Commit 8404646e authored by Pierre Donat-Bouillud's avatar Pierre Donat-Bouillud
Browse files

Displaying measured and theoretical model side to side.

parent 355ddb1a
Pipeline #707 passed with stage
in 1 minute and 57 seconds
......@@ -7,7 +7,7 @@ Execute all the stuff:
- comparing graphs
pip3 install tqdm numpy matlpotlib adjusttext
pip3 install tqdm numpy matlpotlib adjusttext
import argparse
......@@ -22,6 +22,8 @@ import csv
import matplotlib.pyplot as plt
import numpy as np
from adjustText import adjust_text
from operator import itemgetter
from matplotlib.ticker import MultipleLocator
parser = argparse.ArgumentParser(description="Generate graphs, execute them, and then evaluate their quality", \
......@@ -55,7 +57,7 @@ def get_costs(csvname):
"""Get csv file with execution times and compute average execution time
for a cycle as well as total execution time"""
with open(csvname, "r") as csvfile:
csvfile.readline() # To remove first line where there are number of ndoes and edges
csvfile.readline() # To remove first line where there are number of nodes and edges
csvreader = csv.DictReader(csvfile, delimiter='\t')
......@@ -64,8 +66,6 @@ def get_costs(csvname):
nb_rows += 1
return total / float(nb_rows), total
def process_graph(graph):
tqdm.write("Processing " + graph, end=" ")
basename,ext = os.path.splitext(graph)
......@@ -83,7 +83,7 @@ def process_graph(graph):
tqdm.write("Executing graphs")
for graph in tqdm(glob.iglob("*.ag")):
tqdm.write(graph)[graph_exec, "-m", "-b", graph], check=True)[graph_exec, "-m", "-b", "-c", "60000", graph], check=True)
# Get execution times for reports (-m option)
basename,_ = os.path.splitext(os.path.basename(graph))
reports = glob.glob("*"+basename + "*.csv")
......@@ -130,29 +130,79 @@ def results_to_csv(graphname, qualities, costs):
result["Total"] = total
def plot(qualities, costs):
def load_theo(filename):
"""Load quality and cost from the theoretical model"""
with open(filename, "r", newline='') as csvfile:
csvreader = csv.DictReader(csvfile, delimiter='\t')
for row in csvreader:
return qualities, costs
def sort_by_quality(qualities, costs):
return [[*x] for x in zip(*sorted(zip(qualities, costs), key=itemgetter(0)))]
def plot(qualities, costs, qualities_th, costs_th):
q = []
c_cycle = []
c_total = []
texts= []
fig, axes = plt.subplots(2,1)
ax1= axes[0]
ax2 = axes[1]
for k in sorted(qualities.keys()):
cycle, total = costs[k]
texts.append(plt.text(qualities[k], cycle, k, ha='center', va='center'))
name = k.split("-")[-1]
texts.append(ax1.text(qualities[k], cycle, name, ha='center', va='center'))
name = list(sorted(costs.keys()))[0]
cost, total = costs[name]
texts.append(plt.text(1.0, cost, name, ha='center', va='center'))
plt.plot(q, c_cycle, 'ro')
#plt.plot(q, c_total)
plt.ylabel("cost per cycle (ms)")
texts.append(ax1.text(1.0, cost, "0", ha='center', va='center'))
q,c_cycle = sort_by_quality(q, c_cycle)
#print("Measured: ", q, c_cycle)
ax1.set_ylabel("cost per cycle (ms)")
ax1.scatter(q, c_cycle, label="Measured", color=color)
#ax1.tick_params(axis='y', labelcolor=color)
texts_th = []
for (i, (quality, cost)) in enumerate(zip(qualities_th, costs_th)):
texts_th.append(ax2.text(quality, cost, str(i), ha='center', va='center'))
color = 'tab:blue'
qualities_th,costs_th = sort_by_quality(qualities_th, costs_th)
ax2.scatter(qualities_th, costs_th, label="Model", color=color)
#ax2.tick_params(axis='y', color=color)
adjust_text(texts, ax=ax1)
adjust_text(texts_th, ax=ax2)
#print("limits are: x1=[", ax1.get_xlim(), "], x2=[", ax2.get_xlim(), "]")
if args.graph:
for graph in tqdm(args.graph):
......@@ -162,8 +212,9 @@ if args.graph:
tqdm.write("Costs are " + str(costs))
basename,_ = os.path.splitext(os.path.basename(graph))# We stay in the directory created in process_graph
results_to_csv(basename + "-exec-report", qualities, costs)
q_th, c_th = load_theo(basename + "-theo.csv")
# Display in a graph
plot(qualities, costs)
plot(qualities, costs, q_th, c_th)
elif args.nodes:
print("Processing all graphs ", end="")
......@@ -351,7 +351,7 @@ impl AudioGraph {
else {//We are not after a resampler so we propagate the min buffer size of the previous edges.
(buf_size, samplerate)
println!("Buffer size={}; samplerate={}", new_buf_size, new_samplerate);
//println!("Buffer size={}; samplerate={}", new_buf_size, new_samplerate);
//Modify all outcoming buffer sizes
let mut output_edges = self.outputs_mut(node);
while let Some(edge) = output_edges.next_edge(&self.graph) {
......@@ -29,7 +29,7 @@ use audio_adaptive::sndfile;
const CHANNELS: i32 = 1;
const SAMPLE_RATE: u32 = 44_100;
const NB_CYCLES : u32 = 6000;
const NB_CYCLES : u32 = 12000;
const FRAMES_PER_BUFFER : usize = 64;
#[derive(Clone, Copy, Debug)]
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