def test_no_reports(tmp_path):
from edalize.reporting import Reporting
rpt = Reporting.report(str(tmp_path))
assert rpt == {"summary": None, "resources": None, "timing": None}
def test_missing_reports(tmp_path):
from edalize.reporting import Reporting
(tmp_path / "top1_resource_report.txt").touch()
rpt = Reporting.report(str(tmp_path))
assert rpt == {"summary": None, "resources": None, "timing": None}
def test_missing_dir():
from edalize.reporting import Reporting
data_dir = Path(tests_dir + "/test_reporting/data/doesntexist")
rpt = Reporting.report(str(data_dir))
assert rpt == {"summary": None, "resources": None, "timing": None}
def report_summary(resources: Dict[str, pd.DataFrame], timing: Dict[str,
Any]):
util = resources["Utilization by Hierarchy"]
# Find a column beginning with DSP since we don't know if it's
# DSP48A1, DSP48E2, etc.
dsp_col = [c for c in util.columns if c.startswith("DSP")]
if len(dsp_col) != 1:
logger.error("Expected 1 column named DSP but found", len(dsp_col))
resource_buckets = {
"lut": "LUTs",
"reg": "Slice Reg",
"blkmem": "BRAM/FIFO",
"dsp": dsp_col[0],
}
# The basic resource data is ints, but the timing information is more
# complex
summary = {} # type: Dict[str, Any]
# Resources in this table are of the form 123/456 and we want the
# second (total) number
for k in resource_buckets.keys():
cell = util.iloc[0].at[resource_buckets[k]]
summary[k] = int(str(cell).split("/")[1])
summary["constraint"] = {}
summary["fmax"] = {}
# Report the constraint and Fmax value for each timegroup
for k, v in timing["constraint"].items():
summary["constraint"][k] = v["constraint"]
summary["fmax"][k] = Reporting.period_to_freq(v.get("min period"))
return summary
def test_period_to_freq():
from edalize.reporting import Reporting
assert Reporting.period_to_freq(10.0) == 100
assert round(Reporting.period_to_freq(125, "ps", "GHZ")) == 8
assert Reporting.period_to_freq(None) is None
assert Reporting.period_to_freq(float("nan")) is None
assert round(Reporting.period_to_freq("9", "ns"), 4) == round(1 / 9e-3, 4)
with pytest.raises(ValueError):
Reporting.period_to_freq(5, "MHz")
Reporting.period_to_freq("invalid")
Reporting.period_to_freq(5, "ps", "ns")
def report_summary(
resources: Dict[str, pd.DataFrame], timing: Dict[str, pd.DataFrame]
) -> Dict[str, Union[int, float, Dict[str, Optional[float]]]]:
summary = {
} # type: Dict[str, Union[int, float, Dict[str, Optional[float]]]]
# Vivado uses different tables and row values for different families.
# This at least works with the Artix 7 and Kintex Ultrascale+
if "Slice Logic" in resources:
table = "Slice Logic"
lut = "Slice LUTs"
reg = "Slice Registers"
elif "CLB Logic" in resources:
table = "CLB Logic"
lut = "CLB LUTs"
reg = "CLB Registers"
else:
logger.error("Can't find a table with LUT information")
return summary
df = resources[table].set_index("Site Type")
summary["lut"] = df.loc[lut, "Used"].item()
summary["reg"] = df.loc[reg, "Used"].item()
if "Memory" in resources:
table = "Memory"
elif "BLOCKRAM" in resources:
table = "BLOCKRAM"
else:
logger.error("Can't find a table with memory information")
return summary
df = resources[table].set_index("Site Type")
summary["blkmem"] = df.loc["Block RAM Tile", "Used"].item()
if "DSP" in resources:
table = "DSP"
elif "ARITHMETIC" in resources:
table = "ARITHMETIC"
else:
logger.error("Can't find a table with memory information")
return summary
df = resources[table].set_index("Site Type")
summary["dsp"] = df.loc["DSPs", "Used"].item()
# Return a dict indexed by the clock name
df = timing["Clock Summary"].set_index("Clock")
summary["constraint"] = df["Frequency(MHz)"].to_dict()
# Loadless clocks won't have a WNS entry which maps to NaN. This will
# be mapped to None by period_to_df which feels more appropriate.
# Pandas.Series.transform or apply feel like the most appropriate
# functions to use, but seem to interpret the returned None as a
# no-op, so the mapping is done when the returned dictionary is
# created.
period = timing["Clock Summary"].set_index("Clock")["Period(ns)"]
wns = timing["Intra Clock Table"].set_index("Clock")["WNS(ns)"]
summary["fmax"] = {
k: Reporting.period_to_freq(v)
for k, v in (period - wns).to_dict().items()
}
return summary