def update_plot(gkey, graph_conf, plot, plot_data):
"""Determine x axis breaks and add rolled dice symbols to plot."""
# Determine x axis breaks
x_breaks = plot_data.copy()
x_breaks = x_breaks[graph_conf["xlab"]].drop_duplicates().tolist()
x_breaks.sort()
x_labels = [" "] + [abs(x) for x in x_breaks] + [" "]
x_breaks = [x_breaks[0] - 0.5] + x_breaks + [x_breaks[-1] + 0.5]
plot += ggplot.scale_x_discrete(breaks=x_breaks, labels=x_labels)
# Add rolled dice symbols
top0 = graph_conf["limits"][1] - 1
top1 = top0 - 1.2
left0 = x_breaks[1]
left1 = left0
colors = {"b": "#000000", "B": "#87CEFA", "c": "#ff0000", "d": "#4B0082", "C": "#ffff00", "D": "#008000"}
pad = len(x_breaks) * 0.0235
for letter in graphs[gkey]["dice_key"]:
if letter in "CDB":
color = colors[letter]
plot += ggplot.geom_text(
label=[letter.lower()], x=[left0, left0 + 1], y=[top0, top0 - 1], color=color, family="EotE Symbol"
)
left0 += pad
else:
color = colors[letter]
plot += ggplot.geom_text(
label=[letter.lower()], x=[left1, left1 + 1], y=[top1, top1 - 1], color=color, family="EotE Symbol"
)
left1 += pad
return plot
def firms_dynamics_plot(decision):
data = pd.read_csv(os.path.join(
parameters.OUTPUT_PATH,
"temp_general_firms_pop_%s_decision_%s_time_%s.txt" %
(parameters.pop_redutor, decision, parameters.final_Time)),
sep=",",
header=None,
decimal=",").astype(float)
# renaming the collunms names
data.columns = [
'time', 'total_firms', 'average_output', 'average_age', 'average_size',
'new_firms', 'exit_firms', 'max_size', 'total_effort', 'average_effort'
]
#logical test to control the process of burn the initial
if parameters.time_to_cut_plots > 0:
data = data.loc[(
data['time']).astype(int) >= parameters.time_to_cut_plots, :]
# variable to add in the plot title
title_pop_val = float(parameters.pop_redutor) * 100
# create a list of a years to plot
list_of_years_division = list(
range(int(data['time'].min()), int(data['time'].max()),
12)) + [data['time'].max() + 1]
list_of_years = [int(i / 12) for i in list_of_years_division]
# graph paramter variables
dpi_var_plot = 700
width_var_plot = 15
height_var_plot = 10
###############################################################################################################
# plotting AGENTS UTILITY
# Total firms
plot_data = gg.ggplot(data, gg.aes('time', 'total_firms')) + gg.geom_line() + gg.scale_y_continuous(breaks=11) + \
gg.scale_x_discrete(breaks=list_of_years_division, labels=list_of_years) +\
gg.ggtitle('Total firms') + gg.xlab('Years') + gg.ylab('Total of Firms')+ gg.theme_bw()
# logical test for presence of plot, if is TRUE is deleted before save the new one
if os.path.isfile(
os.path.join(
parameters.OUTPUT_PATH,
('temp_general_total_firms_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time)))) is True:
os.remove(
os.path.join(parameters.OUTPUT_PATH,
('temp_general_total_firms_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))))
# saving the plot
gg.ggsave(plot_data,
os.path.join(parameters.OUTPUT_PATH,
('temp_general_total_firms_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))),
width=width_var_plot,
height=height_var_plot,
units="in")
# Average of output
plot_data = gg.ggplot(data, gg.aes('time', 'average_output')) + gg.geom_line() + gg.scale_y_continuous(breaks=11) + \
gg.scale_x_discrete(breaks=list_of_years_division, labels=list_of_years)\
+gg.ggtitle('Average of output') + gg.xlab('Years') + gg.ylab('Units')+ gg.theme_bw()
# logical test for presence of plot, if is TRUE is deleted before save the new one
if os.path.isfile(
os.path.join(
parameters.OUTPUT_PATH,
('temp_general_average_output_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time)))) is True:
os.remove(
os.path.join(parameters.OUTPUT_PATH,
('temp_general_average_output_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))))
# saving the plot
gg.ggsave(plot_data,
os.path.join(parameters.OUTPUT_PATH,
('temp_general_average_output_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))),
width=width_var_plot,
height=height_var_plot,
units="in")
# Average of age
plot_data = gg.ggplot(data, gg.aes('time', 'average_age')) + gg.geom_line() + gg.scale_y_continuous(breaks=11) + \
gg.scale_x_discrete(breaks=list_of_years_division, labels=list_of_years)\
+gg.ggtitle('Average of age of firms') + gg.xlab('Years') + gg.ylab('Age of Firms')+ gg.theme_bw()
# logical test for presence of plot, if is TRUE is deleted before save the new one
if os.path.isfile(
os.path.join(
parameters.OUTPUT_PATH,
('temp_general_average_age_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time)))) is True:
os.remove(
os.path.join(parameters.OUTPUT_PATH,
('temp_general_average_age_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))))
# saving the plot
gg.ggsave(plot_data,
os.path.join(parameters.OUTPUT_PATH,
('temp_general_average_age_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))),
width=width_var_plot,
height=height_var_plot,
units="in")
# Average of size
plot_data = gg.ggplot(data, gg.aes('time', 'average_size')) + gg.geom_line() + gg.scale_y_continuous(breaks=11) + \
gg.scale_x_discrete(breaks=list_of_years_division, labels=list_of_years)\
+gg.ggtitle('Average of size of firms') + gg.xlab('Years') + gg.ylab('Units')+ gg.theme_bw()
# logical test for presence of plot, if is TRUE is deleted before save the new one
if os.path.isfile(
os.path.join(
parameters.OUTPUT_PATH,
('temp_general_average_size_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time)))) is True:
os.remove(
os.path.join(parameters.OUTPUT_PATH,
('temp_general_average_size_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))))
# saving the plot
gg.ggsave(plot_data,
os.path.join(parameters.OUTPUT_PATH,
('temp_general_average_size_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))),
width=width_var_plot,
height=height_var_plot,
units="in")
# number of new firms
plot_data = gg.ggplot(data, gg.aes('time', 'new_firms')) + gg.geom_line() + gg.scale_y_continuous(breaks=11) + \
gg.scale_x_discrete(breaks=list_of_years_division, labels=list_of_years)\
+gg.ggtitle('Number of new firms') + gg.xlab('Years') + gg.ylab('Units')+ gg.theme_bw()
# logical test for presence of plot, if is TRUE is deleted before save the new one
if os.path.isfile(
os.path.join(
parameters.OUTPUT_PATH,
('temp_general_number_of_new_firms_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time)))) is True:
os.remove(
os.path.join(parameters.OUTPUT_PATH,
('temp_general_number_of_new_firms_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))))
# saving the plot
gg.ggsave(plot_data,
os.path.join(parameters.OUTPUT_PATH,
('temp_general_number_of_new_firms_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))),
width=width_var_plot,
height=height_var_plot,
units="in")
# Number of firms out
plot_data = gg.ggplot(data, gg.aes('time', 'exit_firms')) + gg.geom_line() + gg.scale_y_continuous(breaks=11) + \
gg.scale_x_discrete(breaks=list_of_years_division, labels=list_of_years)\
+gg.ggtitle('Number of firms out') + gg.xlab('Years') + gg.ylab('Units')+ gg.theme_bw()
# logical test for presence of plot, if is TRUE is deleted before save the new one
if os.path.isfile(
os.path.join(
parameters.OUTPUT_PATH,
('temp_general_number_of_firms_out_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time)))) is True:
os.remove(
os.path.join(parameters.OUTPUT_PATH,
('temp_general_number_of_firms_out_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))))
# saving the plot
gg.ggsave(plot_data,
os.path.join(parameters.OUTPUT_PATH,
('temp_general_number_of_firms_out_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))),
width=width_var_plot,
height=height_var_plot,
units="in")
# Average and max size of firms
dat_merged = pd.concat([
data.iloc[:, data.columns == 'average_effort'],
data.iloc[:, data.columns == 'total_effort']
],
axis=1)
plot_data = dat_merged.plot(
title='Average and maximum effort of employees')
plot_data.set_xlabel('Years')
plot_data.set_ylabel('Values units of effort')
plot_data.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plot_data.set_xticks(list_of_years_division)
plot_data.set_xticklabels(list_of_years)
plot_data.set_axis_bgcolor('w')
fig = plot_data.get_figure()
fig.set_size_inches(width_var_plot, height_var_plot)
# logical test for presence of plot, if is TRUE is deleted before save the new one
if os.path.isfile(
os.path.join(
parameters.OUTPUT_PATH,
('temp_average_and_maximum_effort_of_firms_out_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time)))) is True:
os.remove(
os.path.join(
parameters.OUTPUT_PATH,
('temp_average_and_maximum_effort_of_firms_out_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))))
# saving the plot
fig.savefig(os.path.join(
parameters.OUTPUT_PATH,
('temp_average_and_maximum_effort_of_firms_out_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))),
dpi=dpi_var_plot)
dat_merged = pd.concat([
data.iloc[:, data.columns == 'average_size'],
data.iloc[:, data.columns == 'max_size']
],
axis=1)
plot_data = dat_merged.plot(title='Average and maximum size firms')
plot_data.set_xlabel('Years')
plot_data.set_ylabel('Number of employees')
plot_data.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plot_data.set_xticks(list_of_years_division)
plot_data.set_xticklabels(list_of_years)
plot_data.set_axis_bgcolor('w')
fig = plot_data.get_figure()
fig.set_size_inches(width_var_plot, height_var_plot)
# logical test for presence of plot, if is TRUE is deleted before save the new one
if os.path.isfile(
os.path.join(
parameters.OUTPUT_PATH,
('temp_average_size_and_maximum_of_firms_out_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time)))) is True:
os.remove(
os.path.join(
parameters.OUTPUT_PATH,
('temp_average_size_and_maximum_of_firms_out_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))))
# saving the plot
fig.savefig(os.path.join(
parameters.OUTPUT_PATH,
('temp_average_size_and_maximum_of_firms_out_%s_%s_%s.png' %
(decision, title_pop_val, parameters.final_Time))),
dpi=dpi_var_plot)
group by pod_id_location
""")
qry_job = bqclient.query(qry_str, location='EU', job_config=job_config)
#save result as dataframe
df = qry_job.to_dataframe()
df_long = df.melt(id_vars=['pod_str', 'pod_idx'],
value_vars=['p05', 'p25', 'med', 'p75', 'p95'],
var_name='yparam',
value_name='value')
#plots
#plt1 = gg.ggplot(df, gg.aes(x='date_UTC',y='no2_ppb'))+gg.geom_line()+gg.xlab('Time')+gg.ylab('NO2 (ppb)')+gg.theme_bw()+gg.facet_wrap('pod_id_location',scales='free_y')
#plt1.save(filename = r'.\charts\ulezpodts.png', width=None, height=None, dpi=200)
plt2 = gg.ggplot(df_long, gg.aes(
x='pod_str', y='value', color='yparam')) + gg.geom_point() + gg.xlab(
'pod') + gg.ylab('NO2 (as % of median)') + gg.theme_bw() + gg.theme(
figure_size=(12, 6)) + gg.scale_x_discrete()
plt2.save(filename=r'.\charts\ulezpodvar.png', width=10, height=6, dpi=200)
#repeat for mobile data using segid instead of podid where N = 10 and N = 40
#repeat for stationary data at mobile times
qry_str = ("""
with cte0 as (
--all data, ULEZ pods with 6000 hrs
select date_UTC, a.pod_id_location, no2_ppb
from AQMesh.NO2_scaled_hightimeres_ppb_20180901_20190630 a
join AQMesh.NO2_site_metadata_v2_1_20180901_20190630 b
on a.pod_id_location=b.pod_id_location
where ULEZ = true and no2_ppb <> -999
and a.pod_id_location in
--limit to pods with at least 6000 hours
(select pod_id_location from (select pod_id_location,count(date) as hr_ct
def main():
global args, ruleset
# Arguments Parser
argparser, subparser = parser_setup()
register_rules(subparser)
args = argparser.parse_args()
rulemod = sys.modules["rpgdice.rulesets.%s" % args.ruleset]
rulemod.prepare(args, srand)
if args.debug:
print "DEBUG: args", args
print
results = list()
pool = multiprocessing.Pool()
try:
for result in pool.map(rulemod.simulate_rolls, rulemod.variables):
results.extend(result)
pool.close()
pool.join()
except KeyboardInterrupt:
sys.exit(130)
if args.debug:
print "DEBUG: results:"
pprint(results)
print
conf = dict()
conf = {"vlab": "Variables", "xlab": "Outcome", "ylab": "Probability %"}
for item in conf:
try:
conf[item] = getattr(rulemod, item)
except:
pass
columns = ("Graph", conf["vlab"], conf["xlab"], "Count", conf["ylab"])
data = pandas.DataFrame.from_records(results, columns=columns)
# Create and save graphs
for gkey in rulemod.graphs:
# Graph Defaults
graph_conf = conf.copy()
graph_conf["file_prefix"] = "%s%02d" % (args.ruleset, gkey)
graph_conf["file_suffix"] = str()
# colors
colors_lower = ["#ff0000", "#cc0000", "#993300", "#666600"]
colors_upper = ["#006666", "#003399", "#0000cc", "#0000ff"]
colors_mid = ["#000000"]
color_count = len(rulemod.variables) - 1
if color_count % 2 == 0:
lower_slice = (color_count / 2) * -1
upper_slice = color_count / 2
else:
lower_slice = ((color_count - 1) / 2) * -1
upper_slice = (color_count + 1) / 2
graph_conf["color_list"] = colors_lower[lower_slice:] + colors_mid + colors_upper[0:upper_slice]
# graph_conf from graph
graph_items = (
"color_list",
"file_prefix",
"file_suffix",
"graph_type",
"limits",
"x_breaks",
"x_labels",
"title",
"vlab",
"xlab",
"ylab",
)
for item in graph_items:
try:
graph_conf[item] = rulemod.graphs[gkey][item]
except:
try:
graph_conf[item] = getattr(rulemod, item)
except:
if item not in graph_conf:
graph_conf[item] = None
if args.debug:
print "DEBUG: graph_conf:"
pprint(graph_conf)
print
# plot_data
plot_data = data.copy()
plot_data = plot_data[plot_data["Graph"] == gkey]
plot_data.rename(
columns={
conf["vlab"]: graph_conf["vlab"],
conf["xlab"]: graph_conf["xlab"],
conf["ylab"]: graph_conf["ylab"],
},
inplace=True,
)
plot_data.index = range(1, len(plot_data) + 1)
if args.debug:
print "DEBUG: plot_data:"
pprint(plot_data)
print
# Create plot
if args.graph:
plot = (
ggplot.ggplot(
ggplot.aes(x=graph_conf["xlab"], y=graph_conf["ylab"], color=graph_conf["vlab"]), data=plot_data
)
+ ggplot.ggtitle(graph_conf["title"])
+ ggplot.theme_gray()
+ ggplot.scale_colour_manual(values=graph_conf["color_list"])
)
plot.rcParams["font.family"] = "monospace"
if graph_conf["x_breaks"] and graph_conf["x_labels"]:
plot += ggplot.scale_x_discrete(breaks=graph_conf["x_breaks"], labels=graph_conf["x_labels"])
if graph_conf["limits"]:
plot += ggplot.ylim(graph_conf["limits"][0], graph_conf["limits"][1])
if graph_conf["graph_type"] == "bars":
plot += ggplot.geom_line(size=20)
text_data = plot_data[plot_data["Count"] > 0]
text_data.index = range(0, len(text_data))
outcomes = dict(text_data[graph_conf["xlab"]])
percents = dict(text_data[graph_conf["ylab"]])
for k in outcomes:
percent = "%4.1f%%" % percents[k]
x = outcomes[k]
y = percents[k] + 4
color = graph_conf["color_list"][k]
plot += ggplot.geom_text(label=[percent], x=[x, x + 1], y=[y, y - 1], color=color)
else:
plot += ggplot.geom_line()
plot += ggplot.geom_point(alpha=0.3, size=50)
if hasattr(rulemod, "update_plot"):
plot = rulemod.update_plot(gkey, graph_conf, plot, plot_data)
if args.dumpsave:
filename = "/dev/null"
else:
filename = "%s%s.png" % (graph_conf["file_prefix"], graph_conf["file_suffix"])
ggplot.ggsave(filename, plot, format="png", dpi=300)
return 0
