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How to use _get_ne method in autotest
Best Python code snippet using autotest_python
synthetic.py
Source:synthetic.py 
...39 ionisation.init_splines(self.altitude)40 self.integrated_block_list = []41 self.block_nx = self.dataset.header['block_nx']42 self.block_nx_int = self._reduce_list_to_2d(self.block_nx)43 def _get_ne(self, block, block_ion):44 """45 Returns the electron density of a single block, based on the interpolated ionisation values.46 :param block: block to calculate the electron density47 :param block_ion: interpolated ionisation values for 'block'48 :return: electron densities as a numpy array with the same shape as 'block'49 """50 block_p = block["p"] * self.dataset.units.unit_pressure51 block_T = block["T"] * self.dataset.units.unit_temperature52 block_ne = block_p / ((1 + 1.1 / block_ion) * self.dataset.units.k_B * block_T)53 return block_ne54 def _integrate_block(self, block, l_edge, r_edge):55 """56 Integrates a given block along the given line of sight.57 :param block: the block to integrate58 :param l_edge: contains the left edge of the block, as a ndim length list [x0(, y0, z0)]59 :param r_edge: contains the right edge of the block, as an ndim length list [x1(, y1, z1)]60 :return: 2D numpy array, containing the integrated block. If the block is originally 2D the block itself61 is returned.62 """63 if self.dataset.header["ndim"] == 2:64 return block65 if self.line_of_sight == 'x':66 x = np.linspace(l_edge[0], r_edge[0], self.block_nx[0])67 result = np.zeros_like(block[0, :, :])68 for i, j in np.ndindex(result.shape):69 col = block[:, i, j]70 integrated_col = np.trapz(col, x)71 result[i, j] = integrated_col72 elif self.line_of_sight == 'y':73 y = np.linspace(l_edge[1], r_edge[1], self.block_nx[1])74 result = np.zeros_like(block[:, 0, :])75 for i, j in np.ndindex(result.shape):76 col = block[i, :, j]77 integrated_col = np.trapz(col, y)78 result[i, j] = integrated_col79 else:80 z = np.linspace(l_edge[2], r_edge[2], self.block_nx[2])81 result = np.zeros_like(block[:, :, 0])82 for i, j in np.ndindex(result.shape):83 col = block[i, j, :]84 integrated_col = np.trapz(col, z)85 result[i, j] = integrated_col86 return result87 def _reduce_list_to_2d(self, list_in):88 """89 Used for integrating the block. Converts a 3D array to 2D, depending on the line of sight.90 :param list_in: list of length 2 or 391 :return: numpy array of dimension 2, if list_in is 2-dim as input it is returned92 """93 if self.dataset.header['ndim'] == 2:94 return np.asarray(list_in)95 if self.line_of_sight == 'x':96 array2d = np.asarray([list_in[1], list_in[2]])97 elif self.line_of_sight == 'y':98 array2d = np.asarray([list_in[0], list_in[2]])99 else:100 array2d = np.asarray(list_in[:-1])101 return array2d102 def _merge_integrated_blocks(self):103 """104 Merges all 2D integrated blocks into a single 2D array depending on their position in the Morton curve.105 All 2D integrated blocks are regridded to the finest level available in the current list of blocks.106 As only 2D matrices have to be regridded instead of 3D, this means a large speedup in runtime.107 :return: 2D numpy array containing the merged integrated view108 """109 self.integrated_block_list = np.asarray(self.integrated_block_list)110 # Initialise merged 2D matrix111 maxlvl = np.max(self.dataset.block_lvls)112 refined_nx = 2**(maxlvl - 1) * self.dataset.header['domain_nx']113 refined_nx = self._reduce_list_to_2d(refined_nx)114 merged_result = np.zeros(tuple(refined_nx))115 # NOTE: one can also add all the blocks at a specific level together, and THEN regrid the resulting 2D116 # matrix to the maximum level. This is faster, however, because the 2D matrix still contains zeros117 # from the blocks not at the level at that moment considered, these are also accounted for during118 # interpolation. This has unintended consequences near the block edges between two different levels,119 # such as clearly visible grid lines and very small values not equal to zero (where they should be zero).120 # iterate over blocks and corresponding levels in dataset121 for ileaf, blocklvl in enumerate(self.dataset.block_lvls):122 grid_power_diff = 2 ** (maxlvl - blocklvl) # power of 2 difference between level and max level123 regrid_width = self.block_nx_int * grid_power_diff # index width of the block at max resolution124 # regrid current block to max level125 block = regridding.regrid_2dmatrix(self.integrated_block_list[ileaf], tuple(regrid_width))126 # retrieve current index in morton curve, reduce to 2D (due to integration)127 block_morton_idx = self._reduce_list_to_2d(self.dataset.block_ixs[ileaf])128 # calculate left and right index positions of the block using morton index129 idx0 = (block_morton_idx - 1) * grid_power_diff * self.block_nx_int130 idx1 = idx0 + tuple(regrid_width)131 # add this block to the integrated result132 merged_result[idx0[0]:idx1[0], idx0[1]:idx1[1]] += block133 return merged_result134 def _plot_synthetic_view(self, view):135 bounds_x, bounds_y = self._reduce_list_to_2d(self.dataset.get_bounds())136 norm = None137 if self.logscale:138 norm = matplotlib.colors.LogNorm()139 im = self.ax.imshow(np.rot90(view), extent=[*bounds_x, *bounds_y], cmap=self.cmap, norm=norm)140 self.colorbar = self.fig.colorbar(im)141 self.ax.set_title("integrated over {}".format(self.line_of_sight))142class h_alpha(_syntheticmain):143 """144 Calculates a H-alpha synthetic image, based on a method described by Heinzel (2005).145 """146 def __init__(self, dataset, **kwargs):147 print(">> Creating H-alpha view...")148 super().__init__(dataset, **kwargs)149 self.cmap = kwargs.get("cmap", "Reds_r")150 self.logscale = kwargs.get("logscale", True)151 self._calculate_halpha_view()152 def _calculate_halpha_view(self):153 for ileaf, offset in enumerate(self.dataset.block_offsets):154 block = datfile_utilities.get_single_block_data(self.dataset.file, offset, self.dataset.block_shape)155 # this adds the temperature and pressure to the block156 block = process_data.create_data_dict(block, self.dataset.header)157 # interpolate ionisation and f parameter for each block158 block_ion, block_fpar = ionisation.block_interpolate_ionisation_f(block, self.dataset)159 block_ne = super()._get_ne(block, block_ion)160 n2 = block_ne**2 / (block_fpar * 1e16) # parameter f is interpolated in units of 1e16 cm-3161 # calculate block opacity162 block_kappa = (np.pi * self.dataset.units.ec**2 / (self.dataset.units.m_e * self.dataset.units.c)) * \163 self.f23 * n2 * self._gaussian(block)164 # integrate block along line of sight to get opacity165 l_edge, r_edge = process_data.get_block_edges(ileaf, self.dataset)166 opacity = super()._integrate_block(block_kappa, l_edge, r_edge)167 S = self._source_function()168 intensity = S * (1 - np.exp(-opacity))169 if self.simulation_type == 'filament':170 Ibgr = 2.2 * S171 intensity += Ibgr * np.exp(-opacity)172 # add integrated block to list, this preserves block index173 self.integrated_block_list.append(intensity)174 # merge all integrated blocks into one single 2D array175 view = super()._merge_integrated_blocks()176 # plot final result177 super()._plot_synthetic_view(view)178 def _gaussian(self, block):179 block_T = block["T"] * self.dataset.units.unit_temperature180 ksi = 5 * 1e5 # microturbulence in cm/s181 nu_0 = self.dataset.units.c / (6562.8 * 1e-8) # H-alpha wavelength is 6562.8 Angstrom182 delta_nu = 0183 delta_nuD = (nu_0 / self.dataset.units.c) * \184 np.sqrt(2 * self.dataset.units.k_B * block_T / self.dataset.units.m_p + ksi ** 2)185 phi_nu = 1.0 / (np.sqrt(np.pi) * delta_nuD) * np.exp(-delta_nu / delta_nuD) ** 2186 return phi_nu187 def _source_function(self):188 H = self.altitude * 1e5 # altitude in cm189 # dilution factor W190 W = 0.5 * ( 1 - np.sqrt(1 - (self.dataset.units.Rsun**2 / (self.dataset.units.Rsun + H)**2)) )191 return W * 0.17 * 4.077 * 1e-5192class faraday(_syntheticmain):193 """194 Calculates the Faraday rotation effect for a given dataset. Only for MHD.195 """196 def __init__(self, dataset, **kwargs):197 print(">> Creating Faraday view...")198 if not dataset.header["physics_type"] == "mhd":199 print("calculating Faraday rotation measure is only possible for an MHD dataset")200 return201 if not dataset.header["ndim"] == 3:202 print("calculating Faraday rotation measure is only possible for a 3D dataset")203 return204 super().__init__(dataset, **kwargs)205 self.cmap = kwargs.get("cmap", "jet")206 self.logscale = kwargs.get("logscale", False)207 self._calculate_faraday_view()208 def _calculate_faraday_view(self):209 for ileaf, offset in enumerate(self.dataset.block_offsets):210 block = datfile_utilities.get_single_block_data(self.dataset.file, offset, self.dataset.block_shape)211 # add pressure and temperature to block212 block = process_data.create_data_dict(block, self.dataset.header)213 block_ion, block_fpar = ionisation.block_interpolate_ionisation_f(block, self.dataset)214 block_ne = super()._get_ne(block, block_ion)215 prefactor = self.dataset.units.ec**3 / (2*np.pi*self.dataset.units.m_e**2 * self.dataset.units.c**4)216 if self.line_of_sight == 'x':217 b_key = 'b1'218 elif self.line_of_sight == 'y':219 b_key = 'b2'220 else:221 b_key = 'b3'222 b_para = block[b_key] * self.dataset.units.unit_magneticfield223 l_edge, r_edge = process_data.get_block_edges(ileaf, self.dataset)224 fara_measure = super()._integrate_block(block_ne*b_para, l_edge, r_edge) * prefactor225 self.integrated_block_list.append(fara_measure)226 view = super()._merge_integrated_blocks()227 super()._plot_synthetic_view(view)228 self.colorbar.set_label("rad cm$^{-2}$")
unidic2cabocha.py
Source:unidic2cabocha.py
...101 if j>=0:102 x[7]=self[t].misc[j+9:]103 x[9]=self[t].upos104 self._cabocha._features.append(",".join(x[0:10]))105 def _get_ne(self,index):106 f=self._cabocha._features[index]107 if f.startswith("åè©,åºæåè©,"):108 t=f.split(",")109 if t[2]=="人å":110 return("B-PERSON")111 if t[2]=="å°å":112 return("B-LOCATION")113 return("B-ORGANIZATION")114 return("O")115 def toString(self,format=4):116 if format==4:117 return str(self)118 if not hasattr(self._cabocha,"_sentences"):119 self._makeChunks()120 if format>0 and not hasattr(self._cabocha,"_features"):121 self._makeFeatures()122 result=""123 for k,s in enumerate(self._cabocha._sentences):124 if format==0 or format==2:125 x=len(self._cabocha._chunkinfo[k])126 l,m,n=[],[],[" "*(x*2)]*x127 for i,d,t,h,f,w,z in self._cabocha._chunkinfo[k]:128 l.append(len(w)+len([c for c in w if ord(c)>127]))129 m.append(w)130 if d<0:131 continue132 n[i]="-"*(d*2-1)+"D"+n[i][d*2:]133 for j in range(i+1,d-1):134 n[j]=n[j][0:d*2-1]+"|"+n[j][d*2:]135 h=max([(x-i)*2+j for i,j in enumerate(l)])136 for i in range(x):137 result+=" "*(h-(x-i)*2-l[i])+m[i]+n[i][i*2:].rstrip()+"\n"138 result+="EOS\n"139 if format==1 or format==2:140 for i,d,t,h,f,w,z in self._cabocha._chunkinfo[k]:141 result+="* "+str(i)+" "+str(d)+"D "+str(h)+"/"+str(f)+" 0.000000\n"142 for t in s[i]:143 result+=self[t].form+"\t"+self._cabocha._features[t]+"\t"+self._get_ne(t)+"\t"+str(self[t].id)+"<-"+self[t].deprel144 if self[t] is self[t].head:145 result+="\n"146 else:147 result+="-"+str(self[t].head.id)+"\n"148 result+="EOS\n"149 if format==3:150 result+="<sentence>\n"151 for i,d,t,h,f,w,z in self._cabocha._chunkinfo[k]:152 result+=' <chunk id="'+str(i)+'" link="'+str(d)+'" rel="D" score="0.000000" head="'+str(self[s[i][h]].id)+'" func="'+str(self[s[i][f]].id)+'">\n'153 for t in s[i]:154 result+=' <tok id="'+str(self[t].id)+'" feature="'+self._cabocha._features[t]+'" head="'+str(self[t].head.id)+'" rel="'+self[t].deprel+'">'+self[t].form+'</tok>\n'155 result+=" </chunk>\n"156 result+="</sentence>\n"157 return result158 def size(self):159 return len(self)-1160 def token_size(self):161 return len(self)-1162 def token(self,index):163 if not hasattr(self._cabocha,"_sentences"):164 self._makeChunks()165 c=None166 for s1,s2 in zip(self._cabocha._sentences,self._cabocha._chunkinfo):167 for c1,c2 in zip(s1,s2):168 i,d,t,h,f,w,z=c2169 if t==index:170 c=Chunk(c1,c2)171 break172 return Token(self,c,index)173 def chunk_size(self):174 if not hasattr(self._cabocha,"_sentences"):175 self._makeChunks()176 return sum(len(s) for s in self._cabocha._sentences)177 def chunk(self,index):178 if not hasattr(self._cabocha,"_sentences"):179 self._makeChunks()180 for s1,s2 in zip(self._cabocha._sentences,self._cabocha._chunkinfo):181 for c1,c2 in zip(s1,s2):182 i,d,t,h,f,w,z=c2183 if z==index:184 return Chunk(c1,c2)185 return None186 def sentence_size(self):187 if not hasattr(self._cabocha,"_sentences"):188 self._makeChunks()189 return len(self._cabocha._sentences)190 def sentence(self,index=0):191 if self._result>"":192 return self._result.split("# text = ")[index+1].split("\n")[0]193 if not hasattr(self._cabocha,"_sentences"):194 self._makeChunks()195 return "".join(w for i,d,t,h,f,w,z in self._cabocha._chunkinfo[index])196 def renew(self):197 self._makeChunks()198 self._makeFeatures()199class Chunk(object):200 additional_info=None201 feature_list_size=0202 score=0.0203 def __init__(self,chunk,chunkinfo):204 i,d,t,h,f,w,z=chunkinfo205 self.token_pos=t206 self.head_pos=h207 self.func_pos=f208 self.link=-1 if d<0 else d-i+z209 self.token_size=len(chunk)210 self._surface=w211 def __repr__(self):212 return self._surface213 def feature_list(self,index):214 return None215class Token(object):216 additional_info=ne=None217 def __init__(self,tree,chunk,index):218 self.surface=self.normalized_surface=tree[index+1].form219 self.chunk=chunk220 if not hasattr(tree._cabocha,"_features"):221 tree._makeFeatures()222 self.feature=tree._cabocha._features[index+1]223 self.feature_list_size=len(self.feature.split(","))224 self.ne=tree._get_ne(index+1)225 def __repr__(self):226 return self.normalized_surface227 def feature_list(self,index):228 return self.feature.split(",")[index]229class Parser(object):230 def __init__(self,UniDic=None):231 self.UniDic2UD=unidic2ud.UniDic2UD(UniDic,UDPipe="japanese-modern")232 def parse(self,sentence):233 t=Tree(self.UniDic2UD(sentence,raw=True))234 t._cabocha._parser=self235 return t236 def parseToString(self,sentence):...
conll_get.py
Source:conll_get.py
...7 """8 if not sentence:9 return []10 if type(sentence[0]) is list:11 return [_get_ne(sent) for sent in sentence]12 else:13 return _get_ne(sentence)14def _get_ne(sentence):15 _b = re.compile("^B")16 _i = re.compile("^I")17 ne = []18 for tup in sentence:19 # add a new tuple or NE20 if _b.search(tup[2]):21 ne.append([tup])22 # add to the most recent B or I tag23 # the additional NE information24 if _i.search(tup[2]):25 ne[-1].append(tup)26 return ne27def get_nouns(sentence):28 """ For either a list of sentences, or a single sentence...
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