使用for循环和枚举将文件绘制到单个图中的子图上

我正在尝试将文件绘制到2个数字的8个子图上 . 我正在使用for循环和枚举运算符,以及axarray来执行此操作 . 我几乎完成了最后一步(使用axarray),但需要指导如何完成它 . 这是我的代码:

'import matplotlib.pyplot as plt import parse_gctoo import glob f,ax1 = plt.subplots()

def histo_plotter(file, plot_title, ax):
    # read in file as string
    GCT_object = parse_gctoo.parse(file)
    # for c in range(9):
    #     print type(GCT_object.data_df.iloc[0][c])
    # computing median of rows in data_df
    # gene_medians = GCT_object.data_df.quantile(q=0.5,axis=1)
    # plot_title = "Gene expression levels for {}".format(cell)
    if plot_title == "ZSPCQNORM":
        gene_means = GCT_object.data_df.mean(axis=1)
        #making histogram of means
        ax.hist(gene_means)
        plt.title("MeanGeneExpressionZSPCQNORM")
        plt.xlabel("MedianGeneExpression")
        plt.ylabel("Count")
    elif plot_title == "QNORM":
        gene_medians = GCT_object.data_df.median(axis=1)
        #making histogram of medians
        ax.hist(gene_medians)
        plt.title("MedianGeneExpressionQNORM")
        plt.xlabel("MedianGeneExpression")
        plt.ylabel("Count")
plt.show()
f.savefig("hist_example1.png")



# plt.ylim(-1, 1)
# plt.xlim(-1,1)

# histo_plotter("/Users/eibelman/Desktop/ZSCOREDATA-    CXA061_SKL_48H_X1_B29_ZSPCQNORM_n372x978.gct.txt", "ZSPCQNORM", ax1)
#     histo_plotter("/Users/eibelman/Desktop/NewLJP005_A375_24H_X2_B19_QNORM_n373x978.gct.txt", "QNORM", ax1)
#########



# Create list of x2 LJP005 cell line files

z_list = glob.glob("/Volumes/cmap_obelix/pod/custom/LJP/roast/LJP005_[A375, A549, BT20, HA1E, HC515, HEPG2, HS578T, HT29]*X2*/zs/*ZSPCQNORM*.gct")
q_list = glob.glob("/Volumes/cmap_obelix/pod/custom/LJP/roast/LJP005_[A375, A549, BT20, HA1E, HC515, HEPG2, HS578T, HT29]*_X2_*/*_QNORM_*.gct")



# for loop which allows plotting multiple files in a single figure

f, axarray = plt.subplots(2, 4)
for n, single_q in enumerate(q_list):
     # axarray = plt.subplot(len(q_list), 1, n+1)
     axarray = histo_plotter(n, "QNORM", ax1)
    # axarray[n].plot()
plt.show()

# f, axarray = plt.subplots(2, 4)
# for n, single_z in enumerate(z_list):
#     # ax = plt.subplot(len(z_list), 1, n+1)
#     histo_plotter(single_z, "ZSPCQNORM", ax1)'

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