def _tabulate(self, tablefmt="simple", rollups=False, rows=10):
"""Pretty tabulated string of all the cached data, and column names"""
if not self.is_valid(): self.fill(rows=rows)
# Pretty print cached data
d = collections.OrderedDict()
# If also printing the rollup stats, build a full row-header
if rollups:
col = next(iter(viewvalues(self._data))) # Get a sample column
lrows = len(col['data']) # Cached rows being displayed
d[""] = ["type", "mins", "mean", "maxs", "sigma", "zeros", "missing"] + list(map(str, range(lrows)))
# For all columns...
for k, v in viewitems(self._data):
x = v['data'] # Data to display
t = v["type"] # Column type
if t == "enum":
domain = v['domain'] # Map to cat strings as needed
x = ["" if math.isnan(idx) else domain[int(idx)] for idx in x]
elif t == "time":
x = ["" if math.isnan(z) else time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime(z / 1000)) for z in x]
if rollups: # Rollups, if requested
mins = v['mins'][0] if v['mins'] and v["type"] != "enum" else None
maxs = v['maxs'][0] if v['maxs'] and v["type"] != "enum" else None
#Cross check type with mean and sigma. Set to None if of type enum.
if v['type'] == "enum":
v['mean'] = v['sigma'] = v['zero_count'] = None
x = [v['type'], mins, v['mean'], maxs, v['sigma'], v['zero_count'], v['missing_count']] + x
d[k] = x # Insert into ordered-dict
return tabulate.tabulate(d, headers="keys", tablefmt=tablefmt)
def _tabulate(self, tablefmt="simple", rollups=False):
"""Pretty tabulated string of all the cached data, and column names"""
if not self.is_valid(): self.fill()
# Pretty print cached data
d = collections.OrderedDict()
# If also printing the rollup stats, build a full row-header
if rollups:
col = next(iter(viewvalues(self._data))) # Get a sample column
lrows = len(col['data']) # Cached rows being displayed
d[""] = ["type", "mins", "mean", "maxs", "sigma", "zeros", "missing"] + list(map(str, range(lrows)))
# For all columns...
for k, v in viewitems(self._data):
x = v['data'] # Data to display
t = v["type"] # Column type
if t == "enum":
domain = v['domain'] # Map to cat strings as needed
x = ["" if math.isnan(idx) else domain[int(idx)] for idx in x]
elif t == "time":
x = ["" if math.isnan(z) else time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime(z / 1000)) for z in x]
if rollups: # Rollups, if requested
mins = v['mins'][0] if v['mins'] else None
maxs = v['maxs'][0] if v['maxs'] else None
x = [v['type'], mins, v['mean'], maxs, v['sigma'], v['zero_count'], v['missing_count']] + x
d[k] = x # Insert into ordered-dict
return tabulate.tabulate(d, headers="keys", tablefmt=tablefmt)
def assert_coltypes(frame, freal, fenum, fint, fbin, ftime, fstring):
# The server does not report columns as binary -- instead they are integer.
fint += fbin
fbin = 0
type_counts = defaultdict(int)
for ft in viewvalues(frame.types):
type_counts[ft] += 1
print("Created table with column counts: {%s}" % ", ".join("%s: %d" % t for t in type_counts.items()))
for ct in ["real", "enum", "int", "time", "string"]:
assert abs(type_counts[ct] - locals()["f" + ct] * frame.ncol) < 1, \
"Wrong column count of type %s: %d" % (ct, type_counts[ct])
def assert_coltypes(frame, freal, fenum, fint, fbin, ftime, fstring):
# The server does not report columns as binary -- instead they are integer.
fint += fbin
fbin = 0
type_counts = defaultdict(int)
for ft in viewvalues(frame.types):
type_counts[ft] += 1
print("Created table with column counts: {%s}" %
", ".join("%s: %d" % t for t in type_counts.items()))
for ct in ["real", "enum", "int", "time", "string"]:
assert abs(type_counts[ct] - locals()["f" + ct] * frame.ncol) < 1, \
"Wrong column count of type %s: %d" % (ct, type_counts[ct])
