From ba51c8c2734b74cce5901d4be930a400580a314a Mon Sep 17 00:00:00 2001 From: z0gSh1u Date: Sat, 3 Feb 2024 01:09:38 +0800 Subject: [PATCH] doc --- docs/crip/lowdose.html | 2 +- docs/crip/metric.html | 75 ++++++++++++------ docs/crip/plot.html | 168 +++++++++++++++++++++-------------------- docs/search.js | 2 +- 4 files changed, 139 insertions(+), 108 deletions(-) diff --git a/docs/crip/lowdose.html b/docs/crip/lowdose.html index b538c83..d26c8da 100644 --- a/docs/crip/lowdose.html +++ b/docs/crip/lowdose.html @@ -162,7 +162,7 @@

96 return mod2 * pixelSize * pixelSize / (h * w) 97 98 - 99def nps1D(roi: TwoOrThreeD, pixelSize: float, n: Or[int, None] = None): + 99def nps2DRadialAvg(roi: TwoOrThreeD, pixelSize: float, n: Or[int, None] = None): 100 ''' 101 Compute the radially averaged noise power spectrum (NPS) of a 2D region of interest (ROI). 102 It's recommended that you provide multiple samples (realizations) of the ROI. diff --git a/docs/crip/metric.html b/docs/crip/metric.html index d8c79f7..b98859e 100644 --- a/docs/crip/metric.html +++ b/docs/crip/metric.html @@ -74,7 +74,7 @@

5''' 6 7import numpy as np - 8from .utils import cripWarning + 8from .utils import cripWarning, cripAssert, is3D, isOfSameShape 9from ._typing import * 10from skimage.metrics import structural_similarity, peak_signal_noise_ratio 11 @@ -89,7 +89,7 @@

20 return np.mean(np.abs(x - y) / (y + eps)) * 100 21 22 -23def calcPSNR(x, y, range_=1): +23def calcPSNR(x: TwoOrThreeD, y: TwoOrThreeD, range_=1): 24 ''' 25 Compute the Peak Signal Noise Ratio (PSNR) (dB). 26 ''' @@ -101,7 +101,7 @@

32 return psnr 33 34 -35def calcSSIM(x, y, range_=1): +35def calcSSIM(x: TwoOrThreeD, y: TwoOrThreeD, range_=1): 36 ''' 37 Compute the Structural Similarity (SSIM). 38 ''' @@ -109,16 +109,40 @@

40 range_ == 1 or range_ == 255, 41 'Common `range_` for SSIM computation is 1 or 255. Make sure you know what you are doing follows your intention.' 42 ) -43 ssim = structural_similarity(x, y, data_range=range_) -44 return ssim -45 +43 cripAssert(isOfSameShape(x, y), 'Input images must have the same shape.') +44 +45 ssim = structural_similarity(x, y, data_range=range_, channel_axis=0 if is3D(x) else None) 46 -47def calcRMSE(x, y): -48 ''' -49 Compute the Root Mean Squared Error. -50 ''' -51 sq = (x - y)**2 -52 return np.sqrt(sq.mean()) +47 return ssim +48 +49 +50def calcRMSE(x, y): +51 ''' +52 Compute the Root Mean Squared Error. +53 ''' +54 sq = (x - y)**2 +55 return np.sqrt(sq.mean()) +56 +57 +58class AverageMeter(): +59 +60 def __init__(self): +61 self.reset() +62 +63 def reset(self): +64 self.val = 0 +65 self.avg = 0 +66 self.sum = 0 +67 self.count = 0 +68 +69 def update(self, val): +70 self.val = val +71 self.sum += val +72 self.count += 1 +73 self.avg = self.sum / self.count +74 +75 def getValue(self): +76 return self.avg @@ -154,13 +178,13 @@

def - calcPSNR(x, y, range_=1): + calcPSNR( x: Union[numpy.ndarray, List[numpy.ndarray]], y: Union[numpy.ndarray, List[numpy.ndarray]], range_=1):
- 
24def calcPSNR(x, y, range_=1):
+            
24def calcPSNR(x: TwoOrThreeD, y: TwoOrThreeD, range_=1):
 25    '''
 26        Compute the Peak Signal Noise Ratio (PSNR) (dB).
 27    '''
@@ -183,13 +207,13 @@ 

 

             
         def
-        calcSSIM(x, y, range_=1):
+        calcSSIM(	x: Union[numpy.ndarray, List[numpy.ndarray]],	y: Union[numpy.ndarray, List[numpy.ndarray]],	range_=1):
 
                 
 
     

     
-            
36def calcSSIM(x, y, range_=1):
+            
36def calcSSIM(x: TwoOrThreeD, y: TwoOrThreeD, range_=1):
 37    '''
 38        Compute the Structural Similarity (SSIM).
 39    '''
@@ -197,8 +221,11 @@ 

 41        range_ == 1 or range_ == 255,
 42        'Common `range_` for SSIM computation is 1 or 255. Make sure you know what you are doing follows your intention.'
 43    )
-44    ssim = structural_similarity(x, y, data_range=range_)
-45    return ssim
+44    cripAssert(isOfSameShape(x, y), 'Input images must have the same shape.')
+45
+46    ssim = structural_similarity(x, y, data_range=range_, channel_axis=0 if is3D(x) else None)
+47
+48    return ssim
 

 
 
@@ -218,12 +245,12 @@ 

 
     

     
-            
48def calcRMSE(x, y):
-49    '''
-50        Compute the Root Mean Squared Error.
-51    '''
-52    sq = (x - y)**2
-53    return np.sqrt(sq.mean())
+            
51def calcRMSE(x, y):
+52    '''
+53        Compute the Root Mean Squared Error.
+54    '''
+55    sq = (x - y)**2
+56    return np.sqrt(sq.mean())
 

 
 
diff --git a/docs/crip/plot.html b/docs/crip/plot.html
index 725ca8a..83a512c 100644
--- a/docs/crip/plot.html
+++ b/docs/crip/plot.html
@@ -212,11 +212,11 @@ 

 131        'vmin0vmax1=False is not recommended because it might cause incorrect windowing. Make sure you know what you are doing.'
 132    )
 133
-134    cripAssert(len(subimages) == ncols * nrows)
-135    cripAssert(crops is None or len(crops) == nrows)
+134    ncols = len(colTitles)
+135    nrows = len(rowTitles)
 136
-137    ncols = len(colTitles)
-138    nrows = len(rowTitles)
+137    cripAssert(len(subimages) == ncols * nrows, 'Number of subimages should be equal to ncols * nrows.')
+138    cripAssert(crops is None or len(crops) == nrows, 'Number of crops should be equal to nrows.')
 139
 140    if isinstance(cropLocs, str):
 141        cropLocs = [cropLocs] * nrows
@@ -245,47 +245,47 @@ 

 164        ax.get_xaxis().set_ticks([])
 165        list(map(lambda x: x.set_visible(False), ax.spines.values()))
 166
-167        if i % ncols == 0:
+167        if len(rects) != 0 and i % ncols == 0:
 168            ax.add_patch(rects[i // ncols])
 169
-170        patch = zoomIn(im, *crops[i // ncols])
-171        patch = resize(patch, (cropSize, cropSize))
-172        cropLocs = cropLocs[i // ncols]
+170            patch = zoomIn(im, *crops[i // ncols])
+171            patch = resize(patch, (cropSize, cropSize))
+172            cropLocs = cropLocs[i // ncols]
 173
-174        if cropLocs == 'bottom right':
-175            im[-patch.shape[0]:, -patch.shape[1]:] = patch
-176            patchRect = matplotlib.patches.Rectangle((im.shape[1] - patch.shape[1], im.shape[0] - patch.shape[0]),
-177                                                     patch.shape[1],
-178                                                     patch.shape[0],
-179                                                     linewidth=1,
-180                                                     edgecolor=cropEdgeColor,
-181                                                     facecolor='none')
-182        elif cropLocs == 'top left':
-183            im[:patch.shape[0], :patch.shape[1]] = patch
-184            patchRect = matplotlib.patches.Rectangle((0, 0),
-185                                                     patch.shape[1],
-186                                                     patch.shape[0],
-187                                                     linewidth=1,
-188                                                     edgecolor=cropEdgeColor,
-189                                                     facecolor='none')
-190        elif cropLocs == 'bottom left':
-191            im[-patch.shape[0]:, :patch.shape[1]] = patch
-192            patchRect = matplotlib.patches.Rectangle((0, im.shape[0] - patch.shape[0]),
-193                                                     patch.shape[1],
-194                                                     patch.shape[0],
-195                                                     linewidth=1,
-196                                                     edgecolor=cropEdgeColor,
-197                                                     facecolor='none')
-198        elif cropLocs == 'top right':
-199            im[:patch.shape[0], -patch.shape[1]:] = patch
-200            patchRect = matplotlib.patches.Rectangle((im.shape[1] - patch.shape[1], 0),
-201                                                     patch.shape[1],
-202                                                     patch.shape[0],
-203                                                     linewidth=1,
-204                                                     edgecolor=cropEdgeColor,
-205                                                     facecolor='none')
+174            if cropLocs == 'bottom right':
+175                im[-patch.shape[0]:, -patch.shape[1]:] = patch
+176                patchRect = matplotlib.patches.Rectangle((im.shape[1] - patch.shape[1], im.shape[0] - patch.shape[0]),
+177                                                        patch.shape[1],
+178                                                        patch.shape[0],
+179                                                        linewidth=1,
+180                                                        edgecolor=cropEdgeColor,
+181                                                        facecolor='none')
+182            elif cropLocs == 'top left':
+183                im[:patch.shape[0], :patch.shape[1]] = patch
+184                patchRect = matplotlib.patches.Rectangle((0, 0),
+185                                                        patch.shape[1],
+186                                                        patch.shape[0],
+187                                                        linewidth=1,
+188                                                        edgecolor=cropEdgeColor,
+189                                                        facecolor='none')
+190            elif cropLocs == 'bottom left':
+191                im[-patch.shape[0]:, :patch.shape[1]] = patch
+192                patchRect = matplotlib.patches.Rectangle((0, im.shape[0] - patch.shape[0]),
+193                                                        patch.shape[1],
+194                                                        patch.shape[0],
+195                                                        linewidth=1,
+196                                                        edgecolor=cropEdgeColor,
+197                                                        facecolor='none')
+198            elif cropLocs == 'top right':
+199                im[:patch.shape[0], -patch.shape[1]:] = patch
+200                patchRect = matplotlib.patches.Rectangle((im.shape[1] - patch.shape[1], 0),
+201                                                        patch.shape[1],
+202                                                        patch.shape[0],
+203                                                        linewidth=1,
+204                                                        edgecolor=cropEdgeColor,
+205                                                        facecolor='none')
 206
-207        ax.add_patch(patchRect)
+207            ax.add_patch(patchRect)
 208
 209        if i < len(colTitles):
 210            ax.set_title(colTitles[i], loc='center', fontdict=fontdict)
@@ -330,6 +330,10 @@ 

 249
 250    ax.xlabel('Energy (keV)')
 251    ax.ylabel('LAC (1/mm)')
+252
+253def savefigTight(fig, path, dpi=200, pad=0.05):
+254    fig.tight_layout()
+255    fig.savefig(path, dpi=dpi, bbox_inches='tight', pad_inches=pad)
 

 
 
@@ -589,11 +593,11 @@ 

 132        'vmin0vmax1=False is not recommended because it might cause incorrect windowing. Make sure you know what you are doing.'
 133    )
 134
-135    cripAssert(len(subimages) == ncols * nrows)
-136    cripAssert(crops is None or len(crops) == nrows)
+135    ncols = len(colTitles)
+136    nrows = len(rowTitles)
 137
-138    ncols = len(colTitles)
-139    nrows = len(rowTitles)
+138    cripAssert(len(subimages) == ncols * nrows, 'Number of subimages should be equal to ncols * nrows.')
+139    cripAssert(crops is None or len(crops) == nrows, 'Number of crops should be equal to nrows.')
 140
 141    if isinstance(cropLocs, str):
 142        cropLocs = [cropLocs] * nrows
@@ -622,47 +626,47 @@ 

 165        ax.get_xaxis().set_ticks([])
 166        list(map(lambda x: x.set_visible(False), ax.spines.values()))
 167
-168        if i % ncols == 0:
+168        if len(rects) != 0 and i % ncols == 0:
 169            ax.add_patch(rects[i // ncols])
 170
-171        patch = zoomIn(im, *crops[i // ncols])
-172        patch = resize(patch, (cropSize, cropSize))
-173        cropLocs = cropLocs[i // ncols]
+171            patch = zoomIn(im, *crops[i // ncols])
+172            patch = resize(patch, (cropSize, cropSize))
+173            cropLocs = cropLocs[i // ncols]
 174
-175        if cropLocs == 'bottom right':
-176            im[-patch.shape[0]:, -patch.shape[1]:] = patch
-177            patchRect = matplotlib.patches.Rectangle((im.shape[1] - patch.shape[1], im.shape[0] - patch.shape[0]),
-178                                                     patch.shape[1],
-179                                                     patch.shape[0],
-180                                                     linewidth=1,
-181                                                     edgecolor=cropEdgeColor,
-182                                                     facecolor='none')
-183        elif cropLocs == 'top left':
-184            im[:patch.shape[0], :patch.shape[1]] = patch
-185            patchRect = matplotlib.patches.Rectangle((0, 0),
-186                                                     patch.shape[1],
-187                                                     patch.shape[0],
-188                                                     linewidth=1,
-189                                                     edgecolor=cropEdgeColor,
-190                                                     facecolor='none')
-191        elif cropLocs == 'bottom left':
-192            im[-patch.shape[0]:, :patch.shape[1]] = patch
-193            patchRect = matplotlib.patches.Rectangle((0, im.shape[0] - patch.shape[0]),
-194                                                     patch.shape[1],
-195                                                     patch.shape[0],
-196                                                     linewidth=1,
-197                                                     edgecolor=cropEdgeColor,
-198                                                     facecolor='none')
-199        elif cropLocs == 'top right':
-200            im[:patch.shape[0], -patch.shape[1]:] = patch
-201            patchRect = matplotlib.patches.Rectangle((im.shape[1] - patch.shape[1], 0),
-202                                                     patch.shape[1],
-203                                                     patch.shape[0],
-204                                                     linewidth=1,
-205                                                     edgecolor=cropEdgeColor,
-206                                                     facecolor='none')
+175            if cropLocs == 'bottom right':
+176                im[-patch.shape[0]:, -patch.shape[1]:] = patch
+177                patchRect = matplotlib.patches.Rectangle((im.shape[1] - patch.shape[1], im.shape[0] - patch.shape[0]),
+178                                                        patch.shape[1],
+179                                                        patch.shape[0],
+180                                                        linewidth=1,
+181                                                        edgecolor=cropEdgeColor,
+182                                                        facecolor='none')
+183            elif cropLocs == 'top left':
+184                im[:patch.shape[0], :patch.shape[1]] = patch
+185                patchRect = matplotlib.patches.Rectangle((0, 0),
+186                                                        patch.shape[1],
+187                                                        patch.shape[0],
+188                                                        linewidth=1,
+189                                                        edgecolor=cropEdgeColor,
+190                                                        facecolor='none')
+191            elif cropLocs == 'bottom left':
+192                im[-patch.shape[0]:, :patch.shape[1]] = patch
+193                patchRect = matplotlib.patches.Rectangle((0, im.shape[0] - patch.shape[0]),
+194                                                        patch.shape[1],
+195                                                        patch.shape[0],
+196                                                        linewidth=1,
+197                                                        edgecolor=cropEdgeColor,
+198                                                        facecolor='none')
+199            elif cropLocs == 'top right':
+200                im[:patch.shape[0], -patch.shape[1]:] = patch
+201                patchRect = matplotlib.patches.Rectangle((im.shape[1] - patch.shape[1], 0),
+202                                                        patch.shape[1],
+203                                                        patch.shape[0],
+204                                                        linewidth=1,
+205                                                        edgecolor=cropEdgeColor,
+206                                                        facecolor='none')
 207
-208        ax.add_patch(patchRect)
+208            ax.add_patch(patchRect)
 209
 210        if i < len(colTitles):
 211            ax.set_title(colTitles[i], loc='center', fontdict=fontdict)
diff --git a/docs/search.js b/docs/search.js
index 58eaec2..847cbce 100644
--- a/docs/search.js
+++ b/docs/search.js
@@ -1,6 +1,6 @@
 window.pdocSearch = (function(){
 /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oCT Recon in Python: An all-in-one tool for Data IO, Pre/Post-process, Physics, Dual Energy, Low Dose and everything.\nhttps://github.com/SEU-CT-Recon/crip
\n"}, "crip.de": {"fullname": "crip.de", "modulename": "crip.de", "kind": "module", "doc": "
Dual-Energy CT module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.de.singleMatMuDecomp": {"fullname": "crip.de.singleMatMuDecomp", "modulename": "crip.de", "qualname": "singleMatMuDecomp", "kind": "function", "doc": "
Decompose single material src's attenuation onto base1 and base2.
\n\n
Return decomposing coefficients along energies when method = 'coeff', or proportion when method = 'prop'.
\n", "signature": "(\tsrc: crip.physics.Atten,\tbase1: crip.physics.Atten,\tbase2: crip.physics.Atten,\tmethod='coeff',\tenergyRange=range(0, 151)) -> numpy.ndarray:", "funcdef": "def"}, "crip.de.calcAttenedSpec": {"fullname": "crip.de.calcAttenedSpec", "modulename": "crip.de", "qualname": "calcAttenedSpec", "kind": "function", "doc": "
Calculate the attenuated spectrum using polychromatic Beer-Lambert law. Supports multiple materials.
\n\n
I.e., \\Omega(E) \\exp (- \\mu(E) L) through all E. L in mm.
\n", "signature": "(\tspec: crip.physics.Spectrum,\tattens: Union[crip.physics.Atten, List[crip.physics.Atten]],\tLs: Union[float, List[float]]) -> crip.physics.Spectrum:", "funcdef": "def"}, "crip.de.calcPostLog": {"fullname": "crip.de.calcPostLog", "modulename": "crip.de", "qualname": "calcPostLog", "kind": "function", "doc": "
Calculate post-log value after attenuation of L length atten. L in mm.
\n", "signature": "(\tspec: crip.physics.Spectrum,\tatten: Union[crip.physics.Atten, List[crip.physics.Atten]],\tL: Union[float, List[float]]) -> float:", "funcdef": "def"}, "crip.de.deDecompGetCoeff": {"fullname": "crip.de.deDecompGetCoeff", "modulename": "crip.de", "qualname": "deDecompGetCoeff", "kind": "function", "doc": "
Calculate the decomposing coefficient (Order 2 with bias term) of two spectra onto two material bases.
\n", "signature": "(\tlowSpec: crip.physics.Spectrum,\thighSpec: crip.physics.Spectrum,\tbase1: crip.physics.Atten,\tlen1: Union[numpy.ndarray, Iterable],\tbase2: crip.physics.Atten,\tlen2: Union[numpy.ndarray, Iterable]):", "funcdef": "def"}, "crip.de.deDecompProj": {"fullname": "crip.de.deDecompProj", "modulename": "crip.de", "qualname": "deDecompProj", "kind": "function", "doc": "
Perform dual-energy decompose in projection domain point-by-point using coeffs.
\n\n
Coefficients can be generated using @see deDecompGetCoeff.
\n", "signature": "(\tlowProj: Union[numpy.ndarray, List[numpy.ndarray]],\thighProj: Union[numpy.ndarray, List[numpy.ndarray]],\tcoeff1: numpy.ndarray,\tcoeff2: numpy.ndarray) -> Tuple[Union[numpy.ndarray, List[numpy.ndarray]], Union[numpy.ndarray, List[numpy.ndarray]]]:", "funcdef": "def"}, "crip.de.deDecompRecon": {"fullname": "crip.de.deDecompRecon", "modulename": "crip.de", "qualname": "deDecompRecon", "kind": "function", "doc": "
Perform dual-energy decompose in reconstruction domain. \\mu values can be calculated using @see calcMu.
\n\n
The values of input volumes should be \\mu value. The outputs are decomposing coefficients.
\n", "signature": "(\tlow: Union[numpy.ndarray, List[numpy.ndarray]],\thigh: Union[numpy.ndarray, List[numpy.ndarray]],\tmuBase1Low: float,\tmuBase1High: float,\tmuBase2Low: float,\tmuBase2High: float,\tcheckCond: bool = True):", "funcdef": "def"}, "crip.de.genMaterialPhantom": {"fullname": "crip.de.genMaterialPhantom", "modulename": "crip.de", "qualname": "genMaterialPhantom", "kind": "function", "doc": "
Generate the phantom of material bases (one is water) from SECT using soft-thresholding.\nzsmooth: smooth window in slice direction.\nsigma: Gaussian smooth sigma in single slice.\n[l, h] defines the fuzzy range of another material, e.g., bone.\nbase: the reference HU of another material.
\n", "signature": "(img, zsmooth=3, sigma=1, l=80, h=300, base=1000):", "funcdef": "def"}, "crip.de.deDecompReconVolCon": {"fullname": "crip.de.deDecompReconVolCon", "modulename": "crip.de", "qualname": "deDecompReconVolCon", "kind": "function", "doc": "
Dual-Energy Triple-Material decomposition with Volume Conservation.\nmuBase* = [low, high]
\n", "signature": "(\tlow: Union[numpy.ndarray, List[numpy.ndarray]],\thigh: Union[numpy.ndarray, List[numpy.ndarray]],\tmuBase1,\tmuBase2,\tmuBase3):", "funcdef": "def"}, "crip.de.teDecompRecon": {"fullname": "crip.de.teDecompRecon", "modulename": "crip.de", "qualname": "teDecompRecon", "kind": "function", "doc": "
Triple-Energy Triple-Material decomposition.\nmuBase* = [low, mid, high]
\n", "signature": "(\tlow: Union[numpy.ndarray, List[numpy.ndarray]],\tmid: Union[numpy.ndarray, List[numpy.ndarray]],\thigh: Union[numpy.ndarray, List[numpy.ndarray]],\tmuBase1,\tmuBase2,\tmuBase3):", "funcdef": "def"}, "crip.de.teDecompReconVolCon": {"fullname": "crip.de.teDecompReconVolCon", "modulename": "crip.de", "qualname": "teDecompReconVolCon", "kind": "function", "doc": "
Triple-Energy Triple-Material decomposition with Volume Conservation.\nmuBase* = [low, mid, high]
\n", "signature": "(\tlow: Union[numpy.ndarray, List[numpy.ndarray]],\tmid: Union[numpy.ndarray, List[numpy.ndarray]],\thigh: Union[numpy.ndarray, List[numpy.ndarray]],\tmuBase1,\tmuBase2,\tmuBase3):", "funcdef": "def"}, "crip.io": {"fullname": "crip.io", "modulename": "crip.io", "kind": "module", "doc": "
I/O module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.io.listDirectory": {"fullname": "crip.io.listDirectory", "modulename": "crip.io", "qualname": "listDirectory", "kind": "function", "doc": "
List files under folder and sort in nat(ural) or dict(ionary) order. \nReturn style can be filename, fullpath or both (path, name) tuple.
\n", "signature": "(\tfolder: str,\tstyle='filename',\tmatch: Union[re.Pattern, str, NoneType] = None,\tsort='nat',\treverse=False) -> Union[List[str], Iterable[Tuple[str, str]]]:", "funcdef": "def"}, "crip.io.imreadDicom": {"fullname": "crip.io.imreadDicom", "modulename": "crip.io", "qualname": "imreadDicom", "kind": "function", "doc": "
Read DICOM file. Return numpy array. Use attrs to supplement DICOM tags for non-standard images.\nYou should be very careful about the whether Rescale Slope is cancelled for CT images.
\n\n
Convert dtype with dtype != None.
\n", "signature": "(\tpath: str,\tdtype=None,\tattrs: Optional[Dict[str, Any]] = None) -> numpy.ndarray:", "funcdef": "def"}, "crip.io.readDicom": {"fullname": "crip.io.readDicom", "modulename": "crip.io", "qualname": "readDicom", "kind": "function", "doc": "
Read DICOM file as pydicom object.
\n", "signature": "(path: str) -> pydicom.dataset.Dataset:", "funcdef": "def"}, "crip.io.imreadRaw": {"fullname": "crip.io.imreadRaw", "modulename": "crip.io", "qualname": "imreadRaw", "kind": "function", "doc": "
Read binary raw file. Return numpy array with shape (nSlice, h, w). offset from head in bytes.\ngap between images in bytes.
\n", "signature": "(\tpath: str,\th: int,\tw: int,\tdtype=<class 'numpy.float32'>,\tnSlice: int = 1,\toffset: int = 0,\tgap: int = 0,\torder='CHW') -> numpy.ndarray:", "funcdef": "def"}, "crip.io.imwriteRaw": {"fullname": "crip.io.imwriteRaw", "modulename": "crip.io", "qualname": "imwriteRaw", "kind": "function", "doc": "
Write raw file. Convert dtype with dtype != None.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tpath: str,\tdtype=None,\torder='CHW'):", "funcdef": "def"}, "crip.io.imreadTiff": {"fullname": "crip.io.imreadTiff", "modulename": "crip.io", "qualname": "imreadTiff", "kind": "function", "doc": "
Read TIFF file. Return numpy array. Convert dtype with dtype != None.
\n", "signature": "(path: str, dtype=None):", "funcdef": "def"}, "crip.io.imwriteTiff": {"fullname": "crip.io.imwriteTiff", "modulename": "crip.io", "qualname": "imwriteTiff", "kind": "function", "doc": "
Write TIFF file. Convert dtype with dtype != None.
\n\n
Note that any floating dtype will be converted to float32.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tpath: str,\tdtype=None):", "funcdef": "def"}, "crip.io.CTPARAMS": {"fullname": "crip.io.CTPARAMS", "modulename": "crip.io", "qualname": "CTPARAMS", "kind": "variable", "doc": "
\n", "default_value": "{'Window Center': {'loc': [40, 4176], 'type': <class 'str'>}, 'Window Width': {'loc': [40, 4177], 'type': <class 'str'>}, 'Manufacturer': {'loc': [8, 112], 'type': <class 'str'>}, 'Manufacturer Model Name': {'loc': [8, 4240], 'type': <class 'str'>}, 'Series Instance UID': {'loc': [32, 14], 'type': <class 'str'>}, 'Body Part Examined': {'loc': [24, 21], 'type': <class 'str'>}, 'Patient Position': {'loc': [24, 20736], 'type': <class 'str'>}, 'KVP': {'loc': [24, 96], 'type': <class 'float'>}, 'X Ray Tube Current': {'loc': [24, 4433], 'type': <class 'float'>}, 'Exposure Time': {'loc': [24, 4432], 'type': <class 'float'>}, 'Exposure': {'loc': [24, 4434], 'type': <class 'float'>}, 'Slice Thickness': {'loc': [24, 80], 'type': <class 'float'>}, 'Data Collection Diameter': {'loc': [24, 144], 'type': <class 'float'>}, 'Reconstruction Diameter': {'loc': [24, 4352], 'type': <class 'float'>}, 'Rows': {'loc': [40, 16], 'type': <class 'int'>}, 'Columns': {'loc': [40, 17], 'type': <class 'int'>}, 'Pixel Spacing': {'loc': [40, 48], 'type': <class 'str'>}, 'Distance Source To Detector': {'loc': [24, 4368], 'type': <class 'float'>}, 'Distance Source To Patient': {'loc': [24, 4369], 'type': <class 'float'>}, 'Rotation Direction': {'loc': [24, 4416], 'type': <class 'str'>}, 'Bits Allocated': {'loc': [40, 256], 'type': <class 'int'>}, 'Table Height': {'loc': [24, 4400], 'type': <class 'float'>}, 'Table Speed': {'loc': [24, 37641], 'type': <class 'float'>}, 'Table Feed Per Rotation': {'loc': [24, 37648], 'type': <class 'float'>}, 'Rescale Intercept': {'loc': [40, 4178], 'type': <class 'float'>}, 'Rescale Slope': {'loc': [40, 4179], 'type': <class 'float'>}, 'Rescale Type': {'loc': [40, 4180], 'type': <class 'str'>}, 'Spiral Pitch Factor': {'loc': [24, 37649], 'type': <class 'float'>}}"}, "crip.io.fetchCTParam": {"fullname": "crip.io.fetchCTParam", "modulename": "crip.io", "qualname": "fetchCTParam", "kind": "function", "doc": "
Fetch CT related parameter from DICOM file. Use readDicom to get DICOM Dataset.
\n\n
@see CTPARAMS in the source code for available keys.
\n", "signature": "(dicom: pydicom.dataset.Dataset, key: str):", "funcdef": "def"}, "crip.io.readEVI": {"fullname": "crip.io.readEVI", "modulename": "crip.io", "qualname": "readEVI", "kind": "function", "doc": "
Read EVI file saved by XCounter Hydra PCD detector. Return the images and metadata.
\n", "signature": "(path: str):", "funcdef": "def"}, "crip.io.imreadEVI": {"fullname": "crip.io.imreadEVI", "modulename": "crip.io", "qualname": "imreadEVI", "kind": "function", "doc": "
Read EVI file saved by XCounter Hydra PCD detector. Return the images only.
\n", "signature": "(path: str):", "funcdef": "def"}, "crip.lowdose": {"fullname": "crip.lowdose", "modulename": "crip.lowdose", "kind": "module", "doc": "
Low Dose CT (LDCT) module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.lowdose.injectGaussianNoise": {"fullname": "crip.lowdose.injectGaussianNoise", "modulename": "crip.lowdose", "qualname": "injectGaussianNoise", "kind": "function", "doc": "
Inject Gaussian noise which obeys distribution N(\\mu, \\sigma^2).
\n", "signature": "(\tprojections: Union[numpy.ndarray, List[numpy.ndarray]],\tsigma: float,\tmu: float = 0) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.lowdose.injectPoissonNoise": {"fullname": "crip.lowdose.injectPoissonNoise", "modulename": "crip.lowdose", "qualname": "injectPoissonNoise", "kind": "function", "doc": "
Inject Poisson noise which obeys distribution P(\\lambda) where \\lambda is the ground-truth quanta in projections.\nprojections must have int type whose value stands for the photon quanta in some way. Floating projections\nshould be manually properly rescaled ahead and scale back as you need since Poisson Distribution only deals with\npositive integers. type_ (postlog or raw) gives the content type of projections, usually you should use\npostlog as input. If you input postlog, the output will also be postlog.
\n", "signature": "(\tprojections: Union[numpy.ndarray, List[numpy.ndarray]],\ttype_: str = 'postlog',\tnPhoton: Union[int, float] = 1,\tsuppressWarning=False) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.lowdose.totalVariation": {"fullname": "crip.lowdose.totalVariation", "modulename": "crip.lowdose", "qualname": "totalVariation", "kind": "function", "doc": "
Computes the Total Variation (TV) of image or images.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]]) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.mangoct": {"fullname": "crip.mangoct", "modulename": "crip.mangoct", "kind": "module", "doc": "
MangoCT integration interface. See https://github.com/SEU-CT-Recon/mandoct
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.mangoct.Mgfbp": {"fullname": "crip.mangoct.Mgfbp", "modulename": "crip.mangoct", "qualname": "Mgfbp", "kind": "class", "doc": "
\n", "bases": "_Mgbin"}, "crip.mangoct.Mgfbp.__init__": {"fullname": "crip.mangoct.Mgfbp.__init__", "modulename": "crip.mangoct", "qualname": "Mgfbp.__init__", "kind": "function", "doc": "
Initialize a handler object to use the FBP tool in mangoct.\nexe is the path to the executable.
\n", "signature": "(exe: str = 'mgfbp', cudaDevice: int = 0, tempDir: str = None)"}, "crip.mangoct.Mgfpj": {"fullname": "crip.mangoct.Mgfpj", "modulename": "crip.mangoct", "qualname": "Mgfpj", "kind": "class", "doc": "
\n", "bases": "_Mgbin"}, "crip.mangoct.Mgfpj.__init__": {"fullname": "crip.mangoct.Mgfpj.__init__", "modulename": "crip.mangoct", "qualname": "Mgfpj.__init__", "kind": "function", "doc": "
Initialize a handler object to use the FPJ tool in mangoct.\nexe is the path to the executable.
\n", "signature": "(exe: str = 'mgfpj', cudaDevice=0, tempDir: str = None)"}, "crip.mangoct.MgfbpConfig": {"fullname": "crip.mangoct.MgfbpConfig", "modulename": "crip.mangoct", "qualname": "MgfbpConfig", "kind": "class", "doc": "
\n", "bases": "_MgConfig"}, "crip.mangoct.MgfbpConfig.setIO": {"fullname": "crip.mangoct.MgfbpConfig.setIO", "modulename": "crip.mangoct", "qualname": "MgfbpConfig.setIO", "kind": "function", "doc": "
\n", "signature": "(\tself,\tInputDir: str,\tOutputDir: str,\tInputFiles: str,\tOutputFilePrefix: str = '',\tOutputFileReplace: List[str] = []):", "funcdef": "def"}, "crip.mangoct.MgfbpConfig.setGeometry": {"fullname": "crip.mangoct.MgfbpConfig.setGeometry", "modulename": "crip.mangoct", "qualname": "MgfbpConfig.setGeometry", "kind": "function", "doc": "
\n", "signature": "(\tself,\tSourceIsocenterDistance: Union[int, float],\tSourceDetectorDistance: Union[int, float],\tTotalScanAngle: Union[int, float],\tDetectorOffcenter: Union[int, float] = 0,\tPMatrixFile: Optional[str] = None,\tSIDFile: Optional[str] = None,\tSDDFile: Optional[str] = None,\tScanAngleFile: Optional[str] = None,\tDetectorOffCenterFile: Optional[str] = None):", "funcdef": "def"}, "crip.mangoct.MgfbpConfig.setSgmFanBeam": {"fullname": "crip.mangoct.MgfbpConfig.setSgmFanBeam", "modulename": "crip.mangoct", "qualname": "MgfbpConfig.setSgmFanBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tSinogramWidth: int,\tSinogramHeight: int,\tViews: int,\tDetectorElementSize: Union[int, float],\tSliceCount: int = 1):", "funcdef": "def"}, "crip.mangoct.MgfbpConfig.setSgmConeBeam": {"fullname": "crip.mangoct.MgfbpConfig.setSgmConeBeam", "modulename": "crip.mangoct", "qualname": "MgfbpConfig.setSgmConeBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tSinogramWidth: int,\tSinogramHeight: int,\tViews: int,\tDetectorElementSize: Union[int, float],\tSliceCount: int,\tSliceThickness: Union[int, float],\tSliceOffCenter: Union[int, float] = 0):", "funcdef": "def"}, "crip.mangoct.MgfbpConfig.setRecFanBeam": {"fullname": "crip.mangoct.MgfbpConfig.setRecFanBeam", "modulename": "crip.mangoct", "qualname": "MgfbpConfig.setRecFanBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tImageDimension: int,\tPixelSize: Union[int, float],\t_Filter: str,\t_FilterParam: Union[float, List[float]],\tImageRotation: Union[int, float] = 0,\tImageCenter: List[float] = [0, 0],\tWaterMu: Optional[float] = None,\tSaveFilteredSinogram: bool = False):", "funcdef": "def"}, "crip.mangoct.MgfbpConfig.setRecConeBeam": {"fullname": "crip.mangoct.MgfbpConfig.setRecConeBeam", "modulename": "crip.mangoct", "qualname": "MgfbpConfig.setRecConeBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tImageDimension: int,\tPixelSize: Union[int, float],\tImageSliceCount: int,\tImageSliceThickness: Union[int, float],\t_Filter: str,\t_FilterParam: Union[float, List[float]],\tImageRotation: Union[int, float] = 0,\tImageCenter: List[float] = [0, 0],\tImageCenterZ: Union[int, float] = 0,\tWaterMu: Optional[float] = None,\tSaveFilteredSinogram: bool = False):", "funcdef": "def"}, "crip.mangoct.MgfpjConfig": {"fullname": "crip.mangoct.MgfpjConfig", "modulename": "crip.mangoct", "qualname": "MgfpjConfig", "kind": "class", "doc": "
\n", "bases": "_MgConfig"}, "crip.mangoct.MgfpjConfig.setIO": {"fullname": "crip.mangoct.MgfpjConfig.setIO", "modulename": "crip.mangoct", "qualname": "MgfpjConfig.setIO", "kind": "function", "doc": "
\n", "signature": "(\tself,\tInputDir: str,\tOutputDir: str,\tInputFiles: str,\tOutputFilePrefix: str = '',\tOutputFileReplace: List[str] = []):", "funcdef": "def"}, "crip.mangoct.MgfpjConfig.setGeometry": {"fullname": "crip.mangoct.MgfpjConfig.setGeometry", "modulename": "crip.mangoct", "qualname": "MgfpjConfig.setGeometry", "kind": "function", "doc": "
\n", "signature": "(\tself,\tSourceIsocenterDistance: Union[int, float],\tSourceDetectorDistance: Union[int, float],\tStartAngle: Union[int, float],\tTotalScanAngle: Union[int, float]):", "funcdef": "def"}, "crip.mangoct.MgfpjConfig.setRecFanBeam": {"fullname": "crip.mangoct.MgfpjConfig.setRecFanBeam", "modulename": "crip.mangoct", "qualname": "MgfpjConfig.setRecFanBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tImageDimension: int,\tPixelSize: Union[int, float],\tSliceCount: int = 1):", "funcdef": "def"}, "crip.mangoct.MgfpjConfig.setRecConeBeam": {"fullname": "crip.mangoct.MgfpjConfig.setRecConeBeam", "modulename": "crip.mangoct", "qualname": "MgfpjConfig.setRecConeBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tImageDimension: int,\tPixelSize: Union[int, float],\tSliceCount: int,\tImageSliceThickness: Union[int, float]):", "funcdef": "def"}, "crip.mangoct.MgfpjConfig.setSgmFanBeam": {"fullname": "crip.mangoct.MgfpjConfig.setSgmFanBeam", "modulename": "crip.mangoct", "qualname": "MgfpjConfig.setSgmFanBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tViews: int,\tDetectorElementCount: int,\tDetectorElementSize: Union[int, float],\tDetectorOffcenter: Union[int, float] = 0,\tOversampleSize: int = 2):", "funcdef": "def"}, "crip.mangoct.MgfpjConfig.setSgmConeBeam": {"fullname": "crip.mangoct.MgfpjConfig.setSgmConeBeam", "modulename": "crip.mangoct", "qualname": "MgfpjConfig.setSgmConeBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tViews: int,\tDetectorElementCount: int,\tDetectorElementSize: Union[int, float],\tDetectorZElementCount: int,\tDetectorElementHeight: Union[int, float],\tDetectorOffcenter: Union[int, float] = 0,\tDetectorZOffcenter: Union[int, float] = 0,\tOversampleSize: int = 2):", "funcdef": "def"}, "crip.metric": {"fullname": "crip.metric", "modulename": "crip.metric", "kind": "module", "doc": "
Metrics calculator module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.metric.calcMAPE": {"fullname": "crip.metric.calcMAPE", "modulename": "crip.metric", "qualname": "calcMAPE", "kind": "function", "doc": "
Compute the Mean Absolution Percentage Error (%) where x is the prediction\nand y is the ground truth.
\n", "signature": "(x, y, eps=1e-06):", "funcdef": "def"}, "crip.metric.calcPSNR": {"fullname": "crip.metric.calcPSNR", "modulename": "crip.metric", "qualname": "calcPSNR", "kind": "function", "doc": "
Compute the Peak Signal Noise Ratio (PSNR) (dB).
\n", "signature": "(x, y, range_=1):", "funcdef": "def"}, "crip.metric.calcSSIM": {"fullname": "crip.metric.calcSSIM", "modulename": "crip.metric", "qualname": "calcSSIM", "kind": "function", "doc": "
Compute the Structural Similarity (SSIM).
\n", "signature": "(x, y, range_=1):", "funcdef": "def"}, "crip.metric.calcRMSE": {"fullname": "crip.metric.calcRMSE", "modulename": "crip.metric", "qualname": "calcRMSE", "kind": "function", "doc": "
Compute the Root Mean Squared Error.
\n", "signature": "(x, y):", "funcdef": "def"}, "crip.physics": {"fullname": "crip.physics", "modulename": "crip.physics", "kind": "module", "doc": "
Physics module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.physics.Spectrum": {"fullname": "crip.physics.Spectrum", "modulename": "crip.physics", "qualname": "Spectrum", "kind": "class", "doc": "
Construct Spectrum object with omega array of every energy.
\n\n
Get \\omega of certain energy (keV):
\n\n
\n
    omega = spec.omega[E]\n
\n
\n"}, "crip.physics.Spectrum.__init__": {"fullname": "crip.physics.Spectrum.__init__", "modulename": "crip.physics", "qualname": "Spectrum.__init__", "kind": "function", "doc": "
\n", "signature": "(omega: numpy.ndarray, unit='keV')"}, "crip.physics.Spectrum.unit": {"fullname": "crip.physics.Spectrum.unit", "modulename": "crip.physics", "qualname": "Spectrum.unit", "kind": "variable", "doc": "
\n"}, "crip.physics.Spectrum.omega": {"fullname": "crip.physics.Spectrum.omega", "modulename": "crip.physics", "qualname": "Spectrum.omega", "kind": "variable", "doc": "
\n"}, "crip.physics.Spectrum.sumOmega": {"fullname": "crip.physics.Spectrum.sumOmega", "modulename": "crip.physics", "qualname": "Spectrum.sumOmega", "kind": "variable", "doc": "
\n"}, "crip.physics.Spectrum.isMonochromatic": {"fullname": "crip.physics.Spectrum.isMonochromatic", "modulename": "crip.physics", "qualname": "Spectrum.isMonochromatic", "kind": "function", "doc": "
\n", "signature": "(self):", "funcdef": "def"}, "crip.physics.Spectrum.fromText": {"fullname": "crip.physics.Spectrum.fromText", "modulename": "crip.physics", "qualname": "Spectrum.fromText", "kind": "function", "doc": "
Parse spectrum text as Spectrum class object.
\n\n
Refer to the document for spectrum text format. @see https://github.com/SEU-CT-Recon/crip
\n", "signature": "(specText: str, unit='keV'):", "funcdef": "def"}, "crip.physics.Spectrum.fromFile": {"fullname": "crip.physics.Spectrum.fromFile", "modulename": "crip.physics", "qualname": "Spectrum.fromFile", "kind": "function", "doc": "
Construct a Spectrum object from spectrum file (first column is energy while second is omega).
\n", "signature": "(path: str, unit='keV'):", "funcdef": "def"}, "crip.physics.Spectrum.monochromatic": {"fullname": "crip.physics.Spectrum.monochromatic", "modulename": "crip.physics", "qualname": "Spectrum.monochromatic", "kind": "function", "doc": "
Construct a monochromatic spectrum.
\n", "signature": "(at: int, unit='keV', omega=100000):", "funcdef": "def"}, "crip.physics.Atten": {"fullname": "crip.physics.Atten", "modulename": "crip.physics", "qualname": "Atten", "kind": "class", "doc": "
Parse atten text as Atten class object. Interpolation is performed to fill DiagEnergyRange.\nRefer to the document for atten text format (NIST ASCII). The density is in g/cm^3.
\n\n
Get \\mu (mm-1) of certain energy (keV):
\n\n
\n
    mu = atten.mu[E]\n
\n
\n"}, "crip.physics.Atten.__init__": {"fullname": "crip.physics.Atten.__init__", "modulename": "crip.physics", "qualname": "Atten.__init__", "kind": "function", "doc": "
\n", "signature": "(muArray: numpy.ndarray, density: Optional[float] = None)"}, "crip.physics.Atten.mu": {"fullname": "crip.physics.Atten.mu", "modulename": "crip.physics", "qualname": "Atten.mu", "kind": "variable", "doc": "
\n"}, "crip.physics.Atten.rho": {"fullname": "crip.physics.Atten.rho", "modulename": "crip.physics", "qualname": "Atten.rho", "kind": "variable", "doc": "
\n"}, "crip.physics.Atten.attenText": {"fullname": "crip.physics.Atten.attenText", "modulename": "crip.physics", "qualname": "Atten.attenText", "kind": "variable", "doc": "
\n"}, "crip.physics.Atten.energyUnit": {"fullname": "crip.physics.Atten.energyUnit", "modulename": "crip.physics", "qualname": "Atten.energyUnit", "kind": "variable", "doc": "
\n"}, "crip.physics.Atten.builtInAttenList": {"fullname": "crip.physics.Atten.builtInAttenList", "modulename": "crip.physics", "qualname": "Atten.builtInAttenList", "kind": "function", "doc": "
Get all built-in materials.
\n", "signature": "() -> List:", "funcdef": "def"}, "crip.physics.Atten.fromBuiltIn": {"fullname": "crip.physics.Atten.fromBuiltIn", "modulename": "crip.physics", "qualname": "Atten.fromBuiltIn", "kind": "function", "doc": "
Get the built-in atten object.\nCall builtInAttenList to get available materials.\nThe density is in g/cm^3.
\n", "signature": "(materialName: str, density: Optional[float] = None):", "funcdef": "def"}, "crip.physics.Atten.fromText": {"fullname": "crip.physics.Atten.fromText", "modulename": "crip.physics", "qualname": "Atten.fromText", "kind": "function", "doc": "
\n", "signature": "(attenText: str, density: float, energyUnit='MeV'):", "funcdef": "def"}, "crip.physics.Atten.fromMuArray": {"fullname": "crip.physics.Atten.fromMuArray", "modulename": "crip.physics", "qualname": "Atten.fromMuArray", "kind": "function", "doc": "
\n", "signature": "(muArray: numpy.ndarray, rho: Optional[float] = None):", "funcdef": "def"}, "crip.physics.Atten.fromFile": {"fullname": "crip.physics.Atten.fromFile", "modulename": "crip.physics", "qualname": "Atten.fromFile", "kind": "function", "doc": "
Construct a new material from file where first column is energy while second\nis \\mu / \\rho.
\n", "signature": "(path: str, rho: float, energyUnit='MeV'):", "funcdef": "def"}, "crip.physics.Material": {"fullname": "crip.physics.Material", "modulename": "crip.physics", "qualname": "Material", "kind": "variable", "doc": "
\n", "default_value": "<class 'crip.physics.Atten'>"}, "crip.physics.calcMu": {"fullname": "crip.physics.calcMu", "modulename": "crip.physics", "qualname": "calcMu", "kind": "function", "doc": "
Calculate the LAC \\mu value (mm-1) of certain atten under a specific spectrum.\nenergyConversion determines the energy conversion efficiency of the detector,\ncan be \"PCD\" (Photon Counting), \"EID\" (Energy Integrating)
\n", "signature": "(\tatten: crip.physics.Atten,\tspec: crip.physics.Spectrum,\tenergyConversion: str) -> float:", "funcdef": "def"}, "crip.physics.DiagEnergyLow": {"fullname": "crip.physics.DiagEnergyLow", "modulename": "crip.physics", "qualname": "DiagEnergyLow", "kind": "variable", "doc": "
\n", "default_value": "0"}, "crip.physics.DiagEnergyHigh": {"fullname": "crip.physics.DiagEnergyHigh", "modulename": "crip.physics", "qualname": "DiagEnergyHigh", "kind": "variable", "doc": "
\n", "default_value": "150"}, "crip.physics.DiagEnergyRange": {"fullname": "crip.physics.DiagEnergyRange", "modulename": "crip.physics", "qualname": "DiagEnergyRange", "kind": "variable", "doc": "
\n", "default_value": "range(0, 151)"}, "crip.physics.DiagEnergyLen": {"fullname": "crip.physics.DiagEnergyLen", "modulename": "crip.physics", "qualname": "DiagEnergyLen", "kind": "variable", "doc": "
\n", "default_value": "151"}, "crip.physics.forwardProjectWithSpectrum": {"fullname": "crip.physics.forwardProjectWithSpectrum", "modulename": "crip.physics", "qualname": "forwardProjectWithSpectrum", "kind": "function", "doc": "
Perform forward projection using spec. lengths is a list of corresponding length [mm] images \n(projection or sinogram) of materials. \nThis function would simulate attenuation and Beam Hardening but no scatter.
\n\n
Set lengths and materials to empty lists to compute the flat field.
\n\n
It's highly recommended to use a monochronmatic spectrum to accelerate if you simulate a lot.
\n", "signature": "(\tlengths: List[numpy.ndarray],\tmaterials: List[crip.physics.Atten],\tspec: crip.physics.Spectrum,\tenergyConversion: str):", "funcdef": "def"}, "crip.physics.brewPowderSolution": {"fullname": "crip.physics.brewPowderSolution", "modulename": "crip.physics", "qualname": "brewPowderSolution", "kind": "function", "doc": "
Generate the Atten of powder solution with certain concentration (mg/mL by default).
\n", "signature": "(\tsolute: crip.physics.Atten,\tsolvent: crip.physics.Atten,\tconcentration: float,\tconcentrationUnit='mg/mL',\trhoSolution: Optional[float] = None) -> crip.physics.Atten:", "funcdef": "def"}, "crip.physics.calcContrastHU": {"fullname": "crip.physics.calcContrastHU", "modulename": "crip.physics", "qualname": "calcContrastHU", "kind": "function", "doc": "
Calculate HU difference resulted by contrast.
\n", "signature": "(\tcontrast: crip.physics.Atten,\tspec: crip.physics.Spectrum,\tenergyConversion: str,\tbase: crip.physics.Atten = <crip.physics.Atten object>):", "funcdef": "def"}, "crip.physics.getCommonDensity": {"fullname": "crip.physics.getCommonDensity", "modulename": "crip.physics", "qualname": "getCommonDensity", "kind": "function", "doc": "
Get the common value of density of a specified material (g/cm^3) from built-in dataset.
\n", "signature": "(materialName: str):", "funcdef": "def"}, "crip.physics.EnergyConversion": {"fullname": "crip.physics.EnergyConversion", "modulename": "crip.physics", "qualname": "EnergyConversion", "kind": "class", "doc": "
An enumeration.
\n", "bases": "enum.Enum"}, "crip.physics.EnergyConversion.EID": {"fullname": "crip.physics.EnergyConversion.EID", "modulename": "crip.physics", "qualname": "EnergyConversion.EID", "kind": "variable", "doc": "
\n", "default_value": "<EnergyConversion.EID: 'EID'>"}, "crip.physics.EnergyConversion.PCD": {"fullname": "crip.physics.EnergyConversion.PCD", "modulename": "crip.physics", "qualname": "EnergyConversion.PCD", "kind": "variable", "doc": "
\n", "default_value": "<EnergyConversion.PCD: 'PCD'>"}, "crip.physics.calcPathLength": {"fullname": "crip.physics.calcPathLength", "modulename": "crip.physics", "qualname": "calcPathLength", "kind": "function", "doc": "
Calculate the X-ray peneration pathlength of an cuboid object based on geometry.\nSID is source-isocenter distance. Currently no offset can be assumed.
\n", "signature": "(\tthickness: float,\tSID: float,\tdetH: int,\tdetW: int,\tdetElementSize: float):", "funcdef": "def"}, "crip.plot": {"fullname": "crip.plot", "modulename": "crip.plot", "kind": "module", "doc": "
Figure artist module of crip.
\n\n
https://github.com/z0gSh1u/crip
\n"}, "crip.plot.smooth": {"fullname": "crip.plot.smooth", "modulename": "crip.plot", "qualname": "smooth", "kind": "function", "doc": "
Smooth an 1D array by moving averaging window. This name follows MATLAB.
\n\n
The implementation is from https://stackoverflow.com/questions/40443020
\n", "signature": "(data: numpy.ndarray, winSize: int = 5):", "funcdef": "def"}, "crip.plot.window": {"fullname": "crip.plot.window", "modulename": "crip.plot", "qualname": "window", "kind": "function", "doc": "
Window img using win (ww, wl) with style 'wwwl' or (left, right) with style 'lr'.\nSet normalize to '0255' to convert to 8-bit image, or '01' to [0, 1] float image.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\twin: Union[Tuple[int], Tuple[float]],\tstyle: str = 'lr',\tnormalize: Optional[str] = None):", "funcdef": "def"}, "crip.plot.average": {"fullname": "crip.plot.average", "modulename": "crip.plot", "qualname": "average", "kind": "function", "doc": "
Average along channel dimension [i - r, i + r].\nUse for example, show CT slices smoother.
\n", "signature": "(imgs: Union[numpy.ndarray, List[numpy.ndarray]], i: int, r: int):", "funcdef": "def"}, "crip.plot.addFont": {"fullname": "crip.plot.addFont", "modulename": "crip.plot", "qualname": "addFont", "kind": "function", "doc": "
Add font files under dir to matplotlib.
\n", "signature": "(dir_: str):", "funcdef": "def"}, "crip.plot.fontdict": {"fullname": "crip.plot.fontdict", "modulename": "crip.plot", "qualname": "fontdict", "kind": "function", "doc": "
\n", "signature": "(family, weight, size):", "funcdef": "def"}, "crip.plot.zoomIn": {"fullname": "crip.plot.zoomIn", "modulename": "crip.plot", "qualname": "zoomIn", "kind": "function", "doc": "
Crop a patch. (row, col) determines the left-top point. (h, w) gives height and width.
\n", "signature": "(img, row, col, h, w):", "funcdef": "def"}, "crip.plot.plotSpectrum": {"fullname": "crip.plot.plotSpectrum", "modulename": "crip.plot", "qualname": "plotSpectrum", "kind": "function", "doc": "
Plot the spectrum in ax. Example\n```\nfig = plt.figure()\nax = fig.add_subplot(111)\nplotSpectrum(ax, spec)
\n", "signature": "(ax: matplotlib.axes._axes.Axes, spec: crip.physics.Spectrum):", "funcdef": "def"}, "crip.plot.makeImageGrid": {"fullname": "crip.plot.makeImageGrid", "modulename": "crip.plot", "qualname": "makeImageGrid", "kind": "function", "doc": "
Make an Image Grid.\n    preprocessor(index, subimage) is applied to each subimage, e.g, perform windowing.\n    figsize accepts that for plt.figure(figsize=...).\n    vmin0vmax1=True guarantees correct windowing when windowed image is not compactly supported in [0, 1].\n    Return the handle of plt.Figure.\n```\n           colTitles\n        +-------------+\n        |             |\nrow     |  subImages  |  ...\nTitles  |       +-----|\n        |       |crop |\n        +-------------+\n              ...
\n", "signature": "(\tsubimages: List[numpy.ndarray],\tcolTitles: List[str],\trowTitles: List[str],\tpreprocessor=None,\tfontdict=None,\tcrops=None,\tcropLocs='bottom right',\tcropEdgeColor='yellow',\tcropSize=64,\tfigsize=None,\tvmin0vmax1=True):", "funcdef": "def"}, "crip.postprocess": {"fullname": "crip.postprocess", "modulename": "crip.postprocess", "kind": "module", "doc": "
Postprocess module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.postprocess.drawCircle": {"fullname": "crip.postprocess.drawCircle", "modulename": "crip.postprocess", "qualname": "drawCircle", "kind": "function", "doc": "
Return points of a circle on center (slice center if None) with radius.
\n\n
This function can be used for preview FOV crop.
\n", "signature": "(\tslice: numpy.ndarray,\tradius: int,\tcenter=None) -> Tuple[numpy.ndarray, numpy.ndarray]:", "funcdef": "def"}, "crip.postprocess.fovCropRadius": {"fullname": "crip.postprocess.fovCropRadius", "modulename": "crip.postprocess", "qualname": "fovCropRadius", "kind": "function", "doc": "
Get the radius (in pixel) of the circle valid FOV of the reconstructed volume.
\n\n
Geometry:\n    - SOD: Source Object Distance\n    - SDD: Source Detector Distance\n    - detWidth: Width of the detector, i.e., nElements * detElementWidth\n    - reconPixSize: Pixel size of the reconstructed image
\n", "signature": "(\tSOD: float,\tSDD: float,\tdetWidth: float,\treconPixSize: float,\troundOff=True) -> float:", "funcdef": "def"}, "crip.postprocess.fovCrop": {"fullname": "crip.postprocess.fovCrop", "modulename": "crip.postprocess", "qualname": "fovCrop", "kind": "function", "doc": "
Crop a circle FOV on reconstructed image img with radius (pixel) \\\nand fill value for outside FOV.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tradius: int,\tfill: Union[int, float] = 0) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.postprocess.muToHU": {"fullname": "crip.postprocess.muToHU", "modulename": "crip.postprocess", "qualname": "muToHU", "kind": "function", "doc": "
Convert \\mu map to HU.
\n\n
HU = (\\mu - \\muWater) / \\muWater * b
\n", "signature": "(\timage: Union[numpy.ndarray, List[numpy.ndarray]],\tmuWater: float,\tb=1000) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.postprocess.huToMu": {"fullname": "crip.postprocess.huToMu", "modulename": "crip.postprocess", "qualname": "huToMu", "kind": "function", "doc": "
Convert HU to mu. (Invert of MuToHU.)
\n", "signature": "(\timage: Union[numpy.ndarray, List[numpy.ndarray]],\tmuWater: float,\tb=1000) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.postprocess.huNoRescale": {"fullname": "crip.postprocess.huNoRescale", "modulename": "crip.postprocess", "qualname": "huNoRescale", "kind": "function", "doc": "
Invert the rescale-slope (y = kx + b) of HU value to get linear relationship between HU and mu.
\n", "signature": "(\timage: Union[numpy.ndarray, List[numpy.ndarray]],\tb: float = -1000,\tk: float = 1) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.postprocess.postlogsToProjections": {"fullname": "crip.postprocess.postlogsToProjections", "modulename": "crip.postprocess", "qualname": "postlogsToProjections", "kind": "function", "doc": "
Invert postlog images to the original projections.
\n", "signature": "(\tpostlogs: Union[numpy.ndarray, List[numpy.ndarray]],\tflat: Union[numpy.ndarray, float]) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.postprocess.binning": {"fullname": "crip.postprocess.binning", "modulename": "crip.postprocess", "qualname": "binning", "kind": "function", "doc": "
Perform binning with rates = (c, h, w) / (h, w).
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\trates: Tuple[int]) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.preprocess": {"fullname": "crip.preprocess", "modulename": "crip.preprocess", "kind": "module", "doc": "
Preprocess module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.preprocess.averageProjections": {"fullname": "crip.preprocess.averageProjections", "modulename": "crip.preprocess", "qualname": "averageProjections", "kind": "function", "doc": "
Average projections. For example, to calculate the flat field.\nProjections can be either (views, H, W) shaped numpy array, or\nviews * (H, W) Python List.
\n", "signature": "(projections: Union[numpy.ndarray, List[numpy.ndarray]]) -> numpy.ndarray:", "funcdef": "def"}, "crip.preprocess.flatDarkFieldCorrection": {"fullname": "crip.preprocess.flatDarkFieldCorrection", "modulename": "crip.preprocess", "qualname": "flatDarkFieldCorrection", "kind": "function", "doc": "
Perform flat field (air) and dark field correction to get post-log value.\nIf flat is 3D and projections is also 3D, the correction will be performed view by view.\nI.e., - log [(X - D) / (F - D)]. Multi projections accepted.\nIf flat is None, air is estimated using the brightest pixel by default.
\n", "signature": "(\tprojections: Union[numpy.ndarray, List[numpy.ndarray]],\tflat: Union[numpy.ndarray, List[numpy.ndarray], float, NoneType] = None,\tdark: Union[numpy.ndarray, List[numpy.ndarray], float] = 0) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.preprocess.projectionsToSinograms": {"fullname": "crip.preprocess.projectionsToSinograms", "modulename": "crip.preprocess", "qualname": "projectionsToSinograms", "kind": "function", "doc": "
Permute projections to sinograms by axes swapping (views, h, w) -> (h, views, w).
\n\n
Note that the width direction is along detector channels of a row.
\n", "signature": "(projections: Union[numpy.ndarray, List[numpy.ndarray]]):", "funcdef": "def"}, "crip.preprocess.sinogramsToProjections": {"fullname": "crip.preprocess.sinogramsToProjections", "modulename": "crip.preprocess", "qualname": "sinogramsToProjections", "kind": "function", "doc": "
Permute sinograms back to projections by axes swapping (h, views, w) -> (views, h, w).
\n\n
Note that the width direction is along detector channels of a row.
\n", "signature": "(sinograms: Union[numpy.ndarray, List[numpy.ndarray]]):", "funcdef": "def"}, "crip.preprocess.padImage": {"fullname": "crip.preprocess.padImage", "modulename": "crip.preprocess", "qualname": "padImage", "kind": "function", "doc": "
Pad the image on four directions using symmetric padding (Up, Right, Down, Left). \\\nmode determines the border value, can be symmetric, edge, constant (zero), reflect. \\\ndecay can be None, cosine, smoothCosine to perform a decay on padded border.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tpadding: Tuple[int, int, int, int],\tmode: str = 'symmetric',\tdecay: Optional[str] = None):", "funcdef": "def"}, "crip.preprocess.padSinogram": {"fullname": "crip.preprocess.padSinogram", "modulename": "crip.preprocess", "qualname": "padSinogram", "kind": "function", "doc": "
Pad sinograms in width direction (same line detector elements) using mode and decay\\\nwith padding (single int, or (right, left)).
\n\n
@see padImage for parameter details.
\n", "signature": "(\tsgms: Union[numpy.ndarray, List[numpy.ndarray]],\tpadding: Union[int, Tuple[int, int]],\tmode='symmetric',\tdecay='smoothCosine'):", "funcdef": "def"}, "crip.preprocess.correctBeamHardeningPolynomial": {"fullname": "crip.preprocess.correctBeamHardeningPolynomial", "modulename": "crip.preprocess", "qualname": "correctBeamHardeningPolynomial", "kind": "function", "doc": "
Apply the polynomial (\\mu L vs. PostLog fit) on postlog to perform basic beam hardening correction.\ncoeffs can be either tuple or np.poly1d. Set bias=True if your coeffs includes the bias (order 0) term.
\n", "signature": "(\tpostlog: Union[numpy.ndarray, List[numpy.ndarray]],\tcoeffs: Union[Tuple, numpy.poly1d],\tbias=True):", "funcdef": "def"}, "crip.preprocess.injectGaussianNoise": {"fullname": "crip.preprocess.injectGaussianNoise", "modulename": "crip.preprocess", "qualname": "injectGaussianNoise", "kind": "function", "doc": "
Inject Gaussian noise which obeys distribution N(\\mu, \\sigma^2).
\n", "signature": "(\tprojections: Union[numpy.ndarray, List[numpy.ndarray]],\tsigma: float,\tmu: float = 0) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.preprocess.injectPoissonNoise": {"fullname": "crip.preprocess.injectPoissonNoise", "modulename": "crip.preprocess", "qualname": "injectPoissonNoise", "kind": "function", "doc": "
Inject Poisson noise which obeys distribution P(\\lambda) where \\lambda is the ground-truth quanta in projections.\nprojections must have int type whose value stands for the photon quanta in some way. Floating projections\nshould be manually properly rescaled ahead and scale back as you need since Poisson Distribution only deals with\npositive integers. type_ (postlog or raw) gives the content type of projections, usually you should use\npostlog as input. If you input postlog, the output will also be postlog.
\n", "signature": "(\tprojections: Union[numpy.ndarray, List[numpy.ndarray]],\ttype_: str = 'postlog',\tnPhoton: Union[int, float] = 1,\tsuppressWarning=False) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.preprocess.binning": {"fullname": "crip.preprocess.binning", "modulename": "crip.preprocess", "qualname": "binning", "kind": "function", "doc": "
Perform binning with rates = (c, h, w) / (h, w).
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\trates: Tuple[int]) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.preprocess.fanToPara": {"fullname": "crip.preprocess.fanToPara", "modulename": "crip.preprocess", "qualname": "fanToPara", "kind": "function", "doc": "
Re-order Fan-Beam sinogram to Parallel-Beam's.\ngammas: fan angles from min to max [rad], e.g., arctan(elementOffcenter / SDD)\nbetas: system rotation angles from min to max [rad]\nsid: Source-Isocenter-Distance [mm]\noThetas: output rotation angle range (min, delta, max) tuple [rad]\noLines: output detector element physical locations range (min, delta, max) tuple [mm], e.g., elementOffcenter array\n```\n       /| <- gamma for detector element X\n      / | <- SID\n     /  |\n====X============ <- detector\n    ^<--^ <- offcenter
\n", "signature": "(\tsgm: numpy.ndarray,\tgammas: numpy.ndarray,\tbetas: numpy.ndarray,\tsid: float,\toThetas: Tuple[float],\toLines: Tuple[float]) -> numpy.ndarray:", "funcdef": "def"}, "crip.preprocess.correctRingArtifactInProj": {"fullname": "crip.preprocess.correctRingArtifactInProj", "modulename": "crip.preprocess", "qualname": "correctRingArtifactInProj", "kind": "function", "doc": "
Apply the ring artifact correction method in projection domain (input postlog sinogram),\nusing gaussian filter in sinogram detector direction [1].\n[1] \u674e\u4fca\u6c5f,\u80e1\u5c11\u5174,\u674e\u4fdd\u78ca\u7b49.CT\u56fe\u50cf\u73af\u72b6\u4f2a\u5f71\u6821\u6b63\u65b9\u6cd5[J].\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66\u5b66\u62a5,2007,(11):1378-1382.DOI:10.13700/j.bh.1001-5965.2007.11.020\nhttps://kns.cnki.net/kcms2/article/abstract?v=xBNwvqFr00JMj5WzBbZMcj9N-kBm9Pi08enuNi8ymtGWVZuwGHdLWwttkgzSivkJSBVk0CVQZxo7DgBmujqhLCaVvvBMoig5RV0B4fDLUnjGQcCPo3O4KfAo4iX4vCwZ&uniplatform=NZKPT&flag=copy
\n", "signature": "(\tsgm: Union[numpy.ndarray, List[numpy.ndarray]],\tsigma: float,\tksize: Optional[int] = None):", "funcdef": "def"}, "crip.preprocess.correctFlatDarkField": {"fullname": "crip.preprocess.correctFlatDarkField", "modulename": "crip.preprocess", "qualname": "correctFlatDarkField", "kind": "function", "doc": "
Perform flat field (air) and dark field correction to get post-log value.\nIf flat is 3D and projections is also 3D, the correction will be performed view by view.\nI.e., - log [(X - D) / (F - D)]. Multi projections accepted.\nIf flat is None, air is estimated using the brightest pixel by default.
\n", "signature": "(\tprojections: Union[numpy.ndarray, List[numpy.ndarray]],\tflat: Union[numpy.ndarray, List[numpy.ndarray], float, NoneType] = None,\tdark: Union[numpy.ndarray, List[numpy.ndarray], float] = 0) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared": {"fullname": "crip.shared", "modulename": "crip.shared", "kind": "module", "doc": "
Shared module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.shared.rotate": {"fullname": "crip.shared.rotate", "modulename": "crip.shared", "qualname": "rotate", "kind": "function", "doc": "
Rotate the image or each image in a volume by deg [DEG] (multiple of 90) clockwise.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tdeg: int) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.verticalFlip": {"fullname": "crip.shared.verticalFlip", "modulename": "crip.shared", "qualname": "verticalFlip", "kind": "function", "doc": "
Vertical flip one image, or each image in a volume.
\n\n
Set copy = True to get a copy of array, otherwise a view only.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tcopy=False) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.horizontalFlip": {"fullname": "crip.shared.horizontalFlip", "modulename": "crip.shared", "qualname": "horizontalFlip", "kind": "function", "doc": "
Horizontal flip one image, or each image in a volume.
\n\n
Set copy = True to get a copy of array, otherwise a view only.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tcopy=False) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.stackFlip": {"fullname": "crip.shared.stackFlip", "modulename": "crip.shared", "qualname": "stackFlip", "kind": "function", "doc": "
Flip a stack w.r.t. z-axis, i.e., reverse slices.
\n\n
Set copy = True to get a copy of array, otherwise a view only.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tcopy=False) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.resize": {"fullname": "crip.shared.resize", "modulename": "crip.shared", "qualname": "resize", "kind": "function", "doc": "
Resize the image or each image in a volume to dsize = (H, W) (if dsize is not None) or scale \nby scale = (facH, facW) using interp (bicubic, linear, nearest available).
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tdsize: Tuple[int] = None,\tscale: Tuple[Union[float, int]] = None,\tinterp: str = 'bicubic') -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.gaussianSmooth": {"fullname": "crip.shared.gaussianSmooth", "modulename": "crip.shared", "qualname": "gaussianSmooth", "kind": "function", "doc": "
Perform Gaussian smooth with kernel size = ksize and Gaussian \\sigma = sigma (int or tuple (x, y)).
\n\n
Leave ksize = None to auto determine to include the majority of Gaussian energy.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tsigma: Union[float, int, Tuple[Union[float, int]]],\tksize: int = None):", "funcdef": "def"}, "crip.shared.stackImages": {"fullname": "crip.shared.stackImages", "modulename": "crip.shared", "qualname": "stackImages", "kind": "function", "doc": "
Stack seperate image into one numpy array. I.e., views * (h, w) -> (views, h, w).
\n\n
Convert dtype with dtype != None.
\n", "signature": "(imgList: List[numpy.ndarray], dtype=None) -> numpy.ndarray:", "funcdef": "def"}, "crip.shared.splitImages": {"fullname": "crip.shared.splitImages", "modulename": "crip.shared", "qualname": "splitImages", "kind": "function", "doc": "
Split stacked image into seperate numpy arrays. I.e., (views, h, w) -> views * (h, w).
\n\n
Convert dtype with dtype != None.
\n", "signature": "(\timgs: Union[numpy.ndarray, List[numpy.ndarray]],\tdtype=None) -> List[numpy.ndarray]:", "funcdef": "def"}, "crip.shared.binning": {"fullname": "crip.shared.binning", "modulename": "crip.shared", "qualname": "binning", "kind": "function", "doc": "
Perform binning with rates = (c, h, w) / (h, w).
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\trates: Tuple[int]) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.transpose": {"fullname": "crip.shared.transpose", "modulename": "crip.shared", "qualname": "transpose", "kind": "function", "doc": "
Transpose vol with axes swapping order.
\n", "signature": "(\tvol: Union[numpy.ndarray, List[numpy.ndarray]],\torder: Tuple[int]) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.permute": {"fullname": "crip.shared.permute", "modulename": "crip.shared", "qualname": "permute", "kind": "function", "doc": "
Permute axes (transpose) from from_ to to, reslicing the reconstructed volume.
\n\n
Valid directions are: 'sagittal', 'coronal', 'transverse'.
\n", "signature": "(\tvol: Union[numpy.ndarray, List[numpy.ndarray]],\tfrom_: str,\tto: str) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.shepplogan": {"fullname": "crip.shared.shepplogan", "modulename": "crip.shared", "qualname": "shepplogan", "kind": "function", "doc": "
Generate the Shepp-Logan phantom.
\n", "signature": "(size: int = 512):", "funcdef": "def"}, "crip.shared.fitPolyV1D2": {"fullname": "crip.shared.fitPolyV1D2", "modulename": "crip.shared", "qualname": "fitPolyV1D2", "kind": "function", "doc": "
Fit a degree-2 polynomial using pseudo-inverse to a variable x1.\nOutput 3 coefficients. If bias is False, the last coefficient will be 0.
\n\n
c[0] * x1**2 + c[1] * x1 + c[2]
\n", "signature": "(x1: numpy.ndarray, y: numpy.ndarray, bias: bool = True):", "funcdef": "def"}, "crip.shared.fitPolyV2D2": {"fullname": "crip.shared.fitPolyV2D2", "modulename": "crip.shared", "qualname": "fitPolyV2D2", "kind": "function", "doc": "
Fit a degree-2 polynomial using pseudo-inverse to a pair of variables x1, x2.\nOutput 6 coefficients. If bias is False, the last coefficient will be 0.
\n\n
c[0] * x1**2 + c[1] * x2**2 + c[2] * x1 * x2 + c[3] * x1 + c[4] * x2 + c[5]
\n", "signature": "(\tx1: numpy.ndarray,\tx2: numpy.ndarray,\ty: numpy.ndarray,\tbias: bool = True):", "funcdef": "def"}, "crip.shared.applyPolyV1D2": {"fullname": "crip.shared.applyPolyV1D2", "modulename": "crip.shared", "qualname": "applyPolyV1D2", "kind": "function", "doc": "
Apply a degree-2 polynomial to a variable x1.
\n\n
c[0] * x1**2 + c[1] * x1 + c[2]
\n", "signature": "(coeff: numpy.ndarray, x1: numpy.ndarray):", "funcdef": "def"}, "crip.shared.applyPolyV2D2": {"fullname": "crip.shared.applyPolyV2D2", "modulename": "crip.shared", "qualname": "applyPolyV2D2", "kind": "function", "doc": "
Apply a degree-2 polynomial to a pair of variables x1, x2.
\n\n
c[0] * x1**2 + c[1] * x2**2 + c[2] * x1 * x2 + c[3] * x1 + c[4] * x2 + c[5]
\n", "signature": "(coeff: numpy.ndarray, x1: numpy.ndarray, x2: numpy.ndarray):", "funcdef": "def"}, "crip.utils": {"fullname": "crip.utils", "modulename": "crip.utils", "kind": "module", "doc": "
Utilities of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.utils.readFileText": {"fullname": "crip.utils.readFileText", "modulename": "crip.utils", "qualname": "readFileText", "kind": "function", "doc": "
\n", "signature": "(path_):", "funcdef": "def"}, "crip.utils.CripException": {"fullname": "crip.utils.CripException", "modulename": "crip.utils", "qualname": "CripException", "kind": "class", "doc": "
The universal expection class for crip.
\n", "bases": "builtins.BaseException"}, "crip.utils.CripException.__init__": {"fullname": "crip.utils.CripException.__init__", "modulename": "crip.utils", "qualname": "CripException.__init__", "kind": "function", "doc": "
\n", "signature": "(*args: object)"}, "crip.utils.cripAssert": {"fullname": "crip.utils.cripAssert", "modulename": "crip.utils", "qualname": "cripAssert", "kind": "function", "doc": "
The only assert method for crip.
\n", "signature": "(cond, hint):", "funcdef": "def"}, "crip.utils.cripWarning": {"fullname": "crip.utils.cripWarning", "modulename": "crip.utils", "qualname": "cripWarning", "kind": "function", "doc": "
The only warning method for crip.
\n", "signature": "(ensure, hint, dumpStack=False):", "funcdef": "def"}, "crip.utils.ConvertListNDArray": {"fullname": "crip.utils.ConvertListNDArray", "modulename": "crip.utils", "qualname": "ConvertListNDArray", "kind": "function", "doc": "
Decorator to convert List[ndarray] to ndarray.
\n", "signature": "(f):", "funcdef": "def"}, "crip.utils.asFloat": {"fullname": "crip.utils.asFloat", "modulename": "crip.utils", "qualname": "asFloat", "kind": "function", "doc": "
Make sure arr has floating type.
\n", "signature": "(arr):", "funcdef": "def"}, "crip.utils.is2D": {"fullname": "crip.utils.is2D", "modulename": "crip.utils", "qualname": "is2D", "kind": "function", "doc": "
\n", "signature": "(x: numpy.ndarray):", "funcdef": "def"}, "crip.utils.is3D": {"fullname": "crip.utils.is3D", "modulename": "crip.utils", "qualname": "is3D", "kind": "function", "doc": "
\n", "signature": "(x: numpy.ndarray):", "funcdef": "def"}, "crip.utils.is2or3D": {"fullname": "crip.utils.is2or3D", "modulename": "crip.utils", "qualname": "is2or3D", "kind": "function", "doc": "
\n", "signature": "(x: numpy.ndarray):", "funcdef": "def"}, "crip.utils.isInt": {"fullname": "crip.utils.isInt", "modulename": "crip.utils", "qualname": "isInt", "kind": "function", "doc": "
\n", "signature": "(n):", "funcdef": "def"}, "crip.utils.isIntDtype": {"fullname": "crip.utils.isIntDtype", "modulename": "crip.utils", "qualname": "isIntDtype", "kind": "function", "doc": "
\n", "signature": "(dtype):", "funcdef": "def"}, "crip.utils.isFloatDtype": {"fullname": "crip.utils.isFloatDtype", "modulename": "crip.utils", "qualname": "isFloatDtype", "kind": "function", "doc": "
\n", "signature": "(dtype):", "funcdef": "def"}, "crip.utils.isIntType": {"fullname": "crip.utils.isIntType", "modulename": "crip.utils", "qualname": "isIntType", "kind": "function", "doc": "
\n", "signature": "(arr: numpy.ndarray):", "funcdef": "def"}, "crip.utils.isFloatType": {"fullname": "crip.utils.isFloatType", "modulename": "crip.utils", "qualname": "isFloatType", "kind": "function", "doc": "
\n", "signature": "(arr: numpy.ndarray):", "funcdef": "def"}, "crip.utils.isType": {"fullname": "crip.utils.isType", "modulename": "crip.utils", "qualname": "isType", "kind": "function", "doc": "
Check if x has type t or isinstance of t.
\n", "signature": "(x, t):", "funcdef": "def"}, "crip.utils.isNumber": {"fullname": "crip.utils.isNumber", "modulename": "crip.utils", "qualname": "isNumber", "kind": "function", "doc": "
\n", "signature": "(a):", "funcdef": "def"}, "crip.utils.isList": {"fullname": "crip.utils.isList", "modulename": "crip.utils", "qualname": "isList", "kind": "function", "doc": "
\n", "signature": "(x):", "funcdef": "def"}, "crip.utils.isListNDArray": {"fullname": "crip.utils.isListNDArray", "modulename": "crip.utils", "qualname": "isListNDArray", "kind": "function", "doc": "
\n", "signature": "(arr):", "funcdef": "def"}, "crip.utils.isOfSameShape": {"fullname": "crip.utils.isOfSameShape", "modulename": "crip.utils", "qualname": "isOfSameShape", "kind": "function", "doc": "
\n", "signature": "(a: numpy.ndarray, b: numpy.ndarray):", "funcdef": "def"}, "crip.utils.inRange": {"fullname": "crip.utils.inRange", "modulename": "crip.utils", "qualname": "inRange", "kind": "function", "doc": "
\n", "signature": "(a, range_=None, low=None, high=None):", "funcdef": "def"}, "crip.utils.inArray": {"fullname": "crip.utils.inArray", "modulename": "crip.utils", "qualname": "inArray", "kind": "function", "doc": "
\n", "signature": "(a, arr):", "funcdef": "def"}, "crip.utils.getAsset": {"fullname": "crip.utils.getAsset", "modulename": "crip.utils", "qualname": "getAsset", "kind": "function", "doc": "
\n", "signature": "(folder, prefix='_asset'):", "funcdef": "def"}, "crip.utils.cvtEnergyUnit": {"fullname": "crip.utils.cvtEnergyUnit", "modulename": "crip.utils", "qualname": "cvtEnergyUnit", "kind": "function", "doc": "
Convert between energy units. (ev, keV, MeV)
\n", "signature": "(arr, from_, to):", "funcdef": "def"}, "crip.utils.cvtLengthUnit": {"fullname": "crip.utils.cvtLengthUnit", "modulename": "crip.utils", "qualname": "cvtLengthUnit", "kind": "function", "doc": "
Convert between length units. (um, mm, cm, m)
\n", "signature": "(arr, from_, to):", "funcdef": "def"}, "crip.utils.cvtMuUnit": {"fullname": "crip.utils.cvtMuUnit", "modulename": "crip.utils", "qualname": "cvtMuUnit", "kind": "function", "doc": "
Convert between mu value units. (um-1, mm-1, cm-1, m-1)
\n", "signature": "(arr, from_, to):", "funcdef": "def"}, "crip.utils.radToDeg": {"fullname": "crip.utils.radToDeg", "modulename": "crip.utils", "qualname": "radToDeg", "kind": "function", "doc": "
\n", "signature": "(x):", "funcdef": "def"}, "crip.utils.degToRad": {"fullname": "crip.utils.degToRad", "modulename": "crip.utils", "qualname": "degToRad", "kind": "function", "doc": "
\n", "signature": "(x):", "funcdef": "def"}, "crip.utils.sysPlatform": {"fullname": "crip.utils.sysPlatform", "modulename": "crip.utils", "qualname": "sysPlatform", "kind": "function", "doc": "
\n", "signature": "():", "funcdef": "def"}, "crip.utils.getHW": {"fullname": "crip.utils.getHW", "modulename": "crip.utils", "qualname": "getHW", "kind": "function", "doc": "
Get height and width of img.
\n", "signature": "(img: numpy.ndarray):", "funcdef": "def"}, "crip.utils.is1D": {"fullname": "crip.utils.is1D", "modulename": "crip.utils", "qualname": "is1D", "kind": "function", "doc": "
\n", "signature": "(x: numpy.ndarray):", "funcdef": "def"}, "crip.utils.as3D": {"fullname": "crip.utils.as3D", "modulename": "crip.utils", "qualname": "as3D", "kind": "function", "doc": "
\n", "signature": "(x: numpy.ndarray):", "funcdef": "def"}, "crip.utils.nextPow2": {"fullname": "crip.utils.nextPow2", "modulename": "crip.utils", "qualname": "nextPow2", "kind": "function", "doc": "
Get the next power of 2 of x.
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+    /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"crip": {"fullname": "crip", "modulename": "crip", "kind": "module", "doc": "
CT Recon in Python: An all-in-one tool for Data IO, Pre/Post-process, Physics, Dual Energy, Low Dose and everything.\nhttps://github.com/SEU-CT-Recon/crip
\n"}, "crip.de": {"fullname": "crip.de", "modulename": "crip.de", "kind": "module", "doc": "
Dual-Energy CT module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.de.singleMatMuDecomp": {"fullname": "crip.de.singleMatMuDecomp", "modulename": "crip.de", "qualname": "singleMatMuDecomp", "kind": "function", "doc": "
Decompose single material src's attenuation onto base1 and base2.
\n\n
Return decomposing coefficients along energies when method = 'coeff', or proportion when method = 'prop'.
\n", "signature": "(\tsrc: crip.physics.Atten,\tbase1: crip.physics.Atten,\tbase2: crip.physics.Atten,\tmethod='coeff',\tenergyRange=range(0, 151)) -> numpy.ndarray:", "funcdef": "def"}, "crip.de.calcAttenedSpec": {"fullname": "crip.de.calcAttenedSpec", "modulename": "crip.de", "qualname": "calcAttenedSpec", "kind": "function", "doc": "
Calculate the attenuated spectrum using polychromatic Beer-Lambert law. Supports multiple materials.
\n\n
I.e., \\Omega(E) \\exp (- \\mu(E) L) through all E. L in mm.
\n", "signature": "(\tspec: crip.physics.Spectrum,\tattens: Union[crip.physics.Atten, List[crip.physics.Atten]],\tLs: Union[float, List[float]]) -> crip.physics.Spectrum:", "funcdef": "def"}, "crip.de.calcPostLog": {"fullname": "crip.de.calcPostLog", "modulename": "crip.de", "qualname": "calcPostLog", "kind": "function", "doc": "
Calculate post-log value after attenuation of L length atten. L in mm.
\n", "signature": "(\tspec: crip.physics.Spectrum,\tatten: Union[crip.physics.Atten, List[crip.physics.Atten]],\tL: Union[float, List[float]]) -> float:", "funcdef": "def"}, "crip.de.deDecompGetCoeff": {"fullname": "crip.de.deDecompGetCoeff", "modulename": "crip.de", "qualname": "deDecompGetCoeff", "kind": "function", "doc": "
Calculate the decomposing coefficient (Order 2 with bias term) of two spectra onto two material bases.
\n", "signature": "(\tlowSpec: crip.physics.Spectrum,\thighSpec: crip.physics.Spectrum,\tbase1: crip.physics.Atten,\tlen1: Union[numpy.ndarray, Iterable],\tbase2: crip.physics.Atten,\tlen2: Union[numpy.ndarray, Iterable]):", "funcdef": "def"}, "crip.de.deDecompProj": {"fullname": "crip.de.deDecompProj", "modulename": "crip.de", "qualname": "deDecompProj", "kind": "function", "doc": "
Perform dual-energy decompose in projection domain point-by-point using coeffs.
\n\n
Coefficients can be generated using @see deDecompGetCoeff.
\n", "signature": "(\tlowProj: Union[numpy.ndarray, List[numpy.ndarray]],\thighProj: Union[numpy.ndarray, List[numpy.ndarray]],\tcoeff1: numpy.ndarray,\tcoeff2: numpy.ndarray) -> Tuple[Union[numpy.ndarray, List[numpy.ndarray]], Union[numpy.ndarray, List[numpy.ndarray]]]:", "funcdef": "def"}, "crip.de.deDecompRecon": {"fullname": "crip.de.deDecompRecon", "modulename": "crip.de", "qualname": "deDecompRecon", "kind": "function", "doc": "
Perform dual-energy decompose in reconstruction domain. \\mu values can be calculated using @see calcMu.
\n\n
The values of input volumes should be \\mu value. The outputs are decomposing coefficients.
\n", "signature": "(\tlow: Union[numpy.ndarray, List[numpy.ndarray]],\thigh: Union[numpy.ndarray, List[numpy.ndarray]],\tmuBase1Low: float,\tmuBase1High: float,\tmuBase2Low: float,\tmuBase2High: float,\tcheckCond: bool = True):", "funcdef": "def"}, "crip.de.genMaterialPhantom": {"fullname": "crip.de.genMaterialPhantom", "modulename": "crip.de", "qualname": "genMaterialPhantom", "kind": "function", "doc": "
Generate the phantom of material bases (one is water) from SECT using soft-thresholding.\nzsmooth: smooth window in slice direction.\nsigma: Gaussian smooth sigma in single slice.\n[l, h] defines the fuzzy range of another material, e.g., bone.\nbase: the reference HU of another material.
\n", "signature": "(img, zsmooth=3, sigma=1, l=80, h=300, base=1000):", "funcdef": "def"}, "crip.de.deDecompReconVolCon": {"fullname": "crip.de.deDecompReconVolCon", "modulename": "crip.de", "qualname": "deDecompReconVolCon", "kind": "function", "doc": "
Dual-Energy Triple-Material decomposition with Volume Conservation.\nmuBase* = [low, high]
\n", "signature": "(\tlow: Union[numpy.ndarray, List[numpy.ndarray]],\thigh: Union[numpy.ndarray, List[numpy.ndarray]],\tmuBase1,\tmuBase2,\tmuBase3):", "funcdef": "def"}, "crip.de.teDecompRecon": {"fullname": "crip.de.teDecompRecon", "modulename": "crip.de", "qualname": "teDecompRecon", "kind": "function", "doc": "
Triple-Energy Triple-Material decomposition.\nmuBase* = [low, mid, high]
\n", "signature": "(\tlow: Union[numpy.ndarray, List[numpy.ndarray]],\tmid: Union[numpy.ndarray, List[numpy.ndarray]],\thigh: Union[numpy.ndarray, List[numpy.ndarray]],\tmuBase1,\tmuBase2,\tmuBase3):", "funcdef": "def"}, "crip.de.teDecompReconVolCon": {"fullname": "crip.de.teDecompReconVolCon", "modulename": "crip.de", "qualname": "teDecompReconVolCon", "kind": "function", "doc": "
Triple-Energy Triple-Material decomposition with Volume Conservation.\nmuBase* = [low, mid, high]
\n", "signature": "(\tlow: Union[numpy.ndarray, List[numpy.ndarray]],\tmid: Union[numpy.ndarray, List[numpy.ndarray]],\thigh: Union[numpy.ndarray, List[numpy.ndarray]],\tmuBase1,\tmuBase2,\tmuBase3):", "funcdef": "def"}, "crip.io": {"fullname": "crip.io", "modulename": "crip.io", "kind": "module", "doc": "
I/O module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.io.listDirectory": {"fullname": "crip.io.listDirectory", "modulename": "crip.io", "qualname": "listDirectory", "kind": "function", "doc": "
List files under folder and sort in nat(ural) or dict(ionary) order. \nReturn style can be filename, fullpath or both (path, name) tuple.
\n", "signature": "(\tfolder: str,\tstyle='filename',\tmatch: Union[re.Pattern, str, NoneType] = None,\tsort='nat',\treverse=False) -> Union[List[str], Iterable[Tuple[str, str]]]:", "funcdef": "def"}, "crip.io.imreadDicom": {"fullname": "crip.io.imreadDicom", "modulename": "crip.io", "qualname": "imreadDicom", "kind": "function", "doc": "
Read DICOM file. Return numpy array. Use attrs to supplement DICOM tags for non-standard images.\nYou should be very careful about the whether Rescale Slope is cancelled for CT images.
\n\n
Convert dtype with dtype != None.
\n", "signature": "(\tpath: str,\tdtype=None,\tattrs: Optional[Dict[str, Any]] = None) -> numpy.ndarray:", "funcdef": "def"}, "crip.io.readDicom": {"fullname": "crip.io.readDicom", "modulename": "crip.io", "qualname": "readDicom", "kind": "function", "doc": "
Read DICOM file as pydicom object.
\n", "signature": "(path: str) -> pydicom.dataset.Dataset:", "funcdef": "def"}, "crip.io.imreadRaw": {"fullname": "crip.io.imreadRaw", "modulename": "crip.io", "qualname": "imreadRaw", "kind": "function", "doc": "
Read binary raw file. Return numpy array with shape (nSlice, h, w). offset from head in bytes.\ngap between images in bytes.
\n", "signature": "(\tpath: str,\th: int,\tw: int,\tdtype=<class 'numpy.float32'>,\tnSlice: int = 1,\toffset: int = 0,\tgap: int = 0,\torder='CHW') -> numpy.ndarray:", "funcdef": "def"}, "crip.io.imwriteRaw": {"fullname": "crip.io.imwriteRaw", "modulename": "crip.io", "qualname": "imwriteRaw", "kind": "function", "doc": "
Write raw file. Convert dtype with dtype != None.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tpath: str,\tdtype=None,\torder='CHW'):", "funcdef": "def"}, "crip.io.imreadTiff": {"fullname": "crip.io.imreadTiff", "modulename": "crip.io", "qualname": "imreadTiff", "kind": "function", "doc": "
Read TIFF file. Return numpy array. Convert dtype with dtype != None.
\n", "signature": "(path: str, dtype=None):", "funcdef": "def"}, "crip.io.imwriteTiff": {"fullname": "crip.io.imwriteTiff", "modulename": "crip.io", "qualname": "imwriteTiff", "kind": "function", "doc": "
Write TIFF file. Convert dtype with dtype != None.
\n\n
Note that any floating dtype will be converted to float32.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tpath: str,\tdtype=None):", "funcdef": "def"}, "crip.io.CTPARAMS": {"fullname": "crip.io.CTPARAMS", "modulename": "crip.io", "qualname": "CTPARAMS", "kind": "variable", "doc": "
\n", "default_value": "{'Window Center': {'loc': [40, 4176], 'type': <class 'str'>}, 'Window Width': {'loc': [40, 4177], 'type': <class 'str'>}, 'Manufacturer': {'loc': [8, 112], 'type': <class 'str'>}, 'Manufacturer Model Name': {'loc': [8, 4240], 'type': <class 'str'>}, 'Series Instance UID': {'loc': [32, 14], 'type': <class 'str'>}, 'Body Part Examined': {'loc': [24, 21], 'type': <class 'str'>}, 'Patient Position': {'loc': [24, 20736], 'type': <class 'str'>}, 'KVP': {'loc': [24, 96], 'type': <class 'float'>}, 'X Ray Tube Current': {'loc': [24, 4433], 'type': <class 'float'>}, 'Exposure Time': {'loc': [24, 4432], 'type': <class 'float'>}, 'Exposure': {'loc': [24, 4434], 'type': <class 'float'>}, 'Slice Thickness': {'loc': [24, 80], 'type': <class 'float'>}, 'Data Collection Diameter': {'loc': [24, 144], 'type': <class 'float'>}, 'Reconstruction Diameter': {'loc': [24, 4352], 'type': <class 'float'>}, 'Rows': {'loc': [40, 16], 'type': <class 'int'>}, 'Columns': {'loc': [40, 17], 'type': <class 'int'>}, 'Pixel Spacing': {'loc': [40, 48], 'type': <class 'str'>}, 'Distance Source To Detector': {'loc': [24, 4368], 'type': <class 'float'>}, 'Distance Source To Patient': {'loc': [24, 4369], 'type': <class 'float'>}, 'Rotation Direction': {'loc': [24, 4416], 'type': <class 'str'>}, 'Bits Allocated': {'loc': [40, 256], 'type': <class 'int'>}, 'Table Height': {'loc': [24, 4400], 'type': <class 'float'>}, 'Table Speed': {'loc': [24, 37641], 'type': <class 'float'>}, 'Table Feed Per Rotation': {'loc': [24, 37648], 'type': <class 'float'>}, 'Rescale Intercept': {'loc': [40, 4178], 'type': <class 'float'>}, 'Rescale Slope': {'loc': [40, 4179], 'type': <class 'float'>}, 'Rescale Type': {'loc': [40, 4180], 'type': <class 'str'>}, 'Spiral Pitch Factor': {'loc': [24, 37649], 'type': <class 'float'>}}"}, "crip.io.fetchCTParam": {"fullname": "crip.io.fetchCTParam", "modulename": "crip.io", "qualname": "fetchCTParam", "kind": "function", "doc": "
Fetch CT related parameter from DICOM file. Use readDicom to get DICOM Dataset.
\n\n
@see CTPARAMS in the source code for available keys.
\n", "signature": "(dicom: pydicom.dataset.Dataset, key: str):", "funcdef": "def"}, "crip.io.readEVI": {"fullname": "crip.io.readEVI", "modulename": "crip.io", "qualname": "readEVI", "kind": "function", "doc": "
Read EVI file saved by XCounter Hydra PCD detector. Return the images and metadata.
\n", "signature": "(path: str):", "funcdef": "def"}, "crip.io.imreadEVI": {"fullname": "crip.io.imreadEVI", "modulename": "crip.io", "qualname": "imreadEVI", "kind": "function", "doc": "
Read EVI file saved by XCounter Hydra PCD detector. Return the images only.
\n", "signature": "(path: str):", "funcdef": "def"}, "crip.lowdose": {"fullname": "crip.lowdose", "modulename": "crip.lowdose", "kind": "module", "doc": "
Low Dose CT (LDCT) module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.lowdose.injectGaussianNoise": {"fullname": "crip.lowdose.injectGaussianNoise", "modulename": "crip.lowdose", "qualname": "injectGaussianNoise", "kind": "function", "doc": "
Inject Gaussian noise which obeys distribution N(\\mu, \\sigma^2).
\n", "signature": "(\tprojections: Union[numpy.ndarray, List[numpy.ndarray]],\tsigma: float,\tmu: float = 0) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.lowdose.injectPoissonNoise": {"fullname": "crip.lowdose.injectPoissonNoise", "modulename": "crip.lowdose", "qualname": "injectPoissonNoise", "kind": "function", "doc": "
Inject Poisson noise which obeys distribution P(\\lambda) where \\lambda is the ground-truth quanta in projections.\nprojections must have int type whose value stands for the photon quanta in some way. Floating projections\nshould be manually properly rescaled ahead and scale back as you need since Poisson Distribution only deals with\npositive integers. type_ (postlog or raw) gives the content type of projections, usually you should use\npostlog as input. If you input postlog, the output will also be postlog.
\n", "signature": "(\tprojections: Union[numpy.ndarray, List[numpy.ndarray]],\ttype_: str = 'postlog',\tnPhoton: Union[int, float] = 1,\tsuppressWarning=False) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.lowdose.totalVariation": {"fullname": "crip.lowdose.totalVariation", "modulename": "crip.lowdose", "qualname": "totalVariation", "kind": "function", "doc": "
Computes the Total Variation (TV) of image or images.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]]) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.mangoct": {"fullname": "crip.mangoct", "modulename": "crip.mangoct", "kind": "module", "doc": "
MangoCT integration interface. See https://github.com/SEU-CT-Recon/mandoct
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.mangoct.Mgfbp": {"fullname": "crip.mangoct.Mgfbp", "modulename": "crip.mangoct", "qualname": "Mgfbp", "kind": "class", "doc": "
\n", "bases": "_Mgbin"}, "crip.mangoct.Mgfbp.__init__": {"fullname": "crip.mangoct.Mgfbp.__init__", "modulename": "crip.mangoct", "qualname": "Mgfbp.__init__", "kind": "function", "doc": "
Initialize a handler object to use the FBP tool in mangoct.\nexe is the path to the executable.
\n", "signature": "(exe: str = 'mgfbp', cudaDevice: int = 0, tempDir: str = None)"}, "crip.mangoct.Mgfpj": {"fullname": "crip.mangoct.Mgfpj", "modulename": "crip.mangoct", "qualname": "Mgfpj", "kind": "class", "doc": "
\n", "bases": "_Mgbin"}, "crip.mangoct.Mgfpj.__init__": {"fullname": "crip.mangoct.Mgfpj.__init__", "modulename": "crip.mangoct", "qualname": "Mgfpj.__init__", "kind": "function", "doc": "
Initialize a handler object to use the FPJ tool in mangoct.\nexe is the path to the executable.
\n", "signature": "(exe: str = 'mgfpj', cudaDevice=0, tempDir: str = None)"}, "crip.mangoct.MgfbpConfig": {"fullname": "crip.mangoct.MgfbpConfig", "modulename": "crip.mangoct", "qualname": "MgfbpConfig", "kind": "class", "doc": "
\n", "bases": "_MgConfig"}, "crip.mangoct.MgfbpConfig.setIO": {"fullname": "crip.mangoct.MgfbpConfig.setIO", "modulename": "crip.mangoct", "qualname": "MgfbpConfig.setIO", "kind": "function", "doc": "
\n", "signature": "(\tself,\tInputDir: str,\tOutputDir: str,\tInputFiles: str,\tOutputFilePrefix: str = '',\tOutputFileReplace: List[str] = []):", "funcdef": "def"}, "crip.mangoct.MgfbpConfig.setGeometry": {"fullname": "crip.mangoct.MgfbpConfig.setGeometry", "modulename": "crip.mangoct", "qualname": "MgfbpConfig.setGeometry", "kind": "function", "doc": "
\n", "signature": "(\tself,\tSourceIsocenterDistance: Union[int, float],\tSourceDetectorDistance: Union[int, float],\tTotalScanAngle: Union[int, float],\tDetectorOffcenter: Union[int, float] = 0,\tPMatrixFile: Optional[str] = None,\tSIDFile: Optional[str] = None,\tSDDFile: Optional[str] = None,\tScanAngleFile: Optional[str] = None,\tDetectorOffCenterFile: Optional[str] = None):", "funcdef": "def"}, "crip.mangoct.MgfbpConfig.setSgmFanBeam": {"fullname": "crip.mangoct.MgfbpConfig.setSgmFanBeam", "modulename": "crip.mangoct", "qualname": "MgfbpConfig.setSgmFanBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tSinogramWidth: int,\tSinogramHeight: int,\tViews: int,\tDetectorElementSize: Union[int, float],\tSliceCount: int = 1):", "funcdef": "def"}, "crip.mangoct.MgfbpConfig.setSgmConeBeam": {"fullname": "crip.mangoct.MgfbpConfig.setSgmConeBeam", "modulename": "crip.mangoct", "qualname": "MgfbpConfig.setSgmConeBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tSinogramWidth: int,\tSinogramHeight: int,\tViews: int,\tDetectorElementSize: Union[int, float],\tSliceCount: int,\tSliceThickness: Union[int, float],\tSliceOffCenter: Union[int, float] = 0):", "funcdef": "def"}, "crip.mangoct.MgfbpConfig.setRecFanBeam": {"fullname": "crip.mangoct.MgfbpConfig.setRecFanBeam", "modulename": "crip.mangoct", "qualname": "MgfbpConfig.setRecFanBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tImageDimension: int,\tPixelSize: Union[int, float],\t_Filter: str,\t_FilterParam: Union[float, List[float]],\tImageRotation: Union[int, float] = 0,\tImageCenter: List[float] = [0, 0],\tWaterMu: Optional[float] = None,\tSaveFilteredSinogram: bool = False):", "funcdef": "def"}, "crip.mangoct.MgfbpConfig.setRecConeBeam": {"fullname": "crip.mangoct.MgfbpConfig.setRecConeBeam", "modulename": "crip.mangoct", "qualname": "MgfbpConfig.setRecConeBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tImageDimension: int,\tPixelSize: Union[int, float],\tImageSliceCount: int,\tImageSliceThickness: Union[int, float],\t_Filter: str,\t_FilterParam: Union[float, List[float]],\tImageRotation: Union[int, float] = 0,\tImageCenter: List[float] = [0, 0],\tImageCenterZ: Union[int, float] = 0,\tWaterMu: Optional[float] = None,\tSaveFilteredSinogram: bool = False):", "funcdef": "def"}, "crip.mangoct.MgfpjConfig": {"fullname": "crip.mangoct.MgfpjConfig", "modulename": "crip.mangoct", "qualname": "MgfpjConfig", "kind": "class", "doc": "
\n", "bases": "_MgConfig"}, "crip.mangoct.MgfpjConfig.setIO": {"fullname": "crip.mangoct.MgfpjConfig.setIO", "modulename": "crip.mangoct", "qualname": "MgfpjConfig.setIO", "kind": "function", "doc": "
\n", "signature": "(\tself,\tInputDir: str,\tOutputDir: str,\tInputFiles: str,\tOutputFilePrefix: str = '',\tOutputFileReplace: List[str] = []):", "funcdef": "def"}, "crip.mangoct.MgfpjConfig.setGeometry": {"fullname": "crip.mangoct.MgfpjConfig.setGeometry", "modulename": "crip.mangoct", "qualname": "MgfpjConfig.setGeometry", "kind": "function", "doc": "
\n", "signature": "(\tself,\tSourceIsocenterDistance: Union[int, float],\tSourceDetectorDistance: Union[int, float],\tStartAngle: Union[int, float],\tTotalScanAngle: Union[int, float]):", "funcdef": "def"}, "crip.mangoct.MgfpjConfig.setRecFanBeam": {"fullname": "crip.mangoct.MgfpjConfig.setRecFanBeam", "modulename": "crip.mangoct", "qualname": "MgfpjConfig.setRecFanBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tImageDimension: int,\tPixelSize: Union[int, float],\tSliceCount: int = 1):", "funcdef": "def"}, "crip.mangoct.MgfpjConfig.setRecConeBeam": {"fullname": "crip.mangoct.MgfpjConfig.setRecConeBeam", "modulename": "crip.mangoct", "qualname": "MgfpjConfig.setRecConeBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tImageDimension: int,\tPixelSize: Union[int, float],\tSliceCount: int,\tImageSliceThickness: Union[int, float]):", "funcdef": "def"}, "crip.mangoct.MgfpjConfig.setSgmFanBeam": {"fullname": "crip.mangoct.MgfpjConfig.setSgmFanBeam", "modulename": "crip.mangoct", "qualname": "MgfpjConfig.setSgmFanBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tViews: int,\tDetectorElementCount: int,\tDetectorElementSize: Union[int, float],\tDetectorOffcenter: Union[int, float] = 0,\tOversampleSize: int = 2):", "funcdef": "def"}, "crip.mangoct.MgfpjConfig.setSgmConeBeam": {"fullname": "crip.mangoct.MgfpjConfig.setSgmConeBeam", "modulename": "crip.mangoct", "qualname": "MgfpjConfig.setSgmConeBeam", "kind": "function", "doc": "
\n", "signature": "(\tself,\tViews: int,\tDetectorElementCount: int,\tDetectorElementSize: Union[int, float],\tDetectorZElementCount: int,\tDetectorElementHeight: Union[int, float],\tDetectorOffcenter: Union[int, float] = 0,\tDetectorZOffcenter: Union[int, float] = 0,\tOversampleSize: int = 2):", "funcdef": "def"}, "crip.metric": {"fullname": "crip.metric", "modulename": "crip.metric", "kind": "module", "doc": "
Metrics calculator module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.metric.calcMAPE": {"fullname": "crip.metric.calcMAPE", "modulename": "crip.metric", "qualname": "calcMAPE", "kind": "function", "doc": "
Compute the Mean Absolution Percentage Error (%) where x is the prediction\nand y is the ground truth.
\n", "signature": "(x, y, eps=1e-06):", "funcdef": "def"}, "crip.metric.calcPSNR": {"fullname": "crip.metric.calcPSNR", "modulename": "crip.metric", "qualname": "calcPSNR", "kind": "function", "doc": "
Compute the Peak Signal Noise Ratio (PSNR) (dB).
\n", "signature": "(\tx: Union[numpy.ndarray, List[numpy.ndarray]],\ty: Union[numpy.ndarray, List[numpy.ndarray]],\trange_=1):", "funcdef": "def"}, "crip.metric.calcSSIM": {"fullname": "crip.metric.calcSSIM", "modulename": "crip.metric", "qualname": "calcSSIM", "kind": "function", "doc": "
Compute the Structural Similarity (SSIM).
\n", "signature": "(\tx: Union[numpy.ndarray, List[numpy.ndarray]],\ty: Union[numpy.ndarray, List[numpy.ndarray]],\trange_=1):", "funcdef": "def"}, "crip.metric.calcRMSE": {"fullname": "crip.metric.calcRMSE", "modulename": "crip.metric", "qualname": "calcRMSE", "kind": "function", "doc": "
Compute the Root Mean Squared Error.
\n", "signature": "(x, y):", "funcdef": "def"}, "crip.physics": {"fullname": "crip.physics", "modulename": "crip.physics", "kind": "module", "doc": "
Physics module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.physics.Spectrum": {"fullname": "crip.physics.Spectrum", "modulename": "crip.physics", "qualname": "Spectrum", "kind": "class", "doc": "
Construct Spectrum object with omega array of every energy.
\n\n
Get \\omega of certain energy (keV):
\n\n
\n
    omega = spec.omega[E]\n
\n
\n"}, "crip.physics.Spectrum.__init__": {"fullname": "crip.physics.Spectrum.__init__", "modulename": "crip.physics", "qualname": "Spectrum.__init__", "kind": "function", "doc": "
\n", "signature": "(omega: numpy.ndarray, unit='keV')"}, "crip.physics.Spectrum.unit": {"fullname": "crip.physics.Spectrum.unit", "modulename": "crip.physics", "qualname": "Spectrum.unit", "kind": "variable", "doc": "
\n"}, "crip.physics.Spectrum.omega": {"fullname": "crip.physics.Spectrum.omega", "modulename": "crip.physics", "qualname": "Spectrum.omega", "kind": "variable", "doc": "
\n"}, "crip.physics.Spectrum.sumOmega": {"fullname": "crip.physics.Spectrum.sumOmega", "modulename": "crip.physics", "qualname": "Spectrum.sumOmega", "kind": "variable", "doc": "
\n"}, "crip.physics.Spectrum.isMonochromatic": {"fullname": "crip.physics.Spectrum.isMonochromatic", "modulename": "crip.physics", "qualname": "Spectrum.isMonochromatic", "kind": "function", "doc": "
\n", "signature": "(self):", "funcdef": "def"}, "crip.physics.Spectrum.fromText": {"fullname": "crip.physics.Spectrum.fromText", "modulename": "crip.physics", "qualname": "Spectrum.fromText", "kind": "function", "doc": "
Parse spectrum text as Spectrum class object.
\n\n
Refer to the document for spectrum text format. @see https://github.com/SEU-CT-Recon/crip
\n", "signature": "(specText: str, unit='keV'):", "funcdef": "def"}, "crip.physics.Spectrum.fromFile": {"fullname": "crip.physics.Spectrum.fromFile", "modulename": "crip.physics", "qualname": "Spectrum.fromFile", "kind": "function", "doc": "
Construct a Spectrum object from spectrum file (first column is energy while second is omega).
\n", "signature": "(path: str, unit='keV'):", "funcdef": "def"}, "crip.physics.Spectrum.monochromatic": {"fullname": "crip.physics.Spectrum.monochromatic", "modulename": "crip.physics", "qualname": "Spectrum.monochromatic", "kind": "function", "doc": "
Construct a monochromatic spectrum.
\n", "signature": "(at: int, unit='keV', omega=100000):", "funcdef": "def"}, "crip.physics.Atten": {"fullname": "crip.physics.Atten", "modulename": "crip.physics", "qualname": "Atten", "kind": "class", "doc": "
Parse atten text as Atten class object. Interpolation is performed to fill DiagEnergyRange.\nRefer to the document for atten text format (NIST ASCII). The density is in g/cm^3.
\n\n
Get \\mu (mm-1) of certain energy (keV):
\n\n
\n
    mu = atten.mu[E]\n
\n
\n"}, "crip.physics.Atten.__init__": {"fullname": "crip.physics.Atten.__init__", "modulename": "crip.physics", "qualname": "Atten.__init__", "kind": "function", "doc": "
\n", "signature": "(muArray: numpy.ndarray, density: Optional[float] = None)"}, "crip.physics.Atten.mu": {"fullname": "crip.physics.Atten.mu", "modulename": "crip.physics", "qualname": "Atten.mu", "kind": "variable", "doc": "
\n"}, "crip.physics.Atten.rho": {"fullname": "crip.physics.Atten.rho", "modulename": "crip.physics", "qualname": "Atten.rho", "kind": "variable", "doc": "
\n"}, "crip.physics.Atten.attenText": {"fullname": "crip.physics.Atten.attenText", "modulename": "crip.physics", "qualname": "Atten.attenText", "kind": "variable", "doc": "
\n"}, "crip.physics.Atten.energyUnit": {"fullname": "crip.physics.Atten.energyUnit", "modulename": "crip.physics", "qualname": "Atten.energyUnit", "kind": "variable", "doc": "
\n"}, "crip.physics.Atten.builtInAttenList": {"fullname": "crip.physics.Atten.builtInAttenList", "modulename": "crip.physics", "qualname": "Atten.builtInAttenList", "kind": "function", "doc": "
Get all built-in materials.
\n", "signature": "() -> List:", "funcdef": "def"}, "crip.physics.Atten.fromBuiltIn": {"fullname": "crip.physics.Atten.fromBuiltIn", "modulename": "crip.physics", "qualname": "Atten.fromBuiltIn", "kind": "function", "doc": "
Get the built-in atten object.\nCall builtInAttenList to get available materials.\nThe density is in g/cm^3.
\n", "signature": "(materialName: str, density: Optional[float] = None):", "funcdef": "def"}, "crip.physics.Atten.fromText": {"fullname": "crip.physics.Atten.fromText", "modulename": "crip.physics", "qualname": "Atten.fromText", "kind": "function", "doc": "
\n", "signature": "(attenText: str, density: float, energyUnit='MeV'):", "funcdef": "def"}, "crip.physics.Atten.fromMuArray": {"fullname": "crip.physics.Atten.fromMuArray", "modulename": "crip.physics", "qualname": "Atten.fromMuArray", "kind": "function", "doc": "
\n", "signature": "(muArray: numpy.ndarray, rho: Optional[float] = None):", "funcdef": "def"}, "crip.physics.Atten.fromFile": {"fullname": "crip.physics.Atten.fromFile", "modulename": "crip.physics", "qualname": "Atten.fromFile", "kind": "function", "doc": "
Construct a new material from file where first column is energy while second\nis \\mu / \\rho.
\n", "signature": "(path: str, rho: float, energyUnit='MeV'):", "funcdef": "def"}, "crip.physics.Material": {"fullname": "crip.physics.Material", "modulename": "crip.physics", "qualname": "Material", "kind": "variable", "doc": "
\n", "default_value": "<class 'crip.physics.Atten'>"}, "crip.physics.calcMu": {"fullname": "crip.physics.calcMu", "modulename": "crip.physics", "qualname": "calcMu", "kind": "function", "doc": "
Calculate the LAC \\mu value (mm-1) of certain atten under a specific spectrum.\nenergyConversion determines the energy conversion efficiency of the detector,\ncan be \"PCD\" (Photon Counting), \"EID\" (Energy Integrating)
\n", "signature": "(\tatten: crip.physics.Atten,\tspec: crip.physics.Spectrum,\tenergyConversion: str) -> float:", "funcdef": "def"}, "crip.physics.DiagEnergyLow": {"fullname": "crip.physics.DiagEnergyLow", "modulename": "crip.physics", "qualname": "DiagEnergyLow", "kind": "variable", "doc": "
\n", "default_value": "0"}, "crip.physics.DiagEnergyHigh": {"fullname": "crip.physics.DiagEnergyHigh", "modulename": "crip.physics", "qualname": "DiagEnergyHigh", "kind": "variable", "doc": "
\n", "default_value": "150"}, "crip.physics.DiagEnergyRange": {"fullname": "crip.physics.DiagEnergyRange", "modulename": "crip.physics", "qualname": "DiagEnergyRange", "kind": "variable", "doc": "
\n", "default_value": "range(0, 151)"}, "crip.physics.DiagEnergyLen": {"fullname": "crip.physics.DiagEnergyLen", "modulename": "crip.physics", "qualname": "DiagEnergyLen", "kind": "variable", "doc": "
\n", "default_value": "151"}, "crip.physics.forwardProjectWithSpectrum": {"fullname": "crip.physics.forwardProjectWithSpectrum", "modulename": "crip.physics", "qualname": "forwardProjectWithSpectrum", "kind": "function", "doc": "
Perform forward projection using spec. lengths is a list of corresponding length [mm] images \n(projection or sinogram) of materials. \nThis function would simulate attenuation and Beam Hardening but no scatter.
\n\n
Set lengths and materials to empty lists to compute the flat field.
\n\n
It's highly recommended to use a monochronmatic spectrum to accelerate if you simulate a lot.
\n", "signature": "(\tlengths: List[numpy.ndarray],\tmaterials: List[crip.physics.Atten],\tspec: crip.physics.Spectrum,\tenergyConversion: str):", "funcdef": "def"}, "crip.physics.brewPowderSolution": {"fullname": "crip.physics.brewPowderSolution", "modulename": "crip.physics", "qualname": "brewPowderSolution", "kind": "function", "doc": "
Generate the Atten of powder solution with certain concentration (mg/mL by default).
\n", "signature": "(\tsolute: crip.physics.Atten,\tsolvent: crip.physics.Atten,\tconcentration: float,\tconcentrationUnit='mg/mL',\trhoSolution: Optional[float] = None) -> crip.physics.Atten:", "funcdef": "def"}, "crip.physics.calcContrastHU": {"fullname": "crip.physics.calcContrastHU", "modulename": "crip.physics", "qualname": "calcContrastHU", "kind": "function", "doc": "
Calculate HU difference resulted by contrast.
\n", "signature": "(\tcontrast: crip.physics.Atten,\tspec: crip.physics.Spectrum,\tenergyConversion: str,\tbase: crip.physics.Atten = <crip.physics.Atten object>):", "funcdef": "def"}, "crip.physics.getCommonDensity": {"fullname": "crip.physics.getCommonDensity", "modulename": "crip.physics", "qualname": "getCommonDensity", "kind": "function", "doc": "
Get the common value of density of a specified material (g/cm^3) from built-in dataset.
\n", "signature": "(materialName: str):", "funcdef": "def"}, "crip.physics.EnergyConversion": {"fullname": "crip.physics.EnergyConversion", "modulename": "crip.physics", "qualname": "EnergyConversion", "kind": "class", "doc": "
An enumeration.
\n", "bases": "enum.Enum"}, "crip.physics.EnergyConversion.EID": {"fullname": "crip.physics.EnergyConversion.EID", "modulename": "crip.physics", "qualname": "EnergyConversion.EID", "kind": "variable", "doc": "
\n", "default_value": "<EnergyConversion.EID: 'EID'>"}, "crip.physics.EnergyConversion.PCD": {"fullname": "crip.physics.EnergyConversion.PCD", "modulename": "crip.physics", "qualname": "EnergyConversion.PCD", "kind": "variable", "doc": "
\n", "default_value": "<EnergyConversion.PCD: 'PCD'>"}, "crip.physics.calcPathLength": {"fullname": "crip.physics.calcPathLength", "modulename": "crip.physics", "qualname": "calcPathLength", "kind": "function", "doc": "
Calculate the X-ray peneration pathlength of an cuboid object based on geometry.\nSID is source-isocenter distance. Currently no offset can be assumed.
\n", "signature": "(\tthickness: float,\tSID: float,\tdetH: int,\tdetW: int,\tdetElementSize: float):", "funcdef": "def"}, "crip.plot": {"fullname": "crip.plot", "modulename": "crip.plot", "kind": "module", "doc": "
Figure artist module of crip.
\n\n
https://github.com/z0gSh1u/crip
\n"}, "crip.plot.smooth": {"fullname": "crip.plot.smooth", "modulename": "crip.plot", "qualname": "smooth", "kind": "function", "doc": "
Smooth an 1D array by moving averaging window. This name follows MATLAB.
\n\n
The implementation is from https://stackoverflow.com/questions/40443020
\n", "signature": "(data: numpy.ndarray, winSize: int = 5):", "funcdef": "def"}, "crip.plot.window": {"fullname": "crip.plot.window", "modulename": "crip.plot", "qualname": "window", "kind": "function", "doc": "
Window img using win (ww, wl) with style 'wwwl' or (left, right) with style 'lr'.\nSet normalize to '0255' to convert to 8-bit image, or '01' to [0, 1] float image.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\twin: Union[Tuple[int], Tuple[float]],\tstyle: str = 'lr',\tnormalize: Optional[str] = None):", "funcdef": "def"}, "crip.plot.average": {"fullname": "crip.plot.average", "modulename": "crip.plot", "qualname": "average", "kind": "function", "doc": "
Average along channel dimension [i - r, i + r].\nUse for example, show CT slices smoother.
\n", "signature": "(imgs: Union[numpy.ndarray, List[numpy.ndarray]], i: int, r: int):", "funcdef": "def"}, "crip.plot.addFont": {"fullname": "crip.plot.addFont", "modulename": "crip.plot", "qualname": "addFont", "kind": "function", "doc": "
Add font files under dir to matplotlib.
\n", "signature": "(dir_: str):", "funcdef": "def"}, "crip.plot.fontdict": {"fullname": "crip.plot.fontdict", "modulename": "crip.plot", "qualname": "fontdict", "kind": "function", "doc": "
\n", "signature": "(family, weight, size):", "funcdef": "def"}, "crip.plot.zoomIn": {"fullname": "crip.plot.zoomIn", "modulename": "crip.plot", "qualname": "zoomIn", "kind": "function", "doc": "
Crop a patch. (row, col) determines the left-top point. (h, w) gives height and width.
\n", "signature": "(img, row, col, h, w):", "funcdef": "def"}, "crip.plot.plotSpectrum": {"fullname": "crip.plot.plotSpectrum", "modulename": "crip.plot", "qualname": "plotSpectrum", "kind": "function", "doc": "
Plot the spectrum in ax. Example\n```\nfig = plt.figure()\nax = fig.add_subplot(111)\nplotSpectrum(ax, spec)
\n", "signature": "(ax: matplotlib.axes._axes.Axes, spec: crip.physics.Spectrum):", "funcdef": "def"}, "crip.plot.makeImageGrid": {"fullname": "crip.plot.makeImageGrid", "modulename": "crip.plot", "qualname": "makeImageGrid", "kind": "function", "doc": "
Make an Image Grid.\n    preprocessor(index, subimage) is applied to each subimage, e.g, perform windowing.\n    figsize accepts that for plt.figure(figsize=...).\n    vmin0vmax1=True guarantees correct windowing when windowed image is not compactly supported in [0, 1].\n    Return the handle of plt.Figure.\n```\n           colTitles\n        +-------------+\n        |             |\nrow     |  subImages  |  ...\nTitles  |       +-----|\n        |       |crop |\n        +-------------+\n              ...
\n", "signature": "(\tsubimages: List[numpy.ndarray],\tcolTitles: List[str],\trowTitles: List[str],\tpreprocessor=None,\tfontdict=None,\tcrops=None,\tcropLocs='bottom right',\tcropEdgeColor='yellow',\tcropSize=64,\tfigsize=None,\tvmin0vmax1=True):", "funcdef": "def"}, "crip.postprocess": {"fullname": "crip.postprocess", "modulename": "crip.postprocess", "kind": "module", "doc": "
Postprocess module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.postprocess.drawCircle": {"fullname": "crip.postprocess.drawCircle", "modulename": "crip.postprocess", "qualname": "drawCircle", "kind": "function", "doc": "
Return points of a circle on center (slice center if None) with radius.
\n\n
This function can be used for preview FOV crop.
\n", "signature": "(\tslice: numpy.ndarray,\tradius: int,\tcenter=None) -> Tuple[numpy.ndarray, numpy.ndarray]:", "funcdef": "def"}, "crip.postprocess.fovCropRadius": {"fullname": "crip.postprocess.fovCropRadius", "modulename": "crip.postprocess", "qualname": "fovCropRadius", "kind": "function", "doc": "
Get the radius (in pixel) of the circle valid FOV of the reconstructed volume.
\n\n
Geometry:\n    - SOD: Source Object Distance\n    - SDD: Source Detector Distance\n    - detWidth: Width of the detector, i.e., nElements * detElementWidth\n    - reconPixSize: Pixel size of the reconstructed image
\n", "signature": "(\tSOD: float,\tSDD: float,\tdetWidth: float,\treconPixSize: float,\troundOff=True) -> float:", "funcdef": "def"}, "crip.postprocess.fovCrop": {"fullname": "crip.postprocess.fovCrop", "modulename": "crip.postprocess", "qualname": "fovCrop", "kind": "function", "doc": "
Crop a circle FOV on reconstructed image img with radius (pixel) \\\nand fill value for outside FOV.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tradius: int,\tfill: Union[int, float] = 0) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.postprocess.muToHU": {"fullname": "crip.postprocess.muToHU", "modulename": "crip.postprocess", "qualname": "muToHU", "kind": "function", "doc": "
Convert \\mu map to HU.
\n\n
HU = (\\mu - \\muWater) / \\muWater * b
\n", "signature": "(\timage: Union[numpy.ndarray, List[numpy.ndarray]],\tmuWater: float,\tb=1000) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.postprocess.huToMu": {"fullname": "crip.postprocess.huToMu", "modulename": "crip.postprocess", "qualname": "huToMu", "kind": "function", "doc": "
Convert HU to mu. (Invert of MuToHU.)
\n", "signature": "(\timage: Union[numpy.ndarray, List[numpy.ndarray]],\tmuWater: float,\tb=1000) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.postprocess.huNoRescale": {"fullname": "crip.postprocess.huNoRescale", "modulename": "crip.postprocess", "qualname": "huNoRescale", "kind": "function", "doc": "
Invert the rescale-slope (y = kx + b) of HU value to get linear relationship between HU and mu.
\n", "signature": "(\timage: Union[numpy.ndarray, List[numpy.ndarray]],\tb: float = -1000,\tk: float = 1) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.postprocess.postlogsToProjections": {"fullname": "crip.postprocess.postlogsToProjections", "modulename": "crip.postprocess", "qualname": "postlogsToProjections", "kind": "function", "doc": "
Invert postlog images to the original projections.
\n", "signature": "(\tpostlogs: Union[numpy.ndarray, List[numpy.ndarray]],\tflat: Union[numpy.ndarray, float]) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.postprocess.binning": {"fullname": "crip.postprocess.binning", "modulename": "crip.postprocess", "qualname": "binning", "kind": "function", "doc": "
Perform binning with rates = (c, h, w) / (h, w).
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\trates: Tuple[int]) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.preprocess": {"fullname": "crip.preprocess", "modulename": "crip.preprocess", "kind": "module", "doc": "
Preprocess module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.preprocess.averageProjections": {"fullname": "crip.preprocess.averageProjections", "modulename": "crip.preprocess", "qualname": "averageProjections", "kind": "function", "doc": "
Average projections. For example, to calculate the flat field.\nProjections can be either (views, H, W) shaped numpy array, or\nviews * (H, W) Python List.
\n", "signature": "(projections: Union[numpy.ndarray, List[numpy.ndarray]]) -> numpy.ndarray:", "funcdef": "def"}, "crip.preprocess.flatDarkFieldCorrection": {"fullname": "crip.preprocess.flatDarkFieldCorrection", "modulename": "crip.preprocess", "qualname": "flatDarkFieldCorrection", "kind": "function", "doc": "
Perform flat field (air) and dark field correction to get post-log value.\nIf flat is 3D and projections is also 3D, the correction will be performed view by view.\nI.e., - log [(X - D) / (F - D)]. Multi projections accepted.\nIf flat is None, air is estimated using the brightest pixel by default.
\n", "signature": "(\tprojections: Union[numpy.ndarray, List[numpy.ndarray]],\tflat: Union[numpy.ndarray, List[numpy.ndarray], float, NoneType] = None,\tdark: Union[numpy.ndarray, List[numpy.ndarray], float] = 0) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.preprocess.projectionsToSinograms": {"fullname": "crip.preprocess.projectionsToSinograms", "modulename": "crip.preprocess", "qualname": "projectionsToSinograms", "kind": "function", "doc": "
Permute projections to sinograms by axes swapping (views, h, w) -> (h, views, w).
\n\n
Note that the width direction is along detector channels of a row.
\n", "signature": "(projections: Union[numpy.ndarray, List[numpy.ndarray]]):", "funcdef": "def"}, "crip.preprocess.sinogramsToProjections": {"fullname": "crip.preprocess.sinogramsToProjections", "modulename": "crip.preprocess", "qualname": "sinogramsToProjections", "kind": "function", "doc": "
Permute sinograms back to projections by axes swapping (h, views, w) -> (views, h, w).
\n\n
Note that the width direction is along detector channels of a row.
\n", "signature": "(sinograms: Union[numpy.ndarray, List[numpy.ndarray]]):", "funcdef": "def"}, "crip.preprocess.padImage": {"fullname": "crip.preprocess.padImage", "modulename": "crip.preprocess", "qualname": "padImage", "kind": "function", "doc": "
Pad the image on four directions using symmetric padding (Up, Right, Down, Left). \\\nmode determines the border value, can be symmetric, edge, constant (zero), reflect. \\\ndecay can be None, cosine, smoothCosine to perform a decay on padded border.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tpadding: Tuple[int, int, int, int],\tmode: str = 'symmetric',\tdecay: Optional[str] = None):", "funcdef": "def"}, "crip.preprocess.padSinogram": {"fullname": "crip.preprocess.padSinogram", "modulename": "crip.preprocess", "qualname": "padSinogram", "kind": "function", "doc": "
Pad sinograms in width direction (same line detector elements) using mode and decay\\\nwith padding (single int, or (right, left)).
\n\n
@see padImage for parameter details.
\n", "signature": "(\tsgms: Union[numpy.ndarray, List[numpy.ndarray]],\tpadding: Union[int, Tuple[int, int]],\tmode='symmetric',\tdecay='smoothCosine'):", "funcdef": "def"}, "crip.preprocess.correctBeamHardeningPolynomial": {"fullname": "crip.preprocess.correctBeamHardeningPolynomial", "modulename": "crip.preprocess", "qualname": "correctBeamHardeningPolynomial", "kind": "function", "doc": "
Apply the polynomial (\\mu L vs. PostLog fit) on postlog to perform basic beam hardening correction.\ncoeffs can be either tuple or np.poly1d. Set bias=True if your coeffs includes the bias (order 0) term.
\n", "signature": "(\tpostlog: Union[numpy.ndarray, List[numpy.ndarray]],\tcoeffs: Union[Tuple, numpy.poly1d],\tbias=True):", "funcdef": "def"}, "crip.preprocess.injectGaussianNoise": {"fullname": "crip.preprocess.injectGaussianNoise", "modulename": "crip.preprocess", "qualname": "injectGaussianNoise", "kind": "function", "doc": "
Inject Gaussian noise which obeys distribution N(\\mu, \\sigma^2).
\n", "signature": "(\tprojections: Union[numpy.ndarray, List[numpy.ndarray]],\tsigma: float,\tmu: float = 0) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.preprocess.injectPoissonNoise": {"fullname": "crip.preprocess.injectPoissonNoise", "modulename": "crip.preprocess", "qualname": "injectPoissonNoise", "kind": "function", "doc": "
Inject Poisson noise which obeys distribution P(\\lambda) where \\lambda is the ground-truth quanta in projections.\nprojections must have int type whose value stands for the photon quanta in some way. Floating projections\nshould be manually properly rescaled ahead and scale back as you need since Poisson Distribution only deals with\npositive integers. type_ (postlog or raw) gives the content type of projections, usually you should use\npostlog as input. If you input postlog, the output will also be postlog.
\n", "signature": "(\tprojections: Union[numpy.ndarray, List[numpy.ndarray]],\ttype_: str = 'postlog',\tnPhoton: Union[int, float] = 1,\tsuppressWarning=False) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.preprocess.binning": {"fullname": "crip.preprocess.binning", "modulename": "crip.preprocess", "qualname": "binning", "kind": "function", "doc": "
Perform binning with rates = (c, h, w) / (h, w).
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\trates: Tuple[int]) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.preprocess.fanToPara": {"fullname": "crip.preprocess.fanToPara", "modulename": "crip.preprocess", "qualname": "fanToPara", "kind": "function", "doc": "
Re-order Fan-Beam sinogram to Parallel-Beam's.\ngammas: fan angles from min to max [rad], e.g., arctan(elementOffcenter / SDD)\nbetas: system rotation angles from min to max [rad]\nsid: Source-Isocenter-Distance [mm]\noThetas: output rotation angle range (min, delta, max) tuple [rad]\noLines: output detector element physical locations range (min, delta, max) tuple [mm], e.g., elementOffcenter array\n```\n       /| <- gamma for detector element X\n      / | <- SID\n     /  |\n====X============ <- detector\n    ^<--^ <- offcenter
\n", "signature": "(\tsgm: numpy.ndarray,\tgammas: numpy.ndarray,\tbetas: numpy.ndarray,\tsid: float,\toThetas: Tuple[float],\toLines: Tuple[float]) -> numpy.ndarray:", "funcdef": "def"}, "crip.preprocess.correctRingArtifactInProj": {"fullname": "crip.preprocess.correctRingArtifactInProj", "modulename": "crip.preprocess", "qualname": "correctRingArtifactInProj", "kind": "function", "doc": "
Apply the ring artifact correction method in projection domain (input postlog sinogram),\nusing gaussian filter in sinogram detector direction [1].\n[1] \u674e\u4fca\u6c5f,\u80e1\u5c11\u5174,\u674e\u4fdd\u78ca\u7b49.CT\u56fe\u50cf\u73af\u72b6\u4f2a\u5f71\u6821\u6b63\u65b9\u6cd5[J].\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66\u5b66\u62a5,2007,(11):1378-1382.DOI:10.13700/j.bh.1001-5965.2007.11.020\nhttps://kns.cnki.net/kcms2/article/abstract?v=xBNwvqFr00JMj5WzBbZMcj9N-kBm9Pi08enuNi8ymtGWVZuwGHdLWwttkgzSivkJSBVk0CVQZxo7DgBmujqhLCaVvvBMoig5RV0B4fDLUnjGQcCPo3O4KfAo4iX4vCwZ&uniplatform=NZKPT&flag=copy
\n", "signature": "(\tsgm: Union[numpy.ndarray, List[numpy.ndarray]],\tsigma: float,\tksize: Optional[int] = None):", "funcdef": "def"}, "crip.preprocess.correctFlatDarkField": {"fullname": "crip.preprocess.correctFlatDarkField", "modulename": "crip.preprocess", "qualname": "correctFlatDarkField", "kind": "function", "doc": "
Perform flat field (air) and dark field correction to get post-log value.\nIf flat is 3D and projections is also 3D, the correction will be performed view by view.\nI.e., - log [(X - D) / (F - D)]. Multi projections accepted.\nIf flat is None, air is estimated using the brightest pixel by default.
\n", "signature": "(\tprojections: Union[numpy.ndarray, List[numpy.ndarray]],\tflat: Union[numpy.ndarray, List[numpy.ndarray], float, NoneType] = None,\tdark: Union[numpy.ndarray, List[numpy.ndarray], float] = 0) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared": {"fullname": "crip.shared", "modulename": "crip.shared", "kind": "module", "doc": "
Shared module of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.shared.rotate": {"fullname": "crip.shared.rotate", "modulename": "crip.shared", "qualname": "rotate", "kind": "function", "doc": "
Rotate the image or each image in a volume by deg [DEG] (multiple of 90) clockwise.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tdeg: int) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.verticalFlip": {"fullname": "crip.shared.verticalFlip", "modulename": "crip.shared", "qualname": "verticalFlip", "kind": "function", "doc": "
Vertical flip one image, or each image in a volume.
\n\n
Set copy = True to get a copy of array, otherwise a view only.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tcopy=False) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.horizontalFlip": {"fullname": "crip.shared.horizontalFlip", "modulename": "crip.shared", "qualname": "horizontalFlip", "kind": "function", "doc": "
Horizontal flip one image, or each image in a volume.
\n\n
Set copy = True to get a copy of array, otherwise a view only.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tcopy=False) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.stackFlip": {"fullname": "crip.shared.stackFlip", "modulename": "crip.shared", "qualname": "stackFlip", "kind": "function", "doc": "
Flip a stack w.r.t. z-axis, i.e., reverse slices.
\n\n
Set copy = True to get a copy of array, otherwise a view only.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tcopy=False) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.resize": {"fullname": "crip.shared.resize", "modulename": "crip.shared", "qualname": "resize", "kind": "function", "doc": "
Resize the image or each image in a volume to dsize = (H, W) (if dsize is not None) or scale \nby scale = (facH, facW) using interp (bicubic, linear, nearest available).
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tdsize: Tuple[int] = None,\tscale: Tuple[Union[float, int]] = None,\tinterp: str = 'bicubic') -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.gaussianSmooth": {"fullname": "crip.shared.gaussianSmooth", "modulename": "crip.shared", "qualname": "gaussianSmooth", "kind": "function", "doc": "
Perform Gaussian smooth with kernel size = ksize and Gaussian \\sigma = sigma (int or tuple (x, y)).
\n\n
Leave ksize = None to auto determine to include the majority of Gaussian energy.
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\tsigma: Union[float, int, Tuple[Union[float, int]]],\tksize: int = None):", "funcdef": "def"}, "crip.shared.stackImages": {"fullname": "crip.shared.stackImages", "modulename": "crip.shared", "qualname": "stackImages", "kind": "function", "doc": "
Stack seperate image into one numpy array. I.e., views * (h, w) -> (views, h, w).
\n\n
Convert dtype with dtype != None.
\n", "signature": "(imgList: List[numpy.ndarray], dtype=None) -> numpy.ndarray:", "funcdef": "def"}, "crip.shared.splitImages": {"fullname": "crip.shared.splitImages", "modulename": "crip.shared", "qualname": "splitImages", "kind": "function", "doc": "
Split stacked image into seperate numpy arrays. I.e., (views, h, w) -> views * (h, w).
\n\n
Convert dtype with dtype != None.
\n", "signature": "(\timgs: Union[numpy.ndarray, List[numpy.ndarray]],\tdtype=None) -> List[numpy.ndarray]:", "funcdef": "def"}, "crip.shared.binning": {"fullname": "crip.shared.binning", "modulename": "crip.shared", "qualname": "binning", "kind": "function", "doc": "
Perform binning with rates = (c, h, w) / (h, w).
\n", "signature": "(\timg: Union[numpy.ndarray, List[numpy.ndarray]],\trates: Tuple[int]) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.transpose": {"fullname": "crip.shared.transpose", "modulename": "crip.shared", "qualname": "transpose", "kind": "function", "doc": "
Transpose vol with axes swapping order.
\n", "signature": "(\tvol: Union[numpy.ndarray, List[numpy.ndarray]],\torder: Tuple[int]) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.permute": {"fullname": "crip.shared.permute", "modulename": "crip.shared", "qualname": "permute", "kind": "function", "doc": "
Permute axes (transpose) from from_ to to, reslicing the reconstructed volume.
\n\n
Valid directions are: 'sagittal', 'coronal', 'transverse'.
\n", "signature": "(\tvol: Union[numpy.ndarray, List[numpy.ndarray]],\tfrom_: str,\tto: str) -> Union[numpy.ndarray, List[numpy.ndarray]]:", "funcdef": "def"}, "crip.shared.shepplogan": {"fullname": "crip.shared.shepplogan", "modulename": "crip.shared", "qualname": "shepplogan", "kind": "function", "doc": "
Generate the Shepp-Logan phantom.
\n", "signature": "(size: int = 512):", "funcdef": "def"}, "crip.shared.fitPolyV1D2": {"fullname": "crip.shared.fitPolyV1D2", "modulename": "crip.shared", "qualname": "fitPolyV1D2", "kind": "function", "doc": "
Fit a degree-2 polynomial using pseudo-inverse to a variable x1.\nOutput 3 coefficients. If bias is False, the last coefficient will be 0.
\n\n
c[0] * x1**2 + c[1] * x1 + c[2]
\n", "signature": "(x1: numpy.ndarray, y: numpy.ndarray, bias: bool = True):", "funcdef": "def"}, "crip.shared.fitPolyV2D2": {"fullname": "crip.shared.fitPolyV2D2", "modulename": "crip.shared", "qualname": "fitPolyV2D2", "kind": "function", "doc": "
Fit a degree-2 polynomial using pseudo-inverse to a pair of variables x1, x2.\nOutput 6 coefficients. If bias is False, the last coefficient will be 0.
\n\n
c[0] * x1**2 + c[1] * x2**2 + c[2] * x1 * x2 + c[3] * x1 + c[4] * x2 + c[5]
\n", "signature": "(\tx1: numpy.ndarray,\tx2: numpy.ndarray,\ty: numpy.ndarray,\tbias: bool = True):", "funcdef": "def"}, "crip.shared.applyPolyV1D2": {"fullname": "crip.shared.applyPolyV1D2", "modulename": "crip.shared", "qualname": "applyPolyV1D2", "kind": "function", "doc": "
Apply a degree-2 polynomial to a variable x1.
\n\n
c[0] * x1**2 + c[1] * x1 + c[2]
\n", "signature": "(coeff: numpy.ndarray, x1: numpy.ndarray):", "funcdef": "def"}, "crip.shared.applyPolyV2D2": {"fullname": "crip.shared.applyPolyV2D2", "modulename": "crip.shared", "qualname": "applyPolyV2D2", "kind": "function", "doc": "
Apply a degree-2 polynomial to a pair of variables x1, x2.
\n\n
c[0] * x1**2 + c[1] * x2**2 + c[2] * x1 * x2 + c[3] * x1 + c[4] * x2 + c[5]
\n", "signature": "(coeff: numpy.ndarray, x1: numpy.ndarray, x2: numpy.ndarray):", "funcdef": "def"}, "crip.utils": {"fullname": "crip.utils", "modulename": "crip.utils", "kind": "module", "doc": "
Utilities of crip.
\n\n
https://github.com/SEU-CT-Recon/crip
\n"}, "crip.utils.readFileText": {"fullname": "crip.utils.readFileText", "modulename": "crip.utils", "qualname": "readFileText", "kind": "function", "doc": "
\n", "signature": "(path_):", "funcdef": "def"}, "crip.utils.CripException": {"fullname": "crip.utils.CripException", "modulename": "crip.utils", "qualname": "CripException", "kind": "class", "doc": "
The universal expection class for crip.
\n", "bases": "builtins.BaseException"}, "crip.utils.CripException.__init__": {"fullname": "crip.utils.CripException.__init__", "modulename": "crip.utils", "qualname": "CripException.__init__", "kind": "function", "doc": "
\n", "signature": "(*args: object)"}, "crip.utils.cripAssert": {"fullname": "crip.utils.cripAssert", "modulename": "crip.utils", "qualname": "cripAssert", "kind": "function", "doc": "
The only assert method for crip.
\n", "signature": "(cond, hint):", "funcdef": "def"}, "crip.utils.cripWarning": {"fullname": "crip.utils.cripWarning", "modulename": "crip.utils", "qualname": "cripWarning", "kind": "function", "doc": "
The only warning method for crip.
\n", "signature": "(ensure, hint, dumpStack=False):", "funcdef": "def"}, "crip.utils.ConvertListNDArray": {"fullname": "crip.utils.ConvertListNDArray", "modulename": "crip.utils", "qualname": "ConvertListNDArray", "kind": "function", "doc": "
Decorator to convert List[ndarray] to ndarray.
\n", "signature": "(f):", "funcdef": "def"}, "crip.utils.asFloat": {"fullname": "crip.utils.asFloat", "modulename": "crip.utils", "qualname": "asFloat", "kind": "function", "doc": "
Make sure arr has floating type.
\n", "signature": "(arr):", "funcdef": "def"}, "crip.utils.is2D": {"fullname": "crip.utils.is2D", "modulename": "crip.utils", "qualname": "is2D", "kind": "function", "doc": "
\n", "signature": "(x: numpy.ndarray):", "funcdef": "def"}, "crip.utils.is3D": {"fullname": "crip.utils.is3D", "modulename": "crip.utils", "qualname": "is3D", "kind": "function", "doc": "
\n", "signature": "(x: numpy.ndarray):", "funcdef": "def"}, "crip.utils.is2or3D": {"fullname": "crip.utils.is2or3D", "modulename": "crip.utils", "qualname": "is2or3D", "kind": "function", "doc": "
\n", "signature": "(x: numpy.ndarray):", "funcdef": "def"}, "crip.utils.isInt": {"fullname": "crip.utils.isInt", "modulename": "crip.utils", "qualname": "isInt", "kind": "function", "doc": "
\n", "signature": "(n):", "funcdef": "def"}, "crip.utils.isIntDtype": {"fullname": "crip.utils.isIntDtype", "modulename": "crip.utils", "qualname": "isIntDtype", "kind": "function", "doc": "
\n", "signature": "(dtype):", "funcdef": "def"}, "crip.utils.isFloatDtype": {"fullname": "crip.utils.isFloatDtype", "modulename": "crip.utils", "qualname": "isFloatDtype", "kind": "function", "doc": "
\n", "signature": "(dtype):", "funcdef": "def"}, "crip.utils.isIntType": {"fullname": "crip.utils.isIntType", "modulename": "crip.utils", "qualname": "isIntType", "kind": "function", "doc": "
\n", "signature": "(arr: numpy.ndarray):", "funcdef": "def"}, "crip.utils.isFloatType": {"fullname": "crip.utils.isFloatType", "modulename": "crip.utils", "qualname": "isFloatType", "kind": "function", "doc": "
\n", "signature": "(arr: numpy.ndarray):", "funcdef": "def"}, "crip.utils.isType": {"fullname": "crip.utils.isType", "modulename": "crip.utils", "qualname": "isType", "kind": "function", "doc": "
Check if x has type t or isinstance of t.
\n", "signature": "(x, t):", "funcdef": "def"}, "crip.utils.isNumber": {"fullname": "crip.utils.isNumber", "modulename": "crip.utils", "qualname": "isNumber", "kind": "function", "doc": "
\n", "signature": "(a):", "funcdef": "def"}, "crip.utils.isList": {"fullname": "crip.utils.isList", "modulename": "crip.utils", "qualname": "isList", "kind": "function", "doc": "
\n", "signature": "(x):", "funcdef": "def"}, "crip.utils.isListNDArray": {"fullname": "crip.utils.isListNDArray", "modulename": "crip.utils", "qualname": "isListNDArray", "kind": "function", "doc": "
\n", "signature": "(arr):", "funcdef": "def"}, "crip.utils.isOfSameShape": {"fullname": "crip.utils.isOfSameShape", "modulename": "crip.utils", "qualname": "isOfSameShape", "kind": "function", "doc": "
\n", "signature": "(a: numpy.ndarray, b: numpy.ndarray):", "funcdef": "def"}, "crip.utils.inRange": {"fullname": "crip.utils.inRange", "modulename": "crip.utils", "qualname": "inRange", "kind": "function", "doc": "
\n", "signature": "(a, range_=None, low=None, high=None):", "funcdef": "def"}, "crip.utils.inArray": {"fullname": "crip.utils.inArray", "modulename": "crip.utils", "qualname": "inArray", "kind": "function", "doc": "
\n", "signature": "(a, arr):", "funcdef": "def"}, "crip.utils.getAsset": {"fullname": "crip.utils.getAsset", "modulename": "crip.utils", "qualname": "getAsset", "kind": "function", "doc": "
\n", "signature": "(folder, prefix='_asset'):", "funcdef": "def"}, "crip.utils.cvtEnergyUnit": {"fullname": "crip.utils.cvtEnergyUnit", "modulename": "crip.utils", "qualname": "cvtEnergyUnit", "kind": "function", "doc": "
Convert between energy units. (ev, keV, MeV)
\n", "signature": "(arr, from_, to):", "funcdef": "def"}, "crip.utils.cvtLengthUnit": {"fullname": "crip.utils.cvtLengthUnit", "modulename": "crip.utils", "qualname": "cvtLengthUnit", "kind": "function", "doc": "
Convert between length units. (um, mm, cm, m)
\n", "signature": "(arr, from_, to):", "funcdef": "def"}, "crip.utils.cvtMuUnit": {"fullname": "crip.utils.cvtMuUnit", "modulename": "crip.utils", "qualname": "cvtMuUnit", "kind": "function", "doc": "
Convert between mu value units. (um-1, mm-1, cm-1, m-1)
\n", "signature": "(arr, from_, to):", "funcdef": "def"}, "crip.utils.radToDeg": {"fullname": "crip.utils.radToDeg", "modulename": "crip.utils", "qualname": "radToDeg", "kind": "function", "doc": "
\n", "signature": "(x):", "funcdef": "def"}, "crip.utils.degToRad": {"fullname": "crip.utils.degToRad", "modulename": "crip.utils", "qualname": "degToRad", "kind": "function", "doc": "
\n", "signature": "(x):", "funcdef": "def"}, "crip.utils.sysPlatform": {"fullname": "crip.utils.sysPlatform", "modulename": "crip.utils", "qualname": "sysPlatform", "kind": "function", "doc": "
\n", "signature": "():", "funcdef": "def"}, "crip.utils.getHW": {"fullname": "crip.utils.getHW", "modulename": "crip.utils", "qualname": "getHW", "kind": "function", "doc": "
Get height and width of img.
\n", "signature": "(img: numpy.ndarray):", "funcdef": "def"}, "crip.utils.is1D": {"fullname": "crip.utils.is1D", "modulename": "crip.utils", "qualname": "is1D", "kind": "function", "doc": "
\n", "signature": "(x: numpy.ndarray):", "funcdef": "def"}, "crip.utils.as3D": {"fullname": "crip.utils.as3D", "modulename": "crip.utils", "qualname": "as3D", "kind": "function", "doc": "
\n", "signature": "(x: numpy.ndarray):", "funcdef": "def"}, "crip.utils.nextPow2": {"fullname": "crip.utils.nextPow2", "modulename": "crip.utils", "qualname": "nextPow2", "kind": "function", "doc": "
Get the next power of 2 of x.
\n", "signature": "(x):", "funcdef": "def"}}, "docInfo": {"crip": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 29}, "crip.de": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 17}, "crip.de.singleMatMuDecomp": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 117, "bases": 0, "doc": 41}, "crip.de.calcAttenedSpec": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 114, "bases": 0, "doc": 34}, "crip.de.calcPostLog": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 104, "bases": 0, "doc": 20}, "crip.de.deDecompGetCoeff": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 146, "bases": 0, "doc": 19}, "crip.de.deDecompProj": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 175, "bases": 0, "doc": 27}, "crip.de.deDecompRecon": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 142, "bases": 0, "doc": 37}, "crip.de.genMaterialPhantom": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 61, "bases": 0, "doc": 49}, "crip.de.deDecompReconVolCon": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 98, "bases": 0, "doc": 13}, "crip.de.teDecompRecon": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 135, "bases": 0, "doc": 11}, "crip.de.teDecompReconVolCon": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 135, "bases": 0, "doc": 14}, "crip.io": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 15}, "crip.io.listDirectory": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 132, "bases": 0, "doc": 41}, "crip.io.imreadDicom": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 70, "bases": 0, "doc": 47}, "crip.io.readDicom": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 9}, "crip.io.imreadRaw": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 127, "bases": 0, "doc": 33}, "crip.io.imwriteRaw": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 80, "bases": 0, "doc": 14}, "crip.io.imreadTiff": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 26, "bases": 0, "doc": 17}, "crip.io.imwriteTiff": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 65, "bases": 0, "doc": 27}, "crip.io.CTPARAMS": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 653, "signature": 0, "bases": 0, "doc": 3}, "crip.io.fetchCTParam": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 36, "bases": 0, "doc": 30}, "crip.io.readEVI": 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