' + # get normalization results + for item in dir(output): + if item in self.reportList: + if item == 'wav_anchor': + val = getattr(output, item) + printFormat = ('{:.2f}, ' * len(val))[:-1] + resultTxt += item + ': ' + printFormat.format(*val) + '\n' + else: + resultTxt += item + ': ' + getattr(output, item) + '\n' + if (item in self.arrayList) or (item in self.reportList): + self.resultDict[item] = getattr(output, item) + + # send text to report info box + self.infoBox.setText(resultTxt) + + def loadData(self): + # get current map idx + if not self.isMapOpen(): + return + # pass the selected map data to plotwidget + self.rawSpectra.setHeader(self.headers[self.selectMapidx], 'spectra') + currentMapItem = self.headermodel.item(self.selectMapidx) + rc2ind = self.rc2indList[self.selectMapidx] + # get current map name + mapName = currentMapItem.data(0) + # get current selected pixels + pixelCoord = currentMapItem.selectedPixels + # get selected specIds + spectraIds = [] + if currentMapItem.selectedPixels is None: # select all + spectraIds = list(range(len(rc2ind))) + else: + for i in range(len(pixelCoord)): + row_col = tuple(pixelCoord[i]) + spectraIds.append(rc2ind[row_col]) + spectraIds = sorted(spectraIds) + # add specitem model + self.specItemModel.clear() + for idx in spectraIds: + item = QStandardItem(mapName + '# ' + str(idx)) + item.idx = idx + self.specItemModel.appendRow(item) + + def removeSpec(self): + # get current selectedSpecRow + if self.specSelectModel.selectedIndexes(): + selectedSpecRow = self.specSelectModel.selectedIndexes()[0].row() + self.specSelectModel.blockSignals(True) + self.specItemModel.removeRow(selectedSpecRow) + self.specSelectModel.blockSignals(False) + # clean up plots + self.rawSpectra.clearAll() + self.resultSpectra.clearAll() + self.infoBox.setText('') + + def batchProcess(self): + # get current map idx + if not self.isMapOpen(): + return + elif self.specItemModel.rowCount() == 0: + MsgBox('No spectrum is loaded.\nPlease click "Load spectra" to import data.') + return + # check if baseline fit OK + if self.out is None: + self.out = Preprocessor(self.wavenumberList[self.selectMapidx], self.dataSets[self.selectMapidx][0]) + baselineOK = self.out.rubber_band(**self.processArgs) + if not baselineOK: + return + # notice to user + userMsg = YesNoDialog(f'Ready to batch process selected spectra.\nDo you want to continue?') + userChoice = userMsg.choice() + if userChoice == QMessageBox.No: # user choose to stop + return + + self.isBatchProcessOn = True + # set plot type to rubberband + self.normBox.setCurrentIndex(1) + + # init resultSetsDict, paramsDict + self.resultSetsDict = {} + self.paramsDict = {} + self.paramsDict['specID'] = [] + self.paramsDict['row_column'] = [] + ind2rc = self.ind2rcList[self.selectMapidx] + filePath = self.pathList[self.selectMapidx] + energy = self.out.energy + n_energy = len(energy) + for item in self.arrayList: + self.resultSetsDict[item] = np.empty((0, n_energy)) + for item in self.reportList: + self.paramsDict[item] = [] + # batch process begins + n_spectra = self.specItemModel.rowCount() + for i in range(n_spectra): + msg.showMessage(f'Processing {i + 1}/{n_spectra} spectra') + # select each spec and collect results + self.specSelectModel.select(self.specItemModel.index(i, 0), QItemSelectionModel.ClearAndSelect) + # get spec idx + currentSpecItem = self.specItemModel.item(i) + self.paramsDict['specID'].append(currentSpecItem.idx) + self.paramsDict['row_column'].append(ind2rc[currentSpecItem.idx]) + # append all results into a single array/list + for item in self.arrayList: + self.resultSetsDict[item] = np.append(self.resultSetsDict[item], self.resultDict[item].reshape(1, -1), + axis=0) + for item in self.reportList: + self.paramsDict[item].append(self.resultDict[item]) + + # result collection completed. convert paramsDict to df + dfDict = {} + dfDict['param'] = pd.DataFrame(self.paramsDict).set_index('specID') + for item in self.arrayList: + # convert resultSetsDict to df + dfDict[item] = pd.DataFrame(self.resultSetsDict[item], columns=energy.tolist(), + index=self.paramsDict['specID']).rename_axis('specID', axis=0) + + # save df to files + msg.showMessage(f'Batch processing is completed! Saving results to csv files.') + saveDataChoice = self.saveResultBox.currentIndex() + if saveDataChoice != 3: # save a single result + saveDataType = self.arrayList[saveDataChoice] + dirName, csvName, h5Name = self.saveToFiles(energy, dfDict, filePath, saveDataType) + if h5Name is None: + MsgBox(f'Processed data was saved as csv file at: \n{dirName + csvName}') + else: + MsgBox( + f'Processed data was saved as: \n\ncsv file at: {dirName + csvName} and \n\nHDF5 file at: {dirName + h5Name}') + else: # save all results + csvList = [] + h5List = [] + for saveDataType in self.arrayList: + dirName, csvName, h5Name = self.saveToFiles(energy, dfDict, filePath, saveDataType) + csvList.append(csvName) + h5List.append(h5Name) + + allcsvName = (', ').join(csvList) + if h5Name is None: + MsgBox(f'Processed data was saved as csv files at: \n{dirName + allcsvName}') + else: + allh5Name = (', ').join(h5List) + MsgBox( + f'Processed data was saved as: \n\ncsv files at: {dirName + allcsvName} and \n\nHDF5 files at: {dirName + allh5Name}') + + # save parameter + xlsName = csvName[:-4] + '_param.xlsx' + dfDict['param'].to_excel(dirName + xlsName) + # batch process completed + self.isBatchProcessOn = False + + def saveToFiles(self, energy, dfDict, filePath, saveDataType): + + ind2rc = self.ind2rcList[self.selectMapidx] + n_spectra = self.specItemModel.rowCount() + + # get dirname and old filename + dirName = os.path.dirname(filePath) + dirName += '/' + oldFileName = os.path.basename(filePath) + + # save dataFrames to csv file + csvName = oldFileName[:-3] + '_' + saveDataType + '.csv' + dfDict[saveDataType].to_csv(dirName + csvName) + + # if a full map is processed, also save results to a h5 file + h5Name = None + if n_spectra == len(ind2rc): + fullMap = ir_map(filename=filePath) + fullMap.add_image_cube() + fullMap.wavenumbers = energy + fullMap.N_w = len(energy) + fullMap.data = np.zeros((fullMap.data.shape[0], fullMap.N_w)) + fullMap.imageCube = np.zeros((fullMap.imageCube.shape[0], fullMap.imageCube.shape[1], fullMap.N_w)) + for i in self.paramsDict['specID']: + fullMap.data[i, :] = self.resultSetsDict[saveDataType][i, :] + row, col = ind2rc[i] + fullMap.imageCube[row, col, :] = fullMap.data[i, :] = self.resultSetsDict[saveDataType][i, :] + # save data as hdf5 + h5Name = oldFileName[:-3] + '_' + saveDataType + '.h5' + fullMap.write_as_hdf5(dirName + h5Name) + + return dirName, csvName, h5Name diff --git a/xicam/BSISB/widgets/spectraplotwidget.py b/xicam/BSISB/widgets/spectraplotwidget.py index 9fc03d0..7e598d3 100644 --- a/xicam/BSISB/widgets/spectraplotwidget.py +++ b/xicam/BSISB/widgets/spectraplotwidget.py @@ -1,4 +1,5 @@ -from pyqtgraph import PlotWidget, TextItem, PlotDataItem +from qtpy.QtCore import Qt +from pyqtgraph import PlotWidget, TextItem, PlotDataItem, mkPen from xicam.core import msg from xicam.core.data import NonDBHeader import numpy as np @@ -11,7 +12,7 @@ class SpectraPlotWidget(PlotWidget): sigEnergyChanged = Signal(object) - def __init__(self, linePos=650, txtPosRatio=0.25, invertX=True, *args, **kwargs): + def __init__(self, linePos=650, txtPosRatio=0.3, invertX=True, *args, **kwargs): """ A widget to display a 1D spectrum :param linePos: the initial position of the InfiniteLine @@ -133,3 +134,129 @@ def plot(self, x, y, *args, **kwargs): self.txt.setPos(r * x[-1] + (1 - r) * x[0], 0.95 * ymax) self.cross.setData([x_val], [y_val]) self.addItem(self.txt) + +class baselinePlotWidget(SpectraPlotWidget): + def __init__(self): + super(baselinePlotWidget, self).__init__() + self.line.setValue(800) + self.txt = TextItem('', anchor=(0, 0)) + self.cross = PlotDataItem([800], [0], symbolBrush=(255, 255, 0), symbolPen=(255, 255, 0), + symbol='+',symbolSize=20) + self.line.sigPositionChanged.connect(self.getMu) + self._mu = None + + def plot(self, x, y, *args, **kwargs): + # set up infinity line and get its position + self.plotItem.plot(x, y, *args, **kwargs) + self.addItem(self.line) + self.addItem(self.cross) + x_val = self.line.value() + if x_val == 0: + y_val = 0 + else: + idx = val2ind(x_val, self.wavenumbers) + x_val = self.wavenumbers[idx] + y_val = y[idx] + + if not self._meanSpec: + txt_html = f'
\ + Spectrum #{self.spectrumInd}
' + else: + txt_html = f'
\ + {self._mean_title}
' + + txt_html += f'
\ + X = {x_val: .2f}, Y = {y_val: .4f}
' + self.txt = TextItem(html=txt_html, anchor=(0, 0)) + ymax = np.max(y) + self._y = y + r = self.txtPosRatio + self.txt.setPos(r * x[-1] + (1 - r) * x[0], 0.95 * ymax) + self.cross.setData([x_val], [y_val]) + self.addItem(self.txt) + + def getMu(self): + if self._mu is not None: + x_val = self.line.value() + if x_val == 0: + y_val = 0 + else: + idx = val2ind(x_val, self._x) + x_val = self._x[idx] + y_val = self._mu[idx] + txt_html = f'
\ + X = {x_val: .2f}, Y = {y_val: .4f}
' + self.txt.setHtml(txt_html) + self.cross.setData([x_val], [y_val]) + + def addDataCursor(self, x, y): + self.addItem(self.line) + self.addItem(self.cross) + ymax = np.max(y) + self.txt.setText('') + r = self.txtPosRatio + self.txt.setPos(r * x[-1] + (1 - r) * x[0], 0.95 * ymax) + self.addItem(self.txt) + self.getMu() + + def clearAll(self): + # remove legend + _legend = self.plotItem.legend + if (_legend is not None) and (_legend.scene() is not None): + _legend.scene().removeItem(_legend) + self.clear() + + def plotBase(self, dataGroup, plotType='raw'): + """ + make plots for Larch Group object + :param dataGroup: Larch Group object + :return: + """ + # add legend + self.plotItem.addLegend(offset=(-1, -1)) + x = self._x = dataGroup.energy # self._x, self._mu for getEnergy + y = self._mu = dataGroup.specTrim + n = len(x) # array length + self._y = None # disable getEnergy func + if plotType == 'raw': + self.plotItem.plot(x, y, name='Raw', pen=mkPen('w', width=2)) + elif plotType == 'base': + self.plotItem.plot(x, y, name='Raw', pen=mkPen('w', width=2)) + self.plotItem.plot(x, dataGroup.rubberBaseline, name='Rubberband baseline', pen=mkPen('g', style=Qt.DotLine, width=2)) + self.plotItem.plot(dataGroup.wav_anchor, dataGroup.spec_anchor, symbol='o', symbolPen='r', symbolBrush=0.5) + elif plotType == 'rubberband': + y = self._mu = dataGroup.rubberDebased + self.plotItem.plot(x, y, name='Rubberband debased', pen=mkPen('r', width=2)) + elif plotType == 'deriv2': + y = self._mu = dataGroup.rubberDebased # for data cursor + scale, offset = self.alignTwoCurve(dataGroup.rubberDebased[n//4:n*3//4], dataGroup.deriv2[n//4:n*3//4]) + deriv2Scaled = dataGroup.deriv2 * scale + offset + ymin, ymax = np.min(y), np.max(y) + self.getViewBox().setYRange(ymin, ymax, padding=0.1) + self.plotItem.plot(x, y, name='Rubberband debased', pen=mkPen('r', width=2)) + self.plotItem.plot(x, deriv2Scaled, name='2nd derivative (scaled)', pen=mkPen('g', width=2)) + elif plotType == 'deriv4': + y = self._mu = dataGroup.rubberDebased + scale, offset = self.alignTwoCurve(dataGroup.rubberDebased[n//4:n*3//4], dataGroup.deriv4[n//4:n*3//4]) + deriv4Scaled = dataGroup.deriv4 * scale + offset + ymin, ymax = np.min(y), np.max(y) + self.getViewBox().setYRange(ymin, ymax, padding=0.1) + self.plotItem.plot(x, y, name='Rubberband debased', pen=mkPen('r', width=2)) + self.plotItem.plot(x, deriv4Scaled, name='4th derivative (normed, scaled)', pen=mkPen('g', width=2)) + # add infinityline, cross + self.addDataCursor(x, y) + + def alignTwoCurve(self, y1, y2): + """ + Align the scale of y2 to that of y1 + :param y1: the main curve + :param y2: the curve to be aligned + :return: + scale: scale factor + offset: y offset + """ + y1Range, y2Range = np.max(y1) - np.min(y1), np.max(y2) - np.min(y2) + scale = y1Range / y2Range + y = y2 * scale + offset = np.max(y1) - np.max(y) + return scale, offset \ No newline at end of file From 512bcc5b21269164047ff26ba6ea86deb51d61e6 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Tue, 5 May 2020 00:37:21 -0700 Subject: [PATCH 28/56] add 'error type' to isbaselineOK msgbox --- xicam/BSISB/widgets/preprocesswidget.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/xicam/BSISB/widgets/preprocesswidget.py b/xicam/BSISB/widgets/preprocesswidget.py index 2bddf7a..e0fccdd 100644 --- a/xicam/BSISB/widgets/preprocesswidget.py +++ b/xicam/BSISB/widgets/preprocesswidget.py @@ -53,15 +53,15 @@ def isBaseFitOK(self, anchors, kind): # decide if num of anchor points is enough for 'quadratic' or 'cubic' fit if len(self.wav_anchor) < 2: MsgBox('Baseline fitting needs at least 2 anchor points.\n' + - 'Please add more "anchor points" to correctly fit the baseline.') + 'Please add more "anchor points" to correctly fit the baseline.', type='error') return False elif len(self.wav_anchor) < 3 and kind == 'quadratic': MsgBox('Quadratic baseline needs more than 2 anchor points.\n' + - 'Please add more "anchor points" to correctly fit the baseline.') + 'Please add more "anchor points" to correctly fit the baseline.', type='error') return False elif len(self.wav_anchor) < 4 and kind == 'cubic': MsgBox('Cubic baseline needs more than 3 anchor points.\n' + - 'Please add more "anchor points" to correctly fit the baseline.') + 'Please add more "anchor points" to correctly fit the baseline.', type='error') return False else: return True From 2dec52ffaa20205b9d59ac1a0001df74da57ff17 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Mon, 11 May 2020 10:40:04 -0700 Subject: [PATCH 29/56] hide mapToH5 stage for review --- xicam/BSISB/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/xicam/BSISB/__init__.py b/xicam/BSISB/__init__.py index 0557f5e..7280c76 100644 --- a/xicam/BSISB/__init__.py +++ b/xicam/BSISB/__init__.py @@ -49,7 +49,7 @@ def __init__(self, *args, **kwargs): self.imageview = BSISBTabview(self.headermodel, self.selectionmodel, MapView, 'image') self.imageview.currentChanged.connect(self.updateTab) - self.stages = {"MapToH5": GUILayout(self.mapToH5), + self.stages = {#"MapToH5": GUILayout(self.mapToH5), "Image View": GUILayout(self.imageview), "Preprocess": GUILayout(self.preprocess), "Factor Analysis": GUILayout(self.FA_widget)} From 07fb319b110f282fb55bc4a297f3f7ea61a9b6e4 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Mon, 25 May 2020 18:16:23 -0700 Subject: [PATCH 30/56] change fonts and button names in FA widget --- xicam/BSISB/widgets/clusteringwidget.py | 262 +++++++++++++++++++++ xicam/BSISB/widgets/factorizationwidget.py | 52 ++-- 2 files changed, 300 insertions(+), 14 deletions(-) create mode 100644 xicam/BSISB/widgets/clusteringwidget.py diff --git a/xicam/BSISB/widgets/clusteringwidget.py b/xicam/BSISB/widgets/clusteringwidget.py new file mode 100644 index 0000000..87b8262 --- /dev/null +++ b/xicam/BSISB/widgets/clusteringwidget.py @@ -0,0 +1,262 @@ +import os +import numpy as np +import pandas as pd +from umap import UMAP +from sklearn import cluster +from functools import partial +from qtpy.QtWidgets import * +from qtpy.QtCore import Qt, QItemSelectionModel, Signal +from qtpy.QtGui import QStandardItemModel, QStandardItem, QFont +from pyqtgraph.parametertree import ParameterTree, Parameter +from xicam.core import msg +from lbl_ir.data_objects.ir_map import val2ind +from xicam.BSISB.widgets.spectraplotwidget import SpectraPlotWidget +from xicam.BSISB.widgets.imshowwidget import SlimImageView +from xicam.BSISB.widgets.uiwidget import MsgBox, YesNoDialog + + +class ClusteringParameters(ParameterTree): + sigParamChanged = Signal(object) + + def __init__(self): + super(ClusteringParameters, self).__init__() + + self.parameter = Parameter(name='params', type='group', + children=[{'name': "Embedding", + 'values': ['PCA', 'UMAP'], + 'value': 'UMAP', + 'type': 'list'}, + {'name': "Components", + 'value': 2, + 'type': 'int'}, + {'name': "Neighbors", + 'value': 15, + 'type': 'int'}, + {'name': "Min Dist", + 'value': 0.1, + 'type': 'float'}, + {'name': "Metric", + 'values': ['euclidean', 'manhattan', 'correlation'], + 'value': 'euclidean', + 'type': 'list'}, + {'name': "Normalization", + 'values': ['L2', 'L1', 'None'], + 'value': 'L2', + 'type': 'list'}, + {'name': "Wavenumber Range", + 'value': '400, 4000', + 'type': 'str'}, + {'name': "Clusters", + 'value': 3, + 'type': 'int'}, + {'name': "X Component", + 'values': [1, 2, 3, 4], + 'value': 1, + 'type': 'list'}, + {'name': "Y Component", + 'values': [1, 2, 3, 4], + 'value': 2, + 'type': 'list'} + ]) + self.setParameters(self.parameter, showTop=False) + self.setIndentation(0) + # change Fonts + self.fontSize = 12 + font = QFont("Helvetica [Cronyx]", self.fontSize) + boldFont = QFont("Helvetica [Cronyx]", self.fontSize, QFont.Bold) + self.header().setFont(font) + for item in self.listAllItems(): + if hasattr(item, 'widget'): + item.setFont(0, boldFont) + item.widget.setFont(font) + item.displayLabel.setFont(font) + item.widget.setMaximumHeight(40) + + # connect signals + self.parameter.child('Embedding').sigValueChanged.connect(self.updateMethod) + self.parameter.child('Components').sigValueChanged.connect(self.setComponents) + + def updateMethod(self): + """ + Toggle parameter menu based on embedding method + :return: None + """ + if self.parameter["Embedding"] == 'UMAP': + self.parameter.child('Neighbors').show() + self.parameter.child('Min Dist').show() + self.parameter.child('Metric').show() + self.parameter.child('Normalization').hide() + else: + self.parameter.child('Neighbors').hide() + self.parameter.child('Min Dist').hide() + self.parameter.child('Metric').hide() + self.parameter.child('Normalization').show() + + def setComponents(self): + N = self.parameter['Components'] + for entry in ['X Component', 'Y Component']: + param = self.parameter.child(entry) + param.setLimits(list(range(1, N + 1))) + + +class ScatterPlotWidget(SpectraPlotWidget): + def __init__(self): + super(ScatterPlotWidget, self).__init__(invertX=False) + self.scene().sigMouseClicked.connect(self.getMousePos) + self.line.hide() + + def getMousePos(self, event): + pos = event.pos() + if self.getViewBox().sceneBoundingRect().contains(pos): + mousePoint = self.getViewBox().mapSceneToView(pos) + x, y = mousePoint.x(), mousePoint.y() + + +class ClusteringWidget(QSplitter): + def __init__(self, headermodel, selectionmodel): + super(ClusteringWidget, self).__init__() + self.headermodel = headermodel + self.mapselectmodel = selectionmodel + self.selectMapidx = 0 + + # split between cluster image and scatter plot + self.image_and_scatter = QSplitter() + # split between image&scatter and spec plot, vertical split + self.leftsplitter = QSplitter() + self.leftsplitter.setOrientation(Qt.Vertical) + # split between params, buttons and map list, vertical split + self.rightsplitter = QSplitter() + self.rightsplitter.setOrientation(Qt.Vertical) + + self.clusterImage = SlimImageView() + self.clusterScatterPlot = ScatterPlotWidget() + self.rawSpecPlot = SpectraPlotWidget() + self.clusterMeanPlot = SpectraPlotWidget() + + # ParameterTree + self.parametertree = ClusteringParameters() + self.parameter = self.parametertree.parameter + + # buttons layout + self.buttons = QWidget() + self.buttonlayout = QGridLayout() + self.buttons.setLayout(self.buttonlayout) + # set up buttons + self.fontSize = 12 + font = QFont("Helvetica [Cronyx]", self.fontSize) + self.computeBtn = QPushButton() + self.computeBtn.setText('Compute clusters') + self.computeBtn.setFont(font) + self.removeBtn = QPushButton() + self.removeBtn.setText('Remove spectra') + self.removeBtn.setFont(font) + # add all buttons + self.buttonlayout.addWidget(self.computeBtn) + self.buttonlayout.addWidget(self.removeBtn) + + # Headers listview + self.headerlistview = QListView() + self.headerlistview.setModel(headermodel) + self.headerlistview.setSelectionModel(selectionmodel) # This might do weird things in the map view? + self.headerlistview.setSelectionMode(QListView.SingleSelection) + + # assemble widgets + self.image_and_scatter.addWidget(self.clusterImage) + self.image_and_scatter.addWidget(self.clusterScatterPlot) + self.leftsplitter.addWidget(self.image_and_scatter) + self.leftsplitter.addWidget(self.rawSpecPlot) + self.leftsplitter.addWidget(self.clusterMeanPlot) + self.leftsplitter.setSizes([200, 50, 50]) + self.rightsplitter.addWidget(self.parametertree) + self.rightsplitter.addWidget(self.buttons) + self.rightsplitter.addWidget(self.headerlistview) + self.rightsplitter.setSizes([200, 50, 50]) + self.addWidget(self.leftsplitter) + self.addWidget(self.rightsplitter) + self.setSizes([500, 100]) + + # Connect signals + self.computeBtn.clicked.connect(self.computeCluster) + self.removeBtn.clicked.connect(self.removeSpec) + + def computeCluster(self): + # get current map idx + if not self.isMapOpen(): + return + # Select wavenumber region + wavROIList = [] + for entry in self.parameter['Wavenumber Range'].split(','): + try: + wavROIList.append(val2ind(int(entry), self.wavenumbers)) + except: + continue + if len(wavROIList) % 2 == 0: + wavROIList = sorted(wavROIList) + wavROIidx = [] + for i in range(len(wavROIList) // 2): + wavROIidx += list(range(wavROIList[2 * i], wavROIList[2 * i + 1] + 1)) + else: + msg.logMessage('"Wavenumber Range" values must be in pairs', msg.ERROR) + MsgBox('Clustering computation aborted.', 'error') + return + self.wavenumbers_select = self.wavenumbers[wavROIidx] + self.N_w = len(self.wavenumbers_select) + # get current dataset + n_spectra = len(self.data) + self.dataset = np.zeros((n_spectra, self.N_w)) + for i in range(n_spectra): + self.dataset[i, :] = self.data[i][wavROIidx] + # get parameters and compute embedding + n_components = self.parameter['Components'] + if self.parameter['Embedding'] == 'UMAP': + n_neighbors = self.parameter['Neighbors'] + metric = self.parameter['Metric'] + min_dist = np.clip(self.parameter['Min Dist'], 0, 1) + self.umap = UMAP(n_neighbors=n_neighbors, + min_dist=min_dist, + n_components=n_components, + metric=metric) + self.low_dim = self.umap.fit_transform(self.dataset) + elif self.parameter['Embedding'] == 'PCA': + pass + + def removeSpec(self): + pass + + def setHeader(self, field: str): + self.headers = [self.headermodel.item(i).header for i in range(self.headermodel.rowCount())] + self.field = field + self.wavenumberList = [] + self.imgShapes = [] + self.rc2indList = [] + self.ind2rcList = [] + self.dataSets = [] + + # get wavenumbers, imgShapes, rc2ind + for header in self.headers: + dataEvent = next(header.events(fields=[field])) + self.wavenumberList.append(dataEvent['wavenumbers']) + self.imgShapes.append(dataEvent['imgShape']) + self.rc2indList.append(dataEvent['rc_index']) + self.ind2rcList.append(dataEvent['index_rc']) + # get raw spectra + data = None + try: # spectra datasets + data = header.meta_array('spectra') + except IndexError: + msg.logMessage('Header object contained no frames with field ''{field}''.', msg.ERROR) + if data is not None: + self.dataSets.append(data) + + def isMapOpen(self): + if not self.mapselectmodel.selectedIndexes(): # no map is open + return False + else: + self.selectMapidx = self.mapselectmodel.selectedIndexes()[0].row() + # get current data + self.wavenumbers = self.wavenumberList[self.selectMapidx] + self.rc2ind = self.rc2indList[self.selectMapidx] + self.ind2rc = self.ind2rcList[self.selectMapidx] + self.imgShape = self.imgShapes[self.selectMapidx] + self.data = self.dataSets[self.selectMapidx] + return True diff --git a/xicam/BSISB/widgets/factorizationwidget.py b/xicam/BSISB/widgets/factorizationwidget.py index bb9ebc1..8c94b77 100644 --- a/xicam/BSISB/widgets/factorizationwidget.py +++ b/xicam/BSISB/widgets/factorizationwidget.py @@ -1,8 +1,8 @@ -from qtpy.QtWidgets import QSplitter, QGridLayout, QWidget, QListView +from qtpy.QtWidgets import * from xicam.core import msg from pyqtgraph import PlotWidget, PlotDataItem, TextItem, mkPen, InfiniteLine, ImageItem, PolyLineROI from qtpy.QtCore import Qt, QItemSelectionModel -from qtpy.QtGui import QStandardItemModel +from qtpy.QtGui import QStandardItemModel, QFont from functools import partial from qtpy.QtCore import Signal from pymcr.mcr import McrAR @@ -35,7 +35,7 @@ def __init__(self, headermodel: QStandardItemModel, selectionmodel: QItemSelecti 'values': ['PCA', 'NMF', 'MCR'], 'value': 'PCA', 'type': 'list'}, - {'name': "# of Components", + {'name': "Components", 'value': 4, 'type': 'int'}, {'name': "Calculate", @@ -56,7 +56,7 @@ def __init__(self, headermodel: QStandardItemModel, selectionmodel: QItemSelecti 'values': [1, 2, 3, 4], 'value': 4, 'type': 'list'}, - {'name': "Wavenumber ROI", + {'name': "Wavenumber Range", 'value': '800,1800', 'type': 'str'}, {'name': "Normalization", @@ -76,10 +76,21 @@ def __init__(self, headermodel: QStandardItemModel, selectionmodel: QItemSelecti #constants self.method = 'PCA' self.field = 'spectra' - + # change Fonts + self.fontSize = 12 + font = QFont("Helvetica [Cronyx]", self.fontSize) + boldFont = QFont("Helvetica [Cronyx]", self.fontSize, QFont.Bold) + self.header().setFont(font) + for item in self.listAllItems(): + if hasattr(item, 'widget'): + item.setFont(0, boldFont) + item.widget.setFont(font) + item.displayLabel.setFont(font) + item.widget.setMaximumHeight(40) + # connect signals self.parameter.child('Calculate').sigActivated.connect(self.calculate) self.parameter.child('Save results').sigActivated.connect(self.saveResults) - self.parameter.child('# of Components').sigValueChanged.connect(self.setNumComponents) + self.parameter.child('Components').sigValueChanged.connect(self.setNumComponents) self.parameter.child('Method').sigValueChanged.connect(self.setMethod) self.parameter.child('C regressor').hide() @@ -127,7 +138,7 @@ def setHeader(self, wavenumbers, imgShapes, rc2indList, ind2rcList): self._dataSets['volume'].append(path) def setNumComponents(self): - N = self.parameter['# of Components'] + N = self.parameter['Components'] for i in range(4): param = self.parameter.child(f'Map {i + 1} Component') @@ -135,11 +146,11 @@ def setNumComponents(self): def calculate(self): - N = self.parameter['# of Components'] + N = self.parameter['Components'] if hasattr(self, '_dataSets'): wavROIList = [] - for entry in self.parameter['Wavenumber ROI'].split(','): + for entry in self.parameter['Wavenumber Range'].split(','): try: wavROIList.append(val2ind(int(entry), self.wavenumbers)) except: @@ -151,7 +162,7 @@ def calculate(self): for i in range(len(wavROIList) // 2): wavROIidx += list(range(wavROIList[2 * i], wavROIList[2 * i + 1] + 1)) else: - msg.logMessage('"Wavenumber ROI" values must be in pairs', msg.ERROR) + msg.logMessage('"Wavenumber Range" values must be in pairs', msg.ERROR) MsgBox('Factorization computation aborted.', 'error') return @@ -287,7 +298,7 @@ def popup_plots(self): ax = plt.gca() ax.set_ylabel('Explained variance ratio', fontsize=16) ax.set_xlabel('Component number', fontsize=16) - ax.set_xticks(np.arange(self.parameter['# of Components'])) + ax.set_xticks(np.arange(self.parameter['Components'])) # loadings plot plt.figure() labels = [] @@ -446,7 +457,6 @@ def __init__(self, headermodel, selectionmodel): partial(self.updateComponents, i)) self.addWidget(self.display) - self.rightsplitter.addWidget(self.parametertree) self.addWidget(self.rightsplitter) self.display.addWidget(self.gridwidget) self.display.addWidget(self.componentSpectra) @@ -463,8 +473,22 @@ def __init__(self, headermodel, selectionmodel): self.headerlistview = QListView() self.headerlistview.setModel(headermodel) self.headerlistview.setSelectionModel(selectionmodel) # This might do weird things in the map view? - self.rightsplitter.addWidget(self.headerlistview) self.headerlistview.setSelectionMode(QListView.SingleSelection) + # add title to list view + self.fontSize = 12 + font = QFont("Helvetica [Cronyx]", self.fontSize) + self.mapListWidget = QWidget() + self.listLayout = QVBoxLayout() + self.mapListWidget.setLayout(self.listLayout) + mapListTitle = QLabel('Maps list') + mapListTitle.setFont(font) + self.listLayout.addWidget(mapListTitle) + self.listLayout.addWidget(self.headerlistview) + + # adjust right splitter + self.rightsplitter.addWidget(self.parametertree) + self.rightsplitter.addWidget(self.mapListWidget) + self.rightsplitter.setSizes([200, 50]) def updateRoiMask(self): if self.selectionmodel.hasSelection(): @@ -576,7 +600,7 @@ def showComponents(self, fac_obj): # show score plots self.drawMap(component_index, i) # update the last image and loading plots as a recalculation complete signal - N = self.parameter['# of Components'] + N = self.parameter['Components'] self.parameter.child(f'Map 4 Component').setValue(N) # clear maps else: From bad8f4a7adbbf3a9ec0b0cce636dd44f3d3e49c4 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Mon, 25 May 2020 18:17:42 -0700 Subject: [PATCH 31/56] add cleanup func in preprocess --- xicam/BSISB/widgets/preprocesswidget.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/xicam/BSISB/widgets/preprocesswidget.py b/xicam/BSISB/widgets/preprocesswidget.py index e0fccdd..0f1b490 100644 --- a/xicam/BSISB/widgets/preprocesswidget.py +++ b/xicam/BSISB/widgets/preprocesswidget.py @@ -342,8 +342,6 @@ def getCurrentSpecid(self): specidx = currentSpecItem.idx return specidx - - def updateSpecPlot(self): # get current map idx and selected spectrum idx specidx = self.getCurrentSpecid() @@ -444,6 +442,12 @@ def removeSpec(self): self.resultSpectra.clearAll() self.infoBox.setText('') + def cleanUp(self): + self.specItemModel.clear() + self.rawSpectra.clearAll() + self.resultSpectra.clearAll() + self.infoBox.setText('') + def batchProcess(self): # get current map idx if not self.isMapOpen(): From ea3c18b6d2da509df909c7821e8f3f80fe88206b Mon Sep 17 00:00:00 2001 From: lchen23 Date: Mon, 25 May 2020 18:18:24 -0700 Subject: [PATCH 32/56] add clustering widget --- xicam/BSISB/__init__.py | 15 +- xicam/BSISB/widgets/clusteringwidget.py | 234 +++++++++++++++++++++--- 2 files changed, 213 insertions(+), 36 deletions(-) diff --git a/xicam/BSISB/__init__.py b/xicam/BSISB/__init__.py index 7280c76..dbc41f2 100644 --- a/xicam/BSISB/__init__.py +++ b/xicam/BSISB/__init__.py @@ -9,6 +9,7 @@ from xicam.BSISB.widgets.spectraplotwidget import SpectraPlotWidget from xicam.BSISB.widgets.factorizationwidget import FactorizationWidget from xicam.BSISB.widgets.preprocesswidget import PreprocessWidget +from xicam.BSISB.widgets.clusteringwidget import ClusteringWidget from xicam.plugins import GUIPlugin, GUILayout from xicam.gui.widgets.tabview import TabView @@ -41,6 +42,7 @@ def __init__(self, *args, **kwargs): self.selectionmodel = QItemSelectionModel(self.headermodel) self.preprocess = PreprocessWidget(self.headermodel, self.selectionmodel) self.FA_widget = FactorizationWidget(self.headermodel, self.selectionmodel) + self.clusterwidget = ClusteringWidget(self.headermodel, self.selectionmodel) # update headers list when a tab window is closed self.headermodel.rowsRemoved.connect(partial(self.FA_widget.setHeader, 'spectra')) @@ -52,8 +54,8 @@ def __init__(self, *args, **kwargs): self.stages = {#"MapToH5": GUILayout(self.mapToH5), "Image View": GUILayout(self.imageview), "Preprocess": GUILayout(self.preprocess), - "Factor Analysis": GUILayout(self.FA_widget)} - # "NMF": GUILayout(self.NMF_widget)} + "Factor Analysis": GUILayout(self.FA_widget), + "Clustering": GUILayout(self.clusterwidget)} super(BSISB, self).__init__(*args, **kwargs) def appendHeader(self, header: NonDBHeader, **kwargs): @@ -85,6 +87,7 @@ def appendHeader(self, header: NonDBHeader, **kwargs): self.preprocess.setHeader(field='spectra') self.FA_widget.setHeader(field='spectra') + self.clusterwidget.setHeader(field='spectra') for i in range(4): self.FA_widget.roiList[i].sigRegionChangeFinished.connect(self.updateROI) @@ -111,8 +114,6 @@ def updateROI(self, roi): self.imageview.widget(selectMapIdx).roiMove(roi) def updateTab(self): - # clear specItemModel - self.preprocess.specItemModel.clear() - self.preprocess.rawSpectra.clearAll() - self.preprocess.resultSpectra.clearAll() - self.preprocess.infoBox.setText('') \ No newline at end of file + # clean up all widgets + self.preprocess.cleanUp() + self.clusterwidget.cleanUp() diff --git a/xicam/BSISB/widgets/clusteringwidget.py b/xicam/BSISB/widgets/clusteringwidget.py index 87b8262..532c94d 100644 --- a/xicam/BSISB/widgets/clusteringwidget.py +++ b/xicam/BSISB/widgets/clusteringwidget.py @@ -1,18 +1,19 @@ -import os -import numpy as np -import pandas as pd -from umap import UMAP -from sklearn import cluster from functools import partial -from qtpy.QtWidgets import * -from qtpy.QtCore import Qt, QItemSelectionModel, Signal -from qtpy.QtGui import QStandardItemModel, QStandardItem, QFont -from pyqtgraph.parametertree import ParameterTree, Parameter -from xicam.core import msg +import numpy as np from lbl_ir.data_objects.ir_map import val2ind +from matplotlib import cm +from pyqtgraph import TextItem, mkBrush, mkPen +from pyqtgraph.parametertree import ParameterTree, Parameter +from qtpy.QtCore import Qt, Signal +from qtpy.QtGui import QFont +from qtpy.QtWidgets import * +from sklearn.cluster import KMeans +from sklearn.neighbors import NearestNeighbors +from umap import UMAP +from xicam.BSISB.widgets.mapviewwidget import MapViewWidget, toHtml from xicam.BSISB.widgets.spectraplotwidget import SpectraPlotWidget -from xicam.BSISB.widgets.imshowwidget import SlimImageView -from xicam.BSISB.widgets.uiwidget import MsgBox, YesNoDialog +from xicam.BSISB.widgets.uiwidget import MsgBox +from xicam.core import msg class ClusteringParameters(ParameterTree): @@ -27,7 +28,7 @@ def __init__(self): 'value': 'UMAP', 'type': 'list'}, {'name': "Components", - 'value': 2, + 'value': 3, 'type': 'int'}, {'name': "Neighbors", 'value': 15, @@ -50,16 +51,17 @@ def __init__(self): 'value': 3, 'type': 'int'}, {'name': "X Component", - 'values': [1, 2, 3, 4], + 'values': [1, 2, 3], 'value': 1, 'type': 'list'}, {'name': "Y Component", - 'values': [1, 2, 3, 4], + 'values': [1, 2, 3], 'value': 2, 'type': 'list'} ]) self.setParameters(self.parameter, showTop=False) self.setIndentation(0) + self.parameter.child('Normalization').hide() # change Fonts self.fontSize = 12 font = QFont("Helvetica [Cronyx]", self.fontSize) @@ -75,6 +77,8 @@ def __init__(self): # connect signals self.parameter.child('Embedding').sigValueChanged.connect(self.updateMethod) self.parameter.child('Components').sigValueChanged.connect(self.setComponents) + for entry in ['Components', 'Clusters', 'X Component', 'Y Component']: + self.parameter.child(entry).sigValueChanged.connect(partial(self.updateClusterParams, entry)) def updateMethod(self): """ @@ -98,26 +102,92 @@ def setComponents(self): param = self.parameter.child(entry) param.setLimits(list(range(1, N + 1))) + def updateClusterParams(self, name): + self.sigParamChanged.emit(name) class ScatterPlotWidget(SpectraPlotWidget): + sigScatterClicked = Signal(object) + def __init__(self): - super(ScatterPlotWidget, self).__init__(invertX=False) - self.scene().sigMouseClicked.connect(self.getMousePos) + super(ScatterPlotWidget, self).__init__(invertX=False, linePos=0) + self.scene().sigMouseClicked.connect(self.setCrossPos) self.line.hide() + self.scatterData = None + self.nbr = None - def getMousePos(self, event): + def setCrossPos(self, event): pos = event.pos() - if self.getViewBox().sceneBoundingRect().contains(pos): - mousePoint = self.getViewBox().mapSceneToView(pos) + if (self.getViewBox().sceneBoundingRect().contains(pos)) and (self.scatterData is not None): + mousePoint = self.getViewBox().mapToView(pos) x, y = mousePoint.x(), mousePoint.y() + _, ind = self.nbr.kneighbors(np.array([[x, y]])) + self.cross.setData(self.scatterData[ind[0], 0], self.scatterData[ind[0], 1]) + self.sigScatterClicked.emit(ind[0, 0]) + + def clickFromImage(self, ind): + self.cross.setData([self.scatterData[ind, 0]], [self.scatterData[ind, 1]]) + + def getNN(self): + msg.showMessage('Training NearestNeighbors model in scatter plot.') + self.nbr = NearestNeighbors(n_neighbors=1, algorithm='auto').fit(self.scatterData) + msg.showMessage('NearestNeighbors model training is finished.') + +class ClusterSpectraWidget(SpectraPlotWidget): + def __init__(self): + super(ClusterSpectraWidget, self).__init__() + + def getEnergy(self, lineobject): + if self._y is not None: + x_val = lineobject.value() + idx = val2ind(x_val, self.wavenumbers) + x_val = self.wavenumbers[idx] + y_val = self._y[idx] + txt_html = toHtml(f'Cluster #{self.spectrumInd}') + txt_html += toHtml(f'X = {x_val: .2f}, Y = {y_val: .4f}') + self.txt.setHtml(txt_html) + self.cross.setData([x_val], [y_val]) + + def setColors(self, colorLUT): + self.colorLUT = colorLUT + + def showClusterSpectra(self, i=0): + if (self._data is not None) and (i < len(self._data)): + self.clear() + self.spectrumInd = i + self.plot(self.wavenumbers, self._data[i], pen=mkPen(self.colorLUT[i], width=4)) + def plot(self, x, y, *args, **kwargs): + # set up infinity line and get its position + self.plotItem.plot(x, y, *args, **kwargs) + self.addItem(self.line) + self.addItem(self.cross) + x_val = self.line.value() + if x_val == 0: + y_val = 0 + else: + idx = val2ind(x_val, self.wavenumbers) + x_val = self.wavenumbers[idx] + y_val = y[idx] + txt_html = toHtml(f'Cluster #{self.spectrumInd}') + txt_html += toHtml(f'X = {x_val: .2f}, Y = {y_val: .4f}') + self.txt = TextItem(html=txt_html, anchor=(0, 0)) + ymax = np.max(y) + self._y = y + r = self.txtPosRatio + self.txt.setPos(r * x[-1] + (1 - r) * x[0], 0.95 * ymax) + self.cross.setData([x_val], [y_val]) + self.addItem(self.txt) class ClusteringWidget(QSplitter): def __init__(self, headermodel, selectionmodel): super(ClusteringWidget, self).__init__() self.headermodel = headermodel self.mapselectmodel = selectionmodel + # init some values self.selectMapidx = 0 + self.low_dim = None + self.labels = None + self.mean_spectra = None # split between cluster image and scatter plot self.image_and_scatter = QSplitter() @@ -128,10 +198,10 @@ def __init__(self, headermodel, selectionmodel): self.rightsplitter = QSplitter() self.rightsplitter.setOrientation(Qt.Vertical) - self.clusterImage = SlimImageView() + self.clusterImage = MapViewWidget() self.clusterScatterPlot = ScatterPlotWidget() self.rawSpecPlot = SpectraPlotWidget() - self.clusterMeanPlot = SpectraPlotWidget() + self.clusterMeanPlot = ClusterSpectraWidget() # ParameterTree self.parametertree = ClusteringParameters() @@ -152,13 +222,21 @@ def __init__(self, headermodel, selectionmodel): self.removeBtn.setFont(font) # add all buttons self.buttonlayout.addWidget(self.computeBtn) - self.buttonlayout.addWidget(self.removeBtn) + # self.buttonlayout.addWidget(self.removeBtn) # Headers listview self.headerlistview = QListView() self.headerlistview.setModel(headermodel) self.headerlistview.setSelectionModel(selectionmodel) # This might do weird things in the map view? self.headerlistview.setSelectionMode(QListView.SingleSelection) + # add title to list view + self.mapListWidget = QWidget() + self.listLayout = QVBoxLayout() + self.mapListWidget.setLayout(self.listLayout) + mapListTitle = QLabel('Maps list') + mapListTitle.setFont(font) + self.listLayout.addWidget(mapListTitle) + self.listLayout.addWidget(self.headerlistview) # assemble widgets self.image_and_scatter.addWidget(self.clusterImage) @@ -169,20 +247,28 @@ def __init__(self, headermodel, selectionmodel): self.leftsplitter.setSizes([200, 50, 50]) self.rightsplitter.addWidget(self.parametertree) self.rightsplitter.addWidget(self.buttons) - self.rightsplitter.addWidget(self.headerlistview) + self.rightsplitter.addWidget(self.mapListWidget) self.rightsplitter.setSizes([200, 50, 50]) self.addWidget(self.leftsplitter) self.addWidget(self.rightsplitter) self.setSizes([500, 100]) # Connect signals - self.computeBtn.clicked.connect(self.computeCluster) + self.computeBtn.clicked.connect(self.computeEmbedding) self.removeBtn.clicked.connect(self.removeSpec) + self.clusterImage.sigShowSpectra.connect(self.rawSpecPlot.showSpectra) + self.clusterImage.sigShowSpectra.connect(self.showClusterMean) + self.clusterImage.sigShowSpectra.connect(self.clusterScatterPlot.clickFromImage) + self.clusterScatterPlot.sigScatterClicked.connect(self.rawSpecPlot.showSpectra) + self.clusterScatterPlot.sigScatterClicked.connect(self.showClusterMean) + self.clusterScatterPlot.sigScatterClicked.connect(self.setImageCross) + self.parametertree.sigParamChanged.connect(self.updateClusterParams) - def computeCluster(self): + def computeEmbedding(self): # get current map idx if not self.isMapOpen(): return + msg.showMessage('Compute embedding.') # Select wavenumber region wavROIList = [] for entry in self.parameter['Wavenumber Range'].split(','): @@ -213,16 +299,100 @@ def computeCluster(self): metric = self.parameter['Metric'] min_dist = np.clip(self.parameter['Min Dist'], 0, 1) self.umap = UMAP(n_neighbors=n_neighbors, - min_dist=min_dist, - n_components=n_components, - metric=metric) + min_dist=min_dist, + n_components=n_components, + metric=metric, + random_state=0) self.low_dim = self.umap.fit_transform(self.dataset) elif self.parameter['Embedding'] == 'PCA': pass + # update cluster map + self.computeCluster() + + def computeCluster(self): + # check if embeddings exist + if self.low_dim is None: + return + msg.showMessage('Compute clusters.') + # get num of clusters + n_clusters = self.parameter['Clusters'] + # set colorLUT + self.colorLUT = cm.get_cmap('viridis', n_clusters).colors[:, :3] * 255 + # compute cluster + cluster_object = KMeans(n_clusters=n_clusters, random_state=0).fit(self.low_dim) + self.labels = cluster_object.labels_ + # update cluster image + self.cluster_map = self.labels.reshape(self.imgShape[0], self.imgShape[1]) + self.cluster_map = np.flipud(self.cluster_map) + self.clusterImage.setImage(self.cluster_map, levels=[0, n_clusters-1]) + # self.clusterImage.setImage(self.cluster_map) + self.clusterImage._image = self.cluster_map + self.clusterImage.rc2ind = self.rc2ind + self.clusterImage.row, self.clusterImage.col = self.imgShape[0], self.imgShape[1] + self.clusterImage.txt.setPos(self.clusterImage.col, 0) + self.clusterImage.cross.show() + #update cluster mean + mean_spectra = [] + for ii in range(n_clusters): + sel = (self.labels == ii) + this_mean = np.mean(self.dataset[sel, :], axis=0) + mean_spectra.append(this_mean) + self.mean_spectra = np.vstack(mean_spectra) + self.clusterMeanPlot.setColors(self.colorLUT) + self.clusterMeanPlot._data = self.mean_spectra + self.clusterMeanPlot.wavenumbers = self.wavenumbers_select + # update scatter plot + self.updateScatterPlot() + + def updateScatterPlot(self): + if (self.low_dim is None) or (self.labels is None): + return + # get scatter x, y values + self.clusterScatterPlot.scatterData = self.low_dim[:, [self.parameter['X Component'] - 1, self.parameter['Y Component'] - 1]] + # get colormapings + brushes = [mkBrush(self.colorLUT[x, :]) for x in self.labels] + # make plots + if hasattr(self, 'scatterPlot'): + self.clusterScatterPlot.removeItem(self.scatterPlot) + self.scatterPlot = self.clusterScatterPlot.plotItem.plot(self.clusterScatterPlot.scatterData, pen=None, symbol='o', symbolBrush=brushes) + self.clusterScatterPlot.getViewBox().autoRange(padding=0.1) + self.clusterScatterPlot.getNN() + + def updateClusterParams(self, name): + if name == 'Components': + self.computeEmbedding() + elif name == 'Clusters': + self.computeCluster() + elif name in ['X Component', 'Y Component']: + self.updateScatterPlot() + + def showClusterMean(self, i): + if self.mean_spectra is None: + return + self.clusterMeanPlot.showClusterSpectra(self.labels[i]) + + def setImageCross(self, ind): + row, col = self.ind2rc[ind] + # update cross + self.clusterImage.cross.setData([col + 0.5], [self.imgShape[0] - row - 0.5],) + # update text + self.clusterImage.txt.setHtml(toHtml(f'Point: #{ind}', size=8) + + toHtml(f'X: {col}', size=8) + + toHtml(f'Y: {row}', size=8) + + toHtml(f'Val: {self.clusterImage._image[self.imgShape[0] - row -1, col] :d}', size=8)) def removeSpec(self): pass + def cleanUp(self): + if hasattr(self, 'imgShape'): + img = np.zeros((self.imgShape[0], self.imgShape[1])) + self.clusterImage.setImage(img=img) + if hasattr(self, 'scatterPlot'): + self.clusterScatterPlot.removeItem(self.scatterPlot) + self.rawSpecPlot.clear() + self.clusterMeanPlot.clear() + def setHeader(self, field: str): self.headers = [self.headermodel.item(i).header for i in range(self.headermodel.rowCount())] self.field = field @@ -247,6 +417,7 @@ def setHeader(self, field: str): msg.logMessage('Header object contained no frames with field ''{field}''.', msg.ERROR) if data is not None: self.dataSets.append(data) + self.isMapOpen() def isMapOpen(self): if not self.mapselectmodel.selectedIndexes(): # no map is open @@ -254,9 +425,14 @@ def isMapOpen(self): else: self.selectMapidx = self.mapselectmodel.selectedIndexes()[0].row() # get current data + self.currentHeader = self.headers[self.selectMapidx] self.wavenumbers = self.wavenumberList[self.selectMapidx] self.rc2ind = self.rc2indList[self.selectMapidx] self.ind2rc = self.ind2rcList[self.selectMapidx] self.imgShape = self.imgShapes[self.selectMapidx] self.data = self.dataSets[self.selectMapidx] + self.rawSpecPlot.setHeader(self.currentHeader, 'spectra') + # reset custer image + img = np.zeros((self.imgShape[0], self.imgShape[1])) + self.clusterImage.setImage(img=img) return True From 39b8268ae7ccd70e2ea96142fec5a1235a08f809 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Tue, 26 May 2020 13:05:35 -0700 Subject: [PATCH 33/56] change scatter plot mouseClick to mouseMove --- xicam/BSISB/widgets/clusteringwidget.py | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/xicam/BSISB/widgets/clusteringwidget.py b/xicam/BSISB/widgets/clusteringwidget.py index 532c94d..5a6e814 100644 --- a/xicam/BSISB/widgets/clusteringwidget.py +++ b/xicam/BSISB/widgets/clusteringwidget.py @@ -110,7 +110,8 @@ class ScatterPlotWidget(SpectraPlotWidget): def __init__(self): super(ScatterPlotWidget, self).__init__(invertX=False, linePos=0) - self.scene().sigMouseClicked.connect(self.setCrossPos) + # self.scene().sigMouseClicked.connect(self.setCrossPos) + self.scene().sigMouseMoved.connect(self.moveCrossPos) self.line.hide() self.scatterData = None self.nbr = None @@ -121,6 +122,16 @@ def setCrossPos(self, event): mousePoint = self.getViewBox().mapToView(pos) x, y = mousePoint.x(), mousePoint.y() _, ind = self.nbr.kneighbors(np.array([[x, y]])) + self.addItem(self.cross) + self.cross.setData(self.scatterData[ind[0], 0], self.scatterData[ind[0], 1]) + self.sigScatterClicked.emit(ind[0, 0]) + + def moveCrossPos(self, pos): + if (self.getViewBox().sceneBoundingRect().contains(pos)) and (self.scatterData is not None): + mousePoint = self.getViewBox().mapSceneToView(pos) + x, y = mousePoint.x(), mousePoint.y() + _, ind = self.nbr.kneighbors(np.array([[x, y]])) + self.addItem(self.cross) self.cross.setData(self.scatterData[ind[0], 0], self.scatterData[ind[0], 1]) self.sigScatterClicked.emit(ind[0, 0]) @@ -353,7 +364,7 @@ def updateScatterPlot(self): brushes = [mkBrush(self.colorLUT[x, :]) for x in self.labels] # make plots if hasattr(self, 'scatterPlot'): - self.clusterScatterPlot.removeItem(self.scatterPlot) + self.clusterScatterPlot.plotItem.clearPlots() self.scatterPlot = self.clusterScatterPlot.plotItem.plot(self.clusterScatterPlot.scatterData, pen=None, symbol='o', symbolBrush=brushes) self.clusterScatterPlot.getViewBox().autoRange(padding=0.1) self.clusterScatterPlot.getNN() @@ -389,7 +400,7 @@ def cleanUp(self): img = np.zeros((self.imgShape[0], self.imgShape[1])) self.clusterImage.setImage(img=img) if hasattr(self, 'scatterPlot'): - self.clusterScatterPlot.removeItem(self.scatterPlot) + self.clusterScatterPlot.plotItem.clearPlots() self.rawSpecPlot.clear() self.clusterMeanPlot.clear() @@ -432,7 +443,6 @@ def isMapOpen(self): self.imgShape = self.imgShapes[self.selectMapidx] self.data = self.dataSets[self.selectMapidx] self.rawSpecPlot.setHeader(self.currentHeader, 'spectra') - # reset custer image - img = np.zeros((self.imgShape[0], self.imgShape[1])) - self.clusterImage.setImage(img=img) + # reset custer image and plots + self.cleanUp() return True From cbeb8bc1f9822d5eaf554d0572ef71fdc59758a7 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Wed, 27 May 2020 10:40:38 -0700 Subject: [PATCH 34/56] add clearAll func --- xicam/BSISB/widgets/spectraplotwidget.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/xicam/BSISB/widgets/spectraplotwidget.py b/xicam/BSISB/widgets/spectraplotwidget.py index 7e598d3..9b03f9a 100644 --- a/xicam/BSISB/widgets/spectraplotwidget.py +++ b/xicam/BSISB/widgets/spectraplotwidget.py @@ -77,7 +77,7 @@ def setHeader(self, header: NonDBHeader, field: str, *args, **kwargs): def showSpectra(self, i=0): if (self._data is not None) and (i < len(self._data)): - self.clear() + self.getViewBox().clear() self._meanSpec = False self.spectrumInd = i self.plot(self.wavenumbers, self._data[i]) @@ -86,9 +86,16 @@ def getSelectedPixels(self, selectedPixels): self.selectedPixels = selectedPixels # print(selectedPixels) + def clearAll(self): + # remove legend + _legend = self.plotItem.legend + if (_legend is not None) and (_legend.scene() is not None): + _legend.scene().removeItem(_legend) + self.getViewBox().clear() + def showMeanSpectra(self): self._meanSpec = True - self.clear() + self.getViewBox().clear() if self.selectedPixels is not None: n_spectra = len(self.selectedPixels) tmp = np.zeros((n_spectra, self.N_w)) @@ -199,13 +206,6 @@ def addDataCursor(self, x, y): self.addItem(self.txt) self.getMu() - def clearAll(self): - # remove legend - _legend = self.plotItem.legend - if (_legend is not None) and (_legend.scene() is not None): - _legend.scene().removeItem(_legend) - self.clear() - def plotBase(self, dataGroup, plotType='raw'): """ make plots for Larch Group object From bd4f9fbd088dba626b39e35750cc5860f820b3ae Mon Sep 17 00:00:00 2001 From: lchen23 Date: Wed, 27 May 2020 10:50:24 -0700 Subject: [PATCH 35/56] show all cluster mean spectra and fix clearAll bug --- xicam/BSISB/widgets/clusteringwidget.py | 107 ++++++++++++++++-------- 1 file changed, 71 insertions(+), 36 deletions(-) diff --git a/xicam/BSISB/widgets/clusteringwidget.py b/xicam/BSISB/widgets/clusteringwidget.py index 5a6e814..3cb651d 100644 --- a/xicam/BSISB/widgets/clusteringwidget.py +++ b/xicam/BSISB/widgets/clusteringwidget.py @@ -105,6 +105,7 @@ def setComponents(self): def updateClusterParams(self, name): self.sigParamChanged.emit(name) + class ScatterPlotWidget(SpectraPlotWidget): sigScatterClicked = Signal(object) @@ -143,51 +144,81 @@ def getNN(self): self.nbr = NearestNeighbors(n_neighbors=1, algorithm='auto').fit(self.scatterData) msg.showMessage('NearestNeighbors model training is finished.') + class ClusterSpectraWidget(SpectraPlotWidget): def __init__(self): super(ClusterSpectraWidget, self).__init__() + self._x = None + self.ymax, self.zmax = 0, 100 + self._plots = [] - def getEnergy(self, lineobject): + def getEnergy(self): if self._y is not None: - x_val = lineobject.value() - idx = val2ind(x_val, self.wavenumbers) - x_val = self.wavenumbers[idx] + x_val = self.line.value() + idx = val2ind(x_val, self._x) + x_val = self._x[idx] y_val = self._y[idx] - txt_html = toHtml(f'Cluster #{self.spectrumInd}') - txt_html += toHtml(f'X = {x_val: .2f}, Y = {y_val: .4f}') + txt_html = toHtml(f'X = {x_val: .2f}, Y = {y_val: .4f}') self.txt.setHtml(txt_html) self.cross.setData([x_val], [y_val]) def setColors(self, colorLUT): - self.colorLUT = colorLUT - - def showClusterSpectra(self, i=0): - if (self._data is not None) and (i < len(self._data)): - self.clear() - self.spectrumInd = i - self.plot(self.wavenumbers, self._data[i], pen=mkPen(self.colorLUT[i], width=4)) + self.colorLUT = colorLUT.copy() + self.colorLUT[0, :] = np.ones(3) * 255 + + def plotClusterSpectra(self): + if self._data is not None: + if self._plots: + self.clearAll() + self._plots = [] + self.ymax = 0 + self.plotItem.addLegend(offset=(1, 1)) + self.nSpectra = len(self._data) + for i in range(self.nSpectra): + name = 'Cluster #' + str(i) + tmp = self.plot(self.wavenumbers, self._data[i], pen=mkPen(self.colorLUT[i], width=2), name=name) + tmp.curve.setClickable(True) + tmp.curve.sigClicked.connect(partial(self.curveHighLight, i)) + self._plots.append(tmp) + self.addItem(self.txt) + + def curveHighLight(self, k): + for i in range(self.nSpectra): + if i == k: + self._plots[i].setPen(mkPen(self.colorLUT[k], width=6)) + self._plots[i].setZValue(50) + else: + self._plots[i].setPen(mkPen(self.colorLUT[i], width=2)) + self._plots[i].setZValue(0) + self._x, self._y = self._plots[k].getData() + ymin, ymax = np.min(self._y), np.max(self._y) + self.getViewBox().setYRange(ymin, ymax, padding=0.1) + r = self.txtPosRatio + self.txt.setPos(r * self._x[-1] + (1 - r) * self._x[0], 0.95 * ymax) + self.getEnergy() def plot(self, x, y, *args, **kwargs): # set up infinity line and get its position - self.plotItem.plot(x, y, *args, **kwargs) + plot_item = self.plotItem.plot(x, y, *args, **kwargs) self.addItem(self.line) self.addItem(self.cross) x_val = self.line.value() - if x_val == 0: - y_val = 0 - else: - idx = val2ind(x_val, self.wavenumbers) - x_val = self.wavenumbers[idx] - y_val = y[idx] - txt_html = toHtml(f'Cluster #{self.spectrumInd}') - txt_html += toHtml(f'X = {x_val: .2f}, Y = {y_val: .4f}') + idx = val2ind(x_val, x) + x_val = x[idx] + y_val = y[idx] + txt_html = toHtml(f'X = {x_val: .2f}, Y = {y_val: .4f}') self.txt = TextItem(html=txt_html, anchor=(0, 0)) + self.txt.setZValue(self.zmax - 1) + self.cross.setData([x_val], [y_val]) + self.cross.setZValue(self.zmax) ymax = np.max(y) - self._y = y + if ymax > self.ymax: + self.ymax = ymax + self._x, self._y = x, y r = self.txtPosRatio self.txt.setPos(r * x[-1] + (1 - r) * x[0], 0.95 * ymax) - self.cross.setData([x_val], [y_val]) - self.addItem(self.txt) + return plot_item + class ClusteringWidget(QSplitter): def __init__(self, headermodel, selectionmodel): @@ -335,14 +366,14 @@ def computeCluster(self): # update cluster image self.cluster_map = self.labels.reshape(self.imgShape[0], self.imgShape[1]) self.cluster_map = np.flipud(self.cluster_map) - self.clusterImage.setImage(self.cluster_map, levels=[0, n_clusters-1]) + self.clusterImage.setImage(self.cluster_map, levels=[0, n_clusters - 1]) # self.clusterImage.setImage(self.cluster_map) self.clusterImage._image = self.cluster_map self.clusterImage.rc2ind = self.rc2ind self.clusterImage.row, self.clusterImage.col = self.imgShape[0], self.imgShape[1] self.clusterImage.txt.setPos(self.clusterImage.col, 0) self.clusterImage.cross.show() - #update cluster mean + # update cluster mean mean_spectra = [] for ii in range(n_clusters): sel = (self.labels == ii) @@ -352,6 +383,7 @@ def computeCluster(self): self.clusterMeanPlot.setColors(self.colorLUT) self.clusterMeanPlot._data = self.mean_spectra self.clusterMeanPlot.wavenumbers = self.wavenumbers_select + self.clusterMeanPlot.plotClusterSpectra() # update scatter plot self.updateScatterPlot() @@ -359,13 +391,15 @@ def updateScatterPlot(self): if (self.low_dim is None) or (self.labels is None): return # get scatter x, y values - self.clusterScatterPlot.scatterData = self.low_dim[:, [self.parameter['X Component'] - 1, self.parameter['Y Component'] - 1]] + self.clusterScatterPlot.scatterData = self.low_dim[:, + [self.parameter['X Component'] - 1, self.parameter['Y Component'] - 1]] # get colormapings brushes = [mkBrush(self.colorLUT[x, :]) for x in self.labels] # make plots if hasattr(self, 'scatterPlot'): self.clusterScatterPlot.plotItem.clearPlots() - self.scatterPlot = self.clusterScatterPlot.plotItem.plot(self.clusterScatterPlot.scatterData, pen=None, symbol='o', symbolBrush=brushes) + self.scatterPlot = self.clusterScatterPlot.plotItem.plot(self.clusterScatterPlot.scatterData, pen=None, + symbol='o', symbolBrush=brushes) self.clusterScatterPlot.getViewBox().autoRange(padding=0.1) self.clusterScatterPlot.getNN() @@ -380,17 +414,18 @@ def updateClusterParams(self, name): def showClusterMean(self, i): if self.mean_spectra is None: return - self.clusterMeanPlot.showClusterSpectra(self.labels[i]) + self.clusterMeanPlot.curveHighLight(self.labels[i]) def setImageCross(self, ind): row, col = self.ind2rc[ind] # update cross - self.clusterImage.cross.setData([col + 0.5], [self.imgShape[0] - row - 0.5],) + self.clusterImage.cross.setData([col + 0.5], [self.imgShape[0] - row - 0.5]) # update text self.clusterImage.txt.setHtml(toHtml(f'Point: #{ind}', size=8) - + toHtml(f'X: {col}', size=8) - + toHtml(f'Y: {row}', size=8) - + toHtml(f'Val: {self.clusterImage._image[self.imgShape[0] - row -1, col] :d}', size=8)) + + toHtml(f'X: {col}', size=8) + + toHtml(f'Y: {row}', size=8) + + toHtml(f'Val: {self.clusterImage._image[self.imgShape[0] - row - 1, col] :d}', + size=8)) def removeSpec(self): pass @@ -401,8 +436,8 @@ def cleanUp(self): self.clusterImage.setImage(img=img) if hasattr(self, 'scatterPlot'): self.clusterScatterPlot.plotItem.clearPlots() - self.rawSpecPlot.clear() - self.clusterMeanPlot.clear() + self.rawSpecPlot.clearAll() + self.clusterMeanPlot.clearAll() def setHeader(self, field: str): self.headers = [self.headermodel.item(i).header for i in range(self.headermodel.rowCount())] From e44e0c20d3e337f2efc237db0329dd2861f9d4aa Mon Sep 17 00:00:00 2001 From: lchen23 Date: Sat, 30 May 2020 21:57:26 -0700 Subject: [PATCH 36/56] change FA to Decomposition --- xicam/BSISB/__init__.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/xicam/BSISB/__init__.py b/xicam/BSISB/__init__.py index dbc41f2..21e88ba 100644 --- a/xicam/BSISB/__init__.py +++ b/xicam/BSISB/__init__.py @@ -54,7 +54,7 @@ def __init__(self, *args, **kwargs): self.stages = {#"MapToH5": GUILayout(self.mapToH5), "Image View": GUILayout(self.imageview), "Preprocess": GUILayout(self.preprocess), - "Factor Analysis": GUILayout(self.FA_widget), + "Decomposition": GUILayout(self.FA_widget), "Clustering": GUILayout(self.clusterwidget)} super(BSISB, self).__init__(*args, **kwargs) @@ -116,4 +116,3 @@ def updateROI(self, roi): def updateTab(self): # clean up all widgets self.preprocess.cleanUp() - self.clusterwidget.cleanUp() From 98d2ae9d31062d53bf9349442b40797ad311a47f Mon Sep 17 00:00:00 2001 From: lchen23 Date: Sat, 30 May 2020 22:03:37 -0700 Subject: [PATCH 37/56] change text position --- xicam/BSISB/widgets/spectraplotwidget.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/xicam/BSISB/widgets/spectraplotwidget.py b/xicam/BSISB/widgets/spectraplotwidget.py index 9b03f9a..193f525 100644 --- a/xicam/BSISB/widgets/spectraplotwidget.py +++ b/xicam/BSISB/widgets/spectraplotwidget.py @@ -12,7 +12,7 @@ class SpectraPlotWidget(PlotWidget): sigEnergyChanged = Signal(object) - def __init__(self, linePos=650, txtPosRatio=0.3, invertX=True, *args, **kwargs): + def __init__(self, linePos=650, txtPosRatio=0.35, invertX=True, *args, **kwargs): """ A widget to display a 1D spectrum :param linePos: the initial position of the InfiniteLine @@ -36,6 +36,7 @@ def __init__(self, linePos=650, txtPosRatio=0.3, invertX=True, *args, **kwargs): symbolSize=20) self.cross.setZValue(100) self.addItem(self.cross) + self.txt = TextItem() self.getViewBox().invertX(invertX) self.spectrumInd = 0 self.selectedPixels = None @@ -134,11 +135,11 @@ def plot(self, x, y, *args, **kwargs): txt_html = toHtml(f'{self._mean_title}') txt_html += toHtml(f'X = {x_val: .2f}, Y = {y_val: .4f}') - self.txt = TextItem(html=txt_html, anchor=(0, 0)) + self.txt.setHtml(txt_html) ymax = np.max(y) self._y = y r = self.txtPosRatio - self.txt.setPos(r * x[-1] + (1 - r) * x[0], 0.95 * ymax) + self.txt.setPos(r * x[-1] + (1 - r) * x[0], ymax) self.cross.setData([x_val], [y_val]) self.addItem(self.txt) From 69f52bcb47762c9ff530d44a953d0ac6f1feb605 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Sat, 30 May 2020 22:12:20 -0700 Subject: [PATCH 38/56] add PCA and multimap clustering --- xicam/BSISB/widgets/clusteringwidget.py | 71 +++++++++++++++++-------- 1 file changed, 49 insertions(+), 22 deletions(-) diff --git a/xicam/BSISB/widgets/clusteringwidget.py b/xicam/BSISB/widgets/clusteringwidget.py index 3cb651d..c7af578 100644 --- a/xicam/BSISB/widgets/clusteringwidget.py +++ b/xicam/BSISB/widgets/clusteringwidget.py @@ -9,6 +9,8 @@ from qtpy.QtWidgets import * from sklearn.cluster import KMeans from sklearn.neighbors import NearestNeighbors +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler, Normalizer from umap import UMAP from xicam.BSISB.widgets.mapviewwidget import MapViewWidget, toHtml from xicam.BSISB.widgets.spectraplotwidget import SpectraPlotWidget @@ -147,7 +149,7 @@ def getNN(self): class ClusterSpectraWidget(SpectraPlotWidget): def __init__(self): - super(ClusterSpectraWidget, self).__init__() + super(ClusterSpectraWidget, self).__init__(txtPosRatio=0.35) self._x = None self.ymax, self.zmax = 0, 100 self._plots = [] @@ -194,7 +196,7 @@ def curveHighLight(self, k): ymin, ymax = np.min(self._y), np.max(self._y) self.getViewBox().setYRange(ymin, ymax, padding=0.1) r = self.txtPosRatio - self.txt.setPos(r * self._x[-1] + (1 - r) * self._x[0], 0.95 * ymax) + self.txt.setPos(r * self._x[-1] + (1 - r) * self._x[0], ymax) self.getEnergy() def plot(self, x, y, *args, **kwargs): @@ -216,7 +218,7 @@ def plot(self, x, y, *args, **kwargs): self.ymax = ymax self._x, self._y = x, y r = self.txtPosRatio - self.txt.setPos(r * x[-1] + (1 - r) * x[0], 0.95 * ymax) + self.txt.setPos(r * x[-1] + (1 - r) * x[0], self.ymax) return plot_item @@ -227,7 +229,7 @@ def __init__(self, headermodel, selectionmodel): self.mapselectmodel = selectionmodel # init some values self.selectMapidx = 0 - self.low_dim = None + self.embedding = None self.labels = None self.mean_spectra = None @@ -259,12 +261,8 @@ def __init__(self, headermodel, selectionmodel): self.computeBtn = QPushButton() self.computeBtn.setText('Compute clusters') self.computeBtn.setFont(font) - self.removeBtn = QPushButton() - self.removeBtn.setText('Remove spectra') - self.removeBtn.setFont(font) # add all buttons self.buttonlayout.addWidget(self.computeBtn) - # self.buttonlayout.addWidget(self.removeBtn) # Headers listview self.headerlistview = QListView() @@ -290,14 +288,13 @@ def __init__(self, headermodel, selectionmodel): self.rightsplitter.addWidget(self.parametertree) self.rightsplitter.addWidget(self.buttons) self.rightsplitter.addWidget(self.mapListWidget) - self.rightsplitter.setSizes([200, 50, 50]) + self.rightsplitter.setSizes([300, 50, 50]) self.addWidget(self.leftsplitter) self.addWidget(self.rightsplitter) self.setSizes([500, 100]) # Connect signals self.computeBtn.clicked.connect(self.computeEmbedding) - self.removeBtn.clicked.connect(self.removeSpec) self.clusterImage.sigShowSpectra.connect(self.rawSpecPlot.showSpectra) self.clusterImage.sigShowSpectra.connect(self.showClusterMean) self.clusterImage.sigShowSpectra.connect(self.clusterScatterPlot.clickFromImage) @@ -305,6 +302,7 @@ def __init__(self, headermodel, selectionmodel): self.clusterScatterPlot.sigScatterClicked.connect(self.showClusterMean) self.clusterScatterPlot.sigScatterClicked.connect(self.setImageCross) self.parametertree.sigParamChanged.connect(self.updateClusterParams) + self.mapselectmodel.selectionChanged.connect(self.updateMap) def computeEmbedding(self): # get current map idx @@ -345,15 +343,29 @@ def computeEmbedding(self): n_components=n_components, metric=metric, random_state=0) - self.low_dim = self.umap.fit_transform(self.dataset) + self.embedding = self.umap.fit_transform(self.dataset) elif self.parameter['Embedding'] == 'PCA': - pass + # normalize and mean center + if self.parameter['Normalization'] == 'L1': # normalize + data_norm = Normalizer(norm='l1').fit_transform(self.dataset) + elif self.parameter['Normalization'] == 'L2': + data_norm = Normalizer(norm='l2').fit_transform(self.dataset) + else: + data_norm = self.dataset + # subtract mean + data_centered = StandardScaler(with_std=False).fit_transform(data_norm) + # Do PCA + self.PCA = PCA(n_components=n_components) + self.PCA.fit(data_centered) + self.embedding = self.PCA.transform(data_centered) + # save embedding to standardModelItem + self.item.embedding = self.embedding # update cluster map self.computeCluster() def computeCluster(self): # check if embeddings exist - if self.low_dim is None: + if self.embedding is None: return msg.showMessage('Compute clusters.') # get num of clusters @@ -361,7 +373,7 @@ def computeCluster(self): # set colorLUT self.colorLUT = cm.get_cmap('viridis', n_clusters).colors[:, :3] * 255 # compute cluster - cluster_object = KMeans(n_clusters=n_clusters, random_state=0).fit(self.low_dim) + cluster_object = KMeans(n_clusters=n_clusters, random_state=0).fit(self.embedding) self.labels = cluster_object.labels_ # update cluster image self.cluster_map = self.labels.reshape(self.imgShape[0], self.imgShape[1]) @@ -388,10 +400,10 @@ def computeCluster(self): self.updateScatterPlot() def updateScatterPlot(self): - if (self.low_dim is None) or (self.labels is None): + if (self.embedding is None) or (self.labels is None): return # get scatter x, y values - self.clusterScatterPlot.scatterData = self.low_dim[:, + self.clusterScatterPlot.scatterData = self.embedding[:, [self.parameter['X Component'] - 1, self.parameter['Y Component'] - 1]] # get colormapings brushes = [mkBrush(self.colorLUT[x, :]) for x in self.labels] @@ -411,6 +423,22 @@ def updateClusterParams(self, name): elif name in ['X Component', 'Y Component']: self.updateScatterPlot() + def updateMap(self): + # get current map idx + if not self.mapselectmodel.selectedIndexes(): # no map is open + return + else: + self.selectMapidx = self.mapselectmodel.selectedIndexes()[0].row() + # get current item + self.item = self.headermodel.item(self.selectMapidx) + if hasattr(self.item, 'embedding'): + # load embedding + self.embedding = self.item.embedding + self.computeCluster() + else: + # reset custer image and plots + self.cleanUp() + def showClusterMean(self, i): if self.mean_spectra is None: return @@ -427,17 +455,17 @@ def setImageCross(self, ind): + toHtml(f'Val: {self.clusterImage._image[self.imgShape[0] - row - 1, col] :d}', size=8)) - def removeSpec(self): - pass - def cleanUp(self): if hasattr(self, 'imgShape'): img = np.zeros((self.imgShape[0], self.imgShape[1])) self.clusterImage.setImage(img=img) if hasattr(self, 'scatterPlot'): self.clusterScatterPlot.plotItem.clearPlots() + self.clusterScatterPlot.scatterData = None self.rawSpecPlot.clearAll() + self.rawSpecPlot._data = None self.clusterMeanPlot.clearAll() + self.clusterMeanPlot._data = None def setHeader(self, field: str): self.headers = [self.headermodel.item(i).header for i in range(self.headermodel.rowCount())] @@ -463,7 +491,7 @@ def setHeader(self, field: str): msg.logMessage('Header object contained no frames with field ''{field}''.', msg.ERROR) if data is not None: self.dataSets.append(data) - self.isMapOpen() + self.cleanUp() def isMapOpen(self): if not self.mapselectmodel.selectedIndexes(): # no map is open @@ -471,6 +499,7 @@ def isMapOpen(self): else: self.selectMapidx = self.mapselectmodel.selectedIndexes()[0].row() # get current data + self.item = self.headermodel.item(self.selectMapidx) self.currentHeader = self.headers[self.selectMapidx] self.wavenumbers = self.wavenumberList[self.selectMapidx] self.rc2ind = self.rc2indList[self.selectMapidx] @@ -478,6 +507,4 @@ def isMapOpen(self): self.imgShape = self.imgShapes[self.selectMapidx] self.data = self.dataSets[self.selectMapidx] self.rawSpecPlot.setHeader(self.currentHeader, 'spectra') - # reset custer image and plots - self.cleanUp() return True From f417e01c4d1ea27739d49b2135d5ab232f9898f6 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Sat, 30 May 2020 22:19:57 -0700 Subject: [PATCH 39/56] move buttons out of parameterlist --- xicam/BSISB/widgets/factorizationwidget.py | 518 +++++++++++---------- 1 file changed, 262 insertions(+), 256 deletions(-) diff --git a/xicam/BSISB/widgets/factorizationwidget.py b/xicam/BSISB/widgets/factorizationwidget.py index 8c94b77..d2873b9 100644 --- a/xicam/BSISB/widgets/factorizationwidget.py +++ b/xicam/BSISB/widgets/factorizationwidget.py @@ -14,6 +14,7 @@ import seaborn as sns from xicam.BSISB.widgets.uiwidget import MsgBox from xicam.BSISB.widgets.imshowwidget import SlimImageView +from xicam.BSISB.widgets.spectraplotwidget import SpectraPlotWidget from lbl_ir.data_objects.ir_map import val2ind from lbl_ir.tasks.preprocessing import data_prep from lbl_ir.tasks.NMF.multi_set_analyses import aggregate_data @@ -23,7 +24,6 @@ class FactorizationParameters(ParameterTree): - sigPCA = Signal(object) def __init__(self, headermodel: QStandardItemModel, selectionmodel: QItemSelectionModel): super(FactorizationParameters, self).__init__() @@ -38,8 +38,6 @@ def __init__(self, headermodel: QStandardItemModel, selectionmodel: QItemSelecti {'name': "Components", 'value': 4, 'type': 'int'}, - {'name': "Calculate", - 'type': 'action'}, {'name': "Map 1 Component", 'values': [1, 2, 3, 4], 'value': 1, @@ -66,9 +64,7 @@ def __init__(self, headermodel: QStandardItemModel, selectionmodel: QItemSelecti {'name': "C regressor", 'values': ['OLS', 'NNLS'], 'value': 'OLS', - 'type': 'list'}, - {'name': "Save results", - 'type': 'action'} + 'type': 'list'} ]) self.setParameters(self.parameter, showTop=False) @@ -88,55 +84,22 @@ def __init__(self, headermodel: QStandardItemModel, selectionmodel: QItemSelecti item.displayLabel.setFont(font) item.widget.setMaximumHeight(40) # connect signals - self.parameter.child('Calculate').sigActivated.connect(self.calculate) - self.parameter.child('Save results').sigActivated.connect(self.saveResults) self.parameter.child('Components').sigValueChanged.connect(self.setNumComponents) self.parameter.child('Method').sigValueChanged.connect(self.setMethod) self.parameter.child('C regressor').hide() def setMethod(self): if self.parameter['Method'] == 'PCA': - self.method = 'PCA' - self.field = 'spectra' self.parameter.child('Normalization').setToDefault() self.parameter.child('Normalization').show() self.parameter.child('C regressor').hide() elif self.parameter['Method'] == 'NMF': - self.method = 'NMF' - self.field = 'volume' self.parameter.child('Normalization').hide() self.parameter.child('C regressor').hide() elif self.parameter['Method'] == 'MCR': - self.method = 'MCR' - self.field = 'spectra' self.parameter.child('Normalization').hide() self.parameter.child('C regressor').show() - def setHeader(self, wavenumbers, imgShapes, rc2indList, ind2rcList): - # get all headers selected - # headers = [self.headermodel.itemFromIndex(i).header for i in self.selectionmodel.selectedRows()] - self.headers = [self.headermodel.item(i).header for i in range(self.headermodel.rowCount())] - - self.wavenumbers = wavenumbers - self.N_w = len(self.wavenumbers) - self.imgShapes = imgShapes - self.rc2indList = rc2indList - self.ind2rcList = ind2rcList - self._dataSets = {'spectra': [], 'volume': []} - - for header in self.headers: - data = None - try: # spectra datasets - data = header.meta_array('spectra') - except IndexError: - msg.logMessage('Header object contained no frames with field ''{field}''.', msg.ERROR) - if data is not None: - self._dataSets['spectra'].append(data) - # NMF path sets - volumeEvent = next(header.events(fields=['volume'])) - path = volumeEvent['path'] # readin filepath - self._dataSets['volume'].append(path) - def setNumComponents(self): N = self.parameter['Components'] @@ -144,216 +107,11 @@ def setNumComponents(self): param = self.parameter.child(f'Map {i + 1} Component') param.setLimits(list(range(1, N + 1))) - def calculate(self): - - N = self.parameter['Components'] - - if hasattr(self, '_dataSets'): - wavROIList = [] - for entry in self.parameter['Wavenumber Range'].split(','): - try: - wavROIList.append(val2ind(int(entry), self.wavenumbers)) - except: - continue - # Select wavenumber region - if len(wavROIList) % 2 == 0: - wavROIList = sorted(wavROIList) - wavROIidx = [] - for i in range(len(wavROIList) // 2): - wavROIidx += list(range(wavROIList[2 * i], wavROIList[2 * i + 1] + 1)) - else: - msg.logMessage('"Wavenumber Range" values must be in pairs', msg.ERROR) - MsgBox('Factorization computation aborted.', 'error') - return - - self.wavenumbers_select = self.wavenumbers[wavROIidx] - # get map ROI selected region - self.selectedPixelsList = [self.headermodel.item(i).selectedPixels for i in - range(self.headermodel.rowCount())] - self.df_row_idx = [] # row index for dataframe data_fac - - msg.showMessage('Start computing', self.method + '. Image shape:', str(self.imgShapes)) - self.dataRowSplit = [0] # remember the starting/end row positions of each dataset - if self.field == 'spectra': # PCA workflow - self.N_w = len(self.wavenumbers_select) - self._allData = np.empty((0, self.N_w)) - - for i, data in enumerate(self._dataSets['spectra']): # i: map idx - if self.selectedPixelsList[i] is None: - n_spectra = len(data) - tmp = np.zeros((n_spectra, self.N_w)) - for j in range(n_spectra): - tmp[j, :] = data[j][wavROIidx] - self.df_row_idx.append((self.ind2rcList[i][j], j)) - else: - n_spectra = len(self.selectedPixelsList[i]) - tmp = np.zeros((n_spectra, self.N_w)) - for j in range(n_spectra): # j: jth selected pixel - row_col = tuple(self.selectedPixelsList[i][j]) - tmp[j, :] = data[self.rc2indList[i][row_col]][wavROIidx] - self.df_row_idx.append((row_col, self.rc2indList[i][row_col])) - - self.dataRowSplit.append(self.dataRowSplit[-1] + n_spectra) - self._allData = np.append(self._allData, tmp, axis=0) - - if len(self._allData) > 0: - if self.method == 'PCA': - self.data_fac_name = 'data_PCA' # define pop up plots labels - # normalize and mean center - if self.parameter['Normalization'] == 'L1':# normalize - data_norm = Normalizer(norm='l1').fit_transform(self._allData) - elif self.parameter['Normalization'] == 'L2': - data_norm = Normalizer(norm='l2').fit_transform(self._allData) - else: - data_norm = self._allData - #subtract mean - data_centered = StandardScaler(with_std=False).fit_transform(data_norm) - # Do PCA - self.PCA = PCA(n_components=N) - self.PCA.fit(data_centered) - self.data_PCA = self.PCA.transform(data_centered) - # pop up plots - self.popup_plots() - elif self.method == 'MCR': - self.data_fac_name = 'data_MCR' # define pop up plots labels - # Do ICA to find initial estimate of ST matrix - self.ICA = FastICA(n_components=N) - self.ICA.fit(self._allData) - # Do MCR - self.MCR = McrAR(max_iter=100, c_regr=self.parameter['C regressor'], st_regr='NNLS', - tol_err_change=1e-6, tol_increase=0.5) - self.MCR.fit(self._allData, ST=self.ICA.components_) - self.MCR.components_ = self.MCR.ST_opt_ - self.data_MCR = self.MCR.C_opt_ - #test ICA - # self.MCR = self.ICA - # self.data_MCR = self.ICA.transform(self._allData) - # pop up plots - self.popup_plots() - else: - msg.logMessage('The data matrix is empty. No PCA is performed.', msg.ERROR) - MsgBox('The data matrix is empty. No PCA is performed.', 'error') - self.PCA, self.data_PCA = None, None - self.MCR, self.data_MCR = None, None - # emit PCA and transformed data - if self.method == 'PCA': - self.sigPCA.emit((self.wavenumbers_select, self.PCA, self.data_PCA, self.dataRowSplit)) - elif self.method == 'MCR': - self.sigPCA.emit((self.wavenumbers_select, self.MCR, self.data_MCR, self.dataRowSplit)) - - elif self.field == 'volume': # NMF workflow - data_files = [] - wav_masks = [] - row_idx = np.array([], dtype='int') - self.allDataRowSplit = [0] # row split for complete datasets - - for i, file in enumerate(self._dataSets['volume']): - ir_data, fmt = read_map.read_all_formats(file) - n_spectra = ir_data.data.shape[0] - self.allDataRowSplit.append(self.allDataRowSplit[-1] + n_spectra) - data_files.append(ir_data) - ds = data_prep.data_prepper(ir_data) - wav_masks.append(ds.decent_bands) - # row selection - if self.selectedPixelsList[i] is None: - row_idx = np.append(row_idx, np.arange(self.allDataRowSplit[-2], self.allDataRowSplit[-1])) - for k, v in self.rc2indList[i].items(): - self.df_row_idx.append((k, v)) - else: - n_spectra = len(self.selectedPixelsList[i]) - for j in range(n_spectra): - row_col = tuple(self.selectedPixelsList[i][j]) - row_idx = np.append(row_idx, self.allDataRowSplit[-2] + - self.rc2indList[i][row_col]) - self.df_row_idx.append((row_col, self.rc2indList[i][row_col])) - - self.dataRowSplit.append(self.dataRowSplit[-1] + n_spectra) # row split for ROI selected rows - - # define pop up plots labels - self.data_fac_name = 'data_NMF' - - if len(self.df_row_idx) > 0: - # aggregate datasets - ir_data_agg = aggregate_data(self._dataSets['volume'], data_files, wav_masks) - col_idx = list(set(wavROIidx) & set(ir_data_agg.master_wmask)) - self.wavenumbers_select = self.wavenumbers[col_idx] - ir_data_agg.data = ir_data_agg.data[:, col_idx] - ir_data_agg.data = ir_data_agg.data[row_idx, :] - # perform NMF - self.NMF = NMF(n_components=N) - self.data_NMF = self.NMF.fit_transform(ir_data_agg.data) - # pop up plots - self.popup_plots() - else: - msg.logMessage('The data matrix is empty. No NMF is performed.', msg.ERROR) - MsgBox('The data matrix is empty. No NMF is performed.', 'error') - self.NMF, self.data_NMF = None, None - # emit NMF and transformed data : data_NMF - self.sigPCA.emit((self.wavenumbers_select, self.NMF, self.data_NMF, self.dataRowSplit)) - - def popup_plots(self): - # component variance ratio plot - if self.method == 'PCA': - plt.plot(getattr(self, self.method).explained_variance_ratio_, 'o-b') - ax = plt.gca() - ax.set_ylabel('Explained variance ratio', fontsize=16) - ax.set_xlabel('Component number', fontsize=16) - ax.set_xticks(np.arange(self.parameter['Components'])) - # loadings plot - plt.figure() - labels = [] - for i in range(getattr(self, self.method).components_.shape[0]): - labels.append(self.method + str(i + 1)) - plt.plot(self.wavenumbers_select, getattr(self, self.method).components_[i, :], '-', - label=labels[i]) - loadings_legend = plt.legend(loc='best') - plt.setp(loadings_legend, draggable=True) - plt.xlim([max(self.wavenumbers_select), min(self.wavenumbers_select)]) - ax = plt.gca() - ax.set_xlabel('Wavenumber$(cm^{-1})$', fontsize=16) - # matrix plot - groupLabel = np.zeros((self.dataRowSplit[-1], 1)) - for i in range(len(self.dataRowSplit) - 1): - groupLabel[self.dataRowSplit[i]:self.dataRowSplit[i + 1]] = int(i) - - df_scores = pd.DataFrame(np.append(getattr(self, self.data_fac_name), groupLabel, axis=1), - columns=labels + ['Group label']) - grid = sns.pairplot(df_scores, vars=labels, hue="Group label") - # change legend properties - legend_labels = [] - for i in range(self.headermodel.rowCount()): - if (self.selectedPixelsList[i] is None) or (self.selectedPixelsList[i].size > 0): - legend_labels.append(self.headermodel.item(i).data(0)) - for t, l in zip(grid._legend.texts, legend_labels): t.set_text(l) - plt.setp(grid._legend.get_texts(), fontsize=14) - plt.setp(grid._legend.get_title(), fontsize=14) - plt.setp(grid._legend, bbox_to_anchor=(0.2, 0.95), frame_on=True, draggable=True) - plt.setp(grid._legend.get_frame(), edgecolor='k', linewidth=1, alpha=1) - plt.show() - - def saveResults(self): - if (hasattr(self, 'PCA') and self.PCA is not None) or (hasattr(self, 'NMF') and self.NMF is not None)\ - or (hasattr(self, 'MCR') and self.MCR is not None): - name = self.method - df_fac_components = pd.DataFrame(getattr(self, name).components_, columns=self.wavenumbers_select) - df_data_fac = pd.DataFrame(getattr(self, self.data_fac_name), index=self.df_row_idx) - df_fac_components.to_csv(name + '_components.csv') - df_data_fac.to_csv(name + '_data.csv') - np.savetxt(name + '_mapRowSplit.csv', np.array(self.dataRowSplit), fmt='%d', delimiter=',') - MsgBox(name + ' components successfully saved!') - else: - MsgBox('No factorization components available.') - -class ComponentPlotWidget(PlotWidget): +class ComponentPlotWidget(SpectraPlotWidget): def __init__(self, *args, **kwargs): - super(ComponentPlotWidget, self).__init__(*args, **kwargs) - self.line = InfiniteLine(pos=800, movable=True) - self.line.sigPositionChanged.connect(self.getEnergy) - self.addItem(self.line) - self.cross = PlotDataItem([800], [0], symbolBrush=(255, 255, 255), symbolPen=(255, 255, 255), symbol='+', symbolSize=25) - self.addItem(self.cross) - self.txt = TextItem() - self.getViewBox().invertX(True) + super(ComponentPlotWidget, self).__init__(linePos=800, txtPosRatio=0.35, *args, **kwargs) + self.cross = PlotDataItem([800], [0], symbolBrush=(255, 255, 255), symbolPen=(255, 255, 255), + symbol='+', symbolSize=25) self._x, self._y = None, None self.ymax, self.zmax = 0, 100 @@ -387,11 +145,14 @@ def plot(self, x, y, *args, **kwargs): if ymax > self.ymax: self.ymax = ymax self._x, self._y = x, y - self.txt.setPos(x[0], 0.95 * self.ymax) + r = self.txtPosRatio + self.txt.setPos(r * x[-1] + (1 - r) * x[0], self.ymax) self.addItem(self.txt) return plot_item class FactorizationWidget(QSplitter): + sigPCA = Signal(object) + def __init__(self, headermodel, selectionmodel): super(FactorizationWidget, self).__init__() self.headermodel = headermodel @@ -451,7 +212,6 @@ def __init__(self, headermodel, selectionmodel): self.parametertree = FactorizationParameters(headermodel, selectionmodel) self.parameter = self.parametertree.parameter - self.parametertree.sigPCA.connect(self.showComponents) for i in range(4): self.parameter.child(f'Map {i + 1} Component').sigValueChanged.connect( partial(self.updateComponents, i)) @@ -469,6 +229,23 @@ def __init__(self, headermodel, selectionmodel): self.setOrientation(Qt.Horizontal) self.display.setOrientation(Qt.Vertical) + # buttons layout + self.buttons = QWidget() + self.buttonlayout = QGridLayout() + self.buttons.setLayout(self.buttonlayout) + # set up buttons + self.fontSize = 12 + font = QFont("Helvetica [Cronyx]", self.fontSize) + self.computeBtn = QPushButton() + self.computeBtn.setText('Decompose') + self.computeBtn.setFont(font) + self.saveBtn = QPushButton() + self.saveBtn.setText('Save Results') + self.saveBtn.setFont(font) + # add all buttons + self.buttonlayout.addWidget(self.computeBtn) + self.buttonlayout.addWidget(self.saveBtn) + # Headers listview self.headerlistview = QListView() self.headerlistview.setModel(headermodel) @@ -487,8 +264,14 @@ def __init__(self, headermodel, selectionmodel): # adjust right splitter self.rightsplitter.addWidget(self.parametertree) + self.rightsplitter.addWidget(self.buttons) self.rightsplitter.addWidget(self.mapListWidget) - self.rightsplitter.setSizes([200, 50]) + self.rightsplitter.setSizes([300, 50, 50]) + + #connect signals + self.computeBtn.clicked.connect(self.calculate) + self.saveBtn.clicked.connect(self.saveResults) + self.sigPCA.connect(self.showComponents) def updateRoiMask(self): if self.selectionmodel.hasSelection(): @@ -579,10 +362,11 @@ def showComponents(self, fac_obj): # get map ROI selected region self.selectedPixelsList = [self.headermodel.item(i).selectedPixels for i in range(self.headermodel.rowCount())] # clear plots and legends - self.componentSpectra.plotItem.clearPlots() + # self.componentSpectra.plotItem.clearPlots() + self.componentSpectra.clearAll() self.componentSpectra.ymax = 0 - for sample, label in self._plotLegends.items[:]: - self._plotLegends.removeItem(label.text) + # for sample, label in self._plotLegends.items[:]: + # self._plotLegends.removeItem(label.text) self.wavenumbers, self._fac, self._data_fac, self._dataRowSplit = fac_obj[0], fac_obj[1], fac_obj[2], fac_obj[3] @@ -650,16 +434,30 @@ def setHeader(self, field: str): self.imgShapes = [] self.rc2indList = [] self.ind2rcList = [] + self._dataSets = {'spectra': [], 'volume': []} # get wavenumbers, imgShapes for header in self.headers: dataEvent = next(header.events(fields=[field])) self.wavenumbers = dataEvent['wavenumbers'] + self.N_w = len(self.wavenumbers) wavenum_align.append( - (round(self.wavenumbers[0]), len(self.wavenumbers))) # append (first wavenum value, wavenum length) + (round(self.wavenumbers[0]), self.N_w)) # append (first wavenum value, wavenum length) self.imgShapes.append(dataEvent['imgShape']) self.rc2indList.append(dataEvent['rc_index']) self.ind2rcList.append(dataEvent['index_rc']) + # load data + data = None + try: # spectra datasets + data = header.meta_array('spectra') + except IndexError: + msg.logMessage('Header object contained no frames with field ''{field}''.', msg.ERROR) + if data is not None: + self._dataSets['spectra'].append(data) + # NMF path sets + volumeEvent = next(header.events(fields=['volume'])) + path = volumeEvent['path'] # readin filepath + self._dataSets['volume'].append(path) # init maps if len(self.imgShapes) > 0: @@ -669,7 +467,215 @@ def setHeader(self, field: str): MsgBox('Length of wavenumber arrays of displayed maps are not equal. \n' 'Perform PCA or NMF on these maps will lead to error.','warn') - self.parametertree.setHeader(self.wavenumbers, self.imgShapes, self.rc2indList, self.ind2rcList) + # self.parametertree.setHeader(self.wavenumbers, self.imgShapes, self.rc2indList, self.ind2rcList) + def calculate(self): + N = self.parameter['Components'] + #set decompose method + if self.parameter['Method'] == 'PCA': + self.method = 'PCA' + self.field = 'spectra' + elif self.parameter['Method'] == 'NMF': + self.method = 'NMF' + self.field = 'volume' + elif self.parameter['Method'] == 'MCR': + self.method = 'MCR' + self.field = 'spectra' + + if hasattr(self, '_dataSets'): + wavROIList = [] + for entry in self.parameter['Wavenumber Range'].split(','): + try: + wavROIList.append(val2ind(int(entry), self.wavenumbers)) + except: + continue + # Select wavenumber region + if len(wavROIList) % 2 == 0: + wavROIList = sorted(wavROIList) + wavROIidx = [] + for i in range(len(wavROIList) // 2): + wavROIidx += list(range(wavROIList[2 * i], wavROIList[2 * i + 1] + 1)) + else: + msg.logMessage('"Wavenumber Range" values must be in pairs', msg.ERROR) + MsgBox('Factorization computation aborted.', 'error') + return + + self.wavenumbers_select = self.wavenumbers[wavROIidx] + # get map ROI selected region + self.selectedPixelsList = [self.headermodel.item(i).selectedPixels for i in + range(self.headermodel.rowCount())] + self.df_row_idx = [] # row index for dataframe data_fac + + msg.showMessage('Start computing', self.method + '. Image shape:', str(self.imgShapes)) + self.dataRowSplit = [0] # remember the starting/end row positions of each dataset + if self.field == 'spectra': # PCA workflow + self.N_w = len(self.wavenumbers_select) + self._allData = np.empty((0, self.N_w)) + + for i, data in enumerate(self._dataSets['spectra']): # i: map idx + if self.selectedPixelsList[i] is None: + n_spectra = len(data) + tmp = np.zeros((n_spectra, self.N_w)) + for j in range(n_spectra): + tmp[j, :] = data[j][wavROIidx] + self.df_row_idx.append((self.ind2rcList[i][j], j)) + else: + n_spectra = len(self.selectedPixelsList[i]) + tmp = np.zeros((n_spectra, self.N_w)) + for j in range(n_spectra): # j: jth selected pixel + row_col = tuple(self.selectedPixelsList[i][j]) + tmp[j, :] = data[self.rc2indList[i][row_col]][wavROIidx] + self.df_row_idx.append((row_col, self.rc2indList[i][row_col])) + + self.dataRowSplit.append(self.dataRowSplit[-1] + n_spectra) + self._allData = np.append(self._allData, tmp, axis=0) + + if len(self._allData) > 0: + if self.method == 'PCA': + self.data_fac_name = 'data_PCA' # define pop up plots labels + # normalize and mean center + if self.parameter['Normalization'] == 'L1':# normalize + data_norm = Normalizer(norm='l1').fit_transform(self._allData) + elif self.parameter['Normalization'] == 'L2': + data_norm = Normalizer(norm='l2').fit_transform(self._allData) + else: + data_norm = self._allData + #subtract mean + data_centered = StandardScaler(with_std=False).fit_transform(data_norm) + # Do PCA + self.PCA = PCA(n_components=N) + self.PCA.fit(data_centered) + self.data_PCA = self.PCA.transform(data_centered) + # pop up plots + self.popup_plots() + elif self.method == 'MCR': + self.data_fac_name = 'data_MCR' # define pop up plots labels + # Do ICA to find initial estimate of ST matrix + self.ICA = FastICA(n_components=N) + self.ICA.fit(self._allData) + # Do MCR + self.MCR = McrAR(max_iter=100, c_regr=self.parameter['C regressor'], st_regr='NNLS', + tol_err_change=1e-6, tol_increase=0.5) + self.MCR.fit(self._allData, ST=self.ICA.components_) + self.MCR.components_ = self.MCR.ST_opt_ + self.data_MCR = self.MCR.C_opt_ + #test ICA + # self.MCR = self.ICA + # self.data_MCR = self.ICA.transform(self._allData) + # pop up plots + self.popup_plots() + else: + msg.logMessage('The data matrix is empty. No PCA is performed.', msg.ERROR) + MsgBox('The data matrix is empty. No PCA is performed.', 'error') + self.PCA, self.data_PCA = None, None + self.MCR, self.data_MCR = None, None + # emit PCA and transformed data + if self.method == 'PCA': + self.sigPCA.emit((self.wavenumbers_select, self.PCA, self.data_PCA, self.dataRowSplit)) + elif self.method == 'MCR': + self.sigPCA.emit((self.wavenumbers_select, self.MCR, self.data_MCR, self.dataRowSplit)) + + elif self.field == 'volume': # NMF workflow + data_files = [] + wav_masks = [] + row_idx = np.array([], dtype='int') + self.allDataRowSplit = [0] # row split for complete datasets + + for i, file in enumerate(self._dataSets['volume']): + ir_data, fmt = read_map.read_all_formats(file) + n_spectra = ir_data.data.shape[0] + self.allDataRowSplit.append(self.allDataRowSplit[-1] + n_spectra) + data_files.append(ir_data) + ds = data_prep.data_prepper(ir_data) + wav_masks.append(ds.decent_bands) + # row selection + if self.selectedPixelsList[i] is None: + row_idx = np.append(row_idx, np.arange(self.allDataRowSplit[-2], self.allDataRowSplit[-1])) + for k, v in self.rc2indList[i].items(): + self.df_row_idx.append((k, v)) + else: + n_spectra = len(self.selectedPixelsList[i]) + for j in range(n_spectra): + row_col = tuple(self.selectedPixelsList[i][j]) + row_idx = np.append(row_idx, self.allDataRowSplit[-2] + + self.rc2indList[i][row_col]) + self.df_row_idx.append((row_col, self.rc2indList[i][row_col])) + + self.dataRowSplit.append(self.dataRowSplit[-1] + n_spectra) # row split for ROI selected rows + + # define pop up plots labels + self.data_fac_name = 'data_NMF' + + if len(self.df_row_idx) > 0: + # aggregate datasets + ir_data_agg = aggregate_data(self._dataSets['volume'], data_files, wav_masks) + col_idx = list(set(wavROIidx) & set(ir_data_agg.master_wmask)) + self.wavenumbers_select = self.wavenumbers[col_idx] + ir_data_agg.data = ir_data_agg.data[:, col_idx] + ir_data_agg.data = ir_data_agg.data[row_idx, :] + # perform NMF + self.NMF = NMF(n_components=N) + self.data_NMF = self.NMF.fit_transform(ir_data_agg.data) + # pop up plots + self.popup_plots() + else: + msg.logMessage('The data matrix is empty. No NMF is performed.', msg.ERROR) + MsgBox('The data matrix is empty. No NMF is performed.', 'error') + self.NMF, self.data_NMF = None, None + # emit NMF and transformed data : data_NMF + self.sigPCA.emit((self.wavenumbers_select, self.NMF, self.data_NMF, self.dataRowSplit)) + + def popup_plots(self): + # component variance ratio plot + if self.method == 'PCA': + plt.plot(getattr(self, self.method).explained_variance_ratio_, 'o-b') + ax = plt.gca() + ax.set_ylabel('Explained variance ratio', fontsize=16) + ax.set_xlabel('Component number', fontsize=16) + ax.set_xticks(np.arange(self.parameter['Components'])) + # loadings plot + plt.figure() + labels = [] + for i in range(getattr(self, self.method).components_.shape[0]): + labels.append(self.method + str(i + 1)) + plt.plot(self.wavenumbers_select, getattr(self, self.method).components_[i, :], '-', + label=labels[i]) + loadings_legend = plt.legend(loc='best') + plt.setp(loadings_legend, draggable=True) + plt.xlim([max(self.wavenumbers_select), min(self.wavenumbers_select)]) + ax = plt.gca() + ax.set_xlabel('Wavenumber$(cm^{-1})$', fontsize=16) + # matrix plot + groupLabel = np.zeros((self.dataRowSplit[-1], 1)) + for i in range(len(self.dataRowSplit) - 1): + groupLabel[self.dataRowSplit[i]:self.dataRowSplit[i + 1]] = int(i) + + df_scores = pd.DataFrame(np.append(getattr(self, self.data_fac_name), groupLabel, axis=1), + columns=labels + ['Group label']) + grid = sns.pairplot(df_scores, vars=labels, hue="Group label") + # change legend properties + legend_labels = [] + for i in range(self.headermodel.rowCount()): + if (self.selectedPixelsList[i] is None) or (self.selectedPixelsList[i].size > 0): + legend_labels.append(self.headermodel.item(i).data(0)) + for t, l in zip(grid._legend.texts, legend_labels): t.set_text(l) + plt.setp(grid._legend.get_texts(), fontsize=14) + plt.setp(grid._legend.get_title(), fontsize=14) + plt.setp(grid._legend, bbox_to_anchor=(0.2, 0.95), frame_on=True, draggable=True) + plt.setp(grid._legend.get_frame(), edgecolor='k', linewidth=1, alpha=1) + plt.show() + + def saveResults(self): + if (hasattr(self, 'PCA') and self.PCA is not None) or (hasattr(self, 'NMF') and self.NMF is not None)\ + or (hasattr(self, 'MCR') and self.MCR is not None): + name = self.method + df_fac_components = pd.DataFrame(getattr(self, name).components_, columns=self.wavenumbers_select) + df_data_fac = pd.DataFrame(getattr(self, self.data_fac_name), index=self.df_row_idx) + df_fac_components.to_csv(name + '_components.csv') + df_data_fac.to_csv(name + '_data.csv') + np.savetxt(name + '_mapRowSplit.csv', np.array(self.dataRowSplit), fmt='%d', delimiter=',') + MsgBox(name + ' components successfully saved!') + else: + MsgBox('No factorization components available.') From 73d1f4be9868caad039fcb407eeb3447b5db9441 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Mon, 1 Jun 2020 12:03:17 -0700 Subject: [PATCH 40/56] add lbl_ir package --- lbl_ir/.gitignore | 2 + lbl_ir/README.md | 30 ++ lbl_ir/lbl_ir/GPR/GPR_engine.py | 143 +++++ lbl_ir/lbl_ir/GPR/GPR_peaks.py | 227 ++++++++ lbl_ir/lbl_ir/GPR/__init__.py | 0 lbl_ir/lbl_ir/GPR/spectral_peak_picker.py | 116 ++++ lbl_ir/lbl_ir/__init__.py | 1 + lbl_ir/lbl_ir/data_objects/__init__.py | 2 + lbl_ir/lbl_ir/data_objects/ir_map.py | 504 ++++++++++++++++++ lbl_ir/lbl_ir/gui_tools/__init__.py | 0 lbl_ir/lbl_ir/gui_tools/ispectrum.py | 85 +++ lbl_ir/lbl_ir/gui_tools/rc2ind_mapping.py | 23 + .../io_tools/Omnic_PyMca5/DataObject.py | 166 ++++++ .../lbl_ir/io_tools/Omnic_PyMca5/OmnicMap.py | 363 +++++++++++++ .../lbl_ir/io_tools/Omnic_PyMca5/__init__.py | 0 lbl_ir/lbl_ir/io_tools/__init__.py | 0 lbl_ir/lbl_ir/io_tools/basic_parser.py | 142 +++++ lbl_ir/lbl_ir/io_tools/map_IO.py | 249 +++++++++ lbl_ir/lbl_ir/io_tools/read_XAS.py | 54 ++ lbl_ir/lbl_ir/io_tools/read_map.py | 47 ++ lbl_ir/lbl_ir/io_tools/read_numpy.py | 42 ++ lbl_ir/lbl_ir/io_tools/read_omnic.py | 51 ++ lbl_ir/lbl_ir/math_tools/__init__.py | 3 + lbl_ir/lbl_ir/math_tools/batched_SVD.py | 467 ++++++++++++++++ lbl_ir/lbl_ir/simulations/__init__.py | 0 .../simulations/spectra_map_simulator.py | 287 ++++++++++ lbl_ir/lbl_ir/tasks/NMF/__init__.py | 0 lbl_ir/lbl_ir/tasks/NMF/basic_NMF.py | 40 ++ lbl_ir/lbl_ir/tasks/NMF/multi_set_analyses.py | 124 +++++ lbl_ir/lbl_ir/tasks/Worms/Shai_Hulut.py | 227 ++++++++ lbl_ir/lbl_ir/tasks/Worms/__init__.py | 0 lbl_ir/lbl_ir/tasks/__init__.py | 0 lbl_ir/lbl_ir/tasks/baseline/__init__.py | 0 lbl_ir/lbl_ir/tasks/baseline/rubberband.py | 29 + lbl_ir/lbl_ir/tasks/preprocessing/__init__.py | 0 .../lbl_ir/tasks/preprocessing/data_prep.py | 96 ++++ lbl_ir/lbl_ir/tasks/preprocessing/denoise.py | 18 + .../tasks/preprocessing/spatial_mask.py | 41 ++ lbl_ir/lbl_ir/tasks/preprocessing/svd_data.py | 16 + .../lbl_ir/tasks/preprocessing/transform.py | 22 + lbl_ir/setup.py | 100 ++++ 41 files changed, 3717 insertions(+) create mode 100644 lbl_ir/.gitignore create mode 100644 lbl_ir/README.md create mode 100644 lbl_ir/lbl_ir/GPR/GPR_engine.py create mode 100644 lbl_ir/lbl_ir/GPR/GPR_peaks.py create mode 100644 lbl_ir/lbl_ir/GPR/__init__.py create mode 100644 lbl_ir/lbl_ir/GPR/spectral_peak_picker.py create mode 100644 lbl_ir/lbl_ir/__init__.py create mode 100644 lbl_ir/lbl_ir/data_objects/__init__.py create mode 100644 lbl_ir/lbl_ir/data_objects/ir_map.py create mode 100644 lbl_ir/lbl_ir/gui_tools/__init__.py create mode 100644 lbl_ir/lbl_ir/gui_tools/ispectrum.py create mode 100644 lbl_ir/lbl_ir/gui_tools/rc2ind_mapping.py create mode 100644 lbl_ir/lbl_ir/io_tools/Omnic_PyMca5/DataObject.py create mode 100644 lbl_ir/lbl_ir/io_tools/Omnic_PyMca5/OmnicMap.py create mode 100644 lbl_ir/lbl_ir/io_tools/Omnic_PyMca5/__init__.py create mode 100644 lbl_ir/lbl_ir/io_tools/__init__.py create mode 100644 lbl_ir/lbl_ir/io_tools/basic_parser.py create mode 100644 lbl_ir/lbl_ir/io_tools/map_IO.py create mode 100644 lbl_ir/lbl_ir/io_tools/read_XAS.py create mode 100644 lbl_ir/lbl_ir/io_tools/read_map.py create mode 100644 lbl_ir/lbl_ir/io_tools/read_numpy.py create mode 100644 lbl_ir/lbl_ir/io_tools/read_omnic.py create mode 100644 lbl_ir/lbl_ir/math_tools/__init__.py create mode 100644 lbl_ir/lbl_ir/math_tools/batched_SVD.py create mode 100644 lbl_ir/lbl_ir/simulations/__init__.py create mode 100644 lbl_ir/lbl_ir/simulations/spectra_map_simulator.py create mode 100644 lbl_ir/lbl_ir/tasks/NMF/__init__.py create mode 100644 lbl_ir/lbl_ir/tasks/NMF/basic_NMF.py create mode 100644 lbl_ir/lbl_ir/tasks/NMF/multi_set_analyses.py create mode 100644 lbl_ir/lbl_ir/tasks/Worms/Shai_Hulut.py create mode 100644 lbl_ir/lbl_ir/tasks/Worms/__init__.py create mode 100644 lbl_ir/lbl_ir/tasks/__init__.py create mode 100644 lbl_ir/lbl_ir/tasks/baseline/__init__.py create mode 100644 lbl_ir/lbl_ir/tasks/baseline/rubberband.py create mode 100644 lbl_ir/lbl_ir/tasks/preprocessing/__init__.py create mode 100644 lbl_ir/lbl_ir/tasks/preprocessing/data_prep.py create mode 100644 lbl_ir/lbl_ir/tasks/preprocessing/denoise.py create mode 100644 lbl_ir/lbl_ir/tasks/preprocessing/spatial_mask.py create mode 100644 lbl_ir/lbl_ir/tasks/preprocessing/svd_data.py create mode 100644 lbl_ir/lbl_ir/tasks/preprocessing/transform.py create mode 100644 lbl_ir/setup.py diff --git a/lbl_ir/.gitignore b/lbl_ir/.gitignore new file mode 100644 index 0000000..cc715cc --- /dev/null +++ b/lbl_ir/.gitignore @@ -0,0 +1,2 @@ +__pycache__/ +.ipynb_checkpoints/ \ No newline at end of file diff --git a/lbl_ir/README.md b/lbl_ir/README.md new file mode 100644 index 0000000..234c43d --- /dev/null +++ b/lbl_ir/README.md @@ -0,0 +1,30 @@ +# README # + + +This README would normally document whatever steps are necessary to get your application up and running. + +### What is this repository for? ### + +* Quick summary +* Version +* [Learn Markdown](https://bitbucket.org/tutorials/markdowndemo) + +### How do I get set up? ### + +* Summary of set up +* Configuration +* Dependencies +* Database configuration +* How to run tests +* Deployment instructions + +### Contribution guidelines ### + +* Writing tests +* Code review +* Other guidelines + +### Who do I talk to? ### + +* Repo owner or admin +* Other community or team contact diff --git a/lbl_ir/lbl_ir/GPR/GPR_engine.py b/lbl_ir/lbl_ir/GPR/GPR_engine.py new file mode 100644 index 0000000..e1c7f75 --- /dev/null +++ b/lbl_ir/lbl_ir/GPR/GPR_engine.py @@ -0,0 +1,143 @@ +import numpy as np +import matplotlib.pyplot as plt +import numba +from scipy.optimize import minimize +from scipy.optimize import basinhopping + +@numba.jit(nopython=True) #,cache=True) +def dmat(X1,X2=None): + if X2 is None: + X2 = X1 + N1,M1 = X1.shape + N2,M2 = X2.shape + + dd = np.zeros( (N2,N1) ) + for ii in range(N2): + for jj in range(N1): + tmp = 0 + for kk in range(M1): + delta = X2[ii,kk]-X1[jj,kk] + tmp += delta*delta + tmp = np.sqrt( tmp ) + dd[ii,jj]=tmp + return dd + +@numba.jit(nopython=True) #,cache=True) +def rbf_kern(dmat, variance, length): + result = np.exp( -dmat*dmat/(1e-8+length*length*2.0))*(1e-8+variance) + return result + +@numba.jit(nopython=True) #,cache=True) +def d_rbf_kern_FD(X2, X, variance, length, h=1e-4): + dmat_plus = dmat(X,X2+h/2) + dmat_minus= dmat(X,X2-h/2) + Kplus = np.exp( -dmat_plus*dmat_plus/(length*length*2.0) )*variance + Kminus = np.exp( -dmat_minus*dmat_minus/(length*length*2.0) )*variance + result = (Kplus - Kminus)/h + return result + +def d_rbf_kern(X2, X, dxx2, Kxx2, variance, length): + # first derivative + result1 = [] + for xx in X2.flatten(): + result1.append( X.flatten()-xx ) + result1 = np.vstack(result1) + result1 = result1*Kxx2 + + # second derivative + result2 = [] + N2,N1 = result1.shape + for ii in range(N2): + xx = X2[ii,:] + dK = result1[ii,:] + K = Kxx2[ii,:] + tmp = -K/(length*length) + tmp2 = (X-xx).flatten()*dK.flatten()/(length*length) + result2.append( tmp.flatten()+tmp2.flatten() ) + result2 = np.vstack(result2) + return result1, result2 + + + +class GPR_exp_fitter(object): + def __init__(self,sigma,length, mu=0,niter=3): + """ + :param sigma: The sigma + :param length: The length scale + :param mu: The mean value + :param niter: the number of iterations in basin hopping to fit the GPR model + """ + + self.sigma = sigma + self.length = length + self.mu = mu + + # to compute later + self.dX1X1 = None + + + def marginal_likelihood(self,hparams): + this_mu = hparams[0] + this_sigma = hparams[1] + this_length= hparams[2] + KX1X1 = rbf_kern( self.dX1X1, this_sigma*this_sigma, this_length ) + + II = np.diag(self.sY*self.sY) + KX1X1_inv = np.linalg.pinv(KX1X1+II) + tY = self.Y - this_mu + tY = tY.reshape(-1,1) + KiY = KX1X1_inv.dot(tY) + term1 = 0.5*tY.transpose().dot(KiY) + term2 = 0.5*np.linalg.slogdet(KX1X1+II)[1] + + result = term1+term2 + result = result.flatten()[0] + return result + + + def fit(self,X,Y,sY): + # first we need to build a distance matrix + self.sY = sY + if type(self.sY) is float: + self.sY = self.Y*0+sY + self.X = X + self.Y = Y + self.dX1X1 = dmat(X.reshape(-1,1)) + fitter = basinhopping( func = self.marginal_likelihood, + x0 = np.array([self.mu, self.sigma, self.length]), + niter = 13) + self.mu = fitter.x[0] + self.sigma = abs(fitter.x[1]) + self.length = abs(fitter.x[2]) + + + self.tY = (Y - self.mu).reshape(-1,1) + self.KX1X1 = rbf_kern( self.dX1X1, self.sigma**2.0, self.length ) + np.eye( self.dX1X1.shape[0])*self.sY + self.KX1X1_inv = np.linalg.pinv(self.KX1X1) + self.KiY = self.KX1X1_inv.dot(self.tY) + + + + def predict(self,X): + dX1X2 = dmat(self.X.reshape(-1,1), X.reshape(-1,1)) + kX1X2 = rbf_kern( dX1X2, self.sigma**2, self.length ) + tmp = kX1X2.dot( self.KiY ) + return tmp+self.mu + + + + +def tst(): + x = np.linspace(-10,10,20, True) + xx = np.linspace(-10,10,200,True) + y = np.sin(x) #10 + x*x + x + + plt.plot(x,y,'.');plt.show() + obj = GPR_exp_fitter(1,1,1) + obj.fit(x,y,0.01) + yy = obj.predict( xx ).flatten() + plt.plot(x,y,'.'); plt.plot(xx,yy);plt.show() + + +if __name__ == "__main__": + tst() diff --git a/lbl_ir/lbl_ir/GPR/GPR_peaks.py b/lbl_ir/lbl_ir/GPR/GPR_peaks.py new file mode 100644 index 0000000..32c7069 --- /dev/null +++ b/lbl_ir/lbl_ir/GPR/GPR_peaks.py @@ -0,0 +1,227 @@ +import numpy as np +import matplotlib.pyplot as plt +import numba +from numpy.polynomial.polynomial import Polynomial +from scipy.optimize import minimize +from lbl_ir.GPR import GPR_engine + +""" +Here I try to use Gaussian process regression for identifying peaks +and estimate their standard deviation. +I build a simple GPR fitter myself because I needed more control over +kernel and hyper parameters. Things need to be cleaned up. + +""" + + +#@numba.jit(nopython=True) +def dmat(X1,X2=None): + if X2 is None: + X2 = X1 + N1,M1 = X1.shape + N2,M2 = X2.shape + + dd = np.zeros( (N2,N1) ) + for ii in range(N2): + for jj in range(N1): + tmp = 0 + for kk in range(M1): + delta = X2[ii,kk]-X1[jj,kk] + tmp += delta*delta + tmp = np.sqrt( tmp ) + dd[ii,jj]=tmp + return dd + +@numba.jit(nopython=True) +def rbf_kern(dmat, variance, length): + result = np.exp( -dmat*dmat/(length*length*2.0))*variance + return result + +def d_rbf_kerni_FD(X2, X, variance, length, h=1e-4): + dmat_plus = dmat(X,X2+h/2) + dmat_minus= dmat(X,X2-h/2) + Kplus = np.exp( -dmat_plus*dmat_plus/(length*length*2.0) )*variance + Kminus = np.exp( -dmat_minus*dmat_minus/(length*length*2.0) )*variance + result = (Kplus - Kminus)/h + return result + +def d_rbf_kern(X2, X, dxx2, Kxx2, variance, length): + # first derivative + result1 = [] + for xx in X2.flatten(): + result1.append( X.flatten()-xx ) + result1 = np.vstack(result1) + result1 = result1*Kxx2 + + # second derivative + result2 = [] + N2,N1 = result1.shape + for ii in range(N2): + xx = X2[ii,:] + dK = result1[ii,:] + K = Kxx2[ii,:] + tmp = -K/(length*length) + tmp2 = (X-xx).flatten()*dK.flatten()/(length*length) + result2.append( tmp.flatten()+tmp2.flatten() ) + result2 = np.vstack(result2) + return result1, result2 + + + +class peak_picker(object): + def __init__(self,X,Y,sY): + self.X = X + self.Y = Y + self.N = X.shape[0] + self.sY = sY + init_scale = np.std( self.X.flatten()) + obj = GPR_engine.GPR_exp_fitter( sigma=np.std(Y.flatten()), length=init_scale) + obj.fit( self.X, self.Y, self.sY ) + + self.lengthscale = obj.length + self.variance = obj.sigma**2.0 + self.mu = obj.mu + + # set things up for peak picking purposes + self.dXX = dmat(self.X) + self.Kxx = rbf_kern(self.dXX,self.variance,self.lengthscale) + self.Keff = np.eye( self.N )*self.sY*self.sY + self.Kxx + self.Keff_inv = np.linalg.pinv(self.Keff,rcond=1e-10) + self.Keff_invY= self.Keff_inv.dot(self.Y-self.mu) + + self.dx = np.mean( np.sort( self.dXX.flatten() )[self.N:self.N*2] ) + + + + + def predict(self,x, grads=True): + # setup stuff + dxx2 = dmat(self.X, x ) + dx2x2 = dmat(x) + Kxx2 = rbf_kern( dxx2, self.variance,self.lengthscale ) + Kx2x2 = rbf_kern( dx2x2, self.variance, self.lengthscale ) + + # mean and variance + cond_mean = Kxx2.dot( self.Keff_invY ) + self.mu + cond_variance = Kx2x2 - Kxx2.dot(self.Keff_inv).dot(Kxx2.transpose()) + + if grads: + # derivatives if requested + dK,ddK = d_rbf_kern(x, self.X, dxx2, Kxx2, self.variance, self.lengthscale) + dmu = dK.dot( self.Keff_invY ) + ddmu = ddK.dot( self.Keff_invY ) + return cond_mean, cond_variance, dmu, ddmu + + else: + return cond_mean, cond_variance + + def f(self,x): + xx = np.array([x]).reshape(-1,1) + result = self.predict(xx)[0][0,0]+self.mu + return -result + + + def find_peak(self, x, eps=1e-2, max_iter=1000): + init_simplex = np.array( [x, x+eps] ).reshape(-1,1) + x_opt = minimize(fun=self.f, method='Nelder-Mead',x0=x, options={'initial_simplex':init_simplex} ) + return x_opt.x + + def find_peak_oof(self, x, eps=1e-8,max_iter=1000, damp=1.0, peak_range=None): + # do a simple root finding starting from x + converged=False + x_in = np.array(x).reshape(-1,1) + iter = 0 + restart = 0 + max_delta = 1.0 + if peak_range is not None: + max_delta = np.max_peak(range) - np.min(peak_range) + max_delta = max_delta / 10.0 + while not converged: + m,v,dm,ddm = self.predict(x_in,True) + tmp = np.random.uniform(0.95,1.05,1)[0] + #print("HERE",m,v,dm,ddm) + delta = tmp*dm/ddm + #if np.abs(delta) > max_delta: + # delta = max_delta*(np.sign(delta)) + #print(x_in, delta, dm, ddm,"DELTA", x_in - delta*damp ) + x_in = x_in - delta*damp + iter+=1 + if np.abs(dm) < eps: + converged=True + if iter > max_iter: + converged = False + print("Early termination in peak finding. Apply damping") + restart += 1 + iter = 0 + x_in = np.array(x).reshape(-1,1) + damp = damp * 0.5 + if restart > 5: + print("Early termination in peak finding. Damping doesn't work") + converged = True + return None + return x_in[0,0] + + + def fst_der_prop(self,x): + x0 = np.array([x]).reshape(-1,1) + # we need to get the vector + M = self.X.flatten()-x + M = np.diag(M/self.lengthscale*self.lengthscale) + + dxx2 = dmat(self.X, x0 ) + dx2x2 = dmat(x0) + Kxx2 = rbf_kern( dxx2, self.variance,self.lengthscale ) + Kx2x2 = rbf_kern( dx2x2, self.variance, self.lengthscale ) + nmu = M.dot(Kxx2.transpose()).transpose().dot(self.Keff_invY) + tmp = M.dot(Kxx2.transpose()).transpose() + nmu = tmp.dot(self.Keff_invY) + nvar = Kx2x2 - tmp.dot(self.Keff_inv).dot(tmp.transpose()) + return nmu[0][0], nvar[0][0] + + def peak_and_std_via_resample(self,x_start,N=10, factor = 0.75): + x_peak = self.find_peak( x_start ) + h = self.dx*factor + x_around = np.array( [x_peak-h, x_peak, x_peak+h] ).reshape(-1,1) + # collect the new posterior mean and variance + mu, var = self.predict(x_around, grads=False) + fs = np.random.multivariate_normal( mu.flatten(), var, N ) + ds = [] + for ii in range(N): + c = Polynomial.fit( x_around.flatten()-x_peak, fs[ii,:], deg = 2 ) + c = c.coef + dx = -0.5*c[1]/c[2] + ds.append(dx) + return x_peak, np.std( ds ), mu[1], np.sqrt(np.abs(var[1][1])) + + +def tst(P=8,S=0.1, show=False): + X = np.random.uniform(-3,3,P).reshape(-1,1) # np.linspace(-3,3,P).reshape((P,1)) + X = np.linspace(-3,3,P).reshape((P,1)) + Y = 10.0*np.exp(-X*X) + np.random.normal(0,S, (P,1) ) + + this_one = np.argmax( Y.flatten() ) + this_x = X.flatten()[this_one] + S = np.zeros( P )+S + obj = peak_picker(X,Y,S) + peak,sigma,val,sig = obj.peak_and_std_via_resample(this_x, N=100) + assert abs(peak/sigma) < 4 + print( "OK" ) + if show: + Xstar = np.linspace(-3,3,1024).reshape(-1,1) + y,v,dm,ddm = obj.predict(Xstar, True) + plt.plot( X.flatten() , Y.flatten(), '.' ) + plt.plot( Xstar.flatten(), y.flatten(), '-' ) + #plt.plot( Xstar.flatten(), dm.flatten(), '--', lw=3) + #plt.plot( Xstar.flatten(), ddm.flatten(), '--', lw=2) + print(peak) + peak_val, peak_std = obj.predict(np.array([[peak[0]]]), False) + print(peak_val, peak_std) + plt.errorbar( peak, peak_val, peak_std, sigma )# '.', markersize=10) + plt.show() + + + + + +if __name__ =="__main__": + tst(P=35,S=0.25,show=True) diff --git a/lbl_ir/lbl_ir/GPR/__init__.py b/lbl_ir/lbl_ir/GPR/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lbl_ir/lbl_ir/GPR/spectral_peak_picker.py b/lbl_ir/lbl_ir/GPR/spectral_peak_picker.py new file mode 100644 index 0000000..45d8fc3 --- /dev/null +++ b/lbl_ir/lbl_ir/GPR/spectral_peak_picker.py @@ -0,0 +1,116 @@ +import numpy as np +import matplotlib.pyplot as plt +from scipy.signal import find_peaks +import sys + +from lbl_ir.io_tools.read_map import read_all_formats +from lbl_ir.tasks.preprocessing.transform import to_absorbance +from lbl_ir.GPR.GPR_peaks import peak_picker + + +class spectral_peak_picker(object): + def __init__(self, + wavenumbers, + spectrum, + sigma=None, + window=5, + peak_height_threshold=0.1, + peak_separation_threshold=3, + peak_prominence=0.001, + ): + """ + + :param wavenumbers: The wavenumbers + :param spectrum: The IR spectrum (Absorbance) + :param sigma: The associated standard deviation. If None, a suitable default will be chosen. + :param window: A parameter that determines the window size when fitting peak using a GPR approach. + The default should be fine. + :param peak_height_threshold: minimum peak height + :param peak_separation_threshold: minimum distance between peaks + :param peak_prominence: peak prominance + """ + + self.wavenumbers = wavenumbers + self.Nwavs = len(wavenumbers) + self.spectrum = spectrum + self.sigma = sigma + self.window = window + self.peak_height_threshold = peak_height_threshold + self.peak_separation_threshold = peak_separation_threshold + self.peak_prominence = peak_prominence + + # first pass: find the peak using a simple approach + self.raw_peaks_indx, self.peak_props = find_peaks(self.spectrum, + height=self.peak_height_threshold, + distance=self.peak_separation_threshold, + prominence=self.peak_prominence ) + self.prominances = self.peak_props['prominences'] + + def refine_peaks(self, level=0.05, sigma_multi=0.0005): + these_peaks = self.prominances > level + these_peak_indx = self.raw_peaks_indx[ these_peaks ] + # now loop over these peaks and do the GPR stuff + peak_locations = [] + peak_sigmas = [] + peak_vals = [] + val_sigmas = [] + ok_flags = [] + for ii in these_peak_indx: + min_indx = max(ii-self.window,0) + max_indx = min(ii+self.window+1,self.Nwavs) + these_waves = self.wavenumbers[min_indx:max_indx] + these_specs = self.spectrum[min_indx:max_indx] + obs_sigma = np.sqrt(np.abs( these_specs ))*sigma_multi + + + + obj = peak_picker(X=these_waves.reshape(-1,1),Y=these_specs.reshape(-1,1),sY=obs_sigma) + x_start = np.mean(these_waves.flatten()) + + peak, sigma, val, val_sig = obj.peak_and_std_via_resample(x_start = x_start ) + if abs(peak - x_start) > 3*(these_waves[1]- these_waves[0]): + peak = x_start + sigma = 2*(these_waves[1]- these_waves[0]) + val = self.spectrum[ii] + val_sig = -1 + ok_flags.append(False) + else: + ok_flags.append(True) + + peak_locations.append(peak) + peak_sigmas.append(sigma) + peak_vals.append(val) + val_sigmas.append(val_sig) + return np.array(peak_locations).flatten(), \ + np.array(peak_sigmas).flatten(), \ + np.array(peak_vals).flatten(), \ + np.array(val_sigmas).flatten(), \ + np.array(ok_flags).flatten() + + + + + + + + + + + +def tst(filename): + map, fmt = read_all_formats(filename) + data,bg = to_absorbance( map.data,map.wavenumbers ) + waves = map.wavenumbers + spec = np.mean(data, axis=0) + #plt.plot(spec);plt.show() + obj = spectral_peak_picker(waves, spec, sigma=0.05, peak_height_threshold=0.1) + peaks, sigma, val, vs, ok = obj.refine_peaks( 0.005) + plt.plot(waves, spec,'.-', markersize=4) + plt.plot(peaks,val,'x',markersize=8);plt.show() + plt.plot( peaks, sigma, '.', markersize=4); plt.show() + + + + +if __name__ =="__main__": + tst(sys.argv[1]) diff --git a/lbl_ir/lbl_ir/__init__.py b/lbl_ir/lbl_ir/__init__.py new file mode 100644 index 0000000..4f1fbed --- /dev/null +++ b/lbl_ir/lbl_ir/__init__.py @@ -0,0 +1 @@ +from . import data_objects, GPR, gui_tools, io_tools, math_tools, simulations, tasks diff --git a/lbl_ir/lbl_ir/data_objects/__init__.py b/lbl_ir/lbl_ir/data_objects/__init__.py new file mode 100644 index 0000000..139597f --- /dev/null +++ b/lbl_ir/lbl_ir/data_objects/__init__.py @@ -0,0 +1,2 @@ + + diff --git a/lbl_ir/lbl_ir/data_objects/ir_map.py b/lbl_ir/lbl_ir/data_objects/ir_map.py new file mode 100644 index 0000000..d5cc26e --- /dev/null +++ b/lbl_ir/lbl_ir/data_objects/ir_map.py @@ -0,0 +1,504 @@ +import numpy as np +import h5py +import datetime +import sys +import os +import matplotlib.pyplot as plt + +def val2ind(val, an_array): + return np.argmin(abs(an_array-val), axis=0) + +class sample_info(object): + """ + A simple class that contains sample info. + + Arguments: + ---------- + + sample_id : A string that identifies the sample, spaces will be + substituted for underscores. + Specifying this is mandatory. + + sample_meta_data : More verbose description of the data. Having this + structured isn't a bad idea, but not enforced at this + level. Not requiered but highly encouraged. + + sample_date : The date at which the data was taken. Default is + today. + + Attributes: + ----------- + + show(out) : prints the contents of id, meta data and date to out. + If out is None, it defaults to sys.stdout + + Examples: + --------- + + si = sample_info(sample_id = 'C_elegans', + sample_date='2004_04_18', + sample_meta_data='Some details.') + si.show() + + """ + def __init__(self, sample_id="Unknown", sample_meta_data="None", sample_date=None): + assert ' ' not in sample_id + self.sample_id = sample_id + + self.sample_meta_data = sample_meta_data + if sample_date is None: + sample_date = str(datetime.date.today().year)+ \ + '_'+str(datetime.date.today().month)+ \ + '_'+str(datetime.date.today().day) + + self.sample_date = sample_date + + def show(self, out=None): + if out is None: + out = sys.stdout + print("Sample_id : %s "%self.sample_id, file=out) + print("Sample date : %s "%self.sample_date, file=out ) + print("Sample description:\n ", self.sample_meta_data, file=out ) + + +class ir_map(object): + """ A simple data object that contains IR data. + + Arguments: + ---------- + wavenumbers : An array of wavenumbers + + sample_info : A sample_info object + + filename : The hdf5 filename where data will be read from. Required for + the 'hdf5' mode. + + data_type : Either 'transmission', 'reflection', or 'absorbance'(default) + + _mode : Choice between 'memory' or 'hdf5' + If 'memory', the dataset currenly resides in memory + If 'hdf5', the dataset currenly resides in an hdf5 file + + _N_obs : The number of rows in the 2D spectral matrix. + + Attributes: + ----------- + As above plus + + add_data(self, spectrum, xy) : add data, provide a numpy array of spectra + and xy positions + + _allocate_space(self) : this is a function that allocates space for + an hdf5 file. no need to call it yourself. + + write_as_hdf5(self, filename) : Writes in-memory data as an hdf5 file. + + """ + + def __init__(self, + wavenumbers = [], + sample_info = sample_info(), + filename = None, + data_type = 'absorbance', + with_image_cube = False, + with_factorization = False): + self.wavenumbers = wavenumbers + self.N_w = len(wavenumbers) + self.sample_info = sample_info + self._h5_filename = filename + self.xy = np.empty( (0,2) ) + self.data = np.empty( (0,self.N_w) ) + self._N_obs = 0 + self._with_image_cube = with_image_cube + self._with_factorization = with_factorization + + assert data_type in ['transmission', 'reflection', 'absorbance'] + self.data_type = data_type + + if self._h5_filename is not None: # hdf5 mode, load data from hdf5 file + self._mode = 'hdf5' + + self._h5= h5py.File(self._h5_filename,'r') + with self._h5: + self._root = list(self._h5.keys())[0] #get sample root group name + self.sample_info.sample_id = self._h5[self._root+'/info/sample_id'][()] + self.sample_info.sample_meta_data = self._h5[self._root+'/info/sample_meta_data'][()] + self.sample_info.sample_date = self._h5[self._root+'/info/sample_date'][()] + else: # memory mode, data is in memory + self._mode = 'memory' + self._h5 = None # this stays None until we allocate space + +# assert self.sample_info.sample_id != "Unknown", "Missing 'sample_info' keyword parameter" + self._root = str(self.sample_info.sample_id) + '_' + str(self.sample_info.sample_date) + + def add_data(self, spectrum=None, xy=None, ind=[]): + """When working in memory mode, append the data in memory into the ir_map object. + + When working in hdf5 mode, load the data from the hdf5 file. + + Arguments: + + ---------- + spectrum : The 2D spectral data matrix + + xy : An array of xy positions + + ind : An array of indices for selecting specific rows in the 2D spectra matrix. + If it is not given(default), full spectra matrix with all data points are loaded + into the ir_map object, 1D int array + + """ + if self._mode == 'memory': + assert spectrum is not None, "please provide a spectrum matrix" + assert xy is not None, "please provide a xy position array" + + if len(ind) == 0: + self.xy = np.append(self.xy, xy, axis = 0) + self.data = np.append(self.data, spectrum, axis = 0) + else: + self.xy = np.append(self.xy, xy[ind,:], axis = 0) + self.data = np.append(self.data, spectrum[ind,:], axis = 0) + + if self._mode == 'hdf5': + self._h5= h5py.File(self._h5_filename,'r') + with self._h5: + self.wavenumbers = self._h5[self._root+'/data/wavenumbers'][:] + if len(ind) == 0: + self.data = self._h5[self._root+'/data/spectra'][:,:] + self.xy = self._h5[self._root+'/data/xy'][:,:] + else: + self.data = self._h5[self._root+'/data/spectra'][ind,:] + self.xy = self._h5[self._root+'/data/xy'][ind,:] + + def add_image_cube(self, imageCube=None, imageMask=None, image_grid_param=None, ind=[]): + """When working in memory mode, load the image cube data in memory into the ir_map object + and flatten the image cube to 2d spectrum matrix. + + When working in hdf5 mode, load the data from the hdf5 file and flatten the image cube + to 2d spectrum matrix. + + Arguments: + + ---------- + imageCube : The spectral image cube, 3D float array + + imageMask : An image mask where non-blank pixels = True, 2D bool array + + image_grid_param : [x0, y0, dx, dy], 1D float list or array + + ind : An array of indices for selecting wavenumber range. If it is not given(default), + full spectrum are loaded into the ir_map object, 1D int array + """ + self._with_image_cube = True + + if self._mode == 'memory': + assert imageCube is not None, "please provide an image cube" + assert imageMask is not None, "please provide an image mask matrix" + assert image_grid_param is not None, "please provide image grid parameters : [x0, y0, dx, dy]" + + self.imageMask = imageMask + self.image_grid_param = image_grid_param + self.N_y, self.N_x = imageMask.shape[0], imageMask.shape[1] + + if len(ind) == 0:# read in full spectra + self.imageCube = imageCube + else:# read in partial spectra + self.imageCube = imageCube[:,:,ind] + assert len(ind) <= len(self.wavenumbers), "The selected wavenumber indices is longer than the full wavenumber range" + self.wavenumbers = self.wavenumbers[ind] + self.N_w = len(self.wavenumbers) + # convert image cube to 2d data matrix and load the data into self.data, self.xy + self.flatten_image_cube(imageCube, imageMask, image_grid_param) + + if self._mode == 'hdf5': + self._h5= h5py.File(self._h5_filename,'r') + with self._h5: + self.imageMask = self._h5[self._root+'/data/image/image_mask'][:,:] + self.image_grid_param = self._h5[self._root+'/data/image/image_grid_param'][:] + self.wavenumbers = self._h5[self._root+'/data/wavenumbers'][:] + if len(ind) == 0:# read in full spectra + self.imageCube = self._h5[self._root+'/data/image/image_cube'][:,:,:] + else:# read in partial spectrum + self.imageCube = self._h5[self._root+'/data/image/image_cube'][:,:,ind] + assert len(ind) <= len(self.wavenumbers), "The selected wavenumber indices is longer than the full wavenumber range" + self.wavenumbers = self.wavenumbers[ind] + self.N_w = len(self.wavenumbers) + # convert image cube to 2d data matrix and load the data into self.data, self.xy + self.flatten_image_cube(self.imageCube, self.imageMask, self.image_grid_param) + + def add_factorization(self, component=None, component_coef=None, prefix='PCA', ind=[]): + """When working in memory mode, load the factorized components data in memory into the ir_map object. + + When working in hdf5 mode, load the data from the hdf5 file. + + Arguments: + + ---------- + component : The spectral image cube, 3D float array + + component_coef : An image mask where non-blank pixels = True, 2D bool array + + prefix : Name of the factorized components, e.g. PCA, MCR + + ind : An array of indices for selecting specific rows in the 2D spectra matrix. + If it is not given(default), full spectra matrix with all data points are loaded + into the ir_map object, 1D int array + """ + self._with_factorization = True + self._factor_prefix = prefix + '_' + + if self._mode == 'memory': + assert component is not None, "please provide a component matrix" + assert component_coef is not None, "please provide a component_coef matrix" + + self.component = component + self.N_component = component.shape[0] + if len(ind) == 0:# read in all data + self.component_coef = component_coef + else:# read in partial data points + self.component_coef = component_coef[ind,:] + + if self._mode == 'hdf5': + self._h5= h5py.File(self._h5_filename,'r') + with self._h5: + self.component = self._h5[self._root + '/data/factorization/' + self._factor_prefix + 'component'][:,:] + self.N_component = self.component.shape[0] + if len(ind) == 0: # read in all data + self.component_coef = self._h5[self._root + '/data/factorization/' + self._factor_prefix + 'component_coef'][:,:] + else: # read in partial data points + self.component_coef = self._h5[self._root + '/data/factorization/' + self._factor_prefix + 'component_coef'][ind,:] + # check the component dimensions match self.data dimensions + assert self.component.shape[1] == self.data.shape[1], "number of wavenumbers in component does not match that of spectra matrix" + assert self.component_coef.shape[0] == self.data.shape[0], "number of rows in component_coef does not match that of spectra matrix" + + def _allocate_space(self): + + self._h5= h5py.File(self._h5_filename,'w') + data_group = self._h5.create_group( self._root ) + data_group.create_dataset( 'data/xy', + (self._N_obs, 2), + dtype='float32') # we just allocate space + data_group.create_dataset('data/wavenumbers', + data = self.wavenumbers, + dtype='float32') # this we can keep in memory + data_group.create_dataset('data/spectra', + (self._N_obs,self.N_w), + dtype='float32') # we just allocate space + + dt = h5py.special_dtype(vlen=str) + data_group.create_dataset('info/sample_id', + data = self.sample_info.sample_id, + dtype= dt ) + + data_group.create_dataset('info/sample_meta_data', + data = self.sample_info.sample_meta_data, + dtype= dt ) + + data_group.create_dataset('info/sample_date', + data = self.sample_info.sample_date, + dtype= dt ) + if self._with_image_cube: + data_group.create_dataset('data/image/image_cube', + (self.N_y, self.N_x, self.N_w), + dtype='float32') # we just allocate space + data_group.create_dataset('data/image/image_mask', + (self.N_y, self.N_x), + dtype='bool') # we just allocate space + data_group.create_dataset('data/image/ind_rc_map', + (self._N_obs, 3), + dtype='int') # we just allocate space + data_group.create_dataset('data/image/image_grid_param', + data = self.image_grid_param, + dtype='float32') # this we keep in memory + + if self._with_factorization: + data_group.create_dataset('data/factorization/' + self._factor_prefix + 'component', + (self.N_component, self.N_w), + dtype='float32') # we just allocate space + data_group.create_dataset('data/factorization/' + self._factor_prefix + 'component_coef', + (self._N_obs, self.N_component), + dtype='float32') # we just allocate space + + def write_as_hdf5(self, filename): + """Save the object out as an hdf5 file. + + Arguments: + + ---------- + filename : The hdf5 filename where data will be written to. + + """ + # prevent overwriting the existing hdf5 files + assert self._h5_filename != filename, \ + "The given hdf5 filename already exists. Please provide a different filename" + + self._h5_filename = filename + self._N_obs = self.data.shape[0] + self._allocate_space( ) + + with self._h5: + self._h5[self._root + '/data/xy'][:,:] = self.xy + self._h5[self._root + '/data/spectra'][:,:] = self.data + + if self._with_image_cube: #save image cube + self._h5[self._root + '/data/image/image_cube'][:,:,:] = self.imageCube + self._h5[self._root + '/data/image/image_mask'][:,:] = self.imageMask + self._h5[self._root + '/data/image/ind_rc_map'][:,:] = self.ind_rc_map + self._h5[self._root + '/data/image/image_grid_param'][:] = self.image_grid_param + + if self._with_factorization: #save factorization + self._h5[self._root + '/data/factorization/' + self._factor_prefix +'component'][:,:] = self.component + self._h5[self._root + '/data/factorization/' + self._factor_prefix +'component_coef'][:,:] = self.component_coef + + print(f'Data is saved as an HDF5 file. Filename : {filename}') + + def to_image_cube(self, N_x=64, N_y=64, x0=0, y0=0, dx=1, dy=1): + """Transform the spectra matrix to 3D image cube. + + The third dimension of the image cube is the spectra data. + + Arguments: + ---------- + (x0, y0) : The starting location of the ir image. + + (N_x, N_y) : The size of the image. + + (dx, dy) : The step size of the image. + + Returns: + -------- + imageCube : The spectral image cube, 3D float array + + imageMask : An image mask where non-blank pixels = True, 2D bool array + + pointCounts : A mask that shows measurement counts in each pixel, 2D int array + """ + + self._with_image_cube = True + + self.N_x = N_x + self.N_y = N_y + self.image_grid_param = np.array([x0, y0, dx, dy], dtype='float32') + + self.imageCube = np.zeros((self.N_y, self.N_x, self.N_w), dtype='float32') + self.pointCounts = np.zeros((self.N_y, self.N_x), dtype='int') + ind_rc_map = np.zeros((self.xy.shape[0], 3), dtype='int') # ind to row-col mapping [i, row, col] + + x = np.arange(self.N_x)*dx + x0 + y = np.arange(self.N_y)*dy + y0 + + for i in range(self.xy.shape[0]): + ind_rc_map[i, 0] = i + ind_rc_map[i, 1] = val2ind(self.xy[i, 1], y) # align y coordinate to get row + ind_rc_map[i, 2] = val2ind(self.xy[i, 0], x) # align x coordinate to get col + self.pointCounts[ind_rc_map[i, 1], ind_rc_map[i, 2]] += 1 # count how many measurements fall in a pixel + self.imageCube[ind_rc_map[i, 1], ind_rc_map[i, 2], :] += self.data[i,:]# add all spectra that fall in a pixel + + self.imageCube /= np.where(self.pointCounts != 0, self.pointCounts,1)[:,:,np.newaxis]# get average spectra per pixel + self.imageMask = self.pointCounts.astype('bool') # convert to boolean matrix + self.ind_rc_map = ind_rc_map + + return self.imageCube, self.imageMask, self.pointCounts + + def flatten_image_cube(self, imageCube, imageMask, image_grid_param): + """Transform a 3D image cube into a spectra matrix using imageMask to filter out blank data points, + and load the matrix into self.data, load the xy positions of the data points into self.xy + + Arguments: + ---------- + imageCube : The spectral image cube, 3D float array + + imageMask : An image mask where non-blank pixels = True, 2D bool array + + image_grid_param : [x0, y0, dx, dy], 1D float list or array + """ + self.N_x = N_x = imageCube.shape[1] + self.N_y = N_y = imageCube.shape[0] + x0 = image_grid_param[0] + y0 = image_grid_param[1] + dx = image_grid_param[2] + dy = image_grid_param[3] + # set up xy grid and use imageMask to pull out non-blank pixel xy-coordinate + x = np.linspace(x0, x0+dx*(N_x-1), N_x) + y = np.linspace(y0, y0+dy*(N_y-1), N_y) + xv, yv = np.meshgrid(x, y) + xy_grid = np.zeros((N_y, N_x, 2)) + xy_grid[:,:,0] = xv + xy_grid[:,:,1] = yv + + # set up image grid and use imageMask to pull out non-blank pixel row, col position + x = np.arange(N_x) + y = np.arange(N_y) + X, Y = np.meshgrid(x,y) + ind_rc_map = np.zeros((len(X[imageMask]), 3), dtype='int') # ind to row-col mapping [i, row, col] + for i, (r,c) in enumerate(zip(Y[imageMask], X[imageMask])): + ind_rc_map[i,:] = [i, r, c] + + self.xy = xy_grid[imageMask,:] + self.data = imageCube[imageMask,:] + self.ind_rc_map = ind_rc_map + +if __name__ == "__main__": + si = sample_info(sample_id = 'C_elegans') + si.show() + #prespare sample data + np.random.seed(3) + N_wav = 100 + N_obs = 100 + waves = np.linspace(500,4000,N_wav) + data = np.random.uniform(0,1, (N_obs, N_wav) ) + xy = np.random.uniform(-5,5, (N_obs, 2) ) + imageCube = data.reshape(-1, 10, N_wav) + imageMask = np.random.random((N_obs//10, 10)) > 0.5 + x0, y0, dx, dy = 0, 0, 1, 1 + image_grid_param = [x0, y0, dx, dy] + component = np.random.random((3, N_wav)) + component_coef = np.random.random((imageMask.sum(), 3)) + # test loading 2d spectra matrix and writing into hdf5 file + ir_data = ir_map( waves, si) + ir_data.add_data( data, xy ) + ir_data.write_as_hdf5('tst_file.h5') + + # test loading 2d spectra matrix from a hdf5 file + ir_data2 = ir_map(filename ='tst_file.h5' ) + ir_data2.add_data() #load full data matrix + print(ir_data2.data.shape) + ir_data2.add_data(ind=np.arange(20)) #load partial data matrix + print(ir_data2.data.shape) + os.remove('tst_file.h5') + + # test loading image cube data + ir_data3 = ir_map( waves, si) + ir_data3.add_image_cube(imageCube, imageMask, image_grid_param) + assert ir_data3.data.shape[0] == imageMask.sum(), "number of rows in ir_data3.data doesn't match non-blank pixels in imageMask" + r , c= np.where(imageMask) + assert np.all(ir_data3.xy[:,0] == c), "x coordinates in ir_data3.xy doesn't match that of non-blank pixels in imageMask" + assert np.all(ir_data3.xy[:,1] == r), "y coordinates in ir_data3.xy doesn't match that of non-blank pixels in imageMask" + + # loading factorization component + ir_data3.add_factorization(component, component_coef) + + # writing into hdf5 file + ir_data3.write_as_hdf5('tst_file2.h5') + # show hdf5 file dataset structure + lst=[] + with h5py.File('tst_file2.h5','r') as f: + root_name = list(f.keys())[0] + h = f[root_name] + f.visit(lst.append) + ind_rc_map = f[root_name + '/data/image/ind_rc_map'][:,:] + print(*lst, sep='\n') + print(ind_rc_map[:20, :]) + plt.imshow(imageMask) + + # test loading image cube and factorization components from a hdf5 file + ir_data4 = ir_map(filename='tst_file2.h5') + ir_data4.add_image_cube() + ir_data4.add_factorization() + print(ir_data4.data.shape) + print(ir_data4.component.shape) + print(ir_data4.component_coef.shape) + + os.remove('tst_file2.h5') + + print('OK') diff --git a/lbl_ir/lbl_ir/gui_tools/__init__.py b/lbl_ir/lbl_ir/gui_tools/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lbl_ir/lbl_ir/gui_tools/ispectrum.py b/lbl_ir/lbl_ir/gui_tools/ispectrum.py new file mode 100644 index 0000000..a0f0bd2 --- /dev/null +++ b/lbl_ir/lbl_ir/gui_tools/ispectrum.py @@ -0,0 +1,85 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Mar 22 17:34:27 2019 + +@author: lchen43 +""" +import matplotlib.pyplot as plt +from matplotlib.patches import Circle +from lbl_ir.data_objects.ir_map import val2ind + +class ispectrum: + + def __init__(self, wavenumbers, y): + self.f, self.ax = plt.subplots(figsize=(8,4)) + self.wavenumbers, self.y = wavenumbers, y + self.ax.plot(wavenumbers, y) + plt.xlim([4000,500]) + plt.title('Click on the spectrum to select peaks; right click to cancel selections'); + plt.show() + + self.pos = [] + self.connect() + + def connect(self): + '''connect to all the events + ''' + self.cidpress = self.f.canvas.mpl_connect('button_press_event', self.onclick) + self.cidmotion = self.f.canvas.mpl_connect('motion_notify_event', self.hover) + self.cidenter_axes = self.f.canvas.mpl_connect('axes_enter_event', self.in_axes) + self.cidleave_axes = self.f.canvas.mpl_connect('axes_leave_event', self.leave_axes) + + def disconnect(self): + '''disconnect all the stored connection ids + ''' + self.f.canvas.mpl_disconnect(self.cidpress) + self.f.canvas.mpl_disconnect(self.cidmotion) + self.f.canvas.mpl_disconnect(self.cidenter_axes) + self.f.canvas.mpl_disconnect(self.cidleave_axes) + return self.pos + + def onclick(self, event): + ind = val2ind(event.xdata, self.wavenumbers) + if event.button == 1: + self.pos.append([self.wavenumbers[ind], self.y[ind]]) + self.ax.plot(self.wavenumbers[ind], self.y[ind],'ro') + self.ax.text(self.wavenumbers[ind], self.y[ind]+0.03,str(len(self.pos))) + self.ax.texts[-1], self.ax.texts[-2] = self.ax.texts[-2], self.ax.texts[-1] + else: + if len(self.pos) > 0: + self.pos.pop() + self.ax.lines[-1].remove() + self.ax.texts[-2].remove() + + def hover(self, event): + ind = val2ind(event.xdata, self.wavenumbers) + self.ax.patches[-1].set_center((self.wavenumbers[ind],self.y[ind])) + self.ax.texts[-1].set_position((self.wavenumbers[ind],self.y[ind]+0.08)) + self.ax.texts[-1].set_text(str(self.wavenumbers[ind]) + ', ' +str(self.y[ind])) + + + def in_axes(self, event): + self.ax.texts = [] + self.ax.patches = [] + if event.inaxes: + ind = val2ind(event.xdata, self.wavenumbers) + self.ax.add_patch(Circle((self.wavenumbers[ind], self.y[ind]), radius = 0.05, color = 'r')) + if len(self.pos) > 0: + for i in range(len(self.pos)): + self.ax.text(self.pos[i][0], self.pos[i][1]+0.03, str(i+1)) + self.ax.text(self.wavenumbers[ind], self.y[ind], str(len(self.ax.texts))+ '-' + str(self.wavenumbers[ind]) + ', ' +str(self.y[ind])) + + def leave_axes(self, event): + self.ax.texts = [] + self.ax.patches = [] + +if __name__ == "__main__": + + import os + from ..io_tools.map_IO import read_spa + + test_data_home = '../test_irdata/' + + spa_file = os.path.join(test_data_home, 'test_data0001.spa') + wavenumbers, y, _, _ = read_spa(spa_file) + spec = ispectrum(wavenumbers, y) \ No newline at end of file diff --git a/lbl_ir/lbl_ir/gui_tools/rc2ind_mapping.py b/lbl_ir/lbl_ir/gui_tools/rc2ind_mapping.py new file mode 100644 index 0000000..a283ef4 --- /dev/null +++ b/lbl_ir/lbl_ir/gui_tools/rc2ind_mapping.py @@ -0,0 +1,23 @@ +import numpy as np + + +def getRC2Ind(imgShape, imageMask=None): + """ + In a 2D image, get a dictionary mapping from (row, col) to linear index i of flattened image, and vice versa, + :param imgShape: the shape of the image + :param imageMask: if the image is sparse, use imageMask to generate the mapping + :return: + """ + if imageMask == None: + imageMask = np.ones(imgShape) > 0 + N_x, N_y = imgShape[1], imgShape[0] + x = np.arange(N_x) + y = np.arange(N_y) + X, Y = np.meshgrid(x, y) + ind_rc_map = np.zeros((imgShape[0] * imgShape[1], 3), dtype='int') + for i, (r, c) in enumerate(zip(Y[imageMask], X[imageMask])): + ind_rc_map[i, :] = [i, r, c] + # make a dictionary + ind2rc = {x[0]: tuple(x[1:]) for x in ind_rc_map} + rc2ind = {tuple(x[1:]): x[0] for x in ind_rc_map} + return ind2rc, rc2ind \ No newline at end of file diff --git a/lbl_ir/lbl_ir/io_tools/Omnic_PyMca5/DataObject.py b/lbl_ir/lbl_ir/io_tools/Omnic_PyMca5/DataObject.py new file mode 100644 index 0000000..d330440 --- /dev/null +++ b/lbl_ir/lbl_ir/io_tools/Omnic_PyMca5/DataObject.py @@ -0,0 +1,166 @@ +#/*########################################################################## +# +# The PyMca X-Ray Fluorescence Toolkit +# +# Copyright (c) 2004-2014 European Synchrotron Radiation Facility +# +# This file is part of the PyMca X-ray Fluorescence Toolkit developed at +# the ESRF by the Software group. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +# THE SOFTWARE. +# +#############################################################################*/ +__author__ = "V. Armando Sole - ESRF Data Analysis" +__contact__ = "sole@esrf.fr" +__license__ = "MIT" +__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" +import numpy + +class DataObject(object): + ''' + Simple container of an array and associated information. + Basically it has the members: + info: A dictionnary + data: An array, usually 2D, 3D, ... + + In the past also incorporated selection methods. + Now each different data source implements its selection methods. + + Plotting routines may add additional members + + x: A list containing arrays to be considered axes + y: A list of data to be considered as signals + m: A list containing the monitor data + ''' + GETINFO_DEPRECATION_WARNING = True + GETDATA_DEPRECATION_WARNING = True + SELECT_DEPRECATION_WARNING = True + + def __init__(self): + ''' + Defaut Constructor + ''' + self.info = {} + self.data = numpy.array([]) + + # all the following methods are here for compatibility purposes + # they are obsolete and bound to disappear. + + def getInfo(self): + """ + Deprecated method + """ + if DataObject.GETINFO_DEPRECATION_WARNING: + print("DEPRECATION WARNING: DataObject.getInfo()") + DataObject.GETINFO_DEPRECATION_WARNING = False + return self.info + + def getData(self): + """ + Deprecated method + """ + if DataObject.GETDATA_DEPRECATION_WARNING: + print("DEPRECATION WARNING: DataObject.getData()") + DataObject.GETDATA_DEPRECATION_WARNING = False + return self.data + + def select(self, selection=None): + """ + Deprecated method + """ + if DataObject.SELECT_DEPRECATION_WARNING: + print("DEPRECATION WARNING: DataObject.select(selection=None)") + DataObject.SELECT_DEPRECATION_WARNING = False + dataObject = DataObject() + dataObject.info = self.info + dataObject.info['selection'] = selection + if selection is None: + dataObject.data = self.data + return dataObject + if type(selection) == dict: + #dataObject.data = self.data #should I set it to none??? + dataObject.data = None + if 'rows' in selection: + dataObject.x = None + dataObject.y = None + dataObject.m = None + if 'x' in selection['rows']: + for rownumber in selection['rows']['x']: + if rownumber is None: + continue + if dataObject.x is None: + dataObject.x = [] + dataObject.x.append(self.data[rownumber, :]) + + if 'y' in selection['rows']: + for rownumber in selection['rows']['y']: + if rownumber is None: + continue + if dataObject.y is None: + dataObject.y = [] + dataObject.y.append(self.data[rownumber, :]) + + if 'm' in selection['rows']: + for rownumber in selection['rows']['m']: + if rownumber is None: + continue + if dataObject.m is None: + dataObject.m = [] + dataObject.m.append(self.data[rownumber, :]) + elif ('cols' in selection) or ('columns' in selection): + if 'cols' in selection: + key = 'cols' + else: + key = 'columns' + dataObject.x = None + dataObject.y = None + dataObject.m = None + if 'x' in selection[key]: + for rownumber in selection[key]['x']: + if rownumber is None: + continue + if dataObject.x is None: + dataObject.x = [] + dataObject.x.append(self.data[:, rownumber]) + + if 'y' in selection[key]: + for rownumber in selection[key]['y']: + if rownumber is None: + continue + if dataObject.y is None: + dataObject.y = [] + dataObject.y.append(self.data[:, rownumber]) + + if 'm' in selection[key]: + for rownumber in selection[key]['m']: + if rownumber is None: + continue + if dataObject.m is None: + dataObject.m = [] + dataObject.m.append(self.data[:, rownumber]) + if dataObject.x is None: + if 'Channel0' in dataObject.info: + ch0 = int(dataObject.info['Channel0']) + else: + ch0 = 0 + dataObject.x = [numpy.arange(ch0, + ch0 + len(dataObject.y[0])).astype(numpy.float)] + if not ("selectiontype" in dataObject.info): + dataObject.info["selectiontype"] = "%dD" % len(dataObject.y) + return dataObject diff --git a/lbl_ir/lbl_ir/io_tools/Omnic_PyMca5/OmnicMap.py b/lbl_ir/lbl_ir/io_tools/Omnic_PyMca5/OmnicMap.py new file mode 100644 index 0000000..a5bf3b3 --- /dev/null +++ b/lbl_ir/lbl_ir/io_tools/Omnic_PyMca5/OmnicMap.py @@ -0,0 +1,363 @@ +#/*########################################################################## +# +# The PyMca X-Ray Fluorescence Toolkit +# +# Copyright (c) 2004-2014 European Synchrotron Radiation Facility +# +# This file is part of the PyMca X-ray Fluorescence Toolkit developed at +# the ESRF by the Software group. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +# THE SOFTWARE. +# +# Note: Last modified by Liang Chen 5/14/2019 +#############################################################################*/ +__author__ = "V.A. Sole - ESRF Data Analysis" +__contact__ = "sole@esrf.fr" +__license__ = "MIT" +__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" +import os +import sys +import re +import struct +import numpy +import copy + +from lbl_ir.io_tools.Omnic_PyMca5 import DataObject #modified + +DEBUG = 0 +SOURCE_TYPE = "EdfFileStack" + + +class OmnicMap(DataObject.DataObject): + ''' + Class to read OMNIC .map files + + It reads the spectra into a DataObject instance. + This class info member contains all the parsed information. + This class data member contains the map itself as a 3D array. + ''' + def __init__(self, filename): + ''' + Parameters: + ----------- + filename : str + Name of the .map file. + It is expected to work with OMNIC versions 7.x and 8.x + ''' + DataObject.DataObject.__init__(self) + if sys.platform == 'win32' or 1: #modified, "added or 1" + fid = open(filename, 'rb') + else: + fid = open(filename, 'rb') + data = fid.read() + fid.close() + + try: + omnicInfo = self._getOmnicInfo(data) + except: + omnicInfo = None + self.sourceName = [filename] + if sys.version < '3.0': + searchedChain = "Spectrum " + else: + searchedChain = bytes("Spectrum", 'utf-8') + firstByte = data.index(searchedChain) + s = data[firstByte:(firstByte + 100 - 16)] + if sys.version >= '3.0': + s = str(s) + if DEBUG: + print("firstByte = %d" % firstByte) + print("s1 = %s " % s) + exp = re.compile('(-?[0-9]+\.?[0-9]*)') + tmpValues = exp.findall(s) + spectrumIndex = int(tmpValues[0]) + self.nSpectra = int(tmpValues[1]) + if "X = " in s: + xPosition = float(tmpValues[2]) + yPosition = float(tmpValues[3]) + else: + # I have to calculate them from the scan + xPosition, yPosition = self.getPositionFromIndexAndInfo(0, omnicInfo) + if DEBUG: + print("spectrumIndex, nSpectra, xPosition, yPosition = %d %d %f %f" %\ + (spectrumIndex, self.nSpectra, xPosition, yPosition)) + if sys.version < '3.0': + chain = "Spectrum" + else: + chain = bytes("Spectrum", 'utf-8') + secondByte = data[(firstByte + 1):].index(chain) + secondByte += firstByte + 1 + if DEBUG: + print("secondByte = ", secondByte) + self.nChannels = int((secondByte - firstByte - 100) / 4) + if DEBUG: + print("nChannels = %d" % self.nChannels) + self.firstSpectrumOffset = firstByte - 16 + + #fill the header + self.header = [] + oldXPosition = xPosition + oldYPosition = yPosition + self.nRows = 0 + for i in range(self.nSpectra): + offset = int(firstByte + i * (100 + self.nChannels * 4)) + if sys.version < '3.0': + s = data[offset:(offset + 100 - 16)] + else: + s = str(data[offset:(offset + 100 - 16)]) + tmpValues = exp.findall(s) + spectrumIndex = int(tmpValues[0]) + if "X = " in s: + xPosition = float(tmpValues[2]) + yPosition = float(tmpValues[3]) + else: + #I have to calculate them from the scan + xPosition, yPosition = self.getPositionFromIndexAndInfo(i, omnicInfo) + if (abs(yPosition - oldYPosition) > 1.0e-6) and\ + (abs(xPosition - oldXPosition) < 1.0e-6): + break + self.nRows = self.nRows + 1 + if DEBUG: + print("DIMENSIONS X = %f Y=%d" %\ + ((self.nSpectra * 1.0) / self.nRows, self.nRows)) + + #arrange as an EDF Stack + self.info = {} + self.__nFiles = int(self.nSpectra / self.nRows) + self.data = numpy.zeros((self.__nFiles, self.nRows, self.nChannels), + dtype=numpy.float32) + + self.__nImagesPerFile = 1 + offset = firstByte - 16 + 100 # starting position of the data + delta = 100 + self.nChannels * 4 + fmt = "%df" % self.nChannels + for i in range(self.__nFiles): + for j in range(self.nRows): + # this approach is inneficient when compared to a direct + # data readout, but it allows to deal with nan at the source + tmpData = numpy.zeros((self.nChannels,), dtype=numpy.float32) + tmpData[:] = struct.unpack(fmt,\ + data[offset:(offset + delta - 100)]) + finiteData = numpy.isfinite(tmpData) + self.data[i, j, finiteData] = tmpData[finiteData] + offset = int(offset + delta) + shape = self.data.shape + for i in range(len(shape)): + key = 'Dim_%d' % (i + 1,) + self.info[key] = shape[i] + + self.info["SourceType"] = SOURCE_TYPE + self.info["SourceName"] = self.sourceName + self.info["Size"] = self.__nFiles * self.__nImagesPerFile + self.info["NumberOfFiles"] = self.__nFiles * 1 + self.info["FileIndex"] = 0 + self.info["Channel0"] = 0.0 + if omnicInfo is not None: + self.info['McaCalib'] = [omnicInfo['First X value'] * 1.0, + omnicInfo['Data spacing'] * 1.0, + 0.0] + else: + self.info["McaCalib"] = [0.0, 1.0, 0.0] + self.info['OmnicInfo'] = omnicInfo + + def _getOmnicInfo(self, data): + ''' + Parameters: + ----------- + data : The contents of the .map file + + Returns: + -------- + A dictionnary with acquisition information + ''' + #additional information + fmt = "I" # unsigned long in 32-bit + offset = 372 # 93*4 unsigned integers + infoBlockIndex = (struct.unpack(fmt, data[offset:(offset + 4)])[0] - 204) / 4. + infoBlockIndex = int(infoBlockIndex) + #infoblock is the position of the information block + offset = infoBlockIndex * 4 + #read 13 unsigned integers + nValues = 13 + fmt = "%dI" % nValues + values = struct.unpack(fmt, data[offset:(offset + 4 * nValues)]) + ddict = {} + ddict['Number of points'] = values[0] + ddict['Number of scan points'] = values[6] + ddict['Interferogram peak position'] = values[7] + ddict['Number of sample scans'] = values[8] + ddict['Number of FFT points'] = values[10] + ddict['Number of background scans'] = values[12] + offset = (infoBlockIndex + 3) * 4 + nFloats = 47 + fmt = "%df" % nFloats + vFloats = struct.unpack(fmt, data[offset:(offset + 4 * nFloats)]) + lastX = vFloats[0] + firstX = vFloats[1] + ddict['First X value'] = firstX + ddict['Last X value'] = lastX + ddict['Identifier for start indices of spectra'] = vFloats[14] + ddict['Laser frequency'] = vFloats[16] + ddict['Data spacing'] = (lastX - firstX) / (ddict['Number of points'] - 1.0) + ddict['Background gain'] = vFloats[10] + if DEBUG: + for key in ddict.keys(): + print(key, ddict[key]) + ddict.update(self.getMapInformation(data)) + return ddict + + def getMapInformation(self, data): + ''' + Internal method to help finding spectra coordinates + Parameters: + ----------- + data : Contents of the .map file + + Returns: + -------- + Dictionnary with map gemoetrical acquisition parameters + ''' + #look for the chain 'Position' (this is the old version, modified by Liang Chen 3/11/2019) +# if sys.version < '3.0': +# chain = 'Position' +# else: +# chain = bytes('Position', 'utf-8') +# offset = data.index(chain) +# positions = [offset] +# while True: +# try: +# a = data[(offset + 1):].index(chain) +# offset = a + offset + 1 +# positions.append(offset) +# except ValueError: +# break + + #look for the chain 'micrometers' (this is the new version, modified by Liang Chen 3/11/2019) + if sys.version < '3.0': + chain = 'micrometers' + else: + chain = bytes('micrometers', 'utf-8') + offset = data.index(chain) + x0_trial = struct.unpack('f', data[offset - 88 : offset - 84])[0] + + #look for the chain 'Spectrum ' + if sys.version < '3.0': + searchedChain = "Spectrum " + else: + searchedChain = bytes("Spectrum", 'utf-8') + firstSpectrum = data.index(searchedChain) + s = str(data[firstSpectrum : (firstSpectrum + 100 - 16)]) + exp = re.compile('(-?[0-9]+\.?[0-9]*)') + tmpValues = exp.findall(s) + x0 = float(tmpValues[2]) + + #look for the chain 'GMT' + if sys.version < '3.0': + chain = "GMT" + else: + chain = bytes("GMT", 'utf-8') + offset_year = data.index(chain) + year_collected = int(data[offset_year - 6 :offset_year - 2].decode('utf-8')) + ddict = {} + #map description position (this is the old version, modified by Liang Chen 3/11/2019) +# if (positions[1] - positions[0]) == 66: # reverse engineered magic number :-) +# mapDescriptionOffset = positions[0] - 90 +# mapDescription = struct.unpack('6f', data[mapDescriptionOffset:mapDescriptionOffset + 24]) +# y0, y1, deltaY, x0, x1, deltaX = mapDescription +# ddict['First map location'] = [x0, y0] +# ddict['Last map location'] = [x1, y1] +# ddict['Mapping stage X step size'] = deltaX +# ddict['Mapping stage Y step size'] = deltaY +# ddict['Number of spectra'] = abs((1 + ((y1 - y0) / deltaY)) * (1 + ((x1 - x0) / deltaX))) + + #map description position (this is the new version, modified by Liang Chen 5/14/2019) + if (round(x0_trial) == round(x0)) or (year_collected >= 2018): # if map was collected in 2018 or later, use default: offset - 100 + mapDescriptionOffset = offset - 100 + else: + mapDescriptionOffset = offset - 96 + + mapDescription = struct.unpack('6f', data[mapDescriptionOffset:mapDescriptionOffset + 24]) + y0, y1, deltaY, x0, x1, deltaX = mapDescription + ddict['First map location'] = [x0, y0] + ddict['Last map location'] = [x1, y1] + ddict['Mapping stage X step size'] = deltaX + ddict['Mapping stage Y step size'] = deltaY + ddict['Mapping stage parameters'] = [x0, y0, deltaX, deltaY] + ddict['Number of spectra'] = round(abs((1 + ((y1 - y0) / deltaY)) * (1 + ((x1 - x0) / deltaX)))) + + if DEBUG: + for key in ddict.keys(): + print(key, ddict[key]) + return ddict + + def getOmnicInfo(self): + """ + Returns a dictionnary with the parsed OMNIC information + """ + return copy.deepcopy(self.info['OmnicInfo']) + + def getPositionFromIndexAndInfo(self, index, info=None): + ''' + Internal method to obtain the position at which a spectrum + was acquired + Parameters: + ----------- + index : int + Index of spectrum + info : Dictionnary + Information recovered with _getOmnicInfo + Returns: + -------- + x, y : floats + Position at which the spectrum was acquired. + ''' + if info is None: + return 0.0, 0.0 + ddict = info + #first variation on X and then on Y + try: + x0, y0 = ddict['First map location'] + except KeyError: + return 0.0, 0.0 + x1, y1 = ddict['Last map location'] + deltaX = ddict['Mapping stage X step size'] + deltaY = ddict['Mapping stage Y step size'] + nX = round(1 + ((x1 - x0) / deltaX)) + x = x0 + (index % nX) * deltaX + y = y0 + int(index / nX) * deltaY + return x, y + +if __name__ == "__main__": + filename = None + if len(sys.argv) > 2: + DEBUG = int(sys.argv[2]) + if len(sys.argv) > 1: + filename = sys.argv[1] + elif os.path.exists("SambaPhg_IR.map"): + filename = "SambaPhg_IR.map" + if filename is not None: + w = OmnicMap(filename) + print(type(w)) + print(type(w.data[0:10])) + print(w.data[0:10]) + print("shape = ", w.data.shape) + print(type(w.info)) + print("INFO = ", w.info['OmnicInfo']) + else: + print("Please supply input filename") diff --git a/lbl_ir/lbl_ir/io_tools/Omnic_PyMca5/__init__.py b/lbl_ir/lbl_ir/io_tools/Omnic_PyMca5/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lbl_ir/lbl_ir/io_tools/__init__.py b/lbl_ir/lbl_ir/io_tools/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lbl_ir/lbl_ir/io_tools/basic_parser.py b/lbl_ir/lbl_ir/io_tools/basic_parser.py new file mode 100644 index 0000000..e61a347 --- /dev/null +++ b/lbl_ir/lbl_ir/io_tools/basic_parser.py @@ -0,0 +1,142 @@ +import configparser +import sys +import ast +import io + +""" +I put the output from configparser through some helper classes and create objects +with names and key / value pairs as laid out in the input file. +I find this a lot easier to work with then the raw config parser objects. + +""" + +class section_object(object): + def __init__(self, items): + for item in items: + key = item[0] + val = item[1].strip() + try: + val = ast.literal_eval(val.strip()) + except: pass + setattr(self, key, val) + + def as_txt(self): + txt = """""" + keys = [] + for key in self.__dict__.keys(): + if '__' not in key: + keys.append(key) + keys.sort() + for key in keys: + txt += key+'='+str(self.__dict__[key])+' \n' + return txt + + +class config_object(object): + def __init__(self, config): + for section in config.sections(): + items = config.items(section) + this_section_object = section_object(items) + setattr(self, section, this_section_object ) + + def as_txt(self): + txt = """""" + keys = [] + for key in self.__dict__.keys(): + if '__' not in key: + keys.append(key) + for key in keys: + txt+='[%s]\n'%key + txt+=self.__dict__[key].as_txt()+' \n' + return txt + + def show(self,f=None): + if f is None: + f = sys.stdout + print(self.as_txt(), file=f) + + +def read_and_parse(inputs,defaults=None): + # make config object + config = configparser.ConfigParser() + + # not sure how the defaults work + if defaults is not None: + default_config = None + if type(defaults) is str: + default_config = configparser.ConfigParser() + default_config.read_string( defaults ) + #default_config.readfp(io.BytesIO(defaults)) + else: + default_config = configparser.ConfigParser() + default_config.read_file(defaults) + # if this aint a config object, we're f-ed anyway + config._sections = default_config._sections + + if inputs is None: + co = config_object( default_config ) + co.show() + raise SystemExit('##--- No inputs provide, use template shown above ---##') + + + if type(inputs) is type(""""""): + config.read_string(inputs) #inputs = inputs + else: + # update the config object accoring to the specified input + config.read_file(inputs) + + # build the object. + co = config_object( config ) + return co + +def tst(): + default_instructions = """ +[data] +experiment = None +run = None +index_start = 0 +index_stop = 500 +index_stride = 1 + +[output] +filename = output.h5 +comments = "No comments" +""" + + instructions = """ +[data] +experiment = amox26916 +run = 56 +index_start = 0 +index_stop = 5000 + +[output] +filename = output_59.h5 +comments = "No soup for you!" +ooops = 9 +""" + + obj = read_and_parse(instructions, default_instructions) + + instructions=""" +[stuff] +coordinates = (1,4) +""" + obj = read_and_parse(instructions) + assert obj.stuff.coordinates[0] == 1 + assert obj.stuff.coordinates[1] == 4 + obj.show() + + can_we_do_comments = """ +[breakfast] +eggs = True +bacon = True +spam = False # i don't like spam +""" + obj = read_and_parse(can_we_do_comments) + obj.show() + print('OK') + + +if __name__ == "__main__": + tst() diff --git a/lbl_ir/lbl_ir/io_tools/map_IO.py b/lbl_ir/lbl_ir/io_tools/map_IO.py new file mode 100644 index 0000000..4b588b8 --- /dev/null +++ b/lbl_ir/lbl_ir/io_tools/map_IO.py @@ -0,0 +1,249 @@ +""" +Created on Tue Dec 18 16:14:43 2018 + +@author: Liang Chen +""" + +import spectral.io.envi as envi +import numpy as np +import re + + +def read_envi(hdr_file): + """Load an ENVI map from the .hdr header file + + Parameters: + ----------- + hdr_file: string + file path for a .hdr file + + Returns: + -------- + out : object + a spectral.io.bipfile.BipFile object + + Examples: + --------- + >>> hdr_file = os.path.join(test_data_home, 'test_envi.hdr') + >>> img = envi.open(hdr_file) + (17, 32, 1738) + """ + + # read in parameter list in the header file + params = [] + + with open(hdr_file, 'r', encoding='utf-8', errors='ignore') as header: + for line in header: + params.append(line.split('=')[0].strip()) + + # if parameter 'byte order' is not found, append 'byte order = 0' to the header file + if 'byte order' not in params: + with open(hdr_file, 'a', encoding='utf-8', errors='ignore') as header: + header.write('byte order = 0\n') + + img = envi.open(hdr_file) + + return img + + +def read_binary(fileObj, byteType='uint8', size=1): + """A helper function to readin values from a binary file + + Parameters: + ----------- + fileObj : object + a binary file object + + byteType : string, optional, default 'uint8' + the type of readin values + + size : int, optional + the number of bytes to readin. Default is 1. + + Returns: + -------- + out : a value or a tuple of values or a string + the readout value from the bytes + """ + import struct + + typeNames = { + 'int8': ('b', struct.calcsize('b')), + 'uint8': ('B', struct.calcsize('B')), + 'int16': ('h', struct.calcsize('h')), + 'uint16': ('H', struct.calcsize('H')), + 'int32': ('i', struct.calcsize('i')), + 'uint32': ('I', struct.calcsize('I')), + 'int64': ('q', struct.calcsize('q')), + 'uint64': ('Q', struct.calcsize('Q')), + 'float': ('f', struct.calcsize('f')), + 'double': ('d', struct.calcsize('d')), + 'char': ('s', struct.calcsize('s'))} + + if size == 1: + return struct.unpack(typeNames[byteType][0], fileObj.read(typeNames[byteType][1]))[0] + elif size > 1: + return struct.unpack(typeNames[byteType][0] * size, fileObj.read(typeNames[byteType][1] * size)) + else: + return None + + +def read_spa(spa_file): + """Load a spectrum from a .spa file + + Parameters: + ----------- + spa_file: string + file path for a .spa file + + Returns: + -------- + wavenumbers : float array + the wavenumber values of the spectrum (x-axis) + + spectrum : float array + the transmission/reflection/absorption coefficient + at wavenumber of the spectrum (y-axis) + + title : string + the title of the spectrum + + comment : string + the comment section of the spectrum, if it exists + + Examples: + --------- + >>> spa_file = os.path.join(test_data_home, 'test_data0001.spa') + >>> wavenumbers, spectrum, title, comment = read_spa(spa_file) + >>> print(title) + C:\\Users\\lchen43\\Documents\\CDIPS_2017\\lbl-ir\\test_data\\test_data.map - Spectrum #1 + Position (X,Y): 499.51, 5715.69 Thu Apr 22 13:57:44 2010 (GMT-07:00) + >>> print(comment) + Split map from: C:\\Users\\lchen43\\Documents\\CDIPS_2017\\lbl-ir\\test_data\\test_data.map + X Range: 499.51, 654.51 + Y Range: 5715.69, 5795.69 + Position (X,Y): 499.51, 5715.69 + Thu Apr 22 13:57:44 2010 (GMT-07:00) + """ + + with open(spa_file, 'rb') as f: + f.seek(30) + readChar = read_binary(f, 'uint8', 255) + title = ''.join([chr(i) for i in filter(lambda x: x > 0, readChar)]) + + f.seek(564) + spectrumPts = read_binary(f, 'int32') + + f.seek(576) + maxWavenum = read_binary(f, 'float') + minWavenum = read_binary(f, 'float') + wavenumbers = np.linspace(maxWavenum, minWavenum, spectrumPts) + + # The starting byte location of the spectrum data is stored in the + # header. It immediately follows a flag value of 3. + # If there is a comment section, the flag value is 27 and it should be in front of the flag value of 3. + f.seek(338) + Flag = 0 + while (Flag != 3 and Flag != 27): + Flag = read_binary(f, 'uint16') + + if Flag == 3: # no comment section, look for data starting position + dataPosition = read_binary(f, 'uint16') + f.seek(dataPosition) + spectrum = read_binary(f, 'float', spectrumPts) + comment = '' + elif Flag == 27: # there is a comment section, look for comment starting position + commentPosition = read_binary(f, 'uint16') + # move forward 14 bytes, that's the data starting position + f.seek(14, 1) + dataPosition = read_binary(f, 'uint16') + commentLength = dataPosition - commentPosition + + f.seek(commentPosition) + readChar = read_binary(f, 'uint8', commentLength) + comment = ''.join([chr(i) + for i in filter(lambda x: x > 0, readChar)]) + f.seek(dataPosition) + spectrum = read_binary(f, 'float', spectrumPts) + else: + raise Exception('The flag value of 3 or 27 cannot be found') + + spectrum = np.array(spectrum) + return wavenumbers, spectrum, title, comment + + +def read_series(file_name, wavLen=1738): + """ + read Ominc series map + :param file_name: path of minc series map + :param wavLen: the length of the wavenumbers vector + :return: + wav: the wavenumbers vector + spectra: all spectra in the series map + xy: all xy coordinate in the series map + """ + + wav = spectra = xy = None + + with open(file_name, 'rb') as fid: + data = fid.read() + # read out firstWav, lastWav, construct wav + fid.seek(0) + s = [] + for i in range(1000): + s.append((read_binary(fid, 'uint32'))) + offset = s.index(wavLen) * 4 + fid.seek(offset + 12) + val = read_binary(fid, 'float', 2) + firstWav, lastWav = val[1], val[0] + wav = np.linspace(firstWav, lastWav, wavLen) + + # read out num of spectra + Chain = bytes('Spectrum', 'utf-8') + firstByte = data.index(Chain) + s1 = str(data[firstByte:(firstByte + 100 - 16)]) + exp = re.compile('(-?[0-9]+\.?[0-9]*)') + tmpValues = exp.findall(s1) + nSpectra = int(tmpValues[1]) + + # read out all spectra + spectra = np.zeros((nSpectra, wavLen)) + delta = wavLen * 4 + 96 + for i in range(nSpectra): + fid.seek(firstByte + delta * i + 80) + spectra[i, :] = read_binary(fid, 'float', wavLen) + + # find xy positions + chain = bytes('Position', 'utf-8') + firstPos = data.index(chain) + secondPos = data[(firstPos + 1):].index(chain) + secondPos += firstPos + 1 + fid.seek(secondPos + 48) + val = read_binary(fid, 'float', 2 * nSpectra) + xy = np.zeros((nSpectra, 2)) + xy[:, 0], xy[:, 1] = val[::2], val[1::2] + + return wav, spectra, xy + + +if __name__ == "__main__": + import os + + test_data_home = '../../test_irdata/' + + hdr_file = os.path.join(test_data_home, 'test_envi.hdr') + img = read_envi(hdr_file) + print('====Envi file====') + print(img.shape) + + spa_file = os.path.join(test_data_home, 'test_data0001.spa') + wavenumbers, spectrum, title, comment = read_spa(spa_file) + print('====' + title + '====') + print(comment) + + map_file = os.path.join(test_data_home, 'typeII-010_12x9.map') + wavenumbers, spectra, xy = read_series(map_file) + print('====Series map====') + print(spectra.shape) + print(xy[:3,:]) + print(spectra[:4, -3:]) diff --git a/lbl_ir/lbl_ir/io_tools/read_XAS.py b/lbl_ir/lbl_ir/io_tools/read_XAS.py new file mode 100644 index 0000000..71e7aec --- /dev/null +++ b/lbl_ir/lbl_ir/io_tools/read_XAS.py @@ -0,0 +1,54 @@ +import os +import numpy as np +import h5py +from lbl_ir.data_objects import ir_map + + +def get_grid_info(coords): + xsorted = sorted(set(coords[:,0])) + ysorted = sorted(set(coords[:,1])) + x0, xmax, y0, ymax = xsorted[0], xsorted[-1], ysorted[0], ysorted[-1] + dx = np.mean(np.diff(xsorted)) + dy = np.mean(np.diff(ysorted)) + if dx > dy: + step = dy / 2 + else: + step = dx / 2 + Nx = int(round((xmax - x0)/step) + 1) + Ny = int(round((ymax - y0)/step) + 1) + return x0, y0, step, Nx, Ny + +def read_xasH5(filePath): + xasTypes = [] + energy = {} + dataSets = {} + coords = {} + xas_maps = {} + + with h5py.File(filePath, 'r') as f: + xasSpectra = f['xas/'] + for k in xasSpectra: + xasTypes.append(k) + for k1 in xasSpectra[k]: + k1 = '/' + k1 + spectra = xasSpectra[k + k1 + '/raw'][:, :, :] + energy[k] = spectra[0, 0, :] + dataSets[k] = spectra[:, 1, :] + dataSets[k] = np.where(dataSets[k] != np.inf, dataSets[k], 0) #filter np.inf + + samples = f['maps/samples/'] + for i, k in enumerate(samples): + coords[xasTypes[i]] = samples[k + '/xas_coords'][:, :] + + for _type in xasTypes: + assert coords[_type].shape[0] == dataSets[_type].shape[0], 'xas and coords sample sequences were mis-aligned.' + + fileName = os.path.basename(filePath) + sample_info = ir_map.sample_info(fileName[:-3]) + xas_maps[_type] = ir_map.ir_map(wavenumbers=energy[_type], sample_info=sample_info) + xas_maps[_type].add_data(spectrum=dataSets[_type], xy=coords[_type]) + x0, y0, step, Nx, Ny = get_grid_info(coords[_type]) + xas_maps[_type].to_image_cube(Nx, Ny, x0, y0, step, step) + + return xas_maps + diff --git a/lbl_ir/lbl_ir/io_tools/read_map.py b/lbl_ir/lbl_ir/io_tools/read_map.py new file mode 100644 index 0000000..0c74394 --- /dev/null +++ b/lbl_ir/lbl_ir/io_tools/read_map.py @@ -0,0 +1,47 @@ +import h5py +import sys + +from lbl_ir.io_tools import read_omnic +from lbl_ir.data_objects import ir_map + +def read_all_formats(filename, sample_info=None): + + ok = False + format = None + if not ok: + try: + data = read_omnic.read_and_convert(filename, sample_info=None) + ok= True + format = "Omnic" + except: pass + + if not ok: + try: + data = ir_map.ir_map(filename=filename) + ok = True + format = "hdf5" + + with h5py.File(filename,'r') as f: + root_name = list(f.keys())[0] + # if there is an image group, load imagecube and data, otherwise load data group + if 'image' in f[root_name + '/data']: + data.add_image_cube() + else: + data.add_data() + # if there is an factorization group, load factorization data + if 'factorization' in f[root_name + '/data']: + data.add_factorization() + except: pass + + + + if not ok: + print("Could not read file; Check input or file formats and header integrity.") + else: + return data, format + + + +if __name__ == "__main__": + data,fmt = read_all_formats(sys.argv[1]) + print("Data read in with format", fmt) diff --git a/lbl_ir/lbl_ir/io_tools/read_numpy.py b/lbl_ir/lbl_ir/io_tools/read_numpy.py new file mode 100644 index 0000000..d6d8c34 --- /dev/null +++ b/lbl_ir/lbl_ir/io_tools/read_numpy.py @@ -0,0 +1,42 @@ +import numpy as np +from lbl_ir.data_objects import ir_map + + +def read_npy(filename, wavenumbers=None, data_type="absorbance", sample_info=None): + if sample_info is None: + sample_info = ir_map.sample_info() + + data = np.load(filename) + + if wavenumbers is None: + wavenumbers = np.arange(data.shape[2]) + + image_grid_param = [0, 0, 1, 1] + image_mask = np.ones(data.shape[0:2]) > 0.5 + + this_ir_map = ir_map.ir_map(wavenumbers=wavenumbers, + sample_info=sample_info, + data_type=data_type) + this_ir_map.add_image_cube(data, image_mask, image_grid_param) + return this_ir_map + +def read_npz(filename, data_type="absorbance", sample_info=None): + if sample_info is None: + sample_info = ir_map.sample_info() + + npzFile = np.load(filename) + + for k in npzFile.files: + if 'energy' in k: + wavenumbers = npzFile[k] + else: + data = npzFile[k] + + image_grid_param = [0, 0, 1, 1] + image_mask = np.ones(data.shape[0:2]) > 0.5 + + this_ir_map = ir_map.ir_map(wavenumbers=wavenumbers, + sample_info=sample_info, + data_type=data_type) + this_ir_map.add_image_cube(data, image_mask, image_grid_param) + return this_ir_map \ No newline at end of file diff --git a/lbl_ir/lbl_ir/io_tools/read_omnic.py b/lbl_ir/lbl_ir/io_tools/read_omnic.py new file mode 100644 index 0000000..e64f649 --- /dev/null +++ b/lbl_ir/lbl_ir/io_tools/read_omnic.py @@ -0,0 +1,51 @@ +import numpy as np +import sys + +from lbl_ir.io_tools.Omnic_PyMca5 import OmnicMap +from lbl_ir.data_objects import ir_map + + +def read_and_convert(filename, start_wav=None, stop_wav=None, data_type="absorbance", sample_info=None): + if sample_info is None: + sample_info = ir_map.sample_info() + + omnic_object = OmnicMap.OmnicMap( filename ) + wavenumbers = None + if wavenumbers is None: + n_wav = omnic_object.data.shape[2] + if (start_wav is None) or ( stop_wav is None) : + if omnic_object.info['OmnicInfo'] is not None: + start_wav = omnic_object.info['OmnicInfo']['First X value'] + stop_wav = omnic_object.info['OmnicInfo']['Last X value'] + n_wav = omnic_object.info['OmnicInfo']['Number of points'] + else: + raise ValueError('There is an issue with the Ominc file or its parser. Make sure that the meta info from the header is present and parsed correctly.') + + wavenumbers = np.linspace( start_wav, stop_wav, n_wav ) + image_grid_param = omnic_object.info['OmnicInfo']['Mapping stage parameters'] + image_mask = np.ones( omnic_object.data.shape[0:2]) > 0.5 + + # build the basis object + this_ir_map = ir_map.ir_map( wavenumbers = wavenumbers, + sample_info = sample_info, + data_type = data_type + ) + this_ir_map.add_image_cube(omnic_object.data, image_mask, image_grid_param) + return this_ir_map + +if __name__ == "__main__": + + map_name = '190519_N2_L2w1_mp2' + '.map' + file_name = '../../ir_data/' + map_name + sample_id = file_name + sample_id = sample_id.replace("../","") + sample_id = sample_id.replace(".map","") + sample_id = sample_id.replace("/","_") + sample_id = sample_id.replace(" ","_") + print(sample_id) + + sample_info = ir_map.sample_info( sample_id = sample_id, sample_meta_data='Hello World') + data = read_and_convert(file_name, sample_info=sample_info) + data.write_as_hdf5('../../ir_data/' + map_name + '.h5') + + diff --git a/lbl_ir/lbl_ir/math_tools/__init__.py b/lbl_ir/lbl_ir/math_tools/__init__.py new file mode 100644 index 0000000..b28b04f --- /dev/null +++ b/lbl_ir/lbl_ir/math_tools/__init__.py @@ -0,0 +1,3 @@ + + + diff --git a/lbl_ir/lbl_ir/math_tools/batched_SVD.py b/lbl_ir/lbl_ir/math_tools/batched_SVD.py new file mode 100644 index 0000000..70ca0f4 --- /dev/null +++ b/lbl_ir/lbl_ir/math_tools/batched_SVD.py @@ -0,0 +1,467 @@ +from scipy.sparse.linalg import svds, eigsh +from scipy.linalg.interpolative import svd as isvd + + +import os +import numpy as np +import matplotlib.pyplot as plt +import time + +from mpi4py import MPI +import h5py + +from tqdm import tqdm + + +def test_function( N, M , noise=0.001): + """ + Generate some test data we can use. The resulting matrix should have a rank of 4. + """ + + x = np.linspace(-1,1,M) + p0 = x*0+1.0 + p1 = x + p2 = 0.5*(3*x*x-1) + p3 = 0.5*(5*x*x*x-3*x) + + result = [] + for ii in range(N): + tmp = np.random.uniform(-1,1,4) + tmp = tmp[0]*p0 + tmp[1]*p1 + tmp[2]*p2 + tmp[3]*p3 + tmp = tmp + np.random.normal(0,1.0,M)*noise + result.append(tmp) + result = np.vstack(result) + + return result + +def invert_permutation(p): + """Given a permutation, provide an array that undoes the permutation. + """ + s = np.empty(p.size, p.dtype) + s[p] = np.arange(p.size) + return s + + +class batched_SVD(object): + """Compute an SVD of all data, but in small batches to overcome memory issues. + + Parameters: + ---------- + + data: A pointer to a data object, say a N,M matrix. + N experimental observations of dimension M + + N_max: The maximum number of entries in sub block of data + + k_singular: The number of singular values to consider + + randomize : A flag which determines if the data will be split in a random fashion. True by default + + + Attributes: + ----------- + self.order : The order in which the data will be examined + + self.inv_order : The inverse of the above array + + self.N_split : The number of batches of data + + self.parts : A list of selection arrays + + self.partial_svd_u : A list of (truncated) svd matrices (U) from individual batches of data + + self.partial_svd_s : A list of (truncated) svd matrices (S) from individual batches of data + + self.partial_svd_vt : A list of (truncated) svd matrices (V^T) from individual batches of data + + self.partial_bases : A list of (truncated) svd matrices (SV^T) from individual batches of data + + + Examples: + --------- + + data = test_function(10000,3000,1.1) + bSVD = batched_SVD(data, 2000, 5, randomize=True) + u,s,vt = bSVD.go_svd() + + """ + + def __init__(self, data, N_max, k_singular, randomize=True): + self.data = data + self.N_max = N_max + self.k_singular = k_singular + + self.randomize = randomize + + self.order = np.arange( self.data.shape[0] ) + if self.randomize: + np.random.shuffle( self.order ) + + self.N_split = int( np.floor(self.data.shape[0] / self.N_max) ) +1 + self.parts = np.array_split( self.order, self.N_split) + + # if we have these numbers in order, we can use them as slices in a hdf5 setting + # this is only requiered when we randomize the lot + if self.randomize: + tmp = [] + for part in self.parts: + part = np.sort(part) + tmp.append(part) + self.parts = tmp + self.order = np.concatenate(self.parts) + self.inv_order = invert_permutation(self.order) + + # here the partial svds are stored + self.partial_svd_u = [] + self.partial_svd_s = [] + self.partial_svd_vt = [] + self.partial_bases = [] + + + def SVD_on_chunk(self, this_chunk): + """Perform an SVD on a subset of the data + + Parameters: + ----------- + + this_chunk: a list of indices that maps back to the self.data array + """ + + tmp_data = self.data[ this_chunk, : ] + + u,s,v = isvd(tmp_data.astype('float64'), self.k_singular) + vt = v.transpose() + self.partial_svd_u.append( u ) + self.partial_svd_s.append( s ) + self.partial_svd_vt.append( vt ) + self.partial_bases.append( np.diag(s).dot(vt) ) + + + def get_u_given_basis(self, sigma, v_transpose, this_chunk): + inv_bases = v_transpose.transpose().dot( np.diag( 1.0 / sigma ) ) + tmp_data = self.data[ this_chunk, : ] + new_u = tmp_data.dot( inv_bases ) + return new_u + + def go_svd(self): + """ Seperate SVD's for individual data chunks are computed and combined into a single, best estimate + of the matrix S and V^T. The data is subsequently revisited to get a new estimate of the matrix U. + + The estimate of U on the basis of the chunked SVD approach is possible, but not as accurate. + """ + + for this_chunk in self.parts: + self.SVD_on_chunk( this_chunk ) + + # do an SVD on the SVD results of the individual chunks. + tmp_bases = np.vstack( self.partial_bases ) + ub,sb,vb = isvd(tmp_bases.astype('float64'), self.k_singular) + vbt = vb.transpose() + ubs = np.array_split(ub,self.N_split) + new_s = sb + new_vt = vbt + + # revisit the data to get the U matrix again + new_us = [] + for part in self.parts: + this_new_u = self.get_u_given_basis(new_s, new_vt, part) + new_us.append(this_new_u) + + # stack it up and unmix the data + new_us = np.vstack(new_us) + if self.randomize: + new_us = new_us[ self.inv_order ] + return new_us, sb, vbt + + +class parallel_SVD_MPI(object): + """ + THSI NEEDS WORK!!! + An MPI based version of an SVD method, splitting the data among a number of cores. + The outline is a bit different than the batched_SVD version. + + The idea is to first split all data equally across all cores. In each core, we compute + the an SVD using the procedure coded up for the batched_SVD approach, and end up with a + SVD estimate on each core. We subsequently pool all these SVD's and reestimate it on + the root node, and scatter these functions back to all subsequent nodes. Subsequently, + we need to compute U on on each node for all data associated with this node. The final + results are stored in a pointer, which is either a hdf5 file or a numpy array. + + + Parameters: + ---------- + + data: A pointer to a data object, say a N,M matrix. + N experimental observations of dimension M + + N_max: The maximum number of entries in sub block of data + + k_singular: The number of singular values to consider + + MPI_COMM_WORLD: an instance of MPI.COMM_WORLD + + randomize : A flag which determines if the data will be split in a random fashion. True by default + + Attributes: + ----------- + + go_svd() : this function performs the svd and returns the SVD results for each single core, including + an array that allows one to map the data back to the original order in which it was presented. + + Example: + -------- + + + + + + """ + + + def __init__(self, data, N_max, k_singular, MPI_COMM_WORLD, randomize=True, selection = None ): + # randomize the order in which we analyze the data + self.randomize = randomize + + # sort out MPI stuff + self.mpi_comm = MPI_COMM_WORLD + self.mpi_rank = self.mpi_comm.Get_rank() + self.mpi_size = self.mpi_comm.Get_size() + + # data etc + self.data = data # the data + self.k_singular = k_singular # the number of singular vectors + self.N_max = N_max # the maximum number of data points per chunk per core + + # split the data amond cores + self.N_obs, self.N_dim = self.data.shape + if selection is not None: + self.Nobs = len(selection) + self.order = np.arange(self.N_obs) + self.selection = selection + if self.selection is not None: + self.order = self.order[selection] + self.inv_order = None + + if self.randomize: + np.random.shuffle( self.order ) + self.rank_splits = np.array_split( self.order, self.mpi_size ) + + if self.randomize: + tmp = [] + for part in self.rank_splits: + part = np.sort(part) # we want this ordered from low to high to enable slicing an hdf5 array + tmp.append(part) + self.rank_splits = tmp + self.order = np.concatenate(self.rank_splits) + self.inv_order = invert_permutation(self.order) + self.inv_order_split = np.array_split( self.inv_order, self.mpi_size ) + self.mpi_comm.Barrier() + + def go_svd(self): + """Do the SVD ihn each core, on several chunks. + """ + + partial_u = [] + partial_s = [] + partial_vt = [] + partial_bases = [] + + rank_selection = self.rank_splits[ self.mpi_rank ] + N_chunks = int( len(rank_selection) / self.N_max ) + 1 + chunks = np.array_split( rank_selection, N_chunks ) + u = None + s = None + vt = None + this_rank_bases = None + for chunk in tqdm(chunks, position=self.mpi_rank): + partial_data = self.data[ chunk, : ] + u,s,vt = isvd(partial_data.astype('float64'), self.k_singular) + partial_u.append( u ) + partial_s.append( s ) + partial_vt.append( vt ) + + partial_bases.append( np.diag(s).dot(vt) ) + # now that we have the partial svd results, we bnring stuff together + if N_chunks > 1: + all_bases = np.vstack( partial_bases ) + uc,sc,vct = isvd(all_bases.astype('float64'), self.k_singular) + # now we need to pass this guy to the main rank + this_rank_bases = np.diag(sc).dot(vct) + else: + this_rank_bases = np.diag(s).dot(vt) + self.mpi_comm.Barrier() + + + gathered_bases = None + if self.mpi_rank == 0: + gathered_bases = np.empty( [self.mpi_size, self.k_singular, self.N_dim], dtype='d' ) + gathered_bases = self.mpi_comm.gather( this_rank_bases, root=0 ) + + final_sg = np.zeros( [ self.k_singular ], dtype='float32' ) + final_vgt = np.zeros( [ self.k_singular, self.N_dim], dtype='float32' ) + + if self.mpi_rank == 0: + gathered_bases = np.vstack( gathered_bases ) + ug,final_sg,final_vgt = isvd(gathered_bases.astype('float64'), self.k_singular) + # we now need to scatter back the sg and vgt matrices + self.mpi_comm.Barrier() + self.mpi_comm.Bcast( final_sg, root=0 ) + self.mpi_comm.Barrier() + self.mpi_comm.Bcast( final_vgt , root=0 ) + self.mpi_comm.Barrier() + # now that we have the final and best sigma and Vt, we need to go back to the data and + # reestimate the the U matrices + inv_multi = final_vgt.transpose().dot( np.diag(final_sg) ) + chunks_of_u = [] + for chunk in chunks: + partial_data = self.data[ chunk, : ] + this_u = partial_data.dot( inv_multi ) + chunks_of_u.append( this_u) + + chunks_of_u = np.vstack( chunks_of_u ) + # we need to return this, including a placement array + return chunks_of_u, final_sg, final_vgt, self.inv_order_split + + + + + + +def tst_batched(): + N = 10000 + M = 2000 + K = 200 + P = 4 + + data = test_function(N,M,0.0001) + print("Data constructed") + e0 = time.time() + u,s,vt = isvd(data.astype('float64'), P) + vt = vt.transpose() + e1 = time.time() + + bSVD = batched_SVD(data, K, P, randomize=False) + e4 = time.time() + us, ss, vst = bSVD.go_svd() + e5 = time.time() + + assert np.std( (s-ss)/ss ) < 1e-3 + print("Singular values match between batch and full approach") + + # checking the reconstructions + da = us.dot(np.diag(ss).dot( vst )) + dc = u.dot(np.diag(s).dot( vt )) + delta_both = np.std( (da-dc) ) + assert np.abs( delta_both ) < 1e-2 + + + print('Reconstruction Error is similar in batched versus full approach') + print ('Time for full: %4.2f batched: %4.2f'%(e1-e0, e5-e4)) + + # Now we want to do this using a random order or data + + bSVD = batched_SVD(data, K, P, randomize=True) + e4 = time.time() + us, ss, vst = bSVD.go_svd() + e5 = time.time() + + assert np.std( (s-ss)/s ) < 1e-3 + print("Singular values match between randomized batch and full approach") + delta_both = np.std( (dc - da) ) + assert np.abs( delta_both ) < 1e-2 + print('Reconstruction Error is decent') + print ('Time for full: %4.2f batched: %4.2f'%(e1-e0, e5-e4)) + + +def tst_MPI(): + N = 10000 # number of observations + M = 2000 # dimension of an observations + P = 4 # number of singular values + + # make the data + mpi_comm = MPI.COMM_WORLD + if mpi_comm.Get_rank() == 0: + data = test_function(N,M,0.0001) + + e0 = time.time() + u,s,vt = isvd(data.astype('float64'),P) + e1 = time.time() + print('Standard svd takes %12.3f seconds'%(e1-e0)) + print('\n') + + e0 = time.time() + # lets quickly see if this works in the batched setup + bSVD = batched_SVD(data, 1000, P, randomize=True) + ub, sb, vbt = bSVD.go_svd() + e1 = time.time() + print('Single core batched version with data in memory takes %12.3f seconds'%(e1-e0) ) + + + # write this to an hdf5 file + print('Creating h5 data file') + f = h5py.File('test_data.h5','w') + dset = f.create_dataset("data", data=data, dtype='float32') + f.close() + del data + + # now we read the data + f = h5py.File('test_data.h5','r') + data = f['/data'] + print(data.shape) + e0 = time.time() + # lets quickly see if this works in the batched setup + bSVD = batched_SVD(data, 1000, P, randomize=False) + ub, sb, vbt = bSVD.go_svd() + e1 = time.time() + print('Single core batched version while reading data from HDF5 takes %12.3f seconds'%( e1-e0 ) ) + print(sb) + f.close() + + mpi_comm.Barrier() + f = h5py.File('test_data.h5','r' ) # lets see if we can get away with this + data = f['data'] + e2 = time.time() + print('build it') + mSVD = parallel_SVD_MPI( data, 1000, P, mpi_comm, False, None) + print('go') + u,s,v,sel = mSVD.go_svd() + e3 = time.time() + print('Core %i with batches version takes %12.3f seconds, while reading data from hdf5'%( mpi_comm.rank , e3-e2) ) + print(s, mpi_comm.rank ) + + #lets read the data into memory + print('Reading data into memory') + data2 = f['data'].value #[:,:] + print(data2.shape) + print( 'done') + + mpi_comm.Barrier() + e2 = time.time() + mSVD = parallel_SVD_MPI( data2, 500, P, mpi_comm, True) + u,s,v,sel = mSVD.go_svd() + e3 = time.time() + print('Core %i with batches version takes %12.3f seconds, with data in memory'%(mpi_comm.rank , e3-e2) ) + + mpi_comm.Barrier() + selection = np.arange(1000) + print('Testing setup with an included selection array') + data2[1000,:]=0. + mSVD = parallel_SVD_MPI( data2, 50000, P, mpi_comm, False, selection = selection) + SVDs = batched_SVD.batched_SVD( ) + up,sp,vp, order_split = mSVD.go_svd() + uf,sf,vf = isvd(data2[0:1000,:].astype('float64'), P) + assert np.mean( ( np.abs(sp-sf)/sf ) ) < 2e-2 + + mpi_comm.Barrier() + if mpi_comm.Get_rank() == 0: + # now remove this file again + print('Removing h5 data file') + os.remove('test_data.h5') + # done + + + + + +if __name__ == "__main__": + tst_batched() diff --git a/lbl_ir/lbl_ir/simulations/__init__.py b/lbl_ir/lbl_ir/simulations/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lbl_ir/lbl_ir/simulations/spectra_map_simulator.py b/lbl_ir/lbl_ir/simulations/spectra_map_simulator.py new file mode 100644 index 0000000..091bda6 --- /dev/null +++ b/lbl_ir/lbl_ir/simulations/spectra_map_simulator.py @@ -0,0 +1,287 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jan 2 16:14:47 2019 + +@author: Liang Chen +""" +import numpy as np +import matplotlib.pyplot as plt +from lbl_ir.data_objects.ir_map import sample_info, ir_map + +def lorentzian(x,x0,gamma=10): + + return 1/(np.power((x-x0)/gamma,2)+1) + +def gaussian(x,x0=0,sigma=10): + + return 1/np.exp(np.power((x-x0)/sigma,2)) + +class spectra_map_simulator: + + """Generate a simulated spectral map + + Parameters: + ----------- + NbaseSpectra: int, optional + The number of basis spectrum. Default is 3. + + Nclusters: int, optional + The number of data point clusters in the spectral map. Default is 4. + + ptsPerCluster: int, optional + The number of data points per cluster. Default is 50. + + Nx: int, optional + The pixel numbers of x-axis. Default is 64. + + Ny: int, optional + The pixel numbers of y-axis. Default is 64. + + cov: int or float matrix, optional + The covariance matrix of 2D gaussian distribution used to generate random data point clusters. + Default is [[20, 10], [10, 25]]. + + sigma: int or float, optional + The standard deviation of 1D gaussian distribution used to generate spectral weights. Default is 10. + + startWavenumber: int or float, optional + The beginning wavenumber of spectral range. Default is 400. + + endWavenumber: int or float, optional + The ending wavenumber of spectral range. Default is 4000. + + Nwavenumber: int, optional + The number of wavenumber values in the spectrum. Default is 1600. + + random_state: int, optional + The seed of the pseudo random number generator to use when generating random numbers. Default to 17. + + Attributes: + -------- + data : float matrix + The final spectral data matrix. Number of rows equals number of non-zero data points; + Number of columns equals number of wavenumbers. + + densityMatCondense : float matrix + The distribution weight matrix of each basis spectrum (only stores non-zero data points, see nonZeroInd). + Number of rows equals number of non-zero data points; + Number of columns equals number of basis spectrum. + + spectraMat : float matrix + The basis spectra matrix. Number of rows equals number of basis spectrum; + Number of columns equals number of wavenumbers. + + wavenumber : float array + The wavenumber values of the spectrum (x-axis). + + nonZeroInd : int array + The linear index of all non-zero data points in the flatterned 2D map. + + Examples: + --------- + >>> s = spectra_map_simulator(random_state=3) + >>> s.spectra_map_gen() + >>> print(s.data.shape) + (446, 1600) + """ + + def __init__(self, NbaseSpectra=3, Nclusters=4, ptsPerCluster=50, Nx=64, cov=[[20, 10], [10, 25]], sigma=10, + startWavenumber=400, endWavenumber=4000, Nwavenumber=1600, random_state=17): + + self.NbaseSpectra = NbaseSpectra + self.Nclusters = Nclusters + self.ptsPerCluster = ptsPerCluster + self.Nx = Nx + self.Ny = Nx + self.cov = cov + self.sigma = sigma + self.startWavenumber = startWavenumber + self.endWavenumber = endWavenumber + self.Nwavenumber = Nwavenumber + self.random_state = random_state + + def cluster_placement(self, random_state=17): + """Generate simulated data point locations and weights in a 2D map + + Returns: + -------- + points : float matrix + The data point location matrix. The first two columns are x-y coordiantes of data points. + The third column are the weight coefficients of data points. + + densityVec : float array + The 1D array generated from flattened 2D weight coefficients matrix (densityMat) + """ + + np.random.seed(seed=random_state) + centroids = np.random.randint(int(self.Nx*0.8), size=(self.Nclusters,2)) + self.points = np.zeros((self.ptsPerCluster*self.Nclusters, 3)) + densityMat = np.zeros((self.Ny, self.Nx)) + + for i in range(self.Nclusters): + self.points[i*self.ptsPerCluster:(i+1)*self.ptsPerCluster, :2] = np.random.multivariate_normal(centroids[i,:], self.cov, self.ptsPerCluster) + self.points[i*self.ptsPerCluster:(i+1)*self.ptsPerCluster, 2] = gaussian(np.sqrt(np.power(self.points[i*self.ptsPerCluster:(i+1)*self.ptsPerCluster, :2] + -centroids[i,:],2).sum(axis = 1)),sigma=self.sigma) + + for i in self.points: + idx = tuple(np.clip(i[1::-1].astype(int), 0, self.Nx-1)) + densityMat[idx] += i[2] + + densityVec = densityMat.flatten() + + return densityVec + + def spectrum_gen(self, Npeaks=3, firstPeakPosition=1200, peakWidth=[30,60,150], random_state=17): + """Generate a simulated spectrum + + Parameters: + ----------- + Npeaks: int, optional + The number of peaks. Default is 3. + + firstPeakPosition: int, optional + The position of the first peak. Default is 1200. + + peakWidth: int or float list, optional + The list of peak widths. Default is [30,60,150]. + + Returns: + -------- + wavenumber : float array + The wavenumber values of the spectrum (x-axis). + + spectrum : float array + The transmission/reflection/absorption coefficients of the spectrum (y-axis) + """ + + if len(peakWidth) != Npeaks: + raise Exception("The number of peak width values doesn't match the number of peaks") + + np.random.seed(seed=random_state) + self.wavenumber = np.linspace(self.startWavenumber,self.endWavenumber,self.Nwavenumber) + peakPositions = np.linspace(firstPeakPosition,self.endWavenumber,Npeaks,endpoint=False) + spectrum = np.zeros(len(self.wavenumber)) + weights = np.random.rand(Npeaks) + + for i in range(Npeaks): + singlePeak = lorentzian(self.wavenumber,peakPositions[i],peakWidth[i]) + spectrum += singlePeak * weights[i] + spectrum /= weights.sum() + + return spectrum + + def spectra_map_gen(self, Npeaks = [3,4,5], positions = [800, 1000, 1200]): + """Generate a spectral map data cube + + Parameters: + ----------- + Npeaks: int list, optional + The list of number of peaks. Default is [3,4,5]. + + positions: int or float list, optional + The list of first peak positions. Default is [800, 1000, 1200]. + + Returns + ------- + self : object + """ + + if len(positions) != len(Npeaks): + raise Exception("The number of first peak positions doesn't match the number of basis spectrum") + + np.random.seed(seed=self.random_state) + random_seeds = np.random.randint(50, size=len(Npeaks)) + + densityVecs = np.zeros((self.Nx*self.Ny,self.NbaseSpectra)) # component coefficients + self.spectraMat = np.zeros((self.NbaseSpectra, self.Nwavenumber)) # components spectra matrix + + for i in range(self.NbaseSpectra): + + densityVecs[:,i] = self.cluster_placement(random_state = random_seeds[i]) + + np.random.seed(seed=random_seeds[i]) + peakWidth = np.random.randint(4,20, Npeaks[i])*10 + self.spectraMat[i,:] = self.spectrum_gen(Npeaks=Npeaks[i], firstPeakPosition = positions[i], + peakWidth=peakWidth, random_state = random_seeds[i]) + + self.nonZeroInd = ~np.all(densityVecs==0, axis=1) + self.densityVecsCondense = densityVecs[self.nonZeroInd] # remove all zero rows + self.data = self.densityVecsCondense.dot(self.spectraMat) # matrix mulplication + + mask = np.zeros((self.Ny, self.Nx), dtype='bool').flatten() + mask[self.nonZeroInd] = True + self.mask = mask.reshape(self.Ny, self.Nx) + + def save(self, sample_id='simulated_dataset'): + """Save the simulated dataset as an hdf5 file. + + Arguments: + + ---------- + sample_id : A string that identifies the sample, spaces will be substituted for underscores. + + """ + si = sample_info(sample_id=sample_id, sample_meta_data=f"This dataset has {self.NbaseSpectra} components and map size is {self.Ny} * {self.Nx}") + si.show() + + self.spectra_map_gen() # generate simulated dataset + y, x = np.where(self.mask) + self.xy = np.c_[x,y] + + ir_data = ir_map(self.wavenumber, si, with_factorization=True) + ir_data.add_data(spectrum=self.data, xy=self.xy) + ir_data.to_image_cube() + ir_data.add_factorization(component=self.spectraMat, component_coef=self.densityVecsCondense, prefix='MCR') + ir_data.write_as_hdf5(f'{sample_id}.h5') + + def load(self, filename='simulated_dataset.h5'): + """load the simulated dataset from an hdf5 file. + + Arguments: + + ---------- + filename : The hdf5 filename where data will be read from. + """ + ir_data = ir_map(filename=filename) + ir_data.add_image_cube() + ir_data.add_factorization(prefix='MCR') + + return ir_data + + def plot_spectra_map(self): + """Plot the spectral distribution map and basis spectra + + """ + + self.spectra_map_gen() # generate simulated dataset + + plt.figure(figsize=(12, 6)) + for i in range(self.NbaseSpectra): + + n_row = 2 + + plt.subplot(n_row,3,i+1) + densityMat = np.zeros((self.Ny, self.Nx)).flatten() + densityMat[self.nonZeroInd] = self.densityVecsCondense[:,i] + densityMat = densityMat.reshape(self.Ny, self.Nx) + plt.imshow(np.flipud(densityMat)) + plt.title(f'Distribution of Component {i+1}') + plt.colorbar() + plt.clim([0, 2]) + + if n_row == 3: + plt.subplot(n_row,3,i+7) + plt.imshow(np.flipud(densityMat>0)) + + plt.subplot(n_row,3,i+4) + plt.subplots_adjust(hspace=0.3) + plt.plot(self.wavenumber, self.spectraMat[i,:]) + plt.title(f'Spectrum of Component {i+1}') + plt.xlim([4000,400]) + + return None + +if __name__ == "__main__": + + s = spectra_map_simulator(random_state=3) + s.plot_spectra_map() \ No newline at end of file diff --git a/lbl_ir/lbl_ir/tasks/NMF/__init__.py b/lbl_ir/lbl_ir/tasks/NMF/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lbl_ir/lbl_ir/tasks/NMF/basic_NMF.py b/lbl_ir/lbl_ir/tasks/NMF/basic_NMF.py new file mode 100644 index 0000000..fa9d2cf --- /dev/null +++ b/lbl_ir/lbl_ir/tasks/NMF/basic_NMF.py @@ -0,0 +1,40 @@ +from sklearn.decomposition import NMF +import matplotlib.pyplot as plt +import pyqtgraph as pq + +import sys +import numpy as np +from lbl_ir.io_tools import read_map +from lbl_ir.tasks.preprocessing import data_prep + + +def simple_NMF(ir_map, wmask, smask,components=40): + """ + A tools to do NMF on a selected region in a map + + :param ir_map: Image data cube + :param wmask: wavelength mask + :param smask: spatial mask + :param components: number of components + :return: spatial maps of components and associated spectra + """ + ori_shape = ir_map.imageCube.shape + data = ir_map.imageCube[:,:,wmask] + data = data.reshape( ori_shape[0]*ori_shape[1], data.shape[2] ) + position_selection = np.where(smask.flatten()>0.5)[0] + data = data[ position_selection, : ] + + + NMF_obj = NMF(n_components=components) + W = NMF_obj.fit_transform( data ) + H = NMF_obj.components_ + + maps = [] + for nn in range(components): + this_map = np.zeros(ori_shape[0:2]) + this_map = this_map.flatten() + this_map[position_selection] = W[:,nn] + this_map = this_map.reshape( ori_shape[0:2]) + maps.append(this_map) + + return maps, H diff --git a/lbl_ir/lbl_ir/tasks/NMF/multi_set_analyses.py b/lbl_ir/lbl_ir/tasks/NMF/multi_set_analyses.py new file mode 100644 index 0000000..edbebcf --- /dev/null +++ b/lbl_ir/lbl_ir/tasks/NMF/multi_set_analyses.py @@ -0,0 +1,124 @@ +from sklearn.decomposition import NMF +import matplotlib.pyplot as plt +#import pyqtgraph as pq + + +import sys +import numpy as np +from lbl_ir.io_tools import read_map +from lbl_ir.tasks.preprocessing import data_prep, transform +#from umap import UMAP + + +class aggregate_data(object): + def __init__(self, names, data, wmask, components=40): + self.names = names + self.Nset = len(names) + self.wmask = wmask + self.master_wmask = self.get_wavenumber_intersection() + self.wavenumbers = data[0].wavenumbers[self.master_wmask] + self.data, self.dims, self.labels = self.get_data(data) + del data + self.components = components + print(self.dims) + + + def get_wavenumber_intersection(self): + result = self.wmask[0] + for ii in range(1,self.Nset): + result = np.intersect1d(result,self.wmask[ii]) + return result + + def get_data(self,data): + result = [] + dims = [] + labels = [] + for kk,cube in enumerate(data): + sel_cube = cube.imageCube[:,:, self.master_wmask ] + dim = sel_cube.shape + sel_cube = sel_cube.reshape( dim[0]*dim[1],dim[2] ) + result.append( sel_cube ) + labs = np.zeros( dim[0]*dim[1] ) + kk + labels.append(labs) + dims.append( dim[:2] ) + result = np.vstack( result ) + labels = np.concatenate(labels).flatten() + return transform.to_absorbance(result,False)[0], dims, labels + + def splitter(self,X,to_map=False): + sets = [] + for kk in range( self.Nset ): + sel = self.labels == kk + sel = np.where(sel)[0] + tmp = X[sel,:] + if to_map: + tmp = tmp.reshape( self.dims[kk] ) + sets.append( tmp ) + return sets + + + + +def multi_set_umap(agg_data, fraction=0.25): + umap_object = UMAP(n_components=2, n_neighbors=5) + Nobs = agg_data.data.shape[0] + these_ones = np.arange(Nobs) + np.random.shuffle(these_ones) + these_ones = these_ones[:int(Nobs*fraction)] + these_ones = np.sort(these_ones) + + low_dim = umap_object.fit_transform( agg_data.data[these_ones,:] ) + low_dim_all = umap_object.transform( agg_data.data) + low_dim_all_split = agg_data.splitter(low_dim_all,False) + + for set in low_dim_all_split: + plt.plot(set[:,0],set[:,1],'.' , markersize=1.5 ) + plt.savefig('UMAP.png') + +def multi_set_NMF(agg_data,components=40): + NMF_obj = NMF(n_components=components) + W = NMF_obj.fit_transform( agg_data.data ) + H = NMF_obj.components_ + spectra = [] + maps = [] + for ii in range(components): + Ws = W[:,ii].reshape(-1,1) + Ws_split = agg_data.splitter( Ws,True ) + spectra.append(H[ii,:]) + maps.append( Ws_split ) + return maps, spectra, agg_data.wavenumbers + + +def single_set_NMF(data, wav_mask, components): + A = transform.fix_data( data.data[:,wav_mask] ) + NMF_obj = NMF(n_components=components) + W = NMF_obj.fit_transform( A ) + H = NMF_obj + return data.wavenumbers[wav_mask],W,H + + + +if __name__ == "__main__": + data_files = [] + wav_masks = [] + names = ['../../../ir_data/test_data.map'] + if len(names)>1: + for fname in names: + data,fmt = read_map.read_all_formats( fname ) + data_files.append( data ) + ds = data_prep.data_prepper(data) + wav_masks.append( ds.decent_bands ) + + ad = aggregate_data(names, data_files, wav_masks) + #multi_set_umap( ad ) + multi_set_NMF( ad ) + else: + data,fmt = read_map.read_all_formats( names[0] ) + print(fmt) + wmask = data_prep.data_prepper(data).decent_bands + wavs,W,H = single_set_NMF(data, wmask, 4 ) + for i in range(4): + plt.plot(wavs, H.components_[i], label='NMF'+str(i)) + plt.xlim([4000, 500]) + plt.legend() + diff --git a/lbl_ir/lbl_ir/tasks/Worms/Shai_Hulut.py b/lbl_ir/lbl_ir/tasks/Worms/Shai_Hulut.py new file mode 100644 index 0000000..55d7b1a --- /dev/null +++ b/lbl_ir/lbl_ir/tasks/Worms/Shai_Hulut.py @@ -0,0 +1,227 @@ +""" +Here are some specific worm processing tools. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +# reading an omnic map +from lbl_ir.io_tools.Omnic_PyMca5.OmnicMap import OmnicMap + +# doing SVD +from lbl_ir.math_tools.batched_SVD import batched_SVD + +# for segmentating of the image +from scipy.ndimage.morphology import binary_closing, binary_dilation, binary_fill_holes +from scipy.ndimage import gaussian_filter + +from skimage.morphology import skeletonize +from skimage import draw +import skimage +import pandas +from skan import csr +from scipy.ndimage.morphology import distance_transform_edt + + + +class little_maker( object ): + def __init__(self, filename, k_singular=10, z_threshold =14): + self.filename = filename + self.k_singular = k_singular + self.z_threshold = z_threshold + + self.omnic_object = OmnicMap(filename) + if self.omnic_object.info['OmnicInfo'] is not None: + start_wav = self.omnic_object.info['OmnicInfo']['First X value'] + stop_wav = self.omnic_object.info['OmnicInfo']['Last X value'] + n_wav = self.omnic_object.info['OmnicInfo']['Number of points'] + else: + start_wav = 699 + stop_wav = 3999 + n_wav = self.omnic_object.data.shape[-1] + self.waves = np.linspace(start_wav, stop_wav, n_wav ) # I'm not sure where to find this in the data object! + + self.data_shape = self.omnic_object.data.shape + self.flattened_data = self.omnic_object.data.reshape(-1, self.data_shape[2] ) + + self.full_frame_svd() + self.z_scores = self.get_background_map() + + def full_frame_svd(self,N_max=1000): + svd_obj = batched_SVD( self.flattened_data, + N_max=N_max, + k_singular=self.k_singular, + randomize=True) + self.U,self.S,self.VT = svd_obj.go_svd() + + + def skeletonize(self, threshold): + fin_mask = (self.z_scores > threshold).astype(int) + skel = skeletonize( fin_mask ).astype(int) + sk_obj = csr.Skeleton(skel, spacing=1, keep_images=True ) + + path_lengths = sk_obj.path_lengths() + this_one = np.argmax(path_lengths) + path_coordinates = sk_obj.path_coordinates(this_one).astype(int) + + pruned_skel = np.zeros( fin_mask.shape ) + for pair in path_coordinates: + pruned_skel[ pair[0], pair[1] ] = 1 + return pruned_skel, path_coordinates[::-1,:] + + def blobber(self): + None + + def extract_extrema(self, N_pixels=20, radius=20, threshold=14): + skel, coords = self.skeletonize(threshold) + side_0_coords = coords[ 0:N_pixels ] + dxy = side_0_coords[0,:]- side_0_coords[-1,:] + angle_0 = np.arctan2(dxy[1], dxy[0]) + print(angle_0*180.0/3.14) + print( side_0_coords ) + nimage = skimage.transform.rotate( self.z_scores , -angle_0*180.0/np.pi, clip=False, resize=True ) + plt.imshow(nimage); plt.show() + + side_1_coords = coords[ -(N_pixels): ] + dxy = side_1_coords[0,:]- side_1_coords[-1,:] + angle_1 = np.arctan2(dxy[1], dxy[0]) + print(angle_1*180.0/3.14) + print( side_1_coords ) + nimage = skimage.transform.rotate( self.z_scores , -angle_1*180.0/np.pi, clip=False, resize=True ) + plt.imshow(nimage); plt.show() + + + + + + + + def get_background_map(self, percentile=75.0, safety1=10, safety2=10, window_MM=6): + """ + We first use a local roughness check to determine edges. We do this on the mean images, + but could easly use the full hypercube if need be. + """ + + + # get a mean image + mean_img = self.U[:,0].reshape( self.data_shape[0:2] ) + mean_img = mean_img - np.mean(mean_img) + + # lets padd the image to avoid FFT periodicity issues + X,Y = mean_img.shape + # we need to fill this with some decent values + fill_sigma = np.std(mean_img) + fill_mean = np.median( mean_img ) + new_img = np.random.normal( fill_mean, fill_sigma, (X+2*safety1,Y+2*safety1)) + # blur it a bit + new_img = gaussian_filter( new_img, sigma=10) + + # paste the real image in here + new_img[ safety1:safety1+X,safety1:safety1+Y ] = mean_img + mean_img = new_img+0 + + + # define a windowing function of MM by MM pixels + MM = window_MM + kernel = np.zeros( mean_img.shape ) + kernel[0:MM,0:MM] = 1.0 + + # compute a local mean + FT_img = np.fft.fft2(mean_img) + FT_kernel = np.fft.fft2(kernel) + local_mean= np.fft.ifft2( FT_img* FT_kernel.conjugate() ).real / ( MM*MM) + + # compute a local variance + mean_img_sq = mean_img * mean_img + FT_img_sq = np.fft.fft2(mean_img_sq) + local_var = np.fft.ifft2( FT_img_sq* FT_kernel.conjugate() ).real / (MM*MM) + local_var = local_var - local_mean*local_mean + local_sigma = np.sqrt( local_var ) + + ############################################################## + ## Now we build a rough mask + ############################################################## + threshold = np.percentile( local_sigma.flatten(), percentile ) + sel = local_sigma > threshold + mask = np.zeros( mean_img.shape ) + mask[sel] = 1.0 + + # The morphological operators need some room on the sides + safety2 = 0 + V,W = mask.shape + nV = V+safety2*2 + nW = W+safety2*2 + new_mask = np.zeros( (nV,nW) ) + # place the mask + new_mask[safety2:safety2+V, safety2:safety2+W ] = mask + + BM=5 + structure = np.ones((BM,BM)) + closed = binary_closing(new_mask,structure) + + done=False + BM=5 + while not done: + BM = BM + 1 + structure = np.ones((BM,BM)) + new_closed = binary_closing(closed,structure).astype(int) + delta = np.sum( np.abs(new_closed.astype(int) - closed.astype(int)) ) + if delta == 0: + done = True + if BM >20: + done = True + closed = new_closed+0 + + new_mask = closed+0 + + # for some reason, the whole mask is shifted along one axis. This has likely to do with some + # origin definition of the structuring elements, but lets solve this using an FFT based shift calculation + # The origin option in the scipy morphology toolbox kill my kernel + + mask = new_mask[safety2:safety2+V, safety2:safety2+W] + ft_mask = np.fft.fft2(mask) + ft_mean = np.fft.fft2(np.abs(mean_img) ) + TF = np.fft.ifft2( ft_mean*ft_mask.conjugate() ).real + dX,dY = np.meshgrid( np.arange(mask.shape[1]), np.arange(mask.shape[0]) ) + here = np.argmax( TF ) + dX = dX.flatten()[here] + dY = dY.flatten()[here] + + # no wild shifts please + if dX > 10: + dX = 0 + if dY > 10: + dY = 0 + + mask = np.roll( mask, dX, axis=0) + mask = np.roll( mask, dY, axis=1) + # lift out the section of the mask that we are interested in + mask = mask[safety1:safety1+X, safety1:safety1+Y] + + + ###################################################### + # Here we build a more fine tuned mask / z_score map + ###################################################### + + + # now we use this mask to define the background + bg_sel = mask.flatten() < 0.5 + background = self.U[bg_sel,:] + mean_bg = np.mean( background, axis=0) + var_covar = np.cov( (background-mean_bg).transpose() ) + inv_vcv = np.linalg.pinv( var_covar ) + t = self.U - mean_bg + z_scores = [] + for tt in t: + z = tt.reshape(1,-1) + z_scores.append( np.sqrt( z.dot(inv_vcv).dot(z.transpose()) ) ) + z_scores = np.array(z_scores).reshape( self.data_shape[0:2] ) + return z_scores + + + + + + + diff --git a/lbl_ir/lbl_ir/tasks/Worms/__init__.py b/lbl_ir/lbl_ir/tasks/Worms/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lbl_ir/lbl_ir/tasks/__init__.py b/lbl_ir/lbl_ir/tasks/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lbl_ir/lbl_ir/tasks/baseline/__init__.py b/lbl_ir/lbl_ir/tasks/baseline/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lbl_ir/lbl_ir/tasks/baseline/rubberband.py b/lbl_ir/lbl_ir/tasks/baseline/rubberband.py new file mode 100644 index 0000000..5d39c33 --- /dev/null +++ b/lbl_ir/lbl_ir/tasks/baseline/rubberband.py @@ -0,0 +1,29 @@ +import numpy as np +from scipy.spatial import ConvexHull + +def rubberband(wavenumbers, spectrum): + """ + A rubberband baseline is essentially the lower half of tyhe convex hull + of the data. See this discussion for more details and code. + + https://dsp.stackexchange.com/questions/2725/how-to-perform-a-rubberband-correction-on-spectroscopic-data + """ + + # First find the convex hull using scipy.spatial tools + tmp = np.vstack([ wavenumbers.flatten(),spectrum.flatten()]).transpose() + v = ConvexHull(tmp).vertices + + # rool it untill the first point is the first point measured + v = np.roll(v, -v.argmin()) + # We don't care aboput the top half of the convex hull + v = v[:v.argmax()] + + # Use interpolation to get the baseline across the whole + # spectrum + return np.interp(wavenumbers, wavenumbers[v], spectrum[v]) + + + + + + diff --git a/lbl_ir/lbl_ir/tasks/preprocessing/__init__.py b/lbl_ir/lbl_ir/tasks/preprocessing/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lbl_ir/lbl_ir/tasks/preprocessing/data_prep.py b/lbl_ir/lbl_ir/tasks/preprocessing/data_prep.py new file mode 100644 index 0000000..afc9901 --- /dev/null +++ b/lbl_ir/lbl_ir/tasks/preprocessing/data_prep.py @@ -0,0 +1,96 @@ +import matplotlib.pyplot as plt +#import pyqtgraph as pq + + +import sys +import numpy as np +from lbl_ir.io_tools import read_map + +from scipy.stats import iqr +import scipy.signal +import scipy.ndimage + +from numba import jit + + +@jit#(nopython=True) +def band_score_numba(map,subsample=1,band=10): + Nx,Ny,Nwav = map.shape + result = np.zeros((Nwav,Nwav)) + norma = np.zeros((Nwav,Nwav)) + for ii in range(Nwav): + for jj in range( ii+1 ,min(ii+band,Nwav)): + slab_0 = map[::subsample,::subsample,ii].flatten() + slab_1 = map[::subsample,::subsample,jj].flatten() + cc = np.corrcoef(slab_0, slab_1)[0][1] + norma[ii,jj]=1.0 + norma[jj,ii]=1.0 + result[ii,jj]=cc + result[jj,ii]=cc + result = np.sum( result, axis=1) + norma = np.sum( norma, axis=1) + return result/norma + + +class data_prepper(object): + """ + Prep the data for further data analyses. + We try to detect systematic issues, such as bad bands and synchortron noise spikes + + + """ + def __init__(self, data_map, band_limit=0.99, threshold=6.0,band=5, additional_selection = None): + self.data_map = data_map + self.waves = self.data_map.wavenumbers + self.threshold = threshold + self.additional_selection = additional_selection + self.band_limit = band_limit + self.band_scores = None + self.bad_bands = None + self.decent_bands = None + self.score_bands(band) + + def score_bands(self,band=10): + # we need to loop over every single frame and detect spikes + self.band_scores = band_score_numba( self.data_map.imageCube,subsample=1,band=band ) + self.bad_bands = np.where( self.band_scores < self.band_limit )[0] + self.decent_bands = np.where( self.band_scores >= self.band_limit)[0] + + def wavenumber_mask(self): + # we have to merge these auto score bands with the manual selection + selections = self.band_scores >= self.band_limit + tmp = np.zeros( len(self.waves) ) > 1.0 + if self.additional_selection is not None: + for this_sel in self.additional_selection: + pair = np.sort( this_sel ) + sel1 = (self.waves > pair[0]) & (self.waves or < + :param delta: selects band size around provided peak values. + :return: + """ + sel1 = (ir_map.wavenumbers > wave1-delta) & (ir_map.wavenumbers < wave1+delta) + sel2 = (ir_map.wavenumbers > wave2-delta) & (ir_map.wavenumbers < wave2+delta) + map1 = np.mean( ir_map.imageCube[:,:,sel1], axis=2 ) + map2 = np.mean( ir_map.imageCube[:,:,sel2], axis=2 ) + + mask1 = map1 > bg_threshold + mask2 = map2 > bg_threshold + bg_mask = mask1*mask2 + + + fin = map1 / map2 + if operator == '<': + fin = fin < peak_threshold + if operator == '>': + fin = fin > peak_threshold + fin = fin & bg_mask + mask = np.zeros( ir_map.imageCube.shape[0:2] ) + mask[fin] = 1.0 + return mask + + + + + + diff --git a/lbl_ir/lbl_ir/tasks/preprocessing/svd_data.py b/lbl_ir/lbl_ir/tasks/preprocessing/svd_data.py new file mode 100644 index 0000000..49ef021 --- /dev/null +++ b/lbl_ir/lbl_ir/tasks/preprocessing/svd_data.py @@ -0,0 +1,16 @@ +import numpy as np + +from lbl_ir.math_tools.batched_SVD import batched_SVD + + + +def svd(data, k_singular, N_max=10000): + svd_obj = batched_SVD( data.data, + N_max=N_max, + k_singular=k_singular, + randomize=True) + U,S,VT = svd_obj.go_svd() + return U,S,VT + + + diff --git a/lbl_ir/lbl_ir/tasks/preprocessing/transform.py b/lbl_ir/lbl_ir/tasks/preprocessing/transform.py new file mode 100644 index 0000000..772e87c --- /dev/null +++ b/lbl_ir/lbl_ir/tasks/preprocessing/transform.py @@ -0,0 +1,22 @@ +import numpy as np +from lbl_ir.tasks.baseline import rubberband + +def to_absorbance(data, wavenumbers, eps=1e-5, rubber_band = False): + tmp = data /100.0 + sel_low = tmp < eps + sel_high = tmp > 1-eps + tmp[sel_low] = eps + tmp[sel_high] = 1.0-eps + result = -np.log(tmp) + bg = result*0 + if rubber_band: + shape = result.shape + for ii in range(shape[0]): + for jj in range(shape[1]): + tmp = result[ii,jj,:] + this_bg = rubberband.rubberband( wavenumbers, tmp ) + bg[ii,jj,:]=this_bg + return result, bg + else: + return result,None + diff --git a/lbl_ir/setup.py b/lbl_ir/setup.py new file mode 100644 index 0000000..322a1b1 --- /dev/null +++ b/lbl_ir/setup.py @@ -0,0 +1,100 @@ +""" +Usage: pip install -e . + python setup.py install + python setup.py bdist_wheel + python setup.py sdist bdist_egg + twine upload dist/* +""" + +from setuptools import setup, find_packages + +setup( + name='lbl_ir', + + # Versions should comply with PEP440. For a discussion on single-sourcing + # the version across setup.py and the project code, see + # https://packaging.python.org/en/latest/single_source_version.html + version='0.1.0', + + description='A toolset to analyze OMNIC map files.', + + # The project's main homepage. + url='https://bitbucket.org/berkeleylab/lbl_ir/src/master/', + + # Author details + author='Liang Chen; Petrus Zwart', + author_email='lchen2@lbl.gov', + + # Choose your license + license='BSD', + + # See https://pypi.python.org/pypi?%3Aaction=list_classifiers + classifiers=[ + # How mature is this project? Common values are + # 3 - Alpha + # 4 - Beta + # 5 - Production/Stable + 'Development Status :: 4 - Beta', + + # Indicate who your project is intended for + 'Intended Audience :: Science/Research', + 'Topic :: Scientific/Engineering :: Physics', + + # Pick your license as you wish (should match "license" above) + 'License :: OSI Approved :: BSD License', + + # Specify the Python versions you support here. In particular, ensure + # that you indicate whether you support Python 2, Python 3 or both. + 'Programming Language :: Python :: 3.6' + ], + + # What does your project relate to? + keywords='Infrared Microscopy Analysis', + + # You can just specify the packages manually here if your project is + # simple. Or you can use find_packages(). + packages=find_packages(), + + package_dir={}, + + # Alternatively, if you want to distribute just a my_module.py, uncomment + # this: + # py_modules=["__init__"], + + # List run-time dependencies here. These will be installed by pip when + # your project is installed. For an analysis of "install_requires" vs pip's + # requirements files see: + # https://packaging.python.org/en/latest/requirements.html + install_requires=['numpy', 'scipy', 'seaborn', 'pandas', 'matplotlib', 'spectral', 'h5py', + 'scikit-learn', 'umap-learn'], + + setup_requires=[], + + # List additional groups of dependencies here (e.g. development + # dependencies). You can install these using the following syntax, + # for example: + # $ pip install -e .[dev,tests] + extras_require={ + # 'dev': ['check-manifest'], + }, + + # If there are data files included in your packages that need to be + # installed, specify them here. If using Python 2.6 or less, then these + # have to be included in MANIFEST.in as well. + package_data={}, + + # Although 'package_data' is the preferred approach, in some case you may + # need to place data files outside of your packages. See: + # http://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files # noqa + # In this case, 'data_file' will be installed into '/my_data' + # data_files=[#('lib/python2.7/site-packages/gui', glob.glob('gui/*')), + # ('lib/python2.7/site-packages/yaml/tomography',glob.glob('yaml/tomography/*'))], + + # To provide executable scripts, use entry points in preference to the + # "scripts" keyword. Entry points provide cross-platform support and allow + # pip to create the appropriate form of executable for the target platform. + entry_points={}, + + ext_modules=[], + include_package_data=True +) From 45615efd6e5c6456bb58723565a3cacf39603f28 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Tue, 2 Jun 2020 18:24:31 -0700 Subject: [PATCH 41/56] add openpyxl dependency --- requirements.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/requirements.txt b/requirements.txt index 934e790..63a273e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,3 +11,6 @@ seaborn scikit-learn umap-learn pyMCR +xlrd +xlwt +openpyxl \ No newline at end of file From 0650e94498b5c474a007702e2cba105e60a4fc67 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Wed, 3 Jun 2020 16:12:33 -0700 Subject: [PATCH 42/56] add mouseclick to select anchor points --- xicam/BSISB/widgets/preprocesswidget.py | 34 ++++++++++++++++++++++--- 1 file changed, 31 insertions(+), 3 deletions(-) diff --git a/xicam/BSISB/widgets/preprocesswidget.py b/xicam/BSISB/widgets/preprocesswidget.py index 0f1b490..ed96331 100644 --- a/xicam/BSISB/widgets/preprocesswidget.py +++ b/xicam/BSISB/widgets/preprocesswidget.py @@ -200,6 +200,7 @@ def __init__(self, headermodel, selectionmodel): self.out = None self.reportList = ['preprocess_method', 'wav_anchor', 'interp_method'] self.arrayList = ['rubberDebased', 'deriv2', 'deriv4'] + self.mousePosList = [] # split between spectrum parameters and viewwindow, vertical split self.params_and_specview = QSplitter() @@ -275,6 +276,7 @@ def __init__(self, headermodel, selectionmodel): self.resultSpectra = baselinePlotWidget() # ParameterTree self.parametertree = PreprocessParameters() + self.parameter = self.parametertree.parameter self.processArgs = self.parametertree.processArgs self.argMap = self.parametertree.argMap @@ -300,6 +302,7 @@ def __init__(self, headermodel, selectionmodel): self.specSelectModel.selectionChanged.connect(self.updateSpecPlot) self.normBox.currentIndexChanged.connect(self.updateSpecPlot) self.parametertree.sigParamChanged.connect(self.updateSpecPlot) + self.rawSpectra.scene().sigMouseClicked.connect(self.setAnchors) def setHeader(self, field: str): self.headers = [self.headermodel.item(i).header for i in range(self.headermodel.rowCount())] @@ -333,6 +336,31 @@ def isMapOpen(self): self.selectMapidx = self.mapselectmodel.selectedIndexes()[0].row() return True + def setAnchors(self, event): + # get current map idx and selected spectrum idx + specidx = self.getCurrentSpecid() + plotChoice = self.normBox.currentIndex() + if (not self.isMapOpen()) or (self.specItemModel.rowCount() == 0) or (specidx is None) or (plotChoice != 1): + return + + pos = event.pos() + button = event.button() + parser = Preprocessor(self.wavenumberList[self.selectMapidx], self.dataSets[self.selectMapidx][specidx]) + parser.parse_anchors(self.parameter['Anchor points']) + anchor_low, anchor_high = parser.wav_anchor[0], parser.wav_anchor[-1] + if self.rawSpectra.getViewBox().sceneBoundingRect().contains(pos): + mousePoint = self.rawSpectra.getViewBox().mapToView(pos) + x = mousePoint.x() + if anchor_low < x < anchor_high: + if button == Qt.LeftButton:# left click, add point to mousePosList + self.mousePosList.append(x) + elif (button == Qt.MidButton) and self.mousePosList :# right click, remove last point from mousePosList + self.mousePosList.pop() + # set anchors list + anchors = [anchor_low] + sorted(self.mousePosList) + [anchor_high] + txt = ', '.join([str(int(round(x))) for x in anchors]) + self.parameter.child('Anchor points').setValue(txt) + def getCurrentSpecid(self): # get selected spectrum idx specidx = None # default value @@ -375,13 +403,13 @@ def updateSpecPlot(self): if plotChoice == 0: # plot raw spectrum self.infoBox.setText('') # clear txt self.rawSpectra.plotBase(self.out, plotType='raw') - elif plotChoice == 1: # plot raw, edges, norm + elif plotChoice == 1: # plot raw, rubberband self.rawSpectra.plotBase(self.out, plotType='base') self.resultSpectra.plotBase(self.out, plotType='rubberband') - elif plotChoice == 2: # plot raw, edges, flattened + elif plotChoice == 2: # plot raw, 2nd derivative self.rawSpectra.plotBase(self.out, plotType='base') self.resultSpectra.plotBase(self.out, plotType='deriv2') - elif plotChoice == 3: # plot raw, edges, Mback + poly normalized + elif plotChoice == 3: # plot raw, 4th derivative self.rawSpectra.plotBase(self.out, plotType='base') self.resultSpectra.plotBase(self.out, plotType='deriv4') From e0f8beae86c3403ae84a6d9d90deac4dfcbcc103 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Thu, 4 Jun 2020 16:48:55 -0700 Subject: [PATCH 43/56] add mousePosList to cleanup --- xicam/BSISB/widgets/preprocesswidget.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/xicam/BSISB/widgets/preprocesswidget.py b/xicam/BSISB/widgets/preprocesswidget.py index ed96331..c401fa0 100644 --- a/xicam/BSISB/widgets/preprocesswidget.py +++ b/xicam/BSISB/widgets/preprocesswidget.py @@ -475,6 +475,8 @@ def cleanUp(self): self.rawSpectra.clearAll() self.resultSpectra.clearAll() self.infoBox.setText('') + self.mousePosList = [] + self.normBox.setCurrentIndex(0) def batchProcess(self): # get current map idx From 2c82ccd9023243a22352f3163e0534b6791eba6b Mon Sep 17 00:00:00 2001 From: lchen23 Date: Thu, 4 Jun 2020 16:56:34 -0700 Subject: [PATCH 44/56] clear image window in clean up --- xicam/BSISB/widgets/clusteringwidget.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/xicam/BSISB/widgets/clusteringwidget.py b/xicam/BSISB/widgets/clusteringwidget.py index c7af578..1c05204 100644 --- a/xicam/BSISB/widgets/clusteringwidget.py +++ b/xicam/BSISB/widgets/clusteringwidget.py @@ -457,8 +457,9 @@ def setImageCross(self, ind): def cleanUp(self): if hasattr(self, 'imgShape'): - img = np.zeros((self.imgShape[0], self.imgShape[1])) - self.clusterImage.setImage(img=img) + self.clusterImage.clear() + # img = np.zeros((self.imgShape[0], self.imgShape[1])) + # self.clusterImage.setImage(img=img) if hasattr(self, 'scatterPlot'): self.clusterScatterPlot.plotItem.clearPlots() self.clusterScatterPlot.scatterData = None From 3339a106b8ad76d9d853e03db780a50a187c1147 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Thu, 4 Jun 2020 16:59:12 -0700 Subject: [PATCH 45/56] restore legend --- xicam/BSISB/widgets/factorizationwidget.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/xicam/BSISB/widgets/factorizationwidget.py b/xicam/BSISB/widgets/factorizationwidget.py index d2873b9..e41e2ed 100644 --- a/xicam/BSISB/widgets/factorizationwidget.py +++ b/xicam/BSISB/widgets/factorizationwidget.py @@ -362,11 +362,10 @@ def showComponents(self, fac_obj): # get map ROI selected region self.selectedPixelsList = [self.headermodel.item(i).selectedPixels for i in range(self.headermodel.rowCount())] # clear plots and legends - # self.componentSpectra.plotItem.clearPlots() - self.componentSpectra.clearAll() + self.componentSpectra.getViewBox().clear() self.componentSpectra.ymax = 0 - # for sample, label in self._plotLegends.items[:]: - # self._plotLegends.removeItem(label.text) + for sample, label in self._plotLegends.items[:]: + self._plotLegends.removeItem(label.text) self.wavenumbers, self._fac, self._data_fac, self._dataRowSplit = fac_obj[0], fac_obj[1], fac_obj[2], fac_obj[3] From 522246452543f6d1c9c0aa73f53ae9f97c7cace6 Mon Sep 17 00:00:00 2001 From: lchen23 Date: Thu, 2 Jul 2020 20:35:20 -0700 Subject: [PATCH 46/56] add installation instructions --- README.md | 108 +++++++++++++++++++----- images/buildtools.png | Bin 0 -> 150534 bytes images/example.png | Bin 0 -> 81775 bytes tests/PC12-NGF-3h.h5 | Bin 0 -> 2736628 bytes tests/__init__.py | 0 xicam/BSISB/widgets/clusteringwidget.py | 2 +- 6 files changed, 89 insertions(+), 21 deletions(-) create mode 100644 images/buildtools.png create mode 100644 images/example.png create mode 100644 tests/PC12-NGF-3h.h5 delete mode 100644 tests/__init__.py diff --git a/README.md b/README.md index c2aec49..b37804c 100644 --- a/README.md +++ b/README.md @@ -1,40 +1,108 @@ -Bsisb (Xi-cam PluginMaker) -=============================== +BSISB (A Xi-cam plugin for FTIR data analysis) +============================================== -version number: 0.1 -author: Liang Chen +Version number: 0.1 -Overview --------- +Author: Liang Chen +## Installation +--------------------- +### Install C++ build tools (Windows OS only): +Download and install "Build Tools for Visual Studio" from [here](https://visualstudio.microsoft.com/downloads/). +See the screenshot below for reference: -Installation / Usage --------------------- -Install Xi-CAM: +![build](images/buildtools.png) -Follow the guide [here](https://xi-cam2.readthedocs.io/en/latest/install.html) +### Install git and python3.7.7: -Install lbl_ir: +You will need to ensure that you have both git and python3 installed on your system for Xi-cam installation. - $ git clone https://lchen2ir@bitbucket.org/berkeleylab/lbl_ir.git - $ cd lbl_ir - $ pip install -e . +**MacOS** + +Open the Terminal application (in Applications/Utilities). In the terminal, check to see if git is installed by typing `git --version`. Either a version number will be printed, indicating git is already installed, or a dialog will open asking The “git” command requires the command line developer tools. Would you like to install the tools now? Click the **Install** button to install the developer tools, which will install git for you. + +You will also need to install python3.7.7. You can download python3.7.7 at [python.org](https://www.python.org/downloads/mac-osx/). You will want to get the macOS 64-bit installer if you are running any macOS version since Mavericks. + +**Windows OS** + +Download git [here](https://git-scm.com/download/win) and follow the installer’s instructions. This will install **Git for Windows**, which provides a **Git Bash** command line interface as well as a **Git GUI** graphical interface for git. + +You will want to go to the python3.7.7 download page at [python.org](https://www.python.org/downloads/release/python-377/). For modern systems, install the **Windows x86-64 executable installer** at the bottom. + +When you run the installer, make sure sure to check the box that says **Add Python 3.x to PATH** to ensure that the interpreter will be placed in your execution path. + +### Create and Activate a Virtual Environment: + +The latest python3 version comes with the venv module, which can be used to create a virtual environment. A virtual environment is a sequestered space where you can install and uninstall packages without modifying your system’s installed packages. + +Create a virtual environment for installing the Xi-cam components and dependencies. You will then want to activate the virtual environment you created so that any packages you install with python’s package manager, **pip**, will be installed into that active virtual environment. In the commands below, create a virtual environment called **venv_xicam** and activate it: + +**Linux/macOS** + + $ python3 -m venv venv_xicam + $ source venv_xicam/bin/actviate + +**Windows OS** + + $ python -m venv venv_xicam + $ venv_xicam\Scripts\activate + +### Install Xi-CAM: +Before installing Xi-cam, run the following commands: + + $ python -m pip install --upgrade pip + $ pip install wheel + $ pip install --upgrade setuptools + $ pip install numcodecs + +Then run the following commands to install Xi-cam: -Clone the repo: + $ pip install xicam + +Run the following commands to install lbl_ir and Xi-cam.BSISB: $ git clone https://github.com/lchen23/Xi-cam.BSISB.git - $ cd Xi-CAM.BSISB + $ cd Xi-cam.BSISB/lbl_ir + $ pip install -e . + $ cd .. $ pip install -e . - $ cd .. + +### Run the Xi-cam.BSISB program: + +Run the following command: + + $ xicam + +### Run the Xi-cam.BSISB GUI from a new terminal: +First, activate the virtual environment, then run xicam: + +**Linux/macOS** + + $ source venv_xicam/bin/actviate $ xicam +**Windows OS** + +In Git bash: + + $ source venv_xicam/Scripts/activate + $ xicam + +In Windows CMD: + + $ venv_xicam\Scripts\activate + $ xicam + +After the Xi-cam GUI program launches, open the +`Xi-cam.BSISB/tests/PC12-NGF-3h.h5` file by double clicking the h5 file +in the file browser as shown below: + +![example](images/example.png) + Contributing ------------ TBD -Example -------- -TBD diff --git a/images/buildtools.png b/images/buildtools.png new file mode 100644 index 0000000000000000000000000000000000000000..6f81aed0c29dcd964fd347a4eb6f9011bde8cdaf GIT binary patch literal 150534 zcmdqJcT`i|_BN`BprT*{1O#4GKtw=5S`bjGQl)p0UK48QMWl%|MXC^x-g`+x3rZ70 z5$Oq4N+2ObgoF|TH=g%B-}%OO&+ni6#~pXvJqBZEZ&ucttIcQ5XU>(lm)dGqF0o%a zbLPyI7tfXT&YYo}J9Flo%mq5?o2aBT5cPJ}M^8=hOwGvcP3i-!(=)ASXU^0oGaTET zr#@fwd~WJ<<_rt(pTDy&^zQvSbLO=8h4M3l0IMxZsHXwOX8#n2px9w#*(vqA6s7Kf zCke3wk3tT+Kb2nsvl(3KReonM&9h@%5w0S7%^y@8!>&+9m*$}r>Vdsp%5rm2sDT4J 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