# cvmcore
## 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')
```
### 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']
```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')
```
#### 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']
#### 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']
### 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')
```
#### 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')
```
#### 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')
```
#### 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')
```
#### 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')
```
### 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')
```
#### 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')
```
#### 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')
```
#### 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')
```
### 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')
```
#### 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')
```
#### 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')
```
#### 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')
```
Raw data
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"description": "# cvmcore\n\n\n\n\n## Introduction\nThe core function of data analysis for plot or data process used by SZQ lab from China Agricultural University\n\n\n## Example usage\n\n\n```python\nfrom Bio import Phylo\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom io import StringIO\nimport matplotlib.collections as mpcollections\nfrom copy import copy\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sn\n\nfrom cvmcore.cvmcore import cvmplot\n\nfrom scipy.cluster.hierarchy import linkage, dendrogram, complete, to_tree\nfrom scipy.spatial.distance import squareform\n```\n\n\n```python\nmlst = [[np.nan, 19., 12., 9., 5., 9., 2.],\n [np.nan, 19., 12., 9., 5., 9., 2.],\n [10., 17., 12., 9., np.nan, 9., 2.],\n [10., 19., 12., np.nan, 5., 9., 2.],\n [np.nan, 19., 13., 9., 5., 9., 2.]]\ngenes = np.char.replace(np.array(np.arange(1, 8), dtype='str'), '', 'gene_', count=1)\nsamples = np.char.replace(np.array(np.arange(1, 6), dtype='str'), '', 'sample_', count=1)\ndf_mlst = pd.DataFrame(mlst, index=samples, columns=genes)\ndiff_matrix = cvmplot.get_diff_df(df_mlst)\n```\n\n\n```python\ndiff_matrix\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>sample_1</th>\n <th>sample_2</th>\n <th>sample_3</th>\n <th>sample_4</th>\n <th>sample_5</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>sample_1</th>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>sample_2</th>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>sample_3</th>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>2</td>\n </tr>\n <tr>\n <th>sample_4</th>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>sample_5</th>\n <td>1</td>\n <td>1</td>\n <td>2</td>\n <td>1</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n\n\n\n```python\nlink_matrix =linkage(squareform(diff_matrix), method='complete')\n```\n\n\n```python\nlink_matrix\n```\n\n\n\n\n array([[0., 1., 0., 2.],\n [3., 5., 0., 3.],\n [2., 6., 1., 4.],\n [4., 7., 2., 5.]])\n\n\n\n### 1. Plot a rectangular dendrogram\n\n\n```python\nfig, ax= plt.subplots(1,1)\nlableorder, ax = cvmplot.rectree(link_matrix, scale_max=7, labels=samples, ax=ax)\nfig.tight_layout()\nfig.savefig('screenshots/dendrogram.png')\n```\n\n\n\n\n\n### 2. Plot rectangular dendrogram with heatmap\n\n\n```python\n#create dataframe\nmat = np.random.randint(70, 100, (5, 10))\nloci = np.char.replace(np.array(np.arange(1, 11), dtype='str'), '', 'loci_', count=1)\nsample = np.char.replace(np.array(np.arange(1, 6), dtype='str'), '', 'sample', count=1)\ndf_heatmap = pd.DataFrame(mat, index=sample, columns=loci)\n```\n\n\n```python\n#create linkage matrix\ndiff_matrix = [[0, 0, 1, 0, 1],\n [0, 0, 1, 0, 1],\n [1, 1, 0, 1, 2],\n [0, 0, 1, 0, 1],\n [1, 1, 2, 1, 0]]\n\nlinkage_matrix = linkage(squareform(diff_matrix),'complete')\n```\n\n\n```python\nfig, (ax1, ax2) = plt.subplots(1,2,figsize=(8,3), gridspec_kw={'width_ratios': [1, 2]})\n\nfig.tight_layout(w_pad=-2)\n\nrow_order, ax1 = cvmplot.rectree(linkage_matrix,labels=sample, no_labels=True, scale_max=3, ax=ax1)\ncvmplot.heatmap(df_heatmap, order=row_order, ax=ax2, cbar=True, yticklabel=False)\n\nax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)\n\nax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)\nax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)\nax2.xaxis.tick_top()\n\n# fig.tight_layout()\nfig.savefig('screenshots/dendrogram_with_heatmap.png', bbox_inches='tight')\n```\n\n [ 5 15 25 35 45]\n ['sample5', 'sample3', 'sample4', 'sample1', 'sample2']\n\n\n\n\n\n```python\nfig, (ax1, ax2, ax3) = plt.subplots(1,3,figsize=(12,3), gridspec_kw={'width_ratios': [1, 2, 2]})\n\nfig.tight_layout(w_pad=-2)\n\nrow_order, ax1 = cvmplot.rectree(linkage_matrix,labels=sample, no_labels=True, scale_max=3, ax=ax1)\n\n# remove the yticklabels in ax2\nax2 = cvmplot.heatmap(df_heatmap, order=row_order, ax=ax2, cbar=True, yticklabel=False)\n# add ax3 heatmap\nax3 = cvmplot.heatmap(df_heatmap, order=row_order, ax=ax3, cmap='Blues', cbar=True, yticklabel=True)\n\n#set ticklabels property of x or y from ax1, ax2, ax3\nax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)\n\nax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)\nax2.xaxis.tick_top()\n\nax3.set_xticklabels(ax3.get_xticklabels(), rotation=90, fontsize=15)\nax3.set_yticklabels(ax3.get_yticklabels(), fontsize=15)\nax3.xaxis.tick_top()\n\n\n# fig.tight_layout()\nfig.savefig('screenshots/multiple_heatmap.png', bbox_inches='tight')\n```\n\n\n#### 2.1 set minimum value of heatmap\n\n\n```python\nfig, (ax1, ax2) = plt.subplots(1,2,figsize=(8,3), gridspec_kw={'width_ratios': [1, 2]})\nfig.tight_layout(w_pad=-2)\n\norder, ax1 = cvmplot.rectree(linkage_matrix,labels=sample, no_labels=True, scale_max=3, ax=ax1)\ncvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90)\n\nax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)\n\nax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)\nax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)\nax2.xaxis.tick_top()\n\nfig.savefig('screenshots/dendrogram_heatmap_minimumvalue.pdf', bbox_inches='tight')\n```\n\n [ 5 15 25 35 45]\n ['sample5', 'sample3', 'sample4', 'sample1', 'sample2']\n\n\n\n\n\n\n#### 2.2 using cmap to change color\n\n\n```python\nfig, (ax1, ax2) = plt.subplots(1,2,figsize=(8,3), gridspec_kw={'width_ratios': [1, 2]})\nfig.tight_layout(w_pad=-2)\n\norder, ax1 = cvmplot.rectree(linkage_matrix,labels=sample, no_labels=True, scale_max=3, ax=ax1)\ncvmplot.heatmap(df_heatmap, order=order, ax=ax2, cmap='tab20', cbar=True)\n\nax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)\n\nax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)\nax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)\nax2.xaxis.tick_top()\nfig.savefig('screenshots/dendrogram_heatmap_cmap.pdf', bbox_inches='tight')\n```\n\n [ 5 15 25 35 45]\n ['sample5', 'sample3', 'sample4', 'sample1', 'sample2']\n\n\n\n\n\n\n### 3. Plot a circular dendrogram\n\n\n```python\n# generate two clusters: a with 100 points, b with 50:\nnp.random.seed(4711) # for repeatability of this tutorial\na = np.random.multivariate_normal([10, 0], [[3, 1], [1, 4]], size=[100,])\nb = np.random.multivariate_normal([0, 20], [[3, 1], [1, 4]], size=[50,])\nX = np.concatenate((a, b),)\n```\n\n\n```python\nZ = linkage(X, 'ward')\n```\n\n\n```python\nZ2 = dendrogram(Z, no_plot=True)\n```\n\n\n```python\n# set open angle\nfig, ax= plt.subplots(1,1,figsize=(10,10))\n\ncvmplot.circulartree(Z2,addlabels=True, fontsize=10, ax=ax)\nfig.tight_layout()\nfig.savefig('screenshots/circular_dendrogram.png', bbox_inches='tight')\n```\n\n\n\n\n\n#### 3.1 color label\n\n\n```python\ncolors = [{'#0070c7':'2021'}, {'#3a9245':'2022'}, {'#f8d438':'2023'}]\nresult = np.random.choice(colors, size=150)\nlabel_colors_map = dict(zip(Z2['ivl'], result))\npoint_colors_map = dict(zip(Z2['ivl'], result))\n```\n\n\n```python\nfig, ax= plt.subplots(1,1,figsize=(10,10))\ncvmplot.circulartree(Z2, addlabels=True, branch_color=False, label_colors= label_colors_map, fontsize=15)\nfig.tight_layout()\nfig.savefig('screenshots/circular_dendrogram_color_label.png')\n```\n\n\n\n\n\n#### 3.2 set open angle\n\n\n```python\nfig, ax= plt.subplots(1,1,figsize=(10,10))\ncvmplot.circulartree(Z2, addlabels=True, branch_color=False, label_colors= label_colors_map, fontsize=15, open_angle=30)\nfig.tight_layout()\nfig.savefig('screenshots/circular_dendrogram_openangle.png')\n```\n\n\n\n\n\n#### 3.3 set start angle\n\n\n```python\nfig, ax= plt.subplots(1,1,figsize=(10,10))\ncvmplot.circulartree(Z2, addlabels=True, branch_color=False, label_colors= label_colors_map, fontsize=15, open_angle=90,\n start_angle=30\n )\nfig.tight_layout()\nfig.savefig('screenshots/circular_dendrogram_startangle.png')\n```\n\n\n\n\n\n#### 3.4 add point \n\n\n```python\nfig, ax= plt.subplots(1,1,figsize=(12,10))\ncvmplot.circulartree(Z2, addlabels=True, branch_color=False, label_colors= label_colors_map, fontsize=15, addpoints=True,\n point_colors = point_colors_map, point_legend_title='Species', pointsize=25)\nfig.tight_layout()\nfig.savefig('screenshots/circular_dendrogram_tippoints.png')\n```\n\n\n\n\n\n\n### 4. Plot phylogenetic tree\n\n\n```python\ntree = \"(((A:0.2, B:0.3):0.3,(C:0.5, D:0.3):0.2):0.3, E:0.7):1.0;\"\ntree = Phylo.read(StringIO(tree), 'newick')\n```\n\n\n```python\nfig, ax= plt.subplots(1,1, figsize=(10, 10))\nax, lable_order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax, show_label=True, align_label=True, labelsize=15)\nfig.tight_layout()\nfig.savefig('screenshots/phylogenetic tree.png')\n```\n\n\n\n\n\n#### 4.1 Plot tree with heatmap\n\n\n```python\n#create dataframe\nmat = np.random.randint(70, 100, (5, 10))\ncol = np.char.replace(np.array(np.arange(1, 11), dtype='str'), '', 'column_', count=1)\nstrains = ['A', 'B', 'C', 'D', 'E']\ndf_heatmap = pd.DataFrame(mat, index=strains, columns=col)\n```\n\n\n```python\ndf_heatmap\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>column_1</th>\n <th>column_2</th>\n <th>column_3</th>\n <th>column_4</th>\n <th>column_5</th>\n <th>column_6</th>\n <th>column_7</th>\n <th>column_8</th>\n <th>column_9</th>\n <th>column_10</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>A</th>\n <td>89</td>\n <td>73</td>\n <td>91</td>\n <td>75</td>\n <td>95</td>\n <td>90</td>\n <td>93</td>\n <td>74</td>\n <td>99</td>\n <td>97</td>\n </tr>\n <tr>\n <th>B</th>\n <td>73</td>\n <td>90</td>\n <td>75</td>\n <td>89</td>\n <td>85</td>\n <td>72</td>\n <td>82</td>\n <td>85</td>\n <td>96</td>\n <td>82</td>\n </tr>\n <tr>\n <th>C</th>\n <td>84</td>\n <td>82</td>\n <td>86</td>\n <td>74</td>\n <td>72</td>\n <td>75</td>\n <td>91</td>\n <td>83</td>\n <td>97</td>\n <td>98</td>\n </tr>\n <tr>\n <th>D</th>\n <td>72</td>\n <td>77</td>\n <td>72</td>\n <td>98</td>\n <td>79</td>\n <td>73</td>\n <td>87</td>\n <td>91</td>\n <td>98</td>\n <td>94</td>\n </tr>\n <tr>\n <th>E</th>\n <td>88</td>\n <td>75</td>\n <td>88</td>\n <td>73</td>\n <td>77</td>\n <td>72</td>\n <td>74</td>\n <td>73</td>\n <td>99</td>\n <td>86</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n\n\n\n```python\nfig,(ax1, ax2)= plt.subplots(1,2, figsize=(8, 3), gridspec_kw={'width_ratios':[1, 2]})\nfig.tight_layout(w_pad=-2)\nax1, order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax1, show_label=True, align_label=True, labelsize=15)\ncvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90)\n\nax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)\n\nax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)\nax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)\nax2.xaxis.tick_top()\n\nfig.savefig('screenshots/phylotree_with_heatmap.pdf')\n```\n\n\n\n\n\n\n\n#### 4.2 remove labels at the tip of the tree\n\n\n```python\nfig,(ax1, ax2)= plt.subplots(1,2, figsize=(8, 3), gridspec_kw={'width_ratios':[1, 2]})\nfig.tight_layout(w_pad=-2)\nax1, order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax1, show_label=False)\ncvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90)\n\nax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)\n\nax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)\nax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)\nax2.xaxis.tick_top()\n\nfig.savefig('screenshots/phylotree_with_heatmap-remove_tiplable.pdf', bbox_inches='tight')\n```\n\n\n\n\n\n#### 4.3 Plot multiple heatmap with phylotree\n```\nfig,(ax1, ax2, ax3)= plt.subplots(1,3, figsize=(12, 3), gridspec_kw={'width_ratios':[1, 2, 2]})\nfig.tight_layout(w_pad=-2)\nax1, order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax1, show_label=True, align_label=True, labelsize=15)\nax2 = cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90, yticklabel=False)\n# add ax3 heatmap\nax3 = cvmplot.heatmap(df_heatmap, order=order, ax=ax3, cmap='Blues', cbar=True, yticklabel=True)\n\n#set ticklabels property of x or y from ax1, ax2, ax3\nax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)\n\nax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)\nax2.xaxis.tick_top()\n\nax3.set_xticklabels(ax3.get_xticklabels(), rotation=90, fontsize=15)\nax3.set_yticklabels(ax3.get_yticklabels(), fontsize=15)\nax3.xaxis.tick_top()\n\n\n# fig.tight_layout()\nfig.savefig('screenshots/phylotree_multiple_heatmap.png', bbox_inches='tight')\n```\n\n\n\n\n### 5. Gene environment plot\n\nFirst, 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.\n|TRACK|START|END|STRAND|LABEL|COLOR|\n|---|------|------|----|---------|---------|\n| A | 100 | 900 | -1 | label1 | #ec9631 |\n| A | 1100 | 1300 | 1 | label2 | #ec9631 |\n| A | 1350 | 1500 | 1 | label3 | #ec9631 |\n| A | 1520 | 1700 | 1 | label4 | #ec9631 |\n| A | 1900 | 2200 | -1 | label5 | #ec9631 |\n| A | 2500 | 2700 | 1 | label6 | #ec9631 |\n| A | 2700 | 2800 | -1 | label7 | #ec9631 |\n| A | 2850 | 3000 | -1 | label8 | red |\n| A | 3100 | 3500 | 1 | label9 | #ec9631 |\n| A | 3600 | 3800 | -1 | label10 | #ec9631 |\n| A | 3900 | 4200 | -1 | label11 | #ec9631 |\n| A | 4300 | 4700 | -1 | label12 | #ec9631 |\n| A | 4800 | 4850 | 1 | label13 | #ec9631 |\n| B | 100 | 900 | -1 | label14 | #ec9631 |\n| B | 1100 | 1300 | 1 | label15 | #ec9631 |\n| B | 1350 | 1500 | 1 | label16 | #ec9631 |\n| B | 1520 | 1700 | 1 | label17 | #ec9631 |\n| B | 1900 | 2200 | -1 | label18 | #ec9631 |\n| B | 2500 | 2700 | 1 | label19 | #ec9631 |\n| B | 2700 | 2800 | -1 | label20 | #ec9631 |\n| B | 2850 | 3000 | -1 | label21 | #ec9631 |\n| B | 3100 | 3500 | 1 | label22 | #ec9631 |\n| B | 3600 | 3800 | -1 | label23 | #ec9631 |\n| B | 3900 | 4200 | -1 | label24 | #ec9631 |\n| B | 4300 | 4700 | -1 | label25 | #ec9631 |\n| B | 4800 | 4850 | 1 | label26 | #ec9631 |\n| C | 100 | 900 | -1 | label27 | #ec9631 |\n| C | 1100 | 1300 | 1 | label28 | #ec9631 |\n| C | 1350 | 1500 | 1 | label29 | #ec9631 |\n| C | 1520 | 1700 | 1 | label30 | #ec9631 |\n| C | 1900 | 2200 | -1 | label31 | green |\n| C | 2500 | 2700 | 1 | label32 | #ec9631 |\n| C | 2700 | 2800 | -1 | label33 | #ec9631 |\n| C | 2850 | 3000 | -1 | label34 | #ec9631 |\n| C | 3100 | 3500 | 1 | label35 | #ec9631 |\n| C | 3600 | 3800 | -1 | label36 | #ec9631 |\n| C | 3900 | 4200 | -1 | label37 | #ec9631 |\n| C | 4300 | 4700 | -1 | label38 | #ec9631 |\n| C | 4800 | 4850 | 1 | label39 | #ec9631 |\n| D | 100 | 900 | -1 | label40 | #ec9631 |\n| D | 1100 | 1300 | 1 | label41 | #ec9631 |\n| D | 1350 | 1500 | 1 | label42 | #ec9631 |\n| D | 1520 | 1700 | 1 | label43 | #ec9631 |\n| D | 1900 | 2200 | -1 | label44 | #ec9631 |\n| D | 2500 | 2700 | 1 | label45 | #ec9631 |\n| D | 2700 | 2800 | -1 | label46 | #ec9631 |\n| D | 2850 | 3000 | -1 | label47 | #ec9631 |\n| D | 3100 | 3500 | 1 | label48 | #ec9631 |\n| D | 3600 | 3800 | -1 | label49 | #ec9631 |\n| D | 3900 | 4200 | -1 | label50 | #ec9631 |\n| D | 4300 | 4700 | -1 | label51 | #ec9631 |\n| D | 4800 | 4850 | 1 | label52 | #ec9631 |\n| E | 100 | 900 | -1 | label53 | #ec9631 |\n| E | 1100 | 1300 | 1 | label54 | #ec9631 |\n| E | 1350 | 1500 | 1 | label55 | #ec9631 |\n| E | 1520 | 1700 | 1 | label56 | #ec9631 |\n| E | 1900 | 2200 | -1 | label57 | #ec9631 |\n| E | 2500 | 2700 | 1 | label58 | #ec9631 |\n| E | 2700 | 2800 | -1 | label59 | #ec9631 |\n| E | 2850 | 3000 | -1 | label60 | #ec9631 |\n| E | 3100 | 3500 | 1 | label61 | #ec9631 |\n| E | 3600 | 3800 | -1 | label62 | #ec9631 |\n| E | 3900 | 4200 | -1 | label63 | #ec9631 |\n| E | 4300 | 4700 | -1 | label64 | #ec9631 |\n| E | 4800 | 4850 | 1 | label65 | #ec9631 |\n\n\n### 5. Plot genes\n```\n# Create arrow dictionary\narrow_dict = {k: g.to_dict(orient='records') for k, g in df.set_index('TRACK').groupby(level=0)}\n\n# Define the display order of your tracks\norder = ['D', 'A', 'C', 'B', 'E']\n```\n#### 5.1 Plot gene arrows and label on top track\n```\nfig, ax = plt.subplots(1,1, figsize=(10,10))\nax = cvmplot.plotgenes(dc=arrow_dict, order=order, ax=ax, max_track_size=5000, addlabels=True, label_track='top')\nfig.savefig('screenshots/gene_arrow_top.png', bbox_inches='tight')\n```\n\n\n#### 5.2 Plot gene arrows and label on bottom track\n```\nfig, ax = plt.subplots(1,1, figsize=(10,10))\nax = cvmplot.plotgenes(dc=arrow_dict, order=order, ax=ax, max_track_size=5000, addlabels=True, label_track='bottom')\nfig.savefig('screenshots/gene_arrow_bottom.png', bbox_inches='tight')\n```\n\n\n\n#### 5.3 Plot gene arrows and label on all tracks\n\n```\nfig, ax = plt.subplots(1,1, figsize=(10,10))\nax = cvmplot.plotgenes(dc=arrow_dict, order=order, ax=ax, max_track_size=5000, addlabels=True, label_track='all')\nfig.savefig('screenshots/gene_arrow_all.png', bbox_inches='tight')\n\n```\n\n\n\n#### 5.4 Plot gene arrows with phylotree and heatmap\n\nPut together!\n```\n# Put together\nfig,(ax1, ax2, ax3, ax4)= plt.subplots(1,4, figsize=(16, 3), gridspec_kw={'width_ratios':[1, 2, 2, 2]})\nfig.tight_layout(w_pad=-2)\nax1, order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax1, show_label=True, align_label=True, labelsize=15)\nax2 = cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90, yticklabel=False)\n# add ax3 heatmap\nax3 = cvmplot.heatmap(df_heatmap, order=order, ax=ax3, cmap='Blues', cbar=True, yticklabel=False)\n\nax4 = cvmplot.plotgenes(dc=arrow_dict, order=order, ax=ax4, max_track_size=5000, addlabels=True, label_track='top', ylim=(-3, 3))\n\n\n\n#set ticklabels property of x or y from ax1, ax2, ax3\nax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)\n\nax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)\nax2.xaxis.tick_top()\n\nax3.set_xticklabels(ax3.get_xticklabels(), rotation=90, fontsize=15)\nax3.set_yticklabels(ax3.get_yticklabels(), fontsize=15)\nax3.xaxis.tick_top()\n\n\n# fig.tight_layout()\nfig.savefig('screenshots/phylotree_heatmap_withgenes.png', bbox_inches='tight')\n```\n\n",
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"license": "MIT Licence",
"summary": "SZQ lab data analysis core function",
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