from daCore import BasicObjects, PlatformInfo
mpr = PlatformInfo.PlatformInfo().MachinePrecision()
mfp = PlatformInfo.PlatformInfo().MaximumPrecision()
-from daCore.PlatformInfo import vfloat
+from daCore.PlatformInfo import vfloat, has_matplotlib
from daCore.NumericObjects import FindIndexesFromNames, SingularValuesEstimation
+from daAlgorithms.Atoms import eosg
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
msgs += "\n" + __marge + "-"*__nbtirets
msgs += ("\n")
#
- cut1pd, cut1pc, cut1pm = 1, 1, 1
+ cut1pd, cut1pc, cut1pm, cut1pi = 1, 1, 1, 1
for ns in range(len(__sv)):
svalue = __sv[ns]
rvalue = __sv[ns] / __sv[0]
rsinfo = 100 * __qisv[ns]
if __s:
msgs += (__marge + " %0"+str(__ordre)+"i | %22."+str(__p)+"e | %22."+str(__p)+"e | %2i%s , %4.1f%s\n")%(ns,svalue,rvalue,vsinfo,"%",rsinfo,"%")
- if rsinfo > 10: cut1pd = ns+2 # 10%
- if rsinfo > 1: cut1pc = ns+2 # 1%
- if rsinfo > 0.1: cut1pm = ns+2 # 1‰
+ if rsinfo > 10: cut1pd = ns+2 # 10%
+ if rsinfo > 1: cut1pc = ns+2 # 1%
+ if rsinfo > 0.1: cut1pm = ns+2 # 1‰
+ if rsinfo > 0.01: cut1pi = ns+2 # 0.1‰
#
if __s:
msgs += __marge + "-"*__nbtirets + "\n"
msgs += (__flech + "Summary of variance cut-off:\n")
msgs += (__marge + "----------------------------\n")
if cut1pd > 0:
- msgs += __marge + "Representing more than 90%s of variance requires at least %i mode(s).\n"%("%",cut1pd)
+ msgs += __marge + "Representing more than 90%s of variance requires at least %i mode(s).\n"%("%",cut1pd)
if cut1pc > 0:
- msgs += __marge + "Representing more than 99%s of variance requires at least %i mode(s).\n"%("%",cut1pc)
- if cut1pc > 0:
- msgs += __marge + "Representing more than 99.9%s of variance requires at least %i mode(s).\n"%("%",cut1pm)
+ msgs += __marge + "Representing more than 99%s of variance requires at least %i mode(s).\n"%("%",cut1pc)
+ if cut1pm > 0:
+ msgs += __marge + "Representing more than 99.9%s of variance requires at least %i mode(s).\n"%("%",cut1pm)
+ if cut1pi > 0:
+ msgs += __marge + "Representing more than 99.99%s of variance requires at least %i mode(s).\n"%("%",cut1pi)
#
- if self._parameters["PlotAndSave"]:
- msgs += ("\n")
- msgs += (__marge + "Plot and save results in a file named \"%s\"\n"%str(self._parameters["ResultFile"]))
- #
- import matplotlib.pyplot as plt
- from matplotlib import ticker
- fig = plt.figure(figsize=(10,15), layout="tight")
- if len(self._parameters["ResultTitle"]) > 0:
- fig.suptitle(self._parameters["ResultTitle"])
- else:
- fig.suptitle("Singular values analysis on an ensemble of %i snapshots\n"%__nsn)
- # ----
- ax = fig.add_subplot(3,1,1)
- ax.set_xlabel("Singular values index, numbered from 1 (first %i ones)"%len(__qisv))
- ax.set_ylabel("Remaining variance to be explained (%, linear scale)", color="tab:blue")
- ax.grid(True, which='both', color="tab:blue")
- ax.set_xlim(1,1+len(__qisv))
- ax.set_ylim(0,100)
- ax.plot(range(1,1+len(__qisv)), 100 * __qisv, linewidth=2, color="b", label="On linear scale")
- ax.tick_params(axis='y', labelcolor="tab:blue")
- ax.yaxis.set_major_formatter('{x:.0f}%')
- #
- rg = ax.twinx()
- rg.set_ylabel("Remaining variance to be explained (%, log scale)", color="tab:red")
- rg.grid(True, which='both', color="tab:red")
- rg.set_xlim(1,1+len(__qisv))
- rg.set_yscale("log")
- rg.plot(range(1,1+len(__qisv)), 100 * __qisv, linewidth=2, color="r", label="On log10 scale")
- rg.set_ylim(rg.get_ylim()[0],101)
- rg.tick_params(axis='y', labelcolor="tab:red")
- # ----
- ax = fig.add_subplot(3,1,2)
- ax.set_ylabel("Singular values")
- ax.set_xlim(1,1+len(__sv))
- ax.plot(range(1,1+len(__sv)), __sv, linewidth=2)
- ax.grid(True)
- # ----
- ax = fig.add_subplot(3,1,3)
- ax.set_ylabel("Singular values (log scale)")
- ax.grid(True, which='both')
- ax.set_xlim(1,1+len(__sv))
- ax.set_xscale("log")
- ax.set_yscale("log")
- ax.plot(range(1,1+len(__sv)), __sv, linewidth=2)
- # ----
- plt.savefig(str(self._parameters["ResultFile"]))
- plt.close(fig)
+ if has_matplotlib and self._parameters["PlotAndSave"]:
+ try:
+ msgs += ("\n")
+ msgs += (__marge + "Plot and save results in a file named \"%s\"\n"%str(self._parameters["ResultFile"]))
+ #
+ import matplotlib.pyplot as plt
+ from matplotlib import ticker
+ fig = plt.figure(figsize=(10,15))
+ plt.tight_layout()
+ if len(self._parameters["ResultTitle"]) > 0:
+ fig.suptitle(self._parameters["ResultTitle"])
+ else:
+ fig.suptitle("Singular values analysis on an ensemble of %i snapshots\n"%__nsn)
+ # ----
+ ax = fig.add_subplot(3,1,1)
+ ax.set_xlabel("Singular values index, numbered from 1 (first %i ones)"%len(__qisv))
+ ax.set_ylabel("Remaining variance to be explained (%, linear scale)", color="tab:blue")
+ ax.grid(True, which='both', color="tab:blue")
+ ax.set_xlim(1,1+len(__qisv))
+ ax.set_ylim(0,100)
+ ax.plot(range(1,1+len(__qisv)), 100 * __qisv, linewidth=2, color="b", label="On linear scale")
+ ax.tick_params(axis='y', labelcolor="tab:blue")
+ ax.yaxis.set_major_formatter('{x:.0f}%')
+ #
+ rg = ax.twinx()
+ rg.set_ylabel("Remaining variance to be explained (%, log scale)", color="tab:red")
+ rg.grid(True, which='both', color="tab:red")
+ rg.set_xlim(1,1+len(__qisv))
+ rg.set_yscale("log")
+ rg.plot(range(1,1+len(__qisv)), 100 * __qisv, linewidth=2, color="r", label="On log10 scale")
+ rg.set_ylim(rg.get_ylim()[0],101)
+ rg.tick_params(axis='y', labelcolor="tab:red")
+ # ----
+ ax = fig.add_subplot(3,1,2)
+ ax.set_ylabel("Singular values")
+ ax.set_xlim(1,1+len(__sv))
+ ax.plot(range(1,1+len(__sv)), __sv, linewidth=2)
+ ax.grid(True)
+ # ----
+ ax = fig.add_subplot(3,1,3)
+ ax.set_ylabel("Singular values (log scale)")
+ ax.grid(True, which='both')
+ ax.set_xlim(1,1+len(__sv))
+ ax.set_xscale("log")
+ ax.set_yscale("log")
+ ax.plot(range(1,1+len(__sv)), __sv, linewidth=2)
+ # ----
+ plt.savefig(str(self._parameters["ResultFile"]))
+ plt.close(fig)
+ except:
+ msgs += ("\n")
+ msgs += (__marge + "Saving figure fail, please update your Matplolib version.\n")
+ msgs += ("\n")
#
msgs += ("\n")
msgs += (__marge + "%s\n"%("-"*75,))
