diff --git a/.pylintrc b/.pylintrc deleted file mode 100644 index c1b664a..0000000 --- a/.pylintrc +++ /dev/null @@ -1,4 +0,0 @@ -[MESSAGES CONTROL] -disable= - too-few-public-methods, - too-many-instance-attributes \ No newline at end of file diff --git a/docs/hsma_changes.md b/docs/hsma_changes.md index 7508b8b..ca31275 100644 --- a/docs/hsma_changes.md +++ b/docs/hsma_changes.md @@ -73,6 +73,8 @@ class Patient: self.time_with_nurse = np.nan ``` +This enables metrics like `count_unseen` and `mean_q_time_unseen`, which can show backlog in the system at the end of the simulation. + ## Seeds The HSMA models use a multiplied version of the `run_number` as a seed when sampling: @@ -111,24 +113,31 @@ class g: g.patient_inter = 10 ``` -To resolve this, the template creates an instance of the parameter class, and never uses or alters it directly. For clarity, the class is called `Defaults()` and instances are typicallyed called `param`. +To resolve this, the template creates an instance of the parameter class, and never uses or alters it directly. For clarity, the class is called `Param()` and instances are typically called `param`. ``` -class Defaults(): - def __init__(self): - self.patient_inter = 5 - self.mean_n_consult_time = 35 - self.number_of_nurses = 9 - self.warm_up_period = 0 - self.data_collection_period = 600 - self.number_of_runs = 5 - self.audit_interval = 5 - self.scenario_name = 0 - -... - -param = Defaults() -param.patient_inter = 10 +class Param: + """ + Default parameters for simulation. + + Attributes are described in initialisation docstring. + """ + def __init__( + self, + patient_inter=4, + mean_n_consult_time=10, + number_of_nurses=5, + warm_up_period=1440*13, + data_collection_period=1440*30, + number_of_runs=31, + audit_interval=120, # every 2 hours + scenario_name=0, + cores=-1, + logger=SimLogger(log_to_console=False, log_to_file=False) + ): + ... + +param = Defaults(patient_inter = 10) model = Model(param) ``` @@ -179,6 +188,51 @@ run_results = { self.run_results_df = pd.DataFrame(run_results_list) ``` +### First arrival at time 0 + +In the HSMA model, there will always be an arrival at time 0, since that is the first action in `generator_patient_arrivals()`: + +``` +def generator_patient_arrivals(self): + while True: + self.patient_counter += 1 + p = Patient(self.patient_counter) + sampled_inter = random.expovariate(1.0 / g.patient_inter) + yield self.env.timeout(sampled_inter) +``` + +This has been altered, so the first patient arrives after a sampled inter-arrival time, by moving the sampling to the start of the method. + +``` +def generate_patient_arrivals(self): + while True: + sampled_inter = self.patient_inter_arrival_dist.sample() + yield self.env.timeout(sampled_inter) + p = Patient(len(self.patients) + 1) + p.arrival_time = self.env.now + ... +``` + +### MonitoredResource and warm-up + +The same metrics can be calculated in several possible ways. In this model, time-weighted averages have been included using the `MonitoredResource`. This requires methods `init_results_variables()` and `warm_up_complete()`, which have meant that implementation of warm-up can be done using `warm_up_complete()` rather than by checking whether warm up is complete every time a metric is saved. + +``` +def warm_up_complete(self): + if self.param.warm_up_period > 0: + yield self.env.timeout(self.param.warm_up_period) + self.init_results_variables() +``` + +As opposed to, for example: + +``` +if self.env.now > g.warm_up_period: + self.results_df.at[patient.id, "Q Time Nurse"] = ( + patient.q_time_nurse + ) +``` + ## Extra features ### Prevent addition of new attributes to the parameter class @@ -191,15 +245,8 @@ The parameter class includes a function that: This is to avoid an error where it looks like a parameter has been changed, but actually the wrong attribute name was used - for example, setting `param.nurses = 3` and thinking this has reduced the number of nurse resources, but actually this is based on `param.number_of_nurses` which remains set to 9. ``` -class Defaults(): - def __init__(self): - object.__setattr__(self, '_initialising', True) - - self.patient_inter = 5 - self.mean_n_consult_time = 35 - ... - - object.__setattr__(self, '_initialising', False) +class Param(): + ... def __setattr__(self, name, value): if hasattr(self, '_initialising') and self._initialising: @@ -242,7 +289,7 @@ for rule, param_names in validation_rules.items(): ### Tests -The files `test_unittest.py` and `test_backtest.py` include various tests which check the model is functioning as expected. For example, checking that: +The files `test_unittest_model.py`, `test_unittest_logger.py` and `test_backtest.py` include various tests which check the model is functioning as expected. For example, checking that: * It is not possible to add new attributes to the parameter class. * Invalid inputs to `Model()` return an `ValueError`. @@ -256,11 +303,15 @@ The `summary_stats()` function is used to find the overall results from across t It can also be applied to other results dataframes if desired. +### Logger + +The `SimLogger` class will generate logs which can be saved to a file or printed to a console. This includes information on when patients arrive and are seen. This can be helpful for understanding the simulation or when debugging. + ### Other minor changes There are a few smaller changes to the model with minimal impact on function. These include: -* **Names** - for example, `Patient.id` was changed to `Patient.patient_id`, as the patient objects are used to create the patient-level results dataframe, and want "patient_id" as the column name. +* **Names** - for example, `Patient.id` was changed to `Patient.patient_id`, as the patient objects are used to create the patient-level results dataframe, and want "patient_id" as the column name. Also, `Trial` was changed to `Runner` with methods changed to e.g. `run_reps()`. * **Comments and docstrings** * **Removed `patient_counter`** - as can just use the length of the `patients` list now. * **Interval audit** - records cumulative mean wait time, as well as utilisation. \ No newline at end of file diff --git a/notebooks/analysis.ipynb b/notebooks/analysis.ipynb index 4d755ba..debf8e3 100644 --- a/notebooks/analysis.ipynb +++ b/notebooks/analysis.ipynb @@ -123,7 +123,10 @@ " 'mean_nurse_utilisation_tw': 'Time-weighted mean nurse utilisation',\n", " 'mean_nurse_q_length': 'Time-weighted mean queue length for nurse (n)',\n", " 'patient_inter': 'Patient inter-arrival time',\n", - " 'number_of_nurses': 'Number of nurses'\n", + " 'number_of_nurses': 'Number of nurses',\n", + " 'count_unseen': 'Patients still unseen at end of simulation (n)',\n", + " 'mean_q_time_unseen': (\n", + " 'Mean wait time of patients unseen by simulation end (minutes)')\n", "}" ] }, @@ -204,6 +207,7 @@ " q_time_nurse\n", " time_with_nurse\n", " run\n", + " q_time_unseen\n", " \n", " \n", " \n", @@ -214,6 +218,7 @@ " 0.0\n", " 7.266791\n", " 0\n", + " NaN\n", " \n", " \n", " 1\n", @@ -222,6 +227,7 @@ " 0.0\n", " 1.551695\n", " 0\n", + " NaN\n", " \n", " \n", " 2\n", @@ -230,6 +236,7 @@ " 0.0\n", " 3.649895\n", " 0\n", + " NaN\n", " \n", " \n", " 3\n", @@ -238,6 +245,7 @@ " 0.0\n", " 8.137105\n", " 0\n", + " NaN\n", " \n", " \n", " 4\n", @@ -246,18 +254,19 @@ " 0.0\n", " 22.057543\n", " 0\n", + " NaN\n", " \n", " \n", "\n", "" ], "text/plain": [ - " patient_id arrival_time q_time_nurse time_with_nurse run\n", - "0 1 18720.556711 0.0 7.266791 0\n", - "1 2 18724.792565 0.0 1.551695 0\n", - "2 3 18725.627670 0.0 3.649895 0\n", - "3 4 18728.774983 0.0 8.137105 0\n", - "4 5 18729.812428 0.0 22.057543 0" + " patient_id arrival_time q_time_nurse time_with_nurse run q_time_unseen\n", + "0 1 18720.556711 0.0 7.266791 0 NaN\n", + "1 2 18724.792565 0.0 1.551695 0 NaN\n", + "2 3 18725.627670 0.0 3.649895 0 NaN\n", + "3 4 18728.774983 0.0 8.137105 0 NaN\n", + "4 5 18729.812428 0.0 22.057543 0 NaN" ] }, "metadata": {}, @@ -302,6 +311,8 @@ " mean_nurse_utilisation\n", " mean_nurse_utilisation_tw\n", " mean_nurse_q_length\n", + " count_unseen\n", + " mean_q_time_unseen\n", " \n", " \n", " \n", @@ -315,6 +326,8 @@ " 0.499589\n", " 0.499688\n", " 0.127467\n", + " 0\n", + " NaN\n", " \n", " \n", " 1\n", @@ -326,6 +339,8 @@ " 0.501977\n", " 0.502039\n", " 0.128875\n", + " 0\n", + " NaN\n", " \n", " \n", " 2\n", @@ -337,6 +352,8 @@ " 0.498094\n", " 0.498251\n", " 0.134689\n", + " 9\n", + " 6.314118\n", " \n", " \n", " 3\n", @@ -348,6 +365,8 @@ " 0.498249\n", " 0.498249\n", " 0.123128\n", + " 0\n", + " NaN\n", " \n", " \n", " 4\n", @@ -359,6 +378,8 @@ " 0.496934\n", " 0.497176\n", " 0.114955\n", + " 0\n", + " NaN\n", " \n", " \n", "\n", @@ -372,12 +393,19 @@ "3 3 0 10831 0.491104 9.938504 \n", "4 4 0 10720 0.463252 10.016611 \n", "\n", - " mean_nurse_utilisation mean_nurse_utilisation_tw mean_nurse_q_length \n", - "0 0.499589 0.499688 0.127467 \n", - "1 0.501977 0.502039 0.128875 \n", - "2 0.498094 0.498251 0.134689 \n", - "3 0.498249 0.498249 0.123128 \n", - "4 0.496934 0.497176 0.114955 " + " mean_nurse_utilisation mean_nurse_utilisation_tw mean_nurse_q_length \\\n", + "0 0.499589 0.499688 0.127467 \n", + "1 0.501977 0.502039 0.128875 \n", + "2 0.498094 0.498251 0.134689 \n", + "3 0.498249 0.498249 0.123128 \n", + "4 0.496934 0.497176 0.114955 \n", + "\n", + " count_unseen mean_q_time_unseen \n", + "0 0 NaN \n", + "1 0 NaN \n", + "2 9 6.314118 \n", + "3 0 NaN \n", + "4 0 NaN " ] }, "metadata": {}, @@ -532,6 +560,8 @@ " mean_nurse_utilisation\n", " mean_nurse_utilisation_tw\n", " mean_nurse_q_length\n", + " count_unseen\n", + " mean_q_time_unseen\n", " \n", " \n", " \n", @@ -543,6 +573,8 @@ " 0.497680\n", " 0.497801\n", " 0.125251\n", + " 0.419355\n", + " 3.482629\n", " \n", " \n", " std_dev\n", @@ -552,6 +584,8 @@ " 0.007508\n", " 0.007544\n", " 0.017212\n", + " 1.688672\n", + " 2.688100\n", " \n", " \n", " lower_95_ci\n", @@ -561,6 +595,8 @@ " 0.494926\n", " 0.495033\n", " 0.118938\n", + " -0.200055\n", + " -3.194981\n", " \n", " \n", " upper_95_ci\n", @@ -570,6 +606,8 @@ " 0.500434\n", " 0.500568\n", " 0.131564\n", + " 1.038765\n", + " 10.160239\n", " \n", " \n", "\n", @@ -588,11 +626,11 @@ "lower_95_ci 0.494926 0.495033 \n", "upper_95_ci 0.500434 0.500568 \n", "\n", - " mean_nurse_q_length \n", - "mean 0.125251 \n", - "std_dev 0.017212 \n", - "lower_95_ci 0.118938 \n", - "upper_95_ci 0.131564 " + " mean_nurse_q_length count_unseen mean_q_time_unseen \n", + "mean 0.125251 0.419355 3.482629 \n", + "std_dev 0.017212 1.688672 2.688100 \n", + "lower_95_ci 0.118938 -0.200055 -3.194981 \n", + "upper_95_ci 0.131564 1.038765 10.160239 " ] }, "metadata": {}, @@ -1885,6 +1923,102 @@ ], "label": "Time-weighted mean queue length for nurse (n)", "method": "update" + }, + { + "args": [ + { + "x": [ + [ + 0, + 0, + 9, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 3, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0 + ] + ] + }, + { + "xaxis": { + "title": "Patients still unseen at end of simulation (n)" + } + } + ], + "label": "Patients still unseen at end of simulation (n)", + "method": "update" + }, + { + "args": [ + { + "x": [ + [ + null, + null, + 6.314118037451408, + null, + null, + null, + null, + null, + null, + null, + null, + null, + 0.9655746818098123, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + 3.1681939577538287, + null, + null, + null, + null, + null + ] + ] + }, + { + "xaxis": { + "title": "Mean wait time of patients unseen by simulation end (minutes)" + } + } + ], + "label": "Mean wait time of patients unseen by simulation end (minutes)", + "method": "update" } ], "direction": "down", @@ -1970,7 +2104,7 @@ " fig = px.histogram(rep_results[column])\n", " fig.update_layout(\n", " xaxis_title=name_mappings.get(column, column),\n", - " yaxis_title='Frequency',\n", + " yaxis_title='Replications',\n", " template='plotly_white',\n", " showlegend=False)\n", "\n", @@ -1989,7 +2123,7 @@ { "data": { "image/svg+xml": [ - "10.5k10.6k10.7k10.8k10.9k11k024681012Patient arrivals (n)Frequency" + "10.5k10.6k10.7k10.8k10.9k11k024681012Patient arrivals (n)Replications" ] }, "metadata": {}, @@ -1998,7 +2132,7 @@ { "data": { "image/svg+xml": [ - "0.350.40.450.50.550.60.650246810Mean wait time for nurse (minutes)Frequency" + "0.350.40.450.50.550.60.650246810Mean wait time for nurse (minutes)Replications" ] }, "metadata": {}, @@ -2007,7 +2141,7 @@ { "data": { "image/svg+xml": [ - "9.89.91010.110.210.30246810Mean consultation time with nurse (minutes)Frequency" + "9.89.91010.110.210.30246810Mean consultation time with nurse (minutes)Replications" ] }, "metadata": {}, @@ -2016,7 +2150,7 @@ { "data": { "image/svg+xml": [ - "0.480.490.50.51012345678Mean nurse utilisationFrequency" + "0.480.490.50.51012345678Mean nurse utilisationReplications" ] }, "metadata": {}, @@ -2098,7 +2232,8 @@ "source": [ "# Run scenarios\n", "scenario_results = run_scenarios(\n", - " {'patient_inter': [3, 4, 5, 6, 7], 'number_of_nurses': [5, 6, 7, 8]}\n", + " scenarios = {'patient_inter': [3, 4, 5, 6, 7],\n", + " 'number_of_nurses': [5, 6, 7, 8]}\n", ")" ] }, @@ -2136,6 +2271,8 @@ " mean_nurse_utilisation\n", " mean_nurse_utilisation_tw\n", " mean_nurse_q_length\n", + " count_unseen\n", + " mean_q_time_unseen\n", " patient_inter\n", " number_of_nurses\n", " \n", @@ -2151,6 +2288,8 @@ " 0.667505\n", " 0.667682\n", " 0.640755\n", + " 0\n", + " NaN\n", " 3\n", " 5\n", " \n", @@ -2164,6 +2303,8 @@ " 0.676714\n", " 0.676991\n", " 0.639532\n", + " 1\n", + " 5.141475\n", " 3\n", " 5\n", " \n", @@ -2177,6 +2318,8 @@ " 0.665035\n", " 0.665125\n", " 0.675016\n", + " 0\n", + " NaN\n", " 3\n", " 5\n", " \n", @@ -2190,6 +2333,8 @@ " 0.667688\n", " 0.667794\n", " 0.660951\n", + " 5\n", + " 12.680044\n", " 3\n", " 5\n", " \n", @@ -2203,6 +2348,8 @@ " 0.664808\n", " 0.664908\n", " 0.594881\n", + " 0\n", + " NaN\n", " 3\n", " 5\n", " \n", @@ -2225,12 +2372,12 @@ "3 0.667688 0.667794 0.660951 \n", "4 0.664808 0.664908 0.594881 \n", "\n", - " patient_inter number_of_nurses \n", - "0 3 5 \n", - "1 3 5 \n", - "2 3 5 \n", - "3 3 5 \n", - "4 3 5 " + " count_unseen mean_q_time_unseen patient_inter number_of_nurses \n", + "0 0 NaN 3 5 \n", + "1 1 5.141475 3 5 \n", + "2 0 NaN 3 5 \n", + "3 5 12.680044 3 5 \n", + "4 0 NaN 3 5 " ] }, "execution_count": 18, @@ -2324,7 +2471,7 @@ { "data": { "image/svg+xml": [ - "3456700.511.52Number of nurses5678Patient inter-arrival timeMean wait time for nurse (minutes)" + "3456700.511.52Number of nurses5678Patient inter-arrival timeMean wait time for nurse (minutes)" ] }, "metadata": {}, @@ -2359,7 +2506,7 @@ { "data": { "image/svg+xml": [ - "345670.20.30.40.50.6Number of nurses5678Patient inter-arrival timeMean nurse utilisation" + "345670.20.30.40.50.6Number of nurses5678Patient inter-arrival timeMean nurse utilisation" ] }, "metadata": {}, @@ -2436,6 +2583,277 @@ " tf.write(table_latex)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Running a basic example (which can compare to R template)\n", + "\n", + "To enable comparison between the templates, this section runs the model with a simple set of base case parameters (matched to R), and then running some scenarios on top of that base case." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 20 scenarios. Running:\n", + "{'patient_inter': 3, 'number_of_nurses': 5}\n", + "{'patient_inter': 3, 'number_of_nurses': 6}\n", + "{'patient_inter': 3, 'number_of_nurses': 7}\n", + "{'patient_inter': 3, 'number_of_nurses': 8}\n", + "{'patient_inter': 4, 'number_of_nurses': 5}\n", + "{'patient_inter': 4, 'number_of_nurses': 6}\n", + "{'patient_inter': 4, 'number_of_nurses': 7}\n", + "{'patient_inter': 4, 'number_of_nurses': 8}\n", + "{'patient_inter': 5, 'number_of_nurses': 5}\n", + "{'patient_inter': 5, 'number_of_nurses': 6}\n", + "{'patient_inter': 5, 'number_of_nurses': 7}\n", + "{'patient_inter': 5, 'number_of_nurses': 8}\n", + "{'patient_inter': 6, 'number_of_nurses': 5}\n", + "{'patient_inter': 6, 'number_of_nurses': 6}\n", + "{'patient_inter': 6, 'number_of_nurses': 7}\n", + "{'patient_inter': 6, 'number_of_nurses': 8}\n", + "{'patient_inter': 7, 'number_of_nurses': 5}\n", + "{'patient_inter': 7, 'number_of_nurses': 6}\n", + "{'patient_inter': 7, 'number_of_nurses': 7}\n", + "{'patient_inter': 7, 'number_of_nurses': 8}\n" + ] + } + ], + "source": [ + "new_base = {\n", + " 'patient_inter': 4,\n", + " 'mean_n_consult_time': 10,\n", + " 'number_of_nurses': 5,\n", + " 'warm_up_period': 0,\n", + " 'data_collection_period': 1440,\n", + " 'number_of_runs': 10,\n", + " 'cores': 1\n", + "}\n", + "\n", + "# Run scenarios\n", + "compare_template_results = run_scenarios(\n", + " scenarios = {'patient_inter': [3, 4, 5, 6, 7],\n", + " 'number_of_nurses': [5, 6, 7, 8]},\n", + " base_param = new_base\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " run_number scenario arrivals mean_q_time_nurse mean_time_with_nurse \\\n", + "5 5 19 217 0.0 10.222827 \n", + "6 6 19 207 0.0 10.401924 \n", + "7 7 19 169 0.0 8.976417 \n", + "8 8 19 206 0.0 9.728157 \n", + "9 9 19 224 0.0 10.386136 \n", + "\n", + " mean_nurse_utilisation mean_nurse_utilisation_tw mean_nurse_q_length \\\n", + "5 0.191125 0.191125 0.0 \n", + "6 0.185337 0.185337 0.0 \n", + "7 0.129216 0.129216 0.0 \n", + "8 0.173133 0.173133 0.0 \n", + "9 0.201134 0.201134 0.0 \n", + "\n", + " count_unseen mean_q_time_unseen patient_inter number_of_nurses \n", + "5 0 NaN 7 8 \n", + "6 0 NaN 7 8 \n", + "7 0 NaN 7 8 \n", + "8 0 NaN 7 8 \n", + "9 0 NaN 7 8 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "compare_template_results.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "3456700.511.522.5Number of nurses5678Patient inter-arrival timeMean wait time for nurse (minutes)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "result, plot = plot_scenario(\n", + " results=compare_template_results,\n", + " x_var='patient_inter',\n", + " result_var='mean_q_time_nurse',\n", + " colour_var='number_of_nurses',\n", + " name_mappings=LABELS)\n", + "\n", + "plot.show()\n", + "\n", + "plot.write_image(os.path.join(\n", + " OUTPUT_DIR, 'scenario_nurse_wait_compare_templates.png'))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "345670.20.30.40.50.60.7Number of nurses5678Patient inter-arrival timeMean nurse utilisation" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "result, plot = plot_scenario(\n", + " results=compare_template_results,\n", + " x_var='patient_inter',\n", + " result_var='mean_nurse_utilisation',\n", + " colour_var='number_of_nurses',\n", + " name_mappings=LABELS)\n", + "\n", + "plot.show()\n", + "\n", + "plot.write_image(os.path.join(\n", + " OUTPUT_DIR, 'scenario_nurse_util_compare_templates.png'))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2452,7 +2870,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2480,13 +2898,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "89101112131415012345Mean consultation time with nurse (minutes)Mean wait time for nurse (minutes)" + "89101112131415012345Mean consultation time with nurse (minutes)Mean wait time for nurse (minutes)" ] }, "metadata": {}, @@ -2509,7 +2927,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -2564,12 +2982,27 @@ "source": [ "## NaN results\n", "\n", - "Note: In this model, if patients are still waiting to be seen at the end of the simulation, they will have NaN results." + "If patients are still waiting to be seen at the end of the simulation, they will have NaN results for `q_time_nurse` and `time_with_nurse`.\n", + "\n", + "These patients are captured in the columns `q_time_unseen`, `mean_q_time_unseen`, and `count_unseen`, which reflect the number of and wait times of patients who were still waiting in the queue at the end of the simulation, and were not seen by a nurse.\n", + "\n", + "These patients will be ignored in calculation of metrics like mean time with nurse (as they don't get to see nurse) - but it's important we still have measures for those unseen, as lots of patients waiting at the end of the simulation reveals large backlogs in the system." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "param = Param(patient_inter=2)\n", + "nan_experiment = Runner(param)\n", + "nan_experiment.run_reps()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -2598,16 +3031,63 @@ " q_time_nurse\n", " time_with_nurse\n", " run\n", + " q_time_unseen\n", " \n", " \n", " \n", " \n", + " 667681\n", + " 21581\n", + " 61906.939440\n", + " NaN\n", + " NaN\n", + " 30\n", + " 13.060560\n", + " \n", + " \n", + " 667682\n", + " 21582\n", + " 61908.106613\n", + " NaN\n", + " NaN\n", + " 30\n", + " 11.893387\n", + " \n", + " \n", + " 667683\n", + " 21583\n", + " 61908.583848\n", + " NaN\n", + " NaN\n", + " 30\n", + " 11.416152\n", + " \n", + " \n", + " 667684\n", + " 21584\n", + " 61908.605235\n", + " NaN\n", + " NaN\n", + " 30\n", + " 11.394765\n", + " \n", + " \n", + " 667685\n", + " 21585\n", + " 61909.339842\n", + " NaN\n", + " NaN\n", + " 30\n", + " 10.660158\n", + " \n", + " \n", " 667686\n", " 21586\n", " 61913.030043\n", " NaN\n", " NaN\n", " 30\n", + " 6.969957\n", " \n", " \n", " 667687\n", @@ -2616,6 +3096,7 @@ " NaN\n", " NaN\n", " 30\n", + " 4.615439\n", " \n", " \n", " 667688\n", @@ -2624,6 +3105,7 @@ " NaN\n", " NaN\n", " 30\n", + " 4.578066\n", " \n", " \n", " 667689\n", @@ -2632,6 +3114,7 @@ " NaN\n", " NaN\n", " 30\n", + " 2.182090\n", " \n", " \n", " 667690\n", @@ -2640,30 +3123,249 @@ " NaN\n", " NaN\n", " 30\n", + " 0.154651\n", " \n", " \n", "\n", "" ], "text/plain": [ - " patient_id arrival_time q_time_nurse time_with_nurse run\n", - "667686 21586 61913.030043 NaN NaN 30\n", - "667687 21587 61915.384561 NaN NaN 30\n", - "667688 21588 61915.421934 NaN NaN 30\n", - "667689 21589 61917.817910 NaN NaN 30\n", - "667690 21590 61919.845349 NaN NaN 30" + " patient_id arrival_time q_time_nurse time_with_nurse run \\\n", + "667681 21581 61906.939440 NaN NaN 30 \n", + "667682 21582 61908.106613 NaN NaN 30 \n", + "667683 21583 61908.583848 NaN NaN 30 \n", + "667684 21584 61908.605235 NaN NaN 30 \n", + "667685 21585 61909.339842 NaN NaN 30 \n", + "667686 21586 61913.030043 NaN NaN 30 \n", + "667687 21587 61915.384561 NaN NaN 30 \n", + "667688 21588 61915.421934 NaN NaN 30 \n", + "667689 21589 61917.817910 NaN NaN 30 \n", + "667690 21590 61919.845349 NaN NaN 30 \n", + "\n", + " q_time_unseen \n", + "667681 13.060560 \n", + "667682 11.893387 \n", + "667683 11.416152 \n", + "667684 11.394765 \n", + "667685 10.660158 \n", + "667686 6.969957 \n", + "667687 4.615439 \n", + "667688 4.578066 \n", + "667689 2.182090 \n", + "667690 0.154651 " ] }, - "execution_count": 26, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "param = Param(patient_inter=2)\n", - "nan_experiment = Runner(param)\n", - "nan_experiment.run_reps()\n", - "nan_experiment.patient_results_df.tail()" + "nan_experiment.patient_results_df.tail(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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run_numberscenariocount_unseenmean_q_time_unseen
000324355.273453
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" + ], + "text/plain": [ + " run_number scenario count_unseen mean_q_time_unseen\n", + "0 0 0 324 355.273453\n", + "1 1 0 332 325.855390\n", + "2 2 0 215 226.156109\n", + "3 3 0 24 25.698068\n", + "4 4 0 198 170.001899" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nan_experiment.run_results_df[\n", + " ['run_number', 'scenario', 'count_unseen', 'mean_q_time_unseen']].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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count_unseenmean_q_time_unseen
mean159.548387160.234201
std_dev129.314562129.323442
lower_95_ci112.115423112.797980
upper_95_ci206.981351207.670423
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" + ], + "text/plain": [ + " count_unseen mean_q_time_unseen\n", + "mean 159.548387 160.234201\n", + "std_dev 129.314562 129.323442\n", + "lower_95_ci 112.115423 112.797980\n", + "upper_95_ci 206.981351 207.670423" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pylint: disable=pointless-statement\n", + "nan_experiment.overall_results_df[['count_unseen', 'mean_q_time_unseen']]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "010020030040050002468101214Patients still unseen at end of simulation (n)Replications" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "010020030040050060002468101214Mean wait time of patients unseen by simulation end (minutes)Replications" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_results_spread(\n", + " rep_results=nan_experiment.run_results_df,\n", + " column='count_unseen',\n", + " file='spread_nan_count_unseen.png',\n", + " name_mappings=LABELS\n", + ")\n", + "\n", + "plot_results_spread(\n", + " rep_results=nan_experiment.run_results_df,\n", + " column='mean_q_time_unseen',\n", + " file='spread_nan_wait_unseen.png',\n", + " name_mappings=LABELS\n", + ")" ] }, { @@ -2675,7 +3377,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 35, "metadata": {}, "outputs": [ { diff --git a/notebooks/choosing_parameters.ipynb b/notebooks/choosing_parameters.ipynb index 512fee7..a76218c 100644 --- a/notebooks/choosing_parameters.ipynb +++ b/notebooks/choosing_parameters.ipynb @@ -292,7 +292,7 @@ { "data": { "image/svg+xml": [ - "00.10.20.30.40500100015002000250030003500400000.10.20.30.40.50.60.7Run time (minutes)Run time (minutes)UtilisationRunning mean nurse wait time (minutes)Suggested warm-up lengthSuggested warm-up lengthSuggested warm-up length" + "00.10.20.30.40500100015002000250030003500400000.10.20.30.40.50.60.7Run time (minutes)Run time (minutes)UtilisationRunning mean nurse wait time (minutes)Suggested warm-up lengthSuggested warm-up lengthSuggested warm-up length" ] }, "metadata": {}, @@ -321,7 +321,7 @@ { "data": { "image/svg+xml": [ - "00.10.20.30.40.5010k20k30k40k50k00.10.20.30.40.50.60.7Run time (minutes)Run time (minutes)UtilisationRunning mean nurse wait time (minutes)Suggested warm-up lengthSuggested warm-up lengthSuggested warm-up length" + "00.10.20.30.40.5010k20k30k40k50k00.10.20.30.40.50.60.7Run time (minutes)Run time (minutes)UtilisationRunning mean nurse wait time (minutes)Suggested warm-up lengthSuggested warm-up lengthSuggested warm-up length" ] }, "metadata": {}, @@ -716,7 +716,7 @@ { "data": { "image/svg+xml": [ - "0510158.599.51010.511variablecumulative_meanlower_ciupper_ciNumber of replicationsMean consultation time with nurse (minutes)" + "0510158.599.51010.511variablecumulative_meanlower_ciupper_ciNumber of replicationsMean consultation time with nurse (minutes)" ] }, "metadata": {}, @@ -1349,7 +1349,7 @@ { "data": { "image/svg+xml": [ - "01020304045505560variablecumulative_meanlower_ciupper_ciNumber of replicationsMean wait time for nurse (*100) (minutes)" + "01020304045505560variablecumulative_meanlower_ciupper_ciNumber of replicationsMean wait time for nurse (*100) (minutes)" ] }, "metadata": {}, @@ -1645,7 +1645,7 @@ { "data": { "image/svg+xml": [ - "05101548.54949.55050.55151.5variablecumulative_meanlower_ciupper_ciNumber of replicationsMean nurse utilisation (*100 - %)" + "05101548.54949.55050.55151.5variablecumulative_meanlower_ciupper_ciNumber of replicationsMean nurse utilisation (*100 - %)" ] }, "metadata": {}, @@ -1690,10 +1690,10 @@ "Running with cores: 7.\n", "Running with cores: 8.\n", " cores run_time\n", - "0 1 5.0\n", - "1 2 3.5\n", - "2 3 2.5\n", - "3 4 2.0\n", + "0 1 5.5\n", + "1 2 4.0\n", + "2 3 3.0\n", + "3 4 2.5\n", "4 5 2.0\n", "5 6 2.0\n", "6 7 2.0\n", @@ -1703,7 +1703,7 @@ { "data": { "image/svg+xml": [ - "1234567811.522.533.544.55Number of coresRun time (rounded to nearest .5 seconds)" + "1234567812345Number of coresRun time (rounded to nearest .5 seconds)" ] }, "metadata": {}, @@ -1757,7 +1757,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Notebook run time: 0m 26s\n" + "Notebook run time: 0m 27s\n" ] } ], diff --git a/notebooks/generate_exp_results.ipynb b/notebooks/generate_exp_results.ipynb index 644535e..3e8180d 100644 --- a/notebooks/generate_exp_results.ipynb +++ b/notebooks/generate_exp_results.ipynb @@ -152,6 +152,7 @@ " q_time_nurse\n", " time_with_nurse\n", " run\n", + " q_time_unseen\n", " \n", " \n", " \n", @@ -162,6 +163,7 @@ " 0.0\n", " 1.664896\n", " 0\n", + " NaN\n", " \n", " \n", " 1\n", @@ -170,6 +172,7 @@ " 0.0\n", " 29.462508\n", " 0\n", + " NaN\n", " \n", " \n", " 2\n", @@ -178,6 +181,7 @@ " 0.0\n", " 7.345594\n", " 0\n", + " NaN\n", " \n", " \n", " 3\n", @@ -186,6 +190,7 @@ " 0.0\n", " 7.986073\n", " 0\n", + " NaN\n", " \n", " \n", " 4\n", @@ -194,6 +199,7 @@ " 0.0\n", " 7.127312\n", " 0\n", + " NaN\n", " \n", " \n", " ...\n", @@ -202,6 +208,7 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 1885\n", @@ -210,6 +217,7 @@ " 0.0\n", " 1.712059\n", " 4\n", + " NaN\n", " \n", " \n", " 1886\n", @@ -218,6 +226,7 @@ " 0.0\n", " 11.023619\n", " 4\n", + " NaN\n", " \n", " \n", " 1887\n", @@ -226,6 +235,7 @@ " 0.0\n", " 8.212316\n", " 4\n", + " NaN\n", " \n", " \n", " 1888\n", @@ -234,6 +244,7 @@ " 0.0\n", " 24.898160\n", " 4\n", + " NaN\n", " \n", " \n", " 1889\n", @@ -242,27 +253,41 @@ " 0.0\n", " 3.353385\n", " 4\n", + " NaN\n", " \n", " \n", "\n", - "

1890 rows × 5 columns

\n", + "

1890 rows × 6 columns

\n", "" ], "text/plain": [ - " patient_id arrival_time q_time_nurse time_with_nurse run\n", - "0 1 504.513444 0.0 1.664896 0\n", - "1 2 507.230909 0.0 29.462508 0\n", - "2 3 508.394374 0.0 7.345594 0\n", - "3 4 512.975372 0.0 7.986073 0\n", - "4 5 521.933116 0.0 7.127312 0\n", - "... ... ... ... ... ...\n", - "1885 365 1978.104484 0.0 1.712059 4\n", - "1886 366 1980.171724 0.0 11.023619 4\n", - "1887 367 1986.506388 0.0 8.212316 4\n", - "1888 368 1988.648731 0.0 24.898160 4\n", - "1889 369 1993.658168 0.0 3.353385 4\n", + " patient_id arrival_time q_time_nurse time_with_nurse run \\\n", + "0 1 504.513444 0.0 1.664896 0 \n", + "1 2 507.230909 0.0 29.462508 0 \n", + "2 3 508.394374 0.0 7.345594 0 \n", + "3 4 512.975372 0.0 7.986073 0 \n", + "4 5 521.933116 0.0 7.127312 0 \n", + "... ... ... ... ... ... \n", + "1885 365 1978.104484 0.0 1.712059 4 \n", + "1886 366 1980.171724 0.0 11.023619 4 \n", + "1887 367 1986.506388 0.0 8.212316 4 \n", + "1888 368 1988.648731 0.0 24.898160 4 \n", + "1889 369 1993.658168 0.0 3.353385 4 \n", + "\n", + " q_time_unseen \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "1885 NaN \n", + "1886 NaN \n", + "1887 NaN \n", + "1888 NaN \n", + "1889 NaN \n", "\n", - "[1890 rows x 5 columns]" + "[1890 rows x 6 columns]" ] }, "metadata": {}, @@ -310,6 +335,8 @@ " mean_nurse_utilisation\n", " mean_nurse_utilisation_tw\n", " mean_nurse_q_length\n", + " count_unseen\n", + " mean_q_time_unseen\n", " \n", " \n", " \n", @@ -323,6 +350,8 @@ " 0.640936\n", " 0.642332\n", " 0.549098\n", + " 0\n", + " NaN\n", " \n", " \n", " 1\n", @@ -334,6 +363,8 @@ " 0.644744\n", " 0.646797\n", " 0.319636\n", + " 0\n", + " NaN\n", " \n", " \n", " 2\n", @@ -345,6 +376,8 @@ " 0.662446\n", " 0.668010\n", " 0.835793\n", + " 0\n", + " NaN\n", " \n", " \n", " 3\n", @@ -356,6 +389,8 @@ " 0.634062\n", " 0.639573\n", " 0.544915\n", + " 0\n", + " NaN\n", " \n", " \n", " 4\n", @@ -367,6 +402,8 @@ " 0.601042\n", " 0.601042\n", " 0.406760\n", + " 0\n", + " NaN\n", " \n", " \n", "\n", @@ -380,12 +417,19 @@ "3 3 0 367 2.227175 10.523889 \n", "4 4 0 369 1.653496 9.809751 \n", "\n", - " mean_nurse_utilisation mean_nurse_utilisation_tw mean_nurse_q_length \n", - "0 0.640936 0.642332 0.549098 \n", - "1 0.644744 0.646797 0.319636 \n", - "2 0.662446 0.668010 0.835793 \n", - "3 0.634062 0.639573 0.544915 \n", - "4 0.601042 0.601042 0.406760 " + " mean_nurse_utilisation mean_nurse_utilisation_tw mean_nurse_q_length \\\n", + "0 0.640936 0.642332 0.549098 \n", + "1 0.644744 0.646797 0.319636 \n", + "2 0.662446 0.668010 0.835793 \n", + "3 0.634062 0.639573 0.544915 \n", + "4 0.601042 0.601042 0.406760 \n", + "\n", + " count_unseen mean_q_time_unseen \n", + "0 0 NaN \n", + "1 0 NaN \n", + "2 0 NaN \n", + "3 0 NaN \n", + "4 0 NaN " ] }, "metadata": {}, @@ -611,6 +655,8 @@ " mean_nurse_utilisation\n", " mean_nurse_utilisation_tw\n", " mean_nurse_q_length\n", + " count_unseen\n", + " mean_q_time_unseen\n", " \n", " \n", " \n", @@ -622,6 +668,8 @@ " 0.636646\n", " 0.639551\n", " 0.531240\n", + " 0.0\n", + " NaN\n", " \n", " \n", " std_dev\n", @@ -631,6 +679,8 @@ " 0.022494\n", " 0.024255\n", " 0.195914\n", + " 0.0\n", + " NaN\n", " \n", " \n", " lower_95_ci\n", @@ -640,6 +690,8 @@ " 0.608716\n", " 0.609435\n", " 0.287981\n", + " 0.0\n", + " NaN\n", " \n", " \n", " upper_95_ci\n", @@ -649,6 +701,8 @@ " 0.664576\n", " 0.669667\n", " 0.774500\n", + " 0.0\n", + " NaN\n", " \n", " \n", "\n", @@ -667,11 +721,11 @@ "lower_95_ci 0.608716 0.609435 \n", "upper_95_ci 0.664576 0.669667 \n", "\n", - " mean_nurse_q_length \n", - "mean 0.531240 \n", - "std_dev 0.195914 \n", - "lower_95_ci 0.287981 \n", - "upper_95_ci 0.774500 " + " mean_nurse_q_length count_unseen mean_q_time_unseen \n", + "mean 0.531240 0.0 NaN \n", + "std_dev 0.195914 0.0 NaN \n", + "lower_95_ci 0.287981 0.0 NaN \n", + "upper_95_ci 0.774500 0.0 NaN " ] }, "metadata": {}, diff --git a/notebooks/time_weighted_averages.ipynb b/notebooks/time_weighted_averages.ipynb index 2b9ae4b..7eef1e9 100644 --- a/notebooks/time_weighted_averages.ipynb +++ b/notebooks/time_weighted_averages.ipynb @@ -8,7 +8,9 @@ "\n", "This notebook provides two simple examples to explain the concept of **time-weighted averages**.\n", "\n", - "These are then related back to the model, with the concept of the \"**area under the curve**\" and some exercepts from the model code." + "These are then related back to the model, with the concept of the \"**area under the curve**\" and some exercepts from the model code.\n", + "\n", + "The **utilisation** metrics in the model are then compared - the time-weighted utilisation, alongside two others - explaining their calculations, purposes, pros and cons." ] }, { @@ -59,7 +61,7 @@ { "data": { "image/svg+xml": [ - "02468101214160123TimeQueue size" + "02468101214160123TimeQueue size" ] }, "metadata": {}, @@ -225,7 +227,7 @@ { "data": { "image/svg+xml": [ - "02468101214160123Queue Size Over Time (Area Under Curve Visualisation)TimeQueue Size" + "02468101214160123Queue Size Over Time (Area Under Curve Visualisation)TimeQueue Size" ] }, "metadata": {}, @@ -298,7 +300,7 @@ { "data": { "image/svg+xml": [ - "024681012141600.511.52TimeNurses busy" + "024681012141600.511.52TimeNurses busy" ] }, "metadata": {}, @@ -349,7 +351,7 @@ { "data": { "image/svg+xml": [ - "024681012141600.51TimeNurse utilisation" + "024681012141600.51TimeNurse utilisation" ] }, "metadata": {}, @@ -521,7 +523,7 @@ { "data": { "image/svg+xml": [ - "05101500.511.5205101500.20.40.60.81Nurses in Use vs Nurse UtilisationTimeTimeNurses in UseNurse Utilisation" + "05101500.511.5205101500.20.40.60.81Nurses in Use vs Nurse UtilisationTimeTimeNurses in UseNurse Utilisation" ] }, "metadata": {}, @@ -594,6 +596,57 @@ "\n", "And you can see this matches the calculated utilisation above." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare utilisation metrics in the model\n", + "\n", + "There are three utilisation metrics in the model.\n", + "\n", + "### Run results `mean_nurse_utilisation`\n", + "\n", + "When nurse are seized, recorded sampled consultation time in `model.nurse_time_used`. At end of simulation, utilisation is calculated as:\n", + "\n", + "$$\n", + "\\text{mean\\_nurse\\_utilisation} = \\frac{\\text{model.nurse\\_time\\_used}}{\\text{param.number\\_of\\_nurses} \\times \\text{param.data\\_collection\\_period}}\n", + "$$\n", + "\n", + "### Run results `mean_nurse_utilisation_tw`\n", + "\n", + "Nurses are set up using `MonitoredResource`. Every time a nurse is seized or released, the \"area under the curve\" for resources busy is updated (see `time_weighted_averages.ipynb` for more detailed explanation of how this method works).\n", + "\n", + "```\n", + "self.area_resource_busy.append(self.count * time_since_last_event)\n", + "```\n", + "\n", + "At the end of the simulation, utilisation is calculated as:\n", + "\n", + "$$\n", + "\\text{mean\\_nurse\\_utilisation\\_tw} = \\frac{\\text{model.nurse.area\\_resource\\_busy}}{\\text{param.number\\_of\\_nurses} \\times \\text{param.data\\_collection\\_period}}\n", + "$$\n", + "\n", + "### Interval audit `utilisation`\n", + "\n", + "At specified intervals (e.g. once an hour), the current utilisation is calculated from:\n", + "\n", + "```\n", + "self.nurse.count / self.nurse.capacity\n", + "```\n", + "\n", + "This is not used to calculated overall mean utilisation (for reasons given below).\n", + "\n", + "### Comparison of methods\n", + "\n", + "Ultimately, we use either `mean_nurse_utilisation` or `mean_nurse_utilisation_tw`, and each will give pretty much the same result.\n", + "\n", + "| Metric | Pros ✅ | Cons 🟥 |\n", + "| - | - | - |\n", + "| Run results `mean_nurse_utilisation` | Simple to implement. | Requires correction for when time with nurse is less than remaining simulation time - `min(patient.time_with_nurse, remaining_time)`. |\n", + "| Run results `mean_nurse_utilisation_tw` | Doesn't require correction for when time with nurse is less than remaining time (though, as part of how this method is introduced, do have call to `update_time_weighted_stats()` at end of simulation). | More complex to implement and understand the method (`time_weighted_averages.ipynb` provided to support). |\n", + "| Interval audit `utilisation` | Helpful for monitoring over time, such as when choosing appropriate warm-up length. | Less accurate for overall mean, can miss details if interval audit intervals are sparse. |" + ] } ], "metadata": { diff --git a/outputs/choose_param_cores.png b/outputs/choose_param_cores.png index 5693345..0264681 100644 Binary files a/outputs/choose_param_cores.png and b/outputs/choose_param_cores.png differ diff --git a/outputs/example_overall.csv b/outputs/example_overall.csv index 7bb4900..7e3071a 100644 --- a/outputs/example_overall.csv +++ b/outputs/example_overall.csv @@ -1,5 +1,5 @@ -arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation,mean_nurse_utilisation_tw,mean_nurse_q_length -10776.741935483871,0.5016901310828316,9.9785879812698,0.49768010202939217,0.497800680610181,0.12525116811287498 -115.80327218806197,0.06742190929332627,0.11502904359180056,0.007507558993998087,0.007544462802101291,0.017211501482469225 -10734.264952313013,0.4769595750951833,9.936394987440801,0.49492630715799685,0.4950333493126889,0.11893793840008443 -10819.21891865473,0.5264206870704798,10.0207809750988,0.5004338969007874,0.5005680119076731,0.13156439782566554 +arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation,mean_nurse_utilisation_tw,mean_nurse_q_length,count_unseen,mean_q_time_unseen +10776.741935483871,0.5016901310828316,9.9785879812698,0.49768010202939217,0.497800680610181,0.12525116811287498,0.41935483870967744,3.4826288923383495 +115.80327218806197,0.06742190929332627,0.11502904359180056,0.007507558993998087,0.007544462802101291,0.017211501482469225,1.6886719347539962,2.688099887068902 +10734.264952313013,0.4769595750951833,9.936394987440801,0.49492630715799685,0.4950333493126889,0.11893793840008443,-0.20005502768130318,-3.194981409998563 +10819.21891865473,0.5264206870704798,10.0207809750988,0.5004338969007874,0.5005680119076731,0.13156439782566554,1.038764705100658,10.160239194675261 diff --git a/outputs/example_run.csv b/outputs/example_run.csv index db7735c..22e8084 100644 --- a/outputs/example_run.csv +++ b/outputs/example_run.csv @@ -1,32 +1,32 @@ 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b/outputs/spread_nurse_util.png differ diff --git a/outputs/spread_nurse_wait.png b/outputs/spread_nurse_wait.png index a4fbe8b..305a5c2 100644 Binary files a/outputs/spread_nurse_wait.png and b/outputs/spread_nurse_wait.png differ diff --git a/simulation/helper.py b/simulation/helper.py index 51bbcba..058fce5 100644 --- a/simulation/helper.py +++ b/simulation/helper.py @@ -25,22 +25,33 @@ def summary_stats(data): Returns: tuple: (mean, standard deviation, CI lower, CI upper). """ - mean = data.mean() + # Remove any NaN from the series + data = data.dropna() + + # Find number of observations count = len(data) - # Cannot calculate some metrics if there is only 1 sample in data - if count == 1: - std_dev = np.nan - ci_lower = np.nan - ci_upper = np.nan + # If there are no observations, then set all to NaN + if count == 0: + mean, std_dev, ci_lower, ci_upper = np.nan, np.nan, np.nan, np.nan + # If there is only one observation, can do mean but not others + elif count == 1: + mean = data.mean() + std_dev, ci_lower, ci_upper = np.nan, np.nan, np.nan + # With more than one observation, can calculate all... else: + mean = data.mean() std_dev = data.std() - # Calculation of CI uses t-distribution, which is suitable for - # smaller sample sizes (n<30) - ci_lower, ci_upper = st.t.interval( - confidence=0.95, - df=count-1, - loc=mean, - scale=st.sem(data)) + # Special case for CI if variance is 0 + if np.var(data) == 0: + ci_lower, ci_upper = mean, mean + else: + # Calculation of CI uses t-distribution, which is suitable for + # smaller sample sizes (n<30) + ci_lower, ci_upper = st.t.interval( + confidence=0.95, + df=count-1, + loc=mean, + scale=st.sem(data)) return mean, std_dev, ci_lower, ci_upper diff --git a/simulation/logging.py b/simulation/logging.py index 79649bb..75418fc 100644 --- a/simulation/logging.py +++ b/simulation/logging.py @@ -123,7 +123,6 @@ def _configure_logging(self): handlers.append(logging.FileHandler(self.file_path, mode='w')) if self.log_to_console: handlers.append(rich_handler) - #handlers.append(logging.StreamHandler(sys.stdout)) # Add handlers directly to the logger for handler in handlers: diff --git a/simulation/model.py b/simulation/model.py index c494aab..fd1ca89 100644 --- a/simulation/model.py +++ b/simulation/model.py @@ -44,6 +44,7 @@ from simulation.helper import summary_stats +# pylint: disable=too-many-instance-attributes,too-few-public-methods class Param: """ Default parameters for simulation. @@ -679,6 +680,13 @@ def run_single(self, run): patient_results = pd.DataFrame(model.results_list) patient_results['run'] = run + # Add a column with the wait time of patients who remained unseen + # at the end of the simulation + patient_results['q_time_unseen'] = np.where( + patient_results['time_with_nurse'].isna(), + model.env.now - patient_results['arrival_time'], np.nan + ) + # Create dictionary recording the run results # Currently has two alternative methods of measuring utilisation run_results = { @@ -694,7 +702,9 @@ def run_single(self, run): (self.param.number_of_nurses * self.param.data_collection_period)), 'mean_nurse_q_length': (sum(model.nurse.area_n_in_queue) / - self.param.data_collection_period) + self.param.data_collection_period), + 'count_unseen': patient_results['time_with_nurse'].isna().sum(), + 'mean_q_time_unseen': patient_results['q_time_unseen'].mean() } # Convert interval audit results to a dataframe and add run column @@ -776,7 +786,7 @@ def run_reps(self): self.overall_results_df = pd.DataFrame(uncertainty_metrics) -def run_scenarios(scenarios): +def run_scenarios(scenarios, base_param=None): """ Execute a set of scenarios and return the results from each run. @@ -784,6 +794,9 @@ def run_scenarios(scenarios): scenarios (dict): Dictionary where key is name of parameter and value is a list with different values to run in scenarios. + base_param (dict): + Dictionary with parameters for base case. Optional, defaults to + use those as set in Param. Returns: pandas.dataframe: @@ -802,9 +815,17 @@ def run_scenarios(scenarios): for index, scenario_to_run in enumerate(all_scenarios_dicts): print(scenario_to_run) - # Pass scenario arguments to Param() - param = Param(scenario_name=index, - **scenario_to_run) + # Create instance of parameter class with any specified base case + # parameters + if base_param is None: + param = Param() + else: + param = Param(**base_param) + + # Update parameter list with the scenario parameters + param.scenario_name = index + for key in scenario_to_run: + setattr(param, key, scenario_to_run[key]) # Perform replications and keep results from each run, adding the # scenario values to the results dataframe diff --git a/tests/exp_results/overall.csv b/tests/exp_results/overall.csv index 6774121..8d08f20 100644 --- a/tests/exp_results/overall.csv +++ b/tests/exp_results/overall.csv @@ -1,5 +1,5 @@ -,arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation,mean_nurse_utilisation_tw,mean_nurse_q_length -mean,378.0,2.114432379323691,10.17019147705724,0.6366457796002349,0.639550942417509,0.5312404456976593 -std_dev,15.198684153570664,0.8065769414595266,0.4803422428799903,0.022494099125355767,0.024254560268967756,0.19591429007197672 -lower_95_ci,359.12834106644124,1.11293482933209,9.573767807256708,0.6087156665442539,0.6094349281392683,0.2879807249816362 -upper_95_ci,396.87165893355876,3.115929929315292,10.766615146857774,0.664575892656216,0.6696669566957497,0.7745001664136824 +,arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation,mean_nurse_utilisation_tw,mean_nurse_q_length,count_unseen,mean_q_time_unseen +mean,378.0,2.114432379323691,10.17019147705724,0.6366457796002349,0.639550942417509,0.5312404456976593,0.0, +std_dev,15.198684153570664,0.8065769414595266,0.4803422428799903,0.022494099125355767,0.024254560268967756,0.19591429007197672,0.0, +lower_95_ci,359.12834106644124,1.11293482933209,9.573767807256708,0.6087156665442539,0.6094349281392683,0.2879807249816362,0.0, +upper_95_ci,396.87165893355876,3.115929929315292,10.766615146857774,0.664575892656216,0.6696669566957497,0.7745001664136824,0.0, diff --git a/tests/exp_results/patient.csv b/tests/exp_results/patient.csv index 34dba7d..a46c8e8 100644 --- a/tests/exp_results/patient.csv +++ b/tests/exp_results/patient.csv @@ -1,1891 +1,1891 @@ -patient_id,arrival_time,q_time_nurse,time_with_nurse,run -1,504.51344428446833,0.0,1.6648958649577223,0 -2,507.230909157429,0.0,29.462507817526664,0 -3,508.3943739037057,0.0,7.345594141189309,0 -4,512.9753720220415,0.0,7.986073087421324,0 -5,521.9331158184133,0.0,7.127311958567695,0 -6,523.3372003115608,0.0,6.654871185934851,0 -7,525.4774467363679,0.0,4.0624435236596765,0 -8,529.085874527684,0.0,5.337218667814203,0 -9,538.9939475235766,0.0,5.172086304364773,0 -10,546.479861195384,0.0,2.057444526970659,0 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-run_number,scenario,arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation,mean_nurse_utilisation_tw,mean_nurse_q_length -0,0,403,2.0437879039607987,9.57743596674643,0.6409356249106212,0.6423324393767569,0.5490976835308012 -1,0,382,1.2551167218327395,10.208371460384479,0.6447441378534594,0.6467972830722329,0.31963639182673764 -2,0,369,3.392586488165945,10.731510270167153,0.6624456885958806,0.6680103775187581,0.8357927073077738 -3,0,367,2.2271750333429026,10.523888711113448,0.6340615768641413,0.6395727423427239,0.5449154914912302 -4,0,369,1.6534957493160707,9.8097509768747,0.6010418697770725,0.6010418697770727,0.4067599543317534 +run_number,scenario,arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation,mean_nurse_utilisation_tw,mean_nurse_q_length,count_unseen,mean_q_time_unseen +0,0,403,2.0437879039607987,9.57743596674643,0.6409356249106212,0.6423324393767569,0.5490976835308012,0, +1,0,382,1.2551167218327395,10.208371460384479,0.6447441378534594,0.6467972830722329,0.31963639182673764,0, +2,0,369,3.392586488165945,10.731510270167153,0.6624456885958806,0.6680103775187581,0.8357927073077738,0, +3,0,367,2.2271750333429026,10.523888711113448,0.6340615768641413,0.6395727423427239,0.5449154914912302,0, +4,0,369,1.6534957493160707,9.8097509768747,0.6010418697770725,0.6010418697770727,0.4067599543317534,0, diff --git a/tests/test_unittest_model.py b/tests/test_unittest_model.py index 8904dd6..cdf16fe 100644 --- a/tests/test_unittest_model.py +++ b/tests/test_unittest_model.py @@ -230,7 +230,7 @@ def helper_warmup(warm_up_period): ) assert first_interval['utilisation'] == 0, ( 'With no warm-up, expect first entry in interval audit to ' + - 'have utilisation of 0 but utilisation was ' + + 'have utilisation of 0 but utilisation was ' + f'{first_interval['utilisation']}.' ) assert first_interval['queue_length'] == 0, ( @@ -476,17 +476,13 @@ def test_valid_cores(cores): def test_consistent_metrics(): """ - Presently, the simulation code includes a few different methods to - calculate the same metrics. We would expect each to return similar results - (with a little tolerance, as expect some deviation due to methodological - differences). + Expect utilisation to be pretty much the same, between the two methods + implemented for calculating the overall mean utilisation. """ # Run default model experiment = Runner(Param()) experiment.run_reps() - # Absolute tolerance (atol) = +- 0.001 - # Check nurse utilisation pd.testing.assert_series_equal( experiment.run_results_df['mean_nurse_utilisation'],