def process_stock(stock_no, days):
"""处理单个stock"""
csv_file = "%s/convert_%s.csv" % (conf.HISTORY_CONVERTED_PATH, stock_no)
rows = common.load_csv(csv_file, fields)
rows = list(rows)
for i, row in enumerate(rows):
print rolling(rows, i)
def plot_timeseries(v):
figname = v.csv_file.replace('.csv', '.png')
if os.path.exists(figname):
return
fig2 = plt.figure(num=2)
plt.clf()
ax2 = fig2.add_subplot(1, 1, 1)
ts = load_csv(v.csv_file)
# convert units for easier reading of graphs
ts.index = convert_time_units_series(ts.index, years=True)
# ts.plot(ax=ax2, label=v.simulation_set)
l, = ax2.plot(ts.index, ts.values, label=v.simulation_set)
ax2.legend()
ax2.set_xlabel(ts.index.name)
ax2.set_ylabel(v.units)
ax2.set_title(name)
# add yearly mean as well
if o.yearly_mean:
yearly_mean = ts.rolling(12).mean()
l2, = ax2.plot(ts.index[::12],
yearly_mean[::12],
alpha=1,
linewidth=2,
color=l.get_color())
if o.png_timeseries:
fig2.savefig(figname, dpi=o.dpi)
def get_freqstats_per_scenario_per_test(sc_list, freq_metrics):
print "get_freqstats_per_scenario_per_test:Enter"
all_freqdist_data = collections.OrderedDict()
for ix, each_scenario in enumerate(sc_list):
print each_scenario
all_freqdist_data[each_scenario] = collections.OrderedDict()
for each_mifint_freq_str in CUSTOM_CROPPING_PARAMS_ALL[
each_scenario].keys():
all_freqdist_data[each_scenario][each_mifint_freq_str] = {}
DEVFREQ_MIFINT_PAIRS = [
int(s) if (("default" not in s) and ("test" not in s)) else s
for s in each_mifint_freq_str.split("-")
]
MIF_FREQ = DEVFREQ_MIFINT_PAIRS[0]
INT_FREQ = DEVFREQ_MIFINT_PAIRS[1]
DATA_DIR = BASE_DATA_DIR + each_scenario + "/"
cpugpu_csv_fname = DATA_DIR + "data_cpugpu-{0}-{1}.csv".format(
MIF_FREQ, INT_FREQ)
mem_csv_fname = DATA_DIR + "data_mem-{0}-{1}.csv".format(
MIF_FREQ, INT_FREQ)
(c, perfdata) = load_csv(mem_csv_fname, cpugpu_csv_fname)
for each_fmetric in freq_metrics:
freq_data_dict = {
'sum':
np.sum(perfdata[each_fmetric]),
'mode':
_counter_mode(perfdata[each_fmetric]),
'mean':
np.mean(perfdata[each_fmetric]),
'min':
np.min(perfdata[each_fmetric]),
'max':
np.max(perfdata[each_fmetric]),
'counter':
collections.Counter(perfdata[each_fmetric]),
'transitions':
np.count_nonzero(np.diff(perfdata[each_fmetric]))
}
all_freqdist_data[each_scenario][each_mifint_freq_str][
each_fmetric] = freq_data_dict
return all_freqdist_data
def compute_corrmatrix_all_scenarios(sc_list, met_list):
MIF_FREQ = "default"
INT_FREQ = "default"
all_sc_corr_mat = collections.OrderedDict()
for each_scenario in sc_list:
DATA_DIR = BASE_DATA_DIR + each_scenario + "/"
cpugpu_csv_fname = DATA_DIR + "data_cpugpu-{0}-{1}.csv".format(
MIF_FREQ, INT_FREQ)
mem_csv_fname = DATA_DIR + "data_mem-{0}-{1}.csv".format(
MIF_FREQ, INT_FREQ)
(count, perfdata) = load_csv(mem_csv_fname, cpugpu_csv_fname)
lbl = "{0}:mif-{1}:int-{2}".format(SCENARIO_ID, MIF_FREQ, INT_FREQ)
corr = compute_correlation_matrix(perfdata, met_list)[2]
all_sc_corr_mat[each_scenario] = corr
return all_sc_corr_mat
def plot_cpugpumem_dist_all_scenarios(sc_list, mif_freq, int_freq):
fig, axs = plt.subplots(5, 3, figsize=(8 * 1.2, 8 * 1.2), sharex=True)
axs = axs.ravel()
for ix, each_scenario in enumerate(sc_list):
DATA_DIR = BASE_DATA_DIR + each_scenario + "/"
cpugpu_csv_fname = DATA_DIR + "data_cpugpu-{0}-{1}.csv".format(
mif_freq, int_freq)
mem_csv_fname = DATA_DIR + "data_mem-{0}-{1}.csv".format(
mif_freq, int_freq)
(count, perfdata) = load_csv(mem_csv_fname, cpugpu_csv_fname)
cpu_util = perfdata['cpu_cost']
gpu_util = perfdata['gpu_cost']
mem_util = perfdata['mem_cost']
bus_util = perfdata['sat_cost']
y_data = [cpu_util, gpu_util, bus_util]
pos = np.arange(1, len(y_data) + 1)
xticklbls = [
'cpu_cost',
'gpu_cost',
#'mem_cost',
'bus_cost'
]
axs[ix].boxplot(y_data, positions=pos)
axs[ix].set_xticks(pos)
ymax = 80.0 if np.max([np.max(m) for m in y_data]) < 80.0 else np.max(
[np.max(m) for m in y_data])
axs[ix].set_ylim([-0.5, ymax])
axs[ix].set_title(each_scenario)
axs[-1].set_xticklabels(xticklbls, rotation=35)
axs[-2].set_xticklabels(xticklbls, rotation=35)
axs[-3].set_xticklabels(xticklbls, rotation=35)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('csv_file', nargs='+')
parser.add_argument('--yearly-mean', action='store_true')
parser.add_argument('--png', help='file name for saving png figure')
o = parser.parse_args()
fig2 = plt.figure()
ax2 = plt.subplot(1, 1, 1)
# compare string to find label
records = [get_metadata(csv_file) for csv_file in o.csv_file]
keep_label = {
field: len(set(r[field] for r in records)) > 1
for field in records[0]
}
for csv_file in o.csv_file:
ts = load_csv(csv_file)
ts.index = convert_time_units_series(ts.index, years=True)
record = get_metadata(csv_file)
label = ', '.join(
str(value) for key, value in record.items() if keep_label.get(key))
# name = ts.columns[1]
path = Path(csv_file)
name = path.name
cname = ts.name
color = None
zorder = None
# units = re.match(r'', ts.columns[1])
try:
units, = re.match(r'.* \((.*)\)', cname).groups()
except:
logging.warning(f'failed to parse units: {cname}')
units = ''
l, = ax2.plot(ts.index,
ts.values,
alpha=0.5,
label=label,
linewidth=1 if o.yearly_mean else 2,
color=color,
zorder=zorder)
# add yearly mean as well
if o.yearly_mean:
yearly_mean = ts.rolling(12).mean()
l2, = ax2.plot(ts.index[::12],
yearly_mean[::12],
alpha=1,
linewidth=2,
color=l.get_color(),
zorder=zorder)
ax2.legend(fontsize='xx-small')
ax2.set_ylabel(units)
ax2.set_xlabel(ts.index.name)
ax2.set_title(name)
# ax2.set_xlim(xmin=start_year, xmax=2100)
mi, ma = ax2.get_xlim()
if mi < 0:
ax2.set_xlim(xmin=0) # start at start_year (i.e. ERA5 start)
if o.png:
figname = o.png
fig2.savefig(figname, dpi=300)
else:
plt.show()
def plot_freq_time_in_state(sc_list,
metric,
TMP_MIF_FREQ="default",
TMP_INT_FREQ="default"):
f, axarr = plt.subplots(2, 8, sharex=True, sharey=True, figsize=(16, 10))
f.canvas.set_window_title('plot_time_in_state -' + metric)
axarr = axarr.ravel()
# get colors and freq list
(freq_list, colsd) = get_allfreq_list(metric)
for ix, each_scenario in enumerate(sc_list):
print each_scenario
DATA_DIR = BASE_DATA_DIR + each_scenario + "/"
cpugpu_csv_fname = DATA_DIR + "data_cpugpu-{0}-{1}.csv".format(
TMP_MIF_FREQ, TMP_INT_FREQ)
mem_csv_fname = DATA_DIR + "data_mem-{0}-{1}.csv".format(
TMP_MIF_FREQ, TMP_INT_FREQ)
try:
axarr[ix].set_title(each_scenario, fontsize=12)
(count, perfdata) = load_csv(mem_csv_fname, cpugpu_csv_fname)
freq_data = perfdata[metric]
freq_data_dict = collections.OrderedDict()
for each_freq in freq_list:
fcount = freq_data.count(each_freq)
if fcount > 0:
freq_data_dict[each_freq] = fcount
print(json.dumps(freq_data_dict, indent=4))
cols = [colsd[f] for f in freq_data_dict.keys()]
pdata = freq_data_dict.values()
lbls = [str(k) for k in freq_data_dict.keys()]
wedges, plt_labels, junk = axarr[ix].pie(pdata,
autopct=my_autopct,
colors=cols)
for t in plt_labels:
t.set_fontsize(10)
except:
continue
axarr[ix].set_title(each_scenario, fontsize=12)
rects_list = []
rect_lbl_list = []
for ix, each_f in enumerate(freq_list):
rec = patches.Rectangle((0.72, 0.1), 0.2, 0.6, facecolor=colsd[each_f])
rects_list.append(rec)
rect_lbl_list.append(str(each_f))
nlines = len(freq_list)
ncol = int(np.ceil(nlines / 2.))
leg = plt.figlegend(rects_list,
rect_lbl_list,
loc='upper center',
ncol=ncol,
labelspacing=0.,
fontsize=14)
leg.draggable()
plt.subplots_adjust(hspace=0.09,
wspace=0.00,
top=0.87,
bottom=0.0,
left=0.0,
right=1.00)
def get_metric_slope_analysis(sc_list):
print "get_freqstats_per_scenario_per_test:Enter"
all_freqdist_data = {}
for ix, each_scenario in enumerate(sc_list):
print each_scenario
all_freqdist_data[each_scenario] = collections.OrderedDict()
DEVFREQ_MIFINT_PAIRS = ["default", "default"]
MIF_FREQ = DEVFREQ_MIFINT_PAIRS[0]
INT_FREQ = DEVFREQ_MIFINT_PAIRS[1]
DATA_DIR = BASE_DATA_DIR + each_scenario + "/"
cpugpu_csv_fname = DATA_DIR + "data_cpugpu-{0}-{1}.csv".format(
MIF_FREQ, INT_FREQ)
mem_csv_fname = DATA_DIR + "data_mem-{0}-{1}.csv".format(
MIF_FREQ, INT_FREQ)
(c, perfdata) = load_csv(mem_csv_fname, cpugpu_csv_fname)
interested_metrics = [
'cpu_util', 'sat_total', 'bus_mif_freq', 'bus_int_freq', 'cpu_freq'
]
# get slope for interested metrics
tmp_dict = {} # 1 or -1 or 0 per tick
for each_metric in interested_metrics:
tmp_dict[each_metric] = []
diff = np.diff(perfdata[each_metric])
# decrease precision
if (each_metric == "cpu_util") or (each_metric == "sat_total"):
diff = [x if abs(x) > 5.0 else 0.0 for x in diff]
else:
pass
for x in diff:
if x == 0: # no change
tmp_dict[each_metric].append(0)
elif x > 0: # positive
tmp_dict[each_metric].append(1)
elif x < 0: # negative
tmp_dict[each_metric].append(-1)
else:
pass
# construct result [1 2 3 4]
samples = len(tmp_dict[interested_metrics[0]])
result = []
for i in xrange(samples):
if (tmp_dict['cpu_util'][i]
== 0) and (tmp_dict['sat_total'][i]
== -1) and (tmp_dict['bus_mif_freq'][i]
== -1) and (tmp_dict['cpu_freq'][i]
== 1):
result.append(1)
elif (tmp_dict['cpu_util'][i]
== -1) and (tmp_dict['sat_total'][i]
== -1) and (tmp_dict['bus_mif_freq'][i]
== -1) and (tmp_dict['cpu_freq'][i]
== 0):
result.append(2)
elif (tmp_dict['cpu_util'][i]
in [0, -1]) and (tmp_dict['bus_mif_freq'][i]
== -1) and (tmp_dict['cpu_freq'][i] == 0):
result.append(3)
elif (tmp_dict['cpu_util'][i]
in [0, -1]) and (tmp_dict['bus_mif_freq'][i]
== -1) and (tmp_dict['cpu_freq'][i] == 1):
result.append(4)
else:
result.append(0)
all_freqdist_data[each_scenario] = {
# 'trans_type1' : result.count(1),
# 'trans_type2' : result.count(2),
# 'trans_type3' : result.count(2),
# 'trans_type4' : result.count(2),
'trans_type1_perc': float(result.count(1)) / float(len(result)),
'trans_type2_perc': float(result.count(2)) / float(len(result)),
'trans_type3_perc': float(result.count(3)) / float(len(result)),
'trans_type4_perc': float(result.count(4)) / float(len(result)),
}
pprint.pprint(all_freqdist_data)
import pandas as pd
from sklearn import svm
import sklearn.preprocessing as pp
from common import load_csv, write_results_table
# Configuration
group_columns = ['LinkRef']
categorial_columns = []
meta_columns = ['JourneyLinkRef', 'JourneyRef', 'DateTime', 'LineDirectionLinkOrder', 'LinkName']
results = pd.DataFrame()
# Load and pre-process data
data = load_csv('data/4A_201701_Consistent.csv',
group_columns = group_columns,
categorial_columns = categorial_columns,
meta_columns = meta_columns,
n_lags = 20,
n_headways = 0)
for group, X, Y, meta in data:
# Split data into train and test
X_train, X_test = np.split(X, [int(.8*len(X))])
Y_train, Y_test = np.split(Y, [int(.8*len(Y))])
meta_train, meta_test = np.split(meta, [int(.8*len(meta))])
# Train
print('Train data set (size, features):', X_train.shape)
# Normalizing X and y:
def plot_all_simulations():
figname = os.path.join(o.output, loc_folder, asset_folder,
'all_' + name + '.png')
if os.path.exists(figname):
return
fig3 = plt.figure(num=3)
plt.clf()
ax3 = fig3.add_subplot(1, 1, 1)
for v in variables:
ts = load_csv(v.csv_file)
ts.index = convert_time_units_series(ts.index, years=True)
if isinstance(v.datasets[0], ERA5):
color = 'k'
zorder = 5
else:
color = None
zorder = None
# add yearly mean instead of monthly mean
if o.yearly_mean:
yearly_mean = ts.rolling(12).mean()
x = ts.index[::12]
y = yearly_mean[::12]
else:
x = ts.index
y = ts.values
l, = ax3.plot(x,
y,
alpha=0.5 if o.ensemble else 1,
label=v.simulation_set,
linewidth=1 if o.ensemble else 2,
color=color,
zorder=zorder)
# Add ensemble mean
if o.ensemble:
for experiment in ensemble_files:
df = load_csv(ensemble_files[experiment])
df.index = convert_time_units_series(df.index,
years=True)
if o.yearly_mean:
yearly_mean = df.rolling(12).mean()
x = df.index[::12]
y = yearly_mean.iloc[::12]
else:
x = df.index
y = df
l, = ax3.plot(x,
y["median"],
alpha=1,
label=f"{experiment} (median)",
linewidth=2,
zorder=4)
ax3.plot(x,
y["lower"],
linewidth=1,
zorder=4,
linestyle="--",
color=l.get_color())
ax3.plot(x,
y["upper"],
linewidth=1,
zorder=4,
linestyle="--",
color=l.get_color())
ax3.fill_between(x,
y["lower"],
y["upper"],
alpha=0.2,
zorder=-1,
color=l.get_color())
ax3.legend(fontsize='xx-small')
ax3.set_ylabel(v.units)
ax3.set_xlabel(ts.index.name)
ax3.set_title(name)
# ax3.set_xlim(xmin=start_year, xmax=2100)
mi, ma = ax3.get_xlim()
if mi < 0:
ax3.set_xlim(
xmin=0) # start at start_year (i.e. ERA5 start)
if o.png_timeseries:
fig3.savefig(figname, dpi=max(o.dpi, 300))
def main():
import argparse
locations = yaml.safe_load(open('locations.yml'))
variables_def = yaml.safe_load(open('indicators.yml'))
assets = yaml.safe_load(open('assets.yml'))
cmip6_yml = yaml.safe_load(open('cmip6.yml'))
parser = argparse.ArgumentParser()
parser.add_argument(
"--max-workers",
type=int,
default=4,
help=
"Number of parallel threads for data download. Hint: use `--max-workers 1` for serial downlaod."
)
# g = parser.add_argument_group('variables or asset')
g = parser.add_mutually_exclusive_group(required=True)
# g.add_argument('--era5', nargs='*', help='list of ERA5-monthly variables to download (original name, no correction)')
# g.add_argument('--cmip6', nargs='*', help='list of CMIP6-monthly variables to download')
g.add_argument('--indicators',
nargs='*',
default=[],
choices=[vdef['name'] for vdef in variables_def],
help='list of custom indicators to download')
g.add_argument(
'--asset',
choices=list(assets.keys()),
help=
'pre-defined list of variables, defined in assets.yml (experimental)')
parser.add_argument(
'--dataset',
choices=['era5', 'cmip6'],
help='dataset in combination with for `--indicators` and `--asset`')
parser.add_argument('-o',
'--output',
default='indicators',
help='output directory, default: %(default)s')
parser.add_argument('--overwrite',
action='store_true',
help=argparse.SUPPRESS)
g = parser.add_argument_group('location')
g.add_argument('--location',
choices=[loc['name'] for loc in locations],
help='location name defined in locations.yml')
g.add_argument('--lon', type=float)
g.add_argument('--lat', type=float)
g = parser.add_argument_group('area size controls')
g.add_argument(
'--area',
nargs=4,
type=float,
help='area as four numbers: top, left, bottom, right (CDS convention)')
g.add_argument(
'--width-km',
type=float,
default=1000,
help=
"Width (km) around the selected location, when not provided by `area`. %(default)s km by default."
)
g.add_argument(
'--view',
nargs=4,
type=float,
help=
'area for plot as four numbers: top, left, bottom, right (CDS convention)'
)
g = parser.add_argument_group('ERA5 control')
# g.add_argument('--year', nargs='+', default=list(range(1979, 2019+1)), help='ERA5 years to download, default: %(default)s')
g.add_argument('--year',
nargs='+',
default=list(range(1979, 2019 + 1)),
help=argparse.SUPPRESS)
g = parser.add_argument_group('CMIP6 control')
g.add_argument('--model',
nargs='*',
default=None,
choices=get_all_models())
g.add_argument('--experiment',
nargs='*',
choices=cmip6_yml["experiments"],
default=['ssp5_8_5'])
# g.add_argument('--period', default=None, help=argparse.SUPPRESS) # all CMIP6 models and future experiements share the same parameter...
# g.add_argument('--historical', action='store_true', help='this flag provokes downloading historical data as well and extend back the CMIP6 timeseries to 1979')
g.add_argument('--historical',
action='store_true',
default=True,
help=argparse.SUPPRESS)
g.add_argument('--no-historical',
action='store_false',
dest='historical',
help=argparse.SUPPRESS)
# g.add_argument('--bias-correction', action='store_true', help='align CMIP6 variables with matching ERA5')
g.add_argument('--bias-correction',
action='store_true',
default=True,
help=argparse.SUPPRESS)
g.add_argument('--no-bias-correction',
action='store_false',
dest='bias_correction',
help='suppress bias-correction for CMIP6 data')
g.add_argument(
'--reference-period',
default=[1979, 2019],
nargs=2,
type=int,
help='reference period for bias correction (default: %(default)s)')
g.add_argument('--yearly-bias',
action='store_true',
help='yearly instead of monthly bias correction')
g.add_argument(
'--ensemble',
action='store_true',
help=
'If `--model` is not specified, default to all available models. Also write a csv file with all models as columns, as well as median, lower and upper (5th and 95th percentiles) fields.'
)
g = parser.add_argument_group('visualization')
g.add_argument('--view-region', action='store_true')
g.add_argument('--view-timeseries', action='store_true')
g.add_argument('--png-region', action='store_true')
g.add_argument('--png-timeseries', action='store_true')
g.add_argument('--dpi',
default=100,
type=int,
help='dop-per-inches (default: %(default)s)')
g.add_argument('--yearly-mean', action='store_true')
o = parser.parse_args()
if not (o.location or (o.lon and o.lat)):
parser.error(
'please provide a location, for instance `--location Welkenraedt`, or use custom lon and lat, e.g. `--lon 5.94 --lat 50.67`'
)
elif o.location:
loc = {loc['name']: loc for loc in locations}[o.location]
o.lon, o.lat = loc['lon'], loc['lat']
if 'area' in loc and not o.area:
o.area = loc['area']
if not o.area:
o.area = make_area(o.lon, o.lat, o.width_km)
print('lon', o.lon)
print('lat', o.lat)
if not o.asset and not o.indicators:
parser.error(
'please provide indicators, for example: `--indicators 2m_temperature` or asset, e.g. `--asset energy`'
)
# assets only contain indicators
if o.asset:
for vname in assets[o.asset]:
if vname not in [v['name'] for v in variables_def]:
parser.error(
f'unknown indicator in assets.yml: {vname}. See indicators.yml for indicator definition'
)
o.indicators.append(vname)
# folder structure for CSV results
loc_folder = o.location.lower() if o.location else f'{o.lat}N-{o.lon}E'
asset_folder = o.asset if o.asset else 'all'
if o.model is None:
if o.ensemble:
o.model = get_all_models()
else:
o.model = 'mpi_esm1_2_lr'
# loop over indicators
vdef_by_name = {v['name']: v for v in variables_def}
for name in o.indicators:
variables = [] # each variable for the simulation set
vdef = vdef_by_name[name]
indicator_def = dict(name=name,
units=vdef.get('units'),
description=vdef.get('description'),
scale=vdef.get('scale', 1),
offset=vdef.get('offset', 0))
vdef2 = vdef.get('era5', {})
era5_kwargs = dict(area=o.area, year=o.year)
era5 = parse_indicator(ERA5,
defs=vdef2,
cls_kwargs=era5_kwargs,
**indicator_def)
era5.simulation_set = 'ERA5'
era5.set_folder = 'era5'
era5.alias = name
if not o.dataset or o.dataset == 'era5' or o.bias_correction:
variables.append(era5)
vdef2 = vdef.get('cmip6', {})
transform = Transform(vdef2.get('scale', 1), vdef2.get('offset', 0))
if not o.dataset or o.dataset == 'cmip6':
for model in o.model:
labels = {
x: "{}-{}.{}".format(*x.split("_"))
for x in cmip6_yml["experiments"]
}
# if o.historical:
# historical_kwargs = dict(model=model, experiment='historical')
# historical = parse_indicator(CMIP6, defs=vdef2, cls_kwargs=historical_kwargs, **indicator_def)
# else:
# historical = None
for experiment in o.experiment:
cmip6_kwargs = dict(model=model,
experiment=experiment,
historical=o.historical,
area=o.area)
cmip6 = parse_indicator(CMIP6,
defs=vdef2,
cls_kwargs=cmip6_kwargs,
**indicator_def)
cmip6.reference = era5
cmip6.simulation_set = f'CMIP6 - {labels.get(experiment, experiment)} - {model}'
cmip6.set_folder = f'cmip6-{model}-{experiment}'
cmip6.alias = name
# print("indicator variable", experiment, [d.name for d in cmip6.datasets])
variables.append(cmip6)
if not variables:
logging.warning(f'no variable for {name}')
continue
if o.max_workers < 2:
variables2 = download_all_variables_serial(variables)
else:
variables2 = download_all_variables(variables)
# Diagnose which variables have been excluded
names = list(set([v.name for v in variables]))
names2 = list(set([v.name for v in variables2]))
models = list(
set([
v.datasets[0].model for v in variables
if isinstance(v.datasets[0], CMIP6)
]))
models2 = list(
set([
v.datasets[0].model for v in variables2
if isinstance(v.datasets[0], CMIP6)
]))
print(f"Downloaded {len(variables2)} out of {len(variables)}")
print(f"... {len(names2)} out of {len(names)} variable types")
print(f"... {len(models2)} out of {len(models)} models")
print("CMIP6 models excluded:",
" ".join([m for m in models if m not in models2]))
print("CMIP6 models included:", " ".join(models2))
variables = variables2
# download and convert to csv
for v in variables:
folder = os.path.join(o.output, loc_folder, asset_folder,
v.set_folder)
v.csv_file = os.path.join(folder, (v.alias or v.variable) + '.csv')
if os.path.exists(v.csv_file):
print("Already exitst:", v.csv_file)
continue
series = v.load_timeseries(o.lon, o.lat, overwrite=o.overwrite)
bias_correction_method = vdef.get('bias-correction')
if o.bias_correction and isinstance(
v.datasets[0],
CMIP6) and bias_correction_method is not None:
era5 = v.reference.load_timeseries(o.lon, o.lat)
#v.set_folder += '-unbiased'
if o.yearly_bias:
series = correct_yearly_bias(series, era5,
o.reference_period,
bias_correction_method)
else:
series = correct_monthly_bias(series, era5,
o.reference_period,
bias_correction_method)
os.makedirs(folder, exist_ok=True)
print("Save to", v.csv_file)
save_csv(series, v.csv_file)
if o.ensemble:
ensemble_files = {}
import cftime, datetime
for experiment in o.experiment:
ensemble_variables = [
v for v in variables if isinstance(v.datasets[0], CMIP6)
and v.datasets[0].experiment == experiment
]
dates = np.array([
cftime.DatetimeGregorian(y, m, 15)
for y in range(1979, 2100 + 1) for m in range(1, 12 + 1)
])
index = pd.Index(cftime.date2num(dates, time_units),
name=time_units)
df = {}
for v in ensemble_variables:
series = load_csv(v.csv_file)
series.index = index[:len(series)]
df[v.datasets[0].model] = series
df = pd.DataFrame(df)
median = df.median(axis=1)
lower = df.quantile(.05, axis=1)
upper = df.quantile(.95, axis=1)
df["median"] = median
df["lower"] = lower
df["upper"] = upper
first = ensemble_variables[0]
folder = os.path.join(
o.output, loc_folder, asset_folder,
first.set_folder.replace(first.datasets[0].model,
"ensemble"))
csv_file = os.path.join(folder, first.alias
or first.name) + '.csv'
ensemble_files[experiment] = csv_file
os.makedirs(folder, exist_ok=True)
print("Save to", csv_file)
save_csv(df, csv_file)
if o.view_region or o.view_timeseries or o.png_region or o.png_timeseries:
import matplotlib.pyplot as plt
cb = None
try:
import cartopy
import cartopy.crs as ccrs
kwargs = dict(projection=ccrs.PlateCarree())
except ImportError:
logging.warning('install cartopy to benefit from coastlines')
cartopy = None
kwargs = {}
if o.view is None:
o.view = o.area
def plot_timeseries(v):
figname = v.csv_file.replace('.csv', '.png')
if os.path.exists(figname):
return
fig2 = plt.figure(num=2)
plt.clf()
ax2 = fig2.add_subplot(1, 1, 1)
ts = load_csv(v.csv_file)
# convert units for easier reading of graphs
ts.index = convert_time_units_series(ts.index, years=True)
# ts.plot(ax=ax2, label=v.simulation_set)
l, = ax2.plot(ts.index, ts.values, label=v.simulation_set)
ax2.legend()
ax2.set_xlabel(ts.index.name)
ax2.set_ylabel(v.units)
ax2.set_title(name)
# add yearly mean as well
if o.yearly_mean:
yearly_mean = ts.rolling(12).mean()
l2, = ax2.plot(ts.index[::12],
yearly_mean[::12],
alpha=1,
linewidth=2,
color=l.get_color())
if o.png_timeseries:
fig2.savefig(figname, dpi=o.dpi)
def plot_region(v):
v0 = v.datasets[0]
figname = v.csv_file.replace('.csv', '-region.png')
if os.path.exists(figname):
return
fig1 = plt.figure(num=1)
plt.clf()
ax1 = fig1.add_subplot(1, 1, 1, **kwargs)
if isinstance(v.datasets[0], ERA5):
y1, y2 = o.reference_period
roll = False
title = f'ERA5: {y1}-{y2}'
else:
y1, y2 = 2071, 2100
roll = True if o.view[1] < 0 else False
title = f'{labels.get(v0.experiment, v0.experiment)} ({v0.model}): {y1}-{y2}'
refslice = slice(str(y1), str(y2))
map = v.load_cube(time=refslice, area=o.view,
roll=roll).mean(dim='time')
h = ax1.imshow(map.values[::-1],
extent=cube_area(map, extent=True))
cb = plt.colorbar(h, ax=ax1, label=f'{name} ({v.units})')
# h = map.plot(ax=ax1, cbar_kwargs={'label':f'{v.units}'}, robust=True)
ax1.set_title(title)
ax1.plot(o.lon, o.lat, 'ko')
if cartopy:
ax1.coastlines(resolution='10m')
if o.png_region:
fig1.savefig(figname, dpi=o.dpi)
for v in variables:
if o.view_timeseries or o.png_timeseries:
plot_timeseries(v)
if o.view_region or o.png_region:
try:
plot_region(v)
except:
logging.warning(f'failed to make map for {v.name}')
# all simulation sets on one figure
def plot_all_simulations():
figname = os.path.join(o.output, loc_folder, asset_folder,
'all_' + name + '.png')
if os.path.exists(figname):
return
fig3 = plt.figure(num=3)
plt.clf()
ax3 = fig3.add_subplot(1, 1, 1)
for v in variables:
ts = load_csv(v.csv_file)
ts.index = convert_time_units_series(ts.index, years=True)
if isinstance(v.datasets[0], ERA5):
color = 'k'
zorder = 5
else:
color = None
zorder = None
# add yearly mean instead of monthly mean
if o.yearly_mean:
yearly_mean = ts.rolling(12).mean()
x = ts.index[::12]
y = yearly_mean[::12]
else:
x = ts.index
y = ts.values
l, = ax3.plot(x,
y,
alpha=0.5 if o.ensemble else 1,
label=v.simulation_set,
linewidth=1 if o.ensemble else 2,
color=color,
zorder=zorder)
# Add ensemble mean
if o.ensemble:
for experiment in ensemble_files:
df = load_csv(ensemble_files[experiment])
df.index = convert_time_units_series(df.index,
years=True)
if o.yearly_mean:
yearly_mean = df.rolling(12).mean()
x = df.index[::12]
y = yearly_mean.iloc[::12]
else:
x = df.index
y = df
l, = ax3.plot(x,
y["median"],
alpha=1,
label=f"{experiment} (median)",
linewidth=2,
zorder=4)
ax3.plot(x,
y["lower"],
linewidth=1,
zorder=4,
linestyle="--",
color=l.get_color())
ax3.plot(x,
y["upper"],
linewidth=1,
zorder=4,
linestyle="--",
color=l.get_color())
ax3.fill_between(x,
y["lower"],
y["upper"],
alpha=0.2,
zorder=-1,
color=l.get_color())
ax3.legend(fontsize='xx-small')
ax3.set_ylabel(v.units)
ax3.set_xlabel(ts.index.name)
ax3.set_title(name)
# ax3.set_xlim(xmin=start_year, xmax=2100)
mi, ma = ax3.get_xlim()
if mi < 0:
ax3.set_xlim(
xmin=0) # start at start_year (i.e. ERA5 start)
if o.png_timeseries:
fig3.savefig(figname, dpi=max(o.dpi, 300))
if o.view_timeseries or o.png_timeseries:
plot_all_simulations()
if o.view_timeseries or o.view_region:
plt.show()
def plot_cpugpumem_dist_all_scenarios(sc_list, mif_freq, int_freq, nrows=2):
cmap = plt.get_cmap('rainbow')
colsd = [cmap(i) for i in np.linspace(0, 1, 6)]
colsd = [
'#08519c',
'#6baed6', # blues
#'#a50f15', '#fb6a4a', # reds
'#006d2c',
'#74c476', # greens
]
nrows = nrows
fig, axs = plt.subplots(nrows,
int(np.ceil(len(sc_list) / float(nrows))),
figsize=(12 * 1.2, 5 * 1.2),
sharex=True,
sharey=True)
axs = axs.ravel()
xticklbls = [
'cpu_util',
'cpu_cost',
#'gpu_util', 'gpu_cost',
'mem_util',
'mem_cost'
]
for ix, each_scenario in enumerate(sc_list):
print each_scenario
DATA_DIR = BASE_DATA_DIR + each_scenario + "/"
cpugpu_csv_fname = DATA_DIR + "data_cpugpu-{0}-{1}.csv".format(
mif_freq, int_freq)
mem_csv_fname = DATA_DIR + "data_mem-{0}-{1}.csv".format(
mif_freq, int_freq)
(count, perfdata) = load_csv(mem_csv_fname, cpugpu_csv_fname)
cpu_util = perfdata['cpu_util']
gpu_util = perfdata['gpu_util']
mem_util = perfdata['sat_total']
cpu_cost = perfdata['cpu_cost']
gpu_cost = perfdata['gpu_cost']
mem_cost = perfdata['sat_cost']
y_data = [
cpu_util,
cpu_cost,
#gpu_util, gpu_cost,
mem_util,
mem_cost
]
pos = np.arange(1, len(y_data) + 1)
bp = axs[ix].boxplot(y_data,
positions=pos,
patch_artist=True,
widths=0.7)
# change col of boxes
for box, c in zip(bp['boxes'], colsd):
box.set(facecolor=c) # change fill color
# change outline color
box.set(color='#000000', linewidth=1)
# change fill color
box.set(facecolor=c)
i += 1
## change color and linewidth of the whiskers
for whisker in bp['whiskers']:
whisker.set(color='#000000', linewidth=1, linestyle='-')
## change color and linewidth of the caps
for cap in bp['caps']:
cap.set(color='#000000', linewidth=1)
## change color and linewidth of the medians
for median in bp['medians']:
median.set(color='#000000', linewidth=1)
## change the style of fliers and their fill
for flier, c in zip(bp['fliers'], colsd):
flier.set(marker='x', color=c)
axs[ix].set_xticks(pos)
ymax = 100.0 if np.max([np.max(m)
for m in y_data]) < 100.0 else np.max(
[np.max(m) for m in y_data])
axs[ix].set_ylim([-0.5, ymax])
axs[ix].set_title(each_scenario, fontsize=14)
axs[ix].tick_params(axis='y', labelsize=14)
axs[ix].xaxis.grid(False)
axs[ix].yaxis.grid(True)
for ix in np.arange(1, int(len(sc_list) / float(nrows)) + 1):
#axs[-1*ix].set_xticklabels(xticklbls, rotation=35, fontsize=12)
axs[-1 * ix].set_xticklabels([])
#axs[8].set_ylim([0,150])
# legend
rect_lbl_list = xticklbls
cols = colsd
rects_list = []
for ix, each_rect in enumerate(rect_lbl_list):
rec = patches.Rectangle((0.72, 0.1), 0.2, 0.6, facecolor=cols[ix])
rects_list.append(rec)
leg = plt.figlegend(rects_list,
rect_lbl_list,
loc='upper center',
ncol=len(rects_list) / 2,
labelspacing=0.,
fontsize=14,
frameon=False)
leg.get_frame().set_facecolor('#FFFFFF')
leg.get_frame().set_linewidth(0.0)
leg.draggable()
plt.subplots_adjust(top=0.88,
left=0.025,
right=0.995,
bottom=0.02,
wspace=0.25)
#################
# MAIN code
#################
SCENARIO_ID = "idle1"
DATA_DIR = BASE_DATA_DIR + SCENARIO_ID + "/"
MIF_FREQ = "default"
INT_FREQ = "default"
#MIF_FREQ = 800000
#INT_FREQ = 800000
cpugpu_csv_fname = DATA_DIR + "data_cpugpu-{0}-{1}.csv".format(
MIF_FREQ, INT_FREQ)
mem_csv_fname = DATA_DIR + "data_mem-{0}-{1}.csv".format(MIF_FREQ, INT_FREQ)
(count, perfdata) = load_csv(mem_csv_fname, cpugpu_csv_fname)
# plot_cross_correlation(perfdata['bus_mif_freq'],
# perfdata['cpu_util_freq'])
# temp crop data
#perfdata = _crop_pefdata(perfdata, 586, -1)
lbl = "{0}:mif-{1}:int-{2}".format(SCENARIO_ID, MIF_FREQ, INT_FREQ)
#corr_matrix = compute_correlation_matrix(perfdata, reduced_target_metrics_order_nogpu)[1]
#plot_corr_matrix(corr_matrix, reduced_target_metrics_order_nogpu, SCENARIO_ID)
# plot_overlapped(perfdata, ['cpu_freq',
# 'bus_mif_freq', 'bus_int_freq',
# 'cpu_util', 'cpu_cost',
import matplotlib.pyplot as plt
from common import load_csv
import sys
x = []
y = []
c = ['r', 'g', 'b', 'c', 'm', 'y', 'k']
KLASS = len(c)
data = load_csv(sys.argv[1])
for i in xrange(0,KLASS):
x = [float(dat[4]) for dat in data if int(dat[0])%KLASS == i]
y = [float(dat[3]) for dat in data if int(dat[0])%KLASS == i]
plt.plot(x, y, c[i])
plt.show()
def plot_scatter_allsc_allfreq(scenario_list, scatter_plot=True, line_plot=True):
mean_mem_cost = []
mean_cpu_cost = []
mean_gpu_cost = []
mean_all_metrics = {}
CPU_METRIC = 'cpu_cost'
MEM_METRIC = 'sat_cost'
GPU_METRIC = 'gpu_cost'
for each_sc in scenario_list:
print each_sc
possible_freqs = CUSTOM_CROPPING_PARAMS_ALL[each_sc].keys()
tmp_mem = []
tmp_cpu = []
tmp_gpu = []
tmp_fix = []
for fix, each_f in enumerate(all_mifint_freqs_macroworkload):
#print possible_freqs
if each_f in possible_freqs:
#print each_sc, each_f
miffreq, intfreq = mif_int_freqstr_to_tuple(each_f)
# get sc data
DATA_DIR = BASE_DATA_DIR + each_sc + "/"
cpugpu_csv_fname = DATA_DIR + "data_cpugpu-{0}-{1}.csv".format(miffreq, intfreq)
mem_csv_fname = DATA_DIR + "data_mem-{0}-{1}.csv".format(miffreq, intfreq)
(count, perfdata) = load_csv(mem_csv_fname, cpugpu_csv_fname)
m = markers_and_cols_per_scenario[each_sc][0]
c = markers_and_cols_per_scenario[each_sc][1]
mean_cpu_cost.append([fix, np.mean(perfdata[CPU_METRIC]), m, c])
mean_gpu_cost.append([fix, np.mean(perfdata[GPU_METRIC]), m, c])
mean_mem_cost.append([fix, np.mean(perfdata[MEM_METRIC]), m, c])
tmp_cpu.append(np.mean(perfdata[CPU_METRIC]))
tmp_gpu.append(np.mean(perfdata[GPU_METRIC]))
tmp_mem.append(np.mean(perfdata[MEM_METRIC]))
tmp_fix.append(fix)
else:
pass
# populate dicts
mean_all_metrics[each_sc] = {
'cpu': tmp_cpu,
'gpu': tmp_gpu,
'mem': tmp_mem,
'fix' : tmp_fix,
'marker' : markers_and_cols_per_scenario[each_sc][0],
'col' : markers_and_cols_per_scenario[each_sc][1]
}
##### only scatters ####
if scatter_plot == True:
fig, axs = plt.subplots(1,3)
fig.canvas.set_window_title("plot_scatter_allsc_allfreq")
axs = axs.ravel()
axi = 0
titles = [MEM_METRIC.replace('sat','mem'), CPU_METRIC, GPU_METRIC]
for each_metric_data in [
mean_mem_cost, mean_cpu_cost,mean_gpu_cost
]:
x,y, m, c = zip(*each_metric_data)
for i, each_x in enumerate(x):
axs[axi].scatter([x[i]],[y[i]],marker=m[i], color=c[i], s=90, linewidth=3.5)
axs[axi].hold(True)
axs[axi].set_title(titles[axi])
axs[axi].xaxis.grid(False)
axs[axi].yaxis.grid(False)
axi+=1
# legend
rect_lbl_list = scenario_list
cols = [markers_and_cols_per_scenario[s][1] for s in scenario_list]
markers = [markers_and_cols_per_scenario[s][0] for s in scenario_list]
artist_list = []
for ix, each_rect in enumerate(rect_lbl_list):
a = plt.Line2D((0,1),(0,0), color=cols[ix], marker=markers[ix], linestyle='', mew=1.5, ms=13, mec=cols[ix])
artist_list.append(a)
leg = plt.figlegend( artist_list, rect_lbl_list, loc = 'upper center',
ncol=len(artist_list)/4, labelspacing=0. , fontsize=13,
frameon=False, numpoints=1)
leg.get_frame().set_facecolor('#FFFFFF')
leg.get_frame().set_linewidth(0.0)
leg.draggable()
##### line and scatters ####
if line_plot == True:
fsize=14
titles = [CPU_METRIC, GPU_METRIC]
metrics = ['cpu', 'gpu']
fig, axs = plt.subplots(1,len(metrics), figsize=(12.1, 5.5))
fig.canvas.set_window_title("plot_line_allsc_allfreq")
axs = axs.ravel()
#titles = [MEM_METRIC.replace('sat','mem'), CPU_METRIC, GPU_METRIC]
#metrics = ['mem', 'cpu', 'gpu']
for axi, each_met in enumerate(metrics):
ax_ymax = 0
for each_sc_k, each_sc_data in mean_all_metrics.iteritems():
m = each_sc_data['marker']
c = each_sc_data['col']
x = each_sc_data['fix']
y = each_sc_data[each_met]
if np.max(y) > ax_ymax: ax_ymax=np.max(y)
print x
print y
print "---"
axs[axi].plot(x,y,marker=m, color=c, linewidth=2, linestyle='', mew=2, ms=10, markeredgecolor=c)
axs[axi].hold(True)
axs[axi].plot(x,y, color=c, linewidth=1, linestyle='-', alpha=0.2)
axs[axi].hold(True)
axs[axi].set_xlim([-1, len(all_mifint_freqs_macroworkload)])
axs[axi].set_ylim([-2, ax_ymax*1.05])
axs[axi].set_title(titles[axi], fontsize=fsize)
axs[axi].xaxis.grid(False)
axs[axi].yaxis.grid(False)
axs[axi].set_xticks(np.arange(0,len(all_mifint_freqs_macroworkload)))
axs[axi].set_xticklabels([f.replace('000','').replace('default-','') for f in all_mifint_freqs_macroworkload], rotation=45, fontsize=fsize)
axs[axi].tick_params(axis='both', labelsize=fsize)
# legend
rect_lbl_list = scenario_list
cols = [markers_and_cols_per_scenario[s][1] for s in scenario_list]
markers = [markers_and_cols_per_scenario[s][0] for s in scenario_list]
artist_list = []
for ix, each_rect in enumerate(rect_lbl_list):
a = plt.Line2D((0,1),(0,0), color=cols[ix], marker=markers[ix], linestyle='', mew=1.5, ms=13, mec=cols[ix])
artist_list.append(a)
leg = plt.figlegend( artist_list, rect_lbl_list, loc = 'upper center',
ncol=len(artist_list)/4, labelspacing=0. , fontsize=fsize,
frameon=False, numpoints=1, columnspacing=1.0)
leg.get_frame().set_facecolor('#FFFFFF')
leg.get_frame().set_linewidth(0.0)
leg.draggable()
plt.subplots_adjust(top=0.83, left=0.03, right=0.99, bottom=0.15, wspace=0.14, hspace=0.18)
