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This commit is contained in:
Sergei Trofimov 2015-08-11 17:02:38 +01:00
parent a6d374bcff
commit 14f5858e3d
1 changed files with 8 additions and 8 deletions
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16
wlauto/instrumentation/energy_model/__init__.py
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@ -305,11 +305,11 @@ def get_cpus_power_table(data, index, opps): # pylint: disable=too-many-locals
if opps[cluster] is None: if opps[cluster] is None:
bs_power_table.loc[bs_power_table[cluster, 1].notnull(), (cluster, 0)] = \ bs_power_table.loc[bs_power_table[cluster, 1].notnull(), (cluster, 0)] = \
(2* power_table[cluster, 1] - power_table[cluster, 2]).values (2 * power_table[cluster, 1] - power_table[cluster, 2]).values
else: else:
df = pd.concat([bs_power_table[cluster], df = pd.concat([bs_power_table[cluster],
opps[cluster].set_index('frequency')], opps[cluster].set_index('frequency')],
axis=1).dropna(subset=[1]).reset_index() axis=1).dropna(subset=[1]).reset_index()
# Create a projection by calculating coefficients from the lowest two OPPs (assume minimal leakage) # Create a projection by calculating coefficients from the lowest two OPPs (assume minimal leakage)
v0 = df.voltage[0] v0 = df.voltage[0]
@ -320,18 +320,18 @@ def get_cpus_power_table(data, index, opps): # pylint: disable=too-many-locals
# Assumption: # Assumption:
# P = k1*v*f + k2*v^2*f # P = k1*v*f + k2*v^2*f
coeffs = np.array([ coeffs = np.array([
[v0 * f0, (v0**2) * f0], [v0 * f0, (v0**2) * f0],
[v1 * f1, (v1**2) * f1] [v1 * f1, (v1**2) * f1]
]) ])
c1pow = np.array([df[1][0], df[1][1]]) c1pow = np.array([df[1][0], df[1][1]])
c2pow = np.array([df[2][0], df[2][1]]) c2pow = np.array([df[2][0], df[2][1]])
c1k1, c1k2 = np.linalg.solve(coeffs, c1pow) c1k1, c1k2 = np.linalg.solve(coeffs, c1pow)
c2k1, c2k2 = np.linalg.solve(coeffs, c2pow) c2k1, c2k2 = np.linalg.solve(coeffs, c2pow)
df['a1'] = pd.Series(df.frequency * df.voltage * c1k1 + df.frequency * df.voltage ** 2 * c1k2, df['a1'] = pd.Series(df.frequency * df.voltage * c1k1 + df.frequency * df.voltage ** 2 * c1k2,
index=df.index) index=df.index)
df['a2'] = pd.Series(df.frequency * df.voltage * c2k1 + df.frequency * df.voltage ** 2 * c2k2, df['a2'] = pd.Series(df.frequency * df.voltage * c2k1 + df.frequency * df.voltage ** 2 * c2k2,
index=df.index) index=df.index)
bs_power_table.loc[bs_power_table[cluster, 1].notnull(), (cluster, 0)] = (2 * df.a1 - df.a2).values bs_power_table.loc[bs_power_table[cluster, 1].notnull(), (cluster, 0)] = (2 * df.a1 - df.a2).values
# re-order columns and rename colum '0' to 'cluster' # re-order columns and rename colum '0' to 'cluster'