# assign tick position and label
# especially for bar plot or time series plot
plt.xticks(range(0, len(compare_cell_ls)), compare_cell_ls, rotation=45)

# Not work: label will off-set 1 (still don't know why)
ax.set_xticklabels(compare_cell_ls, rotation=45)

# auto get & rotate
ax[0].set_xticklabels(ax[0].xaxis.get_majorticklabels(), rotation=45)

# work for last row graph, not Every plots (in FacetGrid)
g.set_xticklabels(rotation=45)

# data format
# each row denote a gene's expression under different condition
[zhangqf7@loginview02 HuR]$ head predict_RBP_binding_combine.compare.txt|cut -f 4-7
egg     1cell   4cell   64cell
0.21742857142857144     0.34700000000000003     0.12    0.13285714285714287
0.22228571428571428     0.1551428571428571      0.03528571428571429     0.04671428571428572
0.12285714285714285     0.07571428571428572     0.027000000000000003    0.026857142857142857
0.41571428571428576     0.5638571428571428      0.34114285714285714     0.2785714285714286
0.4587142857142856      0.3832857142857143      0.40771428571428575     0.3097142857142857
0.217   0.2868571428571429      0.13699999999999998     0.14914285714285716
0.21757142857142855     0.4165714285714285      0.1558571428571429      0.15371428571428572
0.33399999999999996     0.3514285714285714      0.1827142857142857      0.17557142857142854
0.32557142857142857     0.3127142857142857      0.19657142857142856     0.2992857142857143

# plot each as trend line
fig,ax=plt.subplots()
for i in df_plot.index:
		ax.plot(range(0, len(col_ls)), df_plot.loc[i, col_ls], color='grey', alpha=0.3, lw=0.3)

# mean value of each state
# axis=0 => mean of each column (add a new row); axis=1 => mean of each row (add a new column)
df_plot_mean = df_plot.loc[:, compare_cell_ls].mean(axis=0)
ax.plot(range(0, len(compare_cell_ls)), df_plot_mean, color='blue')

ax.legend_.remove()

# show legend
plt.legend()

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(10)

fig = plt.figure()
ax = plt.subplot(111)

for i in xrange(5):
    ax.plot(x, i * x, label='$y = %ix$'%i)

# Shrink current axis by 20%
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])

# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()

# Use plot with tight mode, or plot will be cut 
plt.legend(bbox_to_anchor=(1, 1), loc=2)
plt.savefig(savefn, bbox_inches='tight')

plt.plot((.1, .3))
ax.axis('square')
ax.set_xlim(0.1, 0.3)

# in seaborn like jointplot also works
g = sns.jointplot(x='col1', y='col2', data=d_g, kind="reg", stat_func=stats.pearsonr, size=10)
g.ax_joint.set_xlim(0.35, 0.9)
g.ax_joint.set_ylim(0.35, 0.9)

lims = [
    np.min([ax.get_xlim(), ax.get_ylim()]),  # min of both axes
    np.max([ax.get_xlim(), ax.get_ylim()]),  # max of both axes
]

# now plot both limits against eachother
ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)
ax.set_aspect('equal')
ax.set_xlim(lims)
ax.set_ylim(lims)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

y = [2.56422, 3.77284, 3.52623, 3.51468, 3.02199]
z = [0.15, 0.3, 0.45, 0.6, 0.75]
n = [58, 651, 393, 203, 123]

fig, ax = plt.subplots()
ax.scatter(z, y)

for i, txt in enumerate(n):
    ax.annotate(txt, (z[i], y[i]))

# with x,y denote absolute coordinates
plt.text(x, y, s, fontsize=12)

# with transform to project the axes
# 这里的x，y就是相对于坐标轴的哪个位置的
plt.text(0.5, 0.5, str, ha='center',va='center', transform=ax.transAxes)

# here is a very useful library to adjust overlapped text
# https://stackoverflow.com/questions/19073683/matplotlib-overlapping-annotations-text
# https://github.com/Phlya/adjustText

from adjustText import adjust_text

texts = []
for x, y, s in zip(xs, ys, ss):
    texts.append(plt.text(x, y, s))
    
adjust_text(texts, only_move={'text': 'y'})

# 这个是画水平或者竖直的线，只指定x或者y时，跨越坐标轴
# 指定比如xmin/xmax时，画对应的比例。这些值取值为[0,1]之间
# https://stackoverflow.com/questions/16930328/vertical-horizontal-lines-in-matplotlib/16930526
plt.axhline(y=0, xmin=0, xmax=1, hold=None, **kwargs)
plt.axvline(x=0, ymin=0, ymax=1, hold=None, **kwargs)

# 直接连接任意两点，指定两点的坐标时
plt.plot((x1, x2), (y1, y2), 'k-')

plt.axvspan(3, 6, color='red', alpha=0.5)

im = plt.imread('grace_hopper.jpg')
newax = fig.add_axes([0.8, 0.8, 0.2, 0.2], anchor='NE', zorder=-1)
newax.imshow(im)
newax.axis('off')

from matplotlib.backends.backend_pdf import PdfPages

def plot_history(history, pdf):
    fig,ax = plt.subplots()
    # Plot training & validation accuracy values
    plt.plot(history['acc'])
    plt.plot(history['val_acc'])
    plt.title('Model accuracy')
    plt.ylabel('Accuracy')
    plt.xlabel('Epoch')
    plt.legend(['Train', 'Test'], loc='upper left')
    pdf.savefig(fig)
    
    fig,ax = plt.subplots()
    # Plot training & validation loss values
    plt.plot(history['loss'])
    plt.plot(history['val_loss'])
    plt.title('Model loss')
    plt.ylabel('Loss')
    plt.xlabel('Epoch')
    plt.legend(['Train', 'Test'], loc='upper left')
    pdf.savefig(fig)
    
pdf = matplotlib.backends.backend_pdf.PdfPages(plot_savefn)
                
for ite in range(n_ite):
    X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.4,)
    clf.fit(X_train)
    history = clf.history_
    plot_history(history, pdf)
plt.close()
pdf.close()

d1 = pd.DataFrame({'0':[1,2,3,4], '1':[4,5,6,5]})

fig,ax=plt.subplots(1,2,figsize=(16,6))

ax[0].plot(d1['0'], marker='.')
for n,i in enumerate(d1['0']):
    ax[0].text(n, i+0.1, '*A*+#', va='bottom', ha='center', rotation='vertical', size='xx-large')
    
ax[1].plot(d1['0'], marker='.')
for n,i in enumerate(d1['0']):
    ax[1].text(n, i+0.1, '*A*+#', va='bottom', ha='center', rotation=0, size='xx-large')

import seaborn as sns, matplotlib.pyplot as plt

tips = sns.load_dataset("tips")
sns.boxplot(x="day", y="total_bill", data=tips, palette="PRGn")

# statistical annotation
x1, x2 = 2, 3   # columns 'Sat' and 'Sun' (first column: 0, see plt.xticks())
y, h, col = tips['total_bill'].max() + 2, 2, 'k'
plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)
plt.text((x1+x2)*.5, y+h, "ns", ha='center', va='bottom', color=col)

plt.show()

import matplotlib.pyplot as plt
import seaborn as sns
from statannot import add_stat_annotation

sns.set(style="whitegrid")
df = sns.load_dataset("tips")

x = "day"
y = "total_bill"
order = ['Sun', 'Thur', 'Fri', 'Sat']
ax = sns.boxplot(data=df, x=x, y=y, order=order)
add_stat_annotation(ax, data=df, x=x, y=y, order=order,
                    boxPairList=[("Thur", "Fri"), ("Thur", "Sat"), ("Fri", "Sun")],
                    test='Mann-Whitney', textFormat='star', loc='outside', verbose=2)

g = sns.jointplot(x='m1',y='m2',data=df,kind='kde', xlim=(0.0,0.5), ylim=(0.0,0.5), height=8, ratio=5)
sns.regplot(df['m1'],df['m2'], scatter=False, ax=g.ax_joint)

r,p = stats.pearsonr(df['m1'],df['m2'])
s = 'R = {:.2f}\nP = {:.2e}\nN = {}'.format(r,p,df.shape[0])
g.ax_joint.text(0.05, 0.9, s, ha='left', va='top', size=20, transform=g.ax_joint.transAxes)

ax.yaxis.set_label_position("right")
ax.yaxis.tick_right()

import numpy as np
import matplotlib.pyplot as plt

# Create some mock data
t = np.arange(0.01, 10.0, 0.01)
data1 = np.exp(t)
data2 = np.sin(2 * np.pi * t)

fig, ax1 = plt.subplots()

color = 'tab:red'
ax1.set_xlabel('time (s)')
ax1.set_ylabel('exp', color=color)
ax1.plot(t, data1, color=color)
ax1.tick_params(axis='y', labelcolor=color)

ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis

color = 'tab:blue'
ax2.set_ylabel('sin', color=color)  # we already handled the x-label with ax1
ax2.plot(t, data2, color=color)
ax2.tick_params(axis='y', labelcolor=color)

fig.tight_layout()  # otherwise the right y-label is slightly clipped
plt.show()

# get python default color list
# https://stackoverflow.com/questions/42086276/get-default-line-colour-cycle
# color_ls = plt.rcParams['axes.prop_cycle'].by_key()['color']

# seaborn color list
color_stages = sns.color_palette('Set1',n_colors=7, desat=0.8)
my_pal = {'egg':color_stages[0], '1cell': color_stages[1], '4cell': color_stages[2], '64cell': color_stages[3], '1K': color_stages[4], 'sphere':color_stages[5], 'shield':color_stages[6]}

sns.boxplot(x='cell', y='gini', data=df_save_all, ax=ax[0], palette=file_info_dict['my_pal'])

### 指定height_ratios，一般根据每个集合具有的feature的数目
fig, ax = plt.subplots(3,1,figsize=(32, 25), gridspec_kw = {'height_ratios':[19, 15, 7]}, sharey=False, sharex=True)

### plot heatmap
h1 = sns.heatmap(df_plot_all[function_ls[1:]].T,linecolor='grey', linewidths=0.1, cbar=False, square=True, cmap="Greens", ax=ax[0])
h2 = sns.heatmap(df_plot_all[localization_ls].T,linecolor='grey', linewidths=0.1, cbar=False, square=True, cmap="Greens", ax=ax[1])
h3 = sns.heatmap(df_plot_all[domain_ls].T,linecolor='grey', linewidths=0.1, cbar=False, square=True, cmap="Greens", ax=ax[2])

### keep one xlabel for all, also keep yticklabels
# h1.set(xlabel='', ylabel='Heatmap1')
# h2.set(xlabel='', ylabel='Heatmap2')
# h3.set(xlabel='Columns', ylabel='Heatmap3')

### keep one xlabel for all, remove yticklabels
h1.set(xlabel='', ylabel='Heatmap1', yticks=[])
h2.set(xlabel='', ylabel='Heatmap2', yticks=[])
h3.set(xlabel='Columns', ylabel='Heatmap3', yticks=[], xticks=[])

### set yticklabels on the right
ax[0].yaxis.tick_right()
ax[0].set_yticklabels(ax[0].yaxis.get_majorticklabels(), rotation=0)

ax[1].yaxis.tick_right()
ax[1].set_yticklabels(ax[1].yaxis.get_majorticklabels(), rotation=0)

ax[2].yaxis.tick_right()
ax[2].set_yticklabels(ax[2].yaxis.get_majorticklabels(), rotation=0)
ax[2].set_xticklabels(ax[2].xaxis.get_majorticklabels(), rotation=90)

### set tick label color individually
### https://stackoverflow.com/questions/21936014/set-color-for-xticklabels-individually-in-matplotlib
# color_ls = ['red']*10+['black']*19
# [t.set_color(i) for (i,t) in zip(color_ls,ax[2].xaxis.get_ticklabels())]

plt.tight_layout()
plt.savefig('./test.png')
plt.close()

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
uniform_data = np.random.rand(10, 12)
ax = sns.heatmap(uniform_data)
ax.invert_yaxis()

fig,ax = plt.subplots(figsize=(10, 16))
sns.heatmap(..., ax=ax)

sns.heatmap(corrmat, 
	vmin=corrmat.values.min(), 
	vmax=1, square=True, cmap="YlGnBu", 
	linewidths=0.1, annot=True, 
	annot_kws={"fontsize":8})  

def merge_symmetry_df_to_diagonal_lower(df=None):
    if df is None:
        df = pd.DataFrame({0:[1, 0.3, 0.8], 1:[0.3, 1, 0.3], 2:[0.8, 0.3, 1]})
    df_twice = df + df.T
    # df_twice.values[[np.arange(df.shape[0])]*2] = df_twice.values[[np.arange(df.shape[0])]*2] / 2
    df_twice.values[[np.arange(df.shape[0])]*2] = np.diagonal(df)
    
    return df_twice

d1 = pd.DataFrame({0:[1, 0.3, 0.8], 1:[0.3, 1, 0.3], 2:[0.8, 0.3, 1]})
d = merge_symmetry_df_to_diagonal_lower()

fig, ax = plt.subplots(1,3, figsize=(12,3))

sns.heatmap(d1, ax=ax[0], square=True, annot=True)
sns.heatmap(d, ax=ax[1], square=True, annot=True)

mask = np.zeros_like(d)
mask[np.triu_indices_from(mask)] = True
mask[np.diag_indices_from(mask)] = False
sns.heatmap(d, ax=ax[2], square=True, annot=True, mask=mask)

plt.tight_layout()

fig,ax=plt.subplots(figsize=(10,20))
g = sns.heatmap(df_3, xticklabels=False, yticklabels=False, cmap="summer")
g.set_facecolor('black')