z iSB SrSSKJr SSKrSSKrSSKJr SSK J r J r SSK J r SSKJr S r/S QrS \0rS \4S jrSSjrSSjr\R.R0SSj5rSrSrSSjrg)z1 Visualization functions for measurement counts. ) OrderedDictN) optionals)QuasiDistributionProbDistribution)VisualizationError)matplotlib_close_if_inlinec~[U5[U5:wa [S5e[S[X555$)zCalculate the Hamming distance between two bit strings Args: str1 (str): First string. str2 (str): Second string. Returns: int: Distance between strings. Raises: VisualizationError: Strings not same length zStrings not same length.c3.# UH upX:gv M g7fN).0s1s2s c/home/james-whalen/.local/lib/python3.13/site-packages/qiskit/visualization/counts_visualization.py #hamming_distance..)s6oFBrxos)lenrsumzip)str1str2s rhamming_distancers5 4yCI !;<< 6c$o6 66)ascdeschammingvalue value_descrreturnc [U[5(dU/nUHUn[U[[45(d7[[ [ UR 555[5(dMU g g)NTF) isinstancelistrrnextitervaluesfloat)datadats r_is_deprecated_data_formatr*0sa dD ! !v c-/?@ A AZ cjjl# $eF F   rc [U[5(dU/nSn UHWn [U [[45(d7[[ [ U R 555[5(dMUSn MY [UUUUUUUUUU U U S9 $)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 AZ cjjl# $eF F "D          rc *[UUUUUUUUUU U SS9 $)a 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.)r0) r(r1r2r3r4r5r6r7r8r9r:s rplot_distributionr=s6b         rc p ^'SSKJn SSKJn U[;a [ S5eU[ ;aUc Sn[ U5e[U[5(aU/nU(a<[U5[U5:wa$[ S[U5S[U5S35eUc!U RSR5S nO[U[5(aU/nU cU RUS 9upOSn[[R "S U[#555nUbUR%S 5 U[ ;ak/nUH*nUR%US :wa[ U"UU5OS5 M, ['[['UU5S S96Vs/sHn[)U5PM snSnOrSU;al0m'[U[5(aUm'O6UH0nUH'nT'R+US5n[-UUU5T'U'M) M2 [T'R/5U'4SjS9n[U5nS[U5S-- n[1UUX;S9unnn/n[3U5GHxunnSn[3UU5H[unn U(d U(aUUnU S:dM!UR%U R5UUU--U UUUU[U5-SS95 SnM] US- US- -n!U R7UUU!-5 U R9UR/5SSSS9 U(dMUHn"U"Hn#U#R;5n$U S:Xa [=U$S5n$U$S:aDU R?U#RA5U#RC5S- -SU$-[U$5SSSS 9 MoU R?U#RA5U#RC5S- -SU$-S!SSSS 9 M M GM{ U S":XaU RES#S$S%9 OU RES&S$S%9 [FRH"U5RK5n%S'n&U S:Xa[MS'[MS(U%555n&U ROU&[MS)[QU%5-[-S*U%55/5/5 S+U;aU RS5 U RTRWU "S,55 U RYS-S.SS/S09 U(aU R[U5 U(aU R]S1S2SSS3S49 U(a [_U5 U cURa5 U$UReU 5$s snf![ba U$f=f)5Nr) MaxNLocatorzgValue of sort option, %s, isn't a valid choice. Must be 'asc', 'desc', 'hamming', 'value', 'value_desc'z6Must define target_string when using distance measure.zLength of legend (z,) doesn't match number of input executions (z).zaxes.prop_cycler2)r1c@URUR55$r )unionkeys)xys r _plotting_core..6s!''!&&(2Crrestc US$)Nrr )pairs rrErF?s SWXYSZrkeyrrc>TU$r r )rKcombined_countss rrErFIs PS@Trr.)labelr2zorderFrightanchor)rotationha rotation_moder-gMbP?g@g?centerbottom)rUvarP0r,Count)fontsizezQuasi-probabilitygc3,# UH nSU-v M g7f皙?Nr rvals rr!_plotting_core..s>Xcc Xrac3,# UH nSU-v M g7fr`r rbs rrrds8WhsshrermajorrDz--)whichaxisrP linestylez upper left)g)\(?g?T)locbbox_to_anchorncol borderaxespadframeon)3matplotlib.pyplotpyplotmatplotlib.tickerr? VALID_SORTSr DIST_MEASr"dictrrcParamsby_keystrsubplotssorted functoolsreducesetappendrr#getmaxrB _plot_data enumeratebar set_xticksset_xticklabels get_heightroundtextget_x get_width set_ylabelnp concatenateravelminset_ylimr invert_xaxisyaxisset_major_locatorgridr8r6r tight_layoutAttributeErrorsavefig)(r(r1r2r3r4r5r6r7r8r9r:r/pltr?err_msgfiglabelsdistitemrCr,count prev_countlengthwidth labels_dict all_pvaluesall_indsrects_rOidxrc bar_centerrectrecheightall_valsmin_ylimrMs( @rr0r0s'$- ;  7   y]2J ))$v #f+T*  V -YZ]^bZcYddf g   } ./668A E3   z,,w,/R I$$%CT35Q RF! f yD KK $mJ-0VE]-KOE*$,,.4TU YF TQ E)3D&.)\&Kh ET?a!+d"34HC6t Qw FFdUl*##D3u:$56  5aiFQJ/  htnz12 ;++-wV^_ :C ^^-F~-!&vq!1~IIK#--/C*?? 6MK''#$  IIK#--/C*?? 6M''#$   -#^ x g + )B 7~~k*002HH ~sC>X>>?KK3c(m 3S8Wh8W5WXYZ[ ~ HH{1~.HH7Q$H?  %  &   "3'      {{8$$E^|    sT"?T'' T54T5cz[UR5SS9n[SUSU*55n[X!*SUS9$)zUKeep only the largest values in a dictionary, and sum the rest into a new key 'rest'.c US$)Nrr )ps rrE%_keep_largest_items..sAaDrrJc3*# UH upUv M g7fr r )rrKrs rr&_keep_largest_items..sG'Fu'FsN)rG)r{itemsrrv) executionr3 sorted_countsrGs r_keep_largest_itemsrsD9??,.AM G}5E~o'FG GD  o./d ;;rc[U5n[5R"SU56nUVs0sHo"S_M nn/nUH5nUR5nUR U5 UR U5 M7 U$s snf)zTMake all dictionaries in data have the same set of keys, using 0 for missing values.c3@# UHoR5v M g7fr )rB)rrs rr _unify_labels..sFI~~//sr)tupler~rAcopyupdater)r( all_labelsrObaseoutr new_executions r _unify_labelsrsw ;DFFGJ", -*1H*D - C  Y' =! J .s A;cJ^[5n/n/n[U[5(aU/nTb[U4SjU55nUHn/nUH;n X;aTcSXI'UR S5 M"M$SXI'UR Xy5 M= US:Xa[ R "U[S9n O2[ R "U[S9n U [ R"U 5-n UR U 5 [U5n [ R"U 5n UR U 5 M XEU4$)aGenerate the data needed for plotting counts. Parameters: data (list or dict): This is either a list of dictionaries or a single dict containing the values to represent (ex {'001': 130}) labels (list): The list of bitstring labels for the plot. number_to_keep (int): The number of terms to plot and rest is made into a single bar called 'rest'. kind (str): One of 'counts' or 'distribution` Returns: tuple: tuple containing: (dict): The labels actually used in the plotting. (list): List of ndarrays for the bars in each experiment. (list): Indices for the locations of the bars for each experiment. c3<># UHn[UT5v M g7fr )r)rrr3s rr_plot_data..sb]aPY0NKK]asrrr,)dtype) rr"rvrrrarrayintr'rrarange) r(rr3r/rrrrr&rKpvaluesnumeleminds ` rrrs$-KKH$v!b]abb C#!)'(K$MM!$*$%   in- 8 hhvS1GhhvU3G rvvg &G7#f+ii %( X --r) NNNrNNTNNN) rgNNrNNTNNN) rNNrNNTNNNr,)r,)__doc__ collectionsrr|numpyr qiskit.utilsr _optionals qiskit.resultrr exceptionsrutilsr rrtruboolr*r;r=HAS_MATPLOTLIBrequire_in_callr0rrrr rrrs$0=*- 7 @ ( )       hZ      ^B **       Y%+Y%x< /.r