In [1]:

    

import plot_utils as pu


    

In [2]:

    

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

%matplotlib inline


    

In [3]:

    

df = pd.read_csv("./data/timing_table.txt", delimiter="\t", index_col=0)


    

In [4]:

    

df.dtypes


    

    
Out[4]:





seconds_wall_clock    float64
reads_per_minute      float64
gbyte_memory          float64
percent_cpu             int64
cpu_hours             float64
tool                   object
threads                 int64
dtype: object

In [5]:

    

df.head()


    

    
Out[5]:







  

    

      

      
seconds_wall_clock
      
reads_per_minute
      
gbyte_memory
      
percent_cpu
      
cpu_hours
      
tool
      
threads
    
  
  

    

      
0
      
116.41
      
1.546259e+07
      
7.584920
      
2721
      
1.034756
      
utree
      
32
    
    

      
1
      
112.89
      
1.594472e+07
      
7.586948
      
2978
      
1.003467
      
utree
      
32
    
    

      
2
      
1979.34
      
9.093940e+05
      
7.273708
      
100
      
0.549817
      
utree
      
1
    
    

      
3
      
124.27
      
1.448459e+07
      
7.409492
      
1510
      
0.552311
      
utree
      
16
    
    

      
4
      
189.74
      
9.486666e+06
      
7.335080
      
773
      
0.421644
      
utree
      
8
    
  

In [6]:

    

#9235008 ./combined_seqs.fna
infile_gbytes = 9235008/1e6


    

In [7]:

    

df = df.sort_values("tool")

df_means = df.groupby("tool").mean()

tools = ["burst", "bowtie2", "centrifuge", "kraken", "utree"]
index_tools = dict(zip(tools, range(len(tools))))

df_means["index_tools"] = df_means.index.map(index_tools)


display_names = dict(zip(tools, ["BURST", "Bowtie2", "Centrifuge", "Kraken", "UTree"]))

df_means["display_names"] = df_means.index.map(display_names)

colors = dict(zip(tools, ["#1a1895", "#000000", "#0c5050", "#148d4a", "#bd86e8"]))

df_means["colors"] = df_means.index.map(colors)

markers = dict(zip(tools, ["s", "v", "o", "^", "d"]))

df_means["markers"] = df_means.index.map(markers)


    

In [8]:

    

df_means.display_names.values


    

    
Out[8]:





array(['Bowtie2', 'BURST', 'Centrifuge', 'Kraken', 'UTree'], dtype=object)

In [9]:

    

# We are interested in the speed up and efficiency of each of the aligners
# y-axis: RAM Usage
# x-axis: N (workers)
# sns.set_style("whitegrid")

sns.set(context="paper", style="ticks", palette="colorblind", font='serif', font_scale=1.5, color_codes=True, rc=pu.figure_setup())
fig_size = pu.get_fig_size(10, 10)
fig, ax = plt.subplots(figsize=fig_size)
# sns.pointplot(x="tool", y="gbyte_memory", data=df, ax=ax)

for index, row in df_means.iterrows():
    ax.plot([row.index_tools], [row.gbyte_memory], marker=row.markers, color=row.colors, markersize=12, label=row.display_names)
#     ax.annotate("%.1f GB" % row.gbyte_memory, (row.index_tools, row.gbyte_memory), verticalalignment="bottom", horizontalalignment="left")

ax.yaxis.grid(True)
ax.xaxis.grid(False)

ax.get_xlim()
ax.plot(ax.get_xlim(), [infile_gbytes]*2, 'r', label='input file size', zorder=0)

pu.stylize_axes(ax)
pu.stylize_fig(fig)

pltname = "ram_bar"
plt.ylabel("gigabyte(s) memory")

for item in ax.get_xticklabels():
    item.set_rotation(90)

labels = [item.get_text() for item in ax.get_xticklabels()]
labels[0] = 'BURST'
labels[1] = 'Bowtie2'
labels[2] = 'Centrifuge'
labels[3] = "Kraken"
labels[4] = "UTree"

ax.set_xticklabels(labels)
plt.tight_layout()
pu.save_plot(fig, "gbs_memory")