コード例 #1
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ファイル: analyze.py プロジェクト: bsu233/microglia_MACH
def Statistics(cellnums, resultfile):
    """
    Get the statistics of the each cell type
    and draw the pie chart
    """
    cells = {"ram": 0, "rod": 0, "hyp": 0, "dys": 0, "amoe": 0}
    for i in cells.keys():
        for j in range(100):
            cells[i] += cellnums[j][i]

    colors = ['green', 'blue', 'yellow', 'cyan', 'red']
    Allcells = cells['ram'] + cells['hyp'] + cells['dys'] + cells[
        'amoe'] + cells['rod']

    sizes = np.asarray([
        cells['ram'], cells['hyp'], cells['dys'], cells['amoe'], cells['rod']
    ]) / Allcells
    plt.figure(figsize=(3, 3))
    plt.pie(sizes, autopct='%1.1f%%',colors=colors,\
        shadow=True, startangle=90)
    plt.savefig("Pie.png")

    f = open(resultfile, 'w')
    f.write('%4s\t%4s\t%4s\t%4s\t%4s\n' % ("ram", "rod", "hyp", "dys", "amoe"))
    f.write('%4d\t%4d\t%4d\t%4d\t%4d\n' % (cells["ram"],cells["rod"],\
            cells["hyp"],cells["dys"],cells["amoe"]))
    f.close()
コード例 #2
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def data_visualization2():
    #subprocess.check_output(['dos2unix', 'teste.txt'])

    hamb = []
    #subprocess.check_output(['dos2unix', 'write.fasta'], cwd='/home/livanski/Documents/danielsundfeld-hpc/seqs')
    #with open("teste.txt") as file:
    with open(
            '/home/ubuntu/ferramenta_final/servidor_apresentacao/ferramenta/media/arquivo.fasta'
    ) as file:
        for line in file:
            hamb.append(line)

    seq1 = Seq(hamb[1])
    seq2 = Seq(hamb[3])
    seq3 = seq1 + seq2

    gc = GC(seq3)

    au = 100 - gc

    pylab.pie([gc, au])  # cria o grafico pizza
    pylab.title('GC Content')  # indica um titulo
    pylab.xlabel('GC: %0.1f\nAT: %01.f' % (gc, au))  # porcentagens/info
    # exibe
    #pylab.savefig('/home/livanski/Pictures/graph.png', dpi=100)
    #pylab.savefig('/home/livanski/Music/upload_final/django-upload-example/static/graph.png', dpi=100)
    pylab.savefig(
        '/home/ubuntu/ferramenta_final/servidor_apresentacao/ferramenta/mysite/static/css/graph_upload.png',
        dpi=100)
    return pylab.show
コード例 #3
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 def save_plt(self):
     x = [
         len(self.plt_1),
         len(self.plt_2),
         len(self.plt_3),
         len(self.plt_4)
     ]
     plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
     plt.pie(
         x,
         labels=['1-5K', '5-8K', '8-15K', '15K-20K'],
         labeldistance=1.2,  # 设置标签距离圆心的距离(0为 圆饼中心数 1为圆饼边缘)
         autopct="%1.1f%%",  # 1.1f% 保留一位小数的百分比
         pctdistance=0.5,  # 比例值文字距离圆心的距离
         explode=[0, 0.2, 0, 0],  # 每一块顶点距圆形的长度
         colors=["lightskyblue", "green", "royalblue", "magenta"],  # 最好一一对应
         shadow=True,  # 是否有阴影
         startangle=60,  # 第一块开始的角度
     )
     plt.axis('equal')  # 该行代码使饼图长宽相等
     # plt.legend(loc="upper right", fontsize=10, bbox_to_anchor=(1.1, 1.05), borderaxespad=0.3)
     # plt.loc =  'upper right'    # 位于右上角
     # plt.bbox_to_anchor=[0.5, 0.5]   # 外边距 上边 右边
     # plt.ncol=2  # 分两列
     # plt.borderaxespad = 0.3 # 图例的内边距
     # plt.title("上海市{}岗位薪资占比".format(self.key))
     # plt.title("Python Min")
     # plt.savefig('Python_Min')
     plt.title("Python Max")
     plt.savefig('Python_Max')
     plt.show()
コード例 #4
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 def plot_tipo_tweet(self, archivo_imagen = ''):
     """
     Esta función toma como entrada el archivo de datos preprocesados y
     devuelve una imagen tipo diagrama de Venn con el tipo de tweets pescados
     """
     #Levantar los datos preprocesados del archivo
 
     originales = set(self.tweets.tw_id.values) #Conjunto de tweets originales
     rt = set(self.tweets[self.tweets.relacion_nuevo_original == 'RT'].or_id.values) #Conjunto de retweets
     citas = set(self.tweets[self.tweets.relacion_nuevo_original == 'QT'].or_id.values) #Conjunto de citas    
     total_tweets = len(originales.union(rt).union(citas)) #Cantidad total de tweets
     
     # Realizar la figura
     labels = ['RT', 'Originales', 'QT'] 
     sizes = [100 * len(rt) / total_tweets, 100 * len(originales) / total_tweets, 100 * len(citas) / total_tweets]
     
     sbn.set_context("paper", font_scale = 1.5)
     
     plt.figure(figsize = (11,8), dpi = 300)
     plt.title('Tipos de Tweets', fontsize = 20)
     plt.pie(sizes, autopct='%1.1f%%')
     plt.legend(labels)
     plt.axis('equal')
     plt.text(-.1,-1.2,'El total de tweets registrados durante el período fue de : {}'.format(total_tweets))
     if archivo_imagen=='':
         plt.show()
     else:
         plt.savefig(archivo_imagen,bbox_inches='tight')
         plt.show()
     plt.clf()
     sbn.reset_orig()
コード例 #5
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def ZipByDemoCuisine(askNum ,Zipcodes, recorddict):
    for zips in Zipcodes:
        if askNum == zips.Zip:
            print "This is located in " + str(zips.Hood) + "!"
            labels = ['White', 'Black', 'AI', 'Asian', 'NH/PI', 'Other', 'Multiple', 'Hispanic']
            sizes = [zips.White, zips.Black, zips.AI_AN, zips.Asian, zips.NHOPI, zips.OthRace, zips.MultRaces, zips.Hispanic]
            colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'darkblue','pink', 'purple' ]
            explode = (0, 0, 0, 0, 0, 0, 0, 0)
            plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=False, startangle=140)
                 
            plt.axis('equal')
            plt.show()
    
    
    xlist = []
    ylist = []
    
    x = Counter(recorddict[askNum])
    for i in x.most_common(10):
        xlist.append(i[0])
        ylist.append(int(i[1]))

        #i[0] = category
        #i[1] = number of category  
    
    labels = xlist
    sizes = ylist
    colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'darkblue', 'pink', 'white', 'silver', 'purple']
    explode = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
    plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=False, startangle=140)
                 
    plt.axis('equal')
    plt.show()
コード例 #6
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def pie_plot_important_activities_2018(
):  # pie_plot_important_activities_2018(kaggleSurveyDF_2018, schemaSurveyDF_2018 ):
    responseCountsList = []
    responseOptionsTextListAnnotated = [
        'Analyze and understand data',
        'Use ML services to improve product or wokflows',
        'Build/run data infrastructure',
        'Apply ML to new areas and build prototypes',
        'Research/advance state of the art in ML',
        'Other / None of these activities are important to my role'
    ]

    responseCountsList = [9532, 5481, 5233, 7233, 4934, 4663]

    plt.figure()
    plt.pie(x=responseCountsList,
            labels=responseOptionsTextListAnnotated,
            startangle=90,
            autopct='%1.1f%%',
            wedgeprops={
                'linewidth': 1,
                'edgecolor': 'white'
            },
            colors=np.array(plt.get_cmap('tab20').colors))
    plt.title(
        'What activities make up an important part of your role at work?')
    plt.show()
コード例 #7
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ファイル: report.py プロジェクト: iamayushdas/yotubeextractor
    def score(self):
        print('Caculating Your Activity Score.....')
        colors = ['#ff3300', '#33cc33']
        scoreValue = round(
            math.log((len(urls) + len(searchClean * 2) + len(allLikes * 3) +
                      len(allLinks * 4)) / 9, 1.12), 1)
        x_0 = [1, 0, 0, 0]
        pl.pie([100 - scoreValue, scoreValue],
               autopct='%1.1f%%',
               startangle=90,
               colors=colors,
               pctdistance=10)
        plt.pie(x_0, radius=0.7, colors='w')
        plt.axis('equal')

        plt.title("Your YouTube Activity Score",
                  fontsize=21,
                  color='steelblue',
                  fontweight="bold",
                  fontname="Comic Sans MS")

        plt.annotate(scoreValue, (0, 0), (115, 154),
                     fontsize=54,
                     color='teal',
                     fontweight="bold",
                     fontname="Comic Sans MS",
                     xycoords='axes fraction',
                     textcoords='offset points',
                     va='top')
        plt.savefig(image_dir + 'score.png', dpi=400)
        plt.clf()
コード例 #8
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ファイル: plots.py プロジェクト: jephdo/ledgermodel
def piechart(data,labels,title=0):
    fig = plt.figure(figsize=(8,8))

    plt.pie(data,labels=labels, autopct='%1.1f%%', colors=colors(len(data)), shadow=True)
    if(title):
        plt.title(title)

    return fig
コード例 #9
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def pie_plot(counts, label, colors, col_name):
    plt.pie(counts,
            labels=label,
            colors=colors,
            startangle=90,
            autopct='%.1f%%')
    plt.savefig(f'../output/graphs/{col_name}_pie_plt.png')
    plt.show()
コード例 #10
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def get_region_chart():
    count = df['区域'].value_counts()
    print(count)
    plt.pie(count, labels=count.keys(), labeldistance=1.4, autopct='%2.1f%%')
    plt.axis('equal')
    plt.title('岗位地区分布图')
    plt.legend(loc='upper left', bbox_to_anchor=(-0.1, 1))
    plt.savefig('pie_chart.jpg')
    plt.show()
コード例 #11
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def rem_nrem(stages):
    timespent_array=time_spent_in_stages(stages)
    time_nrem=timespent_array[1]+timespent_array[2]+timespent_array[3]+timespent_array[4]
    time_rem=timespent_array[5]
    split=[(time_rem*100/(time_nrem+time_rem)),(time_nrem*100/(time_nrem+time_rem))]
    plt.figure()
    labels=['REM Sleep', 'NREM Sleep']
    plt.pie(split,labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
    plt.title('Subject 2- Bad Sleep - REM/NREM Splitup')
    plt.show()
コード例 #12
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    def default_value(self):
        df = self.df
        #统计数量,比率
        count = df.isnull().sum().sort_values(
            ascending=False)  #统计出每个属性的缺省数量,再根据缺省数量倒排序
        ratio = count / len(df)  #每个属性的缺省占自己总数的比率
        nulldata = pd.concat([count, ratio], axis=1, keys=['count', 'ratio'])

        #饼状图
        explode = [0]
        explode = explode * len(nulldata.index)
        explode[0] = 0.1
        plt.pie(x=count,
                labels=nulldata.index,
                autopct='%1.1f%%',
                shadow=True,
                startangle=90,
                explode=explode,
                pctdistance=0.8,
                textprops={
                    'fontsize': 16,
                    'color': 'w'
                })  #饼状图画出每个属性的缺省在整体缺省数据的占比
        plt.title('属性缺省占比图')
        plt.legend(loc="upper right",
                   bbox_to_anchor=(1.4, 1.1),
                   borderaxespad=0.3)
        # plt.savefig("picture/default_value_pie.jpg")
        plt.show()

        #水平柱状图
        plt.figure()
        plt.barh(nulldata.index, count, 0.5, color='#FFFF00',
                 label="缺省")  #每个属性的缺省样本数
        plt.barh(nulldata.index,
                 df.shape[0] - count,
                 0.5,
                 color='#97FFFF',
                 left=count,
                 label="不缺省")  #每个属性的样本值不缺省的数量
        plt.xlabel("属性")
        plt.ylabel("样本数")
        plt.legend()
        plt.title("每个属性的缺省情况")
        # plt.savefig("picture/default_value_bar.jpg")
        plt.show()

        # 填充缺省,字符串类型的属性用None填充,数值型用众数填充。简单填充,可能会产生更多的误差
        for index in nulldata.index:
            if type(index) is not object:
                df[index].fillna("None", inplace=True)
            else:
                df[index].fillna(df[index].mode()[0], inplace=True)
        self.df = df
        return nulldata, df
コード例 #13
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ファイル: paint.py プロジェクト: kayzhou/character_analysis
def badge_bar(index):
    data1 = pd.read_csv('data/split_class/large_IGNORE_404_badge_+1.txt', sep=' ', header=None)
    data2 = pd.read_csv('data/split_class/large_IGNORE_404_badge_-1.txt', sep=' ', header=None)

    col1 = data1[index]
    col2 = data2[index]

    print(col1.value_counts() / len(col1))
    print(col2.value_counts() / len(col2))
    plt.pie(col1.value_counts() / len(col1), colors=('b', 'r'), labels=['badge', 'non-badge'])
    plt.show()
コード例 #14
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def DemosForZips(Zipcodes):
    for zips in Zipcodes:
        
        labels = ['White', 'Black', 'AI', 'Asian', 'NH/PI', 'Other', 'Multiple', 'Hispanic']
        sizes = [zips.White, zips.Black, zips.AI_AN, zips.Asian, zips.NHOPI, zips.OthRace, zips.MultRaces, zips.Hispanic]
        colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'blue', 'purple', 'silver']
        explode = (0, 0, 0, 0, 0, 0, 0, 0)
        plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=False, startangle=140)
         
        plt.axis('equal')
        plt.show()
        print zips.Zip + " is located in " + str(zips.Hood) + "!"
コード例 #15
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ファイル: test13_饼状图.py プロジェクト: Calen0wong/calen1
 def compute_initial_figure(self):
     UsersPerCountry, UsersPerPlatform = Analytics.UsersPerCountryOrPlatform()
     labels = []
     sizes = []
     print(UsersPerPlatform)
     for p, c in sorted(UsersPerPlatform.iteritems()):
         labels.append(p)
         sizes.append(c)
         colors = ['turquoise', 'yellowgreen', 'firebrick', 'lightsteelblue', 'royalblue']
     pylab.pie(sizes, colors=colors, labels=labels, autopct='%1.1f%%', shadow=True)
     pylab.title('Users Per Platform')
     pylab.gca().set_aspect('1')
     pylab.show()
コード例 #16
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def pie_chart(vintage_results, year=40):
    '''
    plot a pie chart show the release break down (by market) for the specific year
    '''
    labels = vintage_results.keys()
    size = []
    explode = (0,0.1,0,0,0,0,0) # only explode the second one
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
    for each_key, each_val in vintage_results.iteritems():
        this_tot_rel_year = each_val['In Use'][40] + each_val['End of Life'][40]
        size.append(this_tot_rel_year)
    
    plt.pie(size,explode=explode,labels=labels,autopct='%1.1f%%',colors=colors,shadow=True)
    plt.show()
コード例 #17
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    def score(self):
        print("Caculating Your Activity Score.....")
        colors = ["#ff3300", "#33cc33"]
        score_value = round(
            math.log(
                (
                    len(urls)
                    + len(search_clean * 2)
                    + len(all_likes * 3)
                    + len(all_links * 4)
                )
                / 9,
                1.12,
            ),
            1,
        )
        x_0 = [1, 0, 0, 0]
        pl.pie(
            [100 - score_value, score_value],
            autopct="%1.1f%%",
            startangle=90,
            colors=colors,
            pctdistance=10,
        )
        plt.pie(x_0, radius=0.7, colors="w")
        plt.axis("equal")

        plt.title(
            "Your YouTube Activity Score",
            fontsize=21,
            color="steelblue",
            fontweight="bold",
            fontname="Arial",
        )

        plt.annotate(
            score_value,
            (0, 0),
            (123, 154),
            fontsize=54,
            color="teal",
            fontweight="bold",
            fontname="Arial",
            xycoords="axes fraction",
            textcoords="offset points",
            va="top",
        )
        plt.savefig(image_dir + "score.png", dpi=400)
        plt.clf()
コード例 #18
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ファイル: grapher.py プロジェクト: AndreasDL/ir-thesis
def genPiePlot(x,y,fname):
    colors = ['yellowgreen', 'red', 'gold', 'lightskyblue', 'white', 'lightcoral', 'blue', 'pink', 'darkgreen', 'yellow', 'grey', 'violet', 'magenta',
              'cyan']

    patches, texts = plt.pie(y, colors=colors, startangle=90, radius=1.2)
    labels = []
    for _x,_y in zip(x,y):
        labels.append(str(_x) + ' - ' + str(_y))

    sort_legend = False
    if sort_legend:
        patches, labels, dummy = zip(*sorted(zip(patches, labels, y),
                                             key=lambda x: x[2],
                                             reverse=True))

    #plt.legend(patches, labels, loc='lower center', bbox_to_anchor=(0, .5),
    #           fontsize=12)

    fig = matplotlib.pyplot.gcf()
    fig.set_size_inches(18.5, 18.5)
    plt.savefig(fname, bbox_inches='tight', dpi=100)
    plt.clf()

    plt.legend(patches, x, fontsize=23, ncol=3, bbox_to_anchor=(1.,1.))
    fig = matplotlib.pyplot.gcf()
    fig.set_size_inches(18.5, 10.5)
    fig.savefig(fname + "legend.png", dpi=100)
    plt.close()
コード例 #19
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ファイル: run.py プロジェクト: RedBrainLabs/ipython-sql
    def pie(self, key_word_sep=" ", title=None, **kwargs):
        """Generates a pylab pie chart from the result set.

        ``matplotlib`` must be installed, and in an
        IPython Notebook, inlining must be on::

            %%matplotlib inline

        Values (pie slice sizes) are taken from the
        rightmost column (numerical values required).
        All other columns are used to label the pie slices.

        Parameters
        ----------
        key_word_sep: string used to separate column values
                      from each other in pie labels
        title: Plot title, defaults to name of value column

        Any additional keyword arguments will be passsed
        through to ``matplotlib.pylab.pie``.
        """
        self.guess_pie_columns(xlabel_sep=key_word_sep)
        import matplotlib.pylab as plt
        pie = plt.pie(self.ys[0], labels=self.xlabels, **kwargs)
        plt.title(title or self.ys[0].name)
        return pie
コード例 #20
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    def getGenderImage(self):
        for i in range(2):
            tBD = self.getDurationBookData(i)

            plotName = "Gender"
            if i == 0:
                plotName += " (Short "
            else:
                plotName += " (Long "
            plotName += "BestSeller)"

            gender = [0,0] # F,M
            label = ['Female','Male']
            for book in tBD:
                if book[10] == 'F':
                    gender[0] += 1
                elif book[10] == 'M':
                    gender[1] += 1
            tempColors = self.colors[:2]

            plt.title(plotName,fontdict={'fontsize':30})
            a,b,c=plt.pie(gender, colors = tempColors, labels = label, autopct='%1.1f%%', startangle=90)
            for text in b:
                text.set_fontsize(20)
            plt.axis('equal')
            plt.show()
コード例 #21
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    def pie(self, key_word_sep=" ", title=None, **kwargs):
        """Generates a pylab pie chart from the result set.

        ``matplotlib`` must be installed, and in an
        IPython Notebook, inlining must be on::

            %%matplotlib inline

        Values (pie slice sizes) are taken from the
        rightmost column (numerical values required).
        All other columns are used to label the pie slices.

        Parameters
        ----------
        key_word_sep: string used to separate column values
                      from each other in pie labels
        title: Plot title, defaults to name of value column

        Any additional keyword arguments will be passsed
        through to ``matplotlib.pylab.pie``.
        """
        self.guess_pie_columns(xlabel_sep=key_word_sep)
        import matplotlib.pylab as plt
        pie = plt.pie(self.ys[0], labels=self.xlabels, **kwargs)
        plt.title(title or self.ys[0].name)
        return pie
コード例 #22
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    def getFirstBSImage(self):
        for i in range(2):
            tBD = self.getDurationBookData(i)

            plotName = "isFirstBestseller"
            if i == 0:
                plotName += " (Short "
            else:
                plotName += " (Long "
            plotName += "BestSeller)"

            isFBS = [0,0] # F,N
            label = ['First','not First']
            for book in tBD:
                if book[11] == '1':
                    isFBS[0] += 1
                elif book[11] == '0':
                    isFBS[1] += 1
            tempColors = self.colors[:2]

            plt.title(plotName,fontdict={'fontsize':30})
            a,b,c=plt.pie(isFBS, colors = tempColors, labels = label, autopct='%1.1f%%', startangle=90)
            for text in b:
                text.set_fontsize(20)
            plt.axis('equal')
            plt.show()
コード例 #23
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def create_piecharts(grade, total_students, met_students, min_threshold, max_threshold):
    for major, total in total_students.items():
        # Make a square figure and axes
        figure(1, figsize=(6,6))
        ax = axes([0.1, 0.1, 0.8, 0.8])
        # The slices will be ordered and plotted counter-clockwise.
        labels = 'Met threshold', 'Did not meet threshold'
        fracs = [met_students[major]/total, (total-met_students[major])/total]
        # Pop the slice of the pie out for emphasis
        explode=(0.1, 0)
        pie(fracs, explode=explode, labels=labels,
               autopct='%1.1f%%', shadow=True, startangle=90)
        title('%s Students Between %s-%s for Assignment %s' % (major, min_threshold, max_threshold, grade),
                 bbox={'facecolor':'0.8', 'pad':5})
        # Save the figure as a png
        savefig("piechart_%s_%s_%s-%s.png" % (grade, major, min_threshold, max_threshold), bbox_inches="tight")
        clf()
コード例 #24
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 def dataDistribution(self):
   print('Distribuição dos Dados')
   objects = np.zeros(self._NUM_CLASSES)
   for i in range(0,self.output_train.shape[0]):
     out  = np.argmax(self.output_train[i])
     objects[out] += 1
   fig=plt.figure(figsize=(10,5))
   fig.add_subplot(1, 2, 1)
   for i in range(0,self._NUM_CLASSES):
     plt.bar(i, objects[i], 0.8)
     plt.xlabel('Classes')
     plt.ylabel('Elementos')
   fig.add_subplot(1, 2, 2)
   for i in range(0,self._NUM_CLASSES):
     plt.pie(objects )
     plt.xlabel('Classes x Elementos')
   plt.show()
コード例 #25
0
def pie_chart(vintage_results, year=40):
    '''
    plot a pie chart show the release break down (by market) for the specific year
    '''
    labels = vintage_results.keys()
    size = []
    explode = (0, 0.1, 0, 0, 0, 0, 0)  # only explode the second one
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
    for each_key, each_val in vintage_results.iteritems():
        this_tot_rel_year = each_val['In Use'][40] + each_val['End of Life'][40]
        size.append(this_tot_rel_year)

    plt.pie(size,
            explode=explode,
            labels=labels,
            autopct='%1.1f%%',
            colors=colors,
            shadow=True)
    plt.show()
コード例 #26
0
 def default_value(self):
     df = self.df
     #统计数量,比率
     count = df.isnull().sum().sort_values(
         ascending=False)  #统计出每个属性的缺省数量,再根据缺省数量倒排序
     ratio = count / len(df)  #每个属性的缺省占自己总数的比率
     nulldata = pd.concat([count, ratio], axis=1, keys=['count', 'ratio'])
     #饼状图
     explode = [0]
     explode = explode * len(nulldata.index)
     explode[0] = 0.1
     plt.figure()
     plt.pie(x=count,
             labels=nulldata.index,
             autopct='%1.1f%%',
             shadow=True,
             startangle=90,
             explode=explode,
             pctdistance=0.8,
             textprops={
                 'fontsize': 16,
                 'color': 'w'
             })  #饼状图画出每个属性的缺省在整体缺省数据的占比
     plt.title('属性缺省占比图')
     plt.legend(loc="upper right",
                bbox_to_anchor=(1.4, 1.1),
                borderaxespad=0.3)
     if self.port == 11111:
         plt.savefig("default_value_pie1.jpg")
     elif self.port == 22222:
         plt.savefig("default_value_pie2.jpg")
     elif self.port == 33333:
         plt.savefig("default_value_pie3.jpg")
     elif self.port == 44444:
         plt.savefig("default_value_pie4.jpg")
     # 填充缺省,字符串类型的属性用None填充,数值型用众数填充。简单填充,可能会产生更多的误差
     for index in nulldata.index:
         if type(index) is not object:
             df[index].fillna("None", inplace=True)
         else:
             df[index].fillna(df[index].mode()[0], inplace=True)
     return nulldata, df
コード例 #27
0
ファイル: graph_output.py プロジェクト: a-tal/git_credit
def pie_chart(all_credit, graph_title):
    """Display the all_credit dictionary as a pie chart with matplotlib."""

    if not ENABLED:
        return

    # magic numbers are magical. at least the user can resize afterwards
    pylab.figure(1, figsize=(6, 6))
    pylab.axes([0.1, 0.1, 0.8, 0.8])

    pylab.pie(
        all_credit.values(),
        labels=all_credit.keys(),
        autopct='%1.1f%%',
        shadow=True,
        startangle=90,
    )

    pylab.title(graph_title)

    pylab.show()
コード例 #28
0
def percentTimeIn():
    """ Funcao que retorna o percentual de tempo em cada intervalo informado.
    """
    if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
        sr.msgBox('EPM Python Plugin - Demo Tools',
                  'Please select a single pen before applying this function!',
                  'Warning')
        return 0
    epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
    global nodes
    runDialogInterval()
    global nodes
    t, y = rmNanAndOutliers2(epmData)
    if len(nodes) < 2:
        minVal = int(np.floor(np.nanmin(y)))
        maxVal = int(np.ceil(np.nanmax(y)))
        step = int((maxVal - minVal) / 3)
        nodes = range(minVal, maxVal, step)
    intervNum = np.size(nodes) + 1
    totTime = np.empty(intervNum, dtype=object)
    totTime.fill(dtt.timedelta(0, 0))
    for i in range(1, np.size(y)):
        dt = t[i] - t[i - 1]
        ix = np.digitize([y[i]], nodes)
        totTime[ix] += dt
    nodesPercents = np.zeros([np.size(totTime), 2])
    totalPeriod = totTime.sum().total_seconds()
    for i in range(np.size(totTime)):
        if i:
            nodesPercents[i, 0] = nodes[i - 1]
        else:
            nodesPercents[i, 0] = -np.inf
        nodesPercents[i, 1] = totTime[i].total_seconds() / totalPeriod
    labels = []
    for item in nodesPercents[:, 0]:
        labels.append(str(item))
    pl.pie(nodesPercents[:, 1], labels=labels, autopct='%1.1f%%', shadow=True)
    pl.show()
    return nodesPercents
コード例 #29
0
def percentTimeIn():
    """ Funcao que retorna o percentual de tempo em cada intervalo informado.
    """
    if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
        sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
        return 0
    epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
    global nodes
    runDialogInterval()
    global nodes
    t,y = rmNanAndOutliers2(epmData)
    if len(nodes) < 2:
       minVal = int(np.floor(np.nanmin(y)))
       maxVal = int(np.ceil(np.nanmax(y)))
       step = int((maxVal-minVal)/3)
       nodes = range(minVal,maxVal,step)
    intervNum = np.size(nodes)+1
    totTime = np.empty(intervNum, dtype=object)
    totTime.fill(dtt.timedelta(0,0))
    for i in range(1,np.size(y)):
        dt = t[i] - t[i-1]
        ix = np.digitize([y[i]], nodes)
        totTime[ix] += dt
    nodesPercents = np.zeros([np.size(totTime),2])
    totalPeriod = totTime.sum().total_seconds()
    for i in range(np.size(totTime)):
        if i:
           nodesPercents[i,0] = nodes[i-1]
        else:
           nodesPercents[i,0] = -np.inf
        nodesPercents[i,1] = totTime[i].total_seconds()/totalPeriod
    labels = []
    for item in nodesPercents[:,0]:
        labels.append(str(item))
    pl.pie(nodesPercents[:,1], labels=labels, autopct='%1.1f%%', shadow=True)
    pl.show()
    return nodesPercents
コード例 #30
0
def responses_pieplot():
    response_data = pd.read_csv('response_percents.csv')
    #response_data = pd.read_csv('response_percents_reordered.csv')
    #print (response_data)
    fig, ax = plt.subplots(figsize=(7, 7))
    explode = [0, 0, 0, 0, 0, 0, 0.1]
    labels = response_data['Hall']
    #labels_gender = response_data['Gender']
    colors = [
        '#ffad30', '#c369ff', '#2ff59c', '#b5b5b5', '#ff4063', '#4dbeff',
        '#ff69b6'
    ]
    colors_gender = ['#2081C3', '#ff6969']
    plt.pie(x=response_data['Percent'],
            explode=explode,
            labels=labels,
            colors=colors,
            labeldistance=1.05,
            autopct='%.1f%%',
            pctdistance=0.64)
    #plt.pie(x=response_data['Percent'], labels=labels_gender, colors=colors_gender, labeldistance=0.5, autopct='%.1f%%', pctdistance=0.6)

    plt.legend(frameon=False)
    plt.show()
コード例 #31
0
    def getPublishYearImage(self,labelNum):
        for durationNum in range(2):
            plotName = "BSYear - PublishedYear"
            if durationNum == 0:
                plotName += " (Short "
            else:
                plotName += " (Long "
            plotName += "BestSeller)"

            tBD = self.getDurationBookData(durationNum)
            yearDict = {}
            for book in tBD:
                bsYear = int(book[0])
                pYear = int(book[4])
                difYear = bsYear - pYear
                if difYear in yearDict:
                    yearDict[difYear] += 1
                else:
                    yearDict[difYear] = 1
            
            yearNumList = []
            for year in yearDict:
                yearNumList.append((year,yearDict[year]))
            yearNumList.sort(reverse = True, key = lambda year: year[0])
            print(yearNumList)

            if labelNum < len(yearNumList):
                etcNum = 0
                for i in range(labelNum,len(yearNumList)):
                    etcNum += yearNumList[i][1]
                yearNumList = yearNumList[0:labelNum]
                yearNumList.append(('etc',etcNum))

            yearList = []
            numList = []
            for i in range(len(yearNumList)):
                yearList.append(yearNumList[i][0])
                numList.append(yearNumList[i][1])
            tempColors = self.colors[:len(numList)]

            plt.title(plotName,fontdict={'fontsize':30})
            a,b,c=plt.pie(numList, colors = tempColors, labels = yearList, autopct='%1.1f%%', startangle=90)
            for text in b:
                text.set_fontsize(20)
            plt.axis('equal')
            plt.show()
コード例 #32
0
    def getSpecificGenreImage(self):
        genreColorDict = {}
        for i in range(20):
            genreColorDict[i] = self.colors[i]

        for genreNum in [2,3,6]:
                
            tBD = self.getDurationBookData(2)
            
            referenceDict = {}
            for i in range(5):
                referenceDict[i] = 0
            
            for book in tBD:
                if int(book[2]) == genreNum:
                    reference = int(book[9])+int(book[10])
                    if reference > 3:
                        referenceDict[4] += 1
                    else:
                        referenceDict[reference] += 1
            genreDict = referenceDict
            
            genreNumList = []
            for genre in genreDict:
                genreNumList.append((genre,genreDict[genre]))
            
            genreNumList.sort(key = lambda genre: genre[0])
            genreList = []
            numList = []
            tempColors = []
            for i in range(len(genreNumList)):
                print(genreNumList[i])
                if genreNumList[i][0] == 4:
                    genreList.append(">3")
                else:
                    genreList.append(genreNumList[i][0])
                numList.append(genreNumList[i][1])
                tempColors.append(genreColorDict[genreNumList[i][0]])

            #plt.title(plotName,fontdict={'fontsize':20})
            a,b,c = a,b,c=plt.pie(numList, colors = tempColors, labels = genreList, autopct='%1.1f%%', startangle=90)
            for text in b:
                text.set_fontsize(20)
            plt.axis('equal')
            plt.show()
コード例 #33
0
 def pie(self, sizes):
     labels = ['Var_Credit(Closing)', 'Var_Interest(Closing)']
     #print(sizes)
     abc = [float(x) * -1 for x in sizes]
     colors = ['lightsage', 'crimson']
     #patches, texts = plt.pie(abc, colors=colors, startangle=90)
     #plt.legend(patches, labels, loc="best")
     plt.axis('equal')
     plt.tight_layout()
     plt.title("VaR Distribution for Closing Book")
     explode = (0, 0.1)
     pie = plt.pie(abc,
                   colors=colors,
                   labels=labels,
                   explode=explode,
                   shadow=True,
                   autopct='%1.3f%%')
     plt.legend(pie[0], labels, loc="best")
     plt.show()
コード例 #34
0
    def getPublisherImage(self,labelNum):
        for durationNum in range(2):
            plotName = "Genre"
            if durationNum == 0:
                plotName += " (Short "
            else:
                plotName += " (Long "
            plotName += "BestSeller)"

            tBD = self.getDurationBookData(durationNum)
            genreDict = {}
            for book in tBD:
                genre = book[5]
                if genre in genreDict:
                    genreDict[genre] += 1
                else:
                    genreDict[genre] = 1
            
            genreNumList = []
            for genre in genreDict:
                genreNumList.append((genre,genreDict[genre]))
            genreNumList.sort(reverse = True, key = lambda genre: genre[1])
            print(genreNumList)
            if labelNum < len(genreNumList):
                etcNum = 0
                for i in range(labelNum,len(genreNumList)):
                    etcNum += genreNumList[i][1]
                genreNumList = genreNumList[0:labelNum]
                genreNumList.append(('etc',etcNum))

            genreList = []
            numList = []
            for i in range(len(genreNumList)):
                genreList.append(genreNumList[i][0])
                numList.append(genreNumList[i][1])
            tempColors = self.colors[:len(numList)]

            plt.title(plotName,fontdict={'fontsize':30})
            a,b,c=plt.pie(numList, colors = tempColors, labels = genreList, autopct='%1.1f%%', startangle=90)
            for text in b:
                text.set_fontsize(20)
            plt.axis('equal')
            plt.show()
コード例 #35
0
    def getGenreImage(self):
        genreColorDict = {}
        for i in range(10):
            genreColorDict[i] = self.colors[i]
        for durationNum in range(3):
            plotName = "Genre"
            if durationNum == 0:
                plotName += " (Short "
            else:
                plotName += " (Long "
            plotName += "BestSeller)"

            tBD = self.getDurationBookData(durationNum)
            
            genreDict = {}
            for i in range(10):
                genreDict[i] = 0
            i = 0
            for book in tBD:
                genre = book[2]
                genreDict[int(genre)] += 1
            
            genreNumList = []
            for genre in genreDict:
                genreNumList.append((genre,genreDict[genre]))
            
            genreNumList.sort(reverse = True, key = lambda genre: genre[1])
            genreList = []
            numList = []
            tempColors = []
            for i in range(8):
                genreList.append(self.genreNumToName[genreNumList[i][0]])
                numList.append(genreNumList[i][1])
                tempColors.append(genreColorDict[genreNumList[i][0]])

           # plt.title(plotName,fontdict={'fontsize':30})
            a,b,c = a,b,c=plt.pie(numList, colors = tempColors, labels = genreList, autopct='%1.1f%%', startangle=90)
            for text in b:
                text.set_fontsize(30)
            plt.axis('equal')
            plt.show()
コード例 #36
0
    def getReferenceByImage(self):
        tempRefList = []
        for i in range(2):
            bD = self.getDurationBookData(i)
            for book in bD:
                if book[1] == 'Literature&Fiction':
                    tempRefList.append((book[1],int(book[8])+int(book[9]),int(book[12])))
        
        tempRefList.sort(reverse = True, key = lambda genre: genre[1])

        refDict = {}
        for book in tempRefList:
            if book[1] in refDict:
                refDict[book[1]] += 1
            else:
                refDict[book[1]] = 1
        
        temp = []
        for ref in refDict:
            temp.append((ref,refDict[ref]))
        temp.sort(key = lambda genre: genre[0])
        
        genreList = []
        numList = []
        tempColors = []
        for i in range(len(temp)):
            genreList.append(temp[i][0])
            numList.append(temp[i][1])
        tempColors = self.colors[:len(numList)]

        numList[6] = sum(numList[6:])
        numList = numList[:7]
        genreList = genreList[:6]
        genreList.append('etc')
        
        tempColors = self.colors[:len(genreList)]
        a,b,c=plt.pie(numList, colors = tempColors, labels = genreList, autopct='%1.1f%%', startangle=90)
        for text in b:
                text.set_fontsize(20)
        plt.axis('equal')
        plt.show()
コード例 #37
0
ファイル: camembert_nb_pg.py プロジェクト: jbtanguy/Partage
    for s in page_grouped:
        n = max(s)
        n = n + (10 - n % 10)
        dic[n] = len(s)
    return dic


dic = getPageSorted(files)
print('le nombre de page pour chaque pdf')
print(dic)
dic = getGroupedPage(dic)
print("nombre de page regrouper")
print(dic)
dic = getRegroupedPage(dic)
print("regrouped pages ")
print(dic)

plt.subplots(figsize=(20, 10))
lists = sorted(dic.items())  # sorted by key, return a list of tuples
plt.pie([float(v) for v in dic.values()],
        autopct='%2.2f%%',
        shadow=True,
        startangle=140)
plt.legend(labels=["page< " + str(k) + " " + str(dic[k]) for k in dic],
           bbox_transform=plt.gcf().transFigure,
           fontsize=30,
           loc="best")

plt.axis('equal')
plt.show()
コード例 #38
0
ファイル: utils.py プロジェクト: cliffckerr/macaqueERP
def checkmem(origvariable, descend=0, order='n', plot=False, verbose=0):
    """
    Checks how much memory the variable in question uses by dumping it to file.
    
    Example:
        from utils import checkmem
        checkmem(['spiffy',rand(2483,589)],descend=1)
    """
    from os import getcwd, remove
    from os.path import getsize
    from cPickle import dump
    from numpy import iterable, argsort
    
    filename = getcwd()+'/checkmem.tmp'
    
    def dumpfile(variable):
        wfid = open(filename,'wb')
        dump(variable, wfid)
        return None
    
    printnames = []
    printbytes = []
    printsizes = []
    varnames = []
    variables = []
    if descend==False or not(iterable(origvariable)):
        varnames = ['']
        variables = [origvariable]
    elif descend==1 and iterable(origvariable):
        if hasattr(origvariable,'keys'):
            for key in origvariable.keys():
                varnames.append(key)
                variables.append(origvariable[key])
        else:
            varnames = range(len(origvariable))
            variables = origvariable
    
    for v,variable in enumerate(variables):
        if verbose: print('Processing variable %i of %i' % (v+1, len(variables)))
        dumpfile(variable)
        filesize = getsize(filename)
        factor = 1
        label = 'B'
        labels = ['KB','MB','GB']
        for i,f in enumerate([3,6,9]):
            if filesize>10**f:
                factor = 10**f
                label = labels[i]
        printnames.append(varnames[v])
        printbytes.append(filesize)
        printsizes.append('%0.3f %s' % (float(filesize/float(factor)), label))
        remove(filename)

    if order=='a' or order=='alpha' or order=='alphabetical':
        inds = argsort(printnames)
    else:
        inds = argsort(printbytes)

    for v in inds:
        print('Variable %s is %s' % (printnames[v], printsizes[v]))

    if plot==True:
        from matplotlib.pylab import pie, array, axes
        axes(aspect=1)
        pie(array(printbytes)[inds], labels=array(printnames)[inds], autopct='%0.2f')

    return None
コード例 #39
0
# 各组销量的总计之和
sizeTotal = size1Count.sum()
# 吧series转换为dataFrame类型
size1 = size1Count.to_frame(name="销量")
# print(size)
# print(type(size))
# 插入比例
scal = 100.0 * size1Count / sizeTotal
# print(scal)
# 设置格式化输出
pandas.options.display.float_format = "{:,.2f}%".format

# 插入比例
size1.insert(0, "比例", scal)
# 修改索引名称
size1.index.names = ['罩杯']
# 打印结果
print(size1)
# 设置标签
lables1 = ["A", "B", "C"]
lables2 = ["A", "B", "C"]
sales_num = size1['销量']
# 绘制病状第一个参数是数据,第二个参数是数据的一个格式化,第三个参数是标签
plb.pie(sales_num, autopct="%.2f%%", labels=lables1)
# 设置圆形并且均匀
plb.axis('equal')
# 设置图的标题
plb.title("罩杯销售量", FontProperties=font)
# 设置示例
plb.legend(lables2)
plb.show()
コード例 #40
0
ファイル: Matplot_Ex5.py プロジェクト: nabomandy/Python_basic
import matplotlib.pylab as plt  # graphic을 위한 모듈
from matplotlib import rc
rc('font', family="Malgun Gothic")  # 한글 가능한 폰트 설정

labels = ['개구리', '돼지', '개', '통나무']  # 이름
sizes = [15, 30, 45, 10]  # 데이터
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
explode = (0, 0.1, 0, 0)  # 중심에서 간격 수치로 지정
plt.title("Pie Chart")
# wedgeprops : 부채꼴 설정
# width : 반지름 대비 비율
# linewidth : 구분선 굵기
wedgeprops = {'width': 0.7, 'edgecolor': 'w', 'linewidth': 2}
#그래프 작성
# autopct : 부채꼴 내부에 표시되는 내용의 형식.
# startangle : 시작되는 위치 각도 지정. 배치순서 반시계 방향.
plt.pie(sizes,
        explode=explode,
        labels=labels,
        colors=colors,
        autopct='%1.1f%%',
        shadow=True,
        startangle=90,
        wedgeprops=wedgeprops)

plt.show()
コード例 #41
0
[106.7882176183174, 51.407011395593209, 344.10490569403436, 322.25600000000003, 106.7882176183174, 410.40199893791083, 5.7000277630265392]]

#weight fraction pie chart
the_grid = GridSpec(2, 3)

for i in range(3): 
    M_fuse_empty = data[i]['fuse_weight.M_tot'] - data[i]['fuse_weight.M_pilots']
    M_empty = data[i]['wing_weight.M_tot'] + M_fuse_empty


    ax = plt.subplot(the_grid[0, i], aspect=1)
    estimated_masses = np.array([data[i]['wing_weight.M_tot'], M_fuse_empty, M_fuse_empty-2.512*(i+1)-3.499*(i+1)])/M_empty
    labels = ['wing','pod','control surf.']
    #print M_empty, estimated_masses
    #print data[i]['wing_weight.s']
    plt.pie(estimated_masses, labels=labels, autopct='%.0f%%',)
    ax.set_title('%d Pilot'%(i+1))


plt.tight_layout()
plt.savefig('pilot_study_pie.pdf')

#total power req
plt.figure()
fig, ax = plt.subplots()
raw_data = np.array(cantilever_data)
ax.plot([1,2,3,4], raw_data[:,5],label="cantilever")
raw_data = np.array(two_wire_data)
ax.plot([1,2,3,4], raw_data[:,5],label="two-wire")
ax.legend(loc="best")
コード例 #42
0
grouped = df.groupby('Expenditure Account Code Description')
total = grouped.sum().sort_values('Amount',ascending=False)

# Pie chart
grouped = df[~df['Directorate'].isnull()].groupby('Directorate')
total = grouped.sum().sort_values('Amount',ascending=False)

plt.figure(figsize=(7,4))
labels = total.index.values
labels[5:] = ''

cmap = plt.cm.jet
colors = cmap(np.linspace(0., 2., len(total)))
explode = tuple(np.linspace(0,0.8,len(total)))

patches, texts = plt.pie(total['Amount'], radius=0.9, startangle=0,
    colors=colors,explode=explode,labels=labels)
_ = [texts[i].set_fontsize(10) for i in range(0,len(total))]

plt.axis('equal')
plt.title('Total Spendings (2013-2016)')
plt.tight_layout()

#plt.legend(labels=labels, loc="best",fontsize=9,bbox_to_anchor=(0.43, 0.6))





コード例 #43
0
plt.plot(data1,data3)

#Plot scatter
plt.cla()
plt.scatter(carDf.PRICE, carDf.WEIGHT, color='r') 

#Plot bar charts
plt.cla()
plt.bar(carDf.ID, carDf.PRICE)
plt.cla()
plt.barh(carDf.ID, carDf.WEIGHT)
plt.yticks(carDf.ID, carDf.MODEL)

#Plot pie chart
plt.cla()
plt.pie(carDf.PRICE, labels=carDf.MODEL, shadow=True, autopct='%1.1f')

#Plot a histogram
plt.cla()
plt.hist(data3, color='g')
plt.title('Demo Histogram')
plt.xlabel('Sin Weights')
plt.ylabel('Frequency')

#Plot a boxplot
plt.cla() #pass a list of lists
plt.boxplot([[carDf.WEIGHT[carDf.MAKE=='Toyota']], [carDf.WEIGHT[carDf.MAKE=='Ford']] ], labels=('Toyota','Ford')) #show show weights change by make of car 



コード例 #44
0
ファイル: GUITab.py プロジェクト: iyadaqel/python_assignment1
    canvas1 = FigureCanvasTkAgg(f, master=tab4)
    canvas1.show()

    #Create the FIRST plot

    tartita=figure(1, figsize=(3,3),facecolor="white")
    ax = axes([0.1, 0.1, 0.8, 0.8])

    # The slices will be ordered and plotted counter-clockwise.
    labels = 'Cash', 'Credit Card'
    pieChartData = pos.generateCCReport()
    fracs = [pieChartData[0], pieChartData[1]]
    explode=(0, 0.1)

    pie(fracs, explode=explode, labels=labels,
                    autopct=pos.make_autopct(pieChartData), shadow=True, startangle=90, colors= ["#E13F29", "#D69A80"])

    canvas = FigureCanvasTkAgg(tartita, master=tab4)
    canvas.show()

    #Create the third plot
    linechartita=figure(3, figsize=(3,3),facecolor="white")
    # x axis
    lineChartData = pos.getLastDaysSales()
    lineChartdays = lineChartData[0]

    # y axis
    lineChartSales = lineChartData[1]
    plot(lineChartdays, lineChartSales)

    xlabel('Days')
コード例 #45
0
def findSimilarZip(askZip, ziplist, recorddict, Zipcodes):
    
    xlist = []
    ylist = []
    ZipList = []
    x = Counter(recorddict[askZip])
    for i in x.most_common(12):
        xlist.append(i[0])
        ylist.append(int(i[1]))
    
    myZip = xlist[0:4]
    nyZip = ylist[0:4]
    
    labels = xlist
    sizes = ylist
    colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'blue', 'purple', 'white', 'silver', 'pink', 'lightsalmon', 'lightsage']
    explode = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
    plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.2f%%', shadow=False, startangle=140)
                 
    plt.axis('equal')
    plt.show()
    
    for zips in Zipcodes:
        if askZip == zips.Zip:
            print "This is located in " + str(zips.Hood) + "!"
            labels = ['White', 'Black', 'AI', 'Asian', 'NH/PI', 'Other', 'Multiple', 'Hispanic']
            sizes = [zips.White, zips.Black, zips.AI_AN, zips.Asian, zips.NHOPI, zips.OthRace, zips.MultRaces, zips.Hispanic]
            colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'darkblue','pink', 'purple' ]
            explode = (0, 0, 0, 0, 0, 0, 0, 0)
            plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=False, startangle=140)
            
            plt.axis('equal')
            plt.show()    
    
    for Zip in ziplist:
        z = Counter(recorddict[Zip])
        y1 = []
        z1 = []
        for i in z.most_common(12):
            y1.append(i[0])
            z1.append(i[1])
        
        y2 = y1[0:4]
        z2 = z1[0:4]
            
        if myZip == y2 and askZip != Zip:

            ZipList.append(Zip)
                    
            labels = y1
            sizes = z1
            colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'blue', 'purple', 'white', 'silver', 'pink', 'lightsalmon', 'lightsage']
            explode = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
            plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.2f%%', shadow=False, startangle=140)
            plt.axis('equal')
            plt.show()
            
            for zips in Zipcodes:
                if Zip == zips.Zip:
                    print str(Zip) + " is similar to " + str(askZip) + "." 
                    print "This is located in " + str(zips.Hood) + "!"
                    labels = ['White', 'Black', 'AI', 'Asian', 'NH/PI', 'Other', 'Multiple', 'Hispanic']
                    sizes = [zips.White, zips.Black, zips.AI_AN, zips.Asian, zips.NHOPI, zips.OthRace, zips.MultRaces, zips.Hispanic]
                    colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'darkblue','pink', 'purple' ]
                    explode = (0, 0, 0, 0, 0, 0, 0, 0)
                    plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=False, startangle=140)
                         
                    plt.axis('equal')
                    plt.show()
                    
                    
                    
        else:
            continue
        
    if len(ZipList) == 0:
        print "This zip code is truly unique!"
    
    
    return ZipList
コード例 #46
0
def first_round_user_query_visualization(df, plot_flag, fig_dir_path):
    if plot_flag == 'min':
        title_prefix = "Minimum"
    else:
        title_prefix = "Preferred"

    df3, category, perc = get_degree(df, plot_flag)
    df4_sorted, color = get_sorted_degree_area(df, plot_flag)
    df5_sorted = get_sorted_skills(df, plot_flag)
    #print df5_sorted
    #df5_sorted = df5_sorted.head(10)

    # Create 2x2 sub plots
    pie_colors = [[183, 183, 183], [230, 252, 209], [252, 230, 174],
                  [168, 247, 239], [255, 188, 122], [214, 198, 255],
                  [255, 226, 242]]

    pie_colors = [[
        pie_colors[i][0] / 255.0, pie_colors[i][1] / 255.0,
        pie_colors[i][2] / 255.0
    ] for i in range(len(pie_colors))]

    gs = gridspec.GridSpec(2, 2)
    #plot pie chart for degree
    fig = pl.figure(figsize=(18, 12))
    ax = pl.subplot(gs[0, 0])  # row 0, col 0
    n = pl.pie(perc,
               labels=category,
               colors=pie_colors[:len(category)],
               startangle=90,
               autopct='%.1f%%',
               textprops={'fontsize': small_fontsize})
    ax.set_aspect('equal')
    pl.title(title_prefix + " Qualifications - Degree",
             fontsize=20,
             fontweight='bold')

    # plot bar chart for degree areas
    ax = pl.subplot(gs[0, 1])  # row 0, col 1
    objects = df4_sorted["Degree_Area"]
    y_pos = np.arange(len(objects))
    performance = df4_sorted["Percent"]
    pl.barh(y_pos, performance, align='center', alpha=0.5, color=tuple(color))
    pl.yticks(y_pos, objects, rotation=0, fontsize=small_fontsize)
    pl.xlabel('Percent %', fontsize=14)
    pl.xlim([0, 100])
    pl.title('Degree Majors', fontsize=20, fontweight='bold')
    #pl.rcParams.update({'font.size': 8})

    ax = pl.subplot(gs[1, :])  # row 1, span all columns
    df5_sorted = df5_sorted.iloc[:15]
    df5_sorted = df5_sorted.sort_values(['Count'], ascending=[1])

    objects = df5_sorted["Exp/Skills"]
    y_pos = np.arange(len(objects))
    performance = df5_sorted["Percent"]

    pl.barh(y_pos, performance, align='center', alpha=0.5)
    pl.yticks(y_pos, objects, rotation=0, fontsize=small_fontsize)
    pl.xlabel('Percent %', fontsize=14)
    pl.xlim([0, 30])
    pl.title('Top 15 Exp/Skills', fontsize=20, fontweight='bold')
    pl.show()
コード例 #47
0
ファイル: analyzeReport.py プロジェクト: ike104/pythonwork
def tradeanalyze4(rpt,fname,outputDir,data):
    
    """ エグジット要因ごとの勝率
    
    """
    print "\n--------------------------------------------" 
    print "- Exit  \n"
    print 'Entry -> Exit : profit rate [win/total]'    
    cc = []
    cd = []
    count = 1
    for t in ['close','t/p','s/l']:
        f = filter(lambda n:n[6]==t ,rpt.buyorder)
        cc.append(len(f))        
        pc,nc = winlossCount(f)
        if pc+nc>0:
            t0 = "%6.2f %% [ %3d / %3d ]" % (100*float(pc)/float(pc+nc),pc,\
            (pc+nc))            
            cd.append(100*float(pc)/float(pc+nc))
            t1 = "Long - %6s : " % (t)            
            print t1 + t0
            t3 = "$$T0040%02d$$" % (count)  
            count = count+1
            data.update({t3:t0})
        else:
            t3 = "$$T0040%02d$$" % (count)
            count = count+1
            data.update({t3:'NA'})
            cd.append(0.)
    for t in ['close','t/p','s/l']:
        f = filter(lambda n:n[6]==t ,rpt.sellorder)
        cc.append(len(f))        
        pc,nc = winlossCount(f)
        if pc+nc>0:
            t0 = "%6.2f %% [ %3d / %3d ]" % (100*float(pc)/float(pc+nc),pc,\
                (pc+nc))           
            cd.append(100*float(pc)/float(pc+nc))
            t1 = "Short- %6s : " % (t)            
            print t1 + t0
            t3 = "$$T0040%02d$$" % (count)  
            count = count+1
            data.update({t3:t0})
        else:
            t3 = "$$T0040%02d$$" % (count)
            count = count+1
            data.update({t3:'NA'})
            cd.append(0.)

    s = np.array([cd[0],cd[1],cd[2]])
    b = np.array([cd[3],cd[4],cd[5]])
    
    x = np.array(range(len(s)))
    xt = ['close','t/p','s/l']
    gw = 0.4
    plt.figure(figsize=(5,2))
    plt.title('Profit rate by exit ')
    plt.xlabel("Profit rate")
    plt.xlim(0,100)
    #plt.ylim(0+0.5,5-0.5)    
    plt.yticks(x,xt)
    plt.grid(True)
    plt.barh(x -gw/2, s, height = gw, align='center',color='b',alpha=0.5,\
            label='Short')
    plt.barh(x +gw/2, b, height = gw, align='center',color='g',alpha=0.5,\
            label='Long'    )
    plt.legend()
    plt.axvline(50,color='r')
    plt.savefig(outputDir+fname+"test41.png",dpi=72)
    data.update({'$$I004001$$':fname+"test41.png"})       
    #plt.show()
    plt.draw()
    

    ac = cc[0]+cc[3]
    tc = cc[1]+cc[4]
    sc = cc[2]+cc[5]
    tt = ac+tc+sc    

    
    print " "
    t0 = "%6.2f %% [ %4d / %4d ]" % (100*float(ac)/float(tt),ac,tt)
    t1 = "%6.2f %% [ %4d / %4d ]" % (100*float(tc)/float(tt),tc,tt)   
    t2 = "%6.2f %% [ %4d / %4d ]" % (100*float(sc)/float(tt),sc,tt)
    print "Exit  : rate [count/total]" 
    print "Close : " + t0
    print "t/p   : " + t1
    print "s/l   : " + t2
    data.update({'$$T005001$$':t0})    
    data.update({'$$T005002$$':t1})    
    data.update({'$$T005003$$':t2})    
    
    from matplotlib import cm
    plt.figure(figsize=(2,2))
    names =[ 'close','t/p', 's/l']
    # それぞれの割合を用意
    ratios = [ac, tc,sc]
    # どれだけ飛び出すか指定
    moves=(0, 0, 0)
    # 適当なカラーをマッピング
    col = cm.Set2(np.arange(3)/3.,0.7)
    # 円グラフを描画(影付き)
    plt.pie(ratios, explode=moves, labels=names, autopct='%1d%%',\
    shadow=True,colors=col)
    # 円グラフ他ので縦横比は等しく
    plt.gca().set_aspect('equal')
    plt.title('Exit count')
    plt.savefig(outputDir+fname+"test42.png",dpi=72)
    data.update({'$$I004002$$':fname+"test42.png"})       
    #plt.show()
    plt.draw()
    plt.close('all')