cvmcore 0.2.2
SZQ lab data analysis core function
License: MIT Licence
Keywords: pip, mlst, cgmlst, plot

# cvmcore
![PyPI - Version](https://img.shields.io/pypi/v/cvmcore)
![Static Badge](https://img.shields.io/badge/OS-_Windows_%7C_Mac_%7C_Linux-steelblue)


## Introduction
The core function of data analysis for plot or data process used by SZQ lab from China Agricultural University


## Example usage


```python
from Bio import Phylo
import matplotlib as mpl
import matplotlib.pyplot as plt
from io import StringIO
import matplotlib.collections as mpcollections
from copy import copy

import pandas as pd
import numpy as np
import seaborn as sn

from cvmcore.cvmcore import cvmplot

from scipy.cluster.hierarchy import linkage, dendrogram, complete, to_tree
from scipy.spatial.distance import squareform
```


```python
mlst = [[np.nan, 19., 12.,  9.,  5.,  9.,  2.],
        [np.nan, 19., 12.,  9.,  5.,  9.,  2.],
        [10., 17., 12.,  9., np.nan,  9.,  2.],
        [10., 19., 12., np.nan,  5.,  9.,  2.],
        [np.nan, 19., 13.,  9.,  5.,  9.,  2.]]
genes = np.char.replace(np.array(np.arange(1, 8), dtype='str'), '', 'gene_', count=1)
samples = np.char.replace(np.array(np.arange(1, 6), dtype='str'), '', 'sample_', count=1)
df_mlst = pd.DataFrame(mlst, index=samples, columns=genes)
diff_matrix = cvmplot.get_diff_df(df_mlst)
```


```python
diff_matrix
```




<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>sample_1</th>
      <th>sample_2</th>
      <th>sample_3</th>
      <th>sample_4</th>
      <th>sample_5</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>sample_1</th>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>1</td>
    </tr>
    <tr>
      <th>sample_2</th>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>1</td>
    </tr>
    <tr>
      <th>sample_3</th>
      <td>1</td>
      <td>1</td>
      <td>0</td>
      <td>1</td>
      <td>2</td>
    </tr>
    <tr>
      <th>sample_4</th>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>1</td>
    </tr>
    <tr>
      <th>sample_5</th>
      <td>1</td>
      <td>1</td>
      <td>2</td>
      <td>1</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
</div>




```python
link_matrix =linkage(squareform(diff_matrix), method='complete')
```


```python
link_matrix
```




    array([[0., 1., 0., 2.],
           [3., 5., 0., 3.],
           [2., 6., 1., 4.],
           [4., 7., 2., 5.]])



### 1. Plot a rectangular dendrogram


```python
fig, ax= plt.subplots(1,1)
lableorder, ax = cvmplot.rectree(link_matrix, scale_max=7, labels=samples, ax=ax)
fig.tight_layout()
fig.savefig('screenshots/dendrogram.png')
```


![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/output_7_0.png)


### 2. Plot rectangular dendrogram with heatmap


```python
#create dataframe
mat = np.random.randint(70, 100, (5, 10))
loci = np.char.replace(np.array(np.arange(1, 11), dtype='str'), '', 'loci_', count=1)
sample = np.char.replace(np.array(np.arange(1, 6), dtype='str'), '', 'sample', count=1)
df_heatmap = pd.DataFrame(mat, index=sample, columns=loci)
```


```python
#create linkage matrix
diff_matrix = [[0, 0, 1, 0, 1],
               [0, 0, 1, 0, 1],
               [1, 1, 0, 1, 2],
               [0, 0, 1, 0, 1],
               [1, 1, 2, 1, 0]]

linkage_matrix = linkage(squareform(diff_matrix),'complete')
```


```python
fig, (ax1, ax2) = plt.subplots(1,2,figsize=(8,3), gridspec_kw={'width_ratios': [1, 2]})

fig.tight_layout(w_pad=-2)

row_order, ax1 = cvmplot.rectree(linkage_matrix,labels=sample, no_labels=True, scale_max=3, ax=ax1)
cvmplot.heatmap(df_heatmap, order=row_order, ax=ax2, cbar=True, yticklabel=False)

ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)
ax2.xaxis.tick_top()

# fig.tight_layout()
fig.savefig('screenshots/dendrogram_with_heatmap.png', bbox_inches='tight')
```

    [ 5 15 25 35 45]
    ['sample5', 'sample3', 'sample4', 'sample1', 'sample2']



![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/output_11_1.png)

```python
fig, (ax1, ax2, ax3) = plt.subplots(1,3,figsize=(12,3), gridspec_kw={'width_ratios': [1, 2, 2]})

fig.tight_layout(w_pad=-2)

row_order, ax1 = cvmplot.rectree(linkage_matrix,labels=sample, no_labels=True, scale_max=3, ax=ax1)

# remove the yticklabels in ax2
ax2 = cvmplot.heatmap(df_heatmap, order=row_order, ax=ax2, cbar=True, yticklabel=False)
# add ax3 heatmap
ax3 = cvmplot.heatmap(df_heatmap, order=row_order, ax=ax3, cmap='Blues', cbar=True, yticklabel=True)

#set ticklabels property of x or y from ax1, ax2, ax3
ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.xaxis.tick_top()

ax3.set_xticklabels(ax3.get_xticklabels(), rotation=90, fontsize=15)
ax3.set_yticklabels(ax3.get_yticklabels(), fontsize=15)
ax3.xaxis.tick_top()


# fig.tight_layout()
fig.savefig('screenshots/multiple_heatmap.png', bbox_inches='tight')
```
![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/multiple_heatmap.png)

#### 2.1 set minimum value of heatmap


```python
fig, (ax1, ax2) = plt.subplots(1,2,figsize=(8,3), gridspec_kw={'width_ratios': [1, 2]})
fig.tight_layout(w_pad=-2)

order, ax1 = cvmplot.rectree(linkage_matrix,labels=sample, no_labels=True, scale_max=3, ax=ax1)
cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90)

ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)
ax2.xaxis.tick_top()

fig.savefig('screenshots/dendrogram_heatmap_minimumvalue.pdf', bbox_inches='tight')
```

    [ 5 15 25 35 45]
    ['sample5', 'sample3', 'sample4', 'sample1', 'sample2']



![png](http://microbe.genesclouds.com.cn/microbe/library/screenshots/output_13_1.png)


#### 2.2 using cmap to change color


```python
fig, (ax1, ax2) = plt.subplots(1,2,figsize=(8,3), gridspec_kw={'width_ratios': [1, 2]})
fig.tight_layout(w_pad=-2)

order, ax1 = cvmplot.rectree(linkage_matrix,labels=sample, no_labels=True, scale_max=3, ax=ax1)
cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cmap='tab20', cbar=True)

ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)
ax2.xaxis.tick_top()
fig.savefig('screenshots/dendrogram_heatmap_cmap.pdf', bbox_inches='tight')
```

    [ 5 15 25 35 45]
    ['sample5', 'sample3', 'sample4', 'sample1', 'sample2']



![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/output_15_1.png)


### 3. Plot a circular dendrogram


```python
# generate two clusters: a with 100 points, b with 50:
np.random.seed(4711)  # for repeatability of this tutorial
a = np.random.multivariate_normal([10, 0], [[3, 1], [1, 4]], size=[100,])
b = np.random.multivariate_normal([0, 20], [[3, 1], [1, 4]], size=[50,])
X = np.concatenate((a, b),)
```


```python
Z = linkage(X, 'ward')
```


```python
Z2 = dendrogram(Z, no_plot=True)
```


```python
# set open angle
fig, ax= plt.subplots(1,1,figsize=(10,10))

cvmplot.circulartree(Z2,addlabels=True, fontsize=10, ax=ax)
fig.tight_layout()
fig.savefig('screenshots/circular_dendrogram.png', bbox_inches='tight')
```


![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/output_20_0.png)


#### 3.1 color label


```python
colors = [{'#0070c7':'2021'}, {'#3a9245':'2022'}, {'#f8d438':'2023'}]
result = np.random.choice(colors, size=150)
label_colors_map = dict(zip(Z2['ivl'], result))
point_colors_map = dict(zip(Z2['ivl'], result))
```


```python
fig, ax= plt.subplots(1,1,figsize=(10,10))
cvmplot.circulartree(Z2, addlabels=True, branch_color=False, label_colors= label_colors_map, fontsize=15)
fig.tight_layout()
fig.savefig('screenshots/circular_dendrogram_color_label.png')
```


![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/output_23_0.png)


#### 3.2 set open angle


```python
fig, ax= plt.subplots(1,1,figsize=(10,10))
cvmplot.circulartree(Z2, addlabels=True, branch_color=False, label_colors= label_colors_map, fontsize=15, open_angle=30)
fig.tight_layout()
fig.savefig('screenshots/circular_dendrogram_openangle.png')
```


![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/output_25_0.png)


#### 3.3 set start angle


```python
fig, ax= plt.subplots(1,1,figsize=(10,10))
cvmplot.circulartree(Z2, addlabels=True, branch_color=False, label_colors= label_colors_map, fontsize=15, open_angle=90,
                     start_angle=30
                    )
fig.tight_layout()
fig.savefig('screenshots/circular_dendrogram_startangle.png')
```


![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/output_27_0.png)


#### 3.4 add point 


```python
fig, ax= plt.subplots(1,1,figsize=(12,10))
cvmplot.circulartree(Z2, addlabels=True, branch_color=False, label_colors= label_colors_map, fontsize=15, addpoints=True,
                     point_colors = point_colors_map, point_legend_title='Species', pointsize=25)
fig.tight_layout()
fig.savefig('screenshots/circular_dendrogram_tippoints.png')
```


![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/output_29_0.png)



### 4. Plot phylogenetic tree


```python
tree = "(((A:0.2, B:0.3):0.3,(C:0.5, D:0.3):0.2):0.3, E:0.7):1.0;"
tree = Phylo.read(StringIO(tree), 'newick')
```


```python
fig, ax= plt.subplots(1,1, figsize=(10, 10))
ax, lable_order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax, show_label=True, align_label=True, labelsize=15)
fig.tight_layout()
fig.savefig('screenshots/phylogenetic tree.png')
```


![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/output_35_0.png)


#### 4.1 Plot tree with heatmap


```python
#create dataframe
mat = np.random.randint(70, 100, (5, 10))
col = np.char.replace(np.array(np.arange(1, 11), dtype='str'), '', 'column_', count=1)
strains = ['A', 'B', 'C', 'D', 'E']
df_heatmap = pd.DataFrame(mat, index=strains, columns=col)
```


```python
df_heatmap
```




<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>column_1</th>
      <th>column_2</th>
      <th>column_3</th>
      <th>column_4</th>
      <th>column_5</th>
      <th>column_6</th>
      <th>column_7</th>
      <th>column_8</th>
      <th>column_9</th>
      <th>column_10</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>A</th>
      <td>89</td>
      <td>73</td>
      <td>91</td>
      <td>75</td>
      <td>95</td>
      <td>90</td>
      <td>93</td>
      <td>74</td>
      <td>99</td>
      <td>97</td>
    </tr>
    <tr>
      <th>B</th>
      <td>73</td>
      <td>90</td>
      <td>75</td>
      <td>89</td>
      <td>85</td>
      <td>72</td>
      <td>82</td>
      <td>85</td>
      <td>96</td>
      <td>82</td>
    </tr>
    <tr>
      <th>C</th>
      <td>84</td>
      <td>82</td>
      <td>86</td>
      <td>74</td>
      <td>72</td>
      <td>75</td>
      <td>91</td>
      <td>83</td>
      <td>97</td>
      <td>98</td>
    </tr>
    <tr>
      <th>D</th>
      <td>72</td>
      <td>77</td>
      <td>72</td>
      <td>98</td>
      <td>79</td>
      <td>73</td>
      <td>87</td>
      <td>91</td>
      <td>98</td>
      <td>94</td>
    </tr>
    <tr>
      <th>E</th>
      <td>88</td>
      <td>75</td>
      <td>88</td>
      <td>73</td>
      <td>77</td>
      <td>72</td>
      <td>74</td>
      <td>73</td>
      <td>99</td>
      <td>86</td>
    </tr>
  </tbody>
</table>
</div>




```python
fig,(ax1, ax2)= plt.subplots(1,2, figsize=(8, 3), gridspec_kw={'width_ratios':[1, 2]})
fig.tight_layout(w_pad=-2)
ax1, order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax1, show_label=True, align_label=True, labelsize=15)
cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90)

ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)
ax2.xaxis.tick_top()

fig.savefig('screenshots/phylotree_with_heatmap.pdf')
```




![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/output_39_1.png)


#### 4.2 remove labels at the tip of the tree


```python
fig,(ax1, ax2)= plt.subplots(1,2, figsize=(8, 3), gridspec_kw={'width_ratios':[1, 2]})
fig.tight_layout(w_pad=-2)
ax1, order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax1, show_label=False)
cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90)

ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)
ax2.xaxis.tick_top()

fig.savefig('screenshots/phylotree_with_heatmap-remove_tiplable.pdf', bbox_inches='tight')
```



![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/output_41_1.png)

#### 4.3 Plot multiple heatmap with phylotree
```
fig,(ax1, ax2, ax3)= plt.subplots(1,3, figsize=(12, 3), gridspec_kw={'width_ratios':[1, 2, 2]})
fig.tight_layout(w_pad=-2)
ax1, order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax1, show_label=True, align_label=True, labelsize=15)
ax2 = cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90, yticklabel=False)
# add ax3 heatmap
ax3 = cvmplot.heatmap(df_heatmap, order=order, ax=ax3, cmap='Blues', cbar=True, yticklabel=True)

#set ticklabels property of x or y from ax1, ax2, ax3
ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.xaxis.tick_top()

ax3.set_xticklabels(ax3.get_xticklabels(), rotation=90, fontsize=15)
ax3.set_yticklabels(ax3.get_yticklabels(), fontsize=15)
ax3.xaxis.tick_top()


# fig.tight_layout()
fig.savefig('screenshots/phylotree_multiple_heatmap.png', bbox_inches='tight')
```
![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/phylotree_multiple_heatmap.png)



### 5. Gene environment plot

First, you shoud prepare a dataframe from the gff file, The columns should include the feature start, end, strand, label(gene name or whatever you want show next to the arrow) and the arrow color.
|TRACK|START|END|STRAND|LABEL|COLOR|
|---|------|------|----|---------|---------|
| A |  100 |  900 | -1 | label1  | #ec9631 |
| A | 1100 | 1300 |  1 | label2  | #ec9631 |
| A | 1350 | 1500 |  1 | label3  | #ec9631 |
| A | 1520 | 1700 |  1 | label4  | #ec9631 |
| A | 1900 | 2200 | -1 | label5  | #ec9631 |
| A | 2500 | 2700 |  1 | label6  | #ec9631 |
| A | 2700 | 2800 | -1 | label7  | #ec9631 |
| A | 2850 | 3000 | -1 | label8  | red     |
| A | 3100 | 3500 |  1 | label9  | #ec9631 |
| A | 3600 | 3800 | -1 | label10 | #ec9631 |
| A | 3900 | 4200 | -1 | label11 | #ec9631 |
| A | 4300 | 4700 | -1 | label12 | #ec9631 |
| A | 4800 | 4850 |  1 | label13 | #ec9631 |
| B |  100 |  900 | -1 | label14 | #ec9631 |
| B | 1100 | 1300 |  1 | label15 | #ec9631 |
| B | 1350 | 1500 |  1 | label16 | #ec9631 |
| B | 1520 | 1700 |  1 | label17 | #ec9631 |
| B | 1900 | 2200 | -1 | label18 | #ec9631 |
| B | 2500 | 2700 |  1 | label19 | #ec9631 |
| B | 2700 | 2800 | -1 | label20 | #ec9631 |
| B | 2850 | 3000 | -1 | label21 | #ec9631 |
| B | 3100 | 3500 |  1 | label22 | #ec9631 |
| B | 3600 | 3800 | -1 | label23 | #ec9631 |
| B | 3900 | 4200 | -1 | label24 | #ec9631 |
| B | 4300 | 4700 | -1 | label25 | #ec9631 |
| B | 4800 | 4850 |  1 | label26 | #ec9631 |
| C |  100 |  900 | -1 | label27 | #ec9631 |
| C | 1100 | 1300 |  1 | label28 | #ec9631 |
| C | 1350 | 1500 |  1 | label29 | #ec9631 |
| C | 1520 | 1700 |  1 | label30 | #ec9631 |
| C | 1900 | 2200 | -1 | label31 | green   |
| C | 2500 | 2700 |  1 | label32 | #ec9631 |
| C | 2700 | 2800 | -1 | label33 | #ec9631 |
| C | 2850 | 3000 | -1 | label34 | #ec9631 |
| C | 3100 | 3500 |  1 | label35 | #ec9631 |
| C | 3600 | 3800 | -1 | label36 | #ec9631 |
| C | 3900 | 4200 | -1 | label37 | #ec9631 |
| C | 4300 | 4700 | -1 | label38 | #ec9631 |
| C | 4800 | 4850 |  1 | label39 | #ec9631 |
| D |  100 |  900 | -1 | label40 | #ec9631 |
| D | 1100 | 1300 |  1 | label41 | #ec9631 |
| D | 1350 | 1500 |  1 | label42 | #ec9631 |
| D | 1520 | 1700 |  1 | label43 | #ec9631 |
| D | 1900 | 2200 | -1 | label44 | #ec9631 |
| D | 2500 | 2700 |  1 | label45 | #ec9631 |
| D | 2700 | 2800 | -1 | label46 | #ec9631 |
| D | 2850 | 3000 | -1 | label47 | #ec9631 |
| D | 3100 | 3500 |  1 | label48 | #ec9631 |
| D | 3600 | 3800 | -1 | label49 | #ec9631 |
| D | 3900 | 4200 | -1 | label50 | #ec9631 |
| D | 4300 | 4700 | -1 | label51 | #ec9631 |
| D | 4800 | 4850 |  1 | label52 | #ec9631 |
| E |  100 |  900 | -1 | label53 | #ec9631 |
| E | 1100 | 1300 |  1 | label54 | #ec9631 |
| E | 1350 | 1500 |  1 | label55 | #ec9631 |
| E | 1520 | 1700 |  1 | label56 | #ec9631 |
| E | 1900 | 2200 | -1 | label57 | #ec9631 |
| E | 2500 | 2700 |  1 | label58 | #ec9631 |
| E | 2700 | 2800 | -1 | label59 | #ec9631 |
| E | 2850 | 3000 | -1 | label60 | #ec9631 |
| E | 3100 | 3500 |  1 | label61 | #ec9631 |
| E | 3600 | 3800 | -1 | label62 | #ec9631 |
| E | 3900 | 4200 | -1 | label63 | #ec9631 |
| E | 4300 | 4700 | -1 | label64 | #ec9631 |
| E | 4800 | 4850 |  1 | label65 | #ec9631 |


### 5. Plot genes
```
# Create arrow dictionary
arrow_dict = {k: g.to_dict(orient='records') for k, g in df.set_index('TRACK').groupby(level=0)}

# Define the display order of your tracks
order = ['D', 'A',  'C', 'B', 'E']
```
#### 5.1 Plot gene arrows and label on top track
```
fig, ax = plt.subplots(1,1, figsize=(10,10))
ax = cvmplot.plotgenes(dc=arrow_dict, order=order, ax=ax, max_track_size=5000, addlabels=True, label_track='top')
fig.savefig('screenshots/gene_arrow_top.png', bbox_inches='tight')
```
![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/gene_arrow_top.png)

#### 5.2 Plot gene arrows and label on bottom track
```
fig, ax = plt.subplots(1,1, figsize=(10,10))
ax = cvmplot.plotgenes(dc=arrow_dict, order=order, ax=ax, max_track_size=5000, addlabels=True, label_track='bottom')
fig.savefig('screenshots/gene_arrow_bottom.png', bbox_inches='tight')
```
![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/gene_arrow_bottom.png)


#### 5.3 Plot gene arrows and label on all tracks

```
fig, ax = plt.subplots(1,1, figsize=(10,10))
ax = cvmplot.plotgenes(dc=arrow_dict, order=order, ax=ax, max_track_size=5000, addlabels=True, label_track='all')
fig.savefig('screenshots/gene_arrow_all.png', bbox_inches='tight')

```
![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/gene_arrow_all.png)


#### 5.4 Plot gene arrows with phylotree and heatmap

Put together!
```
# Put together
fig,(ax1, ax2, ax3, ax4)= plt.subplots(1,4, figsize=(16, 3), gridspec_kw={'width_ratios':[1, 2, 2, 2]})
fig.tight_layout(w_pad=-2)
ax1, order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax1, show_label=True, align_label=True, labelsize=15)
ax2 = cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90, yticklabel=False)
# add ax3 heatmap
ax3 = cvmplot.heatmap(df_heatmap, order=order, ax=ax3, cmap='Blues', cbar=True, yticklabel=False)

ax4 = cvmplot.plotgenes(dc=arrow_dict, order=order, ax=ax4, max_track_size=5000, addlabels=True, label_track='top', ylim=(-3, 3))



#set ticklabels property of x or y from ax1, ax2, ax3
ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.xaxis.tick_top()

ax3.set_xticklabels(ax3.get_xticklabels(), rotation=90, fontsize=15)
ax3.set_yticklabels(ax3.get_yticklabels(), fontsize=15)
ax3.xaxis.tick_top()


# fig.tight_layout()
fig.savefig('screenshots/phylotree_heatmap_withgenes.png', bbox_inches='tight')
```
![image](http://microbe.genesclouds.com.cn/microbe/library/screenshots/phylotree_heatmap_withgenes.png)