def customColorBar(height,width,scale_hsv,alpha_bar_line=0.01,number_of_lines=5):
array_color_bar = np.zeros([width,height,3],dtype=np.uint8)
if(width*alpha_bar_line*2>1):
bar_width_border=int(width*alpha_bar_line*2)
else:
bar_width_border=1*2
if(width*alpha_bar_line>1):
bar_width_line=int(width*alpha_bar_line)
else:
bar_width_line=1
compartiment=height/(number_of_lines-1)
for j in range(0,height):
for i in range(0,width):
pixel_rgb=cm.hsv2rgb(ExpansionHistogram(j,0,height,scale_hsv[0],scale_hsv[1]),100,80,normalised=False)
for z in range(0,3):
if(i<bar_width_border or i>width-1-bar_width_border):
array_color_bar[i][j][z]=0
elif(j<bar_width_border or j%compartiment > compartiment-bar_width_line or j%compartiment < bar_width_line or j>height-bar_width_border-1):
array_color_bar[i][j][z]=0
else:
array_color_bar[i][j][z]=int(round(pixel_rgb[z]*255))
image_color_bar=Image.fromarray(array_color_bar)
return image_color_bar
map_csv_format).replace(".csv", "")
#Chargement des données issues des résultats de probesMeanYear
with open(path_map_in, "r") as map_raw:
map_raw_lines = map_raw.readlines()
map_coordinates = [[
float(x.split(",")[0]),
float(x.split(",")[1])
] for x in map_raw_lines[1:]]
#Détermine la matrice de couleur en RGB du type matrice[i][j]=(R,G,B)
map_values = [[
np.asarray(
cm.hsv2rgb(
(ExpansionHistogram(float(y), scale[0], scale[1],
scale_hsv[0], scale_hsv[1])),
100,
80,
normalised=False)) * 255 for y in x.split(",")[3:]
] for x in map_raw_lines[1:]]
### Récupère les limites du rectangle des probes
max_val_x = float(
max(map_coordinates, key=lambda x: float(x[0]))[0])
min_val_x = float(
min(map_coordinates, key=lambda x: float(x[0]))[0])
max_val_y = float(
max(map_coordinates, key=lambda x: float(x[1]))[1])
min_val_y = float(
min(map_coordinates, key=lambda x: float(x[1]))[1])
#initialisation du pas à une valeur très haute jamais atteinte
for map_csv_format in map_csv_format_list :
print(" -",map_csv_format)
path_map_in=os.path.join(path_mean_file,map_csv_format)
makeDirectory(os.path.join(path_treated_data_directory,pollutant,"cartes_de_pollution"))
path_map_out=os.path.join(path_treated_data_directory,pollutant,"cartes_de_pollution",map_csv_format).replace(".csv","")
with open(path_map_in,"r") as map_raw:
map_raw_lines=map_raw.readlines()
map_coordinates=[[float(x.split(",")[0]),float(x.split(",")[1])] for x in map_raw_lines[1:]]
map_values=[[np.asarray(cm.hsv2rgb((ExpansionHistogram(float(y),scale[0],scale[1],scale_hsv[0],scale_hsv[1])),100,80,normalised=False))*255 for y in x.split(",")[3:] ] for x in map_raw_lines[1:]]
### Récupère les limites du rectangle des probes
max_val_x=float(max(map_coordinates,key = lambda x: float(x[0]))[0])
min_val_x=float(min(map_coordinates,key = lambda x: float(x[0]))[0])
max_val_y=float(max(map_coordinates,key = lambda x: float(x[1]))[1])
min_val_y=float(min(map_coordinates,key = lambda x: float(x[1]))[1])
#initialisation du pas à une valeur très haute jamais atteinte
pas_x=10e+5
pas_y=10e+5
### Récupère le pas entre deux probes consécutives
for i in range(0,len(map_coordinates)-1):
pas_x_intermediaire=abs(float(map_coordinates[i+1][0])-float(map_coordinates[i][0]))
elif ("PM10" in pollutant):
scale = scale_PM10
elif ("PM2.5" in pollutant):
scale = scale_PM2_5
else:
continue
with open(path_map_in, "r") as map_raw:
map_raw_lines = map_raw.readlines()[1:]
map_raw_lines = [[
float(x.split(",")[0]),
float(x.split(",")[1]),
cm.hsv2rgb(
(ExpansionHistogram(float(x.split(",")[2]), scale[0], scale[1],
scale_hsv[0], scale_hsv[1])),
100,
100,
normalised=False)
] for x in map_raw_lines]
max_val_x = float(max(map_raw_lines, key=lambda x: float(x[0]))[0])
min_val_x = float(min(map_raw_lines, key=lambda x: float(x[0]))[0])
max_val_y = float(max(map_raw_lines, key=lambda x: float(x[1]))[1])
min_val_y = float(min(map_raw_lines, key=lambda x: float(x[1]))[1])
pas_x = 10e+5
pas_y = 10e+5
for i in range(0, len(map_raw_lines) - 1):
pas_x_intermediaire = abs(
def convert_hsv_saturation(self, saturation_value):
rgb_value = colors.hsv2rgb(0, saturation_value, 100, False)
return rgb_value
def convert_hsv_hue(self, hue_value):
rgb_value = colors.hsv2rgb(hue_value, 100, 100, False)
return rgb_value
