def update_year_zone(year, day_range): """ Generate daily tally count by area/year """ (tally, tally_zone, tally_zone_date_ranges) = data.fetch_data() # Clip to day range sliced = tally_zone.loc[(tally_zone.doy >= day_range[0]) & (tally_zone.doy <= day_range[1])] # Subset by selected year. de = sliced.loc[(sliced.FireSeason == year)] data_traces = [] grouped = de.groupby("ProtectionUnit") for name, group in grouped: group = group.sort_values(["date_stacked"]) group["TotalAcres"] = group["TotalAcres"].round(2) data_traces.extend([{ "x": group.date_stacked, "y": group.TotalAcres, "mode": "lines", "name": luts.zones[name], "line": { "shape": get_line_mode(day_range), "width": 2 }, "hovertemplate": hover_conf, }]) graph_layout = go.Layout( title="<b>Alaska Daily Tally Records by Year, " + str(year) + "</b><br>" + get_title_date_span(day_range), xaxis=xaxis_conf, yaxis=yaxis_conf, ) return {"data": data_traces, "layout": graph_layout}
def update_tally(day_range): """ Generate daily tally count """ (tally, tally_zone, tally_zone_date_ranges) = data.fetch_data() data_traces = [] # Slice by day range. sliced = tally.loc[(tally.doy >= day_range[0]) & (tally.doy <= day_range[1])] grouped = sliced.groupby("FireSeason") for name, group in grouped: group = group.sort_values(["date_stacked"]) if name in luts.important_years: hovertemplate = hover_conf hoverinfo = "" showlegend = True else: hovertemplate = None hoverinfo = "skip" showlegend = False data_traces.extend([{ "x": group.date_stacked, "y": group.TotalAcres, "mode": "lines", "name": str(name), "line": { "color": luts.years_lines_styles[str(name)]["color"], "shape": get_line_mode(day_range), "width": luts.years_lines_styles[str(name)]["width"], }, "showlegend": showlegend, "hoverinfo": hoverinfo, "hovertemplate": hovertemplate, }]) # Add dummy trace with legend entry for non-big years data_traces.extend([ go.Scatter( x=[None], y=[None], mode="lines", name="Other years", line={ "color": luts.default_style["color"], "width": luts.default_style["width"], }, ) ]) graph_layout = go.Layout( title="<b>Alaska Statewide Daily Tally Records, 2004-Present,</b><br>" + get_title_date_span(day_range), xaxis=xaxis_conf, yaxis=yaxis_conf, ) return {"data": data_traces, "layout": graph_layout}
def test_sat3cell_tree(self): w = tf.random_normal([self.n_worlds, self.num_units]) sat3 = csat.Sat3Cell(self.n_ops, self.num_units, self.batch_size, self.n_worlds) nn = treenn.TreeNN(sat3, self.parser, self.batch_size) A, B, E = next(data.fetch_data(self.batch_size)) y = nn(w, [nn.parser(a) for a in A]) self.assertEqual(y.shape, [self.batch_size, self.num_units, self.n_worlds])
def __init__(self): self.all_data = data.fetch_data() self.find_countries() self.country_params = {} self.state_params = {} self.get_real_data() self.simulator = simulate.simulate_epidemic(5, 1.9, 2) self.state_simulator = simulate.simulate_epidemic(6.5, 0.9, 1.6) self.colors = { "active": "#FFA500", "deaths": "#B22222", "recovered": "#008000", } self.get_india_data()
def update_tally_zone(area, day_range): """ Generate daily tally count for specified protection area """ (tally, tally_zone, tally_zone_date_ranges) = data.fetch_data() # Slice by day range. sliced = tally_zone.loc[(tally_zone.doy >= day_range[0]) & (tally_zone.doy <= day_range[1])] # Spatial clip de = sliced.loc[(sliced["ProtectionUnit"] == area)] data_traces = [] grouped = de.groupby("FireSeason") for name, group in grouped: group = group.sort_values(["date_stacked"]) group["TotalAcres"] = group["TotalAcres"].round(2) data_traces.extend([{ "x": group.date_stacked, "y": group.TotalAcres, "mode": "lines", "name": name, "line": { "color": luts.years_lines_styles[str(name)]["color"], "shape": get_line_mode(day_range), "width": luts.years_lines_styles[str(name)]["width"], }, "hovertemplate": hover_conf, }]) # Add dummy trace with legend entry for non-big years data_traces.extend([ go.Scatter( x=[None], y=[None], mode="lines", name="Other years", line={ "color": luts.default_style["color"], "width": luts.default_style["width"], }, ) ]) graph_layout = go.Layout( title="<b>Alaska Daily Tally Records, " + luts.zones[area] + ", 2004-Present</b><br>" + get_title_date_span(day_range), xaxis=xaxis_conf, yaxis=yaxis_conf, ) return {"data": data_traces, "layout": graph_layout}
def test_sat3_output_shape(self): """integration test with sat3 and treenn""" d_world = 10 n_worlds = 64 n_ops = 32 d_embed = 8 batch_size = 50 parser = data.Parser(led_parser.propositional_language()) sat3 = csat.Sat3Cell(n_ops, d_world, batch_size, n_worlds) nn = treenn.TreeNN(sat3, parser, batch_size) possibleworldsnet = pwn.PossibleWorlds(nn, n_worlds, d_world) A, B, E = next(data.fetch_data(batch_size)) y = possibleworldsnet(A, B) self.assertEqual(y.get_shape().as_list(), [batch_size])
def update_tally(community): """ Generate precipitation scatter chart """ std = fetch_data(community) return go.Figure( data=[ go.Scatter( name="pcpt", x=std["doy"], y=std["pcpt"], mode="markers", marker=dict(line_width=1), ) ], layout=go.Layout( title="<b>Daily Precipitiation, [date range] (Anchorage)</b>"), )
def update_tally_zone(community): """ Generate large bubble chart of precip info """ std = fetch_data(community) std["bubble_size"] = np.interp(std["pcpt"], (std["pcpt"].min(), std["pcpt"].max()), (0, 50)) std = std.loc[(std["bubble_size"] > 0)] print(std) return go.Figure( data=[ go.Scatter(x=std["doy"], y=std["year"], mode="markers+text", marker=dict(size=std["bubble_size"])), ], layout=go.Layout( title="<b>Daily Precipitiation, [date range] (Anchorage)</b>"), )
# coding: utf-8 from flask import Flask, send_file import statistics import data import simpleplot app = Flask(__name__) datapoints = data.fetch_data() @app.route("/") def index(): """ Renders static/index.html """ return app.send_static_file('index.html') @app.route("/aapl-in-gold") def aapl_in_gold(): """ Should render a plot of the price of aapl stock in gold, with time as the x axis and value as the y axis """ pass @app.route("/all-as-usd") def all_as_usd(): """ Should render a plot of the value of aapl stock, bitcoin and gold in usd, with time as the x axis and value as the y axis """ pass
from fit_any_country import fit_country from data import fetch_data, get_data if __name__ == "__main__": data = fetch_data() for country in data.keys(): time, time_number_days, cases_ref, deaths_ref = get_data(country) if len(time) > 15: print(country) time_sim, cases_sim, healthy_sim, recovered_sim, deaths_sim = \ fit_country(country, save_to_json=True)
saver.restore(sess, tf.train.latest_checkpoint(LOG_PATH)) endings = [] for story, true_ending in tqdm( zip(stories, true_endings), desc='Conditional Ending Generation'): ending = self._story_continuation(sess, story, true_ending) endings.append(ending) return endings if __name__ == '__main__': # Load data dataloader = data.fetch_data() train_stories = dataloader['train'] valid_stories, valid_labels = dataloader['valid'] # Construct the vocabulary vocab, inverse_vocab, max_len = data.construct_vocab(train_stories) encoded_train_context_, _ = data.encode_text(train_stories, max_len, vocab) # Append max_len tokens to the training context (for consistency during training) train_pads = np.full(shape=(encoded_train_context_.shape[0], max_len), fill_value=vocab['<pad>'], dtype=int) encoded_train_context = np.hstack((encoded_train_context_, train_pads))
def test_fetches_datapoints_in_correct_order(): points = data.fetch_data() assert isinstance(points, OrderedDict) keys = list(points.keys()) assert keys == list(sorted(keys))
def test_fetches_correct_amount_of_data(): assert len(data.fetch_data()) == 848
# pylint: disable=C0103,C0301 """ GUI for app """ import os from datetime import datetime import dash_core_components as dcc import dash_html_components as html import dash_dangerously_set_inner_html as ddsih import luts import data (tally, tally_zone, tally_zone_date_ranges) = data.fetch_data() # For hosting path_prefix = os.getenv("REQUESTS_PATHNAME_PREFIX") or "/" # Used to make the chart exports nice fig_download_configs = dict(filename="Daily_Tally_Count", width="1000", height="650", scale=2) fig_configs = dict( displayModeBar=True, showSendToCloud=False, toImageButtonOptions=fig_download_configs, modeBarButtonsToRemove=[ "zoom2d", "pan2d", "select2d",
import plotly.express as px import pandas from data import fetch_data if __name__ == "__main__": cases_list = fetch_data() for i, data in enumerate(cases_list): data["day"] = i + 1 data["active"] = data["confirmed"] - data["recovered"] - data["deaths"] wide_df = pandas.DataFrame(cases_list) tidy_df = wide_df.melt(id_vars="day", value_vars=("confirmed", "deaths", "recovered", "active"), var_name="type", value_name="cases") fig = px.line(tidy_df, x="day", y="cases", color="type") fig.update_layout(title="Covid-19 cases in Poland", xaxis_title="Days since first case", yaxis_title="Number of cases", font=dict(family="Arial, monospace", size=18, color="#7f7f7f")) fig.show()
# -*- coding: utf-8 -*- """ Created on Thu Sep 28 13:01:32 2017 @author: Jeetu """ import numpy as np import pandas as pd from imblearn.over_sampling import SMOTE import os from data import fetch_data,feature_engineering from sklearn import preprocessing root=os.getcwd() x_train,y_train,x_test,y_test = fetch_data(root,remove_duplicates=True,binary=False) x_train = feature_engineering(x_train,do_normalization=False) l=list(x_train) x_test = feature_engineering(x_test,do_normalization=False) print('Shape of training data after feature engineering is {}'.format(x_train.shape)) print ('Shape of test data after feature engineering is {}'.format(x_test.shape)) (y_train).value_counts().plot.barh() maxcount=y_train.value_counts().max() mincount=y_train.value_counts().min() le=preprocessing.LabelEncoder() y_train=le.fit_transform(y_train)+1 datax=pd.DataFrame() y=pd.Series()