From 76b0129b6ff445e9e18e5e7a751029f4bff4435b Mon Sep 17 00:00:00 2001 From: Tschan Date: Tue, 7 Jul 2026 22:01:39 +0200 Subject: [PATCH] Making the analysis of data easier by including everything in a single dataframe. What remains is to clean up the remaining functions in the OFRAnalyser definition. --- defs/computing.py | 93 +++++++------- .../202607023_OFR_Analysis_01.ipynb | 120 ++++++++---------- 2 files changed, 100 insertions(+), 113 deletions(-) diff --git a/defs/computing.py b/defs/computing.py index cb8f20f..16dfcb5 100644 --- a/defs/computing.py +++ b/defs/computing.py @@ -141,7 +141,7 @@ class OFRSample: self.SOA_density = float(line.split(self.__data_separator)[1]) continue - self.sample_time = self.scan_up_time + self.scan_down_time + self.sampling_time = self.scan_up_time + self.scan_down_time data_lines.pop(-1) for line in data_lines: @@ -169,7 +169,7 @@ class OFRSample: self.scan_up_time = None # Time to scan up the voltage range in DMA, in (s) self.scan_down_time = None # Time to scan down the voltage range in DMA, in(s) - self.sample_time = None # Total time to scan voltage up and back down in DMA, in (s) + self.sampling_time = None # Total time to scan voltage up and back down in DMA, in (s) self.lower_diameter_bound = None # Lower Diameter bound of CPC, in (nm) self.upper_diameter_bound = None # Upper Diameter bound of CPC, in (nm) @@ -238,10 +238,10 @@ class OFRAnalyser: stopping_times = [] sampling_times = [] dry_air_flows = [] - ozone_flows = [] species_flows = [] - total_flows = [] - cnc_flows = [] + ozone_flows = [] + total_flows = None + cpc_flows = [] for sample_number in self.sample_number_list: sample = OFRSample(source_file=self.data_source_file, sample_number = sample_number, data_separator = self.__data_separator) @@ -256,63 +256,66 @@ class OFRAnalyser: if sample_number == 1: starting_times.append(0.00) - stopping_times.append(sample.sample_time) + stopping_times.append(sample.sampling_time) - starting_times.append(stopping_times[-1]) - stopping_times.append(stopping_times[-1] + sample.sample_time) + elif sample_number >= 1: + starting_times.append(stopping_times[-1]) + stopping_times.append(stopping_times[-1] + sample.sampling_time) - local_dry_air_flows = [] - local_ozone_flows = [] - local_species_flows = [] - local_cpc_flows = [] + sampling_times.append(sample.sampling_time) out_flag = False - with open(self.flow_source_file, 'r', errors='ignore') as file: + with open(self.flows_source_file, 'r', errors='ignore') as file: for line_counter, line in enumerate(file.readlines()): if out_flag == True: continue - i = 0 + if line_counter >= 6: - if line_counter == 6: + if line.startswith('-'): + out_flag = True + continue + + i = 0 items = line.split() - while i <= items: - pass + while i < int(items[-1]): + + dry_air_flows.append(float(items[0])) + species_flows.append(float(items[1])) + ozone_flows.append(float(items[2])) + cpc_flows.append(float(items[3])) + + i += 1 - dry_air_flows.append(float(items[0])) - species_flows.append(float(items[1])) - ozone_flows.append(float(items[2])) - cnc_flows.append(float(items[-1])) - starts.append(0) - stops.append(float(items[3]) * float(items[4])) - smpls.append(float(items[4])) - i += 1 - elif line_counter > 6 and not line.startswith('-'): - items = line.split() - dry_air_flows.append(float(items[0])) - species_flows.append(float(items[1])) - ozone_flows.append(float(items[2])) - cnc_flows.append(float(items[-1])) - starts.append(stops[i-1]) - stops.append(stops[i-1] + float(items[3]) * float(items[4])) - smpls.append(float(items[4])) - i += 1 - if line_counter > 6 and line.startswith('-'): - out_flag = True else: continue - self.data = pd.DataFrame({'Sample': self.sample_number_list, - 'Number (#/cm^3)': numbers, - 'Length (mm/cm^3)': lengths, - 'Surface (mm^2/cm^3)': surfaces, - 'Volume (μm^3/cm^3)': volumes, - 'Density (pg/μm^3)': densities, - 'Mass (mg/cm^3)': masses, - 'Time (s)': times}) + total_flows = [(flow_1 + flow_2 + flow_3) * 1E3 for flow_1, flow_2, flow_3 in zip(dry_air_flows, species_flows, ozone_flows)] + mass_production_rate = [(mass * flow) for mass, flow in zip(mass_concentrations, total_flows)] + mass_produced = [(mpr * duration) for mpr, duration in zip(mass_production_rate, sampling_times)] + cumulative_mass_produced = np.cumsum(np.array(mass_produced)).tolist() + + self.data = pd.DataFrame({'Sample (-)': numbers_of_sample, + 'Number (#/cm^3)': number_concentrations, + 'Length (mm/cm^3)': length_concentrations, + 'Surface (mm^2/cm^3)': surface_concentrations, + 'Volume (μm^3/cm^3)': volume_concentrations, + 'Density (pg/μm^3)': aerosol_densities, + 'Mass (mg/cm^3)': mass_concentrations, + 'Start (s)': starting_times, + 'Stop (s)': stopping_times, + 'Duration (s)': sampling_times, + 'Dry Air Flow (L/min)': dry_air_flows, + 'Species Flow (L/min)': species_flows, + 'Ozone Flow (L/min)': ozone_flows, + 'Total Flow (cm^3/s)': total_flows, + 'CPC Flow (L/min)': cpc_flows, + 'Mass Production Rate (mg/s)': mass_production_rate, + 'Mass Produced (mg)': mass_produced, + 'Cumulative Mass Produced (mg)': cumulative_mass_produced}) def GetPlottableData(self, which_data = 'Mass'): diff --git a/run/OFR_Analysis/202607023_OFR_Analysis_01.ipynb b/run/OFR_Analysis/202607023_OFR_Analysis_01.ipynb index a28a95a..ff229b2 100644 --- a/run/OFR_Analysis/202607023_OFR_Analysis_01.ipynb +++ b/run/OFR_Analysis/202607023_OFR_Analysis_01.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "8769766b", "metadata": {}, "outputs": [], @@ -26,8 +26,8 @@ "import pandas as pd\n", "\n", "# Specify the source file for this analysis\n", - "source_file = Path(r\"Z:\\People\\David_Tschan\\data\\OFR_SMPS\\2026\\processed\\06_June\\20260612\\20260612_SET02_EXP01_full.txt\")\n", - "flow_source_file = Path(r\"Z:\\People\\David_Tschan\\data\\OFR_SMPS\\2026\\processed\\06_June\\20260612\\20260612_SET02_EXP01_full_flows.txt\")" + "source_file = Path(r\"C:\\Users\\tschan_d\\Documents\\data_local\\20260612_SET02_EXP01_full.txt\")\n", + "flows_source_file = Path(r\"C:\\Users\\tschan_d\\Documents\\data_local\\20260612_SET02_EXP01_full_flows.txt\")" ] }, { @@ -40,21 +40,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "76a9b026", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# First load and get the data of sample 33 (arbitrary) and create a sample object\n", "sample_number = 33\n", @@ -96,16 +85,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "8db19b57", "metadata": {}, "outputs": [], "source": [ "sample_number_list = [int(i) for i in range(1, 157)]\n", "\n", - "analyser = OFRAnalyser(data_source_file = source_file, sample_number_list=sample_number_list)\n", + "analyser = OFRAnalyser(data_source_file = source_file, flows_source_file = flows_source_file, sample_number_list = sample_number_list)\n", "analyser.GetData()\n", - "analyser.GetFlowData(flow_source_file = flow_source_file)" + "\n", + "analyser.data.to_csv(path_or_buf=r\"C:\\Users\\tschan_d\\Documents\\data_local\\data.csv\", index=False)" ] }, { @@ -118,40 +108,26 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "a4477003", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig = plt.figure(figsize=(10,4), constrained_layout = True)\n", "ax = fig.add_subplot(111)\n", "ax_2 = plt.twinx(ax)\n", "\n", - "times, numbers = analyser.GetPlottableData(which_data='Number')\n", - "_, masses = analyser.GetPlottableData(which_data='Mass')\n", - "\n", - "ax.plot(times, numbers, color='red', label='Number')\n", - "ax_2.plot(times, masses, color='blue', label='Mass')\n", + "ax.plot(analyser.data['Sample (-)'], analyser.data['Number (#/cm^3)'], color='red', label='Number')\n", + "ax_2.plot(analyser.data['Sample (-)'], analyser.data['Mass (mg/cm^3)'], color='blue', label='Mass')\n", "\n", "ax.set_title('2026-06-12 SET02/EXP01: Experiment Overview')\n", - "ax.set_xlabel(r'Time $t$ $(\\unit{\\second})$')\n", + "ax.set_xlabel(r'Sample $(-)$')\n", "ax.set_ylabel(r'Number Density $N$ $(\\unit{\\per\\cubic\\centi\\meter})$')\n", "ax_2.set_ylabel(r'Mass Density $m$ $(\\unit{\\milli\\gram\\per\\cubic\\centi\\meter})$')\n", - "ax.xaxis.set_minor_locator(MultipleLocator(base=180, offset=0))\n", - "ax.xaxis.set_major_locator(MultipleLocator(base=1800, offset=0))\n", + "ax.xaxis.set_minor_locator(MultipleLocator(base=1, offset=0))\n", + "ax.xaxis.set_major_locator(MultipleLocator(base=10, offset=0))\n", "\n", - "ax.set_xlim([0, 30000])\n", + "ax.set_xlim([1, 156])\n", "ax.set_ylim([0, 1.2E6])\n", "ax_2.set_ylim([0, 15])\n", "\n", @@ -173,43 +149,26 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "a5af245c", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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3794dq6yCfO/oba5ckj7zmQvm9+3bl+6aAAAAAACQgv7+fpvETmPHKwA/cODA2GuqeXdBebmyoufU712v673+1zOfAK9eK9QI6vegcQWd1atX28kv7nNJytbq/VlcJ7aJ49SK5161n8825eM4Vft+tonj1KhzL8n51+h1YpsqP07V7tMsHqdqP59tysdxSlrWS03mNTZ8ENWqexPYRZXVa4ODg6Ya7777rmnoOPP1Giuvnt5++22zYMECmzzB3w8jr9SHRcMInj17NnAYwbxqxu1im/KB45QPHKd84DjlA8cpHzhO+cBxqq2TJ0+a22+/3SbMC+pvn0VvvfWWbbYflFn/2WefTbSslk6ABwAAAABAPTz99NO2yb5aBrjEe9UgmAcAAAAAZN4rr7xiJ+fjjz82efLUU0+ZgYEBs2bNmlSWRzAPAAAAAMg8f26zkydP2uR1eaEe7osXL05teS2dAG/y5MljzR3cmIJRCfDysk1bt24d27Zm0YzbxTblA8cpHzhO+cBxygeOUz5wnPKB4wSvrq4uOyrc5s2bTUMT4DWDPCZMAAAAAACY3MVzakWwZcsWs2TJEttvfubMmSWvJw3yJ2UlI9/atWvtMkZGRuyGqS9BWFKAcmXVdEHj/zkdHR22DAAAAAAAjaAh7hSrqj7dP9ydxrGvezCfRkY+BeVLly4d6++wfft2s2rVqsCbA3HLapzAlStXVrQ+AAAAAACkaefOnakur+pm9moasHv37tQy8rmAvb29PVa//KCyutvR19dXNpjPW7MMAAAAAEBBq8dzk7KWkU/UhD5uLX9YWTXFV6CvgF6BvVoOhDl//rw5d+5c2eQVzZR8DQAAAADyJO9D06Wt6pp5deC/+eabU8vIpwB8zpw5thm9+rpXUlb95V3wri4AajkwPDwceicnDmVSf+KJJ0KzEgYtH7Whlhg6pgAAAABa18kc1Mz39vbabuIPPPCAnY+iLuR1rZmfN2+eDejVRz2NjHwrVqywwXG5QD6qrLcWXrXyCvy8Ab7f0aNHzaxZsyI/K6pWXoE8CfbqR60uAAAAACDrlOhOMbObD6MEeElNylJGPiWyU5p+BeBpli1n6tSpZtq0aVUvBwAAAAAA59VXXw2cT8OkrGTkc7Wtu3btCnxdteuqhS9XVk3vDx8+PJb8Ts3sJarPPAAAAAAA9aCh3c+cOTOuInzZsmX1H2e+WseOHTP79u0b6w/tqKZfye3URF5BuYL5cmVFZfWe6dOn29r7AwcO1H2bAAAAAABwFJd2dnYG5ltTMH/lyhVT82A+7U78CsKj8vCpVt1tcLmyCuCj+iIE2bZtm21q76xevdpOAAAAAACkobu727YyT6t1+6SsdeJvBGWqz2r2w2qpe4K6I6hFg26E6ARy3RUAAAAAAPWhpvVKHp+WSVnrxI/07NixwwbyyrSv1g0K6JU4UHkFenp62NUAAAAAciPv48x3dnaa1157zaxfvz4b48znWVrjEqqpRMnQdJ98El544kRjrrsuXtm2NmOmTCkte8MNsddLLSM0ZKBLBijKJeBGH9CJpJp6R8G+gnyNEKByGzZsGKvR7+/vH0siqFYZKqPcBLq7pK4W7uaAymuZ+l90M8H7+WkYt78BAAAAtJyTORhnPiigv/nmmwMTtCcN8ifVMiOfLF++3LScG28Mf+2++4x56aXi41tuMebCheCy3/iGMT//efHx7NnG/Pa3sVZBwbj4A2mdNMoroNf12tDQkH1ewbeCfBfcr1ixwgbwHR0dNuGgHru8BSr73HPP2fe6mwNquq/lqubfm6xQgb/ep9cAAAAAoFU9//zzY8ncXfJ2b0Vs3YP5tDPyNUIzJsBzNepBZsyYYV/33g1SEK7g3Y0eoDIK5EX/K0BXTb67OeASH2oZeo+GA9R7dEPH9cnX8yqv97llAQAAAEAr2rJli+0KvXnz5lSWNylrGfmaIgFeVN8NNbP3+uij6Gb2XidOxF4FBdKuqbufAm5voK9jqKDcBdxBNwL0WH3tvY/983qfyqimHgAAAABQdPr06VQrOSdlLSNfU0jQr71WZV2tu2rZvTXweqyA2wXgauahmnPX3F7UJN5/I0CPvU3lg5rNa5makg4NCAAAAADNrqury8ZfmamZTzsjH9KjJHVqNaEkeK6mXo/1vAvYleTOH3yrabxu0uhEc33m9bhcIjt300DD4bmm9rp5oM+mzzwAAACAVjZv3jxbEa74TLHTzJkzS15PGuRXHcyreb0Cen8NsEOQ3zjKMO+S0ikYV592l5zOZZpXLb0CfMeNQ69cCAr0VV7L0OM4dGJqGbphIArkyTwPAAAAoNWHphscHBwbWcxfoap8c0mD+aqHplNGPhcMBmXke+edd0zWhzLQDYdqEuAxVFp9sb8BAAAAnMzh0HRpmpS1jHxNkQAPAAAAAIAa8qVLb3xGPgAAAAAAUONg3mXkAwAAAAAgC0a+yA3W3t5u+6krx1u1ZZX4XV3J1TI9TWfPnjWbNm2qfzP7tDPyAQCAYMpyc+GCctIYc/31xec//dSYq1fjjWyapOzFi8ZcuZJOWa2v1lsuXTLm88/TKTtlijFtX1RNXL5szGefpVP2uuuMmTgxeVmVU/kwkycbM2lS8rLaB9oXYa691phrroku692vyM73uZJjGVRW3z99D8OonMonLavrha4baZTV+azzOmr7Kymr75++h84nn6RTVtcLXTcqKav1DctM5r+GJylbyfU+yUjYzWTDhg02Afjw8LCtfF6xYoWdr6askoMHJX2vlpKVa0SwZ599NtkbR6vU3d09umrVqsDpnnvuGc2yDz74QF+b0fXr14/+1V/91dj08ssvJ1pOR0dHzdYR7G8AkKtXR0f/9E/1c2909I47SvfJkiWF54Omm28uLfuNb4SXvf760rL33Rde1v8LQn8Ko8p+/HGx7EMPRZf96KNi2Ycfji57/Hix7ObN0WV//eti2a1bo8sePFgsu2NHdNmf/axY9gc/iC77D/9QLPujH0WX3bu3WFbzUWW1LEefEVTmq18tnEfI1ve5kmPpJp1vjs7DqLI6jx2d31Fl9f1w9L2JKqvvnaPvY1RZfZ8dfc+jyuo64ej6EVXW/1M8qqyua1667oWV1fXSS9fTsLK6DnvpOh1WdsGC0rJ6HFY2jet9Xl28eHH07NmzkdPRo0dtPKe4zk/PDw8Pjz2eO3fu6P79+wM/K07Zvr6+0Z6eHhv7aT5Nx44dG21ra0v8vlSGpss7EuAVqFmJmpiIhqPr7e21w9u5144fP8548QDQIKq5+dWv2P2o3C9/WTiPWrWWLkv4PgPlbd++3Wzbtq2iXXXs2LGxmMZRjbp7PmlZxUga1ntoaKhkWO9Gq3pourRop6gPgnaUdp7GJvcPdeeojJpCqLzK9Pf3lzR3KPd62kMZNMtQaQrYNZ689pX6iai/iLpPrFy50u5T7wneSM2yvwEgCTXvvPHGwvxHHxnzO79TfI1m9oX9QDP74KbZOnduvbUwryGZCeaz9X3+zW9KjwnN7AtoZl/Qys3sL126ZKcoH374oVmwYMG4eE6xjL+pvGIIxYZ9fX0ly4hTVo/XrVtnE79rfunSpWOVnmlQpen8+fPNlaj+agGqrplPgwJF7RAF3e4uzKpVq+ydj0r6NCTpH1ELteynk/TLqH4d2gfqh9HZ2Wm6u7vtcwrSoyigd0G9gvk5c+aM1cwrT4JObC1Hy/XW4OsOlj7D3cnSHSy9Xyp9HwCglPeHnXj/VpSTpKz371WaZdUP1vWFTbOsgiDXd7dRZdV/2PVhTrOsAhvXfz5pWa37D35QnEfj6Zzeu7cwP2NG+LFNctz1GzPu78QkZfV7tBZlFaTWoqxkoaz/Op1W2Vpd77No8uTJdopy7ty5wOcVs7gWx16KR5KWVTykGKWaEdxWr14d+bpio4qM1sjIyMjoxo0bK3qv+itErVq5Pg1x+0e4PvNBfSyq6TNfy346SXR1dY2tm9unixYtGt21a1dg+enTp48ODg7aef2v8m6/6TW3T/W8W64r517TvnbLHxoaKnmt0vf5kaMAQCvy9hn19j8HADQg6YEuxN7pk09Ky+ixv0xY2QsXwsv6L/hJyn76adP/wfggIp5Lq8+8YhS97p8Ua8W1du3aWFNSNQvmK+3ELwMDA3YHBnGBnj+48weCYa8HHXwlTiiXXEEJGPIUzAfdENE+VVAeRq/pPfpfkxI8eF/zBuUu6Pee7HrOv/yVK1fa41nN+/wI5gG0IoJ5AMhw9sJ6Z7VLksW0RYP5jo6OsYBbMYc/3vDGh+XK+pebdgK8SmWimb2X6+/umtwHvR7Ud9s1yS/3ehD1s4iTJO+JJ54wcag/Whg3dI2jfo9h3PA5zokTJra9e/eOa6au/aKm7KKhD9TcXbq6usb6gwwODsYabsGbz8DNq/mJ9r/62qf9PgBodWpm6v5cMbwYklAXzDfeKMx//evjf4ug/pTX4MUXC/P33x+/KT0ygOyFudHf3z82drziDuUGc9R0XrGQ4qByZbMsc5cO9W/XTg3rk1CuT0OS/hHO0aNHzaxZsyLXq1x/jaz10/Hf1FDA7Pq/i/axO3krEXTDRCe+Jt0QSPt9ANDq1Kfy3/6t0WuBPNJ44suWFeZJgJcNyunV2Vk8JgTzOeXNXui/y3roUKG+PIi/7C9+EZ3VzuunP41f9vnnC3fzWtT06dND84Sp8tKbUy2qrF/SRNybNm2yNw5mz54dq7zylG3ZssU899xzZcv66n4bS0nvlixZMi7DoJerzfUG7ApS3fPlXg8ydepUM23atMgpSTCfBTpBlc3f0Z2msOEY0vxMV+vv3fdBN1fSeB8AAABQVwrE77ijMGlIAgXzmvxZ7PTYveafgrKYhpX11+YlKavMpHlNZd9E5syZY2Ox9957r2zZEydO2KTlSg5f05r5tDPyufH6lMU8iAI9V5OsWnvd3VBZBaz6LG+T8nKvtwJtr04ENRVRZn/tE90sCdu/adEdLTXldzdkdBMlzt2rSt8HAAAA1I0C8SR9X5GqV155xU7Ox1H9mzNCo3cp1lF8o0pMVV6r1bhrsawKTHUJP3z48FhL6s2bN9d2nHkFinH7bpej2mJvM3lHza610f6x/7TBCv61wWHjzEe97jDOfD4xzjyAVu2m6W7Uq/VmkqGM0Nq8Y5rTzD4bOCZAOk6ePGluv/32cePMZ5GGC1fFalh/fAXxqtxcs2ZN7GVWXDMfJ0iPSwF31D2FpH0akvR5kG3bttmm9t5WB+VaHgAAUE/6M3n0aHEeAADkh1pKu7xwZ8+eta3HVQmtWPimm25qjgR4jaBM9Vm/kwMAAACgwT791Ji77y4mrlMfdiAhBe+VBvBVJ8BTRj51zo9LGfnWrVtXyUcBAAAAQDYok/zhw4UpblZ5oEYmVZORT+3+71AmxxgZ+QjmAQAA6u+aa4zZsaM4j8a79lpjfvSj4jwA1C2Yr2VGPgAAAKRHweKjj7JHs0Q3Vb71rUavBYC8q7jPvIJ5l5EvbLgzBfFKlJckI18jVJsAT8O/uaH1UHva3wAAAABaSx6Hpqulioem80srI1895WkoAwBAa9PQdAsWFOaV1Z6h6RDXlSvGHDlSmNdIvRMnsu8a7fPPFZQU5lV/NImU1PnBuIKZcrIJ4jl1S589e3ZF752UtYx8AABgPAXvCXLPAmMuXjTmK18pzDPOfDZcumTMN79ZPCYE80Dr6urqMjNmzDB9fX02H925c+dsJXmcAL+ibPYAAAAA0JJuvrkwASlQEK9cc2rdPnPmTNul+LHHHov1Xhr1AAAAAEAcN9xgzG9/y75CajTym4J3JZU/dOiQ2bFjh+nu7o71XoL5FBLgAQBQa59+aszddxfmf/ELY6ZMYZ8DAJB3SmG3YcMGO79w4UIb3K9atcoG9uUQzBtjtm7dmtuECQCA1nD1qjGHDxfnAQBA/mmo9xdffNHcf//99rGGex8eHo71XoJ5AAAAAIjbTOreewvzP/0pzaRQtf7+frNixQrz5JNPmnXr1pmDBw/a/vMNC+aVgU/jy6vdv1ZE49ArI19vb6+ZNm1aLT4SAAAAAGpLTaP+6Z+K86irvI8z//rrr5vly5eXPKcR4ZQA77XXXrOTAvo1a9bUd5x5L62A68Cv8ec7Ojrs8/v27SvZ+Y3WDOMSAgBaA0Mbo1KXLxvz5JOF+e98x5hrr2VfNtpnnxmze3dhvqvLmGuuafQaITYuxplyMmfx3MSJE83Q0FDF48rXpWZ+ZGTEPProo3b+nnvuGevQPzAwYLKIBHgAAKBZKXh/4olGrwW8FLz/5V+yT4BWs2LFCnPs2LFsB/Peyn6Nm5d1JMADAAAAANSShqDTpOb23tHUKtVmakB95B2l13e04kE0lt68efPMhAkTzG7X5iiEyvgn9S1wFi9eXPLa2rVrU9kmAAAa7eabCxOQhLr1/tu/FSa6+GbDlSvG/PznhUnzAFrDY489ZgYHB23GenVN37Nnj3nrrbeyVTM/Z86c0GYFQVySvLBgP6zW/8iRI3aZK1euLCmzf//+cc8BAJBnN9xgzG9/2+i1QF6Tb//hHxbmlStK5xIa6+JFY5Yta5Fjot/uFy4E9zVwCRx0l0knaphJk4yZPDl6eZWUnTjRmOuuK+0PH7csUIEDBw7YZHeKY5Vf7qmnnrLN7lUJrSHq4owtX/NgXncYdMfB1ZSvX78+srxLkDdjxoxEn6MbAJ2dnaZa58+ftxn4o0yePNlOAAAAAGJQMP21rxnzq1+Nf23zZmOefrow//77qg0MX87DDxvzN39TmD91yphbbgkv+9BDxvzd3xXmFcjfeGN4WcUg3pxeUWXvu8+Yl14qzF9/fXg5IIIy16sy2l/JreDexc8NC+aVuV618kuXLh2rGdcQdapx1wrecccdaX6cXbbubvipab2S8Gkd1GdfdzmiLFiwIFa/+ifIHgMAAADEo2A6KJDPMzWjiKrBR03lfWi6MIpXy8WsNQ/mVUuu4NllrxdltdeQdF1dXakOS+f6yfs3ur+/f+w53UTQXY/h4eHIZR09etTMmjUrsgy18gCARlIL1HvvLcz/9KfGTJnC8QCQI7/5TWl/Au94fF/6UqG/QVTTeUeJQ+KWVQ16VFk1nfdKUhYNsXr1ajt5h6br7+9v2aORajCv9v7eQN7bjL63tzfNj7JN7HWDwM8b3OvGghLqqVVA1J0OZRKcNm1aqusHAECa1KX0n/6pOA8Amae7jr/+dTEIbwvJva3n4yYOmDChNmWlqZMXoBmlGsx7k9Mlea0Squ0fGhpKdZkAAAAAUqIg/Q/+gN0J1EiqQ9Opj/qmTZvGPa/nqsku7x+uTo+VAV+Tl/rJe4epc9nxK+l/AAAAAADIp5GREZtLrb293SZlV2vtSsu615R1XkOqe2POpgnmd+7caU6dOmVmzpw51p9B82p+r9fCKOjWztFO6e7utvNuB+p//5B1AwMDtlzYsrST3TKCEuQBAAC0CnVNVuJwTd5uymgcHYcdOwpTUx+Ty5eNUQJpTZoH6mjDhg12tDTlT1OX77Bh0uOUVeyp19TaXPOrVq0yWTBhtMr276+//rpZvnx5yXPHjx8fC8ZVKx427nyjKWHC7bffbofOU7/5sMQKAFpX2BC16gbouv7p98lnn4UvI0lZDWHrcuwkKatyUb+TNLKmywmUpOznnxtz6VJ4WQ0R7H6IJil75UphnOUw3uGHk5QtN1Rxnoc1Vjk3alLTj0sNBNB1QNeDMMp1pi7SomuRrklplK3H9b5pceFCneK5Dz74wNx2220lr6mCVwH49OnT7WPVqCvvWlCL8SRlg8on8dZbb9mKbsXLBw8etM+98MILttX5XXfdlWxho1Vqa2sbPX78+GgeffDBB7qRYf8HAL+rV0dH//RPFZqNn7yXvc2bg8u46de/LpbdujW67MGDxbI7dkSX/dnPimV/8IPosv/wD8WyP/pRdNm9e4tlNR9VVsty9BlRZbWOjtY9qqy23dE+iSqrfepoX0eV1bFydAyjyj78cLHsRx9Fl33ooWLZjz+OLtvRUXqeRZW9777g5WoeaDX67kR9X7zfC30no8rqO+3oux5Vth7X+6bFhQtVuHjx4ujZs2cjp6NHjwbGc0NDQ/Z5r46OjtFdu3aN+5wkZYeHh0f7+vpGFy1aVNE2vfbaazZ+7u7utv87u3fvHr3nnnsSL6/qZvZqgqBm9ADQbJpxeFzkl2riVZv41a8W/gfiUiuUEycKEyMhAMiL7du3m5tuuilyWrBgQWgf+KBa86AE6nHLun7zWi91+67Eli1b7HtVM+9tIK+Y+vDhw/VvZq8+6VopNbf3NlXPe7MMAPC2DvQPj0sz+wKa2devmb0r620eDLRSS2ea2edQs5x8aIhLly7ZKcqHH35oA3p/PKfKZjWV94a6CsbV190/vHmSsm5UNfWxVzP7pNra2uzNAw2LPnHiRHPli75DZ8+etX323eO6DU2nhHNq76+7GRpPXhu9ZMmS5O39ASBjFKA9/HBh/qabisFaUEDr+mGXU6uy6gseN4lSkrLaB67/fJplFaTG/U2XpGySoYrzOKwxv4PRyrw3tsrR9Trsml1N2VpdwwGMN3nyZDtFOXfuXODzbtQzb627YtagJOpJyopiXtXOK3l70hHb9FmDg4Nm2bJlJTcPtKxK8sxV3cxeNfP79+83Tz31lF0h/a+kd7rTsHTpUpMH27ZtM4888sjY9MorrzR6lQBkgP5+/M3fFKa4P/QAAOlX7uommaaoliwA4A+63ahoCpbPnDlTEnx7hz+PKqsg3ztUnV7XY/8w6XH09PTY2n4lwVMSvffee8/s2bPHdHZ22tbuSVVdM6++Cmrj70/frw3UXYc82Lp1K83sAQAAAKBJ9Pf3j/VzV+DtHbLcDX/umtFHlRU1q3d54vS6KrMrCeb1eeqLr8pvcbXxfX19doS1uveZzzP6zAOIoqvjqVOF+Ztvpp8ygHzKe7flvK9/S1P/X1ejqeCl6cfiQ72dzFkONHULUH95efPNN22zfhfQe1+rW808ADQrJTG75ZbCPD8gAQBISMF7TrrdAvWgmn+X5G7hwoWhr8VFMA8AAAAAyDzlNvPmN/tYtS05EtYoXtnsK2kwTzD/RQI877B6q1evthMAAEAzjcwRd8QJIBWXLxvz/e8X5v/n/yS9P6rmj9NOnjxp+7tnmQJ1JYkXJb3r7e0dV0Z9+BcvXpx42VzSSYAHAABaYGQOoO4++0zpuwvzuqPEWH1oUYNfJIZX7XtQkngl09u5c2fi5RLMAwAAINPdru+7rzgPAHly0003mVdffdXOb9y4saKgvabBvMbJ00qpecDBgwftcy+88IK9w3DXXXel8REAAABowZE5rrvOmJdeavRaAED1XCCvzPVuqDtHme1nz56daHlt1a6QxuBz7fu9TQZOnz5tx+4DAABA40fm0KR5AEDj+s8vWbLEBu6KoTXevP7XpHHu614zv2XLFjMwMGAeeOABs3v37rHnV6xYYV/LAxLgAQiiRFEPPVScBwAAACq1YcMGM3PmTHPmzBkzY8YMMzw8bA4dOmTj5koS+U0YrSQHvkdbW5sZGRmxA9xPnDhxbGw83XXQCsYZK2/Hjh1m165dtqmB/u/q6gotq7sWas7vdHR02JsJjtZFO+m1116zzfy1U3THI4iyH95+++3mgw8+MLfddlvCLQcAAMi+Tz4x5sYbC/MaxemGG0zu1l+tCuSjj/K3/i0t7ycfMu9kzuI5xcf79u0zy5cvL4mdFd92d3fbwD6JquuaFDCref2yZctKxsZTMD1nzpzYy1AQH7dZ/v79+83KlSsDX1Mg7+5yaEephYDmAQAAkE90DwDQDOPMj4yM2Gb2LjGe+s6rUnzevHklFdZ1C+Z7enpsTbpqxzVu3nvvvWeDbd1ZUIAeh2rXRUF4tRTAu+Bdy9UNAt1YCAv+ASCM7k+6H5DXX5+/pFEAEFhTqouZLmqOLnRhDTX9ZT/91JirV8N3rLfmNUnZixeNCWvN+YktHK+s/4J96ZIxn3+eTtkpU9QktTh+uoZdS6OsMvy5NP1JyqqcykeNSej6iCUpq32gfRFGw8tdc028slrXn/2suO5AC44z76UW44cPH7Y18wrqNf68Yum+vj5bwZ1U1Qnw9OHqL68VU828auP1nFZs/fr1phaUHEA3DlatWlVyB8NlBFRCAUfr5c8U6Hf+/Hl7VyRquhR1oQLQlPT7Vq0DNVErBCC3vIH6rbcYs3Rp6et67C52/mnBgtKyd98dXtafhfnee8PLunbzzpo14WW1zl4PPhhe1n/B7u6OLuvS/Msjj0SXff/9YtnHH48u+/bbxbJPPhld1lsb9/3vR5d9441iWeWqiirrqb00P/5xdNkXXyyW1XxUWS3L0WdElf3bvzXmv/7XwsS4goDp7e21tfMus70m10q9kiHrqg7mFejqTsLVq1dtc/uhoSE7/+ijj9rX0qY7L6p5140DBepqRu9ox3gDeUfrFGXBggW2mUPUtH379tS3BQAAoOa4GwkAmbBmzRpbES4K4pUIz8XP3ri2bgnwvB33k7wWRDXtqnWPSoDn197ebofHczXw6m/g3SQtT8sNWqZLmHD06FEza9asyM+ZPHmynQC0DvL2AGgGl858YrpnFpIF73r/PjN55o25amZvr8W33lDMoTaRZva5aWbvLQvUwMmcJcALs2fPHts9XMO717XPfNi9AGWzr/I+QWKun4G3ht5lBowydepUm3gAAACg2ShO+zvzF4UHMz4uDc7F/ziK+oLXomyS/tRJymrj41bGJCmrIFVTI8sqSI4bKCcpq6A+7nisScoCLe6ZZ54xzz33nM0T19nZab797W+bt956y+Z5U6W0ctElVdG3T4G6+sSL+q6r7b+fgmgNI5cGjV+vmnUF6UoY4JLZuez33qHnXNI79TtQ4js1XSD5HQAAQD4pj9w3vlGcB4C82bJlix2O3SV+V2yrruMumby6ratrd1IV30pT/3hR7bub99eSx+3Er+BbAbuCdQXgeuyazuumgB67ZvKa13OqeVcGQJXz96lX03o1v9c6+F8HAABoJaNmgrlw+3+289ebCSZvA3Oogv/nP2/0WgDIgrwOTbdv3z4bzG/evNk+1ryrgNbQ6pWqus/8xo0bK8q8lwXN0scCQG3QZx5AM+BaBqBZncxJPNfW1mab0s/+YtQPzd955522dr6a7t5Vd3Jxgbwy1/uHgFPtuVvhLNu2bZvtNx82fiGA1qRRdL5oDcWIOgAAAKiIuqbPnDlz7LGbrzZvW9XBvPrPK42+mr5rJVXRr/9FzeQPHTpksm7r1q2ZvpMDoDGUY2mgkAAaANDAlgWubujEidIk+ACQB4qRly9fbpPfeZ/zVyB7uxDUJZhXG3/dWVCiOa2cmgoogFcnf/VfBwAAQANpiDgzpTh/Q4Is8xlx6lSj1wAAqhtf3lV4Oy4ZXjWqDuaVsE4d+tWkXiuoLHzKHq+Edgr081AzDwAA0LS8Y71HjfsOAKiJgRo19ax6gA9loFdWeVEgr77zMm/ePNv0HgDy3LRTN1E1aR4AAADIiqpr5tUvXmO/qw+AgnqNP+/GytPQcHlAAjwAAAAAQEsF8729vbZ23mW2dwG97N+/3+QBCfAAAEBTj8xhCk08J068r9GrAwAtN858rVQ9znyQ48ePmzlz5pisy8u4hAAag7GZATSFnF/Mcr76AGroZIvHc1X3mQ+iQH7Pnj0lY+kBAAAASbW1GaP0TJo0DwAoqOqS+Mwzz5ilS5fa8fH+9m//1j731ltvmfnz59t+88pmDwAAgAa7+ebClENTphijwZE0aR4A4hgZGTFr16417e3tZvHixZHJ2cuVda9p9Da9fuzYsYoOwqZNm8yLL744ljS+YX3mNY78jh07xsbHU/CuMeZ7enrGEuApu30ekAAPAAA0q0/MDebGU7+18+pdSit1AK1gw4YNZsaMGTZG1VDqK1assPNJyyrQVwV2f3+/fbx9+3azatUqMzQ0lHidTp06Zb797W+bs2fP2mTxGtJdy9L/06ZNq1+fedW+b9y40WzevNk+VmCvZHhKgpeXGvlW72PRDD7/3JhLl8Jfv/ZaY665pjB/5YoxFy+Gl1U5lU9aVkP2fvppOmUnTTJm8uTCvL6ZFy6kU1bJj667rvg4api1JGXV3NFbS5KkrNY37OqjoeCuv76ystq/UcMoe/taliurfbFmTWH++edL9wsA5LHP+W9+U7wO5u16T195AEniuQkTJtiAfPr06WNDp+/atcsGzuOvRfHLKrhXLX01qedUs3/gwAGbMF7/a5n6zH//93+vTzN7rYCrlRc3v27dukoXCSSmZJb6gRI27d5dLPvGG9Flv//9Ylm1rIkq++STxbJvvx1d9vHHi2Xffz+67COPFMueOhVdtru79MdPVNkHHyzdb1FlXfDq3HJLeNl77y0tO3t2eNm77y4tu2BBeNmlS0vL6nFYWS3HS58TVlbr56X1Dyur7daP3JdeKkwE8gCawa235vd6r8+MunENoLlcunTJNkePms6fPx/4XtcM3gXnbkj1oObxScrKa6+9VvUQ7Hq/avs1qdm+bgxcjaphSjuY190Lb4I7N19J8wAAAADUxvUTPjVfnfb/5n73qtKNPvNA61BzdnXbjpoW+Gt1vqCabm9w7gQ1jU9aVq3Q1aW80pxzyjc3ceJE2w9fn9Hd3W1bBbz77rv16zOvuwfLly+3fQu8z2nlvLzjAAJp0+kWNbyka94uX/96dFnXHF8WLYpf9stfjl/2S1+KLqum847yFMUtqyaKUWXVlNIrSdmPPgov688qfOJE/LJHj0Y3u/RS0qO4ZX/xi+im814//Wn8sgCQVxNGr5o3zv2RuWCuN+Y3H421V8/b9V5/6/zlATQvdeF+xNtsNcCHH34YGNArOFfg7aem7NWUVV965YfztlBPQvnl1ERfNwO0jNn+ZqP16jOvOwmqnS9n7969Jut9LNavX2+mTp069rxuSPhvSiBbPvvMmB//uDD/P/5HacAMAAA8GKgdQJM6Wcc+80pUp+bxer5Sb775pm2mr77y+l+BvTcJXtLgvuJgPk1Knqedon4J+l93O6JuImjDdfdEfRkGBgZK+iz4hxLQHQ+VCUICvPzidwkAAPzRBNDaTkYE84ob1Ypc8aXiRz32ZrPfvXv3WNxZrqweK/5UEJ4mBffqTvD888/bGwqfK7t3vcaZT4u7w6HgPIobFuD48eN257q7GH7aybpHoSkskAcAAAAANKf+/n5bWaza78cee8xmjXdU+avn4pTV8xquztWku6nSseZPnDhh9uzZYxPHK57VsufMmWNbi9etz3yaXJ8Db//7IGr2oH4G3n4UqtUHcq3ZxtejUyOyhDEeGz/Go7esrkm6NsUpCwBAFaZPnx5ak65KZG/Ne1RZVTyn1Zhd8a7GmF+4cKFZsmSJbR2ggF7J/CqRiZr5SoUNC+D686vW3tvkPoyGNCg37IGGRgBqotnG11Omwcb33gEKGOOx8WM8emkstKjrBwAATWxgYMCcOXPGHD582OzcudOsWbOm4kA+MzXzlXDDAqhJhJceu+b6aiKhjIPeuy5BwoY08Nq6dat54oknqlxroAX8l/9izOXLxkye3Og1AZBHqr1/8MHC/N//vTHXXdfoNWoO3pYXAJBTGinNO1rax1FDNGWQYlNVFGuIOjf03fz5821cW8kQ75lIgOeoJl216lEJ8LyJ7tQkodwYf+rPoD4PQf3xXcKEo0ePmlmzZkUuZ/LkyXZCNuQ+AZ43Hf+f/Vl0s9O8NbPP3cFAU6OZff6a2ef+Ag8AqJeTEQnwski53xTHisuUr9bmanqvPvh33HFHfWvmN23aZO655x57l6GSuwmVBv3qY1AukI9Lw9LVa90BSzXXf/EXhfm1a+P/WNWgwLUoq0GBa1EWaBR1jXJj037ve/FaiiigTXJuZ6FsktrWJGWnTKlNWWrZAQAtbOPGjaazs9M2sffq7u6208svv1zfPvOnTp0y3/72t20N+J133mmD+xdeeME2H0iLEgM4qrmXoPH91PRedzYcl6GwXJZ85I9+l+/dW5hoMJERqvH77W8LU3Ya/KCVE0v+8IeFKeEwLwAAALVw6NAhs2XLlnHPq5K6kmHv2tLqxP/OO+/YTPOnT5+2bf4V3P/e7/1erGUo6FZ5BeK6I6F5l7jOO2xAnGEBVFbJ79wyvMMKoHlMmlSo0NakeWSAmvAq2ZUmzQMAskHdG/7bfytMUd2vAAA1pWz2amrvp+c0PF1SqYVByiqv2njVjrsa8qtRfeh8dyLCmsx7hw0oNyyAhhQYHBxMvO7btm2zTe2d1atX2wkAACD3lKfgH/+xOA8AaAhlr9ew7BpnXt3URXGzKqRdC/S6BvPKxKcmAW6YOK2UatdVY19Nmv16Uqb6PCRMQJFazb74YmH+/vupnQcAAACQbarAVsW3gnq1JhdVVisB/Pbt2+sfzKtpvZq0a8V0l2G2f/xZoEa5rTo7i8mOaWoPAAAAIOuU+02Ta25fSfP61IJ5NWtXrfyrr746Ftgrzb4yzut/gnsAAJAo674bN5ix0QEATTTOvFc1QXzNxpl/8803bROB559/3jYd+DzDWYTzNi4hinI/DHEz9hPI/UFBU+F8BPg+AGh6J3MQz/X29sYum7SpfSoRxIkTJ2ztvOs7r4R16j+vmnkAEen4AdSGxj532WKTjIMOAACQorgJ2l0f+roG80qvf/bsWbNw4UKzZMkSOya8gvi8JL8TstkDKd2geOih4jzQSG1txpDDJb9JUbq7C/O7dhkzeXKj1wgAgIqpO3qtVN3MXuO4K4jPU/Cep2YZaNIWtM3YzB4A0pD7CzwAoF5O5iSeU0v2WuSSqzqC0FB0Gl9eQ9QNDQ3Z5+bPn282bNhgpk2blsY6As2HdPxAbV2+bMzjjxfm//qvjbn2WvY4AABoiHnz5tlY2QX0q1evtkO5VxsvVx3MK6X+4sWL7bzrI//cc8/ZzPbHjh0zd9xxR7UfAYyj3+U/+lFxHhmgRj4XLhQzUFfQ7wdIzWefGfPMM4X5J57gQgEAABrG3xheuebOnDnT+GB+48aNprOz0+zcubPk+e7ubju9/PLL1X4EMM411xjzrW+xYzJFgTxNYwEgey5eNObBBwvzf//3xlx3XaPXCABMqw9Nl4k+80qAd+TIkXF9AEZGRszMmTPNlStXTNb7WKxfv95MnTp17Hk1e9AE1Ewz9gltxm1CfnE+5hfHjn0KAE3WZ76trc22aHet1v2PK5VKNnutiD+Y13Nz5swxebB169ZMH3wE549zN+V034X8cQAAAACyqqOjw8bOYY/F2+qgLsH8mjVr7Irs2bPHJsMTjTX/2GOPmbWMo40a5o/75jeLlcAE8wAAAACySDGzdxx5xc9pqDqY7+vrs03qvSuolvtdXV1m+/btaawjAABoFUqg+dFHxXkAAHJuYGCgJstNZXDrXbt22UlN6yUvzeuBhiEdPwAEU8XA7/wOewcAgHoE805eg/ht27aRAA/1RTp+oLamTDHm178uzgMAADSZioL53t7e2GXjNLXfsWOHrdnXuPT6X030w6hJ/4YNG2y//Llz55r+/n6zaNGi2K8HIQEekIKJE9UBqDgPNFJbmzF/8Accg7wmRXnkkcL8975nzOTJjV4jAACaJ5gfHByMVc7byT+Kgm4F8UqaV44CdWX9Gx4eNvv27bNJ9zQf93UgE5oxHb/GLa5RfyAALXZ9/OEPC/M7dhDMp0G5B9xYzOQhAJBjjDOf8jjzaVq1apXNgB9VM68bBArOp0+fbh/PmzfP3ghYuXJlrNcDxyX8P//H3DZr1vgPU+2iAhTv2LdRtUDeppxJyl64oKyBYRtc+oc3SdlPPzXm6tXw9fCOA56k7MWLxly5kk5Zra+76aPaGP2Ii1H2kzOXzI0zC7U1H//mk/FDmmv/aj/L5cvGfPZZ+HKTlNX54Gqdk5RVOZX3nh+33lqYZ0x2NCtdq/SdVrcS0by+51G5JFxZXTd0/QijciofVlbft+9/vzD/ne8UyyJf48z/5jfFvylZv677qUWBu1Hrynr/5gEAWmacea+33nrL7Ny50xw5csQcPHjQPvfCCy/YCu677rrLJPHFX7rkTpw4YepNzfDFBeqiJvTu+XKvh/r93y/8cPBNn/33/27OnTtnp0v6AXrLLYHl7HTvvaXLnD07vOzdd5eWXbAgvOzSpaVl9TisrJbjpc8JK6v189L6h5XVdnutWRNe1v0Acx58MLqsbk443d3RZU+dGit67Xc2mx+Yv7TTtbdOH1/2/feLy3388ejlvv12seyTT0aXPXKkWFaBQlTZN94olt29u/Q1F8gDzRzIf+1rxvz4x8Xn1Bol6juj74mj709UWReoi76X/tc1buu2bYUpKjhDtulamZfrun/yjhWs74Ge+/rXw2/IAwCa3oEDB8zixYvHtXY/ffp0rFbqqQXzqvH2BvSrV6+2QW8tqT+8N1B3hoaGYr2e1KuvvmpuuukmOzHMXrZcM/Gq+UvzQztdYyJq87Puq19tniaPqk1TjZOmqJYpaA26UferXxWaSTfyfGim71ir0PHScWsm7jvwy1+W3sSuF1VIfOtbhSmqdQwANJGRkRHb6ru9vd0G0KoJr7Sscrwp/lUr8N3eyoeEtmzZYoepU828t4G8uoYfPny4fs3s29rabI337C9qeP2Pa9HMXsvXTvSussrrfXpPudfDmmUcHRw0s/7Tf4psZj958mQzOaoJOM3s69rMvmzZvDTHbKYml96msXQdQND5UM9m9s34HWsl+jvuD3rzcl0PamY/MmJMe3vjro9cnwG0YDP7tWvX2lxq6nKtXGrKrRaWS61cWT2nSmPVnnd3d0d2C4+imFk3DqZNm2YmTpxornwRJ509e9Z+vnscV66ybqkfgb8GXndNtEPjvB5m6i23mGlBwbxfkoy6Sf5QJ6k1SlI2yXBMScp68wikWVb7N+Y+vjJpsnnj/ymUVavFyOTp+sEft79srcoq8HBBCtCqFNjETfaoL3Xc62iSssgH3YCJOqZ5u65z/QeAutu3b99YQN7R0WEDcY14FpRLrVxZPScKuKuheFXN65ctW1ZSAa3PqmSY94qb2Qdlq4+bvT4pb1MGt3PdRp85c6bkgJR7Hc1BlXDLlhWmqMo7AAAAAPmjnGUuf1nYdP78+cD3JsmlVnHetQr09PTYWn0lwVPs/N5775k9e/aYzs5O2wQ/qapq5hU4e+9O+B+74QPKUfCtgF016grA9VjJAbQTVbOux64pg8aNd/0ZdGdD5bzKvQ4AAAAAyDblLNumRLYVGEmQSy3tvGtRFNNquYpzxdXG9/X1mfXr19cvmF+zZk1JTbxrelAJrbymINpQb38F7ej9+/eHLqvc60F0kkydOrUkmZ8mAAAAAED99fb2mkceeSSyzIcffmgW+Ef0+iImVJDup/xq1ZRNg4t933zzTfvZlTSvrzqYVxa+ZrF169bcjEsIAAByRvlg9u4tzgMAYlw6J9spyrmQ0dSS5FKrNO9atRYuXFj1MnKVAA9AhikJ2X33FefR2ghegCIlfly7lj0CAHXU8UUuNWWoD8qlpm7erit3ubJpUn95fUZQM/5nn322fgnwAKBk5IKXXipMSUYxQHMHL5riZrAHUBsaCeejjwpTklFxACDH+vv7bRI75VJzOdkcl5ctTlnRc3pNQbhq7DUfNW59mKefftp2I//JT35iTp8+PW6q2zjzzT4uIbKNIXMBALnx+efGvPhiYf7++7nBBQAtGs/NnDnTtghQ/rk0UF1CArxc0pC9O3YU5wFkDMELUHTpkjGdnYX5jz8mmAeAFjU6OmoWL16c2vKomc/RnRwg09Rc4pZbCvNqynnDDY1eIzQSzWeA7HwfdDPBZYT+3vdIwgegaZzMWc28xpK/+eabzebNm7NTM69O/Dt37rT9Bg4ePGife+GFF2xmwLvuuiuNjwCQBxcuNHoNAABBLWV++MPCvJq1kVEfQE698sordnI+1g3SHNFwdwroNZS6+s6r2b1X0iC/6mBeyQHuueces2HDBjM4ODj2vDrwKxugd2cDablyRYkrCvOLFpE8HQAAAGh2q1evtpO3Zr6/v9/kheJlNbNXc3tv7CwTJkyofzCvOwsac/6BBx6wnfmdFStW2NeAWrh40ZivfKVxLRYBAAAAIAm1Zk9T1UPT6Y6CG4NPdxMcNRkYGRmpdvEAAAAAACDtmnn1i1dAv2zZMttcwNEYfHPmzDF5sG3bNjN16tTQ5hsAAAAAAGQp31zVwXxPT4/p6uqyTe1VM//ee+/ZDv3d3d22z3webN26NRfZDwEAQA5de60xP/pRcR4A0JIOpJxvrupgXoH80NCQzcYnrja+r6/PrF+/vtrFA8iLtjZjvvGN4jxaG8ELUHTNNcZ861vsEQBocVtSzjeX6jjzb775ppk+fXpumtfnbVxCZGfIXgAAcuPqVWPef78w/6UvccMVQFMNTdff35+beK6trc3mlZs2bZqZOHGiuaIhuowxZ8+eNTNmzBh7XNdx5p2FCxemuTgAAIDmGOfd/fhUTp5Jqf78Kk+tpWbPru9nAkAN5H1ourkp55ublFYnfq2Amtv7PfvssybrSICXzxaLW7cW5wFkTKODFyBLLl0y5pvfLDYn4/sAAC2pJ+V8c1X/unr66afNY489ZvvM605DpdTcQIkAdFNAy9EdFtcP3887BJ6jneCGyFu8eLHNDuh0dHTYHRaGBHj57I77xBONXguM6/vgan5OnKDvQ6sjeAGy4/JlYx5/vDD/139NEj4AaJC0881V3Wde48mr8/6aNWuqWYxZu3at7SegOxL79u2zgf3w8HDZ9yloV8IAb1kF89ohLrgPQ595IEUkMgDnA5DN62OjPx8AauRkjnOgpZFvruqaed0LUPBcLQXwLiBXTbpq+1VLXy4gV/Df2dlZ9ecjf7l83n67MP/lL5PLJ3P0w9H105wypfj8hQu6aAS/Ry1urr++srKfflo4KcJ4f7gmKXvxojFRiUiSlNX6ulZFqrVWM/Q0ymr/utEDVPv22WfplL3uOmMmTkxeVuVU3p0DAErpu6HmZa6PmK4bun6EUTk3nF2SsrrO6XrnPhMAkClp5JtrS6OpgALxahw7dsz+rzsTjpoeuOej7N271/YxCKrpV3P8VatWlTS5D3L+/Hlz7ty5yOmSflAjM/T75A//sDC53yrIkFtvLdQC3X136fMLFhSeD5qWLi0tq8dhZbUcL31OWFl/0qd77w0ve8stpWXV4iisrKvlch58MLqsbk44umZFlT11qlj2kUeiy7oM1aJmtFFl3R0wefLJ6LLe6+b3vx9d9o03imU1zIqe0zkAYDx9NzzDEdnvT9T3S98/R9/LqLL6Xjv6vrvn+T4CQGYo39wzzzxjNm3aNG6qe838vHnz7Jh46rOuAFzN7r02b94cq7+8N5B3ghLqeanmXvx967397VXD72+G77fAHxiE9Kt/gk7aQHRt8le/aswvf8leQpHOCW8rCqAVZeX6yPcRABoqrXxzqfWZ37hxY2gNumrGveMAhtH7dVPAuyqqWVetumr+w6iMdoL6x0dpb283Bw4cGBf0uz4WR48eNbNmzYpcxuTJk+2EbKD7X0bpO+ytgaaZfes2sw/aFqCVea+P9W5m7/B9BJBzeR9nfmZK+eZSC+bTosBfteeuhl7BvfrDR/WZ13tUe1/urka5YD4vBx9FBPMAAABAazuZs3hOCd/VBXy2vxtoo/rMi/qUe9v9f/e737X90JNwSe9c8/kzZ86UBPK6g+Glxwri/YG8muy75vfilhk2zB0AAAAAALWWRr65VPvMHz9+fCybvQu+n3vuOdPT02Obz99xxx2xlqPmEWo2r1p0BeiqSXd090JBubfJvcaND0p8Jyqr96iWf8mSJSXLAgAAAACg3tLIN5dqM/vVq1fbsfF27txZ8rwC7ffee8+8/PLLJuvNMtavX2+mTp1ask2akF00swcAAABa28mcNbNPI99cqs3sDx06ZO8u+Ckpne44ZNnlL5I09fb2mu9973tjU54DeQ2hp6z7zTaUnn+7lONHN640uRxCedOMx4ptygeOUz5wnPKB45QPHKd84DjlQzMep3pRBfirr74aOCUN5FOpmZ8/f75tIr9s2bKS5998803bbP7dd981WfX222/bYemUzf7LX/6yaQbKX3DTTTeZs2fPmmnTpplm0YzbxTblA8cpHzhO+cBxygeOUz5wnPKB41RbJ3NWM5+2qvvMK62+ktft2bPHjucuSkCnfusK5gEAAAAAqMXQdFmnVuBLly41DzzwgJ2Psn379voG82pOrwzyCurVzl9U2a9kdUlXBohLw+e+/35h/ktfKg6dDQAAAKA5+XObnTx50rYSz7LBwUGb+E4OHz48FjP7hT1f02BeNB68JmW2FyXEyxPdkNCYfw4J8LLv0091nhXmdUPuhhsavUYAAAAAUEr94Z20c8pVXZ+5bt068+KLL44F8d5AXq/lgboE+BPgBSUgiPtckrK1en8W16kW21TN56SxThyndI9JMx6naj+fbcrHcar2/WwTx6lR516S86/R68Q2VX6cqt2nWTxO1X4+25SP45S0bJY988wztrm9pu9+97vZCOY13rv6zD/88MPjXtu3b5/JqyxeCAjm4+E4ce5l6ftU7edn8RpR7ec34zZV+362iePUqHMvyfnX6HVimwh80zxPsnjuVfv5zbhNSct6qSu4cri1t7ebxYsXmyNHjlRcNsmygjz99NOmp6dnrPL70UcfNd/5zndMw4N5te1Xxnrt0K985Svm/PnzY69VmSgfAAAAAIDENmzYYLtSDw8P28RzLll7JWWTLCuIuqTv2LHD7N27105PPfWUrRSvVippw3R3QR37NXTY7Nmzzb/8y79U3Im/ET79dIL55BNTMn322bXm4sXScnouqJz+Vx/uSsteuGBCy+q1qLLez/GX1eeEldUUVjZo3b20X9Iq673fo6Eqw8r5l3v5cujhBAAAANDi9u3bZ3OjiVqSKxjXqGuVlE2yrCDHjh2z73M0r+eqlUoCPJk+fbrt0K/+54sWLbJ3H7LuqlKiG2MWL75Ro0D6Xv2OOXr0tPlf/6sYeT/77P9tfvCD8eX03J/8yedmYKAYTff3/1+hZf/oj66Yl14qRKdqyfD7vz9iTp5sCyx7551XzOuvF8veddeweeediePKyW23XTWvvvr/2fkPP/zQ/NmfXTH/+q/BZdvbr5p//deP7TKVBXLt2uvN//7fk8aVkylTRs2///v5sbIPPTTFvP76NYFlZf36Qjnp7p5i/vEfw8s+9NDFsbKPPHKdGRi4NrBcwQy7XRqv8/HHrzPGXPvFtp4z119fLOXW0yvouSRla/F+14rFbVMW1oltSv84pbFPs3ac2Kb67JNGHye2qTWOU9DzbFPzHadqP59t4jg14ty7fPmyuXTpkjl9+rR5++23S8q65/7jP/6jJK5zXKCsGNVRjBoUQJcrm2RZYVTJPXPmzLHH3vlqTBitsi18W1vbuJ2nOxednZ12pa9cuWKy6pe//KX52te+1ujVAAAAAABU6ODBgzaxnKM+7WoKr2bxjvq8z507d6yGPW7ZJMuKipnV1947gpoqwletWlVVYr+qa+bVvN5PzQbUj/7AgQMmy/74j//YHvhbb73V7mAAAAAAQOO5mvkoV69etUH2woULS55XLbqS1vm58d6TlE2yrDBr1qwZ1wXd2+y+YTXzAAAAAABkyYQJE2yg75rHK/hWV/CVK1cmLptkWfXUlsZ4eXfeeaeZOHGinTSf1rh5AAAAAAAk1dHRYfO5iZLVnTlzpiT43r17d+yy5V6v1Ouvv964mvklS5bYPgRdXV22D4AcPnzY9Pf329fUhB0AAAAAgHoa+WJseMWn6t+uGFWJ68TfDz6qbJzXK3H27Fnbh76qHHOjFdqxY8fovHnzRkdGRsa9Njw8bF/r7e0drTd9dkdHx+j06dNHFy1aNDo4OFhx2STLqqUk69HX1zc6d+5c3aAZ3bVr17jX9V4tQ8tSuaAyWdsmV07bpHXev3//uDLaDrfdWl7Wt0mva13dpPcF0bbqdR3XrG+Td3vc5D1W3uOoZQ0NDY1mfZu835egdc7CNaLcdz7J+mbl+pBkm+KcV1m4PiTZprjf/0ZfHyrZrqhjkZVrRJJtKvedycI1opptCjoOWblOJD1n4uyDLFwrkmxTnN9HWbhWJNmmcr+PsnKdSLIe5X4fZeU6kWSbyl0HsnSdyDLF0W1tbVUto+Jm9hrsXgPfa2x5P/UleOqpp2yGvnrbsGGDvcOhuyy9vb32jkulZZMsq5aSrIfuFKn/RtidIt1RUpMQLUvluru7UxnjsJbbpHVUOV0PNe/P+qgmMsokqfNNZXSnrBGSni9ufTUNDAwElnFDPTZKkm1y26JJiTF1HXDNj3Q3UxlGjx8/bpel5/3HMYvbpNfc+Rd07mXhGlHuO59kfbNyfYi7TXHOq6xcH5Icp7jf/0ZfH5JuV9SxyNI1Isk2lfvOZOEakXSbyl33snKdSHrOlNsHWbhWJN2mcr+PsnCtqOS7Hfb7KCvXiaTrEfX7KCvXiaTbVO46kJXrRB5Unb6u0rsAEyZMGD1+/Hjo68eOHav6TkMltEm6w+VE3aksVzbJsmqpkvVYuXJl4F0wLct7p013zBpxB7CafZuV41LNeuluZbl11h30np4ee6e0UXfTK93XXV1ddgqjZVZx+anLNul74V9HPfZ+X7J0LoZ955Ne87JwfUiyTeXOq6xcH5JuU7nvfxauD0m3K8mxaOQ1Iul3Kuo7k6VrRJxtinvdy9J1Iuk5E7YPGn1s0vge+M+3LF4rym1TnN9HcZdVL0nWI+j3UdauE3G2Kc61L4vXiSzat29fY2rmdYdTd2/C6O6LytSTu+PjsgyK7kQG3QkqVzbJsmop7fVQ8gbdedYduH379o0tLw/bpHVWaxCVde/Vc3qfJuVt0OS2K+vbpLuWyoypO5/qt+Ol7dKdzLhjV2bt3FPLHd2FDaPEIfW+PqT1fVJfqbSWVU9x1jcL14dq+M+rrFwfkir3/c/C9SGppMeiUdeIpKK+M3m7RsS57mX5OlHNOZPVa0XcbQr6fZTVa0WcbYr6fZR0WfWQZD38v4+yep0ot03lrgNZvU5kwZ49e8y5c+dKhqyrRsXjzOuDe3p6zKFDhwJf37JlS91T9euE8X4ZnKGhocRlkyyrltJeDzUb0x+p9vZ2+1jNfeqtkm3ShV0XA73Pu87KJClqhnXgwIGxP8QqU8+LRtJt8ibNUPM3bwIO1+Sq0X98Kz339AdAwva/lqvta1QTxrjb5H4U6QeSrnX63y0j6bKyIM76ZuH6UKmg8yor14ekyn3/s3B9SCrJsWjkNSKpqO9M3q4Rca57Wb1OVHvOZPFaEXebwn4fZfFaEWebyv0+SrKsekiyHkG/j7J4nYh7nKKuA1m8TmTFK6+8Ym/o6Px+8sknx55XgK9r0ezZsxMtr+KaeV0c9IFuKDql1dekuw16ThfCZ5991tSTvgzePziOxgFMWjbJsmop7fXQF0snj+u3o8f1vvtXyTbpD6zrw+ZGThD1MRJtk5arC6RuIrkLZla3yXshd39o3d1n/a9joruajVTpuadaAI1wEUZ/mPV6I7Yv6TbpO6K+e3pdf1h13Nyd6qxcI+KKs75ZuD5UKui8ysr1IYly3/+sXB+SSnIsGnmNSCrqO5O3a0Sc615WrxPVnjNZvFbE3aaw30dZvFbE2aao30dJl1UPSdYj6PdRFq8Tcbap3HUgi9eJrNB31t1omzlzps1VoP9140M35xKrNgOf+n2o/7x3Wrt2bWCW+3qgz3x4nzD1XfEfcpVrRB+qavoH+ftT+bOC5nGbvH2JdNyCMp9G9UHP0jb5+0l56dg0YjvSPE7ebWumPvNZuj4k7V8edV5l5foQd5vKff+zdH2o5PwrdyyycI2Iu01xvjNZukZUkofCf93L6nUiyTkTlVMoK9eKSr8H3t9HWbtWVLpNQX2ts3KdSLoeYb+PsnSdiLNN5a4DWbxOZMnGjRttvKyR3xQz63891vOVxM+pZY1QwjtNjaYEH+4k1BdBFwEv7wW8XNlyr2dxm+L8sXLPuy9bIy4YcbdJFzfvRdwNr+K9GGo5bugSt02NSLKRZJu8+1yJaaLOrUYmrUl67rlhfcKWpfOy0ZJsU9h5FndZ9RT2nU9yzcvK9SHpNkWdV1m5PiTZpiTf/6wktYqzXeWORVauEUm2qdx3JkvXiLjbVO66l6XrRLlzJsnvo6xcK+JuU5zfR1n6LRF3m8r9PsrKdSLpuVfu91EWrhNJtqncdSBL14msaW9vH33++edLntO+UkC/Z8+exMtrfArIlOlCoBMxaKxGzXu/IFFl47yexW1yFz53B9Z/R1NfJF1M9LymRo5fHmebVM6NU+nKBl0M3NiYjRzLMuk2ueOj90SdW438A5zk3Iu68+ou5G6b3dSI8WGTbJP+uLp1DbpTnYVrRNR3Puk1LyvXh7jbFPe8ysL1IclxylMwn3S7wo5Flq4RSbap3HcmC9eIpNtU7rqXletEuXMm6e+jLFwrkmxT3N9Hjb5WVLJNYb+PsnKdSHrulauZzsJ1Iuk2lbsOZOU6kdVgPmhEuIGBgdH58+cnXt4E/VOL/gAAAAAAAKCgs7PTzJ8/vyT5nZw9e9bm77hy5YqpSzZ7AAAAAAAQz1NPPWWDeSU+VJLAO+64Y+z5SoZaJJgHAAAAAKDGFLC/++67toZ+zpw5ZsKECfZ5NZbXMJNJ0cweAAAAAIA6UtN6DQWqgH7NmjUVLYNgHgAAAACQea+99ppZtWqVGR4eNtOnTzetrq3RKwAAAAAAQDm7du0yK1euZEd9gWAeAAAAAFBzO3bsMPPmzbNNy3fv3j3udSWGW7t2rWlvbzeLFy82R44cGXtNCeN6e3s5Sh4E8wAANJCaC+oHSiPoR5OmJD/Curu7y5bTjy/9GFNzSAAAvAngVLu+aNGiwJ2yYcMGO0SbmtErcF+xYoV9/tixY/bvVdj7WhV95gEAqNUf2S+y1Ibp6+uzff70w6Wjo6Pux0GB+ZkzZ8zAwECsmw6ubJzhc5SVVwG9yjdi2wAA2aW/Kfob0dXVNe7vprc/vGrxFfzrb8nhw4ft30v9v2TJEps8rtUxNB0AADUyNDQ0Nq8fI2pSODg4OPacfpQ0MoGPfhCtW7cuVmCuWnZtT9xxcBXA9/T02FoWgnkAaH7ub5z+3on+buimddygW7Xv4v27qJp4Pe+W6W4ExLkJ3QpoZg8AQI0o8HXTzJkzxz2nHyz+ZvaqLdcPIj2nPoOqlXBjz+o195y/Cbve4/ohqp9huSbuKqfm8PoczWs9wrgmkf5AXp/hPtP1b/Q229eNAj2uZOxcAEC+qJZdgbf+Vum6r/+9QXg5+nsRdIPbe2McpQjmAQDIEP0QUoCt4F81HPoxpKaImhQs6zll8tVjR+VV+6EfTa6foYJzb+KgsB9HKj86OhpZc6KgXU0a/fQZWj8tS+vlT0zk+jbSFBIAWoOu92r1pb9Rmo/bmksUyAflcdFNY/9nMCxdAcE8AAAZo8BZTdT1I0j/uyb5qvVwyYP0g8cF60pMpyaHCvL1A0fN2lU2qkZE71XZcj+I3A8r/48p1xxSn+VaGmjevzw9Vl97AEDz081f/d3Q3yPdZE7CBf7egF5/q5LcEGg1BPMAAGSMP1uvfsioVt5PQbIL6PW6mru7Sc3uVTsS5tChQ4G17UGfIf4gXeukH2sK8lVDr88LqlHRTQgX+AMAmjuQV2st1Zxrck3uk9BNYdf1TMvT3yDGlQ9HMA8AQMa4/vVxuCBbTd3VXN47eZPt+ekmQJwhfqKGrnM/2LQc1cDMmTOHwB0AWpRu8nqb1mve2yVMXD4YF/hr3tslrL+/3/4d0fMqe+DAgbpvR54QzAMAkGPuR1PSMd1Va7906dLYyw8L6l1TStcH35/sTrUqNJEEgObnulx5+WvV9ffC5WrRpHnvjWXdoNZNAD2vG9KMKx+NYB4AgJzTjyOXPVhBt2o11Oy9XBZ5NbVPUvPvpc9QX3397zLW63//Dy89p6b2AAAgXQTzAADknJLkKdmda76o/vNKiBdVI64bAAr41b/eOzReENWsBPW/V+2JPss1h9QyvbUwrulk1LB3AACgMhNG1b4BAAAghGrd1e9RtfNJmswrwNcNAzWXBAAA6SKYBwAAZal2Xf3fy9X4+28AqLyyEwMAgHTRzB4AAJSlJvXr1q0r2yTfNa9/7rnn7HsI5AEAqA1q5gEAAAAAyBlq5gEAAAAAyBmCeQAAAAAAcoZgHgAAAACAnCGYBwAAAAAgZwjmAQAAAADIGYJ5AAAAAAByhmAeAAAAAICcIZgHAAAAAMDky/8P93Nzqe61SLAAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Plotting the relevant data:\n", - "# Note that the dry air flow is not plotted because it is constant at 5 L/min.\n", - "\n", - "times, ozones = analyser.GetPlottableFlowData(which_flow_rate='Ozone')\n", - "_, species = analyser.GetPlottableFlowData(which_flow_rate='Species')\n", - "\n", "fig = plt.figure(figsize=(10,2.5), constrained_layout = True)\n", "ax = fig.add_subplot(111)\n", - "ax_2 = plt.twinx(ax)\n", + "ax_2 = plt.twinx(ax=ax)\n", "\n", - "ax.plot(times, ozones, color='red', linestyle='--', label='Ozone')\n", - "ax_2.plot(times, species, color='blue', linestyle='--', label=r'$\\alpha$-Pinene')\n", + "ax.step(analyser.data['Sample (-)'], analyser.data['Ozone Flow (L/min)'], color='forestgreen', linestyle='--', label='Ozone')\n", + "ax_2.step(analyser.data['Sample (-)'], analyser.data['Species Flow (L/min)'], color='magenta', linestyle='--', label=r'$\\alpha$-Pinene')\n", "\n", "ax.set_title('2026-06-12 SET02/EXP01: Flow Rates Overview ')\n", - "ax.set_xlabel(r'Time $t$ $(\\unit{\\second})$')\n", + "ax.set_xlabel(r'Sample $(-)$')\n", "ax.set_ylabel(r'Ozone Flow Rate $r_{\\ce{O3}}$ $(\\unit{\\liter\\per\\minute})$')\n", "ax_2.set_ylabel(r'$\\alpha$-Pinene Flow Rate $r_{\\alpha}$ $(\\unit{\\liter\\per\\minute})$')\n", - "ax.xaxis.set_minor_locator(MultipleLocator(base=180, offset=0))\n", - "ax.xaxis.set_major_locator(MultipleLocator(base=1800, offset=0))\n", + "ax.xaxis.set_minor_locator(MultipleLocator(base=1, offset=0))\n", + "ax.xaxis.set_major_locator(MultipleLocator(base=10, offset=0))\n", "\n", - "ax.set_xlim([0, 30000])\n", + "ax.set_xlim([1, 156])\n", "ax.set_ylim([0.6, 2.4])\n", "ax_2.set_ylim([0.0, 0.05])\n", "\n", @@ -235,7 +194,32 @@ "id": "e2ae6dbb", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "fig = plt.figure(figsize=(10,2.5), constrained_layout = True)\n", + "ax = fig.add_subplot(111)\n", + "ax_2 = plt.twinx(ax=ax)\n", + "\n", + "ax.step(analyser.data['Sample (-)'], analyser.data['Mass (mg/cm^3)'], color='forestgreen', linestyle='--', label='Ozone')\n", + "ax_2.step(analyser.data['Sample (-)'], analyser.data['Species Flow (L/min)'], color='magenta', linestyle='--', label=r'$\\alpha$-Pinene')\n", + "\n", + "ax.set_title('2026-06-12 SET02/EXP01: Flow Rates Overview ')\n", + "ax.set_xlabel(r'Sample $(-)$')\n", + "ax.set_ylabel(r'Ozone Flow Rate $r_{\\ce{O3}}$ $(\\unit{\\liter\\per\\minute})$')\n", + "ax_2.set_ylabel(r'$\\alpha$-Pinene Flow Rate $r_{\\alpha}$ $(\\unit{\\liter\\per\\minute})$')\n", + "ax.xaxis.set_minor_locator(MultipleLocator(base=1, offset=0))\n", + "ax.xaxis.set_major_locator(MultipleLocator(base=10, offset=0))\n", + "\n", + "ax.set_xlim([1, 156])\n", + "ax.set_ylim([0.6, 2.4])\n", + "ax_2.set_ylim([0.0, 0.05])\n", + "\n", + "lines_1, labels_1 = ax.get_legend_handles_labels()\n", + "lines_2, labels_2 = ax_2.get_legend_handles_labels()\n", + "\n", + "ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n", + "\n", + "plt.savefig(\"20260723_OFR_Analysis_01_Experiment_Overview_bottom.pdf\", format='pdf', dpi=400)" + ] } ], "metadata": {