#gets config file:

config = get_config_file(localpath=path.dirname(path.realpath(__file__))+'/')

#IMPORT VARIABLES

if not source:

source = config.get('Source', 'source')

data_path = config.get('Addresses', source)

filter_dir = config.get('Paths', "filter_dir")

filter_name = config.get("Filters", use_filter)

#END IMPORT VARIABLES

filter_path = path.dirname(path.realpath(__file__)) + '/' + filter_dir + filter_name + '.csv'

filters = DataFrame.from_csv(filter_path, header=None).transpose() # transpose filters because of .csv file formatting

all_data = DataFrame.from_csv(data_path + data_file + ".csv")

all_data = all_data.reset_index(level=0)

#~ print filters["metadata"]

#clean data of respondents who only ckeck extreme answers:

all_data = all_data[map(lambda y: len(set(y)) > clean,np.array(all_data))]

if drop_metadata == True:

# drops metadata

all_data = all_data.drop(filters["metadata"][Series.notnull(filters["metadata"])], axis=1)

drop_list = []

for drop_item in drop:

# compile list of column names to be dropped:

drop_list += list(filters[drop_item][Series.notnull(filters[drop_item])])

#get unique column names (the list may contain duplicates if overlaying multiple filters):

drop_list = list(set(drop_list))

all_data = all_data.drop(drop_list, axis=1)

if participant_subset == "odd":

# selects only odd indexes (keep the other dataset half for validation)

keep_rows = all_data.index.values[1::2]

filtered_data = all_data.ix[keep_rows]

elif participant_subset == "even":

# selects only even indexes (keep the other dataset half for validation)

keep_rows = all_data.index.values[0::2]

filtered_data = all_data.ix[keep_rows]

elif participant_subset == "male":

# selects only male participants

filtered_data = all_data[all_data['My legal gender:'] == 'Male']

elif participant_subset == "female":

# selects only female participants

filtered_data = all_data[all_data['My legal gender:'] == 'Female']

else:

filtered_data = all_data

#convert to correct type for analysis:

filtered_data_array = np.array(filtered_data, dtype='float64')

filtered_data_array = filtered_data_array / 100

fit = r.factanal(filtered_data_array, factors, rotation='promax')

load = r.loadings(fit)

load = numpy2ri.ri2numpy(load)

load = r.t(load)

remapped_cmap = remappedColorMap(cm.PiYG, start=(np.max(load)-abs(np.min(load)))/(2*np.max(load)), midpoint=abs(np.min(load))/(np.max(load)+abs(np.min(load))), name='shrunk')

fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(17.5, 5), facecolor=facecolor)

graphic = ax.imshow(load, cmap = remapped_cmap, interpolation='none')

ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(base=1.0))

ax.yaxis.set_major_locator(matplotlib.ticker.MultipleLocator(base=1.0))

ax.set_xticklabels([0]+filtered_data.columns.tolist(),fontsize=8,rotation=90)

ax.set_yticklabels(np.arange(factors+1))

ax.set_ylabel('Factors')

ax.set_title("Question Loadings on Factors")

#Recolor plot spines:

for spine_side in ["bottom", "top", "left", "right"]:

ax.spines[spine_side].set_color("#777777")

#Remove ticks:

plt.tick_params(axis='both', which='both', left="off", right="off", bottom='off', top='off')

divider = make_axes_locatable(ax)

#calculate width for cbar so that it is equal to the question column width:

cbar_width = str(100/np.shape(load)[1])+ "%"

cax = divider.append_axes("right", size=cbar_width, pad=0.05)

cbar = colorbar(graphic, cax=cax, drawedges=True)

#Limit the number of ticks:

tick_locator = ticker.MaxNLocator(nbins=6)

cbar.locator = tick_locator

cbar.update_ticks()

#Align ticklabels so that negative values are not misaligned (meaning right align):

for t in cbar.ax.get_yticklabels():

t.set_horizontalalignment('right')

t.set_x(0.045*(np.shape(load)[1]+6))

#Tweak color bar borders

cbar.outline.set_color("#666666")