[Python] How to change Labels distance from x and y ticks and choose one color to the...

I am doing a correlation matrix using a seaborn heatmap, I need to :

1. Change the distance between the ticks and the labels of x and y.
2. Also, change the distance between the title and the heatmap.
3. Unifying the color of annotation to be either white or black.

I am using the following code:

from matplotlib import pyplot as plt
import matplotlib
import numpy as np
import seaborn as sns

#call data frame and apply correlation:

#data =
#df = pd.DataFrame(data, columns = features)
#df_small = df.iloc[:,:]#if only few parameters are needed
#correlation_mat = df_small.corr()

#Create color pallete:

def NonLinCdict(steps, hexcol_array):
cdict = {'red': (), 'green': (), 'blue': ()}
for s, hexcol in zip(steps, hexcol_array):
rgb =matplotlib.colors.hex2color(hexcol)
cdict['red'] = cdict['red'] + ((s, rgb[0], rgb[0]),)
cdict['green'] = cdict['green'] + ((s, rgb[1], rgb[1]),)
cdict['blue'] = cdict['blue'] + ((s, rgb[2], rgb[2]),)
return cdict

#https://www.december.com/html/spec/colorshades.html

hc = ['#e5e5ff', '#C7DDF2', '#8EBAE5', '#407FB7', '#00549F']#ffffff #e5e5ff
th = [0, 0.25, 0.5, 0.75, 1]
hc = hc[:0:-1] + hc # prepend a reversed copy, but without repeating the central value
cdict = NonLinCdict(np.linspace(0, 1, len(hc)), hc)
cm = matplotlib.colors.LinearSegmentedColormap('test', cdict)
corr=np.random.uniform(-1, 1, (6,6))

#plot correlation matrix:

plt.figure(figsize = (10,8))
ax=sns.heatmap(corr,center=0, linewidths=1, annot = True,cmap=cm ,square=True, vmin=-1, vmax=1,
robust=True, annot_kws={'size':16}, cbar=True,linecolor='#F6A800',xticklabels=True,
yticklabels=True)


cbar = ax.collections[0].colorbar
cbar.ax.tick_params(labelsize=10, axis='both', which='both', length=0)
cbar.set_ticks(np.linspace(-1, 1, 11))
plt.title("title", y=-1.5,fontsize = 18,)
plt.xlabel("X_parameters",fontsize = 18)
plt.ylabel("Y_paramaters",fontsize = 18)
ax.tick_params(axis='both', which='both', length=0)

ax.axhline(y=0, color='#F6A800',linewidth=4)
ax.axhline(y=corr.shape[1], color='#F6A800',linewidth=4)
ax.axvline(x=0, color='#F6A800',linewidth=4)
ax.axvline(x=corr.shape[0], color='#F6A800',linewidth=4)

#change position of lables and titles and assign colors.

plt.show()


My current output is:

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