gA dZddlmZddlZddlZddlmZddl m Z m Z ddl m Z ddlmZd Zgd Zd eiZd efd Z ddZ ddZejj ddZdZdZddZdS)z1 Visualization functions for measurement counts. ) OrderedDictN) optionals)QuasiDistributionProbDistribution)VisualizationError)matplotlib_close_if_inlinect|t|krtdtdt||DS)zCalculate the Hamming distance between two bit strings Args: str1 (str): First string. str2 (str): Second string. Returns: int: Distance between strings. Raises: VisualizationError: Strings not same length zStrings not same length.c3(K|] \}}||kVdSN).0s1s2s _/granary/edev/quantum/lib/python3.11/site-packages/qiskit/visualization/counts_visualization.py z#hamming_distance..)s*66FBrRx666666)lenrsumzip)str1str2s rhamming_distancersO 4yyCII !;<<< 66c$oo666 6 66r)ascdeschammingvalue value_descrreturnc t|ts|g}|D]b}t|ttfsAtt t |trdScdS)NTF) isinstancelistrrnextitervaluesfloat)datadats r_is_deprecated_data_formatr)0s dD ! !v c-/?@ A A Z cjjll## $ $eF F  44  5rrTc 4t|ts|g}d} |D]a} t| ttfsAtt t | trd} bt|||||||||| | |  S)a Plot a histogram of input counts data. Args: data (list or dict): This is either a list of dictionaries or a single dict containing the values to represent (ex ``{'001': 130}``) figsize (tuple): Figure size in inches. color (list or str): String or list of strings for histogram bar colors. number_to_keep (int): The number of terms to plot per dataset. The rest is made into a single bar called 'rest'. If multiple datasets are given, the ``number_to_keep`` applies to each dataset individually, which may result in more bars than ``number_to_keep + 1``. The ``number_to_keep`` applies to the total values, rather than the x-axis sort. sort (string): Could be `'asc'`, `'desc'`, `'hamming'`, `'value'`, or `'value_desc'`. If set to `'value'` or `'value_desc'` the x axis will be sorted by the number of counts for each bitstring. Defaults to `'asc'`. target_string (str): Target string if 'sort' is a distance measure. legend(list): A list of strings to use for labels of the data. The number of entries must match the length of data (if data is a list or 1 if it's a dict) bar_labels (bool): Label each bar in histogram with counts value. title (str): A string to use for the plot title ax (matplotlib.axes.Axes): An optional Axes object to be used for the visualization output. If none is specified a new matplotlib Figure will be created and used. Additionally, if specified there will be no returned Figure since it is redundant. filename (str): file path to save image to. Returns: matplotlib.Figure: A figure for the rendered histogram, if the ``ax`` kwarg is not set. Raises: MissingOptionalLibraryError: Matplotlib not available. VisualizationError: When legend is provided and the length doesn't match the input data. VisualizationError: Input must be Counts or a dict Examples: .. plot:: :alt: Output from the previous code. :include-source: # Plot two counts in the same figure with legends and colors specified. from qiskit.visualization import plot_histogram counts1 = {'00': 525, '11': 499} counts2 = {'00': 511, '11': 514} legend = ['First execution', 'Second execution'] plot_histogram([counts1, counts2], legend=legend, color=['crimson','midnightblue'], title="New Histogram") # You can sort the bitstrings using different methods. counts = {'001': 596, '011': 211, '010': 50, '000': 117, '101': 33, '111': 8, '100': 6, '110': 3} # Sort by the counts in descending order hist1 = plot_histogram(counts, sort='value_desc') # Sort by the hamming distance (the number of bit flips to change from # one bitstring to the other) from a target string. hist2 = plot_histogram(counts, sort='hamming', target_string='001') counts distributionkind) r!r"rrr#r$r%r&_plotting_core) r'figsizecolornumber_to_keepsort target_stringlegend bar_labelstitleaxfilenamer.r(s rplot_histogramr:;sd dD ! !v D"" c-/?@ A A "Z cjjll## $ $eF F  ""D           rc 8t|||||||||| | d S)aq Plot a distribution from input sampled data. Args: data (list or dict): This is either a list of dictionaries or a single dict containing the values to represent (ex {'001': 130}) figsize (tuple): Figure size in inches. color (list or str): String or list of strings for distribution bar colors. number_to_keep (int): The number of terms to plot per dataset. The rest is made into a single bar called 'rest'. If multiple datasets are given, the ``number_to_keep`` applies to each dataset individually, which may result in more bars than ``number_to_keep + 1``. The ``number_to_keep`` applies to the total values, rather than the x-axis sort. sort (string): Could be `'asc'`, `'desc'`, `'hamming'`, `'value'`, or `'value_desc'`. If set to `'value'` or `'value_desc'` the x axis will be sorted by the maximum probability for each bitstring. Defaults to `'asc'`. target_string (str): Target string if 'sort' is a distance measure. legend(list): A list of strings to use for labels of the data. The number of entries must match the length of data (if data is a list or 1 if it's a dict) bar_labels (bool): Label each bar in histogram with probability value. title (str): A string to use for the plot title ax (matplotlib.axes.Axes): An optional Axes object to be used for the visualization output. If none is specified a new matplotlib Figure will be created and used. Additionally, if specified there will be no returned Figure since it is redundant. filename (str): file path to save image to. Returns: matplotlib.Figure: A figure for the rendered distribution, if the ``ax`` kwarg is not set. Raises: MissingOptionalLibraryError: Matplotlib not available. VisualizationError: When legend is provided and the length doesn't match the input data. Examples: .. plot:: :alt: Output from the previous code. :include-source: # Plot two counts in the same figure with legends and colors specified. from qiskit.visualization import plot_distribution counts1 = {'00': 525, '11': 499} counts2 = {'00': 511, '11': 514} legend = ['First execution', 'Second execution'] plot_distribution([counts1, counts2], legend=legend, color=['crimson','midnightblue'], title="New Distribution") # You can sort the bitstrings using different methods. counts = {'001': 596, '011': 211, '010': 50, '000': 117, '101': 33, '111': 8, '100': 6, '110': 3} # Sort by the counts in descending order dist1 = plot_distribution(counts, sort='value_desc') # Sort by the hamming distance (the number of bit flips to change from # one bitstring to the other) from a target string. dist2 = plot_distribution(counts, sort='hamming', target_string='001') r,r-)r/) r'r0r1r2r3r4r5r6r7r8r9s rplot_distributionr?s@b           rr+c  &ddlm} ddlm} |tvrt d|t vr|d}t |t|tr|g}|rPt|t|kr0t dt|dt|d|&| j d d }nt|tr|g}| | | \}} nd}ttjd |t#}||d |t vrpg}|D]3}||d krt |||nd4d t'tt'||dDd}n}d|vryi&t|tr|&n9|D]6}|D]1}&|d}t+|||&|<27t&&fd}t|}dt|dzz }t/|||| \}}}g}t1|D]\}}d}t1||D]c\}}|s |r||}|dkrL|| |||zz|||||t|zdd}d|dz |dz z} | ||| z| |ddd|r|D]}!|!D]}"|"}#| dkrt;|#d}#|#dkrV| |"|" dz zd|#zt|#dd d!| |"|" dz zd|#zd"dd d!ь֐| d#kr| !d$d%&n| !d'd%&tEj#|$}$d(}%| dkr'tKd(tKd)|$D}%| &|%tKd*tO|$zt+d+|$Dggd,|vr| (| j)*| d-| +d.d/dd01|r| ,||r| -d2d3ddd45|rt]|| |S|/| S)6Nr) MaxNLocatorzgValue of sort option, %s, isn't a valid choice. Must be 'asc', 'desc', 'hamming', 'value', 'value_desc'z6Must define target_string when using distance measure.zLength of legend (z,) doesn't match number of input executions (z).zaxes.prop_cycler1)r0cP||Sr )unionkeys)xys rz _plotting_core..6s!''!&&((2C2Crrestc,g|]}t|Sr )r")rrEs r z"_plotting_core..?s]]]a$q'']]]rc|dS)Nrr )pairs rrGz _plotting_core..?s SWXYSZrkeyrrc|Sr r )rNcombined_countss rrGz _plotting_core..Is PS@Trr-)labelr1zorderFrightanchor)rotationha rotation_moder,gMbP?g@g?centerbottom)rXvarS0r+Count)fontsizezQuasi-probabilitygc3 K|] }d|zV dS皙?Nr rvals rrz!_plotting_core..s&>>cc >>>>>>rrdc3 K|] }d|zV dSrcr res rrz!_plotting_core..s&8W8Wss8W8W8W8W8W8Wrrr=majorrFz--)whichaxisrS linestylez upper left)g)\(?g?T)locbbox_to_anchorncol borderaxespadframeon)0matplotlib.pyplotpyplotmatplotlib.tickerrA VALID_SORTSr DIST_MEASr!dictrrcParamsby_keystrsubplotssorted functoolsreducesetappendrgetmaxrD _plot_data enumeratebar set_xticksset_xticklabels get_heightroundtextget_x get_width set_ylabelnp concatenateravelminset_ylimr invert_xaxisyaxisset_major_locatorgridr7r5r savefig)'r'r0r1r2r3r4r5r6r7r8r9r.pltrAerr_msgfiglabelsdistitemr+count prev_countlengthwidth labels_dict all_pvaluesall_indsrects_rRidxrf bar_centerrectrecheightall_valsmin_ylimrPs' @rr/r/s$#####------ ;  7    y]2J )))$v  #f++T** gV g gZ]^bZcZc g g g    } ./6688A E3   z,,w,//RR I$%C%CT355QQ R RF! f y W WD KK $m<<!>J-0VE]-K-KOE**L,,..4T4T4T4TUUU YYF TQ E)3D&.W[)\)\)\&Kh ET??,,a!+d"344  HC %6 %t Qww FFdUl*##D3u::$56    aiFQJ/  htnz1222 ;++--wV^___    C ^^--F~--!&vq!1!1~~IIKK#--//C*?? 6MKK''#$  IIKK#--//C*?? 6M''#$  0 x g ++++ )B 777~k**0022HH ~sC>>X>>>>>??KK3c(mm 3S8W8Wh8W8W8W5W5WXYYZ[[[ ~~ H{{1~~...HH7Q$H???  %   &     ("3''' {{8$$$rct|d}td|d| D}t|| d|S)zUKeep only the largest values in a dictionary, and sum the rest into a new key 'rest'.c|dS)Nrr )ps rrGz%_keep_largest_items..s AaDrrMc3 K|] \}}|V dSr r )rrNrs rrz&_keep_largest_items..s&GGeuGGGGGGrN)rH)r{itemsrrv) executionr2 sorted_countsrHs r_keep_largest_itemsrsi9??,,..AAAM GG}5E~o5E'FGGG G GD  ~o../d ; ; ;;rct|}tjd|D}d|D}g}|D]@}|}||||A|S)zTMake all dictionaries in data have the same set of keys, using 0 for missing values.c3>K|]}|VdSr )rD)rrs rrz _unify_labels..s,FFIy~~//FFFFFFrci|]}|dS)rr )rrRs r z!_unify_labels..s - - -E1 - - -r)tupler~rCcopyupdater)r' all_labelsbaseoutr new_executions r _unify_labelsrs ;;DFFFFFGJ - -* - - -D C""  Y''' =!!!! Jrcxt}g}g}t|tr|g}tfd|D}|D]}g}|D]C} | |vrd|| <|d#d|| <||| D|dkrt j|t} n2t j|t} | t j | z} || t|} t j | } || |||fS)aGenerate the data needed for plotting counts. Parameters: data (list or dict): This is either a list of dictionaries or a single dict containing the values to represent (ex {'001': 130}) labels (list): The list of bitstring labels for the plot. number_to_keep (int): The number of terms to plot and rest is made into a single bar called 'rest'. kind (str): One of 'counts' or 'distribution` Returns: tuple: tuple containing: (dict): The labels actually used in the plotting. (list): List of ndarrays for the bars in each experiment. (list): Indices for the locations of the bars for each experiment. Nc38K|]}t|VdSr )r)rrr2s rrz_plot_data..s/bbPY0NKKbbbbbbrrrr+)dtype) rr!rvrrrarrayintr&rrarange) r'rr2r.rrrrr%rNpvaluesnumeleminds ` rrrse$--KKH$v!bbbb]abbbbb  . .C)##!)'(K$MM!$$$#$ C  in---- 8  hvS111GGhvU333G rvg &G7###f++i    X --r) NNNrNNTNNN) r;NNrNNTNNN) r;NNrNNTNNNr+)r+)__doc__ collectionsrr|numpyr qiskit.utilsr _optionals qiskit.resultrr exceptionsrutilsr rrtruboolr)r:r?HAS_MATPLOTLIBrequire_in_callr/rrrr rrrs$#####000000========******------ 7 7 7 @?? ( )       hhhhZ      ^^^^B *       U%U%U%+*U%p<<<   /./././././.r