def readRates( infile ):
"""read rates and G+C from a tab-separated file."""
from rpy import r as R
import rpy
handle, name = tempfile.mkstemp()
os.close(handle)
outfile = open(name, "w")
first = True
headers = []
for line in infile:
if line[0] == "#": continue
data = line[:-1].split("\t")
if first:
headers = data
first = False
continue
outfile.write( line )
outfile.close()
assert len(headers) == 3, "malformatted file of rates, please supply id, g+c, rate"
rpy.set_default_mode(rpy.NO_CONVERSION)
matrix = R.read_table( name, na_string = ("NA", "na"), col_names=headers )
rpy.set_default_mode(rpy.BASIC_CONVERSION)
os.remove( name )
return matrix, headers
def randomForest_predict(self, fit_model, data):
"""
03-17-06
2006-10-30, add avg_degree(vertex_gradient) and unknown_cut_off
"""
if self.debug:
sys.stderr.write("Predicting by randomForest...\n")
data = array(data)
set_default_mode(NO_CONVERSION)
data_frame = r.as_data_frame(
{
"p_value": data[:, 0],
"recurrence": data[:, 1],
"connectivity": data[:, 2],
"cluster_size": data[:, 3],
"gradient": data[:, 4],
"avg_degree": data[:, 5],
"unknown_ratio": data[:, 6],
"is_correct": r.factor(data[:, -1]),
}
)
set_default_mode(BASIC_CONVERSION)
pred = r.predict(fit_model, data_frame)
del data_frame
if self.debug:
sys.stderr.write("Done randomForest prediction.\n")
return pred
def rpart_predict(self, fit_model, data):
"""
11-23-05
split from rpart_fit_and_predict()
"""
if self.debug:
sys.stderr.write("Doing rpart_predict...\n")
data = array(data)
set_default_mode(NO_CONVERSION)
data_frame = r.as_data_frame(
{
"p_value": data[:, 0],
"recurrence": data[:, 1],
"connectivity": data[:, 2],
"cluster_size": data[:, 3],
"gradient": data[:, 4],
"is_correct": data[:, -1],
}
)
set_default_mode(BASIC_CONVERSION)
pred = r.predict(fit_model, data_frame, type=["class"]) # 11-17-05 type=c("class")
del data_frame
if self.debug:
sys.stderr.write("Done rpart_predict.\n")
return pred
def readRates(infile):
"""read rates and G+C from a tab-separated file."""
from rpy import r as R
import rpy
handle, name = tempfile.mkstemp()
os.close(handle)
outfile = open(name, "w")
first = True
headers = []
for line in infile:
if line[0] == "#":
continue
data = line[:-1].split("\t")
if first:
headers = data
first = False
continue
outfile.write(line)
outfile.close()
assert len(
headers) == 3, "malformatted file of rates, please supply id, g+c, rate"
rpy.set_default_mode(rpy.NO_CONVERSION)
matrix = R.read_table(name, na_string=("NA", "na"), col_names=headers)
rpy.set_default_mode(rpy.BASIC_CONVERSION)
os.remove(name)
return matrix, headers
def generateCorStructForGLSFromVarianceMatrix(cls, variance_matrix):
"""
2009-12-23
generate the corStruct for gls()
"""
sys.stderr.write("Generating corStruct for gls() from variance_matrix ...")
rpy.set_default_mode(rpy.NO_CONVERSION) # 04-07-05
rpy.r.library("nlme")
# bring the lower-triangle of variance_matrix into a list, row by row
no_of_rows, no_of_cols = variance_matrix.shape
lower_triangle_cor_vector = []
for i in range(1, no_of_rows):
for j in range(i):
lower_triangle_cor_vector.append(
variance_matrix[i][j] / math.sqrt(variance_matrix[i][i] * variance_matrix[j][j])
)
csSymm = rpy.r.corSymm(value=lower_triangle_cor_vector)
data_frame = rpy.r.as_data_frame({"fakedata": [1] * no_of_rows})
csSymm = rpy.r.Initialize(csSymm, data=data_frame)
rpy.set_default_mode(rpy.BASIC_CONVERSION)
sys.stderr.write("Done.\n")
return csSymm
def regress(dv, iv):
# Performs regression using R's linear model function (lm)
if type(dv.values()) is list and all(type(x) is list for x in iv.values()):
# First check that all of the data is in list form, otherwise RPy will throw an error
rpy.set_default_mode(
rpy.NO_CONVERSION) # Keeps values in R format until we need them
R_string, frame = make_R_strings(
dv, iv) # Create strings used by RPy to run regression
# R runs the linear regression
OLS_model = eval('rpy.r.lm(R_string, data=rpy.r.data_frame(' + frame +
'))')
rpy.set_default_mode(
rpy.BASIC_CONVERSION) # Now convert back to usable format
model_summary = rpy.r.summary(OLS_model) # Store resultss
# Extract all of the data of interest
coeff = model_summary['coefficients'][:, 0] # Regression coeffecients
std_err = model_summary['coefficients'][:, 1] # Standard Errors
t_stat = model_summary['coefficients'][:, 2] # t-statistics
p_val = model_summary['coefficients'][:, 3] # p-values
r_sqr = model_summary['r.squared'] # R-squred
asj_r_sqr = model_summary['adj.r.squared'] # Adjusted R-squared
return coeff, std_err, t_stat, p_val, r_sqr, asj_r_sqr
else:
raise TypeError("All variables must all be of type 'list'")
def lm(self, l, h):
for i in range(l, h + 1):
data_frame, data_model = self.mount_reg_params(i)
print data_model
rpy.set_default_mode(rpy.NO_CONVERSION)
linear_model = r.lm(r(data_model), data=data_frame)
rpy.set_default_mode(rpy.BASIC_CONVERSION)
print r.summary(linear_model)['r.squared']
def VegetationClassify(Elev_arr, River_arr):
rpy.r.library("rpart")
# Read the dictionary from the pickle file
pkl_file = open('decision_tree.pkl','rb')
rpy.set_default_mode(rpy.NO_CONVERSION)
traing_data = pickle.load(pkl_file)
pkl_file.close()
# Create Decision tree for predicting landcover class
# create the decision tree using rpart
fit = rpy.r.rpart(formula='Class ~ Elevation + RiverDistance + Slope \
+ Aspect_x + Aspect_y',data = traing_data, method = "class")
# calculate River distance using River_arr
River_dist_arr = dist.CityBlock(River_arr)
# claculate slope and aspect
(Slope_arr, Aspect_arr) = Slope_aspect.Slope_aspect(Elev_arr)
(x_len, y_len) = Elev_arr.shape
# Alloctae vegetation array for holding predicted landcover values
Veg_arr = numpy.zeros((x_len, y_len), dtype = "uint8")
# Normalize the elevation data
minimum_elev = numpy.min(Elev_arr)
factor = numpy.max(Elev_arr) - minimum_elev
Elev_arr = (Elev_arr[:,:] - minimum_elev)*100/factor
# Create various list to hold test data
Elevation = []
Slope = []
RiverDistance = []
Aspect_x = []
Aspect_y = []
# Append the data into respective list
for i in range(0,x_len):
for j in range(0,y_len):
Elevation.append(int(Elev_arr[i][j]))
Slope.append(int(Slope_arr[i][j]))
RiverDistance.append(int(River_dist_arr[i][j]))
Aspect_x.append(int(Aspect_arr[i][j][0]))
Aspect_y.append(int(Aspect_arr[i][j][1]))
# Create dictionary so as to apply R's predict command on it
Test_data ={'Elevation':Elevation ,'Slope':Slope ,'RiverDistance':RiverDistance,\
'Aspect_x':Aspect_x,'Aspect_y':Aspect_y}
rpy.set_default_mode(rpy.BASIC_CONVERSION)
# values contain probability values of the predicted landcover classes
values = rpy.r.predict(fit,newdata=Test_data,method="class")
for i in range(0,x_len):
for j in range(0,y_len):
# Get the class having max probability for each test data point
a = ndimage.maximum_position(values[i*x_len + j])
Veg_arr[i,j] = (a[0]*25) # Assign them some value to facilitate visualization
return Veg_arr
def randomForest_fit(self, known_data, parameter_list, bit_string="1111111"):
"""
03-17-06
2006-10-302006-10-30, add avg_degree(vertex_gradient) and unknown_cut_off
"""
if self.debug:
sys.stderr.write("Fitting randomForest...\n")
mty = parameter_list[0]
from rpy import r
r._libPaths(
os.path.join(lib_path, "R")
) # better than r.library("randomForest", lib_loc=os.path.join(lib_path, "R")) (see plone doc)
r.library("randomForest")
coeff_name_list = [
"p_value",
"recurrence",
"connectivity",
"cluster_size",
"gradient",
"avg_degree",
"unknown_ratio",
] # 2006-10-30
formula_list = []
for i in range(len(bit_string)):
if bit_string[i] == "1":
formula_list.append(coeff_name_list[i])
formula = r("is_correct~%s" % "+".join(formula_list))
known_data = array(known_data)
set_default_mode(NO_CONVERSION)
data_frame = r.as_data_frame(
{
"p_value": known_data[:, 0],
"recurrence": known_data[:, 1],
"connectivity": known_data[:, 2],
"cluster_size": known_data[:, 3],
"gradient": known_data[:, 4],
"avg_degree": known_data[:, 5],
"unknown_ratio": known_data[:, 6],
"is_correct": r.factor(known_data[:, -1]),
}
) # 03-17-06, watch r.factor #2006-10-30
if mty > 0:
fit = r.randomForest(formula, data=data_frame, mty=mty)
else:
fit = r.randomForest(formula, data=data_frame)
del data_frame
if self.debug:
sys.stderr.write("Done fitting randomForest.\n")
return fit
def interpolazionelineare(x, y):
rpy.set_default_mode(rpy.NO_CONVERSION) #serve per l'ultima parte in R
linear_model = rpy.r.lm(rpy.r("y ~ x"), data=rpy.r.data_frame(x=x, y=y))
rpy.set_default_mode(rpy.BASIC_CONVERSION)
summary = rpy.r.summary(linear_model)
#pendenza,errpendenza,intercetta,errintercetta
risultati = (summary['coefficients'][0][0], \
summary['coefficients'][0][1], \
summary['coefficients'][1][0], \
summary['coefficients'][1][1])
return risultati
def _setup(self):
try:
import rpy
self.__rpy = rpy
self.__rpy_version = 1
except:
import rpy2.rpy_classic as rpy
rpy.set_default_mode(rpy.BASIC_CONVERSION)
self.__rpy = rpy
self.__rpy_version = 2
self._process_events()
globals()[self.__name] = rpy.r
def _setup(self):
try:
import rpy
self.__rpy = rpy
self.__rpy_version = 1
except:
import rpy2.rpy_classic as rpy
rpy.set_default_mode(rpy.BASIC_CONVERSION)
self.__rpy = rpy
self.__rpy_version = 2
self._process_events()
globals()[self.__name] = rpy.r
def estimate_pi0(self, lambda_list, pi0_list):
"""
01-19-06
Storey2003, (natural) cubic spline, df=3
"""
sys.stderr.write("Estimating pi0...\n")
rpy.set_default_mode(rpy.NO_CONVERSION)
s = r.smooth_spline(lambda_list, pi0_list, df=3)
rpy.set_default_mode(rpy.BASIC_CONVERSION)
estimated_pi0 = r.predict(s, 1)['y']
print "\t estimated_pi0:", estimated_pi0
sys.stderr.write("Done.\n")
return estimated_pi0
def interpolazionelineare(self, other):
"""x.interpolazionelineare(y) esegue l'i.l. con x in ascissa e y in ordinata.
x e y devono essere due oggetti della classe DatiSperimentali."""
rpy.set_default_mode(rpy.NO_CONVERSION)
linear_model = rpy.r.lm(rpy.r("y ~ x"), data = rpy.r.data_frame(x=self.valori, y=other.valori))
rpy.set_default_mode(rpy.BASIC_CONVERSION)
summary = rpy.r.summary(linear_model)
#pendenza,errpendenza,intercetta,errintercetta
risultati = (summary['coefficients'][0][0], \
summary['coefficients'][0][1], \
summary['coefficients'][1][0], \
summary['coefficients'][1][1])
return risultati
def estimate_pi0(self, lambda_list, pi0_list):
"""
01-19-06
Storey2003, (natural) cubic spline, df=3
"""
sys.stderr.write("Estimating pi0...\n")
rpy.set_default_mode(rpy.NO_CONVERSION)
s = r.smooth_spline(lambda_list, pi0_list, df=3)
rpy.set_default_mode(rpy.BASIC_CONVERSION)
estimated_pi0 = r.predict(s,1)['y']
print "\t estimated_pi0:", estimated_pi0
sys.stderr.write("Done.\n")
return estimated_pi0
def interpolazionelineare(self, other):
rpy.set_default_mode(rpy.NO_CONVERSION)
linear_model = rpy.r.lm(rpy.r("y ~ x"),
data=rpy.r.data_frame(x=self.valori,
y=other.valori))
rpy.set_default_mode(rpy.BASIC_CONVERSION)
summary = rpy.r.summary(linear_model)
#pendenza,errpendenza,intercetta,errintercetta
risultati = (summary['coefficients'][0][0], \
summary['coefficients'][0][1], \
summary['coefficients'][1][0], \
summary['coefficients'][1][1])
return risultati
def _train(self, dataset):
"""Train the classifier using `data` (`Dataset`).
"""
# process the labels based on the model family
if self.params.family == 'gaussian':
# do nothing, just save the labels as a list
labels = dataset.labels.tolist()
pass
elif self.params.family == 'multinomial':
# turn lables into list of range values starting at 1
labels = _label2indlist(dataset.labels,
dataset.uniquelabels)
self.__ulabels = dataset.uniquelabels.copy()
# process the pmax
if self.params.pmax is None:
# set it to the num features
pmax = dataset.nfeatures
else:
# use the value
pmax = self.params.pmax
# train with specifying max_steps
# must not convert trained model to dict or we'll get segfault
rpy.set_default_mode(rpy.NO_CONVERSION)
self.__trained_model = rpy.r.glmnet(dataset.samples,
labels,
family=self.params.family,
alpha=self.params.alpha,
nlambda=self.params.nlambda,
standardize=self.params.standardize,
thresh=self.params.thresh,
pmax=pmax,
maxit=self.params.maxit,
type=self.params.model_type)
rpy.set_default_mode(rpy.NO_DEFAULT)
# get a dict version of the model
self.__trained_model_dict = rpy.r.as_list(self.__trained_model)
# save the lambda of last step
self.__last_lambda = self.__trained_model_dict['lambda'][-1]
# set the weights to the last step
weights = rpy.r.coef(self.__trained_model, s=self.__last_lambda)
if self.params.family == 'multinomial':
self.__weights = N.hstack([rpy.r.as_matrix(weights[str(i)])[1:]
for i in range(1,len(self.__ulabels)+1)])
elif self.params.family == 'gaussian':
self.__weights = rpy.r.as_matrix(weights)[1:]
def make_L(data,direction='S',z=None,):
""" Define the along track distance from one reference
direction define the cardinal direction priority (N,S,W or E).
S means that the reference will be the southern most point
z define the bathymetry, if defined, the closest point to that
bathymetry will be the reference. In case of cross this bathymetry
more than once, the direction criteria is used to distinguish.
"""
from fluid.common.distance import distance
all_cycles_data = join_cycles(data)
if z==None:
import rpy
#for t in topex.invert_keys(data):
for t in all_cycles_data:
rpy.set_default_mode(rpy.NO_CONVERSION)
linear_model = rpy.r.lm(rpy.r("y ~ x"), data = rpy.r.data_frame(x=all_cycles_data[t]['Longitude'], y=all_cycles_data[t]['Latitude']))
rpy.set_default_mode(rpy.BASIC_CONVERSION)
coef=rpy.r.coef(linear_model)
if direction=='S':
lat0=all_cycles_data[t]['Latitude'].min()-1
lon0 = (lat0-coef['(Intercept)'])/coef['x']
L_correction = distance(all_cycles_data[t]['Latitude'],all_cycles_data[t]['Longitude'],lat0,lon0).min()
for c in invert_keys(data)[t]:
data[c][t]['L'] = distance(data[c][t]['Latitude'],data[c][t]['Longitude'],lat0,lon0)- L_correction
# This bathymetric method was only copied from an old code. This should be atleast
# changed, if not removed.
elif method=='bathymetric':
import rpy
for t in all_cycles_data:
# First define the near coast values.
idSouth=numpy.argmin(all_cycles_data[t]['Latitude'])
L_tmp = distance(all_cycles_data[t]['Latitude'],all_cycles_data[t]['Longitude'],all_cycles_data[t]['Latitude'][idSouth],all_cycles_data[t]['Longitude'][idSouth])
idNearCoast = L_tmp.data<400e3
if min(all_cycles_data[t]['Bathy'][idNearCoast]) > -z:
idNearCoast = L_tmp.data<600e3
# Then calculate the distance to a reference
rpy.set_default_mode(rpy.NO_CONVERSION)
linear_model = rpy.r.lm(rpy.r("y ~ x"), data = rpy.r.data_frame(x=all_cycles_data[t]['Longitude'], y=all_cycles_data[t]['Latitude']))
rpy.set_default_mode(rpy.BASIC_CONVERSION)
coef=rpy.r.coef(linear_model)
lat0 = all_cycles_data[t]['Latitude'].min()-1
lon0 = (lat0-coef['(Intercept)'])/coef['x']
#L = distance(,lon,lat0,lon0)
#
#id0 = numpy.argmin(numpy.absolute(all_cycles_data[t]['Bathy'][idNearCoast]))
idref=numpy.argmin(numpy.absolute(all_cycles_data[t]['Bathy'][idNearCoast]+z))
#L_correction = distance(all_cycles_data[t]['Latitude'][idNearCoast][idref],all_cycles_data[t]['Longitude'][idNearCoast][idref],all_cycles_data[t]['Latitude'][idNearCoast][idref],all_cycles_data[t]['Longitude'][idNearCoast][idref])
L_correction = distance(all_cycles_data[t]['Latitude'][idNearCoast][idref],all_cycles_data[t]['Longitude'][idNearCoast][idref],lat0,lon0)
for c in topex.invert_keys(data)[t]:
#data[c][t]['L'] = distance(data[c][t]['Latitude'],data[c][t]['Longitude'],all_cycles_data[t]['Latitude'][idNearCoast][id0],all_cycles_data[t]['Longitude'][idNearCoast][id0]) - L_correction
data[c][t]['L'] = distance(data[c][t]['Latitude'],data[c][t]['Longitude'],lat0,lon0) - L_correction
#
return
def pure_linear_model_via_R(cls, non_NA_genotype_ls, non_NA_phenotype_ls, non_NA_phenotype2count=None):
"""
2010-2-25
use createDesignMatrix() to generate a design matrix
2009-8-28
split out of pure_linear_model(). same functionality as pure_linear_model(), but invoke R to run regression.
"""
genotype_matrix = cls.createDesignMatrix(non_NA_genotype_ls)
# 2008-11-10 do linear regression by R
genotype_var = numpy.var(genotype_matrix[:, 0]) # 2008-11-10 var=\sum(x_i-\bar{x})^2/(n-1)
rpy.set_default_mode(rpy.NO_CONVERSION) # 04-07-05
# data_frame = rpy.r.as_data_frame({"phenotype":non_NA_phenotype_ls, "genotype":rpy.r.as_factor(genotype_matrix[:,1])})
formula_list = []
data_frame_dict = {"phenotype": non_NA_phenotype_ls}
for i in range(genotype_matrix.shape[1]):
var_name = "genotype%s" % i
formula_list.append(var_name)
data_frame_dict.update({var_name: genotype_matrix[:, i]})
data_frame = rpy.r.as_data_frame(data_frame_dict)
formula = "phenotype~%s" % "+".join(formula_list)
if non_NA_phenotype2count and len(non_NA_phenotype2count) == 2: # binary phenotype, use logistic regression
lm_result = rpy.r.glm(rpy.r(formula), data=data_frame, family=rpy.r("binomial"))
else:
lm_result = rpy.r.glm(rpy.r(formula), data=data_frame)
rpy.set_default_mode(rpy.BASIC_CONVERSION)
# 04-07-05 r.summary() requires lm_result in NO_CONVERSION state
summary_stat = rpy.r.summary(lm_result)
# 06-30-05 index 0 in summary_stat['coefficients'] is intercept
coeff_list = []
coeff_p_value_list = []
for i in range(len(summary_stat["coefficients"])):
coeff_list.append(summary_stat["coefficients"][i][0]) # 0 is the coefficient
coeff_p_value_list.append(summary_stat["coefficients"][i][-1]) # -1 is the corresponding p-value
# 06-30-05 fill in other efficients based on bit_string, NOTE i+1
pvalue = coeff_p_value_list[1]
residuals = summary_stat["deviance"]
geno_effect_var = genotype_var * coeff_list[1] * coeff_list[1] * (no_of_rows - 1)
var_perc = geno_effect_var / (residuals + geno_effect_var)
pdata = PassingData(
pvalue=pvalue, var_perc=var_perc, coeff_list=coeff_list, coeff_p_value_list=coeff_p_value_list
)
return pdata
def plot(self,filename=None,format='pdf',**kwargs):
"""Plot the heatmap and save to an image file.
plot() # display using windowing system
plot('hm') # --> hm.pdf
plot('hm.png') # --> hm.png
plot('hm','png') # --> hm.png
By default a clustered heat map is constructed using R's heatmap.2
function. If R cannot be found, an unclustered heat map is
plotted. **kwargs can be used to customize the output.
:Arguments:
filename name of the image file; may contain extension
If empty use the windowing system.
format eps,pdf,png... whatever matplotlib understands
**kwargs for R:
scale Determines the coloring. Choose between 'none' (the
actual values in the heat map (possibly already normalized)),
'row' or 'column' (z-score across the dimension)
N_colors Number of color levels; default is 32.
**kwargs for matplotlib:
The kwargs are applied to the matplotlib.text() method and
are typically used to set font properties. See the
pylab/matplotlib documentation.
"""
if filename:
format = hop.utilities.fileextension(filename,default=format)
labels = self.labels()
try:
try:
import rpy
except ImportError:
from rpy2 import rpy_classic as rpy
# http://www.mail-archive.com/[email protected]/msg01893.html
rpy.set_default_mode(rpy.BASIC_CONVERSION)
self._heatmap_R(labels,filename=filename,format=format,**kwargs)
except ImportError:
msg(0,"rpy package missing: cannot plot clustered heat map, defaulting to "
"an unclustered heat map")
self._heatmap_matplotlib(labels,filename=filename,format=format,**kwargs)
if filename:
msg(1,"Wrote image to file %s.\n" % self.filename(filename,ext=format))
def check_R(model,g):
import rpy
from rpy import r
from numpy import array,allclose
vars = [ v.replace(':','.').replace('+','p').replace('-','m').replace('_','.') for v in model.vars[1:] ]
frame = dict( (v,model.X[:,i+1].reshape(-1)) for i,v in enumerate(vars) )
frame['y'] = model.y.reshape(-1)
formula = 'y ~ ' + ' + '.join(v.replace(':','.') for v in vars)
rpy.set_default_mode(rpy.NO_CONVERSION)
mod = r.glm(r(formula),data=r.data_frame(**frame),family=r.binomial('logit'))
rpy.set_default_mode(rpy.BASIC_CONVERSION)
pmod = mod.as_py()
coef = r.coefficients(mod)
coef = array([coef['(Intercept)']] + [ coef[v] for v in vars ],dtype=float)
coef2 = g.beta.reshape(-1)
def _predict(self, data):
"""
Predict the output for the provided data.
"""
# predict with standard method
values = rpy.r.predict(self.__trained_model,
newx=data,
type='link',
s=self.__last_lambda)
# predict with the final state (i.e., the last step)
classes = None
if self.params.family == 'multinomial':
# remove last dimension of values
values = values[:,:,0]
# get the classes too (they are 1-indexed)
rpy.set_default_mode(rpy.NO_CONVERSION)
class_ind = rpy.r.predict(self.__trained_model,
newx=data,
type='class',
s=self.__last_lambda)
rpy.set_default_mode(rpy.NO_DEFAULT)
class_ind = rpy.r.as_vector(class_ind)
# convert the strings to ints and subtract 1
class_ind = N.array([int(float(c))-1 for c in class_ind])
# convert to actual labels
classes = self.__ulabels[class_ind]
else:
# is gaussian, so just remove last dim of values
values = values[:,0]
# values need to be set anyways if values state is enabled
self.values = values
if classes is not None:
# set the values and return none
return classes
else:
# return the values as predictions
return values
def _mcmc_betas_same_sources(self, tag_list):
"""
The given tag_list contains tags that all have the same features
available. Train on the tags in tag_list using only the songs
in self.only_these_songs, or all available songs if
self.only_these_songs is None.
"""
if not self.production_run:
self.mcmc_reps = 75 # save time
rc.library("bayesm")
data = []
for tag in tag_list:
data.append(rc.list(X=self.X[tag],y=self.y[tag]))
rpy.set_default_mode(rpy.NO_CONVERSION) # Turn off conversion so that lm returns Robj.
data = rc.list(*data)
if self.regtype in ["Hierarchical Linear", "Hierarchical Mixture"]:
Data = rc.list(regdata=data)
elif self.regtype=="Hierarchical Logistic":
Data = rc.list(lgtdata=data)
if self.regtype=="Hierarchical Mixture":
Prior = rc.list(ncomp=self.ncomp)
Mcmc=rc.list(R=self.mcmc_reps)
rpy.set_default_mode(rpy.BASIC_CONVERSION)
try:
if self.regtype=="Hierarchical Linear":
output = rc.rhierLinearModel(Data=Data,Mcmc=Mcmc)
elif self.regtype=="Hierarchical Logistic":
output = rc.rhierBinLogit(Data=Data,Mcmc=Mcmc)
elif self.regtype=="Hierarchical Mixture":
output = rc.rhierLinearMixture(Data=Data,Prior=Prior,Mcmc=Mcmc)
except:
#pdb.set_trace()
self._info_about_r_error(tag_list)
return
beta_matrix = output['betadraw'].mean(axis=2) # nregressions x ncoeffs, averaged along third dim
matrix_index = 0
for tag in tag_list:
cur_tag_beta_vec = beta_matrix[matrix_index,:]
beta_dict_list = [dict([('beta', coeff)]) for coeff in cur_tag_beta_vec]
self.beta[tag] = dict(zip(self.sorted_sources[tag],beta_dict_list))
self.stats[tag] = dict() # I'm not currently storing any stats for hierarchical regressions.
matrix_index += 1
def _independent_betas_same_sources(self, tag_list, remove_tags_when_bad_regression, n_times_show_summary=3):
times_showed_summary = 0 # This allows us to print out some summary statistics without producing an overwhelming amount of output.
SUMMARY_STATS = ["beta", "stderr", "tstat", "pval"]
for tag in tag_list:
self._progress("Computing betas for tag %s." % tag, newline=True) # rmme: newline make false
rpy.set_default_mode(rpy.NO_CONVERSION) # Turn off conversion so that lm returns Robj.
data = rc.list(y=self.y[tag],X=self.X[tag])
model = "y~X-1" # Use -1 because X has an intercept already
if self.regtype=="Independent Linear":
try:
result = rc.lm(model,data=data)
except:
pdb.set_trace()
elif self.regtype=="Independent Logistic":
result = rc.glm(model,family=rc.binomial("logit"),data=data)
rpy.set_default_mode(rpy.BASIC_CONVERSION) # Return to normal conversion mode.
summary = rc.summary(result,correlation=rc.TRUE)
self._record_regression_stats(tag, summary)
beta_dict = dict()
sorted_sources = self.sorted_sources[tag]
coeff_matrix = summary["coefficients"]
for i in range(len(sorted_sources)):
try:
cur_source_dict = dict(zip(SUMMARY_STATS,coeff_matrix[i,:]))
except IndexError:
util.info("\tWARNING: Regression for %s didn't end up using all variables." % tag)
if remove_tags_when_bad_regression:
self._remove_tag(tag)
break # break from for-loop over sorted_sources; we don't continue out of the per-tag for loop until later when we check if tag is in self.features....
continue
try:
cur_source_dict["-log10(pval)"] = -log(cur_source_dict["pval"], 10)
except OverflowError:
pass
beta_dict[sorted_sources[i]] = cur_source_dict
if tag not in self.features: # We've removed this tag a few lines above, so skip it.
continue
self.beta[tag] = beta_dict
if times_showed_summary < n_times_show_summary:
self._print_regression_summary(tag, summary)
times_showed_summary += 1
def regress(dv,iv):
# Performs regression using R's linear model function (lm)
if type(dv.values()) is list and all(type(x) is list for x in iv.values()):
# First check that all of the data is in list form, otherwise RPy will throw an error
rpy.set_default_mode(rpy.NO_CONVERSION) # Keeps values in R format until we need them
R_string,frame=make_R_strings(dv,iv) # Create strings used by RPy to run regression
# R runs the linear regression
OLS_model=eval('rpy.r.lm(R_string, data=rpy.r.data_frame('+frame+'))')
rpy.set_default_mode(rpy.BASIC_CONVERSION) # Now convert back to usable format
model_summary=rpy.r.summary(OLS_model) # Store resultss
# Extract all of the data of interest
coeff=model_summary['coefficients'][:,0] # Regression coeffecients
std_err=model_summary['coefficients'][:,1] # Standard Errors
t_stat=model_summary['coefficients'][:,2] # t-statistics
p_val=model_summary['coefficients'][:,3] # p-values
r_sqr=model_summary['r.squared'] # R-squred
asj_r_sqr=model_summary['adj.r.squared'] # Adjusted R-squared
return coeff,std_err,t_stat,p_val,r_sqr,asj_r_sqr
else:
raise TypeError("All variables must all be of type 'list'")
def rpart_fit_and_predict(self, all_data, known_data, rpart_cp, loss_matrix, prior_prob, bit_string='11111'):
"""
11-09-05
1st use known_data to get the fit model
2nd use the fit model to do prediction on all_data, result is prob for each class
11-09-05 add rpart_cp
11-17-05
add loss_matrix, prior_prob
return two pred
"""
sys.stderr.write("rpart fitting and predicting...\n")
r.library("rpart")
coeff_name_list = ['p_value', 'recurrence', 'connectivity', 'cluster_size', 'gradient']
formula_list = []
for i in range(len(bit_string)):
if bit_string[i] == '1':
formula_list.append(coeff_name_list[i])
#11-17-05 transform into array
all_data = array(all_data)
known_data = array(known_data)
set_default_mode(NO_CONVERSION)
data_frame = r.as_data_frame({"p_value":known_data[:,0], "recurrence":known_data[:,1], "connectivity":known_data[:,2], \
"cluster_size":known_data[:,3], "gradient":known_data[:,4], "is_correct":known_data[:,-1]})
if prior_prob:
prior_prob = [prior_prob, 1-prior_prob] #get the full list
fit = r.rpart(r("is_correct~%s"%'+'.join(formula_list)), data=data_frame, method="class", control=r.rpart_control(cp=rpart_cp),\
parms=r.list(prior=prior_prob, loss=r.matrix(loss_matrix) ) )
else:
fit = r.rpart(r("is_correct~%s"%'+'.join(formula_list)), data=data_frame, method="class", control=r.rpart_control(cp=rpart_cp),\
parms=r.list(loss=r.matrix(loss_matrix) ) )
set_default_mode(BASIC_CONVERSION)
pred_training = r.predict(fit, data_frame, type=["class"])
del data_frame
set_default_mode(NO_CONVERSION)
all_data_frame = r.as_data_frame({"p_value":all_data[:,0], "recurrence":all_data[:,1], "connectivity":all_data[:,2], \
"cluster_size":all_data[:,3], "gradient":all_data[:,4], "is_correct":all_data[:,-1]})
set_default_mode(BASIC_CONVERSION)
pred = r.predict(fit, all_data_frame, type=["class"]) #11-17-05 type=c("class")
del all_data_frame
sys.stderr.write("Done rpart fitting and predicting.\n")
return pred, pred_training
# if you have rpy installed, use it to test the results
have_rpy = False
try:
print("\n")
print("="*30)
print("Validating OLS results in R")
print("="*30)
import rpy
have_rpy = True
except ImportError:
print("\n")
print("="*30)
print("Validating OLS-class results in R")
print("="*30)
print("rpy is not installed")
print("="*30)
if have_rpy:
y = data[:,0]
x1 = data[:,1]
x2 = data[:,2]
x3 = data[:,3]
x4 = data[:,4]
rpy.set_default_mode(rpy.NO_CONVERSION)
linear_model = rpy.r.lm(rpy.r("y ~ x1 + x2 + x3 + x4"), data = rpy.r.data_frame(x1=x1,x2=x2,x3=x3,x4=x4,y=y))
rpy.set_default_mode(rpy.BASIC_CONVERSION)
print(linear_model.as_py()['coefficients'])
summary = rpy.r.summary(linear_model)
print(summary)
def __init__(self, y, design, model_type=r.lm, **kwds):
""" Set up and estimate R model with data and design """
r.library("MASS") # still needs to be in test, but also here for
# logical tests at the end not to show an error
self.y = np.array(y)
self.design = np.array(design)
self.model_type = model_type
self._design_cols = ["x.%d" % (i + 1) for i in range(self.design.shape[1])]
# Note the '-1' for no intercept - this is included in the design
self.formula = r("y ~ %s-1" % "+".join(self._design_cols))
self.frame = r.data_frame(y=y, x=self.design)
rpy.set_default_mode(rpy.NO_CONVERSION)
results = self.model_type(self.formula, data=self.frame, **kwds)
self.robj = results # keep the Robj model so it can be
# used in the tests
rpy.set_default_mode(rpy.BASIC_CONVERSION)
rsum = r.summary(results)
self.rsum = rsum
# Provide compatible interface with scipy models
self.results = results.as_py()
# coeffs = self.results['coefficients']
# self.beta0 = np.array([coeffs[c] for c in self._design_cols])
self.nobs = len(self.results["residuals"])
if isinstance(self.results["residuals"], dict):
self.resid = np.zeros((len(list(self.results["residuals"].keys()))))
for i in list(self.results["residuals"].keys()):
self.resid[int(i) - 1] = self.results["residuals"][i]
else:
self.resid = self.results["residuals"]
self.fittedvalues = self.results["fitted.values"]
self.df_resid = self.results["df.residual"]
self.params = rsum["coefficients"][:, 0]
self.bse = rsum["coefficients"][:, 1]
self.bt = rsum["coefficients"][:, 2]
try:
self.pvalues = rsum["coefficients"][:, 3]
except:
pass
self.rsquared = rsum.setdefault("r.squared", None)
self.rsquared_adj = rsum.setdefault("adj.r.squared", None)
self.aic_R = rsum.setdefault("aic", None)
self.fvalue = rsum.setdefault("fstatistic", None)
if self.fvalue and isinstance(self.fvalue, dict):
self.fvalue = self.fvalue.setdefault("value", None) # for wls
df = rsum.setdefault("df", None)
if df: # for RLM, works for other models?
self.df_model = df[0] - 1 # R counts intercept
self.df_resid = df[1]
self.bcov_unscaled = rsum.setdefault("cov.unscaled", None)
self.bcov = rsum.setdefault("cov.scaled", None)
if "sigma" in rsum:
self.scale = rsum["sigma"]
elif "dispersion" in rsum:
self.scale = rsum["dispersion"]
else:
self.scale = None
self.llf = r.logLik(results)
if model_type == r.glm:
self.getglm()
if model_type == r.rlm:
self.getrlm()
from rpy import r, set_default_mode, NO_CONVERSION, PROC_CONVERSION
import numpy as N
from scipy.interpolate import interp1d
from scipy.ndimage import gaussian_filter1d
from ppgplot_spb import *
import gaussian
set_default_mode(PROC_CONVERSION)
def bootdensity(data, min, max, nboot, ci):
""" Calculate density and confidence intervals on density
for a 1D array of points. Bandwidth is selected automatically.
"""
r("""
limdensity <- function(data, weights=NULL, bw="nrd0")
{
density(data, from=%f, to=%f, weights=weights, bw=bw)
}
"""%(min, max))
density = r.limdensity(data)
xdens = N.array(density['x'])
ydens = N.array(density['y'])
bw = density['bw']
#print 'bandwidth:', bw
ydensboot = N.zeros((nboot, len(xdens)), N.float)
ndata = len(data)
ran = N.random.uniform(0, ndata, (nboot,ndata)).astype(N.int)
for i in range(nboot):
den = r.limdensity(data[ran[i]])
def DecisionTree(output_dir, elev_filename, landcover_filename, river_filename):
"""
This module generate decision tree used to allocate landcover classes.
It imports rpart library from rpy package.
Reads the training data, creates a sample data and use rpart libray to build decision tree.
"""
rpy.r.library("rpart") # rpart library used for creating Decision tree
# Read Elevation Data from ascii file
file_name = "training_data/%s" % (elev_filename)
Elev_arr = numpy.loadtxt(file_name, unpack=True)
# Read Landcover Data from ascii file
file_name = "training_data/%s" % (landcover_filename)
Landcover = numpy.loadtxt(file_name, unpack=True)
# Read River Data from ascii file
file_name = "training_data/%s" % (river_filename)
River = numpy.loadtxt(file_name, unpack=True)
# Compute City block distance from River data
River_dist_arr = city_block_dist.CityBlock(River)
# Compute Slope and Aspect from Elevation data
(Slope_arr, Aspect_arr) = Slope_aspect.Slope_aspect(Elev_arr)
(x_len, y_len) = Elev_arr.shape
no_of_veg_class = 10 # no of vegetation class in Landcover matrix
# Generating Lists for differnt Landcover classes
# Create list of lists to hold pixels of each landcover class - no of list in
# list L is equal to no_of_veg_class
L = []
for i in range(0, no_of_veg_class):
L.append([])
# Now append the pixel co-ordinates into respective list of lists
for i in range(1, x_len - 1): # Ignoring boundary cells
for j in range(1, y_len - 1): # because we don't have slope and aspect for them
# nodata values already gets handled since we are ignoring it
if Landcover[i][j] == 0:
L[0].append((i, j))
elif Landcover[i][j] == 1:
L[1].append((i, j))
elif Landcover[i][j] == 2:
L[2].append((i, j))
elif Landcover[i][j] == 3:
L[3].append((i, j))
elif Landcover[i][j] == 4:
L[4].append((i, j))
elif Landcover[i][j] == 5:
L[5].append((i, j))
elif Landcover[i][j] == 6:
L[6].append((i, j))
elif Landcover[i][j] == 7:
L[7].append((i, j))
elif Landcover[i][j] == 8:
L[8].append((i, j))
elif Landcover[i][j] == 9:
L[9].append((i, j))
# Sample Data for decision tree
# normalizing elevation data
minimum_elev = numpy.min(Elev_arr)
factor = numpy.max(Elev_arr) - minimum_elev
Elev_arr = (Elev_arr[:, :] - minimum_elev) * 100 / factor
# Create various list to hold sample training data
Elevation = []
Slope = []
RiverDistance = []
Aspect_x = []
Aspect_y = []
Class = []
# Now sampling the data
for i in range(0, no_of_veg_class):
if len(L[i]) < 500:
limit = len(L[i])
else:
limit = 500
for j in range(0, limit):
Elevation.append(int(Elev_arr[L[i][j][0]][L[i][j][1]]))
Slope.append(int(Slope_arr[L[i][j][0]][L[i][j][1]]))
RiverDistance.append(int(River_dist_arr[L[i][j][0]][L[i][j][1]]))
Aspect_x.append(int(Aspect_arr[L[i][j][0]][L[i][j][1]][0]))
Aspect_y.append(int(Aspect_arr[L[i][j][0]][L[i][j][1]][1]))
Class.append(i)
# create dictionary of sample data which will be needed to generate decision tree
traing_data = {
"Elevation": Elevation,
"Slope": Slope,
"RiverDistance": RiverDistance,
"Aspect_x": Aspect_x,
"Aspect_y": Aspect_y,
"Class": Class,
}
# write dictionary into pickle file for further use(reusability)
output = open("decision_tree.pkl", "wb")
pickle.dump(traing_data, output)
output.close()
rpy.set_default_mode(rpy.NO_CONVERSION)
print "Creating Decision tree"
# Using rpart create the decision tree
fit = rpy.r.rpart(
formula="Class ~ Elevation + RiverDistance + Slope + Aspect_x + Aspect_y", data=traing_data, method="class"
)
# output a png image of the decision tree
file_name = "%s/DecisionTree.png" % (output_dir)
rpy.r.png(file_name)
rpy.r.plot(fit)
rpy.r.text(fit)
rpy.r.dev_off()
def calc_stratified_rates(summset,
popset,
conflev=0.95,
basepop=100000,
timeinterval='years',
ci_method='dobson',
popset_popcol='_freq_',
debug=False):
"""
Calculate stratified population rates
summset is a straified summary dataset of counts of events for
the population-of-interest
popset is the stratified population counts for the
population-of-interest
"""
from rpy import r, get_default_mode, set_default_mode, BASIC_CONVERSION
alpha = get_alpha(conflev)
if ci_method not in ('dobson', 'ff'):
raise Error('Only Dobson et al. (dobson) and Fay-Feuer (ff) '
'methods for confidence intervals currently '
'implemented')
if not popset.has_column(popset_popcol):
raise Error('Denominator population dataset %r does not have a '
'%r column' % (popset.label or popset.name, popset_popcol))
st = time.time()
r_mode = get_default_mode()
try:
set_default_mode(BASIC_CONVERSION)
# We turn the summset into an Ncondcols-dimensional matrix
summtab = CrossTab.from_summset(summset)
# The population dataset must have at least as many dimensions as
# summary dataset. Any additional axes are eliminated by summing.
# any missing axes are created by replication.
poptab = CrossTab.from_summset(popset, shaped_like=summtab)
poptab.collapse_axes_not_in(summtab)
poptab.replicate_axes(summtab)
popfreq = poptab[popset_popcol].data.astype(Numeric.Float64)
# Manufacture a CrossTab for the result
result = summtab.empty_copy()
basepop = float(basepop)
for table, name, n_add, l_add in just_freq_tables(summtab):
# avoid integer overflows...
summfreq = table.data.astype(Numeric.Float64)
strata_rate = summfreq / popfreq
result.add_table('summfreq' + n_add,
data=summfreq,
label='Events' + l_add)
result.add_table('popfreq' + n_add,
data=popfreq,
label='Person-' + timeinterval + ' at risk' +
l_add)
result.add_table('sr' + n_add,
data=strata_rate * basepop,
label='Strata-specific Rate per ' +
'%d' % basepop + ' person-' + timeinterval +
l_add)
if alpha is not None:
# CIs for stratified rates
summfreq_shape = summfreq.shape
summfreq_flat = MA.ravel(summfreq)
assert popfreq.shape == summfreq.shape
popfreq_flat = MA.ravel(popfreq)
sr_ll = Numeric.empty(len(summfreq_flat),
typecode=Numeric.Float64)
sr_ul = Numeric.empty(len(summfreq_flat),
typecode=Numeric.Float64)
sr_ll_mask = Numeric.zeros(len(summfreq_flat),
typecode=Numeric.Int8)
sr_ul_mask = Numeric.zeros(len(summfreq_flat),
typecode=Numeric.Int8)
for i, v in enumerate(summfreq_flat):
try:
if v == 0:
sr_ll[i] = 0.0
else:
sr_ll[i] = (
(r.qchisq(alpha / 2., df=2.0 * v) / 2.0) /
popfreq_flat[i]) * basepop
sr_ul[i] = (
(r.qchisq(1. - alpha / 2., df=2.0 *
(v + 1)) / 2.0) /
popfreq_flat[i]) * basepop
except:
sr_ll[i] = 0.0
sr_ul[i] = 0.0
sr_ll_mask[i] = 1
sr_ul_mask[i] = 1
sr_ll = MA.array(sr_ll, mask=sr_ll_mask, typecode=MA.Float64)
sr_ul = MA.array(sr_ul, mask=sr_ul_mask, typecode=MA.Float64)
sr_ll.shape = summfreq_shape
sr_ul.shape = summfreq_shape
sr_base = 'Stratified rate %s%%' % (100.0 * conflev)
result.add_table('sr_ll' + n_add,
data=sr_ll,
label=sr_base + ' lower confidence limit ' +
l_add)
result.add_table('sr_ul' + n_add,
data=sr_ul,
label=sr_base + ' upper confidence limit ' +
l_add)
finally:
set_default_mode(r_mode)
soom.info('calc_stratified_rates took %.03f' % (time.time() - st))
name = 'stratified_rates_' + summset.name
label = 'Stratified Rates for ' + (summset.label or summset.name)
if conflev:
label += ' (%g%% conf. limits)' % (conflev * 100)
if debug:
global vars
vars = Vars(locals())
return result.to_summset(name, label=label)
def gls_via_R(cls, non_NA_genotype_ls, non_NA_phenotype_ls, non_NA_phenotype2count=None, variance_matrix=None):
"""
2009-12-23
general least square model via calling equivalent function in R.
"""
genotype_matrix = cls.createDesignMatrix(non_NA_genotype_ls) # no need to add a constant vector.
if hasattr(cls, "corStruct"):
corStruct = cls.corStruct
else:
if variance_matrix is not None:
corStruct = cls.generateCorStructForGLSFromVarianceMatrix(variance_matrix)
setattr(cls, "corStruct", corStruct)
else:
corStruct = None
# 2008-11-10 do linear regression by R
genotype_var = numpy.var(genotype_matrix[:, 0]) # 2008-11-10 var=\sum(x_i-\bar{x})^2/(n-1)
rpy.set_default_mode(rpy.NO_CONVERSION) # 04-07-05
rpy.r.library("nlme")
# data_frame = rpy.r.as_data_frame({"phenotype":non_NA_phenotype_ls, "genotype":rpy.r.as_factor(genotype_matrix[:,1])})
formula_list = []
data_frame_dict = {"phenotype": non_NA_phenotype_ls}
for i in range(genotype_matrix.shape[1]):
var_name = "genotype%s" % i
formula_list.append(var_name)
data_frame_dict.update({var_name: genotype_matrix[:, i]})
data_frame = rpy.r.as_data_frame(data_frame_dict)
formula = "phenotype~%s" % "+".join(formula_list)
lm_result = rpy.r.gls(rpy.r(formula), data=data_frame, correlation=corStruct)
rpy.set_default_mode(rpy.BASIC_CONVERSION)
# 04-07-05 r.summary() requires lm_result in NO_CONVERSION state
summary_stat = rpy.r.summary(lm_result)
rpy.set_default_mode(rpy.NO_CONVERSION)
summary_stat1 = rpy.r.summary(lm_result)
rpy.set_default_mode(rpy.VECTOR_CONVERSION)
summary_stat2 = rpy.r.summary(lm_result)
rpy.set_default_mode(rpy.TOP_CONVERSION)
summary_stat3 = rpy.r.summary(lm_result)
# 06-30-05 index 0 in summary_stat['coefficients'] is intercept
coeff_list = []
coeff_p_value_list = []
for i in range(len(summary_stat["coefficients"])):
coeff_list.append(summary_stat["coefficients"][i][0]) # 0 is the coefficient
coeff_p_value_list.append(summary_stat["coefficients"][i][-1]) # -1 is the corresponding p-value
# 06-30-05 fill in other efficients based on bit_string, NOTE i+1
pvalue = coeff_p_value_list[1]
residuals = summary_stat["deviance"]
geno_effect_var = genotype_var * coeff_list[1] * coeff_list[1] * (no_of_rows - 1)
var_perc = geno_effect_var / (residuals + geno_effect_var)
pdata = PassingData(
pvalue=pvalue, var_perc=var_perc, coeff_list=coeff_list, coeff_p_value_list=coeff_p_value_list
)
return pdata
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
parser = E.OptionParser(
version=
"%prog version: $Id: data2phylocontrasts.py 2782 2009-09-10 11:40:29Z andreas $",
usage=globals()["__doc__"])
parser.add_option("-c",
"--columns",
dest="columns",
type="string",
help="columns to take for calculating histograms.")
parser.add_option("-t",
"--filename-tree",
dest="filename_tree",
type="string",
help="filename with tree(s).")
parser.add_option("--skip-header",
dest="add_header",
action="store_false",
help="do not add header to flat format.")
parser.add_option("--write-header",
dest="write_header",
action="store_true",
help="write header and exit.")
parser.add_option("--debug",
dest="debug",
action="store_true",
help="debug mode")
parser.add_option("--display-tree",
dest="display_tree",
action="store_true",
help="display the tree")
parser.add_option("-m",
"--method",
dest="methods",
type="choice",
action="append",
choices=("contrasts", "spearman", "pearson", "compute"),
help="methods to perform on contrasts.")
parser.set_defaults(
columns="all",
filename_tree=None,
add_header=True,
write_header=False,
debug=False,
methods=[],
value_format="%6.4f",
pvalue_format="%e",
display_tree=False,
)
(options, args) = E.Start(parser, quiet=True)
if options.columns not in ("all", "all-but-first"):
options.columns = map(lambda x: int(x) - 1, options.columns.split(","))
phylip = WrapperPhylip.Phylip()
if options.debug:
phylip.setLogLevel(options.loglevel)
phylip.setProgram("contrast")
##########################################################
##########################################################
##########################################################
# retrieve data and give to phylip
data = []
headers = []
first = True
for line in sys.stdin:
if line[0] == "#":
continue
d = line[:-1].strip().split("\t")
if first:
first = False
headers = d[1:]
continue
data.append(d)
phylip.setData(data)
ncolumns = len(headers)
nrows = len(data)
##########################################################
##########################################################
##########################################################
# read trees
nexus = None
if options.filename_tree:
nexus = TreeTools.Newick2Nexus(open(options.filename_tree, "r"))
if not nexus:
raise ValueError("please provide trees with branchlenghts")
##########################################################
##########################################################
##########################################################
# set up phylip
phylip_options = []
# print out contrasts
phylip_options.append("C")
phylip_options.append("Y")
phylip.setOptions(phylip_options)
##########################################################
##########################################################
##########################################################
# main loop
##########################################################
for tree in nexus.trees:
if options.display_tree:
tree.display()
# compute this before giving the tree to the phylip module,
# as it remaps taxon names.
map_node2data = {}
for x in range(nrows):
taxon = data[x][0]
map_node2data[tree.search_taxon(taxon)] = x
phylip.setTree(tree)
result = phylip.run()
for method in options.methods:
if method in ("pearson", "spearman"):
options.stdout.write("header1\theader2\tr\tp\tcode\n")
n = len(result.mContrasts)
columns = []
for c in range(ncolumns):
columns.append(map(lambda x: x[c], result.mContrasts))
for x in range(0, ncolumns - 1):
for y in range(x + 1, ncolumns):
# phylip value
phy_r = result.mCorrelations[x][y]
import rpy
from rpy import r as R
# Various ways to calculate r. It is not possible to use
# cor.test or lsfit directly, as you have to perform a
# regression through the origin.
# uncomment to check pearson r against phylip's value
## r = calculateCorrelationCoefficient( columns[x], columns[y] )
# for significance, use linear regression models in R
rpy.set_default_mode(rpy.NO_CONVERSION)
linear_model = R.lm(R("y ~ x - 1"),
data=R.data_frame(x=columns[x],
y=columns[y]))
rpy.set_default_mode(rpy.BASIC_CONVERSION)
ss = R.summary(linear_model)
# extract the p-value
p = ss['coefficients'][-1][-1]
if p < 0.001:
code = "***"
elif p < 0.01:
code = "**"
elif p < 0.05:
code = "*"
else:
code = ""
options.stdout.write("\t".join(
(headers[x], headers[y], options.value_format %
phy_r, options.pvalue_format % p, code)) + "\n")
elif method == "contrasts":
options.stdout.write("\t".join(headers) + "\n")
for d in result.mContrasts:
options.stdout.write(
"\t".join(map(lambda x: options.value_format % x, d)) +
"\n ")
elif method == "compute":
# make room for all internal nodes and one dummy node
# for unrooted trees.
max_index = TreeTools.GetMaxIndex(tree) + 2
variances = [None] * max_index
values = [[None] * nrows for x in range(max_index)]
contrasts = []
for x in range(max_index):
contrasts.append([None] * ncolumns)
branchlengths = [None] * max_index
def update_data(
node_id,
bl,
c1,
c2,
):
b1, b2 = branchlengths[c1], branchlengths[c2]
rb1 = 1.0 / b1
rb2 = 1.0 / b2
# compute variance
variance = math.sqrt(b1 + b2)
# extend branch length of this node to create correct
# variance for parent
branchlengths[node_id] = bl + (b1 * b2) / (b1 + b2)
variances[node_id] = variance
for c in range(ncolumns):
v1, v2 = values[c1][c], values[c2][c]
# save ancestral value as weighted mean
values[node_id][c] = (
(rb1 * v1 + rb2 * v2)) / (rb1 + rb2)
# compute normalized contrast
contrasts[node_id][c] = (v1 - v2) / variance
def update_contrasts(node_id):
"""update contrasts for a node."""
node = tree.node(node_id)
if node.succ:
if len(node.succ) == 2:
c1, c2 = node.succ
update_data(node_id, node.data.branchlength, c1,
c2)
else:
assert (node_id == tree.root)
assert (len(node.succ) == 3)
update_data(node_id, node.data.branchlength,
node.succ[0], node.succ[1])
update_data(max_index - 1, node.data.branchlength,
node_id, node.succ[2])
else:
for c in range(ncolumns):
values[node_id][c] = float(
data[map_node2data[node_id]][c + 1])
branchlengths[node_id] = node.data.branchlength
tree.dfs(tree.root, post_function=update_contrasts)
options.stdout.write("node_id\tvariance\t%s\n" %
"\t".join(headers))
for node_id in range(max_index):
if variances[node_id] is None:
continue
options.stdout.write("%s\t%s\t%s\n" % (
node_id,
options.value_format % variances[node_id],
"\t".join(
map(lambda x: options.value_format % x,
contrasts[node_id])),
))
E.Stop()
def calc_indirectly_std_ratios(summset,
popset,
stdsummset,
stdpopset,
conflev=0.95,
baseratio=100,
timeinterval='years',
popset_popcol='_freq_',
stdpopset_popcol='_stdpop_',
ci_method='daly',
debug=False):
"""
Calculate Indirectly Standardised Population Event Ratios
- summset is a summary dataset of counts of events for the
population-of-interest being compared to the standard population.
- popset is the stratified population counts for the
population-of-interest
- stdsummset is a summary dataset of counts of events for the
standard population
- stdpopset is the stratified population counts for the standard
population
"""
from rpy import r, get_default_mode, set_default_mode, BASIC_CONVERSION
alpha = get_alpha(conflev)
if ci_method != 'daly':
raise Error("Only Daly method for confidence intervals "
"currently implemented")
if not popset.has_column(popset_popcol):
raise Error('Denominator population dataset %r does not have a '
'%r column' % (popset.label or popset.name, popset_popcol))
if not stdpopset.has_column(stdpopset_popcol):
raise Error('Standard population dataset %r does not have a '
'%r column' %
(stdpopset.label or stdpopset.name, stdpopset_popcol))
st = time.time()
r_mode = get_default_mode()
try:
set_default_mode(BASIC_CONVERSION)
shape = shape_union(stdsummset, summset)
summtab = CrossTab.from_summset(summset, shaped_like=shape)
stdsummtab = CrossTab.from_summset(stdsummset, shaped_like=shape)
stdpoptab = CrossTab.from_summset(stdpopset, shaped_like=shape)
stdpoptab.collapse_axes_not_in(stdsummtab)
stdsummtab.replicate_axes(shape)
stdpoptab.replicate_axes(shape)
poptab = CrossTab.from_summset(popset, shaped_like=shape)
poptab.collapse_axes_not_in(shape)
if poptab.get_shape() != stdsummtab.get_shape():
raise Error(
'Observed population does not have all the required columns')
popfreq = poptab[popset_popcol].data.astype(MA.Float64)
result = stdsummtab.empty_copy()
result.add_table('popfreq',
data=popfreq,
label='Total person-' + timeinterval + ' at risk')
expected_cols = []
for table, name, n_add, l_add in just_freq_tables(stdsummtab):
stdsummfreq = stdsummtab[name].data.astype(MA.Float64)
stdpopfreq = stdpoptab[stdpopset_popcol].data.astype(MA.Float64)
std_strata_rates = stdsummfreq / stdpopfreq
strata_expected_freq = std_strata_rates * popfreq
# print stdsummfreq[0,0,0], stdpopfreq[0,0,0], popfreq[0,0,0]
result.add_table('expected' + n_add,
data=strata_expected_freq,
label='Expected events' + l_add)
expected_cols.append('expected' + n_add)
result.collapse_axes_not_in(summtab)
axis = 0
baseratio = float(baseratio)
for table, name, n_add, l_add in just_freq_tables(summtab):
observed = table.data.astype(Numeric.Float64)
result.add_table('observed' + n_add,
data=observed,
label='Observed events' + l_add)
expected = result['expected' + n_add].data
isr = observed / expected
result.add_table('isr' + n_add,
data=isr * baseratio,
label='Indirectly Standardised Event Ratio')
# Confidence Intervals
if alpha is None or name != '_freq_':
# Can only calculate confidence intervals on freq cols
continue
conflev_l = (1 - conflev) / 2.0
conflev_u = (1 + conflev) / 2.0
# get shape of observed
observed_shape = observed.shape
# flattened version
observed_flat = MA.ravel(observed)
# sanity check on shapes - should be the same!
assert expected.shape == observed.shape
# flattened version of expecetd
expected_flat = MA.ravel(expected)
# lists to hold results
isr_ll = Numeric.empty(len(observed_flat),
typecode=Numeric.Float64)
isr_ul = Numeric.empty(len(observed_flat),
typecode=Numeric.Float64)
isr_ll_mask = Numeric.zeros(len(observed_flat),
typecode=Numeric.Int8)
isr_ul_mask = Numeric.zeros(len(observed_flat),
typecode=Numeric.Int8)
obs_mask = MA.getmaskarray(observed_flat)
exp_mask = MA.getmaskarray(expected_flat)
for i, v in enumerate(observed_flat):
if obs_mask[i] or exp_mask[i]:
isr_ll[i] = 0.0
isr_ul[i] = 0.0
isr_ll_mask[i] = 1
isr_ul_mask[i] = 1
else:
if v == 0.:
obs_ll = 0.0
obs_ul = -math.log(1 - conflev)
else:
obs_ll = r.qgamma(conflev_l, v, scale=1.)
obs_ul = r.qgamma(conflev_u, v + 1., scale=1.)
isr_ll[i] = obs_ll / expected_flat[i]
isr_ul[i] = obs_ul / expected_flat[i]
isr_ll = MA.array(isr_ll, typecode=MA.Float64, mask=isr_ll_mask)
isr_ul = MA.array(isr_ul, typecode=MA.Float64, mask=isr_ul_mask)
isr_ll.shape = observed_shape
isr_ul.shape = observed_shape
isr_base = 'ISR %d%%' % (100.0 * conflev)
result.add_table('isr_ll' + n_add,
data=isr_ll * baseratio,
label=isr_base + ' lower confidence limit' +
l_add)
result.add_table('isr_ul' + n_add,
data=isr_ul * baseratio,
label=isr_base + ' upper confidence limit' +
l_add)
finally:
set_default_mode(r_mode)
soom.info('calc_indirectly_std_ratios took %.03f' % (time.time() - st))
name = 'indir_std_ratios_' + summset.name
label = 'Indirectly Standardised Ratios for ' + (summset.label
or summset.name)
if conflev:
label += ' (%g%% conf. limits)' % (conflev * 100)
if debug:
global vars
vars = Vars(locals())
return result.to_summset(name, label=label)
def main(parameter_file):
"""
It performs the following actions:
1. Gets the parameters, required for simulation, from parameter.yaml file.
2. calls DEM_creator() --> for generating DEM grid
3. Erosion modelling
4. Flow modelling
5. Landcover class allocation using decision tree
6. Geometric feature development
7. road mapping
"""
time1 = time.time()
#*****************parameter handling *************************************
# Get the parameters from parameter.yaml file
yaml_file = open(parameter_file, 'r')
stream = yaml.load(yaml_file)
resolution = stream['resolution']
H = stream['H']
H_wt = stream['H_wt']
seed = stream['seed']
sigma = stream['sigma']
elev_range = stream['elev_range']
max_level = stream['max_level']
DEMcreator_option = stream['DEMcreator_option']
output_dir = stream['output_dir']
river_drop = stream['river_drop']
Erosion_permission = stream['Erosion_permission']
decision_tree = stream['decision_tree']
counter = stream['counter']
elev_filename = stream['training_elev_filename']
landcover_filename = stream['training_landcover_filename']
river_filename = stream['training_river_filename']
no_of_veg_class = stream['no_of_veg_class']
min_area = stream['min_area']
max_area = stream['max_area']
aspect_ratio = stream['aspect_ratio']
agri_area_limit = stream['agri_area_limit']
yaml_file.close()
#**************************print statistics***********************************
print ("Running simulation with follwing parameters")
print ("H: %s" % H)
print ("H_wt: %s" % H_wt)
print ("seed: %s" % seed)
print ("sigma: %f" % sigma)
print ("elev_range: %s" % elev_range)
print ("max_level: %s" % max_level)
print ("DEMcreator_option: %s" % DEMcreator_option)
print ("output_dir: %s" % output_dir)
print ("River drop: %d" % river_drop)
print ("counter: %d" % counter)
print ("no of vegetation class %d" % no_of_veg_class)
print ("min area: %f" % min_area)
print ("max area: %f" % max_area)
print ("aspect ratio: %f" % aspect_ratio)
print ("agricultural area limit: %f" % agri_area_limit)
gradient = 0 #fixed for now TODO incorporate gradient in next version
#*****************************DEM genaration************************************
# Generate DEM using FM2D/SS algorithm by calling DEM_creator(args...) function
DEM_Result = DEM_generator.DEM_creator(H, H_wt, seed, elev_range,sigma,gradient,max_level, DEMcreator_option)
pathname = os.path.dirname(sys.argv[0])
fullpath = os.path.abspath(pathname)
filename = fullpath + "/" + output_dir
if not os.path.exists(filename):
os.makedirs(filename) # create output directory if it doesn't exist
DEM_arr = DEM_Result[0]
DEM_Result = 0 #free space
#****************************region adjustment***********************************
# We create a temporary region that is only valid in this python session
g.use_temp_region()
rows = DEM_arr.shape[0]
cols = DEM_arr.shape[1]
n = 4928050 #some arbitrary value
s = n - resolution*rows
e = 609000 #some arbitrary value
w = e - resolution*cols
g.run_command('g.region', flags = 'ap', n = n ,s = s, e = e, w = w,res = resolution, rows = rows ,cols = cols)
#*************************Flow accumulation with Erosion modelling****************************
filename = fullpath + "/ascii_files"
if not os.path.exists(filename):
os.makedirs(filename)
if not Erosion_permission:
counter = 0
DEM_arr_to_ascii(DEM_arr,resolution)
g.run_command('r.in.ascii', overwrite = True, flags='i', input = fullpath +'/'+'ascii_files' +'/DEM.asc', output='test_DEM')
#Flow computation for massive grids (float version)
g.run_command('r.terraflow', overwrite = True, elevation = 'test_DEM@user1', filled = 'flooded_DEM',\
direction = 'DEM_flow_direction',swatershed = 'DEM_sink_watershed', accumulation = 'DEM_flow_accum', tci = 'DEM_tci')
g.run_command('r.out.ascii',flags='h',input='DEM_flow_accum@user1',output=fullpath +'/ascii_files'+ '/DEM_flow_accum',null='0')
f = open(fullpath +'/ascii_files'+ '/DEM_flow_accum', 'r')
Flow_accum_arr = numpy.loadtxt(f)
f.close()
for iteration in range(0,counter):
DEM_arr_to_ascii(DEM_arr,resolution)
#Input the DEM ascii file into grass
g.run_command('r.in.ascii', overwrite = True, flags='i', input = fullpath +'/'+'ascii_files' +'/DEM.asc', output='test_DEM')
#Flow computation for massive grids (float version)
g.run_command('r.terraflow', overwrite = True, elevation = 'test_DEM@user1', filled = 'flooded_DEM',\
direction = 'DEM_flow_direction',swatershed = 'DEM_sink_watershed', accumulation = 'DEM_flow_accum', tci = 'DEM_tci')
g.run_command('r.out.ascii',flags='h',input='DEM_flow_accum@user1',output=fullpath +'/ascii_files'+ '/DEM_flow_accum',null='0')
f = open(fullpath +'/ascii_files'+ '/DEM_flow_accum', 'r')
Flow_accum_arr = numpy.loadtxt(f)
f.close()
#call erosion modelling function
DEM_arr = Erosion(Flow_accum_arr, DEM_arr, river_drop)
output=fullpath +'/'+output_dir+ '/DEM.asc'
arr_to_ascii(DEM_arr,output)
output=fullpath +'/'+output_dir+ '/flow_accum.asc'
arr_to_ascii(Flow_accum_arr,output)
#****************************landcover allocation using decision tree********************************
# Get slope and Aspect using grass functions
g.run_command('r.slope.aspect',overwrite=True,elevation='test_DEM@user1',slope='DEM_Slope',aspect='DEM_Aspect')
g.run_command('r.out.ascii',flags='h',input='DEM_Slope@user1',output=fullpath + '/ascii_files'+'/DEM_Slope',null='0')
f = open('ascii_files/DEM_Slope', 'r')
DEM_Slope_arr = numpy.loadtxt(f)
f.close()
g.run_command('r.out.ascii',flags='h',input='DEM_Aspect@user1',output=fullpath +'/ascii_files'+'/DEM_Aspect',null='0')
f = open('ascii_files/DEM_Aspect', 'r')
DEM_Aspect_arr = numpy.loadtxt(f)
f.close()
Distance_arr = dist.CityBlock(Flow_accum_arr,flag = 0)
# Normalize the elevation values to use decision tree
minimum_elev = numpy.min(DEM_arr)
factor = numpy.max(DEM_arr) - minimum_elev
Elev_arr = (DEM_arr[:,:] - minimum_elev)*100/factor
# Create various list to hold test data
Elevation = []
Slope = []
RiverDistance = []
Aspect = []
# Append the data into respective list
x_len = DEM_arr.shape[0]
y_len = DEM_arr.shape[1]
for i in range(0,x_len):
for j in range(0,y_len):
Elevation.append(int(Elev_arr[i][j]))
Slope.append(int(DEM_Slope_arr[i][j]))
RiverDistance.append(int(Distance_arr[i][j]))
Aspect.append(int(DEM_Aspect_arr[i][j]))
Elev_arr = 0 #free space
DEM_slope_arr = 0 #free space
DEM_Aspect_arr = 0 #free space
Distance_arr = 0 #free space
# Create dictionary to apply R's predict command on it
Test_data = {'Elevation':Elevation ,'Slope':Slope ,'RiverDistance':RiverDistance,'Aspect':Aspect}
#free spaces
Elevation = []
Slope = []
RiverDistance = []
Aspect = []
# create decision tree from training data
fit = DecisionTree(no_of_veg_class,elev_filename, landcover_filename, river_filename,decision_tree)
g.run_command('g.region', flags = 'ap', n = n ,s = s, e = e, w = w,res = resolution, rows = rows ,cols = cols)
# Alloctae vegetation array for holding predicted landcover values
Veg_arr = numpy.zeros(DEM_arr.shape, dtype = "uint8")
rpy.r.library("rpart")
rpy.set_default_mode(rpy.BASIC_CONVERSION)
# values contain probability values of the predicted landcover classes
values = rpy.r.predict(fit,newdata=Test_data,method="class")
Test_data = 0 #free space
x_len = Veg_arr.shape[0]
y_len = Veg_arr.shape[1]
for i in range(0,x_len):
for j in range(0,y_len):
# Get the class having max probability for each test data point
a = ndimage.maximum_position(values[i*y_len + j])
Veg_arr[i,j] = (a[0]) # Assign them some value to facilitate visualization
values = 0 #free space
filename=fullpath +'/'+output_dir+ "/landcover.asc"
arr_to_ascii(Veg_arr,filename)
# Allocate and initialize Suitabilty map
Suitability = numpy.zeros( DEM_arr.shape, dtype = "uint8")
for i in range(0,DEM_arr.shape[0]):
for j in range(0,DEM_arr.shape[1]):
#TODO can use mask here, needs to be generalised
if Veg_arr[i][j] == 0: # Ignore
Suitability[i][j] = 0
elif Veg_arr[i][j] == 25: # Deciduous woodland
Suitability[i][j] = 60
elif Veg_arr[i][j] == 50: # Coniferous woodland
Suitability[i][j] = 55
elif Veg_arr[i][j] == 75: # Agriculture including pasture
Suitability[i][j] = 98
elif Veg_arr[i][j] == 100: # Semi-natural grassland
Suitability[i][j] = 90
elif Veg_arr[i][j] == 125: # Bog and swamp
Suitability[i][j] = 50
elif Veg_arr[i][j] == 150: # Heath
Suitability[i][j] = 75
elif Veg_arr[i][j] == 175: # Montane habitat
Suitability[i][j] = 20
elif Veg_arr[i][j] == 200: # Rock and quarry
Suitability[i][j] = 30
elif Veg_arr[i][j] == 225: # Urban
Suitability[i][j] = 80
Display_fields = Geometry.GeometricFeature(Suitability, min_area,max_area ,aspect_ratio ,agri_area_limit)
f = open('fields_arr', 'w')
numpy.save(f,Display_fields)
f.close()
pylab.imsave(output_dir+"/fields.png",Display_fields)
time2 = time.time()
print "time taken", time2-time1
shutil.rmtree(fullpath+'/ascii_files')
def DecisionTree(no_of_veg_class, elev_filename, landcover_filename, river_filename,decision_tree):
"""
Generates a decision tree given the training data
Input:
no_of_veg_class: No of landcover class in training data
elev_filename : Name of training file having elevation values
landcover_filename: Name of training file having landcover values
river_filename: Name of training file having river presence absence info
"""
rpy.r.library("rpart")
g.use_temp_region()
#TODO generalize no of rows and columns for training data
rows = 2001
cols = 1201
resolution = 50
n = 4928050 #some arbitrary value
s = n - resolution*rows
e = 609000 #some arbitrary value
w = e - resolution*cols
g.run_command('g.region', flags = 'ap', n = n ,s = s, e = e, w = w,res = 50, rows = 2001 ,cols = 1201)
pathname = os.path.dirname(sys.argv[0])
fullpath = os.path.abspath(pathname)
if decision_tree:
# Convert ascii DEM into grass raster map that will help in getting slope and aspect
file_name = "/Training/%s" % elev_filename
g.run_command('r.in.ascii', overwrite = True, flags='i', input = fullpath + file_name, output='training_DEM')
# TODO read training DEM into array without writing another file
g.run_command('r.out.ascii',flags='h',input='training_DEM@user1',output=fullpath + '/ascii_files'+'/training_DEM',null='0')
f = open('ascii_files/training_DEM', 'r')
Elev_arr = numpy.loadtxt(f)
f.close()
file_name = "Training/%s" % (landcover_filename)
Landcover = numpy.loadtxt(file_name) # Read Landcover Data from ascii file
file_name = "Training/%s" % (river_filename)
River = numpy.loadtxt(file_name) # Read River Data from ascii file
River_dist_arr = dist.CityBlock(River,flag = 1) #Compute distance from River data
g.run_command('r.slope.aspect',overwrite=True,elevation='training_DEM@user1',slope='Slope',aspect='Aspect')
g.run_command('r.out.ascii',flags='h',input='Slope@user1',output=fullpath + '/ascii_files'+'/Slope',null='0')
f = open('ascii_files/Slope', 'r')
Slope_arr = numpy.loadtxt(f) #Get Slope into an array
f.close()
g.run_command('r.out.ascii',flags='h',input='Aspect@user1',output=fullpath +'/ascii_files'+ '/Aspect',null='0')
f = open('ascii_files/Aspect', 'r')
Aspect_arr = numpy.loadtxt(f) #Get Aspect into an array
f.close()
(x_len,y_len) = Elev_arr.shape
L = [ [] for i in range(0,no_of_veg_class)]
for i in range(1,x_len-1): # Ignoring boundary cells
for j in range(1,y_len-1):
# Append the pixel co-ordinates into respective list of lists
# nodata values already gets handled since we are ignoring it
for k in range(0, no_of_veg_class):
if Landcover[i][j] == k:
L[k].append( (i,j) )
break
minimum_elev = numpy.min(Elev_arr)
factor = numpy.max(Elev_arr) - minimum_elev # normalize elevation data
Elev_arr = (Elev_arr[:,:]-minimum_elev)*100/factor
# Sample training Data for decision tree, we can't take entire data as it take longer processing time
# various lists to hold sample training data
Elevation = []
Slope = []
RiverDistance = []
Aspect = []
Class = []
# Sample the data
for i in range(0,no_of_veg_class):
if len(L[i]) < 1000:
limit = len(L[i])
else:
limit = 1000
for j in range(0,limit):
Elevation.append( int(Elev_arr[ L[i][j][0] ][ L[i][j][1] ]))
Slope.append(int(Slope_arr[ L[i][j][0] ][ L[i][j][1] ]))
RiverDistance.append(int(River_dist_arr[ L[i][j][0] ][ L[i][j][1] ]))
Aspect.append(int(Aspect_arr[ L[i][j][0] ][ L[i][j][1] ]))
Class.append(i)
#free space
Elev_arr = 0
Slope_arr = 0
River_dist_arr = 0
Aspect_arr = 0
# create dictionary of sample data which will be needed to generate decision tree
training_data = {'Elevation':Elevation,'Slope':Slope,'RiverDistance':RiverDistance,'Aspect':Aspect,'Class':Class}
#free space
Elevation = []
Slope = []
RiverDistance = []
Aspect = []
Class = []
f = open( 'save.p', 'w' )
pickle.dump(training_data, f )
f.close()
else:
f = open( 'save.p', 'r' )
training_data = pickle.load( f )
f.close()
rpy.set_default_mode(rpy.NO_CONVERSION)
#Using rpart create the decision tree
fit = rpy.r.rpart(formula='Class ~ Elevation + RiverDistance + Slope + Aspect',data=training_data,method="class")
training_data = 0
#rpy.r.png("DecisionTree.png") # Output a png image of the decision tree
#rpy.r.plot(fit)
#rpy.r.text(fit)
#rpy.r.dev_off()
return fit
def __init__(self, y, design, model_type=r.lm, **kwds):
''' Set up and estimate R model with data and design '''
r.library('MASS') # still needs to be in test, but also here for
# logical tests at the end not to show an error
self.y = np.array(y)
self.design = np.array(design)
self.model_type = model_type
self._design_cols = [
'x.%d' % (i + 1) for i in range(self.design.shape[1])
]
# Note the '-1' for no intercept - this is included in the design
self.formula = r('y ~ %s-1' % '+'.join(self._design_cols))
self.frame = r.data_frame(y=y, x=self.design)
rpy.set_default_mode(rpy.NO_CONVERSION)
results = self.model_type(self.formula, data=self.frame, **kwds)
self.robj = results # keep the Robj model so it can be
# used in the tests
rpy.set_default_mode(rpy.BASIC_CONVERSION)
rsum = r.summary(results)
self.rsum = rsum
# Provide compatible interface with scipy models
self.results = results.as_py()
# coeffs = self.results['coefficients']
# self.beta0 = np.array([coeffs[c] for c in self._design_cols])
self.nobs = len(self.results['residuals'])
if isinstance(self.results['residuals'], dict):
self.resid = np.zeros((len(self.results['residuals'].keys())))
for i in self.results['residuals'].keys():
self.resid[int(i) - 1] = self.results['residuals'][i]
else:
self.resid = self.results['residuals']
self.fittedvalues = self.results['fitted.values']
self.df_resid = self.results['df.residual']
self.params = rsum['coefficients'][:, 0]
self.bse = rsum['coefficients'][:, 1]
self.bt = rsum['coefficients'][:, 2]
try:
self.pvalues = rsum['coefficients'][:, 3]
except:
pass
self.rsquared = rsum.setdefault('r.squared', None)
self.rsquared_adj = rsum.setdefault('adj.r.squared', None)
self.aic_R = rsum.setdefault('aic', None)
self.fvalue = rsum.setdefault('fstatistic', None)
if self.fvalue and isinstance(self.fvalue, dict):
self.fvalue = self.fvalue.setdefault('value', None) # for wls
df = rsum.setdefault('df', None)
if df: # for RLM, works for other models?
self.df_model = df[0] - 1 # R counts intercept
self.df_resid = df[1]
self.bcov_unscaled = rsum.setdefault('cov.unscaled', None)
self.bcov = rsum.setdefault('cov.scaled', None)
if 'sigma' in rsum:
self.scale = rsum['sigma']
elif 'dispersion' in rsum:
self.scale = rsum['dispersion']
else:
self.scale = None
self.llf = r.logLik(results)
if model_type == r.glm:
self.getglm()
if model_type == r.rlm:
self.getrlm()
if __name__ == '__main__':
modules = os.listdir('.')
if '--random' in sys.argv:
shuffle=True
sys.argv.remove('--random')
else:
shuffle=False
if '--loop' in sys.argv:
niter = 1000
sys.argv.remove('--loop')
else:
niter = 1
modules = filter( lambda x: not x.endswith('.pyc'), modules)
modules = filter( lambda x: x.startswith('test_'), modules)
modules = filter( lambda x: x.endswith('.py'), modules)
print "Modules to be tested:", modules
for iter in range(niter):
if shuffle: random.shuffle(modules)
for module in modules:
name = module[:-3]
print 'Testing:', name
rpy.set_default_mode(rpy.NO_DEFAULT) # reset to base case
run(name)
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-c", "--columns", dest="columns", type="string",
help="columns to take for calculating histograms.")
parser.add_option("-t", "--tree-nh-file", dest="filename_tree",
type="string",
help="filename with tree(s).")
parser.add_option("--skip-header", dest="add_header", action="store_false",
help="do not add header to flat format.")
parser.add_option("--output-with-header", dest="write_header",
action="store_true",
help="write header and exit.")
parser.add_option("--debug", dest="debug", action="store_true",
help="debug mode")
parser.add_option("--display-tree", dest="display_tree",
action="store_true",
help="display the tree")
parser.add_option("-m", "--method", dest="methods", type="choice",
action="append",
choices=("contrasts", "spearman", "pearson",
"compute"),
help="methods to perform on contrasts.")
parser.set_defaults(
columns="all",
filename_tree=None,
add_header=True,
write_header=False,
debug=False,
methods=[],
value_format="%6.4f",
pvalue_format="%e",
display_tree=False,
)
(options, args) = E.Start(parser, quiet=True)
if options.columns not in ("all", "all-but-first"):
options.columns = map(lambda x: int(x) - 1, options.columns.split(","))
phylip = WrapperPhylip.Phylip()
if options.debug:
phylip.setLogLevel(options.loglevel)
phylip.setProgram("contrast")
##########################################################
##########################################################
##########################################################
# retrieve data and give to phylip
data = []
headers = []
first = True
for line in sys.stdin:
if line[0] == "#":
continue
d = line[:-1].strip().split("\t")
if first:
first = False
headers = d[1:]
continue
data.append(d)
phylip.setData(data)
ncolumns = len(headers)
nrows = len(data)
##########################################################
##########################################################
##########################################################
# read trees
nexus = None
if options.filename_tree:
nexus = TreeTools.Newick2Nexus(open(options.filename_tree, "r"))
if not nexus:
raise ValueError("please provide trees with branchlenghts")
##########################################################
##########################################################
##########################################################
# set up phylip
phylip_options = []
# print out contrasts
phylip_options.append("C")
phylip_options.append("Y")
phylip.setOptions(phylip_options)
##########################################################
##########################################################
##########################################################
# main loop
##########################################################
for tree in nexus.trees:
if options.display_tree:
tree.display()
# compute this before giving the tree to the phylip module,
# as it remaps taxon names.
map_node2data = {}
for x in range(nrows):
taxon = data[x][0]
map_node2data[tree.search_taxon(taxon)] = x
phylip.setTree(tree)
result = phylip.run()
for method in options.methods:
if method in ("pearson", "spearman"):
options.stdout.write("header1\theader2\tr\tp\tcode\n")
# n = len(result.mContrasts)
columns = []
for c in range(ncolumns):
columns.append(map(lambda x: x[c], result.mContrasts))
for x in range(0, ncolumns - 1):
for y in range(x + 1, ncolumns):
# phylip value
phy_r = result.mCorrelations[x][y]
import rpy
from rpy import r as R
# Various ways to calculate r. It is not
# possible to use cor.test or lsfit directly,
# as you have to perform a regression through
# the origin.
# uncomment to check pearson r against
# phylip's value r =
# calculateCorrelationCoefficient(columns[x],
# columns[y])
# for significance, use linear regression models in R
rpy.set_default_mode(rpy.NO_CONVERSION)
linear_model = R.lm(
R("y ~ x - 1"), data=R.data_frame(x=columns[x],
y=columns[y]))
rpy.set_default_mode(rpy.BASIC_CONVERSION)
ss = R.summary(linear_model)
# extract the p-value
p = ss['coefficients'][-1][-1]
if p < 0.001:
code = "***"
elif p < 0.01:
code = "**"
elif p < 0.05:
code = "*"
else:
code = ""
options.stdout.write("\t".join(
(headers[x], headers[y],
options.value_format % phy_r,
options.pvalue_format % p,
code)) + "\n")
elif method == "contrasts":
options.stdout.write("\t".join(headers) + "\n")
for d in result.mContrasts:
options.stdout.write(
"\t".join(
map(lambda x: options.value_format % x, d)) + "\n")
elif method == "compute":
# make room for all internal nodes and one dummy node
# for unrooted trees.
max_index = TreeTools.GetMaxIndex(tree) + 2
variances = [None] * max_index
values = [[None] * nrows for x in range(max_index)]
contrasts = []
for x in range(max_index):
contrasts.append([None] * ncolumns)
branchlengths = [None] * max_index
def update_data(node_id, bl, c1, c2, ):
b1, b2 = branchlengths[c1], branchlengths[c2]
rb1 = 1.0 / b1
rb2 = 1.0 / b2
# compute variance
variance = math.sqrt(b1 + b2)
# extend branch length of this node to create correct
# variance for parent
branchlengths[node_id] = bl + (b1 * b2) / (b1 + b2)
variances[node_id] = variance
for c in range(ncolumns):
v1, v2 = values[c1][c], values[c2][c]
# save ancestral value as weighted mean
values[node_id][c] = (
(rb1 * v1 + rb2 * v2)) / (rb1 + rb2)
# compute normalized contrast
contrasts[node_id][c] = (v1 - v2) / variance
def update_contrasts(node_id):
"""update contrasts for a node."""
node = tree.node(node_id)
if node.succ:
if len(node.succ) == 2:
c1, c2 = node.succ
update_data(
node_id, node.data.branchlength, c1, c2)
else:
assert(node_id == tree.root)
assert(len(node.succ) == 3)
update_data(
node_id, node.data.branchlength,
node.succ[0], node.succ[1])
update_data(
max_index - 1, node.data.branchlength,
node_id, node.succ[2])
else:
for c in range(ncolumns):
values[node_id][c] = float(
data[map_node2data[node_id]][c + 1])
branchlengths[node_id] = node.data.branchlength
tree.dfs(tree.root, post_function=update_contrasts)
options.stdout.write(
"node_id\tvariance\t%s\n" % "\t".join(headers))
for node_id in range(max_index):
if variances[node_id] is None:
continue
options.stdout.write("%s\t%s\t%s\n" % (
node_id,
options.value_format % variances[
node_id],
"\t".join(
map(lambda x: options.value_format % x,
contrasts[node_id])),
))
E.Stop()
def rpart_fit(self, known_data, parameter_list, bit_string="11111"):
"""
11-09-05
1st use known_data to get the fit model
2nd use the fit model to do prediction on all_data, result is prob for each class
11-09-05 add rpart_cp
11-17-05
add loss_matrix, prior_prob
return two pred
11-23-05
split fit and predict. rpart_fit_and_predict() is split into rpart_fit() and rpart_predict()
11-27-05
r cleanup
03-17-06
use parameter_list instead
"""
if self.debug:
sys.stderr.write("Doing rpart_fit...\n")
# 03-17-06
rpart_cp, loss_matrix, prior_prob = parameter_list
# 11-27-05 r cleanup
from rpy import r
r.library("rpart")
coeff_name_list = ["p_value", "recurrence", "connectivity", "cluster_size", "gradient"]
formula_list = []
for i in range(len(bit_string)):
if bit_string[i] == "1":
formula_list.append(coeff_name_list[i])
# 11-17-05 transform into array
known_data = array(known_data)
set_default_mode(NO_CONVERSION)
data_frame = r.as_data_frame(
{
"p_value": known_data[:, 0],
"recurrence": known_data[:, 1],
"connectivity": known_data[:, 2],
"cluster_size": known_data[:, 3],
"gradient": known_data[:, 4],
"is_correct": known_data[:, -1],
}
)
if prior_prob:
prior_prob = [prior_prob, 1 - prior_prob] # get the full list
fit = r.rpart(
r("is_correct~%s" % "+".join(formula_list)),
data=data_frame,
method="class",
control=r.rpart_control(cp=rpart_cp),
parms=r.list(prior=prior_prob, loss=r.matrix(loss_matrix)),
)
else:
fit = r.rpart(
r("is_correct~%s" % "+".join(formula_list)),
data=data_frame,
method="class",
control=r.rpart_control(cp=rpart_cp),
parms=r.list(loss=r.matrix(loss_matrix)),
)
del data_frame
if self.debug:
sys.stderr.write("Done rpart_fit.\n")
return fit
def _run_():
if len(sys.argv) == 1:
print __doc__
sys.exit(2)
long_options_list = [
"rFile=",
"chr=",
"delim=",
"missingval=",
"BoundaryStart=",
"removeOutliers=",
"addConstant=",
"logTransform",
"BoundaryEnd=",
"phenotypeFileType=",
"help",
"parallel=",
"parallelAll",
"LRT",
"minMAF=",
"kinshipDatafile=",
"phenotypeRanks",
"onlyMissing",
"onlyOriginal96",
"onlyOriginal192",
"onlyBelowLatidue=",
"complement",
"negate",
"srInput=",
"sr",
"srOutput=",
"srPar=",
"srSkipFirstRun",
"testRobustness",
"permutationFilter=",
"useLinearRegress",
"regressionCofactors=",
"FriLerAsCofactor",
"FriColAsCofactor",
"memReq=",
"walltimeReq=",
]
try:
opts, args = getopt.getopt(sys.argv[1:], "o:c:d:m:h",
long_options_list)
except:
traceback.print_exc()
print sys.exc_info()
print __doc__
sys.exit(2)
phenotypeRanks = False
removeOutliers = None
addConstant = -1
phenotypeFileType = 1
rFile = None
delim = ","
missingVal = "NA"
help = 0
minMAF = 0.0
boundaries = [-1, -1]
chr = None
parallel = None
logTransform = False
negate = False
parallelAll = False
lrt = False
kinshipDatafile = None
onlyMissing = False
onlyOriginal96 = False
onlyOriginal192 = False
onlyBelowLatidue = None
complement = False
sr = False
srOutput = False
srInput = False
srSkipFirstRun = False
srTopQuantile = 0.95
srWindowSize = 30000
testRobustness = False
permutationFilter = 0.002
useLinearRegress = False
regressionCofactors = None
FriLerAsCofactor = False
FriColAsCofactor = False
memReq = "5g"
walltimeReq = "150:00:00"
for opt, arg in opts:
if opt in ("-h", "--help"):
help = 1
print __doc__
elif opt in ("-o", "--rFile"):
rFile = arg
elif opt in ("--phenotypeFileType"):
phenotypeFileType = int(arg)
elif opt in ("--BoundaryStart"):
boundaries[0] = int(arg)
elif opt in ("--BoundaryEnd"):
boundaries[1] = int(arg)
elif opt in ("--addConstant"):
addConstant = float(arg)
elif opt in ("--parallel"):
parallel = arg
elif opt in ("--minMAF"):
minMAF = float(arg)
elif opt in ("--parallelAll"):
parallelAll = True
elif opt in ("--onlyMissing"):
onlyMissing = True
elif opt in ("--onlyOriginal96"):
onlyOriginal96 = True
elif opt in ("--onlyOriginal192"):
onlyOriginal192 = True
elif opt in ("--onlyBelowLatidue"):
onlyBelowLatidue = float(arg)
elif opt in ("--complement"):
complement = True
elif opt in ("--logTransform"):
logTransform = True
elif opt in ("--negate"):
negate = True
elif opt in ("--removeOutliers"):
removeOutliers = float(arg)
elif opt in ("--LRT"):
lrt = True
elif opt in ("-c", "--chr"):
chr = int(arg)
elif opt in ("-d", "--delim"):
delim = arg
elif opt in ("-m", "--missingval"):
missingVal = arg
elif opt in ("--kinshipDatafile"):
kinshipDatafile = arg
elif opt in ("--phenotypeRanks"):
phenotypeRanks = True
elif opt in ("--sr"):
sr = True
elif opt in ("--srSkipFirstRun"):
srSkipFirstRun = True
elif opt in ("--srInput"):
srInput = arg
elif opt in ("--srOutput"):
srOutput = arg
elif opt in ("--srPar"):
vals = arg.split(",")
srTopQuantile = float(vals[0])
srWindowSize = int(vals[1])
elif opt in ("--testRobustness"):
testRobustness = True
elif opt in ("--permutationFilter"):
permutationFilter = float(arg)
elif opt in ("--FriLerAsCofactor"):
FriLerAsCofactor = True
elif opt in ("--FriColAsCofactor"):
FriColAsCofactor = True
elif opt in ("--useLinearRegress"):
useLinearRegress = True
elif opt in ("--regressionCofactors"):
regressionCofactors = arg
elif opt in ("--memReq"):
memReq = arg
elif opt in ("--walltimeReq"):
walltimeReq = arg
else:
if help == 0:
print "Unkown option!!\n"
print __doc__
sys.exit(2)
if len(args) < 3 and not parallel:
if help == 0:
print "Arguments are missing!!\n"
print __doc__
sys.exit(2)
print "Emma is being set up with the following parameters:"
print "output:", rFile
print "phenotypeRanks:", phenotypeRanks
print "phenotypeFileType:", phenotypeFileType
print "parallel:", parallel
print "parallelAll:", parallelAll
print "minMAF:", minMAF
print "LRT:", lrt
print "delim:", delim
print "missingval:", missingVal
print "kinshipDatafile:", kinshipDatafile
print "chr:", chr
print "boundaries:", boundaries
print "onlyMissing:", onlyMissing
print "onlyOriginal96:", onlyOriginal96
print "onlyOriginal192:", onlyOriginal192
print "onlyBelowLatidue:", onlyBelowLatidue
print "complement:", complement
print "negate:", negate
print "logTransform:", logTransform
print "addConstant:", addConstant
print "removeOutliers:", removeOutliers
print "sr:", sr
print "srSkipFirstRun:", srSkipFirstRun
print "srInput:", srInput
print "srOutput:", srOutput
print "srTopQuantile:", srTopQuantile
print "srWindowSize:", srWindowSize
print "testRobustness:", testRobustness
print "permutationFilter:", permutationFilter
print "useLinearRegress:", useLinearRegress
print "regressionCofactors:", regressionCofactors
print "FriLerAsCofactor:", FriLerAsCofactor
print "FriColAsCofactor:", FriColAsCofactor
print "walltimeReq:", walltimeReq
print "memReq:", memReq
def runParallel(phenotypeIndex, phed):
#Cluster specific parameters
print phenotypeIndex
phenName = phed.getPhenotypeName(phenotypeIndex)
outFileName = resultDir + "Emma_" + parallel + "_" + phenName
shstr = "#!/bin/csh\n"
shstr += "#PBS -l walltime=" + walltimeReq + "\n"
shstr += "#PBS -l mem=" + memReq + "\n"
shstr += "#PBS -q cmb\n"
shstr += "#PBS -N E" + phenName + "_" + parallel + "\n"
shstr += "set phenotypeName=" + parallel + "\n"
shstr += "set phenotype=" + str(phenotypeIndex) + "\n"
if useLinearRegress:
outFileName = resultDir + "LR_" + parallel + "_" + phenName
shstr += "(python " + emmadir + "Emma.py -o " + outFileName + " "
if useLinearRegress:
shstr += " --useLinearRegress "
if regressionCofactors:
shstr += " --regressionCofactors=" + str(regressionCofactors) + " "
if FriLerAsCofactor:
shstr += " --FriLerAsCofactor "
if FriColAsCofactor:
shstr += " --FriColAsCofactor "
if onlyOriginal96:
shstr += " --onlyOriginal96 "
elif onlyOriginal192:
shstr += " --onlyOriginal192 "
if onlyBelowLatidue:
shstr += " --onlyBelowLatidue=" + str(onlyBelowLatidue) + " "
if logTransform:
shstr += " --logTransform "
if negate:
shstr += " --negate "
if removeOutliers:
shstr += " --removeOutliers=" + str(removeOutliers) + " "
if phenotypeRanks:
shstr += " --phenotypeRanks "
if testRobustness:
shstr += " --testRobustness "
shstr += " --permutationFilter=" + str(permutationFilter) + " "
if sr:
shstr += " --sr "
if not srOutput:
output = resultDir + "Emma_" + parallel + "_" + phenName + ".sr.pvals"
shstr += " --srOutput=" + str(output) + " "
if srSkipFirstRun:
if not srInput:
output = resultDir + "Emma_" + parallel + "_" + phenName + ".pvals"
shstr += " --srInput=" + str(output) + " "
shstr += " --srSkipFirstRun "
shstr += " --srPar=" + str(srTopQuantile) + "," + str(
srWindowSize) + " "
if kinshipDatafile:
shstr += " --kinshipDatafile=" + str(kinshipDatafile) + " "
shstr += " --addConstant=" + str(addConstant) + " "
shstr += snpsDataFile + " " + phenotypeDataFile + " " + str(
phenotypeIndex) + " "
shstr += "> " + outFileName + "_job" + ".out) >& " + outFileName + "_job" + ".err\n"
f = open(parallel + ".sh", 'w')
f.write(shstr)
f.close()
#Execute qsub script
os.system("qsub " + parallel + ".sh ")
snpsDataFile = args[0]
phenotypeDataFile = args[1]
if parallel: #Running on the cluster..
phed = phenotypeData.readPhenotypeFile(
phenotypeDataFile, delimiter='\t') #Get Phenotype data
if parallelAll:
for phenotypeIndex in phed.phenIds:
if onlyMissing:
phenName = phed.getPhenotypeName(phenotypeIndex)
pvalFile = resultDir + "Emma_" + parallel + "_" + phenName + ".pvals"
res = None
try:
res = os.stat(pvalFile)
except Exception:
print "File", pvalFile, "does not exist."
if res and res.st_size > 0:
print "File", pvalFile, "already exists, and is non-empty."
if sr:
srInput = resultDir + "Emma_" + parallel + "_" + phenName + ".sr.pvals"
srRes = None
try:
srRes = os.stat(srInput)
except Exception:
print "File", srInput, "does not exist."
if srRes and srRes.st_size > 0:
print "File", srInput, "already exists, and is non-empty."
else:
runParallel(phenotypeIndex, phed)
else:
print "Setting up the run."
runParallel(phenotypeIndex, phed)
else:
runParallel(phenotypeIndex, phed)
else:
phenotypeIndex = int(args[2])
runParallel(phenotypeIndex, phed)
return
else:
phenotypeIndex = int(args[2])
print "phenotypeIndex:", phenotypeIndex
print "\nStarting program now!\n"
snpsds = dataParsers.parseCSVData(snpsDataFile,
format=1,
deliminator=delim,
missingVal=missingVal)
#Load phenotype file
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile,
delimiter='\t') #Get Phenotype data
numAcc = len(snpsds[0].accessions)
#Removing outliers
if removeOutliers:
print "Remoing outliers"
phed.naOutliers(phenotypeIndex, removeOutliers)
#If onlyOriginal96, then remove all other phenotypes..
if onlyOriginal96:
print "Filtering for the first 96 accessions"
original_96_ecotypes = phenotypeData._getFirst96Ecotypes_()
original_96_ecotypes = map(str, original_96_ecotypes)
keepEcotypes = []
if complement:
for acc in phed.accessions:
if not acc in original_96_ecotypes:
keepEcotypes.append(acc)
else:
keepEcotypes = original_96_ecotypes
phed.filterAccessions(keepEcotypes)
print "len(phed.accessions)", len(phed.accessions)
if onlyOriginal192:
print "Filtering for the first 192 accessions"
original_192_ecotypes = phenotypeData._getFirst192Ecotypes_()
original_192_ecotypes = map(str, original_192_ecotypes)
keepEcotypes = []
if complement:
for acc in phed.accessions:
if not acc in original_192_ecotypes:
keepEcotypes.append(acc)
else:
keepEcotypes = original_192_ecotypes
phed.filterAccessions(keepEcotypes)
print "len(phed.accessions)", len(phed.accessions)
if onlyBelowLatidue:
print "Filtering for the accessions which orginate below latitude", onlyBelowLatidue
eiDict = phenotypeData._getEcotypeIdInfoDict_()
print eiDict
keepEcotypes = []
for acc in phed.accessions:
acc = int(acc)
if eiDict.has_key(acc) and eiDict[acc][
2] and eiDict[acc][2] < onlyBelowLatidue:
keepEcotypes.append(str(acc))
elif eiDict.has_key(acc) and eiDict[acc][2] == None:
keepEcotypes.append(str(acc))
phed.filterAccessions(keepEcotypes)
print "len(phed.accessions)", len(phed.accessions)
sys.stdout.write("Finished prefiltering phenotype accessions.\n")
sys.stdout.flush()
phenotype = phed.getPhenIndex(phenotypeIndex)
accIndicesToKeep = []
phenAccIndicesToKeep = []
#Checking which accessions to keep and which to remove .
for i in range(0, len(snpsds[0].accessions)):
acc1 = snpsds[0].accessions[i]
for j in range(0, len(phed.accessions)):
acc2 = phed.accessions[j]
if acc1 == acc2 and phed.phenotypeValues[j][phenotype] != 'NA':
accIndicesToKeep.append(i)
phenAccIndicesToKeep.append(j)
break
print "\nFiltering accessions in genotype data:"
#Filter accessions which do not have the phenotype value (from the genotype data).
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
snpsd.removeAccessionIndices(accIndicesToKeep)
print ""
print numAcc - len(
accIndicesToKeep
), "accessions removed from genotype data, leaving", len(
accIndicesToKeep), "accessions in all."
print "\nNow filtering accessions in phenotype data:"
phed.removeAccessions(
phenAccIndicesToKeep
) #Removing accessions that don't have genotypes or phenotype values
print "Verifying number of accessions: len(phed.accessions)==len(snpsds[0].accessions) is", len(
phed.accessions) == len(snpsds[0].accessions)
if len(phed.accessions) != len(snpsds[0].accessions):
raise Exception
#Filtering monomorphic
print "Filtering monomorphic SNPs"
for snpsd in snpsds:
print "Removed", str(snpsd.filterMonoMorphicSnps()), "Snps"
#Remove minor allele frequencies
if minMAF != 0:
sys.stdout.write("Filterting SNPs with MAF<" + str(minMAF) + ".")
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
snpsd.filterMinMAF(minMAF)
#Removing SNPs which are outside of boundaries.
if chr:
print "\nRemoving SNPs which are outside of boundaries."
snpsds[chr - 1].filterRegion(boundaries[0], boundaries[1])
snpsds = [snpsds[chr - 1]]
#Ordering accessions in genotype data to fit phenotype data.
print "Ordering genotype data accessions."
accessionMapping = []
i = 0
for acc in phed.accessions:
if acc in snpsds[0].accessions:
accessionMapping.append((snpsds[0].accessions.index(acc), i))
i += 1
#print zip(accessionMapping,snpsds[0].accessions)
print "len(snpsds[0].snps)", len(snpsds[0].snps)
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
snpsd.orderAccessions(accessionMapping)
print "\nGenotype data has been ordered."
#Converting format to 01
newSnpsds = []
sys.stdout.write("Converting data format")
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
newSnpsds.append(snpsd.getSnpsData(missingVal=missingVal))
print ""
print "Checking kinshipfile:", kinshipDatafile
if kinshipDatafile: #Is there a special kinship file?
kinshipSnpsds = dataParsers.parseCSVData(kinshipDatafile,
format=1,
deliminator=delim,
missingVal=missingVal)
accIndicesToKeep = []
#Checking which accessions to keep and which to remove (genotype data).
sys.stdout.write(
"Removing accessions which do not have a phenotype value for " +
phed.phenotypeNames[phenotype] + ".")
sys.stdout.flush()
for i in range(0, len(kinshipSnpsds[0].accessions)):
acc1 = kinshipSnpsds[0].accessions[i]
for j in range(0, len(phed.accessions)):
acc2 = phed.accessions[j]
if acc1 == acc2 and phed.phenotypeValues[j][phenotype] != 'NA':
accIndicesToKeep.append(i)
break
print accIndicesToKeep
for snpsd in kinshipSnpsds:
sys.stdout.write(".")
sys.stdout.flush()
snpsd.removeAccessionIndices(accIndicesToKeep)
print ""
print numAcc - len(
accIndicesToKeep
), "accessions removed from kinship genotype data, leaving", len(
accIndicesToKeep), "accessions in all."
print "Ordering kinship data accessions."
accessionMapping = []
i = 0
for acc in snpsds[0].accessions:
if acc in kinshipSnpsds[0].accessions:
accessionMapping.append(
(kinshipSnpsds[0].accessions.index(acc), i))
i += 1
print zip(accessionMapping, snpsds[0].accessions)
print "len(snpsds[0].snps)", len(snpsds[0].snps)
for snpsd in kinshipSnpsds:
sys.stdout.write(".")
sys.stdout.flush()
snpsd.orderAccessions(accessionMapping)
print "Kinship genotype data has been ordered."
newKinshipSnpsds = []
sys.stdout.write("Converting data format")
for snpsd in kinshipSnpsds:
sys.stdout.write(".")
sys.stdout.flush()
newKinshipSnpsds.append(snpsd.getSnpsData(
missingVal=missingVal)) #This data might have NAs
print ""
kinshipSnpsds = newKinshipSnpsds
else:
kinshipSnpsds = newSnpsds
print "Found kinship data."
#Ordering accessions according to the order of accessions in the genotype file
# accessionMapping = []
# i = 0
# for acc in snpsds[0].accessions:
# if acc in phed.accessions:
# accessionMapping.append((phed.accessions.index(acc),i))
# i += 1
# phed.orderAccessions(accessionMapping)
#Negating phenotypic values
if negate:
phed.negateValues(phenotypeIndex)
if logTransform and not phed.isBinary(
phenotypeIndex) and phed.getMinValue(phenotypeIndex) <= 0:
addConstant = 0
#Adding a constant.
if addConstant != -1:
if addConstant == 0:
addConstant = math.sqrt(phed.getVariance(phenotypeIndex)) / 10
addConstant = addConstant - phed.getMinValue(phenotypeIndex)
print "Adding a constant to phenotype:", addConstant
phed.addConstant(phenotypeIndex, addConstant)
#Log-transforming
if logTransform:
print "Log transforming phenotype"
phed.logTransform(phenotypeIndex)
#Converting phenotypes to Ranks
elif phenotypeRanks:
phed.transformToRanks(phenotypeIndex)
if not chr:
snpsDataset = snpsdata.SNPsDataSet(newSnpsds, [1, 2, 3, 4, 5])
kinshipSnpsDataset = snpsdata.SNPsDataSet(kinshipSnpsds,
[1, 2, 3, 4, 5])
else:
snpsDataset = snpsdata.SNPsDataSet(newSnpsds, [chr])
kinshipSnpsDataset = snpsdata.SNPsDataSet(kinshipSnpsds, [chr])
phenotypeName = phed.getPhenotypeName(phenotypeIndex)
sys.stdout.flush()
if testRobustness:
print "Starting a robustness test"
allSNPs = []
for snpsd in snpsDataset.snpsDataList:
allSNPs += snpsd.snps
phenVals = phed.getPhenVals(phenotypeIndex)
_robustness_test_(allSNPs, phenVals, rFile, filter=permutationFilter)
sys.exit(0)
if useLinearRegress:
phenVals = phed.getPhenVals(phenotypeIndex)
d0 = {}
d0["phen"] = phenVals
dh = {}
dh["phen"] = phenVals
import rpy, gc
if regressionCofactors: #Adds ler and col as cofactors
import pickle
f = open(regressionCofactors, "r")
co_factors = pickle.load(f)
f.close()
#inserting co factors into model
for factor in co_factors:
d[factor] = co_factors[factor]
import analyzeHaplotype as ah
(ler_factor, col_factor) = ah.getLerAndColAccessions(newSnpsds, True)
if FriColAsCofactor:
d0["col"] = col_factor
dh["col"] = col_factor
if FriLerAsCofactor:
d0["ler"] = ler_factor
dh["ler"] = ler_factor
chr_pos_pvals = []
stats = []
sys.stdout.write("Applying the linear model")
sys.stdout.flush()
for i in range(0, len(newSnpsds)): #[3]:#
snpsd = newSnpsds[i]
sys.stdout.write("|")
sys.stdout.flush()
gc.collect(
) #Calling garbage collector, in an attempt to clean up memory..
for j in range(0, len(snpsd.snps)):
if j % 5000 == 0:
sys.stdout.write(".")
sys.stdout.flush()
#if snpsd.positions[j]>1700000:
# break
snp = snpsd.snps[j]
d0["snp"] = snp
try:
rpy.set_default_mode(rpy.NO_CONVERSION)
aov0 = rpy.r.aov(r("phen ~ ."), data=d0)
aovh = rpy.r.aov(r("phen ~ ."), data=dh)
rpy.set_default_mode(rpy.BASIC_CONVERSION)
s0 = rpy.r.summary(aov0)
sh = rpy.r.summary(aovh)
#print s0,sh
rss_0 = s0['Sum Sq'][-1]
if type(sh['Sum Sq']) != float:
rss_h = sh['Sum Sq'][-1]
else:
rss_h = sh['Sum Sq']
f = (rss_h - rss_0) / (rss_0 /
(len(phenVals) - len(d0) + 1))
pval = rpy.r.pf(f, 1, len(phenVals), lower_tail=False)
except Exception, err_str:
print "Calculating p-value failed" #,err_str
pval = 1.0
#print "dh:",dh
#print "d0:",d0
#print "rss_h,rss_0:",rss_h,rss_0
#print "f,p:",f,pval
chr_pos_pvals.append([i + 1, snpsd.positions[j], pval])
mafc = min(snp.count(snp[0]), len(snp) - snp.count(snp[0]))
maf = mafc / float(len(snp))
stats.append([maf, mafc])
sys.stdout.write("\n")
#Write out to a result file
sys.stdout.write("Writing results to file\n")
sys.stdout.flush()
pvalFile = rFile + ".pvals"
f = open(pvalFile, "w")
f.write("Chromosome,position,p-value,marf,maf\n")
for i in range(0, len(chr_pos_pvals)):
chr_pos_pval = chr_pos_pvals[i]
stat = stats[i]
f.write(
str(chr_pos_pval[0]) + "," + str(chr_pos_pval[1]) + "," +
str(chr_pos_pval[2]) + "," + str(stat[0]) + "," +
str(stat[1]) + "\n")
f.close()
#Plot results
print "Generating a GW plot."
phenotypeName = phed.getPhenotypeName(phenotypeIndex)
res = gwaResults.Result(pvalFile,
name="LM_" + phenotypeName,
phenotypeID=phenotypeIndex)
res.negLogTransform()
pngFile = pvalFile + ".png"
plotResults.plotResult(res,
pngFile=pngFile,
percentile=90,
type="pvals",
ylab="$-$log$_{10}(p)$",
plotBonferroni=True,
usePylab=False)
def calc_directly_std_rates(summset,
popset,
stdpopset=None,
conflev=0.95,
basepop=100000,
timeinterval='years',
ci_method='dobson',
popset_popcol='_freq_',
stdpopset_popcol='_stdpop_',
axis=0,
debug=False):
"""
Calculate Directly Standardised Population Rates
summset is a summary dataset of counts of events for the
population-of-interest being compared to the standard
population.
popset is the stratified population counts for the
population-of-interest
stdpopset is the stratified population counts for the standard
population
"""
from rpy import r, get_default_mode, set_default_mode, BASIC_CONVERSION
alpha = get_alpha(conflev)
if ci_method not in ('dobson', 'ff'):
raise Error('Only Dobson et al. (dobson) and Fay-Feuer (ff) methods '
'for confidence intervals currently implemented')
if not popset.has_column(popset_popcol):
raise Error('Denominator population dataset %r does not have a '
'%r column' % (popset.label or popset.name, popset_popcol))
if stdpopset is not None and not stdpopset.has_column(stdpopset_popcol):
raise Error('Standard population dataset %r does not have a '
'%r column' %
(stdpopset.label or stdpopset.name, stdpopset_popcol))
st = time.time()
r_mode = get_default_mode()
try:
set_default_mode(BASIC_CONVERSION)
# We turn the summset into an Ncondcols-dimensional matrix
summtab = CrossTab.from_summset(summset)
if stdpopset is not None:
# Then attempt to do the same to the stdpop data, summing any
# axes not required and replicate any missing until we have an
# array the same shape as the summtab array.
stdtab = CrossTab.from_summset(stdpopset, shaped_like=summtab)
stdtab.collapse_axes_not_in(summtab)
stdtab.replicate_axes(summtab)
stdpop = stdtab[stdpopset_popcol].data.astype(Numeric.Float64)
# The population dataset must have at least as many dimensions as
# summary dataset. Any additional axes are eliminated by summing.
# any missing axes are created by replication.
poptab = CrossTab.from_summset(popset, shaped_like=summtab)
poptab.collapse_axes_not_in(summtab)
poptab.replicate_axes(summtab)
popfreq = poptab[popset_popcol].data.astype(Numeric.Float64)
# Manufacture a CrossTab for the result, with one less axis (the first)
result = summtab.empty_copy()
del result.axes[axis]
if stdpopset is not None:
sum_stdpop = sumaxis(stdpop)
stdwgts = stdpop / sum_stdpop
stdpop_sq = stdpop**2
sum_stdpop_sq = sum_stdpop**2
ffwi = stdwgts / popfreq
ffwm = MA.maximum(MA.ravel(ffwi))
basepop = float(basepop)
for table, name, n_add, l_add in just_freq_tables(summtab):
# avoid integer overflows...
summfreq = table.data.astype(Numeric.Float64)
strata_rate = summfreq / popfreq
result.add_table('summfreq' + n_add,
data=sumaxis(summfreq, axis),
label='Total events' + l_add)
result.add_table('popfreq' + n_add,
data=sumaxis(popfreq, axis),
label='Total person-' + timeinterval +
' at risk' + l_add)
if stdpopset is not None:
std_strata_summfreq = summfreq * Numeric.where(
MA.getmask(stdwgts), 0., 1.)
wgtrate = strata_rate * stdwgts
result.add_table('std_strata_summfreq' + n_add,
data=sumaxis(std_strata_summfreq, axis),
label="Total events in standard strata" +
l_add)
# Crude rate
cr = sumaxis(summfreq, axis) / sumaxis(popfreq, axis) * basepop
result.add_table('cr' + n_add,
data=cr,
label='Crude Rate per ' + '%d' % basepop +
' person-' + timeinterval + l_add)
if alpha is not None:
# CIs for crude rate
count = sumaxis(summfreq, axis)
count_shape = count.shape
count_flat = MA.ravel(count)
totpop = sumaxis(popfreq, axis)
assert totpop.shape == count.shape
totpop_flat = MA.ravel(totpop)
cr_ll = Numeric.empty(len(count_flat),
typecode=Numeric.Float64)
cr_ul = Numeric.empty(len(count_flat),
typecode=Numeric.Float64)
cr_ll_mask = Numeric.zeros(len(count_flat),
typecode=Numeric.Int8)
cr_ul_mask = Numeric.zeros(len(count_flat),
typecode=Numeric.Int8)
for i, v in enumerate(count_flat):
try:
if v == 0:
cr_ll[i] = 0.0
else:
cr_ll[i] = (
(r.qchisq(alpha / 2., df=2.0 * v) / 2.0) /
totpop_flat[i]) * basepop
cr_ul[i] = (
(r.qchisq(1. - alpha / 2., df=2.0 *
(v + 1)) / 2.0) /
totpop_flat[i]) * basepop
except:
cr_ll[i] = 0.0
cr_ul[i] = 0.0
cr_ll_mask[i] = 1
cr_ul_mask[i] = 1
cr_ll = MA.array(cr_ll, mask=cr_ll_mask, typecode=MA.Float64)
cr_ul = MA.array(cr_ul, mask=cr_ul_mask, typecode=MA.Float64)
cr_ll.shape = count_shape
cr_ul.shape = count_shape
cr_base = 'Crude rate %d%%' % (100.0 * conflev)
result.add_table('cr_ll' + n_add,
data=cr_ll,
label=cr_base + ' lower confidence limit ' +
l_add)
result.add_table('cr_ul' + n_add,
data=cr_ul,
label=cr_base + ' upper confidence limit ' +
l_add)
if stdpopset is not None:
# Directly Standardised Rate
dsr = sumaxis(wgtrate, axis)
result.add_table('dsr' + n_add,
data=dsr * basepop,
label='Directly Standardised Rate per ' +
'%d' % basepop + ' person-' + timeinterval +
l_add)
# Confidence Intervals
if alpha is None or name != '_freq_':
# Can only calculate confidence intervals on freq cols
continue
if ci_method == 'dobson':
# Dobson et al method
# see: Dobson A, Kuulasmaa K, Eberle E, Schere J. Confidence intervals for weighted sums
# of Poisson parameters, Statistics in Medicine, Vol. 10, 1991, pp. 457-62.
# se_wgtrate = summfreq*((stdwgts/(popfreq/basepop))**2)
se_wgtrate = summfreq * ((stdwgts / (popfreq))**2)
stderr = stdpop_sq * strata_rate * (1.0 - strata_rate)
se_rate = sumaxis(se_wgtrate, axis)
sumsei = sumaxis(stderr, axis)
total_freq = sumaxis(std_strata_summfreq, axis)
# get shape of total_freq
total_freq_shape = total_freq.shape
total_freq_flat = MA.ravel(total_freq)
# flat arrays to hold results and associated masks
l_lam = Numeric.empty(len(total_freq_flat),
typecode=Numeric.Float64)
u_lam = Numeric.empty(len(total_freq_flat),
typecode=Numeric.Float64)
l_lam_mask = Numeric.zeros(len(total_freq_flat),
typecode=Numeric.Int8)
u_lam_mask = Numeric.zeros(len(total_freq_flat),
typecode=Numeric.Int8)
conflev_l = (1 - conflev) / 2.0
conflev_u = (1 + conflev) / 2.0
for i, v in enumerate(total_freq_flat):
try:
if v == 0.:
u_lam[i] = -math.log(1 - conflev)
l_lam[i] = 0.0
else:
l_lam[i] = r.qgamma(conflev_l, v, scale=1.)
u_lam[i] = r.qgamma(conflev_u,
v + 1.,
scale=1.)
except:
l_lam[i] = 0.0
u_lam[i] = 0.0
l_lam_mask[i] = 1
u_lam_mask[i] = 1
l_lam = MA.array(l_lam,
mask=l_lam_mask,
typecode=MA.Float64)
u_lam = MA.array(u_lam,
mask=u_lam_mask,
typecode=MA.Float64)
l_lam.shape = total_freq_shape
u_lam.shape = total_freq_shape
dsr_ll = dsr + (((se_rate / total_freq)**0.5) *
(l_lam - total_freq))
dsr_ul = dsr + (((se_rate / total_freq)**0.5) *
(u_lam - total_freq))
elif ci_method == 'ff':
# Fay and Feuer method
# see: Fay MP, Feuer EJ. Confidence intervals for directly standardized rates:
# a method based on the gamma distribution. Statistics in Medicine 1997 Apr 15;16(7):791-801.
ffvari = summfreq * ffwi**2.0
ffvar = sumaxis(ffvari, axis)
dsr_flat = Numeric.ravel(MA.filled(dsr, 0))
dsr_shape = dsr.shape
ffvar_flat = Numeric.ravel(MA.filled(ffvar, 0))
# flat arrays to hold results and associated masks
dsr_ll = Numeric.empty(len(dsr_flat),
typecode=Numeric.Float64)
dsr_ul = Numeric.empty(len(dsr_flat),
typecode=Numeric.Float64)
dsr_ll_mask = Numeric.zeros(len(dsr_flat),
typecode=Numeric.Int8)
dsr_ul_mask = Numeric.zeros(len(dsr_flat),
typecode=Numeric.Int8)
for i, y in enumerate(dsr_flat):
try:
dsr_ll[i] = (ffvar_flat[i] / (2.0 * y)) * r.qchisq(
alpha / 2., df=(2.0 * (y**2.) / ffvar_flat[i]))
dsr_ul[i] = ((ffvar_flat[i] + (ffwm**2.0)) /
(2.0 * (y + ffwm))) * r.qchisq(
1. - alpha / 2.,
df=((2.0 * ((y + ffwm)**2.0)) /
(ffvar_flat[i] + ffwm**2.0)))
except:
dsr_ll[i] = 0.0
dsr_ul[i] = 0.0
dsr_ll_mask[i] = 1
dsr_ul_mask[i] = 1
dsr_ll = MA.array(dsr_ll,
mask=dsr_ll_mask,
typecode=MA.Float64)
dsr_ul = MA.array(dsr_ul,
mask=dsr_ul_mask,
typecode=MA.Float64)
dsr_ll.shape = dsr_shape
dsr_ul.shape = dsr_shape
result.add_table('dsr_ll' + n_add,
data=dsr_ll * basepop,
label='DSR ' + '%d' % (100.0 * conflev) +
'% lower confidence limit' + l_add)
result.add_table('dsr_ul' + n_add,
data=dsr_ul * basepop,
label='DSR ' + '%d' % (100.0 * conflev) +
'% upper confidence limit' + l_add)
finally:
set_default_mode(r_mode)
soom.info('calc_directly_std_rates took %.03f' % (time.time() - st))
if stdpopset is not None:
name = 'dir_std_rates_' + summset.name
label = 'Directly Standardised Rates for ' + (summset.label
or summset.name)
else:
name = 'crude_rates_' + summset.name
label = 'Crude Rates for ' + (summset.label or summset.name)
if conflev:
label += ' (%g%% conf. limits)' % (conflev * 100)
if debug:
global vars
vars = Vars(locals())
return result.to_summset(name, label=label)
def lm_fit(self, lm_instance, go_no2prediction_space, bit_string, curs=None, lm_table=None):
"""
02-28-05
linear model fitting here
03-08-05
grouping and accumulating before do linear model fitting, see log of 2005,
section 'linear model overfitting' for detail.
03-27-05
Use glm of R to do logistic regression
06-30-05
add cluster_size
add bit_string to control which parameter should be enabled.
07-04-05
add connectivity_2nd
07-06-05
add logistic
11-09-05 extend coeff_list and coeff_p_value_list
restructure the list, go_no2lm_results[go_no]
--data_prepare
--submit
"""
sys.stderr.write("Linear Model Fitting...\n")
go_no2lm_results = {}
#06-30-05 setup the formula_list based on bit_string
coeff_name_list = ['p_value', 'recurrence', 'connectivity', 'cluster_size', 'connectivity_2nd']
formula_list = []
for i in range(len(bit_string)):
if bit_string[i] == '1':
formula_list.append(coeff_name_list[i])
for (go_no,data) in go_no2prediction_space.iteritems():
sys.stderr.write("%s prediction entries from %s.\n"%(len(data), go_no))
#11-09-05 extend coeff_list and coeff_p_value_list
coeff_list = [0]*7 #intercept, p_value, recurrence, connectivity, cluster_size
coeff_p_value_list = [1]*7
index = 0 #06-30-05 the pointer for summary_stat
if len(data)<=50:
#two few data
continue
#convert it to a 2d array
data = array(data)
"""
data_frame = r("d=data.frame(p_value=c(%s),recurrence=c(%s),connectivity=c(%s), is_correct=c(%s))"%(repr(list(data[:,0]))[1:-1], \
repr(list(data[:,1]))[1:-1], repr(list(data[:,2]))[1:-1], repr(list(data[:,3]))[1:-1]))
lm_result = r("lm_result=glm(is_correct~p_value+recurrence+connectivity, data=d,family=binomial)")
significance_dict = r("summary(lm_result)")
print significance_dict['coefficients']
"""
set_default_mode(NO_CONVERSION) #04-07-05
data_frame = r.as_data_frame({"p_value":data[:,0], "recurrence":data[:,1], "connectivity":data[:,2], \
"cluster_size":data[:,3], "connectivity_2nd":data[:,4], "is_correct":data[:,-1]}) #06-30-05 -1 denotes is_correct
if self.logistic:
lm_result = r.glm(r("is_correct~%s"%'+'.join(formula_list)), data=data_frame, family=r("binomial"))
else:
lm_result = r.glm(r("is_correct~%s"%'+'.join(formula_list)), data=data_frame) #06-30-05 use formula_list
set_default_mode(BASIC_CONVERSION) #04-07-05
#04-07-05 r.summary() requires lm_result in NO_CONVERSION state
summary_stat = r.summary(lm_result)
if self.debug:
print "everything about coefficients from function", go_no, "is"
print summary_stat['coefficients'] #p-values of coefficients
"""
#04-07-05 convert to python dictionary form
lm_result = lm_result.as_py()
coeff_list = [lm_result["coefficients"]["(Intercept)"], lm_result["coefficients"]["p_value"], \
lm_result["coefficients"]["recurrence"], lm_result["coefficients"]["connectivity"], \
lm_result["coefficients"]["cluster_size"], \
summary_stat['coefficients'][0][-1], summary_stat['coefficients'][1][-1],\
summary_stat['coefficients'][2][-1], summary_stat['coefficients'][3][-1],\
summary_stat['coefficients'][4][-1], 1]
#the last entry is score_cut_off, replaced later in get_score_cut_off()
#06-30-05 add corresponding p-values
"""
#06-30-05 0 in summary_stat['coefficients'] is intercept
coeff_list[0] = summary_stat['coefficients'][0][0] #0 is the coefficient
coeff_p_value_list[0] = summary_stat['coefficients'][0][-1] #-1 is the corresponding p-value
#06-30-05 fill in other efficients based on bit_string, NOTE i+1
for i in range(len(bit_string)):
if bit_string[i] == '1':
index+=1
coeff_list[i+1] = summary_stat['coefficients'][index][0] #0 is the coefficient
coeff_p_value_list[i+1] = summary_stat['coefficients'][index][-1] #-1 is the corresponding p-value
#11-09-05 restructure the following list
go_no2lm_results[go_no] = [coeff_list, coeff_p_value_list, 1] #the last entry is score_cut_off, replaced later in get_score_cut_off()
sys.stderr.write("done.\n")
return go_no2lm_results
def make_L(
data,
direction='S',
z=None,
):
""" Define the along track distance from one reference
direction define the cardinal direction priority (N,S,W or E).
S means that the reference will be the southern most point
z define the bathymetry, if defined, the closest point to that
bathymetry will be the reference. In case of cross this bathymetry
more than once, the direction criteria is used to distinguish.
"""
from fluid.common.distance import distance
all_cycles_data = join_cycles(data)
if z == None:
import rpy
#for t in topex.invert_keys(data):
for t in all_cycles_data:
rpy.set_default_mode(rpy.NO_CONVERSION)
linear_model = rpy.r.lm(rpy.r("y ~ x"),
data=rpy.r.data_frame(
x=all_cycles_data[t]['Longitude'],
y=all_cycles_data[t]['Latitude']))
rpy.set_default_mode(rpy.BASIC_CONVERSION)
coef = rpy.r.coef(linear_model)
if direction == 'S':
lat0 = all_cycles_data[t]['Latitude'].min() - 1
lon0 = (lat0 - coef['(Intercept)']) / coef['x']
L_correction = distance(all_cycles_data[t]['Latitude'],
all_cycles_data[t]['Longitude'], lat0,
lon0).min()
for c in invert_keys(data)[t]:
data[c][t]['L'] = distance(data[c][t]['Latitude'],
data[c][t]['Longitude'], lat0,
lon0) - L_correction
# This bathymetric method was only copied from an old code. This should be atleast
# changed, if not removed.
elif method == 'bathymetric':
import rpy
for t in all_cycles_data:
# First define the near coast values.
idSouth = numpy.argmin(all_cycles_data[t]['Latitude'])
L_tmp = distance(all_cycles_data[t]['Latitude'],
all_cycles_data[t]['Longitude'],
all_cycles_data[t]['Latitude'][idSouth],
all_cycles_data[t]['Longitude'][idSouth])
idNearCoast = L_tmp.data < 400e3
if min(all_cycles_data[t]['Bathy'][idNearCoast]) > -z:
idNearCoast = L_tmp.data < 600e3
# Then calculate the distance to a reference
rpy.set_default_mode(rpy.NO_CONVERSION)
linear_model = rpy.r.lm(rpy.r("y ~ x"),
data=rpy.r.data_frame(
x=all_cycles_data[t]['Longitude'],
y=all_cycles_data[t]['Latitude']))
rpy.set_default_mode(rpy.BASIC_CONVERSION)
coef = rpy.r.coef(linear_model)
lat0 = all_cycles_data[t]['Latitude'].min() - 1
lon0 = (lat0 - coef['(Intercept)']) / coef['x']
#L = distance(,lon,lat0,lon0)
#
#id0 = numpy.argmin(numpy.absolute(all_cycles_data[t]['Bathy'][idNearCoast]))
idref = numpy.argmin(
numpy.absolute(all_cycles_data[t]['Bathy'][idNearCoast] + z))
#L_correction = distance(all_cycles_data[t]['Latitude'][idNearCoast][idref],all_cycles_data[t]['Longitude'][idNearCoast][idref],all_cycles_data[t]['Latitude'][idNearCoast][idref],all_cycles_data[t]['Longitude'][idNearCoast][idref])
L_correction = distance(
all_cycles_data[t]['Latitude'][idNearCoast][idref],
all_cycles_data[t]['Longitude'][idNearCoast][idref], lat0,
lon0)
for c in topex.invert_keys(data)[t]:
#data[c][t]['L'] = distance(data[c][t]['Latitude'],data[c][t]['Longitude'],all_cycles_data[t]['Latitude'][idNearCoast][id0],all_cycles_data[t]['Longitude'][idNearCoast][id0]) - L_correction
data[c][t]['L'] = distance(data[c][t]['Latitude'],
data[c][t]['Longitude'], lat0,
lon0) - L_correction
#
return
is_correct_list = []
for row in reader:
p_value, recurrence, connectivity, cluster_size, gradient, gene_no, go_no, is_correct = row
data.append([float(p_value), float(recurrence), float(connectivity), float(cluster_size), float(gradient), int(gene_no), int(go_no), int(is_correct)])
del reader
return data, is_correct_list
known_fname = '/tmp/hs_fim_92m5x25bfsdfl10q0_7gf1.known'
unknown_fname = '/tmp/hs_fim_92m5x25bfsdfl10q0_7gf1.unknown'
known_data, known_is_correct_list = read_data(known_fname)
unknown_data, unknown_is_correct_list = read_data(unknown_fname)
from numarray import array
from rpy import r, set_default_mode,NO_CONVERSION,BASIC_CONVERSION
set_default_mode(NO_CONVERSION)
#pack data into data_frame
known_data = array(known_data)
known_data_frame = r.as_data_frame({"p_value":known_data[:,0], "recurrence":known_data[:,1], "connectivity":known_data[:,2], \
"cluster_size":known_data[:,3], "gradient":known_data[:,4]})
unknown_data = array(unknown_data)
unknown_data_frame = r.as_data_frame({"p_value":unknown_data[:,0], "recurrence":unknown_data[:,1], "connectivity":unknown_data[:,2], \
"cluster_size":unknown_data[:,3], "gradient":unknown_data[:,4]})
#start to call randomF.r to run randomForest
r.library('randomForest')
r.source('randomF.r')
#rf_model still needs to be in pure R object
rf_model = r.randomF(known_data_frame, known_data[:,-1])
set_default_mode(BASIC_CONVERSION)
unknown_pred = r.predictRandomF(rf_model, unknown_data_frame)
# if you have rpy installed, use it to test the results
have_rpy = False
try:
print "\n"
print "="*30
print "Validating OLS results in R"
print "="*30
import rpy
have_rpy = True
except ImportError:
print "\n"
print "="*30
print "Validating OLS-class results in R"
print "="*30
print "rpy is not installed"
print "="*30
if have_rpy:
y = data[:,0]
x1 = data[:,1]
x2 = data[:,2]
x3 = data[:,3]
x4 = data[:,4]
rpy.set_default_mode(rpy.NO_CONVERSION)
linear_model = rpy.r.lm(rpy.r("y ~ x1 + x2 + x3 + x4"), data = rpy.r.data_frame(x1=x1,x2=x2,x3=x3,x4=x4,y=y))
rpy.set_default_mode(rpy.BASIC_CONVERSION)
print linear_model.as_py()['coefficients']
summary = rpy.r.summary(linear_model)
print summary
def krige_to_grid(grid_fname, obs_x, obs_y, obs_data, vgm_par):
"""Interpolate point data onto a grid using Kriging.
Interpolate point data onto a regular rectangular grid of square cells using
Kriging with a predefined semi-variogram. The observed data locations must
be specified in the same projection and coordinate system as the grid, which
is defined in an ArcGIS raster file.
Parameters
----------
grid_fname : string
Filename of an ArcGIS float grid raster defining the required grid to
Krige onto. All cells are included regardless of their value.
obs_x : array_like
The x coordinates of the observation locations.
obs_y : array_like
The y coordinates of the observation locations.
obs_data : array_like
The data values at the observation locations.
vgm : dict
A dictionary describing the semi-variogram model. Required keys are:
'model' can be one of {'Lin', 'Exp', 'Sph', 'Gau'}
'nugget' must be a scalar
'range' must be a scalar
'sill' must be a scalar
Returns
-------
kriged_est : 2darray
A 2D array containing the Kriged estimates at each point on the
specified rectangular grid.
Notes
-----
This function requires that R, RPy and the R gstat library are correctly
installed.
"""
grid, headers = arcfltgrid.read(grid_fname)
cols = headers[0]
rows = headers[1]
x0 = headers[2]
y0 = headers[3]
cell_size = headers[4]
# TO DO: adjust x0, y0 by 0.5*cell_size if llcorner..
# define the grid (pixel centre's)
xt, yt = np.meshgrid(
np.linspace(x0, x0 + (cols - 1) * cell_size, num=cols),
np.linspace(y0 + (rows - 1) * cell_size, y0, num=rows))
xt = xt.flatten()
yt = yt.flatten()
# Krige using gstat via RPy
r.library('gstat')
rpy.set_default_mode(rpy.NO_CONVERSION)
obs_frame = r.data_frame(x=obs_x, y=obs_y, data=obs_data)
target_grid = r.data_frame(x=xt, y=yt)
v = r.vgm(vgm_par['sill'], vgm_par['model'], vgm_par['range'],
vgm_par['nugget'])
result = r.krige(r('data ~ 1'),
r('~ x + y'),
obs_frame,
target_grid,
model=v)
rpy.set_default_mode(rpy.BASIC_CONVERSION)
result = result.as_py()
kriged_est = np.array(result['var1.pred'])
kriged_est = kriged_est.reshape(rows, cols)
return kriged_est