# # # # dd = """ title = request.GET.get("title") # 标题 # # # # title_size = request.GET.get("title_size") # 标题字号 # # # # xticks = request.GET.get("xticks") # x轴旋转角度 # # # # xlabel = request.GET.get("xlabel") # x轴名称 # # # # yticks = request.GET.get("yticks") # y旋转角度 # # # # ylabel = request.GET.get("ylabel") # y轴名称 # # # # file_id = request.GET.get("file_id") # 工作编号 # # # # range_column = request.GET.get("range_column") # 范围列 # # # # range_data = request.GET.get("range_data") # 范围 # # # # x_col = request.GET.get("x_col") # x列 # # # # y_col = request.GET.get("y_col") # y列 # # # # k = request.GET.get("k") # 增加编号解决缓存问题""" # # # # a = [] # # # # for i in dd.split("\n"): # # # # ddd = i.split(" ")[0] # # # # print(f'{ddd}: (document.getElementById("{ddd}").value),') # # # # a.append(ddd) # # # # # # # # print(",".join(a)) # # # # # # # # # import matplotlib.pyplot as plt # # # import numpy as np # # # import pandas as pd # # # import seaborn as sns # # # # # # # # # def Bubble_plot_with_Encircling(file_path, chart_out_path, out_path, range_column, range_data, x_col, y_col, title, # # # xticks, yticks, x_name, y_name): # # # plt.figure(figsize=(9.4, 4.25), dpi=200) # # # df = pd.read_csv(file_path) # # # df_select = df.loc[df[range_column].isin(range_data), :] # 用那一列规范 范围 # # # # # # gridobj = sns.lmplot( # # # x=x_col, # # # y=y_col, # # # hue=range_column, # # # data=df_select, # # # height=3, # # # palette='Set1', # # # scatter_kws=dict(s=60, linewidths=.7, edgecolors='black')) # # # # # # # Decorations # # # sns.set(style="whitegrid", font_scale=1.5) # # # gridobj.set(xlim=(0.5, 7.5), ylim=(10, 50)) # # # gridobj.fig.set_size_inches(10, 6) # # # plt.title(title) # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # # # # plt.xticks(rotation=xticks, fontsize=10) # # # plt.yticks(rotation=xticks, fontsize=10) # # # # # # # xy轴的名称 # # # plt.xlabel(x_name, fontdict={'fontsize': 10}) # # # plt.ylabel(y_name, fontdict={'fontsize': 10}) # # # # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # def Each_regression_line_in_its_own_column(file_path, chart_out_path, out_path, range_column, range_data, x_col, y_col, # # # title, xticks, yticks, x_name, y_name): # # # df = pd.read_csv(file_path) # # # df_select = df.loc[df[range_column].isin(range_data), :] # # # plt.figure(figsize=(5, 3), dpi=200) # # # gridobj = sns.lmplot(x=x_col, # # # y=y_col, # # # data=df_select, # # # robust=True, # # # palette='Set1', # # # col=range_column, # # # scatter_kws=dict(s=60, linewidths=.7, edgecolors='black')) # # # # # # # Decorations # # # sns.set(style="whitegrid", font_scale=1.5) # # # gridobj.fig.set_size_inches(10, 6) # # # # # # plt.title(title) # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # # # # plt.xticks(rotation=xticks, fontsize=10) # # # plt.yticks(rotation=yticks, fontsize=10) # # # # # # # xy轴的名称 # # # plt.xlabel(x_name, fontdict={'fontsize': 10}) # # # plt.ylabel(y_name, fontdict={'fontsize': 10}) # # # # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # # Each_regression_line_in_its_own_column(file_path="./datasets/mpg_ggplot2.csv", chart_out_path="./Marginal_Boxplot.png", # # # # out_path="./ss", range_column="cyl", range_data=[4, 8], x_col='displ', # # # # y_col='hwy', title='ddd', xticks=3, yticks=3, x_name='fsfs', y_name='sfsf') # # # # # # # # # # 5、抖动图（Jittering with stripplot # # # # # # def Jittering_with_stripplot(file_path, chart_out_path, out_path, x_col, y_col, # # # title, xticks, yticks, x_name, y_name): # # # df = pd.read_csv(file_path) # # # fig, ax = plt.subplots(figsize=(5, 3), dpi=180) # # # print(fig, ax) # # # sns.stripplot(df[x_col], # # # df[y_col], # # # jitter=0.25, # # # size=8, # # # ax=ax, # # # linewidth=.5, # # # palette='Set1') # # # # # # sns.set(style="whitegrid", font_scale=1.1) # # # plt.title(title) # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # # # # plt.legend(fontsize=10) # # # # # # plt.xticks(rotation=xticks, fontsize=10) # # # plt.yticks(rotation=yticks, fontsize=10) # # # # # # # xy轴的名称 # # # plt.xlabel(x_name, fontdict={'fontsize': 10}) # # # plt.ylabel(y_name, fontdict={'fontsize': 10}) # # # # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # # Jittering_with_stripplot(file_path="./datasets/mpg_ggplot2.csv", chart_out_path="./Marginal_Boxplot.png", # # # # out_path="./ss", range_column="cyl", range_data=[4, 8], x_col='displ', # # # # y_col='hwy', title='ddd', xticks=3, yticks=3, x_name='fsfs', y_name='sfsf') # # # # # # # # # def Marginal_Histogram(file_path, chart_out_path, out_path, x_col, y_col, # # # title, title_s, xticks, yticks, x_name, y_name, color1, color2): # # # df = pd.read_csv(file_path) # # # # # # # Create Fig and gridspec # # # fig = plt.figure(figsize=(9.4, 4.25), dpi=200) # # # # # # grid = plt.GridSpec(4, 4, hspace=0.5, wspace=0.2) # # # # # # # Define the axes # # # ax_main = fig.add_subplot(grid[:-1, :-1]) # # # ax_right = fig.add_subplot(grid[:-1, -1], xticklabels=[], yticklabels=[]) # # # ax_bottom = fig.add_subplot(grid[-1, 0:-1], xticklabels=[], yticklabels=[]) # # # # # # # Scatterplot on main ax # # # ax_main.scatter(x_col, # # # y_col, # # # c=df.manufacturer.astype('category').cat.codes, # # # alpha=.9, # # # data=df, # # # cmap="Set1", # # # edgecolors='gray', # # # linewidths=.5) # # # # # # # histogram on the right # # # ax_bottom.hist(df[x_col], # # # 40, # # # histtype='stepfilled', # # # orientation='vertical', # # # color=color1) # # # ax_bottom.invert_yaxis() # # # # # # # histogram in the bottom # # # ax_right.hist(df[y_col], # # # 40, # # # histtype='stepfilled', # # # orientation='horizontal', # # # color=color2) # # # # # # ax_main.title.set_fontsize(fontsize=int(title_s)) # # # # # # ax_main.set(title=title, # # # xlabel=x_name, # # # ylabel=y_name) # # # # # # for item in ([ax_main.xaxis.label, ax_main.yaxis.label] + # # # ax_main.get_xticklabels() + ax_main.get_yticklabels()): # # # item.set_fontsize(10) # # # # # # xlabels = ax_main.get_xticks().tolist() # # # ax_main.set_xticklabels(xlabels) # # # # # # plt.savefig(chart_out_path) # 保存图片 # # # # # # plt.legend(fontsize=10) # # # # # # plt.xticks(rotation=xticks, fontsize=10) # # # plt.yticks(rotation=yticks, fontsize=10) # # # # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # # Marginal_Histogram(file_path="./datasets/mpg_ggplot2.csv", chart_out_path="Marginal_Boxplot.png", # # # # out_path="./ss", x_col='displ', # # # # y_col='hwy', title='title', title_s=12, xticks=3, yticks=3, x_name='x_name', y_name='y_name', color1="#098154", color2='#098154') # # # # # # # # # def Correllogram(file_path, chart_out_path, out_path, # # # title, title_s, xticks, yticks, x_name, y_name, size, color, weight): # # # df = pd.read_csv(file_path) # # # plt.figure(figsize=(5, 3), dpi=200) # # # sns.heatmap( # # # df.corr(), # # # xticklabels=df.corr().columns, # # # yticklabels=df.corr().columns, # # # cmap='Set1', # # # center=0, # # # annot=True, # # # annot_kws={ # # # 'size': size, # # # 'weight': weight, # # # 'color': color # # # }, # # # ) # # # # # # plt.title(title, fontsize=int(title_s)) # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # # # # plt.legend(fontsize=10) # # # # # # plt.xticks(rotation=xticks, fontsize=10) # # # plt.yticks(rotation=yticks, fontsize=10) # # # # # # # xy轴的名称 # # # plt.xlabel(x_name, fontdict={'fontsize': 10}) # # # plt.ylabel(y_name, fontdict={'fontsize': 10}) # # # # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # # Correllogram(file_path="./datasets/mtcars.csv", # # # # chart_out_path="Correllogram.png", # # # # out_path="./ss", # # # # title='title', title_s=12, xticks=3, yticks=3, x_name='x_name', y_name='y_name', size=7, color='#098154', weight=5) # # # # # # # # # def Marginal_Boxplot(file_path, chart_out_path, out_path, x_col, y_col, # # # title, title_s, xticks, yticks, x_name, y_name): # # # # 边缘箱图（Marginal Boxplot） # # # df = pd.read_csv(file_path) # # # # # # fig = plt.figure(figsize=(9.4, 4.25), dpi=100) # # # grid = plt.GridSpec( # # # 4, 4, hspace=0.5, wspace=0.2 # # # ) # # # # # # # Define the axes # # # ax_main = fig.add_subplot(grid[:-1, :-1]) # # # ax_right = fig.add_subplot(grid[:-1, -1], xticklabels=[], yticklabels=[]) # # # ax_bottom = fig.add_subplot(grid[-1, 0:-1], xticklabels=[], yticklabels=[]) # # # # # # # Scatterplot on main ax # # # ax_main.scatter(x_col, # # # y_col, # # # c=df.manufacturer.astype('category').cat.codes, # # # alpha=.9, # # # data=df, # # # cmap="Set1", # # # edgecolors='black', # # # linewidths=.5) # # # # # # # Add a graph in each part # # # sns.boxplot(df[y_col], ax=ax_right, orient="v", linewidth=1, palette='Set1') # # # sns.boxplot(df[x_col], ax=ax_bottom, orient="h", linewidth=1, palette='Set1') # # # # # # ax_bottom.set(xlabel='') # # # ax_right.set(ylabel='') # # # # # # ax_main.title.set_fontsize(fontsize=int(title_s)) # # # ax_main.set(title=title, # # # xlabel=x_name, # # # ylabel=y_name) # # # # # # for item in ([ax_main.xaxis.label, ax_main.yaxis.label] + # # # ax_main.get_xticklabels() + ax_main.get_yticklabels()): # # # item.set_fontsize(11) # # # # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # # # # plt.legend(fontsize=10) # # # # # # plt.xticks(rotation=xticks, fontsize=10) # # # plt.yticks(rotation=yticks, fontsize=10) # # # # # # # xy轴的名称 # # # plt.xlabel(x_name, fontdict={'fontsize': 10}) # # # plt.ylabel(y_name, fontdict={'fontsize': 10}) # # # # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # # Marginal_Boxplot(file_path="./datasets/mpg_ggplot2.csv", chart_out_path="Marginal_Boxplot.png", # # # # out_path="./ss", x_col='displ', # # # # y_col='hwy', title='title', title_s=12, xticks=3, yticks=3, x_name='x_name', y_name='y_name') # # # # # # # # # # 10、矩阵图 （Pairwise Plot） # # # def Pairwise_Plot(file_path, chart_out_path, out_path, title, title_s, features): # # # df = pd.read_csv(file_path) # # # # # # plt.figure(figsize=(9.4, 4.25), dpi=100) # # # # # # plt.title(title, fontsize=int(title_s)) # # # sns.pairplot(df, # # # hue=features, # # # palette='Set1', # # # plot_kws=dict(s=80, edgecolor="white", linewidth=2.5)) # # # # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # # Pairwise_Plot(file_path="./datasets/iris_test.csv", chart_out_path="Pairwise_Plot.png", # # # # out_path="./ss", title='title', title_s=12,features="Species") # # # # # # # # # # plt.title(title, fontsize=int(title_s)) # # # def Diverging_Bars(file_path, chart_out_path, out_path, x_col, y_col, # # # title, title_s, xticks, yticks, x_name, y_name): # # # df = pd.read_csv(file_path) # # # x = df.loc[:, [x_col]] # # # df[x_col + "_z"] = (x - x.mean()) / x.std() # # # df['colors'] = ['red' if x < 0 else 'green' for x in df[x_col + "_z"]] # # # df.sort_values(x_col + "_z", inplace=True) # # # df.reset_index(inplace=True) # # # # # # # Draw plot # # # plt.figure(figsize=(9.4, 4.25), dpi=80) # # # plt.hlines(y=df.index, # # # xmin=0, # # # xmax=df.mpg_z, # # # color=df.colors, # # # alpha=0.8, # # # linewidth=5) # # # # # # # Decorations # # # plt.gca().set(ylabel=y_name, xlabel=x_name) # # # plt.yticks(df.index, df[y_col], fontsize=12) # # # plt.xticks(fontsize=12) # # # # # # plt.xticks(rotation=xticks, fontsize=10) # # # plt.yticks(rotation=yticks, fontsize=10) # # # # # # plt.title(title, fontsize=int(title_s)) # # # # # # plt.grid(linestyle='--', alpha=0.5) # # # # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # # Diverging_Bars(file_path="./datasets/mtcars.csv", # # # # chart_out_path="Diverging_Bars_vertical.png", # # # # out_path="./ss", # # # # x_col='mpg', # # # # y_col='cars', # # # # title='title', # # # # title_s=12, # # # # xticks=3, # # # # yticks=3, # # # # x_name='x_name', # # # # y_name='y_name') # # # # # # def Diverging_Bars_vertical(file_path, chart_out_path, out_path, x_col, y_col, # # # title, title_s, xticks, yticks, x_name, y_name): # # # df = pd.read_csv(file_path) # # # x = df.loc[:, [y_col]] # # # # # # df[y_col + 'z'] = (x - x.mean()) / x.std() # # # df['colors'] = ['red' if x < 0 else 'green' for x in df[y_col + 'z']] # # # df.sort_values(y_col + 'z', inplace=True) # # # df.reset_index(inplace=True) # # # plt.gca().set(ylabel=y_name, xlabel=x_name) # # # # Draw plot # # # plt.figure(figsize=(10, 6), dpi=80) # # # plt.vlines(x=df.index, # # # ymin=0, # # # ymax=df[y_col + 'z'], # # # color=df.colors, # # # alpha=0.8, # # # linewidth=5) # # # for y, x, tex in zip(df[y_col + 'z'], df.index, df[y_col + 'z']): # # # t = plt.text(x, # # # y + 0.2, # # # round(tex, 1), # # # horizontalalignment='center', # # # fontdict={ # # # 'color': 'black' if x < 0 else 'black', # # # 'size': 8 # # # }) # # # # # # # Decorations # # # plt.xticks(df.index, df[x_col], fontsize=12, rotation=90) # # # plt.yticks(fontsize=12) # # # plt.title(title, fontdict={'size': int(title_s)}) # # # plt.grid(linestyle='--', alpha=0.5) # # # # # # plt.xticks(rotation=xticks, fontsize=10) # # # plt.yticks(rotation=yticks, fontsize=10) # # # # # # plt.title(title, fontsize=int(title_s)) # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # # Diverging_Bars_vertical(file_path="./datasets/mtcars.csv", # # # # chart_out_path="Diverging_Bars_vertical.png", # # # # out_path="./ss", # # # # x_col='cars', # # # # y_col='mpg', # # # # title='title', # # # # title_s=12, # # # # xticks=90, # # # # yticks=3, # # # # x_name='x_name', # # # # y_name='y_name') # # # # # # # # # def Ordered_Bar_Chart(file_path, chart_out_path, out_path, x_col, y_col, # # # title, title_s, xticks, yticks, x_name, y_name, color): # # # df_raw = pd.read_csv(file_path) # # # df = df_raw[[x_col, y_col]].groupby(y_col).apply(lambda x: x.mean()) # # # df.sort_values(x_col, inplace=True) # # # df.reset_index(inplace=True) # # # # # # import matplotlib.patches as patches # # # # # # plt.gca().set(ylabel=y_name, xlabel=x_name) # # # # # # fig, ax = plt.subplots(figsize=(9.4, 4.25), facecolor='white', dpi=80) # # # ax.vlines(x=df.index, # # # ymin=0, # # # ymax=df.cty, # # # color=color, # # # alpha=0.7, # # # linewidth=20) # # # # # # # Annotate Text # # # for i, cty in enumerate(df.cty): # # # ax.text(i, cty + 0.5, round(cty, 1), horizontalalignment='center') # # # # # # # Title, Label, Ticks and Ylim # # # # # # plt.xticks(df.index, # # # df.manufacturer.str.upper(), # # # rotation=60, # # # horizontalalignment='right', # # # fontsize=10) # # # plt.yticks(fontsize=12) # # # plt.ylabel(y_name, fontsize=12) # # # plt.ylabel(x_name, fontsize=12) # # # # # # plt.ylim = (0, 30) # # # # # # # 添加底纹 # # # p1 = patches.Rectangle((.57, -0.005), # # # width=.33, # # # height=.13, # # # alpha=.1, # # # facecolor='green', # # # transform=fig.transFigure) # # # p2 = patches.Rectangle((.124, -0.005), # # # width=.446, # # # height=.13, # # # alpha=.1, # # # facecolor='red', # # # transform=fig.transFigure) # # # fig.add_artist(p1) # # # fig.add_artist(p2) # # # # # # plt.ylabel(y_name, fontsize=12) # # # plt.ylabel(x_name, fontsize=12) # # # plt.title(title, fontsize=int(title_s)) # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # # Ordered_Bar_Chart(file_path="./datasets/mpg_ggplot2.csv", # # # # chart_out_path="Ordered_Bar_Chart.png", # # # # out_path="./ss", # # # # x_col='cty', # # # # y_col='manufacturer', # # # # title='title', # # # # title_s=12, # # # # xticks=90, # # # # yticks=3, # # # # x_name='x_name', # # # # y_name='y_name', # # # # color="#e66765") # # # # # # # # # # 18、棒棒糖图(Lollipop Chart) # # # # 棒棒糖图(Lollipop Chart) # # # # Prepare Data # # # def Lollipop_Chart(file_path, chart_out_path, out_path, x_col, y_col, # # # title, title_s, xticks, yticks, x_name, y_name, color): # # # df_raw = pd.read_csv(file_path) # # # df = df_raw[[x_col, # # # y_col]].groupby(y_col).apply(lambda x: x.mean()) # # # df.sort_values(x_col, inplace=True) # # # df.reset_index(inplace=True) # # # # # # # Draw plot # # # fig, ax = plt.subplots(figsize=(9.4, 4.25), dpi=200) # # # # # # ax.vlines(x=df.index, # # # ymin=0, # # # ymax=df.cty, # # # color=color, # # # alpha=0.7, # # # linewidth=4) # # # # # # ax.scatter(x=df.index, y=df[x_col], s=85, color=color, alpha=0.7) # # # # # # # Title, Label, Ticks and Ylim # # # # # # ax.set_xticks(df.index) # # # ax.set_xticklabels(df.manufacturer.str.upper(), # # # rotation=60, # # # fontdict={ # # # 'horizontalalignment': 'right', # # # 'size': 11 # # # }) # # # ax.set_ylim(0, 30) # # # plt.yticks(fontsize=12) # # # # # # # Annotate # # # for row in df.itertuples(): # # # ax.text(row.Index, # # # row.cty + .5, # # # s=round(row.cty, 2), # # # horizontalalignment='center', # # # verticalalignment='bottom', # # # fontsize=12) # # # # # # plt.xticks(rotation=xticks, fontsize=10) # # # plt.yticks(rotation=yticks, fontsize=10) # # # # # # plt.ylabel(y_name, fontsize=12) # # # plt.ylabel(x_name, fontsize=12) # # # # # # plt.title(title, fontsize=int(title_s)) # # # # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # # Lollipop_Chart(file_path="./datasets/mpg_ggplot2.csv", # # # # chart_out_path="Lollipop_Chart.png", # # # # out_path="./ss", # # # # x_col='cty', # # # # y_col='manufacturer', # # # # title='title', # # # # title_s=12, # # # # xticks=90, # # # # yticks=3, # # # # x_name='x_name', # # # # y_name='y_name', # # # # color="#e66765") # # # # # # # # # def Dot_Plot(file_path, chart_out_path, out_path, x_col, y_col, # # # title, title_s, xticks, yticks, x_name, y_name, color1, color2): # # # df_raw = pd.read_csv(file_path) # # # df = df_raw[[x_col, # # # y_col]].groupby(y_col).apply(lambda x: x.mean()) # # # df.sort_values(x_col, inplace=True) # # # df.reset_index(inplace=True) # # # # # # # Draw plot # # # fig, ax = plt.subplots(figsize=(9.4, 4.25), dpi=200) # # # ax.hlines(y=df.index, # # # xmin=11, # # # xmax=26, # # # color=color1, # # # alpha=0.7, # # # linewidth=1, # # # linestyles='dashdot') # # # ax.scatter(y=df.index, x=df.cty, s=75, color=color2, alpha=0.7) # # # # # # ax.set_yticks(df.index) # # # ax.set_yticklabels(df.manufacturer.str.title(), # # # fontdict={ # # # 'horizontalalignment': 'right', # # # 'fontsize': 12, # # # }) # # # # # # ax.set_xlim(10, 27) # # # # # # plt.xticks(rotation=xticks, fontsize=10) # # # plt.yticks(rotation=yticks, fontsize=10) # # # # # # plt.ylabel(y_name, fontsize=12) # # # plt.ylabel(x_name, fontsize=12) # # # # # # plt.title(title, fontsize=int(title_s)) # # # # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # # Dot_Plot(file_path="./datasets/mpg_ggplot2.csv", # # # # chart_out_path="Dot_Plot.png", # # # # out_path="./ss", # # # # x_col='cty', # # # # y_col='manufacturer', # # # # title='title', # # # # title_s=12, # # # # xticks=90, # # # # yticks=3, # # # # x_name='x_name', # # # # y_name='y_name', # # # # color2='#3dd0b5', # # # # color1='#d5d5d5') # # # # # # # # # def Slope_Chart(file_path, chart_out_path, out_path, col_1, col_2, col_3, # # # title, title_s, xticks, yticks, x_name, y_name): # # # import matplotlib.lines as mlines # # # df = pd.read_csv(file_path) # # # # # # def newline(p1, p2): # # # ax = plt.gca() # # # l = mlines.Line2D([p1[0], p2[0]], [p1[1], p2[1]], # # # color='red' if p1[1] - p2[1] > 0 else 'green', # # # marker='o', # # # markersize=6) # # # ax.add_line(l) # # # return l # # # # # # fig, ax = plt.subplots(1, 1, figsize=(9.4, 4.25), dpi=80) # # # # # # # Vertical Lines # # # ax.vlines(x=1, # # # ymin=500, # # # ymax=13000, # # # color='black', # # # alpha=0.7, # # # linewidth=1, # # # linestyles='dotted') # # # ax.vlines(x=3, # # # ymin=500, # # # ymax=13000, # # # color='black', # # # alpha=0.7, # # # linewidth=1, # # # linestyles='dotted') # # # # # # ax.scatter(y=df[col_1], # # # x=np.repeat(1, df.shape[0]), # # # s=10, # # # color='black', # # # alpha=0.7) # # # ax.scatter(y=df[col_2], # # # x=np.repeat(3, df.shape[0]), # # # s=10, # # # color='black', # # # alpha=0.7) # # # # # # # Line Segmentsand Annotation # # # for p1, p2, c in zip(df[col_1], df[col_2], df[col_3]): # # # newline([1, p1], [3, p2]) # # # ax.text(1 - 0.05, # # # p1, # # # c + ', ' + str(round(p1)), # # # horizontalalignment='right', # # # verticalalignment='center', # # # fontdict={'size': 14}) # # # ax.text(3 + 0.05, # # # p2, # # # c + ', ' + str(round(p2)), # # # horizontalalignment='left', # # # verticalalignment='center', # # # fontdict={'size': 14}) # # # # # # # 'Before' and 'After' Annotations # # # ax.text(1 - 0.05, # # # 13000, # # # 'BEFORE', # # # horizontalalignment='right', # # # verticalalignment='center', # # # fontdict={ # # # 'size': 15, # # # 'weight': 700 # # # }) # # # ax.text(3 + 0.05, # # # 13000, # # # 'AFTER', # # # horizontalalignment='left', # # # verticalalignment='center', # # # fontdict={ # # # 'size': 15, # # # 'weight': 700 # # # }) # # # # # # # Decoration # # # # # # ax.set(xlim=(0, 4), ylim=(0, 14000)) # # # # # # ax.set_xticks([1, 3]) # # # ax.set_xticklabels([col_1, col_2], fontdict={'size': 15, 'weight': 700}) # # # plt.yticks(np.arange(500, 13000, 2000), rotation=yticks, fontsize=10) # # # # # # # Lighten borders # # # plt.gca().spines["top"].set_alpha(.0) # # # plt.gca().spines["bottom"].set_alpha(.0) # # # plt.gca().spines["right"].set_alpha(.0) # # # plt.gca().spines["left"].set_alpha(.0) # # # # # # plt.xticks(rotation=xticks, fontsize=10) # # # # # # plt.ylabel(y_name, fontsize=12) # # # plt.ylabel(x_name, fontsize=12) # # # # # # plt.title(title, fontsize=int(title_s)) # # # # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # # Slope_Chart(file_path="./datasets/gdppercap.csv", # # # # chart_out_path="Slope_Chart.png", # # # # out_path="./ss", # # # # col_1="1952", # # # # col_2='1957', # # # # col_3='continent', # # # # title='title', # # # # title_s=12, # # # # xticks=90, # # # # yticks=90, # # # # x_name="x_name", # # # # y_name="y_name") # # # # # # # # # def Dumbbell_Plot(file_path, chart_out_path, out_path, col_1, col_2, col_3, # # # title, title_s, xticks, yticks, x_name, y_name): # # # import matplotlib.lines as mlines # # # # # # # Import Data # # # df = pd.read_csv(file_path) # # # df.sort_values(col_1, inplace=True) # # # df.reset_index(inplace=True) # # # # # # # Func to draw line segment # # # def newline(p1, p2): # # # ax = plt.gca() # # # l = mlines.Line2D([p1[0], p2[0]], [p1[1], p2[1]], color='#d5695d') # # # ax.add_line(l) # # # return l # # # # # # # Figure and Axes # # # fig, ax = plt.subplots(1, 1, figsize=(9.4, 4.25), facecolor='#f8f2e4', dpi=80) # # # # # # # Vertical Lines # # # ax.vlines(x=.05, # # # ymin=0, # # # ymax=26, # # # color='black', # # # alpha=1, # # # linewidth=1, # # # linestyles='dotted') # # # ax.vlines(x=.10, # # # ymin=0, # # # ymax=26, # # # color='black', # # # alpha=1, # # # linewidth=1, # # # linestyles='dotted') # # # ax.vlines(x=.15, # # # ymin=0, # # # ymax=26, # # # color='black', # # # alpha=1, # # # linewidth=1, # # # linestyles='dotted') # # # ax.vlines(x=.20, # # # ymin=0, # # # ymax=26, # # # color='black', # # # alpha=1, # # # linewidth=1, # # # linestyles='dotted') # # # # # # # Points # # # ax.scatter(y=df['index'], x=df[col_1], s=50, color='#dc2624') # # # ax.scatter(y=df['index'], x=df[col_2], s=50, color='#e87a59') # # # # # # # Line Segments # # # for i, p1, p2 in zip(df['index'], df[col_1], df[col_2]): # # # newline([p1, i], [p2, i]) # # # # # # # Decoration # # # ax.set_facecolor('#f8f2e4') # # # # # # ax.set(xlim=(0, .25), ylim=(-1, 27)) # # # plt.yticks(fontsize=15) # # # # # # plt.xticks(rotation=xticks, fontsize=10) # # # plt.yticks(rotation=yticks, fontsize=10) # # # # # # plt.ylabel(y_name, fontsize=12) # # # plt.ylabel(x_name, fontsize=12) # # # # # # plt.title(title, fontsize=int(title_s)) # # # # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # # Dumbbell_Plot(file_path="./datasets/health.csv", # # # # chart_out_path="Dumbbell_Plot.png", # # # # out_path="./ss", # # # # col_1="pct_2014", # # # # col_2='pct_2013', # # # # col_3='continent', # # # # title='title', # # # # title_s=12, # # # # xticks=90, # # # # yticks=90, # # # # x_name="x_name", # # # # y_name="y_name") # # # # # # # # # def Stacked_Histogram_for_Continuous_Variable(file_path, chart_out_path, out_path, x_col, groupby_col, # # # title, title_s, xticks, yticks, x_name, y_name): # # # df = pd.read_csv(file_path) # # # # # # # Prepare data # # # x_var = x_col # # # groupby_var = groupby_col # # # df_agg = df.loc[:, [x_var, groupby_var]].groupby(groupby_var) # # # vals = [df[x_var].values.tolist() for i, df in df_agg] # # # # # # # Draw # # # plt.figure(figsize=(9.4, 4.25), dpi=180) # # # colors = [plt.cm.Set1(i / float(len(vals) - 1)) for i in range(len(vals))] # # # n, bins, patches = plt.hist(vals, # # # 30, # # # stacked=True, # # # density=False, # # # color=colors[:len(vals)]) # # # # # # # Decoration # # # plt.legend({ # # # group: col # # # for group, col in zip( # # # np.unique(df[groupby_var]).tolist(), colors[:len(vals)]) # # # }) # # # # # # plt.xlabel(x_var) # # # # # # # plt.ylim(0, 25) # # # plt.xticks(ticks=bins[::3], labels=[round(b, 1) for b in bins[::3]]) # # # # # # plt.xticks(rotation=xticks, fontsize=10) # # # plt.yticks(rotation=yticks, fontsize=10) # # # # # # plt.ylabel(y_name, fontsize=12) # # # plt.ylabel(x_name, fontsize=12) # # # # # # plt.title(title, fontsize=int(title_s)) # # # # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # (9.4, 4.25) # # # # # # # # # # Stacked_Histogram_for_Continuous_Variable(file_path="./datasets/mpg_ggplot2.csv", # # # # chart_out_path="Stacked_Histogram_for_Continuous_Variable.png", # # # # out_path="./ss", # # # # x_col="displ", # # # # groupby_col='class', # # # # title='title', # # # # title_s=12, # # # # xticks=0, # # # # yticks=0, # # # # x_name='x_name', # # # # y_name='y_name') # # # # # # # # # def Density_Plot(file_path, chart_out_path, out_path, x_col, groupby_col, # # # title, title_s, xticks, yticks, x_name, y_name): # # # df = pd.read_csv(file_path) # # # # # # # Draw Plot # # # plt.figure(figsize=(10, 8), dpi=80) # # # sns.kdeplot(df.loc[df['cyl'] == 4, "cty"], # # # shade=True, # # # color="#01a2d9", # # # label="Cyl=4", # # # alpha=.7) # # # sns.kdeplot(df.loc[df['cyl'] == 5, "cty"], # # # shade=True, # # # color="#dc2624", # # # label="Cyl=5", # # # alpha=.7) # # # sns.kdeplot(df.loc[df['cyl'] == 6, "cty"], # # # shade=True, # # # color="#C89F91", # # # label="Cyl=6", # # # alpha=.7) # # # sns.kdeplot(df.loc[df['cyl'] == 8, "cty"], # # # shade=True, # # # color="#649E7D", # # # label="Cyl=8", # # # alpha=.7) # # # # # # # Decoration # # # sns.set(style="whitegrid", font_scale=1.1) # # # # # # plt.legend() # # # plt.xticks(rotation=xticks, fontsize=10) # # # plt.yticks(rotation=yticks, fontsize=10) # # # # # # plt.ylabel(y_name, fontsize=12) # # # plt.ylabel(x_name, fontsize=12) # # # # # # plt.title(title, fontsize=int(title_s)) # # # # # # plt.savefig(f"{chart_out_path}") # 保存图片 # # # plt.savefig(f"{out_path}.png") # # # plt.savefig(f"{out_path}.svg") # # # plt.savefig(f"{out_path}.pdf") # # # # # # # # # from Bio.Seq import Seq # # # # # # # my_sequence = Seq(">fjjj/n ATGACGTTGCATG") # # # # # # # # print ("The sequence is：", my_sequence) # # # # #len(my_sequence) 长度 # # # # print("The length of the sequence is：", len(my_sequence)) # # # # #反向互补序列 # # # # print ("Reverse complementary sequence：",my_sequence.reverse_complement()) # # # # #计算核苷酸的出现次数 # # # # print("The number of As in the sequence:", my_sequence.count("A")) # # # # print("The number of Ts in the sequence:", my_sequence.count("T")) # # # # print("The number of Gs in the sequence:", my_sequence.count("G")) # # # # print("The number of Cs in the sequence:", my_sequence.count("C")) # # # # #计算GC含量 # # # # C_count=my_sequence.count("C") # # # # G_count=my_sequence.count("G") # # # # Length=len(my_sequence) # # # # GC=(C_count+G_count)/Length # # # # print ("GC percentage:","%.2f" % GC) # # # # #查找子序列的起始索引 # # # # print("Found TTG in the sequence at index:", my_sequence.find("TTG")) # # # # #翻译 # # # # print ("protein translation is:",my_sequence.translate()) # # # tst = """>fjjj # # # ATGACGTTGCATG # # # >fjjj # # # ATGACGTTGCATG # # # >fjjj # # # ATGACGTTGCATG # # # >fjjj # # # ATGACGTTGCATG""" # # # # # # def 反向互补序列(in_seq): # # # def in_seq(a): # # # a = a.replace("\r", "") # # # a_list = a.split(">")[1:] # # # data_ = [] # # # for i in a_list: # # # all_seq = [] # # # for s, j in enumerate(i.split("\n")): # # # if s == 0: # # # data_.append(f">{j}") # # # all_seq.append(j) # # # data_.append("".join(all_seq)) # # # return data_ # # # # # # data_all = [] # # # # # # for i, data in enumerate(in_seq(tst)): # # # if i % 2 == 0: # # # data_all.append(data + "\n") # # # else: # # # my_sequence = Seq(data) # # # data_all.append(str(my_sequence.reverse_complement())+"\n") # # # return "".join(data_all) # # # # url = '' # # # class Tool(object): # def __init__(self): # self.jg = { # 'Basics': {}, # 'senior': {}, # } # # def ex_file(self, file_name): # self.jg["ex_file"] = url + file_name # # def Basics(self, title, option, label, vale): # # if self.jg["Basics"].get(title): # self.jg["Basics"][title].append({'name': option, 'data': label, 'vale': vale}) # else: # self.jg["Basics"][title] = [{'name': option, 'data': label, 'vale': vale}] # # def Senior(self, title, option, label, vale): # # if self.jg["senior"].get(title): # self.jg["senior"][title].append({'name': option, 'data': label, 'vale': vale}) # else: # self.jg["senior"][title] = [{'name': option, 'data': label, 'vale': vale}] # # def row_back(self, lable, value): # return f""" #
#
# #
# #
#
#
# """ # # def head_back(self, title): # return f""" #
#
# #
#
# """ # # def back_(self, sort): # tmp = [] # for i in self.jg[sort].keys(): # tmp.append(self.head_back(i)) # for j in self.jg[sort][i]: # tmp.append(self.row_back(j["data"], j['vale'])) # return '\n'.join(tmp) # # def html_out(self): # print("基础------->") # print(self.back_("Basics")) # print('高级------->') # print(self.back_("senior")) # # def js_out(self): # js_list = [] # all_dict = dict(self.jg["Basics"], **self.jg["senior"]) # for i in all_dict.keys(): # for j in all_dict[i]: # js_list.append(f'{j["data"]}: (document.getElementById("{j["data"]}").value),') # # print("\n".join(js_list)) # # def view_out(self): # py_list = [] # all_dict = dict(self.jg["Basics"], **self.jg["senior"]) # for i in all_dict.keys(): # for j in all_dict[i]: # py_list.append(f'{j["data"]} = request.GET.get("{j["data"]}", "{j["vale"]}")') # # print("\n".join(py_list)) # # on_num = ' oninput = "value=value.replace(/[^\d]/g,'').slice(0,3)" ' # # def html_out(data): # all_list = [] # for i in data: # title = f""" #
#
# #
#
# """ # all_list.append(title) # # for j in i['data']: # code = f""" #
#
# #
# #
#
#
# """ # all_list.append(code) # # return all_list # # # def js_out(data): # all_list = [] # new_list = data["Basics"] + data["senior"] # for i in new_list: # for j in i['data']: # all_list.append(f'{j["name"]}: (document.getElementById("{j["name"]}").value),') # # return "\n".join(all_list) # # # def py_out(data): # all_list = [] # new_list = data["Basics"] + data["senior"] # for i in new_list: # for j in i['data']: # all_list.append(f'{j["name"]}: (document.getElementById("{j["name"]}").value),') # return "\n".join(all_list) # # # # # # # # # # # # data = { # 'Basics': [{'title': 'lable', 'data': [ # {'name': 'x_name', "value": 'x', 'astrict': ''}, # {'name': 'x_name', "value": 'x', 'astrict': ''}, # {'name': 'x_col', "value": 'x_col', 'astrict': ''}, # {'name': 'x_col', "value": 'x_col', 'astrict': ''}, # ]}, # {'title': 'title', 'data': [ # {'name': 'title', "value": 'title', 'astrict': ''}, # {'name': 'title_size', "value": '12', # 'astrict': ' oninput = "value=value.replace(/[^\d]/g,'').slice(0,2)" '}, # ]}, # {'title': 'data', 'data': [ # {'name': 'range_column', "value": 'range_column', 'astrict': ''}, # {'name': 'range_data', "value": '1-2', 'astrict': ''}, # ]}, # # ], # # 'Senior': [{'title': 'lable', 'data': [ # {'name': 'xticks', "value": '0', 'astrict': on_num}, # {'name': 'yticks', "value": '0', 'astrict': on_num}] # }] # } # # a = "font_color, x_color, x_axis_color, y_color, y_axis_color, line_color, color, line_color_bar, color_bar, y_color_bar, y_axis_color_bar, x_axis_color_bar, background" a = a.split(",") for i in a: i = i.strip() print(f"{{'name': '{i}', 'value': '', 'astrict': ''}},")