Visual Comparison of Flat vs Hierarchical DNS Namespaces 1. Flat DNS Namespace 2. Hierarchical DNS Namespace Key Differences Feature Flat Namespace Hierarchical Namespace Structure Single level Multi-level (tree-like) Scalability Poor Excellent Delegation Not possible Each …
Here’s a copyright-free and plagiarism-free version of Which Router to Use in GNS3: Which Router to Use in GNS3 When you start building labs in GNS3, one of the first choices you’ll face is which …
Perfect 👍 I’ll rewrite that into a fresh, copyright-free and plagiarism-free version while keeping all the important details intact. You can safely post this on your FB/blog. 🚀 Why Do We Need the GNS3 VM? …
All proceeds from Medium will go to Justetc Social Services ( non-profit). Justetc Social Services provides services in the Training and Education Areas.
import pandas as pd import matplotlib.pyplot as plt %matplotlib inlinefrom __future__ import print_function from ipywidgets import interact, interactive, fixed, interact_manual import ipywidgets as widgetsaccess_to_care = pd.read_excel('../data/access-to-care.xls') access_to_care.head()
# find all indicators access_to_care.set_index(['Indicator']) indicators = pd.Index(access_to_care['Indicator']).unique()
# find all years years = access_to_care['Data year'].dropna().unique() indicators[0], years('Inability to Pay for Medical Bills', array(['2016', 2016, 'Not applicable', '2015', '2013'], dtype=object))# sort years years = [ int(aYear) for aYear in years if (aYear != 'Not applicable') and len( str(aYear).split(' ')) <= 1 ] years = sorted(years) years
import pandas as pd import matplotlib.pyplot as plt %matplotlib inlinefrom __future__ import print_function from ipywidgets import interact, interactive, fixed, interact_manual import ipywidgets as widgetshealth_status = pd.read_excel('../data/health-status.xls') health_status.head()
# find all indicators health_status.set_index(['Indicator']) indicators = pd.Index(health_status['Indicator']).unique()
# find all years years = health_status['Data year'].unique() indicators[0], years[0]('Cancer Mortality (F)', '2013')# sort years years = [ int(aYear) for aYear in years if (aYear != 'Not applicable') and len( str(aYear).split(' ')) <= 1 ] years = sorted(years) years
import pandas as pd import matplotlib.pyplot as plt %matplotlib inlinefrom __future__ import print_function from ipywidgets import interact, interactive, fixed, interact_manual import ipywidgets as widgetsnon_med_determinants = pd.read_excel('../data/non-med-determinants.xls') non_med_determinants.head()
# find all indicators non_med_determinants.set_index(['Indicator']) indicators = pd.Index(non_med_determinants['Indicator']).unique()
# find all years years = non_med_determinants['Data year'].dropna().unique() indicators[0], years('Alcohol Consumption: Adults', array(['2014', '2015', '2013', 2015, 'Not applicable', '2016'], dtype=object))# sort years years = [ int(aYear) for aYear in years if (aYear != 'Not applicable') and len( str(aYear).split(' ')) <= 1 ] years = sorted(years) years
Indicator visualizations will be in a separate ipynb file (visualize-indicators-final.ipynb)
Visualizations can be created using the UI interface i.e. Select options and execute the code block after to get the visualizations.
I have placed a separate section at the end of this file where research questions and plots as I placed on my documents and presentations are placed (Code there will generate the plots for that section)
For research question section, corresponding health status will need to be selected and data reloaded to re — execute (UI selections will not matter)
#!conda install basemapimport pandas as pd import matplotlib.pyplot as plt %matplotlib inline import numpy as np
from __future__ import print_function from ipywidgets import interact, interactive, fixed, interact_manual import ipywidgets as widgets
import pandas as pd import matplotlib.pyplot as plt %matplotlib inline# it is important # from geopy.geocoders import Nominatim
# for geo maps # !conda install basemap import conda import os if 'PROJ_DIR' in os.environ: pyproj_datadir = os.environ['PROJ_DIR'] else: conda_file_dir = conda.CONDA_PACKAGE_ROOT conda_dir = conda_file_dir.split('lib')[0] pyproj_datadir = os.path.join(os.path.join(conda_dir, 'Library'),'share') os.environ['PROJ_LIB'] = pyproj_datadir
Data folder will have data files, one for one aspect of healthcare performance. I will load data file names(aspects) in a drop down — to act upon that. Hence, any new data files with similar structures (likely) can be easily integrated with the visualizations.
Find all the performance indicators under this aspect/measure
irrespective we have data for Canada or not
# find details on Indicators # find all indicators measure_data.set_index(['Indicator']) indicators = pd.Index(measure_data['Indicator']).unique() indicatorsIndex(['Inability to Pay for Medical Bills', 'Poor Weekend/Evening Care', 'Regular Doctor', 'Same or Next Day Appt', 'Wait Time: Cataract Surgery', 'Wait Time: Hip Replacement', 'Wait Time: Knee Replacement', 'Wait Time: Specialist'], dtype='object', name='Indicator')
Find the performance indicators under this aspect/measure when Canada must have data
# indicators that must have canadian data
# first where canada do exist data_with_canada = measure_data[measure_data['Region'] == 'Canada']
#indicators when canada also have data indicators_with_canada = data_with_canada['Indicator'].unique()
print('All indicators\n', indicators) print('Indicators where canada also has Data\n', indicators_with_canada) 'Indicator Counts', len(indicators_with_canada), len(indicators)All indicators Index(['Inability to Pay for Medical Bills', 'Poor Weekend/Evening Care', 'Regular Doctor', 'Same or Next Day Appt', 'Wait Time: Cataract Surgery', 'Wait Time: Hip Replacement', 'Wait Time: Knee Replacement', 'Wait Time: Specialist'], dtype='object', name='Indicator') Indicators where canada also has Data ['Inability to Pay for Medical Bills' 'Poor Weekend/Evening Care' 'Regular Doctor' 'Same or Next Day Appt' 'Wait Time: Cataract Surgery' 'Wait Time: Hip Replacement' 'Wait Time: Knee Replacement' 'Wait Time: Specialist']
('Indicator Counts', 8, 8)
Find years as we can see in the data
All years, also seprately when we have data for Canada
# find all years years = measure_data['Data year'].dropna().unique() years_with_canada = data_with_canada['Data year'].dropna().unique()
print('Years we have data\n', years) print('Years where canada also has data\n', years_with_canada)Years we have data ['2016' 2016 'Not applicable' '2015' '2013'] Years where canada also has data [2016]
Years when we have data for Canada
# sort years for all data years = [ int(aYear) for aYear in years if (aYear != 'Not applicable') and len( str(aYear).split(' ')) <= 1 ] years = sorted(years) years
all_years = [0] + years all_years[0, 2013, 2015, 2016, 2016]
Sort the years so that we can show them in drop down in ascending format
0 means all years selected
# sort years years_with_canada = [ int(aYear) for aYear in years_with_canada if (aYear != 'Not applicable') and len( str(aYear).split(' ')) <= 1 ] years_with_canada = sorted(years_with_canada) years_with_canada
countries = measure_data[ (measure_data['Type of region'] == 'Country') | (measure_data['Type of region'] == 'Canada') ]['Region'] countries = sorted (countries.unique()) #print(countries) #countries = ['Canada'] + countries
# bring Canada at the top if 'Canada' in countries: countries = ['Canada'] + countries
countries[:1]['Canada']
unique color code for each country
color will be used as a third dimension in some plots
# countries unique color code all_regions = measure_data['Region'].unique() region_colors = [] region_colors_dict = {} import random random.seed(0) for aRegion in all_regions: region_colors_dict[aRegion] = np.random.randint(0, 255)
list(region_colors_dict.keys())[:5], list(region_colors_dict.values())[:5](['United Kingdom', 'Saskatchewan', 'Germany', 'British Columbia', 'Australia'], [227, 16, 56, 193, 232])# latitude and longitude information geolocator = Nominatim(user_agent="chrome") lats, lons = [], [] lats_dict = {} lons_dict = {} #for aCountry in countries: for aRegion in all_regions: try: location = geolocator.geocode(aRegion) lats.append(location.latitude) lons.append(location.longitude) lats_dict[aRegion] = location.latitude lons_dict[aRegion] = location.longitude except: continue
<ipython-input-17-1521376715a3> in <module>() 1 # latitude and longitude information ----> 2 geolocator = Nominatim(user_agent="chrome") 3 lats, lons = [], [] 4 lats_dict = {} 5 lons_dict = {}
NameError: name 'Nominatim' is not defined# Province unique color code all_provinces = measure_data[ measure_data['Type of region'] == 'Province' ]['Region'].unique() province_colors = [] province_colors_dict = {} import random random.seed(0) for aProvince in all_provinces: province_colors_dict[aProvince] = np.random.randint(0, 255)
#print(indicator_data[ ['Indicator', 'Value', 'Data year' ]]) #print(indicator_data['Value'])Indicator Value Data year 17 Inability to Pay for Medical Bills 8.7 Not applicable Indicator Value Data year 27 Poor Weekend/Evening Care 54.1 Not applicable Indicator Value Data year 57 Regular Doctor 94.6 Not applicable Indicator Value Data year 84 Same or Next Day Appt 59.2 Not applicable Indicator Value Data year 110 Wait Time: Cataract Surgery 98.0 Not applicable Indicator Value Data year 128 Wait Time: Hip Replacement 115.2 Not applicable Indicator Value Data year 161 Wait Time: Knee Replacement 204.6 Not applicable Indicator Value Data year 174 Wait Time: Specialist 41.9 Not applicable
END-RELOAD-DATA
Method to plot over years or only for a year.
#Note: All combinations of UI selections might not work as that will require extensive testing and adjust (adjust with data and real life) #country might be represented by colors when applicable #supports plots such as: Bubble, Line, Bar, Hor Bar, Pie #other plot types can also be added. Once implemented will work for all measures and indicators (with proper data format provided) #Note: it is known to me that code can be improved here. similar codes are in multiple places — could be reduced
# redundend code to address a last minute bug # countries unique color code all_regions = measure_data['Region'].unique() region_colors = [] region_colors_dict = {} import random random.seed(0) for aRegion in all_regions: region_colors_dict[aRegion] = np.random.randint(0, 255)
# one improvement that can be made: usually I kept, two plots side by side. where the right one shows country and color # as in other cases we do not need the right one, code to hide the right one or just to create and use one subplot is more # appropriate # if (sec_fig == True): fig, axs = plt.subplots(1, 2, figsize=fig_size, sharey=False, gridspec_kw = {'width_ratios':ratios}) #else: #axs = plt.subplots(1, 1, figsize=fig_size )
# for country color codes if (provincial_only == False): c = [ region_colors_dict[x] for x in indicator_data['Region'] ] m = [ x for x in indicator_data['Region'] ] c_code = [ x[0:3] for x in indicator_data['Region'] ] else: #province_colors_dict c = [ province_colors_dict[x] for x in indicator_data['Region'] ] m = [ x for x in indicator_data['Region'] ] c_code = [ x[0:3] for x in indicator_data['Region'] ] region_colors_dict = province_colors_dict
# this block might not apply to anything for one single year plot
color_as_a_dimension = False if color_as_a_dimension == True: axs[1].scatter([1]*len(c), [i*5 for i in range(len(c))], s=300, c=c, marker='^') count = list(region_colors_dict.keys()) #print(count) for j in range(len(c)): axs[1].annotate(count[j], (1.0001, j*5))
axs[1].set_xlabel('Country and Colors') axs[1].set_ylabel('')
axs[1].set_xticks([]) axs[1].set_yticks([])
if chart_type == 'Line': # best to use only for one year
axs[0].plot(indicator_data['Value'], indicator_data['Region']) #, c=c axs[0].set_xticklabels(indicator_data['Value'], rotation=90) # can be turned off # https://stackoverflow.com/questions/10998621/rotate-axis-text-in-python-matplotlib plt.suptitle(indicator + ' For a Year') axs[0].set_xlabel('Values') axs[0].set_ylabel('Countries/Regions')
axs[0].pie(indicator_data['Value'], labels=indicator_data['Region']) #, c=c axs[0].set_xticklabels(indicator_data['Value'], rotation=90) # can be turned off # https://stackoverflow.com/questions/10998621/rotate-axis-text-in-python-matplotlib plt.suptitle(indicator + ' For a Year') axs[0].set_xlabel('Values') axs[0].set_ylabel('Countries/Regions')
#print(indicator_data) # for country color codes if (provincial_only == False): c = [ region_colors_dict[x] for x in indicator_data['Region'] ] m = [ x for x in indicator_data['Region'] ]
else: #province_colors_dict c = [ province_colors_dict[x] for x in indicator_data['Region'] ] m = [ x for x in indicator_data['Region'] ] #c_code = [ x[0:3] for x in indicator_data['Region'] ] region_colors_dict = province_colors_dict
plt.suptitle(indicator + ' Over a Year ' + str(year) ) axs[0].set_xlabel('Regions') axs[0].set_ylabel('Values') axs[0].set_xticklabels(indicator_data['Region'], rotation=90) # can be turned off
axs[1].scatter([1]*len(c), [i*5 for i in range(len(c))], s=300, c=c, marker='^') count = list(region_colors_dict.keys()) #print(count) for j in range(len(c)): axs[1].annotate(count[j], (1.0001, j*5))
axs[1].set_xlabel('Country and Colors') axs[1].set_ylabel('')
#plt.show() else: # for multiple years # though this method is primarily to use for one year, unless in some specific cases """ plt.ion() f = plt.figure() #ax = f.gca()
#top box f.show() """
# redundend code to address a last minute bug # countries unique color code all_regions = measure_data['Region'].unique() region_colors = [] region_colors_dict = {} import random random.seed(0) for aRegion in all_regions: region_colors_dict[aRegion] = np.random.randint(0, 255)
#print(indicator_data) # for country color codes #print(region_colors_dict) if (provincial_only == False): c = [ region_colors_dict[x] for x in indicator_data['Region'] ] m = [ x for x in indicator_data['Region'] ]
else: #province_colors_dict c = [ province_colors_dict[x] for x in indicator_data['Region'] ] m = [ x for x in indicator_data['Region'] ] #c_code = [ x[0:3] for x in indicator_data['Region'] ] region_colors_dict = province_colors_dict
#c = [ region_colors_dict[x] for x in indicator_data['Region'] ] #m = [ x for x in indicator_data['Region'] ]
# was here axs[0].plot(indicator_data['Data year'], indicator_data['Value']) #, c=c # was here axs[0].set_xticks(indicator_data['Value'])
# brought from one year # best to use only for one year #plt.plot(indicator_data['Data year'], indicator_data['Value'], c=c) #plt.xticks(indicator_data['Value'])
#axs[0].plot(indicator_data['Data year'], indicator_data['Value']) #, c=c axs[0].plot(indicator_data['Data year'], indicator_data['Value']) #, c=c #axs[0].plot(165, 'OECD', color='Red') #axs[0].set_xticklabels(indicator_data['Data year'], rotation=90) # can be turned off # https://stackoverflow.com/questions/10998621/rotate-axis-text-in-python-matplotlib #plt.suptitle(indicator + ' For a Year') #axs[0].set_xlabel('Years') #axs[0].set_ylabel('Values')
# for country color codes c = [ region_colors_dict[x] for x in indicator_data['Region'] ] m = [ x for x in indicator_data['Region'] ]
axs[0].scatter(indicator_data['Data year'], indicator_data['Value'], s = indicator_data['Value'] * bubble_scale, c=c)
plt.suptitle(indicator + ' Over Years \n Values are in multiples of Benchmark (' + str(benchmark_value) + ')' ) axs[0].set_xlabel('Years') axs[0].set_ylabel('Values/Becnhmark Value(' + str(benchmark_value) +')' )
# show country colors axs[1].scatter([1]*len(c), [i*5 for i in range(len(c))], s=300, c=c, marker='o')
count = list(m) #list(region_colors_dict.keys()) #print(count) for j in range(len(c)): axs[1].annotate(count[j], (1, j*5))
axs[1].set_xlabel('Country and Colors') axs[1].set_ylabel('')
axs[1].set_xticks([]) axs[1].set_yticks([])
if (animate): plt.pause(0.01) #f.canvas.draw()
#plt.show()
Plot over a region for different countries
Some of the plots as can be generated from this method can also be generated using the method above. The use of this method will be mostly averaging over years. However, in the dataset, data for all years might not be available; hence, to make the visualization, the years selected need to make sense consideringreal-world Ideally, I could give options to select multiple years individually and show info to the users what data are there and what are appropriate (I am considering that out of scope for now)
plt.rcParams['figure.figsize'] = [10, 10] def plot_measure_by_regions(year, indicator, bubble_scale, chart_type = '', ratios=[3,1], provincial_only=False, all_years=all_years): #print('year, indicator', year, indicator) plt.ion() # now f = plt.figure() # now ax = f.gca() #plt.xticks([]) # now f.show()
# for country color codes c = [ region_colors_dict[x] for x in indicator_data['Region'] ] m = [ x for x in indicator_data['Region'] ] c_code = [ x[0:3] for x in indicator_data['Region'] ]
axs[0].plot(indicator_data['Region'], indicator_data['Value']) #, s=indicator_data['Value'] * bubble_scale, c=c ) plt.suptitle(indicator + ' Over Regions') axs[0].set_xlabel('Regions and Countries') axs[0].set_ylabel('Values') axs[0].set_xticklabels(indicator_data['Region'], rotation=90) # can be turned off
else: axs[0].scatter(indicator_data['Region'], indicator_data['Value'], s=indicator_data['Value'] * bubble_scale, c=c ) plt.suptitle(indicator + ' Over Regions') axs[0].set_xlabel('Regions and Countries') axs[0].set_ylabel('Values') axs[0].set_xticklabels(indicator_data['Region'], rotation=90) # can be turned off
# plt.show() # show country colors axs[1].scatter([1]*len(c), [i*5 for i in range(len(c))], s=300, c=c, marker='o')
count = list(m) #list(region_colors_dict.keys()) #print(count) for j in range(len(c)): axs[1].annotate(count[j], (1, j*5))
axs[1].set_xlabel('Country and Colors') axs[1].set_ylabel('')
# for country color codes c = [ region_colors_dict[x] for x in indicator_data['Region'] ] m = [ x for x in indicator_data['Region'] ] c_code = [ x[0:3] for x in indicator_data['Region'] ]
axs[0].plot(x.index, x['Value']) #, s = x['Value'] * bubble_scale, c=c )
# though I am repeating this block of code - this can be just placed at the end of the else block # just trying to save on debug time plt.suptitle(indicator + ' Over Regions') axs[0].set_xlabel('Regions and Countries') axs[0].set_ylabel('Values') axs[0].set_xticklabels(x.index, rotation=90) # can be turned off
x = x.sort_values(by=['Value']) axs[0].bar(x.index, x['Value']) #, s = x['Value'] * bubble_scale, c=c )
# though I am repeating this block of code - this can be just placed at the end of the else block # just trying to save on debug time plt.suptitle(indicator + ' Over Regions') axs[0].set_xlabel('Regions and Countries') axs[0].set_ylabel('Values') axs[0].set_xticklabels(x.index, rotation=90) # can be turned off
x = x.sort_values(by=['Value']) axs[0].barh(x.index, x['Value']) #, s = x['Value'] * bubble_scale, c=c )
# though I am repeating this block of code - this can be just placed at the end of the else block # just trying to save on debug time plt.suptitle(indicator + ' Over Regions') #axs[0].set_xlabel('Regions and Countries') axs[0].set_xlabel('Values') axs[0].set_ylabel('Regions and Countries') #axs[0].set_xticklabels(x.index, rotation=90) # can be turned off fig.savefig('./saved_images_from_visualizations/' + 'hor_bar_' + indicator.replace(' ', '_')[0:10] + '_' + str(np.random.randint(0, 99999)) + '.png')
axs[0].pie(x['Value'], labels=x.index) #, c=c axs[0].set_xticklabels(x['Value'], rotation=90) # can be turned off # https://stackoverflow.com/questions/10998621/rotate-axis-text-in-python-matplotlib plt.suptitle(indicator + ' Over Regions') #axs[0].set_xlabel('Values') axs[0].set_ylabel('Countries/Regions')
else: #axs[0].scatter(indicator_data['Region'], indicator_data['Value'], s = indicator_data['Value'] * bubble_scale, c=c ) axs[0].scatter(x.index, x['Value'], s = x['Value'] * bubble_scale, c=c )
plt.suptitle(indicator + ' Over Regions') axs[0].set_xlabel('Regions and Countries') axs[0].set_ylabel('Values') #axs[0].set_xticklabels(indicator_data['Region'], rotation=90) # can be turned off axs[0].set_xticklabels(x.index, rotation=90) # can be turned off
#plt.show() # show country colors axs[1].scatter([1]*len(c), [i*5 for i in range(len(c))], s=300, c=c, marker='o')
count = list(m) #list(region_colors_dict.keys()) #print(count) for j in range(len(c)): axs[1].annotate(count[j], (1, j*5))
axs[1].set_xlabel('Country and Colors') axs[1].set_ylabel('')
min_marker_size = 2.5 #* bubble_scale #for lon, lat, mag in zip(lons, lats, magnitudes): #for i in range(indicator_data.shape[0]): for reg, val in zip(magnitudes['Region'], magnitudes['Value']): #try: #reg = magnitudes[i:i+1]['Region'] #reg = magnitudes['Region'][i] #print(reg) lat = lats_dict[reg] lon = lons_dict[reg]
#print(lat, lon) x, y = eq_map(lon, lat) mag = val #magnitudes['Value'][i] #magnitudes[i:i+1]['Value'] #print(mag) msize = mag * min_marker_size/bubble_scale #print(msize, msize) #marker_string = get_marker_color(mag) #eq_map.plot(x, y, marker_string, markersize=msize) eq_map.plot(x, y, marker='o', markersize=msize)
#x, y = eq_map(0, 0) #eq_map.plot(x, y, marker='D',color='m')
#plt.show()
#except: #print('hello') #continue
title_string = indicator title_string += ' for year ' + str(year) plt.title(title_string) #plt.show()
#plt.suptitle(indicator + 'Over Regions and Years') #plt.xlabel('Regions and Countries') #plt.ylabel('Values') #plt.show()
else: # this is to plot over multiple years # average taken over multiple years will be plotted # I could make this more user friendly and data friendly # by giving users the option to select years, countries; also by informing to what extent data are available (skipping as that will extend the work much)
#print(indicator_data) # for country color codes """ c = [ region_colors_dict[x] for x in indicator_data['Region'] ] m = [ x for x in indicator_data['Region'] ] c_code = [ x[0:3] for x in indicator_data['Region'] ] """
indicator_data = indicator_data.set_index(['Region']) x = indicator_data.groupby(['Region']).mean()
#print(x) #print(x['Value'][0])
#print(indicator_data) # not used magnitudes = pd.DataFrame() magnitudes['Region'] = x.index magnitudes['Value'] = x['Value']
#print(magnitudes)
# Make this plot larger. plt.figure(figsize=(16,12))
min_marker_size = 2.5 #* bubble_scale #for lon, lat, mag in zip(lons, lats, magnitudes): #for i in range(indicator_data.shape[0]): #for reg, val in zip(magnitudes['Region'], magnitudes['Value']): for reg, val in zip(x.index, x['Value']): try: #reg = magnitudes[i:i+1]['Region'] #reg = magnitudes['Region'][i] #print(reg, val) lat = lats_dict[reg] lon = lons_dict[reg]
#print(lat, lon) x, y = eq_map(lon, lat) mag = val #magnitudes['Value'][i] #magnitudes[i:i+1]['Value'] #print(mag) msize = mag * min_marker_size/bubble_scale #print(msize, msize) #marker_string = get_marker_color(mag) #eq_map.plot(x, y, marker_string, markersize=msize) eq_map.plot(x, y, marker='o', markersize=msize)
#x, y = eq_map(0, 0) #eq_map.plot(x, y, marker='D',color='m')
#plt.show()
except: #print('hello') continue
title_string = indicator title_string += ' Average for selected years ' plt.title(title_string) #plt.show()
Heatmap to compare across indicators and countries
Options implemented:
Heatmap for one year, one indicator for a health-aspect across countries Heatmap for one year, all indicator for a health-aspect across countries
Heatmap for one year, one indicator for a health-aspect across Canadian province Heatmap for one year, all indicator for a health-aspect across Canadian province
Heatmap for all years with mean values, one indicator for a health-aspect across countries Heatmap for all years with mean values, all indicator for a health-aspect across countries
Heatmap for all years with mean values, one indicator for a health-aspect across Canadian province Heatmap for all years with mean values, all indicator for a health-aspect across Canadian province
Whether taking mean over multiple years is a pragmatic approach or not: — it can be an effective way of measurements provided data exist for all those years (otherwise the years with data will dominate) — in my case, data will not be available for all indicators for all years — we can then just plot for one year or I could give an interface to select years, indicators, countries for custom comparison — that can be a long task. Hence, I am giving the tool that can be extended in different ways
# https://stackoverflow.com/questions/48470251/move-tick-marks-at-the-top-of-the-seaborn-plot?noredirect=1&lq=1 ax.xaxis.set_ticks_position('top') ax.set_xticklabels(indicator_data_heatmap['Indicator'], rotation=90) # can be turned off
#plt.show()
title_string = measure_file.result[0:len(measure_file.result)-4] + ':' + indicator title_string += ' for year ' + str(year) plt.title(title_string) #plt.show()
else: # this is to plot over multiple years # average taken over multiple years will be plotted # I could make this more user friendly and data friendly # by giving users the option to select years, countries; also by informing to what extent data are available (skipping as that will extend the work much)
heatmap_data = measure_data.loc[ (measure_data['Type of region'].isin(['Country', 'Canada']) ) ] if ( provincial_only == True ): heatmap_data = measure_data.loc[ (measure_data['Type of region'].isin(['Province']) ) ]
if ( indicator != '' ): heatmap_data = measure_data.loc[ (measure_data['Indicator'] == indicator) & (measure_data['Type of region'].isin(['Country', 'Canada']) ) ] if ( provincial_only == True ): heatmap_data = measure_data.loc[ (measure_data['Indicator'] == indicator) & (measure_data['Type of region'].isin(['Province']) ) ]
all_years_str = '' for aYear in all_years: if (aYear > 0): all_years_str += str(aYear) + ', '
year_str = ' for year ' + str(year) if year > 0 else ' Mean over years \n' + all_years_str title_string += year_str plt.title(title_string) #plt.show()
Users will be interact with the system to generate custom visualizations
START-RELOAD-DAT
this UI component creation might not be the must
# create the interactive interface def f(indicator): return indicator
#print ('Measure' + measure_file.result) #print('Select parameters\n') #print('Select Indicator with or without Canadian data') indicator_country = interactive(f, indicator=indicators); #display(indicator_country) indicator_country.result
def f(canada_indicator): return canada_indicator
#print('Select Indicator with Canadian data') indicator_canada = interactive(f, canada_indicator=indicators_with_canada); #display(indicator_canada) indicator_canada.result
#print('Select what to plt:') what_to_plot = {} # this can come from an excel file as well what_to_plot['health-status.xls'] = ['Indicator Values over years', 'Indicator Values over countries', 'Geo plot', 'Heatmap'] what_to_plot['access-to-care.xls'] = ['Indicator Values over years', 'Indicator Values over countries', 'Geo plot', 'Heatmap'] #what_to_plot['indicator-methodology.xls'] = ['Indicator Values over years', 'Indicator Values over countries'] what_to_plot['non-med-determinants.xls'] = ['Indicator Values over years', 'Indicator Values over countries', 'Geo plot', 'Heatmap'] what_to_plot['patient-safety.xls'] = ['Indicator Values over years', 'Indicator Values over countries', 'Geo plot', 'Heatmap'] what_to_plot['prescribing-primary.xls'] = ['Indicator Values over years', 'Indicator Values over countries', 'Geo plot', 'Heatmap'] what_to_plot['quality-of-care.xls'] = ['Indicator Values over years', 'Indicator Values over countries', 'Geo plot', 'Heatmap']
Note: use_data_with_canada — is to indicate that when canadian data are available. Also, Canada_Indicator and Year from the right will be used
com_with_cdn and animate controls are not used so far
Must as part of reload data
# Reference: https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20Styling.html print('Indicator and year list on the right, represent where Canadian data exist') from ipywidgets import Button, GridBox, Layout, ButtonStyle GridBox(children=[ use_data_with_canada, compare_canada, indicator_country, indicator_canada, year_country, year_canada, plots, chart, bubble_scale_country, provincial_only_plot, heatmap_consider_indicator_, animate_ ],
layout=Layout( width='100%', grid_template_rows='auto auto', grid_template_columns='50%50%', ) )Indicator and year list on the right, represent where Canadian data exist
<ipython-input-28-5b8e7f56abb0> in <module>() 1 # Reference: https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20Styling.html 2 print('Indicator and year list on the right, represent where Canadian data exist') ----> 3 from ipywidgets import Button, GridBox, Layout, ButtonStyle 4 GridBox(children=[ 5 use_data_with_canada, compare_canada,
ImportError: cannot import name 'GridBox'
END-RELOAD-DATA
# assign a default value bubble_scale_country.result = 10
Create the Plot Based on User Selections
plt.rcParams['figure.figsize'] = [10, 8] #print(chart.result) #provincial_only = False heatmap_consider_indicator = heatmap_consider_indicator_.result # Data irrespective Canada has data or not if use_data_with_canada.result == False: indicator = indicator_country.result year = year_country.result bubble_scale = bubble_scale_country.result
# when we are saying data for canada must exist there else: indicator = indicator_canada.result year = year_canada.result bubble_scale = bubble_scale_country.result #provincial_only = provincial_only_plot.result
Please Select the related measure and reload all data (as marked with Start-Reload, End-Reload). Otherwise the following visualizations might not work unless that is for currently selected measures
A better solution could be: I could place the measure selection here and could execute all the data reload code
Visualizations plotted independently for the visualizations used in the detail prsentation document
All these can be generated using the UI, I am just showing specific cases as I plotted using UI and provided on my report
How does Canada compare for a health status indicator such as : Cancer Mortality (F) for 2017 (per 100k)? Example: Cancer Mortality, 2017:
year = 2017 indicator = 'Cancer Mortality (F)'
# does not matter bubble_scale = 11
chart_type = 'Line'
# subplot ratios ratios = [1000, 1]
# not implemented animate = False
# if for Provinces - Canada provincial_only = False
# figure size fig_size = [5, 5]
# not important sec_fig = True
print('The right line on the plot does not count; comes from the right subplot; that is not relevant for this case') plot_measure_by_years(year, indicator, bubble_scale, chart_type, ratios, animate, provincial_only, fig_size=fig_size)The right line on the plot does not count; comes from the right subplot; that is not relevant for this case
Research Question: how does an indicator such as Transport Mortality changed over time for different countries?
year = 0 indicator = 'Transport Accident Mortality (M)'
# does not matter bubble_scale = 100
chart_type = 'Bubble'
# subplot ratios # as we will show countries by colors, ratios are useful ratios = [3, 1]
# not implemented animate = False
# if for Provinces - Canada provincial_only = False
# figure size fig_size = [20, 10]
# not important sec_fig = True
print('Note: Y axis values need to be multiplied by Benchmark value to get actual values') plot_measure_by_years(year, indicator, bubble_scale, chart_type, ratios, animate, provincial_only, fig_size=fig_size)Note: Y axis values need to be multiplied by Benchmark value to get actual values
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:346: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead
Research Question: How do Canadian provinces perform for Transport Mortality (M) for 2017?
year = 2017 indicator = 'Transport Accident Mortality (M)'
# does not matter bubble_scale = 100
# will plot for other chart types as well chart_type = 'Line'
# subplot ratios # as we will show countries by colors, ratios are useful ratios = [10, 1]
# not implemented animate = False
# Matters as we are plotting for Canadian provinces provincial_only = True
# figure size fig_size = [10, 8]
# not important sec_fig = True
print('Note: Y axis values need to be multiplied by Benchmark value to get actual values') plot_measure_by_years(year, indicator, bubble_scale, chart_type, ratios, animate, provincial_only, fig_size=fig_size)Note: Y axis values need to be multiplied by Benchmark value to get actual values
Research, Analysis, and Visualization Concern:
How does the transport mortality compare against countries on 2017 based on the data we have? Visualize in different format
year = 2017 indicator = 'Transport Accident Mortality (M)'
# does not matter bubble_scale = 32
# will plot for other chart types as well chart_type = 'Line'
# subplot ratios # as we will show countries by colors, ratios are useful ratios = [10, 1]
# not implemented animate = False
# Matters as we are plotting for Canadian provinces provincial_only = False
# figure size fig_size = [5, 5]
# not important sec_fig = True
for chart_type in chart_types: plot_measure_by_years(year, indicator, bubble_scale, chart_type, ratios, animate, provincial_only, fig_size=fig_size)
How does the transport mortality compare against Canadian provinces on 2017 based on the data we have? Visualize in different format
Note: the code from the above cell need to be executed first as I am reusing some variables
# Matters as we are plotting for Canadian provinces provincial_only = True
# figure size fig_size = [10, 8]
# not important sec_fig = True
ratios = [3, 1]
for chart_type in chart_types: plot_measure_by_years(year, indicator, bubble_scale, chart_type, ratios, animate, provincial_only, fig_size=fig_size)
For Research Question: What are average alcohol consumption across countries over last couple of years
# you can add or remove years, to get average measures over those years year = 0 # 0 indicates all years for the list all_years all_years = [2013, 2014, 2015, 2016, 2017] indicator = 'Alcohol Consumption: Adults' bubble_scale = 91 # NA chart_type='Bar' # change to Line, Bubble, Pie, 'Hor Bar' provincial_only = False # if you set true only provincial data will be plotted
#year = 0 # 0 indicates all years for the list all_years #all_years = [2013, 2014, 2015, 2016, 2017] indicator = 'Alcohol Consumption: Adults' #bubble_scale = 91 # NA #chart_type='Bar' # change to Line, Bubble, Pie, 'Hor Bar' provincial_only = False # if you set true only provincial data will be plotted
print('if you want for only one year change as follows') print('For Research Question: What are average alcohol consumption across countries for 2015') # if you want for only one year change as follows year = 2015 # other chart type sych as bar will work though will have issues chart_type='Bar' #plot_measure_by_regions(year, indicator, bubble_scale, chart_type, ratios=[3,1], provincial_only) plot_measure_by_regions(year, indicator, bubble_scale, chart_type, ratios=[3,1], provincial_only=provincial_only, all_years=all_years)if you want for only one year change as follows For Research Question: What are average alcohol consumption across countries for 2015
Research Question: For 2015, which country smoked the most? Used Geo plot. However, plots like above sections could also be used
# the plot on the presentation used 2 # note : this is inverse size of the bubble bubble_scale = 3 chart_type = '' # are not relevant # chart_type = '', all_years=all_years, also provincial_only=False
Research Question: Where in the world obesity are more common?
year = 0 indicator = 'Obesity Reported: Adults' # the plot on the presentation used 2 # note : this is inverse size of the bubble bubble_scale = 2 chart_type = '' # are not relevant # chart_type = '', all_years=all_years, also provincial_only=False
year, indicator, bubble_scale(0, 'Same or Next Day Appt', 10)year = 0 indicator = 'Same or Next Day Appt' bubble_scale = 10 chart_type = 'Bubble' provincial_only=True ratios=[3,1]
What I was looking for is: A DQN (Deep Q Learning Neural Network) or a Reinforcement Learning example that can learn from existing simulation data, and then can use that learning to interactively optimize an objective. The challenge will be: Whether my data can be learned from (whether the format/structure of the data is usable in DQN/RL) by the DQN/RL, also what to define as the actions, and how to define, utilize, and optimize the reward. Came across misc. stuff as below:
Came across: Did not really check: Reinforcement Learning – A Simple Python Example and A Step Closer to AI with Assisted Q-Learning
