def plot_matches(df_in,
date,
filename_out,
x_var='date_time',
y_var="shorthand_search_vol"):
"""
Plot y-var and save based on specified variables.
Assumes that df has already been filtered using dplyr's sift mechanism.
Also assumes that a date has been passed in.
"""
# basic data processing for viz
df_in['date_time'] = date + " " + df_in['time'].astype(str)
df_in['date_time'] = pd.to_datetime(df_in['date_time'],
errors="coerce",
infer_datetime_format=True)
# build layers for plot
p = ggplot(aes(x=x_var, y=y_var, group="match_id", color="match_id"),
data=df_in)
p += geom_line(size=2)
# informative
p += labs(x="time (gmt)", y="search volume (scaled to 100)")
# p += ggtitle("man. city (h) vs. chelsea (a)\naug. 8 '16, etihad stadium")
p += scale_x_date(labels=date_format("%H:%M:%S"), date_breaks="30 minutes")
# visual
t = theme_gray()
t._rcParams['font.size'] = 8
t._rcParams['font.family'] = 'monospace'
p += t
# done
p.save(filename_out, width=16, height=8)
def boxplot(self, conn, column, table_chosen, title):
data_df = dfile.single_selector(conn=conn,
table=table_chosen,
column=column)
box_plot = ggplot(
aes(x=column),
data=data_df) + geom_boxplot() + theme_gray() + labs(title=title)
now = datetime.datetime.now()
b = now
print(b)
print(b - a)
print(box_plot)
def line_chart(self, conn, column1, column2, table_chosen, title):
data_df = dfile.double_selector(conn=conn,
table=table_chosen,
col1=column1,
col2=column2)
line_plot = ggplot(
aes(y=column2, x=column1),
data=data_df) + geom_line() + theme_gray() + labs(title=title)
now = datetime.datetime.now()
b = now
print(b)
print(b - a)
print(line_plot)
def plot_predictions(date_times, actual_values, predictions, match_id,
feature_set_in, filename):
"""
Plot y-var and save based on specified variables.
Assumes that df has already been filtered using dplyr's sift mechanism.
Also assumes that a date has been passed in.
"""
actual_df = pd.DataFrame()
actual_df['date_time'] = pd.to_datetime(date_times,
errors="coerce",
infer_datetime_format=True)
actual_df['search_vol'] = actual_values
actual_df['match_id'] = "actual" + match_id
predict_df = pd.DataFrame()
predict_df['date_time'] = pd.to_datetime(date_times,
errors="coerce",
infer_datetime_format=True)
predict_df['search_vol'] = list(predictions)
predict_df['match_id'] = "predictedby_" + str(feature_set_in) + match_id
plotting_df = pd.concat([actual_df, predict_df], axis=0, ignore_index=True)
# build layers for plot
p = ggplot(aes(x='date_time',
y='search_vol',
group="match_id",
color="match_id"),
data=plotting_df)
p += geom_line(size=2)
# informative
p += labs(x="time (gmt)", y="search volume (scaled to 100)")
# p += ggtitle("man. city (h) vs. chelsea (a)\naug. 8 '16, etihad stadium")
p += scale_x_date(labels=date_format("%H:%M:%S"), date_breaks="30 minutes")
# visual
t = theme_gray()
t._rcParams['font.size'] = 8
t._rcParams['font.family'] = 'monospace'
p += t
# done
p.save(filename, width=16, height=8)
def area_chart(self, conn, column1, column2, table_chosen, title):
data_df = dfile.double_selector(conn=conn,
table=table_chosen,
col1=column1,
col2=column2)
ymin = float(
input("Enter the minimum value that should be plotted: "))
ymax = float(
input("Enter the maximum value that should be plotted: "))
area_plot = ggplot(
aes(x=column2, ymin=ymin, ymax=ymax),
data=data_df) + geom_area() + theme_gray() + labs(title=title)
now = datetime.datetime.now()
b = now
print(b)
print(b - a)
print(area_plot)
df = pd.melt(df)
df['feature'] = feature
dfs_to_concat.append(df)
master_df = pd.concat(dfs_to_concat)
# histogram
p = ggplot(aes(x='value', fill='variable', color='variable'), data=master_df)
p += geom_histogram(bins=25, alpha=0.5)
p += scale_x_continuous(limits=(-25, 25))
p += ggtitle("sarimax coefficient magnitude distribution")
p += facet_wrap("feature", ncol=3, scales="free")
p += labs(x=" ", y=" ")
# visuals
t = theme_gray()
t._rcParams['font.size'] = 10
t._rcParams['font.family'] = 'monospace'
p += t
p.save("arima_1/" + "histogram.png")
# boxplot
p = ggplot(aes(x='variable', y='value'), data=master_df)
p += geom_boxplot()
p += scale_y_continuous(limits=(-25, 25))
p += ggtitle("sarimax coefficient magnitudes")
p += facet_wrap("feature", ncol=3)
p += labs(x=" ", y=" ")
# visuals
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
def density_chart(self, conn, column, table_chosen, title): data_df = dfile.single_selector(conn=conn, table=table_chosen, column=column) density_plot = ggplot(aes(x=column), data=data_df) + geom_density() + theme_gray() + labs(title=title) print(density_plot)
def hist_chart(self, conn, column, table_chosen, title): data_df = dfile.single_selector(conn = conn, table = table_chosen, column = column) hist_plot = ggplot(aes(x=column), data=data_df) + geom_histogram() + theme_gray() + labs(title=title) print(hist_plot)
def point_chart(self, conn, column1, column2, table_chosen, title): data_df = dfile.double_selector(conn=conn, table=table_chosen, col1=column1, col2=column2) point_plot = ggplot(aes(x=column1, y=column2), data=data_df) + geom_point() + theme_gray() + labs(title=title) print(point_plot)