prediction_array=[get_prediction_all_features(all_states[i],input_features_dic,default_year) for i in range(len(all_states))] prediction_by_state={'AL': prediction_array[0],'AK':prediction_array[1],'AZ': prediction_array[2],'AR': prediction_array[3], 'CA': prediction_array[4],'CO': prediction_array[5],'CT': prediction_array[6],'DE': prediction_array[7], 'DC': prediction_array[8],'FL': prediction_array[9],'GA': prediction_array[10],'HI':prediction_array[11],'ID': prediction_array[12], 'IL': prediction_array[13],'IN': prediction_array[14],'IA': prediction_array[15],'KS': prediction_array[16], 'KY': prediction_array[17],'LA': prediction_array[18],'ME': prediction_array[19],'MD': prediction_array[20], 'MA': prediction_array[21],'MI': prediction_array[22],'MN': prediction_array[23],'MS': prediction_array[24], 'MO': prediction_array[25],'MT': prediction_array[26],'NE': prediction_array[27],'NV': prediction_array[28],'NH': prediction_array[29], 'NJ': prediction_array[30],'NM': prediction_array[31],'NY': prediction_array[32],'NC': prediction_array[33],'ND': prediction_array[34],'OH': prediction_array[35], 'OK': prediction_array[36],'OR': prediction_array[37],'PA': prediction_array[38],'RI': prediction_array[39], 'SC': prediction_array[40],'SD': prediction_array[41],'TN': prediction_array[42],'TX': prediction_array[43],'UT': prediction_array[44],'VT': prediction_array[45], 'VA': prediction_array[46],'WA': prediction_array[47],'WV': prediction_array[48],'WI': prediction_array[49], 'WY': prediction_array[50]} state_xs = [us_states[key]["lons"] for key in us_states.keys()] state_ys = [us_states[key]["lats"] for key in us_states.keys()] Map = figure(toolbar_location="left",plot_width=1100, plot_height=700) Map.patches(state_xs, state_ys, fill_color=[color_class(prediction_by_state[key],Reds) for key in us_states.keys()],fill_alpha=0.7, line_color="black", line_width=0.5) Map.xgrid.grid_line_color = None Map.ygrid.grid_line_color = None Map.axis.visible = False show(Map) print('Darker shades of red in the map indicate higher percentages of unresolved homicide.') vis_bool=input('Would you like to see a heatmap for all selected states and features? Please enter yes or no:') if vis_bool=='yes': heatmap_generate(all_states,input_features_dic,default_year) print('Darker shades of red indicate a higher value for the percentage of unresolved homicide.')
from bokeh.models.widgets import CheckboxButtonGroup, Select from pyproj import Proj, transform # Prep data accidents = pd.read_pickle("fars_accidents.pickle") drivers = pd.read_pickle("final_drivers.pickle") #us_states = us_states.data.copy() with open("us_states.pickle", "rb") as file: us_states = pickle.load(file) state_xs = [us_states[code]["lons"] for code in us_states] state_ys = [us_states[code]["lats"] for code in us_states] state_names = us_states.keys() id_to_st = { 1: "AL", 2: "AK", 4: "AZ", 5: "AR", 6: "CA", 8: "CO", 9: "CT", 10: "DE", 11: "DC", 12: "FL", 13: "GA", 15: "HI", 16: "ID",
from bokeh.models.widgets import CheckboxButtonGroup, Select from pyproj import Proj, transform # Prep data accidents = pd.read_pickle("fars_accidents.pickle") drivers = pd.read_pickle("final_drivers.pickle") #us_states = us_states.data.copy() with open("us_states.pickle", "rb") as file: us_states = pickle.load(file) state_xs = [us_states[code]["lons"] for code in us_states] state_ys = [us_states[code]["lats"] for code in us_states] state_names = us_states.keys() id_to_st = {1:"AL", 2:"AK", 4:"AZ", 5:"AR", 6:"CA", 8:"CO", 9:"CT", 10:"DE", 11:"DC", 12:"FL", 13:"GA", 15:"HI", 16:"ID", 17:"IL", 18:"IN", 19:"IA", 20:"KS", 21:"KY", 22:"LA", 23:"ME", 24:"MD", 25:"MA", 26:"MI", 27:"MN", 28:"MS", 29:"MO", 30:"MT", 31:"NE", 32:"NV", 33:"NH", 34:"NJ", 35:"NM", 36:"NY", 37:"NC", 38:"ND", 39:"OH", 40:"OK", 41:"OR", 42:"PA", 44:"RI", 45:"SC", 46:"SD", 47:"TN", 48:"TX", 49:"UT", 50:"VT", 51:"VA", 53:"WA", 54:"WV", 55:"WI", 56:"WY"} with open("id_to_state.pickle", "wb") as f: pickle.dump(id_to_st, f) st_to_id = {v: k for k, v in id_to_st.items()}
# -*- coding: utf-8 -*- from bokeh.sampledata import us_states from bokeh.plotting import figure, show, output_file us_states = us_states.data.copy() ### we'll ignore Alaska and Hawai for now. del us_states["HI"] del us_states["AK"] state_xs = [us_states[code]["lons"] for code in us_states.keys()] state_ys = [us_states[code]["lats"] for code in us_states.keys()] # let's run a simple loop that will paint NY, CA and FL in blue, and all the rest in red. state_colors= [] for state in us_states.keys(): if state=='NY' or state=="CA" or state=="FL": state_colors.append("blue") else: state_colors.append("red") output_file("us_map.html", title="us_map.py example") p = figure(title="US States", toolbar_location="left", plot_width=1100, plot_height=700) p.patches(state_xs, state_ys, fill_color=state_colors, fill_alpha=0.7, line_color="white", line_width=0.5) show(p)
# -*- coding: utf-8 -*- from bokeh.sampledata import us_states from bokeh.plotting import figure, show, output_file import csv import os us_states = us_states.data.copy() ### let's ignore Alaska and Hawai for now.. del us_states["HI"] del us_states["AK"] state_xs = [us_states[code]["lons"] for code in us_states.keys()] state_ys = [us_states[code]["lats"] for code in us_states.keys()] os.chdir("C:\\Users\\Itamar_account\\Documents\\Python Scripts\\maps") ## set your working director here results = dict() with open('elections.csv') as csvfile: reader = csv.reader(csvfile, delimiter=',') for row in reader: ## first column - state Code, 2nd - obama, 3rd - romney state_code=row[0] obama_res=row[1] romney_res=row[2] if state_code in us_states.keys(): if obama_res=="1": results[state_code] = "OBAMA" else: results[state_code] = "ROMNEY"
sld_rate={} num1={} denom1={} for state_key in us_states: num1[state_key]=loan_defs[loan_defs['State'] == state_key.lower()]['Num 1'].sum() denom1[state_key]=loan_defs[loan_defs['State'] == state_key.lower()]['Denom 1'].sum() sld_rate[state_key] = num1[state_key]/denom1[state_key] sld_min, sld_max=np.min(sld_rate.values()), np.max(sld_rate.values()) for state_key in us_states: col_idx=(sld_rate[state_key]-sld_min)/(sld_max-sld_min) state_colors.append(rgb_to_hex(blues_grad(col_idx))) state_keys=us_states.keys() state_sld_rates=np.round(np.array(sld_rate.values())*100) source = ColumnDataSource(data=dict( state_key=state_keys, sld_rate=state_sld_rates, students_failed=num1.values(), students_entered=denom1.values() )) TOOLS="pan,wheel_zoom,box_zoom,reset,hover,save" p = figure(title="Student Loan Defaults - 2012", toolbar_location="left", tools=TOOLS, plot_width=1100, plot_height=700) p.patches(state_xs, state_ys, fill_color=state_colors, fill_alpha=0.8,