def output_rarity(self, rarity, data_path, data, criteria=None):
'''
Outputs csv files containing rarity measures
Parameters
----------
rarity : a CompareRarity object
data_path : str
data_path string for identifying data in csv file
data : list
A list of observed species abundance distributions
criteria : dict or None
The criteria for how the plot was split
'''
folder_name = 'rarity_values_' + self.out_dir
make_directory(folder_name)
keys = list(rarity.viewkeys())
dtype = [(kw, np.int) for kw in keys]
dtype.insert(0, ('criteria', 'S90')) # arbitrary length
dtype.insert(0, ('data_name', 'S90')) # arbitrary length
# Get a list of my minimums
rare_list = []
mins = list(rarity['observed'].viewkeys())
for mn in mins:
rarity_array = np.empty(len(data), dtype=dtype)
rarity_array['criteria'] = criteria
nm = os.path.split(data_path)[1].split('.')[0]
rarity_array['data_name'] = np.repeat(nm, len(rarity_array))
for kw in keys:
rarity_array[kw] = rarity[kw][mn]
rare_list.append(rarity_array)
# Output results
count = 0
for i, rare in enumerate(rare_list):
filename = os.path.join(folder_name, self.out_dir + '_rarity_<=_' +
str(mins[i]))
logging.info('Saving rarity data ' + filename)
output_form(rare, filename)
count += 1
fout = open(os.path.join(folder_name, 'README'), 'w')
fout.write(readme_info_rarity.format(folder_name, count))
fout.close()
def plot_reds(self, reds, criteria=None):
"""
Saves plot and csv file with predicted and empirical rank energy data
Parameters
----------
reds : tuple
The output from the CompareSED.compare_rads method with
return_spp=True.
criteria : list or None
A list of dicts with the criteria for divisions. See Patch.sad
Output
------
This method outputs both a plot and a csv that compare observed and
predicted species-level rank energy curves.
"""
folder_name = "sed_rank_energy_plots_" + self.out_dir
make_directory(folder_name)
if type(reds) != type((1,)):
raise TypeError("Input reds must be a tuple. Set return_spp=True" + " in CompareSED.compare_rads")
spp = reds[1]
tot_reds = len(reds[0]["observed"])
recs = make_rec_from_dict(reds[0], tot_reds)
if criteria != None:
assert len(criteria) == tot_reds, "len(criteria) must equal" + " number of reds under consideration"
count = 0
for i, data in enumerate(recs):
plot_rec_columns(data)
plt.semilogx()
plt.ylabel("Energy")
plt.xlabel("Log Rank")
if spp != None:
if criteria != None:
plt.title("Rank Energy Distribution for species " + str(spp[i]))
plt.figtext(
0.97,
0.5,
"Criteria for plot: " + str(criteria[i]),
rotation="vertical",
size=8,
horizontalalignment="center",
verticalalignment="center",
)
else:
plt.title("Rank Energy Distribution for species " + str(spp[i]))
filename = os.path.join(folder_name, self.out_dir + "_sed_rank_energy_" + str(spp[i]) + "_" + str(i))
logging.info("Saving figure " + filename)
plt.savefig(filename)
output_form(recs[i], filename)
count += 2
elif spp == None:
if criteria != None:
plt.title("Criteria: " + str(criteria[i]))
else:
plt.title("Plot number " + str(i))
filename = os.path.join(folder_name, self.out_dir + "_sed_rank_energy_" + str(i))
logging.info("Saving figure " + filename)
plt.savefig(filename)
output_form(recs[i], filename)
plt.clf()
fout = open(os.path.join(folder_name, "README"), "w")
fout.write(
readme_info_plots.format(
count,
count / 2,
count / 2,
folder_name,
"species-level energy distribution (SED) rank energy plots",
"sed_rank_energy",
)
)
fout.close()
def plot_reds(self, reds, criteria=None):
"""
Saves plot and csv file with predicted and empirical rank energy data
Parameters
----------
reds : tuple
The output from the CompareIED.compare_rads method
criteria : list or None
A list of dicts with the criteria for divisions. See Patch.sad
Output
------
This method outputs both a plot and a csv that compare observed and
predicted individual rank energy curves for the entire community at the
given subset.
"""
folder_name = "ied_rank_energy_plots_" + self.out_dir
make_directory(folder_name)
tot_reds = len(reds["observed"])
recs = make_rec_from_dict(reds, tot_reds)
if criteria != None:
assert len(criteria) == tot_reds, "len(criteria) must equal" + " number of reds under consideration"
count = 0
for i, data in enumerate(recs):
# Plot all data in a single rec array
plot_rec_columns(data)
# Make appropriate title for figure
if criteria != None:
plt.title("Rank Energy Distribution")
plt.figtext(
0.97,
0.5,
"Criteria for plot: " + str(criteria[i]),
rotation="vertical",
size=8,
horizontalalignment="center",
verticalalignment="center",
)
else:
plt.title("Rank Energy Distribution")
plt.figtext(
0.97,
0.5,
"Plot number: " + str(i),
rotation="vertical",
size=8,
horizontalalignment="center",
verticalalignment="center",
)
plt.loglog()
plt.ylabel("Log Energy")
plt.xlabel("Log Rank")
filename = os.path.join(folder_name, self.out_dir + "_ied_rank_energy_" + str(i))
logging.info("Saving figure " + filename)
plt.savefig(filename)
plt.clf()
output_form(recs[i], filename)
count += 2
fout = open(os.path.join(folder_name, "README"), "w")
fout.write(
readme_info_plots.format(
count,
count / 2,
count / 2,
folder_name,
"individual energy distribution (IED) rank energy plots",
"ied_rank_energy",
)
)
fout.close()
def plot_reds(self, reds, criteria=None, species=None):
"""
Plotting the observed and predicted rank abundance distributions
Parameters
----------
reds : dict
A dictionary that is returned from the function compare_reds in the
CompareASED class.
criteria : list of objects
If not none, the objects in criteria will be printed a strings in
the plots and file names.
Notes
-----
Saves RAD plots to given out_dir. Saves as many plots as there are
observed distributions.
"""
folder_name = "ased_rank_energy_plots_" + self.out_dir
make_directory(folder_name)
tot_sad = len(reds["observed"])
recs = make_rec_from_dict(reds, tot_sad, species=species)
if criteria != None:
assert len(criteria) == tot_sad, "len(criteria) must equal" + " number of data arrays under consideration"
count = 0
for i, data in enumerate(recs):
# Plot all columns of the rec array
plot_rec_columns(data)
plt.semilogy()
plt.ylabel("Log Energy")
plt.xlabel("Rank")
if criteria != None and np.all([type(crt) != dict for crt in criteria]):
plt.title("ASED rank energy distribution for " + str(criteria[i]))
filename = os.path.join(folder_name, self.out_dir + "_rank_abundance_plot_" + str(criteria[i]))
logging.info("Saving figure and csv " + filename)
plt.savefig(filename)
output_form(recs[i], filename)
count += 2
elif criteria != None and np.all([type(crt) == dict for crt in criteria]):
plt.title("ASED rank energy distribution")
plt.figtext(
0.97,
0.5,
"Criteria for plot: " + str(criteria[i]),
rotation="vertical",
size=8,
horizontalalignment="center",
verticalalignment="center",
)
filename = os.path.join(folder_name, self.out_dir + "_ased_rank_energy_plot_" + str(i))
logging.info("Saving figure " + filename)
plt.savefig(filename)
output_form(recs[i], filename)
count += 2
else:
plt.title("ASED rank energy distribution: plot number " + str(i))
filename = os.path.join(folder_name, self.out_dir + "_ased_rank_energy_plot_" + str(i))
logging.info("Saving figure " + filename)
plt.savefig(filename)
output_form(recs[i], filename)
count += 2
plt.clf()
fout = open(os.path.join(folder_name, "README"), "w")
fout.write(
readme_info_plots.format(
count,
count / 2,
count / 2,
folder_name,
"average species energy distribution (ASED) rank" + " energy plots",
"ased_rank_energy_plot",
)
)
fout.close()
def plot_sars(self, sars, names=[], form="sar"):
"""
Plots observed vs predicted sars
Parameters
----------
sars : list of dicts
The output of CompareSARCurve method compare_curves
names : list or strings
If not None, names is a list of the same length as sars. Gives the
desired names for the plots.
"""
if form == "sar":
file_str = "_SAR_plot_"
ylab = "log(Species Number)"
stype = "species"
folder_name = "sar_plots_" + self.out_dir
make_directory(folder_name)
elif form == "ear":
file_str = "_EAR_plot_"
ylab = "log(Endemic Species Number)"
stype = "endemic_species"
folder_name = "ear_plots_" + self.out_dir
make_directory(folder_name)
else:
raise ValueError("Parameter 'form' must be 'ear' or 'sar' not '%s'" % form)
if len(names) != 0:
assert len(names) == len(sars)
"Length of names must equal" + "length of sars"
count = 0
for i, sar in enumerate(sars):
filename = os.path.join(folder_name, self.out_dir + file_str + str(i))
legend = []
for kw in sar.iterkeys():
legend.append(kw)
if kw == "observed":
fig = plt.plot(sar[kw]["area"], sar[kw]["items"], "-o")
else:
fig = plt.plot(sar[kw]["area"], sar[kw]["items"])
# Change dtype names and output
defnm = sar[kw].dtype.names
sar[kw].dtype.names = (stype, "area_fraction")
output_form(sar[kw], filename + "_" + kw)
sar[kw].dtype.names = defnm
# Plot formatting
fig[0].axes.xaxis.tick_bottom()
fig[0].axes.yaxis.tick_left()
plt.loglog()
plt.legend(tuple(legend), loc="best")
plt.xlabel("log(Area Fraction)")
plt.ylabel(ylab)
if len(names) != 0:
plt.title(names[i])
else:
plt.title(form.upper() + " plot %i" % (i))
filename = os.path.join(folder_name, self.out_dir + file_str + str(i))
logging.info("Saving figure " + filename)
plt.savefig(filename)
plt.clf()
count += 1
fout = open(os.path.join(folder_name, "README"), "w")
fout.write(readme_info_sar.format(folder_name, count, count * len(sar)))
fout.close()
def plot_cdfs(self, cdfs, obs_data, criteria=None, species=None):
"""
Plots observed vs predicted cdfs and returns a csv file with values
used for plotting.
Parameters
----------
cdfs : dict
A dictionary that is returned from the function compare_cdfs in the
CompareDistribution class.
obs_data : list
A list of arrays. The observed data
(CompareDistribution.observed_data)
criteria : dict or None
The criteria for splitting the data. Can be species names. If not
None, the criteria will be printed on the plots
species : array-like object or None
The species names that will be added to the csv files.
"""
# Make directory
folder_name = self.dist_name + "_cdf_plots_" + self.out_dir
make_directory(folder_name)
# SEDOutput could pass in tuple
spp = None
if type(cdfs) == type((1,)) and len(cdfs) == 2:
spp = cdfs[1]
cdfs = cdfs[0]
tot_sad = len(cdfs["observed"])
recs = make_rec_from_dict(cdfs, tot_sad, add_rank=False)
if criteria != None:
assert len(criteria) == tot_sad, "len(criteria) must equal" + " number of data arrays under consideration"
count = 0
for i, data in enumerate(recs):
names = data.dtype.names
for nm in names:
fig = plt.plot(np.sort(obs_data[i]), np.sort(data[nm]), "-o")
# Formatting
fig[0].axes.xaxis.tick_bottom()
fig[0].axes.yaxis.tick_left()
ylim = list(plt.ylim())
if ylim[0] == 0:
ylim[0] = -0.1
plt.ylim((ylim[0], 1.1))
xlim = plt.xlim()
plt.xlim((0.9, xlim[1] + 10))
plt.legend(names, loc="best")
plt.semilogx()
plt.ylabel(self.cdf_y_axis)
plt.xlabel("Log " + self.cdf_x_axis)
# Add observed to cdf array
if species != None:
sorted_ab, sorted_spp = sort_rank_abund([obs_data[i]], [species[i]])
n_rec = add_field(data, [(self.variable, np.float)])
n_rec = add_field(n_rec, [("species", "S40")])
n_rec[self.variable] = sorted_ab[0]
n_rec["species"] = sorted_spp[0]
else:
n_rec = add_field(data, [(self.variable, np.float)])
n_rec[self.variable] = np.sort(obs_data[i])
# Used for SSAD
if criteria != None and spp == None and np.all([type(crt) != dict for crt in criteria]):
plt.title("Cumulative density function for species " + str(criteria[i]))
filename = os.path.join(folder_name, self.out_dir + "_cdf_plot_" + str(criteria[i]))
logging.info("Saving figure and csv " + filename)
plt.savefig(filename)
output_form(n_rec, filename)
count += 2
# Used for SAD
elif criteria != None and spp == None and np.all([type(crt) == dict for crt in criteria]):
plt.title("Cumulative Density Function")
plt.figtext(
0.97,
0.5,
"Criteria for plot: " + str(criteria[i]),
rotation="vertical",
size=8,
horizontalalignment="center",
verticalalignment="center",
)
filename = os.path.join(folder_name, self.out_dir + "_cdf_plot_" + str(i))
logging.info("Saving figure " + filename)
plt.savefig(filename)
output_form(n_rec, filename)
count += 2
# Used for SED
elif criteria != None and spp != None and np.all([type(crt) == dict for crt in criteria]):
plt.title("Cumulative Density Function for species " + str(spp[i]))
plt.figtext(
0.97,
0.5,
"Criteria for plot: " + str(criteria[i]),
rotation="vertical",
size=8,
horizontalalignment="center",
verticalalignment="center",
)
filename = os.path.join(folder_name, self.out_dir + "_cdf_plot_" + str(spp[i]) + "_" + str(i))
logging.info("Saving figure " + filename)
plt.savefig(filename)
output_form(n_rec, filename)
count += 2
else:
plt.title("CDF: plot number " + str(i))
filename = os.path.join(folder_name, self.out_dir + "_cdf_plot_" + str(i))
logging.info("Saving figure and csv " + filename)
plt.savefig(filename)
output_form(n_rec, filename)
count += 2
plt.clf()
fout = open(os.path.join(folder_name, "README"), "w")
fout.write(
readme_info_plots.format(
count, count / 2, count / 2, folder_name, "cumulative density plots (cdf)", "cdf_plot"
)
)
fout.close()
def write_summary_table(self, smry, criteria=None, species=None):
"""
Parameters
---------
smry : dict
A dictionary as returned by the function compare_summary within the
CompareDistribution class.
criteria : array-like object
An array-like object in which contains either string or dicts that
tell how each dataset was generated. Describes the subsetting of
an sad and the species ID of an ssad.
species : array_like object
If not None, must be the an array-like object of the same length as
criteria, but containing species strings. Can only be used if
criteria is also not None.
Notes
-----
Writes out a formatted txt file
"""
# Make output folder
folder_name = self.dist_name + "_summary_statistics_" + self.out_dir
make_directory(folder_name)
tot_sad = len(smry["observed"]["balls"])
if criteria != None:
assert len(criteria) == tot_sad, "len(criteria) must equal" + " number of data arrays under consideration"
if species != None:
assert len(species) == tot_sad, "len(species) must equal" + " number of data arrays under consideration"
ob = smry["observed"]
count = 0
for i in xrange(tot_sad):
if criteria != None and species != None:
filename = os.path.join(
folder_name, self.out_dir + "_summary_table_" + str(species[i]) + "_" + str(i) + ".txt"
)
filename_aic = os.path.join(folder_name, self.out_dir + "_AIC_table_" + str(species[i]) + "_" + str(i))
elif criteria != None and np.all([type(crt) != dict for crt in criteria]):
filename = os.path.join(folder_name, self.out_dir + "_summary_table_" + str(criteria[i]) + ".txt")
filename_aic = os.path.join(folder_name, self.out_dir + "_AIC_table_" + str(criteria[i]))
else:
filename = os.path.join(folder_name, self.out_dir + "_summary_table_" + str(i) + ".txt")
filename_aic = os.path.join(folder_name, self.out_dir + "_AIC_table_" + str(i))
fout = open(filename, "w")
logging.info("Writing summary table %s" % filename)
if criteria != None and species != None:
fout.write("CRITERIA: " + str(criteria[i]) + "\n" + "SPECIES: " + str(species[i]) + "\n\n")
elif criteria != None:
fout.write("CRITERIA: " + str(criteria[i]) + "\n\n")
else:
fout.write("CRITERIA: NONE " + str(i) + "\n\n")
# Getting rarity
ob_rare = {}
for mins in ob["tot_min"].iterkeys():
ob_rare["<=" + str(mins)] = ob["tot_min"][mins][i]
fout.write(
"EMPIRICAL VALUES:\n"
+ self.urns
+ " = "
+ str(ob["urns"][i])
+ "\n"
+ self.balls
+ " = "
+ str(ob["balls"][i])
+ "\nObserved "
+ self.Nmax
+ " = "
+ str(ob["max"][i])
+ "\nObserved Rarity = "
+ str(ob_rare)
+ "\n\n"
)
# Also output AIC values in for each table. Could add other
# measures to this table as well.
# Might break this out later
aic_vals = {}
for kw in smry.iterkeys():
if kw != "observed":
dt = smry[kw]
# set relevant aic values for table output
aic_vals[kw] = {
"AIC_weights": dt["aic_w"][i],
"Delta_AIC": dt["aic_d"][i],
"Parameter_number": dt["par_num"][i],
"Corrected_AIC": dt["aic"][i],
}
# Getting rarity
dt_rare = {}
for mins in dt["tot_min"].iterkeys():
dt_rare["<=" + str(mins)] = dt["tot_min"][mins][i]
dt_vars = {}
for key in dt["vars"].iterkeys():
dt_vars[key] = dt["vars"][key][i]
fout.write(
"PREDICTED DISTRIBUTION : "
+ kw
+ "\n"
+ self.urns
+ " = "
+ str(dt["urns"][i])
+ "\n"
+ self.balls
+ " = "
+ str(dt["balls"][i])
+ "\nAIC = "
+ str(dt["aic"][i])
+ "\nDelta_AIC = "
+ str(dt["aic_d"][i])
+ "\nAIC_weight = "
+ str(dt["aic_w"][i])
+ "\nNumber of Parameters = "
+ str(dt["par_num"][i])
+ "\nPredicted "
+ self.Nmax
+ " = "
+ str(dt["max"][i])
+ "\nPredicted Rarity = "
+ str(dt_rare)
+ "\nOther Variables = "
+ str(dt_vars)
+ "\n\n"
)
fout.close()
count += 1
# Make and print AIC table
dtype = [
("Model", "S30"),
("Parameter_number", np.float),
("Corrected_AIC", np.float),
("AIC_weights", np.float),
("Delta_AIC", np.float),
]
aic_array = np.empty(len(aic_vals), dtype=dtype)
for j, model_name in enumerate(aic_vals.iterkeys()):
aic_array["Model"][j] = model_name
aic_array["Parameter_number"][j] = aic_vals[model_name]["Parameter_number"]
aic_array["Corrected_AIC"][j] = aic_vals[model_name]["Corrected_AIC"]
aic_array["AIC_weights"][j] = aic_vals[model_name]["AIC_weights"]
aic_array["Delta_AIC"][j] = aic_vals[model_name]["Delta_AIC"]
output_form(aic_array, filename_aic)
fout = open(os.path.join(folder_name, "README"), "w")
fout.write(readme_info_summary.format(folder_name, count))
fout.close()
def plot_reds(self, reds, criteria=None, species=None):
'''
Plotting the observed and predicted rank abundance distributions
Parameters
----------
reds : dict
A dictionary that is returned from the function compare_reds in the
CompareASED class.
criteria : list of objects
If not none, the objects in criteria will be printed a strings in
the plots and file names.
Notes
-----
Saves RAD plots to given out_dir. Saves as many plots as there are
observed distributions.
'''
folder_name = 'ased_rank_energy_plots_' + self.out_dir
make_directory(folder_name)
tot_sad = len(reds['observed'])
recs = make_rec_from_dict(reds, tot_sad, species=species)
if criteria != None:
assert len(criteria) == tot_sad, "len(criteria) must equal" + \
" number of data arrays under consideration"
count = 0
for i, data in enumerate(recs):
# Plot all columns of the rec array
plot_rec_columns(data)
plt.semilogy()
plt.ylabel('Log Energy')
plt.xlabel('Rank')
if criteria != None and np.all([type(crt) != dict for crt in
criteria]):
plt.title('ASED rank energy distribution for ' +
str(criteria[i]))
filename = os.path.join(folder_name, self.out_dir +
'_rank_abundance_plot_' + str(criteria[i]))
logging.info('Saving figure and csv ' + filename)
plt.savefig(filename)
output_form(recs[i], filename)
count += 2
elif criteria != None and np.all([type(crt) == dict for crt in
criteria]):
plt.title('ASED rank energy distribution')
plt.figtext(.97, .5, 'Criteria for plot: ' + str(criteria[i]),
rotation='vertical', size=8,
horizontalalignment='center',
verticalalignment='center')
filename = os.path.join(folder_name, self.out_dir +
'_ased_rank_energy_plot_' + str(i))
logging.info('Saving figure ' + filename)
plt.savefig(filename)
output_form(recs[i], filename)
count += 2
else:
plt.title('ASED rank energy distribution: plot number ' + str(i))
filename = os.path.join(folder_name, self.out_dir +
'_ased_rank_energy_plot_' + str(i))
logging.info('Saving figure ' + filename)
plt.savefig(filename)
output_form(recs[i], filename)
count += 2
plt.clf()
fout = open(os.path.join(folder_name, 'README'), 'w')
fout.write(readme_info_plots.format(count, count /2, count/2,
folder_name,
'average species energy distribution (ASED) rank' +
' energy plots', 'ased_rank_energy_plot'))
fout.close()
def plot_sars(self, sars, names=[], form='sar'):
'''
Plots observed vs predicted sars
Parameters
----------
sars : list of dicts
The output of CompareSARCurve method compare_curves
names : list or strings
If not None, names is a list of the same length as sars. Gives the
desired names for the plots.
'''
if form == 'sar':
file_str = '_SAR_plot_'
ylab = 'log(Species Number)'
stype = 'species'
folder_name = 'sar_plots_' + self.out_dir
make_directory(folder_name)
elif form == 'ear':
file_str = '_EAR_plot_'
ylab = 'log(Endemic Species Number)'
stype = 'endemic_species'
folder_name = 'ear_plots_' + self.out_dir
make_directory(folder_name)
else:
raise ValueError("Parameter 'form' must be 'ear' or 'sar' not '%s'"
% form)
if len(names) != 0:
assert len(names) == len(sars); "Length of names must equal" + \
"length of sars"
count = 0
for i, sar in enumerate(sars):
filename = os.path.join(folder_name, self.out_dir + file_str +
str(i))
legend = []
for kw in sar.iterkeys():
legend.append(kw)
if kw == 'observed':
fig = plt.plot(sar[kw]['area'], sar[kw]['items'], '-o')
else:
fig = plt.plot(sar[kw]['area'], sar[kw]['items'])
# Change dtype names and output
defnm = sar[kw].dtype.names
sar[kw].dtype.names = (stype, 'area_fraction')
output_form(sar[kw], filename + '_' + kw)
sar[kw].dtype.names = defnm
# Plot formatting
fig[0].axes.xaxis.tick_bottom()
fig[0].axes.yaxis.tick_left()
plt.loglog()
plt.legend(tuple(legend), loc='best')
plt.xlabel('log(Area Fraction)')
plt.ylabel(ylab)
if len(names) != 0:
plt.title(names[i])
else:
plt.title(form.upper() + ' plot %i' % (i))
filename = os.path.join(folder_name, self.out_dir + file_str +
str(i))
logging.info('Saving figure ' + filename)
plt.savefig(filename)
plt.clf()
count += 1
fout = open(os.path.join(folder_name, 'README'), 'w')
fout.write(readme_info_sar.format(folder_name, count, count * len(sar)))
fout.close()
def write_summary_table(self, smry, criteria=None, species=None):
'''
Parameters
---------
smry : dict
A dictionary as returned by the function compare_summary within the
CompareDistribution class.
criteria : array-like object
An array-like object in which contains either string or dicts that
tell how each dataset was generated. Describes the subsetting of
an sad and the species ID of an ssad.
species : array_like object
If not None, must be the an array-like object of the same length as
criteria, but containing species strings. Can only be used if
criteria is also not None.
Notes
-----
Writes out a formatted txt file
'''
# Make output folder
folder_name = self.dist_name + '_summary_statistics_' + self.out_dir
make_directory(folder_name)
tot_sad = len(smry['observed']['balls'])
if criteria != None:
assert len(criteria) == tot_sad, "len(criteria) must equal" + \
" number of data arrays under consideration"
if species != None:
assert len(species) == tot_sad, "len(species) must equal" + \
" number of data arrays under consideration"
ob = smry['observed']
count = 0
for i in xrange(tot_sad):
if criteria != None and species != None:
filename = os.path.join(folder_name, self.out_dir + \
'_summary_table_' + str(species[i]) + '_' + str(i) +
'.txt')
filename_aic = os.path.join(folder_name, self.out_dir + \
'_AIC_table_' + str(species[i]) + '_' + str(i))
elif criteria != None and np.all([type(crt) != dict for crt in
criteria]):
filename = os.path.join(folder_name, self.out_dir + \
'_summary_table_' + str(criteria[i]) + '.txt')
filename_aic = os.path.join(folder_name, self.out_dir + \
'_AIC_table_' + str(criteria[i]))
else:
filename = os.path.join(folder_name, self.out_dir +
'_summary_table_' + str(i) + '.txt')
filename_aic = os.path.join(folder_name, self.out_dir +
'_AIC_table_' + str(i))
fout = open(filename, 'w')
logging.info('Writing summary table %s' % filename)
if criteria != None and species != None:
fout.write('CRITERIA: ' + str(criteria[i]) + '\n' +
'SPECIES: ' + str(species[i]) + '\n\n')
elif criteria != None:
fout.write('CRITERIA: ' + str(criteria[i]) + '\n\n')
else:
fout.write('CRITERIA: NONE ' + str(i) + '\n\n')
# Getting rarity
ob_rare = {}
for mins in ob['tot_min'].iterkeys():
ob_rare['<=' + str(mins)] = ob['tot_min'][mins][i]
fout.write('EMPIRICAL VALUES:\n' + self.urns + ' = ' +
str(ob['urns'][i]) + '\n' + self.balls + ' = ' +
str(ob['balls'][i]) + '\nObserved ' + self.Nmax + ' = ' +
str(ob['max'][i]) + '\nObserved Rarity = ' +
str(ob_rare) + '\n\n')
# Also output AIC values in for each table. Could add other
# measures to this table as well.
# Might break this out later
aic_vals = {}
for kw in smry.iterkeys():
if kw != 'observed':
dt= smry[kw]
# set relevant aic values for table output
aic_vals[kw]={'AIC_weights' : dt['aic_w'][i], 'Delta_AIC' :
dt['aic_d'][i], 'Parameter_number' :
dt['par_num'][i], 'Corrected_AIC' :
dt['aic'][i]}
# Getting rarity
dt_rare = {}
for mins in dt['tot_min'].iterkeys():
dt_rare['<=' + str(mins)] = dt['tot_min'][mins][i]
dt_vars = {}
for key in dt['vars'].iterkeys():
dt_vars[key] = dt['vars'][key][i]
fout.write('PREDICTED DISTRIBUTION : ' + kw + '\n' +
self.urns + ' = ' + str(dt['urns'][i]) + '\n' +
self.balls + ' = ' + str(dt['balls'][i]) +
'\nAIC = ' + str(dt['aic'][i]) + '\nDelta_AIC = ' +
str(dt['aic_d'][i]) + '\nAIC_weight = ' +
str(dt['aic_w'][i]) + '\nNumber of Parameters = ' +
str(dt['par_num'][i]) + '\nPredicted '+ self.Nmax + ' = ' +
str(dt['max'][i]) + '\nPredicted Rarity = ' +
str(dt_rare) + '\nOther Variables = ' +
str(dt_vars) + '\n\n')
fout.close()
count += 1
# Make and print AIC table
dtype = [('Model', 'S30'), ('Parameter_number', np.float),
('Corrected_AIC', np.float), ('AIC_weights', np.float),
('Delta_AIC', np.float)]
aic_array = np.empty(len(aic_vals), dtype=dtype)
for j, model_name in enumerate(aic_vals.iterkeys()):
aic_array['Model'][j] = model_name
aic_array['Parameter_number'][j] =\
aic_vals[model_name]['Parameter_number']
aic_array['Corrected_AIC'][j] =\
aic_vals[model_name]['Corrected_AIC']
aic_array['AIC_weights'][j] =\
aic_vals[model_name]['AIC_weights']
aic_array['Delta_AIC'][j] =\
aic_vals[model_name]['Delta_AIC']
output_form(aic_array, filename_aic)
fout = open(os.path.join(folder_name, 'README'), 'w')
fout.write(readme_info_summary.format(folder_name, count))
fout.close()
